Metadata_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WrEG4FZOmlVe"
   },
   "source": [
    "## Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Nk-RqOYE0okd",
    "outputId": "63e2d4c5-c5c1-4e9e-a431-f17e475c803c"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import zipfile\n",
    "import numpy as np\n",
    "import squarify\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import ticker\n",
    "import seaborn as sns  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UzHh4HZ1jr8T"
   },
   "source": [
    "# EDA\n",
    "\n",
    "The purpose of our EDA is to find insights that will help us later to how clean our data and ultimately create our models.\n",
    "\n",
    "\n",
    "**Note**: \n",
    "\n",
    "1.This is the EDA for baseline MRI data, which is our data of focus in the first experiment. In the first experiment, we plan to do a cross-sectional prediction, namely predicting whether the patient has Alzheimer or not. We will take care of the temporal effect in the second experiment.\n",
    "\n",
    "2.This is the EDA for meta data/data dictionary only. The EDA for images themselves is in {TODO: fill in the blank} file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/accounts/grad/khern045/NeuroimagingCapstone/scripts'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "To54QyYA9ByX"
   },
   "source": [
    "Creating data paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "_etWhaUjBNdZ"
   },
   "outputs": [],
   "source": [
    "base_path = os.path.join('/accounts', 'grad', 'khern045', 'NeuroimagingCapstone')\n",
    "bl_3t_md_file = 'ADNI1_Baseline_3T.csv'  # data dictionary\n",
    "bl_path = os.path.join(base_path, 'data', 'adni-data', bl_3t_md_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3LOvlKc6R-2M"
   },
   "source": [
    "Read in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "jsboYxXXdAsF",
    "outputId": "04390ea6-71f3-4a72-bcc8-5d4925f67a66"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>Image Data ID</th>\n",
       "      <th>Subject</th>\n",
       "      <th>Group</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Visit</th>\n",
       "      <th>Modality</th>\n",
       "      <th>Description</th>\n",
       "      <th>Type</th>\n",
       "      <th>Acq Date</th>\n",
       "      <th>Format</th>\n",
       "      <th>Downloaded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I119721</td>\n",
       "      <td>136_S_0195</td>\n",
       "      <td>MCI</td>\n",
       "      <td>M</td>\n",
       "      <td>80</td>\n",
       "      <td>bl</td>\n",
       "      <td>MRI</td>\n",
       "      <td>MPR; ; N3; Scaled_2</td>\n",
       "      <td>Processed</td>\n",
       "      <td>2006-04-04</td>\n",
       "      <td>NiFTI</td>\n",
       "      <td>3/01/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I34416</td>\n",
       "      <td>127_S_0397</td>\n",
       "      <td>MCI</td>\n",
       "      <td>M</td>\n",
       "      <td>75</td>\n",
       "      <td>bl</td>\n",
       "      <td>MRI</td>\n",
       "      <td>MPR; GradWarp; B1 Correction; N3; Scaled</td>\n",
       "      <td>Processed</td>\n",
       "      <td>2006-06-06</td>\n",
       "      <td>NiFTI</td>\n",
       "      <td>2/15/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I39197</td>\n",
       "      <td>130_S_0505</td>\n",
       "      <td>MCI</td>\n",
       "      <td>M</td>\n",
       "      <td>80</td>\n",
       "      <td>bl</td>\n",
       "      <td>MRI</td>\n",
       "      <td>MPR; ; N3; Scaled</td>\n",
       "      <td>Processed</td>\n",
       "      <td>2006-10-18</td>\n",
       "      <td>NiFTI</td>\n",
       "      <td>3/01/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I40657</td>\n",
       "      <td>002_S_0413</td>\n",
       "      <td>CN</td>\n",
       "      <td>F</td>\n",
       "      <td>76</td>\n",
       "      <td>bl</td>\n",
       "      <td>MRI</td>\n",
       "      <td>MPR; ; N3; Scaled</td>\n",
       "      <td>Processed</td>\n",
       "      <td>2006-05-19</td>\n",
       "      <td>NiFTI</td>\n",
       "      <td>3/01/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I51981</td>\n",
       "      <td>023_S_0078</td>\n",
       "      <td>MCI</td>\n",
       "      <td>F</td>\n",
       "      <td>76</td>\n",
       "      <td>bl</td>\n",
       "      <td>MRI</td>\n",
       "      <td>MPR; GradWarp; B1 Correction; N3; Scaled</td>\n",
       "      <td>Processed</td>\n",
       "      <td>2006-01-12</td>\n",
       "      <td>NiFTI</td>\n",
       "      <td>2/15/2023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Image Data ID     Subject Group Sex  Age Visit Modality  \\\n",
       "0       I119721  136_S_0195   MCI   M   80    bl      MRI   \n",
       "1        I34416  127_S_0397   MCI   M   75    bl      MRI   \n",
       "2        I39197  130_S_0505   MCI   M   80    bl      MRI   \n",
       "3        I40657  002_S_0413    CN   F   76    bl      MRI   \n",
       "4        I51981  023_S_0078   MCI   F   76    bl      MRI   \n",
       "\n",
       "                                Description       Type   Acq Date Format  \\\n",
       "0                       MPR; ; N3; Scaled_2  Processed 2006-04-04  NiFTI   \n",
       "1  MPR; GradWarp; B1 Correction; N3; Scaled  Processed 2006-06-06  NiFTI   \n",
       "2                         MPR; ; N3; Scaled  Processed 2006-10-18  NiFTI   \n",
       "3                         MPR; ; N3; Scaled  Processed 2006-05-19  NiFTI   \n",
       "4  MPR; GradWarp; B1 Correction; N3; Scaled  Processed 2006-01-12  NiFTI   \n",
       "\n",
       "  Downloaded  \n",
       "0  3/01/2023  \n",
       "1  2/15/2023  \n",
       "2  3/01/2023  \n",
       "3  3/01/2023  \n",
       "4  2/15/2023  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(bl_path, parse_dates=['Acq Date'])\n",
    "df.head()\n",
    "# shit data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cDWX4YkrjMvo",
    "outputId": "5114464e-9bc6-45a5-ae71-f80af89e039c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 199 entries, 0 to 198\n",
      "Data columns (total 12 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   Image Data ID  199 non-null    object        \n",
      " 1   Subject        199 non-null    object        \n",
      " 2   Group          199 non-null    object        \n",
      " 3   Sex            199 non-null    object        \n",
      " 4   Age            199 non-null    int64         \n",
      " 5   Visit          199 non-null    object        \n",
      " 6   Modality       199 non-null    object        \n",
      " 7   Description    199 non-null    object        \n",
      " 8   Type           199 non-null    object        \n",
      " 9   Acq Date       199 non-null    datetime64[ns]\n",
      " 10  Format         199 non-null    object        \n",
      " 11  Downloaded     199 non-null    object        \n",
      "dtypes: datetime64[ns](1), int64(1), object(10)\n",
      "memory usage: 18.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ou18PWkD9Zy1"
   },
   "source": [
    "Here we can see that there are no null values. Most are of type object. We can also see that there are unnecessary variables like Format, Downloaded, Modality, Visit. We can plan to remove those"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4iZ63j4uSmE-"
   },
   "source": [
    "Drop unused columns\n",
    "\n",
    "Split up date into year, month, day columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "q4GqWR2sSEsa",
    "outputId": "61865139-6408-4b02-d7cc-6e55dc2710b2"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Image Data ID</th>\n",
       "      <th>Subject</th>\n",
       "      <th>Group</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Description</th>\n",
       "      <th>Acq_year</th>\n",
       "      <th>Acq_month</th>\n",
       "      <th>Acq_day</th>\n",
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       "      <th>0</th>\n",
       "      <td>I119721</td>\n",
       "      <td>136_S_0195</td>\n",
       "      <td>MCI</td>\n",
       "      <td>M</td>\n",
       "      <td>80</td>\n",
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       "      <td>4</td>\n",
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       "      <th>1</th>\n",
       "      <td>I34416</td>\n",
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       "      <td>6</td>\n",
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       "      <th>2</th>\n",
       "      <td>I39197</td>\n",
       "      <td>130_S_0505</td>\n",
       "      <td>MCI</td>\n",
       "      <td>M</td>\n",
       "      <td>80</td>\n",
       "      <td>MPR; ; N3; Scaled</td>\n",
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       "      <td>10</td>\n",
       "      <td>18</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I40657</td>\n",
       "      <td>002_S_0413</td>\n",
       "      <td>CN</td>\n",
       "      <td>F</td>\n",
       "      <td>76</td>\n",
       "      <td>MPR; ; N3; Scaled</td>\n",
       "      <td>2006</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I51981</td>\n",
       "      <td>023_S_0078</td>\n",
       "      <td>MCI</td>\n",
       "      <td>F</td>\n",
       "      <td>76</td>\n",
       "      <td>MPR; GradWarp; B1 Correction; N3; Scaled</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  Image Data ID     Subject Group Sex  Age  \\\n",
       "0       I119721  136_S_0195   MCI   M   80   \n",
       "1        I34416  127_S_0397   MCI   M   75   \n",
       "2        I39197  130_S_0505   MCI   M   80   \n",
       "3        I40657  002_S_0413    CN   F   76   \n",
       "4        I51981  023_S_0078   MCI   F   76   \n",
       "\n",
       "                                Description  Acq_year  Acq_month  Acq_day  \n",
       "0                       MPR; ; N3; Scaled_2      2006          4        4  \n",
       "1  MPR; GradWarp; B1 Correction; N3; Scaled      2006          6        6  \n",
       "2                         MPR; ; N3; Scaled      2006         10       18  \n",
       "3                         MPR; ; N3; Scaled      2006          5       19  \n",
       "4  MPR; GradWarp; B1 Correction; N3; Scaled      2006          1       12  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['Visit', 'Modality', 'Type', 'Format', 'Downloaded'], axis=1, errors = 'ignore')\n",
    "\n",
    "df['Acq_year'] = pd.DatetimeIndex(df['Acq Date']).year\n",
    "df['Acq_month'] = pd.DatetimeIndex(df['Acq Date']).month\n",
    "df['Acq_day'] = pd.DatetimeIndex(df['Acq Date']).day\n",
    "df = df.drop(['Acq Date'], axis=1, errors = 'ignore')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SpTlWU3N-d7Q"
   },
   "source": [
    "Change the names of some columsn to remove spaces and make for easier coding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "cIhi0uCK-nCb",
    "outputId": "1586d1bd-98db-4c74-a12d-d72c4d5213ad"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Img_ID</th>\n",
       "      <th>Subject</th>\n",
       "      <th>Group</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Desc</th>\n",
       "      <th>Acq_year</th>\n",
       "      <th>Acq_month</th>\n",
       "      <th>Acq_day</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>I119721</td>\n",
       "      <td>136_S_0195</td>\n",
       "      <td>MCI</td>\n",
       "      <td>M</td>\n",
       "      <td>80</td>\n",
       "      <td>MPR; ; N3; Scaled_2</td>\n",
       "      <td>2006</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I34416</td>\n",
       "      <td>127_S_0397</td>\n",
       "      <td>MCI</td>\n",
       "      <td>M</td>\n",
       "      <td>75</td>\n",
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       "      <th>2</th>\n",
       "      <td>I39197</td>\n",
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       "      <td>MCI</td>\n",
       "      <td>M</td>\n",
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       "      <th>3</th>\n",
       "      <td>I40657</td>\n",
       "      <td>002_S_0413</td>\n",
       "      <td>CN</td>\n",
       "      <td>F</td>\n",
       "      <td>76</td>\n",
       "      <td>MPR; ; N3; Scaled</td>\n",
       "      <td>2006</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I51981</td>\n",
       "      <td>023_S_0078</td>\n",
       "      <td>MCI</td>\n",
       "      <td>F</td>\n",
       "      <td>76</td>\n",
       "      <td>MPR; GradWarp; B1 Correction; N3; Scaled</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Img_ID     Subject Group Sex  Age  \\\n",
       "0  I119721  136_S_0195   MCI   M   80   \n",
       "1   I34416  127_S_0397   MCI   M   75   \n",
       "2   I39197  130_S_0505   MCI   M   80   \n",
       "3   I40657  002_S_0413    CN   F   76   \n",
       "4   I51981  023_S_0078   MCI   F   76   \n",
       "\n",
       "                                       Desc  Acq_year  Acq_month  Acq_day  \n",
       "0                       MPR; ; N3; Scaled_2      2006          4        4  \n",
       "1  MPR; GradWarp; B1 Correction; N3; Scaled      2006          6        6  \n",
       "2                         MPR; ; N3; Scaled      2006         10       18  \n",
       "3                         MPR; ; N3; Scaled      2006          5       19  \n",
       "4  MPR; GradWarp; B1 Correction; N3; Scaled      2006          1       12  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.rename({'Image Data ID': 'Img_ID', 'Description':\"Desc\"}, axis=1) \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KZqwvOjD_cgE"
   },
   "source": [
    "Split description column into binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4nfH4s0P_aBN",
    "outputId": "bbf84b77-9bf6-4aa7-b4f6-988b87156846"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['MPR; ; N3; Scaled_2', 'MPR; GradWarp; B1 Correction; N3; Scaled',\n",
       "       'MPR; ; N3; Scaled', 'MPR-R; GradWarp; B1 Correction; N3; Scaled',\n",
       "       'MPR; GradWarp; B1 Correction; N3; Scaled_2',\n",
       "       'MPR-R; ; N3; Scaled_2', 'MPR-R; ; N3; Scaled',\n",
       "       'MPR-R; GradWarp; B1 Correction; N3; Scaled_2',\n",
       "       'MPR; GradWarp; N3; Scaled'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Desc.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 261
    },
    "id": "tDXDUh44Wltm",
    "outputId": "f7e9b19d-a68e-4ea5-f753-a4b8d28f3104"
   },
   "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>Img_ID</th>\n",
       "      <th>Subject</th>\n",
       "      <th>Group</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Acq_year</th>\n",
       "      <th>Acq_month</th>\n",
       "      <th>Acq_day</th>\n",
       "      <th>B1 Correction</th>\n",
       "      <th>GradWarp</th>\n",
       "      <th>MPR</th>\n",
       "      <th>MPR-R</th>\n",
       "      <th>N3</th>\n",
       "      <th>Scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I119721</td>\n",
       "      <td>136_S_0195</td>\n",
       "      <td>MCI</td>\n",
       "      <td>M</td>\n",
       "      <td>80</td>\n",
       "      <td>2006</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I34416</td>\n",
       "      <td>127_S_0397</td>\n",
       "      <td>MCI</td>\n",
       "      <td>M</td>\n",
       "      <td>75</td>\n",
       "      <td>2006</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I39197</td>\n",
       "      <td>130_S_0505</td>\n",
       "      <td>MCI</td>\n",
       "      <td>M</td>\n",
       "      <td>80</td>\n",
       "      <td>2006</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I40657</td>\n",
       "      <td>002_S_0413</td>\n",
       "      <td>CN</td>\n",
       "      <td>F</td>\n",
       "      <td>76</td>\n",
       "      <td>2006</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I51981</td>\n",
       "      <td>023_S_0078</td>\n",
       "      <td>MCI</td>\n",
       "      <td>F</td>\n",
       "      <td>76</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Img_ID     Subject Group Sex  Age  Acq_year  Acq_month  Acq_day  \\\n",
       "0  I119721  136_S_0195   MCI   M   80      2006          4        4   \n",
       "1   I34416  127_S_0397   MCI   M   75      2006          6        6   \n",
       "2   I39197  130_S_0505   MCI   M   80      2006         10       18   \n",
       "3   I40657  002_S_0413    CN   F   76      2006          5       19   \n",
       "4   I51981  023_S_0078   MCI   F   76      2006          1       12   \n",
       "\n",
       "   B1 Correction  GradWarp  MPR  MPR-R  N3  Scaled  \n",
       "0              0         0    1      0   1       1  \n",
       "1              1         1    1      0   1       1  \n",
       "2              0         0    1      0   1       1  \n",
       "3              0         0    1      0   1       1  \n",
       "4              1         1    1      0   1       1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://stackoverflow.com/a/29036042/16800940\n",
    "desc_df = pd.get_dummies(df.Desc.replace({'(Scaled_2)':'Scaled'}, regex=True).str.split(\"; \").apply(pd.Series).stack()).sum(level=0)\n",
    "desc_df.rename({'B1 Correction':'B1'})\n",
    "\n",
    "dfc = pd.concat([df, desc_df.iloc[:, 1:]], axis=1).drop('Desc', axis=1)\n",
    "dfc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hCUvFjQsA_WX"
   },
   "source": [
    "MRI images from the ADNI dataset, we think, come partially preprocessed. \n",
    "\n",
    "ADNI Pipeline uses is a preprocessing pipeline used to perform a series of steps to convert the raw image into a preprocessed image. \n",
    "\n",
    "These steps can include N3/N4 bias correction, skull stripping, registration, intensity normalization, scaling and spatial normalization.\n",
    "\n",
    "Preprocessing Definitions:\n",
    "  > **N3**: Correct for intensity non-uniformity in the image, which can be caused by a variety of factors including differences in coil sensitivity, magnetic field inhomogeneities, and tissue properties.\n",
    "\n",
    "  > **N4**: Corrects for intensity non-uniformity in the MRI data caused by variations in the magnetic field and other factors\n",
    "\n",
    "  > **Gradwarp**: Corrects for intensity variations in the magnetic field of the scnaner, which can cause distortions in the image. These distortions can cause errors in subsequent image processing steps\n",
    "\n",
    "  > **Distortion correction**: Used to correct for spatial distortions in the image that are caused by factors such as gradient nonlinearities, susceptibility artifacts, and motion.\n",
    "\n",
    "  > **B1 Correction**: is a type of correction for radiofrequency (RF) inhomogeneity in MRI images. RF inhomogeneity occurs when the RF field is not perfectly uniform across the image.\n",
    "\n",
    "  > **Skull Stripping**: Removes non-brain tissue from the MRI images\n",
    "\n",
    "  > **Registration**: Aligns the MRI images to a standard space, such as the MNI (Montreal Neurological Institute) template\n",
    "\n",
    "  > **Spatial Normalizaiton**: Warps the images to fit the template space, correcting for differences in brain size and shape between individuals\n",
    "  \n",
    "  > **Intensity Normalization**: Rescales the intensity values of the images to a common range.\n",
    "\n",
    "  > **Scaled**: Not too sure what this is. On the documentation they have parameters scaled(1.00002, 1, 1.0003) or something similar.\n",
    "\n",
    "  > **Motion correction**: Motion correction is typically performed to correct for subject motion during the acquisition of the images. These artifacts can cause blurring or ghosting in the image, which can affect subsequent image processing steps.\n",
    "\n",
    "  > **Smoothing**: Spatial smoothing is performed to improve the signal-to-noise ratio of the images and to reduce noise and other unwanted variability.\n",
    "\n",
    "We think that they become preprocessed, because the images are not intensity normalized or skull stripped. \n",
    "\n",
    "We know for sure that they are N3 bias corrected, Gradwarped, scaled. I want to say that they're also registered. \n",
    "\n",
    "**MPR and MPR_R**:\n",
    "In ADNI preprocessing, MPR and MPR-R refer to two different acquisitions of the same imaging modality, specifically the magnetization-prepared rapid gradient echo (MPRAGE) sequence. MPR refers to the first acquisition of the MPRAGE sequence, while MPR-R refers to a repeat acquisition that is obtained immediately after the first acquisition.\n",
    "\n",
    "The purpose of acquiring two MPRAGE scans in quick succession is to improve the quality of the image by reducing the effects of noise, motion artifacts, and other sources of variability. The two scans are then averaged or combined in some way to produce a single high-quality image.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wTdNDmItTKHY"
   },
   "source": [
    "Unique Subjects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 435
    },
    "id": "W9Jc1x3yjwvP",
    "outputId": "5d4a862f-9fcf-420e-b3e7-837d5313bcc4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique_vals = len(dfc.Subject.unique())\n",
    "n = dfc.shape[0]\n",
    "\n",
    "############# PLOT\n",
    "color = sns.color_palette(\"pastel\")\n",
    "fig, ax = plt.subplots(figsize=(10, 7))\n",
    "squarify.plot(sizes=[unique_vals, n-unique_vals], label=[f'Unique: {unique_vals}', f'Non-Unique: {n-unique_vals}'], alpha=.8, color=color)\n",
    "ax.axis('off')\n",
    "\n",
    "# remove padding left and right, add some to top and bot\n",
    "fig.subplots_adjust(left=0, right=1, top=0.8, bottom=0.1)\n",
    "\n",
    "# Add title\n",
    "fig.text(\n",
    "    0, 0.87, \"Unique Values\", \n",
    "    fontsize=22, fontweight=\"bold\"\n",
    ")\n",
    "\n",
    "# Add subtitle\n",
    "fig.text(\n",
    "    0, 0.82, \"Number of unique and non-unique MRI images, 199 total\", \n",
    "    fontsize=15\n",
    ")\n",
    "plt.show()\n",
    "#############"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sJQDkoLY2YDw"
   },
   "source": [
    "31.78% of the data is not unique. This means that some of our data will be of the exact same person. A person could have 5 MRI scans of them taken, or 2. Having the same photo taken of a person but 5 days apart, could be useless and make cause bias in the model. We might have to remove them later or find a new dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 491
    },
    "id": "PBUJsaSELpSO",
    "outputId": "a641e0d6-6a90-4908-e80a-33a2c3ca5450"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "duplicates = dfc.groupby(['Subject']).size().value_counts()\n",
    "names = list(map(str, duplicates.index))\n",
    "counts = duplicates.values\n",
    "\n",
    "############# PLOT\n",
    "fig, ax = plt.subplots(figsize=(10, 7))\n",
    "ax.barh(names, counts, height=0.55)\n",
    "\n",
    "# remove padding left and right, add some to top and bot\n",
    "fig.subplots_adjust(left=0, right=1, top=0.8, bottom=0.1)\n",
    "\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.get_yaxis().set_visible(False)\n",
    "\n",
    "# xlabels\n",
    "ax.xaxis.set_ticklabels([i * 20 for i in range(0, 6)], size=16, fontweight=100)\n",
    "\n",
    "# y labels\n",
    "for name, count in zip(names, counts):\n",
    "  if count < 8:\n",
    "    x = count\n",
    "    color = 'teal'\n",
    "  else:\n",
    "     x = 1\n",
    "     color = 'white'\n",
    "  ax.text(\n",
    "      x + 0.5, int(name) - 1, name + ' scan(s)', fontsize=15, color=color, va=\"center\",\n",
    "  )\n",
    "\n",
    "# Add title\n",
    "fig.text(\n",
    "    0, 0.87, \"Returning Users\", \n",
    "    fontsize=22, fontweight=\"bold\"\n",
    ")\n",
    "\n",
    "# Add subtitle\n",
    "fig.text(\n",
    "    0, 0.82, \"Number of patients that took n scans\", \n",
    "    fontsize=15\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 510
    },
    "id": "fvEFPmOCk1Q_",
    "outputId": "843ca081-a847-4bde-98b6-d09707effbac"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dfc.groupby(['Subject']).size().apply(lambda x: (x > 1)).index\n",
    "dup_rows = dfc[dfc['Subject'].duplicated(keep=False)].loc[:,['Img_ID','Subject','Acq_year','Acq_month','Acq_day','MPR','MPR-R']]\n",
    "# duplicate rows group by yr and day\n",
    "dup_row_gb_yrd = dup_rows.groupby(['Subject', 'Acq_year','Acq_day']).size()\n",
    "count_dups = np.unique((dup_row_gb_yrd.sort_values(ascending=False) > 1).values, return_counts=True)\n",
    "\n",
    "names = np.append(count_dups[0],'False')\n",
    "counts = np.append(count_dups[1], 0)\n",
    "\n",
    "############# PLOT\n",
    "fig, ax = plt.subplots(figsize=(10, 7))\n",
    "ax.barh(names, counts, height=0.55)\n",
    "\n",
    "# remove padding left and right, add some to top and bot\n",
    "fig.subplots_adjust(left=0, right=1, top=0.8, bottom=0.1)\n",
    "\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.get_yaxis().set_visible(False)\n",
    "\n",
    "# xlabels\n",
    "ax.xaxis.set_ticklabels([i * 10 for i in range(0, 5)], size=16, fontweight=100)\n",
    "\n",
    "# y labels\n",
    "for name, count in zip(names, counts):\n",
    "  if count < 8:\n",
    "    x = count\n",
    "    color = 'teal'\n",
    "  else:\n",
    "     x = 1\n",
    "     color = 'white'\n",
    "  ax.text(\n",
    "      x + 0.5, np.where(names == name), name, fontsize=15, color=color, va=\"center\",\n",
    "  )\n",
    "\n",
    "ax.set_xlabel(\"Count\", fontsize=15)\n",
    "\n",
    "# Add title\n",
    "fig.text(\n",
    "    0, 0.87, \"Multiple Images\", \n",
    "    fontsize=22, fontweight=\"bold\"\n",
    ")\n",
    "\n",
    "# Add subtitle\n",
    "fig.text(\n",
    "    0, 0.82, \"Of the duplicate subjects, were the MRI images taken on the same year/month/day?\", \n",
    "    fontsize=15\n",
    ")\n",
    "\n",
    "plt.show()\n",
    "#######################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PY7NRZOqnNf_"
   },
   "source": [
    "Every single duplicate subject had two (three for one individual) images taken on the same day. This could be due to MPR/MPR-R acquisitions where researchers take multiple MRI scans in order to improve the quality of the image. They take the average of the two images in hopes to reduce the noise and motion effects. \n",
    "\n",
    "This is something we will have to deal with later. We can remove the datapoints or we can do the MPR/MPR-R preprocessing talked about in the previous paragraph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8zysJ9VmTVfo"
   },
   "source": [
    "Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "UhTpsUPhgPDs",
    "outputId": "a1045894-3f27-4073-d70b-563ce72d654b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3794878/2898986754.py:7: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(a=ser, hist=True, kde=True, rug=False, bins=bins,\n",
      "/accounts/grad/khern045/.local/lib/python3.8/site-packages/seaborn/distributions.py:2511: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  kdeplot(**{axis: a}, ax=ax, color=kde_color, **kde_kws)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3794878/2898986754.py:7: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(a=ser, hist=True, kde=True, rug=False, bins=bins,\n",
      "/accounts/grad/khern045/.local/lib/python3.8/site-packages/seaborn/distributions.py:2511: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  kdeplot(**{axis: a}, ax=ax, color=kde_color, **kde_kws)\n"
     ]
    },
    {
     "data": {
      "image/png": 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dxu9IeV7HHkALYGHaslw/142AW4nuKI0COgB3A/fF8Y8gSmwfkNQord5viH5jbo23/1/gHkmpz+QEIPDFZpEjgRVETehyilPSIUS/L2/E8TwGjMvt6JiZWYULIXhKm4CriP4IljTNTCs7Ol7WOJ6/CRhfyrZPjst3ylj+B+AzYM+0ZYfHZUfF8yfF84ellWkHbAWezxL/aWW8zkLgS3HZDmnLA9FJe2q+AFgGrM6IbxzwShn7eDl9W/GynxKd3O8bz3eK93lyGdtKlcuc3gHalVKvIH6tc4Er4mWt4roHlFBHwEfAnRnLvwZsBFqmHeuVaeuPjrfbJ+N4Ts3YzgTg5bT53wKfAi3SljUnOnn/dhnH5avxPvrH8z+O5zunlcn1s3M30clzvbRl3eL366R4/nHghjx8z54HHixhXY843pHZjnPGsf1O2vyHwCri72O87DygGOhZxnv0nbTPyxLgoRxfh+LP1y+AD9KWl/RdL/O9pozfkRLiGB3vr2kcTzvgP8AioPUufK4DMCitzLfiZVekLesVLxsWz7cA1gNXZmz/SaJmjqn5R4CnMsrMA24qZ5zjgDmA0sr8Mo5p9O5+Rj158uTJ0+5NvpOR3RrgsCzT42XUmw2cKOlqSf0VdWrORX/g6RC16QcghPAK0UnTUfGiw4DlIYRX08osAWZl2V4AJmYulPRVSa9L+pzoBDPVB6J7RtFn0/ZRTHQldFZ6fERXVtuV9ILi134IX7xTcj/RidyAkuqW4YdEx6I/0dXQtcBE7dxkraeiJjwriE6QtxKduKZe5yqiuxu3SBopaa+MfXQnunI7LuNO1hSiq/zlHT3q6Yz5OcC+afODia7er03b1zqi97YfpRtFdHI7I56/n+j9PyetTK6fncHAw0BxWhwLiT6HqThmA6Ml/VTSgWl3OPJpd7Y5OezcN+fheHuH5Vi/B9FdpztLKiCpQfwdnw9sJvp8XQPsFx+z0uTyXu/q7whEFyu2EvXFOAM4M0R9x6B8n+stwItp8/Pjf6dkWZb6HehDdAck23e+u3aMAnc/cJyklhA1FY1ju7+ccfYHHg0hhLR9PZT9sJiZWWVzkpHdthDCzMyJ6Apkae4guqJ5NvAKsELS73I4SdiHqKlAphVEVwcB2pC9k3m2ZatDCFvSF0gaTtSEaDpRM6Ij2NFkIbNvxWcZ81tKWFZan4xWRE1eMl9Xar4Fu2Z+/H68GkKYQNSUojfRldxU86Gnidqh/wgYSHSC+UYq3jhxGgIsJ3rPlkt6UdLBabFDdAV2a9qUanayUxv3HJR17FoRNRfZmjEdU9q+JLUiarL0mKRmcTv3dcCr7NxkKtfPTiuiJj+ZcXROi+N3wP8RXdl+A1gs6fvkV+qkNdt3oiyfpM+EEDYAnxN9x3LRMv63tI7z1xHdMbqVqLnUYUTHBUr/TkBu7/Wu/o5ANOpaf6LmbquAsXGzqdS+IbfP9br4e5KS+j3532c57Tcm9ZpTx7is7/yj8X7PiOdHEiVFqYseucbZhoz3O8u8mZklxKNL5VH8R/lG4EZJ7YmaalxD9Af0llKqLgMyr6YD7M2Oq83L2dEmPF1ros7dO4WSpdxZRM2bvpVaIGlQKTHtrpVEJwaZryvVmX1VPnYSQihS1Cm4Z7xoANFdguND1EcBAGV0/o3XnRF3JB1IdOL4hKR902K7GHg9y24XZlm2O1YRnXj9Nsu6daXUO5PoO/x90vp4pEjqE0J4m9w/O6uIrvxn68OwEiBEo5RdAVyhaGjUS4C/SJoXQniqlFjLYwjRZyf12d8E7NSxXFLzEurulVGuEVH/olxH20pdSCgtKTkL+HsI4fq0/ZyU4/bLfK9343cE4PX4Ts6rkhYDLxANCHAdFf+5Th3jvdj5gsxO3/kQwueSniBKLm4lSqYeSLsjkWucy/ni70u231EzM0uA72RUkBDC4hDCH4iaFPSKF2de+Ut5BTghvgoPgKTDiPohpK7uvQq0kdQ/rUw74NAcQ2pI1LQj3Xk51i23EMJ2opPEszJWnU3URn56PvajaASuVuwYpSrVUXpzWpkjiY5ltji3hhCmEHWy3YfouQbziNrld8p2RyuEUNYdrfJ6luhuzDtZ9jWvlHqjgHeJroKnT0OJTtJTdzNy/eyk4piVJY4PM3ceQnif6Ir+ZnZ8xneLpAOBbwP/CSGkEqyPgSZxzClDStjE8dr54ZjDiftS5RhC6r2/oJQyO32X4jsM52SUKem7Xq73uoTfkZyEEKYS3Q34gaLRzCr6c/020TODsn3n30trtgUwFhgk6RSiO2Vj09blGuerwKkZTfZGYGZmVYLvZOSRpH8SXYV7mahfxzFEHWdTo86kTiK+KWkssCGE8BbRCe6lRA/Ouo7oyusfgLeA8XGdJ4map4yTdDlRB8griZoipDdrKMlk4P8k/ZIoqTkROG7XX21OriR6TXcSnUQcQHQF97YQwse7uM0e8Z0LETWr+QnRFeD74vUvEzWPuS0eEWtfoo6sS1IbiE9k/0TUBvwDoo63PwPeCCGsistcBtytaBjViUQnjZ2B04nauef6AMZc/JmoecsUSX+PY92baISxl0II92VWiO+4DAQuDyE8n2X9U0Qnvr8k98/OVUSj+Twh6Q6iuxftiJpkjQkhPC/pYaLk8fV4O6m7KVPT9h2Aq0MIV5XxultIOoLoYkcrok7Z3yAayelHaeWeivd1h6QbgP2I7qBkszGO/49ESeMfgYdDCHPKiAWI7iJI+inRiEj3EH2uAtFIbPfFzSYnA9+O+2SsIkqKMp9SXtp3vdT3OoffkfK4hmiEp6+EEP5VkZ/rEMIqSX8BfiVpG1FiN4LotybzIZFPEiUk/wQWpvUpSr0HucR5HdFv2ThJ/yLqq/H1XY3fzMzyrKJ7lle3iRJGsonXjaH00aVGE/1BX0X0B/RN4OsZ27iMaOSUbcCHacsPJurYuIGo3fO9wN4ZdTsSnXBtirdxMVH/gwllxU80xOifiNosryVKXlIjWJ2cVm6nEXviZc+TMRJQaccpo9xIomRpC9EV6WuAwrT1nTJjKGE7qXLp03Kizvh9M8oOJbqqujF+D05Mfw1ETSruJkowNsXbuY+0UbbicsOIOr+uj4/ZbKK294XZjgFljFxU2rFjR2fjFURXyT8kGh2odwnH4zLSRunKsv7seN+H5/rZicvtDzxI9BneSHQF/Z/sGA3sJ0Qnj2uIkrtXSBvJjKjjbwC+Vcb7+Xza+7iVqH/IFOIT9izlhxGNJLYhfk96Zh7b+JjdEB/fFfH7dh/QbBfeoxFEydQmoqY/TwAd43V7EzUrWxvv53qi5Oh/vwVlfNdLfa/J4Xcky/EZnbn/tHVTiO54aVc+1yUdt2zHjuh35mqiO4tbiAY5OK+EmP8T17+2hPWlxhmXOYvoM7qJ6K7vYXh0KU+ePHmqElPqj45VQ3E/gw+Ihn68Mul4rPqoqM+OpGOIhuhtH3YejazCKXrg3YMhhB+XVdbMzMwqlptLVSOSLiFq3vI+UafdHxE107gjybis6qvEz86RwO2VnWCYmZlZ1eIko3rZRNQuuyNRk4AZwOAQwkeJRmXVQaV8dkII1+Rze2ZmZlY9ubmUmZmZmZnllYewNTMzMzOzvHKSYWZmZmZmeeUkw8zMzMzM8spJhpmZmZmZ5ZWTDDMzMzMzyysnGWZmZmZmlleV/pwMSb2AvwMDgM+A24GrQwjby6jXFPgLcDpRcvQ48L0QwqdpZUoaj3dLCKF+WbENHTo0PPXUUzm9DjMzMzMzQ9kWVmqSIak58AwwBzgN6ALcQJQ0/KqM6uOA7sBFRE8uvg6YAAxMKzMgS73HgP/mEt/KlStzKWZmZmZmZqWo7DsZlwANgREhhLXAZEl7AldJuj5e9gWSBgBDgEEhhKnxsiXAK5IGhxCeAQghvJxR7zCgFXBfhb0iMzMzMzPbSWX3yRgGTMpIJsYSJR6Dyqi3IpVgAIQQZgAL43UlGQWsJ7qbYWZmZmZmlaCyk4z9gbnpC0IIi4AN8bqc68XeLameJAFnA4+EEDbsUrRmZmZmZlZulZ1kNCfq7J1pdbwun/UGAu2I7pSYmZmZmVklqclD2I4iSkImlVZI0sWSZkqaWVRUVDmRmZmZmZnVYJWdZKwGmmZZ3jxel5d6kgqBM4DxIYQtpQUUQrg1hNAvhNCvdevWpRU1MzMzM7McVHaSMZeMPhSS2gONyN7nosR6sZL6ahwHtMajSpmZmZmZVbrKTjImAidIapK2bCSwEXihjHptJB2VWiCpH9A5XpdpFLAMeH53AzYzMzMzs/Kp7CTjFmAz8JCkwZIuBq4C/pw+rK2k+ZL+lZoPIUwHngbukjRC0unAPcBLqWdkpNWtT/RU8HEhhOIKf0VmZmZmZraTSk0yQgiriZoy1SF6dsXVwI3AlRlFC+My6UYS3e24A7gLmAUMz7KbYUT9NzyqlJmZmZlZAhRCSDqGKqNfv35h5syZSYdhZmZmZlZdKNvCmjyErZmZmZmZJcBJhpmZmZmZ5ZWTDDMzMzMzyysnGWZmZmZmlleFSQdgZmZWHd37yqKkQ8jq3MM7JB2CmZnvZJiZmZmZWX45yTAzMzMzs7xykmFmZmZmZnnlJMPMzMzMzPLKSYaZmZmZmeWVkwwzMzMzM8srJxlmZmZmZpZXTjLMzMzMzCyvnGSYmZmZmVleOckwMzMzM7O8cpJhZmZmZmZ55STDzMzMzMzyykmGmZmZmZnllZMMMzMzMzPLKycZZmZmZmaWV04yzMzMzMwsr5xkmJmZmZlZXjnJMDMzMzOzvHKSYWZmZmZmeeUkw8zMzMzM8spJhpmZmZmZ5ZWTDDMzMzMzyysnGWZmZmZmlldOMszMzMzMLK+cZJiZmZmZWV45yTAzMzMzs7xykmFmZmZmZnnlJMPMzMzMzPLKSYaZmZmZmeWVkwwzMzMzM8srJxlmZmZmZpZXTjLMzMzMzCyvKj3JkNRL0rOSNkhaKuk3kurkUK+ppDslrZa0RtI9klpmKddS0j8lLZe0UdJcSedXzKsxMzMzM7NMhZW5M0nNgWeAOcBpQBfgBqJk51dlVB8HdAcuAoqB64AJwMC07e8JTAU+B74LrAR6AfXy+TrMzMzMzKxklZpkAJcADYERIYS1wOQ4MbhK0vXxsi+QNAAYAgwKIUyNly0BXpE0OITwTFz0F0B9oF8IYWO87LkKfD1mZmZmZpahsptLDQMmZSQTY4kSj0Fl1FuRSjAAQggzgIXxupQLgX+lJRhmZmZmZlbJKjvJ2B+Ym74ghLAI2BCvy7le7N1UPUn7AXsBn0l6UtIWSUWS/izJzaXMzMzMzCpJZScZzYHPsixfHa/bnXpt4n+vB5YAQ4HfA5cCvytpw5IuljRT0syioqLSozczMzMzszLVpCFsFf/7TgjhGyGEKSGEG4Frge9JapStUgjh1hBCvxBCv9atW1dasGZmZmZmNVVlJxmrgaZZljeP1+1OvdS/mR29pxB1Bu+Se5hmZmZmZrarKjvJmEtG3wtJ7YFGZO9zUWK9WHpfjQXAFnbc0fjfLuJ/i8sbrJmZmZmZlV9lJxkTgRMkNUlbNhLYCLxQRr02ko5KLZDUD+gcryOEsAWYDByTUfc4oo7l83c7ejMzMzMzK1NlJxm3AJuBhyQNlnQxcBXw5/RhbSXNl/Sv1HwIYTrwNHCXpBGSTgfuAV5Ke0YGwG+Ag+Mngw+R9GPg58DvQwibK/zVmZmZmZlZ5SYZIYTVRHcW6gCPAVcDNwJXZhQtjMukG0l0t+MO4C5gFjA8Y/szgFOAg+Ltfx+4hqjzt5mZmZmZVYLKfuI3IYQ5wLFllOmUZdlnRA/bu7CMupOASbsRopmZmZmZ7YaaNIStmZmZmZlVAU4yzMzMzMwsr5xkmJmZmZlZXjnJMDMzMzOzvHKSYWZmZmZmeeUkw8zMzMzM8spJhpmZmZmZ5ZWTDDMzMzMzyysnGWZmZmZmlldOMszMzMzMLK+cZJiZmZmZWV45yTAzMzMzs7wqTDoAMzOzilYcilmwagHzV82naEMRReuLWL1pNSEEJFGgAprUa0KLhi1o2aglbRq3oXPzzrRu1BpJSYdvZlbtOMkwM7MaZ8v2Lbzw4Qs89t5jvPLxK6y/2AcAACAASURBVLxT9A4tG7Wkc/POtGzYkhYNW9C0flMKCgoIxYEQAkXri3ht2Wt8tukzlq9fzqI1i9i8bTOdm3fmgL0OoG+bvhzU5iD6t+tPswbNkn6JZmZVmpMMMzOrEUIITFs8jZtm3MRTC56iS/MuHNf5OC4feDn7t9qfPevvWe5trtu8jg9Wf8DclXOZUzSHCfMm8OaKN+nYtCMd6o6iR6v96dWqJ3vUa1wBr8jMrPpSCCHpGKqMfv36hZkzZyYdhpmZlcP24u08MOcBbph+A59u+JQL+17IKT1OYa899qqQ/W3dvpU5RXO4++WFLFg9nw9WL6RN473ps9cBHLrPobRt0rZC9purcw/vkOj+zazWydqm1HcyzMys2pqxZAaXPnEpBRRw6WGXcnzn46lTUKdC91m3Tl0OanMQCzu046gOR7GteBsfffYh7658l7+88hcaFjag3z79GND+SFo1alWhsZiZVVVOMszMrNpZtXEVP3/m5zw671F+MfAXnNHzjMQ6aBcWFNKlRVe6tOjKSd1PZvGaRcxe/ga/f/Ea2jftwMAOR9G3zcEUFvhPrpnVHv7FMzOzamXW0lmMGDeCYzsdy3MXPEfTBk2TDul/hOjQtCMdmnbkxG7DeOeTd3hu4XOMm/MAg/cbzJc7DqRBYcOkwzQzq3BOMszMrNq44/U7+Onkn3LtcddyUveTkg6nVIUFdTmoTV8OatOXJeuW8OJHU5k4fyKDOg7ihC4n0LCukw0zq7mcZJiZWZVXHIr57sTv8vSCpxl/9ni6teyWdEjl0q5JO87pM4pVG1cxZeGz/Oq5XzGs61AGdTqaugV1kw7PzCzvnGSYmVmVtr14Oxc+ciELVi3g8VGP06R+k6RD2mUtGrbgzF5nsfzz5Uxa8BRTFk7h3APOpc9eByQdmplZXjnJMDOzKmtb8TbOf/h8Fq9dzL+H/5tGdRslHVJetGnchgsOGs37n77HvW/fR8em0zin9zlVqn+Jmdnu8HMy0vg5GWZW2937yqKkQ/if4lDMv167nbVb1jHu/O9VuT4ME15bmZftbN2+lSkLn+WVJTMY2Wckh7c7PC/brUr87A6zGs3PyTAzs+rjgTnjWLN5DaP7jq5yCUY+1a1TlxO6DuWAvQ/kvrfvZU7RO4zqcy4NChskHZqZ2S4rSDoAMzOzTFM/msrbn7zNeQd+hcJa0jG6bZO2fLf/9ygOxfxu6m9ZvKbq3FUyMysvJxlmZlalzF35Lo++9yijDxpNw1r2TIl6depxRs8zOb7L8dz48l94bdmspEMyM9slbi5lZmZVxifrV3Dba7dz3gHn0rJRq6TDScxBe/eldaO9+Pcb/2bpuqWc1P1klL3Zs5lZleQ7GWZmViVsK97Gba/dxuDOx9G5eZekw0lc2yZt+U7/7/Dmije5/bXb2Va8LemQzMxy5iTDzMyqhEfnPcoedRszYN8BSYdSZTSp14SLD72YjVs38I+ZN7N1+5akQzIzy4mTDDMzS9y8lfOY/vF0zux1JiWMhlhrFRbU5bwDv0Ldgnr8dcbf2LRtU9IhmZmVyUmGmZklav3W9dw5+w7O7HUmjes1TjqcKqmO6jCy99m0aNiCG1/+Mxu3bkw6JDOzUjnJMDOzRN331n30at2bHi17JB1KlSYVMGL/4bRt0o6/z/g7W9x0ysyqMCcZZmaWmHeL5rBg9XyGdR2WdCjVhDi1x6k0a9CUW2b+w53BzazKcpJhZmaJ2Fq8lXveuofTepxG3Tq144F7+SDEmb3OQirg9tdupzhsTzokM7Mv8HMyzMwsEU+9P5E2jduwf6ueZZad8NrKSoio+ihQAaP6jGLMG2O4/537GdXn3KRDMjPbSaXfyZDUS9KzkjZIWirpN5Lq5FCvqaQ7Ja2WtEbSPZJaZpQZIylkmfavuFdkZmbltWL9Cp778DlO7nFK0qFUW4UFhXzlgK/wbtFcpix8NulwzMx2Uql3MiQ1B54B5gCnAV2AG4iSnV+VUX0c0B24CCgGrgMmAAMzys0FLsxY9uHuxG1mZvkTCNz71r0cs9+xNKvfLOlwqrUGhQ0Y3Xc0/5j5D/baYy/67HVA0iGZmQGV31zqEqAhMCKEsBaYLGlP4CpJ18fLvkDSAGAIMCiEMDVetgR4RdLgEMIzacXXhxBertiXYWZmu+rtFW+zeuMqvtT+S0mHUiO0aNiCrxz4Fe6cfSc/OuIy2u3ZLumQzMwqvbnUMGBSRjIxlijxGFRGvRWpBAMghDADWBivMzOzaqA4FDN+7niGdh1GgTz2SL50bNqRk7ufws0zb2bD1g1Jh2NmVulJxv5EzZn+J4SwCNgQr8u5XuzdLPV6SVorabOklySVlryYmVklmv7xdBoWNqBX67I7e1v5HNzmYHq07MGds+8kUJx0OGZWy1V2ktEc+CzL8tXxut2t9zpwGXAKcB5Qh6hJVv9ditbMzPJmy/YtPDLvkfiZGEo6nBrppO4nsmbTZ0yaPynpUMyslqtR96pDCH8NIfwjhPBCCOFB4DhgCfCLkupIuljSTEkzi4qKKi1WM7Pa5tmFz9CxaQc6NO2YdCg1Vh0Vcu6B5/HMwmd579N5SYdjZrVYZScZq4GmWZY3j9fltV4IYQPwJHBIKWVuDSH0CyH0a926dSkhmJnZrvp8y+dMXjCZE7oMTTqUGq9Z/WaM7D2S21+7nXVb1iUdjpnVUpWdZMwlow+FpPZAI7L3uSixXqykvhrpQjyZmVlCnl7wNH32PoBWjVolHUqt0K1FNw5uczB3zf43wX8CzSwBlZ1kTAROkNQkbdlIYCPwQhn12kg6KrVAUj+gc7wuK0kNgZOAWbsTtJmZ7br1W9fz4qIXOabjMUmHUqsM6TqEVZtW8eJHLyYdipnVQpWdZNwCbAYekjRY0sXAVcCf04e1lTRf0r9S8yGE6cDTwF2SRkg6HbgHeCn1jIz4ieAvSvqmpOMkjQSeA9oCv6+sF2hmZjt75oNn6NO6N80blja+h+VbHRVyTp9zmDD3YZZ/vjzpcMyslqnUJCOEsJqoM3Yd4DHgauBG4MqMooVxmXQjie523AHcRXR3Ynja+s1AEdGTw58EbiUakWpQCGFmXl+ImZnlZMPWDbzw4fMcvZ/vYiShdaO9GNJlCLe/djvbirclHY6Z1SKV/cRvQghzgGPLKNMpy7LPgAvjKVudTcCIPIRoZmZ5MmXhs/Rs3ZOWDVsmHUqtdcS+RzB35Vwmvv8kp/Q4NelwzKyWqFFD2JqZWdWxadtGpix8jqM7+S5GssTwniN4/qPn+Xjtx0kHY2a1hJMMMzOrEM99+DzdW3andSMPD560pvWbMqzrMMbMHsP24u1Jh2NmtYCTDDMzy7ttxVuZsnAKgzoNSjoUi/Vr24+GdRsyaYGfBm5mFc9JhpmZ5d2MJa/SpvHe7NN4n6RDsf8RZ/Q8g2c+eIal65YmHYyZ1XBOMszMLK8CgckfPM2XO3w56VAsQ7MGzTi+y2D+8+bdBIqTDsfMajAnGWZmlldziuYQAnRr2S3pUCyLI/YdwLbibfx30X+TDsXMajAnGWZmlleTFzzNwI5HAUo6FMtCiNP3H87Dcx9m3ZZ1SYdjZjWUkwwzM8ubj9d+zNJ1S+nbpm/SoVgp2jZpyyH7HMKDcx5IOhQzq6GcZJiZWd5M/uBpjmx/JHVU6c96tXI6vvPxzF05j3kr5yUdipnVQE4yzMwsL9ZtWcsby9+kf7vDkw7FclCvTn1O7XEq97x1D9uKtyUdjpnVME4yzMwsL1786CUO2LsPjeo2SjoUy1Hv1r1o3qAZUxZOSToUM6thnGSYmdluKw7bmfrRCwzY98ikQ7FyESf3OIWn5k9kzaY1SQdjZjWIkwwzM9ttb6x4k6YNmtG2SdukQ7Fyat2oNYe1PYyH5o5POhQzq0GcZJiZ2W57buEUjtx3QNJh2C46tvNxvFv0LgtWL0g6FDOrIZxkmJnZbln2+VKWfb6MPnv3SToU20X169RnWLcTue+teykOfhK4me0+JxlmZrZbnv/wefq36+9ha6u5g9v0pU5BIdMXT0s6FDOrAZxkmJnZLtu0bRMzlrzK4e2OSDoU223ilO4nM2HeI2zatinpYMysmnOSYWZmu2zGkhl0ad6ZPevvmXQolgf77tmeri268tT8iUmHYmbVnJMMMzPbZVM/msrhfvhejTK06wm88NFUPt24MulQzKwac5JhZma75KM1H7F+6+d0bdkt6VAsj5rWb8aR7Y/koTkPJR2KmVVjTjLMzGyXvPjRVPq37Y9Q0qFYng3qOIj3V8/3kLZmtsucZJiZWblt2raJmctm0a9tv6RDsQpQr049TugyhAfnPEAgJB2OmVVDTjLMzKzcXl36Kl2ad6GJO3zXWIe0OYRN2zbz+rLXkg7FzKohJxlmZlZuL340lf7t+icdhlUgqYATu53I+HcfYlvxtqTDMbNqxkmGmZmVy+I1i1i7eR3dW7jDd03XrUU3WjVqxQsfvZB0KGZWzTjJMDOzcnlx0Ysc1rYfkv+E1AbDug7jyfefZMPWDUmHYmbViP9CmJlZzrZu38LMpTM51B2+a402jdvQq3VPJvoBfWZWDk4yzMwsZ7OXz6bdnvvSrEGzpEOxSnR85yG8tOglVm9clXQoZlZNOMkwM7OcvbToJfq1PTTpMKyS7Vl/T45odziPzHsk6VDMrJpwkmFmZjn5dONKFq9dTO/WvZMOxRIwqNPRvPXJ2yxZuyTpUMysGnCSYWZmOZm2aBp92/SlsKBu0qFYAhoUNuCYTscwfu74pEMxs2rASYaZmZUpUMx/F0+jX9vDkg7FEnTEvkewfN0y5q2cl3QoZlbFOckwM7MyzV05j0Z1G9K2SdukQ7EEFRYUMqTLCYx/90ECIelwzKwKc5JhZmZl+u+il+jnYWsNOKjNQWwP23lt6aykQzGzKsxJhpmZlWrD1g289cnb9G3TN+lQrAoQYmjXYTw892G2F29POhwzq6KcZJiZWalmLZtJ9xbdaFR3j6RDsSqiW4tuNG/YnBcXvZh0KGZWRTnJMDOzUk1bPI1D2h6SdBhWxQztOown3n+czds3JR2KmVVBlZ5kSOol6VlJGyQtlfQbSXVyqNdU0p2SVktaI+keSS1LKX+apCBpZn5fgZlZ7bHi8+UUbVhJ95Y9kg7Fqph2TdrRpXkXJi+YnHQoZlYFVWqSIak58AwQgNOA3wCXAVfnUH0ccDRwETAaOAyYUMJ+GgA3Ait2N2Yzs9ps+sfTObhNX+qUfS3IaqEhXYbw7MIprNuyNulQzKyKqew7GZcADYERIYTJIYRbiBKMH0nas6RKkgYAQ4ALQgjjQwgPA18BjpI0OEuVnwBLgKfy/grMzGqJ4lDM9I9f5pB9Dk06FKuiWjRsycFtDuaJ955IOhQzq2LKlWRIGi/pREm7mpwMAyaFENIveYwlSjwGlVFvRQhhampBCGEGsDBelx5jB+CnwPd3MUYzMwPmrZxL43p7sE/jfZIOxaqw4/Y7jhlLZlC0vijpUMysCilvstASeAz4WNIfJJW3ke7+wNz0BSGERcCGeF3O9WLvZql3AzAuhPBaOWMzM7M0/108jUN9F8PKsEe9PfhSh6OYMC9rC2Yzq6XKlWSEEI4GugG3AyOBOZKmSbpIUpMcNtEc+CzL8tXxut2qJ+lYomZVv8ghFjMzK8GmbRt5+5O3/GwMy8nADgN579P3+GjNh0mHYmZVRLmbPYUQPgghXBFC2I/ohH4+USfrZZL+LenoPMeYE0mFwN+Aa0IIOXf4lnSxpJmSZhYV+VavmRnArKWz6NKii5+NYTmpV6cegzsfx/g54wmEpMMxsypgdzt+TweeA+YBjYBjgSmSZks6OEv51UDTLMubx+tKkku9b8RlxkhqJqkZUA+oE8/XzbbhEMKtIYR+IYR+rVu3LiUEM7PaY9riae7wbeVyWNv+rN60mjlFc5IOxcyqgF1KMiQNknQnsJyoD8QM4LAQQnugD/ApcFeWqnPJ6EMhqT1RgpKtz0WJ9WLpfTV6APsSDVu7Op5GAX3j/4/M5bWZmdV2KzcUsezz5ezfys/GsNwVqIChXYYyfs6DFIfipMMxs4SVd3SpKyTNJ7p7sR/wbaBtCOFbIYRZACGEOcCvgV5ZNjEROCGj/8ZIYCPwQim7ngi0kXRUWiz9gM7xOoCbgGMypknAe/H//bQgM7McTP/4Zfq2OYg6Kkw6FKtmeu/Vm7p16vHyxy8nHYqZJay8dzK+SfRQvO4hhKNDCHeHEDZlKTcX+FqW5bcAm4GHJA2WdDFwFfDn9GFtJc2X9K/UfAhhOvA0cJekEZJOB+4BXgohPBOXmR9CeD59IrrTsi6e94P5zMzKEAi8/PF0N5WyXSRO7HYij8x7hC3btyQdjJklqLxJRvsQwi9CCPNLKxRCWBVC+HeW5auB44A6REPhXk3UafzKjKKFcZl0I4nudtxB1BRrFjC8nPGbmVkp5q+aTx0Vsu+e7ZIOxaqpjk070qFpe55d+EzSoZhZgsp7L3yrpAHxg/B2IulQYEYIITM52EncnOrYMsp0yrLsM+DCeMpJCGF0rmXNzAymL57OoW0PAZR0KFaNndBlKDe/+n8c1WEgTerlMsK9mdU05b2TUdpfnbrAtt2IxczMErRx60ZeX/46fdtkGxzQLHetGrWib5uDefy9x5MOxcwSUuadDEkdgE5piw6W1CCjWAPgAmBh/kIzM7PKNGHuBPbdc1+a1s82YrhZ+Ry333HcMP0Gjul0DNAh6XDMrJLl0lzqQqI+EyGe/lFCuY3ARXmKy8zMKtmY2WM4dJ9zkw7Daog96u3B0Z0GMX7Og/zouP5Jh2NmlSyX5lI3AwcABxE1lzovnk+fegAtQgj3VVCcZmZWgZauW8qMpTPo3bpP0qFYDXJk+y+xZN0Snv3g2aRDMbNKVuadjBBCEVAEIGk/YFkIwePSmZnVIP958z+c2PVE6tapm3QoVoMUFhQyrNswfjjph7z+zdepU1Dq2DBmVoOUeSdDUqO02SKgUFKjkqaKC9XMzCpCCIExs8dwZq8zkw7FaqAD9jqARnUbMWb2mKRDMbNKlEtzqXWSUo0pPwfWlTGZmVk1MmvZLDZs3UD/dm43bxVBXDHoCn713K9Yt9mnCWa1RS4dv78GLEj7f6i4cMzMrLKNmT2GM3qegeRnY1jF6NumLwM7DOR3U3/Hdcdfl3Q4ZlYJcumT8e+0/4+p0GjMzKxSbdm+hbFvj+WxUY8lHYrVcJcfdTmD7x7MRYdcRLeW3ZIOx8wqWLkexiepUFL9jGVDJP1A0iH5Dc3MzCraE+89QbeW3ejYrGPSoVgNt3fjvbm036X8cNIPkw7FzCpBeZ/4fT9pz8mQ9D3gKeBa4GVJJ+cxNjMzq2BjZo/hzJ7u8G2V4+sHf525K+cy8f2JSYdiZhWsvEnGEcCTafM/AW4IITQEbgd+ma/AzMysYn2y/hNe+OgFTulxStKhWC1Rv7A+Vwy6gu8/9X22bPdo+GY1WXmTjJbAcgBJBwBtgVvidQ8AvfIXmpmZVaR73ryHIV2G0Lhe46RDsVpkcOfBdGzakRun35h0KGZWgcqbZKwAOsX/Hwp8FEJIjTzVECjOU1xmZlaBQgjcMfsOzup1VtKhWC109TFXc/2061m0ZlHSoZhZBSlvkvEAcJ2kPwI/A+5KW3cw8H6+AjMzs4oze/ls1m5ey4D2A5IOxWqhTs068bW+X+N7E7+XdChmVkHKm2T8HPgnsD9RB/Br09YdStQx3MzMqrg7Zt/BmT3PpEDl/TNglh+XHnYpb614i8ffezzpUMysAuTyML7/CSFsA35TwroReYnIzMwq1OZtm7nvrft4/Fyf3FlyGhQ24HfH/o7vTvwux+53LI3qNko6JDPLo12+hCWpjqRGmVM+gzMzs/x7/L3H2b/V/nRo2iHpUKyWG9RpEAfudSC/m/q7pEMxszwr78P49pR0k6SlwGZgXZbJzMyqsDtn38mZvfxsDKsarjr6Km577TbeXPFm0qGYWR6Vq7kUUX+Mk4meiTEH8CDXZmbVyNJ1S3lp0UvceIKHD7WqYe/Ge/OzL/2Mrz3yNV6+6GUKC8p7amJmVVF5v8knAD8MIdxeEcGYmVnFGjN7DCd1P4k96u2RdChm/zOqzygmzJ3AX17+Cz8+8sdJh2NmeVDePhnrgY8rIhAzM6tYIQTueP0Ozul9TtKhmO1EEtcffz3XvnQtC1YtKLuCmVV55U0ybgC+JXnMQzOz6ubFRS9SWFDIIfscknQoZl/QqVknvn3Yt/nGY9+gOPjZvmbVXXmbS7UDDgLmSXoO+CxjfQgh/CwvkZmZWV7d/trtjOwzEklJh2KW1UWHXMST7z/Jza/ezHf6fyfpcMxsN5Q3yTgTKI7rHZ9lfSB6EriZmVUhazat4dF5j/LihS8mHYpZiQoLCrlx6I2cPvZ0ju98PD1a9Ug6JDPbReV9GN9+FRWImZlVnLFvj2Vgh4G0bNQy6VDMStWleRcuG3AZX334q0z7+jSPNmVWTblvhZlZLZBqKmVWHZx/0Pk0qtuIa1+8NulQzGwXlTvJkHSgpPslLZC0WdIh8fJrJA3Lf4hmZrY73lzxJks/X8qgjoOSDsUsJwUq4E9D/sTfZvyNV5e8mnQ4ZrYLyvvE72HALKANcBdQN231ZuC7+QvNzMzy4ZaZt3BO73OoU1An6VDMcta2SVuuOfYazn7wbNZsWpN0OGZWTuVt6HgtMCaE8A1JhcCVaetmA5fkLTIzM9tt67esZ+zbY3n6q08nHYpZuZ3c/WSmLZ7GRY9exLizxuV9ZLR7X1mU1+3lw7mHd0g6BLO8KG9zqf2B++P/h4x1a4EWux2RmZnlzdi3x3JYu8No26Rt0qGY7ZIrBl3Buyvf5dZZtyYdipmVQ3mTjE+AziWs6w1UvUsCZma12C2zbuG8A85LOgyzXdagsAE3n3Qzv5zyS95Y/kbS4ZhZjsqbZIwFfiPpqLRlQVJ3oudj3JO3yMzMbLe8vux1ln++nGM6HZN0KGa7pWuLrlx99NUMv384qzauSjocM8tBeZOMXwMzgansuGvxCPA28Cbw+/yFZmZmu+OWWbcwqs8od/i2GmF4z+Ec3+V4znnwHLYXb086HDMrQ7mSjBDC5hDCycBg4N/A7cC9wEkhhJNDCFsrIEYzMyundZvXMe6dcYzs7WdjWM3xy4G/ZOO2jVz+7OVJh2JmZch5dClFQzocDxwB7B0v/hiYDjyT/9DMzGxX3ff2fRyx7xHs02SfpEMxy5vCgkL+cdI/OOnekzhkn0M4p885SYdkZiXIKcmQdDBRf4yuwHZgJSCgJVAHeF/SOSGE2RUVqJmZ5SaEwE0zbuIXA3+RdChmedeiYQtuO+U2zh1/Lh2bdmRA+wFJh2RmWZTZXErS3sAkYBNwItAkhNA2hLAP0AQ4GdgCTJK0Vw7b6yXpWUkbJC2V9BtJZTYYltRU0p2SVktaI+keSS0zylwt6S1JayWtkzRTktsKmFmt8tKil9i4bSNHdTiq7MJm1VCfvfrwl6F/Yfj9w3n/0/eTDsfMssilT8Z3gY3AwBDCpBDC5tSKuI/GRODLcZnvlLYhSc2JmlYF4DTgN8BlwNU5xDEOOBq4CBgNHAZMyCizJzAGGAmcAbwGjJV0Zg7bNzOrEf4+4+9ccNAFFKi8Y3uYVR/H7ncslw24jKH3DOWT9Z8kHY6ZZciludQQ4OYQwtqSCoQQPpP0D6IT+ytK2dYlQENgRLy9yZL2BK6SdH1J+5A0II5jUAhharxsCfCKpMEhhGfiOH6YUfVpSb2B84EHc3itZmbV2pK1S5j8wWR+e8xvkw7FrMKdd+B5LFm3hFPuO4Up509hj3p7JB2SmcVyuczVleiOQFlmxWVLMwyYlJFMjCVKPAaVUW9FKsEACCHMABbG60rzKVCvjDJmZjXCP2f9k9N7nE6T+k2SDsWsUvzkyJ/QuVlnTh17Kpu2bUo6HDOL5ZJkNAXW5FBuHVFzpdLsD8xNXxBCWARsiNflXC/2brZ6kgolNZN0HtEdkFvKiMvMrNrbsn0Lt866lQv6XpB0KGaVRhLXH389Teo14YxxZ7Bl+5akQzIzcksyRNSHIhcqY31z4LMsy1fH63a7nqQjgK3xujHA90MImX030stfHHcQn1lUVFR69GZmVdj4OePp3rI73Vt2TzoUs0pVp6AOfx36V4qLixn14Ci2FW9LOiSzWi/XXoGTJH1S2gRMrMhAy+Etok7hxwM3ATdJGlVS4RDCrSGEfiGEfq1bt66sGM3M8u6vr/yVCw7yXQyrnerWqcvNJ93MZ5s+Y9SDo3xHwyxhuXT8zmXkp1ytJmp+lal5vK60etkygC/UCyGsB2bGs89IagpcB9xX7mjNzKqJaYunsWL9CoZ0GZJ0KGaJqV9Yn9tOvY1vPfEtht8/nPFnj6dBYYOkwzKrlcpMMkII+Uwy5pLRh0JSe6AR2ftcpNcbmGX5/nxxGNtMrwEXSioMIfj+qZnVSDdMu4GvH/x16hSU+dghsxqtQWED/nnyP/nBUz/gxHtO5NFRj9K4XuOkwzKrdSp7EPX/b+++46Oq8jeOf76pJNSE3nuRJk00IkWqgFKUqqhYUFfBtqzlp+66yrorCgoqgq6iKEpxUUERECwUQQQbiBQBQXoLCQRC2vn9MYM7G4MgJHOTzPP2Na9xzj03PIPHJN+5557zEdDNzAKXPRmIb4+Nz09zXgUz+3VnKTNrBdTi9NO02gA7VGCISGG1JXELn237jIGNtPeoCPimTo3vPp6KxSrS8fWO2kdDxAPBLjImAieAWWbW2cxuAR4FxgYua2tmP5nZKydfO+eWAwuAKWZ2pZn1AaYCS0/ukWFm1f07iQ8zs45m1svMJgODgH8E7R2KiATZsyueZXDjwdojQCRAeFg4o7uMpk3VNiS8ksDGgxu9jiQSUs7kQhbv1wAAIABJREFUnoxc45xLNLNO+G7InoNvxahn8BUa2XNlv+Y/0N/3VXzF0QfAnQHHDwO7gP8DKvpfrwN6Oufm5uobERHJJw6nHubN799kwbULvI4iku+YGX9p8xeqlKhC28ltmTVgFm2qtfE6lkhICGqRAeCcWwd0PE2fGjm0HQZu8D9yOicJuDYXIoqIFBiTVk2iU81OVCpeyesoIvnW4CaDqVi8In2m9+GpLk8xtNlQryOJFHrBni4lIiK5JC0zjedWPsfNLW72OopIvtehRgdm9p/JY58/xl3z7tJeGiJ5TEWGiEgB9eb3b1Invg5NyjfxOopIgVCvdD0+uPoD1u5dS9c3unIk7YjXkUQKLRUZIiIFUGZWJv9c+k9GtB7hdRSRAqVUkVK81uc1GpVtxD8Wj2Jz4mavI4kUSioyREQKoJnrZhJXJI6LqlzkdRSRAic8LJwH2z5I7wa9mfDVBBZsXoDDeR1LpFBRkSEiUsA45/jHkn8wovUIzMzrOCIF1nllGjL8guGs3LmSCV9NICXtqNeRRAoNFRkiIgXMBxs/wDA61vzdhfpE5AzExcTxpwtuo1SRkjy2+HE2HtzgdSSRQkFFhohIAeKcY9SSUdzR+g5dxRDJJeEWQc+6l3PleVfy8tf/5v0N75HlMr2OJVKgqcgQESlAPtn6CYeOH6JHnR5eRxEpdOqXrs+dF97J5kObGb1sNPtT9nsdSaTAUpEhIlJAOOd4+NOHueeiewgPC/c6jkihVDyqODc0v5HG5Zrwz6X/ZMWO5bopXOQsBH3HbxEROTsf/fQRSalJ9Krfy+soIoWaYVxS7RJqx9fm7TVvs3bfWq5pMoSYyBivo4kUGLqSISJSAGS5LB765CH+nPBnwkzfukWCoWKxioxoPYLIsEgeX/yY9tQQ+QP0k0pEpAB498d3yXJZXFbnMq+jiISUyPBIejfow+X1ruDFVS/y4cYPyHJZXscSyfc0XUpEJJ/LzMrkkU8f4f/a/p9WlBLxSMOyDalcojLT105jw8EN3NT8ZkoWKel1LJF8S1cyRETyuWlrp1EsqhiX1rjU6ygiIa1kdEmGtRhG1ZLVGLXkcdbtX+d1JJF8S0WGiEg+lpqRykOfPMT9be7XVQyRfMAsjC61ujCo0SBe+3YyH2z8AIemT4lkpyJDRCQfG7diHA3LNiShaoLXUUQkQO34OgxvPYK1+9bw/MrnSUlP8TqSSL6iIkNEJJ/an7Kf0V+M5sG2D3odRURyUCK6BMNa3kJcTDz/WPIPdh3Z6XUkkXxDRYaISD71t8/+Rt8GfakdV9vrKCJyCuEWzuV1L6dLrS6MWT6G7/d+53UkkXxBq0uJiORDP+7/kRk/zOCzoZ95HUVEzkDzCs0pHVOaN79/g041d9O1TjcM3UcloUtXMkRE8qGRH49keOvhxMfEex1FRM5QtZLVuKP1HXy580ve+G4KWS7T60ginlGRISKSz8zeMJsNBzZw/fnXex1FRP6gktGluLXVbRw4doAXvprAicwTXkcS8YSKDBGRfORY+jFGfDSCUR1HER0R7XUcETkL0eHRXN9sKNHhUYxdPoYjaUe8jiQSdCoyRETykVGLR9GiQgvaVW/ndRQROQfhFs6ARgOoWaoWTy17isOph72OJBJUKjJERPKJ9QfWM2n1JB5p/4jXUUQkVxiX1bmMFhWb8/QXT3Ho+CGvA4kEjYoMEZF8wDnH7R/ezl0X3kWFYhW8jiMiuahDjUu5qEoCT3/xFPtT9nsdRyQotIStiBR6b3253esIObr6wmq//vvr373O/mP7GdpsqHeBRCTPXFLtEiLDIxiz/GlGXvwXysSW8TqSSJ7SlQwREY/tTN7JyAUjGdN1DBFh+uxHpLC6sPJFtK/RgbErxpKYmuh1HJE8pSJDRMRDzjmGzRnG9edfT+Nyjb2OIyJ5LKFKAgmVL2Ls8rEkn0j2Oo5InlGRISLioSnfTWF70nZGXDjC6ygiEiRtq7ejWfnzeWbFWFLSU7yOI5InVGSIiHhkZ/JORn48krHdxhIVHuV1HBEJok61OlEnvg4vrHyB9Kx0r+OI5DoVGSIiHnBkcf1713Nd0+s0TUokJBk96vakeFQxXvn6FbJclteBRHKVigwREQ8s2PwxR9KOcNdFd3kdRUQ8Yhj9Gw0g+UQSM36YjsN5HUkk16jIEBEJsp8P/8yCzfN5rvtzWk1KJMRFhEVw7fnXsf7ABhZu/tjrOCK5RkWGiEgQpWak8vLXL9O7QR+qlKjidRwRyQdiImIY2mwoC7Z8zNxNc72OI5IrVGSIiASJwzF1zZvUiqtJ03JNvY4jIvlIqSKlGNL0Gq5/73rW7V/ndRyRc6YiQ0QkSD7b+ik7kndwRb1eXkcRkXyoeskaPNT2Ia54+woOHjvodRyRcxL0IsPMGprZIjM7Zma7zOwxMws/g/NKmtlkM0s0syQzm2pmpQOOh5vZ/Wa2xMwO+h8LzOyCvH1HIiKnt/nQT3yw6UOGNL1Wy9WKyCkNaDSAbrW70X9mfzKyMryOI3LWgnrHoZnFAQuBdUBvoDYwBl+x8/BpTp8B1ANuBrKAJ4H3gLb+4zHAA8Bk4J+AA4YDS83sYufc6lx9MyIiZygpNYlJqyfRv2F/SseUPv0JIoXMe18f8DpCgfLgJQ8y5N0hPLToIZ7s8qTXcUTOSrCXNbkNXzFwpXMuGfjYzEoAj5rZaH/bb5hZAtAVaO+cW+xv2wl8aWadnXMLgeNALedcYsB5i4CN+IqNG/LyjYmI5CQjK4NJqydyYeULaVCmgddxRKQACA8L5/nuz9PjrR4kVE2gT4M+XkcS+cOCPV2qOzA/WzExDV/h0f405+09WWAAOOdWAlv9x3DOZQYWGP62NOAHoFLuxBcROXMOxxvfv0FMZAwda3XyOo6IFCClY0vzYs8XGTZnGJsObvI6jsgfFuwiowGwPrDBObcdOOY/dsbn+f34e+eZWTTQAt/VDBGRoJr300fsSP6FgY0GYZjXcUSkgGlRsQV/Tvgzfaf35Vj6Ma/jiPwhwS4y4oDDObQn+o/l9nkPAfHA82caUEQkN6zetYpPf/6M686/Xjd6i8hZu7bptdQvXZ+7593tdRSRP6TQLmFrZj3xFRn3O+c2/E6/W8xslZmt2r9/f/ACikihtSVxC1PXvMXQ84dSMrqk13FEpAAzM57o9ASLtixixg8zvI4jcsaCXWQkAjn9xI3zH8uV8/zL1k4HJjrnnv29QM65l5xzrZxzrcqWLft7XUVETmv30d1MWDWBAY36U6m4bgcTkXNXPLo4L/R8gTvm3sGWxC1exxE5I8EuMtaT7R4KM6sKxJLzPRenPM/vN/dqmFk94ENgEXDnuYQVEfkjElMTGbdiHN3rXEaDMud5HUdECpGm5ZsyovUIBr4zkLTMNK/jiJxWsIuMj4BuZlY8oG0gvuVnPz/NeRXM7JKTDWbWCqjlP3ayrSIwH9gMDHbOZeZidhGRU0pJT2HcimdJqJpAy4qtvI4jIoXQTc1vIq5IHH/99K9eRxE5rWAXGROBE8AsM+tsZrcAjwJjA5e1NbOfzOyVk6+dc8uBBcAUM7vSzPoAU4Gl/j0yMLMYfAVHHDAKaGpmF/kfzYP0/kQkBKVmpDL+y/HULV2X9tV/bzVuEZGzZ2Y83fVpXv/udZZsW+J1HJHfFdQiw7+PRScgHJgD/B14Bvhbtq4R/j6BBuK72vEqMAVYDfQNOF4eOB/fvRsfAMsDHu/m5vsQETkpLTON51c+R/li5elZt6fXcUSkkCsTW4YnOz/JkHeHkJSa5HUckVMK9o7fOOfWAR1P06dGDm2H8e3anePO3c65n0EL0YtI8KRnpTPhqxcoHl2Cvg36om9BIhIMnWt1ZtHWRQyfO5w3rnzD6zgiOSq0S9iKiOSljKwMXlo1icjwKPo37K/N9kQkqB5p9wjLdyzXsraSb6nIEBH5gzKyMpi0ehKZLotBjQcSZvpWKiLBFRsZy/ju4xnx0Qj2HN3jdRyR39BPRhGRPyAjK51JqyaS5bK4punVhFvQZ52KiADQrEIzBjcezC1zbsE553Uckf+hIkNE5Az5CoxJZOG4uokKDBHx3t0X3c2WxC1M+W6K11FE/oeKDBGRM5CemcaErybgfi0wsi+AJyISfFHhUYztNpaRH49kR/IOr+OI/EpFhojIaZzIPMFzK58jKjyaq5tcowJDRPKVxuUac0OzG7hp9k2aNiX5hooMEZHfkZpxnHErxlEiugQDGw3QTd4iki/dccEd7Dm6h9e+fc3rKCKAigwRkVNKSTvK2BXPUK5oWa5q2A9TgSEi+VRkeCRPd32a+xbex87knV7HEVGRISKSk6TUJJ5e/jQ1SlanT4M+2gdDRPK9RmUbcW3Ta7n1g1s1bUo8pyJDRCSbg8cP8tQXo2lSrgk96vZAO3mLSEFx54V3siVxC2+vfdvrKBLiVGSIiATYe3QPTy17iouqJNCxZidUYIhIQRIVHsWYrmO4e97d7EvZ53UcCWEqMkRE/H5J/oWnl4+hc61OXFLtEq/jiIiclfMrnE//hv0ZMXeE11EkhKnIEBEBNidu5tkVz9Kr/hW0qnSB13FERM7JvQn38tWur5izYY7XUSREqcgQkZD34/51vLDyBQY0GkCTck29jiMics5iImN4svOT/OnDP5GUmuR1HAlBKjJEJKR9s+cb/v3Nv7nu/GupX7q+13FERHJNm2ptaF+jPfcvvN/rKBKCVGSISMha/ssXvLVmKjc2v4kapWp6HUdEJNc93PZhZm+YzeJti72OIiFGRYaIhKRPt37Cexve55aWt1C5eGWv44iI5ImSRUry+KWPc/Psm0nNSPU6joQQFRkiElIcjrmbPmTh1oX8qdVtlI0t53UkEZE81b1ud+rG12XU4lFeR5EQoiJDREKGwzFr3X9YuXMlt7W8jVJF4ryOJCISFI93fJyJqyaydt9ar6NIiFCRISIhIctlMfX7N1l/YD23tLyV4tElvI4kIhI0FYpVYOTFI7lp9k1kZmV6HUdCgIoMESn0MrMyefWbV9l1ZBc3txxGbGSs15FERIJuSNMhGMaEryZ4HUVCgIoMESnUUjNSmbR6IkfTjnBDsxuJDo/2OpKIiCfCLIzRXUbz6OeP8kvSL17HkULOnHNeZ8g3WrVq5VatWuV1DBHJJSlpKfSe1pvjyS0Z1Hgg4RbhdSQREc8t2rKQ3Ud3c0fr4Rh2RudcfWG1PE4lBViOg0hXMkSkUEpKTaLrG10pE1uGqxsPVoEhIuLXoWYH9qXsZ/Wu1V5HkUJMRYaIFDoHjh2g45SONCjTgKe7Po2ZvtWJiJwUbhFc1fBKpv8wnWPpKV7HkUJKP3lFpFDZc3QP7V9rT0KVBB679DHCVGCIiPxG9ZI1aFy2Ee+se8frKFJI6aeviBQa25O203ZyWy6vezkPXvIgZmc211hEJBRdVucy1u7/gQ0HNngdRQohFRkiUij8dOgn2k5uy5AmQ7jrortUYIiInEZ0RBH61O/DG99PIT0zzes4UsioyBCRAm/d/nW0f609d1xwB8NaDvM6johIgdGwbEMqFa/EnI1zvI4ihYyKDBEp0L7Z/Q0dX+/I/W3uZ0jTIV7HEREpcHrV78WyX5axPWm711GkEFGRISIF1vJfltPtzW48funj9GvYz+s4IiIFUrGo4nSv24Mp371Olsv0Oo4UEioyRKRA+nTrp/Sa1oux3cbSs15Pr+OIiBRorSq2pEhkDAs2L/A6ihQSKjJEpMD5cOOHDHhnAC/2fJGONTt6HUdEpBAwrmxwJQs2L2Bvyl6vw0ghoCJDRAqUmT/M5Ib3b2By78lcXPVir+OIiBQa8THxdKrZide/fR1HltdxpIBTkSEiBcbkbyZz57w7mXrVVFpUbOF1HBGRQufiqheT6TL4/OfPvY4iBZyKDBEpEMatGMcjnz7C9H7TaVS2kddxREQKJbMwrjqvH7M3zObg8YNex5ECTEWGiORrzjn+/tnfGfflOGYNnEWd+DpeRxIRKdTKFS1H22pteeO7KTic13GkgAp6kWFmDc1skZkdM7NdZvaYmYWfwXklzWyymSWaWZKZTTWz0tn6dDGzt83sZzNzZvZonr0REclzzjnunX8vM9fNZNbAWVQpUcXrSCIiIaF9jQ4cSTvK0u1LvI4iBVRQiwwziwMWAg7oDTwG/Bn4+xmcPgPoANwMDAUuAN7L1ucyoCmwCDiWG5lFxBsZWRnc8P4NLNm+hBn9Z1CuaDmvI4mIhIwwC2NAwwG8++N7mjYlZyXYVzJuA2KAK51zHzvnJuIrMO41sxKnOsnMEoCuwPXOuf84594FhgCXmFnngK5/cc41cs7dBBzPu7chInnpePpx+k7ry/ak7UzrN41SRUp5HUlEJOSUL1aedtXbMuW713FO06bkjwl2kdEdmO+cSw5om4av8Gh/mvP2OucWn2xwzq0EtvqPnWzTemsiBdzh1MN0fbMrkeGRvNr7VWIjY72OJCISstpVb09K2jEmrZ7kdRQpYIJdZDQA1gc2OOe245va1OCPnOf342nOE5ECZGfyTtpNbkf90vUZ3308UeFRXkcSEQlpYRbGgEb9efiTh9l8aLPXcaQACXaREQcczqE90X8st887LTO7xcxWmdmq/fv3n8uXEpFzsG7/OhJeSeDyepfz9w5/J8y0+J2ISH5Qrmh5hrceznXvXkdmVqbXcaSACPmf4s65l5xzrZxzrcqWLet1HJGQtGTbEjq81oGRF49keOvhmJnXkUREJMDNLW7GzBi9bLTXUaSACHaRkQiUzKE9zn8st88TkXxu2tppXDnjSsZ3H0+/hv28jiMiIjkIszCe6fYMY1eM5ds933odRwqAiCD/eevJdg+FmVUFYsn5novA89rm0N6A3y5jK5Lr3vpyu9cRfuPqC6t5HSFHZ/p35XB8tGkui7ct5rZmb3LoYEXeO3ggj9OJiMjZqlyiMg+3e5hrZl3D6ltWUySiiNeRJB8L9pWMj4BuZlY8oG0gvuVmPz/NeRXM7JKTDWbWCqjlPyYiBUhGVgavf/saq3d/zR0X3EHFYhW9jiQiImeg33n9qBVXi/s+vs/rKJLPBbvImAicAGaZWWczuwV4FBgbuKytmf1kZq+cfO2cWw4sAKaY2ZVm1geYCix1zi0MOK+6mfUzs35AFNDQ//rXZW5FxFvJJ5IZu3wMR9KOcGvLWykefcotckREJJ8xM57s/CTvrn+XDzd+6HUcyceCWmQ45xKBTkA4MAffRnzPAH/L1jXC3yfQQHxXO14FpgCrgb7Z+lwKzPQ/igP9/f/+Yq69CRE5a9uTtvPEkieoGVeTIU2v1RK1IiIFUKkipRh/2Xhumn0Tu4/s9jqO5FPBvicD59w6oONp+tTIoe0wcIP/carzXgNeO6eAIpInVu78kmlrp9P3vD40KdfU6zgiInIOLqxyIYMbD+a6965j/pD5WnZcfkMjQkTyVEZWOm+tmcr7G2YzrOUwFRgiIoXEPQn3kJSaxFPLnvI6iuRDKjJEJM8cPH6Q0V88xcHjhxjReoRu8BYRKUQiwiJ4occLjFk+hqXbl3odR/IZFRkikie+3r2aJ5Y8QZOyjbm26RAtdSgiUghVLlGZp7s+zaB3BrE/Zb/XcSQfCfo9GSJSuJ3ITGXa2ulsPLiBoecPpWrJql5HEhGRPNS5VmdW7VrF1bOuZt418wgPy752j4QiXckQkVzzxS9fMGrxKNIz07jzwrtUYIiIhIiRF4/kWNoxHl/8uNdRJJ9QkSEi5ywlLYW75t3FVTOuolvtbvRr2J/o8GivY4mISJBEhEXwfI/neWn1S8zZMMfrOJIPqMgQkXPy8eaPafJiE3Yl7+Ljaz+mcbkmXkcSEREPlC9WnkmXT+LG2Tey/sB6r+OIx3RPhoiclW2Ht3HP/Hv4evfXPHbpY3Su1dl/5ICnuURExDstK7XkgTYP0Htab1bevJKSRUp6HUk8oisZIvKHHE07yqOfPUrzSc2pHVebRdctCigwREQk1A1uMpg2Vdtw9ayryczK9DqOeERFhoickbTMNJ778jnqjK/Dmr1rmHvNXO5JuIeYyBivo4mISD7zt/Z/43DqYUYuGOl1FPGIpkuJyO86nn6cyd9OZvSy0dSJr8OUvlNoXK6x17FERCQfiwyP5OUrXqb3tN7Ujq/N8NbDvY4kQaYiQ0RydODYASatmsT4leNpXqE547qP44JKF3gdS0RECohSRUoxpc8U+k7vS624WvSo28PrSBJEKjJE5FfOOZZuX8rEVRP5cNOHdKvTjWlXTaN+mfpeRxMRkQKoeqnqvHzFy1z/3vXMHzKfFhVbeB1JgkRFhoiwbv863l7zNtN/mI7DcU2Ta1jWdhlxMXFeRxMRkQKuZaWW/KvTv+j5Vk8+H/o59UrX8zqSBIGKDJEQlJaZxrLty5i7aS5zf5pL4vFEetXvxbOXPcv55c/HzLyOKCIihUj3ut1JOpFElze6sOzGZVQpUcXrSJLHVGSIhIDkE8ms2rWKpduXsnT7Ur7c+SW142rToUYH/tXpXzSv2Jww02JzIiKSdwY1HsTh1MN0ntKZpTcupUxsGa8jSR5SkSFSSDjnSExNZNPBTWw6tIlNBzfx/d7v+Xbvt+xL2Ufjso1pWaklVze5mrHdxhIfE+91ZBERCTG3tbqNw6mH6fJGFxZeu5DSsaW9jiR5REWGSD6W5TI5ln6clPQUUtL8j/SjHDlxhG+SNrA3ZS+7juxiR/IOdiTvINzCqRlXkxqlalCzVE261unKPQn3ULNUTcLDwr1+OyIiItzf5n4ysjLoOKUji65bpCsahZSKDJEgSstMIzE1kaTUJJJPJJN8IomjaUc5knaUo/7H8fRjpKSncCz9GGmZaRSJiCE2MoaYiFiKRhUlNjKWopFFqVQ+iuYVmtO1dlcqFa9EpeKVKBFdwuu3KCIi8rvMjIfaPsS/lv6Ljq/7Co2yRct6HUtymYoMkVx2JC2ZXcm72Zuyl/0p+9ibso+Dxw+SePwQJzLTKFWkJCWiSlAsujjFoopRLLIo8TFxVClRmdhIXxERExFDTGQMRSKKYOR8E3afFvrkR0RECiYz44FLHiDMwrj09UtZcO0CKhWv5HUsyUUqMkTOkiOLfUf3sS1pG9uStrE9aTs7k3eR5TKpUKwCZWPLUjq2NE3KNyY+Jp5S0XEUjYqFUxQNIiIiocTMuK/NfRSNKsrFr1zM/CHztS9TIaIiQ+QMZWRl8PPhn9l0aCM/HdrM1sQtRIVHU7VEFSqVqETbam2pUKwiJaKLo0JCRETk9MyM4a2HUza2LO1fa8/swbNpXbm117EkF6jIEDkF5xxr961l3k/zeGfVbrYc3kKZ2DLULFWLZhWa0ad+b4rrHggREZFzNrDxQOJj4+kxtQev9HqF3g16ex1JzpGKDJEAx9OPs3DLQmZvnM1Hmz4iPCycS2tcSqtKXenfaACxkbFeRxQRESmUutTqwpS+Uxg2Zxhr9q3hobYPaXPYAkxFhoS8w6mHmb1hNv9Z9x8+2/YZTco1oXOtzrx11VvUjquNmfHe1we8jikiIlLoNavQjDmD5zBs9jC+3/s9r/V5TR/wFVAqMiQkJR5P5N317zLzh5ks+2UZbaq14bI6l/GPTv/QJnUiIiIeqlCsAjMHzOSBhQ9wwcsXMLP/TBqWbeh1LPmDVGRIyDiadpT317/P22vfZsn2JVxS7RJ61e/FuO7jKBZVzOt4IiIi4lckogjPdHuG6T9Mp93kdjzZ+UlubH6jpk8VICoypFBLy0xj3k/zmPr9VOZtnkfrSq3pVb8XY7qOoXh0ca/jiYiIyCmYGYMaD6JFxRbc/uHtzN88nwk9J2iH8AIizOsAIrkty2WxZNsSbv3gViqOqcgTS56gecXmLLtxGa/3fZ2rGl6lAkNERKSAqFe6HnMGzyE+Jp7GExoz44cZOOe8jiWnoSsZUmis2buGqWum8taatygaVZQ+Dfow9+q5VC1Z1etoIiIicg5iImP4a/u/0rNuT0Z+PJK31rzF+O7jqVaymtfR5BRUZEiBtjVxK9PWTuOttW+ReDyRPg368GrvV0PiBjGteCUiIqGmZaWWzLtmHs+vfJ5mE5tx54V3cl+b+7QCVT5kutz0X61atXKrVq3yOoacxs7knbyz7h2m/TCNTQc3cXm9y+ldvzcXVL6AMMubGYD6hV5EREJZnxb57z6IHck7GLV4FN/u+ZZRHUdxTZNrCA8L9zpWKMrxbnwVGQFUZORfvyT9wqwfZzFz3Ux+2P8D3Wp34/J6l9O2WlsiwyPz/M9XkSEiIqEsPxYZJ63YsYLRy0aTmJrIo+0fZUCjASo2gktFxumoyMg/nHOsP7Det0nej/9hc+JmutTqQve63WlfvT1R4VFBzaMiQ0REQll+LjLA93vDku1LGLt8LEknkrjnonu47vzrNI0qOHIsMnRPhuQbJzJOsGT7Ej7c+CFzNs4hNSOVrrW7cm/CvSRUSQjKFQsREREpeMyMdtXb0bZaW1bsWMG/v/k3D3/yMDe3uJlhLYZRO7621xFDjooM8czJqxWLti5i/k/zWbx9MXXj69KhRgcm9JxAo7KNtOmOiIiInDEzI6FqAglVE9iSuIUp303holcuomGZhtzY/Eb6nteXEtElvI4ZEoI+XcrMGgLPAQnAYeDfwN+dc5mnOa8k8CzQB9/+Hh8AdzrnDmbr1xsYBdQFtvi/9vQzyabpUnnLOcePB35kybYlLN62mE9//pTwsHDaVmtLm6ptaF+jPfEx8V7HzJGmS4mISCjL79Olfk9aZhoLtyxk5rqZrNixgvbV29OvYT8ur3d5vv29o4DxfrqUmcUBC4F1QG+gNjAGX9Hw8GlOnwHUA24GsoAngfeAtgFf/xLgP8AE4E6gB/C2mSU65xbk6puR09qfsp/Vu1fz5Y4vWbFjBSt3raR4VHGjkoNtAAANDElEQVRaV27NhZUv5PYLbqdGqRq6WiEiIiJ5Jio8ih51e9Cjbg+SUpNYuGUhb699m+Fzh9OwbEO61e5G19pdaVWpFdER0V7HLTSCeiXDzB4E7gOqO+eS/W33AY8CFU625XBeAvAF0N45t9jf1hr4EujinFvob5sPRDrnOgacOxco4Zy75HT5dCXj7KRlprHx4EbW7lvLmr1r+G7vd3y751uOpB2hSbkmNKvQjBYVW9C8QnPKFyvvddyzoisZIiISygrylYxTOZFxgpW7VvL5z5+zdPtStiRuoXmF5rSp1obWlVvTomILqpesrg9DT8/71aXMbDGwyzk3KKCtGrAN6OWcm3OK8x4DbnHOVcjWvgV41zn3ZzOLBo7gm0I1MaDPdcBkIN45l/R7+VRk5Mw5R9KJJLYnbWdr4lZ+PvwzmxM3s+ngJjYe2sjO5J1UK1mN+qXrU7d0XRqUaUCTck2oVrJaofkfU0WGiIiEssJYZGR3NO0oX+/+mq92fuX74HTfGk5knKBxucacV/Y8GpVtRP0y9akVV4tqJasFfaXLfMz76VJAA+CTwAbn3HYzO+Y/lmOR4T+2Pof2H/3HwDf1KjKHfj/im45VD/jq7GIXLs45jqUf43DqYQ6nHiYxNZEDxw78+thzdA97ju5h79G97Dqyi51HdmJmVC5emWolq1G1RFWqlKzCwMYD9T+aiIiIFArFoorRrno72lVv92vbvpR9bDi4gU0HN7F692pmrpvJ9qTt7D66m/JFy1OlRJVfHxWLVaR8sfKUK1qOsrFliY+JJz4mnhLRJQrNh65/RLCLjDh8N3tnl+g/djbn1QroQw79ErMdz7fW7V/HjB9mkJmVSZbLItNlkpGVQWZWJpkuk/TMdDKyMkjPSic9K520zDTSM9M5kXmCExn+R+YJjqcf53iG75GSlkJKegpZLuuUf25UeBSlY0oTHxNPXJE4ysSWoWzRstSJr0O5ouWoWKwixaOL53jusfRjrD+QU/1XuOw8cuq/PxERkcLu+727vI7gmZLRJWlVqRWtKrX6tS09M51dR3ex5+gedh/ZzebEzazYseLXD2wTUxNP+fUiwyIpFlWMopFFiY2MJSYyhpiIGIpEFCE6IproiGiiwqOIDIskMjySqLAoIsIiiAyPJCIsgnALJzwsnHALp2vtrrSv0T4Yfw1/WMgvYWtmtwC3+F8eNbMNnoUpQUWKUimPvrrDkYUj0//862/NaaSx2//P/zhOBDFk5FEeKYg0JiSQxoME0niQ7DQmfAzDCD/5SCedRP8/5+qJE08kcogtuZDyXMxzzl2WvTHYRUYiUDKH9jj43b/pRKDsac47+Zz968dlO/4/nHMvAS/9zp8dssxslTviWp2+p4QKjQkJpPEggTQeJDuNidAWFuQ/bz3/vYcCADOrCsSS8z0XpzzPL/Bejc1Aeg79GuBb8nbjWeQVEREREZE/KNhFxkdANzMLnOA/EDgOfH6a8yr498EAwMxa4bsf4yMA59wJ4FOgf7ZzBwLLT7eylIiIiIiI5I5gFxkTgRPALDPr7L8f4lFgbOAeGWb2k5m9cvK1c245sACYYmZXmlkfYCqw9OQeGX6PAx3M7Fkz62Bmo/FtyPdYnr+zwknTyCQ7jQkJpPEggTQeJDuNiRAW1H0yAMysIfA8kIBvJah/A4865zID+vwMfOacGxrQVgp4BuiLrzj6AN+eGP+zgYG/ABkF1AW2+r/2tDx8SyIiIiIiEiDoRYaIiIiIiBRuwZ4uJfmImQ01M5fD47aAPmZm/2dmv5jZcTNbbGbNvMwtecfMIszsATPbZGYnzGyHmT2TrY/GRIgws89O8T3CmVmCv4/GQwgxs0Fm9rWZHTWznWY2xcwqZeujMREizKyPmX3v/3mx1czuzaGPxkOIUpEhAB3xTV87+ZgVcOwB4BHgSeAK4Ciw0MwqBDukBMVrwJ3A00BXfP/9j2frozEROm7nf783JAAfAweAr/x9NB5ChJn1At4GvgB6A/cD7YAPzSzw9wmNiRBgZm3w/b6wEt9/51eBJ83s7mxdNR5ClKZLhTAzGwpMBoo7547mcLwIsBcY45x7zN9WFPgZmOScezh4aSWvmdllwBzgfOfculP00ZgIYWYWBewBpjvn/qTxEFrMbBpQ1znXMqCtF/A+0NA596PGROgws/lArHOubUDbGOAGoIJzLk3jIbTpSob8nouBEsCMkw3OuRR8v4h29yqU5JkbgU9OVWD4aUyEtsvwbXD6tv+1xkNoiQSyLwd/2P9s/meNidDRDN+VzUAL8H2PSPC/1ngIYSoyBGCzmWWY2QYzuzWgvQGQCWzK1v9Hct4cUQq2C4GNZva8mSWb2TEzm5VtvrXGRGgbBOwAlvhfazyElleBtmZ2nZmVMLN6+FZzDPxwQmMidBQB0rK1nXx9nv9Z4yGEqcgIbbvxzZO8Ft88yRXARDO7x388DjgauLywXyIQ6586IYVHBWAovk+nBuG75N0SeNfMTn5KqTERoswsFugFzHD/nWer8RBCnHMf4vse8RK+KxobgHDgqoBuGhOh4yfggmxtrf3P8f5njYcQFuF1APGOc24+MD+g6SP//MmHzWycR7HEO+Z/9HbOHQQws93A5/gWB1jkYTbx3hVAUf47VUpCjJldim9T3XHAR0B5fBvqvmtmnXP4RVIKt4n4PpgcBryDr8A4ubpUlmepJN/QlQzJ7h18n0DUwPdJQzEzC8/WJw445pzLfplUCrZEYM3JAsNvKb7L3w0D+mhMhKZBwE/OuVUBbRoPoWUMMNs5d79z7jPn3HSgD9AB32pToDERSl4FXvQ/DuFbaepx/7E9/meNhxCmIkOycwHP6/FdCq+TrU8D/zEpXH7kvzdvBjL++6mUxkQIMrOS+G7SzH4VQ+MhtDQAvg1scM5twLfMdW1/k8ZEiHDOZTrnhgNlgab4rmyt8B8++azxEMJUZEh2/fCtgb8N31royUD/kwf987KvwHepXAqXD4AmZlYmoK0dvhVlvvO/1pgITX2BaH5bZGg8hJZtQIvABjM7D4jBtyQpaEyEHOdconNujX8p/NuBL5xzJwsIjYcQpnsyQpiZ/QffJjrf4/ukYaD/cadzLgtINbN/AY+YWSK+Tx3uxVecPudNaslDL+HbiG+OmT0BFMe3edJC59xSAOecxkRoGgR855z7MbBR4yHkTASeMbNd/PeejL/iKzDmgsZEKDGzi4BL8F3dKgEMBrr52wCNh1CnIiO0bcC3N0JVfFNi1gHXOefeCOjzL3zfDB4ESgOrgC7Oub1Bzip5zDmXbGYdgfHANHz3YrwP3JOtq8ZECPFf2eqEbyW6nGg8hI7x+L4v/Am4Dd8eGUuBB/17H5ykMREa0vF9MPkovim1S4A2zrk12fppPIQo7fgtIiIiIiK5SvdkiIiIiIhIrlKRISIiIiIiuUpFhoiIiIiI5CoVGSIiIiIikqtUZIiIiIiISK5SkSEiIiIiIrlKRYaIiHjCzLaamTOzOl5nERGR3KUiQ0REgs7MEoAa/peDPYwiIiJ5QEWGiIh4YTCQAnyJigwRkUJHRYaIiASVmYUDA4DZwKvAeWZ2frY+HczsezNLNbOvzKy1mR0ws0ez9ettZqv8/faY2WgziwzamxERkRypyBARkWC7FCgPTAPeAdIJuJphZpWBucA+oB8wCZgKxAR+ETMbAMwCVgK9gL8DtwD/zPN3ICIivyvC6wAiIhJyBgOHgXnOuTQzWwAMMrMHnXMOuBs4BlzhnDsOYGbJwPSTX8DMDHgKmOKcuz2g/QTwgpn90zl3MHhvSUREAulKhoiIBI2ZRQFXAu8659L8zdOA6kCC//UFwMcnCwy/2dm+VD2gGjDDzCJOPoBPgCJA47x6DyIicnoqMkREJJi6A6WAuWZWysxKAZ8BJ/jvlKkKwP7Ak5xzqcDRgKYy/ue5+KZbnXxs9bdXzYvwIiJyZjRdSkREgulkITEzh2P9zexuYA9QNvCAmRUBigU0HfI/3wJ8k8PX2ppDm4iIBImKDBERCQozKwpcAbwNvJTtcHNgLNAR+Aq4wcxiAqZM9crWfwOwE6jhnHs571KLiMjZUJEhIiLB0huIBcY5574MPGBmy4CH8F3peAS4A5hjZs/gmz71AL6bwbMAnHNZZvZn4A0zKwF8BKQBtYA+QD/n3LGgvCsREfkN3ZMhIiLBMhjYlL3AAHDOpQMz8N0UfgDoCZTDt0TtCOBGIBxIDjhnOr7CpRm+6VezgNuBr/EVHCIi4hHzrRYoIiKSf5nZJcASoKNz7lOv84iIyO9TkSEiIvmOmT2J74buPUB9fFOoDgLNnXNZXmYTEZHT0z0ZIiKSH0Xj22yvPHAEWADcqwJDRKRg0JUMERERERHJVbrxW0REREREcpWKDBERERERyVUqMkREREREJFepyBARERERkVylIkNERERERHKVigwREREREclV/w8T8wGwpsb3OgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df dupes removed\n",
    "dfdr = dfc.drop_duplicates('Subject')\n",
    "\n",
    "def plot_hist(ser, bins, sub_title='Histogram of Baseline Ages'):\n",
    "  ############# PLOT\n",
    "  fig, ax = plt.subplots(figsize=(10, 7))\n",
    "  sns.distplot(a=ser, hist=True, kde=True, rug=False, bins=bins, \n",
    "               kde_kws={\"color\": \"g\", \"alpha\": 0.1, \"linewidth\": 1, \"shade\": True})\n",
    "  # sns.histplot(data=dfc['Age'], kde=True, stat='density', ax=ax, bins=20)\n",
    "\n",
    "\n",
    "  # remove padding left and right, add some to top and bot\n",
    "  fig.subplots_adjust(left=0, right=1, top=0.8, bottom=0.1)\n",
    "\n",
    "  ax.spines[\"top\"].set_visible(False)\n",
    "  ax.spines[\"right\"].set_visible(False)\n",
    "\n",
    "  # y and x ticks\n",
    "  ax.tick_params(axis='both', which='major', labelsize=15)\n",
    "\n",
    "  # y labels\n",
    "  ax.set_ylabel(\"Density\", fontsize=15)\n",
    "  ax.set_xlabel(\"Age\", fontsize=15)\n",
    "\n",
    "  # Add title\n",
    "  fig.text(\n",
    "      0, 0.87, \"Age Density\", \n",
    "      fontsize=22, fontweight=\"bold\"\n",
    "  )\n",
    "\n",
    "  # Add subtitle\n",
    "  fig.text(\n",
    "      0, 0.82, sub_title, \n",
    "      fontsize=15\n",
    "  )\n",
    "\n",
    "  plt.show()\n",
    "  #######################\n",
    "\n",
    "plot_hist(dfc['Age'], 12)\n",
    "plot_hist(dfdr['Age'], 12, \"Histogram of Baseline Ages, Duplicates Removed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 352
    },
    "id": "s46nROg47-qr",
    "outputId": "3150ecc1-05e3-4202-d5cb-4626f1349513"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x324 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"white\", rc={\"axes.facecolor\": (0, 0, 0, 0)})\n",
    "\n",
    "# Initialize the FacetGrid object\n",
    "pal = sns.cubehelix_palette(n_colors=3, start=0.4, rot=-.25, light=.75, dark=0.25)\n",
    "g = sns.FacetGrid(dfdr, row=\"Group\", hue=\"Group\", aspect=9, height=1.5, palette=pal, row_order=['AD', 'MCI', 'CN'])\n",
    "\n",
    "# Draw the densities in a few steps\n",
    "g.map(sns.kdeplot, \"Age\",\n",
    "      bw_adjust=.5, clip_on=False,\n",
    "      fill=True, alpha=1, linewidth=1.5)\n",
    "g.map(sns.kdeplot, \"Age\", clip_on=False, color=\"w\", lw=2, bw_adjust=.5)\n",
    "\n",
    "# passing color=None to refline() uses the hue mapping\n",
    "g.refline(y=0, linewidth=2, linestyle=\"-\", color=None, clip_on=False)\n",
    "\n",
    "# Define and use a simple function to label the plot in axes coordinates\n",
    "def label(x, color, label):\n",
    "    ax = plt.gca()\n",
    "    ax.text(0, .2, label, fontweight=\"bold\", color=color,\n",
    "            ha=\"left\", va=\"center\", fontsize=16, transform=ax.transAxes)\n",
    "    plt.xlabel('Age', fontweight='bold', fontsize=15)\n",
    "    plt.setp(ax.get_xticklabels(), fontsize=13.5)\n",
    "\n",
    "g.map(label, \"Age\")\n",
    "\n",
    "# subtitle\n",
    "g.fig.suptitle('Age Distributions for Each Stage of Cognitive Impairment',\n",
    "               ha='right',\n",
    "               fontsize=20,\n",
    "               fontweight=20)\n",
    "\n",
    "# Set the subplots to overlap\n",
    "g.figure.subplots_adjust(hspace=-.25)\n",
    "\n",
    "# Remove axes details that don't play well with overlap\n",
    "g.set_titles(\"\")\n",
    "g.set(yticks=[], ylabel=\"\")\n",
    "g.despine(bottom=True, left=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jtxW8FgP491n"
   },
   "source": [
    "Age group is roughly normal with and without the duplicates. This doesn't really tell you anything tho."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Snl5q4MpTXx4"
   },
   "source": [
    "### Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 305
    },
    "id": "eT6VSsGYf7Vn",
    "outputId": "2449a767-57cc-4195-8f36-6c59f43da85d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd25f2c5940>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfc.value_counts('Group').plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "crzuVz3cmRtA"
   },
   "source": [
    "From the data, we can see that most of subjects have Mild Cognitive Impairment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-eSxo3jFTaMF"
   },
   "source": [
    "#### Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "vyaNNU9S62jc",
    "outputId": "8d2fe5a3-3267-4a99-e6da-6aaffb0fa98c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfc.value_counts('Sex').plot.bar()\n",
    "len(dfc[dfc['Sex'] == 'F']) - len(dfc[dfc['Sex'] == 'M'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pJetHPfUnzzV"
   },
   "source": [
    "There are more women subjects in the data, thouogh the difference is not very big. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eOnLImMCJ85g"
   },
   "source": [
    "Group/Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 385
    },
    "id": "dV-_5joKJ_Xr",
    "outputId": "b2861324-99d7-4e8e-bdc4-ddf0135cf1ac"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 409.1x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfc.groupby(['Sex', 'Group']).size().unstack(fill_value=0)\n",
    "sns.catplot(x = \"Group\",       # x variable name\n",
    "            hue = \"Sex\",  # group variable name\n",
    "            data = df,     # dataframe to plot\n",
    "            kind = \"count\").set(title=\"TESTINGTESTING\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qxvsUaX5oZPw"
   },
   "source": [
    "Interesting, we find that actually men subjects have more Mild Cognitive Impairment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MyLrE1uRTmh4"
   },
   "source": [
    "#### Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "id": "3eZqrz1hTn6g",
    "outputId": "daa701b8-c833-4405-e85d-6c83c71e0f02"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd25eeb7d60>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfc.value_counts('Acq_year').sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5YSxSra2p-KS"
   },
   "source": [
    "Most frequent acquisition date is 2006."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qW5AkCT-OVGR"
   },
   "source": [
    "### Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 454
    },
    "id": "vFIpJsFHqnLG",
    "outputId": "598e9c40-01ce-4c1e-8374-847af63d4f2d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(9, 6))\n",
    "fig.subplots_adjust(left=0, right=1, top=0.8, bottom=0.1)\n",
    "pal = sns.cubehelix_palette(start=.5, rot=-.5, as_cmap=True)\n",
    "\n",
    "# all the values of N3 and Scaled are 1, thus dropping them\n",
    "sns.heatmap(dfdr.corr(),annot=True,linewidths=0.95, cmap=pal, annot_kws={\"size\": 10}, ax=ax)\n",
    "  # Add title\n",
    "fig.text(\n",
    "    0, 0.87, \"Correlation Heatmap\", \n",
    "    fontsize=22, fontweight=\"bold\"\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Zik8FP0IrcdK",
    "outputId": "4e2dac69-e76b-4ccc-b21e-b013826e02c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n",
      "[1]\n"
     ]
    }
   ],
   "source": [
    "print(dfc['N3'].unique())\n",
    "print(dfc['Scaled'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 400
    },
    "id": "vbnS35-THZ6d",
    "outputId": "70ffe848-874f-4cd0-80c9-2fb7b86b42ed"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(9, 6))\n",
    "fig.subplots_adjust(left=0, right=1, top=0.8, bottom=0.1)\n",
    "pal = sns.cubehelix_palette(start=.5, rot=-.5, as_cmap=True)\n",
    "\n",
    "# all the values of N3 and Scaled are 1, thus dropping them\n",
    "sns.heatmap(dfdr.drop(['Acq_month', 'Acq_day', 'N3', 'Scaled','MPR-R'], axis = 1).corr(),\\\n",
    "            annot=True,linewidths=0.95, cmap=pal, annot_kws={\"size\": 10}, ax=ax)\n",
    "  # Add title\n",
    "fig.text(\n",
    "    0, 0.87, \"Correlation Heatmap\", \n",
    "    fontsize=22, fontweight=\"bold\"\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "27roB2kOJToC"
   },
   "source": [
    "base_path = os.path.join('/accounts', 'grad', 'khern045', 'NeuroimagingCapstone')\n",
    "bl_3t_md_file = 'ADNI1_Baseline_3T.csv'  # data dictionary\n",
    "bl_path = os.path.join(base_path, 'data', 'adni-data', bl_3t_md_file)Everything has almost 0 correlation. B1 Correction and GradWarp seem to be connected because they both correct image intensities caused by the MRI machine. We get NA values for N3 and Scaled because the values don't vary. If $x_i - \\bar{x} = 0 \\ ∀ \\ x ∈ X$ then $𝜌 = \\text{undefined}$. In this case, all x values are 1.\n",
    "\n",
    "$$\n",
    "𝜌_{xy} = \\frac{\\sum(x_i - \\bar{x})(y_i - \\bar{y})}{\\sqrt{\\sum(x_i - \\bar{x})^2 \\sum(y_i - \\bar{y})^2}}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "FK3FpTOHU1hp"
   },
   "outputs": [],
   "source": [
    "# clean with duplicates\n",
    "dfc.to_parquet(os.path.join(base_path, 'data', 'adni-data', 'ADNI1_Baseline_3T_cwd.parquet'))\n",
    "# clean with out duplicates\n",
    "dfdr.to_parquet(os.path.join(base_path, 'data', 'adni-data', 'ADNI1_Baseline_3T_cwod.parquet'))"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}