From 3ad7f9aa038c13dca7a28146cd4c4c1f674523bf Mon Sep 17 00:00:00 2001
From: Corbett Bennett <corbettb@alleninstitute.org>
Date: Mon, 9 Jan 2023 13:58:28 -0800
Subject: [PATCH 1/3] Add tutorials for LFP and behavior-only sessions

---
 ...al_behavior_neuropixels_LFP_analysis.ipynb | 2481 +++++++++++++
 ...opixels_analyzing_behavior_only_data.ipynb | 3204 +++++++++++++++++
 2 files changed, 5685 insertions(+)
 create mode 100644 doc_template/examples_root/examples/nb/visual_behavior_neuropixels_LFP_analysis.ipynb
 create mode 100644 doc_template/examples_root/examples/nb/visual_behavior_neuropixels_analyzing_behavior_only_data.ipynb

diff --git a/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_LFP_analysis.ipynb b/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_LFP_analysis.ipynb
new file mode 100644
index 000000000..39d086e28
--- /dev/null
+++ b/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_LFP_analysis.ipynb
@@ -0,0 +1,2481 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ae902862",
+   "metadata": {
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+     "duration": 0.011796,
+     "end_time": "2022-12-13T23:26:12.136657",
+     "exception": false,
+     "start_time": "2022-12-13T23:26:12.124861",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# LFP Analysis\n",
+    "\n",
+    "## Tutorial overview\n",
+    "\n",
+    "This Jupyter notebook will demonstrate how to access and analyze LFP data from the Neuropixels Visual Behavior dataset. LFP, which stands for \"local field potential,\" contains information about low-frequency (0.1-500 Hz) voltage fluctations around each recording site. It's complementary to the spiking activity, and can be analyzed on its own or in conjunction with spikes.\n",
+    "\n",
+    "This tutorial will cover the following topics:\n",
+    "\n",
+    "* <a href='#Learning-about-the-available-probes'>Selecting probes to analyze</a>\n",
+    "* <a href='#Loading-LFP-data'>Loading LFP data</a>\n",
+    "* <a href='#Aligning-LFP-data-to-a-stimulus'>Aligning data in time</a>\n",
+    "* <a href='#Aligning-LFP-data-to-units'>Aligning data in space</a>\n",
+    "* <a href='#Current-Source-Density'>Exploring pre-computed CSD plots</a>\n",
+    "\n",
+    "This tutorial assumes you've already created a data cache, or are working with the files on AWS. If you haven't reached that step yet, we recommend going through the [data access tutorial](https://allensdk.readthedocs.io/en/latest/visual_behavior_neuropixels.html) first.\n",
+    "\n",
+    "Functions related to analyzing spike data will be covered in other tutorials. For a full list of available tutorials, see the [SDK documentation](https://allensdk.readthedocs.io/en/latest/visual_behavior_neuropixels.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fcb744dc",
+   "metadata": {},
+   "source": [
+    "## Learning about the available probes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7afeb83",
+   "metadata": {},
+   "source": [
+    "First let's import the `VisualBehaviorNeuropixelsProjectCache` and a few other helpful packages:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "94f6902c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\svc_ccg\\AppData\\Local\\Continuum\\anaconda3\\envs\\allensdk_38_dev\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from allensdk.brain_observatory.behavior.behavior_project_cache.\\\n",
+    "    behavior_neuropixels_project_cache \\\n",
+    "    import VisualBehaviorNeuropixelsProjectCache\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "227af228",
+   "metadata": {},
+   "source": [
+    "Now let's instantiate a cache from the Amazon S3 bucket. The `cache_dir` should be a path specifying where to download data on your local system:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "20f8cbc4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cache_dir = \"/path/to/vbn_cache\"\n",
+    "\n",
+    "cache = VisualBehaviorNeuropixelsProjectCache.from_s3_cache(\n",
+    "            cache_dir=cache_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15d56488",
+   "metadata": {},
+   "source": [
+    "**Finding LFP data of interest**\n",
+    "\n",
+    "We can look at the cache probes table to identify which probe insertions have valid LFP data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9b32147e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fraction of insertions with valid LFP:  0.9602209944751381\n"
+     ]
+    }
+   ],
+   "source": [
+    "probes = cache.get_probe_table()\n",
+    "valid_lfp = probes[probes['has_lfp_data']]\n",
+    "\n",
+    "print('Fraction of insertions with valid LFP: ', len(valid_lfp)/len(probes))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b667fb5",
+   "metadata": {},
+   "source": [
+    "Now let's find a probe insertion with good LFP data that passed through VISp."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ddfb4b53",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ecephys_session_id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sampling_rate</th>\n",
+       "      <th>lfp_sampling_rate</th>\n",
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+       "      <th>has_lfp_data</th>\n",
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+       "      <th>channel_count</th>\n",
+       "      <th>structure_acronyms</th>\n",
+       "      <th>file_id</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ecephys_probe_id</th>\n",
+       "      <th></th>\n",
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+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1044506934</th>\n",
+       "      <td>1044385384</td>\n",
+       "      <td>probeC</td>\n",
+       "      <td>30000.049852</td>\n",
+       "      <td>2500.004154</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>307</td>\n",
+       "      <td>384</td>\n",
+       "      <td>['MB', 'MRN', 'POST', 'SCig', 'VISp', 'root']</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1044520064</th>\n",
+       "      <td>1044389060</td>\n",
+       "      <td>probeC</td>\n",
+       "      <td>30000.146997</td>\n",
+       "      <td>2500.012250</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>417</td>\n",
+       "      <td>384</td>\n",
+       "      <td>['MB', 'NB', 'POL', 'POST', 'PRE', 'SUB', 'TH'...</td>\n",
+       "      <td>7</td>\n",
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+       "    <tr>\n",
+       "      <th>1044791095</th>\n",
+       "      <td>1044594870</td>\n",
+       "      <td>probeC</td>\n",
+       "      <td>30000.054069</td>\n",
+       "      <td>2500.004506</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>365</td>\n",
+       "      <td>384</td>\n",
+       "      <td>['HPF', 'MB', 'MRN', 'POST', 'SCig', 'VISp', '...</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1044827086</th>\n",
+       "      <td>1044597824</td>\n",
+       "      <td>probeC</td>\n",
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+       "      <td>2500.012413</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>378</td>\n",
+       "      <td>384</td>\n",
+       "      <td>['DG', 'MB', 'PIL', 'POST', 'PRE', 'SGN', 'SUB...</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1046469927</th>\n",
+       "      <td>1046166369</td>\n",
+       "      <td>probeC</td>\n",
+       "      <td>30000.150477</td>\n",
+       "      <td>2500.012540</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>519</td>\n",
+       "      <td>384</td>\n",
+       "      <td>['HPF', 'MB', 'MRN', 'POST', 'SCig', 'VISp', '...</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
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+       "</div>"
+      ],
+      "text/plain": [
+       "                  ecephys_session_id    name  sampling_rate  \\\n",
+       "ecephys_probe_id                                              \n",
+       "1044506934                1044385384  probeC   30000.049852   \n",
+       "1044520064                1044389060  probeC   30000.146997   \n",
+       "1044791095                1044594870  probeC   30000.054069   \n",
+       "1044827086                1044597824  probeC   30000.148958   \n",
+       "1046469927                1046166369  probeC   30000.150477   \n",
+       "\n",
+       "                  lfp_sampling_rate  phase  has_lfp_data  unit_count  \\\n",
+       "ecephys_probe_id                                                       \n",
+       "1044506934              2500.004154    1.0          True         307   \n",
+       "1044520064              2500.012250    1.0          True         417   \n",
+       "1044791095              2500.004506    1.0          True         365   \n",
+       "1044827086              2500.012413    1.0          True         378   \n",
+       "1046469927              2500.012540    1.0          True         519   \n",
+       "\n",
+       "                  channel_count  \\\n",
+       "ecephys_probe_id                  \n",
+       "1044506934                  384   \n",
+       "1044520064                  384   \n",
+       "1044791095                  384   \n",
+       "1044827086                  384   \n",
+       "1046469927                  384   \n",
+       "\n",
+       "                                                 structure_acronyms  file_id  \n",
+       "ecephys_probe_id                                                              \n",
+       "1044506934            ['MB', 'MRN', 'POST', 'SCig', 'VISp', 'root']        1  \n",
+       "1044520064        ['MB', 'NB', 'POL', 'POST', 'PRE', 'SUB', 'TH'...        7  \n",
+       "1044791095        ['HPF', 'MB', 'MRN', 'POST', 'SCig', 'VISp', '...       12  \n",
+       "1044827086        ['DG', 'MB', 'PIL', 'POST', 'PRE', 'SGN', 'SUB...       18  \n",
+       "1046469927        ['HPF', 'MB', 'MRN', 'POST', 'SCig', 'VISp', '...       24  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "valid_lfp[valid_lfp['structure_acronyms'].str.contains(\"'VISp',\")].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd6a8bff",
+   "metadata": {},
+   "source": [
+    "Below we'll select an insertion from this table (1064735073) and grab the session data for it. We can get the session ID from the probes table:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "156b0a05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "session_id = probes.loc[1064735073]['ecephys_session_id']\n",
+    "session = cache.get_ecephys_session(\n",
+    "            ecephys_session_id=session_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a724fa05",
+   "metadata": {},
+   "source": [
+    "## Loading LFP data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1bf8448",
+   "metadata": {},
+   "source": [
+    "Once we've gotten the session data, we can use `get_lfp` to grab the LFP data for our selected probe insertion. If you haven't tried to access this data previously, you'll have to wait while the LFP NWB file downloads. Even if you already have the data stored locally, it may still take a minute to load, since the LFP data is quite large (this example file is 4.5 GB).\n",
+    "\n",
+    "Once the data is loaded, we can take a closer look at the lfp object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "78341edd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lfp = session.get_lfp(1064735073)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
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+       "\n",
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+       "  display: contents;\n",
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+       "\n",
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+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
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+       "  padding-right: 5px;\n",
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+       ".xr-var-list > li:nth-child(odd) > div,\n",
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+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
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+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
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+       "\n",
+       ".xr-attrs dt:hover span {\n",
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+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'LFP'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 11924764</li><li><span class='xr-has-index'>channel</span>: 96</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5f459647-7a90-4611-b9da-454fe602460b' class='xr-array-in' type='checkbox' ><label for='section-5f459647-7a90-4611-b9da-454fe602460b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>9.0675e-05 7.9364996e-05 6.4545e-05 ... -3.159e-05 7.8e-07</span></div><pre class='xr-array-data'>array([[ 9.0674999e-05,  7.9364996e-05,  6.4544998e-05, ...,\n",
+       "        -2.4179999e-05, -7.8000003e-06, -5.8500000e-06],\n",
+       "       [ 8.0339996e-05,  3.7829999e-05,  9.1649999e-06, ...,\n",
+       "        -3.6074998e-05,  1.5015000e-05,  2.1255000e-05],\n",
+       "       [ 3.0420000e-05, -3.1979998e-05, -3.7635000e-05, ...,\n",
+       "        -1.7939999e-05,  8.7749995e-06,  2.2619999e-05],\n",
+       "       ...,\n",
+       "       [ 9.7694996e-05,  1.0803000e-04,  3.1004998e-05, ...,\n",
+       "        -2.3985000e-05, -5.8695001e-05,  5.8500000e-06],\n",
+       "       [ 1.2109500e-04,  1.2012000e-04,  5.5575001e-05, ...,\n",
+       "        -2.0865000e-05, -4.1535000e-05,  5.4600000e-06],\n",
+       "       [ 8.4239997e-05,  9.9254998e-05,  5.2259998e-05, ...,\n",
+       "        -1.7549999e-05, -3.1590000e-05,  7.7999999e-07]], dtype=float32)</pre></div></li><li class='xr-section-item'><input id='section-71e7d084-c3f9-483c-a81c-9afd19363c8d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-71e7d084-c3f9-483c-a81c-9afd19363c8d' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.97 4.971 ... 9.545e+03 9.545e+03</div><input id='attrs-573396b2-f910-43ce-b17f-e69e07bab26f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-573396b2-f910-43ce-b17f-e69e07bab26f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5c58335-8310-4165-aae1-aa92c6b7240c' class='xr-var-data-in' type='checkbox'><label for='data-d5c58335-8310-4165-aae1-aa92c6b7240c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([4.970398e+00, 4.971198e+00, 4.971998e+00, ..., 9.544757e+03,\n",
+       "       9.544758e+03, 9.544759e+03])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>channel</span></div><div class='xr-var-dims'>(channel)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1066253838 ... 1066254232</div><input id='attrs-2fe639d4-8e7b-4ea0-a68c-df3e3b01f8ca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2fe639d4-8e7b-4ea0-a68c-df3e3b01f8ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c754783c-d38b-4905-b07f-61bbb423f34a' class='xr-var-data-in' type='checkbox'><label for='data-c754783c-d38b-4905-b07f-61bbb423f34a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([1066253838, 1066253842, 1066253846, 1066253851, 1066253855, 1066253860,\n",
+       "       1066253864, 1066253868, 1066253872, 1066253876, 1066253880, 1066253884,\n",
+       "       1066253888, 1066253892, 1066253896, 1066253901, 1066253905, 1066253909,\n",
+       "       1066253913, 1066253918, 1066253922, 1066253927, 1066253931, 1066253935,\n",
+       "       1066253939, 1066253943, 1066253947, 1066253951, 1066253955, 1066253960,\n",
+       "       1066253964, 1066253968, 1066253973, 1066253978, 1066253982, 1066253986,\n",
+       "       1066253990, 1066253994, 1066253998, 1066254003, 1066254007, 1066254011,\n",
+       "       1066254015, 1066254019, 1066254023, 1066254027, 1066254031, 1066254036,\n",
+       "       1066254040, 1066254045, 1066254049, 1066254053, 1066254057, 1066254061,\n",
+       "       1066254065, 1066254069, 1066254073, 1066254077, 1066254081, 1066254085,\n",
+       "       1066254089, 1066254093, 1066254097, 1066254101, 1066254105, 1066254109,\n",
+       "       1066254113, 1066254118, 1066254122, 1066254126, 1066254130, 1066254134,\n",
+       "       1066254138, 1066254142, 1066254146, 1066254150, 1066254154, 1066254158,\n",
+       "       1066254163, 1066254167, 1066254171, 1066254176, 1066254180, 1066254184,\n",
+       "       1066254188, 1066254192, 1066254196, 1066254200, 1066254204, 1066254208,\n",
+       "       1066254212, 1066254216, 1066254220, 1066254224, 1066254228, 1066254232],\n",
+       "      dtype=int64)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-0808d08f-d567-4b70-8619-6bb9b157be89' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0808d08f-d567-4b70-8619-6bb9b157be89' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'LFP' (time: 11924764, channel: 96)>\n",
+       "array([[ 9.0674999e-05,  7.9364996e-05,  6.4544998e-05, ...,\n",
+       "        -2.4179999e-05, -7.8000003e-06, -5.8500000e-06],\n",
+       "       [ 8.0339996e-05,  3.7829999e-05,  9.1649999e-06, ...,\n",
+       "        -3.6074998e-05,  1.5015000e-05,  2.1255000e-05],\n",
+       "       [ 3.0420000e-05, -3.1979998e-05, -3.7635000e-05, ...,\n",
+       "        -1.7939999e-05,  8.7749995e-06,  2.2619999e-05],\n",
+       "       ...,\n",
+       "       [ 9.7694996e-05,  1.0803000e-04,  3.1004998e-05, ...,\n",
+       "        -2.3985000e-05, -5.8695001e-05,  5.8500000e-06],\n",
+       "       [ 1.2109500e-04,  1.2012000e-04,  5.5575001e-05, ...,\n",
+       "        -2.0865000e-05, -4.1535000e-05,  5.4600000e-06],\n",
+       "       [ 8.4239997e-05,  9.9254998e-05,  5.2259998e-05, ...,\n",
+       "        -1.7549999e-05, -3.1590000e-05,  7.7999999e-07]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * time     (time) float64 4.97 4.971 4.972 ... 9.545e+03 9.545e+03 9.545e+03\n",
+       "  * channel  (channel) int64 1066253838 1066253842 ... 1066254228 1066254232"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lfp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2124cd0c",
+   "metadata": {
+    "papermill": {
+     "duration": 0.175397,
+     "end_time": "2022-12-13T23:38:18.207467",
+     "exception": false,
+     "start_time": "2022-12-13T23:38:18.032070",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "The LFP data is stored as an [xarray.DataArray](http://xarray.pydata.org/en/stable/) object, with coordinates of `time` and `channel`. The xarray library simplifies the process of working with N-dimensional data arrays, by keeping track of the meaning of each axis. If this is your first time encountering xarrays, we strongly recommend reading through the [documentation](http://xarray.pydata.org/en/stable/quick-overview.html) before going further. Getting used to xarrays can be frustrating, especially when they don't behave like numpy arrays. But they are designed to prevent common mistakes when analyzing multidimensional arrays, so they are well worth learning more about. Plus, the syntax is modeled on that of the [pandas](https://pandas.pydata.org/) library, so if you're familiar with that you already have a head start.\n",
+    "\n",
+    "The print-out above already tells us a lot about what the `lfp` object contains. It stores an array with around 12 million points along the `time` axis and 96 points along the `channel` axis. The `time` axis ranges from 5 to around 9600 seconds, while the `channel` axis ranges from 1066253838 to 1066254232 (these are the unique IDs for each channel).\n",
+    "\n",
+    "Let's use the `DataArray.sel()` method to select a slice through this array between 100 and 101 seconds:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "78adcd35",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-12-13T23:38:18.555561Z",
+     "iopub.status.busy": "2022-12-13T23:38:18.554885Z",
+     "iopub.status.idle": "2022-12-13T23:38:19.437417Z",
+     "shell.execute_reply": "2022-12-13T23:38:19.436602Z"
+    },
+    "papermill": {
+     "duration": 1.060802,
+     "end_time": "2022-12-13T23:38:19.440396",
+     "exception": false,
+     "start_time": "2022-12-13T23:38:18.379594",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
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+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'LFP'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1250</li><li><span class='xr-has-index'>channel</span>: 96</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-921ce118-ac55-4e6e-b738-56ba7803b65b' class='xr-array-in' type='checkbox' ><label for='section-921ce118-ac55-4e6e-b738-56ba7803b65b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-0.000394875 -0.000362115 -0.000344175 ... 2.5545e-05 9.75e-06</span></div><pre class='xr-array-data'>array([[-3.94875009e-04, -3.62115010e-04, -3.44175001e-04, ...,\n",
+       "        -1.46249995e-05,  1.81350006e-05, -3.00299998e-05],\n",
+       "       [-3.47294990e-04, -2.96595012e-04, -2.78265012e-04, ...,\n",
+       "        -7.99499958e-06,  3.90000014e-06, -3.27599992e-05],\n",
+       "       [-3.03420005e-04, -2.76704988e-04, -3.07320006e-04, ...,\n",
+       "        -2.18400000e-05,  9.75000034e-07, -2.92499990e-05],\n",
+       "       ...,\n",
+       "       [ 4.40699987e-05,  1.31624998e-04,  1.63019999e-04, ...,\n",
+       "        -1.55999999e-06,  3.64650004e-05,  1.55999999e-06],\n",
+       "       [ 1.42739998e-04,  2.26004995e-04,  2.24054995e-04, ...,\n",
+       "         8.58000021e-06,  4.15350005e-05, -9.36000015e-06],\n",
+       "       [ 1.99485003e-04,  2.26395001e-04,  1.89929997e-04, ...,\n",
+       "        -2.34000004e-06,  2.55449995e-05,  9.74999966e-06]], dtype=float32)</pre></div></li><li class='xr-section-item'><input id='section-01488bd0-6c71-4074-9292-771ae0c03de9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-01488bd0-6c71-4074-9292-771ae0c03de9' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>100.0 100.0 100.0 ... 101.0 101.0</div><input id='attrs-3bb43be8-8bff-4396-b16e-fc39e9f5a8a8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3bb43be8-8bff-4396-b16e-fc39e9f5a8a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbff9332-573b-4b27-b3f6-35162acb9dd3' class='xr-var-data-in' type='checkbox'><label for='data-cbff9332-573b-4b27-b3f6-35162acb9dd3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([100.000581, 100.001381, 100.002181, ..., 100.998179, 100.998979,\n",
+       "       100.999779])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>channel</span></div><div class='xr-var-dims'>(channel)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1066253838 ... 1066254232</div><input id='attrs-5d785321-2b85-4ed5-a213-b7384573828b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d785321-2b85-4ed5-a213-b7384573828b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b38da1c-71e3-4611-aefe-f841561d416f' class='xr-var-data-in' type='checkbox'><label for='data-2b38da1c-71e3-4611-aefe-f841561d416f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([1066253838, 1066253842, 1066253846, 1066253851, 1066253855, 1066253860,\n",
+       "       1066253864, 1066253868, 1066253872, 1066253876, 1066253880, 1066253884,\n",
+       "       1066253888, 1066253892, 1066253896, 1066253901, 1066253905, 1066253909,\n",
+       "       1066253913, 1066253918, 1066253922, 1066253927, 1066253931, 1066253935,\n",
+       "       1066253939, 1066253943, 1066253947, 1066253951, 1066253955, 1066253960,\n",
+       "       1066253964, 1066253968, 1066253973, 1066253978, 1066253982, 1066253986,\n",
+       "       1066253990, 1066253994, 1066253998, 1066254003, 1066254007, 1066254011,\n",
+       "       1066254015, 1066254019, 1066254023, 1066254027, 1066254031, 1066254036,\n",
+       "       1066254040, 1066254045, 1066254049, 1066254053, 1066254057, 1066254061,\n",
+       "       1066254065, 1066254069, 1066254073, 1066254077, 1066254081, 1066254085,\n",
+       "       1066254089, 1066254093, 1066254097, 1066254101, 1066254105, 1066254109,\n",
+       "       1066254113, 1066254118, 1066254122, 1066254126, 1066254130, 1066254134,\n",
+       "       1066254138, 1066254142, 1066254146, 1066254150, 1066254154, 1066254158,\n",
+       "       1066254163, 1066254167, 1066254171, 1066254176, 1066254180, 1066254184,\n",
+       "       1066254188, 1066254192, 1066254196, 1066254200, 1066254204, 1066254208,\n",
+       "       1066254212, 1066254216, 1066254220, 1066254224, 1066254228, 1066254232],\n",
+       "      dtype=int64)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-ef6219a2-e558-4fd3-8866-afc12ba9249b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ef6219a2-e558-4fd3-8866-afc12ba9249b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'LFP' (time: 1250, channel: 96)>\n",
+       "array([[-3.94875009e-04, -3.62115010e-04, -3.44175001e-04, ...,\n",
+       "        -1.46249995e-05,  1.81350006e-05, -3.00299998e-05],\n",
+       "       [-3.47294990e-04, -2.96595012e-04, -2.78265012e-04, ...,\n",
+       "        -7.99499958e-06,  3.90000014e-06, -3.27599992e-05],\n",
+       "       [-3.03420005e-04, -2.76704988e-04, -3.07320006e-04, ...,\n",
+       "        -2.18400000e-05,  9.75000034e-07, -2.92499990e-05],\n",
+       "       ...,\n",
+       "       [ 4.40699987e-05,  1.31624998e-04,  1.63019999e-04, ...,\n",
+       "        -1.55999999e-06,  3.64650004e-05,  1.55999999e-06],\n",
+       "       [ 1.42739998e-04,  2.26004995e-04,  2.24054995e-04, ...,\n",
+       "         8.58000021e-06,  4.15350005e-05, -9.36000015e-06],\n",
+       "       [ 1.99485003e-04,  2.26395001e-04,  1.89929997e-04, ...,\n",
+       "        -2.34000004e-06,  2.55449995e-05,  9.74999966e-06]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * time     (time) float64 100.0 100.0 100.0 100.0 ... 101.0 101.0 101.0 101.0\n",
+       "  * channel  (channel) int64 1066253838 1066253842 ... 1066254228 1066254232"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lfp_slice = lfp.sel(time=slice(100,101))\n",
+    "\n",
+    "lfp_slice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e659b5c2",
+   "metadata": {
+    "papermill": {
+     "duration": 0.17422,
+     "end_time": "2022-12-13T23:38:19.837136",
+     "exception": false,
+     "start_time": "2022-12-13T23:38:19.662916",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "We see that this new DataArray is smaller than before; it contains the same number of channels, but only 1250 samples, due to the LFP sample rate of ~1250 Hz.\n",
+    "\n",
+    "Let's plot the data for one of the channels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4b201aed",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-12-13T23:38:20.187826Z",
+     "iopub.status.busy": "2022-12-13T23:38:20.187195Z",
+     "iopub.status.idle": "2022-12-13T23:38:21.004693Z",
+     "shell.execute_reply": "2022-12-13T23:38:21.002888Z"
+    },
+    "papermill": {
+     "duration": 0.996076,
+     "end_time": "2022-12-13T23:38:21.006575",
+     "exception": false,
+     "start_time": "2022-12-13T23:38:20.010499",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'LFP (V)')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,2))\n",
+    "_ = plt.plot(lfp_slice.time, lfp_slice.sel(channel=lfp_slice.channel[10]))\n",
+    "plt.xlabel('Time (s)')\n",
+    "plt.ylabel('LFP (V)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c78a1ab",
+   "metadata": {
+    "papermill": {
+     "duration": 0.174601,
+     "end_time": "2022-12-13T23:38:21.363670",
+     "exception": false,
+     "start_time": "2022-12-13T23:38:21.189069",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Alternatively, we can visualize this slice  of data using matplotlib's `imshow` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "4406d13d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-12-13T23:38:21.716611Z",
+     "iopub.status.busy": "2022-12-13T23:38:21.715946Z",
+     "iopub.status.idle": "2022-12-13T23:38:22.698335Z",
+     "shell.execute_reply": "2022-12-13T23:38:22.697600Z"
+    },
+    "papermill": {
+     "duration": 1.167738,
+     "end_time": "2022-12-13T23:38:22.705956",
+     "exception": false,
+     "start_time": "2022-12-13T23:38:21.538218",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8,8))\n",
+    "im = plt.imshow(lfp_slice.T,aspect='auto',origin='lower',vmin=-1e-3, vmax=1e-3)\n",
+    "_ = plt.colorbar(im, fraction=0.036, pad=0.04)\n",
+    "_ = plt.xlabel('Sample number')\n",
+    "_ = plt.ylabel('Channel index')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2aee4be5",
+   "metadata": {
+    "papermill": {
+     "duration": 0.179739,
+     "end_time": "2022-12-13T23:38:23.123444",
+     "exception": false,
+     "start_time": "2022-12-13T23:38:22.943705",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Note that we've transposed the original array to place the time dimension along the x-axis. We've also configured the plot so that the origin of the array is in the lower-left, so that that channels closer to the probe tip are lower in the image.\n",
+    "\n",
+    "A few things to note about this plot:\n",
+    "\n",
+    "* The units of the LFP are volts, so the color scale ranges from -1 to +1 mV\n",
+    "* Even though there are 384 channels on the Neuropixels probe, there are only 96 channels in this plot. That's because only every 4th channel is included in the NWB file (resulting in 40 micron vertical spacing). In addition, the reference channels and channels far outside the brain have been removed.\n",
+    "* The top of the plot is relatively flat. This corresponds to channels that are outside the brain. The LFP channels are originally referenced to the tip reference site on the Neuropixels probe. Before NWB packaging, the LFP data is digitally referenced to the channels outside the brain.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cf3c13d",
+   "metadata": {},
+   "source": [
+    "## Aligning LFP data to a stimulus"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd96a7b8",
+   "metadata": {
+    "papermill": {
+     "duration": 0.18031,
+     "end_time": "2022-12-13T23:38:25.150271",
+     "exception": false,
+     "start_time": "2022-12-13T23:38:24.969961",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "In the above example, we selected LFP data based on an arbitrary time span (100 to 101 seconds). For many analyses, however, you'll want to align the data to the onset of a particular type of stimulus.\n",
+    "\n",
+    "First, we need to select some stimulus presentations to use. Below, we'll use the full field flashes presented in stimulus block 4 of the Visual Behavior Neuropixels experiments. To learn more about the stimuli used in these experiments, check out the [documentation](http://portal.brain-map.org/explore/circuits/visual-behavior-neuropixels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "43ddc9c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stim_presentations = session.stimulus_presentations\n",
+    "flashes = stim_presentations[stim_presentations['stimulus_name'].str.contains('flash')]\n",
+    "presentation_times = flashes.start_time.values\n",
+    "presentation_ids = flashes.index.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c955de89",
+   "metadata": {},
+   "source": [
+    "First, let's make a convenience function that helps us align the LFP to times of interest. Because we're using xarrays, the alignment operation is fast, and doesn't require any for loops! There's a lot going on here, so we recommend referring to the pandas and xarray documentation if anything is confusing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "c3c98319",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def align_lfp(lfp, trial_window, alignment_times, trial_ids = None):\n",
+    "    '''\n",
+    "    Aligns the LFP data array to experiment times of interest\n",
+    "    INPUTS:\n",
+    "        lfp: data array containing LFP data for one probe insertion\n",
+    "        trial_window: vector specifying the time points to excise around each alignment time\n",
+    "        alignment_times: experiment times around which to excise data\n",
+    "        trial_ids: indices in the session stim table specifying which stimuli to use for alignment.\n",
+    "                    None if aligning to non-stimulus times\n",
+    "    \n",
+    "    OUTPUT:\n",
+    "        aligned data array with dimensions channels x trials x time\n",
+    "    '''\n",
+    "    \n",
+    "    time_selection = np.concatenate([trial_window + t for t in alignment_times])\n",
+    "    \n",
+    "    if trial_ids is None:\n",
+    "        trial_ids = np.arange(len(alignment_times))\n",
+    "        \n",
+    "    inds = pd.MultiIndex.from_product((trial_ids, trial_window), \n",
+    "                                      names=('presentation_id', 'time_from_presentation_onset'))\n",
+    "\n",
+    "    ds = lfp.sel(time = time_selection, method='nearest').to_dataset(name = 'aligned_lfp')\n",
+    "    ds = ds.assign(time=inds).unstack('time')\n",
+    "\n",
+    "    return ds['aligned_lfp']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "9a41ddea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aligned_lfp = align_lfp(lfp, np.arange(-0.5, 0.5, 1/500), presentation_times, presentation_ids)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8cebc63",
+   "metadata": {},
+   "source": [
+    "`aligned_lfp` is a DataArray with dimensions of channels x trials x time. It's been downsampled to 500 Hz by changing the time step in the `trial_window` argument of the `align_lfp` function.\n",
+    "\n",
+    "Note that we can get the channels IDs for each channel in this DataArray. Let's use the session channels table to map these to the probe and mark the surface of the brain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d65ee0f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chans = session.get_channels()\n",
+    "lfp_chan_depths = [chans.loc[c]['probe_vertical_position'] for c in lfp.channel.values]\n",
+    "\n",
+    "chans_in_brain = chans[(chans['probe_id']==1064735073)&(~chans['structure_acronym'].str.contains('root'))]\n",
+    "first_channel_in_brain_position = chans_in_brain['probe_vertical_position'].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "3e397d0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\svc_ccg\\AppData\\Local\\Temp\\ipykernel_238860\\759061444.py:3: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
+      "  im = ax.pcolor(aligned_lfp.time_from_presentation_onset.values, lfp_chan_depths, aligned_lfp.mean(dim='presentation_id').data)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(-0.4, 3570, 'brain surface')"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "fig.suptitle('Flash aligned mean LFP')\n",
+    "im = ax.pcolor(aligned_lfp.time_from_presentation_onset.values, lfp_chan_depths, aligned_lfp.mean(dim='presentation_id').data)\n",
+    "_ = plt.colorbar(im, fraction=0.036, pad=0.04)\n",
+    "_ = plt.xlabel('Time from flash onset (s)')\n",
+    "_ = plt.ylabel('Channel Position from Tip (um)')\n",
+    "\n",
+    "ax.axvline(0, c='w', ls='dotted')\n",
+    "ax.axvline(0.25, c='w', ls='dotted')\n",
+    "ax.axhline(first_channel_in_brain_position, c='w')\n",
+    "ax.text(-0.4, first_channel_in_brain_position+50, 'brain surface', c='w')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "178cb17f",
+   "metadata": {},
+   "source": [
+    "Here we see the effect of a 250 ms flash stimulus on the LFP. There are two large responses in cortex (the first ~700 um below the brain surface), one corresponding to the stimulus onset (around 50 ms), and one corresponding to the stimulus offset (around sample 300 ms).\n",
+    "\n",
+    "You can use the code sample above to align the LFP to any type of event (e.g. spike times, running onset, optogenetic stimuli) just by changing the `trial_window` and `time_selection` variables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84b9a411",
+   "metadata": {
+    "papermill": {
+     "duration": 0.184171,
+     "end_time": "2022-12-13T23:38:42.628683",
+     "exception": false,
+     "start_time": "2022-12-13T23:38:42.444512",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Aligning LFP data to units"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ddba938",
+   "metadata": {
+    "papermill": {
+     "duration": 0.188486,
+     "end_time": "2022-12-13T23:38:43.002780",
+     "exception": false,
+     "start_time": "2022-12-13T23:38:42.814294",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "The previous section demonstrated how to align the LFP in time. What if we want to extract the LFP at a particular location in space, corresponding to the location of a unit we're analyzing?\n",
+    "\n",
+    "Let's start by finding a well-isolated unit whose peak channel is included in our LFP data. \n",
+    "\n",
+    "Once we've selected a unit of interest, we can align the LFP data to its spike times:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "9b73cad1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sess_units  = session.get_units()\n",
+    "\n",
+    "#Grab units whose peak channels are in the LFP data, have relatively low isi violations and high amplitude spikes\n",
+    "units_on_lfp_chans = sess_units[(sess_units.peak_channel_id.isin(lfp.channel.values)) &\n",
+    "                                (sess_units.isi_violations < 0.5) &\n",
+    "                                (sess_units.amplitude > 200)]\n",
+    "\n",
+    "#Merge this curated unit table with the channel table to get CCF locations for these units\n",
+    "units_on_lfp_chans = units_on_lfp_chans.merge(chans, left_on='peak_channel_id', right_index=True)\n",
+    "\n",
+    "#Select a unit in V1\n",
+    "v1_units = units_on_lfp_chans[units_on_lfp_chans.structure_acronym.str.contains('VISp')]\n",
+    "unit_id = v1_units.index.values[5]\n",
+    "\n",
+    "#Get the peak channel ID for this unit (the channel on which it had the greatest spike amplitude)\n",
+    "peak_chan_id = units_on_lfp_chans.loc[unit_id]['peak_channel_id']\n",
+    "peak_probe_position = units_on_lfp_chans.loc[unit_id]['probe_vertical_position']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65aaae41",
+   "metadata": {
+    "papermill": {
+     "duration": 0.187469,
+     "end_time": "2022-12-13T23:40:23.489925",
+     "exception": false,
+     "start_time": "2022-12-13T23:40:23.302456",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Using `unit_id` and `peak_chan_id`, we can select the spikes and LFP within an arbitrary time interval. Note that we can use `method='nearest'` when selecting the LFP data channel if our peak channel isn't in the LFP DataArray. Here this is unnecessary since we've filtered for units with peak channels in the LFP data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "9e414a5b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-12-13T23:40:23.862043Z",
+     "iopub.status.busy": "2022-12-13T23:40:23.861355Z",
+     "iopub.status.idle": "2022-12-13T23:41:38.784790Z",
+     "shell.execute_reply": "2022-12-13T23:41:38.782836Z"
+    },
+    "papermill": {
+     "duration": 75.11299,
+     "end_time": "2022-12-13T23:41:38.788714",
+     "exception": false,
+     "start_time": "2022-12-13T23:40:23.675724",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\svc_ccg\\AppData\\Local\\Continuum\\anaconda3\\envs\\allensdk_38_dev\\lib\\site-packages\\xarray\\core\\indexing.py:189: FutureWarning: Passing method to Int64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
+      "  indexer = index.get_loc(\n"
+     ]
+    }
+   ],
+   "source": [
+    "start_time = 500\n",
+    "end_time = 510\n",
+    "\n",
+    "spike_times = session.spike_times[unit_id]\n",
+    "\n",
+    "times_in_range = spike_times[(spike_times > start_time) & (spike_times < end_time)]\n",
+    "\n",
+    "lfp_data = lfp.sel(time = slice(start_time, end_time))\n",
+    "lfp_data = lfp_data.sel(channel = peak_chan_id, method='nearest')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8499cf74",
+   "metadata": {},
+   "source": [
+    "Let's also find the stimulus presentations in this window"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "8b5593a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stims_in_window = stim_presentations[(stim_presentations.start_time>start_time)&(stim_presentations.start_time<end_time) &\n",
+    "                                    (stim_presentations.omitted==False)]\n",
+    "stim_times_in_window = stims_in_window.start_time.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1649d08",
+   "metadata": {
+    "papermill": {
+     "duration": 0.235302,
+     "end_time": "2022-12-13T23:41:39.214563",
+     "exception": false,
+     "start_time": "2022-12-13T23:41:38.979261",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Finally, we can plot the spike times and stim times along with the LFP for this interval:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "d4e2f340",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-12-13T23:41:39.587303Z",
+     "iopub.status.busy": "2022-12-13T23:41:39.586716Z",
+     "iopub.status.idle": "2022-12-13T23:41:39.799260Z",
+     "shell.execute_reply": "2022-12-13T23:41:39.798237Z"
+    },
+    "papermill": {
+     "duration": 0.402103,
+     "end_time": "2022-12-13T23:41:39.803104",
+     "exception": false,
+     "start_time": "2022-12-13T23:41:39.401001",
+     "status": "completed"
+    },
+    "scrolled": false,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1f2046c3340>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = plt.plot(lfp_data.time, lfp_data)\n",
+    "_ = plt.plot(times_in_range, np.ones(times_in_range.shape)*3e-4, '.r')\n",
+    "_ = plt.xlabel('Time (s)')\n",
+    "_ = plt.ylabel('LFP (V)')\n",
+    "\n",
+    "_ = plt.plot(stim_times_in_window, np.ones(stim_times_in_window.size)*4e-4, 'vg')\n",
+    "\n",
+    "plt.legend(['LFP', 'spikes', 'stim times'])\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae1fd4e3",
+   "metadata": {},
+   "source": [
+    "This plot shows clear troughs in the LFP associated with every stimulus presentation. We also see that the spiking for our selected unit is highly entrained to the stimulus presentations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a472384d",
+   "metadata": {},
+   "source": [
+    "Now let's calculate a spike triggered average of the LFP using a subset of spikes for our unit of interest and the `align_lfp` function we defined above:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "58bc794f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spikes_to_use = np.random.choice(spike_times, min((spike_times.size, 1000)), replace=False)\n",
+    "spike_triggered_lfp = align_lfp(lfp, np.arange(-0.1, 0.1, 1/1250), spikes_to_use)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ecd94221",
+   "metadata": {},
+   "source": [
+    "Let's plot this spike-triggered LFP for a region of the probe centered on this unit's peak channel:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "9bed263b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Channel depth')"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "im = ax.pcolor(spike_triggered_lfp.time_from_presentation_onset.values, lfp_chan_depths, \n",
+    "               spike_triggered_lfp.mean(dim='presentation_id').data, shading='auto')\n",
+    "\n",
+    "ax.plot(-0.01, peak_probe_position, '>w')\n",
+    "ax.text(-0.015, peak_probe_position, 'peak channel', c='w', va='center', ha='right')\n",
+    "ax.set_ylim([peak_probe_position-300, peak_probe_position+300])\n",
+    "ax.set_xlabel('Time from spike (s)')\n",
+    "ax.set_ylabel('Channel depth')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89fd0d32",
+   "metadata": {},
+   "source": [
+    "Aligning the LFP to spikes from our example unit reveals a low resolution spike waveform (note the thin dark stripe at the center of the plot). As an added exercise, you can compare this plot to the high resolution waveform for this unit stored in the session `mean_waveforms` dictionary. This plot also gives us some information about the network state that was conducive to spiking for this unit. Note the blue LFP depression in the middle of the plot, indicating that this unit tended to spike during periods of relative network excitability. How might this look for other cortical units? What if we limited our analysis to spikes during a particular stimulus or spontaneous activity?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d21ccc7",
+   "metadata": {
+    "papermill": {
+     "duration": 0.184966,
+     "end_time": "2022-12-13T23:41:40.549473",
+     "exception": false,
+     "start_time": "2022-12-13T23:41:40.364507",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Current Source Density"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3733ad8",
+   "metadata": {
+    "papermill": {
+     "duration": 0.183898,
+     "end_time": "2022-12-13T23:41:40.961825",
+     "exception": false,
+     "start_time": "2022-12-13T23:41:40.777927",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "LFP data is commonly used to generate current source density (CSD) plots, which show the location of current sources and sinks along the probe axis. CSD analysis benefits from high spatial resolution, since it involves taking the second spatial derivative of the data. Because of Neuropixels dense site spacing, these probes are optimal for computing the CSD. However, the LFP data available through the AllenSDK has been spatially downsampled prior to NWB packaging.\n",
+    "\n",
+    "To provide access to a high-resolution CSD plot, we've pre-computed the CSD in response to a flash stimulus for all probes with LFP."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "cda6bb92",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-12-13T23:41:41.334332Z",
+     "iopub.status.busy": "2022-12-13T23:41:41.333496Z",
+     "iopub.status.idle": "2022-12-13T23:41:41.449136Z",
+     "shell.execute_reply": "2022-12-13T23:41:41.448228Z"
+    },
+    "papermill": {
+     "duration": 0.307092,
+     "end_time": "2022-12-13T23:41:41.453446",
+     "exception": false,
+     "start_time": "2022-12-13T23:41:41.146354",
+     "status": "completed"
+    },
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
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+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'CSD'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>virtual_channel_index</span>: 384</li><li><span class='xr-has-index'>time</span>: 876</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6a64c5ef-c47a-49ad-a47f-5b7de39abdf7' class='xr-array-in' type='checkbox' ><label for='section-6a64c5ef-c47a-49ad-a47f-5b7de39abdf7' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-6.851e+03 -3.913e+03 -838.2 ... -1.298e+04 -8.213e+03 -3.431e+03</span></div><pre class='xr-array-data'>array([[ -6851.39437989,  -3913.14433717,   -838.16058113, ...,\n",
+       "        -53515.63831491, -29278.10117822,  -3749.31503814],\n",
+       "       [ 13243.57677227,   8740.01659952,   4313.56271629, ...,\n",
+       "         22739.01313458,  14183.82539278,   5204.40438723],\n",
+       "       [ 20996.49997869,  14830.58764115,   8758.41292695, ...,\n",
+       "         27012.55385223,  16988.00078316,   6538.44099997],\n",
+       "       ...,\n",
+       "       [  6343.70177858,  10211.65003108,  13976.14628462, ...,\n",
+       "          -864.07565883,  -2487.33239344,  -4367.92360728],\n",
+       "       [-25405.87596708, -38171.87082796, -49758.37596587, ...,\n",
+       "          8405.71892336,   8860.94179133,   9921.50032083],\n",
+       "       [ 17426.22217689,  22564.83211026,  26815.27213176, ...,\n",
+       "        -12976.83226892,  -8212.91142969,  -3430.54626923]])</pre></div></li><li class='xr-section-item'><input id='section-67271b3e-146d-45f3-80ed-7b9c5b7d6c38' class='xr-section-summary-in' type='checkbox'  checked><label for='section-67271b3e-146d-45f3-80ed-7b9c5b7d6c38' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>virtual_channel_index</span></div><div class='xr-var-dims'>(virtual_channel_index)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 379 380 381 382 383</div><input id='attrs-cdfbf506-4ccf-4c3e-8158-513fe68b2bf4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cdfbf506-4ccf-4c3e-8158-513fe68b2bf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2f2c51d-d2a7-482b-b3ad-c0dff1dc6b9f' class='xr-var-data-in' type='checkbox'><label for='data-f2f2c51d-d2a7-482b-b3ad-c0dff1dc6b9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([  0,   1,   2, ..., 381, 382, 383])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1 -0.0996 ... 0.2496 0.25</div><input id='attrs-1e27ac22-323b-44a9-83f0-67caa5e0d150' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e27ac22-323b-44a9-83f0-67caa5e0d150' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfbe0187-eaea-4716-9dbd-63875b10b5db' class='xr-var-data-in' type='checkbox'><label for='data-cfbe0187-eaea-4716-9dbd-63875b10b5db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([-0.1     , -0.0996  , -0.0992  , ...,  0.249199,  0.249599,  0.249999])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>vertical_position</span></div><div class='xr-var-dims'>(virtual_channel_index)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 10 20 30 ... 3800 3810 3820 3830</div><input id='attrs-41a6f62b-c6f8-4239-a9bf-4e4fd361d2c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-41a6f62b-c6f8-4239-a9bf-4e4fd361d2c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a695dd78-52ca-4523-833a-a6cb9c9cd8c3' class='xr-var-data-in' type='checkbox'><label for='data-a695dd78-52ca-4523-833a-a6cb9c9cd8c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([   0,   10,   20,   30,   40,   50,   60,   70,   80,   90,  100,\n",
+       "        110,  120,  130,  140,  150,  160,  170,  180,  190,  200,  210,\n",
+       "        220,  230,  240,  250,  260,  270,  280,  290,  300,  310,  320,\n",
+       "        330,  340,  350,  360,  370,  380,  390,  400,  410,  420,  430,\n",
+       "        440,  450,  460,  470,  480,  490,  500,  510,  520,  530,  540,\n",
+       "        550,  560,  570,  580,  590,  600,  610,  620,  630,  640,  650,\n",
+       "        660,  670,  680,  690,  700,  710,  720,  730,  740,  750,  760,\n",
+       "        770,  780,  790,  800,  810,  820,  830,  840,  850,  860,  870,\n",
+       "        880,  890,  900,  910,  920,  930,  940,  950,  960,  970,  980,\n",
+       "        990, 1000, 1010, 1020, 1030, 1040, 1050, 1060, 1070, 1080, 1090,\n",
+       "       1100, 1110, 1120, 1130, 1140, 1150, 1160, 1170, 1180, 1190, 1200,\n",
+       "       1210, 1220, 1230, 1240, 1250, 1260, 1270, 1280, 1290, 1300, 1310,\n",
+       "       1320, 1330, 1340, 1350, 1360, 1370, 1380, 1390, 1400, 1410, 1420,\n",
+       "       1430, 1440, 1450, 1460, 1470, 1480, 1490, 1500, 1510, 1520, 1530,\n",
+       "       1540, 1550, 1560, 1570, 1580, 1590, 1600, 1610, 1620, 1630, 1640,\n",
+       "       1650, 1660, 1670, 1680, 1690, 1700, 1710, 1720, 1730, 1740, 1750,\n",
+       "       1760, 1770, 1780, 1790, 1800, 1810, 1820, 1830, 1840, 1850, 1860,\n",
+       "       1870, 1880, 1890, 1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970,\n",
+       "       1980, 1990, 2000, 2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080,\n",
+       "       2090, 2100, 2110, 2120, 2130, 2140, 2150, 2160, 2170, 2180, 2190,\n",
+       "       2200, 2210, 2220, 2230, 2240, 2250, 2260, 2270, 2280, 2290, 2300,\n",
+       "       2310, 2320, 2330, 2340, 2350, 2360, 2370, 2380, 2390, 2400, 2410,\n",
+       "       2420, 2430, 2440, 2450, 2460, 2470, 2480, 2490, 2500, 2510, 2520,\n",
+       "       2530, 2540, 2550, 2560, 2570, 2580, 2590, 2600, 2610, 2620, 2630,\n",
+       "       2640, 2650, 2660, 2670, 2680, 2690, 2700, 2710, 2720, 2730, 2740,\n",
+       "       2750, 2760, 2770, 2780, 2790, 2800, 2810, 2820, 2830, 2840, 2850,\n",
+       "       2860, 2870, 2880, 2890, 2900, 2910, 2920, 2930, 2940, 2950, 2960,\n",
+       "       2970, 2980, 2990, 3000, 3010, 3020, 3030, 3040, 3050, 3060, 3070,\n",
+       "       3080, 3090, 3100, 3110, 3120, 3130, 3140, 3150, 3160, 3170, 3180,\n",
+       "       3190, 3200, 3210, 3220, 3230, 3240, 3250, 3260, 3270, 3280, 3290,\n",
+       "       3300, 3310, 3320, 3330, 3340, 3350, 3360, 3370, 3380, 3390, 3400,\n",
+       "       3410, 3420, 3430, 3440, 3450, 3460, 3470, 3480, 3490, 3500, 3510,\n",
+       "       3520, 3530, 3540, 3550, 3560, 3570, 3580, 3590, 3600, 3610, 3620,\n",
+       "       3630, 3640, 3650, 3660, 3670, 3680, 3690, 3700, 3710, 3720, 3730,\n",
+       "       3740, 3750, 3760, 3770, 3780, 3790, 3800, 3810, 3820, 3830])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>horizontal_position</span></div><div class='xr-var-dims'>(virtual_channel_index)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>24 24 24 24 24 ... 24 24 24 24 24</div><input id='attrs-bf7814c2-f48d-408c-bbe3-05db94ed3f5e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bf7814c2-f48d-408c-bbe3-05db94ed3f5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-932a952b-1dd1-445b-a1b6-346d4f31bd4d' class='xr-var-data-in' type='checkbox'><label for='data-932a952b-1dd1-445b-a1b6-346d4f31bd4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "       24, 24, 24, 24, 24, 24, 24, 24, 24, 24])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-e19b4507-0989-4205-90b8-2c20d355b961' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e19b4507-0989-4205-90b8-2c20d355b961' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'CSD' (virtual_channel_index: 384, time: 876)>\n",
+       "array([[ -6851.39437989,  -3913.14433717,   -838.16058113, ...,\n",
+       "        -53515.63831491, -29278.10117822,  -3749.31503814],\n",
+       "       [ 13243.57677227,   8740.01659952,   4313.56271629, ...,\n",
+       "         22739.01313458,  14183.82539278,   5204.40438723],\n",
+       "       [ 20996.49997869,  14830.58764115,   8758.41292695, ...,\n",
+       "         27012.55385223,  16988.00078316,   6538.44099997],\n",
+       "       ...,\n",
+       "       [  6343.70177858,  10211.65003108,  13976.14628462, ...,\n",
+       "          -864.07565883,  -2487.33239344,  -4367.92360728],\n",
+       "       [-25405.87596708, -38171.87082796, -49758.37596587, ...,\n",
+       "          8405.71892336,   8860.94179133,   9921.50032083],\n",
+       "       [ 17426.22217689,  22564.83211026,  26815.27213176, ...,\n",
+       "        -12976.83226892,  -8212.91142969,  -3430.54626923]])\n",
+       "Coordinates:\n",
+       "  * virtual_channel_index  (virtual_channel_index) int32 0 1 2 3 ... 381 382 383\n",
+       "  * time                   (time) float64 -0.1 -0.0996 -0.0992 ... 0.2496 0.25\n",
+       "    vertical_position      (virtual_channel_index) int32 0 10 20 ... 3820 3830\n",
+       "    horizontal_position    (virtual_channel_index) int32 24 24 24 ... 24 24 24"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "csd = session.get_current_source_density(1064735073)\n",
+    "csd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a2f58ea",
+   "metadata": {
+    "papermill": {
+     "duration": 0.190746,
+     "end_time": "2022-12-13T23:41:41.835409",
+     "exception": false,
+     "start_time": "2022-12-13T23:41:41.644663",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "The `CSD` object is a DataArray with dimensions of channels x time. Note that the channels are actually \"virtual channels,\" based on interpolated signals along the central axis of the probe, with 10 micron inter-site spacing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "1efaa3bb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-12-13T23:41:42.260778Z",
+     "iopub.status.busy": "2022-12-13T23:41:42.259944Z",
+     "iopub.status.idle": "2022-12-13T23:41:45.386871Z",
+     "shell.execute_reply": "2022-12-13T23:41:45.385019Z"
+    },
+    "papermill": {
+     "duration": 3.364218,
+     "end_time": "2022-12-13T23:41:45.390242",
+     "exception": false,
+     "start_time": "2022-12-13T23:41:42.026024",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\svc_ccg\\AppData\\Local\\Temp\\ipykernel_150440\\216337481.py:1: DeprecationWarning: Please use `gaussian_filter` from the `scipy.ndimage` namespace, the `scipy.ndimage.filters` namespace is deprecated.\n",
+      "  from scipy.ndimage.filters import gaussian_filter\n",
+      "C:\\Users\\svc_ccg\\AppData\\Local\\Temp\\ipykernel_150440\\216337481.py:9: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
+      "  _ = ax.pcolor(csd[\"time\"], csd[\"vertical_position\"], filtered_csd, vmin=-3e4, vmax=3e4)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(-0.075, 2750, 'end of cortex')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x1000 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.ndimage.filters import gaussian_filter\n",
+    "\n",
+    "_ = plt.figure(figsize=(10,10))\n",
+    "\n",
+    "filtered_csd = gaussian_filter(csd.data, sigma=(5,1))\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(6, 6))\n",
+    "\n",
+    "_ = ax.pcolor(csd[\"time\"], csd[\"vertical_position\"], filtered_csd, vmin=-3e4, vmax=3e4)\n",
+    "\n",
+    "_ = ax.set_xlabel(\"time relative to stimulus onset (s)\")\n",
+    "_ = ax.set_ylabel(\"vertical position (um)\")\n",
+    "\n",
+    "\n",
+    "chans_in_v1 = chans[(chans['probe_id']==1064735073)&(chans['structure_acronym'].str.contains('VISp'))]\n",
+    "last_cortex_channel_position = chans_in_v1['probe_vertical_position'].min()\n",
+    "\n",
+    "ax.axhline(first_channel_in_brain_position, c='w')\n",
+    "ax.text(-0.075, first_channel_in_brain_position+50, 'brain surface', c='w')\n",
+    "ax.axhline(last_cortex_channel_position, c='w')\n",
+    "ax.text(-0.075, last_cortex_channel_position+50, 'end of cortex', c='w')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20507760",
+   "metadata": {},
+   "source": [
+    "The CSV for this probe insertion reveals that visual activation is nicely confined to visual cortex (between the white lines). The trajectory for this probe likely missed other visually-responsive areas. Let's check to see which other areas were recorded:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "d9fcb808",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"['HPF', 'MB', 'MRN', 'NB', 'POST', 'VISp', 'root']\""
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "probes.loc[1064735073]['structure_acronyms']"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_analyzing_behavior_only_data.ipynb b/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_analyzing_behavior_only_data.ipynb
new file mode 100644
index 000000000..7c779c769
--- /dev/null
+++ b/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_analyzing_behavior_only_data.ipynb
@@ -0,0 +1,3204 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "659789fc",
+   "metadata": {},
+   "source": [
+    "# Analyzing behavior-only sessions from the Visual Behavior Neuropixels dataset\n",
+    "\n",
+    "In addition to the behavior sessions that accompany Neuropixels recordings, we have also released the training sessions for each mouse as standalone NWB files.\n",
+    "\n",
+    "The following tutorial will demonstrate how to load these sessions for an example mouse. We'll cover the following topics:\n",
+    "\n",
+    "* <a href='#Finding-behavior-sessions-to-analyze'>Finding behavior sessions to analyze</a>\n",
+    "* <a href='#Loading-behavior-data'>Loading behavior data</a>\n",
+    "* <a href='#Understanding-behavior-session-contents'>Understanding the behavior session contents</a>\n",
+    "\n",
+    "\n",
+    "This tutorial assumes you've already created a data cache, or are working with the files on AWS. If you haven't reached that step yet, we recommend going through the [data access tutorial](https://allensdk.readthedocs.io/en/latest/visual_behavior_neuropixels.html) first.\n",
+    "\n",
+    "You can read more about the stages of behavioral training in the [white paper](https://brainmapportal-live-4cc80a57cd6e400d854-f7fdcae.divio-media.net/filer_public/f7/06/f706855a-a3a1-4a3a-a6b0-3502ad64680f/visualbehaviorneuropixels_technicalwhitepaper.pdf) accompanying this release"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3864bc02",
+   "metadata": {},
+   "source": [
+    "## Finding behavior sessions to analyze\n",
+    "\n",
+    "First let's import the `VisualBehaviorNeuropixelsProjectCache` as well as a few other helpful packages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6832c96a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\svc_ccg\\AppData\\Local\\Continuum\\anaconda3\\envs\\allensdk_38_dev\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from allensdk.brain_observatory.behavior.behavior_project_cache.\\\n",
+    "    behavior_neuropixels_project_cache \\\n",
+    "    import VisualBehaviorNeuropixelsProjectCache\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from matplotlib import pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa753025",
+   "metadata": {},
+   "source": [
+    "Now we'll instantiate the cache to begin downloading data from the S3 bucket. Note that the `cache_dir` should point to a local directory where you would like to download the data. If you've already downloaded the required data, the cache will pull the local files from this directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4ea2afb8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cache_dir = \"/path/to/vbn_cache\"\n",
+    "\n",
+    "cache = VisualBehaviorNeuropixelsProjectCache.from_s3_cache(\n",
+    "            cache_dir=cache_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05563abe",
+   "metadata": {},
+   "source": [
+    "Once we have a cache instance, we can grab the `behavior_session_table` to see what behavior sessions are available for analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f1bdd1b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>equipment_name</th>\n",
+       "      <th>genotype</th>\n",
+       "      <th>mouse_id</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age_in_days</th>\n",
+       "      <th>session_number</th>\n",
+       "      <th>prior_exposures_to_session_type</th>\n",
+       "      <th>prior_exposures_to_image_set</th>\n",
+       "      <th>prior_exposures_to_omissions</th>\n",
+       "      <th>ecephys_session_id</th>\n",
+       "      <th>date_of_acquisition</th>\n",
+       "      <th>session_type</th>\n",
+       "      <th>image_set</th>\n",
+       "      <th>file_id</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>behavior_session_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1055198750</th>\n",
+       "      <td>NP.1</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>531237</td>\n",
+       "      <td>F</td>\n",
+       "      <td>158</td>\n",
+       "      <td>45</td>\n",
+       "      <td>5</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.055182e+09</td>\n",
+       "      <td>2020-10-07 09:40:30.407000+00:00</td>\n",
+       "      <td>HABITUATION_5_images_G_handoff_ready_3uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1052162513</th>\n",
+       "      <td>NP.1</td>\n",
+       "      <td>Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
+       "      <td>530862</td>\n",
+       "      <td>M</td>\n",
+       "      <td>143</td>\n",
+       "      <td>41</td>\n",
+       "      <td>2</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.051331e+09</td>\n",
+       "      <td>2020-09-18 10:18:25.503000+00:00</td>\n",
+       "      <td>HABITUATION_5_images_G_handoff_ready_3uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1056100734</th>\n",
+       "      <td>NP.1</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>531237</td>\n",
+       "      <td>F</td>\n",
+       "      <td>163</td>\n",
+       "      <td>48</td>\n",
+       "      <td>8</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.056090e+09</td>\n",
+       "      <td>2020-10-12 12:31:57.133000+00:00</td>\n",
+       "      <td>HABITUATION_5_images_G_handoff_ready_3uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1055397049</th>\n",
+       "      <td>NP.1</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>531237</td>\n",
+       "      <td>F</td>\n",
+       "      <td>159</td>\n",
+       "      <td>46</td>\n",
+       "      <td>6</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.055387e+09</td>\n",
+       "      <td>2020-10-08 10:46:16.623000+00:00</td>\n",
+       "      <td>HABITUATION_5_images_G_handoff_ready_3uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1052162543</th>\n",
+       "      <td>NP.1</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>128</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.052096e+09</td>\n",
+       "      <td>2020-09-22 10:54:33.351000+00:00</td>\n",
+       "      <td>HABITUATION_5_images_G_handoff_ready_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1026</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    equipment_name  \\\n",
+       "behavior_session_id                  \n",
+       "1055198750                    NP.1   \n",
+       "1052162513                    NP.1   \n",
+       "1056100734                    NP.1   \n",
+       "1055397049                    NP.1   \n",
+       "1052162543                    NP.1   \n",
+       "\n",
+       "                                                          genotype  mouse_id  \\\n",
+       "behavior_session_id                                                            \n",
+       "1055198750                                                   wt/wt    531237   \n",
+       "1052162513           Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt    530862   \n",
+       "1056100734                                                   wt/wt    531237   \n",
+       "1055397049                                                   wt/wt    531237   \n",
+       "1052162543                                                   wt/wt    533537   \n",
+       "\n",
+       "                    sex  age_in_days  session_number  \\\n",
+       "behavior_session_id                                    \n",
+       "1055198750            F          158              45   \n",
+       "1052162513            M          143              41   \n",
+       "1056100734            F          163              48   \n",
+       "1055397049            F          159              46   \n",
+       "1052162543            M          128              30   \n",
+       "\n",
+       "                     prior_exposures_to_session_type  \\\n",
+       "behavior_session_id                                    \n",
+       "1055198750                                         5   \n",
+       "1052162513                                         2   \n",
+       "1056100734                                         8   \n",
+       "1055397049                                         6   \n",
+       "1052162543                                         0   \n",
+       "\n",
+       "                     prior_exposures_to_image_set  \\\n",
+       "behavior_session_id                                 \n",
+       "1055198750                                   38.0   \n",
+       "1052162513                                   29.0   \n",
+       "1056100734                                   41.0   \n",
+       "1055397049                                   39.0   \n",
+       "1052162543                                   23.0   \n",
+       "\n",
+       "                     prior_exposures_to_omissions  ecephys_session_id  \\\n",
+       "behavior_session_id                                                     \n",
+       "1055198750                                      0        1.055182e+09   \n",
+       "1052162513                                      0        1.051331e+09   \n",
+       "1056100734                                      0        1.056090e+09   \n",
+       "1055397049                                      0        1.055387e+09   \n",
+       "1052162543                                      0        1.052096e+09   \n",
+       "\n",
+       "                                  date_of_acquisition  \\\n",
+       "behavior_session_id                                     \n",
+       "1055198750           2020-10-07 09:40:30.407000+00:00   \n",
+       "1052162513           2020-09-18 10:18:25.503000+00:00   \n",
+       "1056100734           2020-10-12 12:31:57.133000+00:00   \n",
+       "1055397049           2020-10-08 10:46:16.623000+00:00   \n",
+       "1052162543           2020-09-22 10:54:33.351000+00:00   \n",
+       "\n",
+       "                                                        session_type  \\\n",
+       "behavior_session_id                                                    \n",
+       "1055198750           HABITUATION_5_images_G_handoff_ready_3uL_reward   \n",
+       "1052162513           HABITUATION_5_images_G_handoff_ready_3uL_reward   \n",
+       "1056100734           HABITUATION_5_images_G_handoff_ready_3uL_reward   \n",
+       "1055397049           HABITUATION_5_images_G_handoff_ready_3uL_reward   \n",
+       "1052162543           HABITUATION_5_images_G_handoff_ready_5uL_reward   \n",
+       "\n",
+       "                    image_set  file_id  \n",
+       "behavior_session_id                     \n",
+       "1055198750                  G     1022  \n",
+       "1052162513                  G     1023  \n",
+       "1056100734                  G     1024  \n",
+       "1055397049                  G     1025  \n",
+       "1052162543                  G     1026  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "behavior_session_table = cache.get_behavior_session_table()\n",
+    "behavior_session_table.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfcf5c81",
+   "metadata": {},
+   "source": [
+    "Now let's grab all the sessions for an example mouse (533537) and order them chronologically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "81fd1207",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>equipment_name</th>\n",
+       "      <th>genotype</th>\n",
+       "      <th>mouse_id</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age_in_days</th>\n",
+       "      <th>session_number</th>\n",
+       "      <th>prior_exposures_to_session_type</th>\n",
+       "      <th>prior_exposures_to_image_set</th>\n",
+       "      <th>prior_exposures_to_omissions</th>\n",
+       "      <th>ecephys_session_id</th>\n",
+       "      <th>date_of_acquisition</th>\n",
+       "      <th>session_type</th>\n",
+       "      <th>image_set</th>\n",
+       "      <th>file_id</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>behavior_session_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1041490591</th>\n",
+       "      <td>BEH.G-Box2</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-08-07 12:50:42.917000+00:00</td>\n",
+       "      <td>TRAINING_0_gratings_autorewards_15min_0uL_reward</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1042048852</th>\n",
+       "      <td>BEH.G-Box6</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>85</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-08-10 14:15:54.490000+00:00</td>\n",
+       "      <td>TRAINING_1_gratings_10uL_reward</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3238</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1042325912</th>\n",
+       "      <td>BEH.G-Box1</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>86</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-08-11 13:29:45.772000+00:00</td>\n",
+       "      <td>TRAINING_1_gratings_10uL_reward</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1042593405</th>\n",
+       "      <td>BEH.G-Box2</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>87</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-08-12 14:59:52.796000+00:00</td>\n",
+       "      <td>TRAINING_1_gratings_10uL_reward</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1042812152</th>\n",
+       "      <td>BEH.G-Box6</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>88</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-08-13 12:29:17.268000+00:00</td>\n",
+       "      <td>TRAINING_2_gratings_flashed_10uL_reward</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1043131651</th>\n",
+       "      <td>BEH.G-Box6</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>89</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-08-14 13:08:22.369000+00:00</td>\n",
+       "      <td>TRAINING_2_gratings_flashed_10uL_reward</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1043758309</th>\n",
+       "      <td>BEH.G-Box6</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>92</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-08-17 13:34:50.885000+00:00</td>\n",
+       "      <td>TRAINING_3_images_G_10uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1044600724</th>\n",
+       "      <td>BEH.G-Box6</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>95</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-08-20 12:57:08.496000+00:00</td>\n",
+       "      <td>TRAINING_3_images_G_10uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>3240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1044996732</th>\n",
+       "      <td>BEH.G-Box6</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>96</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-08-21 12:52:08.583000+00:00</td>\n",
+       "      <td>TRAINING_3_images_G_10uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>3237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1045550754</th>\n",
+       "      <td>BEH.G-Box4</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>99</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-08-24 13:46:04.209000+00:00</td>\n",
+       "      <td>TRAINING_4_images_G_training_7uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>3180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1045904194</th>\n",
+       "      <td>BEH.G-Box4</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>100</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-08-25 13:54:59.520000+00:00</td>\n",
+       "      <td>TRAINING_5_images_G_epilogue_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>3182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1046247654</th>\n",
+       "      <td>BEH.G-Box4</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>101</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-08-26 14:09:48.255000+00:00</td>\n",
+       "      <td>TRAINING_5_images_G_epilogue_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>3178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1046638304</th>\n",
+       "      <td>BEH.G-Box3</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>102</td>\n",
+       "      <td>13</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-08-27 15:11:14.334000+00:00</td>\n",
+       "      <td>TRAINING_5_images_G_epilogue_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1861</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1046925699</th>\n",
+       "      <td>BEH.G-Box3</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>103</td>\n",
+       "      <td>14</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-08-28 14:03:54.270000+00:00</td>\n",
+       "      <td>TRAINING_5_images_G_epilogue_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>3174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1047535739</th>\n",
+       "      <td>BEH.G-Box3</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>106</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-08-31 13:30:04.082000+00:00</td>\n",
+       "      <td>TRAINING_5_images_G_epilogue_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1047772100</th>\n",
+       "      <td>BEH.G-Box3</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>107</td>\n",
+       "      <td>16</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-09-01 14:08:24.307000+00:00</td>\n",
+       "      <td>TRAINING_5_images_G_epilogue_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1048001337</th>\n",
+       "      <td>BEH.G-Box3</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>108</td>\n",
+       "      <td>17</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-09-02 14:10:04.936000+00:00</td>\n",
+       "      <td>TRAINING_5_images_G_epilogue_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1048222926</th>\n",
+       "      <td>BEH.G-Box3</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>109</td>\n",
+       "      <td>18</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-09-03 14:17:25.770000+00:00</td>\n",
+       "      <td>TRAINING_5_images_G_epilogue_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2842</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1048430639</th>\n",
+       "      <td>BEH.G-Box3</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>110</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-09-04 14:39:44.830000+00:00</td>\n",
+       "      <td>TRAINING_5_images_G_handoff_ready_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2427</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1049116555</th>\n",
+       "      <td>BEH.G-Box3</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>114</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-09-08 15:14:50.618000+00:00</td>\n",
+       "      <td>TRAINING_5_images_G_handoff_ready_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>3175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1049302025</th>\n",
+       "      <td>BEH.G-Box3</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>115</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-09-09 15:12:37.224000+00:00</td>\n",
+       "      <td>TRAINING_5_images_G_handoff_ready_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1049536543</th>\n",
+       "      <td>BEH.G-Box3</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>116</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-09-10 14:27:28.767000+00:00</td>\n",
+       "      <td>TRAINING_5_images_G_handoff_lapsed_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>3253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1049778048</th>\n",
+       "      <td>BEH.G-Box3</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>117</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-09-11 13:53:13.805000+00:00</td>\n",
+       "      <td>TRAINING_5_images_G_handoff_lapsed_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>3265</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1050311775</th>\n",
+       "      <td>BEH.G-Box3</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>120</td>\n",
+       "      <td>24</td>\n",
+       "      <td>2</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-09-14 14:20:13.789000+00:00</td>\n",
+       "      <td>TRAINING_5_images_G_handoff_lapsed_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2283</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1050668465</th>\n",
+       "      <td>BEH.G-Box3</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>121</td>\n",
+       "      <td>25</td>\n",
+       "      <td>3</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-09-15 14:16:26.923000+00:00</td>\n",
+       "      <td>TRAINING_5_images_G_handoff_ready_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1050968995</th>\n",
+       "      <td>BEH.G-Box3</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>122</td>\n",
+       "      <td>26</td>\n",
+       "      <td>4</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-09-16 13:44:44.321000+00:00</td>\n",
+       "      <td>TRAINING_5_images_G_handoff_ready_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>3283</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1051180953</th>\n",
+       "      <td>BEH.G-Box3</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>123</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-09-17 14:34:43.127000+00:00</td>\n",
+       "      <td>TRAINING_5_images_G_handoff_ready_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>3291</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1051380286</th>\n",
+       "      <td>BEH.G-Box3</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>124</td>\n",
+       "      <td>28</td>\n",
+       "      <td>6</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-09-18 13:55:37.816000+00:00</td>\n",
+       "      <td>TRAINING_5_images_G_handoff_ready_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2544</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1051943283</th>\n",
+       "      <td>BEH.G-Box3</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>127</td>\n",
+       "      <td>29</td>\n",
+       "      <td>7</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-09-21 13:54:49.139000+00:00</td>\n",
+       "      <td>TRAINING_5_images_G_handoff_ready_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2508</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1052162543</th>\n",
+       "      <td>NP.1</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>128</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.052096e+09</td>\n",
+       "      <td>2020-09-22 10:54:33.351000+00:00</td>\n",
+       "      <td>HABITUATION_5_images_G_handoff_ready_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1052316794</th>\n",
+       "      <td>NP.1</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>129</td>\n",
+       "      <td>31</td>\n",
+       "      <td>1</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-09-23 10:44:14.554000+00:00</td>\n",
+       "      <td>HABITUATION_5_images_G_handoff_ready_5uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1052547131</th>\n",
+       "      <td>NP.1</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>130</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.052509e+09</td>\n",
+       "      <td>2020-09-24 11:23:42.644000+00:00</td>\n",
+       "      <td>HABITUATION_5_images_G_handoff_ready_3uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1034</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1052772626</th>\n",
+       "      <td>NP.1</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>131</td>\n",
+       "      <td>33</td>\n",
+       "      <td>1</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.052713e+09</td>\n",
+       "      <td>2020-09-25 11:11:55.135000+00:00</td>\n",
+       "      <td>HABITUATION_5_images_G_handoff_ready_3uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1053320526</th>\n",
+       "      <td>NP.1</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>134</td>\n",
+       "      <td>34</td>\n",
+       "      <td>2</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.053288e+09</td>\n",
+       "      <td>2020-09-28 11:39:25.202000+00:00</td>\n",
+       "      <td>HABITUATION_5_images_G_handoff_ready_3uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1053566897</th>\n",
+       "      <td>NP.1</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>135</td>\n",
+       "      <td>35</td>\n",
+       "      <td>3</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.053491e+09</td>\n",
+       "      <td>2020-09-29 11:15:07.235000+00:00</td>\n",
+       "      <td>HABITUATION_5_images_G_handoff_ready_3uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1053736353</th>\n",
+       "      <td>NP.1</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>136</td>\n",
+       "      <td>36</td>\n",
+       "      <td>4</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.053701e+09</td>\n",
+       "      <td>2020-09-30 11:09:16.230000+00:00</td>\n",
+       "      <td>HABITUATION_5_images_G_handoff_ready_3uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1053932834</th>\n",
+       "      <td>NP.1</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>137</td>\n",
+       "      <td>37</td>\n",
+       "      <td>5</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.053891e+09</td>\n",
+       "      <td>2020-10-01 11:25:06.431000+00:00</td>\n",
+       "      <td>HABITUATION_5_images_G_handoff_ready_3uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1054166851</th>\n",
+       "      <td>NP.1</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>138</td>\n",
+       "      <td>38</td>\n",
+       "      <td>6</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.054140e+09</td>\n",
+       "      <td>2020-10-02 11:31:34.080000+00:00</td>\n",
+       "      <td>HABITUATION_5_images_G_handoff_ready_3uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1054753938</th>\n",
+       "      <td>NP.1</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>141</td>\n",
+       "      <td>39</td>\n",
+       "      <td>7</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.054725e+09</td>\n",
+       "      <td>2020-10-05 12:51:41.150000+00:00</td>\n",
+       "      <td>HABITUATION_5_images_G_handoff_ready_3uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1056</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1055021129</th>\n",
+       "      <td>NP.1</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>142</td>\n",
+       "      <td>40</td>\n",
+       "      <td>8</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.054993e+09</td>\n",
+       "      <td>2020-10-06 13:14:17.981000+00:00</td>\n",
+       "      <td>HABITUATION_5_images_G_handoff_ready_3uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1079</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1055253879</th>\n",
+       "      <td>NP.1</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>143</td>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.055222e+09</td>\n",
+       "      <td>2020-10-07 14:01:41.373000+00:00</td>\n",
+       "      <td>EPHYS_1_images_G_3uL_reward</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1055431030</th>\n",
+       "      <td>NP.1</td>\n",
+       "      <td>wt/wt</td>\n",
+       "      <td>533537</td>\n",
+       "      <td>M</td>\n",
+       "      <td>144</td>\n",
+       "      <td>42</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.055404e+09</td>\n",
+       "      <td>2020-10-08 15:12:55.061000+00:00</td>\n",
+       "      <td>EPHYS_1_images_H_3uL_reward</td>\n",
+       "      <td>H</td>\n",
+       "      <td>-999</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    equipment_name genotype  mouse_id sex  age_in_days  \\\n",
+       "behavior_session_id                                                      \n",
+       "1041490591              BEH.G-Box2    wt/wt    533537   M           82   \n",
+       "1042048852              BEH.G-Box6    wt/wt    533537   M           85   \n",
+       "1042325912              BEH.G-Box1    wt/wt    533537   M           86   \n",
+       "1042593405              BEH.G-Box2    wt/wt    533537   M           87   \n",
+       "1042812152              BEH.G-Box6    wt/wt    533537   M           88   \n",
+       "1043131651              BEH.G-Box6    wt/wt    533537   M           89   \n",
+       "1043758309              BEH.G-Box6    wt/wt    533537   M           92   \n",
+       "1044600724              BEH.G-Box6    wt/wt    533537   M           95   \n",
+       "1044996732              BEH.G-Box6    wt/wt    533537   M           96   \n",
+       "1045550754              BEH.G-Box4    wt/wt    533537   M           99   \n",
+       "1045904194              BEH.G-Box4    wt/wt    533537   M          100   \n",
+       "1046247654              BEH.G-Box4    wt/wt    533537   M          101   \n",
+       "1046638304              BEH.G-Box3    wt/wt    533537   M          102   \n",
+       "1046925699              BEH.G-Box3    wt/wt    533537   M          103   \n",
+       "1047535739              BEH.G-Box3    wt/wt    533537   M          106   \n",
+       "1047772100              BEH.G-Box3    wt/wt    533537   M          107   \n",
+       "1048001337              BEH.G-Box3    wt/wt    533537   M          108   \n",
+       "1048222926              BEH.G-Box3    wt/wt    533537   M          109   \n",
+       "1048430639              BEH.G-Box3    wt/wt    533537   M          110   \n",
+       "1049116555              BEH.G-Box3    wt/wt    533537   M          114   \n",
+       "1049302025              BEH.G-Box3    wt/wt    533537   M          115   \n",
+       "1049536543              BEH.G-Box3    wt/wt    533537   M          116   \n",
+       "1049778048              BEH.G-Box3    wt/wt    533537   M          117   \n",
+       "1050311775              BEH.G-Box3    wt/wt    533537   M          120   \n",
+       "1050668465              BEH.G-Box3    wt/wt    533537   M          121   \n",
+       "1050968995              BEH.G-Box3    wt/wt    533537   M          122   \n",
+       "1051180953              BEH.G-Box3    wt/wt    533537   M          123   \n",
+       "1051380286              BEH.G-Box3    wt/wt    533537   M          124   \n",
+       "1051943283              BEH.G-Box3    wt/wt    533537   M          127   \n",
+       "1052162543                    NP.1    wt/wt    533537   M          128   \n",
+       "1052316794                    NP.1    wt/wt    533537   M          129   \n",
+       "1052547131                    NP.1    wt/wt    533537   M          130   \n",
+       "1052772626                    NP.1    wt/wt    533537   M          131   \n",
+       "1053320526                    NP.1    wt/wt    533537   M          134   \n",
+       "1053566897                    NP.1    wt/wt    533537   M          135   \n",
+       "1053736353                    NP.1    wt/wt    533537   M          136   \n",
+       "1053932834                    NP.1    wt/wt    533537   M          137   \n",
+       "1054166851                    NP.1    wt/wt    533537   M          138   \n",
+       "1054753938                    NP.1    wt/wt    533537   M          141   \n",
+       "1055021129                    NP.1    wt/wt    533537   M          142   \n",
+       "1055253879                    NP.1    wt/wt    533537   M          143   \n",
+       "1055431030                    NP.1    wt/wt    533537   M          144   \n",
+       "\n",
+       "                     session_number  prior_exposures_to_session_type  \\\n",
+       "behavior_session_id                                                    \n",
+       "1041490591                        1                                0   \n",
+       "1042048852                        2                                0   \n",
+       "1042325912                        3                                1   \n",
+       "1042593405                        4                                2   \n",
+       "1042812152                        5                                0   \n",
+       "1043131651                        6                                1   \n",
+       "1043758309                        7                                0   \n",
+       "1044600724                        8                                1   \n",
+       "1044996732                        9                                2   \n",
+       "1045550754                       10                                0   \n",
+       "1045904194                       11                                0   \n",
+       "1046247654                       12                                1   \n",
+       "1046638304                       13                                2   \n",
+       "1046925699                       14                                3   \n",
+       "1047535739                       15                                4   \n",
+       "1047772100                       16                                5   \n",
+       "1048001337                       17                                6   \n",
+       "1048222926                       18                                7   \n",
+       "1048430639                       19                                0   \n",
+       "1049116555                       20                                1   \n",
+       "1049302025                       21                                2   \n",
+       "1049536543                       22                                0   \n",
+       "1049778048                       23                                1   \n",
+       "1050311775                       24                                2   \n",
+       "1050668465                       25                                3   \n",
+       "1050968995                       26                                4   \n",
+       "1051180953                       27                                5   \n",
+       "1051380286                       28                                6   \n",
+       "1051943283                       29                                7   \n",
+       "1052162543                       30                                0   \n",
+       "1052316794                       31                                1   \n",
+       "1052547131                       32                                0   \n",
+       "1052772626                       33                                1   \n",
+       "1053320526                       34                                2   \n",
+       "1053566897                       35                                3   \n",
+       "1053736353                       36                                4   \n",
+       "1053932834                       37                                5   \n",
+       "1054166851                       38                                6   \n",
+       "1054753938                       39                                7   \n",
+       "1055021129                       40                                8   \n",
+       "1055253879                       41                                0   \n",
+       "1055431030                       42                                0   \n",
+       "\n",
+       "                     prior_exposures_to_image_set  \\\n",
+       "behavior_session_id                                 \n",
+       "1041490591                                    NaN   \n",
+       "1042048852                                    NaN   \n",
+       "1042325912                                    NaN   \n",
+       "1042593405                                    NaN   \n",
+       "1042812152                                    NaN   \n",
+       "1043131651                                    NaN   \n",
+       "1043758309                                    0.0   \n",
+       "1044600724                                    1.0   \n",
+       "1044996732                                    2.0   \n",
+       "1045550754                                    3.0   \n",
+       "1045904194                                    4.0   \n",
+       "1046247654                                    5.0   \n",
+       "1046638304                                    6.0   \n",
+       "1046925699                                    7.0   \n",
+       "1047535739                                    8.0   \n",
+       "1047772100                                    9.0   \n",
+       "1048001337                                   10.0   \n",
+       "1048222926                                   11.0   \n",
+       "1048430639                                   12.0   \n",
+       "1049116555                                   13.0   \n",
+       "1049302025                                   14.0   \n",
+       "1049536543                                   15.0   \n",
+       "1049778048                                   16.0   \n",
+       "1050311775                                   17.0   \n",
+       "1050668465                                   18.0   \n",
+       "1050968995                                   19.0   \n",
+       "1051180953                                   20.0   \n",
+       "1051380286                                   21.0   \n",
+       "1051943283                                   22.0   \n",
+       "1052162543                                   23.0   \n",
+       "1052316794                                   24.0   \n",
+       "1052547131                                   25.0   \n",
+       "1052772626                                   26.0   \n",
+       "1053320526                                   27.0   \n",
+       "1053566897                                   28.0   \n",
+       "1053736353                                   29.0   \n",
+       "1053932834                                   30.0   \n",
+       "1054166851                                   31.0   \n",
+       "1054753938                                   32.0   \n",
+       "1055021129                                   33.0   \n",
+       "1055253879                                   34.0   \n",
+       "1055431030                                    0.0   \n",
+       "\n",
+       "                     prior_exposures_to_omissions  ecephys_session_id  \\\n",
+       "behavior_session_id                                                     \n",
+       "1041490591                                      0                 NaN   \n",
+       "1042048852                                      0                 NaN   \n",
+       "1042325912                                      0                 NaN   \n",
+       "1042593405                                      0                 NaN   \n",
+       "1042812152                                      0                 NaN   \n",
+       "1043131651                                      0                 NaN   \n",
+       "1043758309                                      0                 NaN   \n",
+       "1044600724                                      0                 NaN   \n",
+       "1044996732                                      0                 NaN   \n",
+       "1045550754                                      0                 NaN   \n",
+       "1045904194                                      0                 NaN   \n",
+       "1046247654                                      0                 NaN   \n",
+       "1046638304                                      0                 NaN   \n",
+       "1046925699                                      0                 NaN   \n",
+       "1047535739                                      0                 NaN   \n",
+       "1047772100                                      0                 NaN   \n",
+       "1048001337                                      0                 NaN   \n",
+       "1048222926                                      0                 NaN   \n",
+       "1048430639                                      0                 NaN   \n",
+       "1049116555                                      0                 NaN   \n",
+       "1049302025                                      0                 NaN   \n",
+       "1049536543                                      0                 NaN   \n",
+       "1049778048                                      0                 NaN   \n",
+       "1050311775                                      0                 NaN   \n",
+       "1050668465                                      0                 NaN   \n",
+       "1050968995                                      0                 NaN   \n",
+       "1051180953                                      0                 NaN   \n",
+       "1051380286                                      0                 NaN   \n",
+       "1051943283                                      0                 NaN   \n",
+       "1052162543                                      0        1.052096e+09   \n",
+       "1052316794                                      0                 NaN   \n",
+       "1052547131                                      0        1.052509e+09   \n",
+       "1052772626                                      0        1.052713e+09   \n",
+       "1053320526                                      0        1.053288e+09   \n",
+       "1053566897                                      0        1.053491e+09   \n",
+       "1053736353                                      0        1.053701e+09   \n",
+       "1053932834                                      0        1.053891e+09   \n",
+       "1054166851                                      0        1.054140e+09   \n",
+       "1054753938                                      0        1.054725e+09   \n",
+       "1055021129                                      0        1.054993e+09   \n",
+       "1055253879                                      0        1.055222e+09   \n",
+       "1055431030                                      1        1.055404e+09   \n",
+       "\n",
+       "                                  date_of_acquisition  \\\n",
+       "behavior_session_id                                     \n",
+       "1041490591           2020-08-07 12:50:42.917000+00:00   \n",
+       "1042048852           2020-08-10 14:15:54.490000+00:00   \n",
+       "1042325912           2020-08-11 13:29:45.772000+00:00   \n",
+       "1042593405           2020-08-12 14:59:52.796000+00:00   \n",
+       "1042812152           2020-08-13 12:29:17.268000+00:00   \n",
+       "1043131651           2020-08-14 13:08:22.369000+00:00   \n",
+       "1043758309           2020-08-17 13:34:50.885000+00:00   \n",
+       "1044600724           2020-08-20 12:57:08.496000+00:00   \n",
+       "1044996732           2020-08-21 12:52:08.583000+00:00   \n",
+       "1045550754           2020-08-24 13:46:04.209000+00:00   \n",
+       "1045904194           2020-08-25 13:54:59.520000+00:00   \n",
+       "1046247654           2020-08-26 14:09:48.255000+00:00   \n",
+       "1046638304           2020-08-27 15:11:14.334000+00:00   \n",
+       "1046925699           2020-08-28 14:03:54.270000+00:00   \n",
+       "1047535739           2020-08-31 13:30:04.082000+00:00   \n",
+       "1047772100           2020-09-01 14:08:24.307000+00:00   \n",
+       "1048001337           2020-09-02 14:10:04.936000+00:00   \n",
+       "1048222926           2020-09-03 14:17:25.770000+00:00   \n",
+       "1048430639           2020-09-04 14:39:44.830000+00:00   \n",
+       "1049116555           2020-09-08 15:14:50.618000+00:00   \n",
+       "1049302025           2020-09-09 15:12:37.224000+00:00   \n",
+       "1049536543           2020-09-10 14:27:28.767000+00:00   \n",
+       "1049778048           2020-09-11 13:53:13.805000+00:00   \n",
+       "1050311775           2020-09-14 14:20:13.789000+00:00   \n",
+       "1050668465           2020-09-15 14:16:26.923000+00:00   \n",
+       "1050968995           2020-09-16 13:44:44.321000+00:00   \n",
+       "1051180953           2020-09-17 14:34:43.127000+00:00   \n",
+       "1051380286           2020-09-18 13:55:37.816000+00:00   \n",
+       "1051943283           2020-09-21 13:54:49.139000+00:00   \n",
+       "1052162543           2020-09-22 10:54:33.351000+00:00   \n",
+       "1052316794           2020-09-23 10:44:14.554000+00:00   \n",
+       "1052547131           2020-09-24 11:23:42.644000+00:00   \n",
+       "1052772626           2020-09-25 11:11:55.135000+00:00   \n",
+       "1053320526           2020-09-28 11:39:25.202000+00:00   \n",
+       "1053566897           2020-09-29 11:15:07.235000+00:00   \n",
+       "1053736353           2020-09-30 11:09:16.230000+00:00   \n",
+       "1053932834           2020-10-01 11:25:06.431000+00:00   \n",
+       "1054166851           2020-10-02 11:31:34.080000+00:00   \n",
+       "1054753938           2020-10-05 12:51:41.150000+00:00   \n",
+       "1055021129           2020-10-06 13:14:17.981000+00:00   \n",
+       "1055253879           2020-10-07 14:01:41.373000+00:00   \n",
+       "1055431030           2020-10-08 15:12:55.061000+00:00   \n",
+       "\n",
+       "                                                         session_type  \\\n",
+       "behavior_session_id                                                     \n",
+       "1041490591           TRAINING_0_gratings_autorewards_15min_0uL_reward   \n",
+       "1042048852                            TRAINING_1_gratings_10uL_reward   \n",
+       "1042325912                            TRAINING_1_gratings_10uL_reward   \n",
+       "1042593405                            TRAINING_1_gratings_10uL_reward   \n",
+       "1042812152                    TRAINING_2_gratings_flashed_10uL_reward   \n",
+       "1043131651                    TRAINING_2_gratings_flashed_10uL_reward   \n",
+       "1043758309                            TRAINING_3_images_G_10uL_reward   \n",
+       "1044600724                            TRAINING_3_images_G_10uL_reward   \n",
+       "1044996732                            TRAINING_3_images_G_10uL_reward   \n",
+       "1045550754                    TRAINING_4_images_G_training_7uL_reward   \n",
+       "1045904194                    TRAINING_5_images_G_epilogue_5uL_reward   \n",
+       "1046247654                    TRAINING_5_images_G_epilogue_5uL_reward   \n",
+       "1046638304                    TRAINING_5_images_G_epilogue_5uL_reward   \n",
+       "1046925699                    TRAINING_5_images_G_epilogue_5uL_reward   \n",
+       "1047535739                    TRAINING_5_images_G_epilogue_5uL_reward   \n",
+       "1047772100                    TRAINING_5_images_G_epilogue_5uL_reward   \n",
+       "1048001337                    TRAINING_5_images_G_epilogue_5uL_reward   \n",
+       "1048222926                    TRAINING_5_images_G_epilogue_5uL_reward   \n",
+       "1048430639               TRAINING_5_images_G_handoff_ready_5uL_reward   \n",
+       "1049116555               TRAINING_5_images_G_handoff_ready_5uL_reward   \n",
+       "1049302025               TRAINING_5_images_G_handoff_ready_5uL_reward   \n",
+       "1049536543              TRAINING_5_images_G_handoff_lapsed_5uL_reward   \n",
+       "1049778048              TRAINING_5_images_G_handoff_lapsed_5uL_reward   \n",
+       "1050311775              TRAINING_5_images_G_handoff_lapsed_5uL_reward   \n",
+       "1050668465               TRAINING_5_images_G_handoff_ready_5uL_reward   \n",
+       "1050968995               TRAINING_5_images_G_handoff_ready_5uL_reward   \n",
+       "1051180953               TRAINING_5_images_G_handoff_ready_5uL_reward   \n",
+       "1051380286               TRAINING_5_images_G_handoff_ready_5uL_reward   \n",
+       "1051943283               TRAINING_5_images_G_handoff_ready_5uL_reward   \n",
+       "1052162543            HABITUATION_5_images_G_handoff_ready_5uL_reward   \n",
+       "1052316794            HABITUATION_5_images_G_handoff_ready_5uL_reward   \n",
+       "1052547131            HABITUATION_5_images_G_handoff_ready_3uL_reward   \n",
+       "1052772626            HABITUATION_5_images_G_handoff_ready_3uL_reward   \n",
+       "1053320526            HABITUATION_5_images_G_handoff_ready_3uL_reward   \n",
+       "1053566897            HABITUATION_5_images_G_handoff_ready_3uL_reward   \n",
+       "1053736353            HABITUATION_5_images_G_handoff_ready_3uL_reward   \n",
+       "1053932834            HABITUATION_5_images_G_handoff_ready_3uL_reward   \n",
+       "1054166851            HABITUATION_5_images_G_handoff_ready_3uL_reward   \n",
+       "1054753938            HABITUATION_5_images_G_handoff_ready_3uL_reward   \n",
+       "1055021129            HABITUATION_5_images_G_handoff_ready_3uL_reward   \n",
+       "1055253879                                EPHYS_1_images_G_3uL_reward   \n",
+       "1055431030                                EPHYS_1_images_H_3uL_reward   \n",
+       "\n",
+       "                    image_set  file_id  \n",
+       "behavior_session_id                     \n",
+       "1041490591                NaN     2232  \n",
+       "1042048852                NaN     3238  \n",
+       "1042325912                NaN     1909  \n",
+       "1042593405                NaN     3168  \n",
+       "1042812152                NaN     3233  \n",
+       "1043131651                NaN     2062  \n",
+       "1043758309                  G     2029  \n",
+       "1044600724                  G     3240  \n",
+       "1044996732                  G     3237  \n",
+       "1045550754                  G     3180  \n",
+       "1045904194                  G     3182  \n",
+       "1046247654                  G     3178  \n",
+       "1046638304                  G     1861  \n",
+       "1046925699                  G     3174  \n",
+       "1047535739                  G     2429  \n",
+       "1047772100                  G     1760  \n",
+       "1048001337                  G     2430  \n",
+       "1048222926                  G     2842  \n",
+       "1048430639                  G     2427  \n",
+       "1049116555                  G     3175  \n",
+       "1049302025                  G     2846  \n",
+       "1049536543                  G     3253  \n",
+       "1049778048                  G     3265  \n",
+       "1050311775                  G     2283  \n",
+       "1050668465                  G     1937  \n",
+       "1050968995                  G     3283  \n",
+       "1051180953                  G     3291  \n",
+       "1051380286                  G     2544  \n",
+       "1051943283                  G     2508  \n",
+       "1052162543                  G     1026  \n",
+       "1052316794                  G     2483  \n",
+       "1052547131                  G     1034  \n",
+       "1052772626                  G     1036  \n",
+       "1053320526                  G     1052  \n",
+       "1053566897                  G     1062  \n",
+       "1053736353                  G     1082  \n",
+       "1053932834                  G     1077  \n",
+       "1054166851                  G     1033  \n",
+       "1054753938                  G     1056  \n",
+       "1055021129                  G     1079  \n",
+       "1055253879                  G     -999  \n",
+       "1055431030                  H     -999  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mouse_id = 533537\n",
+    "mouse_session_table = behavior_session_table[behavior_session_table['mouse_id']==mouse_id].sort_values(by='date_of_acquisition')\n",
+    "mouse_session_table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "61beb666",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(mouse_session_table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b5be0de",
+   "metadata": {},
+   "source": [
+    "Looks like this mouse had 42 behavior sessions. You can see that these start with the earliest training stage (TRAINING_0), when the mouse is first introduced to the lick spout. After that, the mouse progresses through the training stages and is handed off to the Neuropixels team for habituation on the experimental rig on 2020-09-22. The final two sessions in this list are recording sessions (EPHYS_1) during which Neuropixels data was collected while the mouse performed the behavior. If we wanted to analyze the ephys data from these sessions, we would use the `ecephys_session_id` from this table to load the corresponding NWB file with `cache.get_ecephys_session`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e8d06d6",
+   "metadata": {},
+   "source": [
+    "## Loading behavior data\n",
+    "\n",
+    "Now that we've identified a few behavior sessions of interest, let's download one for further analysis using the `behavior_session_id` from the table above and the `get_behavior_session` method of the cache. If you haven't already downloaded this data, the following line will download it from the S3 bucket to your local cache directory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ab5da6a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "behavior_session = cache.get_behavior_session(1052316794)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e46eda9",
+   "metadata": {},
+   "source": [
+    "We can view all of the attributes of this new object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d20d687d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['behavior_session_id',\n",
+       " 'eye_tracking',\n",
+       " 'eye_tracking_rig_geometry',\n",
+       " 'get_performance_metrics',\n",
+       " 'get_reward_rate',\n",
+       " 'get_rolling_performance_df',\n",
+       " 'licks',\n",
+       " 'metadata',\n",
+       " 'raw_running_speed',\n",
+       " 'rewards',\n",
+       " 'running_speed',\n",
+       " 'stimulus_presentations',\n",
+       " 'stimulus_templates',\n",
+       " 'stimulus_timestamps',\n",
+       " 'task_parameters',\n",
+       " 'trials']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "behavior_session.list_data_attributes_and_methods()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f7a6887",
+   "metadata": {
+    "papermill": {
+     "duration": 0.160133,
+     "end_time": "2022-12-14T00:21:04.025289",
+     "exception": false,
+     "start_time": "2022-12-14T00:21:03.865156",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Note that any attribute can be followed by a `?` in a Jupyter Notebook to see the docstring. For example, running the cell below will make a frame appear at the bottom of your browser with the docstring for the `running_speed` attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "17525cab",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-12-14T00:21:04.293581Z",
+     "iopub.status.busy": "2022-12-14T00:21:04.293206Z",
+     "iopub.status.idle": "2022-12-14T00:21:04.300265Z",
+     "shell.execute_reply": "2022-12-14T00:21:04.299642Z"
+    },
+    "papermill": {
+     "duration": 0.143286,
+     "end_time": "2022-12-14T00:21:04.301737",
+     "exception": false,
+     "start_time": "2022-12-14T00:21:04.158451",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "behavior_session.running_speed?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b9d7a9f",
+   "metadata": {},
+   "source": [
+    "We can also look at some basic task parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5e4175dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'auto_reward_volume': 0.005,\n",
+       " 'blank_duration_sec': [0.5, 0.5],\n",
+       " 'n_stimulus_frames': 72054,\n",
+       " 'omitted_flash_fraction': nan,\n",
+       " 'response_window_sec': [0.15, 0.75],\n",
+       " 'reward_volume': 0.005,\n",
+       " 'session_type': 'HABITUATION_5_images_G_handoff_ready_5uL_reward',\n",
+       " 'stimulus': 'images',\n",
+       " 'stimulus_distribution': 'geometric',\n",
+       " 'stimulus_duration_sec': 0.25,\n",
+       " 'task': 'change detection'}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "behavior_session.task_parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c43a035d",
+   "metadata": {},
+   "source": [
+    "## Understanding behavior session contents\n",
+    "\n",
+    "Now that we've grabbed a session, let's take a look at a few of its attributes in more detail:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "820d6b7e",
+   "metadata": {
+    "papermill": {
+     "duration": 0.130613,
+     "end_time": "2022-12-14T00:21:05.694894",
+     "exception": false,
+     "start_time": "2022-12-14T00:21:05.564281",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Stimuli\n",
+    "A pandas dataframe with one row for every distinct stimulus describing what was shown and when it was presented to the mouse."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a0e09d3d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-12-14T00:21:05.960342Z",
+     "iopub.status.busy": "2022-12-14T00:21:05.959628Z",
+     "iopub.status.idle": "2022-12-14T00:21:05.978717Z",
+     "shell.execute_reply": "2022-12-14T00:21:05.978100Z"
+    },
+    "papermill": {
+     "duration": 0.153847,
+     "end_time": "2022-12-14T00:21:05.980286",
+     "exception": false,
+     "start_time": "2022-12-14T00:21:05.826439",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>duration</th>\n",
+       "      <th>end_frame</th>\n",
+       "      <th>flashes_since_change</th>\n",
+       "      <th>image_index</th>\n",
+       "      <th>image_name</th>\n",
+       "      <th>is_change</th>\n",
+       "      <th>is_image_novel</th>\n",
+       "      <th>omitted</th>\n",
+       "      <th>start_frame</th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stimulus_block</th>\n",
+       "      <th>stimulus_name</th>\n",
+       "      <th>end_time</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>stimulus_presentations_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.250247</td>\n",
+       "      <td>75</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>im036_r</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1.021993</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Natural_Images_Lum_Matched_set_ophys_G_2019</td>\n",
+       "      <td>1.272240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.250204</td>\n",
+       "      <td>120</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>im036_r</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>105</td>\n",
+       "      <td>1.772656</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Natural_Images_Lum_Matched_set_ophys_G_2019</td>\n",
+       "      <td>2.022860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.250225</td>\n",
+       "      <td>165</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>im036_r</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>150</td>\n",
+       "      <td>2.523272</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Natural_Images_Lum_Matched_set_ophys_G_2019</td>\n",
+       "      <td>2.773497</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.250249</td>\n",
+       "      <td>210</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>im036_r</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>195</td>\n",
+       "      <td>3.273877</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Natural_Images_Lum_Matched_set_ophys_G_2019</td>\n",
+       "      <td>3.524126</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.250260</td>\n",
+       "      <td>255</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>im036_r</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.024551</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Natural_Images_Lum_Matched_set_ophys_G_2019</td>\n",
+       "      <td>4.274811</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           duration  end_frame  flashes_since_change  \\\n",
+       "stimulus_presentations_id                                              \n",
+       "0                          0.250247         75                   0.0   \n",
+       "1                          0.250204        120                   1.0   \n",
+       "2                          0.250225        165                   2.0   \n",
+       "3                          0.250249        210                   3.0   \n",
+       "4                          0.250260        255                   4.0   \n",
+       "\n",
+       "                           image_index image_name  is_change  is_image_novel  \\\n",
+       "stimulus_presentations_id                                                      \n",
+       "0                                    0    im036_r      False           False   \n",
+       "1                                    0    im036_r      False           False   \n",
+       "2                                    0    im036_r      False           False   \n",
+       "3                                    0    im036_r      False           False   \n",
+       "4                                    0    im036_r      False           False   \n",
+       "\n",
+       "                           omitted  start_frame  start_time  stimulus_block  \\\n",
+       "stimulus_presentations_id                                                     \n",
+       "0                            False           60    1.021993               0   \n",
+       "1                            False          105    1.772656               0   \n",
+       "2                            False          150    2.523272               0   \n",
+       "3                            False          195    3.273877               0   \n",
+       "4                            False          240    4.024551               0   \n",
+       "\n",
+       "                                                         stimulus_name  \\\n",
+       "stimulus_presentations_id                                                \n",
+       "0                          Natural_Images_Lum_Matched_set_ophys_G_2019   \n",
+       "1                          Natural_Images_Lum_Matched_set_ophys_G_2019   \n",
+       "2                          Natural_Images_Lum_Matched_set_ophys_G_2019   \n",
+       "3                          Natural_Images_Lum_Matched_set_ophys_G_2019   \n",
+       "4                          Natural_Images_Lum_Matched_set_ophys_G_2019   \n",
+       "\n",
+       "                           end_time  \n",
+       "stimulus_presentations_id            \n",
+       "0                          1.272240  \n",
+       "1                          2.022860  \n",
+       "2                          2.773497  \n",
+       "3                          3.524126  \n",
+       "4                          4.274811  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stimulus_presentations = behavior_session.stimulus_presentations\n",
+    "stimulus_presentations.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3911ef5",
+   "metadata": {},
+   "source": [
+    "Here's a quick explanation for the important columns in this table:\n",
+    "\n",
+    "`stimulus_name`: Indicates the stimulus category for this stimulus presentation. \n",
+    "\n",
+    "`duration`: Duration of stimulus in seconds\n",
+    "\n",
+    "`start_time`: Experiment time when stimulus started. This value is corrected for display lag and therefore indicates when the stimulus actually appeared on the screen.\n",
+    "\n",
+    "`end_time`: Experiment time when stimulus ended, also corrected for display lag.\n",
+    "\n",
+    "`start_frame`: Stimulus frame index when this stimulus started. This can be used to sync this table to the behavior trials table, for which behavioral data is collected every frame.\n",
+    "\n",
+    "`end_frame`: Stimulus frame index when this stimulus ended.\n",
+    "\n",
+    "`flashes_since_change`: Indicates how many flashes of the same image have occurred since the last stimulus change.\n",
+    "\n",
+    "`image_name`: Indicates which natural image was flashed for this stimulus presentation. To see how to visualize this image, check out [this tutorial](https://allensdk.readthedocs.io/en/latest/_static/examples/nb/visual_behavior_neuropixels_data_access.html).\n",
+    "\n",
+    "`is_change`: Indicates whether the image identity changed for this stimulus presentation. When both this value and 'active' are TRUE, the mouse was rewarded for licking within the response window.\n",
+    "\n",
+    "`omitted`: Indicates whether the image presentation was omitted for this flash. Most image flashes had a 5% probability of being omitted (producing a gray screen). Flashes immediately preceding a change or immediately following an omission could not be omitted.\n",
+    "\n",
+    "`is_image_novel`: Indicates whether this image has been shown to the mouse in previous training sessions. If `True`, then this image is novel to the mouse."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbe586f5",
+   "metadata": {
+    "papermill": {
+     "duration": 0.130844,
+     "end_time": "2022-12-14T00:21:06.243924",
+     "exception": false,
+     "start_time": "2022-12-14T00:21:06.113080",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Licks\n",
+    "A pandas dataframe with one row for every detected lick onset time. For behavior-only sessions, licks times were not recorded on an independent DAQ. Instead, each lick is assigned the time of the stimulus frame during which it occurred."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "853bb2ef",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-12-14T00:21:06.561946Z",
+     "iopub.status.busy": "2022-12-14T00:21:06.560951Z",
+     "iopub.status.idle": "2022-12-14T00:21:06.575049Z",
+     "shell.execute_reply": "2022-12-14T00:21:06.574324Z"
+    },
+    "papermill": {
+     "duration": 0.201218,
+     "end_time": "2022-12-14T00:21:06.576698",
+     "exception": false,
+     "start_time": "2022-12-14T00:21:06.375480",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamps</th>\n",
+       "      <th>frame</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1542</th>\n",
+       "      <td>1504.821639</td>\n",
+       "      <td>90207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>50.242960</td>\n",
+       "      <td>3012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2751</th>\n",
+       "      <td>2467.429411</td>\n",
+       "      <td>147914</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>83.754964</td>\n",
+       "      <td>5021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1719</th>\n",
+       "      <td>1615.116088</td>\n",
+       "      <td>96819</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       timestamps   frame\n",
+       "1542  1504.821639   90207\n",
+       "92      50.242960    3012\n",
+       "2751  2467.429411  147914\n",
+       "145     83.754964    5021\n",
+       "1719  1615.116088   96819"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "behavior_session.licks.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51c4fcaa",
+   "metadata": {
+    "papermill": {
+     "duration": 0.134096,
+     "end_time": "2022-12-14T00:21:06.846209",
+     "exception": false,
+     "start_time": "2022-12-14T00:21:06.712113",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Rewards\n",
+    "A pandas dataframe with one row for every reward that was delivered, assigned the time of the corresponding visual stimulus frame. `Autorewarded` is True if the reward was delivered without requiring a preceding lick."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "47edbecd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-12-14T00:21:07.112407Z",
+     "iopub.status.busy": "2022-12-14T00:21:07.111555Z",
+     "iopub.status.idle": "2022-12-14T00:21:07.122718Z",
+     "shell.execute_reply": "2022-12-14T00:21:07.122113Z"
+    },
+    "papermill": {
+     "duration": 0.146195,
+     "end_time": "2022-12-14T00:21:07.124282",
+     "exception": false,
+     "start_time": "2022-12-14T00:21:06.978087",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "      <th></th>\n",
+       "      <th>volume</th>\n",
+       "      <th>timestamps</th>\n",
+       "      <th>auto_rewarded</th>\n",
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+       "  </thead>\n",
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+       "      <td>False</td>\n",
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+       "      <th>2</th>\n",
+       "      <td>0.005</td>\n",
+       "      <td>40.184356</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     volume   timestamps  auto_rewarded\n",
+       "76    0.005  1548.725919          False\n",
+       "103   0.005  2099.330771          False\n",
+       "114   0.005  2276.265681          False\n",
+       "55    0.005  1014.985537          False\n",
+       "2     0.005    40.184356           True"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "behavior_session.rewards.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28da6b4e",
+   "metadata": {
+    "papermill": {
+     "duration": 0.13344,
+     "end_time": "2022-12-14T00:21:07.394238",
+     "exception": false,
+     "start_time": "2022-12-14T00:21:07.260798",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Running data\n",
+    "A pandas dataframe with one row for each read of the analog input line monitoring the encoder voltage, polled at ~60 Hz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d533cd8e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-12-14T00:21:07.663126Z",
+     "iopub.status.busy": "2022-12-14T00:21:07.662383Z",
+     "iopub.status.idle": "2022-12-14T00:21:07.671514Z",
+     "shell.execute_reply": "2022-12-14T00:21:07.670867Z"
+    },
+    "papermill": {
+     "duration": 0.145929,
+     "end_time": "2022-12-14T00:21:07.673203",
+     "exception": false,
+     "start_time": "2022-12-14T00:21:07.527274",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamps</th>\n",
+       "      <th>speed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.064051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.016644</td>\n",
+       "      <td>6.003349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.033363</td>\n",
+       "      <td>11.664898</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.050056</td>\n",
+       "      <td>16.788652</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.066662</td>\n",
+       "      <td>21.144286</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   timestamps      speed\n",
+       "0    0.000000   0.064051\n",
+       "1    0.016644   6.003349\n",
+       "2    0.033363  11.664898\n",
+       "3    0.050056  16.788652\n",
+       "4    0.066662  21.144286"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "behavior_session.running_speed.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99fbd519",
+   "metadata": {
+    "papermill": {
+     "duration": 0.13377,
+     "end_time": "2022-12-14T00:21:07.944487",
+     "exception": false,
+     "start_time": "2022-12-14T00:21:07.810717",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Now we can make a simple plot where we combine running, licking and stimuli for an example stretch of the session."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1451597b",
+   "metadata": {
+    "papermill": {
+     "duration": 0.13331,
+     "end_time": "2022-12-14T00:21:08.211772",
+     "exception": false,
+     "start_time": "2022-12-14T00:21:08.078462",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### First, add a column to the stimulus_presentations table that assigns a unique color to every stimulus"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "91138ca3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-12-14T00:21:08.479817Z",
+     "iopub.status.busy": "2022-12-14T00:21:08.479233Z",
+     "iopub.status.idle": "2022-12-14T00:21:08.496251Z",
+     "shell.execute_reply": "2022-12-14T00:21:08.495524Z"
+    },
+    "papermill": {
+     "duration": 0.153711,
+     "end_time": "2022-12-14T00:21:08.498038",
+     "exception": false,
+     "start_time": "2022-12-14T00:21:08.344327",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "unique_stimuli = [stimulus for stimulus in behavior_session.stimulus_presentations['image_name'].unique()]\n",
+    "colormap = {image_name: sns.color_palette()[image_number] for image_number, image_name in enumerate(np.sort(unique_stimuli))}\n",
+    "\n",
+    "stim_colors = stimulus_presentations.apply(lambda row: colormap[row['image_name']], axis=1)\n",
+    "stimulus_presentations['color'] = stim_colors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e196d97e",
+   "metadata": {
+    "papermill": {
+     "duration": 0.132178,
+     "end_time": "2022-12-14T00:21:08.809858",
+     "exception": false,
+     "start_time": "2022-12-14T00:21:08.677680",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### now make some simple plotting functions to plot these datastreams"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "4dc57491",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-12-14T00:21:09.078092Z",
+     "iopub.status.busy": "2022-12-14T00:21:09.077451Z",
+     "iopub.status.idle": "2022-12-14T00:21:09.089271Z",
+     "shell.execute_reply": "2022-12-14T00:21:09.088574Z"
+    },
+    "papermill": {
+     "duration": 0.14795,
+     "end_time": "2022-12-14T00:21:09.090977",
+     "exception": false,
+     "start_time": "2022-12-14T00:21:08.943027",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def plot_running(ax, initial_time, final_time):\n",
+    "    '''\n",
+    "    a simple function to plot running speed between two specified times on a specified axis\n",
+    "    inputs:\n",
+    "        ax: axis on which to plot\n",
+    "        intial_time: initial time to plot from\n",
+    "        final_time: final time to plot to\n",
+    "    '''\n",
+    "    running_sample = behavior_session.running_speed.query('timestamps >= @initial_time and timestamps <= @final_time')\n",
+    "    ax.plot(\n",
+    "        running_sample['timestamps'],\n",
+    "        running_sample['speed']\n",
+    "    )\n",
+    "\n",
+    "def plot_licks(ax, initial_time, final_time):\n",
+    "    '''\n",
+    "    a simple function to plot licks as dots between two specified times on a specified axis\n",
+    "    inputs:\n",
+    "        ax: axis on which to plot\n",
+    "        intial_time: initial time to plot from\n",
+    "        final_time: final time to plot to\n",
+    "    '''\n",
+    "    licking_sample = behavior_session.licks.query('timestamps >= @initial_time and timestamps <= @final_time')\n",
+    "    ax.plot(\n",
+    "        licking_sample['timestamps'],\n",
+    "        np.zeros_like(licking_sample['timestamps']),\n",
+    "        marker = 'o',\n",
+    "        color = 'black',\n",
+    "        linestyle = 'none'\n",
+    "    )\n",
+    "    \n",
+    "def plot_rewards(ax, initial_time, final_time):\n",
+    "    '''\n",
+    "    a simple function to plot rewards between two specified times as blue diamonds on a specified axis\n",
+    "    inputs:\n",
+    "        ax: axis on which to plot\n",
+    "        intial_time: initial time to plot from\n",
+    "        final_time: final time to plot to\n",
+    "    '''\n",
+    "    rewards_sample = behavior_session.rewards.query('timestamps >= @initial_time and timestamps <= @final_time')\n",
+    "    ax.plot(\n",
+    "        rewards_sample['timestamps'],\n",
+    "        np.zeros_like(rewards_sample['timestamps']),\n",
+    "        marker = 'd',\n",
+    "        color = 'blue',\n",
+    "        linestyle = 'none',\n",
+    "        markersize = 12,\n",
+    "        alpha = 0.5\n",
+    "    )\n",
+    "    \n",
+    "def plot_stimuli(ax, ti, tf):\n",
+    "    '''\n",
+    "    a simple function to plot stimuli as colored vertical spans on a s\n",
+    "    inputs:\n",
+    "        ax: axis on which to plot\n",
+    "        intial_time: initial time to plot from\n",
+    "        final_time: final time to plot to\n",
+    "    '''\n",
+    "    stimulus_presentations_sample = stimulus_presentations.query('end_time >= @initial_time and start_time <= @final_time')\n",
+    "    for idx, stimulus in stimulus_presentations_sample.iterrows():\n",
+    "        ax.axvspan(stimulus['start_time'], stimulus['end_time'], color=stimulus['color'], alpha=0.25)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca252172",
+   "metadata": {
+    "papermill": {
+     "duration": 0.13274,
+     "end_time": "2022-12-14T00:21:09.357180",
+     "exception": false,
+     "start_time": "2022-12-14T00:21:09.224440",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Now make the plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "8841065a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-12-14T00:21:09.623083Z",
+     "iopub.status.busy": "2022-12-14T00:21:09.622396Z",
+     "iopub.status.idle": "2022-12-14T00:21:09.729580Z",
+     "shell.execute_reply": "2022-12-14T00:21:09.728978Z"
+    },
+    "papermill": {
+     "duration": 0.242244,
+     "end_time": "2022-12-14T00:21:09.731298",
+     "exception": false,
+     "start_time": "2022-12-14T00:21:09.489054",
+     "status": "completed"
+    },
+    "scrolled": false,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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4+59++um0a9eOU089lfj4eLZs2cKrr77K9OnT3dVjTz31FPPnz2fkyJFce+219OnTh5ycHFavXs3cuXPJycmp07Hy8vLo0KEDF1xwAQMHDiQkJIS5c+eyYsWKapWiRzrzzDMZP348DzzwAHv27GHgwIHMmTOH//73v8yYMaPa4AMRERE5wXlj1KmIiIiceHJzc82rrrrKjImJMUNCQszJkyebW7duNZOTk80rrriixn0///xz8/TTTzfj4uJMPz8/s2PHjub1119vHjx40L3Nu+++awLmihUrqu07f/58EzDnz59f7fZXX33V7NWrl+nr62vGx8ebN954o5mbm1ttm7Fjx5p9+/Y9ZkyHDh0yp0+fboaGhpqAOXbs2EbFb5qmWVhYaN53331mt27dTD8/PzMmJsY85ZRTzOeee86sqKhwb2ez2cxnn33W7NWrl+nn52fGxsaaU6dONVetWuXeZuvWreaYMWPMwMBAE3A/xq7HKSUlxWOPxxVXXGEmJycf9+d3qaysNB999FGzc+fOpq+vr5mUlGTed999ZllZ2VHHCw4OrvV4pmmaRUVF5sUXX2xGRESYgDsO1//7Z599Vm37lJQUEzDffffdarevWbPGPO+888zo6GjT39/fTE5ONi+88ELz559/rvH8//znP80xY8a49+vatat59913m/n5+dW2S09PN2+++WYzKSnJ9PX1Ndu1a2eedtpp5ptvvlnnY5WXl5t33323OXDgQDM0NNQMDg42Bw4caL722mtHPX5H/n8UFhaat99+u5mQkGD6+vqa3bt3N5999lnT4XBU2w4wb7755qN+zrr8nYqIiEjTM0xTXVNFREREREREREQaSz3aREREREREREREPECJNhEREREREREREQ9Qok1ERERERERERMQDlGgTERERERERERHxACXaREREREREREREPECJNhEREREREREREQ/w8XYATc3hcHDgwAFCQ0MxDMPb4YiIiIiIiIiIiJeYpklhYSEJCQlYLJ6vP2v1ibYDBw6QlJTk7TBEREREREREROQEsW/fPjp06ODx47b6RFtoaCjgfADDwsKwFxVTtHgxhq8vhp9fjfuaFRWYlZWEjBqFNSTY47EplmMrKrfxy7YMfK0W/HysNW5bYbNTaXcwpmccIf6e/XWuTxxNHcuJFo9iaXwsJ9Lvi2Jp/lha6u+tYvFOLPV5jYamfZ1WLI2PpS29p1MsjYtDsSgWxXJsFWU2UjdmY/GxYPWtuRrJXunAYXPQsV80fgGef2+pWE7sWI4XR1FxIadMGujOF3laq0+0uZaLhoWFORNtFiuWoCAsISFYAgNr3NdRWoqjqIiQsLCmecFXLMdkKbcRFFJKiL8vgX41f7gprbBTVF5JWFiYxz/Y1CeOpo7lRItHsTQ+lhPp90WxNH8sLfX3VrF4J5b6vEZD075OK5bGx9KW3tMplsbFoVgUi2I5tgo/GyEhFfgF+uBby2t0ZYWdilIbYWFhTZNQUiwndCzHi8PXz5l0a6r2YhqGICIiIiIiIiIi4gFKtImIiIiIiIiIiHiAEm0iIiIiIiIiIiIe0Op7tImIiIiIiIjIic9ut1NZWVnjNhXlNuxUYjdNDIe95uOZDuzYKCsvw9EE6Q/FcuLG4uvr67Fj1ZcSbSIiIiIiIiLiVUVFRezfvx/TNGvczjRNbH4ObCZQWUsze9PE9IP9aYVN0vhesZy4sRiGQbu49h47Xn0o0SYiIiIiIiIiXmO329m/fz9BQUHExsbWmHBxOEwqy2wYBmCpJTHjMDFN8A3wwVLbtg2gWE7MWEzTJDMzk4OHDmAh3CPHrA8l2kRERERERETEayorKzFNk9jYWAIDA2vc1uEwsZjOJI5RS2LGrEri+DVhQkmxnJixxMbGUlRUhEnNS1ebgoYhiIiIiIiIiIjXNcUyRmmbXL9LNS9EbhpKtImIiIiIiIiIiHiAEm0iIiIiIiIiIiIeoESbiIiIiIiIiEgLM27cOGbMmOHtMLymU6dOzJw509thHEXDEEREREREREREWpgvv/wSX19fb4chR1CiTURERERERESkkSoqKvDz82u280VFRTXbuaTutHRURERERESkFcovreSFn7Zz/utLuPHfq9h8oMDbIYnUiWmalFTYvPJlmnWfUzlhwnhm3PEX7rr7ThI6tOOMM6exZ+8e/AN9WbdurXu7vLw8AoL9+OWXhQAsWLAAwzD4+eefGTZsGEFBQZxyyils27bNvc8jjzzCoEGD+OCDD+jUqRPh4eFcdNFFFBYWurc5culozz7defrZp7ju+muIjo2kW/cuvP2vt6rFvHTpEkacNIyI6FBGjBjO119/jWEYrF27luN57bXX6N69OwEBAcTHx3PBBRdUi+GWW27hlltuITw8nJiYGB566G/VHsfy8nL+eu89dO6STGR0OKNGn8LCqsfCZfHixYwePZrAwECSkpK47bbbKC4udt+fkZHBmWeeSWBgIJ07d+bDDz+s+T/Hi1TRJiIiIiIi0sqsSc3lpg9XczC/zH3b/G0ZfH7DKfRLDPdiZCK1K6200+ehH71y7s1/n0yQX91TJR9+9AHXXXMd8+ctrH3jIzzwwAM8//zzxMbGcsMNN/DnP/+ZX3/91X3/rl27+Prrr/n222/Jzc3lwgsv5KmnnuKJJ5447jFfenkmDz/0CPfccy9fffklt952C6NHj6Fnj54UFBRw3gXnMvn0Kbz3zvscSE/jjjturzHGlStXctttt/HBBx9wyimnkJOTw6JFi6ptM2vWLK6++mqWL1/OypUrue6660ho14Gr/3w1ADNuv40tW7bwwfsf0j6hPf/9738586zprFq5hm5durF79y6mTZvK448/zjvvvENmZqY7effuu+8CcOWVV3LgwAHmz5+Pr68vt912GxkZGfV+zJuDEm0iIiIiIiKthGmavLdkD09+v4VKu0mn6CCuH9uVr9eksSwlhzv/s47v/zLa22GKtBrdunbjySeewrAYAOzZu6fO+z7xxBOMHTsWgHvvvZfp06dTVlZGQEAAAA6Hg/fee4/Q0FAALrvsMn7++ecaE22TT5/CDdffCMBdd93Ny6++xMKFC+jZoyeffPoxhmHw+v+9gb9/AAOHDODgwQNce+21xz1eamoqwcHBnHHGGYSGhpKcnMzgwYOrbZOUlMSLL76IYRj07NmT9evX88qrL3H1n68mNTWVWe/PYuf23SQkJABwx+13MOenH3n//Vn8/ZHHePb5Z7j44ovd1Xndu3fn5ZdfZuzYsbz++uukpqYye/Zsli9fzvDhwwH417/+Re/evev8WDcnJdpERERERERamAqbg+82HGB7ehFxof50jArC7jD5cFkqC7dnAjC1XzueuWAAoQG+TO7bjgnPL2BbeiE/bjrE5M5hXv4JRI4v0NfK5r9PPuZ9DodJRakNw8Cd3Doe02FimuAX6IOllm0PP3d9DB48pF7bH27AgAHuf7dv3x5wLpHs2LEj4Jyq6UqyubaprYqrf7/+7n8bhkF8fDyZmc7nhO3bt9O/X38CAgJwrewcMWJEjcebNGkSycnJdOnShSlTpjBlyhTOPfdcgoKC3NucdNJJGIZx2Pcn88ILL2C329m4aSN2u51+A/pUO255eTnRUdEArN+wno0bN/DRRx+57zdNE4fDQUpKCtu3b8fHx4ehQ4e67+/VqxcRERE1xu4tSrSJiIiIiIi0INlF5Vz+znI2Hafnmp/VwgPTe3P5ycnuD79RwX5cflIyL8/byTuLU5jceWBzhixSL4ZhHHf5psNh4mOnfok2v7on2uorKCi42vcWw9kK//AeZZWVlcfc9/CJoa6/VYfDccz7Xdscfn9tx6zrPjUJDQ1l9erVLFiwgDlz5vDQQw/xyCOPsGLFijoluoqLirBarSxdsgyrtXoSMyQ4xL3Nddddx1/+8pej9u/YsSPbt29vcPzeoESbiIiIiIiIh5Xb7OzMKCIyyI+EiECPHdfuMLnx36vZdKCAyCBfpvRrT25xBftySwAYlBTBVad2pltcyFH7XnJSMq/M38nKvbkcyC9DNW0inhcbGwvAwUOHGFR127r167wWz+F69OjBx598RHl5OX5+/gCsWLGi1v18fHyYOHEiEydO5OGHHyYiIoJ58+Zx3nnnAbBs2bJq2y9b9hvdunbDarUycNAg7HY7mRmZjBo16qhjmw6TQYMGs2XLFrp163bM8/fq1QubzcaqVavcS0e3bdtGXl5efX78ZqNEm4iIiIiIiAd9u/4AD3y1kfxSZxXLwA7h3DOlFye1a3zC7d+/7WX5nhxC/H347IaT6RYXWvtOVeLDAhjRKYplKTl8tzmDPzU6GhE5UmBgICNHjOS5556hU6dOZGZm8sgjD3k7LAAu+uOfePiRh7jplhu56467OZhxgOeeew6g2tLPw3377bfs3r2bMWPGEBkZyffff4/D4aBnz57ubVJTU7njjju4/vrrWb16Na+++ipPPfkMAD269+BPF/2JP19zFc889QwDBw0iKzOT+Qvm069ff6ZOnsqdd9zF2PGjueWWW7jmmmsIDg5m8+bN/PTTT7z66qv07NmTKVOmcP311/P666/j4+PDjBkzCAz03EUMT7J4OwAREREREZHWYtGOTG79eA35pZWE+PtgtRis25/PJW8v49YvN5F/7BVkdZJfWskLPzmXUP11Ss96JdlcpvV39oFasDOn4YGISI3++c+3sNlsnHzKSO66+w4eeeTv3g4JgLCwML78/CvWr1/HyFOG87e/PchDDzmTgK4BDEeKiIjgyy+/ZMKECfTu3Zs33niDjz/+mL59+7q3ufzyyyktLWXEiBHcfPPN3HbbbVz952vc97/15r+49JJLuefee+g/oC9/uPACVq5cScekJAD69xvA/PkL2L59O6NHj2bw4ME89NBD7uEJAO+++y4JCQmMHTuW8847j+uuu464uLimeJgaTRVtIiLSaPtySlhRdXX91G4xBPvr5UVERNqecpude7/YgGnCeUMSeeb8AeSVVvLqvJ38+7e9fL85k5V+8MpQO/0aUIjxz4W7yC+tpEd8CBePTG5QjKO6xwCwen8+ZQkQVMv2InJ88+bNp6LUdtTtvXv1ZuGCRdVuKyuucA8gGDduXLUebgCDBg2qdtsjjzzCI488Um2bGTNmuCdzAixYsKDa/ds27+DIwrQVy1ZV+/7kk09hxbJV7iERH3/8Eb6+vu4BDEcaNWrUUec5kq+vLzNnzuT1118Hfh9Ycfj9D/3tYR7628NH7Ws6nD/z8OHDmTNnznHP0a5dO7799ttqt1122WU1xuUt+iQkIiINVlZp56nZW5m1dI/7jUNUsB+PntWXMwcm1LyziIhIK/Ppin2k5ZXSLiyAJ8/tj4/VQkyIP4+c1ZfzhiQy4+PV7M4u5crlpcwc6ceoeL86Hzu9oIx3fk0B4O7JvbA2sLF7l5hg2oUFcKigjLWFcEp4gw4jIi3Uvz/8gE7JnUlon8DmbZv461//yoUXXnjCLsNsibR0VEREGiSjsIwL3ljCe0ucSbYhHSNIjAgkp7iCWz9ew+PfbsbhMGs/kIiISCvxxeo0AK4f24UA3+rT9QZ0iODLPw/lpAgotcNfluWzKbfu60ifn7ONskoHQ5Mjmdi74culDMPg1G7OqrZleQ0+jIi0UIcOpXPV1VcyaOgA7rzzDv7whz/w5ptvejusVkUVbSIiUm/7c0u49O1l7MkuISrYj+f/MJDxveKotDt44aftvL5gF28vTqHc5uCeKT1rP6CIiEgLtz+3hHX78jAMOGPAsau6Q/19eLUP/GW7lV+z7NzyWwGfjosgLtB6zO1dtqQX8dmq/QDcP633cZuW19Wp3aL5YvV+JdpE2qC77ryLO2+/07101NLA6liX2paVtkVerWiz2+387W9/o3PnzgQGBtK1a1cee+yxauuSTdPkoYceon379gQGBjJx4kR27NjhxahFRNq21OwSLnxjKXuyS+gQGchXN53C+F7OK+u+Vgt/ndKLFy4ciGHAB7/t5bUFu7wcsYiISNP7YeMhAEZ0iiI21P+42/la4NmBAXQLtZJR5uDeVYXYzeNXgDtMeGzOTkwTpg9oz9DkyEbH6qpo21wE+ZWqPhcR8SSvJtqefvppXn/9dV599VW2bNnC008/zTPPPMMrr7zi3uaZZ57h5Zdf5o033mDZsmUEBwczefJkysrKvBi5iEjbVFhWyZ9nreBAfhnd4kL4/IZTSI4OPmq784Z04Ilz+gPwxsJd7Mwoau5QRUREmtX3Gw4Cv0/1rEmor8HMkWEEWmFZZiXvbC897rYfHoBle/MI8LXw18m9PBJrfFgAnaMCMYH1eXaPHFNERJy8mmhbsmQJZ599NtOnT6dTp05ccMEFnH766SxfvhxwVrPNnDmTBx98kLPPPpsBAwbw/vvvc+DAAb7++mtvhi4i0uaYpsntn65lZ0YR7cIC+OiakbQLP/YYcICLR3bkwmEdME34bNV+7OrXJiIirdTB/FJWp+YBMKVfuzrt0znUhwcGhgLwypZi1uUc3a9tY76dl/Y4//3g9D50jPbcjNDBHcIAJdpERDzNq4m2U045hZ9//pnt27cDsG7dOhYvXszUqVMBSElJ4dChQ0ycONG9T3h4OCNHjmTp0qXHPGZ5eTkFBQXVvkREpPF+2HiIuVsy8POx8OblQ4kLO36SzeX+ab0JD/TlYH4Zv+3OboYoRUREmp9r2eiw5Eji6/D66HJOR3+mdvDHbsLdKwooqHC479uUW8l1K0qpNOG07tFcMrKjR2MenOhKtDlq2VJEROrDq4m2e++9l4suuohevXrh6+vL4MGDmTFjBpdccgkAhw45X7Di4+Or7RcfH+++70j/+Mc/CA8Pd38lJSU17Q8hItIGlFXaeeL7LQDcMLYrAzpE1Gm/iCA//nxqJwC+33gQRw09aERERFqq2Rucn02m1mHZ6OEMw+DhQSF0CLKQVuLgml/z+TW9gvd2lHDFojwKbTAoDF44p/EDEI40sCrRtiHfrtdnEREP8mqi7T//+Q8ffvghH330EatXr2bWrFk899xzzJo1q8HHvO+++8jPz3d/7du3z4MRi4i0Te8v3cP+3FLahwdww9gu9dr3j8M74u9j4WB+GdsPFTYqjtIKO3//32ZOe34Bt3+6lrySikYdT0REpLEyCspYsTcHqPuy0cOF+lqYOTKcSD+DTXk2rluSz7Mbiym1w0nRVl7rAyH+Pp4Om55xwQRYoNAGKYVaPioi4ileTbTdfffd7qq2/v37c9lll3H77bfzj3/8A4B27ZwvVOnp6dX2S09Pd993JH9/f8LCwqp9iYhIw9kdJrOW7AXg9ok9CPKr35v9kAAfBidFAPDLjswGx2GaJvd8sZ53fk1hV2YxX61J46I3f6OkwtbgY4qIiDTWj5sOYZowKCmCxIjABh2jd4QPn46L5MwkfxKDLAyI9OGRQSG8MSyAYM/n2ADwsVjoG+L89/pcvZZK62C321n4y0I+/fQTFv6yELu9aZPIEyaM56577gSgR89uvPzKS3Xa77333iMiIqIJIxNv8mqiraSkBIuleghWqxWHw9knoHPnzrRr146ff/7ZfX9BQQHLli3j5JNPbtZYRUTaqvlbM0jLKyUiyJezBiU06BjDO0cB8NvuHCpsDesFM39bBv9bdwAfi8HtE3sQE+LP1kOFPPHdlgYdT0RExBO+r1o2Or2ey0aPlBhs5alhYcyZHM3H4yL5Q+dArB5eLnqk/s5ZDMccxCDS0nz99Vd079mN0ydP5PIrL+P0yRPp0bsbX//3q2Y5/6+Ll3LN1dc2y7nkxObVRNuZZ57JE088wXfffceePXv46quveOGFFzj33HMBZ8+CGTNm8Pjjj/PNN9+wYcMGLr/8chISEjjnnHO8GbqISJvx/m/OarY/DksiwNfaoGN0jQ0mJsSP0ko7K/bk1Ht/0zR58acdAPx5VGf+MrE7L100CIAPl6Wyam/9jykiItJYWUXlLEtxDvtpyLJRbxugRJu0El9//RUXXfxH0tL2V7v9wIEDXHzpRXz55ZdNHkNsbCxBQZ6bDCwtl1cTba+88goXXHABN910E7179+auu+7i+uuv57HHHnNvc88993Drrbdy3XXXMXz4cIqKivjhhx8ICKj7NB8REWmYtLxSftmeiWHAJSOTG3wci2FwUpdogAZNH12zL48Nafn4+1i4YWxXAE7tFsOFwzoA8MBXG6m0a2qaiIg0rx82HsJhwoAO4SRFtbwP2K6Ktp0Fdoor9ToqLZPdbueOu+7APMZQD9dtd9xxe5MvIz1y6WheXh433XIjScmJhEWEMGTYIL6f/d0x983MzGTYsGGce+65lJeXk5ubyyWXXEJsbCyBgYF0796dd999t0njF89pohX/dRMaGsrMmTOZOXPmcbcxDIO///3v/P3vf2++wEREBIA5m5zLYYYnR9ExunEfIIZ2jOTb9QfZkJZPhc2Bn0/dr/V8sjwVgDMGJBAV7Oe+/d6pvflpczpbDxXy7q8pXDema6NiFBERqY8vVzurZ84Y0Lhlo94S5w/tAwwOlplszLMxMtav9p1ETjCLf118VCXb4UzTZN++fSxatIhx48Y1S0wOh4Ozzj6DwqIi3n1nFl26dGHL5s1YLEevDtm3bx+TJk3ipJNO4l//+hdWq5U777yTzZs3M3v2bGJiYti5cyelpaXNErs0nlcTbSIicmL7YaMz0XZ63/hGH6tjVBBRwX7kFFew6UA+gztG1mm/cpud2VX9b1wVbC5RwX7cN60393y+nhd/2sGEXvF0iwtpdKwiIiK12ZlRyOrUPKwWg3MGJXo7nAYbEGHl4CEb63OUaJOW6dDBg3Xa7mAdt/OEn+f9zIqVK1i3dgM9uvcAoHNyZ44sutu2bRuTJk3i3HPPZebMmRhVfRlTU1MZPHgww4YNA6BTp07NFrs0nleXjoqIyIkru6jc3U9tct/G950xDIMhVcm1VXtz67zf4h1ZFJbbiA/zZ3inqKPu/8PQDpzSNZrSSjs3fbhKU0hFRKRZvPnLbgBO6xVHXFjLbWvTP9z5kXBDrvq0ScvUrn3dKkrb13E7T1i3bh0dEju4k2zHUlpayujRoznvvPN46aWX3Ek2gBtvvJFPPvmEQYMGcc8997BkyZLmCFs8RIk2ERE5pp+3ZOAwoU/7MI/1nRmaHAHA6tTcY/bROBZXVd3Ufu2xWI6evmYYBjMvGkRsqD/b04u45/P1OBx1O7aIiEhD7Egv5MvVaQBcP7Zlty3oH+FcyrYhVxeqpGUadeooEhM7VEtUHc4wDJKSkhg9enSzxRQYWHvy3d/fn4kTJ/Ltt9+SlpZW7b6pU6eyd+9ebr/9dg4cOMBpp53GXXfd1VThiocp0SYiIsc0d0s64JlqNpc+7cPx97GQW1JJSlZxrds7HCbzt2UCMKnP8ZevxoUG8MqfBuNjMfh2/UGe/2mbx2IWERE5XEmFjTs/W4fNYTKxdxxDk+vWCuFE1SvUgtWAjDIH6aVN2yxepClYrVZeeO4FgKOSba7vX3jhRazWo/ujNZX+/QewP20/23dsP+42FouFDz74gKFDhzJ+/HgOHDhQ7f7Y2FiuuOIK/v3vfzNz5kzefPPNpg5bPESJNhEROYrdYbqng47uEeOx4/r5WOifGA7A6tS8WrffkJZPVlE5If4+x1w2eriTukTzj/P6A/B/83e5ByiIiIh4SkpWMX96axnr9+cTGeTLo2f383ZIjRbkY9AtTFVt0rKdc865fPLRpyQkVO+XmJiYyEf//oTzzjuvWeMZM3oMo0eN5qI/Xcjcn+eSsieFH3/8gTk//VhtO6vVyocffsjAgQOZMGEChw45V3I89NBD/Pe//2Xnzp1s2rSJb7/9lt69ezfrzyANp0SbiIgcZcvBAgrKbAT7Wd2JMU8ZUnXlf01q7X3aft6aAcCYHjF1mlL6h2FJ3DqhGwAPfL2RxTuyGhGpiIiIk83u4PUFu5g88xfW7csjPNCXt68YTmJEoLdD84j+kb6A+rRJy3bOOeeyY9tO5vw4l/ff+4A5P85l2+YdnHP2uV6J55OP/8OwocO4/IpLGTR4APc/eB92+9FVoz4+Pnz88cf07duXCRMmkJGRgZ+fH/fddx8DBgxgzJgxWK1WPvnkEy/8FNIQmjoqIiJHWbrLWc02vHMUvlbPXpMZnBQBwO6sYnKKK4gKPv6Es3lbnctXx/eMq/Px75jUg9ScEv679gB3/Gctc+8cS1iAb6NiFhGRtqus0s6N/17lbmUwpkcsT5zTz2P9S08EAyJ9+HwPrM9RRZu0bFarlbFjxrq/Nx3mUZM+PWnevPlUlDr/brZv21ntvqioKN7859vHjOXKK6/kyiuvdN/n4+PDF1984f7+wQcf5MEHH2y6wKVJqaJNRESO4lo2enKXaI8fOyLIj66xwQCs3Zd33O3SC8rYmFaAYcC4eiTaDMPg6fMH0DkmmIzCcl6Yc/zeGCIiIjUxTZP7v9zA/G2ZBPhaePaCAcy6anirSrLB7xVtm/Js2JsyKyEi0gYo0SYiItXY7A6Wp+QAcHJXzyfaAIZ0dC4fXV3D8tGfNjur2QYnRRAb6l+v4wf4Wnmsqm/OrKV72HQgv4GRiohIWzZ74yG+XJOG1WLwryuG84dhScedbNiSdQ2zEmiFYptJSqEGIoiINIYSbSIiUs2Wg4UUltsIDfChb4Jn+7O5uPq0bdifT4XNccxtXIm2SX0aNvV0VPcYzhjQHtNEVW0iIlJvReU2/v6/zQDcPK4rp3bz3HCgE43VMOgb4erTpuWj0rJt2w7PPGuwTW//xEuUaBMRkWrWp+UBMLBDBFZL01y1T44KIirYjwq7g43HqDYrKrO5+8RN6hPf4PPcMakHFsM5VKGm6jkREZEjvbJoL4cKyugYFcRN47t5O5wm1z/S2b5bAxGkJbPZ4JtvDOYvMPjmGwOb8sbiBUq0iYhINRvTnImvfh6eNno4wzAYWlXV5lqmerjFO7OosDvoEhNMt7iQBp+nS2wI5w/pAMCLP+mypoiI1M2OYnh32T4AHj2rLwG+Vi9H1PT6R6miTVq+336DNWsMenSHtWsNflvW+pZ6y4lPiTYREalmQ1WirX8TJtoATqkatLA8Jeeo5aM/bT4ENK6azeW207rjYzFYtCOLVXtV1SYiIjUzTZN/7AK7Caf3iWd8r7oP5GnJXBVt2/NtlNk1EEFanoIC+N93Bn5+EBMDvr7w7XcGBQXejkzaGiXaRETErdxmZ9uhQqDpE2092oUSE+JHaaXdPeUUoLjcxrytGQCcOTCh0edJigriwuFJALzy8w5MTVMTEZEafJRayaoCCPCx8NCZfbwdTrNpH2gh2t/AZsLWPFW1Scvz01yDlN0GycnO75OTISXF4Od5qmqT5qVEm4iIuG0/VESl3SQ80JekqMAmPZfFMDitt7NibfbGg+4E2JLd2VTaTfomhHls+eot47vhZ7Wwcm8u29OLPHJMERFpXWwOk493l/Ls1goA7prQhQ6RQV6OqvkYhkH/SNfyUfVpk5YlNRV++skgJgZ8nMWZ+PhAdIzJT3Mt7Nvn3fikbVGiTURE3Da4+7OFYRhNf/XvtF5x+PtY2JNdwpJd2RSV25i3xVnNdtWpnT12noSIQC4e2RGA7zYcbLKqNpvdwZ6sYlan5rJsdzb7c0ua5DwiIuIZueUO/rW9hGsW53Hyd9k8vq4IuwnnxsOVwxO9HV6z+30ggirapOVwOJxLRHNyoN0Rw+rbxUN2NnzzDWhRgzQXH28HICIiJ44NzTAI4XChAb6cPSiR/6zcx5u/7CYs0IfiCjudY4I5Z1Djl40e7qZxXfl4eSp7sktYsD2Taf3be+S4DofJ9xsP8sWq/fy6K/uofnMDOoTz+Dn9GNAhwiPnExERz1ifU8ktv+WTXf77p+8wX4PruvhySUxFs1xwOtG4KtrWq6JNWpB16+C33ww6JsORf7aGAR2TTJYsMTj1VBgyxDsxnggW/rKQ0ydPJP1gJuFhzfNev61SRZuIiLhtbKZBCIc7Y0B7+iWEUWF3kFVUgZ+PhWf/MAAfq2dfouLCArjttO4AVQm34kYfM7e4govf/o1bPlrD/G2ZVNgc+PtY6BAZSKfoIHwsBuv353PRm7+xOlWDGEREThSHSu3ctNSZZOsSauX+ASF8OSGSxdOjuaKzH5a2l2MDoF9VRdu+Ygd55Y5athbxvrIy+O83Bg6HSXjYsbcJD3dWvX39tXN7kaamijYREQGg0uZwD0IYkBjRbOf1tVq4e3IvFu3IJKOwnEFJ4fRqd5x3So10+cnJfL/hIJsOFPDcj9t49Ky+RIf4N+hYxeU2Lv3XMjYdKCDIz8rVozpz5sAEusWGYKn6hJZdVM5fPlnL4p1ZzPhkLT/OGEOgn9WTP5KIiDTAMxuKya0w6R3uw/tjIgjy+T2z1pbTS+F+FjqFWNlTZGdDbiWj2zXsNVKkuSz8xWDLVoPu3WrernNn2LwZFi6EyZObLp6Kigr8/Pya7gR1jME/QH+73qSKNhERAWBHRhEVdkezDEI4kp+PhdN6x3POoERiGpj4qgvDMLh0ZEfiwwLILq7gH7O3UljWsOUxj/5vE5sOFBAd7MfXN5/Knaf3pEd8qDvJBhAd4s8blw2lfXgAqTklvLdkj4d+EhERaahNuZX8mFaOBXh8aGi1JJvAoChnLcaKLC0flRNbZiZ8/z2EhYF/LW8f/f0hNBT+9z/nfp4yYcJ4ZtzxF+66+04SOrTjjDOnsWnTRs48+wyiYiJISk7kqj9fQVZWFgDfz/6OqKhI7HY7AGvXrsUwDO699173Ma+55houvfRSALKzs/nTn/5EYmIiQUFB9O/fn48//rhaDOPGjeOWW27h9ttn0KFje844ezoAs3+YTd/+fQiPDOX0yRPZu3dPtf32pu7lrLPOIjIykuDgYPr27cv333/vuQenDVOiTUREANh8oHkHIXhLsL8Pd07qQVSwH2l5pTzz4zbKKu31OsaSXVn8Z+V+DAP+75Ih9IgPPe62If4+3Hl6TwDe/TWFclv9ziUiIp71rx2lAExP8qdXuBb4HGlkrLMa57dMJdrkxGWaMPsHgwMHDTrUcW5JUhKkpcG333p2MMKHH32An58v8+ct5PHHnmDy1NMZNHAQS379jf/991vSMzK45NI/AXDqKaMoLCxkzZo1ACxcuJCYmBgWLFjgPt7ChQsZN24cAGVlZQwdOpTvvvuOjRs3ct1113HZZZexfPnyajHMmjULPz8/5s1dwKsvvcq+ffv440V/YPq06SxftpKrrvwzD/7tgWr73H7HXygvL+eXX35hw4YNPP3004SEhHjugWnDlGgTEREANh0sAJpvEII3xYT4c9/UXoT4+7Azo4gX527HZq/bYiGHw+TJ77cAcMnIjpzUJbrWfc4amEB8mD8ZheXM3+rBy6giIlIvWeUO5h4oB+Cq7kFejubENDLWORBhc56N/Iq2vJBWTmRZWbBihUFkJFjqmNWwWCAqCpYvd+7vKd26duPJJ56iZ4+e/DzvZwYOHMRjf3+cXj17MWjQYN584y0WLFzAjh3bCQ8PZ9CgQe7E2oIFC7j99ttZs2YNRUVFpKWlsXPnTsaOHQtAYmIid911F4MGDaJLly7ceuutTJkyhf/85z/VYujevTtPP/0MPXr0pEePnrz51j/p0qUrzzz9LD179ORPf7qYyy69vNo++/alcuqpp9C/f3+6dOnCGWecwZgxYzz3wLRhSrSJiAgAmw84E23NOQjBmzpEBnHP5J74+1hYvz+ffy9LrdN+P21JZ2NaASH+Ptw+sUed9vHzsXD2IOfl1v+tO9DgmEVEpHH+m2bDbjqXR/ZUNdsxxQda6RZqxQQWpVd4OxyRY4qJgeHDTXJznYMO6sLhgJwcGDHCub+nDB78+yjT9evXs3DhAqJiItxfAwb1A2D37t0AjBkzhgULFmCaJosWLeK8886jd+/eLF68mIULF5KQkED37s4BXna7nccee4z+/fsTFRVFSEgIP/74I6mp1d+3Dh06tNr3W7duZcTw4dVuGznypGrf33TjLTzxxBOceuqpPPzww6xfv94zD4go0SYiImCzO9ie7hyE0FYSbQDd40Pdk0jnbDrkHgZxPKZp8sq8HQBccUpyvQYpnDkgAYCft6ZTXs+lqiIicnzlNjsH8koxa1kLZprw1X7ncsjzkwOaI7QWa0J75+vbvINKtMmJyTBg6hSThPYm+9Pqts++fZCYCGec4dzfU4KCgt3/LiouYvq0M1i+bGW1r00btzBq1GgAxo4dx+LFi1m3bh2+vr706tWLcePGsWDBAhYuXOiuZgN49tlneemll/jrX//K/PnzWbt2LZMnT6aiovrfZnBwMPV11ZV/ZufOXVx22WVs2LCBYcOG8corrzTwUZDDKdEmIiIcKiij0m4SFuBDx6i2tZRmSMdIxvWIxQTeXLSLyhqWkC7YnsnGtAICfa38+dTO9TpPv8Qw4sP8Kat0sDo1r3FBi4gIAB8u28uwx+ZyylPzOP3FX9wXjY5ldQHsLTEJ8jGY3EGJtppMSHD2afvlUDnFlVo+Kiem2FiYNg0KCqC8vOZty8uhsBDOPNO5X1MZPGgwm7dsplNyJ7p17Vbty5UMGz16NIWFhbz44ovupJor0bZgwQJ3fzaAX3/9lbPPPptLL72UgQMH0qVLF7Zv315rHL169WLFypXVblu+fNlR2yUlJXHDDTfw5Zdfcuedd/LWW2814qcXFyXaRESEfTnOxtD9EsNb9SCE47nkpGQiAn05kFfGDxsPHXMb0zR58SfnG5tLT+pYr2o2cE48Hd3d+c5uyS4PNgYREWmj/rU4hQe+2khhuQ1wTs+++K3fyCgoO+b2X1Q9vU9N9CdYk0Zr1C/Ch84hVkrt8ENaLRkMES8aO8akT2+TPXtqrmhNSYE+feCwYrEmccP1N5Kbm8Nll1/KypUr2LV7F3N+msO1113tnjQaGRnJgAED+PDDD91JtTFjxrB69Wq2b99eraKte/fu/PTTTyxZsoQtW7Zw/fXXk56eXmsc115zHTt37uDe+/7Ktu3b+OSTj/ng3+9X2+aue+7kxx9/JCUlhdWrVzN//nx69+7tuQejDVOiTURE2JdbArStZaOHC/H34U8jOgLw1Zo08kqOXirz46ZDrN+fT5CflevHdm3QeUZ3dzYEWbY7p+HBiogIn6/az2PfbgbgtgndWPHARHrGh5JVVMG9X244ahlpXmklP1Vd4/hDZ1Wz1cYwDM6tWl77/s5S7J4c0SjiQQEBcNaZJhaLQX7BsbfJz3cOQjjnHOf2TSkhIYH58xZit9uZfuY0hg4bzF1330l4eASWw6Y2jB07Frvd7k60RUVF0adPH9q1a0fPnj3d2z344IMMGTKEyZMnM27cONq1a8c555xTaxwdO3bkk4//wzf/+4bhI4by1ttv8vdHH6u2jd1u59Zbb6F3795MmTKFHj168Nprr3nkcWjr1AFURESqVbS1VaO6xzBn8yF2ZRbzyYp9XHFyJ/d9NruD5+Y4q9muGdWZmHpWs7mM6BwFwLb0Qsor7YT4+zY6bhGRtmb+1gz++oWzafc1ozpz+6QeGIbBKxcP5oyXFzNvawY/bDzE1P7t3ft8tSGdChN6hVroF6GPQHXxh04BvL29hJ2Fdr7aW8Z58d6OSOTYBg6Ek04yWbDAoF+/6v3XTBNS9xlMnASDB3v+3PPmzaei1Fbttu7duvOfTz87alvTYeLKWc+cOZOZM2dWu3/t2rVH7RMVFcXXX39dYwyuCaYOR/WE+PRp05k+bXq12664/Ep3LC8+PxO/QB8sFlX4epoq2kRE2jibw0FanjPR1lYr2gAshuFOri3cnsmuzCL3fW8tSmFnRhERQb5cM6ZLg8/RPjyQhPAA7A6TvTkljQ1ZRKTNWZOay00frsbuMDlvSCIPTO/tbnnQIz6UG8Y6n6Mf/d9miqqWlDocJp+sdk58viDJt022SGiIMD8L1/V09m19an0R/ztQyZYi+Hj1Ae76bB3TXlrEH/+5lLd+2U1phYb8iPdYLHDGdJOoKDh0RAeQQ+kQHQ1nneXZAQgiNVGiTUSkjTuQV4bNYRIa4ENydNsahHCk7vGhjO7mXN755i+7ySupYN7WDJ6bsw2AB6f3ISygcVVoQ5IjAUjJKm5csCIibcy+nBKumbWS0ko7Y3rE8vT5A45Kmt00vhtJUYEcKijj5Z+dU6K/23CQnVklhFhhWntVs9XH5d0CGR3vR6kd7l9fzkVr4cHvt/P5qv1sPljAspQcnvh+C9NeXsTOjKJajyfSVDp2hEmTTLKywFZVYGazQXaWwaSJDpKSvBuftC1KtImItHF7s50Jn97tw3SVH7j8lE7EhfqTWVTOQ99s5raP12B3mJw7OJHzhyQ2+vgDO0QAsD+3tNHHEhFpK4rKbVwzayXZxRX0TQjj9UuG4Gs9+qNMgK+Vv5/VD3AOS3h13g4e/Z+zl9tliRDqq9e5+rAaBi+fFMZ1PYNICDCI9IGTkiO4eXxX3rh0KI+d0492YQGkZBXzp7d+Y5+qtcWLJk006dLFZO9e5/d790LnzianTVCPQWleSrSJiLRxe7Kdb4r7tA/zciQnhhB/Hx6Y1puOUc7qPsOAy09OPmblREP0SXA+zq7luiIiUrt/fL+FbemFxIX68/YVwwj2P35l2vhecZwzKAG7w+S5OdvJKiqnV1wwVzT+Wkmb5Gcx+EufYH4cF8yCk+DDywZx9+ReTOnXjstOSua720bRq10omYXl3PzRaipsDm+HLG1UWJhzCWlFBWRlQWWl8/swvcWVZqZEm4hIG+eqaHMlgATiwgJ4+Iw+/G16bxbdM56/n90PPx/PvGT2rkpoZhVVqKeNiEgdbE8v5KPlqQC8dNFg2ocH1rrPs38YyDWjOpMcHcTkvvG8fVF/Aq1NHWnbFB3izztXDic80Jf1+/N5a9Fub4ckLdiRE4Pr66STYPBgk+07YNAgk5NGqpqtrXL9LnmjjlmJNhGRNqzS7iC1aplHPyXaqjEMg9hQfyKC/Dx63KhgP+LDnFNL9+dpiY2ISG3eWZyCacLkvvGc3DW6Tvv4Wi08eEYfFt49nn9eNoz2YQFNHGXblhARyCNn9QHglXk7yCgs83JE0tJYrc5MeEVFRaOO4+MDZ51lMn6cyVlnmfioLWOb9fvvUvOnvfRrJyLShu3NLsHmMAn2s5IU1bYHITSnnu3CSC/IJDWnhAFVPdtERORohWWVfLUmDYDrGjH1WZreOYMSeW/JXtbty+Nfi1O4b2pvb4ckLYiPjw9BQUFkZmbi6+uLxXL85IjDYVJZbnNOEbUcXa+UnAy33eb8d3m5iWmCw/DBcoxtG6u2WKpvrFiaKxaHw0FmZiaBAYFUFCnRJiIizWhnRiEAnWKCNQihGfWMD+WX7ZlqGi0iUou5W9IptznoGhvMkI6R3g5HamAYBrdN6MbVs1by76V7uXFsV49XhUvrZRgG7du3JyUlhb2uaQbHYZomtgqHM4lT2/tX05nE8fGzNMl7XcVy4sZisVhoH59IWla+x45ZV0q0iYi0YTsyigDoFK1qtubUq30oAPtyNBBBRKQm360/BMD0AQm6INQCTOgVR+/2YWw5WMB7S/YwY2IPb4ckLYifnx/du3evdfloRbmNtG25+AX44ONbc7WSrdJBRbmNxM6R+NUwRKWhFMuJG4ufnx+2Cu8MZ1GiTUSkDdvpTrQFezmStqVXO2eibX9eCXaHibUJSvZFRFq6CpuDJbuyADi9T7yXo5G6MAyDG8d15baP1/DhslRuHt8NX6vagkvdWSwWAgJq7qlowYYVX6yGDz6WmqecmIYdKwYB/gH4BXg+/aFYTvRYvJNo07OeiEgblVdSQUZhOQbQUf3ZmlVSZBB+VguVdpOMAjWMFhE5ljWpuZRU2IkO9qNPew3saSmm9G1HTIg/mYXlzN2c7u1wRESanRJtIiJt1M5MZzVbQkQggX41X2kSz7JYDOKqJo+m5Wn5qIjIsfy601nNdmq3mCZp1i1Nw8/Hwh+HdwDgw2WpXo5GRKT5KdEmItJGbTlQAEDXWC0b9Yb4MOeyCCXaRESObd1+ZwPr4Z2jvByJ1NdFwztiGLB4ZxYpWcXeDkdEpFkp0SYi0kZtqEq0aTmOd7RTRZuIyHGZpsmmA85EW78EvU61NElRQYzvGQfAR8tqniApItLaKNEmItIG5ZVUsC+nBIBeSrR5haui7YASbSIiR8koLCerqAKrxaC3XqdapEtGdgTgs1X7KbfZvRyNiEjzUaJNRKQN2lRVzZYcHURYgK+Xo2mbfk+0lWGappejERE5sWxMc1azdYsNIcBXfURbonE944gP8yevpJL5WzO8HY6ISLNRok1EpA1yfYDpnxju5UjartgQPywGlFbaySmu8HY4IiInlI1pzgtCfbVstMWyWgzOGZwIwOer0rwcjYhI81GiTUSkjTFNkw1prr43SrR5i4/VQlyoBiKIiByLqz9bX10QatHOH+KcPrpgWwbZReVejkZEpHko0SYi0sbsziomu7gCfx8LvdqHejucNq19uPq0iYgci6vFgQYhtGw94kPpnxiOzWHyzboD3g5HRKRZKNEmItLGLN2VDcCQjpH4+6jvjTclRAQCqmgTETlcbnGF+3mxjxJtLZ5r+ejsDYe8HImISPNQok1EpA1xmCa/7XYm2k7uEu3laMRV0aZEm4jI71zVbJ2igwjVwJ4Wb0q/dgCs2JtDZqGWj4pI66dEm4hIG7Izo4js4goCfa0MTIrwdjhtXvtwV0VbmZcjERE5cWxUf7ZWJTEikIEdwjFNmLNZVW0i0vop0SYi0ob8sj0TgKHJkfj56CXA21wVbQWllRSV2bwcjYjIicE1GVsTR1uPyVVVbT9sVKJNRFo/fcoSEWkjispsLNqRBcCEXnFejkYAAnytRAX7AVo+KiListk9CEEVba3FlL7ORNvSXdnkl1Z6ORoRkaalRJuISBsxb1sGFXYHydFB9GqnaaMnig4aiCAi4lZYbmN3VjGgirbWpEtsCF1ig7E5TJbuyvJ2OCIiTUqJNhGRNsDuMPmpqi/KlL7tMAzDyxGJS0KkEm0iIi5b04sA59L66BB/L0cjnjSmeywAC7cr0SYirZsSbSIibcDinVlkFVUQFuDDKV1jvB2OHMZd0ZZb4uVIRES8b9MhZ6Ktr5aNtjpjezgTbb9sz8Q0TS9HIyLSdJRoExFp5ewOk6/XpAEwfUCChiCcYBJV0SYi4rbpUCEA/RK1bLS1GdklCj+rhbS8UvfyYBGR1kiftkREWrmlu7M5VFBGiL8Pp/eJ93Y4coTEqoq2rKIKSivsXo5GRMS7VNHWegX5+TC8cyTw+xR0EZHWSIk2EZFWzHF4NVv/9gT4Wr0ckRwpNMCX8EBfAA7kq6pNRNqucgfszHRWOqmirXVy9WlzTUEXEWmNlGgTEWnFVuzNIS2vlGA/K6f3VTXbicpV1bY/V4k2EWm7dhSD3YToYD/ahQV4OxxpAqd2c/aJXZ6Sg83u8HI0IiJNQ4k2EZFWyu4w+aqqmm1q//YE+fl4OSI5ng6RGoggIrLFuWqUPglhmo7dSvVpH0Z4oC9F5TbWp+V7OxwRkSahRJuISCv12+5sMgrLCQvwYVq/9t4OR2qggQgiIrC1qj9+v0T1Z2utLBaDk7tEA7Bkp5aPikjrpESbiEgrVFZp54dN6QCcMziRQD/1ZjuRaemoiMjvFW39NAihVTu1W1WibVe2lyMREWkaWkckItIKfbw8lfzSSqKD/ZjYW73ZTnSuRFtmYTnlNjv+PvVPjOaXVPLekj0czC9lTI9YpvRth8WipVci0jJUOkx2uCvaNAihNTu5q7NP28q9uZRV2jWoSURaHSXaRERamYKySt5elALAWQMT8LWqePlEFx7oS4i/D0XlNg7kldE5Jrhe+xeV2zj/jSXszHCWg3yyYh+ju8cw84+DiA7xb4qQRUQ8anexgwoTQvytJEUGeTscaUJdY4OJD/MnvaCc1XtzOaVqQIKISGuhT18iIq3M27/sJr+0kvgwf07pqjevLYFhGO6qtgMN6NP23I/b2JlRREyIH5ee1JEAXwuLdmRxxiuL2ZFe6OlwRUQ8bn2ecwJl//ahqsZt5QzDcL8/+XWX+rSJSOujRJuISCuSVVTO24ud1WzT+7fHqg8rLYZr8mh9+7RlFZXz8fJUAF64cBCPn9Of/948ii6xwRzML+MP/1zK+v15ng5XRMSj1uXZARisZaNtwild1adNRFovJdpERFqR/5u/k5IKO30TwhjYQc2kW5JEd6KtpF77fb5qP+U2BwOTIhjd3Vkh0LNdKF/ccAqDkiLIK6nkz++tYF9O/Y4rItKcXIm2IXrtahNcy0XX78+nsKzSy9GIiHiWEm0iIq3EvpwSPvzNWdk0Y2IPDEPVbC2JqydRaj0TYt+sPQDARcOTqv2fRwb78eE1I+mbEEZWUQXXzFpJSYXNcwGLiHhIXoWDPcUmAINU0dYmJEYE0ik6CLvDZHlKjrfDERHxKCXaRERaiad+2EqF3cGobjGcXLUkQ1qOTtHOAQgZheUUl9ctIbYnq5jNBwvwsRhM6dvuqPuD/X14+4phxIX6sy29kBd/2u7RmEVEPGF9jrOiKTkQIoN8vRyNNBfX9NFfd2r5qIi0Ll5PtKWlpXHppZcSHR1NYGAg/fv3Z+XKle77TdPkoYceon379gQGBjJx4kR27NjhxYhFRE48q/bm8t36gxgG3D+tt7fDkQYICfAhJsQPgL11rGpbuD0TgGGdIokM9jvmNu3DA3n6/AEAvPPrHlKztYRURE4sa3OcFxcGhno5EGlWp3Zz9WnTQAQRaV28mmjLzc3l1FNPxdfXl9mzZ7N582aef/55IiMj3ds888wzvPzyy7zxxhssW7aM4OBgJk+eTFlZmRcjFxE5cZimyePfbQbgD0M70CdBy25aKldV256s4jptv2iHM9E2pkdsjduN7xXH6O4x2B0mry3Y2bggRUQ8bE22s6JtgBJtbcrJXZyJtq2HCskqrvByNCIinuPVRNvTTz9NUlIS7777LiNGjKBz586cfvrpdO3aFXB+eJw5cyYPPvggZ599NgMGDOD999/nwIEDfP31194MXUTkhPH12jTWpOYR6GvlztN7ejscaYTkaGeftj3ZtSfaKmwOllZNaxvTveZEG8CtE7oDzt+X/FI1nhaRE0OxzWRN1dLR4ZqD0KZEh/jTq50zu7psb553gxER8SCvJtq++eYbhg0bxh/+8Afi4uIYPHgwb731lvv+lJQUDh06xMSJE923hYeHM3LkSJYuXXrMY5aXl1NQUFDtS0SktcoqKufv/3NWs90yoRvxYQFejkgaw1XRtrcOyztXp+ZSXGEnJsSPPu1rr2Ic3imSHvEhlFU6+O/atEbHKiLiCcszK6h0QGKgQXKgt6OR5nZq1fTRBerTJiKtiFcTbbt37+b111+ne/fu/Pjjj9x4443cdtttzJo1C4BDhw4BEB8fX22/+Ph4931H+sc//kF4eLj7KykpqWl/CBERL3rkm03kllTSq10o143p4u1wpJE6xTgTbWm5pVTaHTVu+0tVf7ZR3WKwWGqfMGsYBhcOc74mfrvuYCMjFRHxjAWHnEsGR8VY0bDstmdib+fnvPk7srGZXg5GRMRDvJpoczgcDBkyhCeffJLBgwdz3XXXce211/LGG280+Jj33Xcf+fn57q99+/Z5MGIRkRPHnE2H+Hb9QawWg2cvGIiv1evzbaSRooP9CPH3wW6a7KtlIMIvdezPdrip/dsDsGJvDhmF6nUqIt5V4TCZk1YOwMR2Pl6ORrxheKdIIoN8yS21sSbf29GIiHiGVz+VtW/fnj59+lS7rXfv3qSmpgLQrl07ANLT06ttk56e7r7vSP7+/oSFhVX7EhFpbTILy7n/qw0AXDu6C/07qLFNa2AYBl2qqtp2ZBQdd7vsonI2pjlbI4zqHlPn4ydGBDIwKQLThPlbMxoXrIhIIy06VEFBpUmMv4XhUVZvhyNe4GO1cFpVVds8rR4VkVbCq4m2U089lW3btlW7bfv27SQnJwPQuXNn2rVrx88//+y+v6CggGXLlnHyySc3a6wiIicK0zS5+/N1ZBVV0KtdKDMmdvd2SOJBvar6rW05ePweo4t3ZgHQu30YcaH168s3tqoCbrH64YiIl32wqxSAszv6Y9W60Tbr9D5Vy0dznO9xRERaOq8m2m6//XZ+++03nnzySXbu3MlHH33Em2++yc033ww4r+zPmDGDxx9/nG+++YYNGzZw+eWXk5CQwDnnnOPN0EVEvOa9JXtYsC0TPx8LL100mABfVQG0Jr2rJrBtPVR43A8cC7ZVLRutRzWby6iqxtNLdmbhcOgDjYh4x6/pFazIqsTHgEu6agpCWzamRyxBvhYOlsOavJr7k4qItAReTbQNHz6cr776io8//ph+/frx2GOPMXPmTC655BL3Nvfccw+33nor1113HcOHD6eoqIgffviBgABN1hORtmfroQL+MXsrAA9M603PqqSMtB5dYkPwtRrkl1ZyKP/oPmp2h8mCbc5ln+N7xdX7+IOSIgjys5JdXMHWQ4WNjldEpL5yyx08utb5/POnLoHEB+qCUVsW4GtlWh/n69nX+yu9HI2ISON5vXP2GWecwYYNGygrK2PLli1ce+211e43DIO///3vHDp0iLKyMubOnUuPHj28FK2IiPeUVdq57eM1VNgcTOgVx+UnJ3s7JGkCfj4WusaGABwzEbZ2Xx65JZWEBvgwNDmyQccf2TkKgF+rlqCKiDSX3zIquHBBLmklDjoEWbi5d5C3Q5ITwB8GOvtv/3DIRrHGj4pIC+f1RJuIiNTONE3+9vVGtqcXERPizzMXDMBQP5tWq7erT9uho/u0uYYYjOkR2+BJs6dWLR9drESbiDST4koHj64p5Opf8zlQ4iAxyMJrJ4cT6quPIwJDk8LpGACldvhxv6Zii0jLplc2EZEW4P/m7+SzVfuxGPDChQOJCfH3dkjShHpVLQnemJaP44g+bfOqEm0TetZ/2aiLK9G2Yk8ONrv64YhI08osc3DJL3n8Z48zgXJR5wC+Oi2KrmE+Xo5MThSGYXCOcyaC+/dERKSlqterm8PhYOHChSxatIi9e/dSUlJCbGwsgwcPZuLEiSQlJTVVnCIibZJpmrz403ZenrcTgEfP7seYqqmR0nr1ahdGgK+F3JJKdmcWkRjhXFq1L6eEzQcLMAwY17Phvwc94kMJ9fehsNzGzowiT4UtInKUSgfMWFPGjgIHsQEWnh4WyshYP2+HJSegc+Lh9VTYkGtjQ24l/SN9vR2SiEiD1KmirbS0lMcff5ykpCSmTZvG7NmzycvLw2q1snPnTh5++GE6d+7MtGnT+O2335o6ZhGRNiG7qJwb/r3KnWT7y2nduewk9WVrC/x8LAxOcvZfW5aS4779m3UHAOfk0OhGVDVaLQaDOkYAzp5vIiJN5cMDsD7fQaivwazREUqyyXFF+8Hp7Zx1IB/vLvVyNCIiDVenRFuPHj1Yv349b731FgUFBSxdupQvvviCf//733z//fekpqaya9cuRo8ezUUXXcRbb73V1HGLiLRaeSUVvLFwFxNfWMiPm9LxsRj847z+3D5Jg2DaklO6RgOwYFsmFTYHlXYHX67eD8D5Qzo0+vhDOjoTeUq0iUhTySmp4J/7nP/+a/8QkkM0XVRq9qdkZxXb7P3l5JartYGItEx1Wjo6Z84cevfuXeM2ycnJ3Hfffdx1112kpqZ6JDgRkbbANE0WbM9kzqZ01qTmsi29EFdbrp7xoTx/4UD6JYZ7N0hpdoM7RhIT4kdWUQXztqZjc5ikF5TTLiyAKf3aNfr4romla/flcXqf+EYfT0TkSLOWp1Fih95hFs7uqN6iUrsB4Rb6RPiwOc/GF3vLuKaHptKKSMtTp0RbbUm2w/n6+tK1a9cGByQi0paUV9q5+7N1zN54qNrtvdqFcs3oLpw9KKHBkyWlZbNaDM4d3IG3Fu3mP6v2u2+/eXxXAnwbXxUyqGMEhgH7c0spKKskxF+9cETEc8ptdj5clQbANV38sGhSttSBYRhc3CWQB1cX8mlKKVd1D8Sq3x0RaWHq/enthx9+YPHixe7v/+///o9BgwZx8cUXk5ub69HgRERau8e/28LsjYfws1q44uRk3rh0CCsemMgPM8ZwwdAOSrK1ceN7xnJyl2j391P6teOSkZ7p0xcW4EuPOOd00z1ZxR45poiIy4+b0skttRHvB6fFa8mo1N3UDv6E+xocKHGw8FCFt8MREam3en+Cu/vuuykoKABgw4YN3HnnnUybNo2UlBTuuOMOjwcoItJabTlYwFdr0rAY8NYVw3j07H5M6dee2FAtrxEnwzC4dUI37pvai79M6MYz5w/AYvHclf0hyREApGSVeOyYIiIAnyx3tpI5Jx5VJEm9BFgNzusUAGgogoi0TPVOtKWkpNCnTx8AvvjiC8444wyefPJJ/u///o/Zs2d7PEARkdbINE33BMkrT+nM2B6xXo5ITlSGYdA9LpSucSEeTbLB7wMRUlTRJiIetCermCW7sjFwJtpE6uuizoEYwJKMSvYU2hp1rNTsEhZuzyS/pNIzwYmI1KLeiTY/Pz9KSpxXvufOncvpp58OQFRUlLvSTUREarbpQAFpeWUE+Vm5dUI3b4cjbZRrIEJqTgk2u6a7iYhnfL3W2ZttdNcoEgK8HIy0SB2CrYxp5wfAxyllDTqGaZo8++NWxjw7nyveWc4pT/3M9xsOejJMEZFjqneibdSoUdxxxx089thjLF++nOnTpwOwfft2OnTo4PEARUROVGv35XH2q4vp+9AP3PDBKrKKyuu8789b0wE4d3AikcF+TRWiSI06xwQTEeSLzWGyN0fLR0XEM36oGvBzRp84L0ciLdnFXQIB+HpvGcU2s977//u3vfzf/F0ARAb5Ulxh59aP17B4R5ZH4xQROVK9E22vvvoqPj4+fP7557z++uskJiYCMHv2bKZMmeLxAEVETkSbDxRw8Vu/sW5/PsUVdn7YdIjL/7Wc4vLalzfklVSwIS0fgItGdGzqUEWOyzAMBnSIAGC3lo+KiAekZBWz9VAhPhaDiT2ia99B5DhOifOlY7CVIpvJd/vqV9V2KL+MJ77fAsB9U3ux4oGJnDs4EbvD5M7P1pJfqmWkItJ06pxomzdvHna7nY4dO/Ltt9+ybt06rr76avf9L774Ii+//HKTBCkiciKptDv4yydrKKmwc3KXaN67ajiJEYGk5pTw6cp9te6/ZFc2DhM6RQfROSa4GSIWOb4BieEA7M4s8nIkItIazN7oXJp3ctdowgN9vRyNtGQWw+BPXZxrjz/aXYpp1r2q7aWfd1BW6WBYciTXju6Cj9XCP87rT+eYYNILynl13o6mCltEpO6JtmuuuYbY2Fguvvhi/vOf/1BYWNiUcYmInLA+Xp7KjowiooP9ePXiwYzrGcc/LxuK1WKwdl8+mw/W3K/ylx2ZAAzvFNUc4YrUaECSK9GmijYRaTzXstGp/dp7ORJpDc7pGECgFXYU2FmbU7ehCClZxfyn6sLnX6f2cg8SCvC18vCZzqF+s5buJb2gYb3fRERqU+dE2+7du1mwYAF9+vThueeeIy4ujkmTJvHKK6+QmpralDGKiJwwisptvDTXeRV0xsTuRIf4A9AvMZw/Dk8C4MNle7E5jt1YPjWnhL3ZJVgtBkM6RjRLzCI16Z8YjgFkFpVrKY2INEpGYRnr9ztbI0zqo3Gj0nhhfhamJDqr2j7bU1qnfV74aTt2h8n4nrFHXdQc2yOWYcmRVNgczFqyx9PhiogA9ezRNmDAAB588EGWL1/Orl27OP/885k9ezY9e/Zk0KBBPPTQQ6xcubKpYhUR8bo3f9lNdnEFnWOCj+qvdvP4bgT7WzmYX8Yv24/daPeX7c5qtoEdwgn292nyeEVqExrgS3yY80PMzgwtHxWRhnM1me+XGEZsqL+Xo5HW4oJOzteoH9PKKaioeUL2pgP5/G/dAQDumtzzqPsNw+Ca0V0A+Gh5KuU2u4ejFRFpwDAEl4SEBG644Qa+//57srKyePDBB9mzZw9TpkzhySef9GSMIiInhIzCMt5etBuAuyf3xNda/Sk0PNCX06uu4H+1Zj82e/U3g3aHyeKdzg8hp3aNaYaIReqmU3QQADsz1BZCRBrOdTFpTPdYL0circnAKB+6h1kps8O3+2ue8P7cj9sAOHNgAn0Two+5zaQ+8bQPDyCvpJKft2R4PF4RkQYn2g4XHBzMBRdcwPvvv096ejrXXnutJw4rInJCeXr2Nkoq7AxKimBqv3bH3ObUrjGEB/qSVVTBwqoPHC7r9ueRX1pJWIAP/Tsc+82fiDckVyXadqiiTUQayHHYxaQxPZRoE88xDMNd1fZZyvGHIszdnM78bZlYLQZ3TOpx3ONZLQbnDk4E4ItV+z0fsIi0eQ1at7RixQrmz59PRkYGjsP6EBmGwfPPP09srF5cRaR1WbU3hy9WO9+MPXxmHwzDOOZ2fj4WpvVrx8cr9vH12jTG9ojFp6ryzXWl/9RuMfhYLICWK8iJoVPV9NvdmcU4HKa7cbSISF1tPlhAVlEFwX5WhnSM9HY40sqckRTACxuL2V5gZ32ujf6B1e/PLirn3i/XA3D1qM61TnU/e1Airy3YxaKdWZRU2AjyUzsPEfGcele0Pfnkk4wcOZJ3332XlStXsmbNmmpfIiKtTWmFnfu/3AjAH4clMbiWDxBje8QREeSsavt5q3NJQnpBGSv25FTdr4sRcmJpHxaAv4+F0ko7aXl1azbdEKUVdlKzS45bjSAiLZdrovbJXaPx8/HIohkRtwg/C1M7OPv+vbmtpNp9FTYHMz5dS1ZRBT3iQ2qsZnPpER9CUlQgFTYHi3Ycu6+uiEhD1Tt1/9JLL/HOO+9w5ZVXNkE4IiInFtM0ueeL9WxLLyQ62I97phzdWPdIfj4Wzh2cyLu/7uE/K/cxpGMkHy9PxWHCgA7hJEcHU1qhajY5cVgsBp1jgtl6qJAdGUUkRQV59PimafL+0r08NXsrpZV2esSH8M6Vw+kQ6dnziIj3LNquZaPStK7tEcQ3qeUsOFTBkiwLJwVApd3BnZ+uZdGOLAJ9rbz4x0EE+FprPZZhGJzWK573luzh5y3pTO577JYgIiINUe/LTRaLhVNPPbUpYhEROaGYpslzc7bxv3UH8LEYvHbJEKJD6jZFbWKveLrGBlNSYee2T9awLCUHq2Hwx2FJTRy1SMN0jQ0BmmYgwr8Wp/DwN5sorXQmmLenF3HluyuosNU8PU5EWobichsr9zqrtkdrEII0kU6hPvypi3PN6F/XlTFrP/zpg7V8t+EgvlaDf1429LgDEI5lYm/nAKt5WzNxOFRpLSKeU+9E2+23387//d//NUUsIiInDNM0eX7Odv5v/i4AHj27LyO7RNd5f4vF4PaJPWgX5mzeazHg8pOT6VKVzBA50XSp6mfj6YEIq/bm8uT3WwC4Y1IPltw7gZgQP3ZmFPH+0j0ePVd9lFXaeeGn7dz68Rp3/0QRaZjlKTlU2k2SogLdU4xFmsKMvsH0jfAhrxJe2ANr9hcQ7GfljUuH1ruackTnKEL9fcgqKmfd/rwmiVdE2qZ6Lx296667mD59Ol27dqVPnz74+vpWu//LL7/0WHAiIt4yc+4OXp2/E4CHzujDJSOT632M6BB/nj5/ADsyCokN8SeuKukmciJyJYHTcks91hi6rNLO3Z+tw2HCuYMTuXVCNwzD4I5JPbn/qw28sziFq07tjLWZhy84HCZXvbuCpbuzAfhu/QHevGwYE/vEN2scIq3FshRnNdspXWKOOyxIxBOCfAzeHR3BO5vzWZtdSc/uiVw7oUeDWhH4+VgY1T2G2RsPsWRXdq09eEVE6qreFW233XYb8+fPp0ePHkRHRxMeHl7tS0Skpfu/+Tt56ecdADw4vTd/HtW5wcfy87HQNyFcSTY54YUH+hIb4o+Jc/qoJ7z08w52ZxUTH+bPI2f2dX8AP29IIhFBvhzIL/NKNdm/l+1l6e5sAn2tDOwQjsOEh7/ZRLnNe70TTdNkya4sZi3ZQ2p2Se07iJxAlqc4k9YjOkd5ORJpC4J9DG7u7s8/+8FDk7s3qt/nSVWrFX6ruvAiIuIJ9b5cPWvWLL744gumT5/eFPGIiHjVm7/s4tkftwHw1ym9uGZ0Fy9HJNJ8usWHkFlUzo6MIvolNu7i2b6cEv61KAWAx87uR3jQ7xXwAb5WzhmUyHtL9vDt+oOM7xXXqHPVR7nNzqvznNWq907txYXDkhj33HzS8kr575oDXDi8+fsomqbJw99s4v2lewF43LqZNy4dymm9VWEnJ76SChvr9+cDSrRJy+NKtK3ck0ul3YGvVRNzRaTx6v1MEhUVRdeuXZsiFhGRRjFNk8zCsgY3WP96TRpPfr8VcPaSunGcnuukbeke57mBCE//sJUKu4NTu0Uz6RhLMl0T3uZtTcdmb76hCP9bd5CMwnLahwfwpxEdCfSzcsUpnQD4fNX+ZovjcF+tSeP9pXsxDEiKCqTSbnLLR2tIyyv1Sjwi9bEmNQ+bwyQhPIAOkYHeDkekXrrHhRAV7Edppd2dMBYRaax6J9oeeeQRHn74YUpKtKxBRE4cOcUV3PfVBm77ZC3XvL+CL1fvx2HWfYLUqr053PP5egCuHd2Z207r3lShipywulX1aduRUYRZj7+fI63fn8e36w9iGPDAtD7H7Nk0vFMkEUG+5JZUsnZfXoPPVV9fVCXTLhnZET8f59ugcwcnYhiwfE8O+3Ob9/1NSYWNJ75zDou4c1IP5t05juGdIimttPPU7K3NGotIQ7j6s43oHKX+bNLiWCwGIzo5KzG1fFREPKXeibaXX36Z2bNnEx8fT//+/RkyZEi1LxGR5mZ3mLz083b2VvU1qrSbfLZqP+/+uqdOyYJ9OSVc9/4qKuwOTu8Tz31Tezd1yCInpE4xwfhYDArLbGQUljf4OC/+tB2Acwcl0ich7Jjb+FgtnNo1BoAlu5rnw83+3BL3AIRzh3Rw394+PJBhyc4m2PO3ZjRLLC4fL99HdnEFHaOCuH5sV3ytFh45qy/gHNKgfm1yovu9P1vdJ3OLnEhO6qJEm4h4Vr17tJ1zzjlNEIaISMOt3JvLvtxSQvx9ePycfmw8kM+/FqUwd0s6/j4WLj3p+BNDC8squWbWSrKLK+jTPowX/zgISzNPQBQ5UfhaLXSKCWZnRhE7M4qIb8AQjzWpuczflonVYnBrLZWhJ3WN5rsNB1m6K7tZqkj/u/YAACd3iSYxovoStwm94lmxJ5d5WzO47OROTR4LOJe7f7B0DwDXj+3i7g3UNyGcsT1iWbg9k1lL93D/uPpPPRZpDuU2O2tS8wD1Z5OWa2RVn7bVe3Ox2R3oXaCINFa9E20PP/xwU8QhbYRpmny+aj+/7sxiROdo/jQiScsMpNHmb3NWoJw5oD3xYQHEhwVgNQz++ctuvttwkOgQP6b2a3/Ufja7g1s/XsO29ELiQv3515XDCPav99OiSKvSLS6EnRlF7Mgo4tRuMfXef+Zc58Tecwcn0jkmuMZtT+nq/HCzKjW3wb0V6+OrNWmAc+rpkcb3iuXpH7aydHc2lc3UM27Fnlz2ZJcQ5OccDnG4y09OZuH2TL5Zd4B7xjT/gAaRutiwP59ym4PoYD+6xtb89y5youoZH0pYgA8FZTY2Hyygb4Rv7TuJiNSg3ktHV6xYwbJly466fdmyZaxcudIjQUnr9cJP27n78/V8vfYA93+1gefmbPN2SNLC7coo4kBeGVaLwYRevzdcH9czjj+N6AjAB0v3sryqh4yLaZo88NVGFmzLJMDXwluXD6N9uJo4izRmIMKqvbks3F5VzTahW63bd4kJJjLIlwqbg62HCup9vvrYnems0vO1Gkzp1+6o+3vGhxIZ5EtZpYONac3TEPuzlfsAOGNA+6OS/GN6xBIZ5EtmYTlLU/KaJR6R+lJ/NmkNLBaDYVV92o58vygi0hD1TrTdfPPN7Nu376jb09LSuPnmmz0SlLRO6/bl8er8ncDvvRBeW7CLbYcaP91O2q4fNx0CoF9CGCEB1T+onjmgPRN7x2MCr87fwbKq3ht2h8lTs7fy6cp9WAx4+aLBDEyKaObIRU5MroEIe7JL6l1lNnOuszfbBUM6kBxde3WLYRj07xABwLomnvY2r6r32sjO0YQGHF2tYBgGQ5Odr00r9jT9B63ichvfbTgIwB+GHV2x5mu1MH2AsxL3vxvTmzwekYZYtTcXgOGdtGxUWjbX7/DKPblejkREWoN6J9o2b958zKEHgwcPZvPmzR4JSlqnmXO3Y5pw9qAEPrnuZKb2a4dpwsvzdng7NGnBXI1rByVFHnWfYRhceUonRnSKotJuMvPnHTz5/Rae/H4LHy5LBeDxc/pzet+jq1tE2qrYUH/CAn2xO0z2ZBfXeb9Ve3NZtCMLH4vBLXWoZnMZ2CEcgPVNPHn05y3ORNuEXnHH3WZEZ+fzyPKUpv+g9d2Gg5RU2OkcE+wexHAk13LSOduyKLU3eUgi9WKapnti8OCOEV6NRaSxhndyPg+v2JPTqKnbIiLQgESbv78/6elHX1k9ePAgPj7qbSTHtiuziPnbMrEYcPvEHgDuxtdzNh0iu6jh0+2k7Sout7G+qgqmd/vQY25jtRjcdlp3pvdvjwHszCwis6iCiCBfXrtkCBeP7NiMEYuc+AzDOGz5aFGd93vzl12As/9ZUlRQnfcb4K5oy6vzPvWVX1rprlI7rffxE22uioZVe3NwOJr2g5Zr2egFQzscd8nd0ORIEiMCKa6w81tek4YjUm+pOSXkFFfgZ7Ucd7qwSEvRv0M4fj4WsosrSMkp9XY4ItLC1TvRdvrpp3PfffeRn//7Eo+8vDzuv/9+Jk2a5NHgpPX4ZLmzemhCrzg6VTXH7t0+jIEdwqm0m3xdNQlOpD5W7s3F5jCJCvYjLvT40xGtFoNLT0rmpYsGcdUpnbjqlE7M/stopvU/ekCCiDgHIgDsqGOftt2ZRczZ7LwId92YLvU6l6uibWdGESXltnrtW1eLdmRic5h0iwupcUlr34RwAnwt5JZUkpJV92q++krJKmbFnlwsBpw/pMNxtzMMg9P7OntPzstusnBEGsRVzdY7IQx/H6t3gxFpJH8fK4Oq2oisqJqkKyLSUPVOtD333HPs27eP5ORkxo8fz/jx4+ncuTOHDh3i+eefb4oYpYUzTZP/rXP2obnwiD405wx2LouZu1n9Z6T+1qQ6l3d1qWWyoUtsaACju8cyuGPEMXs0iYhTfSva/rU4BdOE03rF0S3u2NWlxxMXFkC7sAAcJmw+2DQDEeZVLRs9rYZlowB+PhYGVy1Dd/Weagqfr3JWs43pEUu78ONfJACY1MeZaPslB2xNXGUnUh9rqpIRg9XjVFqJEa4+bfuaZyCOiLRe9U60JSYmsn79ep555hn69OnD0KFDeemll9iwYQNJSRo/L0fbdKCAQwVlBPpaGdMjttp9rl45K/bkUFBW6Y3wpAVzXU3vFF33ZWoiUrsuMSEYQFZRBbklFTVuW1Zp55uqquSrR3du0PkGVFW1bWiCaZ92h8n8bbX3Z3MZVtWnp6mWslbYHPxn5X4A/jC09vdNIzpFER7gQ54N1ubVbziFSFNSfzZpbVzP/ytTlWgTkcZpUFO14OBgrrvuOk/HIq3U3C3OarVR3WMI8K2+tCA5OpiuscHsyixm0fYs94Q1kdqYpsm6qjf5dZluKCJ1F+hnpUNUEPtyStiZUVTjRMGF2zMpLLfRPjyAkzpHN+h8A5MimLM5nS0HC0iuR3+3uliTmktuSSXhgb4MPc7QgcMN6ejcZv3+/For4Brih02HyCwsJzbU312tVhMfq4UJ3aP5akM68zNsjEj0eEgi9VZus7P5gLMCdZAq2qSVGJocicWA1LwyMsqhXYi3IxKRlqpOFW2//fZbnQ9YUlLCpk2bGhyQtD6uSW8Tj9OA2lVh8PNWLR+VutufW0puSSU+VoOEiJqXXolI/dV1+ei3652tAab3b4/Fcuym/rVxDTPZdqhuPeHq4+etztegcT1j8bHW/rbHlTRIySqmuAl6xr2/ZA8Al4zsiJ9P3RYWTOoZA8C8dJum4ckJYfOBAirsDqKC/ejo4eS4iLeEBvjSu71zsMfqpulkICJtRJ3e4V122WVMnjyZzz77jOLiYzcH3rx5M/fffz9du3Zl1apVHg1SWq70gjL3UqDxx6kMmNDLeUV/wbZM7Oo/I3W0teoDedfYEHzr8OFZROqnLgMRSivs/FxVtXzGwIQGn8v1wWZPdgmVds8uj3T1Z6vLslGAyGA/Olf1fdybXeLRWDam5bNyby4+FoOLR9R94vHoLlH4W2B/qcmOArtHYxJpCNey0UFJEcedmivSErkquNco0SYijVCnT6ebN29m+vTpPPjgg0RERNC3b18mTZrEmWeeyahRo4iJiWHIkCGkpKQwZ84cLr/88qaOW1qIeVWVBAOTIo47FXJYp0iC/azkFFew9ZBe1aRutlQ1Te8ZX7/G6yJSN66Ktt2Zxce9CDJvawYlFXaSogLd00Mbol1YABFBvtgdJgfzyxp8nCPtyylhW3ohVovB2CN6hNbE1XNqT7ZnJ4/OnLsDgOkD2hMXVvdK3CA/KyOrHt55B8s9GpNIQxyeaBNpTZRoExFPqFOizdfXl9tuu41t27axdOlSrr32Wvr160diYiLjxo3jn//8JwcOHODjjz+mf//+TR2ztCCuSoeJNVQS+FotDKt6UVu2O6dZ4pKWz5WU7aFEm0iTSIgIJNDXSrnNwb7cY1d2fbveOQRhev+ERlW1GIZB73bOqra0vNIGH+dIros9Q5MjiQjyq/N+g6v6tKVkea6ibU1qLnO3pGMx4LbTutd7//FV7e/mHax5OIVIc1CiTVqr4Z2dz//bi6GgsulW2qTllfLKzzt45oetbGyCQUAi4l31HoYwbNgwhg0b1hSxSCtTVmln8c4sAE7rXXPD55Fdoli4PZNlKdlcOFzTa6V2Ww86l7P1aBdCWYWWUol4msUw6BoXwsa0fHZmFNHpiKEjxeU2dyLrDA8MsundPoylu7M54MFEm6s/2/F6hB7P4Krkwd6cYhwe6IlmmibP/rgNgPOGdKBrbP07bI+NAgPYlGfjYImd9kHWWvcRaQr5pZXuZdUDGlHJKnIiigsNIDkykL25pazNszMuzPPnWLwji2vfX0lppfP96+sLd/HAtN5cM7qL508mIl6hxkbSZH7dmUVZpYOE8AB3o+vjGVk1qW5ZSg4O9WmTWpRW2EmpWtKlijaRplPTQIQF2zIotznoEhNM34TGfxJxvU6k5Xom0VZcbuO3XdnA771A66pXu1ACfa2UVTo8spR19sZDLNmVjZ/Vwl8aUM0GEO0HgyKcb9vmH1JVm3jP1qrWDYkRgfWqFBVpKYZ3dCaQ1+R6/kLuvpwSrvvAmWQbmBTBxN5xmCY8/t0WvqsaLiQiLZ8SbdJk5lY1oD6td3ytS4oGdAgn0NdKXkklOzNrnnAnsi29ENOEmBB/YkL8vR2OSKvlHoiQfvRAhNkbDwHOajZPNEN3DURIyyvzyGTNxTuzqLA7SI4OomtscO07HMbHaqFvojOeXY18TSout/HYt5sBuGFsF5IaMaFxQrxzIcK8A+rTJt6zuSrR5vqbFWlthiU5E22rcjyfaHv4m02UVNgZ3imSz64/mbevGM71Y5yVbA9+vYHcYl1IEWkNlGiTJmGaJvO2OvuzTajDkh1nnzZnT4SVe9SnTWq21f0mX9VsIk2pe1wIBnAgv4ycw978l1TY3K0BGjNttNq54kPwsRiUVtqrnauhXD1CJ/SKa1AicGCHCAB2NzLR9vqCXRzMLyMpKpCbxndr1LHGxzkTbSuyKimo8Ox0VpG6cg0j6qPXYGmlRlQNxNmQ76DU5rmVNhv25zNvawZWi8FT5w/Az8f5UfyuyT3pGR9Kbkkl/zd/p8fOJyLeo0SbNImNaQWkF5QT5Gfl5C7RddpnZGfnQIQVe3KbMjRpBbYeclbX9GqnN/kiTSk0wNdd1bYm9ffn5g3787HZTXrEh3hs+ba/j5XOVZVn+xq5fNThMJm3NROA0+q5bNRlYFWftl2ZDZ88ejC/lLcX7wbggWl9CPBtXF+15GALXUOt2Ez4JV1VD+Idroq2Ph5YMi5yIuoYGUC8H9hMWJdb6bHjvvtrCgBnDmhfrVenr9XCfdN6AfDvZXvJKlLVskhLp0SbNIm5VZUEo7rF1PmDxciqhNyqvbkeWTYkrZfranrPdnqTL9LUhlRN4Fy59/dEm+vfZwzwTDWbS8+qpN2+nMZN+9yQlk9WUTkh/j6MqLqIU1+uJu8H8kopqbA16BjPz9lOWaWDYcmRTO7bsITfkU5r71wur+mj4g2Vdgfb051Vnn3aaxCCtE6GYTC06td7RaZnEm1F5TZ3y4XLT+l01P1je8TSPzGcskoHn67Y55FztnSmafLhsr2c/epirpm1kp0ZR7exEDlR1Wnq6Msvv1znA952220NDkZaj3nuSW91/2AxoEM4Ab4WcoorOFRQTmiAGuzK0UzTZFu6KtpEmsvIzlF8unIf6/bnkVNcQUFpJdvSizAMOG9IokfP1bNdKN+uP8i+3MYl2lzTRsf0iHEvzamvmBB/ooP9yC6uYFdmMf0T65dUSMsr5cvV+wG4f3pvj/SxAxjX3o83t5ewNKMCu2li9dBxRepid2YxFTYHIf4+dIgM9HY4Ik1meDh8nwkrszyTaPtp8yFKK+10iQl2T7Y+nGEYXHFKJ+76bB0fLUvlxrFdsVja9vP7q/N28vxP26u+y2f9/jx+mDGGqGB9RpQTX50SbS+++GK17zMzMykpKSEiIgKAvLw8goKCiIuLU6JNSC8oY0NaPoYB43vV3p/Nxd/HypCOkSzZlc3OjCK6xymJIkfLKCwnr6QSq8WgW1wINk2pFWlS7SMC6Rkfyrb0Qn7YeJCcYueHjlHdYugQ2fDG/sfSsyp5ntrIijZ3j9AGLht16RQdRHZxBTvSC+udaPt4WSoOE07uEu2uCvSEvhE+BPsYFFSabMu30SfC12PHFqnN5oP5gLNHaltPAkjr5qpoW5dbSZndJMDauN/3nzY7X5em1zBA6IwB7Xn0m02k5ZWycm9ugyuyW4P1+/N4ca4zyXblKZ1YsC2DPdklPDdnG0+e29/L0YnUrk6XeVNSUtxfTzzxBIMGDWLLli3k5OSQk5PDli1bGDJkCI899lhTxystwM9V00YHdoggNrR+EyFPqlo+ujNDk0fl2FzLRjtFBzW635GI1M0ZA9sD8L/1B/l1l3MIwg1ju3r8PK6lo5mF5ZRVNmza26H8MjamFWAYMK5nbKPi6RTj7Bm3o56vSQ6H6a5mu+Skjo2K4Ug+FoNhMc7k2jIPLWkSqatth5x/C73UukFauY4BEOtvUOmA9TmNe64tt9lZuM3ZN7Sm1T4BvlYmVbUZ+G79gUadsyUzTZMnvtuCw4QzBybwyFl9efr8AQB8sWo/2ephJy1AvddT/O1vf+OVV16hZ8+e7tt69uzJiy++yIMPPujR4FqDCpsDexuruHFNeptYh2mjRzo80aY+bXIs29yDEPQmX6S5DEuOYnT3GPf3E3vHuYcFeFJ0iD9hAT6YNLyqzdW6YHBSBDEh9bvYc6RO0c5EW31fk9btz+NAfhnBftZ6tVCoq5GxSrSJd7im8HatGlwi0loZBgyLcl7QXdHI5aOr9+ZRXGEnJsS/1uroM6t6n36/8VCb+wzpsjwlh2UpOfhZLdw71TkkYkTnKAZ0CKfc5uDrtW03CSktR70TbQcPHsRmO7opsN1uJz093SNBtQamaTJz7nb6PfwjQx//ibmb28ZjU1phZ/FOZ7XDaQ34cDEwKRx/HwuF5TYO5pd5OjxpBbZp4qiIV9w4tit/O6MPf53ci7MGenYIwuESIpx9n/ZmNzTR5ny9bchr0JESIwLwsRgUlds4VI/XpB82ORteT+gd3ySVtyNjnf1pVmZVUNlGP4iJd6RkOafwdjlsYqJIazUs0jOJtuUpOQCc3DW61iXXp3aLITzQl8zCcpalZDfqvHVlsztOqAKHd6qms54/NJHEqvcEhmFw7mBnX9gfNh70WmwidVXvRNtpp53G9ddfz+rVq923rVq1ihtvvJGJEyd6NLiW7IvVacycu4MKu4O8kkpu+nC1+81Ja/brzizKbQ4SwgMalAjx97EyqKpKwpVQETnc1qrfi55KtIk0K8Mw6NM+rMn/9lxvqlNz6v+aWW6z8+tO5weT8T3rX1V9JB+rxV3Vtr0e085+XyLU+BiOpUeYlQg/g1I7bMht2ERUkfqyO0x3ArxzjCrapPUbXlXRti6nknJ7wxNRroRZXXqu+flY3FOqf6iaUtpUKu0O/v6/zfR5+EcGPjqHj5alNun56iKjoMzdz+7Pp3audt+Ufu0A5+TzLC0flRNcvRNt77zzDu3atWPYsGH4+/vj7+/PiBEjiI+P5+23326KGFuc0go7//h+C+DsYXNyl2gq7A6en7PNy5E1vZ8PqyRo6IS14Z2cL0Jb0ws8Fpe0DpV2h7t/n5aOirROiY2oaFu9N4/SSufynN7tPZMQ7B7vrNzZerBuibbMwnK2HirEMGB098b1iDsei2EwLNq5fHRdI3sHidTV/twSKuwO/Hws7r9TkdasU7BBtL9BhQM25DbsubbC5mB1ai4AJ9VxuMGkPs6E0oJtmU1WaWaaJn/9Yj3v/JpChc1BQZmN+7/a0OTJvdp8tSYNhwlDkyPpHl/9dbx9eCB92odhmvBbVZWgyImq3om22NhYvv/+e7Zu3cpnn33GZ599xpYtW/j++++Ji2uaK7ctzVdr0sguriApKpA7JvXgb2f0AeD7DQfJKGi9yyEdDtM9COG0RlzFH171IrTtUOEJVcYs3rcnq5gKu4MgPysdIvUmX6Q1SowMAJw92hz1fA34tap1wahu0Q2+2HMk14CGLYfqdvHHVbnQNyGMqGA/j8RwLP0inYm2japok2ayu2plRufoYE0clTbBMAxGxDifx5c3cPnohrQ8yiodRAX70S2ubkuuT+kajZ/VQmpOCSk5pQ06b22+WXeAL1en4WMxeOmiQVxaNbjnof9ubPAwosYyTZPPVjkHCV0wtMMxtzm5q7Of97I9SrTJia3eiTaXTp060bNnT6ZNm0aPHj08GVOL9+/f9gJwxcmd8POx0CchjCEdI3CY8PXaNC9H13Q2Hsgno7CcID+re6hBQ/RPDMfXalBQZuNAXutNTEr9uZaN9ogP1Zt8kVYqLtTZF63c5iCjoH5LQxa5Em0erCTrFheCYUB6QTk5xRW1br9qr7Ny4eRGvA7WRf9IHwA2NrDKQqS+dme6+rNp2ai0Ha4pzysbmGj7bbczITSiU1SdLwAF+/u4l5ku3On5Pm1llXaerFp9deuE7pw9KJEHp/chMSKQjMJyvlrjnc+r6/bnszOjiABfC9MHtD/mNq7PmMuVaJMTXL0TbSUlJVx99dUEBQXRt29fUlOda7lvvfVWnnrqKY8H2NKkZBWz+WABVovB+UN+z8SfV/Xv79a33uaNrmq20d1jGtX82c/HQueqnjibD+Z7JDZpHVx9+zy1JExETjxWi0FiVcXq3nr0acsvqWTD/jwARnWLqXnjegjy83H3adtah6o2V6LN1QahqfSJcCba9pc4yC13NOm5RABSspytG9SfTdqS4VWJtrXZlVQ0oE+baxDCyC71e00Y19N5wWjBTs8nlD5Yupf0gnISIwK5YVwXAAJ8rVx1aicAZi3Z4/Fz1sVXq53VbJP7tiMswPeY24zoHIVhQEp2CdmltV/8EvGWeifa7rvvPtatW8eCBQsICAhw3z5x4kQ+/fRTjwbXEs2umoJyStdoIg9bMjKpj7Op5br9+WQWts7mjT97cNKbq7R680H1aZPfuT7k9oxXok2kNUuKDAIgtR592pbuzsJhOl8/2oUH1L5DPbiG+2yppU9bUbnNXfXT1Im2MD8LScHOt3HbC7R8VJre7xVtmjgqbUeXUCvR/gbljvoPn7HZHaysqryqyyCEw42rGuizPDWPEg+u5Cwqt/H6wl0A3HZaN/x9fi+O+MOwJPysFrYeKmRLM38Gs9kdfLfB+Tn6nKrposcSHuhL3wRnn+a1mRqcJyeueifavv76a1599VVGjRpVrfy1b9++7Nq1y6PBtUSzNzgbSE7tV73cNT4sgP6J4QAs2JbR7HE1tUP5ZWxMK8AwYEKvxvfq6+5OtKlPm/zu94mjGoQg0polRTkTbXtz6p5ocy3POaWr55ds9q56zqmtom13prPip0d8SLWLbU2le5izqm1ngXf66UjbkuLq0aaKNmlDDMNgWFWfthVZ9aug2nSggOIKO2EBPvUe4tU1NpikqEAq7CbL8+q1a43+s2IfOcUVdIoOqrb6CpxJLNfnuP+uPeC5k9bBkl3ZZBVVEBnkW2tVuqs1wzol2uQEVu9EW2Zm5jGHHhQXF3us8XBLtS+nhA1p+VgMOL3v0VVdY3s4S4BdHwZaE1c126CkCGJC/Bt9vI7RQc4+baWV6tMmAOSVVLA/19kQVktHRVq3jq6lo9l1Xzq6cq/ztbUpKsl6VlW07c8tpaDs+H16djZTNZtLt6pE2w5VtEkTK6mwcTDf+X6sq3q0SRvj6tO2Krt+fdpcw3GGd4rCWs/ewoZhML6qqu3X3HrtelwOh8mspXsAuHp0F3ysR6cCplX1RptX9dmuuXyzzpnYm9a/Pb7HiOtwIzs7E20bs4uaPC6Rhqp3om3YsGF899137u9dybW3336bk08+2XORtUBztzifkEZ0jjpmssk1TXP5Hs83tfQ2V3+2iR5YNgrga7XQrWppwib1aRNg7b48wHklPSKo6StFRMR7OlQtHc0qqqC4vPYkUnG5jc0HnNVmwzpFejyesEBfEiOcyT9Xr8hjcVW01XeJUEN1D3Mu+VGiTZqaq5otMshXr8HS5rj6tK3JrqTSUfeVNkt2OT/zndzASmtXn7ZFuXhkhc+C7RnszS4hNMCH84cce3nm2O6xWC0G29OL2FePqvLGKKu08+NG56qwswcdf9moy9Bk5+v8/qJy8mq4+CXiTfVOtD355JPcf//93HjjjdhsNl566SVOP/103n33XZ544ommiLHF+GV7JoD76sORhnSMwGLAvpxSDuW3niqt0go7v1ZNevPEslEX1/LA5u4RICemdfucCdeBHcK9HImINLVgfx9iQpwf5uuyfHT9/nwcJiRGBNI+PLBJYurdvubXpNJKO/uqqm6braIt1FnRtqvQrjYL0qTUn03asq6hVsJ9DUrtsCWvbhc2KmwOlrlbGjRsQM/JXWLwsxocLIfdxY1/jp+1ZC8AFw1PIsjP55jbhAf5MrSjM5E1v5naHS3YlkFhuY324QEMS679YllksB9dq5awb9TyUTlB1TvRNmrUKNauXYvNZqN///7MmTOHuLg4li5dytChQ5sixhah3GZ3Lwkd3T32mNuEBvjSp6p5Y2saSbx4ZxblNgeJEYHuhtGe4Gp4v0V92gRYu89ZNz8oKcK7gYhIs+gY5XwTnVqH5aOrU12TPj1fzebSp2rJ+sYDx0607cwowjShQ2QgCRFNk+w7UscQKwZQWGmSW6HXSWk6roq2LurPJm2QxTAYWlXVtjKrbhVUa/flUVppJzrYr8GfjwL9rJxU9bq2KLNxlcsZhWUs2uEsCrlkZHKN246vKpyYt7V5Em2ufnBnDUzAUscltq5k4MZMLR+VE1O9E20AXbt25a233mL58uVs3ryZf//73/Tv39/TsbUoq/bmUlppJybEv8b+Ua6r3CtSWk+ibe5m55LZib3jPNqnr0tsML5Wg/zSSndfEGmbTNNk3f6qijYl2kTahOToqoEIdZg8uqYq0TasCSvJ+iaGY+Dsx5pbcnRD7G1VgxLqcjXeUwKsBu2DnG/l9hRpIII0Hdey6M7qzyZt1NDoqkRbHfu0uVb7nNw1us7Jo2MZ3835urYos3HP8d+uO4jDhMEdI+hUS8LctUJpya5sSiua9rWlsKySn6sSemcOTKjzfkM6RgCwQRVtcoJqUKJt165dPPjgg1x88cVkZDj/MGbPns2mTZs8GlxLsmiH88l0TPeYGpNNI1yJtlZS0eZwmO5BCJP6tPPosX2tFrpVTR/V8tG2bV9OKTnFFfhaDXdVqIi0bsl1nDxqd/yeiG+K/mwuYQG+7mmLG/Yf3Tt0W3phVQzNs2zUpVOIs0/bXiXapAntdle0aemotE2uPm2rsyux12GljSvRdmotEzRrM66bs7/b6lw7hZWOBh/nv1XDBs6uQzKrR3wIiRGBzuWvKU3bW3zOpnQqbA66xAbTtx7v8YdVVbRtzymm3Nbwx0WkqdQ70bZw4UL69+/PsmXL+OKLLygqcl7hWrduHQ8//LDHA2wpXP3ZRveo+cnUNRBh66HCGieXtRRr9uWRVVRBqL9PkzR/rq0njrQNa/fnAdCnfRj+PlbvBiMizSI52pnU2p9bgs1+/DfRaXmllFbYCQvwoUdc004kHlDVI3J9WvVEW1mlnT1ZzoRgUyb7jiW5KtG2p0gDEaRpmKZJirtHmyrapG3qGe5DsI9BYaXJjoKaL2zkl1a6h3iNamSirWNkIJ0CwWbC0oyGfXbck1XMun15WC0G0wfUnmgzDMMdt6uYpKn8ngBMrNfKqKTIQCL9fah0mGzV9FE5AdU70Xbvvffy+OOP89NPP+Hn9/vUoQkTJvDbb795NLiWIquonE1VPVtGdTt2fzaXmBB/93KYdWnNV+pqmia/7c7mqdlbeX7ONrane+bcrkmrY3vG4ufToALJGvV2DUQ4pD5tbdmqqgpQ9WcTaTviw/wJDfCh0m6yK/P4fdp2VS1pG9YpqlHLc+qif4cIADak5eM47DVpR0YRdtMkMuj36aTNJTnYlWhTRZs0jcyicgrLbRjG70u6RdoaH4vB4GjnAIGVWUe3Dzjc3M3p2Bwm3eJCSIpq/N/MqKrrN4vSaz7v8bh6oJ3aLYbYUP867eMqHlnchIm27KJyd+XfWYPqvmwUnMnAvtHOCtv16SrIkBNPvTMjGzZs4Nxzzz3q9ri4OLKyGv6H+NRTT2EYBjNmzHDfVlZWxs0330x0dDQhISGcf/75pKenN/gcTcX1BNGnfVidnrwGVyUL1qY1z5NCaYWdGZ+u5aI3f+ONhbt4Zd5Opr20iP+s3NfoY7v6s03qE9/oYx1L9/gQrIZBTnEF2f/f3n2Ht1We/x9/H0m2vPeO7ey9BwlJSBogBCijbAqUTRdQVumgLfBraYEuChQKJV+gg1IohdBC2YGEQEIIISE7IdMZ3nvLls7vD43EiZNYtmTZ0ud1Xb7AR0fS4yTyI93nHo3d21yk//u4h+PRRaT/MQzjkIstR98vvUG43pj0OSIrAbvNQl1zG7srDgb/1nuybodlJQS0V2lXDPAE2oqbVDojweHNZstPjVVWuUS0aenuJJPjDUR4Y30xAF8dnxuQ553j2d4+LHH4nXhgmib/+WI/0LWyUa/ZQzMwDHdbhNK64PTKfmN9MU6XyYT8ZF9rBn94A23ry9SnTfoevwNtKSkpFBcXH3F8zZo1DBgwoFuLWLVqFX/+85+ZMGFCh+O33347r732Gi+99BJLly7lwIEDXHDBBd16jmD6cJs70Ha8slGvyZ6a8s97IdBWUtvCBU8s5z9rD2CzGFwwZQBzR2TS7jK565X1rNzZ/br73VVNfFnWgM1iMG9kVgBXfZDdZqXQc/V0R5nSgiNRaV0L28saMAw4cYgCbSKRxDtc6GjtA0zTZKcnCDB9cPBLNm1Wi28gy8pDhhqt9gxj8Ke/TKDkxbkDHwealNEmwaH+bCJu0w6ZPOo6SsCrpLaFJZ6WQudODEygbUoSxFqhotXFxhr/2gRsPFDHzvJG7DYLC8Z2PTEiNT6a8QPc7RKCldV26LTR7hjny2irP+rfh0io+B1o+/rXv86PfvQjSkpKMAwDl8vFxx9/zJ133slVV13l9wIaGhq44oorWLhwIampB98k19bW8vTTT/PQQw9xyimnMHXqVJ599lmWL1/ep0pUTdP0ZbTNOU7ZqNdkz5SUL/bX4Qri74TtZQ1c+MRyNhfXkZEQzfPfPJGHLpnEX689gfMm5eF0mfzs1Q20HaP3zbEs3uYO0s0YkkZybFQgl97BUE8/kO0a3xyRvK+v8QOSSYmLPs7ZIhJOvH06t5XW4+xkwyypa6GhtZ1om4Vxng8EwXbiYHfA/5OdlbhMkwM1zRyoacF6SAZeb8qLdb+Vq3aYNLXrg4YE3i5PoK07GSci4WRcqo0Em0G1w2RDdecBr+dX7sHpMpk+KI1hAeobGm2BkzLcF1XeO9Dq133/s9adzTZ/TDaJMf59XjvYp63cr/t1xb7qJj7bU41hwNld6BvXmaEpccRYLdQ72tlT2xzgFYr0jN+Btvvvv59Ro0ZRUFBAQ0MDY8aMYe7cucyaNYuf/exnfi/gpptu4qyzzmL+/Pkdjq9evZq2trYOx0eNGkVhYSErVqw46uO1trZSV1fX4SuYdpQ3UlLXgt1m6XID5NG5SdhtFmpb2ikK0u+ENUXVXPzkcvbXNDMkI55FN872DSswDIOfnzuOtPhovixr4PV1B7r1HG9sdk+cnT86OGWjXkMz3VcrdijQFpE+3u4O6M4a2rNmsiLS/xSkxREfbaWlzeX7sH+oL0vd+8KEAcm9VtI2uTCF2CgrZfWtrC2qYfEW9144Ni+J2OjeL6tLiraQYHOXqxYrq02CYKfn/ddQDUKQCBdlMTgp233R94OSI1va1Da38ezy3QBcPWtQQJ97fra7P9y7B7pePup0mfzXj2mjh5sz3J1E8tH2SlwBzg557Qt3hdyMwWnkJMd06zFsFoPRGerTJn2T34G26OhoFi5cyI4dO3j99dd57rnn2LJlC3//+9+xWv17g/nCCy/w+eef88ADDxxxW0lJCdHR0aSkpHQ4np2dTUlJyVEf84EHHiA5Odn3VVBQ4Nea/OXNtpk2KJWYqK79/FFWy8HJZUEoKV+ytYzLF66kuqmNifnJvPSdmUc04kyOi+L6kwYD8OelO/2u99/dDGv312O1GN2+CtFV3iuoRZVNGogQYUzTZPkO73h0lY2KRBqLYTDSkyW2qZPyUW/vtqkDe2/SZ0yUlVNHu9slPPTeNl8vnpNHBaeFQlfkxbnfzh1oVp82Cbydvow2lY6KnJzrDrS9ua/liM8lf1i6i/qWdkZmJ3LmuJyAPu/cLBtRFvfgm+31XbuosnJXJaV1rSTF2PjKyK5VXh1qykD3haWKhla2lAT2Q+trX3jLRrvXesprXObB8lGRvqTbYyILCws588wzufjiixk+fLjf99+7dy+33nor//jHP4iJ6V4UuzN33XUXtbW1vq+9e3ve8P9YPtruDQL4l23j7dP2RYB/J3xeVM23/raa5jYnc0dk8vw3TyQ9ofMBDd84cSB2m4UtJfVsKPEvW+x/7gv4zBne9ek13ZWXEovVMGh0OKnSQISIsqO8keLaFqJtFqYNDH6jcxHpe8YPcAfa1nj6oHm5XCYb9tcC/u/BPfXV8bkkxdh85azDsxIYn9c7paudyY3zDkRQRpsEVpvTRVFlEwBDlNEmwim5duJtBnsbXXx6yFCE5dXw3Gfu4NE954wJ+BTsBJvBrCx3kO/d/V0rH/2vpwfaWRNyu5X1bbdZOXGI+/33R9sDVz66t6qJTcV1WAx6HJAcn+kuz11Xpow26Vu6FWh7+umnGTduHDExMcTExDBu3Dj+7//+z6/HWL16NWVlZUyZMgWbzYbNZmPp0qU8+uij2Gw2srOzcTgc1NTUdLhfaWkpOTlHf0Ha7XaSkpI6fAVLu9PFJ55piCf5+SZ/iqdP2/oABtqaHU5u+ecaHE4X80dn839XTSPebjvq+cmxUSwY6/6zXLTu6FmChzNN0xdoO39yz65CdEWU1UJuijsYW1TVFPTnk77D2xNi+qC0kJRkiUjoTfUE2beW1lPbfPBDzZdlDTQ6nMRGWX1Z4r0lNS6aH54xign5ycwcks5t80cE/EOVP3wZbQq0SYDtq26m3WUSE2UhJylwF8ZF+qs4m8HZBe4kg0c3NeIyTTbWOvnhFjCBy2cUBu3iz4I89/O+24U+ba3tLl/GdU+yxrzlo8sCOBDhvc2lgHtaeGp8z/ovj8lIwAAO1LdS0aSEDOk7/A603XPPPdx6662cc845vPTSS7z00kucc8453H777dxzzz1dfpxTTz2V9evXs3btWt/XtGnTuOKKK3z/HxUVxeLFi3332bp1K0VFRcycOdPfZQfFuv211Le2kxwbxVg/r2R7M9q+bCRgzYuf/mgn+6qbyU2O4eGvTyLadvy/3gumuH/xvraxjLYuVpysqHSyvxXio60sGBPYtOijKfSUvirQFlm8m/qc4erPJhKpMhPtDM6IxzRhxY6Dk7K9ZeVj8pKwWbudoN9tQzMTuOvM0dxy6nDSevhBoafyYr2TR1U6KoHl7c82OCMhpMFkkb7kWyPjiLXC2qp2zl9czZWfNFPvhKn5Sdxz9pigPe/JudHYDNhW52R3/bGnjy7dUUldSzs5STHMGNz9qhDve/BPd1XR0haYizneQNtpY3re5zsh2sZQz+fE9cpqkz7k6OlOR/HEE0+wcOFCLrvsMt+xc889lwkTJvC9732PX/ziF116nMTERMaNG9fhWHx8POnp6b7j119/PXfccQdpaWkkJSXxve99j5kzZ3LiiSf6u+yg+NgTBJg1NB2rn28+spNiyE2yU1zXysZaJzN6OJSmtd3JXzzNN390xigSjpHJdqg5wzLISLBT0dDKx9VwShcSAJ/Z5c4ouHBiTq9lGRWkxgGV7KvWRJlI0dru9H2o9l5NE5HING9EJrsqGnlvcymnj82mzWmy3PP7YXoXBxGFM2W0SbB4B1GpbFTkoJxYKz+fnMhPVtf7+qXNSYU/Xjahyz27uyM52sL0zCiWl7Xx9oFWvj3y6J/3Xlrrrlb62qS8HgXJh2UlkJMUQ0ldCyv21HBCtx/Jrba5jZU7q4DABNoAJmQlsb2qiS9K6zl5kC7OS9/g9yXgtrY2pk2bdsTxqVOn0t5+7Mi6v/7whz9w9tlnc+GFFzJ37lxycnJ45ZVXAvocPdHd/mxekz19Z76o6fkV6Lc2lFDR4CA3OYazJuR2+X42q4VzPVNo3u5CRvD66jZWVjqxGXDDjOAOmjhUrmcaTUldS689p4TW6j3VNLc5yUiwMzo3MOPRRaR/Oml4BjFRFvbXNPPhlxW8taGYhtZ20uKjGZmt3w953h5tGoYgAba9zB1oG5apQQgihzqrIIaXT0nl3kkJPHNCDH8cA4ldTHToia/muz8TLdrTgusoQ+JKWmHJdvfFqEtP6NnnNcMwWDDWHRB7a3PP+7Qt2VpGu8tkeFYCA9MDE8Cf4HkfoIw26Uv8DrRdeeWVPPHEE0ccf+qpp7jiiit6tJglS5bw8MMP+76PiYnh8ccfp6qqisbGRl555ZVj9mfrTU2OdtYU1QD+92fzmpzvCbTV9vwKtHdyy0VT84nys4Tm7InuwNySKmhxHruM9c9b3KWbZ2bCgJTe69XhHftcUqtAW6Twlo3OHZ6BYahcRSSSxUXbOH+Su9XBk0t38M9V7kFH500aoHI2Dg5DKGt20ebSdG4JnB3l7omjw7IUaBM53LAkG5cMjuWEdBu99Vb19AF2ErzDGMrbOj1nUQm4TDhxSBpDAhAkP3Oc+7Piu1srutxq6Gje3eQuG50foGw2gPFZ7s/UX1Y20hyg8laRnurRMIQbbriBG264gfHjx7Nw4UIsFgt33HGH76svKa8PbIBm1e5qHE4XA1JiGZge163HmOTJaFtX4zpiPLQ/6lraWLrNfYXhHE92mj8mF6SQl2SnyQkfVxz9l9NHpQ4+KHFgM+D6/G4vt1u8gbaG1nbqWzrfVCS8fOj5Nz1nhFLARcQ96dM7SAjc2eSzhqaHbkF9SLrdINoCLqBUWW0SIKZp+jLahiqjTaRPiLMZnOUZxvDv3Ud+vm1zmSxyx7K4bHphQJ5z+uA0MhLs1La082lt9x/H0e5i6Vb3+/tAlY0C5CTYyY6PxmnCJk+5u0io+R1o27BhA1OmTCEzM5MdO3awY8cOMjIymDJlChs2bGDNmjWsWbOGtWvXBmG53fevVfsC+ngf+8pG07udbTM2JwGbAVUOk/09aGC8cmcVbU6TQelxjOhGCY1hGJw5xt0D6+2Szst/HS6TB9a5f3FdVhjF4O7FFrvNbrP6mk0rqy38VTS0svGAO/37pGHqzyYi7lYH3z9tJD87azT3nj2GG+cNxaJsVwAshkFOrLd8VFfzJTAqGhzUNrdhGOrRJtKXXDzInYDw7oFWSg/7nf/agXZKHZARH8XpYwNTCWa1GJwxzh0Ye6cHw0c/3VVFfWs7GQl2JuWnBGRtXt6stnUqH5U+wu9C8g8++CAY6wi6lz/fy53nTOpeCl8nvNk2PRnfbLdZGRUPGxpgbVUb+fHda57pbRg/qwdrOWt0Fk9/so8lZe00tZvE2Tp+eHluezO7G5yk2w2+OywaWns/qywnKYaqRgcldS0MV0+eXmGaJjvKGymrb2FIRoIvszDYPvKUjY7JTSIz0d4rzykifZ/FYvg95TtSZMdaKGp0Ut7iAsVEJAC82WwFqXFBbfAuIv4ZnRLFlHQbn1e289TWJu6e5P5c1Njm4vEvHQB8e1ZhQF+3Z0/I47lPini7HO5sM0mN9f8x3t3kHtAwf3RWwNs+TMhO5L1dFawrrQ/o44p0V4/jTnV1dbz66qts2bIlEOsJmrJ6B4s9o4R7al91E1tK6rEYMLeH0xAneqZ8rqvq/iCJ5TvcQYmZQ7pfQjMhL5HCGGh2wtv7WzvcdqDJyRNb3b3Z7hibQGJUaDIIvAGXigZHSJ4/0hRVNnHRkyuY/9BSLl+4khMfWMx1f1nFvuqmoD/3h1+6A9lzRyibTUSkKzJi3G/pyltUOiqB4Z04qv5sIn3PzaPdV1T+tauFNZVtmKbJA+saKWs1yY+By6f4307oWGYMTmNUVjzNLnh5r/8JF6Zp8t7mMgDmjw5c2aiXN6NtQ3k9TvUqlT7A70DbJZdcwmOPPQZAc3Mz06ZN45JLLmH8+PG8/PLLAV9gIP39kz0BeZzFnl8S0walkeopZ+yu8Z7ErLVV3csQq2p0sKXEHbk/sQeBNsMw+Jrnd96Lu5p9PeNM0+Sez+tpajeZkm7j3MLQZRdlJHgDba3HOVN6qri2mYueXM7qPdVEWy0MyYjHMOD9LWWc9/jHvjffwWCaZodBCCIicnxZ3kCberRJgPgmjirQJtLnzMiM5uwCOy7g28tr+caHNSwqasEC3DOMgGehGobBtTPcTbqfL2rze/DO+v217K9pJjbK2qOKsKMZmhpHXJSVpjYnO3ohKUDkePwOtH344YfMmTMHgEWLFmGaJjU1NTz66KP88pe/DPgCA8Uw4OPtlWzzTE/qifc8mXHzR2f1+LEmezLaNte0U+Pw/83xyp3ustHhWQk9LrE7LxvsFlhf3c7yMnfg7/mdLawobyPGCr+ckhTSfjgZCe6gZkW9Am3BZJomt72wlrL6VkZkJ7DkB/N4/855LL7jK4zJTaKiwcFN//icliBN9dlSUk95fSuxUVamDkoNynOIiISbTG+grVWBNgkMX0abBiGI9Ek/m5jApDQbje0ma6vasRlw91g7M1KC83znjM0mPQpKW0z+U+Rfz+w31rvLRk8ZlUVsdOBL0a0Wg3GZ7gyW9erTJn2A34G22tpa0tLSAHjrrbe48MILiYuL46yzzuLLL78M+AIDxRsU+/PHRT16nPqWNj7xBLcCkfaaY4dhCRZcwPIy/0siV3jWEojJaxnRcHFBFAD3rKnn8c2N/Ga9+03W7WPjGZgQ2v4cvoy2RgXagum1dcWs3FVFXLSVhVdNIy/F3YRhSGYCz90wgwEpseyrbua1dcVBef5lnrLRE4ekYbepJ4yISFd4A21lymiTAPFNHM1S0z+RvigxysKzc1L4zbREvj82npdPSeUiz2e5YLDbLFzrTmrj0U2NVHXxwo5pmryx3v254avjc4O1PMZ7enirT5v0BX4H2goKClixYgWNjY289dZbLFiwAIDq6mpiYnqnUXp33HDSEABe21jKvh4MrfxgazltTpMhmfEMCdAVvjmZ7mDCshL/A23LPYMQZgYg0AZw47BoCuOtlDS7+NOWJtpNOKfAzhVDutHxMsB8gbZ6h6+0VQLL5TJ5+N1tAHznK0MZmN7xzXVafDS/uWgC4J68W1Yf+Amw3rLROT3sfygiEkm8gbYK9WiTAKhudFDsmfKuAVQifVe0xeCsghiuGxHHsCS/5xz67eu5MDTBQmWryU9W1+PqwmeyjSUNFFU1ERNl4eRRwXt/P9HTp00ZbdIX+B1ou+2227jiiivIz88nLy+PefPmAe6S0vHjxwd6fQEzdkAyc0dk4jThL/u6/zivrtkPwFfHBS4aPyfT/UtxWakDpx8BpLL6FraXNWAYMGNwYAJtiVEGT5+UzFfz7YxLtXH72Hh+NTURI4Qlo17pntJRh9NFfUv3h0fI0b23uZSdFY0kxdi4/qTBnZ4ze1gGs4el4zLhf+sDm9XW7HCyclcVAHNHqD+biEhX+TLaFGiTANh4wP1BdWB6HEkxwcuQEZH+JcoCv5lox25xf3Z9Ysvx+6G9vsnd3/zkkVnERQcvGDgqw50gUNrooLale/3PRQLF70DbjTfeyCeffMIzzzzDRx99hMXifoghQ4b06R5tADfNGwrAq6Xdm8pV0dDK0m3usrbzpwwI2LompVhIsBlUO0w2Vnc9gPTJTndAYlROUo+HMhwqL87Kb09I4sV5qdwwIg5rHwiyAURZLSTHut/sVTVp8mgwvLhqLwCXzSgk3n70jfC784YBsHx7JWV1gctq+3R3FY52F3nJMQxVTxgRkS7zDkNoaDdpalfWt/TMpuJaAMbmJYV4JSLS14xItPLTie736X/a0sRTW4/eA73FCS+tdV+YP39y4D4/dyY+2kZ+orvCbltVz/uyi/SE34E2gKlTp3L++eeTkHDwg/BZZ53F7NmzA7awYJg+OI2p+Um0mfC33f5HuV//4gBOl8nE/OSABgGiLAazs92BsncPdL3/2IodgevP1l+kxLkDbdWNCrQFWnWjwxdIvnhq/jHPnVSQwsjsRJymGdCstmWe558zPLNPZFGKiPQX8TaDWE9by4pWBdqkZ7wZbWPzkkO8EhHpiy4cFMttY9wZZI9sauLeNfU4nEfuPS+VQE1zO/mpsZwagP7mxzPC0/ZmW6UCbRJa3Qq09VeGYfDdkwYC8OLeNr+nfC7ylI2eF4Ro/JkD3P3H/re3tcvloyt2uHtZzRwSOYG21Dh3QLKmSenAgfa/9cW0u0zG5CYxLOv4/Vi8A0aWbiunIUClvB96BiHMUdmoiIhfDMMgM8YdaStXoE16yBtoG5OrjDYR6dw3R8bxg3HxGMC/d7dw6ZJqvqg6+Bltd6OLP3vmEN588jCsluBfRB+uQJv0EREVaAOYNzSNkfHQ7ITndzR3+X47yhv4Yl8tVovBORPzAr6ur+REkxRlUNri4qPS42drFdc2s7uyCYsB04ekBXw9fVWqN6NNpaMB95+13kBy1/59j8hOoCA1ltZ2F4u3lPb4+UtqW9hW6u45eNIwBdpERPzl7dNW3sVJcCKdaXY42Vnunjiq0lEROZZrhsfx5KxkUqINttU5uXxpDdd9VMMv1tZz5SdN1Dth8oAkLp5W0CvrGekNtKl0VEIs4gJthmFwvacq7rkdzbR2kuLaGe8QhK+MyPRNvwykaKvBBQPdNeV/2378AODSre7Mn/H5KRHVpNab0VatjLaAKqtvYdXuagyDLgeSDcNgwdgcAN7eWEK7s2cf7LzZbBPyU0iJC1zPQRGRSJHlC7Qpo026b3NJHS7TPe09Kykm1MsRkT7upOxoXp+fxnmFdgxgZXkbL+5qoaYNRsTBkxeP65VsNoARae5A297aZpranL3ynCKdibhAG8D8DMiyG9S2mXxYcvzMKNM0+e8XBwD42qTAZ7N5XTE0FqsBn5S3sbri2Ot6f4t7esspI7OCtp6+KMVXOqqMtkBavt3d729sXhK5ybFdvt+MQWmkxEVR3dTGck/PwO5avNmdFfeVEcEb+y0iEs4yFGiTADjYn03ZbCLSNal2C7+amsSbC9L48fh4vjkilgcn2Hl+EmQk9N4F9NTYaDLjojGB7cpqkxDye77uunXrOj1uGAYxMTEUFhZitwc+4yuQrAaclWfj2V1tvL63hdMGHHu9G0oa2FPZRGyUldPGBK+JY16clQsHxvCv3S08sK6Rf86LIqqT6H+zw8lH29392U4ZFVmBNpWOBof339NsP0s2bVYLp4/N4cVVe3ljfTFzhmd0a4hBs8PpG8SwIIivMRGRcKaMNgmE1bvdU+0n5msQgoj4pyDeypXD4gBwNTfjauj6oL9AGZEeT3mTgy+rGpmQrQsGEhp+Z7RNmjSJyZMnH/E1adIkRo0aRXJyMldffTUtLS3BWG/AnJXnjjEuLXVQe5yhCP/b5MkeG51FXLTfsUm/3Dg6nqQog8217TyysfMo/DubSmhyOMlPjY24q42p8SodDTTTNFnuDbQN9b832vxR2dhtFvZUNbHBcxXcXx9tr6ClzcWAlMj7Ny0iEii+Hm0tCrRJ95imycpd7kDb9MGRM2xLRMLH4BR3oG93Tdf7sYsEmt+BtkWLFjF8+HCeeuop1q5dy9q1a3nqqacYOXIkzz//PE8//TTvv/8+P/vZz4Kx3oAZmWhlRJKVNhe8s//okXbThP9tdAfazpmQG/R1ZcZYuGdSAgDPbm/mb9ubjjjnn5+6x7dcMCUfSy/Vu/cVybHujLa65jbMLk5nlWPbVdHIgdoWoq0WThjk/2CNhBibr9zzxVVFuLrx9/L2xhIAThuT3a2MOBEROVg6WunQ/ijds6+6meLaFmwWgykDU0K9HBERvw30tMEpqlWgTULH7/SsX/3qVzzyyCOcfvrpvmPjx48nPz+fu+++m08//ZT4+Hi+//3v87vf/S6giw20r+bHsG1TI2/vb+XiwZ33pVpXDwfqWomPtjKvl/qhnZkfw75GFw9vauTX6xspbnJx21ALVuCjnVV8srOKKKvBJdPye2U9fYl38EO7y6S5zYmBgjI99bGnt9qUgSnERlu79RjnTx7Asi8r2FHeyIfbyv16rbQ5Xb7+bAvGqmxURKS70u3uQFuVAm3STZ96stnG5ycHvYpDRCQYBqa4P9fvUaBNQsjvjLb169czcODAI44PHDiQ9evXA+7y0uLi4p6vLsgWeHqzfVrRRk1r5+Wjb7sr6jhtTDYxUd0LQnTHDSNi+fZId9rr33Y0c+nyZh7bA7ct2gTA5dMLyU+N67X19BXRNgsxUe5/trXNKh8NhJU73YG2Wd0oG/VKiYvm/MkDAPjnqr1+/d28t6mU6qY2MhKimd6NjDoREXFL8wTaqh0mXRyqLtLBp76yUe3HItI/FSa5A23lTQ4aNXlUQsTvQNuoUaN48MEHcTgONqNva2vjwQcfZNSoUQDs37+f7Oy+n5kyMMHKyGQrThPeLz6yfNRpmrzjCbSdPSF400Y7YxgGt4yJ5+HpSSRFGXzZ4GLhXqhubmfcgCR+eMaoXl1PX+LNaqtvaQ/xSsLDhv21AEwqSOnR45w5LocBKbHUNbfxyOJtONqP3fvQ628r9gBwybQCbNaIHIQsIhIQKdHuPG8TqNG1KOmGTz2DEGYo0CYi/VSi3Uaap92QykclVPz+VPv444/z+uuvk5+fz/z585k/fz75+fm8/vrrPPHEEwDs3LmTG2+8MeCLDYYFee6stncOHDnF8vMqJ+UOSLRbmTOi+9k+PXHaADtvnpbG7SOi+Wom/PCUIbzwrZnE2yM3nT/J84tTGW09V9fSxu5Kdx/A8QN6Nl3MZrVw66nDiYmysLm4nvv+t4ldFcceq/3JzkpW7KzEZjG4fEZhj55fRCTS2SwGKdHulgpV2iLFT3urmthV0YjFgKkDFWgTkf5Lfdok1PyO1syaNYtdu3bxj3/8g23btgFw8cUXc/nll5OYmAjAlVdeGdhVBtGCAXb+uLmJFWUO6hwukqIPxh7fKHZnTJ05OhO7rffKRg+XYrdw3ZBoXA0OEmYVYo3gIBtAUoz7569r0aeInvJmsw1IifVNdO2JgrQ4fnTGKH73zla2lzXwk0XrGZObxNSBqYzMSehwbmNrOz9Z5C43//r0gogshRYRCbQ0u4Vqh1OBNvGbdzDRjMHpvuFTIiL9UWFyLGtK6thT0wwFoV6NRKJuRWwSExP5zne+E+i1hMSQRBtDE63sqHeypMTBuYUxADhcJu+WugNtZ6tBe5/iLR2ta1bpaE95A209zWY71KicJB44fzz/+mwfH++oYFNxHZuK6zAM+M/aA5w1PpfU+Gj+b9kudpY3kp1k5/unjQzY84uIRLI0u4Ud9Qq0if+8gbbT9b5XRPo5b0bbntqmEK9EIlW3Am1ffvklH3zwAWVlZbhcHfsw3XPPPQFZWG9aMMDOE1uaeHt/qy/QtqLMQW0bZETBiRpv3qd4S0eV0dZz6/fXAe7pYoGUmRjDTScP45Jp+azYWcUnOyrZVdnIqt3VrNpd7TsvJS6KP185LSDZdCIicsjkUW2R4oeKBgef7XHvzwvG5oR4NSIiPXMw0KbSUQkNvwNtCxcu5Lvf/S4ZGRnk5ORgGIbvNsMw+mWg7XRPoO2jUgdlzU6yYq38e3cLAAsywGoxjvMI0psOZrTpU0RPeTPaxgUwo+1QmYkxnDsxj9NGZ7OnsoGmNhef7Kyk2eFkQn4KN8wZTJ5nBLeIiPRcmt39nqVaW6T44d1tFZgmTMhP1r4sIv2eN9C2r64Fp0tjuKX3+R1o++Uvf8mvfvUrfvSjHwVjPSExPMnG1PQoVle28fcdzZxXGMMHxe7hCJfkhnhxcoRET482TR3tmbqWNt+wgkCWjh5NeoKdi0dnc9PJw4L+XCIikSrNm9F25IwnkaN6Z2sFAKcrm01EwkBWvB2rYdDmMqlsdpBiCV2/dYlMfk8dra6u5uKLLw7GWkLqmuHuqPczXzZz6ZJqTODkLCuD1Z+9z0nwDINobFWgrSe+LK0HIDc5hjSVboqIhAWVjoq/6tthxS532agCbSISDqwWg+wE9+eb4obWEK9GIpHfgbaLL76Yd955JxhrCamTc6KZn+d+MTY7ISnK4Cej7SFelXQmwZPR1qBAW4/sKHNnsw3LSjjOmSIi0l+kKdAmflpWBW0uk6GZ8XpPICJhIy/B3Xu9uFGBNul9fpeODhs2jLvvvptPPvmE8ePHExXVcfz3LbfcErDF9SbDMPj1tCSe29HM/kYnVw+LJcfWhqsh1CuTw8Uroy0gdlS4/3EPyYgP8UpERCRQFGgTf71f6f7vGeOUzSYi4SM30Q7FymiT0PA70PbUU0+RkJDA0qVLWbp0aYfbDMPot4E2gBirwQ0jDtaKutRsv0+Kj3bX2Dc5nLjU3LLbdpa7M9qGZOrqtYhIuEj3DENQoE26osVpsswzDFxloyISTnI9GW0lCrRJCPgdaNu1a1cw1iHSZd4ebSbQ1OYM7WL6sZ3lnoy2TGW0iYiEC29GW6MTWtqc6De8HMuKSictLshNsvfKYCQRkd6Sl+huA1Wi0lEJAb97tImEms1qISbK80FC5aPd0uZ0saeyCYChymgTEQkbCTaDKHdSG5WNSmuTY1tc6n4fddrIDAzDCPFqREQCx5vRptJRCYUuZbTdcccd3HfffcTHx3PHHXcc89yHHnooIAsTOZb4aBstbQ6aHO3E2zWu2V97q5pod5nERlnJSYoJ9XJERCRADMMg3W5Q0mJS2eSgMNQLkj7LaZosLXMH2haMzAjxakREAsub0Vbe5KDN5QrxaiTSdCnQtmbNGtra2nz/fzS6Eia9JcFuo7LRQWOrk8zEUK+m//H2ZxucEY/FotetiEg4SYv2BNqU0SbHsL6qnZo2SLTCCYUqGxWR8JIaE0WMzUJLu4uyJgfDQ70giShdCrR98MEHnf6/SKh4J482qHS0W3ZWqD+biEi4Sot2X0CpbHSEeCXSly0tdf/7mJkKNou6yYhIeDEMg5wEO7trminRfii9TLuq9EsJMe5AW6NDgbbu0MRREZHw5Qu0NSmjTY5uWYn7g+fc1BAvREQkSPK8k0eb1KdNepffU0cbGxt58MEHWbx4MWVlZbgOq3feuXNnwBYncjTx0e5/uk2tmjraHTs8E0eHKqNNRCTspNuV0SbHVtbsZHNtOwYwS4E2EQlTOQneyaPaD6V3+R1ou+GGG1i6dClXXnklubm56ssmIZHgGYCgjLbu8Wa0aeKoiEj4OVg6qow26dxHnrLRcckW0qPVJFxEwpM30FbRrECb9C6/A21vvvkm//vf/5g9e3Yw1iPSJQmeHm2N6tHmt9qmNl+Ww+AMZbSJiISbg6Wj+mAhnVtZ4Q7CzsywAgq0iUh4yoqPBhRok97nd4+21NRU0tLSgrEWkS6L9/VoU+mov3Z4BiHkJMX4hkqIiEj4UEabHItpmqwqd//bmJ5mDfFqRESCJzPOndFW3qz9UHqX34G2++67j3vuuYempqZgrEekSxKildHWXTvKNHFURCScKaNNjqWo0Ulpi4soC0xMUaBNRMKXN6OtvNmBaZohXo1EEr/TWX7/+9+zY8cOsrOzGTRoEFFRUR1u//zzzwO2OJGjiVfpaLftrFB/NhGRcJZmP5jRZpqm+ulKB596stkmpkYRYzVUOCoiYSszzh1oa3OZVDe1kRMbdZx7iASG34G28847LwjLEPFPgkpHu21nuTLaRETCmTejrd1lUtvcRorng4YIwCpPf7YTMvWBU0TCW5TVQmpMFNUtbZTUtZCTHhfqJUmE8DvQdu+99wZjHSJ+iT+kdFRpwP7xThwdoow2EZGwFG0xSLRCvRMqGx0KtImPaZoHA20ZUYAuWIpIeMuMi/YF2kR6S7c7oTscDsrKynC5OiacFxYW9nhRIsfjnTra7jJpcyrQ1lXtThd7Kt39FYdo4qiISNhKjfIE2hocDM0M9WqkrzjQ5KKsxYXNgIlpUaDKABEJc5lx0WyralSgTXqV34G2bdu2cf3117N8+fIOx709QJxObdgSfDFRFqyGgdM0aXSoT1tX7atuxuF0YbdZGJASG+rliIhIkKRFQVELVDW2hnop0oesq3Zns41Mtqk/m4hEhCxPVndJnfZD6T1+B9quvfZabDYbr7/+Orm5uWqwKyFhGAbxMTbqmtto1tXYLttZ4e7PNjgjHotFr10RkXCV6mm/VdGgyaNy0Loq98XJ8andLmoREelXMn2BNmW0Se/xe5ddu3Ytq1evZtSoUcFYj0iXJURbqWtu00AEP3j7s2niqIhIeEvzBNqqGhVok4O8GW0T0jQIQUQigy/QVqtAm/Qei793GDNmDBUVFcFYi4hf4jx92pTR1nU7fIMQ1J9NRCScpXnmH1Q2qFRG3NpcJptr3BltE5TRJiIRQhltEgp+B9p+/etf88Mf/pAlS5ZQWVlJXV1dhy+R3hIXbQWgpU2Btq7aUe4uHVWgTUQkvHnjKJXKaBOPbbXttLogKcpgYII11MsREekVWYcE2kxTQ/Skd/h9OWv+/PkAnHrqqR2OaxiC9DZvoK1ZgbYu85aODslQ6aiISDg7mNGmQJu4rat2Z7ONS7VhUY9lEYkQ6Z5AW0u7i9rmNlI834sEk9+Btg8++CAY6xDxW3y0Skf9UdvcRoWnhEgZbSIi4S1VPdrkMOuqPP3ZUtWfTUQih91qIdluo7a1nQM1LQq0Sa/wO9D2la98JRjrEPFbrDLa/LK9zF02mpMUQ2KM3mSLiIQzb6/7ykb1aBO39Z6Mtglp6s8mIpElMyaK2tZ2SuqaGZOXFOrlSATwe6f98MMPj3n73Llzu70YEX8oo80/OzyBtmFZKhsVEQl3h2a0uVwmFotKBSNZrcPFrgb3+6XxymgTkQiTGRfN9tpmijV5VHqJ34G2efPmHXHMOKTPg3q0SW+JsyujzR9fltUDCrSJiESCFM87PJcJNc1tpMWrVCaSbfBksxXEW0iz+z0LTUSkX8uIde+BxTUKtEnv8Hunra6u7vBVVlbGW2+9xQknnMA777wTjDWKdCrOm9GmQFuXbFdGm4hIxIiyQHKMe5+sbFD5aKRbV63+bCISuTJi3b/7lNEmvcXvjLbk5OQjjp122mlER0dzxx13sHr16oAsTOR4vFNHWxRo65IvPYG24Qq0iYhEhPT4KGpb2qlsdDA81IuRkFpXpf5sIhK5Mj0ZbSV1zSFeiUSKgOWOZ2dns3Xr1kA9nMhxeQNt6tF2fE2OdvbXuDcWZbSJiESGNM9ktcoGTR6NZKZpKqNNRCJapkpHpZf5fVlr3bp1Hb43TZPi4mIefPBBJk2aFKh1iRyXbxhCmyvEK+n7dpY3YpqQFh9NeoI91MsREZFekB7vDqpUafJoRNvb6KLGYRJlgVHJymgTkcjjLR0tqWvBNM0OPeZFgsHv3XbSpEkYhoFpmh2On3jiiTzzzDMBW5jI8fgy2tqcR/x7lI7Un01EJPKkxbk/WFQooy2iebPZRiXbiLbqw6WIRJ70GHdGW5PDSV1LO8mxyu6V4PI70LZr164O31ssFjIzM4mJiQnYokS6wjsMwekyaWlzkah/gkeliaMiIpEn3VM6WtWoQFskW++ZOKqyURGJVDE2CymxUdQ0t1Fa16JAmwSdXz3a2trauO6663A4HAwcOJCBAwdSUFCgIJuEREyUBW/Wb0Nre2gX08dtLXEH2kYo0CYiEjHSPKWjlSodjWjrqjz92TQIQUQiWHaiu32OJo9Kb/Ar0BYVFXVEjzaRUDEMg7god/loXUtbiFfTt206UAfA2AFHTg0WEZHwlK5hCBHP4TTZXOu+GDleGW0iEsGyk9zJQSW1mjwqwef31NFvfOMbPP3008FYi4jfYj3low0tymg7mpomBwc8V25G5SSGeDUiItJbvD3aKlU6GrG21LbT5oKUaIPCeL/f9ouIhI0cX6BNWd4SfH7nkLe3t/PMM8/w3nvvMXXqVOLj4zvc/tBDDwVscSLH4x2IUK+MtqPa4ikbHZgeR2KMrmaLiESKg1NHFWiLVN7+bONTozRlT0QiWnaSu3S0pE4ZbRJ8fgfaNmzYwJQpUwDYtm1bh9u0gUtv8wba6pTRdlRbit2BtrF5SSFeiYiI9Kb0eHfpaHWTA6fLxGrR+7RI4504OiFV/dlEJLIdzGhTjzYJPr933Q8++CAY6xDpllhPjzaVjh7d5hJ3f7YxuQq0iYhEktTYKAwDTNMdbMtIsId6SdLL1lS6A22T0pXRLiKRTcMQpDepWYP0ayodPT7vxNExymgTEYkoVotBqgYiRKySFhf7m1xYgInKaBORCOfLaKtToE2CL6SBtgceeIATTjiBxMREsrKyOO+889i6dWuHc1paWrjppptIT08nISGBCy+8kNLS0hCtWPoa7zCE+lZltHXG0e5iV0UjAGNyNXFURCTSpMV7A21q/hxp1lY7ARiVYiM+StfWRSSyeQNtNU1ttLQ5Q7waCXch3XWXLl3KTTfdxCeffMK7775LW1sbCxYsoLGx0XfO7bffzmuvvcZLL73E0qVLOXDgABdccEEIVy19ycGMNgXaOlNc24LTZZIeH+1rACoiIpHD26dNk0cjz+fVLgAmp6lsVEQkMcbmazukPm0SbCHNI3/rrbc6fP+Xv/yFrKwsVq9ezdy5c6mtreXpp5/m+eef55RTTgHg2WefZfTo0XzyySeceOKJoVi29CFxUSodPZb9Ne6pOmPykjSsREQkAqUnKKMtUq2tcWdsTElX2aiIiGEY5CbHsLOikeLaFgZlxId6SRLG+lQeeW1tLQBpaWkArF69mra2NubPn+87Z9SoURQWFrJixYpOH6O1tZW6uroOXxK+YpXRdkz7qpsADUIQEYlU6fHubOYqZbRFlIZ22FrnyWjTIAQREQCyPeWjperTJkHWZwJtLpeL2267jdmzZzNu3DgASkpKiI6OJiUlpcO52dnZlJSUdPo4DzzwAMnJyb6vgoKCYC9dQkilo8e2r/pgRpuIiEQeb4+2CgXa+rXNxXVc/cynnPCr97j22U/ZXHzsC8mf14ELyI+zkB1r7Z1Fioj0cbnJ7kCbJo9KsPWZQNtNN93Ehg0beOGFF3r0OHfddRe1tbW+r7179wZohdIXxXmGITRoGMIRXC6TA55NZKwCbSIiESnDUzpapamj/daG/bVc8uQKlm4rp7y+lQ+2lnPhE8v5dFfVUe/zcbX7v7OyontplSIifV9OsjLapHf0iUDbzTffzOuvv84HH3xAfn6+73hOTg4Oh4OampoO55eWlpKTk9PpY9ntdpKSkjp8Sfg6WDqqHm2HK61vwdHuIibKwuCMhFAvR0REQiDNUzpa2agebf1Rs8PJ9/65hvrWdk4YlMpz189g1tB0mhxObvzH55R18mHRNE2WewJtJ2Ur0CYi4pXjy2hrDvFKJNyFNNBmmiY333wzixYt4v3332fw4MEdbp86dSpRUVEsXrzYd2zr1q0UFRUxc+bM3l6u9EEHhyEoo+1we6vc/dlGZCditWgQgohIJPINQ1DpaL/09Ec72VXRSE5SDAuvmsZJwzN4+uoTGJWTSEVDK9/75xqcLrPDfbaUNVLUAtEWmJGp/mwiIl45nh5tmjoa2VrbXfxlzb6gPkdIA2033XQTzz33HM8//zyJiYmUlJRQUlJCc7M7wpycnMz111/PHXfcwQcffMDq1au59tprmTlzpiaOCnAwo63J4aTd6QrxavqWIk+gbWROYohXIiIioZIe7506qkBbf1PX0saTS3cCcNdXR5ES5/67jI228qcrphAfbWXlriqeXLqjw/3+u6EUgLmZVhKi+kTxiohIn+DNaCtR6WhEe393Bc9vPBDU5wjp7vvEE09QW1vLvHnzyM3N9X29+OKLvnP+8Ic/cPbZZ3PhhRcyd+5ccnJyeOWVV0K4aulLvIE2UFbb4byBtlE5Kp8WEYlU6Qnu0tHa5jbadEGqX/nnyiIaWtsZnpXAORPyOtw2JDOBn3/NPTzsD+9uY8P+WgAaW9t5aW0xAGflKptNRORQ3kBbeX2rkjQi2KItnQ/WDKSQl4529nXNNdf4zomJieHxxx+nqqqKxsZGXnnllaP2Z5PIY7NYiLa5/xkr0NaRN9A2OlcZbSIikSolNgpv94BqlY/2G452F89+vBuAb84dgqWTFhAXThnAGWNzaHeZfPvvq9lf08wf399OdXM7hTEwL0vTRkVEDpURb8dmMXCZUN6g3qWRaFdNExvLG7AFubWS8sml34v19Gmr00AEn5omB3Ut7RgGDM9SoE1EJFJZLAZp8erT1t+89sUBSupayEq087VJeZ2eYxgGD144nsEZ8eyvaWb2g+/7yki/N4igf4gQEelvLBaD7CTvQASVj0aipbsrAZiakxzU51GgTfo9BdqOtLuyEYCsRHuH8loREYk8aerT1q+4XCZ//tAdMLtm9iDstqPv4ylx0Tx3wwwmFqQAYLUY3DZ3EAsyemOlIiL9j69PmwJtEenDoioAZhemBvV5bEF9dJFeEONp9FvXrNJRr92V7rLR/JTYEK9ERERCLT3eDjRQ2agymf5gybYytpU2kGC3ccWMgcc9f0BKLIu+O4udFY0kxdpIN5w0LNkd/IWKiPRDCrRFrupmB1s9CSknDkgJ6nMp0Cb9Xly0MtoOt8fzC2RAqgJtIiKRLj1BGW39hWmaPLnEPWn0ihmFJMd2baCBxWIwLCsBAGdDY9DWJyLS3+UkafJopFpdXAfA8LR4Urq4v3aXSkel3/OWjmoYwkG7K9wZbQOU0SYiEvHSfT3alNEWDC99tpeTH/+EH26BitauTbEzTdhf00Jtc8eLhO9tLuPT3VVEWQ2unT04GMsVEYloucnq0RapVh2oAWBaXnD7s4Ey2iQMxHgz2pqV0QbQ7HBS6rlCk6+MNhGRiJeeYAegSsMQAm7ptnJ+8O91ABQBxZ+38Ld5cUQdYxDBnkYX318LWz/+BMOAeSMyuXrWIDIT7dz1ynoAbpgzxFfeJCIigeMdhlCqQFvE2VBWD8DknKSgP5cCbdLvKaOto73VTZhAcmwUiTHBTYkVEZG+zzsMoUKlowFlmiYPvLEZgLE5Cewpb2BdrYtFe1q4ZHDnF7pqHS6+uaqZ4hbvY8AHW8v5YGu575xROYl875RhQV+/iEgk8mW01TWHeCXSmxoc7eypdf+dj8lMhPbg/v2rdFT6PU0d7WiPZxBCYVpciFciIiJ9QYanR5sy2gLri321bCmpJzbKyt+umMiNnrkFT2xpwuE0O73P7zc0UtxiUhADK26dyZI753Hd7MEk2t3Xvk8emcnfrptOXLSuhYuIBIM3W7i0thWXq/Pf1RJ+tlQ2YgJ5CXZSeyEZRbu49HuxKh3toKjKM3FUZaMiIgKkxbtLRysb1KMtkP6zdj8Ap43JJiU2iotz4C/7DcpaXPx3bwsXDeq4D++ub+fVIncq2y+GQ1aiHWtCPPecM4affHUU7S6TGM/FQxERCY7spBgsBjicLioaW8lKVJl+JNhc2QB4stl6gTLapN9T6WhHez2BtoJUZbSJiMghU0eV0RZQ724qBeDsCbkARFvgqkHuq+TPbGvGaXbMlHhsSxNOE76SaWXKYX2YbVaLgmwiIr0gymrx9Wk7UKM+bZFic4U70DY2M6FXnk+BNun3VDp6kGmaymgTEZEOMjwZbfUt7bS0OUO8mvBQVNnEvupmbBaD2cMyfMcvKogiKcpgT6OT9w4cDGxuqW3nzX3ujMLvDY/u9fWKiMhBeSnuz0kHatSnLRKYpqmMNhF/eUtHldEGFQ2tNLc5sVoMTSsTEREAkmJtRNvcb/nK61U+Gggf76gAYHJhCvH2g51Y4m0Glw9xf4B7elsTpmlimiaPbGwE4Mx8OyOTlLkmIhJK3kDb/moF2iJBSZOD6pZ2bBaD4WnxvfKcCrRJvxejjDafPd5stpRYbBa9vEVEBAzDICvRndVWpkBbQHy6qwqAmUMzjrjtiqGxxFhhY007/9vXylv7W/mw1IHNgJtHqa2DiEioDfAG2pTRFhG2VLkvdg1Pi8du653PyPokLv1e3CEZbaYZ2ZNjijRxVEREOuENtJXXqx9NIHxeVA3A1IGpR9yWZrdw7XD3Pvyjz+q5c1U9ANeNiGNQouaQiYiE2oAUb482BdoiwZZqd6BtTC/1ZwMF2iQMxES5/xk7XSZNjsjuPePtz1aYrkCbiIgc5J2qpoy2nqtoaGVPZROGAZMKUjo95zsj45ife7AX2+kD7NyobDYRkT4hTxltEWWrJ6NtbC/1ZwPQZTXp96KtFmwWg3aXSV1LW4deKZHGO3FUGW0iInKoTG/paJ0CbT21tqgGgOFZCSTHRnV6js1i8PCMJDbWtGMAY1JsGIbRe4sUEZGjGpCqYQiRwtHu4ssa92fkMRnKaBPpMsMwSIxxB9cieSBCa7uT4jp3SZACbSIicqiDPdpUOtpTGw/UATB+QMoxzzMMg3GpUYxNjVKQTUSkD/FmtFU3tdHkiNzPj5Fga2k9bS6TpGgb+Um9NyxQgTYJCwmeQFtdc+QORNhX3YxpQlKM7ahX2EVEJDJlJWkYQqBsPFALwJi8pBCvREREuiMpJopETxWUstrC2xf73Xv26Iz4Xr3opUCbhIXEGHdgKZIz2ooOKRvVlXMRETmUt0dbuQJtPbap2J3RNiZXgTYRkf7KWz66v0aZ3uFs7b4aAEan917ZKCjQJmHCe0WiriVyM9qK1J9NRESOwtejTYG2HqltbmNftTv7QRltIiL9l7d8VBlt4W3dPm9GW+8G2iK3a7yElUSVjlJUqYmjIiLSOW/paGVDK06XidWizOfu2OTpz5afGqs2DSIi/VheijvTe3+1Am193f6aZp5etovKxlbOnZjHqaOzu3S/miYHuz3JKL2d0aZAm4QFb+loXYSWjpqmeUhGW3yIVyMiIn1NerwdiwEu0x1sy+rFhsDhRGWjIiLhoSDVnZywt7opxCuRY9l4oJbLF66k1pNQ85+1B7j77DFcf9Lg49537d4aAAYk2Emy927oS6WjEhZ8GW0RWjpa09xGQ2s7hgEDPGnQIiIiXlaLQUaCykd7ypvRNjYvOcQrERGRnvC22/EmK0jfU9vcxg1//Yza5jbG5iVx/uQBANz/xmbWeXqvHYs30DYqtfcTURRok7DgDbRF6jAEb9lobnIM0Ta9rEVE5EgHJ4+q8XN3+TLa1J9NRKRfK/AE2vZWqXS0r3rwzS0U17YwKD2O5795In+4dBJnT8jF6TL5+WubME3zmPf/dFcVAGN7uWwUFGiTMJFg95SORmiPNu+VGG8KtIiIyOEyvRltdcpo647WdidfltYDCrSJiPR33kBbRUMrTY7ITNboy74srefFVUUA/PrCCb6+qHefPQa7zcLqPdV8tL3iqPdvbXeyek81ABMyE4O/4MMo0CZhISnWWzoamb8kvYG2genqzyYiIp3LSnT3ZVPpaPd8WdpAu8skJS6KvGT1uBMR6c+SY6N8wRtltfU9j3+wHZcJp4/NZsaQdN/x7KQYLpteCMDCZbuOev8v9tbS2u4iIz6aAs+Fxt6kQJuEBe8whPoI7dF2cBCCMtpERKRz2Z7gUGmdSke749BBCIahqa0iIv1dQZq7t/Ve9WnrUyoaWnljfQkAN5087Ijbr5s9GMOAD7eVs72sodPHWLmzEoATBqWFZM9WoE3CQoJnikgklo62O13sr3FfhVGgTUREjibXE2grrlWgrTu8gxA0cVREJDxoIELf9K/P9uJwuphYkMKE/JQjbi9Mj+PUUdkA/HX57k4f45Nd7kDb9IGpwVrmMSnQJmEhKYKHIRyobcHpMomNspKREB3q5YiISB+Vo0Bbj/gCberPJiISFrz9rRVo6zucLpPnV7p7s31jRuFRz7tu9iAAXv58H7WHJdsc2p9txqC04Cz0OBRok7CQEOPt0RZ5GW2Hlo2qlEVERI4mL9ldIlNcq140/nK5TF/p6Ni85BCvRkREAsE7EGFftQJtfcWq3VXsq24mKcbGORPzjnrezKHpjMxOpMnh5F+r9na4bfmOSlraXGQm2hmaGZoe5gq0SVjw9mhraXPhaHeFeDW9q6iyEXCn0IqIiByNN6OtpqmNZoczxKvpX/ZVN9PQ2k60zcKQEL1pFxGRwFLpaN/z/pYyAOaPziYmynrU8wzD4BpPVttfV+zG6TJ9t73l6e92xtickCWiKNAmYcHbow0ibyCCBiGIiEhXJMXYiI92v2lVVpt/Nh6oBWBkdiJRVr19FhEJB96Mtr1VzZimeZyzpTe8t7kUgFNHZx/33PMmDSAlLop91c28u8l9v8bWdt7cUAzAmeNygrfQ49A7BQkLVotBoifYdniNdrhToE1ERLrCMAxfVluJ+rT5ZaMGIYiIhJ0BKbFYDGhuc1Je3xrq5US83ZWN7CxvxGYxmDMi47jnx0ZbuXy6u4/b797ZSpvTxUuf7aWupZ1B6XGcOCQ92Es+KgXaJGykxLvLR6ubIifQVtfS5vt5vc08RUREjibX06ftgAJtflm/353RNi5f/dlERMJFtM1Cvucz1K6KxhCvRt7fWg7AjCFpJHlaQx3Pt+cOJS0+mu1lDXz9qU/4zdtbAbj+pMFYLKHrX65Am4SN1Dj3xM2aJkeIV9J79nqy2bIS7cRGH72GXUREBCDXl9Gm0tGuMk2TDZ5A2/gBCrSJiISTwRnuvpsKtIXekm3uQNupo45fNuqVHBfFry+cgMWA1XuqaXI4mTowlcumH31iaW+wHf8Ukf4hxRNoi6SMNm/Z6EANQhARkS7wBtqU0dZ1JXUtVDY6sFoMRuUkhno5IiISQIMz4lm6rVyBthBrcLTzWVE1AKeOzvLrvqeNyeav103nxVV7GZASy43zhmELcT9VBdokbKTGudNLIymjrahS/dlERKTrclPcpaPq0dZ16/e5s9mGZyUccwKaiIj0P95J0jsVaAupVaV1tLtMhmUlMDDd/+nec4ZnMmd4ZhBW1j0qHZWwkerLaIucQNsOz4bQnV9GIiISebzDEA7UqHS0qzZ4BiGMU9moiEjYUelo3/BJifuilr/ZbH2VAm0SNlI8GW1VjZFROtrS5mRftTujbVhWQohXIyIi/UGeZxhCSZ0y2rpK/dlERMLXkEz356g9lY04XWaIVxOZ2l0mn3kCbfNHd70/W1+mQJuEjUgbhrCzvAHThPT4aN/PLiIicizejLaapjaaHc4Qr6bvM03z4MTRAUkhXo2IiARablIMdpuFNqfpS2KQ3rWxvJ76NicpsVFMKUwN9XICQoE2CRvejLZIKR3dXtYAKJtNRES6LinGRoLd3aJ3f40+UBzPvupmyutbsVkMxuQqo01EJNxYLIavfFR92kJj+f4aAOYOz8BqMUK7mABRoE3CxsGMtsgoHd1erkCbiIj4xzAMCjwDdLyTq+XoPt1VBbj7s8VGaxCCiEg48vVpK1egLRSW73dPGz1lRHj0ZwMF2iSMRNowBF9GW6YCbSIi0nWFae4+bXurNBDheFbtdgfapg9OC/FKREQkWA5mtDWEeCWRZ29tM3vrWrAaMGdYeqiXEzAKtEnYSE9wB9qqGh2YZng3sqxsaKW6qQ2LAYMzNXFURES6riBVGW1d9akn0HbCIAXaRETClbdC6MtSBdp628f73NlsEzISSYyJCvFqAkeBNgkb3kBbm9Oktjm8y0c3l9QDMDA9HrtNpSwiItJ1hekKtHVFRUMrOz1lRNMGhkdzZhEROdKI7EQAtpXWh33CRl/zcZH7gtaMMOuDqkCbhA27zUpijLvBc0VDeJePrttXA8D4AeH1C0lERILP26NtrwJtx/SZJ5ttRHYCqfGa7i0iEq6GZSVgMaC6qS3sP0f2JdUtbawtrQNgZm5KaBcTYAq0SVjJTLAD7qvQ4cplmqzfXwso0CYiIv7zlo7urWrSlftjWLGjElDZqIhIuIuJsjIw3d2OZ1tpfYhXEzmWFVXhMmFEWhy58fZQLyegFGiTsJIRAYG2HeUN1DS1ERtlZWROYqiXIyIi/Ux+qnsYQqPDSVWjrtx3xjRN3t9aBsDcEZkhXo2IiATbiGx3n7atJQq09ZYlu90XtOYWhN8FLQXaJKxkJLpLOyrDOOV35U53Kcu0QalEWfUSFhER/8REWclJigFgb7Umj3ZmR3kDe6uaibZaOGlYRqiXIyIiQTbykD5tEnx1re18dsBdpfWVQgXaRPq09PjwzmhrcrSzfGcFALOH6o2/iIh0T0GaO6tNAxE698b6EgBmDk0n3m4L8WpERCTYRngqhbYq0NYrPiqqwmmaDE2NoyApNtTLCTgF2iSshHvp6FsbS2lpc1GYFsf4fPVnExGR7tFAhKMzTZP/rN0PwLkT80K8GhER6Q2+jLYSTR7tDUv2uMtG5w1MD/FKgkOX6CSsZCa6A21ldf070Fbf0saLq/ayancVbU4Tu81CbXMbyz2NmS+dVoDFMEK8ShER6a8KPYG2okoF2g63anc1O8obsdssLBibHerliIhILxiUEU+U1aDR4WRfdbPvgpQEXqOjnU/31wAwb5ACbSJ9Xm6yu+dMcW1LiFfSfRv21/Ltv69mf03nfXPOHp/LlIGpvbwqEREJJ4Mz3NPVdlY0hHglfc8zH+0C4PzJA0iMiQrxakREpDdEWS0Mz0pkU3EdGw/UKtAWREv2VNHmMhmYHMvglFja21yhXlLAKdAmYSU3xR1oK6nrn4G29ftquXzhJ9S3tlOQFsvVMweRFBNFa7uTupZ2WtqcTC5QkE1ERHpmaKZ7utr2MgXaDrV2bw1vbSzBMOC6kwaHejkiItKLJuQns6m4ji/21XLGuNxQLydsvbOjHIDTh2ZihGmVlgJtElZyPY0UqxodtLQ5iYmyhnhFXVdU2cS1f/mU+tZ2pg9O4/+unkbSIVfSG1rbeX9zaQhXKCIi4WJIpjujrbqpjapGB2nx0SFeUeg1Odr54b+/AOCCyfmM8PTrERGRyDAhP4UXVu1l3b6aUC8lbJU3OVhd7J42etqQ8B3up2EIElaSYm3ERbuDa/2pfLSsroWrnllJRYODMblJPH1YkE1ERCSQ4qJtDEhxX5xSVhvsr2nmmmdWsa20gcxEOz8+c1SolyQiIr1sgmfY3Lp9tbhcGogQDO/tLMcEJmQlkpcYE+rlBI0CbRJWDMMgx9enrfMeZ31NeX0r33h6JbsrmyhIi+Uv156gnjAiIhJ0w7JUPtrudLHww52c9tBSPt1dRaLdxp+vnOobriQiIpFjZE4idpuF+pZ29mgqd1C8s6MCgAVDM0O8kuBS6aiEnbzkWHaWN1Jc07cy2lwuk73VTTS0tmNgEGU1+Lyomofe3UZpXSs5STE8f8OJZCWFb2RfRET6jmFZCSzdVs6O8sgMtJXUtnDLP9fw6e4qAE4YlMoDF4xnWJZKRkVEIlGU1cKYvCTWFNWwbl+Nb3CQBMaumia2VTViNQxOCdNpo14KtEnYyfMMRNhb3TeuQjhdJn9dvps/f7iD0rrWTs8ZkhnP01efoOk2IiLSayJ5IMK+6iYu+NNyyupbSbDbuPvs0Vw8tQCLJTybMouISNdMzE9hTVENX+yt5WuTBgT1uUzTZMWOSvbXNDNneKavMitceYcgnJifQnKYV3Ap0CZhZ3CG+4PDrorGEK/E3XvtthfXsnxHJQB2m4WkWPcvFUe7i/zUWM6ZmMc1swb1q8ENIiLS/4VT6eiXde1sqG5nVLKN0SnHfnvb0NrODX//grL6VoZlJbDwqmnKWhAREQAmFrj7tK3eUxXU53G5TG59cS2vfXEAcH9OfOTrk8J22qnLNHlnp7ts9PQwLxsFBdokDHknqe0sD12gzTRN3t5Ywl2vrKe6qY24aCs/+epoLplWQLRNrRFFRCT0vIG2/TXNNDnaiYvun28LH/+ylSd3HAwWXjgwhrsnJRDVSXaa04TvL9rElpJ6MhPt/O266eR5hkKIiIjM9EzCXLe/lpomBylxwZnK/dSynbz2xQGsFoP81Fj2VDbxvX+u4ZXvxjHeM5QhnKwrraekoZW4KCsnFaSGejlBp0/8EnaG+gJtDZhm70yLcZkmuyoaWb6jkt+8tYWz//gR33nuc6qb2hibl8Rr3zuJb5w4UEE2ERHpM9Lio0mLd3+A6K9Zba+WwpM72gAYlWzDAF7e08I9n9fj6uQ9wB92wQfbq7DbLCy8apqCbCIi0kFOcgwjshMwTfh4e2VQnqO2uY3H3t8OwK/OG8f735/H/NHZtDlNfvDvL3CG4cTTd3a6y0bnDUzDbgv/Si596pewU5AWh8WARoeT8vrOe6IFiqPdxb9X7+Omf3zOff/bxAur9vK3FXvYeKCOaJuFG+cNZdGNs319cERERPqSMblJAGw8UNfh+Nat8OCD7v/2VZWNDn630/3/N4+O4+VTUnn0xCSsBvx3byu/Xt/Y4YLbS3vb+Lu7QoffXzKRSQUpvb9oERHp8+YMd5c2frS9PCj74fOrimhobWdUTiKXTCvAajH4zUUTSI6NYktJva+cNFy0trt4f5c7aBnu00a9FGiTsGO3WRmY7s5q21xSH7TnqWlycO9/N/Dy5/uoaW4jJsrCqJxEvnFiIb+5aAIrfnwKPzxjlLLYRESkzxo7wB1o27C/1nesvR0WLYLFi93/bW8P1eqO7U8f76HeCaOTLHxrpHuY0Cm5du6f6p4a+tyOZu5f10BFi4u/bm/ivo3ui2+3f2UQZ0/IC9m6RUSkb5sz3F0+unRzJYsWmQHdD03T5JW17kDaDXOG+IbwpMVH8625QwB4dPGXtDtdPX+yPuKD3RXUO9rJjo9mSk74lcV2pn824xA5jkkFKeyqaGT17iq+MiLwUfOWNif3v7GZvdXNJMXYuHrWIMbnJdPS7uSU0dkk2PXSEhGRvm/8APcb3kMDbStWwOefw4gRsGaN+/s5c0K1ws5VNrTywufFANw2IhqrcbAf29kFMdS1mfzqiwae39nC8ztbfLddnAM3nTSw19crIiL9x4zB6URbLezcZOfDhnZGjIgK2H64pbqJPVVNxEVbOXNcTofbrp41iP9btpOdFY28tu4A50/O79mT9RGvbCkB4Gsjc7BGyHRvpdpIWJo60N1g8bM91UF5/L9/soe91c2kxEXxi6+NY9bQDGxWvZxERKR/8QbaNpfU0+Z0UVcH//kPREdDZiZERcF//wt1dcd5oF72l+W7aWl3MTYBZqYf2evl8iGx/PHEJAYluG/LsFu4a3Q0Px0KhhEZb/JFRKR7YqOtTM7NpPnLbMoamwO6H64qcV/YOnlkFvGHJWck2G3cMMed1fbnpTt7pd+4y2WyancVz32yh9e+OMDuisaAPu+2qkY2ljdgsxicMyIrYI/b1yntRsLSCYPSAFhTVOObpOZ0mXy4rZx9Nc3MGZbBoIz4bj32qt1VvL+lDAO4+eRhZCfFBHDlIiIivacwLY7EGBv1Le18WdrAxo+T2LkTxoxx3z5oEGzaBO+8Axdd1PPnK61r4YklOyivb+X0cTmcMyHX78BXfUsbf12+G4Dr8o8eODsl184puXaa2k1irEBLC64GRw9/AhERiQT5jYNpq3FQnliNaSYyaJARkP3w8zJ3pM5bnnq4b8wYyOMfbGdLST0fba/w9YsLht11zdz21Ao2HdZuaUBKLF8Zmcm8EZmMzk0iM9GO3Wbp1oWqFza5s89PHpROWmxwJrj2RQq0SVganpVAYVocRVVNvP5FMTOHpvP9f33Bp7urALBaDP7fuWO58kT/ykcc7S7+tmI3AGdPyGVsXmTUmIuISHgyDINxecms2FnJks8bWPNOEpmZYPO8Q7TZ3Jltb78NM2ZAXmr3n+tATTNfe/xj36Ci/60vZsWOCn513nhfj5queH5lEXUt7QxNj+OU9Kbjnh9ncz92+HS7ERGRYCoqgsqtqdgT9lPb2kpRVRMD0+M77IcFBf4/bmNbO1uqGwGYPazzQFtyXBSXTCvgL8t389SHO4MWaNtYXs8Plmyhqd1Fgt3GiUPSqGhwsKm4jv01zTy/sojnVxZ1uE+01UJSrI2pA1NZMCaHsyfmHnOC6Lryet7fU4kBXD4usnqjqtZNwpLFYnD5jEIAfvzKOuY/tJRPd1cRF21l3IAknC6Tu1/dwLubSv163Dc2FFPR4CAtPpoLp4ZHzbyIiES2cQOSME3473+gshJyOraMISfHffw//4HuVpOYpskd/1pLeX0rQzLjuWbWICwG/PPTvTyy+MsuP05DazsLl+0C4FuzCoiQVi8iItJLXC73fldXY2HiCHfl0sqdVZim2eP9cG1pPS4TBqbFUZAWd9Tzrj9pMBYDln1ZwZaSwPdu2FvbzI+WbKWp3cW0wlSW/GAe/3f1Cbx602y+uGcBz15zAlfNHMjQzHiiD2mP5HC6qGhw8PbGUr7/0hfMfvB9Hnp3G3sqG6lvaaO8vpUN+2t5b1MpTy/fzX0r3aPBzxqexYj0hID/HH2ZMtokbF0+o5BFn+9na2k9re0upg5M5Q+XTKIgLZZ7/7uRv63Ywx0vruU/N89mSObxX/iV9c3889U3aa6t5PSTxmEzJuJ0Ovli1QoqyktJTM1k8LipvvOdTifLli2juLiY3Nxc5syZg9Vq9fu4iIhIMI3PT8FRmsT6DTYumQOm6WLPniLq6+tJTEyksLCQgQMtLF8OMyZZGOG5n9Pp5OO1aymprCAnPYPZkyYBdDg2c+RIXE4nD/3zdRa//jkxyek88/0bKUiLwzywkT+98Rm/LlrH8MwrOXN8bod9cNasWSxfvrzDvvjnpTsor2sitW4HLVtKWVZWyknTZ2A5ynq8++vHa9dSXFxMVlwsC+bMwYr/+7SIiIS/tWvdQw8GDoRB0cms2bSN3eVNvOOs5rTp4w7uhzOcNDYuY++efdAUx6yZs4mi417hdDpZ+dkKyspLycrM5pOWdFqK1pHSYmHJEoM5nskKh+85ecl2Jlj28/H67dzzRDH/vOfaTs87/NiME2b6nnfVyoPPO23ydD5b8yll5aUkpmbwf8XxNDicjEyO4ZqBDbz72iu+x4yNtjJ3eDpGySZG5xWTPTmHyTNm4mh3sWzZMrbu3EtxWwyftWRRUu/gkXe38JtnX8bZUI01IRV7/lgAWvdtxNlQzcDcXL43bdox/1ymTJh+zL+T/rgv94tA2+OPP85vf/tbSkpKmDhxIn/84x+ZPv3YfxkiSTFR/Os7M3lrQzGJMVGcPvbglJOfnTWGTQfq+GxPNTf+43MW3Tib2Oijv1iXvP06v/zZD2mqLgPgqdfghQfc9TN1NQcHLqRn5/LII48QG2Xl1ltvZd++fb7b8vPzueyyy/jnP//Z5eOPPPIIF1xwQWD+QERERDoxLjuV5i+zaW9qZ8/ejbz3ztvU1x+8gp6YmMQZZ5xBSd0g/t+fG/nNWRbWfv4hP3r8MfaXlfnOS0tKAsOgqvbgBNPUpCQM06Sq/mD/l2lv/haAyspK37GvLXqApNgoaqqrfMe8QS+v3LwBtAw8kboNSymqr+CaP7uP52VmcsmC03np3Xc6rGdAVhYXn7bgiOP5jz/OZZdfrv1YREQ6aGmBRYvcWW0HDmzmzTff9O2Hn2yDz5a+zfApc6ivSuWVr7xOa/NPAXc7hJzsPH7x0wf46oJzAHjjnde49/67KC45cPAJDAuYLv4L/PcRSE9PBzruh4cfe+k1ePfRH2K1Wo55HkBGdi6nn3wuiz94jZLSg89rsVhwuQ42ULAmZpAx4WTWbvuIc8qLfcePtg929lwD8vOZceo5vPmfl2muObjHWmMTsVgM2hrdf24VwMn//R0//4n7z6azP5ec7Dx+evsv+NaEq4/4O3nllVc6/Vzd1/flPh9oe/HFF7njjjt48sknmTFjBg8//DCnn346W7duJSsrcqZWSPckx0Zx6QmFRxyPtll4/IopnPXoMraU1PPTV9fz+4sndtrgcdm7/+P/3Xo90DE/+NAAm1dlWQnf+Polna5l3759/Pa3v+3y8f3793PRRRfx73//u0//EhERkf5t29pYbHUpONo3sejlpRy+39XX1/PSSy8RO+wrsGskp+0uoeSjezi8aqaqk1Fs1Z0cO/SNuperpZ6alo7HDg2yARQf2A8HXj7ivgfKy3n4H88dcXx/WVmnx/ft36/9WEREjrBkCWzeDC7XVhYt+heH74ftLY1sXv4WYAeGA18B3gGgtKyYb996DX9+5C8AfPvWa46c3ml27Bba2X7Y2bGazj53dnJeRWkx/3jhz0ccPzTIBuCsr6D045eOOO9o+2Bnz7V/3z5e+esTRxx3NtfjPOxYSan7z+bb193Mn5957Ig/l9KyYm758TfJGpjEJV+/2Hf8lVde4aKLLjri/P6wL/f5Hm0PPfQQ3/zmN7n22msZM2YMTz75JHFxcTzzzDOhXpr0c9lJMTx62WQsBrzy+X5+9uoGHO2H/RJyOnns/p9y+C/ZowrgKGTvL5TbbrvtiA8bIiIigVBeDq+/DtnpFlr2fErn+50JmLQUfYolqpWSlU5MOm/iHI60H4uIhD/vfpiQ4OK99/7HsT//tQJ1wDng2Q+9e8W9v7qLe39115FBtghmmiamafLUs493+ufiPfb9H9zh22edTie33nrrMc/vy/tynw60ORwOVq9ezfz5833HLBYL8+fPZ8WKFZ3ep7W1lbq6ug5fIkcza2gG958/HoB/rCzilN8v4e8rdlPZ4E4B/uKzFVSUFh/rIYLKNE327t3LsmXLQrYGEREJT6bp/lCxfz9kJpRiOo49wdN0NDE+ZQ9mWzpwVu8sso/QfiwiEr4O3Q9drqIO7ROObh8wgEP3Q9M0KS49QPEhZZty0OGZdYcyMdm3b59vn122bFmHctEjzu/j+3KfDrRVVFTgdDrJzs7ucDw7O5uSkpJO7/PAAw+QnJzs+yroztxdiShfn17IwqumkZFgZ191M3f/ZyMn/Oo9zn50GQvfWhPq5QFQXBy6YJ+IiISnigr49FNIS4MkW3uX7tPeXAdUAdMhgrLavLQfi4iEn0P3w8bG+uPfAQAXkbwfBot3n+3qfttX9+U+HWjrjrvuuova2lrf1969e0O9JOkHThuTzbIfnsw9Z49h3IAkXCbsrmzCGZsc6qUBkJubG+oliIhImMnIgOnToaoKUpK6tt8lJ6cCacCnuFscRxbtxyIi4efQ/TA+PrGL97IQyfthsHj32a7ut311X+7TwxAyMjKwWq2UlpZ2OF5aWkpOTk6n97Hb7djt9t5YnoSZ2Ggr1500mOtOGkxJbQubimtZXzSUX77/RyrKSgLaf62rDMMgPz+fOXPm0NyuOn8REQkcw4Czz4bVq6GhoZDY+ESaj3ol3yApKYncnBOIs/+dptb/9epaQ+3Q/VhERMLL4fthYmIS9fX1HLtPWz6wHzh0PzTIzXYHfkrKitWn7TAWi8XXr+1wBgYD8gf49tk5c+aQn5/P/v37Oz+/j+/LfTqjLTo6mqlTp7J48WLfMZfLxeLFi5k5c2YIVybhLic5humD0xmclcjNP/mV+2AnE0mPcMg5nU0w9Yf3/g8//DBWq7VHjyUiItKZzEz3h4uGBgsL5h+t75p7Pzr11K/S1GjlxvOsRNLVe+3HIiLh79D9cL5vPzz885z3ezuQBLxGx/3Q5Oc/uZ+f//QB99k9/DwYLgzDwDAMvnXtTb7vD78d4Pe/fci3z1qtVh555JFjnt+X9+U+HWgDuOOOO1i4cCF//etf2bx5M9/97ndpbGzk2muvDfXSJELMOe0s7n/sWbKyO6alJqWkkZSS2uFYRlYuz73wL15++WUGDBjQ4baCggJ+8IMfkJ+f36Xj+fn5fXpksYiIhId582DMGIiJGcXJZ56HER3X4fakpCQuueQSoqNHMma0izsvyeGm792DNbFjT5r05GTSkjuWoFpiEomJ61iGk56eTnp6+nGPHf7m+Wj75YDMLG674hsMyMrqcDw/O7vz4wPytR+LiMgRvPthdPRILrnkEhITO+5f7v3wUmbP/gaJiXuBpb7bbIkZZJ73EwZOOZmvLjiHPz/yF3IO+/xosXQMv3R1P+zqsfz8fK7/xnfJyc475vPm5QzgW9fcRE5Wx/UdbR/s7Ln8OTc3O48/P/IXfvaDn3f655KTncejDy7kvPPO73D8ggsu4N///vcRn6v7w77cp0tHAS699FLKy8u55557KCkpYdKkSbz11ltHDEgQCaZ5p5/NnPln8sWqFVSUl5KRmc3EE9xZld5jiamZDB43ldPG5ZFgt/G1r32NZcuWUVxcTG5uLnPmzMFqtfLAAw/4dVxERCSYYmLgvPPgd7+DiSMnMWzECD7bsI1Y2hg2IIOBAwdSX2+htBTOPdtJTI2LH559Mm/ETqVp70Z+MMTBmLwsZk+aBMDHa9eyr7yC+7dH4codw/+NB0tGFGU1Nb79DThizzv82KxZs1i+fPlR98v9u3aRXFLCSdNnEJWQwM9vvJGP166lpLKCnPQMZk+ahNVq9R0vLi4mKy6WBTfeRHRykvZjERHp4ND9MDt7NLfdNpKioiLq6+tJTEyksLCQ+noLViv84Q+30Ng4mb179kFTHEvMbBYX1fCfraWMzUzkqwvO4fRTv8pf33qXR5esITUtk9duvpxPV62EuCYKBuZ3eT/s6rEZJ8xk78Zq7vr+vXy+7lPKykvJysxm2uTpfLbm4Pczps3E5YTbvnUX++q3UFFZdtx9sLPnP9a57y9ewvqV28gbkMesE2f79lHvn8vKz1b41jNlwnScjs7LbC+44IKjfq7uy/p8oA3g5ptv5uabbw71MiTCWa1Wppx40hHHvceaHU4aWts6nD9v3rxOH8ef4yIiIsE2aRLMnAkffADjx8dx7kmTfLeZJuzZA6ecApMnumhcCqnRBtOzYvjEmEDUyHjmjjiYBTd36lTe3d+K2VxHbozBtBSTpDlzsSbEd3jOzva8w48da790TjuBhiVLsBxSZjJ36tROz587dSqu5mZcDQ0dylK0H4uIyKE67ocWBg0a5Lvt0P1w2jQrhjEPR0s7u9dVkFLfzOKiGt7bWcH3ThhEot2G1WqlOm0E8WOSOH1EFna7nRlTZzFoQgbRMQdDMV3ZD7tyzNHiniButVqZNaPj59bDv3c5nVitVr4y9ysd1uK9f1ef/2jnfmXuVxiYMpboWNsRQbHD19fmcOLk6NPP++O+3OdLR0VEREQkuCwW+NrXID0dSko63lZS4j7+ta91bFe6YIB7+NQ7B1qPeLz/7WsB4MxcGxa1qBERkX6iO/shwLjMBAanxNLqdPH2jnIAnC6Tj4qqAJhTmNYby5c+QoE2EREREaGwEBYsgPJyaPdcWG5vd39/+ulQUNDx/FNz7RjAhup2DjQ5fcfr21wsLXEA8NXcflE8ISIi4uPvfgjuBv1fG5kDwH+2lmKaJiv311DW5CAx2sa03JTe+wEk5BRoExERERHA/QFi6FDYvdv9/e7d7u8XLDjy3IwYC9MyogB4Y9/BrLZFe1pwuGBYopWRiXqrKSIi/Y8/+6HXGUMzsVst7Kxp4qOiap7fsB+Arw7PxG7TfhhJ9LctIiIiIgAkJcG554LD4b5y39bm/j4pqfPzzy1wl4++sLOZdpeJw2Xyjx3NAFw+NBbj8NoaERGRfsDf/RAg0W7jwtHurLYfv7+FNSV12K0WLhqde/Q7SVhSoE1EREREfGbOhClTYNs2mDzZ/f3RnFUQQ1q0QXGzi3/sbObZL5vY1+Qi3W5wTkFM7y1aREQkwPzZD71umFzI+KxEAAzgthmDyUvUfhhp1DhDRERERHxsNjj/fPf/n3+++/ujsVsNbhodz31fNPCb9Y2+47ePTSDOZuBqO/p9RURE+jJ/9kMvu83C42eOY+X+avKTYilMjg3uIqVPUqBNRERERDoYORJ+/OOunXvJ4Bg21bTz8h73pNFrhsVyXqE9iKsTERHpHf7sh15Wi8GsAk0ZjWQKtImIiIhIt1kMg19MSeT6EbHYDIMB8dZQL0lEREQkZBRoExEREZEeG5igt5UiIiIiGoYgIiIiIiIiIiISAAq0iYiIiIiIiIiIBIACbSIiIiIiIiIiIgGgQJuIiIiIiIiIiEgAKNAmIiIiIiIiIiISAAq0iYiIiIiIiIiIBIACbSIiIiIiIiIiIgGgQJuIiIiIiIiIiEgAKNAmIiIiIiIiIiISAAq0iYiIiIiIiIiIBIACbSIiIiIiIiIiIgGgQJuIiIiIiIiIiEgAKNAmIiIiIiIiIiISAAq0iYiIiIiIiIiIBIAt1AsINtM0AairqwPA2dBIQ1MTRlsbRmPjse/rcGC2teGqq8PqcgZ8bVpL5xpa22lqqKet2UKjzXrMcx3tTtqcLurqYnHZA/vP2Z91BHstfW09WkvP19KX/r1oLb2/lv7671ZrCc1a/NmjIbj7tNbS87VE0ns6raVn69BatBatpXOOlnYaGuqxtFiwRh07d8jZ5sLV7qKuLppoR+DfW2otfXstR1tHQ2M9cDBeFGhhH2irrKwEoKCgIMQrERERERERERGRvqCyspLk5OSAP27YB9rS0tIAKCoqCsofoIh0X11dHQUFBezdu5ekpKRQL0dEDqPXqEjfpdenSN+m16hI31VbW0thYaEvXhRoYR9os1jc6YHJycn6BSfSRyUlJen1KdKH6TUq0nfp9SnSt+k1KtJ3eeNFAX/coDyqiIiIiIiIiIhIhFGgTUREREREREREJADCPtBmt9u59957sdvtoV6KiBxGr0+Rvk2vUZG+S69Pkb5Nr1GRvivYr0/DDNY8UxERERERERERkQgS9hltIiIiIiIiIiIivUGBNhERERERERERkQBQoE1ERERERERERCQAFGgTEREREREREREJgH4ZaBs0aBCGYRzxddNNN7F79+5ObzMMg5deesn3GJ3d/sILL4TwpxIJD8d6fQKUlJRw5ZVXkpOTQ3x8PFOmTOHll1/u8BhVVVVcccUVJCUlkZKSwvXXX09DQ0MofhyRsBOI12hnj/Hggw+G4scRCSvHe33u2LGD888/n8zMTJKSkrjkkksoLS3t8BjaQ0WCJxCvUe2hIsHhdDq5++67GTx4MLGxsQwdOpT77ruPQ+d/mqbJPffcQ25uLrGxscyfP58vv/yyw+MEYh/tl4G2VatWUVxc7Pt69913Abj44ospKCjocFtxcTE///nPSUhI4Mwzz+zwOM8++2yH884777wQ/DQi4eVYr0+Aq666iq1bt/Lf//6X9evXc8EFF3DJJZewZs0a32NcccUVbNy4kXfffZfXX3+dDz/8kG9961sh+XlEwk0gXqMAv/jFLzo8zve+971e/1lEws2xXp+NjY0sWLAAwzB4//33+fjjj3E4HJxzzjm4XC7fY2gPFQmeQLxGQXuoSDD8+te/5oknnuCxxx5j8+bN/PrXv+Y3v/kNf/zjH33n/OY3v+HRRx/lySefZOXKlcTHx3P66afT0tLiOycg+6gZBm699VZz6NChpsvl6vT2SZMmmdddd12HY4C5aNGiXlidSGQ7/PUZHx9v/u1vf+twTlpamrlw4ULTNE1z06ZNJmCuWrXKd/ubb75pGoZh7t+/v/cWLhIh/H2NmqZpDhw40PzDH/7Qm8sUiUiHvj7ffvtt02KxmLW1tb7ba2pqTMMwzHfffdc0Te2hIr3N39eoaWoPFQmWs84664i4zwUXXGBeccUVpmmapsvlMnNycszf/va3vttrampMu91u/vOf/zRNM3D7aL/MaDuUw+Hgueee47rrrsMwjCNuX716NWvXruX6668/4rabbrqJjIwMpk+fzjPPPNMhpVBEeq6z1+esWbN48cUXqaqqwuVy8cILL9DS0sK8efMAWLFiBSkpKUybNs33OPPnz8disbBy5cpQ/BgiYas7r1GvBx98kPT0dCZPnsxvf/tb2tvbQ/ATiISvw1+fra2tGIaB3W73nRMTE4PFYuGjjz4CtIeK9KbuvEa9tIeKBN6sWbNYvHgx27ZtA+CLL77go48+8lU27tq1i5KSEubPn++7T3JyMjNmzGDFihVA4PZRWyB+oFB69dVXqamp4Zprrun09qeffprRo0cza9asDsd/8YtfcMoppxAXF8c777zDjTfeSENDA7fccksvrFokMnT2+vzXv/7FpZdeSnp6Ojabjbi4OBYtWsSwYcMAd3+orKysDo9js9lIS0ujpKSkN5cvEva68xoFuOWWW5gyZQppaWksX76cu+66i+LiYh566KEQ/BQi4enw1+eJJ55IfHw8P/rRj7j//vsxTZMf//jHOJ1OiouLAe2hIr2pO69R0B4qEiw//vGPqaurY9SoUVitVpxOJ7/61a+44oorAHz7YHZ2dof7ZWdn+24L1D7a7wNtTz/9NGeeeSZ5eXlH3Nbc3Mzzzz/P3XfffcRthx6bPHkyjY2N/Pa3v1WgTSSAOnt93n333dTU1PDee++RkZHBq6++yiWXXMKyZcsYP358CFcrEnm6+xq94447fOdPmDCB6Ohovv3tb/PAAw90uJIvIt13+OszMzOTl156ie9+97s8+uijWCwWLrvsMqZMmYLF0u+LVET6ne6+RrWHigTHv/71L/7xj3/w/PPPM3bsWNauXcttt91GXl4eV199da+upV8H2vbs2cN7773HK6+80unt//73v2lqauKqq6467mPNmDGD++67j9bWVv2CEwmAzl6fO3bs4LHHHmPDhg2MHTsWgIkTJ7Js2TIef/xxnnzySXJycigrK+vwWO3t7VRVVZGTk9OrP4NIOOvua7QzM2bMoL29nd27dzNy5MheWb9IODvae9wFCxawY8cOKioqsNlspKSkkJOTw5AhQwC0h4r0ku6+RjujPVQkMH7wgx/w4x//mK9//esAjB8/nj179vDAAw9w9dVX+/bB0tJScnNzffcrLS1l0qRJQOD20X59+evZZ58lKyuLs846q9Pbn376ac4991wyMzOP+1hr164lNTVVQTaRAOns9dnU1ARwxJV3q9Xqm8Y0c+ZMampqWL16te/2999/H5fLxYwZM3ph5SKRobuv0c6sXbsWi8VyRKq9iHTP8d7jZmRkkJKSwvvvv09ZWRnnnnsuoD1UpLd09zXaGe2hIoHR1NR0zPewgwcPJicnh8WLF/tur6urY+XKlcycORMI3D7abzPaXC4Xzz77LFdffTU225E/xvbt2/nwww954403jrjttddeo7S0lBNPPJGYmBjeffdd7r//fu68887eWLpI2Dva63PUqFEMGzaMb3/72/zud78jPT2dV1991Tc6GWD06NGcccYZfPOb3+TJJ5+kra2Nm2++ma9//eudloiLiP968hpdsWIFK1eu5OSTTyYxMZEVK1Zw++23841vfIPU1NRQ/UgiYeNY73GfffZZRo8eTWZmJitWrODWW2/l9ttv92XBaA8VCb6evEa1h4oEzznnnMOvfvUrCgsLGTt2LGvWrOGhhx7iuuuuA8AwDG677TZ++ctfMnz4cAYPHszdd99NXl4e5513HhDAfbTbs1ND7O233zYBc+vWrZ3eftddd5kFBQWm0+k84rY333zTnDRpkpmQkGDGx8ebEydONJ988slOzxUR/x3r9blt2zbzggsuMLOyssy4uDhzwoQJ5t/+9rcO51RWVpqXXXaZmZCQYCYlJZnXXnutWV9f31vLFwl7PXmNrl692pwxY4aZnJxsxsTEmKNHjzbvv/9+s6WlpTd/BJGwdazX549+9CMzOzvbjIqKMocPH27+/ve/N10uV4dztIeKBFdPXqPaQ0WCp66uzrz11lvNwsJCMyYmxhwyZIj505/+1GxtbfWd43K5zLvvvtvMzs427Xa7eeqppx7xWg7EPmqYpmkGNo4oIiIiIiIiIiISefp1jzYREREREREREZG+QoE2ERERERERERGRAFCgTUREREREREREJAAUaBMREREREREREQkABdpEREREREREREQCQIE2ERERERERERGRAFCgTUREREREREREJAAUaBMREREREREREQkABdpEREREgCVLlmAYBjU1Nb3+3IZh8Oqrr/b68/bU7t27MQyDtWvXBv25HA4Hw4YNY/ny5V06d9CgQXz22WdBX5eIiIjIoQzTNM1QL0JERESkN82bN49Jkybx8MMP+445HA6qqqrIzs7GMIxeXU9JSQmpqanY7fZefd6ecjqdlJeXk5GRgc1mC+pzPfroo7z22mu8++67XTr/scceY9GiRSxevDio6xIRERE5lDLaRERERIDo6GhycnJ6PcgGkJOT0++CbABWq5WcnJygB9lM0+Sxxx7j+uuv7/J9rrjiCj766CM2btwYxJWJiIiIdKRAm4iIiESUa665hqVLl/LII49gGAaGYbB79+4jSkf/8pe/kJKSwuuvv87IkSOJi4vjoosuoqmpib/+9a8MGjSI1NRUbrnlFpxOp+/xW1tbufPOOxkwYADx8fHMmDGDJUuWHHNNh5aOessxX3nlFU4++WTi4uKYOHEiK1asOOr9TdPk//2//0dhYSF2u528vDxuueWWLq9pz549nHPOOaSmphIfH8/YsWN54403AKiuruaKK64gMzOT2NhYhg8fzrPPPtthrYeWji5dupTp06djt9vJzc3lxz/+Me3t7b7b582bxy233MIPf/hD0tLSyMnJ4f/9v/93zD+f1atXs2PHDs466yzfMYfDwc0330xubi4xMTEMHDiQBx54wHd7amoqs2fP5oUXXjjmY4uIiIgEUnAvP4qIiIj0MY888gjbtm1j3Lhx/OIXvwAgMzOT3bt3H3FuU1MTjz76KC+88AL19fVccMEFnH/++aSkpPDGG2+wc+dOLrzwQmbPns2ll14KwM0338ymTZt44YUXyMvLY9GiRZxxxhmsX7+e4cOHd3mdP/3pT/nd737H8OHD+elPf8pll13G9u3bO80ee/nll/nDH/7ACy+8wNixYykpKeGLL77w3X68Nd100004HA4+/PBD4uPj2bRpEwkJCQDcfffdbNq0iTfffJOMjAy2b99Oc3Nzp2vev38/X/3qV7nmmmv429/+xpYtW/jmN79JTExMh2DaX//6V+644w5WrlzJihUruOaaa5g9ezannXZap4+7bNkyRowYQWJiou/Yo48+yn//+1/+9a9/UVhYyN69e9m7d2+H+02fPp1ly5Z1+c9cREREpKcUaBMREZGIkpycTHR0NHFxceTk5Bzz3La2Np544gmGDh0KwEUXXcTf//53SktLSUhIYMyYMZx88sl88MEHXHrppRQVFfHss89SVFREXl4eAHfeeSdvvfUWzz77LPfff3+X13nnnXf6Mrh+/vOfM3bsWLZv386oUaOOOLeoqIicnBzmz59PVFQUhYWFTJ8+3Xfb8dZUVFTEhRdeyPjx4wEYMmRIh8eePHky06ZNA2DQoEFHXfOf/vQnCgoKeOyxxzAMg1GjRnHgwAF+9KMfcc8992CxuIspJkyYwL333gvA8OHDeeyxx1i8ePFRA2179uzxrf3QdQ0fPpyTTjoJwzAYOHDgEffLy8tjz549R12viIiISKCpdFRERETkKOLi4nxBNoDs7GwGDRrky/byHisrKwNg/fr1OJ1ORowYQUJCgu9r6dKl7Nixw6/nnjBhgu//c3NzAXzPc7iLL76Y5uZmhgwZwje/+U0WLVrkK9fsyppuueUWfvnLXzJ79mzuvfde1q1b53vs7373u7zwwgtMmjSJH/7wh8ec+rl582ZmzpzZoc/d7NmzaWhoYN++fZ3+bN6f72g/G0BzczMxMTEdjl1zzTWsXbuWkSNHcsstt/DOO+8ccb/Y2FiampqO+rgiIiIigaZAm4iIiMhRREVFdfjeMIxOj7lcLgAaGhqwWq2sXr2atWvX+r42b97MI4880u3n9gauvM9zuIKCArZu3cqf/vQnYmNjufHGG5k7dy5tbW1dWtMNN9zAzp07ufLKK1m/fj3Tpk3jj3/8IwBnnnkme/bs4fbbb+fAgQOceuqp3HnnnX79LMf62bw/39F+NoCMjAyqq6s7HJsyZQq7du3ivvvuo7m5mUsuuYSLLrqowzlVVVVkZmb2aK0iIiIi/lCgTURERCJOdHR0hwEGgTJ58mScTidlZWUMGzasw9fxylR7KjY2lnPOOYdHH32UJUuWsGLFCtavX9/lNRUUFPCd73yHV155he9///ssXLjQd1tmZiZXX301zz33HA8//DBPPfVUp2sYPXo0K1aswDRN37GPP/6YxMRE8vPzu/2zTZ48mS1btnR4XICkpCQuvfRSFi5cyIsvvsjLL79MVVWV7/YNGzYwefLkbj+viIiIiL/Uo01EREQizqBBg1i5ciW7d+8mISGBtLS0gDzuiBEjuOKKK7jqqqv4/e9/z+TJkykvL2fx4sVMmDChw9TMQPrLX/6C0+lkxowZxMXF8dxzzxEbG8vAgQNJT08/7ppuu+02zjzzTEaMGEF1dTUffPABo0ePBuCee+5h6tSpjB07ltbWVl5//XXfbYe78cYbefjhh/ne977HzTffzNatW7n33nu54447fP3ZuuPkk0+moaGBjRs3Mm7cOAAeeughcnNzmTx5MhaLhZdeeomcnBxSUlJ891u2bBn33Xdft59XRERExF/KaBMREZGIc+edd2K1WhkzZgyZmZkUFRUF7LGfffZZrrrqKr7//e8zcuRIzjvvPFatWkVhYWHAnuNwKSkpLFy4kNmzZzNhwgTee+89XnvtNdLT07u0JqfTyU033cTo0aM544wzGDFiBH/6058Ad/bfXXfdxYQJE5g7dy5Wq5UXXnih03UMGDCAN954g08//ZSJEyfyne98h+uvv56f/exnPfr50tPTOf/88/nHP/7hO5aYmMhvfvMbpk2bxgknnMDu3bt54403fAG9FStWUFtbe0Q5qYiIiEgwGebhOfgiIiIiIn3MunXrOO2009ixY0eHYRRHc+mllzJx4kR+8pOf9MLqRERERNyU0SYiIiIifd6ECRP49a9/za5du457rsPhYPz48dx+++29sDIRERGRg5TRJiIiIiIiIiIiEgDKaBMREREREREREQkABdpEREREREREREQCQIE2ERERERERERGRAFCgTUREREREREREJAAUaBMREREREREREQkABdpEREREREREREQCQIE2ERERERERERGRAFCgTUREREREREREJAAUaBMREREREREREQmA/w+nCiQ8HcU8/QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "initial_time = 775 # initial time for plot, in seconds\n",
+    "final_time = 800 # final time for plot, in seconds\n",
+    "\n",
+    "plt.clf()\n",
+    "fig, ax = plt.subplots(figsize = (15,5))\n",
+    "plot_running(ax, initial_time, final_time)\n",
+    "plot_licks(ax, initial_time, final_time)\n",
+    "plot_rewards(ax, initial_time, final_time)\n",
+    "plot_stimuli(ax, initial_time, final_time)\n",
+    "\n",
+    "ax.legend(['running speed', 'licks', 'rewards'])\n",
+    "\n",
+    "ax.set_ylabel('running speed (cm/s)')\n",
+    "ax.set_xlabel('time in session (s)')\n",
+    "ax.set_xlim(initial_time, final_time)\n",
+    "ax.set_title('a short section of the session');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4463800e",
+   "metadata": {
+    "papermill": {
+     "duration": 0.049068,
+     "end_time": "2022-12-13T23:44:49.090306",
+     "exception": false,
+     "start_time": "2022-12-13T23:44:49.041238",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Introduction to the behavior trials table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6503eace",
+   "metadata": {
+    "papermill": {
+     "duration": 0.04959,
+     "end_time": "2022-12-13T23:44:49.189664",
+     "exception": false,
+     "start_time": "2022-12-13T23:44:49.140074",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Now let's explore the behavior trials table. This table contains lots of useful information about every trial in the change detection task."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "25e0cc41",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-12-13T23:44:49.289706Z",
+     "iopub.status.busy": "2022-12-13T23:44:49.289034Z",
+     "iopub.status.idle": "2022-12-13T23:44:49.310727Z",
+     "shell.execute_reply": "2022-12-13T23:44:49.309997Z"
+    },
+    "papermill": {
+     "duration": 0.073785,
+     "end_time": "2022-12-13T23:44:49.312356",
+     "exception": false,
+     "start_time": "2022-12-13T23:44:49.238571",
+     "status": "completed"
+    },
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>initial_image_name</th>\n",
+       "      <th>change_image_name</th>\n",
+       "      <th>is_change</th>\n",
+       "      <th>change_time</th>\n",
+       "      <th>go</th>\n",
+       "      <th>catch</th>\n",
+       "      <th>lick_times</th>\n",
+       "      <th>response_time</th>\n",
+       "      <th>...</th>\n",
+       "      <th>reward_time</th>\n",
+       "      <th>reward_volume</th>\n",
+       "      <th>hit</th>\n",
+       "      <th>false_alarm</th>\n",
+       "      <th>miss</th>\n",
+       "      <th>correct_reject</th>\n",
+       "      <th>aborted</th>\n",
+       "      <th>auto_rewarded</th>\n",
+       "      <th>change_frame</th>\n",
+       "      <th>trial_length</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trials_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.000843</td>\n",
+       "      <td>12.777525</td>\n",
+       "      <td>im036_r</td>\n",
+       "      <td>im078_r</td>\n",
+       "      <td>True</td>\n",
+       "      <td>8.528410</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>[9.74160359997768, 9.875109600019641, 9.975196...</td>\n",
+       "      <td>9.741604</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.674043</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>510.0</td>\n",
+       "      <td>11.776682</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.011130</td>\n",
+       "      <td>14.412321</td>\n",
+       "      <td>im078_r</td>\n",
+       "      <td>im078_r</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>[13.928571700002067, 14.112001699977554]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.401190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>14.512404</td>\n",
+       "      <td>15.379752</td>\n",
+       "      <td>im078_r</td>\n",
+       "      <td>im078_r</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>[15.07956089999061]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.867348</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16.013679</td>\n",
+       "      <td>17.831895</td>\n",
+       "      <td>im078_r</td>\n",
+       "      <td>im078_r</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>[17.264757600030862, 17.514946899958886]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.818216</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>18.265613</td>\n",
+       "      <td>29.291666</td>\n",
+       "      <td>im078_r</td>\n",
+       "      <td>im044_r</td>\n",
+       "      <td>True</td>\n",
+       "      <td>25.042558</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>[25.421669999952428, 25.58848069992382, 25.705...</td>\n",
+       "      <td>25.421670</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.188221</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1500.0</td>\n",
+       "      <td>11.026053</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           start_time  stop_time initial_image_name change_image_name  \\\n",
+       "trials_id                                                               \n",
+       "0            1.000843  12.777525            im036_r           im078_r   \n",
+       "1           13.011130  14.412321            im078_r           im078_r   \n",
+       "2           14.512404  15.379752            im078_r           im078_r   \n",
+       "3           16.013679  17.831895            im078_r           im078_r   \n",
+       "4           18.265613  29.291666            im078_r           im044_r   \n",
+       "\n",
+       "           is_change  change_time     go  catch  \\\n",
+       "trials_id                                         \n",
+       "0               True     8.528410  False  False   \n",
+       "1              False          NaN  False  False   \n",
+       "2              False          NaN  False  False   \n",
+       "3              False          NaN  False  False   \n",
+       "4               True    25.042558  False  False   \n",
+       "\n",
+       "                                                  lick_times  response_time  \\\n",
+       "trials_id                                                                     \n",
+       "0          [9.74160359997768, 9.875109600019641, 9.975196...       9.741604   \n",
+       "1                   [13.928571700002067, 14.112001699977554]            NaN   \n",
+       "2                                        [15.07956089999061]            NaN   \n",
+       "3                   [17.264757600030862, 17.514946899958886]            NaN   \n",
+       "4          [25.421669999952428, 25.58848069992382, 25.705...      25.421670   \n",
+       "\n",
+       "           ...  reward_time  reward_volume    hit  false_alarm   miss  \\\n",
+       "trials_id  ...                                                          \n",
+       "0          ...     8.674043          0.005  False        False  False   \n",
+       "1          ...          NaN          0.000  False        False  False   \n",
+       "2          ...          NaN          0.000  False        False  False   \n",
+       "3          ...          NaN          0.000  False        False  False   \n",
+       "4          ...    25.188221          0.005  False        False  False   \n",
+       "\n",
+       "           correct_reject  aborted  auto_rewarded  change_frame  trial_length  \n",
+       "trials_id                                                                      \n",
+       "0                   False    False           True         510.0     11.776682  \n",
+       "1                   False     True          False           NaN      1.401190  \n",
+       "2                   False     True          False           NaN      0.867348  \n",
+       "3                   False     True          False           NaN      1.818216  \n",
+       "4                   False    False           True        1500.0     11.026053  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trials = behavior_session.trials\n",
+    "trials.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b798d127",
+   "metadata": {
+    "papermill": {
+     "duration": 0.049003,
+     "end_time": "2022-12-13T23:44:49.413415",
+     "exception": false,
+     "start_time": "2022-12-13T23:44:49.364412",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Unlike the stimulus presentations table in which every row corresponded to a visual stimulus presentation, for the behavior trials table every row corresponds to one trial of the change detection task. Here is a quick summary of the columns:\n",
+    "\n",
+    "`start_time`: Experiment time when this trial began in seconds.\n",
+    "\n",
+    "`end_time`: Experiment time when this trial ended.\n",
+    "\n",
+    "`initial_image_name`: Indicates which image was shown before the change (or sham change) for this trial\n",
+    "\n",
+    "`change_image_name`: Indicates which image was scheduled to be the change image for this trial. Note that if the trial is aborted, a new trial will begin before this change occurs.\n",
+    "\n",
+    "`is_change`: Indicates whether an image change occurred for this trial. \n",
+    "\n",
+    "`change_time`: Experiment time when the task-control computer commanded an image change. This change time is used to determine the response window during which a lick will trigger a reward. Note that for behavior-only sessions, a constant display lag of 20 ms was applied (unlike for behavior+ephys sessions for which this lag was empirically measured for each experiment).\n",
+    "\n",
+    "`go`: Indicates whether this trial was a 'go' trial. To qualify as a go trial, an image change must occur and the trial cannot be autorewarded.\n",
+    "\n",
+    "`catch`: Indicates whether this trial was a 'catch' trial. To qualify as a catch trial, a 'sham' change must occur during which the image identity does not change. These sham changes are drawn to match the timing distribution of real changes and can be used to calculate the false alarm rate.\n",
+    "\n",
+    "`lick_times`: A list indicating when the behavioral control software recognized a lick (sampled at 60 Hz). Note that for behavior-only sessions these are the only lick times provided (unlike for behavior+ephys sessions for which lick times are also recorded at 100 KHz). \n",
+    "\n",
+    "`response_time`: Indicates the time when the first lick was registered by the task control software for trials that were not aborted (go or catch). NaN for aborted trials.\n",
+    "\n",
+    "`response_latency`: Indicates the latency at which the first lick was registered by the task control software relative to the change (or sham change) for trials that were not aborted (go or catch). NaN for aborted trials. Inf for trials without a response.\n",
+    "\n",
+    "`reward_time`: Indicates when the reward command was triggered for hit trials. NaN for other trial types. \n",
+    "\n",
+    "`reward_volume`: Indicates the volume of water dispensed as reward for this trial. \n",
+    "\n",
+    "`hit`: Indicates whether this trial was a 'hit' trial. To qualify as a hit, the trial must be a go trial during which the stimulus changed and the mouse licked within the reward window (150-750 ms after the change time).\n",
+    "\n",
+    "`false_alarm`: Indicates whether this trial was a 'false alarm' trial. To qualify as a false alarm, the trial must be a catch trial during which a sham change occurred and the mouse licked during the reward window.\n",
+    "\n",
+    "`miss`: To qualify as a miss trial, the trial must be a go trial during which the stimulus changed but the mouse did not lick within the response window.\n",
+    "\n",
+    "`correct_reject`: To qualify as a correct reject trial, the trial must be a catch trial during which a sham change occurred and the mouse withheld licking.\n",
+    "\n",
+    "`aborted`: A trial is aborted when the mouse licks before the scheduled change or sham change.\n",
+    "\n",
+    "`auto_rewarded`: During autorewarded trials, the reward is automatically triggered after the change regardless of whether the mouse licked within the response window. These always come at the beginning of the session to help engage the mouse in behavior.\n",
+    "\n",
+    "`change_frame`: Indicates the stimulus frame index when the change (on go trials) or sham change (on catch trials) occurred. \n",
+    "\n",
+    "`trial_length`: Duration of the trial in seconds."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b27d73fc",
+   "metadata": {},
+   "source": [
+    "You can use the `get_performance_metrics` method for a helpful summary of the behavior during this session:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "02b96e82",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'trial_count': 503,\n",
+       " 'go_trial_count': 309,\n",
+       " 'catch_trial_count': 45,\n",
+       " 'hit_trial_count': 136,\n",
+       " 'miss_trial_count': 173,\n",
+       " 'false_alarm_trial_count': 1,\n",
+       " 'correct_reject_trial_count': 44,\n",
+       " 'auto_reward_count': 5,\n",
+       " 'earned_reward_count': 136,\n",
+       " 'total_reward_count': 141,\n",
+       " 'total_reward_volume': 0.7050000000000001,\n",
+       " 'maximum_reward_rate': 4.314648233692634,\n",
+       " 'engaged_trial_count': 354,\n",
+       " 'mean_hit_rate': 0.5204850997120982,\n",
+       " 'mean_hit_rate_uncorrected': 0.5218975290906293,\n",
+       " 'mean_hit_rate_engaged': 0.5895975463227386,\n",
+       " 'mean_false_alarm_rate': 0.07724001259157509,\n",
+       " 'mean_false_alarm_rate_uncorrected': 0.018134469696969694,\n",
+       " 'mean_false_alarm_rate_engaged': 0.08078944987519368,\n",
+       " 'mean_dprime': 1.569242030995491,\n",
+       " 'mean_dprime_engaged': 1.7775127852098909,\n",
+       " 'max_dprime': 2.2113065796444684,\n",
+       " 'max_dprime_engaged': 2.2113065796444684}"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "behavior_session.get_performance_metrics()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa3a96f3",
+   "metadata": {},
+   "source": [
+    "Let's calculate the hit rate for each image individually:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "80bc0150",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hit</th>\n",
+       "      <th>miss</th>\n",
+       "      <th>hit_rate</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>change_image_name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>im012_r</th>\n",
+       "      <td>15</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.394737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>im036_r</th>\n",
+       "      <td>7</td>\n",
+       "      <td>33</td>\n",
+       "      <td>0.175000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>im044_r</th>\n",
+       "      <td>25</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.609756</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>im047_r</th>\n",
+       "      <td>19</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>im078_r</th>\n",
+       "      <td>21</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.567568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>im083_r</th>\n",
+       "      <td>5</td>\n",
+       "      <td>34</td>\n",
+       "      <td>0.128205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>im111_r</th>\n",
+       "      <td>19</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.487179</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>im115_r</th>\n",
+       "      <td>25</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.675676</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   hit  miss  hit_rate\n",
+       "change_image_name                     \n",
+       "im012_r             15    23  0.394737\n",
+       "im036_r              7    33  0.175000\n",
+       "im044_r             25    16  0.609756\n",
+       "im047_r             19    19  0.500000\n",
+       "im078_r             21    16  0.567568\n",
+       "im083_r              5    34  0.128205\n",
+       "im111_r             19    20  0.487179\n",
+       "im115_r             25    12  0.675676"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hit_miss_table = trials.pivot_table(index='change_image_name', values=['hit', 'miss'], aggfunc=sum)\n",
+    "hit_miss_table['hit_rate'] = hit_miss_table['hit']/(hit_miss_table['hit'] + hit_miss_table['miss'])\n",
+    "hit_miss_table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ebbd0d77",
+   "metadata": {},
+   "source": [
+    "These hit rates are for the entire session, but some mice lose motivation before the end of the session. For these mice, some analyses might benefit from considering only the epochs of the session for which the mouse was engaged. Let's look at a simple engagment index (reward rate) for this session:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "62df72c7",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Trials')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "rolling_performance = behavior_session.get_rolling_performance_df()\n",
+    "\n",
+    "ax.plot(rolling_performance['reward_rate'])\n",
+    "ax.set_ylabel('Rewards per minute')\n",
+    "ax.set_xlabel('Trials')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3d36331",
+   "metadata": {},
+   "source": [
+    "Looks like the mouse lost motivation around trial 350. Let's recompute our image-wise hit rates using only engaged trials. We'll define this as trials for which the reward rate was > 2 rewards per minute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "3ec0f3af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hit</th>\n",
+       "      <th>miss</th>\n",
+       "      <th>hit_rate</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>change_image_name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>im012_r</th>\n",
+       "      <td>15</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.555556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>im036_r</th>\n",
+       "      <td>6</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.230769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>im044_r</th>\n",
+       "      <td>20</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.769231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>im047_r</th>\n",
+       "      <td>18</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>im078_r</th>\n",
+       "      <td>20</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.769231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>im083_r</th>\n",
+       "      <td>4</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.153846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>im111_r</th>\n",
+       "      <td>19</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.678571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>im115_r</th>\n",
+       "      <td>24</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.888889</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   hit  miss  hit_rate\n",
+       "change_image_name                     \n",
+       "im012_r             15    12  0.555556\n",
+       "im036_r              6    20  0.230769\n",
+       "im044_r             20     6  0.769231\n",
+       "im047_r             18     9  0.666667\n",
+       "im078_r             20     6  0.769231\n",
+       "im083_r              4    22  0.153846\n",
+       "im111_r             19     9  0.678571\n",
+       "im115_r             24     3  0.888889"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "engaged_trials = trials[rolling_performance['reward_rate']>2]\n",
+    "\n",
+    "hit_miss_table_engaged = engaged_trials.pivot_table(index='change_image_name', values=['hit', 'miss'], aggfunc=sum)\n",
+    "hit_miss_table_engaged['hit_rate'] = hit_miss_table_engaged['hit']/(hit_miss_table_engaged['hit'] + hit_miss_table_engaged['miss'])\n",
+    "hit_miss_table_engaged"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2c8de09",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From ccc0151c03d6bae7b38e61a874735ab896ce407d Mon Sep 17 00:00:00 2001
From: Corbett Bennett <corbettb@alleninstitute.org>
Date: Mon, 9 Jan 2023 14:09:01 -0800
Subject: [PATCH 2/3] update vbn readme doc to include LFP/behavior-only
 notebooks

---
 doc_template/visual_behavior_neuropixels.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/doc_template/visual_behavior_neuropixels.rst b/doc_template/visual_behavior_neuropixels.rst
index 5b9a0cee0..96d29d3ee 100644
--- a/doc_template/visual_behavior_neuropixels.rst
+++ b/doc_template/visual_behavior_neuropixels.rst
@@ -18,6 +18,8 @@ To get started, check out these jupyter notebooks:
 3) `Aligning behavioral data to task events with the stimulus and trials tables <_static/examples/nb/aligning_behavioral_data_to_task_events_with_the_stimulus_and_trials_tables.html>`_ `(download .ipynb) <_static/examples/nb/aligning_behavioral_data_to_task_events_with_the_stimulus_and_trials_tables.ipynb>`_
 4) `Plot quality metrics for the 'units' identified in these experiments <_static/examples/nb/visual_behavior_neuropixels_quality_metrics.html>`_ `(download .ipynb) <_static/examples/nb/visual_behavior_neuropixels_quality_metrics.ipynb>`_
 5) `Visual Behavior Neuropixels Quickstart <_static/examples/nb/visual_behavior_neuropixels_quickstart.html>`_ `(download .ipynb) <_static/examples/nb/visual_behavior_neuropixels_quickstart.ipynb>`_
+6) `Analyzing LFP data <_static/examples/nb/visual_behavior_neuropixels_LFP_analysis.html>`_ `(download .ipynb) <_static/examples/nb/visual_behavior_neuropixels_LFP_analysis.ipynb>`_
+7) `Analyzing behavior-only data for one mouse's training history <_static/examples/nb/visual_behavior_neuropixels_analyzing_behavior_only_data.html>`_ `(download .ipynb) <_static/examples/nb/visual_behavior_neuropixels_analyzing_behavior_only_data.ipynb>`_
 
 You may also find `these tutorials <https://github.com/AllenInstitute/swdb_2022/tree/main/DynamicBrain>`_ helpful, 
 which were made for students in the Summer Workshop for the Dynamic Brain.

From c4265dc769c531649ff3d8a5304ede2fc2201aeb Mon Sep 17 00:00:00 2001
From: Corbett Bennett <corbettb@alleninstitute.org>
Date: Tue, 10 Jan 2023 11:26:39 -0800
Subject: [PATCH 3/3] set numpy random seed

---
 ...sual_behavior_neuropixels_LFP_analysis.ipynb | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_LFP_analysis.ipynb b/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_LFP_analysis.ipynb
index 39d086e28..af9e1940b 100644
--- a/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_LFP_analysis.ipynb
+++ b/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_LFP_analysis.ipynb
@@ -1764,12 +1764,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 32,
    "id": "58bc794f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "spikes_to_use = np.random.choice(spike_times, min((spike_times.size, 1000)), replace=False)\n",
+    "rng = np.random.default_rng(seed=42) #set seed for deterministic results\n",
+    "spikes_to_use = rng.choice(spike_times, min((spike_times.size, 1000)), replace=False)\n",
     "spike_triggered_lfp = align_lfp(lfp, np.arange(-0.1, 0.1, 1/1250), spikes_to_use)"
    ]
   },
@@ -1783,7 +1784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 33,
    "id": "9bed263b",
    "metadata": {},
    "outputs": [
@@ -1793,13 +1794,13 @@
        "Text(0, 0.5, 'Channel depth')"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1822,7 +1823,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "89fd0d32",
+   "id": "d8d0b12c",
    "metadata": {},
    "source": [
     "Aligning the LFP to spikes from our example unit reveals a low resolution spike waveform (note the thin dark stripe at the center of the plot). As an added exercise, you can compare this plot to the high resolution waveform for this unit stored in the session `mean_waveforms` dictionary. This plot also gives us some information about the network state that was conducive to spiking for this unit. Note the blue LFP depression in the middle of the plot, indicating that this unit tended to spike during periods of relative network excitability. How might this look for other cortical units? What if we limited our analysis to spikes during a particular stimulus or spontaneous activity?"
@@ -2429,7 +2430,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "20507760",
+   "id": "09f347f6",
    "metadata": {},
    "source": [
     "The CSV for this probe insertion reveals that visual activation is nicely confined to visual cortex (between the white lines). The trajectory for this probe likely missed other visually-responsive areas. Let's check to see which other areas were recorded:"
@@ -2438,7 +2439,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "d9fcb808",
+   "id": "3a0355de",
    "metadata": {},
    "outputs": [
     {