tableone.ipynb

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    "<a href=\"https://colab.research.google.com/github/tompollard/tableone/blob/master/tableone.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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   "source": [
    "# Demonstrating the `tableone` package"
   ]
  },
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   "source": [
    "In research papers, it is common for the first table (\"Table 1\") to display summary statistics of the study data. The `tableone` package is used to create this table. For an introduction to basic statistical reporting in biomedical journals, we recommend reading the [SAMPL Guidelines](http://www.equator-network.org/wp-content/uploads/2013/07/SAMPL-Guidelines-6-27-13.pdf). For more reading on accurate reporting in health research, visit the [EQUATOR Network](http://www.equator-network.org/)."
   ]
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   "source": [
    "## Contents\n",
    "\n",
    "Set up:\n",
    "\n",
    "- [Suggested citation](#citation)\n",
    "- [Installation](#installation)\n",
    "\n",
    "Example usage:\n",
    "\n",
    "- [Creating a simple Table 1](#simple-example)\n",
    "- [Creating a stratified Table 1](#stratified-example)\n",
    "- [Adding p-values and standardized mean differences](#pval-smd)\n",
    "- [Using a custom hypothesis test to calculate P-Values](#custom-htest)\n",
    "\n",
    "Exporting the table:\n",
    "\n",
    "- [Exporting to LaTex, Markdown, HTML etc](#export)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## A note for users of `tableone`"
   ]
  },
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   "source": [
    "While we have tried to use best practices in creating this package, automation of even basic statistical tasks can be unsound if done without supervision. We encourage use of `tableone` alongside other methods of descriptive statistics and, in particular, visualization to ensure appropriate data handling. \n",
    "\n",
    "It is beyond the scope of our documentation to provide detailed guidance on summary statistics, but as a primer we provide some considerations for choosing parameters when creating a summary table at: http://tableone.readthedocs.io/en/latest/bestpractice.html. \n",
    "\n",
    "*Guidance should be sought from a statistician when using `tableone` for a research study, especially prior to submitting the study for publication*."
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "## Suggested citation <a name=\"citation\"></a>\n",
    "If you use tableone in your study, please cite the following paper:\n",
    "\n",
    "> Tom J Pollard, Alistair E W Johnson, Jesse D Raffa, Roger G Mark; *tableone*: An open source Python package for producing summary statistics for research papers, *JAMIA Open*, Volume 1, Issue 1, 1 July 2018, Pages 26–31, https://doi.org/10.1093/jamiaopen/ooy012\n",
    "\n",
    "Download the BibTex file from: https://academic.oup.com/jamiaopen/downloadcitation/5001910?format=bibtex"
   ]
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   "source": [
    "## Installation <a name=\"installation\"></a>\n",
    "\n",
    "To install the package with pip, run the following command in your terminal: ``pip install tableone``. To install the package with Conda, run: ``conda install -c conda-forge tableone``. For more detailed installation instructions, refer to the [documentation](http://tableone.readthedocs.io/en/latest/install.html)."
   ]
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   "source": [
    "## Importing libraries\n",
    "\n",
    "Before using the `tableone` package, we need to import it. We will also import `pandas` for loading our sample dataset and `matplotlib` for creating plots."
   ]
  },
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   "source": [
    "# Import numerical libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
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   "source": [
    "# Import tableone\n",
    "try:\n",
    "    from tableone import TableOne, load_dataset\n",
    "except (ModuleNotFoundError, ImportError):\n",
    "    # install on Colab\n",
    "    !pip install tableone\n",
    "    from tableone import TableOne, load_dataset"
   ]
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   "source": [
    "## Loading sample data\n",
    "\n",
    "We begin by loading the data that we would like to summarize into a Pandas DataFrame. \n",
    "- Variables are in columns\n",
    "- Encounters/observations are in rows."
   ]
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   "source": [
    "# Load PhysioNet 2012 sample data\n",
    "data = load_dataset('pn2012')"
   ]
  },
  {
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       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>SysABP</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>ICU</th>\n",
       "      <th>MechVent</th>\n",
       "      <th>LOS</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SICU</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76</td>\n",
       "      <td>105.0</td>\n",
       "      <td>175.3</td>\n",
       "      <td>80.6</td>\n",
       "      <td>CSRU</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44</td>\n",
       "      <td>148.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.7</td>\n",
       "      <td>MICU</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.3</td>\n",
       "      <td>84.6</td>\n",
       "      <td>MICU</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MICU</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   Age  SysABP  Height  Weight   ICU  MechVent  LOS  death\n",
       "0   54     NaN     NaN     NaN  SICU         0    5      0\n",
       "1   76   105.0   175.3    80.6  CSRU         1    8      0\n",
       "2   44   148.0     NaN    56.7  MICU         0   19      0\n",
       "3   68     NaN   180.3    84.6  MICU         0    9      0\n",
       "4   88     NaN     NaN     NaN  MICU         0    4      0"
      ]
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   "source": [
    "data.head()"
   ]
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   "source": [
    "## Example 1: Simple summary of data with Table 1 <a name=\"simple-example\"></a>\n",
    "\n",
    "In this example we provide summary statistics across all of the data."
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "# View the tableone docstring\n",
    "TableOne??"
   ]
  },
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   "source": [
    "# Create a simple Table 1 with no grouping variable\n",
    "# Test for normality, multimodality (Hartigan's Dip Test), and far outliers (Tukey's test)\n",
    "\n",
    "# for versions >= 0.7.9\n",
    "table1 = TableOne(data, dip_test=True, normal_test=True, tukey_test=True)\n",
    "\n",
    "# for versions < 0.7.9\n",
    "table1 = TableOne(data)"
   ]
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       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Missing</th>\n",
       "      <th>Overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <th></th>\n",
       "      <td></td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>0</td>\n",
       "      <td>65.0 (17.2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SysABP, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>291</td>\n",
       "      <td>114.3 (40.2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Height, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>475</td>\n",
       "      <td>170.1 (22.1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>302</td>\n",
       "      <td>82.9 (23.8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">ICU, n (%)</th>\n",
       "      <th>CCU</th>\n",
       "      <td>0</td>\n",
       "      <td>162 (16.2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSRU</th>\n",
       "      <td></td>\n",
       "      <td>202 (20.2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MICU</th>\n",
       "      <td></td>\n",
       "      <td>380 (38.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SICU</th>\n",
       "      <td></td>\n",
       "      <td>256 (25.6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MechVent, n (%)</th>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>540 (54.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>460 (46.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOS, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>0</td>\n",
       "      <td>14.2 (14.2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">death, n (%)</th>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>864 (86.4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>136 (13.6)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br />[1] Hartigan's Dip Test reports possible\n",
       "                                  multimodal distributions for: Age, SysABP, Height, LOS.<br />[2] Normality test reports non-normal\n",
       "                                  distributions for: Age, SysABP, Height, Weight, LOS.<br />[3] Tukey test indicates far outliers\n",
       "                                  in: Height, LOS.<br />"
      ],
      "text/plain": [
       "                       Missing       Overall\n",
       "n                                       1000\n",
       "Age, mean (SD)               0   65.0 (17.2)\n",
       "SysABP, mean (SD)          291  114.3 (40.2)\n",
       "Height, mean (SD)          475  170.1 (22.1)\n",
       "Weight, mean (SD)          302   82.9 (23.8)\n",
       "ICU, n (%)        CCU        0    162 (16.2)\n",
       "                  CSRU            202 (20.2)\n",
       "                  MICU            380 (38.0)\n",
       "                  SICU            256 (25.6)\n",
       "MechVent, n (%)   0          0    540 (54.0)\n",
       "                  1               460 (46.0)\n",
       "LOS, mean (SD)               0   14.2 (14.2)\n",
       "death, n (%)      0          0    864 (86.4)\n",
       "                  1               136 (13.6)\n",
       "[1] Hartigan's Dip Test reports possible\n",
       "                                  multimodal distributions for: Age, SysABP, Height, LOS.\n",
       "[2] Normality test reports non-normal\n",
       "                                  distributions for: Age, SysABP, Height, Weight, LOS.\n",
       "[3] Tukey test indicates far outliers\n",
       "                                  in: Height, LOS."
      ]
     },
     "execution_count": 7,
     "metadata": {},
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    }
   ],
   "source": [
    "# View Table 1 (note the remarks below the table)\n",
    "table1"
   ]
  },
  {
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    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The pd.DataFrame object can be accessed using the `tableone` attribute\n",
    "type(table1.tableone)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "**Summary of the table**:\n",
    "- the first row ('`n`') displays a count of the encounters/observations in the input data.\n",
    "- the '`Missing`' column displays a count of the null values for the particular variable.\n",
    "- if categorical variables are not defined in the arguments, they are detected automatically.\n",
    "- continuous variables (e.g. '`age`') are summarized by '`mean (std)`'.\n",
    "- categorical variables (e.g. '`ascites`') are summarized by '`n (% of non-null values)`'.\n",
    "- if `label_suffix=True`, \"mean (SD); n (%);\" etc are appended to the row label."
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Exploring the warning raised by Hartigan's Dip Test\n",
    "\n",
    "Hartigan's Dip Test is a test for multimodality. The test has suggested that the `Age`, `SysABP`, and `Height` distributions may be multimodal. We'll plot the distributions here."
   ]
  },
  {
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    {
     "data": {
      "text/plain": [
       "(-30, 250)"
      ]
     },
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IcET47D522rFjR2fo9Ud33HEHp512GrfccottNTQ2NjJv3jzWrFnT5aZRX6qrqyM+Pp6Kigpmz57NmjVryMjI4PHHHycxMbHL70tX/02NMRssy5rZ1zrUyiIiIiJdem7bc8RHxLN0zFKf3udr479GWWMZqw6u8ul9pGszZsxgy5YtXHfddbbWERMTw/3338/hw4cH/N6XXHIJ06ZNY+7cufzgBz/oXCEfNGgQN97ou3+UHk+tLCIiInKC4vpi3t3/LjdOvJH4yJ5PSOyPs4eezZC4Iby480Xmj5jf8wvEqzZs2GB3CZ0WLFhgy32P7yv38PTcDxStmIuIiMgJXs97HZfl4l/G/YvP7xXmCOPKcVeyrngde6u8fxy7SKBQMBcREZFjWJbF6/mvMytjFlkJWQNyzyWjlxDuCOcvu/8yIPcT8UcK5iIiInKMz0s/52DtQZ9NYulKSkwK52ady9sFb9Puau/5BSJBSMFcREREjvFa3mvEhsdy4fCBHVu3MHsh5Y3lfF76+YDeV8RfKJiLiIhIp6a2Jv5R8A8WZC8gNiJ2QO99TtY5xITH8Pa+twf0vqEiPv7YTbzPPfccd9xxx0lf88Ybb/Dggyc/qXTVqlVccsklXX7uiSeeoKGh+8OjrrjiCvbu7f++gquvvvqYA5QClYK5iIiIdFpbuJaGtgYW5Swa8HvHRsRybta5vLv/Xdpc/T9KXfpv8eLF3HvvvX1+/cmC+bZt22hvb2fkyJF9vr7HbbfdxsMPP9zv69hNwVxEREQ6vX/gfRIjE5mZ0eczUvplYc5CjjQf4dOiT225f6gqKytj6dKlzJo1i1mzZrFmzRrg2FX1/Px8zjjjDHJzc7nvvvuOWYGvq6vjiiuuYPz48Vx77bVYlsUvf/lLCgsLmTdvHvPmzTvhns8//zyXXXZZ58dvv/0206dPZ+rUqVxwwQUA/OhHP+LGG29k7ty5jBgxgldffZW7776b3NxcFi5cSGtrKwBz587lvffeo60tsP9BpznmIiIiAkCrq5VVB1dx3rDzbDuB8+yhZxMbHst7B97jrKFn2VKDrz306UPsrNzp1WuOTx7PPbPvOelzGhsbmTZtWufHlZWVLF68GIDly5fz3e9+l7PPPpsDBw6wYMECduzYcczrly9fzvLly7nmmmv4zW9+c8znNm7cyLZt2xgyZAhz5sxhzZo1fPvb3+axxx7jgw8+IDU19YR61qxZwzXXXAO4/2Fw66238uGHH5KTk0NlZWXn8/Lz8/nggw/Yvn07Z555Jq+88goPP/wwS5Ys4c033+Tyyy/H4XAwevRoNm/ezIwZM07tm+dHFMxFREQEgPXF66lpqeGC4RfYVkNUWBRnZJ7B6sOrsSwLY4xttQSbmJgYNm3a1Pnxc889x/r16wF477332L59e+fnampqqKurO+b1H3/8Ma+99hoAX/va1/je977X+bnZs2eTleUerTlt2jQKCgo4++yzT1pPUVERTqcTgE8++YRzzjmHnJwcAJKTkzuft2jRIiIiIsjNzaW9vZ2FCxcCkJubS0FBQefz0tLSKCwsVDAXERGRwPf+gfeJCY/hrCH2rlTPzZrLyoMrya/KZ/Tg0bbW4gs9rWzbweVy8cknnxAdHd2n10dFRXW+HxYW1quWkpiYGJqamnp9bYfDQUREROc/1hwOxzH3aWpqIiYm5lRL9yvqMRcREREsy+KDgx8wZ8gcosP7Fs685eyh7pXW1YdX21pHKLnooov47//+786Pj15Z9zjjjDN45ZVXAHjhhRd6dd2EhARqa2u7/NyECRPIy8vrvPaHH37Ivn37AI5pZemt3bt3M3ny5FN+nT9RMBcRERHyq/IpbShlbtZcu0shIy6DMYPH8NHhj+wuJWT88pe/ZP369UyZMoWJEyee0EMO7gkrjz32GFOmTCEvL4+kpKQer/uNb3yDhQsXdrn58+KLL2bVqlUAOJ1OnnrqKb761a8ydepUrrrqqlOqv6SkhJiYGDIyMk7pdf7GWJZldw0+N3PmTMvTQyUiIiIn+uO2P/LI+kf4x9J/kBmfaXc5PLbhMf60/U98dNVHxEfG9/wCP7djxw4mTJhgdxn90tDQQExMDMYYXnjhBVasWMHrr7/e5+s1NjYyb9481qxZQ1hYWL9qe/zxx0lMTOSWW27p13VORVf/TY0xGyzL6vNII62Yi4iICGuL1pKTlOMXoRxg7tC5tLnaWFe0zu5SpMOGDRuYNm0aU6ZM4de//jW/+MUv+nW9mJgY7r//fg4fPtzv2gYNGsSNN97Y7+vYTZs/RUREQlxzezMbijewdOxSu0vpNC1tGjHhMXxW8hkXjLBvSox8ae7cuWzevNmr11ywYIFXrrNs2TKvXMduWjEXEREJcRtLN9LU3mT7NJajRTgimOKcwvpitaJK6NCKuYgEnI0HjvDmliJKa5vJTo3jq6cNJTs1zu6yRALW2sK1hDvCmZluz2mf3ZmZPpNfb/o11c3VJEX1vNHQ32kue/Dw1R5NrZiLSMCob27jOy9sZMmv1/KnT/bz+YEj/GrlHs7/xSoefWcX7a7g38wu4gvritYx1TmV2IhYu0s5xoz0GVhYbCzdaHcp/RYdHU1FRYXPAp0MHMuyqKio6PPM95PRirmIBITaplZueOZTthyqZvkFY7j1nJHER4VTWtvEw2/v4lcf5HGgsoHHr5pGmEMrUiK9Vd9az87Kndyae6vdpZxginMKEY4I1hev57xh59ldTr9kZWVx6NAhysrK7C5FvCA6OrrzpFNvUjAXEb/X7rJY/sImthyq5smvTWfh5C/n1KYlRPPolVMZ5Yznobd34kyI4geXTLSxWpHAsrl0My7LxfT06XaXcoKosChyU3PZULLB7lL6LSIiovO4eZHuqJVFRPzef6/cw8qdpfzw0onHhPKj3XbeKG48cwRPr97HO9uKB7hCkcC1oXQDYSaMqc6pdpfSpRnpM9hRuYP61nq7SxHxOZ8Gc2PMQmPMLmNMnjHm3i4+H2WMebHj8+uMMdkdj883xmwwxnzR8ef5R71mVcc1N3W8pfnyaxARe+0oquFXK/O4fNoQrj9jxEmf+58XT2Ty0ETue20r1Y2tA1ShSGDbWLqRccnjiIvwzw3UMzNm0m61s7nUu2P6RPyRz4K5MSYMeBJYBEwErjHGHP/75VuAI5ZljQYeBx7qeLwcuNSyrFzgRuBPx73uWsuypnW8lfrqaxARe7W1u7jnlS0kxUTww0sn9TjNIDLcwc+XTKGirplH39k1QFWKBK7W9la2lG1hepr/tbF45KbmAvBF+Rc2VyLie75cMZ8N5FmWtdeyrBbgBeCy455zGfCHjvdfBi4wxhjLsjZallXY8fg2IMYYE+XDWkXED6347CBbDlXzo8WTGBwX2avX5GYlccOZ2Ty/bj95pXU+rlAksG2v3E5ze7Nf9pd7JEQmkJOUw9byrXaXIuJzvgzmQ4GDR318qOOxLp9jWVYbUA2kHPecpcDnlmU1H/XYsx1tLD8w3SyhGWO+YYxZb4xZrx3QIoGnoaWNX76/h9nZyVwy5dSOCL/z/NFER4Tx+Hu7fVSdSHD4vORzAE5LO83mSk4uNzWXL8q/0KhBCXp+vfnTGDMJd3vLN496+NqOFpe5HW/Xd/Vay7KesixrpmVZM51Op++LFRGv+sPa/ZTVNvP9heNO+UCOlPgobp6Tw5tbithRVOOjCkUC38bSjQxPGE5qTKrdpZzU5NTJVDRVUFyvjd0S3HwZzA8Dw476OKvjsS6fY4wJB5KAio6Ps4C/AjdYlpXveYFlWYc7/qwF/hd3y4yIBJHqxlZ+88985o1zMis7uU/XuHXuSGIjw/jdR3u9XJ1I8NhWvo1cZ67dZfRIfeYSKnwZzD8DxhhjcowxkcDVwBvHPecN3Js7Aa4AVlqWZRljBgFvAvdalrXG82RjTLgxJrXj/QjgEkBNZyJB5ncf7qW6sZXvLRjX52skxUZw5Yws/m9zIaU1TV6sTiQ4lNSXUNpYyuSUyXaX0qOxg8cS4YhQn7kEPZ8F846e8TuAd4AdwEuWZW0zxvzYGLO442lPAynGmDzgLsAzUvEOYDTwX8eNRYwC3jHGbAE24V5x/52vvgYRGXhltc08s2Yfl04dwqQhSf261rI5ObS5LP70yX4vVScSPLZWuEPu5FT/D+aRYZGMTx6vFXMJej49+dOyrLeAt4577L+Oer8JuLKL1z0APNDNZWd4s0YR8S9PfpBHc5uLu+aP7fe1slPjOH9cGi9+dpDlF4whPMyvt9WIDKht5dsIM2GMTx5vdym9Mjl1Mq/lvUa7q50wR5jd5Yj4hH5KiYjfOHSkgf9dd4B/mZlFTqp3Dju5cuYwSmub+WhPuVeuJxIstpZvZczgMUSHR9tdSq9MTp1MY1sj+6r32V2KiM8omIuI33jivT1g4NsXjPHaNc8fn0ZyXCR/2XCw5yeLhAjLsthWsY1JKZPsLqXXPCv7u47o8DAJXgrmIuIX8kprefXzQ9xwxggyk2K8dt3IcAeXTxvKe9tLOVLf4rXrigSyg7UHqWmpCYj+co+cpBwiHBEK5hLUFMxFxC88+s5uYiLCuO28UV6/9pUzs2hpd/F/Wwp7frJICPBMNwmkYB7hiGDUoFHsrtTBYRK8FMxFxHafHzjC29uK+cY5o0iJj/L69SdkJjI6LZ6/f6HDSUQAtlVsIyosilGDvP8PYV8aO3gsOyt32l2GiM8omIuIrSzL4sG/7yQ1PpKvz83x2X0WTc5g3b4KKuqafXYPkUCxq3IXoweNJsIRYXcpp2Tc4HFUNFVQ3qjN3BKcFMxFxFardpXx6b5Kvn3BGOKifDfBdeHkDFwWvLu9xGf3EAkElmWx68iugBmTeDRPzWpnkWClYC4itml3WTz09k6GJ8dy9azhPr3XxMxEhifH8vetameR0FbaUEpVcxVjB/f/rICBNi7ZfRqwNoBKsFIwFxHb/O+6/ewsruWeheOJDPftX0fGGBZOzmBtfjnVja0+vZeIP/OEWk/IDSRJUUmkx6YrmEvQUjAXEVtU1DXzyDu7OGtUCl/JzRiQe86fmE5ru8XaPPWnSujaVekOtYG4Yg7uf1B4vgaRYKNgLiK2ePQfu2hoaef+xZMwxgzIPacNG0RCVDj/3F02IPcT8Ue7juxiaPxQEiIT7C6lT8YNHse+6n20tOtcAgk+CuYiMuA2H6zihc8OctNZ2YxJH7hwEBHm4KzRKXy4uwzLsgbsviL+ZFflroBdLQcYPWg07VY7BTUFdpci4nUK5iIyoNraXfzHX7/AGR/Fty8cM+D3P2esk8LqJvLL6gb83iJ2a2htYH/N/oCcyOLhmb2+t3qvzZWIeJ+CuYgMqOfWFrCtsIYfLZ5EYvTAz1A+Z4wTgH/uVp+5hJ68qjwsLMYNDryNnx4jEkfgMA72VimYS/BRMBeRAXO4qpHH3t3N+ePTWDR5YDZ8Hm9YciwjnXF8qD5zCUG7j7jnf49NDtxWlujwaIbGDyW/Kt/uUkS8TsFcRAaEZVn88PWtWBYDuuGzK3NHp/Lpvkpa21221SBih7yqPGLCYxgaP9TuUvplVNIotbJIUFIwF5EB8c62Yt7bUcp3549hWHKsrbXMzkmhsbWdbYU1ttYhMtDyq/IZmTQShwnsH/8jB42koKaANleb3aWIeFVg/58pIgGhqbWdH//fdsZnJLBsTo7d5TArZzAAn+6rsLkSkYG1t2pv5+bJQDYyaSRtrjYO1h60uxQRr1IwFxGfe3r1Pgqrm/jR4klEhNn/105aQjQ5qXF8uu+I3aWIDJialhpKG0uDIph3TmbRBlAJMvb/hBSRoFZW28yvP8hj/sR0zhiZYnc5nWZlD+azgkpcLs0zl9DgCbGjkgI/mOckuX/zpj5zCTYK5iLiU09+kEdTm4t7F/nX3ORZ2clUN7ayp1TzzCU05FXlAQTFinlcRBwZcRnkV2syiwQXBXMR8ZnyumZe+OwAS04byihnvN3lHOP0HPfqvfrMJVTkV+UTEx7DkPghdpfiFaOSRqmVRYKOgrmI+Myza/bR3ObiX8/1vxW6YckxpCVEsWG/+swlNOyt3ktOUk7AT2TxyEnKYV/1PlyWxp5K8AiO/ztFxO80ttZz8zIAACAASURBVLTzp4/3s3BSBqPT/Gu1HMAYw9Rhg9hyqNruUkQGRF5VXlD0l3tkJ2bT1N5EaUOp3aWIeI2CuYj4xJtfFFHT1MaNZ2XbXUq3pmYlsbe8nurGVrtLEfGp2pZaShuCYyKLx/DE4QAcqDlgcyUi3qNgLiI+seLTA4x0xnF6TrLdpXRrStYgAL7QqrkEOc/x9cEUzLMTswEoqCmwtQ4Rb1IwFxGv211Sy4b9R/ja7OEYY+wup1tTspIA2HyoyuZKRHxrX/U+wH0wT7BIj0snKixKK+YSVBTMRcTr3thUSJjDsOS0oXaXclKDYiPJTolli4K5BLmCmgLCHeFBM5EFwGEcDEsYxv6a/XaXIuI1CuYi4lWWZfHmF0WcOTKFlPgou8vp0ZQsbQCV4FdQXcDwhOGEO8LtLsWrRiSOYH+tgrkEDwVzEfGq7UU17Cuv5+IpmXaX0itTspIoqm6itLbJ7lJEfGZ/zf7OnuxgMiJxBAdrD9Luare7FBGvUDAXEa96c0sRYQ7DgkkZdpfSK1OHaQOoBLd2VzsHag8wImmE3aV43YjEEbS52iisL7S7FBGvUDAXEa96f0cps7OTSY6LtLuUXhmfkQDAjqIamysR8Y3CukJaXa3kJObYXYrXjUh0/2NDG0AlWCiYi4jXFFU3squklvPGOe0updcSoiMYlhzDjuJau0sR8QnPOMHspGxb6/AFTzDXyEQJFgrmIuI1H+4uA+C8cWk2V3JqJmQkslMr5hKkPKHVE2KDSUp0CnERcVoxl6ChYC4iXrNqVxkZidGMTY+3u5RTMj4zkX3l9TS1agOZBJ+C6gISIxMZHDXY7lK8zhjD8IThmswiQUPBXES8oq3dxeq8cs4d6/TrQ4W6MiEjAZcFe0rq7C5FxOv21+wnOyk74P6/7K0RiSO0Yi5BQ8FcRLxie1ENtU1tzBmTancpp2x8ZiIAO4rVziLBZ1/NvqAcleiRlZBFUV2RRiZKUFAwFxGv+HRfJQCzs5NtruTUjUiOJSYiTJNZJOg0tDZQ2lAa3ME8Pos2q43ihmK7SxHpNwVzEfGKdfsqGZESS0ZStN2lnDKHwzAuI4GdRZrMIsHFc1x9MG789MhKyALgUO0hmysR6T8FcxHpN5fLYn1BZUCulntMyExgZ3ENlmXZXYqI1xyodfdeB3MwH5YwDFAwl+CgYC4i/ZZXVseRhlZm5QRuMB+XnsCRhlbK6prtLkXEaw7WHgS+XFUORumx6YSbcA7VKZhL4FMwF5F+C+T+co9Rae4Rj/ml9TZXIuI9B2sPds76DlZhjjCGxA/RirkEBQVzEem3zQerSI6LZERKrN2l9NloTzAv08hECR4Hag4wPHG43WX4XFZCloK5BAUFcxHpty2HqpmSlRTQc5IzEqOJjQwjr1TBXILHwdqDnT3YwSwrPkutLBIUFMxFpF8aWtrYU1rLlKxBdpfSL8YYRjnjtWIuQaOprYmShpLQCOYJWVQ1V1HboslKEtgUzEWkX7YersFlwdSsJLtL6bfRafHka8VcgsThusMADE8IjVYW0GQWCXwK5iLSL1sOVQEE/Io5wChnHIXVTdQ3t9ldiki/eY6pD4kV8/iOYK52FglwCuYi0i+bDlYxdFAMzoQou0vpt1FO9wbQfeWazCKBzzMqMVQ2f4JWzCXwKZiLSL94Nn4GA89kFm0AlWBwoPYACZEJJEUFx/+fJ+P5OhXMJdApmItInx2pb+FAZUNQtLEADE+JJcxhtAFUgsLB2oMh0V/uocksEgwUzEWkz3YU1QAweWiizZV4R1R4GMOTY7ViLkEh1IL5kPghFNYV2l2GSL8omItIn+0odo8mG58RHMEcYGRqnHrMJeC1uloprCvs7L0OBUPihlBUX4RlWXaXItJnCuYi0me7imtIjY8Mio2fHiNS4thf0aAf7hLQiuuLabfaQ2Iii8eQ+CE0tzdT0VRhdykifaZgLiJ9trO4NqhWywGyU2NpbG2nrLbZ7lJE+syzCTKkVszjhwConUUCmoK5iPRJu8tiV3Et4zMS7C7Fq4YnxwJQUNFgcyUifec5XGho/FCbKxk4ncG8XsFcApeCuYj0SUFFPc1tLsZnBtmKeUocAPsr1Gcugetw3WHCTTjpsel2lzJghsRpxVwCn4K5iPTJziLPxs/gWjEfOjiGMIdhv1bMJYAdrj1MZnwmYY4wu0sZMPGR8SRGJiqYS0BTMBeRPtlZXIPDfHkoT7CICHMwdFAMBVoxlwB2qO5QSLWxeAyJd09mEQlUCuYi0ic7imoZ6YwnOiL4VuRGpMRyoFIr5hK4DtcdDs1gHqdZ5hLYFMxFpE92FtcEXRuLR3aKe5a5RiZKIGpobaCyqTKkJrJ4eA4Z0v+7EqgUzEXklNU2tXLoSCMTgmzjp8eIlFhqm9qoami1uxSRU+aZyJIVH5rBvKGtgermartLEekTBXMROWW7S9xH1o9LD84V8xEdk1nUZy6ByDPDPFRbWUAjEyVw+TSYG2MWGmN2GWPyjDH3dvH5KGPMix2fX2eMye54fL4xZoMx5ouOP88/6jUzOh7PM8b80hhjfPk1iMiJ8krdE1nGpAfXxk+P7BT3LHP1mUsg6pxhnhCCwVyHDEmA81kwN8aEAU8Ci4CJwDXGmInHPe0W4IhlWaOBx4GHOh4vBy61LCsXuBH401Gv+R/gVmBMx9tCX30NItK1/LJ6IsMdZA2OtbsUnxjmOWSoXMFcAs/husPEhscyOGqw3aUMOAVzCXS+XDGfDeRZlrXXsqwW4AXgsuOecxnwh473XwYuMMYYy7I2Wpbl+b9qGxDTsbqeCSRalvWJ5d7Z8Ufgch9+DSLShbzSOkamxhHmCM5fWEVHhJGWEMXhKgVzCTyHag8xNGEoofgL5cTIROIi4tTKIgHLl8F8KHDwqI8PdTzW5XMsy2oDqoGU456zFPjcsqzmjucf6uGaABhjvmGMWW+MWV9WVtbnL0JETpRXWseoIJtffryswTEcOtJodxkipyxUZ5gDGGPIiM2guL7Y7lJE+sSvN38aYybhbm/55qm+1rKspyzLmmlZ1kyn0+n94kRCVFNrOwePNDDaGezBPFbBXAKOZVkU1hWG5EQWj4w4BXMJXL4M5oeBYUd9nNXxWJfPMcaEA0lARcfHWcBfgRssy8o/6vlH/23T1TVFxIfc872D78TP4w0dHENhVSPtLs1DlsBR3VxNQ1sDmXGZdpdim/S4dEoaSuwuQ6RPfBnMPwPGGGNyjDGRwNXAG8c95w3cmzsBrgBWWpZlGWMGAW8C91qWtcbzZMuyioAaY8wZHdNYbgBe9+HXICLHySt1j0ocFfQr5jG0uSxKaprsLkWk1zzH0Xs2QYaijNgMKhoraG3XOQQSeHwWzDt6xu8A3gF2AC9ZlrXNGPNjY8zijqc9DaQYY/KAuwDPSMU7gNHAfxljNnW8pXV87nbg90AekA/83Vdfg4icKK+0DmNgpDPO7lJ8yjNx5nCV2lkkcHg2PWbGh+6KeUZcBhaWVs0lIIX78uKWZb0FvHXcY/911PtNwJVdvO4B4IFurrkemOzdSkWkt/LK6hg2OJboiDC7S/GprMExABw60sCs7GSbqxHpnaI694p5qLeyABTXF5OVELq99hKY/Hrzp4j4n/zSuqDvLwcYOqgjmFdqxVwCR1F9EdFh0SE5w9wjIzYDQCvmEpAUzEWk19pdFnvL6xkV5G0s4J5l7kyI0mQWCShF9UVkxmeG5Axzj4w4dzDXZBYJRArmItJrh4400NLmCokVc+iYZa5DhiSAFNYVhnQbC0BsRCwJkQkK5hKQFMxFpNc8E1lCJ5hrlrkElqL6opAP5tAxy7xBwVwCj4K5iPRafllHMHcm2FzJwMjqmGXu0ixzCQBNbU1UNlWG9KhEj4zYDErq1WMugUfBXER6La+0jtT4KJJiI+wuZUBkDY6htd2itLbZ7lJEeuSZYa4Vcx0yJIFLwVxEei2vtC4kNn56dE5mOaI+c/F/CuZfyojNoLKpkuZ2/aNaAouCuYj0imVZ5JfVh0x/OUBmkjuYF1Xr9E/xf54Z5mpl+XIyi9pZJNAomItIr1TUt1Dd2MooZwgF80HRABRVawOo+L/C+kIcxoEz1ml3KbbTyEQJVArmItIre8vqARgZQq0sidERxEeFa8VcAkJxfTFpsWlEOEJjD8jJpMe6T/9Un7kEGgVzEemVvR0TWUJpxRwgIymaoioFc/F/hXWFDIlTGwu4N3+CVswl8CiYi0iv7C2vJzLcwZCODZGhIjMpWq0sEhA8p34KxITHMChqkIK5BBwFcxHplb1ldeSkxBHmCK2jvt3BXCvm4t/aXe2U1JdoIstRdMiQBCIFcxHplb1l9SHVX+6RmRRDWV0zLW0uu0sR6VZZYxltVpuC+VHSY9O1Yi4BR8FcRHrU0uZif2VDSAbzIYOisSwoqdGqufgvzwxzjUr8UkZchjZ/SsBRMBeRHh2obKDdZYXcxk+AjI5Z5sUK5uLHPDPMtWL+pYy4DKqbq2ls0x4RCRwK5iLSI89ElpEhGMyHJLlnmRdW6Ye7+K/C+kJAwfxonpGJameRQKJgLiI92lseejPMPTIH6fRP8X9FdUUMihpEbESs3aX4DR0yJIFIwVxEerS3rI7U+CgSo0Pv4JL4qHASosIpVjAXP1ZUX6TV8uNkxCqYS+BRMBeRHoXqRBaPzEHRamURv6ZgfiLPIUPaACqBRMFcRHq0t7yeUSEczDOSYtTKIn7Lsiz3qZ+ayHKMyLBIkqOTtWIuAUXBXEROqqqhhcr6Fkamht7GT48hOmRI/FhNSw0NbQ2dPdXyJR0yJIFGwVxETiq/LHQ3fnpkJsVQXtdMc1u73aWInEAzzLuXHptOSb1aWSRwKJiLyEmF8qhEj8yOkYmlNc02VyJyIs8M8yFxCubHy4jLUDCXgKJgLiIntbe8nogww7DBMXaXYpvMQZplLv7LM8NcrSwnyojLoLa1lvrWertLEekVBXMROam9ZXUMT44lPCx0/7rISHQH85JarZiL/ymqKyI6LJrk6GS7S/E7GpkogSZ0f9KKSK/kl9UzKoTbWADSEj2tLNoAKv6nqL6IjLgMjDF2l+J3PCMTFcwlUCiYi0i32tpd7K+oD+n+coDE6HCiIxyUKJiLHyquL1YbSzc83xfNMpdAoWAuIt06dKSR1nYrpCeyABhjSE+MpkSbP8UPFTcomHfHGeMEoLSh1OZKRHpHwVxEurW33D2RJZQPF/JIS4jSirn4nTZXG+WN5aTHpttdil+KDItkcNRgBXMJGArmItKtvZ4Z5iF8uJBHWmI0Zdr8KX6mvLEcl+XSivlJpMWmKZhLwFAwF5Fu5ZfVMzg2gsFxkXaXYrv0hGitmIvf8Wxq1Ip595yxTgVzCRgK5iLSrb1ldSG/8dMjPTGK+pZ26prb7C5FpJPnuHmtmHcvPTZdwVwChoK5iHRrb3k9I1PVXw6Q7pllrlVz8SOeUy09YwHlRM5YJ5VNlbS6Wu0uRaRHCuYi0qWaplbKapu1Yt4hLTEKUDAX/1JcX0xseCwJEQl2l+K30mLTsLCoaKywuxSRHimYi0iX8ks1keVo6Z2HDGkDqPiPkoYS0uPSdbjQSXj67zXLXAKBgrmIdGlPRzAfm66VOHCPSwStmIt/Kakv6Tx2XrrmmWVe1lBmcyUiPVMwF5Eu5ZXWERnuYFhyrN2l+IX4qHBiI8Mo1chE8SPF9cXqL+9BWmwaoBVzCQwK5iLSpT0ltYxyxhPm0K/I4ejTP7ViLv6h1dVKWWOZJrL0YHD0YMId4ZrMIgFBwVxEurSntI4xadr4ebS0hCj1mIvfKG8ox8LSDPMeOIwDZ4xTrSwSEBTMReQEDS1tHDrSqGB+nPTEaEpqtWIu/sHTmqEV857p9E8JFArmInKC/NJ6AMakK5gfLT0xipKaJizLsrsUEZ36eQrSYtPUYy4BQcFcRE6wp7QWgNFpmshytPTEaJpaXdQ06fRPsZ9WzHsvLTaNska1soj/UzAXkRPsKa0jIswwIkUTWY7m7BiZWKZ2FvEDnsOF4iP0m62epMWmUd9aT31rvd2liJyUgrmInGBPSR05qXFEhOmviKN5Dhkq0QZQ8QMlDSVkxGXocKFe8MwyV5+5+Dv91BWRE+SV1jJGbSwn+DKYa8Vc7FdcX6z+8l7yfJ8UzMXfKZiLyDGaWts5UNnAaE1kOcGXp39qxVzsV1Jfov7yXvIcMqRgLv5OwVxEjpFfVofL0kSWrsRFhZMQFa4Vc7Gd53AhnfrZOwrmEigUzEXkGDuK3BNZxmck2lyJf0pLjKJUmz/FZjpc6NTERrg3ySqYi79TMBeRY+wsqiEq3EFOapzdpfglZ0IUZbVqZRF7FTe4Z5irlaX3dMiQBAIFcxE5xo7iGsZlJBDm0KSHrjgToimva7G7DAlxJfXuGeZaMe89Z6yT0kYFc/FvCuYi0smyLHYU1TJBbSzdcsZrxVzs5zn1UyvmvZcem64Vc/F7CuYi0qmstpnK+hbGZ2pUYndSEyKpa26joUWnf4p9ShpKdLjQKUqLTaO8oRyX5bK7FJFuKZiLSKcdxe6NnxMytWLeHWe8e2Riea3aWcQ+xfXFOlzoFDljnLRZbVQ2Vdpdiki3FMxFpNOOohoAtbKchLNjlnlZnSaziH1KGkrUX36KdMiQBAIFcxHptLOohiFJ0STFRthdit/qDOZaMRcbeVbMpfecsU4AyhrKbK5EpHvhdhcgIv5jR1Et49XGclJfrphrA2in9jY4sg+aa8DVDolDISETHFr78YVWVyvljeU6XOgUeQ4ZKmkosbkSke4pmIsIAI0t7eSV1XHRJP2wP5nk2EiMQZNZWhpg26uw5UU4tAFa64/9fGQ8jDofJl4GExZDeKQ9dQahsoYyLCwyYrVifipSY1IxGLWyiF9TMBcRALYXVdPuspiSNcjuUvxaeJiDlLjI0A3m7a3w6VPwz4egqRpSxsBp18GQ0yA2GYwDqg5A8Rew6++w4w1IzIKzvwMzlkGYfuz0l2fFVyvmpybcEU5KTApljWplEf+lvyFFBIDNB6sBmJKVZHMl/i81PoryUGxlKf4CXr4FynfBqAvgnO/B8DOhu8kgFz8G+e/Dh4/AW9+DTc/D4l9BxuSBrTvIdM4w14r5KUuLTVMri/g1NQCKCABfHK4mPTGK9MRou0vxe86EEDxk6PM/we8vdPeRX/MCXPcKjDir+1AO7h7zMfPh5nfgimeh6iA8dR5s+MOAlR2MOk/91Ir5KUuLTVMri/g1BXMRAWDzoSpyh6qNpTdC6vRPy4KVP4U37oDhZ8A3P4Jxi04eyI9nDEz+KnzrU8iZC//3bXjz39wbReWUlTSUEBcRR0KkDgI7VWkxaZrKIn7Np8HcGLPQGLPLGJNnjLm3i89HGWNe7Pj8OmNMdsfjKcaYD4wxdcaYXx33mlUd19zU8Zbmy69BJBTUNLWyt6yeqWpj6RVnQhRldc1YlmV3Kb5lWfD2v8OHD8Np18N1r0K8s+/Xi0uBa1+Gs+6Ez34Pr3zd3bMup6S4vlgzzPsoLTaNquYqmttD5B/WEnB8FsyNMWHAk8AiYCJwjTFm4nFPuwU4YlnWaOBx4KGOx5uAHwDf6+by11qWNa3jTb+TEumnrYc7+suHacW8N5wJUbS0uahparO7FN/64Kew7n/gjNth8X+DI6z/13SEwUUPwPwfu6e6vHSDwvkpKmko0QzzPvKMTNSqufgrX66YzwbyLMvaa1lWC/ACcNlxz7kM8DQbvgxcYIwxlmXVW5a1GndAFxEf23LIHcxzh2rFvDc8s8yDegPop79zb9qcfgMs+Nmpta70xpzl8JVHYddb8Pod4HJ59/pBTCvmfecJ5uozF3/Vq2BujHnVGHOxMeZUgvxQ4OBRHx/qeKzL51iW1QZUAym9uPazHW0sPzDG2z8tRELPpgNVDEuOITlOs6Z7wxnvOf0zSIN5/gfw97th7CK4+HHvh3KP2bfCvPtgywvw7g98c48g4zlcSCvmfaNgLv6ut0H718DXgD3GmAeNMeN8WFNPrrUsKxeY2/F2fVdPMsZ8wxiz3hizvqxMv7IS6Y5lWazfX8msEcl2lxIwUhOCOJhXHYRXboHUsbD0976fO37O92D2N+DjX8Hnf/TtvYKA53AhrZj3jYK5+LteBXPLst6zLOtaYDpQALxnjFlrjFlmjIno5mWHgWFHfZzV8ViXzzHGhANJQEUPtRzu+LMW+F/cLTNdPe8py7JmWpY10+nsx2YlkSBXUNFAeV0LM7MVzHsraFfM21vhLzdCWwtc9WeIivf9PY2BBT+HkfPgb3fBgXW+v2cA88zg1op53yRGJhIVFqVgLn6r160pxpgU4Cbg68BG4P/hDurvdvOSz4AxxpgcY0wkcDXwxnHPeQO4seP9K4CV1knGHBhjwo0xqR3vRwCXAFt7+zWIyIk+21cJwOycwTZXEjiSYiKICDPB12P+z4fh8Aa47FeQOmbg7hsWDlc+C4OGwUvXQ51+y9kdz+FCWjHvG2OMZpmLX+ttj/lfgY+AWOBSy7IWW5b1omVZdwJdLql09IzfAbwD7ABesixrmzHmx8aYxR1PexpIMcbkAXcBnSMVjTEFwGPATcaYQx0TXaKAd4wxW4BNuFfcf3eqX7SIfOmzgkoGx0YwyjkAq6NBwuEwpAbbLPODn8FHj8LUr8Gkywf+/jGD3av0jVXw2m3aDNoNHS7Uf84YJ6WNCubin3rbPPg7y7LeOvoBY0yUZVnNlmXN7O5FHa9567jH/uuo95uAK7t5bXY3l53Ry5pFpBfW7z/CjBHJaB/1qfHMMg8KrY3w129C4lBY9KB9daRPggU/hbe+B+t+A2febl8tfqq4oViHC/VTemw6Wyv0y3bxT71tZXmgi8c+9mYhIjLwiqub2FderzaWPgiqFfOPfgGV+e5Z5dE2j8yc9XUY9xV474dQtNneWvxQSX2J2lj6ydPKEvQHhElAOmkwN8ZkGGNmADHGmNOMMdM73s7D3dYiIgFsdV45AHNGp9pcSeBxBkswL90Jq5+AKVfDqHl2V+PeDLr4VxCb4j4ZtFXHWRytuL5YGz/7yRnrpLm9mZqWGrtLETlBTyvmC4BHcU9UeQz4RcfbXcB/+LY0EfG11XvKSImLZEJGot2lBBxnQhQV9S24XAG86mZZ8LfvuqevLPip3dV8KS4FLnsSynfDhw/bXY1fKWnQinl/eb5/2gAq/uikPeaWZf0B+IMxZqllWa8MUE0iMgAsy2J1XgVzRqficKi//FQ5E6Jod1kcaWghpWN8YsDZ/hocWAuX/j+I87Pfmoy+AKZd617Nn3gZZE61uyLbtbbrcCFv8MwyL2soY8zgAZw+JNILPbWyXNfxbrYx5q7j3wagPhHxkZ3FtZTXNXP2GD8LZAEi1TPLPFA3gLY1w7s/hLRJcFqX57TZ76IH3C0tr3/LPWM9xJU16nAhb3DGus828cyEF/EnPbWyxHX8GQ8kdPEmIgHqn7vds6LnKpj3iTPQT/9c91uo2g8LHgBHmN3VdC02GS7+BRR/AWt/aXc1tvPMMNeKef/o9E/xZz21svy248/7B6YcERko724vYfLQRDKTYuwuJSB5gnlAHjJUXw4fPgJjLoJR59tdzclNXAwTLnUffjR5KQzOtrsi23hWeLVi3j9RYVEkRSVR1qiDrMT/9PaAoYeNMYnGmAhjzPvGmLKj2lxEJMCU1Tbz+YEjzJ+glbe+So2PBKC0JgCD+aoHoaUe5v/E7kp6Z+GDYBzwdmjPHNCKufekxaaplUX8Um/nmF9kWVYNcAlQAIwGvu+rokTEt97fUYJlwfyJWnnrq/iocKIjHIG3Yn5kP2x4FqbfAGnj7a6md5Ky4Jzvw643Yfc/7K7GNiUNJcRFxBEfqVN6+8szy1zE3/Q2mHtaXi4G/mJZVrWP6hGRAfCP7SUMHRTDhExtFekrYwyp8VGU17XYXcqp+ehR9+rzOQG2tnLmHZAyGv5+d8jONi+uLyYjVqvl3pAWk0ZZg1pZxP/0Npj/zRizE5gBvG+McQKh+TejSICrrG/hw91lXDwlE2M0JrE/3ME8gFbMK/fBxudhxjJIGmp3NacmPBIWPQxH9sHa/7a7GluU1JeQHqffcnlDWmwa5Y3ltLna7C5F5Bi9CuaWZd0LnAXMtCyrFagHLvNlYSLiG29uKaTNZbHktAALZn7ImRBgp39++CiERcDZ37W7kr4ZfYF7I+jqx6CmyO5qBlxxg0799Ja02DQsLMoby+0uReQYvV0xBxgPXGWMuQG4ArjINyWJiC+9uvEw4zMSmJCp0z77K6BWzCvyYfMKmHkzJGbaXU3fzf+xe6b5Bw/YXcmAam1vpaKxQhNZvESnf4q/6u1Ulj8BjwJnA7M63mb6sC4R8YGC8no2HqjSarmXOOMjqaxvod1l2V1Kzz76BYRFwpzv2F1J/ySPhNO/6W7JKf7C7moGTGljKRaWVsy9RLPMxV+ddI75UWYCEy3LCoCfPiLSnb9sOIgxsHjaELtLCQqpCVG4LHffvmeuuV+qOghbXoTZ34CEIFhxPed7sOl5+Md9cP1rEAJ7JUrqNcPcmzynfyqYi7/pbSvLVkD/TBcJYA0tbfz5kwNcNDFdhwp5iTM+QE7//PhJ959nfsveOrwlZjCcew/sXQV73rW7mgGhGebelRydTLgjXMFc/E5vg3kqsN0Y844x5g3Pmy8LExHvennDIaobW/nGOSPtLiVopAbC6Z8NlfD5HyD3Svc88GAx8xZ3W8s/7oP24J+soVM/vcthHDhjnArm4nd628ryI18WISK+1e6y+P1H+zht+CBmjEi2u5ygkRofAMH8s99DawOc9W27K/Gu8Ej3RtAXr3P/w2PWLXZX5FPF9cXER8TrcCEv0iFD4o96Oy7xn7hP/IzoeP8z4HMf1iUiXvTu9mIOVDZw61ytaWU7wwAAIABJREFUlntTanwk4MfBvKUB1v0GxiyA9Il2V+N94y+B4WfBqgehpd7uanyqpKFEq+Velhab1vmbCBF/0dupLLcCLwO/7XhoKPCar4oSEe966sO9DEuOYcEk9ad6U3xUONERDv/tMd/0PDRUwNkBPomlO8bAhT+C+lL45H/srsaniuuLdbiQl6XHpmvFXPxOb3vMvwXMAWoALMvaA6T5qigR8Z4N+4/w+YEqbpmTQ5gj+KdXDCRjTMcs8xa7SzmRy+Xe9Dl0Jgw/0+5qfGf46TB2Eaz5pbufPkiVNJRo46eXpcWm0dDWQF1Lnd2liHTqbTBvtiyr8yePMSYc0OhEkQDw+4/2khgdzpUzh9ldSlDy20OG8t51H19/5u3BP07wgh9Acw2secLuSnxChwv5Rucs80atmov/6G0w/6cx5j+AGGPMfOAvwP/5riwR8Yb9FfW8va2Y684YQVxUb/d6y6lwJkT5ZyvLut9AQiZMWGx3Jb6XPgmm/Aus+y3UFNpdjdfpcCHf0CFD4o96G8zvBcqAL4BvAm8B9/mqKBHxjmdW7yPcYbjxrGy7SwlafrliXrYb8le6RwqGRdhdzcA479/B1Q7/fNjuSrzOM8NcK+bepWAu/qhXS2iWZbmMMa8Br1mWVebjmkTEC6oaWnhp/SEumzaU9MRou8sJWs74SCrrW2h3Wf7Tw//pUxAWCTNusruSgZOc4/561z8DZ90JKaPsrshrdLiQbyiYiz866Yq5cfuRMaYc2AXsMsaUGWP+a2DKE5G+en7dARpb2/n63By7SwlqqQlRuCyorPeTDaBN1bDpf2HyUoh32l3NwDrn+xAeBR/81O5KvErB3DdiwmNIiEygpF4jE8V/9NTK8l3c01hmWZaVbFlWMnA6MMcY812fVycifdLc1s5zawuYOyaV8RmJdpcT1Jwdhwz5TZ/5xuehtR5O/6bdlQy8hHQ443bY+goUbba7Gq8pri8mISKBuIg4u0sJOhqZKP6mp2B+PXCNZVn7PA9YlrUXuA64wZeFiUjfvb6pkLLaZr5xjg4U8rXUBD86/dPlcrexDDsdhpxmdzX2mPNtiB4E7//E7kq8prhBM8x9Rad/ir/pKZhHWJZVfvyDHX3mIbKjSCSwWJbFM6v3MT4jgbNHp9pdTtBLjfejYO4ZkRiKq+Ue0Ulw9nfd34v9H9tdjVeU1JeQGZdpdxlBKS02TeMSxa/0FMxP1jTpJw2VInK0j/Mr2Flcy81zcjDBPr/aD6TGRwJ+Esw/fSp0RiSezOxvQHw6vP9j+P/s3Xd8lfX5//HXfXJyMs7J3mQCAWSviCwVQVGs4qh11LpHXa2102qXra217a9+rVq3bbVaF4o4UVwgCDLCniFk771zknPu3x93goABknDO+dzn5Ho+HnkEk3Pu+0Igeedzrs/10f3/yI2K1grpL/eSxPBEattrcbldqksRAjh+MJ+saVpTH2/NwERfFCiEGJjnVhcQa7exeMow1aUMCY4QK6HBFvU95vWFkPcxTLt66IxIPBpbuLERtGgN7P9YdTUnpKO7g/rOegnmXpIUnoRLd1HbUau6FCGA4wRzXdeDdF2P7OMtQtf1If6VXwjzKaxt5ePdlVx5SgahwUGqyxkSNE3rmWWu+EXE3BeM91OvUluHWUy7BqIz/H7VXCayeFdCmDG5SPrMhVn094AhIYQf+M+aQoI0je/NzFRdypCi/JAhVzdsegFGnQXR6erqMBOrDebdY0xn2bVMdTWDVtHWE8zDJZh7Q6LdmGVe2SYjE4U5SDAXIkB0dLlYsqmERRNT5EAhH4t3hKhtZdm3HFoqhtaBQv0x6VKIHwOf3G+cCuqHZMXcu3pPU5UVc2EWEsyFCBDLd1TQ2N7F5SfLiqmvJUQoXjHf+G9wJMOos9XVYEaWIJh/L9Tsha2vqK5mUHqDuYxL9I7Y0FismlWCuTANCeZCBIhXNxSTHhvGrBFxqksZchIcNupanbjcCnqZG4ph30cw7SoIsvr+/mY3djGkTIFPH4BuE0zOGaCK1gpiQ2MJCQpRXUpAsmgW4sPjJZgL05BgLkQAKK5rY3VeLd+Zno7FIiMSfS0+IgS3DnWtCjaAyqbPY9M0WPBraCyCTc+rrmbAKtpkVKK3ySFDwkwkmAsRAF7bUIymwSXT01SXMiQl9Bwy5PM+895Nn9kLIEY2/B7VyAWQOQdW/hWcbaqrGZDK1krZ+OllSeFJEsyFaUgwF8LP6brOkk2lnDoqgWHRYarLGZLiIxSd/pn3ETSXyabP49E0mP9raKk0DmHyI3K4kPfJirkwEwnmQvi5TUUNlDa0c6EcKKRMvENRMN/4b+OEy9Hn+Pa+/ihzFoxaCF88BB2Nqqvpl2ZnMy1dLRLMvSwxPJGWrhbauvzr1RQRmCSYC+Hn3tlahs1q4axxMrVBlXiHDfBxMG8sgX0fwtTvyUmf/TX/V9DRAGseVV1Jv8ioRN/oPWRIZpkLM5BgLoQfc7l13t1azhljEogIlXCmiiPESmiwxbc95rn/Bd0N06723T39XcpkGH8RfPkYtFSrrua4JJj7hswyF2YiwVwIP/bVgTqqmjs5f7K0saikaVrP6Z8+msridsPml2D46RCT5Zt7Booz7oXudqOlxeTk1E/fSAw3Tv+UYC7MQIK5EH7sna1lhAUHMf+kRNWlDHlGMPfRinnRGmgohClX+uZ+gSR+FEz5Lqx/xmgHMrGK1gosmoWE8ATVpQQ0CebCTCSYC+GnXG6dD7ZXsGBsIuE2OVhGtXhHiO9aWTa/BLYIGHueb+4XaE7/BaDD539RXckxVbRWkBCWgNUi/769KTw4nIjgCAnmwhQkmAvhpzYX11Pb6uTs8fIytxkkRPhoxbyzBXYshfEXgs3u/fsFougMyLne6NOv3a+6mqOqbK2U/nIfkZGJwiwkmAvhpz7aWYXVonH6GHmZ2wwSHDbqWp243Lp3b7TrbehqlTaWE3XqT8AaAp/+SXUlRyWnfvpOYniiTGURpiDBXAg/tWJXJaeMiCVSprGYQnxECG4d6lq9vAF084sQMxwyZnr3PoHOkQgzb4Xtr0PFdtXVfIOu68bhQrLx0yeS7clUtkowF+pJMBfCDxXUtJJX1cKZY2V2uVkk9Bwy5NU+8/pCKFhlbF7UNO/dZ6iY/QMIjYJP/6i6km9o6Gyg09UpK+Y+kmxPprq9mi53l+pSxBAnwVwIP7Ril7GyI8HcPOIjfHD655aXjfeTL/fePYaSsBiYcyfseQ+K16uu5jDlreWAzDD3lWR7Mjo61W3mn28vApsEcyH80IpdlYxJiiA9Nlx1KaJHvMPLwVzXYctLMPw0Y/Oi8IxTbgF7Anzye9WVHKb3cKEUe4riSoaG3kOGev+/C6GKBHMh/ExjWxfrC+o5c5zMLjeTeIcN8GIwL/oS6gtk06en2exw2s/gwErI/0x1NQf1BsQku7wq5gu9r0xIMBeqSTAXws98vq8al1tngbSxmIojxEposMV7PeabXwSbA8ae753rD2XTr4WodPj498YrEyZQ0VZBsCWY2NBY1aUMCQeDeZsEc6GWBHMh/MyqvdVEhQUzOS1adSniEJqm9Zz+6YWpLM5WmV3uTdYQmHc3lG40xlGaQEVrBUnhSVg0+TbtC/ZgOxHBEbJiLpSTf/FC+BFd11m1r4Y52XEEWWQqh9kYwdwLK+a73gZnC0z+ruevLQyTLoeEsbDit9Dt5ZGX/SCHC/lekj1JgrlQToK5EH4kr6qFiqYOTh0lhwqZUbwjxDutLJtfhJgsyJjl+WsLQ5AVFv4B6vJhw7OqqzFmmEsw96lke7IEc6GcBHMh/MiqfTUAzM2OV1yJ6EtChBdWzBuK4MAqY7XcIl+yvSr7TBhxBnz+ILTXKyvD5XZR1VYlwdzHku3JcvqnUE6+ygvhR1btq2Z4vF3GJJpUgsNGXasTl9uDGwi3vALoMrvcFzQNFt4P7Q2w8m/Kyqhur6Zb75ZRiT6WHJ5MXUcdnS4vnkUgxHFIMBfCT3R2u1ibX8epo2S13KziI0Jw61DX6qEeZV032liyToWYTM9cUxxb8gSY+j1Y9yTU7ldSQu/hQsMcw5Tcf6jqfYWislVWzYU6EsyF8BObChto73JJf7mJefyQoaK1UH8ApsimT5+a/ysIssGK3ym5fWlLKSDB3Nd6Z8ZLn7lQSYK5EH5i1b5qgiwaM0fIXGOzSogwgrnHNoBueQmC7TB2sWeuJ/onIhnm3Am7lkHhlz6/fXmLsWIurSy+lRwus8yFehLMhfATq/bVMC0jmojQYNWliKPw6Iq5sw22v2nMLg9xnPj1xMDMvgMiUuDDe8Ht9umtS1tKiQ2NJcwa5tP7DnW9K+bSyiJU8mow1zTtHE3T9mialqdp2t19fD5E07RXej6/TtO0rJ6Px2ma9qmmaS2apj16xHOma5q2rec5/9A0TYY5i4BX3+pke1kjc7OljcXM4h02wEPBfPc74GyWNhZVbHaY/2vj0KFtr/r01uWt5QyzSxuLr4VZw4gOiZZWFqGU14K5pmlBwGPAImAccIWmaeOOeNgNQL2u69nAQ8CDPR/vAH4N/LSPSz8O3ASM6nk7x/PVC2Eu6w7UoeswOztOdSniGBwhVkKDLZ5pZdn8IkRnQsbsE7+WGJzJV8CwafDRb6CjyWe3LWspI8UhbSwqJNuTpZVFKOXNFfMZQJ6u6/m6rjuBl4ELjnjMBcB/en79OrBA0zRN1/VWXde/wAjoB2malgJE6rq+Vtd1HXgeuNCLvwchTGFtfi2hwRYmpUWpLkUcg6ZpPad/nuBUlsYSyP/cWC2X2eXqWCzwrb9BS5Ux29wHdF2nvLWcVEeqT+4nDpccLocMCbW8+RU/FSg+5L9Lej7W52N0Xe8GGoFjLQmm9lznWNcUIuCsza9lemYMIdYg1aWI4zCC+QmumG95GZldbhKp02HaVbDuCaja7fXb1XbU0unqlI2fiiTZkySYC6UCdilG07SbNU3boGnahurqatXlCDFo9a1Odlc0M3O4tLH4g3hHyIm1sug6bH4JMudCTJbH6hInYMFvjZ7z939u/Pl4UVlLGSCjElVJtifT5GyiratNdSliiPJmMC8F0g/577Sej/X5GE3TrEAUUHuca6Yd55oA6Lr+lK7rObqu5yQkyIY54b++KqgDYOZICeb+ICHiBFfMi7+Cuv2y6dNM7PHGRtADn8POt7x6q7JWCeYq9R4yJH3mQhVvBvP1wChN04ZrmmYDLgeWHfGYZcA1Pb++BPikp3e8T7qulwNNmqbN7JnGcjXg3a+SQigm/eX+JcFho67Vics9yJXVzS8as8vHHbklRyg1/TpImgjL7wVnq9duc3DFXKayKJEULocMCbW8Fsx7esbvAJYDu4BXdV3foWna7zVN6z0t41kgTtO0PODHwMGRipqmFQB/B67VNK3kkIkutwHPAHnAfuB9b/0ehDCDtfl10l/uR+IjQnDrUNc6iA2gXe2w400jlMvscnMJssK5f4WmEvjiIa/dpqyljEhbJA6b/Pmr0LtiLrPMhSpWb15c1/X3gPeO+NhvDvl1B/Cdozw36ygf3wBM8FyVQphXQ5uT3RVN/PjM0apLEf106CFDvSeB9tvud6GzCaZc4YXKxAnLnAWTLoPVD8OkyyE+2+O3KG8tlzYWhWTFXKgWsJs/hQgEvfPLpb/cf/SG8UFtAN38IkRlGBs/hTmd9XuwhsE7P/LKRtCyljJpY1HIFmQjLjROesyFMhLMhTAx6S/3P4eumA9IYyns/9RYLZfZ5eYVkQxn/Q4KVhnTczxI13UjmMuKuVLJdpllLtSRr/5CmJj0l/ufeIcNGEQw3yqzy/3GtGshfSZ8eC+01njsso2djbR1t8kMc8UkmAuVJJgLYVK9/eUyv9y/OEKshAZbBtbKcnB2+RyIHeG94oRnWCxw/sPQ2QLL7/HYZXtHJcqpn2r1BvNjDIkTwmskmAthUhsK6tF1mDE8VnUpYgA0Tes5/XMAU1mKv4LaPJld7k8ST4K5d8HWVyDvY49csndUYopDVsxVSg5Ppq27jeauZtWliCFIgrkQJrW+sI7gII3J6dGqSxEDZATzAayYH5xdfqH3ihKed+pPIC4b3rkLnCd+UmRvMJcVc7VkZKJQSYK5ECa1saCeialRhAZLf7m/iXeE9L+Vxdkms8v9VXCo0dLSUAif//mEL1feWk64NZxIW6QHihODlWSXkYlCHQnmQphQR5eLrSWN5GRJG4s/SogYwIr5wdnl0sbil7LmwtTvwZpHoSz3hC5V2lLKMMcwjIOthSrJ4caKuYxMFCpIMBfChLaXNuJ0ucnJjFFdihiEBIeNulYnLnc/No9t/i9EZxgbP4V/Wng/OBJh6W3QPYj59T3kcCFzSAhPwKJZKG8pV12KGIIkmAthQusL6gGYLsHcL8VHhODWoa71OBtAG4oh/3OYcqXMLvdnYTFGS0vVTvj8L4O+TGlLqYxKNAGrxUpieKK0sggl5DuBECa0sbCOEQl24hwDPNJdmEK/DxmS2eWBY/TZMPm78MVDULppwE9vdjbT7GyWjZ8mMcw+jNKWUtVliCFIgrkQJuN262worJc2Fj+WEGEE82NuAO2dXZ51KsRk+aYw4V3nPDDolhYZlWguqY5UylullUX4ngRzIUwmv6aFhrYu2fjpx/q1Yl60FuryZdNnIAmLhsWPQPUu+GxgU1p6Q2CqXVbMzSDFkUJlWyVd7i7VpYghRoK5ECbT218uK+b+K95hA44TzHtnl49d7KOqhE+MOgumfA9W/x+Ubuz302TF3FxSHam4dbfMMhc+J8FcCJPZUFBPnN3G8Hi76lLEIDlCrIRYLUdvZXG2GrPLx18ks8sD0dl/BEcyvHkrdLX36yllLWWEBIUQFxrn5eJEf/ROx5F2FuFrEsyFMJkNhXXkZMXILGM/pmlazyzzo0xl2fU2OFukjSVQhUXDBY9AzR5YcV+/nlLWWkaKPUX+3ZvEMLsRzGUDqPA1CeZCmEhVcweFtW3kZEp/ub+LdxzjkKHNLxobPjNm+bQm4UPZZ8KM78O6x2H/J8d9eFlLmcwwN5FkezIamswyFz4nwVwIE9nY21+eJf3l/i7eEdJ3K0tDERxYaYzWk9nlge2s+yB+jDGlpa3umA8taSkhzZHmo8LE8diCbCSEJ8iKufA5+a4ghIlsKKwnxGph/LAo1aWIE2S0svQRzDf/z3g/5QrfFiR8LzgMLn4KWqvhnbuMEZl9aHI20djZSFqEBHMzGWYfJj3mwuckmAthIhsK6piSHo3NKv80/V2Cw0ZdqxOX+5Aw5nYbbSzDT4PoDHXFCd8ZNgXOuBd2LoWtr/T5kJLmEgDSI9J9WZk4jmEOOWRI+J589xfCJDq6XOwoa2K6jEkMCPERIbh1qGs9ZANo0RpoKDTaWMTQMedOyJgN7/4U6gu/8eneYC4r5uaS6kilsrUSl9uluhQxhEgwF8Iktpc20u3WmZYhwTwQ9HnI0KbnISQSxl2gqCqhhCUILnrC+PWbt8ARQa+kpSeYS4+5qaQ4UujWu6lur1ZdihhCJJgLYRK5RQ0ATMmIVlyJ8ISECCOYH9wA2l4PO9+Cid8BW7jCyoQSMZlw7l+NV01WP3zYp0qaS4gJicFhk5n2ZtJ7Cqu0swhfkmAuhEnkFteTERt+cKVV+LdvrJhvfQ26O2D6NQqrEkpNvhzGXQif/gnKtxz8cHFzsbSxmFDv+MreU1mF8AUJ5kKYRG5RA1PSZbU8UMQ7bEBPMNd12PQfSJlsvImhSdPgvIfAHg9LbgJnG2CsmEsbi/mkOFLQ0GTFXPiUBHMhTKC8sZ3yxg6mShtLwHCEWAmxWoxWlrJNULkdpl2tuiyhWngsXPi4cSroR7+h291NeWu5rJibUEhQCInhiRQ3F6suRQwhVtUFCCFgc09/+VTZ+BkwNE3rmWXuNDZ9WsOM/nIhRp4Bs+6ALx+lIn0aLt0loxJNKi0i7eDUHCF8QVbMhTCBzcUN2KwWxqVEqi5FeFC8I4TmpgbY9jqMvwhC5eAo0WPBbyBpAsWf/A6QUYlmleZIOzg1RwhfkGAuhAnkFjUwYVikHCwUYOIdIYyr+xicLdLGIg5nDYFvP0OJbmwOTpcec1NKi0ijqq2KTlcfp/gK4QWSAoRQrMvlZmtpA1PSpY0l0CREhLCg/QOIHw0ZM1WXI8wmcSwl2fMI1nUSdixTXY3oQ+8rGbIBVPiKBHMhFNtT0UxHl1s2fgagkyzFTGYv7qlXGRM5hDhCcUQcqZqNoI9+A9V7VJcjjtDb+y995sJXJJgLoVhuUT2ABPMANKP+XZx6ELUjL1ZdijCpoqYi0pOmgs0OS26AbmmZMJPeMZYymUX4igRzIRTLLW4gISKE1Ogw1aUIT+ruZGT5O3zozqG8W050FN+k6zpFzUVkxo6GCx6Dim3wyf2qyxKHiA2NJcwaJivmwmckmAuh2Oaeg4U0aXUILDvfwuZs4GXXfCqbZBVUfFNNew3t3e1kRGbAmEWQcz2seQTyP1ddmuihaZoxMlEmswgfkWAuhEL1rU7ya1qljSUQrX+W7ujhrHaPp6KpQ3U1woQKmwoByIzIND6w8I8Qlw1v3gJtdQorE4dKc8gsc+E7EsyFUGhzSc/BQjKRJbBUbIfitVhOvgGLJYjKRgnm4puKmosASI/sOVzIFg7ffgZaq+GdH4GuK6xO9EqLSKO0pRRd/jyED0gwF0Kh3KIGLBpMSpODZwLKhmfBGopl6pUkOEJkxVz0qbCpEKvFSoo95esPDpsC8++FnW/B5pfUFScOSnOk0d7dTm1HrepSxBAgwVwIhXKL6hmTHIk9xKq6FOEpHU2w9VUYfzGEx5IUFUqlBHPRh6KmItIcaVgtR/z7n/1DyJwL7/8c6vLVFCcO6h2ZKJNZhC9IMBdCEbdbZ0uxsfFTBJCtrxgnfZ58IwDJkSFUSCuL6ENhcyGZkZnf/IQlCC5+0nj/xs3g6vZ9ceIgCebClySYC6FIfk0rTR3dsvEzkOg6bHgOUiZD6jQAkiNDpZVFfIOu6xQ3FRsTWfoSlQbnPQQl62HlX31bnDhMakQqQVrQwc26QniTBHMhFOk9WGiaBPPAUbQWqnYaq+U94y+TokJp7uimzSmrnuJrVW1VdLg6yIg4SjAHmPBtmHwFrPwLFK3zXXHiMMGWYFIdqRLMhU9IMBdCkdziBiJCrYyIl8NnAsaGZyEkyghUPZIjQwGknUUcpnciy1FXzHst+gtEpcMbNxn7F4QSGZEZFDUVqS5DDAESzIVQJLfnYCGLRQ4WCggtVbBjKUy5wjhevcfBYC7tLOIQB2eY99VjfqjQSLj4aWgsNjaDCiWyIrMoaCqQkYnC6ySYC6FAm7ObPRVNTJWNn4Fjw3Pg7oKTbzrsw4k9wVwms4hDFTUVEWwJJjk8+fgPzjgFTvsZbPkfbH/D+8WJb8iIzKC9u52a9hrVpYgAJ8FcCAW2ljTi1mFqhhwsFBC6O2H9szBqIcRnH/ap5KjeYN6pojJhUgeaDpAZmUmQJah/Tzjt55B2snHwUKOcQulrva9sFDQVqC1EBDwJ5kIokFtknPgpoxIDxI43obUKTrnlG59yhFhxhFilx1wcpqCxgOFRw/v/hCArXPwUuF3w5i3Ge+EzvcFc+syFt0kwF0KB3KJ6hsfbibHbVJciTpSuw9p/QvwYGDm/z4ckRYZIK4s4qMvVRXFzMVmRWQN7YuwIWPQgFKyCLx/1Sm2ib8nhydgsNpnMIrxOgrkQPqbrOrnFDdJfHiiK1kL5Fjjl+wdHJB4pOUpmmYuvFTcX49JdA1sx7zXlShh7PnxyP1Tt8nxxok9BliDSI9IlmAuvk2AuhI+VNXZQ3dzJFJlfHhjWPQ6hUTD58qM+JCkylEppZRE9DjQeAGBE1IiBP1nT4FsPQUgkvPl9cHV5uDpxNBmRGRLMhddJMBfCx3oPFpqaLhs//V5DEex6G6Zfe9iIxCMlR4ZS1dyJ2y2j1oSx8RMgKyprcBdwJBingpZvgVX/z3OFiWPKisyiuLkYt+5WXYoIYBLMhfCx3KIGQqwWTkqJUF2KOFFfPQ1o3xiReKTkqFC63To1rTKZRRgr5onhidiDj/7D3HGNWwyTLoOVf4WyXM8VJ44qIzIDp9tJeWu56lJEAJNgLoSP5RbVMyktiuAg+efn1zpbYNN/YOx5EJ1+zIcmRvSMTGyUYC6MYD6o/vIjLXoQ7Anw5q3QJa1S3tb7Z9bbiiSEN0gyEMKHnN1utpc1yfzyQLDpeehohFl3HPehvbPMZQOo0HXdCOaRHgjmYTGw+FGo3gWf/enEryeOqXdPQH5DvuJKRCCTYC6ED+0qb8LZ7Zb55f7O1QVfPgYZsyF9xnEfnhwpwVwYatpraOlq8cyKOcCoM2HaNbD6H1C0zjPXFH2KCY0hNjSW/EYJ5sJ7JJgL4UMHN37KRBb/tn0JNJXA3B/16+EJESEEWTQqGtu9XJgwu942CI8Fc4Cz/2i0Uy29BZytnruu+IbhUcMlmAuvkmAuhA/lFjeQHBlKSlSY6lLEYOk6rH4YEsfBqIX9ekqQRSM5MpSyBlkxH+p6Q51Hg3lIBFzwT6jLhxX3ee664htGRI1gf8N+dF0mLAnvkGAuhA/lFjXIarm/2/chVO2E2T886oFCfUmNDqO0XlbMh7q8hjwcwQ6SwpM8e+Hhp8Ipt8JXT0L+5569tjhoZPRImpxN1HbUqi5FBCgJ5kL4SG1LJ0V1bRLM/d3qhyEyDSZeMqCnpcaEUdogwXyoy2vIIzs6G20AP9T124LfQFw2vHU7dDR5/vri4CsdsgFUeIsEcyF8ZHNxA4BMZPFnxeuhcDXMuh2Cggf01GHRoVQ0ddDtksNJhipd141gHpPtnRvYwuHCJ6CpFJbf4517DHEjo0YCsL9xv+JKRKCSYC6Ej+QWNRBk0ZgwLEp1KWLIcxoAAAAgAElEQVSwVv8fhEbDtKsH/NTU6HBcbp2qZpllPlTVtNfQ2NlIdrSXgjlA+skw507IfQH2LvfefYao3oOhZMVceIsEcyF8JLe4nrEpEYTZglSXIgajcifsfgdm3AwhjgE/fVi0MTKxTNpZhqx9DfsAGBU9yrs3mvdLSBwPy34AbXXevdcQo2kaI6NGymQW4TUSzIXwAZdbZ0txI1PTpY3Fb33+INgiYOatg3p6arQxiUf6zIeuvPo8AO+1svSyhsBFj0NbLbz/c+/eawiSkYnCm7wazDVNO0fTtD2apuVpmnZ3H58P0TTtlZ7Pr9M0LeuQz/2y5+N7NE07+5CPF2iatk3TtM2apm3wZv1CeEpeVQstnd2y8dNfVe6EnW/BKd+H8NhBXWKYBPMhL68hj9jQWGJDB/d3aEBSJsNpP4Ntr8HOZd6/3xAyMnrkwbYkITzNa8Fc07Qg4DFgETAOuELTtHFHPOwGoF7X9WzgIeDBnueOAy4HxgPnAP/suV6vM3Rdn6Lreo636hfCkzYX9x4sJCvmfmnlX8BmNzZ9DpI9xEp0eLCMTBzC9jfs934by6FO/YkR0N+5C1prfHffADcqxvgz3Fu/V3ElIhB5c8V8BpCn63q+rutO4GXggiMecwHwn55fvw4s0IwZUhcAL+u63qnr+gEgr+d6Qvil3KIGosODyYoLV12KGKiq3bBjqdFbPsjV8l6p0WHSYz5EuXW3dyey9CUo2JjS0tkE7/zIOBxLnLAxMWMACebCO7wZzFOB4kP+u6TnY30+Rtf1bqARiDvOc3XgQ03TNmqadrMX6hbC43KLGpiSHu2d2cXCuw6ult9xwpcaFi2zzIeq8tZy2rrbvDuRpS9J4+CMe2DX27Dtdd/eO0DFh8UTGxrLnro9qksRAcgfN3/O1XV9GkaLzO2app3W14M0TbtZ07QNmqZtqK6u9m2FQhyisb2LvVXNsvHTH1Xthu1vwIybwB53wpfrPf1TjvMeevbVGxNZfB7MwTilNu1keO+n0FTu+/sHGE3TGBUzSlbMhVd4M5iXAumH/Hdaz8f6fIymaVYgCqg91nN1Xe99XwW8yVFaXHRdf0rX9Rxd13MSEhJO+DcjxGDlFtWj63BylgRzv7PyrxAcDrN+4JHLpUaH0ep00dTe7ZHrCf+xu243GtrB/mSfsgTBhY9Ddwe8fae0tHjAmJgx5DXk0e2Wf8vCs7wZzNcDozRNG65pmg1jM+eRW8OXAdf0/PoS4BPdWEpaBlzeM7VlODAK+ErTNLumaREAmqbZgYXAdi/+HoQ4YRsK6gmyaEyRiSz+pWIbbF8Cp9zskdVygNQYmcwyVO2u201GZAb2YLuaAuJHwYLfwr7lsPlFNTUEkNExo+l0dVLUVKS6FBFgvBbMe3rG7wCWA7uAV3Vd36Fp2u81TVvc87BngThN0/KAHwN39zx3B/AqsBP4ALhd13UXkAR8oWnaFuAr4F1d1z/w1u9BCE/YUFjH+GGRhNusqksRA7HiPgiNgjk/8tgle0cmygbQoWd33W5Oij1JbRGn3AKZc+CDX0JD8fEfL45qTKxsABXe4dUec13X39N1fbSu6yN1Xf9jz8d+o+v6sp5fd+i6/h1d17N1XZ+h63r+Ic/9Y8/zxui6/n7Px/J1XZ/c8za+95pCmJWz283m4gZyMn0wt1h4TsEXkPcRzL0Lwjz3SoccMjQ0NTmbKG0pVR/MLRa44DFwu2DZHdLScgJGRI3AqlnZUy8bQIVn+ePmTyH8xo6yRjq63ORIf7n/0HVY8TuIGGYcKORBcXYbNquFkvo2j15XmFvv9A7lwRwgdjgs/APkfwYbnlVdjd+yBdnIisqSFXPhcRLMhfCijYXGwUI5mRLM/cbud6FkPcy7G4LDPHppi0UjPSaM4jpZMR9KdtftBkwSzAFyrocRZ8CHv4G6A6qr8VtjYsfIyEThcRLMhfCi9QV1ZMSGkxgZqroU0R+uLvj4PogbBVOu9MotMuPsFNbJivlQsrtuNwlhCcSHxasuxaBpcMGjxrSWt24Ht1t1RX5pbOxYKtsqqW2vVV2KCCASzIXwEl3X2VhYL20s/mTDc1Cz13ipP8g7m3UzYsMpqm2VWeZDyK66XQc3C5pGVBqc8wAUroZ1T6iuxi+NjxsPwI7aHYorEYFEgrkQXlJQ20ZNi1M2fvqLtjr49E8wYh6MPsdrt8mIDafV6aK21em1ewjz6HR1cqDhAGNjx6ou5ZumXGn8Xf/4PqjZp7oavzMubhwWzcL2GpnaLDxHgrkQXrL+QB2ArJj7i8/+DJ1NcPYDxkv9XpIZFw5AkbSzDAl59Xl0693m6S8/lKbB+Q+DNRTevAVccljOQIQHhzMiaoQEc+FREsyF8JIv82uJs9sYlehQXYo4nuo9sP4ZmH4dJI3z6q0OBvNaCeZDwbaabQBMiJ+guJKjiEiGb/0/KN0Aa/6huhq/Mz5uPDtqd0hrmvAYCeZCeIGu63y5v5aZI+PQvLj6KjxA1+GDu8HmgDPu9frt0mKMYF4owXxI2FazjdjQWFLsKapLOboJ34axi+GzB4wTb0W/TYifQF1HHeWt5apLEQFCgrkQXnCgppWKpg5mj/TMUe7Ci3Yuhf2fwPx7we79P6/Q4CCSI0OllWWI2FazjUnxk8z9A7qmwXn/B2ExsORG6JJxnv3V+0qItLMIT5FgLoQXfJlvjM+aNUKCual1NBnHkydPgpNv9NltM+LCKapr9dn9hBrNzmYONB4wbxvLoexxcOHjUL0bPvy16mr8xuiY0VgtVrbXSjAXniHBXAgv+HJ/LUmRIQyPt6suRRzLZw9Ac4WxWmgJ8tltM2PDpZVlCOhdRZ2YMFFxJf2UvQBm3g7rn4Y9H6iuxi/YgmyMjhnNjhoZmSg8Q4K5EB6m6zpr82uZPTLe3C9fD3XlW435zTnXQdp0n946IzacquZO2p0un95X+FZvMPeLFfNeC34DSROMg4daqlRX4xcmxU9iW802ut0y1UacOAnmQnjYvqoWalqc0sZiZq5uePtOCIs1goiPZfRMZimul1XzQLa1ZitZkVlE2iJVl9J/waHw7WfA2QJLbzM2R4tjmp40nfbudvbU7VFdiggAEsyF8LDVeTUAzJKNn+b15SNQtgnO/Yux4c3HsuKMFqf8aukzD1S6rrOtehuTEiapLmXgEsfCwvsh7yP46inV1Zje1MSpAGys3Ki4EhEIJJgL4WGf761meLyd9Nhw1aWIvlTvhU8fgJPOg/EXKylhREJPMK9pUXJ/4X1lrWXUdtT6VxvLoU6+EUadbWwELd+quhpTS7InkepIZVPVJtWliAAgwVwID+rocvHl/lpOH52guhTRF7cL3roNbOHwrb979YTPY4kIDSYpMoT9VbJiHqg2VRohbVriNMWVDJKmwYX/hPA4eO0aY4KROKrpSdPJrcqVg4bECZNgLoQHrc2vpbPbzbwxEsxNae0/oWQ9nPMgRCQpLSU70UFetayYB6qNlRuJsEUwKmaU6lIGzx4PlzwH9YWw7AfSb34M0xKnUddRR0FTgepShJ+TYC6EB322p5oQq4WZsvHTfMq3wse/hzHnwqRLVVfDyAQH+VUtssIWoDZWbmRa4jQsmp9/m82cBQt+bRzEtf4Z1dWY1rQk45WR3ldKhBgsP/+KIYS5fL63mlkj4wgN9t1MbNEPzjbjRMOwGFj8iLIWlkONTHDQ3NlNdXOn6lKEh9W011DQVMD0JN+O4fSa2XfCqIWw/B4oy1VdjSllRWYRGxorfebihEkwF8JDCmpaOVDTyjzpLzefD38FNXvgoieMl+dNYGSCA0DaWQJQ76ppwARziwUuehLsifDqNdDeoLoi09E0jZykHNaWr5VXwcQJkWAuhIe8v70CgLPGJyuuRBxm93uw4VmYdQeMnK+6moNGJhqTWfbLyMSAs7FyI2HWMMbGjVVdiueEx8J3/gVNpcbhQxI+v2H2sNlUtVWxv2G/6lKEH5NgLoSHfLC9nMlpUaRGh6kuRfSqOwBLb4HkSUoOEjqW5MhQ7LYg9lfJinmg2VS1iUkJkwi2BKsuxbPSZ8BZv4fd78DKv6muxnRmD5sNwJqyNYorEf5MgrkQHlBS38aWkkYWTUxRXYro1dUOr15l/PrS58EaoraeI2iaxshEB/ullSWgNHQ0sKduT+C0sRxp5m0w8VL49H7Y877qakwlxZHC8KjhEszFCZFgLoQHfNDTxrJogrSxmIKuw7s/hYptcNFTEDtcdUV9GpngkBXzALO2fC06+sHV04CjabD4H5AyGZbcZBzYJQ6aPWw2Gyo30OmSTd1icCSYC+EB720rZ1xKJJk9R60LxTb+Czb/F077GYw5R3U1R5Wd6KCssYOmji7VpQgPWV22mghbBBPi/PTEz/4IDoPLXjRehXr5CtkMeojZw2bT6epkY+VG1aUIPyXBXIgTVFDTyqaiBs6bLG0sppD/Gbz3M8g+E+b9UnU1x3RScgQAeyqaFVciPEHXddaUrWFmykyCLAE+MjU6HS57AeoLjFGkrm7VFZlCTlIOwZZgVpeuVl2K8FMSzIU4Qa9vLMGiwbenpakuRdTsg1evhrhRxomFJg9HY1MiAdhdLsedB4L8xnyq2qqYM2yO6lJ8I3M2nPs3yPsI3v+ZTGoBwoPDmZEyg0+LP5WxiWJQJJgLcQJcbp0lm0o4bXQCSZGhqssZ2trq4KVLwRIM330ZQqNUV3RcKVGhRIUFs7NcVswDQe8qacD2l/cl5zqYcydseA7W/EN1NaZwZsaZFDcXs7de+u/FwEkwF+IErNxbTXljB5dMl9VypZytRihvLIHLX4KYLNUV9YumaZyUHMHuClkxDwRrytcwPGo4KY4h1ta24Hcw/mL46Dew403V1Sh3RvoZWDQLK4pWqC5F+CEJ5kKcgOdWHyAxIoSF42QaizLdTnjlKijdaLSvZJyiuqIBGZsSyZ6KZtxuednbn7V2tbK+fP3QaWM5lMUCFz4O6TPhje9D0VrVFSkVFxbHtMRprCiUYC4GToK5EIO0p6KZVftquGZ2Fjar/FNSwu2CN78P+z+G8/8BY89XXdGAjU2JoM3poqiuTXUp4gR8UfoFTreTBRkLVJeiRnAoXPE/iEqDly6Diu2qK1LqzMwzyWvI40DjAdWlCD8jaUKIQXpqZT6hwRauPCVDdSlDk9sNb/8QdrxhnEY47SrVFQ1K7wbQnbIB1K99XPQxsaGxTE2cqroUdcJj4ao3ITgcXrgIavJUV6RM7w9oHxZ8qLgS4W8kmAsxCHsrm3kzt4SrZmYSHW5TXc7Q43bBsjsg979w+t3G5jM/NTopguAgjW2ljapLEYPkdDlZWbKSeenzAn9M4vHEZMLVb4HuhucvgIZi1RUpkWxPZnrSdJbtXybTWcSASDAXYhD+8sEe7DYrt83LVl3K0ON2wVu3w+YXjTnlZ5h7VvnxhAYHMTYlki3FckiLv1pXvo7Wrtah28ZypITRcPVScDbD84uhuVJ1RUpcmH0hRc1F5Fblqi5F+BEJ5kIM0Ec7K1mxq5Jb5o0kxi6r5T7V1QGvXQtb/gdn3Avz7lZdkUdMTotma0mjbAD1Ux8XfYw92M4pKf618dirkifCla8bofw/50NTueqKfG5h5kLCrGEszVuquhThRySYDyVuN9QXwr6PjBaA1f+ALx6CNY/C5v/B/k+NL57ysttRNbQ5uefNbZyUHMFNp45QXc7Q0tEIL14Cu5bB2X+C03+uuiKPmZQWRUtnN/k1LapLEQPkdDlZUbSC09JOIyQoRHU55pI+A658DZpK4V+LoKFIdUU+FR4cztlZZ7O8YDltXbK5W/SPVXUBwsuaK2DHUuOY8sI10NmPPlZ7gnGi28j5cNL5YI/zepn+oNvl5gf/y6W+1cm/rj1ZJrH4UlMZvHgpVO+Ci5+GSZeqrsijpqRHA7C5uJHsxAjF1YiBWFmyksbORhaPXKy6FHPKmmP0nP/3YnhuEVyzDOJGqq7KZy7MvpCleUv5oOADLh51sepyhB+QYB6Iup3GpIrNL0HBKmMTTuwIGH8BDJsKCSdBZCqExYDFCi4ntNUa4adqF5TlwoHPYedb8O5PYNTZMOUKGL0Igvzvr0xjWxe7K5qob3Nis1qItYcwKtGBPaT/v5cul5ufvLqFVftqePDbE5mQav5TJQNGyUZ4+bvgbIHvvgrZgdfHOyLBgSPEypbiBjmsys8s27+M+LB4ZqbMVF2KeaXlwDXvwAsXwr/ONfrPE8eqrsonpiVOY3TMaF7Y+QIXZV+EpmmqSxIm538pSxxdRyNs+BesewKayyFmOJz6U5h4CSSMOcYTwyEs2ljFGH6q8SFdh8rtsOVl2Poq7HnXOE1xzo9gynfBau6XbJ3dbpZsKuGV9cVs7mNTnaZBdoKD6ZkxzBgey8lZsaTFhPX5RTO/uoVfLNnK+oJ6fn7OGC47WcYj+sy2142Nno5EuOojSBqnuiKvCLJoTEqLYmNhvepSxADUd9SzqmQV3xv3PawW+XZ6TCmT4Nr3jEktz50Nl7349febAKZpGteMv4Z7v7iX1WWrmZs6V3VJwuS0oTDGJycnR9+wYYPqMrynuxO+egpW/g06GmD46TDnhzBygZFAT5SrG/a8Z/Sjl20CRzKccQ9M/R6YcDTY53ur+fXS7RTVtTE6ycF5k4YxOT2aeIeNLpdOdXMnO8oa2VLcwMbCepo6ugFIiQrl5KxYpmZE4wixUt3SycaCej7bW01YcBD3XziBC6emKv7dDRHdnfDRb2Hd45A5By59Huzxqqvyqv9bsZeHP97H5l8vJCo8WHU5oh9e2vUSD3z1AEsWL2F0zGjV5fiH+kJ46VKo3Q+LHzFejQ1wXa4uzllyDiOiR/D0wqdVlyO8TNO0jbqu5wz2+fIjvj/TddjxJqz4rbGpZuQCWPBro13Fk4KsMG6xcarigc/h0z8ZB7usexLOvt/oRTeBLpebB9/fzTNfHGBUooN/X3cyp49O6HMV/KxxSQC43Tp7KptZX1DHugN1rM2vZdmWsoOPS4sJ46ZTR3DD3OEkRJj7VYKAUXfAmLxSvhlmfB8W3g/WwJ9+c8rwOHR9H+sL6jiz5++nMC9d13lt72uMjR0roXwgYjLh+uXw6lWw9BaoLzCmKwVwi0dwUDBXjL2Chzc9zM7anYyLC8xX/oRnyIq5v6ovMPq/81YYY6nO+gOMPMM399Z12LnUWNFsKITxF8OiB412A0XanN3c9uImPttTzdWzMrnn3LGEBg98NV/XdWpanHR0uYix23AMoA9deMCOpbDsB8Y36Qv+CWPPU12Rz3R0uZh034dcMyuTe78l37jN7qvyr7jhwxv4/ezfc9Goi1SX43+6nfDOj4zzCMZfZKyehwTuxucmZxOLlixiYsJEnjjzCdXlCC+SFfOhxtUFXz4Gn/3ZaCNZ9Bc4+UbftpRomvGFdMy5sPphWPlX2P+JsbI59Xs+X/lo6ezm6mfXsbm4gQcunsgVMwbfA65pmqyMq9BWBx/cDVtfgdQcuOQ5Y2VtCAkNDmJKejRr8+tUlyL64b+7/ktMSAznjjhXdSn+yWqDCx6D+NHw8X1QucNoWQvQTaGRtkhunHgjf9/4d9ZXrOfk5JNVlyRMSua9+ZOq3fD0fKN1JXsB3P4VnPJ9dX3e1hBjlvQtqyFxnHFE+vOLfTqrtqPLxY3/Wc+Wkkb+eeW0EwrlQpHd78I/Z8L2JXD63XDd+0MulPeaOSKOHWWNNHV0qS5FHENJcwmfFX/GJaMvkdnlJ0LTYO6P4Opl0N5gfH/b+prqqrzmipOuICk8iYc2PsRQ6FYQgyPB3B+43fDlP+HJ04yRhpe+AJe/CFEm2YiYMBqufRfOewhKc+Gfs40Di7z8hafb5eaOlzax7kAdf790MudMSPHq/YSHNVfC6zcYoxDtiXDTp3DGL4dEP/nRzM2Ox63DF/tqVJcijuG/u/5LkBbEZWMuU11KYBh+KtyyClKmwBs3wtLbjSljASbUGsrtU25nW8023tr/lupyhElJMDe7hmJ44QJY/ktjlfy2tcZGTLOxWCDnerj1C6Pnfektxuae1lqv3fKB93ezYlcVv188ngummOSHFHF8ri7jtNlHphuneM77Jdz8qTFObYiblhFNVFgwn+yuUl2KOIrK1kpe2/Mai7MXk2SXTboeE5EM17wNp/4EtrxkLPDs/0R1VR53QfYFTE2cyt82/I3adu99fxT+S4K5Wek6bHkFHp8NpZuMjTGXvwSOBNWVHVtMFlz7Dpx5H+z5wGhR2Puhx2/z6vpinv3iANfOzuKqWVkev77wkv2fwuNz4MN7IWOm8YPmvLshSMYDAliDLJw+OoHP9lThdstL3Wb0zLZncOtubp50s+pSAk+QFRb8Bm74CILD4IWL4J27oLNZdWUeY9Es/HbWb2nrauPBrx5UXY4wIQnmZtRWB69dA2/ebPRu3/IFTLvaf8ZJWYKMvsGbPwV7Arz0HXj7R9DZ4pHLry+o496l2zh1VDy/+lZgbhQKOGWbjW+yL1wIrk644mW48rUhdTR3fy0Ym0hNi5OtpYH3Ur6/q2itYMm+JVw46kJSHfIqndek5RitLbPuMA7NeyTHOOguQPqyR0aP5KZJN/F+wfu8vf9t1eUIk5FgbjZ7PzRWmXe/B2f+Dq57D2KHq65qcJInGuF89g9h47/hiblQtO6ELllS38YtL2wkLSacR6+YhjVI/gqbWu1+eO06eOp0KMs1Jvfctg7GLPKfHzR97PTRCVg0+GhnhepSxBEe2vgQGho3T5TVcq8LDoOz/wg3roDIFHjjJvjXIijfqroyj7hp4k3kJOXwh7V/IK8+T3U5wkQk1ZhFZwu8faexuhweBzd9AnPvMuXJmgNiDYGFfzA2h+ou+Nc5sOJ3xsmOA9Ta2c1Nz2/E6XLzzDU5cjqimVVsNzZ2PpoDez+A034Gd26B2T+A4FDV1ZladLiNOdnxLNtSJpMbTOSr8q9478B7XD/xelIcstHcZ9Jy4MZP4Px/QM1e44f8N2+BunzVlZ0Qq8XKX077C2HWMO767C4aOhpUlyRMQoK5GRStM1aTN/7HWF2++bPA2wiXNQduXQNTroQvHjLGYlXu6PfT3W6dn7y6hT0VTTxyxVRGJji8WKwYtKJ18OKl8MQcI5DPugN+uBnm/wpCo1RX5zcunJJKcV07m4rqVZcigC53F39c90dSHancMOEG1eUMPRYLTL8GfrARZt5mnHj9SA68dYdPx/N6WkJ4An+f93fKWsq445M7aO9uV12SMAEJ5ip1O43V43+dY6wmX/eesbpsDdC5uCERcMGjRn9xSyU8NQ+++D9wu4771Ic/3scHOyq459yxzBuj7oRR0QdXF2x/A547B55bCCXr4Yx74a7txt/nCJlcMVBnT0gmNNjCm7mlqksRwJNbniS/MZ+7Z9xNqFVe8VEmLMZob7lzi3Gw3tZX4B/T4I2bjSEJfmh60nT+fNqf2Vq9lbs+u4uO7g7VJQnFtKHwUmlOTo6+YcMG1WUcrmgdvP1DqN4NU6+Ccx4I6OOIv6G1xjiOedfbkH4KnP/wUU98e3drObe/tIlLpqfx10smoUlvsjk0lRt7Bzb+G1oqIDoTTrnFWNmy2VVX5/d++L9cPt9bzZe/nE+4TQ5pVmVDxQZu+PAGzh9xPvfPvV91OeJQjaWw5h+Q+19wtkD6TJh5C4z5lt+dh7Bk7xLu+/I+pidN55H5j+CwyavC/krTtI26rucM+vkSzH2so8k4fnj9sxCVBt/6O4xeqLoqNXTdWPH44G6jx37OnUYv8iE9yNtLG/nOE18yNiWC/908kxCrn/fc+ztXF+R9DJtfhD3vgbsbss+CGTdB9pn+vyfCRNYX1PGdJ77kjxdN4MpThuZJqKrVd9Rz6TuXEmwJ5rXzX8MeLD9wmlJHI+S+COuegIZCCIuFiZfA5Mth2DS/2Wj+bv673PvFvYyMHsnDZzxMWkSa6pLEIEgw7wdTBHNdhx1vwPJfQXO5sbI4/1cQIj8V01oDy++FrS9D7Aj41v+DkfMprmvj4sfXYLVovHXHHBIj5CVkZSq2w+aXYNur0FoN4fHGN72c62XkoZfous7iR1fT5uzmo7tOx2Lxj3ARKDpdndy4/EZ21u7kP4v+w4T4CapLEsfjdhkLB1teMiabuTohfjSMPR9O+hakTDX61U1sdelqfrbyZ1g0C38+9c/MTZ2ruiQxQBLM+0F5MC/dBB/8EorXQtJEo20jbbq6esxq/6fGYRL1B3COWMiN5YvZ3J7I67fOZnTSEGrzMQNdN9qsdr5lvFXtBEswjDkHJn8XRp0lhwL5wJu5Jdz1yhaevGo6Z49PVl3OkOFyu/jFql+wvGA5fzv9b5yddbbqksRAtTcYm0S3L4HCNcY+rogUGH0OjJgHWaeCPU51lX0qairizk/vJK8hj8vHXM5d0+8iPDhcdVminySY94OyYN5QBJ/92XjZ354A838NU78nL/cfS1cH7V88hnvl3whxd1A79nsknf9bsMerrizw6TpUbPs6jNfuAzTImAXjL4IJ3zbtN7JA1eVyc/b/rUQDlv/oNJnb7wNd7i7uWXUPHxR8wE+m/4RrJ1yruiRxotrqYN+HsPsdYwHI2XPYXdJEY2JYag6kTjNesTVJ20unq5OHNz3MCztfIMWewi9m/IL56fNlj5UfkGDeDz4P5vWF8MXfjZ43TYOZt8KpP4XQSN/V4KdqWzq56tmvqKsq5fWxn5O2/xVjSk3O9cYoSZnw4VntDXDgc8hbAXmfQFMJaEGQNRfGLYaTzpf/54ot31HB91/YKL3mPtDibOEXq37BypKV/Hj6j7luwnWqSxKe5uoyTiI+8DkcWAnFX0HvmMLQaCOgp0yBhJMgYYzRCmNTt1q9qXKTcQhRQx7TEqdxx9Q7ODn5ZGX1iOOTYN4PPgvmVbth7WNGL65mgSOXlU0AABIkSURBVGlXG4cERckGjv4ormvj+n+vp6iujSevmm6MRazeC6v+BttegyAbTLsGZt0OMRJQBqXbCeU935TyPja+KekuCIk0Xt4ddZYx0UBWxk1D13Uuf2otO8uaWH7XaQyLDlNdUkDaV7+PH3/2Y4qbi7nnlHu4dMylqksSvuDqhupdRstp6UbjffUuY2M7ABpEZxghPS7bmD4VnWG8xWT6ZJpal7uLJXuX8PTWp6lqr2JG8gxunXwr05Omywq6CUkw7wevBnNXF+x+F9Y/AwWrICjkkECe6p17BqC1+bXc9uImul1unrwqh1kjjwiGtfuNg4m2/M/Y4JN9prGKPmohBMkouaNythpzxQu/hMLVULLh69WhlMnGRJXsM43T9aRn3LSKattY9PBKJqVF88INM6SlxYOcLifPbn+Wp7c+TVRIFH897a/kJA/6e6oIBK4u42TR6t1Qvefr93X50NV2+GPDYoyQHpkGEclGH/uR78NjPdIi0+nq5PW9r/P01qep7ahlTMwYLh1zKeeNOE960E1Egnk/eDyY67rxk/X2N4yNJS0VEJUBJ18PU6+W1cYB6HK5eezTPB79JI+MuHCeuTqHEcc61bOxBDY9b5yS2lIBkakw8Tsw7gIYNtU0/YFKdDuhageU5X79VtWz8qNZIGkCZM6BzNlG37gjQXXFYgBe21DMz17fyhUz0vnTRRNlpewEdbm7eGf/Ozy19SlKWkpYlLWIn8/4OfFhsp9FHIWuQ1utMZKxochoW20oMv67qdyYuNZe983nBdnAkdwT1JPBkdTzlnj4e3tCv+avt3e38/b+t3l1z6vsqd+DPdjOouGLWJS1iOlJ0wmSfWxKSTDvB48E864OY6pK3gpjY1xDkfGPLftMY4V81ELZ1HmEtq42SlpKqGqrorqtmur2auo76mnrbqO9q53y5kb2VDTS1KEzLMrOlLR4YsMiiQmNITokmrjQOIY5hpEWkUZMSMzhQcTVZRz5vvHfxmYe3WX8cDRusdGOkX4KBAfoS/5uNzQW96zi9KzkVO4wJqe4nMZjwmKMH1SGTTVCePoMCI1SW7c4YX9dvpvHPt3P1bMy+e354wmSEYoDous6+Y35vJv/LkvzllLdXs24uHHcOfVOZqfOVl2eCARdHcbJ1s0VRlDv631LJXQ09P38sJgjQnvPr+2Jh38sPA5d09has5VX97zKR4Uf0d7dTlxoHGdmnsn89PlMS5omJ9UqYOpgrmnaOcDDQBDwjK7rfz7i8yHA88B0oBa4TNf1gp7P/RK4AXABP9R1fXl/rtmXQQXzjiajF7d0I+R/DkVfQneHMTJuxDyYcLExF3WIhx2X20VZaxkFjQUUNhVS0FRgvDUWUNlW+Y3H24PtWLVQOpxBtHUEYbUEkRxlI8xmrGA1O5tp7GxER//G89IcaaRFpJEekU5mZCaZkZlkRWYRr1vQ9n5g/MC0/xNwdxk/NKWdbIzESsuB5En+tYnR7YKmMmMlpr6w532BEcJr9h7+cqoj2eh/TJlsbFwaNtXog5QV1YCj6zp/em8XT686wKmj4vnztyeRKj3nR9XW1caBxgPsrd/LluotfFXxFcXNxVg0C3NT53LZmMs4NfVUefVB+F53J7RUQWuV8b6l8pD3lYd/7Mj2GTA26dsTDob1dnscq6wulnfVsLKlkA69mxCLjemJU5iVOpepSdMYGzsWW5B/nYjqj0wbzDVNCwL2AmcBJcB64Apd13ce8pjbgEm6rt+iadrlwEW6rl+mado44H/ADGAYsAIY3fO0Y16zL8cM5p3NRv9ybV7P+31QvgVq9kFvOEw4CUacASPPMFoBhtihQC63i8q2SoqaiyhqKqK4uZjCpkKKmoooai6iy9118LERwRFkRWWRFZl1MDwnhSfR1BJGboGLD7bVsruimchQK98/fSTXzxlOmC3oG/drdDZS215LWUsZxc3FlLSUUNJcYvy6uQSn23nw8eHW8IMhPdOeQpazk6y6YjJKtxJRvo2Df46OJCOgx2UbY7Fih0PMcOMLW0iEb4Ksq8s4pa61xmjF6f3i29z76wpoKDZadg75/wqa0bYTP8r4+5h4kvE+frTRvyiGlP99VcQf3jG+7F01K5OrZ2WZKqB3ubto6myi0dlovO9spMlpvG90NhrvOxtp626j292Ny+2iW//6PTpomoaGdth7i2ZB4/CPWzQLaBz8eGtXKzXtNdR21NLa1XqwpghbBFMTp3J62unMS59HYniiwv9DQgxAZ8s3w3pvgG+tPvxj7m7aNY2NoSGsCQtlTVgo+21GGLfqMNYSxgRbHNnhKYyMzCI7bgxR0T3fB8PjwOaQRZ0TZOZgPgv4na7rZ/f89y8BdF1/4JDHLO95zJeaplmBCiABuPvQx/Y+rudpx7xmX3LGjdA3PPtTaKuB5koj/DT3vLXVHFq1MUElaULPymPP6mOA9Yzruk57dzvNzmbjrav54Ep1bXstlW2VVLdXU9VWdfDt0PBts9hIj0gnIzLj6xAekUliWDp6t52yxg7KGto5UNPKttJGtpc2UtNiBOkp6dFcMSOd8ycPI9w2uE2bvT8oFDQZq/QHV+obCyhrKTtstT02JJqk4AgSdAsJzg4S2hpIaKklztmOw+3G4XYT4dZxWIKJCIsn2B5vnGpps3/9Fvz/27u32DiuOo7j3//sxdfEdnOjTtIkjSJQ4SGUilbcVHiAFiQCUoXSh1JuCg+JBBIvpS8gnhASICFBpUCjlkupKkFFJAoFFSSeSlNQlDapKtKSNglp0qaJYyf27s7On4cz6107u44br73j9e8jjc7MmdnxSf57Zv6eOTPuD8NiLCJkAOmEAR6ufMRTYYx3PFVfrlwJryOcGqtPDYnCDPne+i3LoU0wsjVc9R7ZEsqhzfMaeygrx8m3r/CDp1/mj0f+R+Khb91+8w3s3DTMljUDjA73MtRXeMdXg+MkplwtU0kqTFQmmChPTJfjlXEuly/POGZcKl+aTsIbE+5WDGOwOMhQcSjcQYvy5KM8OctNl2aGu5OQgIPjJJ7gOO4+XdbW19YlnjBYHGRN7xrW9q1lTd8atq7eys3DN7N19daQxIt0qyQJQ2Smr7qHpP3c2AmOXDrBkamzHKmOc8xiJhuGwY1Uq9wYx4zGVd4VV1lnBYZzvQzl+xkprmK4OMTq3hH6e0co9g6T71kVzom1c+NVZTqfK4aXM0SF8HKBKL8ikv4sJ+b3AHe5+9fS5fuA2919X8M2L6bbnEqXXwFuJyThz7r7r9P6h4E/pR+bc5/NrN3S6595cDNg6RelgKcluZ7wJcr34vmeNPmqmz2kolVdrWp8qsLZ8dLMyobZpp+9xpprbtMkhg64VXDi+mQxTgWsOudPMS+SS4bJJUNEHsp8dR35ZB256nqiZAgjIk4SLpeqXC7FTJTjq5qRi4wd6wd538Yhbtsywifes571qxd3vFu5Wubk+MnppP31S6/z5uSb02Pcz0+en/N/OgcUHQpA0Z1C4hQ9oeAJBYcIx4Ao3UVI1R0jwiyqX9WzCLMcFuWxKF8/KEX59EBVDIl2rieUlg95vixbtYQRGnrqrH4/XXrz5el9tdquyX5LccKFKyUuTVaYrFTTfhg2MAv9MDKIzDBzwi+XCTQcE2rHiIQK2PzOCeY5cgySo588A+R8gBwD5OlvmA9lzvvr8/RjLCxB3rVzI/d8QK+hFbkeiSe8cekUr5w7zKtvHeXE2KucuXyWM6ULnInHmfS5c4S8Oz2NU+IUcCIP58QonTfqyza9rn63C4vSC12144Gl58H0ZDidxDecHK+qm/WZWbPNqxb3ZLv/S4cWlJh37XvmzGwPsAdgaMsqCjd9KCRF1wze1ZVN65r81mcYlUoZr1rD7mf+PJve8hrtnzVnMxZnrZ39pUxFlse8QEQeI5QRBaIkT0QPeetPT6ppaX0UWE3O+rGcYQ3fjukuUOsvGLnIGOjJMdhTYLAnx2BvnuG+IqPDfYwO9zI63EdvYWkfiC3mimwf3s724e1N18dJzPnJ81woXZi+YzBRmQhleYJStUS5WqaclKevGlaqFcrVEpWkEq7SeQKNV/AIaVBtOZ59RW9OCVSn2vy/IEsqzXWhPpwC6seI2b29sb42JKM2FGPG9g37nFHO3m8PrBsIy0niXClXmaokTMUJ5Tihmng6QeKOe7jbY54Hz6dlDgjzRgFL5yN6ibwvTLV5wnLYbn4nOAcq6QRJOl2/Ujx34iAirUUWMTp0E6NDN/HRHZ+dsa52R/1i6SIXShcYmxrjYukiY+UxpuIppuJJyuXLTFUuU65cYSq+QimepBKXSJIYT2ISj0mSKu5VkiTBSUg8THFahisIfnVZb0hjqxqKVvXJVdX1xTZegF6C96UsZmJ+GtjcsLwprWu2zal0KMsQ4SHQuT57rX0C4O77gf0Qxpj/4u5Hr+9fIV0lH+XZMLCBDQPL6EFQERGRJWBm9Bf66S/0Mzo42unmLEuPfWVhV+QXc8DdIWCHmW0zsyKwGzg4a5uDwP3p/D3A3zzcuz0I7DazHjPbBuwAnpvnPkVERERElp1Fu2Lu7rGZ7QOeJgzdPeDuR83se8Dz7n4QeBj4lZkdB94mJNqk2z0BHANiYK97GPTUbJ+L9W8QEREREVkq+gNDIiIiIiJtsNC3sujdUSIiIiIiGaDEXEREREQkA5SYi4iIiIhkgBJzEREREZEMUGIuIiIiIpIBSsxFRERERDJAibmIiIiISAYoMRcRERERyQAl5iIiIiIiGaDEXEREREQkA5SYi4iIiIhkgBJzEREREZEMUGIuIiIiIpIBSsxFRERERDJAibmIiIiISAaYu3e6DYvOzN4EXut0OzJqLfBWpxshi0bx7W6Kb/dSbLub4tu93u3uq673w/l2tiSr3H1dp9uQVWb2vLvf1ul2yOJQfLub4tu9FNvupvh2LzN7fiGf11AWEREREZEMUGIuIiIiIpIBSsxlf6cbIItK8e1uim/3Umy7m+LbvRYU2xXx8KeIiIiISNbpirmIiIiISAYoMV/BzOwuM3vZzI6b2QOdbo8snJmdMLMXzOxw7clwM7vBzP5qZv9Jy5FOt1OuzcwOmNk5M3uxoa5pLC34SdqXj5jZrZ1rucxHi/h+18xOp/33sJl9umHdt9P4vmxmn+pMq2U+zGyzmf3dzI6Z2VEz+0Zar/7bBeaIb1v6rxLzFcrMcsBPgbuBW4B7zeyWzrZK2uTj7r6z4VVcDwDPuPsO4Jl0WbLvEeCuWXWtYnk3sCOd9gAPLVEb5fo9wtXxBfhx2n93uvtTAOmxeTfw3vQzP0uP4ZJNMfAtd78FuAPYm8ZQ/bc7tIovtKH/KjFfuT4IHHf3V929DDwO7Opwm2Rx7AIeTecfBT7XwbbIPLn7P4C3Z1W3iuUu4JcePAsMm9mNS9NSuR4t4tvKLuBxdy+5+3+B44RjuGSQu59x93+n8+PAS8BG1H+7whzxbeUd9V8l5ivXRuBkw/Ip5v5iyfLgwF/M7F9mtiet2+DuZ9L5N4ANnWmatEGrWKo/d4996XCGAw3DzhTfZcrMtgLvB/6J+m/XmRVfaEP/VWIu0l0+4u63Em6N7jWzjzWu9PAaJr2KqQsoll3pIWA7sBM4A/yws82RhTCzQeB3wDfd/VLjOvXf5a9JfNvSf5WYr1yngc0Ny5vSOlnG3P10Wp4DniTcLjtbuy2aluc610JZoFaxVH/uAu5+1t2r7p4AP6d+u1vxXWbMrEBI2n7j7r9Pq9V/u0Sz+Lar/yoxX7kOATvMbJuZFQkPJhzscJtkAcxswMxW1eaBTwIvEuJ6f7rZ/cAfOtNCaYNWsTwIfDF9u8MdwFjDLXNZJmaNK/48of9CiO9uM+sxs22EhwSfW+r2yfyYmQEPAy+5+48aVqn/doFW8W1X/823v8myHLh7bGb7gKeBHHDA3Y92uFmyMBuAJ8MxgzzwmLv/2cwOAU+Y2VeB14AvdLCNMk9m9lvgTmCtmZ0CvgN8n+axfAr4NOGhoivAl5e8wfKOtIjvnWa2kzDE4QTwdQB3P2pmTwDHCG+E2Ovu1U60W+blw8B9wAtmdjitexD1327RKr73tqP/6i9/ioiIiIhkgIayiIiIiIhkgBJzEREREZEMUGIuIiIiIpIBSsxFRERERDJAibmIiIiISAYoMRcRERERyQAl5iIiIiIiGaDEXEREREQkA/4PoNAFK13Fx0QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['Age','SysABP','Height']].dropna().plot.kde(figsize=[12,8])\n",
    "plt.legend(['Age (years)', 'SysABP (mmHg)', 'Height (cm)'])\n",
    "plt.xlim([-30,250])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "naKuFh2xleoL"
   },
   "source": [
    "## Exploring the warning raised by Tukey's rule\n",
    "\n",
    "Tukey's rule has found far outliers in Height, so we'll look at this in a boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "id": "hFgwrRh0leoL",
    "outputId": "56d1d828-73d9-47af-ecdf-38baf808fd62"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['Age','Height','SysABP']].boxplot(whis=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dFlN43fhleoO"
   },
   "source": [
    "In both cases it seems that there are values that may need to be taken into account when calculating the summary statistics. For `SysABP`, a clearly bimodal distribution, the researcher will need to decide how to handle the peak at ~0, perhaps by cleaning the data and/or describing the issue in the summary table. For `Height`, the researcher may choose to report median, rather than mean."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rJcqGPO-leoO"
   },
   "source": [
    "## Example 2: Table 1 with stratification <a name=\"stratified-example\"></a>\n",
    "\n",
    "In this example we provide summary statistics across all of the data, specifying columns, categorical variables, and non-normal variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 427
    },
    "colab_type": "code",
    "id": "2TuCwYPHleoQ",
    "outputId": "36a88bee-dce3-4cb4-bd41-699830aa1bff"
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">Grouped by Mortality</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Missing</th>\n",
       "      <th>Overall</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <th></th>\n",
       "      <td></td>\n",
       "      <td>1000</td>\n",
       "      <td>864</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age, median [Q1,Q3]</th>\n",
       "      <th></th>\n",
       "      <td>0</td>\n",
       "      <td>68 [53,79]</td>\n",
       "      <td>66 [53,78]</td>\n",
       "      <td>75 [62,83]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SysABP, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>291</td>\n",
       "      <td>114.3 (40.2)</td>\n",
       "      <td>115.4 (38.3)</td>\n",
       "      <td>107.6 (49.4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Height, mean [min,max]</th>\n",
       "      <th></th>\n",
       "      <td>475</td>\n",
       "      <td>170.1 [13.0,406.4]</td>\n",
       "      <td>170.3 [13.0,406.4]</td>\n",
       "      <td>168.5 [144.8,188.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>302</td>\n",
       "      <td>82.9 (23.8)</td>\n",
       "      <td>83.0 (23.6)</td>\n",
       "      <td>82.3 (25.4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">ICU, n (%)</th>\n",
       "      <th>CCU</th>\n",
       "      <td>0</td>\n",
       "      <td>162 (16.2)</td>\n",
       "      <td>137 (15.9)</td>\n",
       "      <td>25 (18.4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSRU</th>\n",
       "      <td></td>\n",
       "      <td>202 (20.2)</td>\n",
       "      <td>194 (22.5)</td>\n",
       "      <td>8 (5.9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MICU</th>\n",
       "      <td></td>\n",
       "      <td>380 (38.0)</td>\n",
       "      <td>318 (36.8)</td>\n",
       "      <td>62 (45.6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SICU</th>\n",
       "      <td></td>\n",
       "      <td>256 (25.6)</td>\n",
       "      <td>215 (24.9)</td>\n",
       "      <td>41 (30.1)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br />[1] Hartigan's Dip Test reports possible\n",
       "                                  multimodal distributions for: Age, Height, SysABP.<br />[2] Normality test reports non-normal\n",
       "                                  distributions for: Age, Height, SysABP, Weight.<br />[3] Tukey test indicates far outliers\n",
       "                                  in: Height, SysABP.<br />"
      ],
      "text/plain": [
       "                            Grouped by Mortality                                                             \n",
       "                                         Missing             Overall                   0                    1\n",
       "n                                                               1000                 864                  136\n",
       "Age, median [Q1,Q3]                            0          68 [53,79]          66 [53,78]           75 [62,83]\n",
       "SysABP, mean (SD)                            291        114.3 (40.2)        115.4 (38.3)         107.6 (49.4)\n",
       "Height, mean [min,max]                       475  170.1 [13.0,406.4]  170.3 [13.0,406.4]  168.5 [144.8,188.0]\n",
       "Weight, mean (SD)                            302         82.9 (23.8)         83.0 (23.6)          82.3 (25.4)\n",
       "ICU, n (%)             CCU                     0          162 (16.2)          137 (15.9)            25 (18.4)\n",
       "                       CSRU                               202 (20.2)          194 (22.5)              8 (5.9)\n",
       "                       MICU                               380 (38.0)          318 (36.8)            62 (45.6)\n",
       "                       SICU                               256 (25.6)          215 (24.9)            41 (30.1)\n",
       "[1] Hartigan's Dip Test reports possible\n",
       "                                  multimodal distributions for: Age, Height, SysABP.\n",
       "[2] Normality test reports non-normal\n",
       "                                  distributions for: Age, Height, SysABP, Weight.\n",
       "[3] Tukey test indicates far outliers\n",
       "                                  in: Height, SysABP."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# columns to summarize\n",
    "columns = ['Age', 'SysABP', 'Height', 'Weight', 'ICU', 'death']\n",
    "\n",
    "# columns containing categorical variables\n",
    "categorical = ['ICU']\n",
    "\n",
    "# non-normal variables\n",
    "nonnormal = ['Age']\n",
    "\n",
    "# limit the binary variable \"death\" to a single row\n",
    "limit = {\"death\": 1}\n",
    "\n",
    "# set the order of the categorical variables\n",
    "order = {\"ICU\": [\"MICU\", \"SICU\", \"CSRU\", \"CCU\"]}\n",
    "\n",
    "# alternative labels\n",
    "labels={'death': 'Mortality'}\n",
    "\n",
    "# set decimal places for age to 0\n",
    "decimals = {\"Age\": 0}\n",
    "\n",
    "# optionally, a categorical variable for stratification\n",
    "groupby = ['death']\n",
    "\n",
    "# rename the death column\n",
    "labels={'death': 'Mortality'}\n",
    "\n",
    "# display minimum and maximum for listed variables\n",
    "min_max = ['Height']\n",
    "\n",
    "table2 = TableOne(data, columns=columns, categorical=categorical, groupby=groupby,\n",
    "                  nonnormal=nonnormal, rename=labels, label_suffix=True,\n",
    "                  decimals=decimals, limit=limit, min_max=min_max)\n",
    "\n",
    "table2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jG3dUr3FleoS"
   },
   "source": [
    "**Summary of the table**:\n",
    "\n",
    "- variables are explicitly defined in the input arguments.\n",
    "- the variables are displayed in the same order as the `columns` argument.\n",
    "- the `limit` argument specifies that only a 1 value should be shown for death.\n",
    "- the order of categorical values is defined in the optional `order` argument.\n",
    "- `nonnormal` continuous variables are summarized by '`median [Q1,Q3]`' instead of `mean (SD)`.\n",
    "- 'sex' is shown as 'gender and 'trt' is shown as 'treatment', as specified in the `rename` argument.\n",
    "- data is summarized across the groups specified in the `groupby` argument.\n",
    "- `min_max` displays \\[minimum, maximum\\] for the variable, instead of standard deviation or upper/lower quartiles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5uAN20wDleoj"
   },
   "source": [
    "## Adding p-values and standardized mean differences <a name=\"pval-smd\"></a>\n",
    "\n",
    "- We can run a test to compute p values by setting the ``pval`` argument to True. \n",
    "- Pairwise standardized mean differences can be added with the ``smd`` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8oric66hleoj"
   },
   "outputs": [],
   "source": [
    "# create grouped_table with p values\n",
    "table3 = TableOne(data, columns, categorical, groupby, nonnormal, pval = True, smd=True,\n",
    "                  htest_name=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 489
    },
    "colab_type": "code",
    "id": "mLeUYSoJleoo",
    "outputId": "7e065fce-7fe6-441d-90ad-7b31960ebf6c"
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"7\" halign=\"left\">Grouped by death</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Missing</th>\n",
       "      <th>Overall</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>P-Value</th>\n",
       "      <th>Test</th>\n",
       "      <th>SMD (0,1)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <th></th>\n",
       "      <td></td>\n",
       "      <td>1000</td>\n",
       "      <td>864</td>\n",
       "      <td>136</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age, median [Q1,Q3]</th>\n",
       "      <th></th>\n",
       "      <td>0</td>\n",
       "      <td>68.0 [53.0,79.0]</td>\n",
       "      <td>66.0 [52.8,78.0]</td>\n",
       "      <td>75.0 [62.0,83.0]</td>\n",
       "      <td>&lt;0.001</td>\n",
       "      <td>Kruskal-Wallis</td>\n",
       "      <td>0.487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SysABP, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>291</td>\n",
       "      <td>114.3 (40.2)</td>\n",
       "      <td>115.4 (38.3)</td>\n",
       "      <td>107.6 (49.4)</td>\n",
       "      <td>0.134</td>\n",
       "      <td>Two Sample T-test</td>\n",
       "      <td>-0.176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Height, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>475</td>\n",
       "      <td>170.1 (22.1)</td>\n",
       "      <td>170.3 (23.2)</td>\n",
       "      <td>168.5 (11.3)</td>\n",
       "      <td>0.304</td>\n",
       "      <td>Two Sample T-test</td>\n",
       "      <td>-0.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>302</td>\n",
       "      <td>82.9 (23.8)</td>\n",
       "      <td>83.0 (23.6)</td>\n",
       "      <td>82.3 (25.4)</td>\n",
       "      <td>0.782</td>\n",
       "      <td>Two Sample T-test</td>\n",
       "      <td>-0.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">ICU, n (%)</th>\n",
       "      <th>CCU</th>\n",
       "      <td>0</td>\n",
       "      <td>162 (16.2)</td>\n",
       "      <td>137 (15.9)</td>\n",
       "      <td>25 (18.4)</td>\n",
       "      <td>&lt;0.001</td>\n",
       "      <td>Chi-squared</td>\n",
       "      <td>0.490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSRU</th>\n",
       "      <td></td>\n",
       "      <td>202 (20.2)</td>\n",
       "      <td>194 (22.5)</td>\n",
       "      <td>8 (5.9)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MICU</th>\n",
       "      <td></td>\n",
       "      <td>380 (38.0)</td>\n",
       "      <td>318 (36.8)</td>\n",
       "      <td>62 (45.6)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SICU</th>\n",
       "      <td></td>\n",
       "      <td>256 (25.6)</td>\n",
       "      <td>215 (24.9)</td>\n",
       "      <td>41 (30.1)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br />[1] Hartigan's Dip Test reports possible\n",
       "                                  multimodal distributions for: Age, Height, SysABP.<br />[2] Normality test reports non-normal\n",
       "                                  distributions for: Age, Height, SysABP, Weight.<br />[3] Tukey test indicates far outliers\n",
       "                                  in: Height, SysABP.<br />"
      ],
      "text/plain": [
       "                         Grouped by death                                                                                           \n",
       "                                  Missing           Overall                 0                 1 P-Value               Test SMD (0,1)\n",
       "n                                                      1000               864               136                                     \n",
       "Age, median [Q1,Q3]                     0  68.0 [53.0,79.0]  66.0 [52.8,78.0]  75.0 [62.0,83.0]  <0.001     Kruskal-Wallis     0.487\n",
       "SysABP, mean (SD)                     291      114.3 (40.2)      115.4 (38.3)      107.6 (49.4)   0.134  Two Sample T-test    -0.176\n",
       "Height, mean (SD)                     475      170.1 (22.1)      170.3 (23.2)      168.5 (11.3)   0.304  Two Sample T-test    -0.099\n",
       "Weight, mean (SD)                     302       82.9 (23.8)       83.0 (23.6)       82.3 (25.4)   0.782  Two Sample T-test    -0.031\n",
       "ICU, n (%)          CCU                 0        162 (16.2)        137 (15.9)         25 (18.4)  <0.001        Chi-squared     0.490\n",
       "                    CSRU                         202 (20.2)        194 (22.5)           8 (5.9)                                     \n",
       "                    MICU                         380 (38.0)        318 (36.8)         62 (45.6)                                     \n",
       "                    SICU                         256 (25.6)        215 (24.9)         41 (30.1)                                     \n",
       "[1] Hartigan's Dip Test reports possible\n",
       "                                  multimodal distributions for: Age, Height, SysABP.\n",
       "[2] Normality test reports non-normal\n",
       "                                  distributions for: Age, Height, SysABP, Weight.\n",
       "[3] Tukey test indicates far outliers\n",
       "                                  in: Height, SysABP."
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view first 10 rows of tableone\n",
    "table3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LlqmAzm9leoq"
   },
   "source": [
    "**Summary of the table**:\n",
    "- the `htest_name` argument can be used to display the name of the hypothesis tests used.\n",
    "- the '`p-value`' column displays the p value generated to 3 decimal places."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using a custom hypothesis test to compute P-Values <a name=\"custom-htest\"></a>\n",
    "\n",
    "Custom hypothesis tests can be defined using the `htest` argument, which takes a dictionary of variable: function pairs (i.e. `htest = {var: custom_func}`, where `var` is the variable and `custom_func` is a function that takes lists of values in each group. The custom function must return a single `pval` value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load PhysioNet 2012 sample data\n",
    "data = load_dataset('pn2012')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the custom tests\n",
    "# `*` allows the function to take an unknown number of arguments\n",
    "def my_custom_test(group1, group2):\n",
    "    \"\"\"\n",
    "    Hypothesis test for test_self_defined_statistical_tests\n",
    "    \"\"\"\n",
    "    my_custom_test.__name__ = \"Custom test 1\"\n",
    "    _, pval= stats.ks_2samp(group1, group2)\n",
    "    return pval\n",
    "\n",
    "\n",
    "# If the number of groups is unknown, use *args\n",
    "def my_custom_test2(*args):\n",
    "    \"\"\"\n",
    "    Hypothesis test for test_self_defined_statistical_tests\n",
    "    \"\"\"\n",
    "    # uncomment the following chunk to view the first 10 values in each group\n",
    "    for n, a in enumerate(args):\n",
    "        print(\"Group {} (total {} values.): {} ...\".format(n, len(a), a[:10]))\n",
    "        \n",
    "    my_custom_test2.__name__ = \"Custom test 2\"\n",
    "    _, pval= stats.ks_2samp(*args)\n",
    "    return pval\n",
    "\n",
    "custom_tests = {'Age': my_custom_test, 'SysABP': my_custom_test2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Group 0 (total 608 values.): [105. 148. 150. 205.  98. 115. 119. 145.   0.  94.] ...\n",
      "Group 1 (total 101 values.): [103. 139. 164. 105.  95.   0. 178. 142.  96. 105.] ...\n"
     ]
    }
   ],
   "source": [
    "# create the table\n",
    "table4 = TableOne(data, groupby=\"death\", pval=True, htest_name=True, htest=custom_tests)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">Grouped by death</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Missing</th>\n",
       "      <th>Overall</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>P-Value</th>\n",
       "      <th>Test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <th></th>\n",
       "      <td></td>\n",
       "      <td>1000</td>\n",
       "      <td>864</td>\n",
       "      <td>136</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>0</td>\n",
       "      <td>65.0 (17.2)</td>\n",
       "      <td>64.0 (17.4)</td>\n",
       "      <td>71.7 (14.0)</td>\n",
       "      <td>&lt;0.001</td>\n",
       "      <td>Custom test 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SysABP, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>291</td>\n",
       "      <td>114.3 (40.2)</td>\n",
       "      <td>115.4 (38.3)</td>\n",
       "      <td>107.6 (49.4)</td>\n",
       "      <td>0.012</td>\n",
       "      <td>Custom test 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Height, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>475</td>\n",
       "      <td>170.1 (22.1)</td>\n",
       "      <td>170.3 (23.2)</td>\n",
       "      <td>168.5 (11.3)</td>\n",
       "      <td>0.304</td>\n",
       "      <td>Two Sample T-test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>302</td>\n",
       "      <td>82.9 (23.8)</td>\n",
       "      <td>83.0 (23.6)</td>\n",
       "      <td>82.3 (25.4)</td>\n",
       "      <td>0.782</td>\n",
       "      <td>Two Sample T-test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">ICU, n (%)</th>\n",
       "      <th>CCU</th>\n",
       "      <td>0</td>\n",
       "      <td>162 (16.2)</td>\n",
       "      <td>137 (15.9)</td>\n",
       "      <td>25 (18.4)</td>\n",
       "      <td>&lt;0.001</td>\n",
       "      <td>Chi-squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSRU</th>\n",
       "      <td></td>\n",
       "      <td>202 (20.2)</td>\n",
       "      <td>194 (22.5)</td>\n",
       "      <td>8 (5.9)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MICU</th>\n",
       "      <td></td>\n",
       "      <td>380 (38.0)</td>\n",
       "      <td>318 (36.8)</td>\n",
       "      <td>62 (45.6)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SICU</th>\n",
       "      <td></td>\n",
       "      <td>256 (25.6)</td>\n",
       "      <td>215 (24.9)</td>\n",
       "      <td>41 (30.1)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MechVent, n (%)</th>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>540 (54.0)</td>\n",
       "      <td>468 (54.2)</td>\n",
       "      <td>72 (52.9)</td>\n",
       "      <td>0.862</td>\n",
       "      <td>Chi-squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>460 (46.0)</td>\n",
       "      <td>396 (45.8)</td>\n",
       "      <td>64 (47.1)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOS, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>0</td>\n",
       "      <td>14.2 (14.2)</td>\n",
       "      <td>14.0 (13.5)</td>\n",
       "      <td>15.4 (17.7)</td>\n",
       "      <td>0.386</td>\n",
       "      <td>Two Sample T-test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br />[1] Hartigan's Dip Test reports possible\n",
       "                                  multimodal distributions for: Age, Height, LOS, SysABP.<br />[2] Normality test reports non-normal\n",
       "                                  distributions for: Age, Height, LOS, SysABP, Weight.<br />[3] Tukey test indicates far outliers\n",
       "                                  in: Height, LOS, SysABP.<br />"
      ],
      "text/plain": [
       "                       Grouped by death                                                                     \n",
       "                                Missing       Overall             0             1 P-Value               Test\n",
       "n                                                1000           864           136                           \n",
       "Age, mean (SD)                        0   65.0 (17.2)   64.0 (17.4)   71.7 (14.0)  <0.001      Custom test 1\n",
       "SysABP, mean (SD)                   291  114.3 (40.2)  115.4 (38.3)  107.6 (49.4)   0.012      Custom test 2\n",
       "Height, mean (SD)                   475  170.1 (22.1)  170.3 (23.2)  168.5 (11.3)   0.304  Two Sample T-test\n",
       "Weight, mean (SD)                   302   82.9 (23.8)   83.0 (23.6)   82.3 (25.4)   0.782  Two Sample T-test\n",
       "ICU, n (%)        CCU                 0    162 (16.2)    137 (15.9)     25 (18.4)  <0.001        Chi-squared\n",
       "                  CSRU                     202 (20.2)    194 (22.5)       8 (5.9)                           \n",
       "                  MICU                     380 (38.0)    318 (36.8)     62 (45.6)                           \n",
       "                  SICU                     256 (25.6)    215 (24.9)     41 (30.1)                           \n",
       "MechVent, n (%)   0                   0    540 (54.0)    468 (54.2)     72 (52.9)   0.862        Chi-squared\n",
       "                  1                        460 (46.0)    396 (45.8)     64 (47.1)                           \n",
       "LOS, mean (SD)                        0   14.2 (14.2)   14.0 (13.5)   15.4 (17.7)   0.386  Two Sample T-test\n",
       "[1] Hartigan's Dip Test reports possible\n",
       "                                  multimodal distributions for: Age, Height, LOS, SysABP.\n",
       "[2] Normality test reports non-normal\n",
       "                                  distributions for: Age, Height, LOS, SysABP, Weight.\n",
       "[3] Tukey test indicates far outliers\n",
       "                                  in: Height, LOS, SysABP."
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the table in custom formats (LaTeX, CSV, Markdown, etc) <a name=\"export\"></a>\n",
    "\n",
    "Tables can be exported to file in various formats, including:\n",
    "\n",
    "- LaTeX\n",
    "- CSV\n",
    "- HTML\n",
    "\n",
    "There are two options for exporting content:\n",
    "\n",
    "1. Print and copy the table using the `tabulate` method\n",
    "2. Call the relevant `to_<format>()` method on the DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Printing your table using tabulate\n",
    "\n",
    "The tableone object includes a `tabulate` method, that makes use of the [tabulate](https://pypi.org/project/tabulate/) package to display the table in custom output formats. Supported table formats include: \"github\", \"grid\", \"fancy_grid\", \"rst\", \"html\", \"latex\", and \"latex_raw\". See the tabulate package for more formats.\n",
    "\n",
    "To export your table in LaTex (for example, to add to your document on [Overleaf.com](https://www.overleaf.com/)), it's simple with the `tabulate` method. Just copy and paste the output below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load PhysioNet 2012 sample data\n",
    "data = load_dataset('pn2012')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the table\n",
    "table5 = TableOne(data, groupby=\"death\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{llllll}\n",
      "\\hline\n",
      "                   &      & Missing   & Overall      & 0            & 1            \\\\\n",
      "\\hline\n",
      " n                 &      &           & 1000         & 864          & 136          \\\\\n",
      " Age, mean (SD)    &      & 0         & 65.0 (17.2)  & 64.0 (17.4)  & 71.7 (14.0)  \\\\\n",
      " SysABP, mean (SD) &      & 291       & 114.3 (40.2) & 115.4 (38.3) & 107.6 (49.4) \\\\\n",
      " Height, mean (SD) &      & 475       & 170.1 (22.1) & 170.3 (23.2) & 168.5 (11.3) \\\\\n",
      " Weight, mean (SD) &      & 302       & 82.9 (23.8)  & 83.0 (23.6)  & 82.3 (25.4)  \\\\\n",
      " ICU, n (\\%)        & CCU  & 0         & 162 (16.2)   & 137 (15.9)   & 25 (18.4)    \\\\\n",
      "                   & CSRU &           & 202 (20.2)   & 194 (22.5)   & 8 (5.9)      \\\\\n",
      "                   & MICU &           & 380 (38.0)   & 318 (36.8)   & 62 (45.6)    \\\\\n",
      "                   & SICU &           & 256 (25.6)   & 215 (24.9)   & 41 (30.1)    \\\\\n",
      " MechVent, n (\\%)   & 0    & 0         & 540 (54.0)   & 468 (54.2)   & 72 (52.9)    \\\\\n",
      "                   & 1    &           & 460 (46.0)   & 396 (45.8)   & 64 (47.1)    \\\\\n",
      " LOS, mean (SD)    &      & 0         & 14.2 (14.2)  & 14.0 (13.5)  & 15.4 (17.7)  \\\\\n",
      "\\hline\n",
      "\\end{tabular}\n"
     ]
    }
   ],
   "source": [
    "print(table5.tabulate(tablefmt = \"latex\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|                   |      | Missing   | Overall      | 0            | 1            |\n",
      "|-------------------|------|-----------|--------------|--------------|--------------|\n",
      "| n                 |      |           | 1000         | 864          | 136          |\n",
      "| Age, mean (SD)    |      | 0         | 65.0 (17.2)  | 64.0 (17.4)  | 71.7 (14.0)  |\n",
      "| SysABP, mean (SD) |      | 291       | 114.3 (40.2) | 115.4 (38.3) | 107.6 (49.4) |\n",
      "| Height, mean (SD) |      | 475       | 170.1 (22.1) | 170.3 (23.2) | 168.5 (11.3) |\n",
      "| Weight, mean (SD) |      | 302       | 82.9 (23.8)  | 83.0 (23.6)  | 82.3 (25.4)  |\n",
      "| ICU, n (%)        | CCU  | 0         | 162 (16.2)   | 137 (15.9)   | 25 (18.4)    |\n",
      "|                   | CSRU |           | 202 (20.2)   | 194 (22.5)   | 8 (5.9)      |\n",
      "|                   | MICU |           | 380 (38.0)   | 318 (36.8)   | 62 (45.6)    |\n",
      "|                   | SICU |           | 256 (25.6)   | 215 (24.9)   | 41 (30.1)    |\n",
      "| MechVent, n (%)   | 0    | 0         | 540 (54.0)   | 468 (54.2)   | 72 (52.9)    |\n",
      "|                   | 1    |           | 460 (46.0)   | 396 (45.8)   | 64 (47.1)    |\n",
      "| LOS, mean (SD)    |      | 0         | 14.2 (14.2)  | 14.0 (13.5)  | 15.4 (17.7)  |\n"
     ]
    }
   ],
   "source": [
    "print(table5.tabulate(tablefmt = \"github\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exporting your table using the `to_<format>()` method\n",
    "\n",
    "Alternatively, the table can be saved to file using the Pandas `to_format()` method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to Excel\n",
    "fn1 = 'tableone.xlsx'\n",
    "table5.to_excel(fn1)\n",
    "\n",
    "# Save table to LaTeX\n",
    "fn2 = 'tableone.tex'\n",
    "table5.to_latex(fn2)\n",
    "\n",
    "# Save table to HTML\n",
    "fn3 = 'tableone.html'\n",
    "table5.to_html(fn3)"
   ]
  }
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