diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 34bffa6f..450a59d1 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -25,7 +25,7 @@ jobs:
         run: pip freeze
 
       - name: Installing requirements
-        run: pip install -r requirements.txt --user
+        run: pip install -r .binder/requirements.txt --user
 
       - name: Installing build dependencies
         run: |
diff --git a/docs/visualization/visualize_unit_metrics.ipynb b/docs/visualization/visualize_unit_metrics.ipynb
new file mode 100644
index 00000000..ed22cd1a
--- /dev/null
+++ b/docs/visualization/visualize_unit_metrics.ipynb
@@ -0,0 +1,1049 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "afa6fc9b",
+   "metadata": {},
+   "source": [
+    "# Visualizing Unit Quality Metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c471992",
+   "metadata": {},
+   "source": [
+    "This content is taken and adapted from the [Allen SDK Documentation](https://allensdk.readthedocs.io/en/latest/_static/examples/nb/ecephys_quality_metrics.html). Further explanation for each metric can be found there."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "813f07b7",
+   "metadata": {},
+   "source": [
+    "This Jupyter notebook will provide a detailed explanation of the unit quality metrics included in ecephys NWB Files. It's important to pay attention to quality metrics, because failing to apply them correctly could lead to invalid scientific conclusions, or could end up hiding potentially useful data. To help you avoid these pitfalls, this tutorial will explore how these metrics are calculated, how they can be biased, and how they should be applied to specific use cases. It's important to keep in mind that none of these metrics are perfect, and that the use of unit quality metrics for filtering ephys data is still an evolving area of research. More work is required in order to establish general-purpose best practices and standards in this domain."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ee9e37f",
+   "metadata": {},
+   "source": [
+    "All of the Allen Institute Neuropixels data has been sorted with [Kilosort2](https://github.com/MouseLand/Kilosort), a template-matching algorithm developed by Marius Pachitariu at HHMI Janelia Research Campus. Because there is no \"ground truth\" information available in these datasets, any sorting algorithm is bound to make mistakes. Quality metrics allow us to understand the types of mistakes that are occurring, and obtain an estimate of their severity. Some common errors that can be identified by quality metrics include:\n",
+    "- Assigning spikes from multiple neurons to the same cluster\n",
+    "- Missing spikes from neurons with waveform amplitude near the spike detection threshold\n",
+    "- Failing to track neurons with waveforms that change as a result of electrode drift\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f44393a6",
+   "metadata": {},
+   "source": [
+    "These mistakes can occur even in units that appear to be extremely well isolated. It's misleading to conceive of units as existing in two distinct categories, one with perfectly clean \"single units\" and one with impure \"multiunits.\" Instead, there's a gradient of qualities, with mostly complete, uncontaminated units at one end, and incomplete, highly contaminated units at the other.\n",
+    "\n",
+    "Despite the fact that there's not a clear division between single-unit and multi-unit activity, we still have to make a binary decision in every analysis we carry out: should this unit be included or not? Ideally this decision should be based on objective metrics that will not bias the end results. By default, the AllenSDK uses three quality metrics, isi_violations, amplitude_cutoff, and presence_ratio, to filter out units that are likely to be highly contaminated or missing lots of spikes. However, the default values of these filters may not be appropriate for your analysis, or you may want to disable them entirely. Reading through this tutorial will give you a better understanding of how these (and other) metrics should be applied, so you can apply them effectively throughout your own explorations of this dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb9fcf18",
+   "metadata": {},
+   "source": [
+    "### Environment Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0746ea40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "import fsspec\n",
+    "import h5py\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from dandi import dandiapi\n",
+    "from fsspec.implementations.cached import CachingFileSystem\n",
+    "from pynwb import NWBHDF5IO\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39f93626",
+   "metadata": {},
+   "source": [
+    "### Streaming an NWB File\n",
+    "The example file we use is from the Allen Institute's `Visual Coding - Neuropixels` dataset. To specify your own file to use, set `dandiset_id` and `dandi_filepath` to be the respective dandiset id and dandi filepath of the file you're interested in. When accessing an embargoed dataset, change `authenticate` to True and set `dandi_api_key` to be your dandi api key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0afc48ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dandiset_id = \"000021\"\n",
+    "dandi_filepath = \"sub-703279277/sub-703279277_ses-719161530.nwb\"\n",
+    "authenticate = False\n",
+    "# dandi_api_key = os.environ['DANDI_API_KEY']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e7c873bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "A newer version (0.48.0) of dandi/dandi-cli is available. You are using 0.46.3\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Retrieved file url https://dandiarchive.s3.amazonaws.com/blobs/ef6/6a8/ef66a8f3-9121-42b5-bdf7-3f892b8cb27c?response-content-disposition=attachment%3B%20filename%3D%22sub-703279277_ses-719161530.nwb%22&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAUBRWC5GAEKH3223E%2F20230112%2Fus-east-2%2Fs3%2Faws4_request&X-Amz-Date=20230112T221610Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=47946b8ceabc2bd977e30a6e6d363a315a056f365dc7c014f33bfec0fc2d9d67\n"
+     ]
+    }
+   ],
+   "source": [
+    "if authenticate:\n",
+    "    client = dandiapi.DandiAPIClient(token=dandi_api_key)\n",
+    "else:\n",
+    "    client = dandiapi.DandiAPIClient()\n",
+    "my_dandiset = client.get_dandiset(dandiset_id)\n",
+    "\n",
+    "file = my_dandiset.get_asset_by_path(dandi_filepath)\n",
+    "base_url = file.client.session.head(file.base_download_url)\n",
+    "file_url = base_url.headers['Location']\n",
+    "\n",
+    "print(f\"Retrieved file url {file_url}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a7c575c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\carter.peene\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\hdmf\\spec\\namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-common' version 1.1.3 because version 1.5.1 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "C:\\Users\\carter.peene\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\hdmf\\spec\\namespace.py:531: UserWarning: Ignoring cached namespace 'core' version 2.2.2 because version 2.5.0 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "fs = CachingFileSystem(\n",
+    "    fs=fsspec.filesystem(\"http\"),\n",
+    ")\n",
+    "\n",
+    "# opening may take awhile on a large file\n",
+    "f = fs.open(file_url, \"rb\")\n",
+    "file = h5py.File(f)\n",
+    "io = NWBHDF5IO(file=file, mode='r', load_namespaces=True)\n",
+    "nwb = io.read()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bc98b54",
+   "metadata": {},
+   "source": [
+    "### Reading a Stimulus File"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0b47a289",
+   "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>waveform_duration</th>\n",
+       "      <th>cluster_id</th>\n",
+       "      <th>peak_channel_id</th>\n",
+       "      <th>cumulative_drift</th>\n",
+       "      <th>amplitude_cutoff</th>\n",
+       "      <th>snr</th>\n",
+       "      <th>recovery_slope</th>\n",
+       "      <th>isolation_distance</th>\n",
+       "      <th>nn_miss_rate</th>\n",
+       "      <th>silhouette_score</th>\n",
+       "      <th>...</th>\n",
+       "      <th>local_index</th>\n",
+       "      <th>spread</th>\n",
+       "      <th>waveform_halfwidth</th>\n",
+       "      <th>d_prime</th>\n",
+       "      <th>presence_ratio</th>\n",
+       "      <th>repolarization_slope</th>\n",
+       "      <th>nn_hit_rate</th>\n",
+       "      <th>spike_times</th>\n",
+       "      <th>spike_amplitudes</th>\n",
+       "      <th>waveform_mean</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>950921187</th>\n",
+       "      <td>0.604355</td>\n",
+       "      <td>4</td>\n",
+       "      <td>850249267</td>\n",
+       "      <td>481.80</td>\n",
+       "      <td>0.425574</td>\n",
+       "      <td>2.209140</td>\n",
+       "      <td>-0.118430</td>\n",
+       "      <td>17.537571</td>\n",
+       "      <td>0.009496</td>\n",
+       "      <td>0.036369</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0.357119</td>\n",
+       "      <td>2.962274</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.381716</td>\n",
+       "      <td>0.473829</td>\n",
+       "      <td>[1.0439430431793884, 1.543311060144649, 2.7287...</td>\n",
+       "      <td>[0.0001908626967721937, 0.00016134635752077775...</td>\n",
+       "      <td>[[0.0, 0.5961149999999966, 5.378099999999993, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>950921172</th>\n",
+       "      <td>0.521943</td>\n",
+       "      <td>3</td>\n",
+       "      <td>850249267</td>\n",
+       "      <td>681.53</td>\n",
+       "      <td>0.390098</td>\n",
+       "      <td>1.959983</td>\n",
+       "      <td>-0.109729</td>\n",
+       "      <td>14.677643</td>\n",
+       "      <td>0.003857</td>\n",
+       "      <td>0.103446</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>0.260972</td>\n",
+       "      <td>2.067810</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.536663</td>\n",
+       "      <td>0.445946</td>\n",
+       "      <td>[10.406435026164546, 17.127986534673788, 18.48...</td>\n",
+       "      <td>[0.00014485615850768024, 0.0001722424107984555...</td>\n",
+       "      <td>[[0.0, -1.341600000000002, -0.4586399999999933...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>950921152</th>\n",
+       "      <td>0.467002</td>\n",
+       "      <td>2</td>\n",
+       "      <td>850249267</td>\n",
+       "      <td>1070.71</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>2.522905</td>\n",
+       "      <td>-0.109867</td>\n",
+       "      <td>15.783665</td>\n",
+       "      <td>0.017776</td>\n",
+       "      <td>0.027818</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0.247236</td>\n",
+       "      <td>2.220043</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.566559</td>\n",
+       "      <td>0.284058</td>\n",
+       "      <td>[1.2775103414155262, 2.3915133536963493, 3.701...</td>\n",
+       "      <td>[0.00014859435856024575, 0.0001531048673600236...</td>\n",
+       "      <td>[[0.0, -0.6427199999999993, -2.836079999999998...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>950921135</th>\n",
+       "      <td>0.467002</td>\n",
+       "      <td>1</td>\n",
+       "      <td>850249267</td>\n",
+       "      <td>253.42</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>2.803475</td>\n",
+       "      <td>-0.150379</td>\n",
+       "      <td>26.666930</td>\n",
+       "      <td>0.023742</td>\n",
+       "      <td>0.076530</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>0.233501</td>\n",
+       "      <td>2.339206</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.669090</td>\n",
+       "      <td>0.590737</td>\n",
+       "      <td>[9.473732504122962, 13.198542576065163, 18.302...</td>\n",
+       "      <td>[0.00032386170367170055, 0.0004518112387675137...</td>\n",
+       "      <td>[[0.0, -3.2800950000000078, -6.087510000000009...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>950921111</th>\n",
+       "      <td>0.439531</td>\n",
+       "      <td>0</td>\n",
+       "      <td>850249267</td>\n",
+       "      <td>141.82</td>\n",
+       "      <td>0.018056</td>\n",
+       "      <td>4.647943</td>\n",
+       "      <td>-0.328727</td>\n",
+       "      <td>66.901065</td>\n",
+       "      <td>0.006595</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>0.219765</td>\n",
+       "      <td>4.395994</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>1.261416</td>\n",
+       "      <td>0.952667</td>\n",
+       "      <td>[1.1677100445138795, 1.1707767194728813, 1.349...</td>\n",
+       "      <td>[0.00015644521007973124, 0.000214412247939483,...</td>\n",
+       "      <td>[[0.0, -0.9291749999999945, -6.120270000000007...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>950927711</th>\n",
+       "      <td>1.455946</td>\n",
+       "      <td>482</td>\n",
+       "      <td>850249273</td>\n",
+       "      <td>2.46</td>\n",
+       "      <td>0.000895</td>\n",
+       "      <td>1.651500</td>\n",
+       "      <td>-0.039932</td>\n",
+       "      <td>39.400278</td>\n",
+       "      <td>0.000033</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>464</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>0.274707</td>\n",
+       "      <td>5.557479</td>\n",
+       "      <td>0.37</td>\n",
+       "      <td>0.467365</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>[2613.8652081509977, 2624.5193369599215, 2734....</td>\n",
+       "      <td>[0.00012946663895843286, 0.0001203425053985725...</td>\n",
+       "      <td>[[0.0, 6.3216435986159105, 10.324204152249129,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>950921285</th>\n",
+       "      <td>2.087772</td>\n",
+       "      <td>11</td>\n",
+       "      <td>850249273</td>\n",
+       "      <td>318.53</td>\n",
+       "      <td>0.036848</td>\n",
+       "      <td>1.379817</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>27.472722</td>\n",
+       "      <td>0.000903</td>\n",
+       "      <td>0.291953</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>0.288442</td>\n",
+       "      <td>2.751337</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>0.372116</td>\n",
+       "      <td>0.258065</td>\n",
+       "      <td>[39.04904580954626, 39.457346913598556, 40.495...</td>\n",
+       "      <td>[7.768399792002802e-05, 8.405736507197006e-05,...</td>\n",
+       "      <td>[[0.0, 5.330324999999991, 2.4261899999999486, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>950921271</th>\n",
+       "      <td>0.947739</td>\n",
+       "      <td>10</td>\n",
+       "      <td>850249273</td>\n",
+       "      <td>1008.50</td>\n",
+       "      <td>0.001727</td>\n",
+       "      <td>1.420617</td>\n",
+       "      <td>-0.008204</td>\n",
+       "      <td>30.027595</td>\n",
+       "      <td>0.000707</td>\n",
+       "      <td>0.406673</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>0.288442</td>\n",
+       "      <td>3.847234</td>\n",
+       "      <td>0.96</td>\n",
+       "      <td>0.498618</td>\n",
+       "      <td>0.796491</td>\n",
+       "      <td>[16.751918851114475, 26.127977537450867, 28.65...</td>\n",
+       "      <td>[0.00016516929470324686, 0.0001501058102103845...</td>\n",
+       "      <td>[[0.0, -3.103230000000032, 5.680349999999983, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>950921260</th>\n",
+       "      <td>0.453266</td>\n",
+       "      <td>9</td>\n",
+       "      <td>850249273</td>\n",
+       "      <td>175.00</td>\n",
+       "      <td>0.000081</td>\n",
+       "      <td>4.969091</td>\n",
+       "      <td>-0.184456</td>\n",
+       "      <td>89.804006</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.223876</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>0.192295</td>\n",
+       "      <td>5.274090</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>1.140487</td>\n",
+       "      <td>0.997333</td>\n",
+       "      <td>[0.9620761551434307, 2.092045877265143, 2.4040...</td>\n",
+       "      <td>[0.0003836112198262231, 0.0004093908262843732,...</td>\n",
+       "      <td>[[0.0, 1.9104149999999982, -7.270770000000016,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>950921248</th>\n",
+       "      <td>0.439531</td>\n",
+       "      <td>8</td>\n",
+       "      <td>850249273</td>\n",
+       "      <td>261.11</td>\n",
+       "      <td>0.065478</td>\n",
+       "      <td>2.147758</td>\n",
+       "      <td>-0.085677</td>\n",
+       "      <td>84.145512</td>\n",
+       "      <td>0.005474</td>\n",
+       "      <td>0.076346</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>0.274707</td>\n",
+       "      <td>3.022387</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>0.463570</td>\n",
+       "      <td>0.976667</td>\n",
+       "      <td>[1.316477113448928, 1.779311698293908, 2.96088...</td>\n",
+       "      <td>[0.00013780619334124896, 0.0001439873831056905...</td>\n",
+       "      <td>[[0.0, -3.6761399999999953, 0.8334300000000014...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           waveform_duration  cluster_id  peak_channel_id  cumulative_drift  \\\n",
+       "id                                                                            \n",
+       "950921187           0.604355           4        850249267            481.80   \n",
+       "950921172           0.521943           3        850249267            681.53   \n",
+       "950921152           0.467002           2        850249267           1070.71   \n",
+       "950921135           0.467002           1        850249267            253.42   \n",
+       "950921111           0.439531           0        850249267            141.82   \n",
+       "950927711           1.455946         482        850249273              2.46   \n",
+       "950921285           2.087772          11        850249273            318.53   \n",
+       "950921271           0.947739          10        850249273           1008.50   \n",
+       "950921260           0.453266           9        850249273            175.00   \n",
+       "950921248           0.439531           8        850249273            261.11   \n",
+       "\n",
+       "           amplitude_cutoff       snr  recovery_slope  isolation_distance  \\\n",
+       "id                                                                          \n",
+       "950921187          0.425574  2.209140       -0.118430           17.537571   \n",
+       "950921172          0.390098  1.959983       -0.109729           14.677643   \n",
+       "950921152          0.500000  2.522905       -0.109867           15.783665   \n",
+       "950921135          0.500000  2.803475       -0.150379           26.666930   \n",
+       "950921111          0.018056  4.647943       -0.328727           66.901065   \n",
+       "950927711          0.000895  1.651500       -0.039932           39.400278   \n",
+       "950921285          0.036848  1.379817             NaN           27.472722   \n",
+       "950921271          0.001727  1.420617       -0.008204           30.027595   \n",
+       "950921260          0.000081  4.969091       -0.184456           89.804006   \n",
+       "950921248          0.065478  2.147758       -0.085677           84.145512   \n",
+       "\n",
+       "           nn_miss_rate  silhouette_score  ...  local_index spread  \\\n",
+       "id                                         ...                       \n",
+       "950921187      0.009496          0.036369  ...            4   50.0   \n",
+       "950921172      0.003857          0.103446  ...            3   40.0   \n",
+       "950921152      0.017776          0.027818  ...            2   50.0   \n",
+       "950921135      0.023742          0.076530  ...            1   40.0   \n",
+       "950921111      0.006595               NaN  ...            0   30.0   \n",
+       "950927711      0.000033               NaN  ...          464  100.0   \n",
+       "950921285      0.000903          0.291953  ...           11  100.0   \n",
+       "950921271      0.000707          0.406673  ...           10  100.0   \n",
+       "950921260      0.000000          0.223876  ...            9   60.0   \n",
+       "950921248      0.005474          0.076346  ...            8   60.0   \n",
+       "\n",
+       "           waveform_halfwidth   d_prime  presence_ratio  repolarization_slope  \\\n",
+       "id                                                                              \n",
+       "950921187            0.357119  2.962274            0.99              0.381716   \n",
+       "950921172            0.260972  2.067810            0.99              0.536663   \n",
+       "950921152            0.247236  2.220043            0.99              0.566559   \n",
+       "950921135            0.233501  2.339206            0.99              0.669090   \n",
+       "950921111            0.219765  4.395994            0.99              1.261416   \n",
+       "950927711            0.274707  5.557479            0.37              0.467365   \n",
+       "950921285            0.288442  2.751337            0.89              0.372116   \n",
+       "950921271            0.288442  3.847234            0.96              0.498618   \n",
+       "950921260            0.192295  5.274090            0.99              1.140487   \n",
+       "950921248            0.274707  3.022387            0.99              0.463570   \n",
+       "\n",
+       "           nn_hit_rate                                        spike_times  \\\n",
+       "id                                                                          \n",
+       "950921187     0.473829  [1.0439430431793884, 1.543311060144649, 2.7287...   \n",
+       "950921172     0.445946  [10.406435026164546, 17.127986534673788, 18.48...   \n",
+       "950921152     0.284058  [1.2775103414155262, 2.3915133536963493, 3.701...   \n",
+       "950921135     0.590737  [9.473732504122962, 13.198542576065163, 18.302...   \n",
+       "950921111     0.952667  [1.1677100445138795, 1.1707767194728813, 1.349...   \n",
+       "950927711     0.000000  [2613.8652081509977, 2624.5193369599215, 2734....   \n",
+       "950921285     0.258065  [39.04904580954626, 39.457346913598556, 40.495...   \n",
+       "950921271     0.796491  [16.751918851114475, 26.127977537450867, 28.65...   \n",
+       "950921260     0.997333  [0.9620761551434307, 2.092045877265143, 2.4040...   \n",
+       "950921248     0.976667  [1.316477113448928, 1.779311698293908, 2.96088...   \n",
+       "\n",
+       "                                            spike_amplitudes  \\\n",
+       "id                                                             \n",
+       "950921187  [0.0001908626967721937, 0.00016134635752077775...   \n",
+       "950921172  [0.00014485615850768024, 0.0001722424107984555...   \n",
+       "950921152  [0.00014859435856024575, 0.0001531048673600236...   \n",
+       "950921135  [0.00032386170367170055, 0.0004518112387675137...   \n",
+       "950921111  [0.00015644521007973124, 0.000214412247939483,...   \n",
+       "950927711  [0.00012946663895843286, 0.0001203425053985725...   \n",
+       "950921285  [7.768399792002802e-05, 8.405736507197006e-05,...   \n",
+       "950921271  [0.00016516929470324686, 0.0001501058102103845...   \n",
+       "950921260  [0.0003836112198262231, 0.0004093908262843732,...   \n",
+       "950921248  [0.00013780619334124896, 0.0001439873831056905...   \n",
+       "\n",
+       "                                               waveform_mean  \n",
+       "id                                                            \n",
+       "950921187  [[0.0, 0.5961149999999966, 5.378099999999993, ...  \n",
+       "950921172  [[0.0, -1.341600000000002, -0.4586399999999933...  \n",
+       "950921152  [[0.0, -0.6427199999999993, -2.836079999999998...  \n",
+       "950921135  [[0.0, -3.2800950000000078, -6.087510000000009...  \n",
+       "950921111  [[0.0, -0.9291749999999945, -6.120270000000007...  \n",
+       "950927711  [[0.0, 6.3216435986159105, 10.324204152249129,...  \n",
+       "950921285  [[0.0, 5.330324999999991, 2.4261899999999486, ...  \n",
+       "950921271  [[0.0, -3.103230000000032, 5.680349999999983, ...  \n",
+       "950921260  [[0.0, 1.9104149999999982, -7.270770000000016,...  \n",
+       "950921248  [[0.0, -3.6761399999999953, 0.8334300000000014...  \n",
+       "\n",
+       "[10 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "units = nwb.units\n",
+    "units[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a74eee80",
+   "metadata": {},
+   "source": [
+    "### Firing Rate\n",
+    "*Firing rate* is equal to the total number of spikes divided by the number of seconds in the recording. Below is a density plot of firing rate across all units in the dataset as well as a log-scaled version."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f3d5b46f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = units['firing_rate']\n",
+    "bins = np.linspace(0,50,100)\n",
+    "hist, bin_edges = np.histogram(data, bins=bins)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(bin_edges[:-1], hist)\n",
+    "\n",
+    "plt.xlabel(\"log$_{10}$ firing rate (Hz)\")\n",
+    "plt.ylabel(\"# units\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c0f49999",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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i7QeQMuyQP2LYISLqZo5prBD5R3a4/Jz8EcMOEVE3c4QdGUd29PaRHW4sSP6IYYeIqJt5RoOyfWSHS8/JDzHsEBF1M48IOyH2kZ0qhh3yPww7RETdzCPCjn0KjSM75I8YdoiIupknLD3XOzYVNMtWA5FcGHaIiLpRndmCmnrbeVSeEHbKa+phlQ7qIvITDDtERN1ImsJSKwWEaFWy1SEtPbdYRVTWNchWB5EcGHaIiLpR034dQRBkq0OrUjrCVmm1SbY6iOTAsENE1I08YfdkSdOpLCJ/wrBDRNSNPGEllkQ6+ZzLz8nfMOwQEXUjTwo73FiQ/BXDDhFRN/KosMMjI8hPMewQEXWjMg/YY0ei52Gg5KcYdoiIulG5B47scGNB8jcMO0RE3ajUo8KObWSnjEvPyc8w7BARdSPHyI5HLD23j+zUcGSH/AvDDhFRJzRYrPhwz3lcNNS2eZ2jQTnEE8IOR3bIPzHsEBF1wrfHCvDU+mO4/4P9EMWWz5qyWsXGQ0A9aGSnnD075GcYdoiIOiGnrAYAcCzPiO2ni1u8xlBrhnTmZoQn9OzYA1eVqQGmBovM1RC5D8MOEVEnlDVZvr1i65kWr8kttwWi0AAV1Er5f9yGBaqgVNjO5+LoDvkT+f/2ERF5oaZhZ192OTLOlTo9L4oi/vHNSQDAuFS9W2trjSAIjjO6eBgo+ROGHSKiTiipsoWFKHvj8YptZ52e/2R/LvacK0OAWoGlNwx2e32tiZQOA+XIDvkRhh0iok6QRnYentoXSoWAHaeLceRCBQCgqLIOf99gG9X546/6IyUySK4ym4mwr8jiyA75E4YdIqJOkMLO8KRwzBqeAKCxd+evX52Asa4BQxN1uHtCT7lKbJHeMbLDIyPIfzDsEBF1kCiKKK2yhYXIEA0enNwbALDpeCH+ve0MNhy9CKVCwD/nDIXKAxqTm5LCDjcWJH/iWX8LiYi8QJWpAfUWKwAgMliLvrGhuHZwHADghY2nAAD3X9kLgxN0stXYGmn5OTcWJH/CsENE1EHSFFagWolAjRIAsGBKH8fzPSKDsGhaX1lquxzHyA6nsciPMOwQEXVQSZMpLMnQJB2uGxoHjVKBf940DAFqpVzltSkxwtYsnVlYJXMlRO4ja9jZsWMHZs6ciYSEBAiCgPXr1zs9L4oili5divj4eAQGBmLatGnIzMx0uqasrAxz585FWFgYwsPDce+996Kqin+Jiaj7SKMikZfsivzabSOx9/9NRXrvSDnKapfRPSIAAJlFVRzdIb8ha9iprq7G8OHDsWLFihaff+GFF/Daa69h1apVyMjIQHBwMKZPn466ujrHNXPnzsXx48exefNmfP3119ixYwfmz5/vri+BiPyQ1O+ivyTsqJQKhHvAGVht0Qdr0CcmBACwL7tM5mqI3EMl55vPmDEDM2bMaPE5URSxfPlyPPXUU5g1axYA4IMPPkBsbCzWr1+P2267DSdPnsTGjRuxb98+jBkzBgDw+uuv47rrrsOLL76IhIQEt30tROQ/GqextDJX0jlpqXqcKarCvqwyTLc3VhP5Mo/t2cnKykJBQQGmTZvmeEyn02HcuHHYvXs3AGD37t0IDw93BB0AmDZtGhQKBTIyMlq9t8lkgtFodPogImqv1qaxvEVaT9vxFXs5skN+wmPDTkFBAQAgNjbW6fHY2FjHcwUFBYiJiXF6XqVSQa/XO65pybJly6DT6RwfycnJLq6eiHyZFHYuncbyFmPtZ3Udzzei2tQgczVE3c9jw053WrJkCQwGg+MjNzdX7pKIyItI52J56zRWYnggEsMDYbGKOJhTLnc5RN3OY8NOXJxtHrmwsNDp8cLCQsdzcXFxKCoqcnq+oaEBZWVljmtaotVqERYW5vRBRNRe3j6NBdj6dgBgbxanssj3eWzYSU1NRVxcHLZs2eJ4zGg0IiMjA+np6QCA9PR0VFRU4MCBA45rfvjhB1itVowbN87tNRORf3CEnRDvDTtjezLskP+QdTVWVVUVzpw54/g8KysLhw8fhl6vR0pKChYtWoTnnnsOffv2RWpqKp5++mkkJCRg9uzZAICBAwfi2muvxf33349Vq1bBbDZj4cKFuO2227gSi4i6RdNzsby1ZwdoHNk5nFsBU4MFWpVnboJI5Aqyhp39+/djypQpjs8XL14MAJg3bx7ee+89PPHEE6iursb8+fNRUVGBiRMnYuPGjQgICHC8Zs2aNVi4cCGmTp0KhUKBOXPm4LXXXnP710JE/uHSc7G8Ve/oYEQGa1BaXY+jFwwYYx/pIfJFgiiKotxFyM1oNEKn08FgMLB/h4jadL60Glf9axuCNEqcePZaucvpkgf+ux+bjhfiiWv74w+T+1z+BUQepr2/vz22Z4eIyBOV+MAUliQt1XasxT727ZCPY9ghIuoAX1iJJZE2F9yfXQ6L1e8H+cmHMewQEXVAqZfvsdPUwPhQBGuUqDQ14JcC7iRPvothh4ioA0q9fPfkplRKBUbbR3c4lUW+jGGHiKgDfGkaCwDSekYA4DlZ5NsYdoiIOqBxGss3wk7j5oLl4OJc8lUMO0REHdA4jeX9PTsAMDw5HBqlAiVVJmSX1shdDlG3YNghIuoAX5vGClArMSjBtj/JiXw2KZNvYtghIuoA6agIX5nGAoC4MNuu9KXVJpkrIeoeDDtERO0kiqJjZMcXVmNJpOAmbZhI5GsYdoiI2slXzsW6VJR9z6CSKo7skG9i2CEiaidpCitIo0SgxndOCY+yj+yUMuyQj2LYISJqJ1/aULApaTfoUk5jkY9i2CEiaifHSiwfOCqiKWllGaexyFcx7BARtZNjQ0EfG9mJCuXIDvk2hh0ionby1WmsKHuzdaWpAXVmi8zVELkeww4RUTs1TmP5VtgJC1RBrRQANH6NRL6EYYeIqJ18dRpLEATHUnr27ZAvYtghImonXzsXq6lIx/JzjuyQ72HYISJqJ1+dxgIaV5hxZId8EcMOEVE7Oc7F8rFpLKBxY0EeGUG+iGGHiKgdfPVcLEmUY2NBjuyQ72HYISJqB189F0sijVaVcjUW+SCGHSKidvDVc7Ek7NkhX8awQ0TUDr66oaCEPTvkyxh2iIjawbHHjo+diyVhzw75MoYdIqJ2cCw799GRHcc+O9X1sFpFmashci2GHSKidvD1aSyp6dpiFWGoNctcDZFrMewQEbWDY48dH9xQEAA0KgXCAlQAgNJqTmWRb2HYISJqh7Jq3zwXqympb6e4kk3K5FsYdoiI2qHU0bPjmw3KQJMmZY7skI9h2CEiagdpGkvvo9NYAA8DJd/FsENE1A6+vhoLaBp2OLJDvoVhh4joMixW3z4XS+Lo2eHIDvkYhh0iosvILKpEvcWKEK0K8bpAucvpNpHcWJB8FMMOEdFlHM6pAAAMTdRBqRDkLaYbRfEwUPJRDDtERJdxOLcCADAiJVzWOrobDwMlX+XRYcdiseDpp59GamoqAgMD0bt3b/ztb3+DKDZuZS6KIpYuXYr4+HgEBgZi2rRpyMzMlLFqIvI2G49dxLE8Q6vPS2FneFK4ewqSSRRXY5GP8uiw8/zzz2PlypV44403cPLkSTz//PN44YUX8PrrrzuueeGFF/Daa69h1apVyMjIQHBwMKZPn466ujoZKycib3G2uAq///Ag7n5vX4tnQlWbGnC6sBIAMNJPRnaqTA2oM1tkrobIdTw67OzatQuzZs3C9ddfj549e+Lmm2/GNddcg7179wKwjeosX74cTz31FGbNmoVhw4bhgw8+QH5+PtavXy9v8UTkFbKKqwEAxZUmHM83Nnv+WJ4BVhGICwtAbFiAu8tzq7AAFTRK268FTmWRL/HosHPFFVdgy5YtOH36NADg559/xo8//ogZM2YAALKyslBQUIBp06Y5XqPT6TBu3Djs3r271fuaTCYYjUanDyLyTwXGxlHgHZnFzZ539Oskh7upIvkIgsCNBckneXTY+dOf/oTbbrsNAwYMgFqtxsiRI7Fo0SLMnTsXAFBQUAAAiI2NdXpdbGys47mWLFu2DDqdzvGRnJzcfV8EEXm0wqZh53TzsPPzhQoAwHA/CDtAk40FeWQE+ZAOh53a2lrU1NQ4Pj9//jyWL1+O7777zqWFAcAnn3yCNWvWYO3atTh48CDef/99vPjii3j//fe7dN8lS5bAYDA4PnJzc11UMRF5mwJDY9g5mFOOKlOD0/PSsnN/GNkBGs/+KuFhoORDOhx2Zs2ahQ8++AAAUFFRgXHjxuGll17CrFmzsHLlSpcW9/jjjztGd4YOHYo777wTjz76KJYtWwYAiIuLAwAUFhY6va6wsNDxXEu0Wi3CwsKcPojIPzWdxjJbRGScK3V8XmSsQ76hDoIADE3SyVGe20m7KJdwZId8SIfDzsGDB3HllVcCAD777DPExsbi/Pnz+OCDD/Daa6+5tLiamhooFM4lKpVKWK1WAEBqairi4uKwZcsWx/NGoxEZGRlIT093aS1E5JukkZ2ekUEAgJ2ZJY7npH6dfjGhCNGq3F6bHLj8nHxRh8NOTU0NQkNDAQDfffcdbrrpJigUCowfPx7nz593aXEzZ87E3//+d2zYsAHZ2dlYt24dXn75Zdx4440AbM10ixYtwnPPPYevvvoKR48exV133YWEhATMnj3bpbUQkW+SRnZuGWPr3WvapNzYr+MfozpAY88OV2ORL+lw2OnTpw/Wr1+P3NxcbNq0Cddccw0AoKioyOXTQa+//jpuvvlm/OEPf8DAgQPx2GOP4YEHHsDf/vY3xzVPPPEEHnroIcyfPx9jx45FVVUVNm7ciIAA314iSkRdV21qQGWdrUfnplGJUCoEnCuuxoVyW19i40qsCLlKdLsox/lYHNkh39HhsLN06VI89thj6NmzJ8aNG+eYLvruu+8wcuRIlxYXGhqK5cuX4/z586itrcXZs2fx3HPPQaNpPHVYEAQ8++yzKCgoQF1dHb7//nv069fPpXUQkW+SRnWCNUrE6wIx0t6E/GNmCaxWEUdybbsq+9fIDo+MIN/T4Unom2++GRMnTsTFixcxfPhwx+NTp07FTTfd5NLiiIi6U6G9XydOZxsJvrJvNPafL8fOzBKM6RmBSlMDAtQK9I8NlbNMt4rkYaDkgzo8snPPPfcgODgYI0eOdGoeHjx4MJ5//nmXFkdE1J2kkR1H2OkXBQD48UwJDp6vAGA76Vyl9OgtyVxKmsYqq65v8fgMIm/U4b/B77//Pmpra5s9Xltb61iSTkTkDaSwIx0DMSxRh7AAFQy1Zvx3j23Bhb/sryPR20d2LFYRFbVmmashco12hx2j0QiDwQBRFFFZWel01EJ5eTm++eYbxMTEdGetREQuJS07j7eP7KiUCkzoYxvdOZon9euEy1KbXDQqBXSBagBAKft2yEe0u2cnPDwcgiBAEIQWG4AFQcBf//pXlxZHRNSdpLAT1+SAzyv7RuPbY43HzfjbyA5gW35uqDWjuMqEvn7Ur0S+q91hZ+vWrRBFEVdffTU+//xz6PV6x3MajQY9evRAQkJCtxRJRNQdCi+ZxgKAK/tGOf4cFaJBYnig2+uSW1SIFueKq7n8nHxGu8POVVddBcB20nhKSgoEQei2ooiI3OHSBmUASNYHITUqGFkl1RiRHO6XP+sad1FunMb6ObcC7+/OxiNT+6JHZLBcpRF1SrvCzpEjRzBkyBAoFAoYDAYcPXq01WuHDRvmsuKIiLpLg8WK4krbL/OmYQcArh0Sh5XbzmJin6iWXurzHIeB2kd2jlyowB3vZKDS1IAgjRLPzR4qZ3lEHdausDNixAgUFBQgJiYGI0aMgCAIEMXmSxIFQYDFYnF5kUREnSGKIg7nVqBfbCiCLznbqrjKBKsIqBQCouy/3CWPTuuHK/tEYVyvSHeW6zEcuyhXm/BLgRF3/WcvKu2nwe/LKpezNKJOaVfYycrKQnR0tOPPRETeYMvJItz3wX7cOiYJL9w83Ok5qTk5JlQLhcJ5qkqjUuAKPx3VARrPx/o514A73slARY0ZgxPCcDzfiFOFlaioqUd4kOYydyHyHO0KOz169Gjxz0REniyzqAoAsOdcWbPnCgzN+3XIRurZOXHRCAAYFB+GtfeNx00rf8LZ4mrsyy7HrwbFylkiUYd0+LgIAMjMzMTWrVtRVFQEq9Xq9NzSpUtdUhgRUVdJDbY5ZTUw1pkRFqB2PNdSczLZSOdjAUDfmBD899406ILUSEuNtIedMoYd8iodDjtvv/02HnzwQURFRSEuLs5ppYIgCAw7ROQxmh5meSLfiPFNenAu3T2ZGvWLCUVogArRoVqsuW+cI/ykpUbg//bmYG9W85EyIk/W4bDz3HPP4e9//zuefPLJ7qiHiMhlmh5mefySsFPYwoaCZKMLUmP3kqnQqhRQNzkXbGxP2/5qx/IMqKlvQJCmU5MDRG7X4bOxysvLccstt3RHLURELlVS1TTsGJyeu8ienTaFaFVOQQcAkiKCkBgeiAariEM5FfIURtQJHQ47t9xyC7777rvuqIWIyKVKL5nGakraPZkjOx0ztmcEAHAqi7xKh8cg+/Tpg6effhp79uzB0KFDoVarnZ5/+OGHXVYcEVFnWa2i0zRWZlEV6swWBKiVEEWRDcqdNDZVj/WH8xl2yKt0OOy89dZbCAkJwfbt27F9+3an5wRBYNghIo9gqDXDYrVtfhoWoIKxrgGnCysxLCkcxtoG1JltK0nZoNwx41JtfTuHcstR32CFRtXhCQIit+tw2OGmgkTkDUqrbVNYukA1hibq8OOZEhzLM2JYUjguGmsBABFBagSolXKW6XV6R4dAH6xBWXU9juUbMColQu6SiC6LkZyIfFJxpW0KKzJEg8EJYQAam5SlDQU5qtNxgiBgTA/27ZB36fDIzj333NPm8//5z386XQwRkatIIztRwVoMcoQdW5NyIft1uiQtVY/vThRiX1YZfn9Vb7nLIbqsDoed8nLnQ+DMZjOOHTuGiooKXH311S4rjIioK0rty86jQjUYnKADAPxSYITFKqLAYAtC8Qw7nZJm79vZl10Gq1VsdrYYkafpcNhZt25ds8esVisefPBB9O7NhE9EnkHaPTkyWIvUqGAEqpWoNVtwrrgKBfaeHU5jdc6g+DAEa5Qw1jXgVGElBsaHyV0SUZtc0rOjUCiwePFivPLKK664HRFRl0kbCkaGaKBUCE5TWQXcPblLVEoFRtn7dvZls2+HPJ/LGpTPnj2LhoYGV92OiKhLpA0FpXOdmjYpFxhtz7Fnp/PS7EdHZLBJmbxAh6exFi9e7PS5KIq4ePEiNmzYgHnz5rmsMCKirpA2FIwO0QBoGnaMbFB2gbFS305WGURRdDoUmsjTdDjsHDp0yOlzhUKB6OhovPTSS5ddqUVE5C4lzUZ2bE3KRy8YUGmyjUJzGqvzRiSHQ6UQUFRpwkVDHRLCA+UuiahVHQ47W7du7Y46iIhcSlqNFRlsG9npGxsClUJwBB2tSgFdoLrV11PbAtRKJIQHIqesBrllNQw75NG4qSAR+Zw6swVV9lATFWob2dGqlOgbG+q4Jl4XwKmXLkq0B5x8Q63MlRC1jWGHiHyONIWlUSoQqm0cwJb6dgAuO3eFxAhb2MkrZ9ghz8awQ0Q+p7TJsvOmozdNww6bk7tOGtnJq2DYIc/GsENEPsdxVIS9OVkiNSkDDDuu0Bh26mSuhKhtDDtE5HOabijY1MD4xp4drsTqusZprBqZKyFqW6fCzsKFC1FWxo2kiMgzNT0qoqnQADVSo4IB8FwsV2g6jSWKoszVELWu3WHnwoULjj+vXbsWVVVVAIChQ4ciNzfX9ZUREXWS4xDQS0Z2AODx6f0xa0QCJvWLdndZPkeaCqwzW1FeY5a5GqLWtXufnQEDBiAyMhITJkxAXV0dcnNzkZKSguzsbJjN/I+ciDyHdFTEpT07AHDd0HhcNzTe3SX5pAC1EtGhWhRXmpBXXgt9cPNwSeQJ2j2yU1FRgU8//RSjR4+G1WrFddddh379+sFkMmHTpk0oLCzszjqJiNqttZ4dcr3GqSz27ZDnanfYMZvNSEtLwx//+EcEBgbi0KFDWL16NZRKJf7zn/8gNTUV/fv3d3mBeXl5uOOOOxAZGYnAwEAMHToU+/fvdzwviiKWLl2K+Ph4BAYGYtq0acjMzHR5HUTkPS49KoK6D1dkkTdo9zRWeHg4RowYgQkTJqC+vh61tbWYMGECVCoVPv74YyQmJmLfvn0uLa68vBwTJkzAlClT8O233yI6OhqZmZmIiIhwXPPCCy/gtddew/vvv4/U1FQ8/fTTmD59Ok6cOIGAADYgEvkj6RDQlnp2yLW4sSB5g3aHnby8POzevRu7du1CQ0MDRo8ejbFjx6K+vh4HDx5EUlISJk6c6NLinn/+eSQnJ2P16tWOx1JTUx1/FkURy5cvx1NPPYVZs2YBAD744APExsZi/fr1uO2221xaDxF5PqtVRJkj7HBkp7txGou8QbunsaKiojBz5kwsW7YMQUFB2LdvHx566CEIgoDHHnsMOp0OV111lUuL++qrrzBmzBjccsstiImJwciRI/H22287ns/KykJBQQGmTZvmeEyn02HcuHHYvXt3q/c1mUwwGo1OH0TkGypqzbBYbcug2TDb/aQDQPM5jUUerNObCup0Otx6661Qq9X44YcfkJWVhT/84Q+urA3nzp3DypUr0bdvX2zatAkPPvggHn74Ybz//vsAgIKCAgBAbGys0+tiY2Mdz7Vk2bJl0Ol0jo/k5GSX1k1E8pFWYoUHqaFWct/U7sYjI8gbdOonwZEjR5CUlAQA6NGjB9RqNeLi4vCb3/zGpcVZrVaMGjUK//jHPzBy5EjMnz8f999/P1atWtWl+y5ZsgQGg8HxwX2CiHyHYyUWR3XcQurZKauuR019g8zVELWsU2EnOTkZCoXtpceOHeu2kZH4+HgMGjTI6bGBAwciJycHABAXFwcAzZa9FxYWOp5riVarRVhYmNMHEfkGrsRyr7AAFULsJ8tzKos8lUeP8U6YMAGnTp1yeuz06dPo0aMHAFuzclxcHLZs2eJ43mg0IiMjA+np6W6tlajObMGB82WOfhGSR+OGghzZcQdBEDiVRR7Po8POo48+ij179uAf//gHzpw5g7Vr1+Ktt97CggULANj+ki1atAjPPfccvvrqKxw9ehR33XUXEhISMHv2bHmLJ7/zyubTmLNyNz4/eOHyF1O3KeVKLLfj8nPydO1eei6HsWPHYt26dViyZAmeffZZpKamYvny5Zg7d67jmieeeALV1dWYP38+KioqMHHiRGzcuJF77JDb/XimBABwrrha5kr8W2uHgFL3SQi3/bzN58gOeSiPDjsAcMMNN+CGG25o9XlBEPDss8/i2WefdWNVRM5q6y34paASAFBRUy9zNf6NR0W4X2J4EABOY5Hn8uhpLCJvcTzf4OjVKWfYkVVbh4BS9+A0Fnk6hh0iFzicW+H4c3mNWb5CiEdFyCDRPo3FkR3yVAw7RC7QNOxwGkteJZVceu5u0jRWgbEODRarzNUQNcewQ+QCzmGHIztyqa23oLreAoAjO+4UE6qFWinAYhVRaA+bRJ6EYYeoi0qqTLjQpFehosYMUeReO3Iorbb9otWoFI6N7qj7KRQC4nRckUWei2GHqIt+to/qSBur1VusqLGPLpB7SSuxooI1EARB5mr8i2NjQTYpkwdi2CHqIinsjO8VCY394EmuyJJHKY+KkA2Xn5MnY9gh6qJD9rAzIiUc4UFqAOzbkUtpFVdiyYUrssiTMewQdYHVKjpGdkYmhyMiyPZLliM73a/ObMH+bOezyEqqObIjF+61Q56MYYeoC7JLq2Gsa4BWpUD/uFDHyA732ul+r27JxM2rdmPh2oOO5c4lldw9WS6cxiJPxrBD1AXSkvMhiTqolYom01gc2elum44XAAC+PVaAJz47AqtVdKzGiubIjttJ52PllddyNSJ5HK7NJOoCaQpreFI4ADimsdiz070ulNfgXHE1FILtfLwvDuVBq1Y2HgLKkR23S7Cvxqo1W1BRY0ZEMP8/IM/BkR2iLjjcpDkZAMLZs+MWOzNtJ8yPSonAK78ZAUEA/m9vDnadLQXAE8/lEKBWOs4j41QWeRqGHaJOMjVYcOKiEQAwwjGyw9VY7rAzsxgAcGXfaPx6eAKenzMMACDNnvAQUHlIK7IusEmZPAzDDlEnncg3wmwRoQ/WIFlvG8LnaqzuZ7GK+NE+snNlvygAwK1jkvHsrMEAAIUAxIYx7MhBWpHFXZTJ07Bnh6iTGvt1dI7derkaq/sduVABY10DwgJUGJaoczx+V3pPxIUFoNZs4dJzmTh2UWbYIQ/DsEPUSY5+neQIx2NSUyZXY3UfqV9nQp8oqJTOg9PXDI6ToySyk5qUz5fWyFwJkTNOYxF10s8XDACA4cmNowtSz055NcNOd2nar0OeZURyOADgxzPFqKzj6CZ5DoYdok44lmdAVkk1gMYf8EDjaixjXYNjoztynco6Mw7mVAAAruwbJW8x1MyI5HD0iQlBndmKr49clLscIgeGHaIOyC6pxiMfHcLMN34EAAxJDHMEHADQBaodfzbWNbi9Pl+3+2wpLFYRqVHBSNYHyV0OXUIQBNw6JgkA8Mn+XJmrIWrEsEPUDiVVJiz54iimvrwdXx7OhygC1w2Nw8q5o52uUysVCNXaWuG4Isv1pH4djup4rhtHJkGpEHAopwKZhZVyl0MEgGGHqF0WfXQY/7c3BxariCn9o/H1QxPx77mjWxxdCA/mkRHdhf06ni86VIurB8QAAD49cEHmaohsGHaILqPK1IDd52w783547zisvjsNQ5oseb6UY6+dajZoulJOaQ2yS2ugUggY30svdznUhlvHJAMAvjh4AWb2rpEHYNghuoyD58thsYpIigjExHZMn/DIiO6x84xtVGdUSgRCA9SXuZrkNLl/NKJCtCipqscPvxTJXQ4Rww7R5ezLLgMApPVs32gCj4zoHjtP2/p1JvVjv46nUysVmDM6EQDwKRuVyQMw7BBdxt4se9hJbW/Y4ciOqzVYrPjprNSczH4db3DLaNtU1tZTxSgy1slcDfk7hh2iNpgaLDhk3yl5bDvDjnRkREUtR3ZcZdupYlTWNSA8SN1mvxR5jj4xIRjdIwIWq4gvDuXJXQ75OYYdojYcvWBAfYMVUSEa9IoKbtdrpJEdrsZyDVEUsWLbGQDAb8YmQ6kQZK6I2qvpnjuidCQ9kQwYdojakGGfwhrbU+847PNyHIeBcjWWS+w+W4pDORXQqhS4b2IvucuhDrh+WAKCNEqcK652nCVHJAeGHaI2SM3JY9vZnAxwNZarNR3ViQ7laebeJESrcvS6nSrgBoMkH4YdolZYrCIOZJcDaH9zMsDVWK50KKccP50phUoh4IGrestdDnVCvM52Enq+gU3KJB+GHaJWnLxoRKWpAaFaFQbGh7X7dVyN5TorttpGdW4cmYjE8ECZq6HOSNAFAAAuVtTKXAn5M4YdolZIU1ijekR0qClW6tkxNVhRW2/pltr8wcmLRnx/sgiCADw4maM63ireHlIvcmSHZMSwQ9QKx2aCHZjCAmx9Cip7OOLoTuf9e9tZAMB1Q+PRKzpE5mqosxwjOwaO7JB8GHaIWiCKYoc3E5QIguBoUmbfTudklVRjw5F8AMCCyX1kroa6ounIDpefk1wYdohakFVSjZKqemhUCgxL6vgmdo1NyhzZ6Yw3t5+FVQSmDojBoIT290uR54m3j+zU1FtgrG2QuRryVww7RC2QprBGJIdDq1J2+PWOvXY4stNhFquIb48VAADuu5L76ni7ALUS+mDbSGc+p7JIJl4Vdv75z39CEAQsWrTI8VhdXR0WLFiAyMhIhISEYM6cOSgsLJSvSPIJ0maC7T3881Lca6fzjuYZYKg1IzRAhbE9I+Quh1wgLox9OyQvrwk7+/btw5tvvolhw4Y5Pf7oo4/if//7Hz799FNs374d+fn5uOmmm2SqknxFZ5uTJZzG6rwdp4sBABN6R0Gl9JofUdSGhHBb2Mmv4IoskodX/CSpqqrC3Llz8fbbbyMiovFfegaDAe+++y5efvllXH311Rg9ejRWr16NXbt2Yc+ePTJWTN7soqEWuWW1UAi2Zeed0bjXDqexOmpnpi3sTOrH0819hbSxYAGXn5NMvCLsLFiwANdffz2mTZvm9PiBAwdgNpudHh8wYABSUlKwe/fuVu9nMplgNBqdPogk++y7Jg9O0CFEq+rUPTiN1TmVdWYczKkAAFzZN0reYshl4qWRHU5jkUw695PcjT766CMcPHgQ+/bta/ZcQUEBNBoNwsPDnR6PjY1FQUFBq/dctmwZ/vrXv7q6VPIRx/MNAGzNyZ3FIyM6Z/fZUlisIlKjgpGsD5K7HHKRBPvIzkVOY5FMPHpkJzc3F4888gjWrFmDgIAAl913yZIlMBgMjo/c3FyX3Zu8n3RgYf+40E7fo3GfHY7sdMTOzBIAHNXxNfHcWJBk5tFh58CBAygqKsKoUaOgUqmgUqmwfft2vPbaa1CpVIiNjUV9fT0qKiqcXldYWIi4uLhW76vVahEWFub0QSQ5bQ87A7oQdjiy0zlSv86Vfdmv40sSuLEgycyjp7GmTp2Ko0ePOj129913Y8CAAXjyySeRnJwMtVqNLVu2YM6cOQCAU6dOIScnB+np6XKUTF7OUGt2nM7cN7YLYSeYPTsdlVNag+zSGqgUAsb36twqOPJMsfal56YGK8qq6xEZopW5IvI3Hh12QkNDMWTIEKfHgoODERkZ6Xj83nvvxeLFi6HX6xEWFoaHHnoI6enpGD9+vBwlk5c7XWgb1UnQBUAXqO70fcLtrzXUmmG1ilB04CBRf7XzjG1UZ1RKBEIDOv+9J8+jUSkQFaJFSZUJFw11DDvkdh4ddtrjlVdegUKhwJw5c2AymTB9+nT8+9//lrss8lKu6NcBGnt2rCJgrDM7PqfW7TzNfh1flhAe4Ag7QxI7fgQLUVd4XdjZtm2b0+cBAQFYsWIFVqxYIU9B5FOksNOvi2FHo1IgWKNEdb0F5TUMO5fTYLHip7P2sMP9dXxSvC4ARy4Y2KRMsvDoBmUidzvlguZkCffaab+fL1Sgsq4BukA1hvJf/T5J2liQuyiTHBh2iOxEUcQpe89Ovy40J0sigpsfGVHfYMWajPPIr+C/bpvaYZ/CmtgnCkr2N/kk6cgIjuyQHBh2iOwKjSYYas1QKgT0iQnp8v0iHHvtNC4/f21LJv7fumN4bsOJLt/flzQuOWe/jq+K58aCJCOv69kh6i7SqE5qVDC0KmWX7xd+yflYxjoz3t+VDcB2JIUoihAE/xvFaLBYUWBs/IVXW2/B4dwKAMBEhh2flcAjI0hGDDtEdqcKbGek9XfBFBbQ/OTz/+4+j0pTAwCguNKEfEMdEu2brfmLvIpa3PFOBrJKqps91ys6GEkRPCLCV8XZR3YKjXXcjoHcjtNYRHa/uGjZuaRpg3JtvQXv/pgFAFArbT/kf7aPZviLImMd5r69B1kl1VAqBGhVCsdHiFaFeyakyl0idaPYUC0UAmC2iCipNsldDvkZjuwQ2Z12YXMy0LixYHmNGf+3Nwdl1fVI1gdiQu8ofLQvF4dzK3Dd0HiXvJenK60yYe47GcgurUGyPhCfPJDu6OEg/6BSKhATGoACYx0uVtQhJtR15x0SXQ5HdogAWKwiMgurALhm2TnQuBqr2GjCWzvOAQB+f1VvjO4RAQCOPhVfZ6gx48539yKzqApxYQFYe994Bh0/Fc8VWSQThh0iANml1TA1WBGgViBF75q+EWkaa292GQqMdYgN0+Lm0UkYkRwOADh6wYAGi9Ul7+UORy8YYOjgwabGOjPmrd6LExeNiArRYM3945Dsou8veZ8E7rVDMuE0FhEaTzrvFxvqssbJiEt2Tb7/yl7QqpToHR2CEK0KVaYGZBZVYWB8mEverzt9fSQfC9ceQqhWhfmTeuHuiakI0bb+46PObMGHe85j5bazKK2uR3iQGh/eNw69o7u+pJ+8V7yOIzskD47sEKFJc7KL+nWAxtVY0p9/Oy4FAKBQCBiWZNsl2Fumsr44mAcAqDQ14KXNpzHpha14Z+c51JktTteZLVb8394cTHlxG57bcBKl1fVIjQrGh/eOw4A4zw911L3i7asP8w0c2SH34sgOEVx3AGhTTc/DumdCKoI0jX/dRiSHY9fZUvycW4Hb01Jc9p6XemvHWXz1cz4+uGcc9MGdO5+rss6MHzNtOxw/cW1/fLr/ArJKqvHchpN4efNpBKgb9ySqb7Ciyr68PkEXgEem9cWcUUlQKfnvKmoyssMdxMnNGHaI0LgSy5VhJyxAhX6xIag2WXDXFT2dnhtu79vp7pGd/+45j9yyWvzwSxFuHp3UqXtsPVWMeosVvaKC8eBVvTH/yl74/OAFvPp9JvINdaipdx7diQrRYMGUPrg9LcUpCBFJYaeAIzvkZgw75PfqzBZkl9o2uXNl2BEEARsevhJmi9VpVAcARtrDzunCSlSbGhDcRv9LZ5ktVkcj6PF8Q6fDzqbjBQCA6UPiIAgCVEoBvxmbghtHJiGnrBqi6Hx9sj6IIYdalGCfxiqsNMFiFXkOGrkNww75vTNFVbCKtr6a6BCtS++tViqgbmEKJyYsAPG6AFw01OFongHje0W69H0BIK+8FharLYkczzd26h51Zgu2/lIEALh2cJzTcxqVAn1iXBcOyfdFhWihUghosIooqqzjFgTkNpxIJ7/XdOdkd55VNaKbp7LOl9U4/nwy3wirVWzj6pb9mFmCmnoL4nUBjqZqos5SKgTEhtnPyOLyc3Ijhh3ye64+E6u9pL6d7jo24nxp4/lTlaYG5JbXtHF1yzZKU1iD4/zy0FJyvQRuLEgyYNghv3fKvnNyfzcvje72kZ1S53DT0akss8WK708WAgCuHRJ3mauJ2keaurrIkR1yI4Yd8nuOkR0XNie3x9BEHRQCcNFQh0Kj63/wS2FHo7L9NT+eb+jQ6/dmlaGixgx9sAZje+pdXh/5p8YjIxh2yH0YdsivVdTUo9BoO4G5X6x7d/cN1qoch452x+hOTpltGuuqftEAOj6yI63C+tXAWK6aIZeJD+M0Frkfww75NWkzwcTwQIQGqC9ztesNTwoHcPm+nTqzBfuzyxyrqy5HFEXk2BuUrxtqm4LqSNixWkVH2OEUFrkSd1EmOTDskF871Q2bCXbEiJRwAJcf2fnXplO4edVuPPLRoXYFnqJKE+rMVigVAqYOjIVCAIorTSiqbN8vmMMXKlBoNCFEq8IVfVy/LJ78l3QYaF45R3bIfRh2yK/90g3HRHSENLJz5IKh1RBjsYr48nA+AODrIxfx5OdHLruMXOrXSQgPQFiAGr3sB3C2d3Rn0zHbqM7VA2KgVXGDQHKdXtHBEASgpMqEkiqT3OWQn2DYIb8mnXY+QKaw0y82BIFqJapMDThbXNXiNYdyylFSZYJWpYBSIeCzAxfwzFfHIV66dXET0rLzHvpgAMDgBNtKs+N5l29SrjY14OsjFwFwCotcL1irQmqU7b/Lzm52SdRRDDvkt0RRdExj9XPzHjsSlVKBMT0jAADfHi1o8ZqN9lGWGUPi8OItwyAItjOv/vntL60GHqlfJyUyCECTsHOZXy51Zgvu/2A/8ipqERGkdjQ3E7nS4ATbBpUdXSFI1FkMO+S3LhrqUFnXAJVCQO9o967EauqmUYkAgE8P5DabnhJF0bGx37VD4nDjyCT848ahAIA3d5zD6z+cafGe0jRWD70UdqRfLq2HnfoGKx788AB2nS1FsEaJ//xubLec2UXU3vBN5CoMO+S3pJVYvaKDHXvRyOHawfEI1apwobwWe86VOj134qIRF8prEaBWYJJ9lOX2tBQsvWEQAOCV70+jqIU9eqSjInpcMrKTU1YDY5252fUNFise+egQtp4qRoBagXd/NxYjUyJc90USNSH993iCYYfchGGH/JbUnCzXFJYkUKPEzBEJAIBP9uc6PSc1Cl/VL9rp5PR7JqZiUHwYRBHIyCprds8ce89Oir1nJzxIg0T7kt9Lf8FYrSIe/+wIvj1WAI1SgbfuHNMtB5MSSaSRxqySalSZGmSuhvwBww75rdOF8jYnN3XrmGQAwLfHCmCobRx52djGXjdpqbZdjfdlO4cdQ60Z5TW2e0g9OwAwqJWpg5c3n8a6Q3lQKQSsmDvKMYJE1F30wRrE62ybC568yNEd6n4MO+S3PGVkBwCGJ+nQPzYUpgYr/vezbZn52eIqnC6sgkoh4OoBsc1eI4WdvZeM7OTY+3WiQjQIadJzM6SFptDj+Qas3H4WAPCvW4bhV4Oavw9Rd5Cmso61Y4UgUVcx7JBfMlusOFtkW+o9wM0HgLZEEATcMiYJAPCpfSpL2sE4vXckdIHNd3eWzqs6VVgJQ03jaND5MmkKK8jp+kv7JBosVvzp86OwWEVcN9TW/EzkLoPa0TRP5CoMO+SXzpdWo95iRZBGiaSIQLnLAQDcODIRKoWAny8Y8EuBEZuOt33ieHSoFr2igiGKwP7zjaM7jpVYkcFO1w9OtIWdzKIq1JktWP1TNo7mGRAWoMJffj24O74kolZxRRa5E8MO+bQT+UYUVzbfpVWawuobGwqFhxxyGRmixbSBtmmk17Zk4ufcCggC2pxakkZ39jbp25GmsS4d2YkLC4A+WAOLVcSWk0V4afMpAMD/u34gYkIDXPq1EF2OFHYyCytharDIXA35OoYd8ln/3Z2N617biTveyWi2+Z5j52QP6Ndp6taxtqmkb+wbDI7pEdFmEGmpb0eaxuoR6Rx2BEFw/IJ54rOfUWe2YnwvvaM5msidEsMDoQtUo8EqIrOw5d3DiVyFYYd80if7c/H0l8cB2Hpa9p8vd3re0ZzsASuxmprUNxoxoVrH59MHt31cgxR2jl4woLbe9q/jnFamsYDGFVnV9RZoVAosu2kYBMEzRrbIvwiCgCGJ0lQWm5SpezHskM/56ud8/OnzIwBsK5IA4JN9zvvXnPKgZedNqZQK3Dy6sVH4cmEnKSIQ8boANFhFHMoth6nBgov2TQYvHdkBGvc3AYBF0/o6zigikkN7dvYmcgWGHfIp3x0vwKMfH4ZVtO00vPKO0QCADUcvOjYvq6lvcJwdJddp5225PS0FoQEqTOoXjWR988DSlCAIjX07WWXILauFKALBGiUigzXNrh/fS48QrQqjUsJx/5W9uqV+ovZikzK5i0eHnWXLlmHs2LEIDQ1FTEwMZs+ejVOnTjldU1dXhwULFiAyMhIhISGYM2cOCgsLZaqY5PRjZgkWrj0Ei1XEjSMT8ffZQzCmRwR6RQWjpt6Cb+wneWcWVkEUgchgDaJCtJe5q/sl64Owe8lUvHPXmHZdP7bJ5oI50rLzyOAWp6diQgOQ8eep+Gh+OtRKj/7rT35ACjsnLxphsbZ8qC2RK3j0T7vt27djwYIF2LNnDzZv3gyz2YxrrrkG1dXVjmseffRR/O9//8Onn36K7du3Iz8/HzfddJOMVZNcnttwAvUWK2YMicO/bh4GhUKw719ja8CVjmKQprA8cVRHEqJVtfu8rnH2sHPwfAXOFtmbk9sYEQruwL2JulNqVAgC1UrU1FuQXVp9+RcQdZJHH2m8ceNGp8/fe+89xMTE4MCBA5g0aRIMBgPeffddrF27FldffTUAYPXq1Rg4cCD27NmD8ePHy1E2ycDUYEGmfZPApTMHQdVk1GLOqES8+N0p7D9fjrPFVY4DQD057HREn+gQhAepUVFjxjfHbKNXLfXrEHkapULAgPhQHMqpwLE8A3pHh8hdEvkor/rnncFg69jX623/kj1w4ADMZjOmTZvmuGbAgAFISUnB7t27W72PyWSC0Wh0+iDvdraoGhariLAAFeLCnJdqx4QFYLL9vKdP9uc2hh0PW3beWQpFY9/OoZwKAM5nYhF5Mp6ATu7gNWHHarVi0aJFmDBhAoYMGQIAKCgogEajQXh4uNO1sbGxKCgoaPVey5Ytg06nc3wkJ3OfEW93qtD2g3JAXFiLvSq3jrX9f/z5gTz8UmC71ldGdgAgzR52JD30XGVF3oErssgdvCbsLFiwAMeOHcNHH33U5XstWbIEBoPB8ZGbm3v5F5FHO1Vgm8JqLcBcPSAGUSEalFSZUFJVD8C2e7KvkJqUJZzGIm/RuCLL0GzzTyJX8Yqws3DhQnz99dfYunUrkpIa9yCJi4tDfX09KioqnK4vLCxEXFzr+5NotVqEhYU5fZB3O2UfrWltk0C1UoEbRyY6Pk/WBzqdCO7tBieEIUijBACoFALidTz+gbxDv9hQKBUCymvMuGiok7sc8lEeHXZEUcTChQuxbt06/PDDD0hNTXV6fvTo0VCr1diyZYvjsVOnTiEnJwfp6enuLpdkJPXhtLVJ4C1NjkXoH+tbAVetVGBUSgQA20aDKi4rJy8RoFaib4ytMZlTWdRdPPon4oIFC/Dhhx9i7dq1CA0NRUFBAQoKClBbWwsA0Ol0uPfee7F48WJs3boVBw4cwN1334309HSuxPIjxjoz8u3/IuzXxtRUv9hQjEgOBwD0j/O9VR/S0REtHRNB5Mmkvp2Mc6UyV0K+yqPDzsqVK2EwGDB58mTEx8c7Pj7++GPHNa+88gpuuOEGzJkzB5MmTUJcXBy++OILGasmd5MO9YzXBUAXqG7z2mdnDcb1Q+Nx5/iebqjMve4c3wM3jUzEw1P7yF0KUYdcO8TWdvDRvlwYas0yV0O+SBDZEQaj0QidTgeDwcD+HS+0JuM8/t+6Y5jcPxrv3Z0mdzlE1EFWq4gZr+7EqcJK/PFX/fDQ1L5yl0Reor2/vz16ZIeoPXxt3xwif6NQCPjDlN4AgP/8lIVq+zl2RK7CsENe7xcf2xGZyB9dPzQePSKDUF5jxv/tzZG7HPIxDDvk1URRxGkvOOuKiNqmUirw4FW20Z23d56DqcEic0XkSxh2yKsVVZpQUWOGUiHwXB0iL3fTqCTE6wJQaDThswMX5C6HfAjDDnk1aQqrZ2QQAtRKmashoq7QqBSYP6kXAGDV9rNosFhlroh8BcMOebXTjs0EuYqOyBfcNjYFkcEa5JbV4n9H8uUuh3wEww55NWlkp63NBInIewRqlLhnom23/Ne3nMH50mqZKyJfwLBDXo3NyUS+5870HggPUuNcSTWmvrQdf153FAU8N4u6gGGHvJbFypVYRL4oLECNj+en46p+0WiwilibkYNJ/9qK574+AWMdd1imjmPYIa91vrQapgYrAtQKpOiD5C6HiFyof1wo3r8nDZ88kI6xPSNQ32DFOz9m4W//OyF3aeSFGHbIa0mjOv1iQ6FUCDJXQ0TdIS1Vj08eSMfzc4YCAHad5WGh1HEMO+S12JxM5B8EQcD0wbbDQvMqalHJqSzqIIYd8lqnHMvOGXaIfF14kAaxYVoAwOnCKpmrIW/DsENe61QhR3aI/In0d12awiZqL4Yd8kp1ZguyS2z7b3Bkh8g/SH/XpVFdovZi2CGPVGdu+xDAM0VVsIpARJAa0aFaN1VFRHKSRnZ+KTDKXAl5G4Yd8jjv7DyHwc9swpeH81q95mBOOQDbDz9B4EosIn8gHQtzqqASoijKXA15E4Yd8jhfHs6HxSpi6ZfHUVTZfNfU8up6vPp9JgDg6gEx7i6PiGTSNzYEggCU15hRXGWSuxzyIgw75FGqTA04nm8AABhqzfhrCxuIPbfhJEqr69E/NhR3T0h1d4lEJJMAtRI9I4MBAKcLuCKL2o9hhzzKgfPlsIqALlANpULAhiMX8f2JQsfzOzOL8fnBCxAEYNmcodCo+J8wkT/pz74d6gT+piCXyS2r6fIJxfuyygAAUwfG4L4rbaM2T395DJV1ZtTUN+DP644CAOal98SolIiuFUxEXqcfV2RRJ6jkLoB8Q25ZDa5dvgMAsPWxyYgJC+jUffZm28JOWk89Zo1IxMZjBThfWoN/bTqFALUSuWW1SNAF4LHp/V1WOxF5D2n5OffaoY7gyA51mSiK+PO6o6iut6C63oJ3f8zq1H1MDRYczq0AAIxN1SNQo8Q/brSdh/PfPefxzs5zAIDnbhyCEC1zOpE/6u8IO1WwWrkii9qHYYcuK7ukGjf9+yf85avjsLTww2XdoTzszCyBtAL8wz3nUVFT3+K9Xtx0CrNX/ITSFlZSHLlgQH2DFVEhGvSKsjUhTugThVtGJ0EUAasI/Hp4Aq4eEOu6L46IvEoPfRA0KgVqzRbklNXIXQ55CYYdatOF8hrMfScDB3Mq8N6ubPz5i6NO/5oqrTLhb1/bVkw9dk1/DIwPQ3W9Be/tym52r52ZxXhj6xkczq3AR/tymz2/196vM7an3mnvnP93/UAkhgciNkyLpTMHufgrJCJvolIq0DcmBEDjkTFEl8OwQ60qNNZh7jsZyKuw9ckoBODj/bl49usTjg29/vb1CZTXmDEgLhTzJ/XCgim9AQCrf8pGlanBca+mzcUA8On+3GabgklhJy1V7/R4eJAGmxdPwg9/nIyoEO6WTOTvpBVZbFKm9mLYoRaVVpkw950MnC+tQbI+EJ//4Qr86+bhAID3dmXj+Y2nsPVUEdYfzodCAJ6fMwxqpQIzhsSjV3QwDLVmrNlz3nG/5d9nOpqLgzVKZJfWOMINAFisIg6et+2KPLanc9gBgCCNCsHs0yEiNPbtcGSH2othh5ox1Jhx57t7caaoCvG6AKy9bzzidYGYMzoJf79xCABg1fazWLDmIADg7gmpGJ4cDgBQKgQ8eJVtdOftnVmoM1tw9ILBqbn4hmEJAIBP9l9wvOfJi0ZUmhoQqlVhYHyYu75UIvJC/bn8nDqIYYeaefijQzhx0YioEA0+vG8ckvVBjufmjuuBp64fCACoqbcgMTwQi3/Vz+n1s0cmIjE8ECVVJvzf3hw8+fkRWEVgpr25+NaxSQCAb45eRGWdGUDjFNbonhFQKnjWFRG1Tgo7WSXVMDW0fWgwEcCwQ5c4cL4M208XQ60U8N97x6F3dEiza+67sheeun4g+sSE4OVbhzebXlIrFXjgql4AbEc7nLhoRHiQGs/Ym4tHpUSgd3Qwas0WbDhyEQCwL7uxOZmIqC1xYQEIC1DBYhVxtqhrG5mSf2DYISdv/HAGAHDTyKQ2p5Puu7IXvl98Fcb1imzx+VvHJCMqROtYqv7/rhvoaC4WBAG3jkkGAHxib1SWws6lzclERJcSBKFJ307bx0acKarC4k8O49ujF3lSuh9j2CGH4/kGbD1VDIUAPDi5d5fuFaBW4vf20Z2JfaJw8+gkp+dvHJUIpULAwZwKfHeiECVV9dCoFBiWpOvS+xKRf5DCzi+X6dtZ8sURfHEwDw+uOYiZb/yIbaeKGHr8EMOOF5BGPurMHZubNtaZsetMCeobrO26/t9bzwIAbhiWgJ72Tf264t6JqfjgnjS8dddop31zACAmNABT+scAAP761XEAwIjkcGhVyi6/LxH5vv5xtpHn022EnYxzpdiXXQ61UkCwRoljeUb8bvU+3Prmbhw4X9bq68j3MOx4gTd+OINbVu3GwrWH2vUvktp6C1ZtP4srn9+K376Tgakvb8MXBy+0uPux5ExRFb45Zuuf+cOUro3qSARBwKR+0QjStLxk/NYxttGefEMdANt5WERE7dGevXZWbLP9A+6WMcnY8cQU3H9lKrQqBfZll+OWVbsdPYPk+xh2PFxmYSVe+yETAPD9yUJsONr6X876Bis+2J2NSf/ain9++wsMtWaoFAJyy2qx+JOfce3yHdh4rOV565XbzkIUgWkDYzEgzj1Lv6cMiHHaJJD9OkTUXlLYyTfUwWhf1dnUkQsV2HG62LEdRmSIFv/v+kHY/vgUXD8sHlYReOSjQ9hystDdpZMMGHZkVGisw1Prj+JXL2/HzsziZs9brSL+9MVRmC0iwoPUAIC/fHW8xXOnskqq8atXtmPpl8dRXGlCUkQgXrxlOA4t/RWeuLY/wgJUyCyqwu8/PIhZK37C9tPFjtCTW1aD9YfzAAALr+7TjV+xM7VSgTmjEgEACgEY1SPCbe9NRN5NF6RGXFgAAOCAfUPSpqRp+VnDE5y2z4jTBeC120Zi1ogENFhFPLjmIH7MLHFP0SQbhh0ZlFXX4x/fnMSkF7biwz05yCyqwv0f7EfGuVKn69bszcGB8+UI1iix/g8T0CcmBCVVttc2daG8BnPf3oPzpTWICdXib7OH4Ic/TsbNo5MQGqDGHyb3wc4nr8ZDV/dBkEaJIxcMmPefvfjNW3uwL7sMb+04B4tVxMQ+URhh3xzQXW5PS0GoVoWpA2N5kjkRdciEPlEAgMUfH8bpJrspZxZWYuPxAgAtL7ZQKgS8eMtwXDMoFvUNVtz/wX7HilDyTT4TdlasWIGePXsiICAA48aNw969e+UuqZnKOjNe2Xwak17Yird2nIOpwYqxPSMwsU8U6sxW3PPePhzKsf0L5aKhFs9/+wsA4IlrB6BnVDD+edNQALadh3edsf1LpNBYh9++nYF8Qx16Rwfjm0euxJ3je0Cjcv6/Vheoxh+v6Y8dT0zBvRNToVEpsDerDLes2o0PM2zHOriqV6cjekYFY/efp+Lfc0e5/b2JyLv95deDMDxJh/IaM377dgaySmx77vzb3qtz7eA49LVPd11KrVTg9d+OxFX9olFrtuDu1fscP1fJ9/hE2Pn444+xePFiPPPMMzh48CCGDx+O6dOno6ioSO7SANgaht/cfhZXvrAVr27JRJWpAYMTwrD67rH45IF0vDNvDCb0iUR1vQXz/rMXx/MNeHr9cVSZGjAyJRx3jO8BABjTU487xqcAAJasO4oL5TX47dt7kFNWgxR9ENbcN/6yB2VGhWjx9A2DsP3xybg9LQVKhQBRBEalhCO9lT1zuluIVgW10if+UyQiNwoNUOP9e9IwIC4UJVUmzH17D3adLcFXP+cDABZMaXtaXqtS4s07R2N8Lz2qTA347TsZuHv1XhzLM7ijfHIjQfSBDQfGjRuHsWPH4o033gAAWK1WJCcn46GHHsKf/vSny77eaDRCp9PBYDAgLMx1zbn1DVZ8vC8Hr/9wBkWVJgBA7+hgLP5Vf8wYEgdFk2MRauobcNe7e7H/fDmCNErU1FugVgrY8PCV6NfkXybGOjOueXkHCox1juvidQH45IF0p3np9souqcaGoxcdRzwQEXmbkioTbn1zN84VV0MQAFEEruoXjffvSWvX66tNDXhuw0l8sj/XsWr1uqFxePCqPogIVndn6X4lNizA5f+wbe/vb68PO/X19QgKCsJnn32G2bNnOx6fN28eKioq8OWXXzZ7jclkgslkcnxuNBqRnJzs0rBTWWfGda/tRG5ZLQAgMTwQj/6qH2aPSICqlf+zjXVm3PFOBo5csP2r4uGr+2DxNf2bXffd8QLM/+8BALaRmk8eGI9eLRzrQETkLwoMdbjlzV2On7mfPJDe4RWeWSXVWP79aXz1cz68+zejZ/rhj1e5/HdVe8OO13eElpSUwGKxIDY21unx2NhY/PLLLy2+ZtmyZfjrX//arXWFBqgxOF6HOrMVD13dB78Zm3zZDfPCAtR4/+40LPr4MJQKAX9oZQj2msFxmD+pF37MLMErvxnBoENEfi9OF4C1943HA/89gAFxoZ3ayiI1Khiv3jYSD07ujZe/O42dmSWwMvW4zKWby7r1vb19ZCc/Px+JiYnYtWsX0tPTHY8/8cQT2L59OzIyMpq9xh0jOwBQVFmHUK0agRruCkxERORqfjOyExUVBaVSicJC542hCgsLERcX1+JrtFottNq2G3ldISY0oNvfg4iIiNrm9UtgNBoNRo8ejS1btjges1qt2LJli9NIDxEREfknrx/ZAYDFixdj3rx5GDNmDNLS0rB8+XJUV1fj7rvvlrs0IiIikplPhJ3f/OY3KC4uxtKlS1FQUIARI0Zg48aNzZqWiYiIyP94fYOyK3TXPjtERETUfdr7+9vre3aIiIiI2sKwQ0RERD6NYYeIiIh8GsMOERER+TSGHSIiIvJpDDtERETk0xh2iIiIyKcx7BAREZFPY9ghIiIin+YTx0V0lbSJtNFolLkSIiIiai/p9/blDoNg2AFQWVkJAEhOTpa5EiIiIuqoyspK6HS6Vp/n2VgArFYr8vPzERoaCkEQXHZfo9GI5ORk5Obm8sytduD3q2P4/eoYfr86ht+vjuH3q2Nc9f0SRRGVlZVISEiAQtF6Zw5HdgAoFAokJSV12/3DwsL4H38H8PvVMfx+dQy/Xx3D71fH8PvVMa74frU1oiNhgzIRERH5NIYdIiIi8mkMO91Iq9XimWeegVarlbsUr8DvV8fw+9Ux/H51DL9fHcPvV8e4+/vFBmUiIiLyaRzZISIiIp/GsENEREQ+jWGHiIiIfBrDDhEREfk0hh03+fWvf42UlBQEBAQgPj4ed955J/Lz8+UuyyNlZ2fj3nvvRWpqKgIDA9G7d28888wzqK+vl7s0j/X3v/8dV1xxBYKCghAeHi53OR5nxYoV6NmzJwICAjBu3Djs3btX7pI81o4dOzBz5kwkJCRAEASsX79e7pI81rJlyzB27FiEhoYiJiYGs2fPxqlTp+Quy2OtXLkSw4YNc2wkmJ6ejm+//dYt782w4yZTpkzBJ598glOnTuHzzz/H2bNncfPNN8tdlkf65ZdfYLVa8eabb+L48eN45ZVXsGrVKvz5z3+WuzSPVV9fj1tuuQUPPvig3KV4nI8//hiLFy/GM888g4MHD2L48OGYPn06ioqK5C7NI1VXV2P48OFYsWKF3KV4vO3bt2PBggXYs2cPNm/eDLPZjGuuuQbV1dVyl+aRkpKS8M9//hMHDhzA/v37cfXVV2PWrFk4fvx497+5SLL48ssvRUEQxPr6erlL8QovvPCCmJqaKncZHm/16tWiTqeTuwyPkpaWJi5YsMDxucViERMSEsRly5bJWJV3ACCuW7dO7jK8RlFRkQhA3L59u9yleI2IiAjxnXfe6fb34ciODMrKyrBmzRpcccUVUKvVcpfjFQwGA/R6vdxlkJepr6/HgQMHMG3aNMdjCoUC06ZNw+7du2WsjHyRwWAAAP6sageLxYKPPvoI1dXVSE9P7/b3Y9hxoyeffBLBwcGIjIxETk4OvvzyS7lL8gpnzpzB66+/jgceeEDuUsjLlJSUwGKxIDY21unx2NhYFBQUyFQV+SKr1YpFixZhwoQJGDJkiNzleKyjR48iJCQEWq0Wv//977Fu3ToMGjSo29+XYacL/vSnP0EQhDY/fvnlF8f1jz/+OA4dOoTvvvsOSqUSd911F0Q/2sC6o98vAMjLy8O1116LW265Bffff79MlcujM98vIpLHggULcOzYMXz00Udyl+LR+vfvj8OHDyMjIwMPPvgg5s2bhxMnTnT7+/K4iC4oLi5GaWlpm9f06tULGo2m2eMXLlxAcnIydu3a5ZYhPE/Q0e9Xfn4+Jk+ejPHjx+O9996DQuFf2bwz/3299957WLRoESoqKrq5Ou9QX1+PoKAgfPbZZ5g9e7bj8Xnz5qGiooKjq5chCALWrVvn9L2j5hYuXIgvv/wSO3bsQGpqqtzleJVp06ahd+/eePPNN7v1fVTdencfFx0djejo6E691mq1AgBMJpMrS/JoHfl+5eXlYcqUKRg9ejRWr17td0EH6Np/X2Sj0WgwevRobNmyxfEL22q1YsuWLVi4cKG8xZHXE0URDz30ENatW4dt27Yx6HSC1Wp1y+9Bhh03yMjIwL59+zBx4kRERETg7NmzePrpp9G7d2+/GdXpiLy8PEyePBk9evTAiy++iOLiYsdzcXFxMlbmuXJyclBWVoacnBxYLBYcPnwYANCnTx+EhITIW5zMFi9ejHnz5mHMmDFIS0vD8uXLUV1djbvvvlvu0jxSVVUVzpw54/g8KysLhw8fhl6vR0pKioyVeZ4FCxZg7dq1+PLLLxEaGuroA9PpdAgMDJS5Os+zZMkSzJgxAykpKaisrMTatWuxbds2bNq0qfvfvNvXe5F45MgRccqUKaJerxe1Wq3Ys2dP8fe//7144cIFuUvzSKtXrxYBtPhBLZs3b16L36+tW7fKXZpHeP3118WUlBRRo9GIaWlp4p49e+QuyWNt3bq1xf+W5s2bJ3dpHqe1n1OrV6+WuzSPdM8994g9evQQNRqNGB0dLU6dOlX87rvv3PLe7NkhIiIin+Z/jRBERETkVxh2iIiIyKcx7BAREZFPY9ghIiIin8awQ0RERD6NYYeIiIh8GsMOERER+TSGHSIiIvJpDDtERETk0xh2iIiIyKcx7BD5mMmTJ2PRokVyl9GMKIqYP38+9Ho9BEHA4cOH21Wrp3493aW0tBQxMTHIzs526X1vu+02vPTSSy69J5G34NlYRD5m8uTJGDFiBJYvXy53KU6+/fZbzJo1C9u2bUOvXr0QFRUFo9EItVqN0NDQVl9XVlZ22Wvk5Orv9+LFi1FZWYm3334bAPC73/0OFRUVWL9+vdN127Ztw5QpU1BeXo7w8PDL3vfYsWOYNGkSsrKyoNPpXFIrkbfgyA4RucXZs2cRHx+PK664AnFxcVCpVNDr9W2GmPr6+ste013q6+vd/p41NTV49913ce+997r83kOGDEHv3r3x4YcfuvzeRJ6OYYfIh5lMJjz88MOIiYlBQEAAJk6ciH379jldU1lZiblz5yI4OBjx8fF45ZVXXD519Lvf/Q4PPfQQcnJyIAgCevbsCaD5FNXkyZOxcOFCLFq0CFFRUZg+fXqL1zz88MN44oknoNfrERcXh7/85S9d/ppaem8A2LhxIyZOnIjw8HBERkbihhtuwNmzZx1f1/bt2/Hqq69CEAQIguCYfrJarVi2bBlSU1MRGBiI4cOH47PPPmvz+/TNN99Aq9Vi/Pjx7fq+NpWdne2ooenH5MmTHdfMnDkTH330UYfvTeTtGHaIfNgTTzyBzz//HO+//z4OHjyIPn36YPr06SgrK3Ncs3jxYvz000/46quvsHnzZuzcuRMHDx50aR2vvvoqnn32WSQlJeHixYvNAldT77//PjQaDX766SesWrWq1WuCg4ORkZGBF154Ac8++yw2b97c5a+ppfeurq7G4sWLsX//fmzZsgUKhQI33ngjrFYrXn31VaSnp+P+++/HxYsXcfHiRSQnJwMAli1bhg8++ACrVq3C8ePH8eijj+KOO+7A9u3bW33/nTt3YvTo0ZetsyXJycmOGi5evIhDhw4hMjISkyZNclyTlpaGvXv3wmQydeo9iLyWSEQ+5aqrrhIfeeQRsaqqSlSr1eKaNWscz9XX14sJCQniCy+8IIqiKBqNRlGtVouffvqp45qKigoxKChIfOSRRxyPzZ49WwwPDxfnzJnj9F7/+9//xH79+ol9+vQR33777TbreuWVV8QePXq0WGvTz0eOHHnZayZOnOh0zdixY8Unn3yyQ1/TpVp675YUFxeLAMSjR4+2WJ8oimJdXZ0YFBQk7tq1y+nxe++9V7z99ttbvfesWbPEe+65x+mxefPmiUqlUgwODnb6CAgIEAGI5eXlze5TW1srjhs3TrzhhhtEi8XiePznn38WAYjZ2dmX/TqJfIlK5qxFRN3k7NmzMJvNmDBhguMxtVqNtLQ0nDx5EgBw7tw5mM1mpKWlOa7R6XTo37+/070eeeQR3HPPPXj//fcdjzU0NGDx4sXYunUrdDodRo8ejRtvvBGRkZFdqrs9IxvDhg1z+jw+Ph5FRUUA2v81tfe9MzMzsXTpUmRkZKCkpARWqxUAkJOTgyFDhrR4nzNnzqCmpga/+tWvnB6vr6/HyJEjW33/2tpaBAQENHt8ypQpWLlypdNjGRkZuOOOO1q8zz333IPKykps3rwZCkXjAH5gYCAAW28QkT9h2CGiy5o8eTK2bdvm9NjevXsxePBgJCYmAgBmzJiB7777DrfffnuX3is4OPiy16jVaqfPBUFwhBBXv/fMmTPRo0cPvP3220hISIDVasWQIUPabGCuqqoCAGzYsMHx/ZFotdpWXxcVFYXy8vIW6+rTp4/TYxcuXGjxHs899xw2bdqEvXv3NmvslqYvo6OjW62ByBexZ4fIR/Xu3dvRfyIxm83Yt28fBg0aBADo1asX1Gq1Uw+NwWDA6dOnL3v//Px8p1/kiYmJyMvLc+FX0Dld+ZouVVpailOnTuGpp57C1KlTMXDgwGZhRKPRwGKxOD02aNAgaLVa5OTkoE+fPk4fUk9PS0aOHIkTJ050uE7J559/jmeffRaffPIJevfu3ez5Y8eOISkpCVFRUZ1+DyJvxJEdIh8VHByMBx98EI8//jj0ej1SUlLwwgsvoKamxrG0OTQ0FPPmzXNcExMTg2eeeQYKhQKCIMj8FXSOK7+miIgIREZG4q233kJ8fDxycnLwpz/9yemanj17IiMjA9nZ2QgJCXEslX/sscfw6KOPwmq1YuLEiTAYDPjpp58QFhaGefPmtfh+06dPx5IlS1BeXo6IiIgO1Xrs2DHcddddePLJJzF48GAUFBQAsIUxvV4PwNYAfc0113TovkS+gCM7RD7sn//8J+bMmYM777wTo0aNwpkzZ7Bp0yanX6Qvv/wy0tPTccMNN2DatGmYMGECBg4c2GLvSFMJCQlOIzl5eXlISEjotq+lIzr7NV1KoVDgo48+woEDBzBkyBA8+uij+Ne//uV0zWOPPQalUolBgwYhOjoaOTk5AIC//e1vePrpp7Fs2TIMHDgQ1157LTZs2IDU1NRW32/o0KEYNWoUPvnkkw5/zfv370dNTQ2ee+45xMfHOz5uuukmAEBdXR3Wr1+P+++/v8P3JvJ23EGZiJxUV1cjMTERL730ktPmdtu2bcMbb7zh2CumoaEBAwcOxLZt2xwNyrt27epyg3J3aO1r8kQbNmzA448/jmPHjjk1F3fVypUrsW7dOnz33XcuuyeRt+A0FpGfO3ToEH755RekpaXBYDDg2WefBQDMmjXLcc20adPw888/o7q6GklJSfj000+Rnp6Ol156CVOmTIHVasUTTzzhMUGnPV+Tp7r++uuRmZmJvLy8Nvt7OkqtVuP111932f2IvAlHdoj83KFDh3Dffffh1KlT0Gg0GD16NF5++WUMHTpU7tI6zRe/JiLqPIYdIiIi8mlsUCYiIiKfxrBDREREPo1hh4iIiHwaww4RERH5NIYdIiIi8mkMO0REROTTGHaIiIjIpzHsEBERkU9j2CEiIiKfxrBDREREPu3/Awy2TXkvLPPRAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = np.log10(units['firing_rate'])\n",
+    "bins = np.linspace(-3,3,100)\n",
+    "hist, bin_edges = np.histogram(data, bins=bins)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(bin_edges[:-1], hist)\n",
+    "\n",
+    "plt.xlabel(\"log$_{10}$ firing rate (Hz)\")\n",
+    "plt.ylabel(\"# units\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2e0ffc2",
+   "metadata": {},
+   "source": [
+    "### Presence Ratio\n",
+    "*Presence ratio* measures the fraction of time during a session in which a unit is spiking, and ranges from 0 to 0.99. This can be helpful for detecting units that drift out of the recording during a session."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8d0c1319",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = units['presence_ratio']\n",
+    "bins = np.linspace(0,1,100)\n",
+    "hist, bin_edges = np.histogram(data, bins=bins)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(bin_edges[:-1], hist)\n",
+    "\n",
+    "plt.xlabel(\"Presence ratio\")\n",
+    "plt.ylabel(\"# units\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02f6a446",
+   "metadata": {},
+   "source": [
+    "### Amplitude Cutoff\n",
+    "*Amplitude cutoff* is a way to check for units that are missing spikes. Unlike presence ratio, which can detect units that drift out of the recording, amplitude cutoff provides an estimate of the false negative rate—e.g., the fraction of spikes below the spike detection threshold. Thus, amplitude cutoff is a measure of unit \"completeness\" that is complementary to presence ratio."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "1a9c70f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = units['amplitude_cutoff']\n",
+    "bins = np.linspace(0,0.5,100)\n",
+    "hist, bin_edges = np.histogram(data, bins=bins)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(bin_edges[:-1], hist)\n",
+    "\n",
+    "plt.xlabel(\"Amplitude cutoff\")\n",
+    "plt.ylabel(\"# units\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "421dfea1",
+   "metadata": {},
+   "source": [
+    "### ISI Violations\n",
+    "*Inter-spike-interval violations* are a classic measure of unit contamination. Because all neurons have a biophysical refractory period, we can assume that any spikes occurring in rapid succession (<1.5 ms intervals) come from two different neurons. Therefore, the more a unit is contaminated by spikes from multiple neurons, the higher its isi_violations value will be.\n",
+    "\n",
+    "The calculation for ISI violations comes from Hill et al. (2011) J Neurosci 31: 8699-8705. Rather than reporting the fraction of spikes with ISI violations, their metric reports the relative firing rate of the hypothetical neurons that are generating these violations. You can interpret an ISI violations value of 0.5 as meaning that contamining spikes are occurring at roughly half the rate of \"true\" spikes for that unit. In cases of highly contaminated units, the ISI violations value can sometimes be even greater than 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6bc82442",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = units['isi_violations']\n",
+    "bins = np.linspace(0,10,100)\n",
+    "hist, bin_edges = np.histogram(data, bins=bins)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(bin_edges[:-1], hist)\n",
+    "\n",
+    "plt.xlabel(\"ISI violations\")\n",
+    "plt.ylabel(\"# units\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2dcfd22e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\carter.peene\\AppData\\Local\\Temp\\ipykernel_2204\\3139914895.py:1: RuntimeWarning: divide by zero encountered in log10\n",
+      "  data = np.log10(units['isi_violations'])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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C7go/+HC5PbD7phibgxX7MiNDKY6BDBGlHH/XUmAQ4T9vKbqMjLIrKXjXkqLYN4KpvkBgNqU9gjoZZVeSMrjiHBlKFwxkiCjlSDUyPdueB3oCttR6rdUIMOr8b5+Ziq0lUfRODW6NsNhXqxGQoZc6jcLPyEgZF71WgFHHYl9KPwxkiCjltPdXIxNlRsZfi6KFIPhbqqViX5dHhMPtCXiNrDADGSC6LEqwQl8AyJRqZJzuAR3JQJToGMgQUcoJVSPj31rqPyPzwuZjeG9vXcBt/nOWegQNiqMBpIJfuf06zK4lwB94RRLIBCv0Va5RFCHX9RClov7HTRIRJZm2EDUy/mLfvjMyh+rbcM+b+2DSa7Bn/Hzotd7f+eRzlnoEMnqtBgatBg63Bx0ON3IygdYIi32B6IbYSXU5yvoYAMjQa6ERvGdLddhdvTI2RKmCGRkiSjmh5shINTLtdhccvk6fYPbX2gAA3U4Pjp3qkG8PdjyBRNrK6fJlbeSTr8Ms9gX8WZWoMjI91iQIQlTXI0o2DGSIKKWIoqiY7Bv4wz3LpIdU2tLaxyyZg3VtQf8cbIKuJFMf2LkU6WRfwL+1FElGJthcG3lNnCVDaYCBDBGllE6HG1Jta88aGY1GkAfU9dW5dNCXkQGAg3X+P8vnLBl6Bw3ydF+7t7g2qozMAIp9lXU6A7keUbJhIENEKUX6oa1sZ1bKDWOWTEBGprZ3RibY1pJ0W5fThTa7C74u7LAn+yqvEVEg4wi93cUWbEoHDGSIKKUo62OULdKS/k7Abu10ora1W/5cGdTI5ywF2VpSzoCRzlnK0GsDZrv0J5rAo6+tJdbIUDpgIENEKcXfsRS8S0fKyLSGyMhIW0lSwFPd0gWbLzgKKyPjcKOlyxFwjXBZosjIhCr29d4W+YA9omTDQIaIUoo8QyZEu7FUfBsqI1NZ783ATC/PRWm2CQDwhS8rI2c/+qiR6XS45PbuSLaVAOXWUuSTfc191Mh0RnGiNlGyYCBDRCnF37EUPIjo77ylA76amLHFVowttnpvkwIZXz1KzzkygL9rqcPhjvicJYklijkyfdXIsNiX0gEDGSJKKXKNTMitpb67lip9W0vjSrIwriQr4La+2q8Dt5aiy8hYjL45N5EcGtnH1hKLfSkdcNQjEaWU/mpkcsyhjynweERU+rIv44ut8gGQUudSX/UoGYoTsP1TfcM/nsB7XW3A64QjvGJf1shQ6mIgQ0Qppb8amSG+QOZ4U2ev+06e7kKHww2DVoOKIWZ5Hk1lXRtEUey72FdRI+M70SDqYt+OCGpapCAl+BwZ/5qIUhW3logopfhPvg4eRMwcmQ+N4G2rPnk6MJiROpZGF1qg12owssAMvVZAm92F6pYu/7lGQYt9pcJad1QnXwP+ACmys5b6yMhwa4nSAAMZIkopUo1MqK2lPLMBZ1fkAQA+2F8fcJ80M2acr8hXr9VgVIHFe19tm2JrKcgRBcqupaiLfXW+ryE2NTIs9qV0wECGiFKKPyMTeud83oQiAMDafYGBjFQfM67EKt82Xir4rW/r+1yjgBoZ6ZylyGpkpOvaXR643KEPtVRq72NNFs6RoTTAQIaIUkp/NTIAMG9CMQDg02PNAd1LB6SOpeIs+TapBXt/jc2/tRQ0kPFvLUXbfq28bjjBh8vtQbfT43v9YFmiyGtuiJINAxkiSin+rqXQQUR5fibGFVvh9oj48GADAKDb6caxUx0A/FtLyj9vP3Favi1YjYw5YGvJGxxF2n5t0Glg0Hnfltvsoc+CknQoTrVm+zWlKwYyRJRSlGct9aXn9tKh+nZ4RG8NTYHVKD9Oys5I5y9pBMCk7/3WqWy/jnayr3Ld4WRkpEJfnUaAUdd7TeYIrkWUrBjIEFFKCadGBgDmTfRuL238ohHdTre8rTS2yBpw2GRRljFgi8gc4jBKaRunucMBu8u73RPp1pL3+uHPklEW+gZbk3zWksMlz8QhSjUMZIgopfQ3EE8ysTQLpdkmdDnd+PjQqaCFvgAgCALGFvlvC5XpURb7AoBWI/SbFQpG2rYKZztIPo07SH2M8lqiCHQ5mZWh1MRAhohShtsjyoFEXzUygDdAkbIya/fXyTNkxisKfSVS5xIQvKg22O3ZGfqgWZL+SAFYpBmZUGuSlsAWbEpVDGSIKGUozygKJxtyma9OZt2BBvkYgrHF1l6PU94W6ro9g4mcKOpjlNeJRSAjCIIiw8OMDKUmHlFARClD6vQxKrp/+jJjRB6yTDo0dXi7jAQBGFPUO5BRdjGFChqMOg0EwbuNAwDZUdTHKK8fztZSRx9Tff3X06Ld7mLnEqUsZmSIKGWEWx8j0Ws1uHR8kfx5Rb5Z7j5SGlNklbdo+sp+ZOr9z42mYwkArFJGJozpvn2dsySJpOaGKBkxkCGilNHfOUvBSG3YQGDmRcls1KE8L9P75z6ChkxFkDPgraUwhtj1NWm45/U4FI9SFQMZIkpKHo+In/9zF1a8vR/dvo6ccGfIKF0wpkDehhoXpNBXIgU5oTIyQGBmJCczsuMJJJFsLXX2UyPjvU9q52aNDKUmBjJElJS+bGzH69tP4m+fHMU3/7IZNS1dEW8tAd4g4KuTSwAAs0fnh3zcuSO99430HSIZTKZi4m+0W0vS+UiRbC31Fchwui+lOhb7ElFSarDZ5T/vPtmKr/3pY1wwpgBAZBkZAFh59WT89NIzUDHEHPIxN8yqwLkj8wNmyvSkzMhEH8h4nxdOBkXuWuqrRoaBDKU4BjJElJQa2rxHBkgzXg7U2vDG9moAkdXIAIBJr+0ziAG8A+6U82SCCdxairZGRjqxOoyMjKP/raVMtl9TiuPWEhElpYY2b0ZmfLEVb9x8Hq6aWirfF8nWUizFIpCxRFCcG06xr7xVFcYhlETJiBkZIkpKjb5ApsBqRIZBi8e+PQ1ThmbjhS3HcOn4QlXWFJsamfDbr0/75t/0FTQVZ2cAAE40d0a1HqJEx0CGiJJSgyKQAbxzXG66YCRuumCkamvKCKiRGVjXUjiTfRt7/B0EM97XbXXQd5YUUarh1hIRJaVGX41MXz/EB5s5lltL/QQyoiiisd0byBRmmUI+Tjpe4XhTJwt+KSUxkCGipCRlZAqtoX+ID7aMGGwt+QfYueHxiCEf19LphNPtvX+IJXT2J99ilIO9L+qZlaHUw0CGiJKStK1SmJV4GRmzQQu9Nrq3V2Whcl8Fv1Igl5Oph1EXuv0a8A/z4/YSpSIGMkSUdLqdbnn4XSJtLUldS9FO9QW8h09qNd6DnfpqmZbrYyz9f/1SIFPJQIZSEAMZIko60g9xk14jH7KYCKStpWi3lQBv0bKU2emr4FeaoxNORko6euFArS3qdRElKgYyRJR0GhSFvoJ0LHUCkA6WHNHPcL3+SAP9+g5kws/ISAW/lfVtEMXQdTdEyShxfpUhIgpTYwIW+gLAORW5eO3Hs3BGYehjDMIRznRff41Q/38Howst0GoEtHQ6UW+zozg7sf7eiAaCGRkiSkh9dexEko0YTIIg4OyKPGRH2XotCWeWjL9rq/+/A5NeK2eJDtZxe4lSCwMZIko4RxrbceZvPsCq/3wR9H7pwMhE6liKpXCm+0Y6R4edS5SqGMgQUcLZdvw0WruceG9vXdD7I+nYSUbhnLfUc7Jxf+RAhgW/lGIYyBBRwrH5MhHHmzqDFqdG0rGTjMLZWmqMYGsJ8HcuMSNDqYaBDBElnLZu70nNXU63/ANbSRrNn0gzZGKpv62lwDk64RXuSp1Lhxvb4XR7YrBKosTAQIaIEk6b4gf48SCnNss1MgnWtRQr/Z23JAV3Bp0GWabwmk+H5WbAYtTB6RZxpLEjNgslSgCqBzLV1dW47rrrkJ+fj4yMDEyePBmff/65fL8oirjnnntQUlKCjIwMzJ07F4cOHVJxxUQUb1JGBvBuLym5PSKaOhwAUjcj499aCj7ZV95ai2COjiAIioJf1slQ6lA1kDl9+jRmz54NvV6Pd999F/v378cjjzyC3Nxc+TEPP/wwHnvsMTz11FPYunUrzGYz5s+fj+7ubhVXTkTxFJCRaQrMHjR3OOD2iBAEIN8c/VEAiczSzxyZxggLfSVj2blEKUjVgXgPPfQQysrK8Oyzz8q3jRgxQv6zKIpYtWoV7rrrLixYsAAA8MILL6CoqAhr1qzBt7/97V7XtNvtsNv9e+o2G3/zIEo2tj4yMtIP8XyzAbooD2ZMdBZT38W+kcyQURpX4iv4ZecSpRBV3wXeeustnH322bj22mtRWFiIM888E88884x8/9GjR1FXV4e5c+fKt2VnZ2PmzJnYvHlz0GuuXLkS2dnZ8kdZWVncvw4iiq2+MjL+4wlSsz4GAMyGvgOZaDMynCVDqUjVQObIkSN48sknccYZZ+D999/HzTffjNtuuw3PP/88AKCuzjtDoqioKOB5RUVF8n09LV++HK2trfJHVVVVfL8IIoq5vop9o/0hnkz6K/aNtthZ2lqqbe1Ga6ezn0cTJQdVt5Y8Hg/OPvts/Pa3vwUAnHnmmdi7dy+eeuopLF68OKprGo1GGI2p+wZHlA6Uxb4tnU60djrlsf/Rbqskk/7myCiLfSORZdJjaE4Gqlu6cLDOhpkj8we2UKIEoGpGpqSkBBMmTAi4bfz48Thx4gQAoLi4GABQX18f8Jj6+nr5PiJKPdJAPK3G25FzvNm/vZQWGZl+amQGMkdnnOIkbKJUoGogM3v2bFRWVgbc9sUXX2D48OEAvIW/xcXFWLdunXy/zWbD1q1bMWvWrEFdKxENjm6nGw6Xd2DbGYUWAMAxRcFvpBNtk5FyaynoZOMBzNGRtpcO1DKQodSgaiDzs5/9DFu2bMFvf/tbfPnll3jppZfw9NNPY8mSJQC8cw9uv/123H///XjrrbewZ88e3HDDDSgtLcXChQvVXDoRxYmyPmaCr8vmhKLgtyHCwxKTkbS15BGBbmfgFN6BztGROpcqOUuGUoSqNTLnnHMOVq9ejeXLl2PFihUYMWIEVq1ahUWLFsmPWbZsGTo6OvDDH/4QLS0tmDNnDt577z2YTKnbsUCUzqT6GItRhxFDzABCZWRS9z0gU6+FIACiCLTZncgwaOX7AuboWCKfozNe2lqqa4PHI0KjCW+gHlGiUjWQAYArr7wSV155Zcj7BUHAihUrsGLFikFcFRGpRcrIZJl0GO4LZE4oApl0KPbVaARYDDq02V2wdblQaPXfJwVyeZkG6KOYozNiiBkGrQYdDjeqTndieL45VssmUkVqTpMioqQlBTJWkx7D8zIBAMd8W0sddhc6Hd6x/am8tQQAFb4grrLHzJeBbq3ptBqMK/FGRnuqWwewQqLEwECGiBKKtLVkNelQ4csWNLTZ0eVwy9kYs0Er15GkqinDsgEAu0+2BNwei64t6dq7qlr6fiBREmAgQ0QJxaYIZLIz9cjO8M6POdHcmRat15Kpw3IAALt6BDINMagRmiJfO7KMzCufncDbu2qCdlIRqSW1f6UhoqSj3FoCgOH5mdh9shXHmjrgdHs7eFK50FcypcybNdlbbYPbI8ozdeRi56zogzkpSNpb3Rpw7b4cb+rAHa/vAQB8dKgRKxZMgkmv7edZRPHHjAwRJRSbHMh4f8+SilFPNHXK81PSISMzusCCDL0W7XYXjjS2y7fLWSlL9H8HowstyDRo0elw47Di2n3ZcaJF/vM/Pz+Jbz29BXWt3VGvgShWGMgQUUKRamSyfFtKyoLfgUy0TTY6rQaTh/pqWRRbQLHIyGg1AiaVRlYns9P3uBkj8pCdoceuqhZc+fjH+PxYc9TrIIoFBjJElFDaemVkvIHMieb0ysgAwQt+5a6lAWRkAGBqmXTt8OpkpDX8z4xyvHXLbIwrtuJUux3feWYLNh9uGtBaiAaCgQwRJRRbl1TsK9XISEPx/BmZVJ4hozSlLAdAYEZGLvbNGlid0JQQxcTBON0e7Kux+Z6XjeH5Zrx+83m4YEwBnG4Rb+6sHtBaiAaCgQwRJRTlQDwAqPBlZKpPd6GmpQtA+mRkpvoyMgdqbHC4PDGdoyMV/B6otcHucvf52C/q22B3eQJa4s1GHa6Y5D28V9ruIlIDAxkiSihtdn/7NeD9gZ2h18IjQi5MTYeuJQAoz8tETqYeDrcHlXVtcjYm06CVD5aMVlleBnIz9XC6RRzs5wBJaftpyrDsgCMNpMxYAwMZUhEDGSJKKP6MjHdrSRAElPsKfqXxJemSkREEQVHw2xLTOTqCIGCyLyvTc+heT1JBsJTFkUjrYEaG1MRAhogSSs85MoC/4BfwdtzkmSM/LDFZTZPqZKpa5ELfWNUITRvWuysqmF1yRiYn4HYpM3aq3Q6Ph0PySB0MZIgoYYiiqCj29W+dKAOZfLMhrAFuqWKKnDVpjflkY7ngt48W7C6HG1/Ue7eepE4nSb7FAEEAXB4RpzsdMVkTUaQYyBBRwuh2euDy/WYfGMj4T2geyPyUZCQV/B5qaMNx3yngsaoRkqYHf9nYjna7K+hj9td6p/8WWI0o7tEppddqkJfpzY6xTobUwkCGiBKGNAxPEACzIXhGZqDzU5JNYZYJxVkmeERgfWUDgNhlZAqtJpRkmyCK3uMKgtlV5b196rBsCELvTFgBC35JZQxkiChhyMcTGHUB3TEVyoxMmnQsKUmD8aSMTCyLnUOdsi2R5sz0rI+RsOCX1MZAhogSRlt34DA8SUm2CTpfYJMuHUtKU30Fv5JYDgScGmTonpKy9ToYf0aG5y6ROhjIEFHC6Hk8gUSn1WBYbgaA9KuRAUK3Pcfy2sEyMq1dThw91QEgdEZGypAxI0NqYSBDRAnDJh0Y2SMjAwBnlucCAMaXZA3qmhLB5B7ZkFhur03yzampau5CU3tgMLLHl40pz8sM2fLOGhlSGwMZIkoYoTIyALDy6sn4z9ILcE5F3mAvS3XZGXqMGOKtE9IIiOkcnewMPUb6rr27R8Gvvz4m+LYS4N/mYkaG1MJAhogShlQjk5XROyNj0msxutA62EtKGFIwMcRijPkcHbngt6pHIBNioq8SAxlSGwMZIkoYfWVk0p1UoxKPYmfp2v/aXYMGm79ot79CX+V6GMiQWhjIEFHCYCAT2uWTilGRn4mvnzk05teeP6kYVpMOhxracdWfPsaOE6fRYOtGna0bGsFfRxNMoW9IXrvdhU5H8KF6RPEUcSDT1dWFzs5O+fPjx49j1apVWLt2bUwXRkTpx388Qe+tpXQ3NCcDG355MX5w/si4XPutW+ZgdKEF9TY7vvWXLVj57kEAwOhCC8x9nLRtNmiRodcCYFaG1BFxILNgwQK88MILAICWlhbMnDkTjzzyCBYsWIAnn3wy5gskovRhY0ZGNSOGmLFmyWzMn1gEh9uD1TuqAYRuu5YIgiC3xLNzidQQcSCzfft2nH/++QCA1157DUVFRTh+/DheeOEFPPbYYzFfIBGlj1AD8WhwWIw6PLloOn5+2RhIpxFM6zGMLxjp2IgGGwMZGnwR/9rT2dkJq9XbObB27VpcffXV0Gg0OPfcc3H8+PGYL5CI0odUI5PFjIxqNBoBt156BqaW5WBDZSMWhlGTI2VkGjndl1QQcUZm9OjRWLNmDaqqqvD+++9j3rx5AICGhgZkZaXfoCoiip02OzMyieKCMQW456oJsPRRHyORMzLcWiIVRBzI3HPPPfjFL36BiooKzJw5E7NmzQLgzc6ceeaZMV8gEaUPZmSSk9S5xGJfUkPE7xbf+MY3MGfOHNTW1mLq1Kny7ZdeeimuvvrqmC6OiNKHKIqK9mtmZJIJMzKkpogzMjfeeCPMZjPOPPNMaDT+p0+cOBEPPfRQTBdHROmj0+GG2yMCYNdSsinI4lA8Uk/Egczzzz+Prq6uXrd3dXXJbdlERJGSsjFajYBMg1bl1VAkmJEhNYX9a4/NZoMoit70b1sbTCb/6atutxv//ve/UVhYGJdFElHq87de6yAIsT1LiOJL6lpq7rDD7RFjfhYUUV/CDmRycnIgCAIEQcCYMWN63S8IAu67776YLo6I0geH4SWvfLMRGgHwiEBTu10u/iUaDGG/Y6xfvx6iKOKSSy7B66+/jry8PPk+g8GA4cOHo7S0NC6LJKLUZ5MyMkYW+iYbrUZAvsWIxjY7GtoYyNDgCjuQufDCCwEAR48eRXl5OVO/RBRTPDAyuRVavYFMshb81rV2o7nDgQmlnIeWbMJ6x9i9ezcmTZoEjUaD1tZW7NmzJ+Rjp0yZErPFEVH6kGpksjKYkUlGBVap4Dc5p/ve8LetONTQjhd/MBPnjRqi9nIoAmEFMtOmTUNdXR0KCwsxbdo0CIIAURR7PU4QBLjd7pgvkohSHzMyya3Qmrwt2C2dDnxR3w4AuGv1Xrx7+/kw6tg5lyzCesc4evQoCgoK5D8TEcWanJHhMLyk5M/IJF8gc7CuTf7zkVMdeGrDEfx07hkqrogiEVYgM3z48KB/JiKKFVsXMzLJrNCavMcUVPoCmTyzAc0dDjyx/ktcNbUEIwssKq+MwhHVO8ahQ4ewfv16NDQ0wOPxBNx3zz33xGRhRJRelHNkKPkkd0bGBgD4zowy7Km24b9fNOKuNXvx4g9msrElCUT8jvHMM8/g5ptvxpAhQ1BcXBzwP1kQBAYyRBQV/4GR3FpKRslcIyNtLY0rzsK3zi7HZY9uxKbDTVi9oxpXnzVM5dVRfyIOZO6//3488MADuOOOO+KxHiJKUzwwMrkpu5ZEUUyaTIbHI8pbS+NLrCjPz8Rtl56B371fifvfOYCLxxYi12xQeZXUl4jPWjp9+jSuvfbaeKyFiNKYjVtLSU0KZLqdHrTbXSqvJnwnT3eh0+GGQadBRb4ZAHDT+SMxpsiC5g4HVr57QOUVUn8iDmSuvfZarF27Nh5rIaI0xvbr5JZp0MFi9P6/S6Y6mQO++pgzCi3Qab0/Eg06DX779ckAgH9+fhKfHm1WbX3Uv4jfMUaPHo27774bW7ZsweTJk6HXB6aBb7vttpgtjojShz8jw62lZFVoNaLd7kJjmx2jkqTj52Ctvz5G6eyKPHxnRhn+8WkVfrV6D/592/kw6CL+3Z8GQcSBzNNPPw2LxYKNGzdi48aNAfcJgsBAhogi5vGI8nZEVgYzMslqiNWII6c6emVkjjS2oyjLBLMx8f7fVtZ7MzLjiq297rvj8nFYu68eXza045mPjmDJxaMHe3kUhoj/VXEgHhHFWofDBWlYOLuWkpfUudRg8x9T8O6eWtz84nbMm1CEp284W62lhSRnZEp6BzI5mQbcfeUE3P7KTjy27hCunFKC4b46GkoczJMRkeqk+hi9VoCR6fukJQ/Fa/dmZGzdTtzz1j4AwLqDDWjtdKq2tmC6HG4ca+oAAIwNkpEBgAXTSjFn9BDYXR7ctWZv0ON5SF0RZ2RuvPHGPu//29/+FvViiCg9KVuvk6Vtl3qTOpcabd5A5vfvV8pzZdweER9W1uPrZybOXJZDDW3wiEC+2YACizHoYwRBwG8WTsL8Vf/FR4dO4a1dNVgwbeggr5T6ElX7tfKjoaEBH374Id544w20tLTEYYlElOrYep0a5KF47XbsrGrB37ccBwDMGpkPAPhgf71qawtGHoRXYu0zgB4xxIxbffUxv/nXgYTLLKW7iN81Vq9e3es2j8eDm2++GaNGjYrJoogovfB4gtQgZWRqWrqw/I09EEXg6rOG4rvnVeBrf/oEGyob0e10w6RPjJOlpfqYsUVZ/TwS+OGFI7F6ZzWONHbgnT21+J+Z5fFeHoUpJpvRGo0GS5cuxaOPPhqLyxFRmuHxBKmhMMsbyBxu7MCBWhtyMvX436+Mx+Sh2SjOMqHT4camw6dUXqWfdMZSsELfnow6LS6bUAQAOFBri+u6KDIxq6o7fPgwXK7kmeZIRInDxmF4KaFnncmvrhiPfIsRgiBg3kRvELB2X2JsL4miqDhjqf9ABgDG+2bNSAEQJYaI3zWWLl0a8LkoiqitrcU777yDxYsXx2xhRJSath0/jRVv78MdV4zDeaOGAFBuLTEjk8xyMw3QaQS4PCJmVOTh2rP9hb2XTSjCC5uP4z8H6uH2iNBq1C3qbmy3o7nDAY0AnFEYXiAjdTYdrGtLqvOkUl3EgcyOHTsCPtdoNCgoKMAjjzzSb0cTEdFr205i18lWLH1lF/7z8wthMepg62JGJhVoNALOKs/FgVobHvj6pIAf9DNH5MNq0uFUuwM7q05j+vA8FVcK+aDIinwzMgzh1eyMKrBApxHQ1u1CTWs3huZkxHOJFKaI3zXWr18fj3UQUZo4eboTAFBn68Yjaytx71UTmZFJIf/vBzPR6XAhJzPwxGiDToNLxhXizZ01WLuvXvVApq9BeKEYdBqMLrTgYF0bDtbaGMgkCE6eIqJBVX26S/7z85uOYc/JVkWxLzMyyc6g0/QKYiTzJhQDANbur1d9sJx0WGQ4HUtKyu0lSgwMZIho0IiiiOoWbyBzTkUuPCKwfPVutHR5MzLsWkptF44tgEGrwdFTHTjc2K7qWirrIs/IAP7DJRnIJA4GMkQ0aBrb7bC7PNAIwGPfORNZJh32Vtvw8aFGAKyRSXUWow7njfYOx3tfxe4ll9uDQw3eQGp8cWQZGanDqZKdSwmDgQwRDZqTvm2lkuwMlGRn4I4rxgEAPL5dBtbIpD7l9pJajjV1wOHyINOgxbDcyOpcpAzO4cYO2F3ueCyPIsRff4ho0EiBzFDfD4/vnFOO17edxPYTLQCYkUkHcycU4n/XALuqWlBv60ZRlinur/nB/nrsqW6VPz96yn9QpCbCNvDiLBOyTDrYul34sqEdE0uzY7pWilxUGZlbbrkFzc3NsV4LEaU4qWNJ+i1YoxHw26snQ+f7YSKNuKfUVWg1YVpZDgBgY2Vj3F/vVLsdP/r753hs3SH54+1dNQCAiaWRbSsB3kMkx5V4n1fJOpmEEHYgc/LkSfnPL730EtrbvfuLkydPRlVV1YAX8uCDD0IQBNx+++3ybd3d3ViyZAny8/NhsVhwzTXXoL4+MaZCElHkpIzMsNxM+bZxxVn463fPwSPXTkUp21nTwowKb+v1rpMtcX+t402d8IjejrgbZg2XP354wUj85KLRUV1zPDuXEkrYedxx48YhPz8fs2fPRnd3N6qqqlBeXo5jx47B6RzYSaCfffYZ/vKXv2DKlCkBt//sZz/DO++8g1dffRXZ2dm45ZZbcPXVV+OTTz4Z0OsRkTrkQKZHwHLhmAI1lkMqmTIsB8DgBDK1rd5/c2OLrVixYFJMrjmWnUsJJeyMTEtLC1599VVMnz4dHo8HX/nKVzBmzBjY7Xa8//77UWdK2tvbsWjRIjzzzDPIzc2Vb29tbcVf//pX/OEPf8All1yC6dOn49lnn8WmTZuwZcuWkNez2+2w2WwBH0SUGHpuLVF6mlrmrSs5WNuGbmd8C2ZrW7oBeAvMY0Uq+D3IwyMTQtiBjNPpxIwZM/Dzn/8cGRkZ2LFjB5599llotVr87W9/w4gRIzB27NiIF7BkyRJ89atfxdy5cwNu37ZtG5xOZ8Dt48aNQ3l5OTZv3hzyeitXrkR2drb8UVZWFvGaiCj2RFGUh+Ept5Yo/QzNyUC+2QCXR4z7SdI1voxMSU7siorHFHkDmYY273lNpK6wA5mcnBzMnDkTS5cuhcPhQFdXF2bPng2dTodXXnkFp0+fxl//+teIXvzll1/G9u3bsXLlyl731dXVwWAwICcnJ+D2oqIi1NXVhbzm8uXL0draKn/Eon6HiAbuVLtDniFTnB3/ThVKXIIgYMowb1Zm98nWfh49MFJGpjSGGRmLUYfyPG8wzpOw1Rd2IFNdXY277roLRqMRLpcL06dPx/nnnw+Hw4Ht27dDEATMmTMn7BeuqqrCT3/6U7z44oswmWL3pmY0GpGVlRXwQUTqk7aVirNMMOg4wirdRVMnI4oinG5PRK8j1ciUxDh4lo8qqGWdjNrCfjcZMmQIrrrqKqxcuRKZmZn47LPPcOutt0IQBPziF79AdnY2LrzwwrBfeNu2bWhoaMBZZ50FnU4HnU6HjRs34rHHHoNOp0NRUREcDgdaWloCnldfX4/i4uKwX4eIEkOwjiVKX1KdTCQZmZte2IY5D32IpnZ72M+pafVlZGLcETdenvDLQEZtUf9alJ2djW9+85vQ6/X48MMPcfToUfzkJz8J+/mXXnop9uzZg507d8ofZ599NhYtWiT/Wa/XY926dfJzKisrceLECcyaNSvaZRORSvyBDAt9yZ+ROdzYLp9+3hdRFPHfQ42ot9nx3r7Q5QVKDpcHp3xBT6wzMtIsGW4tqS+qMZq7d+/G0KFDAQDDhw+HXq9HcXExvvWtb4V9DavVikmTAlvhzGYz8vPz5du///3vY+nSpcjLy0NWVhZuvfVWzJo1C+eee240yyYiFbFjiZSGWIwYmpOB6pYu7KluxXmjhvT5eFuXCw6Xd1tp7b56LJo5vN/XqLd1QxQBo06DPHPwE7mjJW0tfVHfDrdHhDbCCcEUO1EFMspOoL1798ZsMT09+uij0Gg0uOaaa2C32zF//nz8+c9/jtvrEVH8cGuJeppalo3qli7sPtl/INPY3i3/edPhU2jrdvZ7NldNi78+RhBiG2hU5Jth1GnQ5XTjRHMnRgwxx/T6FL6EqrjbsGEDVq1aJX9uMpnwxBNPoLm5GR0dHXjjjTdYH0OUpKSMzFBmZMhH2l7aHUbBb4PNXxfjdIvYEMbxBrWtsZ8hI9FqBLkNm/Nk1JVQgQwRpSZRFFkjQ71ILdi7qvov+G1oCyzw/SCM07PjMUNGaRyPKkgIDGSIKO6kGTKCEJ/fjik5TR6aDUEAqlu65KLcUBp9gczwfO/W5PqDDXLNTCjxmCGjJLdgs+BXVQxkiCjuqn21CpwhQ0pWkx4jfbUl/W0vNbR5g5K544swxGJEm92FLUea+nxObZwzMuN5CnZC4DsKEcUdO5YolKnSYLx+tpekjExRlhGXTSgEAKzd33cbdk2cMzKjCy0AgOPNnfB4xLi8BvWPgQwRxR07liiUqWU5AMLJyHgDmUKrCfMmeJs+/rO/oc8AIt4ZmewMb9eUKALtDldcXoP6x0CGiOKOGRkKRXnmkiiGDkqkjEyB1YhZo/JhNmhRZ+vGnurgmZwuhxunO72D9uJVl2XSa+WtUltX/0P9KD4YyBBR3LFjiUIZX5IFnUZAU4dDrqUKxp+RMcKk1+KisX1vL0kdS2aDFlmmqEamhSXLN8umrZsZGbUwkCGiuOPWEoVi0msxrsTb/ROqTsbucqPVl/EotHq3iS6bUATAO+U3GKljqSQnI+bD8JSyMrxBEjMy6mEgQ0Rx5Z0h4xuGF+OD+yg19DcYT9pWMug0cuBw8dhC6DQCDjW04+ipjl7PqYnTqdc9SRkZGzMyqmEgQ0Rx1dThQLfTN0MmTkWXlNymSoPxQgQy0rZSgcUoZ1eyM/U4d2Q+AOCDINtL8Z4hI8nyFfwyI6MeBjJEFFfStlKR1QSjTqvyaigRSRmZvdW2oAW/ykJfpXkTvdtL/9nf0Os58e5Ykkj1N7YwTvCm+GAgQ0RxxY4l6s/IAjM0AtBud6ExyIRfZaGv0gVnFAAAdla1oMvhDrivpnWwMzLcWlILAxkiiqtqdixRP4w6rdwifaKps9f9oTIyw/MzUZxlgsPtwY4TpwPuq20ZrIyMVCPDjIxaGMgQUVyxY4nCUTHE++/jWNBAxptdkTqWJIIg4NyReQDQ67iCeJ58rcSuJfUxkCGiuOLWEoWjPM975tKJpt4dSKEyMgAw01fwu+Vos3ybrduJdrt3q6eUGZmUx0CGiOKKGRkKh3Sq9fHm3hmZUDUyAOTOpZ0nWtDt9NbJSB1L2Rl6ZBriNwwPYI1MImAgQ0Rx450hwxoZ6l9Ffl9bS6EzMhX5mSjKMsLh9mC7r05msGbIAOxaSgQMZIjSnN3lxoInPsGdr++O+bWbOxzocro5Q4b6FWpryeMR5UCmMKt3IOOtk/FtLx3xbi/JM2QGYQCjnJFhIKMaBjJEaa6yrg27qlrw2raTcPdxknA0pN+ui7M4Q4b6Jm0tne50yscRAEBLlxMu37/LfHPvQAbwby9t9RX81g5qRoZbS2pjIEOU5upt3t92XR4Rp4LM8BiIw43tAIDRhZaYXpdSj9mowxCLN1BRtmA3+DqW8swG+aTpnmaO8HYu7ajy1snUDGpGxru11NbthCfGvwhQeBjIEKW5Olu3/OeaPk4fjoYUyIwcYo7pdSk1+etk/NtLDTb/8QShjBhiRqHVCIfLgx0nWlTJyHhEoMMR36yMxyNif40NLrcnrq+TbBjIEKW5+lZ/IFOr+HMsHG7w/kAaxYwMhaHcF8icUHQu9VUfIwmsk2katBkygPf0bilTFO+DI1/+rApfeewjPLnhcFxfJ9kwkCFKc/VxzMgc8WVkRhUwkKH+VeR7M3fHFKdZKw+M7IsykJH+Hcd7hozEXycT34Lfjw41AgD219ri+jrJhoEMUZpTbi3FMiPjcHnkmSAMZCgcwWbJyK3XfWRkAMgTfj8/fhp2l3frpXgQtpaAwZvuu/tkK4DA71liIEOU9qQaBMDf7RELJ5o74PaIMBu0KOrnhxARAAz3ZWSOK2tkfMW+/WVkRgwxo8BqlDvvhlgMg9Yp55/uG7+tpcY2O6p9mSbl9ywxkCFKe4HFvrH7Te9wo78+RhCEmF2XUtfwPG9Gpt5ml0+z9tfI9J1dUdbJAINTHyORZsm0xXGWzO6TLfKf623d7JBSYCBDlMa6ne6AmR2xzMgcZn0MRSgnUw+rb1KuVPDbGGaNDODfXgIGp2NJIq05nltLu3zbSoB3VEJzpyNur5VsGMgQpTGp0FdKmDS02eGMUWun1LHE1msKlyAIcsGvtL0UTteSRJmRGYwZMpLB2FpSZmQAoC7GHYbJjIEMURqT3gzLcjOh1woQxcAupoGQMzJsvaYIKFuwuxxutPlOsQ52YGRPI311MsDgZmTiXewriqJc6Cu1esfq+zQVMJAhSmP1vt92i7NNcodHLDqXRFHk1hJFRTkUTyr0Nek1sBj7P8VaEAQsnFYKQQBmKrIz8ebPyMQnkDl5ugvNHQ7otYI8xbieBb+y+J5vTkQJTRqGV+wrpKxq7orJLJnGdjvaul3QCP6WWqJwDM+TtpY6/dtKVlPYBeN3XD4OSy4ejZxMQ9zW2JN8cGSczlva5dtWGlechWG53u8ntmD7MZAhSmNSero42wSN7+dELDIyUn1MWV4mTHoeFknhk2fJNHX6h+GFsa0k0Wk1gxrEAECWVOwbIiPT7XSj0+FGnjm6dUnbSlOGZaPQ6v2lo4GBjIxbS0RpTPqtrtBqRImvOLI2BhkZbitRtKRZMtUt/uxgOPUxapIzMiECmRv+9inOe3Bd1MHHrqoWAMDUYTnyTCZmZPwYyBClMWVGptRXI1MTi4yMHMiwY4kiU2g1wqTXwO0RsdP3AzySjIwa/EcU9N5aEkXv19Ht9GDL0eaIr+32iNhb7cvIlGWjyPd9yhoZPwYyRGlMejMsyjLJA8RiMUvmiDQMjxkZipBGI6DcNxjv82OnASR+RiY7I/TW0ulOJxy+IxN2+wKzSBxpbEeHw40MvRajCyxyPRu7lvwYyBClKVEU5fR0cZYJJb4D9mpjMN1XysiMZCBDUZC2l6R/n8mTkXFCFAMn7ioDjt2KoXbhkgbhTRqaBZ1WgyJfINPc4YDd5Y52ySmFgQxRmmrt8v+mWGA1otSXkWnqcKDbGf0bZJfDLZ8Jw60lioZ0VIFEKnBNVFKNjEcEOhyB3zvKWpY91a1wRThwUlkfAwC5mXoYtN4f3TxzyYuBDFGakt5gczP1MOm1yMnUw6T3viUMZGro0VMdEEXvuPlouzQovfVs2U/0jIxRp5GDi55D8eoV30tdTje+9GUrwyVN9J1SlgPAOytHmnLM7SUvBjJEaUpZHwN43yClrEzNAOpklB1LPCySoiFtLUkSvUZGEAT/dN8edTI9i3J3V4W/veRweXCgtg0AMHVYtny7v06GGRmAgQxR2pJ+UyxSnCocizoZdizRQCkzMoKApMjshepckjKfOt+gpl09zkzqy8E6GxxuD3Iy9XIBNAC5c4kt2F4MZIjSlLLQVxKLzqXD7FiiARqakyH/4M83G6HTJv6PKmuGv+BXSZodM2uU98iESAp+pULfyUOzA7KbRRyKFyDx/3UQUVxI++tFilOFYzFL5nADh+HRwOi0GgzN9QbViV4fIwk13Vf6hWHehCIA3ixLuN1Gu3sU+kqKszkUT4mBDFGakgOZbOXW0sCm+3o8Io6cklqvubVE0ZPqZBK9PkaSFSIjI32fnVmeizyzAU63KNe99EfahpqiqI8B/NvBAynKTyUMZIjSlFzsa1VuLQ3sBOxaWze6nR7otQLK8nhYJEVPasFOnoyMdEyBv0bG6fbgVLsDgHd6thSQ7ApjMF6H3YUvfdnNqb6OJYkUyEhnUaU7BjJEaapOcTyBpNSXkYn2BGxpW2l4vhn6JKhroMT1lcklGJabgSsmFau9lLDIXUuKjIwUaOi1AvIyDfIWUTgFv9uOn4ZH9NYLKQvygcCMTM8BfOmIp18TpSGX24NT7YHt14A/I2PrdqHD7oLZGNlbBDuWKFZmjcrHx3dcovYywiZlZNoUGZl6+VBWEzQaAVPLvBmZcAp+txxpAgDMHJnX6z6pQL/L6Uab3SW/drrir0xEaaix3Q5R9LaE5itaW60mPay+4CWaziWeek3pKlixr3/EgXd7bIovI3O4sR3t9t4HTCpJgcy5I/N73Zdh0MqvV886GQYyROlIKhIstBqh0QQOrZNmydREMUtGOiySZyxRupGLfRWBTM/t2yEWI4bmZEAUgT19ZGU6HS45azMrSCAD+DOpHIrHQIYoLUlvfoU99t6Bgc2SOd7UCQAYMYSFvpRegg3Ek7/PFAX1UsHv7j7qZLYdPw2XR8TQnAwM87Wh91TMoXgyBjJEaag+yDA8SWmUGRm7yy0fbdBzxDxRqgt2REF9kIL6KWEU/CrrY0Id8yEFRzxviYEMUVoKNgxPEioj89wnR/HwewdDdklUNXdBFAGzQRtQd0OUDvwZmSCBjOIXBqngd1cfZy5tOdIMIHh9jEQaisdAhl1LRGmpLsgwPEmwWTKbvjyFX7+9HwDwtWmlGFec1et5x5u89THD8808LJLSjr9GxgVRFCEIgvx9Vqj4hcF73ABQ3dKFpnY78i2Bv0x0OlzynJlzR/QRyHAonowZGaI01ODbuw++tRQ4S6bb6cb/rtkr33+4oSPoNaX6GOWBf0TpQsrIuD0iOh3eIwikjiLl95nVpMfIId6t12Bt2NuPt8DlEVGabUJZXvD6GMBf31bPoXgMZIiSmccT3TAsOSMTtNjXn5ERRRFPbjiMo6f8wYvUYt2TMiNDlG5Meg30Wm8m0tbtRLvdhQ5fQNPz+6yvwXjKtuu+MptScMT2awYyREnrX7trMOnX7+Ph9w7CHWFA459vEbprqdPhxo6qFjy54TAA4KzyHAB9BDLNzMhQ+hIEIaBzSdrysRp1vQZLSp1Lnx5t7nWdvubHKEnfu43t9oi//1MNAxmiJPXWzhp0Otz484bDuPG5z9Da6ez/SfCe4dLmG8YVrNg3w6BFbqb3DfmnL++Aw+3BxWML8KMLRwHoKyPjC2R4xhKlKeUsmfog9TGS88cUQCMAmw43YX1lg3x7p8MlZ2n6C2SGWAzQCN6trKb29N5eYiBDlKSk/XVBADZ+0YivPfExvqjv/1Rd6Q3WbNDCGmK0uZSVqWrugkmvwYoFkzC60Dvk7nBDR68tLZfbg5OnfYHMEG4tUXqSp/t2OYO2XktGFVhw4+wRAIC71+xFl28LavvxFjjd/dfHAIBOq5EP1Ez3WTIMZIiSUIOtG3W2bmgE4J8/moWhORk43tSJhU98gr9+fBRv7qyWP/61uwanOxzyc+VTr4O8wUqkWTIAcPvcMSjLy0R5XiZ0GgFdTnevN87a1m443SIMWk3QAmKidKDMyPRVhwYAP7tsDEqzTTh5uguPfXgIALD1aHj1MRJO9/Vi+zVREtrly8acUWjFORV5ePvWObjlpe3YdLgJv/nX/l6PL7Qa8dT103FWea5/how1dMAx1Ne5NK7Yiu/P8f7mqNdqUJ6fiSONHTjS2CF3NwH+baWyvAxoNWy9pvSkrJGROgNDBTJmow73LZiEm174HM/89wgWThva50GRwXiv3Zr2GRkGMkRJSBpvLg3XyjMb8MKNM/DkhsPY4vutTnK8qRMnT3fh23/ZghULJqLFN7ArWMpbcsN5FWizu/CTi0ZBr/UnbkcVWHCksQOHG9sx54wh8u3H2LFE5J/u2+WUi337ylBeNqEI8yYUYe3+eix7fTf213h/QemvPkYi1bg1MJAhomSz0zcwSxp3Dnj3zG+99AzcijMCHttud+EX/9yF9/bV4c439mCIbwBXsCJEyagCC/7wzWlBb/8A9b0Kfk+wY4nIn5EJ2FoK/X0GAL/+2kR88uUpeQheSbYJ5WEWzHMonhdrZIiSjCiK2FPt/c1tqiKQCcVi1OHPi87CL+aNgSAAp9pDD8Prz6gCb8alZyBzzDdnhh1LlM7kGpkul5wlCbW1JCnNycDP542VPw+3PkZ57XTfWlI1kFm5ciXOOeccWK1WFBYWYuHChaisrAx4THd3N5YsWYL8/HxYLBZcc801qK+vV2nFROo70dyJlk4nDFoNxhZbw3qORiPglkvOwF8Xnw2rb6bFyAJLxK89StG51HNNADuWKL1JXUstXQ40+Cbu9rWFK1l8XgUmD/VuE18wZkg/j/aTApmGNC/2VTWQ2bhxI5YsWYItW7bggw8+gNPpxLx589DR4X+T/NnPfoa3334br776KjZu3IiamhpcffXVKq6aSF1Soe/40iwYdJF9C18yrgjv/ewCPHXddJw/Ovw3TMmoId5Aps7WjXbfLBpRFDlDhgj+jMyxU51weUQIAuSt3L5oNYK3xm3RWVgwdWjYrycFSemekVG1Rua9994L+Py5555DYWEhtm3bhgsuuACtra3461//ipdeegmXXHIJAODZZ5/F+PHjsWXLFpx77rlqLJtIVbt9e+nTfNNBIzU0J0PuSopUdqYeQyxGnGq340hjO6YMy0Fjmx1dTjc0AjAsl4EMpS+pRkY60mOIxRhQLN+XXLMBV0wuiej1pM7D1i4nup1umPTaiJ6fKhKqRqa11fubZl6et/Vs27ZtcDqdmDt3rvyYcePGoby8HJs3bw56DbvdDpvNFvBBlEqkQXhTwqiPiYeRvjqZI43eN2vpaILSnIyIM0REqUTqWnK4PQD6L/SNxeuZ9N7vuW3HT8f1tRJZwrzreDwe3H777Zg9ezYmTZoEAKirq4PBYEBOTk7AY4uKilBXVxf0OitXrkR2drb8UVZWFu+lEw0al9vjL/Qtiy4jM1CjfLU1UsGvVOhbwdZrSnNZPSZlx3s4pCAIOG+Ud4v4e899hn9+XhXX10tUCRPILFmyBHv37sXLL788oOssX74cra2t8kdVVXr+j6XU9GVjO7qcbliMOowcEnmxbiz07FySCn3L2XpNaa7nkR/9dSzFwh+/PQ2XTSiCw+XBstd249dv7YPTlxFKFwkRyNxyyy3417/+hfXr12PYsGHy7cXFxXA4HGhpaQl4fH19PYqLi4Ney2g0IisrK+CDKFXsrvJmYyYNzYJGpQm6PTuXjrHQlwiAf2tJMhiBjNWkx1+um47b53rnRz236Riu+7+t8piFdKBqICOKIm655RasXr0aH374IUaMGBFw//Tp06HX67Fu3Tr5tsrKSpw4cQKzZs0a7OUSqU46GTec+THxMtq3tXT0VAfcHhEnONWXCACQoddCp/gFY7DOHdNoBNw+dwyevn46LEYdth5txp2v7xmU104EqnYtLVmyBC+99BLefPNNWK1Wue4lOzsbGRkZyM7Oxve//30sXboUeXl5yMrKwq233opZs2axY4nSklToO7UsR7U1lOZkwKjTwO7ynngtZ2S4tURpThAEZGXo0ew7pLWv6dnxMG9iMf66WI9vPb0FW482QRTFsIfrJTNVMzJPPvkkWltbcdFFF6GkpET+eOWVV+THPProo7jyyitxzTXX4IILLkBxcTHeeOMNFVdNpI5upxsHar1deFOibL2OBa1GwAjf4LvtJ06j1Xd2EwMZIv9QPCC8YXixdmZ5LnQaAW3dLtSkydEFqmZkRFHs9zEmkwlPPPEEnnjiiUFYEVHiOlBrg8sjIt9siHoOTKyMKrDgYF0bPjzYCAAosBqRaeDRbUTSUDxg8LaWlAw6DUYVWFBZ34aDtTbV3ysGQ0IU+xJR//zzY7JVTxdLnUsbKxsAABXMxhAB8LdgG3QaZGfo+3l0fIwr8R5dcrCuTZXXH2wMZIiShFToq9YgPCWpc8nW7T2moDyPhb5EgL9zqTjLpNovHNIZbAxkiCih+At91auPkYzqceAkMzJEXlJGJt5Tffsyvtg7dqSyLj0m2zOQIUoC7XaXPIAuETIyI3qccs1heEReUo3MYMyQCUXKyBxu7IDd5VZtHYOFgQxREjjS2A5RBAqtxrBO0403s1GHUkVHBo8nIPKaWOrNhpxZnqvaGkqyTcgy6eD2iPLgylTGNgOiJFDT4m2jLE2gDoRRhRa5vZOt10ReC6YNxayR+SiwqvcLhyAIGFeShU+PNuNgnQ0TSlN7wj0zMkRJoLa1CwBQmqNeurqnkb7tpSyTDjmZBpVXQ5Q4ClUs9JWM820vVaZBwS8DGaIkUOvLfJRkJ1ZGBuDRBESJaJyv4PcAAxkiSgQ1Ld6MTIkKk0JDmTehGNPKcnDDrOFqL4WIepBbsGtTv3OJNTJESUDKyCRSjUxxtglrlsxWexlEFIQUyDS02dHc4UCeOXW3f5mRIUoCtQmYkSGixGUx6lCe5y3CP5ji82QYyBAlOLdHRH2bHUBiZWSIKLGNTZOCXwYyRAmuoa0bbo8InUZIiBkyRJQcxst1MgxkiEhF0gyZoiwTtBp1WzqJKHmM9XUuHaxnIENEKkrEGTJElPikU7C/qGuD2yOqvJr4YSBDlOBqWxJvhgwRJb6KfDOMOg26nG6caO5Uezlxw0CGKMHV+DIyJczIEFEEtBoBY4qkgt/U7VxiIEOU4KSMTCkzMkQUIemoggM9Cn7tLjecbo8aS4o5BjJECU6qkeEMGSKKVLAW7I8PncJ5Kz/Egj99AlFM/toZTvYlSnA1CTjVl4iSw/gSX+dSnQ2iKOL/PjqKle8egEcEmjocaGy3o9Ca3L8kMZAhSmAOlwen2r3D8JiRIaJISVtLx5s7ccs/duCd3bUAAI0AeETgcENH0gcy3FoiSmD1tm6IImDUaVL6rBQiio98ixFDLEaIIvDO7lpoNQLu+9pEXDimAABwuLFd5RUOHAMZogSmPPVaEDgMj4giN6HUu72UbzbgxR/MxOLzKjC60AIgNQIZbi0RJTDp1GvOkCGiaP1i3hiMLrDg++ePwFBfrd2oAimQ6VBzaTHBQIYogXGGDBEN1JRhOZgyLCfgtpG+QOZICmRkuLVENMj+8ekJPPrBF2G1PXKGDBHFw6gCMwCguqULXQ63yqsZGAYyRIOo0+HCXWv24o/rDmH3ydZ+H1/LjAwRxUGe2YCcTD1EETh6Krm3lxjIEA2ifTU2+fC2rUeb+n18DTMyRBQHgiAo6mSSe3uJgQzRINpV1SL/ecuR5n4fz4wMEcWLtL3EQIaIwqbcTvrsaLOcnQmmy+HG6U4nAHYtEVHspUrnEgMZokG0+2SL/Oc2uwv7a0KfSCtlY8wGLbJMbDAkotiSA5kGZmSIKAytnU4ca+oEAEwfngsA2HIkdJ2MPEMmJ4PD8Igo5kb5huIdOdUOTx/Z4UTHQIYohg7U2lDtm8bb0+7qFgDA8PxMXD6xGEDfgYxyqi8RUayV5WZArxXQ7fSg1tat9nKixkCGKEY+PdqMrz72Eb799Oagv91Ihb5ThuXg3JH53uccC10nI2Vk2LFERPGg02owPN9X8JvE20sMZIhiwOHy4Fer98AjAlXNXdih6E6S7PIV+k4dlo0JpVmwGnVo63bhQG3wOhl2LBFRvKVC5xIDGaIYeOajI/hS8RvN2v11vR4jFfpOGZYDrUbAOSPyAITeXuIMGSKKt1SYJcNAhmiAjjd14LF1hwAA8ycWAQA+2F8f8Jh6WzfqbXZoBGDSUO9JtOeO7DuQYUaGiOLN37mUvC3YDGSIBkAURdy1Zi/sLg/mjB6C3187FXqtgCONHQEZGqk+ZkyRFZkGbyu1VCezNcQ8GemcJc6QIaJ4kTqXmJEhSlNv7arBR4dOwaDT4DcLJ8Fq0uO8UUMABG4vSYPwpgzLlm+bUBK6Tqat24k2uwsAUMqMDBHFyUhfjUxDmx22bqfKq4kOAxmiKLV2OvGbfx0AANxy8WiMGOJ9Q5jn215au8+/vbRLUR8j0Wk1OLsi+DwZqWMpO0MvZ3CIiGIty6RHodUIADgSxYTfDrtL9Rk0fIckCsO7e2qx4l/70dzhkG/ziCKcbhGjCsz40YUj5dsvG1+E/129FzurWlBv60ah1ShnZKYqAhnAu720vrIRW4404wfn+6/BGTJENFhGFVjQ0GbHkcZ2TCvLiei597+zH2/urMHyr4zH9ecOj88C+8GMDFEf3B4Rv3+/Eje/uB21rd2wuzzyh9MtQq8VsPLqKTDqtPJzCrNM8pvBfw7U43hTJ1q7nDDoNBhbbA24vjxP5mhTQJ2MPEMmh/UxRBRfIwfQgn2wrg2dDjdyMvSxXlbYmJEhCqG1y4mfvbITHx5sAAD8YM4IfHd2RcBxARajDtlBvoHnTSzCzqoWrN1XD4vR+202oSQLBl3g7w4TS7NgMepg63bhYJ0NE0u9NTS1zMgQ0SCJtnPJ4xFRWdcGABhfYu3n0fHDQIaSgscj4ouGNowtsg7KuUNfNrThphe24eipDhh1Gjx0zRQsPHNo2M+fN6EYD79XiU2HT6E4yxuMTFUU+kqkOpkNlY149fOTaBrn3bqShucxI0NE8Raqc6m104nmTodc/9fTydNd6HS4YdBpUJEf/DGDgYEMJYU/b/gSv1/7BVYsmIgbZlXE9bW6nW4s+r+tqLfZMTQnA3+5fjomDe0dhPRldKEFIwvMONLYgTd2nAQQWOirdO7IfGyobMRzm47huU3HAu5jRoaI4k2a7nusqQMutwc6rQabvjyFJS9tR1u3C+t/cRHK8jJ7Pe9Anbfb8oxCC3Ra9SpVGMhQwhNFEa9t8wYDr287GfdA5t29tai32VGcZcJbt8xGvsUY1XXmTSjGUxsPw+n21r5MLQseDF1z1jB8fOhUQCExABRYjbhkXGFUr01EFK7S7AyY9Bp0Oz040dyJ9ZWN+O2/D8h1e1uPNgcNZA7WereVetb+DTYGMpTwvmxox7GmTgDeLZe61m4UxzFT8ffNxwEAi2aWRx3EAMBlE4rw1MbDALy1NCOHWII+rsBqxP/7wcyoX4eIaCA0GgEjh1iwv9aGW17agf2+uVZDLAacandg98kWfGP6sF7Pq6z3Pm58cdagrrcndi1RwlvbY9z/BwfqQzwyPP/49AQ2ftEY9L691a3YfqIFeq2Ab80oG9DrnFmWgyG+QGjy0GxoNPGv7SEiioZUJ7O/1gatRsC9V03A3VdOAOCv2etJysiMU7HQF2AgQ0lACmRG+77R1u7rfSBjuLYdb8byN/bgpuc/D9pq+P+2eLMxl08qQaF1YFkfjUbAFZOKAQDn+AbfERElIqnrKM9swP/7/kx8b/YIeYzEgRobHC5PwOO7HG4ca/J2Oam9tcRAhhJaXWs3dlW1QBCABxZOAgBsPtyE1q7oRmm/75u263B78L+r90AU/bNbWrucWLOzGgBiNtjpjivG4YGvT8KPLhwVk+sREcXD9ecOx28WTMS/bp2DWaO8863K8zKRk6mHw+3BwbrAY1QONbTBIwL5ZgMKBrAFHwsMZCihSdtIZ5blYObIfIwqMMPlEbGhsiHia4miiPcV2ZwtR5rx+vZq+fPXt51Et9ODsUXWmGVQLEYdFs0cDrOR5WhElLisJj2un1URMPJBEARM9nVs9txeUhb6DsZIjL4wkKGE9oFvW2nexOKA/36wv3edzOHGdnz9z5/g1c+rgl7rUEM7jjd1wqDT4NZLRgMAHnjHe+yAxyPK20rXzxqu+jcmEVEikI5V2V3VEnD7Qd8gvHEqF/oCDGQogdm6ndh8+BQAYN6EooD/bqhshN3llh/r8YhY9tpu7DjRgpXvHgy4TyIFP3NGD8Ftl56BccVWnO50YuW/D2DT4SYcOdUBi1EX0eA7IqJUNsU3yHN3z4yMb6tpnMr1MQADGUpgGyob5UMZR/pGaE8dloNCqxHtdhc2H/afGP3K51XYdvw0AKC5w4F39/QuCJaKhC+bUAS9VoMHvj4ZAPDqtpO47+19AICrzxoqHylARJTupvoKfg81tKHT4QLg3aaXMzIqdywBDGQogUmBh7SdBHg7gS7zZWWkbqbGNjtW/vsAAGBMkTfgeWHzsYBr1bZ2YdfJVggCcOl475C56cNzsWhmOQDvthMQuyJfIqJUUJRlQnGWCR4R2FvtzcI0ttvR3OGARgDOKGQgQxSU3eXGhkrvrBdpO0kiBTL/2V8Pj0fEA+/sh63bhYmlWXjhxpnQawVsP9GCvdX+VOh/fEHPWeW5AW3Vyy4fJ896OXdkHs4oUv+bkogokfi3l1oA+At9K/LNyDBo1VqWjIEMJaQtR5rRbneh0GqUi80ks0blw2LUoaHNjifWf4k1O2ugEYCVV09GcbYJl08qAeCfCQP4szeX9QiKsjP0+MM3p2JiaRZ+OX9cfL8oIqIkJG0vSZ1LlQm0rQQwkKEEJW0rzZ1Q1GsirlGnxUVjCwAAj3zwBQDghlkV8qGMN8zybg+t2VmN1i4nWruccj1Nz+wOAFwwpgDv3HY+pg/n0Doiop6kjMwuX+eSdFjk2CL1O5YAnrVECeBArQ1r99XDrRhO995eX31MkMAD8NbN/Gt3LQCgKMuIn88bI9939vBcjCu24mBdG17fdhL5FgNcHtF3InXw846IiCi4KUNzAAAnmjtxusORcBkZBjKkqn9+VoW71uyFw+3pdZ/VqJMnTPZ00dgCGLQaONwe/PqqibCa9PJ9giDgunOH4641e/H/thzH+BLvbw09t5WIiKh/2Zl6VORn4lhTJ3ZUncahem9zhNqHRUoYyJAqnG4PfvOv/XjBd9L07NH5GNUjWzJ3fBGMuuCFZFkmPZ66/iycanfg8knFve7/+plD8eC7B3HkVAeO+s4DCZXdISKivk0ty8Gxpk68ubMGDrcHmQYthuVm9P/EQcBAhgZdY5sdS17cjk+PNUMQgKVzx2DJxaMjPh36knGhAxOzUYdrzhqK5zcfhygiaNEwERGFZ8qwHLy5swbv+rb9xxZbI37PjhcGMhSV2tYu/OZf+1GWm4mfzxsLg6533Xinw4UH3z2Iz46d7vXclk4nrEYdHv3WNMyNU6bkunOH43lfxueyIEXDREQUnqm+gl/pFOxEmOgrSYqupSeeeAIVFRUwmUyYOXMmPv30U7WXlNY+PdqMqx7/GP/eU4e//PcIvvPMFjTYugMeU9XciWue3IwXNh/HgVpbwEdLpxMjC8xYc8vsuAUxAHBGkRWXjiuEIIDHDhARDcDE0mxoFb8MJsIZS5KEz8i88sorWLp0KZ566inMnDkTq1atwvz581FZWYnCwkK1l5dWRNF7sOJ9b++HyyPijEIL6mzd2Hb8NK7608d48rrpOKs8Fx8fOoVb/rEdLZ1ODLEYcPeVE5CbaZCvo9MKOKs8FyZ9/AcpPf4/Z6LeZseIIea4vxYRUarKMGhxRqFFcVhk4mRkBFFU9LwmoJkzZ+Kcc87Bn/70JwCAx+NBWVkZbr31Vtx55539Pt9msyE7Oxutra3IyopdBHm6w4EO37kT6UAUgT99+CVe8Z0sfeWUEjz8jSmot9nxwxc+x6GGdhi0GnxtWine2H4SHtE7e+Cp66YHHAtPRETJ6Y7Xdss/A3bdMw/Zmfp+njEw4f78TuiMjMPhwLZt27B8+XL5No1Gg7lz52Lz5s1Bn2O322G32+XPbTZbXNb2u7WVeGnribhcO5FpBO9Y/x9dMBKCIGDEEB1WL5mNn/9zJ97fV4/Xtp0EAFxz1jA88PVJg5J1ISKi+JtSlo1XPq9CSbYp7kFMJBI6kDl16hTcbjeKigLrKIqKinDw4MGgz1m5ciXuu+++uK9NrxFgDFLgmsoKs4y4f+FkXDimIOB2i1GHJxdNx5MbD+OlrSdw0/kjsPi8CggCi2uJiFLF5ROL8Y9PT+DKKaVqLyVAQm8t1dTUYOjQodi0aRNmzZol375s2TJs3LgRW7du7fWcYBmZsrKymG8tERERUfykxNbSkCFDoNVqUV9fH3B7fX09iot7D0EDAKPRCKPROBjLIyIiIpUl9N6IwWDA9OnTsW7dOvk2j8eDdevWBWRoiIiIKD0ldEYGAJYuXYrFixfj7LPPxowZM7Bq1Sp0dHTge9/7ntpLIyIiIpUlfCDzrW99C42NjbjnnntQV1eHadOm4b333utVAExERETpJ6GLfWMhXnNkiIiIKH7C/fmd0DUyRERERH1hIENERERJi4EMERERJS0GMkRERJS0GMgQERFR0mIgQ0REREmLgQwRERElLQYyRERElLQYyBAREVHSSvgjCgZKGlxss9lUXgkRERGFS/q53d8BBCkfyLS1tQEAysrKVF4JERERRaqtrQ3Z2dkh70/5s5Y8Hg9qampgtVohCELMrmuz2VBWVoaqqqq0OcOJXzO/5lSUbl8vwK+ZX3NyEEURbW1tKC0thUYTuhIm5TMyGo0Gw4YNi9v1s7KykvIfyEDwa04P6fY1p9vXC/BrThfJ/DX3lYmRsNiXiIiIkhYDGSIiIkpaDGSiZDQace+998JoNKq9lEHDrzk9pNvXnG5fL8CvOV2ky9ec8sW+RERElLqYkSEiIqKkxUCGiIiIkhYDGSIiIkpaDGSIiIgoaTGQiZGKigoIghDw8eCDD6q9rLiz2+2YNm0aBEHAzp071V5OXH3ta19DeXk5TCYTSkpKcP3116OmpkbtZcXNsWPH8P3vfx8jRoxARkYGRo0ahXvvvRcOh0PtpcXVAw88gPPOOw+ZmZnIyclRezlx8cQTT6CiogImkwkzZ87Ep59+qvaS4ua///0vrrrqKpSWlkIQBKxZs0btJcXVypUrcc4558BqtaKwsBALFy5EZWWl2suKKwYyMbRixQrU1tbKH7feeqvaS4q7ZcuWobS0VO1lDIqLL74Y//znP1FZWYnXX38dhw8fxje+8Q21lxU3Bw8ehMfjwV/+8hfs27cPjz76KJ566in86le/UntpceVwOHDttdfi5ptvVnspcfHKK69g6dKluPfee7F9+3ZMnToV8+fPR0NDg9pLi4uOjg5MnToVTzzxhNpLGRQbN27EkiVLsGXLFnzwwQdwOp2YN28eOjo61F5a/IgUE8OHDxcfffRRtZcxqP7973+L48aNE/ft2ycCEHfs2KH2kgbVm2++KQqCIDocDrWXMmgefvhhccSIEWovY1A8++yzYnZ2ttrLiLkZM2aIS5YskT93u91iaWmpuHLlShVXNTgAiKtXr1Z7GYOqoaFBBCBu3LhR7aXEDTMyMfTggw8iPz8fZ555Jn73u9/B5XKpvaS4qa+vx0033YS///3vyMzMVHs5g665uRkvvvgizjvvPOj1erWXM2haW1uRl5en9jIoSg6HA9u2bcPcuXPl2zQaDebOnYvNmzeruDKKl9bWVgBI6e9bBjIxctttt+Hll1/G+vXr8aMf/Qi//e1vsWzZMrWXFReiKOK73/0ufvzjH+Pss89WezmD6o477oDZbEZ+fj5OnDiBN998U+0lDZovv/wSjz/+OH70ox+pvRSK0qlTp+B2u1FUVBRwe1FREerq6lRaFcWLx+PB7bffjtmzZ2PSpElqLyduGMj04c477+xVwNvz4+DBgwCApUuX4qKLLsKUKVPw4x//GI888ggef/xx2O12lb+K8IX79T7++ONoa2vD8uXL1V7ygEXy/xgAfvnLX2LHjh1Yu3YttFotbrjhBohJNhw70q8ZAKqrq3H55Zfj2muvxU033aTSyqMXzddMlOyWLFmCvXv34uWXX1Z7KXHFIwr60NjYiKampj4fM3LkSBgMhl6379u3D5MmTcLBgwcxduzYeC0xpsL9er/5zW/i7bffhiAI8u1utxtarRaLFi3C888/H++lxsxA/h+fPHkSZWVl2LRpE2bNmhWvJcZcpF9zTU0NLrroIpx77rl47rnnoNEk3+8/0fx/fu6553D77bejpaUlzqsbPA6HA5mZmXjttdewcOFC+fbFixejpaUl5TOMgiBg9erVAV97qrrlllvw5ptv4r///S9GjBih9nLiSqf2AhJZQUEBCgoKonruzp07odFoUFhYGONVxU+4X+9jjz2G+++/X/68pqYG8+fPxyuvvIKZM2fGc4kxN5D/xx6PBwCSKusGRPY1V1dX4+KLL8b06dPx7LPPJmUQAwzs/3MqMRgMmD59OtatWyf/MPd4PFi3bh1uueUWdRdHMSGKIm699VasXr0aGzZsSPkgBmAgExObN2/G1q1bcfHFF8NqtWLz5s342c9+huuuuw65ublqLy/mysvLAz63WCwAgFGjRmHYsGFqLCnutm7dis8++wxz5sxBbm4uDh8+jLvvvhujRo1KqmxMJKqrq3HRRRdh+PDh+P3vf4/Gxkb5vuLiYhVXFl8nTpxAc3MzTpw4AbfbLc9HGj16tPxvPZktXboUixcvxtlnn40ZM2Zg1apV6OjowPe+9z21lxYX7e3t+PLLL+XPjx49ip07dyIvL6/Xe1kqWLJkCV566SW8+eabsFqtcu1TdnY2MjIyVF5dnKjaM5Uitm3bJs6cOVPMzs4WTSaTOH78ePG3v/2t2N3drfbSBsXRo0dTvv169+7d4sUXXyzm5eWJRqNRrKioEH/84x+LJ0+eVHtpcfPss8+KAIJ+pLLFixcH/ZrXr1+v9tJi5vHHHxfLy8tFg8EgzpgxQ9yyZYvaS4qb9evXB/3/uXjxYrWXFhehvmefffZZtZcWN6yRISIioqSVnBveRERERGAgQ0REREmMgQwRERElLQYyRERElLQYyBAREVHSYiBDRERESYuBDBERESUtBjJERESUtBjIEBERUdJiIENERERJi4EMUYq46KKLcPvtt6u9jIQR6d9HrP7++P+BaHAxkCGiuPrud7+LhQsXyp83Njbi5ptvRnl5OYxGI4qLizF//nx88sknIZ8TjTfeeAO/+c1vBnSNvoQKWOL9ukQUSKf2AogovVxzzTVwOBx4/vnnMXLkSNTX12PdunVoamqK6evk5eXF9HqJ/rpE6YoZGaIUZLfbcdttt6GwsBAmkwlz5szBZ599FvCYtrY2LFq0CGazGSUlJXj00Ufjvi3S0tKCjz76CA899BAuvvhiDB8+HDNmzMDy5cvxta99LaxrPP300ygtLYXH4wm4fcGCBbjxxhvlz5VfSzh/Hz299957mDNnDnJycpCfn48rr7wShw8fBuDNGG3cuBF//OMfIQgCBEHAsWPHer1uOK990UUX4bbbbsOyZcuQl5eH4uJi/PrXvw5Yy2uvvYbJkycjIyMD+fn5mDt3Ljo6OsL6+yJKdQxkiFLQsmXL8Prrr+P555/H9u3bMXr0aMyfPx/Nzc3yY5YuXYpPPvkEb731Fj744AN89NFH2L59e1zXZbFYYLFYsGbNGtjt9qiuce2116KpqQnr16+Xb2tubsZ7772HRYsWBX1OOH8fPXV0dGDp0qX4/PPPsW7dOmg0Gnz961+Hx+PBH//4R8yaNQs33XQTamtrUVtbi7Kysqhf+/nnn4fZbMbWrVvx8MMPY8WKFfjggw8AALW1tfjOd76DG2+8EQcOHMCGDRtw9dVXQxTFaP76iFKPSEQp4cILLxR/+tOfiu3t7aJerxdffPFF+T6HwyGWlpaKDz/8sCiKomiz2US9Xi+++uqr8mNaWlrEzMxM8ac//al828KFC8WcnBzxmmuuCXitt99+WxwzZow4evRo8ZlnnulzXYsXLxYXLFggf/7aa6+Jubm5oslkEs877zxx+fLl4q5du/p8Tk8LFiwQb7zxRvnzv/zlL2Jpaanodruj+vtQPj6UxsZGEYC4Z8+ePh+vvD2c177wwgvFOXPmBFzjnHPOEe+44w5RFEVx27ZtIgDx2LFjIddGlM6YkSFKMYcPH4bT6cTs2bPl2/R6PWbMmIEDBw4AAI4cOQKn04kZM2bIj8nOzsbYsWMDrvXTn/4UL7zwQsBtLpcLS5cuxYcffogdO3bgd7/7XUT1Lddccw1qamrw1ltv4fLLL8eGDRtw1lln4bnnngv7GosWLcLrr78uZ3VefPFFfPvb34ZG0/stLZy/j2AOHTqE73znOxg5ciSysrJQUVEBADhx4kTY6wz3tadMmRLwvJKSEjQ0NAAApk6diksvvRSTJ0/Gtddei2eeeQanT58Oew1EqY6BDBGFdNFFF8FqtQbc9umnn2LixIkYOnQoLBYLrrjiCqxduzai65pMJlx22WW4++67sWnTJnz3u9/FvffeG/bzr7rqKoiiiHfeeQdVVVX46KOPQm4rReuqq65Cc3MznnnmGWzduhVbt24FADgcjpi+DuANbpQEQZBrgLRaLT744AO8++67mDBhAh5//HGMHTsWR48ejfk6iJIRAxmiFDNq1CgYDIaAdman04nPPvsMEyZMAACMHDkSer0+oOi0tbUVX3zxRb/Xr6mpwdChQ+XPhw4diurq6gGtecKECREVr5pMJlx99dV48cUX8Y9//ANjx47FWWedFfSx4fx99NTU1ITKykrcdddduPTSSzF+/PheWRCDwQC3293nOqN57WAEQcDs2bNx3333YceOHTAYDFi9enXYzydKZWy/JkoxZrMZN998M375y18iLy8P5eXlePjhh9HZ2Ynvf//7AACr1YrFixfLjyksLMS9994LjUYDQRDitrampiZce+21uPHGGzFlyhRYrVZ8/vnnePjhh7FgwYKIrrVo0SJceeWV2LdvH6677rqQjwvn76On3Nxc5Ofn4+mnn0ZJSQlOnDiBO++8M+AxFRUV2Lp1K44dOwaLxYK8vLxeW1vRvHZPW7duxbp16zBv3jwUFhZi69ataGxsxPjx48N6PlGqYyBDlIIefPBBeDweXH/99Whra8PZZ5+N999/H7m5ufJj/vCHP+DHP/4xrrzySmRlZWHZsmWoqqqCyWTq89qlpaUBGZjq6uqAWpu+WCwWzJw5E48++qhcP1JWVoabbroJv/rVryL6Gi+55BLk5eWhsrIS//M//9PnY8P5+1DSaDR4+eWXcdttt2HSpEkYO3YsHnvsMVx00UXyY37xi19g8eLFmDBhArq6unD06FG5jmYgr91TVlYW/vvf/2LVqlWw2WwYPnw4HnnkEVxxxRVhPZ8o1QmiyB4+IvK2Gw8dOhSPPPJIQLZgw4YN+NOf/oTXXnsNgLfYd/z48diwYQOys7Mxffp0bNq0Cfn5+WotnYjSGDMyRGlqx44dOHjwIGbMmIHW1lasWLECAAK2eObOnYtdu3aho6MDw4YNw6uvvopZs2bhkUcewcUXXwyPx4Nly5YxiCEi1TAjQ5SmduzYgR/84AeorKyEwWDA9OnT8Yc//AGTJ09We2lERGFjIENERERJi+3XRERElLQYyBAREVHSYiBDRERESYuBDBERESUtBjJERESUtBjIEBERUdJiIENERERJi4EMERERJS0GMkRERJS0GMgQERFR0vr/MjWqEJjZLKgAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = np.log10(units['isi_violations'])\n",
+    "bins = np.linspace(-5,2.5,100)\n",
+    "hist, bin_edges = np.histogram(data, bins=bins)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(bin_edges[:-1], hist)\n",
+    "\n",
+    "plt.xlabel(\"log$_{10}$ ISI violations\")\n",
+    "plt.ylabel(\"# units\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26b12eb4",
+   "metadata": {},
+   "source": [
+    "### SNR\n",
+    "*Signal-to-noise ratio*, or SNR, is another classic metric of unit quality. It measures the ratio of the maximum amplitude of the mean spike waveform to the standard deviation of the background noise on one channel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d7684b48",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = units['snr']\n",
+    "bins = np.linspace(0,10,100)\n",
+    "hist, bin_edges = np.histogram(data, bins=bins)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(bin_edges[:-1], hist)\n",
+    "\n",
+    "plt.xlabel(\"SNR\")\n",
+    "plt.ylabel(\"# units\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97ac5d3a",
+   "metadata": {},
+   "source": [
+    "### Isolation Distance\n",
+    "*Isolation distance* is a metric based on the principal components (PCs) of a unit's waveforms. After the spike sorting step is complete, the waveforms for every spike are projected into a lower-dimensional principal component space. The higher the isolation distance, the more a unit is separated from its neighbors in PC space, and therefore the lower the likelihood that it's contamined by spikes from multiple units. For more information on this, read the [Allen SDK Documentation](https://allensdk.readthedocs.io/en/latest/_static/examples/nb/ecephys_quality_metrics.html#isolation_distance)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "cdcc2923",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = units['isolation_distance']\n",
+    "bins = np.linspace(0,200,100)\n",
+    "hist, bin_edges = np.histogram(data, bins=bins)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(bin_edges[:-1], hist)\n",
+    "\n",
+    "plt.xlabel(\"Isolation distance\")\n",
+    "plt.ylabel(\"# units\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f29df1c5",
+   "metadata": {},
+   "source": [
+    "### d-prime\n",
+    "Like isolation distance, *d-prime* is another metric calculated for the waveform PCs. It uses linear discriminant analysis to calculate the separability of one unit's PC cluster and all of the others. A higher d-prime value indicates that the unit is better isolated from its neighbors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "8be58216",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = units['d_prime']\n",
+    "bins = np.linspace(0,20,100)\n",
+    "hist, bin_edges = np.histogram(data, bins=bins, density=True)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(bin_edges[:-1], hist)\n",
+    "\n",
+    "plt.xlabel(\"d-prime\")\n",
+    "plt.ylabel(\"# units\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a5e1e0a",
+   "metadata": {},
+   "source": [
+    "### Nearest-neighbors hit-rate\n",
+    "*Nearest-neighbors hit rate* is another PC-based quality metric. It's derived from the 'isolation' metric originally reported in Chung, Magland et al. (2017). This metric looks at the PCs for one unit and calculates the fraction of their nearest neighbors that fall within the same cluster. If a unit is highly contaminated, then many of the closest spikes will come from other units. Nearest-neighbors hit rate is nice because it always falls between 0 and 1, making it straightforward to compare across different datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "f350106e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = units['nn_hit_rate']\n",
+    "bins = np.linspace(0,1,100)\n",
+    "hist, bin_edges = np.histogram(data, bins=bins, density=True)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(bin_edges[:-1], hist)\n",
+    "\n",
+    "plt.xlabel(\"Nearest-neighbors hit rate\")\n",
+    "plt.ylabel(\"# units\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e72554e7",
+   "metadata": {},
+   "source": [
+    "### Summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "5398bb96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 500x1000 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "metrics = ['firing_rate', 'presence_ratio', 'amplitude_cutoff', 'isi_violations', 'snr', 'isolation_distance', 'd_prime', \n",
+    "           'nn_hit_rate']\n",
+    "ranges = [[0,20], [0.9,0.995], [0,0.5], [0,2], [0,8], [0,200], [0,10], [0,1]]\n",
+    "_ = plt.figure(figsize=(5,10))\n",
+    "\n",
+    "for idx, metric in enumerate(metrics):\n",
+    "    data = units[metric]\n",
+    "    # remove NaNs\n",
+    "    data = data[np.invert(np.isnan(data))]\n",
+    "\n",
+    "    plt.subplot(len(metrics),1,idx+1)\n",
+    "    plt.boxplot(data, showfliers=False, showcaps=False, vert=False)\n",
+    "\n",
+    "    plt.ylim([0.8,1.2])\n",
+    "    plt.xlim(ranges[idx])\n",
+    "    plt.yticks([])\n",
+    "    \n",
+    "    plt.title(metric)\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  }
+ ],
+ "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": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}