{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6d432475",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Data Analysis with Pyrfume: A Case Study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d0711d00",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pyrfume\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import Image\n",
    "import os\n",
    "\n",
    "os.environ[\"OMP_NUM_THREADS\"] = \"1\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21a27d97",
   "metadata": {},
   "source": [
    "One of the major virtues of `Pyrfume` is that it allows for the straightforward comparison of experimental results across species, measurement types, and experimental systems. Here, we walk the user through an example of such a cross-modal analysis. \n",
    "\n",
    "Suppose we are interested in the question of whether human odor categories correspond to discrete patterns of glomerular activity. The data for addressing this directly do not exist, but it is certainly the case that plenty of experiments have been done where both mice and humans have smelled the same compounds. `Pyrfume` allows us to quickly compare human psychophysics measurements to mouse glomerular imaging results for sets of common test odorants -- an analysis that would otherwise be cumbersome and time consuming. \n",
    "\n",
    "In our case study, we will compare experimental results obtained from the classic [Dravnieks (1985)](https://www.astm.org/ds61-eb.html) study of human odor qualities, as well as [Chae et al's (2019)](https://www.nature.com/articles/s41593-019-0442-z) glomerular imaging study. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6811369c",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Fetching data and identifying common stimuli"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b80b02e9",
   "metadata": {},
   "source": [
    "The relevant data sets can be fetched from the `Pyrfume` data archive straightforwardly, as shown below. Note that 'behavior', below (and in the entire `Pyrfume` ecosystem), refers generically to any measurable experimental outcome (a reaction time, rating, verbal report, firing rate, calcium transient, etc)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5717bf6d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Molecules\n",
    "dravnieks_molecules = pyrfume.load_data(\"dravnieks_1985/molecules.csv\")\n",
    "chae_molecules = pyrfume.load_data(\"chae_2019/molecules.csv\")\n",
    "\n",
    "# Behavior\n",
    "dravnieks_behavior = pyrfume.load_data(\"dravnieks_1985/behavior_1.csv\")\n",
    "chae_behavior = pyrfume.load_data(\"chae_2019/behavior_1.csv\")\n",
    "\n",
    "# Stimuli\n",
    "dravnieks_stimuli = pyrfume.load_data(\"dravnieks_1985/stimuli.csv\")\n",
    "chae_stimuli = pyrfume.load_data(\"chae_2019/stimuli.csv\")\n",
    "\n",
    "# Subjects\n",
    "chae_subjects = pyrfume.load_data(\"chae_2019/subjects.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0b8011a",
   "metadata": {},
   "source": [
    "Finding common molecules across experiments is achieved with standard filtering operations in [pandas](https://pandas.pydata.org/). For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0cb61916",
   "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>MolecularWeight</th>\n",
       "      <th>IsomericSMILES</th>\n",
       "      <th>IUPACName</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>148.20</td>\n",
       "      <td>CC(C)C1=CC=C(C=C1)C=O</td>\n",
       "      <td>4-propan-2-ylbenzaldehyde</td>\n",
       "      <td>4-isopropylbenzaldehyde</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>79.10</td>\n",
       "      <td>C1=CC=NC=C1</td>\n",
       "      <td>pyridine</td>\n",
       "      <td>pyridine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2758</th>\n",
       "      <td>154.25</td>\n",
       "      <td>CC1(C2CCC(O1)(CC2)C)C</td>\n",
       "      <td>1,3,3-trimethyl-2-oxabicyclo[2.2.2]octane</td>\n",
       "      <td>eucalyptol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6184</th>\n",
       "      <td>100.16</td>\n",
       "      <td>CCCCCC=O</td>\n",
       "      <td>hexanal</td>\n",
       "      <td>hexanal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7410</th>\n",
       "      <td>120.15</td>\n",
       "      <td>CC(=O)C1=CC=CC=C1</td>\n",
       "      <td>1-phenylethanone</td>\n",
       "      <td>acetophenone</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MolecularWeight         IsomericSMILES  \\\n",
       "CID                                            \n",
       "326            148.20  CC(C)C1=CC=C(C=C1)C=O   \n",
       "1049            79.10            C1=CC=NC=C1   \n",
       "2758           154.25  CC1(C2CCC(O1)(CC2)C)C   \n",
       "6184           100.16               CCCCCC=O   \n",
       "7410           120.15      CC(=O)C1=CC=CC=C1   \n",
       "\n",
       "                                      IUPACName                     name  \n",
       "CID                                                                       \n",
       "326                   4-propan-2-ylbenzaldehyde  4-isopropylbenzaldehyde  \n",
       "1049                                   pyridine                 pyridine  \n",
       "2758  1,3,3-trimethyl-2-oxabicyclo[2.2.2]octane               eucalyptol  \n",
       "6184                                    hexanal                  hexanal  \n",
       "7410                           1-phenylethanone             acetophenone  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_molecules = chae_molecules.loc[chae_molecules.index.intersection(dravnieks_molecules.index)]\n",
    "\n",
    "common_molecules.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82b27fcc",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Formatting behavioral data for comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3836d271",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Formatting Chae data\n",
    "\n",
    "Several experimental conditions are encoded in the string tokens of the 'Subject' column in Chae."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b4ff86a3",
   "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>Subject</th>\n",
       "      <th>DeltaF/F</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stimulus</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>G_-1</th>\n",
       "      <td>G_1_left_00</td>\n",
       "      <td>-0.000211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G_-1</th>\n",
       "      <td>G_1_left_01</td>\n",
       "      <td>-0.000046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G_-1</th>\n",
       "      <td>G_1_left_02</td>\n",
       "      <td>-0.000266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G_-1</th>\n",
       "      <td>G_1_left_03</td>\n",
       "      <td>-0.000144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G_-1</th>\n",
       "      <td>G_1_left_04</td>\n",
       "      <td>-0.000222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Subject  DeltaF/F\n",
       "Stimulus                       \n",
       "G_-1      G_1_left_00 -0.000211\n",
       "G_-1      G_1_left_01 -0.000046\n",
       "G_-1      G_1_left_02 -0.000266\n",
       "G_-1      G_1_left_03 -0.000144\n",
       "G_-1      G_1_left_04 -0.000222"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chae_behavior.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36210c27",
   "metadata": {},
   "source": [
    "An example entry for the 'Subject' column is: \n",
    "\n",
    "**G_1_left_01**\n",
    "\n",
    "Which indicates that the data are:\n",
    " - from a glomerulus (G) (as opposed to a mitral cell (M), or a tufted cell (T))\n",
    " - from animal 1 (out of 5)\n",
    " - from the left hemibulb (as opposed to the right)\n",
    " - obtained from glomerulus number 01 (out of typically several dozen; variable across conditions)\n",
    " \n",
    "Similarly, the 'Stimulus' index encodes for glomerulus/mitral cell/tufted cell, CID for compound used, and high vs low concentration (when applicable).\n",
    "\n",
    "For easier and more explicit filtering we can join the behavior data and stimulus/subject identifiers into a single dataframe. At the same time we'll also invert the sign on the deltaF/F measurements for later visualization purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "56aafb89-e0f4-4f5c-9290-f8e01779ca53",
   "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>Subject</th>\n",
       "      <th>DeltaF/F</th>\n",
       "      <th>Animal</th>\n",
       "      <th>Hemibulb</th>\n",
       "      <th>Glom</th>\n",
       "      <th>Cell</th>\n",
       "      <th>FOV</th>\n",
       "      <th>CID</th>\n",
       "      <th>High/Low Conc.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stimulus</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>G_-1</th>\n",
       "      <td>G_1_left_00</td>\n",
       "      <td>0.000211</td>\n",
       "      <td>1.0</td>\n",
       "      <td>left</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G_-1</th>\n",
       "      <td>G_1_left_01</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>1.0</td>\n",
       "      <td>left</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G_-1</th>\n",
       "      <td>G_1_left_02</td>\n",
       "      <td>0.000266</td>\n",
       "      <td>1.0</td>\n",
       "      <td>left</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G_-1</th>\n",
       "      <td>G_1_left_03</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>1.0</td>\n",
       "      <td>left</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G_-1</th>\n",
       "      <td>G_1_left_04</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>1.0</td>\n",
       "      <td>left</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Subject  DeltaF/F  Animal Hemibulb  Glom  Cell  FOV  CID  \\\n",
       "Stimulus                                                                 \n",
       "G_-1      G_1_left_00  0.000211     1.0     left   0.0   NaN  NaN   -1   \n",
       "G_-1      G_1_left_01  0.000046     1.0     left   1.0   NaN  NaN   -1   \n",
       "G_-1      G_1_left_02  0.000266     1.0     left   2.0   NaN  NaN   -1   \n",
       "G_-1      G_1_left_03  0.000144     1.0     left   3.0   NaN  NaN   -1   \n",
       "G_-1      G_1_left_04  0.000222     1.0     left   4.0   NaN  NaN   -1   \n",
       "\n",
       "         High/Low Conc.  \n",
       "Stimulus                 \n",
       "G_-1                NaN  \n",
       "G_-1                NaN  \n",
       "G_-1                NaN  \n",
       "G_-1                NaN  \n",
       "G_-1                NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join behavior, stimuli, and subjects\n",
    "chae = chae_behavior.join(chae_subjects, on=\"Subject\")\n",
    "chae = chae.join(chae_stimuli)\n",
    "\n",
    "# Invert sign of deltaF/F for later visualization\n",
    "chae[\"DeltaF/F\"] = -chae[\"DeltaF/F\"]\n",
    "\n",
    "chae.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa98848e",
   "metadata": {},
   "source": [
    "For purposes of illustration, we'll contrain our analysis here to only glomerular (G) measurements from one animal (#5) and one hemibulb (right). We will also restrict our analysis to the leading glomeruli that contribute most to the variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "59329bf7",
   "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>Glom</th>\n",
       "      <th>8</th>\n",
       "      <th>12</th>\n",
       "      <th>20</th>\n",
       "      <th>24</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>39</th>\n",
       "      <th>...</th>\n",
       "      <th>51</th>\n",
       "      <th>54</th>\n",
       "      <th>57</th>\n",
       "      <th>68</th>\n",
       "      <th>70</th>\n",
       "      <th>74</th>\n",
       "      <th>76</th>\n",
       "      <th>77</th>\n",
       "      <th>84</th>\n",
       "      <th>91</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CID</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>326</th>\n",
       "      <td>-0.000131</td>\n",
       "      <td>0.001743</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.000703</td>\n",
       "      <td>-0.000060</td>\n",
       "      <td>-0.001210</td>\n",
       "      <td>-0.000448</td>\n",
       "      <td>0.000556</td>\n",
       "      <td>0.000410</td>\n",
       "      <td>-0.000613</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>-0.000738</td>\n",
       "      <td>0.000206</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>-0.000196</td>\n",
       "      <td>-0.000976</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>-0.000896</td>\n",
       "      <td>-0.000145</td>\n",
       "      <td>-0.001292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>0.000279</td>\n",
       "      <td>0.000997</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.000384</td>\n",
       "      <td>0.004435</td>\n",
       "      <td>0.003597</td>\n",
       "      <td>0.000684</td>\n",
       "      <td>-0.000423</td>\n",
       "      <td>0.002670</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.001275</td>\n",
       "      <td>0.000832</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>-0.000104</td>\n",
       "      <td>0.004382</td>\n",
       "      <td>0.000660</td>\n",
       "      <td>0.004369</td>\n",
       "      <td>0.000733</td>\n",
       "      <td>0.005166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6184</th>\n",
       "      <td>0.000871</td>\n",
       "      <td>0.001584</td>\n",
       "      <td>0.001369</td>\n",
       "      <td>0.000932</td>\n",
       "      <td>0.000867</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>0.000919</td>\n",
       "      <td>0.000697</td>\n",
       "      <td>0.000513</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000987</td>\n",
       "      <td>-0.000599</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>0.000736</td>\n",
       "      <td>0.000364</td>\n",
       "      <td>0.000741</td>\n",
       "      <td>0.001329</td>\n",
       "      <td>0.000983</td>\n",
       "      <td>0.000924</td>\n",
       "      <td>0.000732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7410</th>\n",
       "      <td>-0.000249</td>\n",
       "      <td>0.000689</td>\n",
       "      <td>-0.000274</td>\n",
       "      <td>0.000951</td>\n",
       "      <td>-0.000212</td>\n",
       "      <td>0.002990</td>\n",
       "      <td>0.003045</td>\n",
       "      <td>0.001338</td>\n",
       "      <td>-0.000421</td>\n",
       "      <td>0.001977</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000345</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.003396</td>\n",
       "      <td>-0.000071</td>\n",
       "      <td>-0.000436</td>\n",
       "      <td>0.002167</td>\n",
       "      <td>0.000166</td>\n",
       "      <td>0.001867</td>\n",
       "      <td>0.000142</td>\n",
       "      <td>0.002050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-0.000201</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>0.000452</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>-0.000224</td>\n",
       "      <td>-0.000280</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>-0.000275</td>\n",
       "      <td>-0.000257</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000148</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>0.000255</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>0.000341</td>\n",
       "      <td>-0.000047</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Glom        8         12        20        24        29        30        31  \\\n",
       "CID                                                                          \n",
       "326  -0.000131  0.001743  0.000253  0.000703 -0.000060 -0.001210 -0.000448   \n",
       "1049  0.000279  0.000997  0.000008  0.000502  0.000384  0.004435  0.003597   \n",
       "6184  0.000871  0.001584  0.001369  0.000932  0.000867  0.000396  0.000177   \n",
       "7410 -0.000249  0.000689 -0.000274  0.000951 -0.000212  0.002990  0.003045   \n",
       "7749 -0.000201  0.000015  0.000138  0.000452  0.000023 -0.000224 -0.000280   \n",
       "\n",
       "Glom        35        36        39  ...        51        54        57  \\\n",
       "CID                                 ...                                 \n",
       "326   0.000556  0.000410 -0.000613  ...  0.000145 -0.000738  0.000206   \n",
       "1049  0.000684 -0.000423  0.002670  ...  0.000093  0.001275  0.000832   \n",
       "6184  0.000919  0.000697  0.000513  ...  0.000987 -0.000599  0.000246   \n",
       "7410  0.001338 -0.000421  0.001977  ...  0.000345  0.000040  0.003396   \n",
       "7749  0.000176 -0.000275 -0.000257  ...  0.000148  0.000189  0.000255   \n",
       "\n",
       "Glom        68        70        74        76        77        84        91  \n",
       "CID                                                                         \n",
       "326   0.000007 -0.000196 -0.000976  0.000046 -0.000896 -0.000145 -0.001292  \n",
       "1049  0.000195 -0.000104  0.004382  0.000660  0.004369  0.000733  0.005166  \n",
       "6184  0.000736  0.000364  0.000741  0.001329  0.000983  0.000924  0.000732  \n",
       "7410 -0.000071 -0.000436  0.002167  0.000166  0.001867  0.000142  0.002050  \n",
       "7749  0.000154  0.000415  0.000094  0.000137  0.000341 -0.000047  0.000005  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter down to common molecules, glomeruli data, animal #5, and the right hemibulb\n",
    "chae_filtered = chae[\n",
    "    chae[\"CID\"].isin(common_molecules.index) & (chae[\"Animal\"] == 5) & (chae[\"Hemibulb\"] == \"right\")\n",
    "]\n",
    "\n",
    "# Drop unneeded columns and format Animal # and Glom # as type int\n",
    "chae_filtered = chae_filtered.drop(columns=[\"Cell\", \"FOV\", \"High/Low Conc.\"], axis=1)\n",
    "chae_filtered = chae_filtered.astype({\"Animal\": int, \"Glom\": int})\n",
    "\n",
    "# Pivot so data are organized in the more neurophys friendly format of stim x glomeruli\n",
    "chae_filtered = pd.pivot_table(chae_filtered, values=\"DeltaF/F\", index=[\"CID\"], columns=[\"Glom\"])\n",
    "\n",
    "# Only retain leading glomeruli that contribute most to the variance\n",
    "chae_filtered = chae_filtered.loc[\n",
    "    :, chae_filtered.var() > chae_filtered.var().quantile(0.75)\n",
    "].sort_index()\n",
    "\n",
    "chae_filtered.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2cd3f5e",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Formatting Dravnieks data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36c621ec",
   "metadata": {},
   "source": [
    "Similar to what we did for Chae, we'll filter out perceptual data that aren't shared between the datasets, and drop low-variance descriptors. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eb3c020a-34cd-495d-bd63-e616ff5ceb30",
   "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>FRUITY,CITRUS</th>\n",
       "      <th>LEMON</th>\n",
       "      <th>ORANGE</th>\n",
       "      <th>FRUITY,OTHER THAN CITRUS</th>\n",
       "      <th>PINEAPPLE</th>\n",
       "      <th>GRAPE JUICE</th>\n",
       "      <th>STRAWBERRY</th>\n",
       "      <th>BANANA</th>\n",
       "      <th>FLORAL</th>\n",
       "      <th>PERFUMERY</th>\n",
       "      <th>...</th>\n",
       "      <th>OILY, FATTY</th>\n",
       "      <th>RANCID</th>\n",
       "      <th>SWEATY</th>\n",
       "      <th>PUTRID, FOUL, DECAYED</th>\n",
       "      <th>FECAL, LIKE MANURE</th>\n",
       "      <th>CADAVEROUS</th>\n",
       "      <th>SICKENING</th>\n",
       "      <th>LIGHT</th>\n",
       "      <th>HEAVY</th>\n",
       "      <th>COOL,COOLING</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CID</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>326</th>\n",
       "      <td>8.39</td>\n",
       "      <td>11.87</td>\n",
       "      <td>3.48</td>\n",
       "      <td>3.91</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.78</td>\n",
       "      <td>5.19</td>\n",
       "      <td>7.32</td>\n",
       "      <td>...</td>\n",
       "      <td>13.20</td>\n",
       "      <td>7.17</td>\n",
       "      <td>7.88</td>\n",
       "      <td>4.79</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.55</td>\n",
       "      <td>14.44</td>\n",
       "      <td>10.61</td>\n",
       "      <td>18.06</td>\n",
       "      <td>9.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>0.56</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.58</td>\n",
       "      <td>...</td>\n",
       "      <td>13.10</td>\n",
       "      <td>20.94</td>\n",
       "      <td>13.26</td>\n",
       "      <td>31.77</td>\n",
       "      <td>5.88</td>\n",
       "      <td>12.76</td>\n",
       "      <td>62.61</td>\n",
       "      <td>2.85</td>\n",
       "      <td>39.46</td>\n",
       "      <td>1.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6184</th>\n",
       "      <td>1.11</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.53</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.64</td>\n",
       "      <td>4.39</td>\n",
       "      <td>2.58</td>\n",
       "      <td>...</td>\n",
       "      <td>17.83</td>\n",
       "      <td>6.52</td>\n",
       "      <td>8.92</td>\n",
       "      <td>4.40</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.96</td>\n",
       "      <td>8.01</td>\n",
       "      <td>13.03</td>\n",
       "      <td>12.93</td>\n",
       "      <td>4.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7410</th>\n",
       "      <td>1.57</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.41</td>\n",
       "      <td>8.29</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.56</td>\n",
       "      <td>16.79</td>\n",
       "      <td>19.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8.66</td>\n",
       "      <td>1.93</td>\n",
       "      <td>6.03</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.68</td>\n",
       "      <td>10.14</td>\n",
       "      <td>12.06</td>\n",
       "      <td>29.34</td>\n",
       "      <td>9.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>5.19</td>\n",
       "      <td>2.95</td>\n",
       "      <td>4.10</td>\n",
       "      <td>29.44</td>\n",
       "      <td>10.11</td>\n",
       "      <td>1.67</td>\n",
       "      <td>3.75</td>\n",
       "      <td>4.55</td>\n",
       "      <td>6.25</td>\n",
       "      <td>9.41</td>\n",
       "      <td>...</td>\n",
       "      <td>3.74</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.47</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.01</td>\n",
       "      <td>15.01</td>\n",
       "      <td>13.17</td>\n",
       "      <td>8.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      FRUITY,CITRUS  LEMON  ORANGE  FRUITY,OTHER THAN CITRUS  PINEAPPLE  \\\n",
       "CID                                                                       \n",
       "326            8.39  11.87    3.48                      3.91       0.64   \n",
       "1049           0.56   0.15    0.00                      0.34       0.30   \n",
       "6184           1.11   0.45    0.00                      7.53       0.90   \n",
       "7410           1.57   0.52    0.41                      8.29       0.52   \n",
       "7749           5.19   2.95    4.10                     29.44      10.11   \n",
       "\n",
       "      GRAPE JUICE  STRAWBERRY  BANANA  FLORAL  PERFUMERY  ...  OILY, FATTY  \\\n",
       "CID                                                       ...                \n",
       "326          0.00        0.96    0.78    5.19       7.32  ...        13.20   \n",
       "1049         0.00        0.00    0.00    0.67       0.58  ...        13.10   \n",
       "6184         0.00        0.32    0.64    4.39       2.58  ...        17.83   \n",
       "7410         1.26        1.74    1.56   16.79      19.62  ...         8.66   \n",
       "7749         1.67        3.75    4.55    6.25       9.41  ...         3.74   \n",
       "\n",
       "      RANCID  SWEATY  PUTRID, FOUL, DECAYED  FECAL, LIKE MANURE  CADAVEROUS  \\\n",
       "CID                                                                           \n",
       "326     7.17    7.88                   4.79                0.00        0.55   \n",
       "1049   20.94   13.26                  31.77                5.88       12.76   \n",
       "6184    6.52    8.92                   4.40                0.32        0.96   \n",
       "7410    1.93    6.03                   1.63                0.58        0.68   \n",
       "7749    1.70    1.47                   0.79                0.00        0.00   \n",
       "\n",
       "      SICKENING  LIGHT  HEAVY  COOL,COOLING  \n",
       "CID                                          \n",
       "326       14.44  10.61  18.06          9.37  \n",
       "1049      62.61   2.85  39.46          1.96  \n",
       "6184       8.01  13.03  12.93          4.29  \n",
       "7410      10.14  12.06  29.34          9.89  \n",
       "7749       5.01  15.01  13.17          8.92  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join CIDs to behavior data\n",
    "dravnieks = dravnieks_stimuli[\"CID\"].to_frame().join(dravnieks_behavior)\n",
    "\n",
    "# Filter for common molecules shared with (filtered) Chae data\n",
    "dravnieks_filtered = (\n",
    "    dravnieks[dravnieks.CID.isin(chae_filtered.index)].set_index(\"CID\").sort_index()\n",
    ")\n",
    "dravnieks_filtered.index = dravnieks_filtered.index.astype(int)\n",
    "\n",
    "# Only retain leading glomeruli that contribute most to the variance\n",
    "dravnieks_filtered = dravnieks_filtered.loc[\n",
    "    :, dravnieks_filtered.var() > dravnieks_filtered.var().quantile(0.75)\n",
    "]\n",
    "\n",
    "dravnieks_filtered.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "369706a3",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Inspection of the data, and co-clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acde658d",
   "metadata": {},
   "source": [
    "Co-clustering involves a permutation of rows and columns to reveal block structure in a matrix, and therefore presumed natural groupings of examples and features. In the case of Dravnieks, for example, co-clustering would reveal whether there are non-overlapping subsets of chemicals for which subsets of chemical descriptors apply uniquely. An example of co-clustering the Dravnieks data can be found in [Castro et al, 2013](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0073289). \n",
    "\n",
    "Once we find the row (chemical ID) permutations on Dravnieks, the same row permutations can be applied to Chae, to see if natural groupings among glomeruli emerge. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1f4c4014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import SpectralBiclustering\n",
    "\n",
    "# Cluster the Dravnieks data\n",
    "percept = dravnieks_filtered.to_numpy()\n",
    "model = SpectralBiclustering(n_clusters=2, random_state=0)\n",
    "model.fit(percept)\n",
    "\n",
    "# Permute rows and columns\n",
    "fit_data = percept[np.argsort(model.row_labels_)]\n",
    "fit_data = fit_data[:, np.argsort(model.column_labels_)]\n",
    "\n",
    "# Show the clustered Dravnieks data\n",
    "plt.plot(figsize=(50, 10))\n",
    "sns.heatmap(data=fit_data, cmap=\"OrRd\", linewidths=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3eaedc01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiEAAAGmCAYAAACqSlR6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAABOxUlEQVR4nO3deVxU5f4H8M8ZlsFQ0SBZUhC3hLT0Dl4CRbyVGFZqaaIWaoqJaIpk2qDmkoqWGbnhkrmW0C9yqasm3ty4oAnikppL0sUUVNSLZTooPL8/JuY2M8zAHIYZtM/79Tqvl5zzPN/5ntn8zvOcRRJCCBARERHZmMLeCRAREdFfE4sQIiIisgsWIURERGQXLEKIiIjILliEEBERkV2wCCEiIiK7YBFCREREdsEihIiIiOyCRQgRERHZBYsQIiIisgsWIURERHXI0qVL4e/vDxcXF6hUKuzfv99s+71790KlUsHFxQUtWrTAsmXLjNqkp6cjMDAQSqUSgYGB2LRpk9726dOnQ5IkvcXLy8uq+1UZFiFERER1RFpaGuLj4zF58mTk5eUhLCwMkZGRKCgoqLR9fn4+evbsibCwMOTl5SExMRFjx45Fenq6rk12djaioqIQHR2No0ePIjo6Gv3798fBgwf1Yj3++OMoLCzULcePH6/VfQUAiTewIyIiqhuCg4Pxt7/9DSkpKbp1AQEB6NOnD5KSkozaT5o0CVu3bsWpU6d062JjY3H06FFkZ2cDAKKionDz5k1s375d1+a5555D48aNsXHjRgDakZDNmzfjyJEjtbRnleNICBERUS3SaDS4efOm3qLRaIzalZaWIjc3FxEREXrrIyIikJWVVWns7Oxso/Y9evRATk4O7t69a7aNYcyzZ8/Cx8cH/v7+GDBgAM6fP2/xvlrKsdYfgYiI6D4zXZKsF2zaNMyYMcNg1TRMnz5db11xcTHKysrg6empt97T0xNFRUWVhi4qKqq0/b1791BcXAxvb2+Tbf4cMzg4GOvWrUObNm1w+fJlzJo1C6GhoThx4gTc3d0t3eNqq1NFSPkPn8rqp2g3DABQOru/7Md2nvyFNocDC+Xl8NRYbf+MWbJzUHSfoo1x7BN5/Z+IAQCI4qOyc5A8ntTmcPZLeTm07qftv2ee7BwU3SZpY2QukB+jSwIAQPz8T1n9pebPa3M4tER+Dp1Ga2McWSGvf4c3atRfL0becnn9O47U9rfC8yAKdsrqL/lqf8GVf79Ifg5/f1Mbo6avxYk18nN4fCgAQBRV/ou2KpJXqDaHPOODDqudQ8dYbQ6X9snLwaerNgeZ39XA/76vxfUfZMeQHm4nu291WXOaYJJajYSEBL11SqXSZHvJoAASQhitq6q94fqqYkZGRur+3b59e4SEhKBly5ZYu3atUe7WVKeKECIiogeNUqk0W3RU8PDwgIODg9Gox5UrV4xGMip4eXlV2t7R0VE3gmGqjamYAODq6or27dvj7NmzVeZdEzwmhIiIyIBkxaW6nJ2doVKpkJGRobc+IyMDoaGhlfYJCQkxar9z504EBQXBycnJbBtTMQHtcSynTp2Ct7e3BXtgOY6EEBERGbDXL/SEhARER0cjKCgIISEhWLFiBQoKChAbq51KU6vVuHjxItatWwdAeybM4sWLkZCQgBEjRiA7OxurVq3SnfUCAOPGjUPXrl0xb9489O7dG1u2bMGuXbuQmZmpazNhwgS8+OKL8PX1xZUrVzBr1izcvHkTQ4YMqdX9ZRFCRERUR0RFReHatWuYOXMmCgsL0a5dO2zbtg1+fn4AgMLCQr1rhvj7+2Pbtm0YP348lixZAh8fHyxcuBB9+/bVtQkNDUVqaiqmTJmCqVOnomXLlkhLS0NwcLCuzS+//IKBAweiuLgYjzzyCJ566ikcOHBA97i1hUUIERGRAXseqxAXF4e4uLhKt61Zs8ZoXXh4OA4fPmw2Zr9+/dCvXz+T21NTUy3K0VpYhBARERmw4gm6ZAYPTCUiIiK74EgIERGRAf5Ctw0WIURERAY4HWMbLEKIiIgMcCTENvg8ExERkV1YPBLyyy+/ICUlBVlZWSgqKoIkSfD09ERoaChiY2PRrFmz2siTiIjIZvgL3TYsKkIyMzMRGRmJZs2aISIiAhERERBC4MqVK9i8eTMWLVqE7du3o3PnzmbjaDQao9sYK5VKOFmePxERkdXxmBDbsKgIGT9+PGJiYvDRRx+Z3B4fH49Dhw6ZjZOUlFTpbY3f7edrSTpERER0H7NoxOmHH37QXb++MiNHjsQPP1R9e2a1Wo2SkhK9Ra1WW5IKERFRrVFYcSHTLBoJ8fb2RlZWFh577LFKt2dnZ1frjnumbmtcbkkyREREtYTFg21YVIRMmDABsbGxyM3NRffu3eHp6QlJklBUVISMjAx88sknSE5OrqVUiYiI6EFiURESFxcHd3d3fPTRR1i+fDnKysoAAA4ODlCpVFi3bh369+9fK4kSERHZCg9MtQ2LT9GNiopCVFQU7t69i+LiYgCAh4cHnJx4bgsRET0YOB1jG7KvmOrk5FSt4z+IiIiIKsPLthMRERngdIxtsAghIiIywOkY22ARQkREZIBFiG3weSYiIiK74EgIERGRAR4TYhssQoiIiAxwmsA2+DwTERGRXXAkhIiIyAB/oduGJIQQ9k6CiIioLlknWe+okMH8b9YkFntERERkF3VqOqY8+2NZ/RQh47T9v5wo+7EV/d7XxjiyQl7/Dm9o++99X34O4dr8y/+9QF7/zgkAgLU1qOCH/FGxH/J7RFb/Tv+5CgAoz5glOwdF9ynaGGe/lB+jdT8AwOX+YbL6e36xX5vDsU/k5/BEjDZGTd9TOUvl5xAUp42xa468/s8mavvnpsjPQTUKAFC2KlZWf4fhy7Q5fPOu/BxemKmNcWixvP6dxmj7H18lP4f2wwEAG2R+Pl/747Mp97UE/vd6fi4zh0EVOex8T34OEVMBANNr8D013QYjC/yFbht1qgghIiKqC1iE2AafZyIiIrILjoQQEREZ4MXKbINFCBERkQFOE9gGixAiIiIDHAmxDRZ7REREZBccCSEiIjLAX+i2wSKEiIjIAIsQ2+DzTERERHbBkRAiIiIDPDDVNliEEBERGeA0gW3weSYiIiK7sHoRcuHCBQwbNsxsG41Gg5s3b+otGo3G2qkQERHJorDiQqZZ/fm5fv061q5da7ZNUlIS3Nzc9JakpCRrp0JERCSLZMWFTLP4mJCtW7ea3X7+/PkqY6jVaiQkJOitUyqVwOFllqZDRERE9ymLi5A+ffpAkiQIIUy2kSTztZ9SqdQWHQbKLU2GiIioFkgKjmHYgsXTMd7e3khPT0d5eXmly+HDh2sjTyIiIpuRJMlqC5lmcRGiUqnMFhpVjZIQERHVdQqFZLXFUkuXLoW/vz9cXFygUqmwf/9+s+337t0LlUoFFxcXtGjRAsuWGR/akJ6ejsDAQCiVSgQGBmLTpk0m4yUlJUGSJMTHx1ucu6UsLkLefvtthIaGmtzeqlUr7N69u0ZJERER/RWlpaUhPj4ekydPRl5eHsLCwhAZGYmCgoJK2+fn56Nnz54ICwtDXl4eEhMTMXbsWKSnp+vaZGdnIyoqCtHR0Th69Ciio6PRv39/HDx40CjeoUOHsGLFCjzxxBO1to9/ZnEREhYWhueee87kdldXV4SHh9coKSIiInuy5nSMJZelWLBgAYYPH46YmBgEBAQgOTkZzZo1Q0pKSqXtly1bBl9fXyQnJyMgIAAxMTEYNmwY5s+fr2uTnJyM7t27Q61Wo23btlCr1XjmmWeQnJysF+u3337Dq6++ipUrV6Jx48ZWey7N4SnMREREBiSFZLWlupelKC0tRW5uLiIiIvTWR0REICsrq9I8s7Ozjdr36NEDOTk5uHv3rtk2hjFHjx6N559/Hs8++6zFz5dcvGw7ERFRLTJ5WQoDxcXFKCsrg6enp956T09PFBUVVRq7qKio0vb37t1DcXExvL29Tbb5c8zU1FQcPnwYhw4dsmjfaopFCBERkQFrntVi6rIU1X1sIYTZfCprb7jeXMwLFy5g3Lhx2LlzJ1xcXKqdpzWwCCEiIjJgj+uEeHh4wMHBwWjU48qVK0YjGRW8vLwqbe/o6Ah3d3ezbSpi5ubm4sqVK1CpVLrtZWVl2LdvHxYvXgyNRgMHB4ca719leEwIERFRHeDs7AyVSoWMjAy99RkZGSbPSg0JCTFqv3PnTgQFBcHJyclsm4qYzzzzDI4fP44jR47olqCgILz66qs4cuRIrRUgAEdCiIiIjNjrImMJCQmIjo5GUFAQQkJCsGLFChQUFCA2NhaA9viSixcvYt26dQCA2NhYLF68GAkJCRgxYgSys7OxatUqbNy4URdz3Lhx6Nq1K+bNm4fevXtjy5Yt2LVrFzIzMwEADRo0QLt27fTycHV1hbu7u9F6a2MRQkREZMBel22PiorCtWvXMHPmTBQWFqJdu3bYtm0b/Pz8AACFhYV61wzx9/fHtm3bMH78eCxZsgQ+Pj5YuHAh+vbtq2sTGhqK1NRUTJkyBVOnTkXLli2RlpaG4OBgm++fIRYhREREdUhcXBzi4uIq3bZmzRqjdeHh4VXeMqVfv37o169ftXPYs2dPtdvWhCR4jXUiIiI9uxu5Wi3WP/57y2qxHjQcCSEiIjIg554vZLk6VYSUfz1FVj/Fi7MAAPfmD5T92I4TtAfxlKUMl9XfYdQqAED5V+/IzkHx8lxtjBo+D+WZC+Tn0EV7QZ3yQ4vl9e80Rtt/c6L8HPrM0cbImCU/Rnftc1i+9315/cMnWi+HQ0vk9e80Wtt/3wfyc+j6NgCgbP1YWf0dohdqc/hyovwc+mlfg/L8b+T1939B2//gIvk5BL+pjbHzPXn9I6Zq+/+7Bp+tzn98tr6dKa9/j3cBAGUrRsjOweGNldocZL6eutfSGp9vma8F8L/Xozbx7re2wVN0iYiIyC7q1EgIERFRXWCvs2P+aliEEBERGeB0jG1wOoaIiIjsgiMhREREBjgdYxssQoiIiAxwOsY2OB1DREREdsGRECIiIgOcjrENFiFEREQGOB1jG5yOISIiIrvgSAgREZEB3jvGNliEEBERGeB0jG1YPB1z+/ZtZGZm4uTJk0bb7ty5g3Xr1lUZQ6PR4ObNm3qLRqOxNBUiIqJaISkkqy1kmkVFyJkzZxAQEICuXbuiffv26NatGwoLC3XbS0pK8Prrr1cZJykpCW5ubnpLUlKS5dkTERHRfcuiImTSpElo3749rly5gtOnT6Nhw4bo3LkzCgoKLHpQtVqNkpISvUWtVlsUg4iIqLZIkmS1hUyz6JiQrKws7Nq1Cx4eHvDw8MDWrVsxevRohIWFYffu3XB1da1WHKVSCaVSabS+3JJkiIiIaonEc0dtwqIi5Pbt23B01O+yZMkSKBQKhIeH4/PPP7dqckRERPTgsqgIadu2LXJychAQEKC3ftGiRRBCoFevXlZNjoiIyB44jWIbFg04vfTSS9i4cWOl2xYvXoyBAwdCCGGVxIiIiOyFZ8fYhkVFiFqtxrZt20xuX7p0KcrLeWQHERERVY0XKyMiIjKg4HSMTbAIISIiMsBpFNvgSUhERERkFxwJISIiMsCzY2yDRQgREZEBTsfYBosQIiIiAxwJsQ0eE0JERER2IQleXYyIiEjPqQ5+VosVcOQ/Vov1oOF0DBERkQFOx9hGnSpCyk+tl9VPERCt7Z8xS/ZjK7pP+SOHDTJzeE3b/6t35Ofw8lxtjJNr5fUPHAIA+Pnp9rJzaP7dcQDA+fDHZfVvsfcEAKBseYzsHBxGfgIAKD/zf7JjKNq8AgC4PfllWf3rzf5Km8PRlfJzeHKENsaxT+T1fyKmRv31YmQukNe/S4K2f9ZH8nMIHa+NkbdMXv+OsQCA44GPys6h/cmL2hxq+lqc/VJ2DorW/QAAvyXIu8dW/QVbtTkcWiI/h06jAQB3pveT1d9lunb/y3OWys8hKA4AcGtSH9kxXOdtlt2X6pY6VYQQERHVBZKCh0zaAosQIiIiAzxF1zZY6hEREZFdcCSEiIjIEA9MtQkWIURERAY4HWMbnI4hIiIiu+BICBERkQGeHWMbLEKIiIgM8GJltsEihIiIyBCPCbEJjjcRERHVIUuXLoW/vz9cXFygUqmwf/9+s+337t0LlUoFFxcXtGjRAsuWGV+dOD09HYGBgVAqlQgMDMSmTZv0tqekpOCJJ55Aw4YN0bBhQ4SEhGD79u1W3a/KsAghIiIyICkUVlsskZaWhvj4eEyePBl5eXkICwtDZGQkCgoKKm2fn5+Pnj17IiwsDHl5eUhMTMTYsWORnp6ua5OdnY2oqChER0fj6NGjiI6ORv/+/XHw4EFdm6ZNm2Lu3LnIyclBTk4Onn76afTu3RsnTpyQ9wRWE4sQIiIiA5IkWW2xxIIFCzB8+HDExMQgICAAycnJaNasGVJSUiptv2zZMvj6+iI5ORkBAQGIiYnBsGHDMH/+fF2b5ORkdO/eHWq1Gm3btoVarcYzzzyD5ORkXZsXX3wRPXv2RJs2bdCmTRvMnj0b9evXx4EDB2Q9f9XFIoSIiKgWaTQa3Lx5U2/RaDRG7UpLS5Gbm4uIiAi99REREcjKyqo0dnZ2tlH7Hj16ICcnB3fv3jXbxlTMsrIypKam4tatWwgJCan2fsphcRFy6tQprF69Gj/++CMA4Mcff8SoUaMwbNgwfPfdd9WKUd0XhIiIyB4khWS1JSkpCW5ubnpLUlKS0WMWFxejrKwMnp6eeus9PT1RVFRUaZ5FRUWVtr937x6Ki4vNtjGMefz4cdSvXx9KpRKxsbHYtGkTAgMDLX7uLGFREbJjxw506NABEyZMQMeOHbFjxw507doV586dQ0FBAXr06FGtQqS6LwgREZFdKCSrLWq1GiUlJXqLWq02+dCGUzhCCLPTOpW1N1xfnZiPPfYYjhw5ggMHDmDUqFEYMmQITp48af55qiGLipCZM2fi7bffxrVr17B69WoMGjQII0aMQEZGBnbt2oWJEydi7ty5Vcax9AUhIiK6XymVSt1ZJxWLUqk0aufh4QEHBwejEYorV64YjWRU8PLyqrS9o6Mj3N3dzbYxjOns7IxWrVohKCgISUlJePLJJ/Hxxx9bvL+WsKgIOXHiBIYOHQoA6N+/P3799Vf07dtXt33gwIE4duxYlXGq+4IQERHZgyQprLZUl7OzM1QqFTIyMvTWZ2RkIDQ0tNI+ISEhRu137tyJoKAgODk5mW1jKmYFIUStHyoh+2JlCoUCLi4uaNSokW5dgwYNUFJSYo28iIiI7MZeN7BLSEhAdHQ0goKCEBISghUrVqCgoACxsbEAtDMJFy9exLp16wAAsbGxWLx4MRISEjBixAhkZ2dj1apV2Lhxoy7muHHj0LVrV8ybNw+9e/fGli1bsGvXLmRmZuraJCYmIjIyEs2aNcOvv/6K1NRU7NmzBzt27KjV/bWoCGnevDnOnTuHVq1aAdAecevr66vbfuHCBXh7e1s3QyIior+IqKgoXLt2DTNnzkRhYSHatWuHbdu2wc/PDwBQWFiod80Qf39/bNu2DePHj8eSJUvg4+ODhQsX6s1ShIaGIjU1FVOmTMHUqVPRsmVLpKWlITg4WNfm8uXLiI6ORmFhIdzc3PDEE09gx44d6N69e63ur0VFyKhRo1BWVqb7u127dnrbt2/fjqeffto6mREREdmJvUZCACAuLg5xcXGVbluzZo3RuvDwcBw+fNhszH79+qFfv34mt69atcqiHK3FoiKkYjjIlNmzZ9coGSIiojrBgmM5SD7ewI6IiMiAPUdC/kpY6hEREZFdcCSEiIjIAEdCbINFCBERkQFLbzxH8nA6hoiIiOyCIyFERESGFPyNbgssQoiIiAzwmBDbkETF7faIiIgIAFDY1/x9VSzhnZ5ltVgPGo6EEBERGeCBqbZRp4qQWxP7yOrn+v5mAED5T5tlP7aipfaxy7+YIK9///kAgMv9w2Tn4PnFfgDAvfcHyOrvODEVAFB+OlV2DorHtI8tfv6nrP5S8+f/6L9Ndg5S854AgOJo+bcA8Fj/HQCg/Fy6rP6KVtr7Lhx5TP69kDqcLgQAnOnUUlb/Nod+0vb/eyvZObT5/hwA4O5cee8pp3e076VLL4XIzsFnUzYAoDxvuaz+io4jtf1lvpbA/15PuZ/Pis/m1Ve7yc7hkc/2AADKDy2R1V/RaTQAoGxhtOwcHMau1+Zw7BN5OTwRo80hZbj8HEZpLw9eflz+ZcIV7eU/fnVJPCbEJvgsExERkV3UqZEQIiKiuoAHptoGixAiIiJDPCbEJliEEBERGeBIiG3wmBAiIiKyC46EEBERGeDZMbbBIoSIiMgArxNiGyz1iIiIyC44EkJERGSIB6baBIsQIiIiAzwmxDb4LBMREZFdWGUkRAjBg3iIiOiBwf/TbMMqIyFKpRKnTp2yRigiIiK7kxSS1RYyzaKRkISEhErXl5WVYe7cuXB3dwcALFiwwGwcjUYDjUajt06pVFqSChEREd3nLCpCkpOT8eSTT6JRo0Z664UQOHXqFFxdXas1hJWUlIQZM2borZs2bRretiQZIiKi2sLpGJuwqAiZPXs2Vq5ciQ8//BBPP/20br2TkxPWrFmDwMDAasVRq9VGoypKpRL3pkZZkg4REVGt4DSKbVhUhKjVajz77LN47bXX8OKLLyIpKQlOTk4WP6hSqax0+uWexZGIiIhqAWsQm7D4wNROnTohNzcXV69eRVBQEI4fP86jiImIiMhisk7RrV+/PtauXYvU1FR0794dZWVl1s6LiIjIfvjj2iZqdJ2QAQMGoEuXLsjNzYWfn5+1ciIiIrIr1iC2UeOLlTVt2hRNmza1Ri5ERET0F8J7xxARERni2TE2wSKEiIjIAKdjbIM3sCMiIiK74EgIERGRIQ6F2ASLECIiIkOcJ7AJPs1ERERkFxwJISIiMsArgduGJIQQ9k6CiIioLvntrd5Wi1X/wy1Wi/Wg4UgIERGRAQ6E2EadKkLK85bJ6qfoGAsAuD35ZdmPXW/2V9ocjn0iL4cnYgAA4mqu7BykR1TaHHJT5OWgGqXt/8UE2Tko+s/XxvjmXXn9X5hpvRy+XyQ/xt/f1Mb41xx5/Z9J1PaX+X4A/veeKD+6Ul7/J0do+8t8PwB/ek/smSevf7dJ2v4yP5vA/z6fP4UFyOrfcv8pbQ7ZH8vPIWScNkZNP1syX0vgT6/nlsny+veere3/7wXyc+icAAD4/Z2XZPV/aO4mbQ6ZH8rPoctbAICyhdGyYziMXS+7L9UtdaoIISIiqhN4xVSb4NkxREREhiQrLhZaunQp/P394eLiApVKhf3795ttv3fvXqhUKri4uKBFixZYtsx45DI9PR2BgYFQKpUIDAzEpk2b9LYnJSWhU6dOaNCgAZo0aYI+ffrg9OnTlidvIRYhREREdURaWhri4+MxefJk5OXlISwsDJGRkSgoKKi0fX5+Pnr27ImwsDDk5eUhMTERY8eORXp6uq5NdnY2oqKiEB0djaNHjyI6Ohr9+/fHwYMHdW327t2L0aNH48CBA8jIyMC9e/cQERGBW7du1er+cjqGiIjIgDVP0dVoNNBoNHrrlEollEqlUdsFCxZg+PDhiInRHlOWnJyMb7/9FikpKUhKSjJqv2zZMvj6+iI5ORkAEBAQgJycHMyfPx99+/bVxejevTvUajUAQK1WY+/evUhOTsbGjRsBADt27NCLu3r1ajRp0gS5ubno2rVrzZ4AMzgSQkREZMiK0zFJSUlwc3PTWyorKEpLS5Gbm4uIiAi99REREcjKyqo0zezsbKP2PXr0QE5ODu7evWu2jamYAFBSUgIAePjhh022sQaOhBAREdUitVqNhIQEvXWVjYIUFxejrKwMnp6eeus9PT1RVFRUaeyioqJK29+7dw/FxcXw9vY22cZUTCEEEhIS0KVLF7Rr167K/asJFiFEREQGJCueHWNq6sXkYxtMBQkhzE4PVdbecL0lMceMGYNjx44hMzOz2jnLxSKEiIjIkB3O0PXw8ICDg4PRCMWVK1eMRjIqeHl5Vdre0dER7u7uZttUFvPNN9/E1q1bsW/fPjRt2rQmu1MtPCaEiIioDnB2doZKpUJGRobe+oyMDISGhlbaJyQkxKj9zp07ERQUBCcnJ7Nt/hxTCIExY8bgq6++wnfffQd/f39r7FKVOBJCRERkyE7XbU9ISEB0dDSCgoIQEhKCFStWoKCgALGx2isPq9VqXLx4EevWrQMAxMbGYvHixUhISMCIESOQnZ2NVatW6c56AYBx48aha9eumDdvHnr37o0tW7Zg165detMto0ePxueff44tW7agQYMGupETNzc31KtXr9b2l0UIERGRAXvdOyYqKgrXrl3DzJkzUVhYiHbt2mHbtm3w8/MDABQWFupdM8Tf3x/btm3D+PHjsWTJEvj4+GDhwoW603MBIDQ0FKmpqZgyZQqmTp2Kli1bIi0tDcHBwbo2KSnaWxp069ZNL5/Vq1dj6NChtba/LEKIiIgM2fGy7XFxcYiLi6t025o1a4zWhYeH4/Dhw2Zj9uvXD/369TO5veJgVlurURFy48YNrF27FmfPnoW3tzeGDBmCZs2aVdnP1IVbnGqSDBEREd1XLDow1cfHB9euXQOgvVRsYGAg5s2bh7Nnz2L58uVo3749fvzxxyrjVPfCLURERPYgSdZbyDSLipCioiKUlZUBABITE9G2bVv89NNP2LlzJ86dO4ewsDBMnTq1yjhqtRolJSV6S8XlZImIiOyOVYhNyJ6OOXjwID755BM89NBDALTTKVOmTDE751TB1IVbyuUmQ0RERPcdi4uQiiusaTSaSi8De/XqVetkRkREZCccwLANi4uQZ555Bo6Ojrh58ybOnDmDxx9/XLetoKAAHh4eVk2QiIjI5ux4dsxfiUVFyLRp0/T+rpiKqfD1118jLCys5lkRERHRA69GRYihDz74oEbJEBER1Qmcj7EJXqyMiIjIAGsQ2+AN7IiIiMguOBJCRERkiEMhNsEihIiIyIDEeQKbYBFCRERkiCMhNsFaj4iIiOyCIyFERESGOBBiEyxCiIiIDEicjrEJSQgh7J0EERFRXVL24SCrxXJ463OrxXrQcCSEiIjIEO8dYxN1qggpGdVTVj+3lG0AgPIfP5P92Iq2rwIAfuzYXFb/tnk/AwDuvNtXdg4uM9MBACWxMp+HZX88D/9eIDsHRecEbYxDi+X17zRG2z//G/k5+L+gjfHlRPkx+r2vjXFgobz+T40FIP89CfzvfVn2yUhZ/R1ilgMA7r0/QHYOjhNTAQAXegbJ6t9sWw4A4MqArrJzaJK6DwBQtjxGVn+HkZ8AAMrPpMnOQdEmCoD8z2fFZ7Ns/VjZOThEa9+L5bvmyOqveDYRAFDQo6PsHHy/zdPmsPM9eTlETAUAXH21m+wcHvlsT41y+HMetYrTMTbBs2OIiIjILurUSAgREVGdwOkYm2ARQkREZIiXTLUJPstERERkFxwJISIiMsQDU22CRQgREZEhHhNiEyxCiIiIDPGYEJvgs0xERER2wZEQIiIiQ5yOsQkWIURERIZ4YKpNcDqGiIiI7IIjIURERIYU/I1uCxY9y3l5ecjPz9f9vWHDBnTu3BnNmjVDly5dkJqaWq04Go0GN2/e1Fs0Go1lmRMREdUWSbLeQiZZVIQMHz4cP//8MwDgk08+wRtvvIGgoCBMnjwZnTp1wogRI/Dpp59WGScpKQlubm56S1JSkqwdICIiovuTRdMxp0+fRsuWLQEAS5cuRXJyMt544w3d9k6dOmH27NkYNmyY2ThqtRoJCQl665RKJe7Ev2RJOkRERLWD0zE2YVERUq9ePVy9ehW+vr64ePEigoOD9bYHBwfrTdeYolQqoVQqjdbfsSQZIiKi2sJpFJuwqNSLjIxESkoKACA8PBxffvml3vYvvvgCrVq1sl52RERE9sBjQmzCopGQefPmoXPnzggPD0dQUBA+/PBD7NmzBwEBATh9+jQOHDiATZs21VauRERE9ACxaCTEx8cHeXl5CAkJwY4dOyCEwPfff4+dO3eiadOm+Pe//42ePXvWVq5ERES2oVBYbyGTLL5OSKNGjTB37lzMnTu3NvIhIiKyP06j2ARLNCIiIrILXjGViIjIgMQb2NkEixAiIiJDEicKbIHPMhERUR2ydOlS+Pv7w8XFBSqVCvv37zfbfu/evVCpVHBxcUGLFi2wbNkyozbp6ekIDAyEUqlEYGCg0Zms+/btw4svvggfHx9IkoTNmzdbc5dMYhFCRERkSCFZb7FAWloa4uPjMXnyZOTl5SEsLAyRkZEoKCiotH1+fj569uyJsLAw5OXlITExEWPHjkV6erquTXZ2NqKiohAdHY2jR48iOjoa/fv3x8GDB3Vtbt26hSeffBKLFy+W93zJxOkYIiIiQ3Y6O2bBggUYPnw4YmJiAADJycn49ttvkZKSUuk91pYtWwZfX18kJycDAAICApCTk4P58+ejb9++uhjdu3eHWq0GoL11yt69e5GcnIyNGzcC0F6MNDIy0gZ7qI8jIURERLWouneOLy0tRW5uLiIiIvTWR0REICsrq9LY2dnZRu179OiBnJwc3L1712wbUzFtiUUIERGRISterKy6d44vLi5GWVkZPD099dZ7enqiqKio0jSLiooqbX/v3j0UFxebbWMqpi1JQghh7ySIiIjqkvL0SVaLdfeFmUYjH5XdyPXSpUt49NFHkZWVhZCQEN362bNnY/369fjxxx+NYrdp0wavv/66bqoFAP7973+jS5cuKCwshJeXF5ydnbF27VoMHDhQ1+azzz7D8OHDceeO8a1jJUnCpk2b0KdPH7m7XG08JoSIiMiQFY8JMXXneEMeHh5wcHAwGqG4cuWK0UhGBS8vr0rbOzo6wt3d3WwbUzFtqU4VIeX758vqpwiboO3/rzmyH1vxTKI2xufx8voPStb2z/xQfg5d3tLG+HqKvP4vzgIAlH04SHYODm99ro2xYZy8/q99DAAo3zNPdg6KbtpfIOXbp8uPEantW/7Nu/L6vzBT23/bNPk59JyhjXE4RV7/v43S9j+wUH4OT40FAJStHyurv0P0QqvlUL7vA3n9u76t7X9I/lH7ik5jtDG+Mx4Cr1b/p7W/Mq3xPNye8rKs/vVmfaXNYctk+Tn0ng0AKFsYLau/w9j12v5LX5edg0PcagDAvQ8GVtHSNMe3N8ruW5c5OztDpVIhIyMDL730km59RkYGevfuXWmfkJAQfP3113rrdu7ciaCgIDg5OenaZGRkYPz48XptQkNDa2EvLFOnihAiIqI6wU43nktISEB0dDSCgoIQEhKCFStWoKCgALGxsQC0Z7ZcvHgR69atAwDExsZi8eLFSEhIwIgRI5CdnY1Vq1bpznoBgHHjxqFr166YN28eevfujS1btmDXrl3IzMzUtfntt99w7tw53d/5+fk4cuQIHn74Yfj6+tba/rIIISIiMmSnU3SjoqJw7do1zJw5E4WFhWjXrh22bdsGPz8/AEBhYaHeNUP8/f2xbds2jB8/HkuWLIGPjw8WLlyoOz0XAEJDQ5GamoopU6Zg6tSpaNmyJdLS0hAcHKxrk5OTg3/84x+6vxMSEgAAQ4YMwZo1a2ptf1mEEBER1SFxcXGIi4urdFtlBUF4eDgOHz5sNma/fv3Qr18/k9u7desGe5ynwiKEiIjIEG9gZxMsQoiIiAzxBnY2wWeZiIiI7IIjIURERIY4HWMTLEKIiIgM2ensmL8aTscQERGRXXAkhIiIyJCdLlb2V8MihIiIyBCnY2yCRQgREZEhFiE2YdF405tvvon9+/fX+EE1Gg1u3ryptxje5piIiIgebBYVIUuWLEG3bt3Qpk0bzJs3z+jWwNWVlJQENzc3vSUpSd7dLYmIiKxOUlhvIZMsfnZ27tyJnj17Yv78+fD19UXv3r3xzTffoLy8vNox1Go1SkpK9Ba1Wm1pKkRERLVDsuJCJllchLRv3x7Jycm4dOkSNmzYAI1Ggz59+qBZs2aYPHmy3q2ATVEqlWjYsKHeolQqZe0AERER3Z9kjxM5OTmhf//+2LFjB86fP48RI0bgs88+w2OPPWbN/IiIiGxPkqy3kElWmazy9fXF9OnTkZ+fjx07dlgjJBERkf2wCLEJi4oQPz8/ODg4mNwuSRK6d+9e46SIiIjowWfRdULy8/NrKw8iIqK6gyMYNsGLlRERERlhEWILPIGZiIiI7IIjIURERIY4EGITLEKIiIgM8ZgQm2ARQkREZIhFiE3wmBAiIiKyC46EEBERGeJIiE1IQghh7ySIiIjqkvLMBVaLpeiSYLVYDxpOxxAREZFd1KnpmPITa2T1Uzw+VNs/b7nsx1Z0HKmNkbNUXv+gOG3//fPl5xA2QRtD5n7o9uHkWvk5BA7Rxji1QV7/gNe0/Q8tlp9DpzE1ykEvj1PrZfaP1vbPTZGfg2qUNkZNX8+jK+Xn8OQIbQyZr4futTi0RH4OnUZbJ4cDC+Xn8NTYP3KQtx+6fcj8UH4OXd7SxpD5ntK9n2R+RwF/+p7K+khe/9Dx2v7HPpGfwxMx2hhHVsiP0eEN2X2rjbMxNlGnihAiIqI6gceE2ASnY4iIiMguOBJCRERkiCMhNsEihIiIyAiLEFvgdAwRERHZBUdCiIiIDHE6xiZYhBARERliEWITLEKIiIgMsQaxCR4TQkRERHbBkRAiIiJDnI6xCRYhRERERliE2AKnY4iIiMguOBJCRERkiNMxNmHxSMiiRYswZMgQfPHFFwCA9evXIzAwEG3btkViYiLu3btXZQyNRoObN2/qLRqNxvLsiYiIaoMkWW8hkywqQt577z1MnjwZt27dwrhx4zBv3jyMHz8er776KoYMGYJPPvkE7733XpVxkpKS4ObmprckJSXJ3gkiIiK6/1g0HbNmzRqsWbMGL7/8Mo4ePQqVSoW1a9fi1VdfBQC0bdsWEydOxIwZM8zGUavVSEhI0FunVCqBcxstTJ+IiKgWcADDJiwaCSksLERQUBAA4Mknn4RCoUCHDh102//2t7/h0qVLVcZRKpVo2LCh3qJUKi3LnIiIqLbYcTpm6dKl8Pf3h4uLC1QqFfbv32+2/d69e6FSqeDi4oIWLVpg2bJlRm3S09MRGBgIpVKJwMBAbNq0qcaPaw0WFSFeXl44efIkAODs2bMoKyvT/Q0AJ06cQJMmTaybIRER0V9EWloa4uPjMXnyZOTl5SEsLAyRkZEoKCiotH1+fj569uyJsLAw5OXlITExEWPHjkV6erquTXZ2NqKiohAdHY2jR48iOjoa/fv3x8GDB2U/rrVYNB0zaNAgDB48GL1798a//vUvTJo0CRMmTMC1a9cgSRJmz56Nfv361VauRERENmK9+RiNRmN08oVSqax0BmDBggUYPnw4YmJiAADJycn49ttvkZKSUumxk8uWLYOvry+Sk5MBAAEBAcjJycH8+fPRt29fXYzu3btDrVYD0B4SsXfvXiQnJ2Pjxo2yHtdaLBoJmTFjBsaNG4eioiKMHDkSc+bMwbx58zBnzhxMnjwZzzzzTLUOTCUiIqrTrDgdU92TMUpLS5Gbm4uIiAi99REREcjKyqo0zezsbKP2PXr0QE5ODu7evWu2TUVMOY9rLRaNhDg4OGDy5Ml66wYMGIABAwZYNSkiIiK7suKptSZPxjBQXFyMsrIyeHp66q339PREUVFRpbGLiooqbX/v3j0UFxfD29vbZJuKmHIe11p4sTIiIqJaZGrqxRTJoAASQhitq6q94frqxLT0ca2BRQgREZEhO1xkzMPDAw4ODkajD1euXDEapajg5eVVaXtHR0e4u7ubbVMRU87jWgvvHUNERFQHODs7Q6VSISMjQ299RkYGQkNDK+0TEhJi1H7nzp0ICgqCk5OT2TYVMeU8rrVwJISIiKiOSEhIQHR0NIKCghASEoIVK1agoKAAsbGxALTHl1y8eBHr1q0DAMTGxmLx4sVISEjAiBEjkJ2djVWrVunOegGAcePGoWvXrpg3bx569+6NLVu2YNeuXcjMzKz249YWFiFERESG7HTPl6ioKFy7dg0zZ85EYWEh2rVrh23btsHPzw+A9qKhf752h7+/P7Zt24bx48djyZIl8PHxwcKFC3Wn5wJAaGgoUlNTMWXKFEydOhUtW7ZEWloagoODq/24tYVFCBERkSE73nguLi4OcXFxlW5bs2aN0brw8HAcPnzYbMx+/fpVeR0vc49bW3hMCBEREdmFJCrO5SEiIiIAQPmJ1VaLpXj8davFetBwOoaIiMgIb6NrC3WqCCk/nCKrn+Jvo7T9c+X1BwCF6o8YP3wqr3+7YQCAsg3jZOfg8NrH2hxk7oduH/bMk52DotskbYzMBfL6d9FeFbA86yP5OYSO18bYNUd+jGcTtTGyP5bXP0T7OpYfWiI/h06jtTGOrJDXv8Mb2v55y+Xn0HGkNsahxfL6dxqj7f9vee8HAFB0/uM9cXyVvP7th1svh4OL5PUPflPb3xqfrfRJ8vr31T52+YGF8nN4aqw2xuZEef37aD+Tcp9H4E/P5Tfvyo/xwkzZfaluqVNFCBERUZ1gxwNT/0pYhBARERmSeN6GLbAIISIiMsKREFtgqUdERER2wZEQIiIiQzwmxCZYhBARERniMSE2wWeZiIiI7IIjIUREREY4HWMLLEKIiIgM8ZgQm+B0DBEREdkFR0KIiIiM8De6LbAIISIiMsTpGJuwuAgpLCxESkoKMjMzUVhYCAcHB/j7+6NPnz4YOnQoHBwcaiNPIiIiesBYNN6Uk5ODgIAAfP3117hz5w7OnDmDv/3tb3B1dcWECRMQFhaGX3/9tco4Go0GN2/e1Fs0Go3snSAiIrIqSbLeQiZZVITEx8dj/PjxyMvLQ1ZWFtauXYszZ84gNTUV58+fx+3btzFlypQq4yQlJcHNzU1vSUpKkr0TRERE1iVZcSFTLCpCDh8+jOjoaN3fgwYNwuHDh3H58mU0btwY77//Pr788ssq46jVapSUlOgtarXa8uyJiIhqg6Sw3kImWXRMSJMmTVBYWIgWLVoAAC5fvox79+6hYcOGAIDWrVvj+vXrVcZRKpVQKpVG68stSYaIiIjuaxaVaH369EFsbCx27NiB3bt349VXX0V4eDjq1asHADh9+jQeffTRWkmUiIjIZnhMiE1YNBIya9YsFBYW4sUXX0RZWRlCQkKwYcMG3XZJknhsBxERPQBYPNiCRUVI/fr1kZaWhjt37uDevXuoX7++3vaIiAirJkdEREQPLlkXK3NxcbF2HkRERHUHDyi1CV4xlYiIyIDEYzlsgqUeERER2QVHQoiIiIxwJMQWWIQQEREZ4jEhNsFnmYiIiOyCIyFERERGOB1jCyxCiIiIDPHsGJuQhBDC3kkQERHVJeLCLqvFkpo9a7VYDxoeE0JERER2UaemY8qzPpLVTxE6Xtt/53uyH1sRMVUb48gKef07vAEAKFszWnYODkOXaHPIWyYvh46xAIC7cwfIzsHpnVQAQNmGcbL6O7z2MQCgdE6U7BycE9O0OSx9XXYMh7jVAIDybdNk9Vf0nKHtL/P9APzvPVGes1Re/6A4bf/sj+XnEKJ9HWv82cpNkZ+DapQ2xoGF8vo/NVbb/+Ai+TkEv6mNcWixvP6dxvzRf4n8HDppvxvKlsfI6u8w8hNtDtnJ8nMIidfG+DxeXv9B2scu/74Gr8Xf/3gtNibIjzFwgey+1cfpGFuoU0UIERFRncBjQmyC0zFERERkFxwJISIiMsSLldkEixAiIiIjnI6xBZZ6REREZBccCSEiIjLEA1NtgkUIERGREU4U2AKfZSIiovvQjRs3EB0dDTc3N7i5uSE6Ohr//e9/zfYRQmD69Onw8fFBvXr10K1bN5w4cUKvjUajwZtvvgkPDw+4urqiV69e+OWXX/TazJ49G6GhoXjooYfQqFEj2fvAIoSIiMiQJFlvqSWDBg3CkSNHsGPHDuzYsQNHjhxBdHS02T7vv/8+FixYgMWLF+PQoUPw8vJC9+7d8euvv+raxMfHY9OmTUhNTUVmZiZ+++03vPDCCygrK9O1KS0txSuvvIJRo0bVaB9kTcfcunULn3/+ObKyslBUVARJkuDp6YnOnTtj4MCBcHV1rVFSREREdlXHjwk5deoUduzYgQMHDiA4OBgAsHLlSoSEhOD06dN47LHHjPoIIZCcnIzJkyfj5ZdfBgCsXbsWnp6e+PzzzzFy5EiUlJRg1apVWL9+PZ59VnvPmw0bNqBZs2bYtWsXevToAQCYMUN7Vek1a9bUaD8sHgk5efIk2rRpg4kTJ+LGjRvw9fVF06ZNcePGDbz99tt47LHHcPLkyRolRUREZF8Kqy0ajQY3b97UWzQaTY2yy87Ohpubm64AAYCnnnoKbm5uyMrKqrRPfn4+ioqKEBERoVunVCoRHh6u65Obm4u7d+/qtfHx8UG7du1Mxq0Ji4uQ0aNHo2vXrrh8+TI2b96M5cuXY8WKFdi8eTMuX76Mrl27YvRo+fdPISIiepAkJSXpjtuoWJKSkmoUs6ioCE2aNDFa36RJExQVFZnsAwCenp566z09PXXbioqK4OzsjMaNG5tsY00WT8ccPHgQOTk5cHZ2Ntrm7OyMxMRE/P3vfzcbQ6PRGFWBSqUSTpYmQ0REVBusOB2jVquRkKB/wz6lUllp2+nTp+umOkw5dOgQAECqJEchRKXr/8xwe3X6VKeNHBYXIY0bN8bZs2cRGBhY6fZz584ZVVCGkpKSjJ7kadOm4d0IN0vTISIiqgXW+w9XqVSaLDoMjRkzBgMGmL8TevPmzXHs2DFcvnzZaNvVq1eNRjoqeHl5AdCOdnh7e+vWX7lyRdfHy8sLpaWluHHjht7/5VeuXEFoaGi19sESFhchI0aMwJAhQzBlyhR0794dnp6ekCQJRUVFyMjIwJw5cxAfH282hsmqMFfeLc+JiIgeBB4eHvDw8KiyXUhICEpKSvD999/rZh8OHjyIkpISk8WCv78/vLy8kJGRgY4dOwLQnuWyd+9ezJs3DwCgUqng5OSEjIwM9O/fHwBQWFiIH374Ae+//741dlGPxUXI9OnTUa9ePSxYsAATJ07UDc8IIeDl5YV33nkHEydONBvDVFVYbmkyREREtaGOnx0TEBCA5557DiNGjMDy5csBAG+88QZeeOEFvTNj2rZti6SkJLz00kuQJAnx8fGYM2cOWrdujdatW2POnDl46KGHMGjQIACAm5sbhg8fjrfeegvu7u54+OGHMWHCBLRv3153tgwAFBQU4Pr16ygoKEBZWRmOHDkCAGjVqhXq169f7f2QdYrupEmTMGnSJN2RtoB2CMff319OOCIiojqmbhchAPDZZ59h7NixujNZevXqhcWLF+u1OX36NEpKSnR/T5w4Ebdv30ZcXBxu3LiB4OBg7Ny5Ew0aNNC1+eijj+Do6Ij+/fvj9u3beOaZZ7BmzRo4ODjo2rz77rtYu3at7u+KkZXdu3ejW7du1d6HGl223d/f36jwuHDhAqZNm4ZPP/20JqGJiIjIjIcffhgbNmww20YIofe3JEmYPn06pk+fbrKPi4sLFi1ahEWLFplss2bNmhpfIwSohSumXr9+Xa86IiIiuu/cB1dMfRBYPBKydetWs9vPnz8vOxkiIqK6gXc1sQWLi5A+ffpAkiSjIZ4/q41ziYmIiOjBYnGp5+3tjfT0dJSXl1e6HD58uDbyJCIish1Ox9iExUWISqUyW2hUNUpCRERU90lWXMgUi6dj3n77bdy6dcvk9latWmH37t01SoqIiMiuOIJhExYXIWFhYWa3u7q6Ijw8XHZCRERE9NdQo+uEEBERPZg4EmILLEKIiIgMcTrGJngiNBEREdmFJHgqCxERkR5x/YTVYkkPP261WA8aTscQEREZ4nSMTdSpIqT8+CpZ/RTth2v7b5sm+7EVPWdoY/wg78Z7inbDtP13z5Wfwz/e0cY4ulJe/ydHaPvLfB6BPz2XNczhd/VLsnN4KGmTNocjK2THUHR4Qxvj2Cfy+j8RU6P+ejFq+npaI4fvTd+Iymz/v7+p7X9gofwcnhqr/ceda/ICuLhrc9jzvvwcuk3UxjixWl7/x1/X9rfGa5G3XF7/jiO1/Q/Key0BQBH8x+uZt0xmDrHa/rkp8nNQjdLGOPN/8mO0eUV2X6pb6lQRQkREVDfwkElbYBFCRERkiNMxNsFSj4iIiOyCIyFERERGOBJiCyxCiIiIjLAIsQUWIURERAYkHhNiEzwmhIiIiOyCIyFERERGOBJiCyxCiIiIDHE6xiasPh1z+fJlzJw509phiYiI6AFj9SKkqKgIM2bMsHZYIiIiG5KsuJApFk/HHDt2zOz206dPy06GiIioTpB43oYtWFyEdOjQAZIkQQhhtK1ifVWnNmk0Gmg0Gr11SqUSTpYmQ0RERPcti0s9d3d3rFy5Evn5+UbL+fPn8c0331QZIykpCW5ubnpLUlKSrB0gIiKyPk7H2ILFIyEqlQqXLl2Cn59fpdv/+9//VjpK8mdqtRoJCQl665RKJXBmg6XpEBERWR/PjrEJi4uQkSNH4tatWya3+/r6YvXq1WZjKJVKbdFhoNzSZIiIiOi+ZXER8tJLL5nd3rhxYwwZMkR2QkRERPbHkRBbsPrhvxcuXMCwYcOsHZaIiMh2JMl6C5lk9SLk+vXrWLt2rbXDEhER2RAPTLUFi6djtm7danb7+fPnZSdDREREfx0WFyF9+vQxeZ2QCrwFMhER3df4/5hNWDwd4+3tjfT0dJSXl1e6HD58uDbyJCIisiFOx9iCxUWISqUyW2hUNUpCREREBMiYjnn77bfNXiekVatW2L17d42SIiIisiveO8YmLC5CwsLCzG53dXVFeHi47ISIiIjsj9MotsBSj4iIiOzC4pEQIiKiBx7PjrEJFiFERERGWITYgiR4KgsREZG+O9esF8vF3XqxHjD3xTEhGo0G06dPh0ajsVsM5sAcmEPdzMEaMZjDg5WDVbi4W28h08R9oKSkRAAQJSUldovBHJgDc6ibOVgjBnN4sHKg+8d9MRJCREREDx4WIURERGQXLEKIiIjILu6LIkSpVGLatGlQKpV2i8EcmANzqJs5WCMGc3iwcqD7B0/RJSIiIru4L0ZCiIiI6MHDIoSIiIjsgkUIERER2QWLECIiIrILFiH3GR5HTERED4o6eRfdX375BSkpKcjKykJRUREkSYKnpydCQ0MRGxuLZs2a2TtFu1EqlTh69CgCAgLsncp9pbCwECkpKcjMzERhYSEcHBzg7++PPn36YOjQoXBwcLB3ikREfzl17hTdzMxMREZGolmzZoiIiICnpyeEELhy5QoyMjJw4cIFbN++HZ07d5b9GBcuXMC0adPw6aefmmxz+/Zt5Obm4uGHH0ZgYKDetjt37uCLL77A4MGDzT7OqVOncODAAYSEhKBt27b48ccf8fHHH0Oj0eC1117D008/bbJvQkJCpes//vhjvPbaa3B3194UacGCBWZz+LMbN25g7dq1OHv2LLy9vTFkyBCzBV1eXh4aNWoEf39/AMCGDRuQkpKCgoIC+Pn5YcyYMRgwYIDZx3zzzTfRv39/hIWFVTtPQ4sWLUJOTg6ef/559O/fH+vXr0dSUhLKy8vx8ssvY+bMmXB0NF1P5+Tk4Nlnn4W/vz/q1auHgwcP4tVXX0VpaSm+/fZbBAQE4Ntvv0WDBg1k50hkD7du3cLnn39u9IOtc+fOGDhwIFxdXWXHvnz5MpYvX4533323yra//PILGjVqhPr16+utv3v3LrKzs9G1a1eTfa9du4Zjx47hySefxMMPP4zi4mKsWrUKGo0Gr7zyCn9wPejseeOaygQFBYn4+HiT2+Pj40VQUFCNHuPIkSNCoVCY3H769Gnh5+cnJEkSCoVChIeHi0uXLum2FxUVme0vhBDbt28Xzs7O4uGHHxYuLi5i+/bt4pFHHhHPPvuseOaZZ4Sjo6P417/+ZbK/JEmiQ4cOolu3bnqLJEmiU6dOolu3buIf//iH2Ry8vb1FcXGxEEKI8+fPCy8vL+Hl5SW6d+8umjZtKtzc3MSpU6dM9u/YsaP47rvvhBBCrFy5UtSrV0+MHTtWpKSkiPj4eFG/fn2xatUqszlUPIetW7cWc+fOFYWFhWbbG5o5c6Zo0KCB6Nu3r/Dy8hJz584V7u7uYtasWWLOnDnikUceEe+++67ZGJ07dxbTp0/X/b1+/XoRHBwshBDi+vXrokOHDmLs2LFV5vLbb7+JFStWiKFDh4rnnntOREZGiqFDh4qVK1eK3377zaL9MlRUVCRmzJhRrbYXLlwQv/76q9H60tJSsXfvXrN9i4uLxXfffSeuXbsmhBDi6tWrYu7cuWLGjBni5MmTlif+B39/f3HmzBlZfUtLS8WmTZvE+++/L9avX1/lc3nhwgVx9epV3d/79u0TgwYNEl26dBGvvvqqyMrKqvIx58+fL37++WdZ+VbYunWrePfdd3WP969//UtERkaKHj16iOXLl1crxu+//y5WrVolXn/9dfHcc8+J559/XowZM0bs2rWryr4nTpwQPj4+olGjRqJ3797ijTfeECNGjBC9e/cWjRo1Eo8++qg4ceKE7P2r6ntSCCEuXbokOnXqJBQKhXBwcBCDBw/We29W9V158OBB4ebmJiRJEo0bNxY5OTnC399ftG7dWrRq1UrUq1dP5Obmyt4HqvvqXBHi4uIifvzxR5PbT506JVxcXMzG2LJli9nlo48+MvvB6NOnj3jhhRfE1atXxdmzZ8WLL74o/P39xX/+8x8hRPWKkJCQEDF58mQhhBAbN24UjRs3FomJibrtiYmJonv37ib7z5kzR/j7+xsVKo6OjtX+YpEkSVy+fFkIIcSAAQNEt27dxK1bt4QQQty5c0e88MILol+/fib7P/TQQ7p97tixo9EX62effSYCAwOrzGHXrl1i3LhxwsPDQzg5OYlevXqJr7/+WpSVlVW5Dy1atBDp6elCCO2XooODg9iwYYNu+1dffSVatWplNka9evXETz/9pPu7rKxMODk5iaKiIiGEEDt37hQ+Pj5mY/ALX+vjjz+udHFwcBBqtVr3tzkhISHixo0bQgghrly5Itq3by+cnZ1F69athYuLi/D19RW//PKL2f7btm0TQgixefNmoVAoRK9evcSkSZPESy+9JJycnMTXX39tNgdJkoSDg4N49tlnRWpqqtBoNGbbG0pJSRGOjo5CpVKJhg0big0bNogGDRqImJgYMXLkSFGvXj2RnJxsNsbZs2eFn5+fcHd3F97e3kKSJPH888+L4OBg4eDgIF555RVx9+5dk/27desmBgwYUGnuGo1GDBw4UHTr1s1k/6NHj5pd0tLSqnxPDh48WDz11FPi0KFDIiMjQwQFBQmVSiWuX78uhNC+JyVJMtn/2WefFTExMeLmzZvigw8+EE2bNhUxMTG67cOHDxd9+vQxmwPd3+pcEeLv7y8+/fRTk9s//fRT4e/vbzZGxa9vSZJMLuY+XE2aNBHHjh3TWxcXFyd8fX3FTz/9VK0ipGHDhuLs2bNCCO1/eo6Ojnpf8MePHxeenp5mY3z//feiTZs24q233hKlpaVCCPlFSGUFzYEDB0TTpk1N9nd3dxc5OTlCCO1zcuTIEb3t586dE/Xq1at2DqWlpSItLU306NFDODg4CB8fH5GYmKh7nipTr149XSEkhBBOTk7ihx9+0P39888/i4ceeshsDn5+fiIzM1P396VLl4QkSeL3338XQgiRn59fZWHLL3wtSZJE06ZNRfPmzfUWSZLEo48+Kpo3b16tz2fFe2LEiBGiQ4cOuhGy4uJiERoaKoYNG2ayf4MGDUR+fr4QQojg4GAxd+5cve2LFi0SHTt2rDKH1atXi969ewsnJyfh7u4uxo0bJ44fP262X4WAgACxYsUKIYQQ3333nXBxcRFLlizRbV+9erUICAgwGyMyMlKMHDlSV4wnJSWJyMhIIYQQZ86cEc2bNxfTpk0z2b9evXpmvwuOHz9u9vNp7nuyYn1V70kfHx9x8OBB3d937twRvXv3Fh06dBDXrl2r8ruycePGuhG40tJSoVAo9OIdPnxYPProo2ZzoPtbnStClixZIpydncXo0aPF5s2bRXZ2tjhw4IDYvHmzGD16tFAqlSIlJcVsDB8fH7Fp0yaT2/Py8sx+MBo0aFDp0PSYMWNE06ZNxb59+ywqQoQQon79+nq/xn/++ecq/+MTQohff/1VDB48WDzxxBPi2LFjwsnJyaIi5MqVK0II7XPy5/+8hdD+56tUKk32f+2118Tw4cOFEEK88sorYsqUKXrb58yZI9q3b19lDhX/4fzZf/7zHzFt2jTh5+dn9rn09/cX27dvF0Jov5gVCoX44osvdNv/+c9/iubNm5vNYdy4caJdu3Zi+/bt4rvvvhP/+Mc/9AqGHTt2iJYtW5qNwS98rTfeeEN06NDB6PMhtzhu06aN+Oabb/S279692+xr6ubmJo4ePSqE0BbHFf+ucO7cuSoL0z/ncPnyZTFv3jzRtm1boVAoRKdOncSKFSvEzZs3TfavrDj+cwGTn59fZQ4PPfSQ3hSWRqMRTk5OuinUzZs3m30efHx8xObNm01u37Rpk9kRPg8PD7Fq1Srx888/V7r885//rPI96erqajQNd/fuXdGnTx/dd5a5GK6urrqCUgjj78n//Oc/1fqepPtXnStChBAiNTVVBAcHC0dHR90XtaOjowgODhZpaWlV9n/xxRfF1KlTTW4/cuSI2V+MnTp1EuvWrat02+jRo0WjRo2q/HA+8cQTuv88hdD+J/XnodX9+/dX+YvxzzZu3Cg8PT2FQqGw6Mu+ffv2omPHjqJ+/friq6++0tu+d+9es//pXLx4UTRv3lx07dpVJCQkiHr16okuXbqIESNGiK5duwpnZ2fxz3/+s8ocKitCKpSXl4udO3ea3D558mTxyCOPiJiYGOHv7y/UarXw9fUVKSkpYtmyZaJZs2Zi/PjxZnP49ddfRf/+/XXvp9DQUHH+/Hnd9m+//VavsKkMv/D/Z9OmTaJZs2Zi0aJFunWWFiEVxXGTJk2M+v38889mi+NevXqJd955RwghRI8ePYymf1auXClat25dZQ6VvS/37dsnhgwZIlxdXYWrq6vJ/hU/RoTQfk4kSdL7LOzZs8fsKKMQ2vfUn0dHb9y4ISRJ0hU/58+fN/s8TJs2Tbi5uYkPPvhAHDlyRBQWFoqioiJx5MgR8cEHH4jGjRubPc6oR48e4r333jO5varvSSGEaN++vfjyyy+N1le8L319fc2+J9u2bas3QvvNN9/oRiiFqHq0lu5/dbIIqVBaWiouXbokLl26pJuOqI59+/bpFQCGfvvtN7Fnzx6T2+fMmaMbFq3MqFGjqvxwpqSkGP3C+7PExETdKEN1XbhwQWzevLnaB0FOnz5db9mxY4fe9gkTJogBAwaYjXHjxg0xadIkERgYKFxcXISzs7Pw8/MTgwYNEocOHaoyh+bNm+t+2clx7949MWvWLPHCCy/oht03btwomjVrJtzd3cXQoUOr/Xzcvn270gM6q4Nf+Pp++eUX8fTTT4vnnntOFBYWWlyE9OzZU7z00kuicePGuuM7KmRnZ5udqjx58qRwd3cXgwcPFu+9956oX7++eO2118Ts2bPF4MGDhVKpFKtXrzabg0KhMFscl5SU6KZbKjN69GjRunVrMWvWLPH3v/9dDBkyRLRt21Zs375d7NixQ7Rv397slJIQQgwZMkSEh4eLU6dOifPnz4uoqCi9aaQ9e/aIZs2amY0xd+5c3fEkCoVCN6rm7e0t5s2bZ7bvV199JdavX29y+/Xr18WaNWvMxpg4caKIiIiodNvdu3dFr169zL4np0+fLjZu3Ghye2Jionj55ZfN5kD3tzpdhBDVJffDF765QsbaX/jl5eVizpw5wsvLSzg4OFS7CBk6dKjeYjgKNWHCBNGjRw+zMc6dOycGDBggGjRooBstdXJyEqGhoWanYitUNUJXld9++03ExMSIdu3aidjYWFFaWio++OAD4ezsLCRJEt26dasy/uXLl8VTTz2lez81b95cHD58WLf9//7v/8TChQurlc/58+dFVlaWyMrK0hvlq213794VJSUlJrffu3evRmch3bp1S9y5c0d2f6r76tx1Qojquvz8fBQVFQEAvLy8dNdRqW337t3D77//joYNG1a6vaysDL/88gv8/Pxkxf/999/h4OAApVJpUb/c3FxkZmZi8ODBaNy4sazH/rNbt27BwcEBLi4uVbYVf1xDqLy8HB4eHnBycqrx49fEnTt3cPfuXYuuOXP27FloNBq0bdvW7PVuiB5EvGw7kYX8/f0REhKCkJAQXQFy4cIFDBs2THbM6vR3dHQ0WYAAwKVLlzBjxgzZOVy7dg2jRo2yuJ9KpcK4cePQuHHjGj8PAHD9+nXExcVVq23Fxbm8vb11BYg1cpAbw8XFBQ0aNLCof+vWrdGuXTujAqQ6MW7fvo3MzEycPHnSaNudO3ewbt26Wu1fV3Kg+5idR2KIHgjVuc5HbfZ/UHKwRoy/Sg41vaiiNS7KWBdyoPsbx/6IqmHr1q1mt58/f75W+z8oOVgjBnPQmjRpEtq3b4+cnBz897//RUJCAjp37ow9e/bA19e3ysevaf+6kgPd33hMCFE1KBQKSJJk9i7GkiShrKysVvo/KDlYIwZz0PL09MSuXbvQvn173brRo0fjm2++we7du+Hq6gofH59a619XcqD7G48JIaoGb29vpKeno7y8vNLl8OHDtdr/QcnhQdmPupDD7du3jY4jWbJkCXr16oXw8HCcOXOmVvvXlRzo/sYihKgaVCqV2f8UqvpFW9P+D0oO1ojBHLTatm2LnJwco/WLFi1C79690atXL7OPX9P+dSUHus/Z8gAUovtVTS+AV9P+D0oO1ojBHLRqelFFa1yUsS7kQPc3HhNCREREdsHpGCIiIrILFiFERERkFyxCiIiIyC5YhBAREZFdsAghIiIiu2ARQkRERHbBIoSIiIjs4v8BqI4hS6pAuZwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Apply the same row permutations to the Chae glomerular data.\n",
    "glom = chae_filtered.to_numpy()\n",
    "glom_shuff = glom[np.argsort(model.row_labels_)]\n",
    "sns.heatmap(glom_shuff, cmap=\"OrRd\", linewidths=0.3, vmax=0.005)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4a84dca-9f2a-44d1-8408-2364a371a740",
   "metadata": {},
   "source": [
    "The final biclustering results can be observed below: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "18646713-36f8-4f81-bfde-ee311758ded1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "image/png": {
       "width": 800
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename=\"case_study.png\", width=800)"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}