{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistics and Machine Learning\n",
    "\n",
    "Some operations are especially useful for statistics\n",
    "\n",
    "- `get_dummies`\n",
    "- `from_dummies` ([someday](https://github.com/pydata/pandas/issues/8745)?)\n",
    "- Categoricals\n",
    "- `sample`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pd.options.display.max_rows = 10\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's quite common to have categorical data (in the statistical sense), which must be transformed before putting them into an algorithm. There are a couple ways to handle this.\n",
    "\n",
    "### Categoricals\n",
    "\n",
    "This basically creates a mapping between the categories and integers. This sometimes makes sense if you're representing soemthing like responses to a survey where the responses are `bad`, `neutral` and `good`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         bad\n",
       "1         bad\n",
       "2         bad\n",
       "3         bad\n",
       "4     neutral\n",
       "       ...   \n",
       "35       good\n",
       "36    neutral\n",
       "37        bad\n",
       "38        bad\n",
       "39    neutral\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(27)\n",
    "s = pd.Series(np.random.choice(['bad', 'neutral', 'good'], size=40))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         bad\n",
       "1         bad\n",
       "2         bad\n",
       "3         bad\n",
       "4     neutral\n",
       "       ...   \n",
       "35       good\n",
       "36    neutral\n",
       "37        bad\n",
       "38        bad\n",
       "39    neutral\n",
       "dtype: category\n",
       "Categories (3, object): [bad, neutral, good]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(27)\n",
    "s = pd.Series(pd.Categorical(np.random.choice(['bad', 'neutral', 'good'], size=40),\n",
    "                             categories=['bad', 'neutral', 'good'], ordered=True))\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Categoricals can put inside Series or DataFrames just like any other column. The have a\n",
    "a special `.cat` namespace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bad', 'neutral', 'good'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.cat.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     1\n",
       "     ..\n",
       "35    2\n",
       "36    1\n",
       "37    0\n",
       "38    0\n",
       "39    1\n",
       "dtype: int8"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.371535</td>\n",
       "      <td>-1.335292</td>\n",
       "      <td>0.158730</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.484234</td>\n",
       "      <td>-0.829893</td>\n",
       "      <td>0.669012</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.036602</td>\n",
       "      <td>1.026895</td>\n",
       "      <td>1.042563</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.867395</td>\n",
       "      <td>-0.556583</td>\n",
       "      <td>2.068296</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.241162</td>\n",
       "      <td>-0.416430</td>\n",
       "      <td>1.738168</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>-0.488712</td>\n",
       "      <td>-0.070807</td>\n",
       "      <td>0.220182</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1.960017</td>\n",
       "      <td>0.600225</td>\n",
       "      <td>0.807731</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.620891</td>\n",
       "      <td>1.151247</td>\n",
       "      <td>-0.158203</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.071992</td>\n",
       "      <td>0.574698</td>\n",
       "      <td>-0.603291</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.298054</td>\n",
       "      <td>1.214289</td>\n",
       "      <td>-0.625867</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           a         b         c        0\n",
       "0   0.371535 -1.335292  0.158730      bad\n",
       "1  -0.484234 -0.829893  0.669012      bad\n",
       "2   0.036602  1.026895  1.042563      bad\n",
       "3  -0.867395 -0.556583  2.068296      bad\n",
       "4  -0.241162 -0.416430  1.738168  neutral\n",
       "..       ...       ...       ...      ...\n",
       "35 -0.488712 -0.070807  0.220182     good\n",
       "36  1.960017  0.600225  0.807731  neutral\n",
       "37  0.620891  1.151247 -0.158203      bad\n",
       "38  0.071992  0.574698 -0.603291      bad\n",
       "39  0.298054  1.214289 -0.625867  neutral\n",
       "\n",
       "[40 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([pd.DataFrame(np.random.randn(40, 3), columns=list('abc')),\n",
    "                s],\n",
    "               axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Categoricals are still quite new. Other packages are starting to support them (notably seaborn and patsy).\n",
    "\n",
    "There is also `get_dummies`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bad</th>\n",
       "      <th>neutral</th>\n",
       "      <th>good</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    bad  neutral  good\n",
       "0     1        0     0\n",
       "1     1        0     0\n",
       "2     1        0     0\n",
       "3     1        0     0\n",
       "4     0        1     0\n",
       "..  ...      ...   ...\n",
       "35    0        0     1\n",
       "36    0        1     0\n",
       "37    1        0     0\n",
       "38    1        0     0\n",
       "39    0        1     0\n",
       "\n",
       "[40 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      A|B\n",
       "1    A|B|C\n",
       "2        C\n",
       "3      B|A\n",
       "4      A|B\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kinds = [\n",
    "    'A|B',\n",
    "    'A|B|C',\n",
    "    'C',\n",
    "    'B|A',\n",
    "    'A|B'\n",
    "]\n",
    "s = pd.Series(kinds)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B  C\n",
       "0  1  1  0\n",
       "1  1  1  1\n",
       "2  0  0  1\n",
       "3  1  1  0\n",
       "4  1  1  0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.str.get_dummies(sep='|')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Integration with other libraries\n",
    "\n",
    "The two foundational libraries for stats in python are statsmodels and scikit-learn.\n",
    "Everyone speaks the *lingua franca* of NumPy arrays, but there is some integration at the higher level of DataFrames."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statsmodels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Brief primer: you give a **estimator** (OLS, WLS, GLM) a **formula** and **dataset**. You then fit that model. The integration with pandas here is fantastic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://nbviewer.ipython.org/urls/umich.box.com/shared/static/zyl08wsmxwoh6ts70v4o.ipynb?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "pd.options.display.max_rows = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've got some longintudinal data (repeated measures of the same individual on children with HIV. There are two treatment arms, and each child receives an anti-retroviral theropy or are  in the control. Here's the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>VDATE</th>\n",
       "      <th>CD4PCT</th>\n",
       "      <th>arv</th>\n",
       "      <th>treatmnt</th>\n",
       "      <th>CD4CNT</th>\n",
       "      <th>baseage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>newpid</th>\n",
       "      <th>VISIT</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>1988-06-29</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>323</td>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1989-01-19</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>610</td>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1989-04-13</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>324</td>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1989-11-30</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>626</td>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1990-06-07</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>220</td>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  VDATE  CD4PCT  arv  treatmnt  CD4CNT  baseage\n",
       "newpid VISIT                                                   \n",
       "1      1     1988-06-29      18    0         1     323     3.91\n",
       "       4     1989-01-19      37    0         1     610     3.91\n",
       "       7     1989-04-13      13    0         1     324     3.91\n",
       "       13    1989-11-30      13    0         1     626     3.91\n",
       "       19    1990-06-07      12    1         1     220     3.91"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/cd4.csv', parse_dates=['VDATE'], index_col=['newpid', 'VISIT'])\n",
    "df = df.dropna()  # estimator can't handle NaNs so ignore for now.\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some basic statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>treatmnt</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arv</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>486</td>\n",
       "      <td>402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "treatmnt    1    2\n",
       "arv               \n",
       "0         486  402\n",
       "1          48   42"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['arv', 'treatmnt']).CD4PCT.count().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "arv\n",
       "0    947.930180\n",
       "1    926.166667\n",
       "Name: CD4CNT, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('arv').CD4CNT.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10f3b9240>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g44qN5Pyfwp83ATdFxODHXPFYklQ2/QNH2Lij69jlpye3dHDo8Oi/e89vnUEsaWH54mZi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f8fad68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='arv', y='CD4CNT', col='treatmnt', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "years_since = df.groupby(level='newpid').VDATE.apply(lambda x: (x - x.min()).dt.days / 365)\n",
    "df['age'] = df.baseage + years_since"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10f9f7898>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AueBpWKG08jk/MbidU8XZCu+nB58E7LfiUMCDkR7EY0R46oi98vru2FvIg/ft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\nEGWRRCKEEKIs/x+ElUk22AfQxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f947438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(\"age\", \"CD4PCT\", data=df, hue=\"arv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These statistics are throwing away information. They don't tie one observation of an individual to the second observation *of the same individual*. We'd expect some people to consistently have higher or lower `CD4PCT` than others. We'll use a GEE model, but we have to extract the age at the time of each observation to get there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>GEE Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>               <td>CD4PCT</td>        <th>  No. Observations:  </th>    <td>978</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                         <td>GEE</td>         <th>  No. clusters:      </th>    <td>226</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                    <td>Generalized</td>     <th>  Min. cluster size: </th>     <td>1</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td>Estimating Equations</td> <th>  Max. cluster size: </th>     <td>7</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Family:</th>                     <td>Gaussian</td>       <th>  Mean cluster size: </th>    <td>4.3</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dependence structure:</th>     <td>Independence</td>     <th>  Num. iterations:   </th>     <td>2</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                   <td>Thu, 23 Jul 2015</td>   <th>  Scale:             </th>  <td>171.820</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance type: </th>            <td>robust</td>        <th>  Time:              </th> <td>14:01:36</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   23.9086</td> <td>    2.997</td> <td>    7.977</td> <td> 0.000</td> <td>   18.034    29.783</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>       <td>   -0.9923</td> <td>    0.373</td> <td>   -2.661</td> <td> 0.008</td> <td>   -1.723    -0.261</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>arv</th>       <td>    1.6919</td> <td>    2.070</td> <td>    0.817</td> <td> 0.414</td> <td>   -2.365     5.749</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>treatmnt</th>  <td>    2.7020</td> <td>    1.635</td> <td>    1.653</td> <td> 0.098</td> <td>   -0.503     5.907</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>      0.2773</td> <th>  Kurtosis:          </th> <td>     -0.3432</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Centered skew:</th> <td>      0.2924</td> <th>  Centered kurtosis: </th> <td>      3.2945</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               GEE Regression Results                              \n",
       "===================================================================================\n",
       "Dep. Variable:                      CD4PCT   No. Observations:                  978\n",
       "Model:                                 GEE   No. clusters:                      226\n",
       "Method:                        Generalized   Min. cluster size:                   1\n",
       "                      Estimating Equations   Max. cluster size:                   7\n",
       "Family:                           Gaussian   Mean cluster size:                 4.3\n",
       "Dependence structure:         Independence   Num. iterations:                     2\n",
       "Date:                     Thu, 23 Jul 2015   Scale:                         171.820\n",
       "Covariance type:                    robust   Time:                         14:01:36\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     23.9086      2.997      7.977      0.000        18.034    29.783\n",
       "age           -0.9923      0.373     -2.661      0.008        -1.723    -0.261\n",
       "arv            1.6919      2.070      0.817      0.414        -2.365     5.749\n",
       "treatmnt       2.7020      1.635      1.653      0.098        -0.503     5.907\n",
       "==============================================================================\n",
       "Skew:                          0.2773   Kurtosis:                      -0.3432\n",
       "Centered skew:                 0.2924   Centered kurtosis:              3.2945\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_basic = sm.GEE.from_formula(\"CD4PCT ~ age + arv + treatmnt\", \"newpid\", df.reset_index())\n",
    "res_basic = mod_basic.fit()\n",
    "res_basic.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>GEE Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>               <td>CD4PCT</td>        <th>  No. Observations:  </th>    <td>978</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                         <td>GEE</td>         <th>  No. clusters:      </th>    <td>226</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                    <td>Generalized</td>     <th>  Min. cluster size: </th>     <td>1</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td>Estimating Equations</td> <th>  Max. cluster size: </th>     <td>7</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Family:</th>                     <td>Gaussian</td>       <th>  Mean cluster size: </th>    <td>4.3</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dependence structure:</th>     <td>Exchangeable</td>     <th>  Num. iterations:   </th>    <td>10</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                   <td>Thu, 23 Jul 2015</td>   <th>  Scale:             </th>  <td>175.419</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance type: </th>            <td>robust</td>        <th>  Time:              </th> <td>14:01:38</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   27.4284</td> <td>    2.801</td> <td>    9.792</td> <td> 0.000</td> <td>   21.939    32.918</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>       <td>   -1.7609</td> <td>    0.336</td> <td>   -5.244</td> <td> 0.000</td> <td>   -2.419    -1.103</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>arv</th>       <td>    1.9451</td> <td>    1.181</td> <td>    1.647</td> <td> 0.100</td> <td>   -0.369     4.260</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>treatmnt</th>  <td>    1.9567</td> <td>    1.569</td> <td>    1.247</td> <td> 0.212</td> <td>   -1.119     5.032</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>      0.2521</td> <th>  Kurtosis:          </th> <td>     -0.3788</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Centered skew:</th> <td>      0.2914</td> <th>  Centered kurtosis: </th> <td>      3.3861</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               GEE Regression Results                              \n",
       "===================================================================================\n",
       "Dep. Variable:                      CD4PCT   No. Observations:                  978\n",
       "Model:                                 GEE   No. clusters:                      226\n",
       "Method:                        Generalized   Min. cluster size:                   1\n",
       "                      Estimating Equations   Max. cluster size:                   7\n",
       "Family:                           Gaussian   Mean cluster size:                 4.3\n",
       "Dependence structure:         Exchangeable   Num. iterations:                    10\n",
       "Date:                     Thu, 23 Jul 2015   Scale:                         175.419\n",
       "Covariance type:                    robust   Time:                         14:01:38\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     27.4284      2.801      9.792      0.000        21.939    32.918\n",
       "age           -1.7609      0.336     -5.244      0.000        -2.419    -1.103\n",
       "arv            1.9451      1.181      1.647      0.100        -0.369     4.260\n",
       "treatmnt       1.9567      1.569      1.247      0.212        -1.119     5.032\n",
       "==============================================================================\n",
       "Skew:                          0.2521   Kurtosis:                      -0.3788\n",
       "Centered skew:                 0.2914   Centered kurtosis:              3.3861\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex = sm.cov_struct.Exchangeable()\n",
    "mod_exchangable = sm.GEE.from_formula(\"CD4PCT ~ age + arv + treatmnt\", \"newpid\", df.reset_index(),\n",
    "                                      cov_struct=ex)\n",
    "res_exchangable = mod_exchangable.fit()\n",
    "res_exchangable.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11076ab70>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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YQUWFj6uusnP48HuAa4Fr6emxsWjRwyb/8kT+7JnGtWTqLx8eoN7RcTgqpi9W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eSihZKOdO9+F/v3z6WtRGLjNesRr9if0Gq9AcwDTgW2ERX1OX7/zYhAMhKNyHdq/tgmpU+hfZNj\nrw18jo7OpqXlGeAhJHdOn/itKojFnHLKJr75ZgHibYwO/BYTM4umpgcQgVgCjCYt7a9s3erF7X5I\nmYNQYphDuVn5Jb6TBxNWzlu7cdTKyK78bnXWOtOa/Gvv+A8miUqkuWytyXIzOehwzGDLlqkh7fXp\nE8vnn3/FqFHP4Xb/DriCpKTXWbZsLHa7nZdfXtMquVVHoPU/E5GTfQiWh4WFq8Lkit+JRJ9A//5z\nePfdHEpL3zWV+5J3rikuwceI8nYF3bo9yc8/CwFbQsICXn/9WrKyynG5TgQ+o3//ejZs+BO9evVS\n5rZ7SH8ldWEIcCExMc/Q1PRn1Hy7oUPjGTv2VEBSFtT8QpX8LJiALZhIzbzvsl8I96y1N19U9XKK\nsiz7gqSkRYHn4JcGK+fNwhGD9sbltyVEWlOiDibaawm02WwsXZrB3Xe/QHm5Mcb7X//6CVGWTgg5\nb//+VCT8MdX0Gur8NDY20q/fdOrqfocUBZ2qnBcPrECSpWcjilENfv91YUbmRw3bEMrin7Hb8/B6\npyt9FEUwKekV/P4WXK4GYDPx8ZvxeAqAD4CTde1twuixu5VvvvlfzGrgNDU9jAifKaievIsvHsLz\nz19CaelGQMtVUPMPMjMvaZcA70qWaQsWLBwZiCRyI9J2OiMvzSyXbPTo2E7xTkWap9aRseTkXBD2\nmF69erFlywPK+ryRzMyxgXyz8nIPmvzx4nDMxucbGgid7AjU/k+ePA+n80EkGqUElfQrObmIlJTL\neeSRjYgRcoNy5nCgF6rHy+9vCW46AJvNFsH8/xuw8fPP9yDRL+cSFbWf++9fhMs1FYnGmUZtLYwa\nNZe33rqJ996rQUhLrglq6yyEsORRmpoeQstju4aPPvozH310KhCFsF1qz8n9989m1KiB9O1boDBT\nX86oUS8oUTBX4HS+wejRcSZ9l5z5cM+a2T5p9OiVJCdrnuxLL32VzMwxgeNdrl+jV0pVD2hXZE09\n1LCUNwuHFZEKwkiEyMEKpzgQkpNw59rtdhITB1FerilBsERRuMYgwmAucK/ym4SOwN+Vz95ALpiZ\npRWaEPKQLxDv21bl+9OR3DRVgZqF5J99i14hk5h5PyKoutG792Z27RqF1ztUOWco/fvnMmnSb8nK\nGovX62XUqPm43ffh8aSieet+0o3DLIz1MsIpq0J48jpwPfHxm0lPf8DwvBxIwv/hIMuxYMGCBT06\nQxHsTBKVSNoOJ1NbG0u4cNPWYNaebOhvQ5SrNcBnuN355ObC6tUHtobb7XaSkwfjdIIYFG8C1pKR\nsZV58+5g8uQXaWqahUStTFfOelT5fx8A6upSKC1dFVbu6w2G6enDDMdIMe4pqARfwvw8gg8+eBVw\nIF4zNRdNiLuSku5g166TEPISvfx+GqlHuxFJUzCeC48je4wNSMqCCi/l5cdRXp4JwH//u4gFC5ZS\nV6eGdpZQUZHJ6NFrDEqX9P2WiOdahWrIVpX+e+65KWy6iwUjLOXNwhGBgx/nHt4T05ZVsTXlrrVz\nbbZjgNHIAgswHngbWbwzgAbi4/+IxzMGuJ64uGdpaPgT0EBc3LMG5qjRo+NM2BY3An9EFmp18X0Y\nUQ7V4wCOQTxiTyDWvnOU87sBa+ndu4pduy5As+z9CNiprc0HVgZqq7ndDwZdvxxwIzlz64D3kdID\naimCYkSgNAMPIHTJk5Xf9CUG7sfjeYSsrGWkpcVjs9lM4+UrKjKZPLl1KmT1HldUfEJFxRTMnifL\nI2fBgoUjGQkJA/n2246zKnc2IvHMtS/lwY7IrfvQr+ElJSuZNKnjjIVGWd6N5OTvDbnTIsOmo8m5\nPIwpA+HHC4TkiV199bH06TOblhY/27Z5qa1tRmTeEsRY+wxixLwUKCDYu7ZrVypCEiapBaJMJiGh\nnEuBFMRYWxdyrmz//6JcQ5W7j6A37kqOXXA+exnV1f9h9OiBjB69AoiivLwJl6sbGkFJ6LPWmiFb\n3cfpuQoyMy9hxYpluFzGKJ/MzLEhbR+NsHLejhB05RyAQ4FI6rMdSA5duMLXbRXM1p/f3g1/cH+H\nDVvId9/toq5uWqD/+j6kpJzHjBmlfPnlf6mpKUBfhDojo0AJ9wiu8+IjOK+uW7c7+fnnvyqfZyLh\nFccTE7OepqbnDMfGx/+RpKRTTOL85wL36erSCS1/W3XsIBexmsYAf0aEcAHQAzgN2ILKtKlZINcC\nV6PPlwuNl/eG/N4WVXVwLTi1jl+kobdH+zvZXlg5b+3GUSsjj/Z3qz3jj6RuWUeNUJ2Vl95ZKQ/G\n/qwlWLaFy6PrjL5+9923XHDBY7S0LA65pts9U+m3+fx4vV4mT34Rp/M8jLLtTWA3qtwaOPAZxo//\nFSUl29ixoy8awctcJCe8BS0aZwki936tfN4KBO8BnkT2CX7gODTPnGo47YZ4MI9B9grbCK5VZ1RO\njQW41Rw0r9fL9OlFgEYGZjaPbT0Hwc/90URYYhXp/oXiaBdmkQqRjhKWhCqHDTgcEgYo1+ucsLrg\nxUtd9FpaWvjvf3vxwQc3AwuIj/+Sd955kJNO6hs0fnPFIz9/BatX7wkKYxiPeNrmYFyMn0BCKUBi\n9t8GriEh4a907x4TYLTq338O2dmn8NFHbsrLc4PaKKdfv38rBUbtSnsLkYVf6tjdd99p5OXdRKgw\nmYa+CCjUExv7FXv2PIgIoyfRipGq4RgqWYoZ8Us2ZoI8WLk3MwDoa8GJpTQ0ETtcMvzR/k62F5by\n1m4ctTLyaH+3DpSwpDM3uAfadiSKWSTG2eD++HzNvPxybQgrZVuFqCPpr5nSoRXZXoqqBJmReQQT\naAlbtUcJ9wQpD3SS8v9QZUmUwWTMCcR+RhSu7sBFwP9DIwCbDdyP3qgr+e5XKm3pZW4zkI5E5dwH\n2ImNzWfPnkHAV2gKYhGi+Mm969HjHhobnzf0KzV1Fp98crxhr6SRnrSPf+Bofu8twhILv0hEmhCt\nuuD79Inlq69+MJBZtG8h36wsRp0XphksxFasWEBUVDSVlQ8qRyxEar5Nw+OBtLS5bNkyMaIi1Cpd\ndUnJSlyuf9PSsh8o5Msv6/n009mIxwtksf4D8E9dzyTRuLr6j+Tnr+C669TyCHGK8uUlOnomLS3C\nfhUT8xj33+/gtdeikZANkNCL21FDQBsa/oTNtpakJI3eWDxsakK3HVG2gj10NkQATVGOG4IIK/M4\n+OLiFKZPL1C8kSN08xMZMjK2kpzc0mXovS1YsGChvWhP7nh7FbEDzcvrWMqD5HT7fD7Aj812DOnp\nw0yUpNXk5q5DZJiE2Tud5+F0XtPuPGbVy7Nw4Y6AIqK2YRyDEGipuXB2uz0Qxl9a+m6YHPRiNNkX\njVoeCFwh/RDFzWxr3ozksJ+N0P0HE4Dl4nA8jNs9W/m8BLgEKfkDepkrBtIngTtxOGYzYcK5rF17\nElVV4xAD6gxECfyT0r5E8ZxxBmxV0+cVrFljJzh8dfr0AioqHqSz9k9WKkMoLOXNwhGD9giR9hJS\nBMdjOxyVuN3tj51vbZExCgAvLpcHCR1UF9+T0XuP3O57g2L4jSxXesUD0AkeUdSSkxfz5pu3c/PN\nK/jgg3uBU4AbgUVoylwBcHegjzbbMWRnX0VR0QbFUqgW9X4AlXq4qWk6n3wyj9pafYjGZILLBdhs\nNpYtGxtQKD/+uBs7dqiFTDXL5ejRl1BTo59FO0KsMhUReBsRgTMHLYxkCT6fTbHuifIrteD+hFpG\nIDjuvq1acOGOOZy5IhYsWDh0+KVsElv3HnWcpOlgzY9x3fUacrpFXlzJ7Nkv0dAw3dD3rKwrWbVq\nhc5AqEZqhGc9NBuP5h37NcGKiGowVs5AlCZISBgQdl6D2T61HHQwKlz3Ih4zkcd2++N4vWpdVY3t\nUgy7HiQsUp2TUMbHnJyhVFcXKOGZtyj9fVo5N1s3R79C9hvvk5PzP9hsx1BVdSOaAXUmkrP+hnLe\nSIYNe4k+ffqwdauRZA0Gh51fM7T3GTKb3/aktPxSYSlvFg4Z6uvrQ+Ki24P2vPRFRZvaZe0L9uyl\np9/FhAl6Ze5p0tNvMj1X37/WhKPP14x41j4DGoBhbY45P/9tXK5PmD79Wuz2JwKx5nFxBYbYcrnu\nSQQLnrKyVaxYcTMlJX9jzpyt7NpVhCzMqvCYht6DpzJiCZNldyRR+h+I52sQmuUuFA7HP3C7UwBj\nMvKkSanYbDbKy9WwGKPlEoQpzBjyOUj5v95a+CZ6cpeamnkG615DwzQyMgqUkJmO0Vl3Fn23BQsW\njiz8Uhhow43jQEm/Ojo/kRjE9OvuRx99TmmpnhTkVmCuorgZ+56ZeYlCz78OKYtzG6pyJSkBkY9H\nFCLzNCIhz1igqznmZeHCWQjVPiHz2rv3TCSPW18U2yx65H0k0mSj0qf7cDgeV3LorkeUu8uBE5GC\n29qcnHPO3ezd+0UgbFSNwLHZbDidasjlJiQFoQWR89uUfjyImnJhs+mVUw1paf+ipqY7dXXrgGY+\n/bSeqqrpSHiq2pbqmTOWVSgoyDYlzOnIM2T23I4aNSNQ3PxIfU8PFJbyZuGQoL6+noSEYhoaxHK0\naVMB1dUTWlXggml19THUB+OFDfbsFRenKItEMm73nUyY8EarFp/WhKPX6+XNN/8POBY1L0xi34tQ\nY9ZPP/2f7N37IR7PI8rvr9LY+CTl5W+xatUC/H4t1ryhYRrTp4ui4vM1K7H4LyjnqQnR2rgmTRqN\nz7efvLxQa11GxlauuspGaqqeEUu18BUgXjU7sAD4fzgc1cyc+YeQxbm4+B7KyiJReuxIDP7WQP/U\n8Ect92885rXkJgWul5AwULHMakhOHtzqZiQS721n1XGyYMHCkYNDwWh8KBBuHAer3UjW0/akPMTG\n2ikt1f/iBb5GFAajbNNKB9gQQ+PzqDT+cXEFpKdPIBzMUxHWoFdEHI6nycyU1IW0tHhcrrGIIrQ8\nUKIgPn4qwXT7mzb50dgdixk2bB/XXivlcFat0lIJtAgfNbqmmSFD7AwZ8jj//OdeamsfBd6nR483\naGw0Mlpu25YKXIHDMYOcnAvIypJ5TU8fxvz5j+B2J6EZWu1Ijts1Sk5dKDOkKNiZwGYcjkqGDj2b\n8nI1Z32NkpNuQ/Lf1pOW1sS335ZQVZWDKJqP0r//foqLpXi4KtNBjPV2u52iog2d8o5JaOmR/Z4e\nKCzlzcJBhaqAvfbaJhoaCghWPl56aUrY8/QWmvnz5+J230mkL2x29giWLDmw8LeysirFuqNR0au1\nzMCoQGreqjTTtkpL36Wq6nSMSci/R4TFWmAjPXv6GDLkRNasMcbwwxf4/cE0vwRi+/v3fwqxhqmJ\n28XAeJKTSw1jttlswBXohVP//nMoKMhm06Z/BpLAjQJtGmIVHIEwRF6L230tt92mhi4YBXKkdX5g\nCU7nFL75ZgkpKXEUF2/D7Z4KvI/D8Q/y8pzYbMcBK5XSAGOVeTRSL+s9dlaIowULFroSulIY5oGE\nhBujMYwKVCRoj0HMKLu9wLNIyD4Ey7ZXXlkLFCJ5ZGegp/FvaJhGWVl7N/VRiCKSBwxlwoS+lJRs\norr6c1pa1Jy1Tehrpnk8s9GHPsITeL2PoVcKr71WS3/IyvKGjfDp2fNJysunAnaSkhYxadImbLZj\naGxMJS8vOFzxeuBdRUnbF9iHTJiwLuCV6t//KU488UWqqm4HRBmdMOFsbDZVrmqK9IIFl3PZZU/h\n8TyC251KcfFcZf6D88jF+HrxxS34fM1UVan7lUepre1GWZl4RPUpDd9+uzjAgNpepKcPY/bsAhoa\nxOhtt+fj9d7dxlm/fFjKm4WDBqMCloaRIbF1BFvF3O57MaunEg4HJ/zNnMREo5efgsSmnwxAYuLX\nZGaOb6PNKCTc4wm2brWzdetCYmP/yZ49ahHQVxGhZFS6JI59MmBTwib0tVhuJS3tcZ5//q6gMfsR\npqzxiMLo4uabf2PwaDoc4RZsY3K0GpIZqWBU78fkyfp4fHC5YnC5MtGE9DTc7uaAAEwAACAASURB\nVGsoLtbIWlR4vV7TNq0QRwsWLBwoOjvf9XCFYbZWT6sj66U2Dn2+VahxsLOg7+d7720NYjq+lYyM\nAubNuwOv18ucOTvQ8sDuQbxLGt57b2tYpTl4nqRUz5fU1QE8SGJiCWvXRlFVpaZLLCQ29gn27BkS\n3GNkK60qMaHpEGI4NVfm1bFWVHyC0zkFlS2ysnIi112nRe6sW7cMl0sNV/z/7b19fFTlmf//BhyY\npU1MS1F3f10hrnK2di1WKUnWArtipUiIiQiNDylZha6rbcFqQY2yLBJtqGCBrag8mBAfAiyZNAQQ\nEHaB+guBbCtl637PF787oG2l8o1gUmGSaTrfP+5zZs6ZOfOYIQ/M9X69eBnn4Zz73DNzX+e6r+v6\nXN8G1mIKe61Y8TQzZ36dhobDtvumEyceYe7cem6/vZ51697B661g8WK3Tf1RCbXsZcWKPbS1vYj1\nnivUBmGCUVMeamNUWlpiXEu4Kmb0KG0qv7GGhsO0t88jlFr6iCW1NHM3bVN23jRNKwHu1HX9HuP/\n81GSc38Eduu6viQ9QxQGKrEUErOzl1FVVZ7U8XJzmyNqqmLR0/S3REVMQtfZjfpJqdcMGrTedqzw\nhpOjRj1HdvYJjh1bTkje9346OhqYNu1p3nqrjc7OHwFvYne69qGaaUY3vHl5fx3FMH8OeAFVU/Y4\n775rrxvzeh+2NAa/2dIYfF+86QIiU13NFFPr38oRdaOijqZDuBurZLLX+7AtshrrRijT0iUEQUg/\n6d4M6qs0TOt1qDrr7KAKYirrpZMdNx2oC+2IDhkyJOpzCxdWW1L5QNkVe3SqsfHLnD7tcXSaIz/v\nmYDZG28Pfv8IKipmWI6vbHNR0S84dkw3NpRBbbB+DyXb/22U4Eqko5OIDfN4nOfT7XazZcss6uoO\ncO7cF3n22Wfp7Az1UG1re5IpU57kuuvchGf/tLYeBzCEzNR9RuTGs/XzDTF79pcZPtyMEs6OyLSJ\n5oyZ9wBO15Hab8ysfQfoYs6c64O1epm6aZuS86Zp2kpU06VfWh5eA9yh67pX07TtmqZdr+v6O+kY\npHDxMHbsbq666lhcwRKnRaGm5qEEa6rSQzwRE3Ohqq19C+VU/TcqdVEtgocO3Rc01ubiqxpOVhkN\nJ+cafcXCr2MokyZdz/PPj2fhwpWWptzmgjieESNW09ameqGNHr2cEydyCOW3b0Q15Azh8/nYtu0T\nVJsAgBry8mod68ba2opQufEraGz8Fjt37sHv77Ll6hcUVFNcPNXWigHs8sgq1UHVyy1atBSf71Gg\nyDBs3wHeQe31TIn7WVws9SiCIPRf0rkZpGTu+wbzxtrJWYCeN+8uKLg24ahdso2a7U7OrTZHyEy3\nP3XqDUaODJ/focC3sIpawUGam293tBXRxmG+zrRt4ef4+tfH8q//OpHaWjOa9QTgJi+vk6IilY7o\n5OhEq/cqLh4f7PV6002/5+235xhzbN+gNr+b1dW76eyMtJle79/i9XZjdWCzs5cZ0Tw3TplPdrs6\nmHDn1+X6jG3enH4bhYXZXHFFFePGXR2su4sVYUv2N+Z0LPM8mUyqkbe3AQ+qsROapmUDw3Rd9xrP\n70J1BhTnLYNx+tFt2vQvCfeWcdqhsUZiEu3h1pO6g/CFJnxMgOEU3RnzOGYPmdbW9wzHbXJwkauv\nX2+TOs7L66S4uIiGhsMUFFxLVVU5s2e/YRQTbwZm09bmCxYqw2gqKgqxGi2Xa4/t/HV1Bzh06H7s\nqZX1lJVNdlB6VFLLXu/DVFQsJS/vWlyuoWzcOC1okIqLp0YIyBQWZtuMkzK4e4Bp+HwVwb/b27/P\niBE/pq1tkXHOV4Bi3O6lxutST4XoTzUmgiBkJj6fj8bGNsJFl8za3d7AacOrtraepqb2pFI5U00n\njSbxHk94zK4U7aK9/ftGL9ApmM5Hc3M5ixdv4t//fSkdHV8F4LOfPcK11/7OENAAFRFzVohOJKU1\nmm0uLZ0VVFEuK5tMXd0e4/WzbO9PxEE5d+5Tm5BbdnYVixfXMXz4Z+JsUIeXUaxE3Y4fxFRzHj78\nDdrb1xHK6gllPuXlrcXvH2FE5W5FzXWk8xt+H2ElfA5PnXqFsjL1XDqj2FIe4UxM503TtPsJdcs1\nKdd1fbOmaX9neSwbpX1u0gFclZYRCgOWnv7oou3QJFNLkO66g/AxVVfvtjhFUwjvYWamS8ycudmQ\nGZ6Px7OPdet+zK5d3yMnJ8fSC+1dYDA33PBXlJU1GipaIaO3cOFKPB4zTcRl5HzXU1Z2C01Nb6RU\nq1FXd4DCwizuumsH//7vxyy7dAA+Ghs/Q2PjjIi5c9pFvOKKqgRncbfhuJmOZDlFRc/w3HNzbLuV\nAGvXNtHa+h5jx46yNfx2usaLRepbEISBTUgF0exT6Wf69Ev7fC1qbT1Oss2TU7XjTs5jas2b3Vx1\n1Z9z9Ki1tkrVkX3pS5dz+LBKp7v22t/x6qvT2bLFGhEb7Ggr4ilDmxuAGzdOY8uWelpbjxuRpdtt\n1x5vQzm8jCDcCf7FL35He3uopq+9fQFHj0YXcjOP7fG8QXNzMarRthfVVHszocjabZw792vCSytK\nSo4xbtx5tm0bYqSEWvujTsDtfi7hDdR42TDpjGJLeYQDgUAgpX9jxoz5uzFjxrxh/J09ZsyYX1ue\nmzdmzJhH4hxDEBLm/PnzgTVrmgJr1jQFVq3yBKArAAHjX2dgzZomx/etWdOU8GtTIfL4nwRKS58N\nrFnTFDh//rzlNQ0BaA/AeuP1XYFrrlkWfM358+cDkyaFnoO1AThvG7PTtVx99eOB8+fPB86fPx9Y\ntao+UFr6bGDVKk/wuNb5U8fvDEBnYMKElwM33bTaGFdDYMKEFwNnzpyxvQYejzp3q1bVG+9tMsbZ\nGVi1qj4wYcKLAfAEwBMYNqwiAJ8Yx1oa/HvkyDkRxy0tfTZivOpYa4NzctNNPw2sWuWxzW1vftZC\nj0nZ3mToP2GA0pO1yGrrrOtcKo9b1/NJk9Yb63bvrJFOc1Ba+mzc8zuN+8yZM4EJE1622a9Y1xJt\nTmKNzXyt1Q5PmrTe8f3hOL0vZE/tj1nH5TQf4bbQiTNnzgSuuWZZ8NhXX/2jQG7uYovtXh+A3xuv\nCc2jOS9O51yzpilifMl+vmJve0RS9iEtapO6rrdrmtaladpVqG2AW1FaqzE5fbojHafPCEaOzMrY\n+QqPqOTmPkWoeFXR0eGzzc/IkVl88MFpdu/+BaG6Krfja3vCtGnjKSiw90dZsuQhcnJy6OjwG/98\nqCD3PqwSw8ePz2P16m3BPPb9+83nfEAO8ENUesRkOjp8lJZOZPTo5zhx4iHjWC28997DrF69ndLS\nibzxxmmamx+hrg7eeCMy6lRbOz24e/rJJ4OorDT7rcHBgzW88MJOamunWxQhI1WzOjp8fPDBaV5/\n/ffY6+c6+eY3i3j99e2Yn8111/2G06eXcPLk14G55OY+w5w51+P3T4iQPb7uulG2z6S6ejcHD16B\nVcnq7bfncPvt2ygtnczp0x3U1W0HQrucap4jx3uhfjeZ/JtMhZEjs+K/SLCRqd+vgf7bCtmFckBF\nMSZPnsqPf7wViJ7SHW7rli1bwa5dd+N2u22Pb9wYql9zetw8tnXNLy2dDijbYB3XtGklMec61VR0\npzlYsuQeTp6Mff6RI7Mixt3R4aerqwul6AhdXV10dHRGnNO63s+YMRGfz8fq1XY7EW1s06aVsHr1\ndosdhv377w3a6FhzUld3IOJ9991Xxf79j9keW7lyEw8+WGyM1c+SJfewY0eopu/SS3/MkiXf5vTp\njpjzXl29l+PH5weP/d57D7N48SZqahYZrQPupKCgjpqaUkupwzdYvXq70fJhItYo5g03XMOMGRPx\n+9W8mePr6AjVFYaPJ9oc9uR3O9B/9z0hWfvYE+ctgL0d/QPAa6iGG7t0XT/Sg2MLQpDItgFPkJu7\nIqj2FDuF7sLKG5sNps3+b17vNGbPthvQkNLkbwg17oyGD5XLXo5qi1BDVtZypk69l9ravXzyiRel\nDfQDlJO0nH37zvLznx+juTmUfuGUjmKmHvh8Pr761ceAkCwwfJvW1irmzi1k5coHOHXKYzikkWmg\n0ernGhoO2x5vbf0OlZX1uFzdwEFKSx8JppKEZI8hL6+TsrLEWkBA9PTIdEt9C4IgpEKk2FX8Wi9w\nbpEzZYqqbY7WeDvZ1LV4KZDhqX6JjDuROUimXYFTeUKoGTe0tHRRVLTV5jzk5j6P338lPp8vaGdC\ndsLHmjWVzJ79ZeBPHD36PlOmjKKwMLLfWTyc7E9hYWI33s8+28zdd/9dUKwtJyeH1tbZwWbWGzZ8\nD79/SEolAMOHD2f//keCaplWnYDwtk2hVEl3QnbSaS7nzLnesddrPKQuPT2k7Lzpur4f2G/5/xag\nIB2DEoTYuJkz58qYUrH2ome4kPLGqpm3c/83c5Fat+5WHnvsFf7jP57i008j+5MUF483GpFrKMct\nNO6Ojh0UFa03Gm/+Gfa+Kj/gzTfnAzcmPN66ugO0tUU6kd3d3VRX76a0dKJhYPfg91sbZSdfP+Zy\nuSJuHqyyxxBZ6K0ei2ytYBb8qxucu1DtBVTz9Lq6PZSX3yqFzYIgXBCSuel0isykqpbr9ebT2nqs\n5xdgEKt+KNxpWLPmKcPupKby63SudNUvuVxD2bSpxKL6WEFFhZumplcMO2Bt4bMFr/dpFi8GqELV\nnqtG2Js3z7BttIZvAIarKjt9loWF9RGO5NixV3LgwNO0tT2KypQ5TGfnYhYufMlW05aTk8PKlQ9Q\nV3eAurq3mTZtfNzvS2QbI+W4grNQSvjx2tsXMHbsfGbNmpiQcqPTXFZUEJxr01mOJyIndenpQ5p0\nC/2edEnFJipv7ES0/mWmRH44fn+XsUjdBezjqaeep7PzXwB3UCXSvAafz0dZWSNe79WYiozheL1f\nQy28Tj9Zc7EOqU+ZRueFF+rxeFoYNeoyKivvYefOXxlpEw8A1ag+a+B2V9LY+CiNjW7q69czffql\nuFxDKSu7xdGxClfhamzspLa2KMKgFBeH1L7Cb2jiFclHtlZQRlb1LtoUHDvUGE5m8jcGsgsoCEI8\neiqSlWhkprR0orGJ90+YN/wwn3HjznPqlHNWQTqzDSIjf/kpHyudxGo87nK5HB3MEHuxliuofqLK\nzlrb+YBzxFSJh30RgK1bN1NUNMJxjOGO5OLFbnJy/hFYDjyJsus1vPfeB6xd2wQMMtoK2KObBQVK\nuTkW5jjDHdef/exFrriijSFDhsRtx3T06BSGD/99UCEyMexz6dwvDtasWcGcOVdG3D9Iy5/0Ic6b\nEJP+cHObitpVeflkNm5Mj1ELN8bW/mWmEmS4YYFsw3HbApTR2Xkbqof99/B6l+BybbPUJbxlKFEW\noRyxpUCFcfaNgJl37gMmY+/FUoOS9z2IkkXeQUmJ6qN3zz1bOXLkD8BKjh6F7dufprv7e9h7re1g\nxIhttLX9BFNS+NCh+zh0aCdwm+NNitvtZvr0S43XXALcS0vLYBoathkppIvwegvweh9k9uw3HGsz\nEtlxU3LMhcydG/7MIELNvTHmoj7qcaIhu4CCICRCouqEiUZmrErE4fa1sfEOvva1Zfh8/wxMIzu7\nipkzZ1NW5na0gRc22+DmuCUKvUEq9wAhh++yhM8T/nmEbLPKVGlpqWHq1E6b8rG5eVlWRpgj6ePs\n2ctQjlvIVh07Noxjxz7F3Hx0im4WFm6N+L6ERwAjHVcfhw//Gea9w969VbS2zjY2PP3k5j4V7Eln\ntlFobh6ckPNUXDzeuO/5G8fnndJ9Kyp20tTk3CBd6DnivAlR6cnNbbqdvmQjKunsDeKUcmDu3DU3\nl9PQsC3iXOra7QIlMM943zeA0Bxt3nwQlcrhMv49YrzvK8C1wFRUA03Vo+XKK3/HH/7wHT7+uBDl\nuD2DSqc8T17e74MpGEeOfAosDJ6/u/tJYAXwGO3tCygpqaKg4Fr8/m9QURE+N5cArqg7Yy7XUOzp\nm100N7+L398VdRc0XTtuLpcrocfiIbuAgiD0hESjbC6Xy7FHqJN93bnzV4bjFrI3DQ1qXXJam9Ip\nox4Z4aqjpubupOuanEilYbeVaNcZKyqnolNvsW5dyAGFZcD3gS5b7z2nz3LkyN8AT2F1vo4erWL6\n9KsjNi/t0T5QUSqnSqLfomx89OimmRIar2Yy8nyhTc329gU8+ugznD79F8b7ZjBixNO0tV2H2uh1\nA10O44ukoeGwsWH9FmoTeh5gr4OPJBA33dO6GeDz+XjxxQNBcTZx+GIjzpsQlVRvbvtLRKM3e4OE\nn0ulvyzH6w2vLfMHd9GsBcQqgmb2aHGjHLcFWJ2jkpJjFBR0U1r6TwBs2PAmq1Y9xscfXw+ouQ4E\n1iY85oKCa4M7x7t317J/v9nwcz3w58B2YILje8MXYdiIxzOfd955ARUhTN6ZSpSeCJNYbxZU+qUg\nCEJsoq05yUTZnEQ4oomQ9BXRNj2TtaPhThlEOqrRGnaHmkqnNubi4qm2c8+dW0hZmY+6um34/X78\n/r/k6NGVtlR8cL7fKSp6JuJ848ZdY2wW2jcvzfOFvid+VKaMtZn284AWdkR7dNNsoF1Xd4Di4vFs\n2fJzZs58lqNHVxL+XQml2T5snM+HWQsOEzh58iOOHg0JmbW1PUlu7iK83tuBroRtp7KV+1CNvOdi\nZviYGgJOpRSqPs6uAB3t+9Vf7hkHEuK8CWmnv0Q00hX9Czfc2dnLDKWm6Iuf2+1m166HmDIltCjn\n5j5v5IFHGn2V+qciayqlYS4qSqbem5+/IUJsZfjw4Xz88RSsRuTw4TnBRX3Llg84cqQK5QTCkCFL\n6e7+bsS43W43b755N6tXb+PcuU9Zvvz/0tExx3huKefOXRVU8LJe36ZNJZa2Asrx9HofNoyDEmUx\nDZH6+2VaWlQOZE/SbxKNqsa7gcjPX5+2MQmCcPGSTCaHU5QtGdtTWjqR7dtDm2mxUiwvBD3d9IwW\njUy0YfcPfzgj5TFHCq6Eaq/iXZNyUHagbosnA4PJy/trPvoo5JTk52+grGxG8LqiRfuUo9jFtm2v\nceiQKmfIzf3/+cIX/oIjRyYD64ARwHFGjz5LY+N32LlzG273YF5/PdRA++mnf0RHxxdQrY7sNDe/\nS2npRObMuZKKip3AH1BNu58CYMiQpyksvIGjR+3vmzPn+phib+H4fD62bfsEuNN4RLUGst6POJVS\nwGBycxdRWvqI7XhO36/+cs84kBDnTYjKQJReNw2cWgR/b0jX92wnJ3JnbzYNDXuA2ItfTk4O+/ff\nZzHi98U8v6o9A7iTrKyVdHRcBfyY0aPPsnHj9wFsOe/ROHfuHG63m61b72HDhh14PPMNwZIydu48\n6Dhuc0Gtrt5NR0fIoPp8FSxevJNdu0K569abiHHjrsbjse5AhoyDMl4hQ5Sfv57Kyq24XEN7XJsR\n7wbD+QYi22YgDh26j8rKembMEHVKQRBi47TmxErZi3fjGeu95maael1qNcN9hdON+BVXVEW8rru7\n+4KfO9HaK5/Px89+doZwB6WsbBZlZVhseChSF81Bt372KuJn3iv8EIDa2u289NJp3n/fBSzgxAmY\nPv05du++l717f2Vrt9PRsRDV224K9gieynQ5deoNamqm0tS006jtC6V4dnc/yfDhkVHgZMXeorUG\nCj+GUynFnDnXx2xFIemRqSPOmxCVVOvG+srps9+w70A1kU59JyeWOmI8xyGeqmKoNUCoELymZjEN\nDYdpbl6JxzMfM33kxIkutmypp6mpPSLtZOvWRlpaXkG1FwDYyPLlH3D33WfJycnhwQfv4L77bqOu\n7gA7d/4qxcXykqiqUk7RK9M4VFfvti36hw7dxx13RP8M0rmoJ3oD4dTKQBAEIRF6Ulsd672JplgO\nlLVr3Lhr+PBDe1rdhx/m2IQ/Ltx9wiVx56u2di+HD8/F6qDceuurMe1RIg6602tcrqG8//5YrI7O\niROPMGXKIh5+OC/akTAFyeDXqJo5t63mft68F/F47ErVPY0CR8OpxjyaKrgV6dGaPsR5E2KSSgpF\nMgbtwt2wJ//VTrQ5aawxJ5K77fP5mD17J17vg8BOcnObqal5iJycnOBcezz2eWhtPR6RYtLQsI0t\nW2YxbVoFx45djpmu0NExmJkz53PPPZOTbrIaWcumVKlMwp2idEWveiPnfdy4q6PKbQuCIKRCT9IM\ne6suuzejHeE2JC9vLTCCyy47BfwMGA7cy5Ejg6ms3Modd6TPsYhnv5zw+Xxs3nwAsKdrvvDC27S1\nvQj0TqTTFC6xRsqysqqMtMkuYDCjR/83J06o5tpW3G43K1c+4Gjfeto+J1HHKpH7vljpkZs2lbB9\n+x5DsKR/RpX7E4MCgUBfnTtw+nRHX517wDFyZBYX23yF37AXFPRsgayu3s2CBUWYsrlq4f62cexq\nx2ObC5Xf76exsY2Wlu8AGLK6IdVE6GLZssjIU/iY7WMIvS98FzXWa0LzUg4o43f55adpbPwqKn3C\nbXvPP/7jT/B4HrMdT6Va3Bb1OsIXc+v3y+fzUVu71+gfo6SF8/LWUlQ0gtbW47aoYLTjOV1HtM8g\n0XlLhmjnBtJ2E3Mx/iYvJCNHZg3q6zEMMDLWRmb6byv8+sPXs9zc55k9+89xuVxGGnr0tSzczubm\nrmDXrruj9gBLh6MXsqtdbNv2SbB8wS7MFd8WpUIs+2X2VTP7tIY2Nz8HfEKoBc9G4FJgpvH/Ifuf\njrmZOXMzLS1/wKxpV/cqd7Jq1T6Ki2+ybSJv2fJzWlvfY9y4a5g58yZjvOVApE3t6WcX7Z4M0mM3\n49n5TP7dJ2sfxXkbIAzUL3WsxeRC37BbF2ynBSd8obIblgZUU0372ICYY06H82aOLbrxm0VBQV1w\n0T579iw33riOjo6vGq/5EFN9UjUI/RucnD4rTt+vaGNQPeLU7l8sh8x6DIi96Kf7u5DMuVNloP4m\n+wpx3pImY21kpv+2nK7/7NmzPProet5++2Pa2ipR63kNcDu5ucuZM+d6ysomR6xzTmvr6NEVHDiw\nAJ/Px8KF1QBUVZXjdrsv4IaqOrcpzBXNdqTrs3e2Xz6ys1casvf7GDFiL21tT6E2I2uAywEYNeq/\nOHlyHtZNyspKe+lC+NwkY298Ph8bNuxg1apWPv54MnAzsJkJE7p57bU7Yr737Nmzts8sViPueDj1\nKEy3HQ4/X7QNXZ/Px/bthzO2VUCy9lHSJoULRrxUOCd1p0SP67RIWsP2WVlupk2bFXMBqK19i+bm\ny1HSupNRu26qf1u05qTO/UxCREsxCE/JjJeGYBUQOXToHkLyv7MoKVkZofT0pS9dzuHDZr77K6jI\nYz2m0qTV6Us0VdA+hlDtmrVHnDW9welzSTRl40LkvPdmqwhBEIQLhZlq39z8VeyiEN8GFuP1Pk1F\nBTQ1RUZK/H5/xPFOnLiEl176GatXn6K9/TFANXX+wQ+ujEhrq62tZ+7caRHHSJVQy5sLmxrnbL92\nG47bFqCMtrbbUKrO81A90t6kpOQYVVXlzJ79hs0eQaRiprVRezJp/263mwcfvAOXaygVFYOAg8C9\nHDw4mHnzTNs6McK2hjKE1Gf2zjshNc2eRtlMUa8LibQKSB/ivAlpx1xompvfpbl5PtEWOyf52dLS\nWXGPHetHbi7YI0dm8cEHpyPUGa0O1Lp17wM/MI5ca4zFj5LSd25OGs/JcFqcwLnHzZYtW4PpENFQ\nDu4mlGFR8zRu3NVASHnS7/eHFVuXoySDrX3ivs3YsfOpqXk8DQuiL/IRB4lmMzUnkR3JnhT+C4Ig\nXMyEaoV2Ozz7GVRPLXfQ2bJGiPLyXjaaMz9pvP5V4HGqqx+kvf0VrJtyKiX+LtvR1617xzGilwhO\n9jK85U3vsw8oI2QbHyYUDfwoOL5wexRr4zb0+XQDe2luvoza2reYO7cw6dF5PF/B47nN1og7MkNI\nfd7R1DQTsbnOPQq3OvYoTCfSKiA9iPMmpBX7TXx48+kQicrPhpPojzzcmaivX08g0B2saVNKjw9a\nzn8nI0Y8zE03/SV5eeeDionhx3W73dTUTGXhQqVcaKaahL8mnlLYli1baWrqoLn5MTye0I5p5PUP\nQjluoXny+zeF1TA8RXix9dixv43o73L06BRmz96Z9I6W3QD7yM5ehcejxm0aGCeJ5ilTFrFr10MJ\nC6ZIpEwQBCEW4U2fNwLfB15HCXMMjhC3ammZy7RpS9i+3ezBpV73+c9/lt/+1n70UaMuo709lHEC\nr+L1PkFd3Z6U1uYLsSmXbDq83X5NYNiwx+jsvM32GqdoYLg9ip8d4sOM6AGsW7eCsjJf1PE5bWCr\nzWNlR61N2yN7wpoZQqDUNEuZN28lBQXXJi1SZsXlGiqbqAOExPLUBCEGPp+P6urdVFfvNlIRzYXG\nhVpo3gS6yM/fgN/fRXX1biOiZMdJfjZVqqv32sZx6NB9tLR8Mfj/yjjtM68A2Exb24s0NlbQ1BQ9\n395MX1HOy2PMnr0Tny8yEhWP1tb3bONTC3Xkzp7TnBw9esL2Xq/3CXJzV6DqCdQ8l5Tk2R5Tu61T\naG4uZ968F6mu3h133D6fj7Vrm5g370WmTHFTWVlPSclK2tsXxh03+PB6s5g581mam+9K4PWCIAiC\nE6WlEykoeAV1y3YnI0b8E0qU6l5UXda9wJsUFFQHMzOs/O3ffpn8/A+BbwCDyc/fwOuvP0J2dhWm\njcjOXsZzz93PnDlXGsfeg3L0ojsfpt2PZUtMJ6i8/Na0OG7f+paHBQuKWLCgiG99yxP13Mp+bTfs\n1zAqK+tZtmwPb7/9ICNGPB28bjMaGG98piO6bNk2li3bZnOISksnkptbSSiip+4x4kXrQhvY5r3S\nZYTPt1Paq5khpOz6TcBmPJ7HWLCgiClTXk/I5oa+U6F5MJ3hdH1eiRJtrjbKHAAAIABJREFULEJ0\nJPIm9AgnJSvlDIWcjpKSY4wb56OxsZuKCrXLFN4fTDl2l1JdvTvmblo6a6Nyc5vxeqeinMtQdCtW\nyD6V8H74mPPzNyTcoNT5vX5UmocpQgLXXfcp119fxdixo9m5s5vFi+8GfIwY8ShtbZMIGeGuiLQM\np7kOKWINA1SULT9/Pd/85mg8HudxhvrW+YDVwAIj+uccfTXPIw07BUEQohMewfL7v0FFhbX2TdnZ\nlSsfAFQmR3i/Ladm062ts21ZJDk5OZSV3UJTkyemje2rGqVkMm9mzdoaFNnyeGrIy/sDtbVFzJ69\nk7a2H2Jt0wPYSixi1ao52Xq3282cOddTUdGz67v66v/kvfemA2rei4unUlbWiLKhSglz/Ph13Hbb\nZ6mpWWSoae7Dev+ibPBO4PaY5+pPpQrmWKRVQOKI2uQAob+qbzmpSeXmLsLrXQKE1IScVIwqK+tx\nuVwRMv3xFK4SueHPynJx880bbU6PSpsMNZOuqZlqNMV+N0JqP5rCUqKqiKZccWvrccaNu5qionEs\nWlRHd3c3H36Yw5Ej96Nq2WK3MrBeb/g8qQW9iOzsl41omI8RIyoMZ20aprNm/TxUmo1dqrm0dGKE\nypO6zkuwF8e3M2rUKk6evAKrIbn99s/hcg1l6tSvUFS0Hq83C3u9nbPCWLpbRfQ2/fU32V8Rtcmk\nyVgbmem/rXjXH68NSzok42O9/0KoA5vEuvZEzxtN5bKk5FiErY+nIpkoTp+JeY8BsXrChl6/Y8cs\n1q7dG3x96L5J1dKBn8rK88ydW2jTFgi/Jqd7sN7otdtTMvl3L2qTQp8zZ871uFzxd3NcLldQDUo5\nJPGjWeFOkdPzdXUHyMpyGwtnaKcRiNhlKi+/ldLSiQk3b04k8ue06/ejH71KR8ejhOSdXSgnagdF\nRe+Ql6dRV3cg5uLZ2nqclpaQAIwpQnL06ErU4r6FtrblxnO1mLUN5uehFvn5WCNgfn+X4w6qM/s4\neXKhca49gJ/f//5/qKh4NvjeXbseYuHC6ojonFNNQX8uUu5PBk0QBMFKvKhJT+uH+2v9cU8zb5wy\nXuw1gj6amy9j3rwXg1HMRO1A+GcyderfM2XKT/F6C4Cb8XjeiBBXC/8Mc3Jyosy7G7Uh2xW8t4p1\n/1JT81CE2JoTovI4cJHI2wChv+5IJNqIOdbrkolmWZ0iU6Fy3bpbg1GtU6dGcPjwA8Y5El+Iku3R\nkviupA+Vlvlr4FqgBLULaBYcm7tkTzuOOXYvui5KSqqMXbfdwK2E7zQWFHzkEOkqN85VTWFhNhUV\nMwif+9LSiZa0SRVly819Eq+3Muwc1vSM8EbmofM4fQ4Xcve2JyQaEeyvv8n+ikTekiZjbWSm/7b6\n+/UnaveTOZ7Zky0r68/w+bqj2uFEbLXP52PixHWcOPGI8chGwM/ixcPZtaszzAZmGeUc3SjhFyU4\nkp9vFzlLJiOouHg8U6a8jtdrV7Netiy28Eu85uzRetemutkYzwb39iZmf//eX0gk8ib0KonmTcd6\nnVNd1yefDOEb31jIqFGX8dxz95OTk+OoUNnSspWvfa0an2+x8VhIRjeZHjXJ7DQm/lofIWMwDfiJ\n8dhgzILj3Nzn8XoriBZ9Co9OKUcqlIJYVVVu7LpdFnF2swYifKevtjZ2i4Kf//wYfr+foqIRTJ3a\nydGjVYwbdw0zZ36P2bNDn5Ma+33A9uD1NDfrlJZOTOg7ka76xXQbmP4cERQEQehr0lkvFXJQ7kKV\nEqi6+J5EgdxuN3PnjqaiYhtwHNCAyQwfvodNm26LaOWjagQvw9pC4NCh+1Cbk4llBNlb5TxlbMia\ndltl2aRyHeY8q2bjQ4zNVvv8pBIp9fl8NDe/i3IDQvXz0a5JonL9C4m8DRAu9h0JMx2ypeV/8ctf\ntvHBB5cBqhFlVtaP+M//LKeh4bBDHvujQCVKcct8zB7V2r//kWDkqTd2kUK7fl8yxmGPhuXnn2L6\n9EtxuYbi93cZu37OO19OO2OhBtkTg9dVW/sW69a9H5R4zs19PthnLXxs1gU5L+9lBg0aYhgqgGqU\n8zsHcI4EmnM4depXuOmmTUa9HSjH+VsUFLxxQSKe0d6f7rq5RCOCF/tvMt1I5C1pMtZGZvpvK5Ou\nP7TeRmaPhK+7yaz3yUQHfT4f8+a9GFE75pRZklgtfAPhtt+8F4HoqZjpqPezXlO089jn0Qc8A9xI\nXt7v2bJlVlIZUekkk7734SRrH6VVgNBvaGpqp7Gxgg8+uAXluCmp246OhSxcqKRjR49+Drv8/VJC\nkv8mIRld1aPmQFISwz3F3PWDYxHPlZQcY/PmGcydW0h5+a2Uld0SUyLXSUI3XNZYna+QXbvuJjd3\nEbATr/dBpkx5nbVrm2zXaY8quWhpmcv06ZdSWroCZaguBf4CZUi7I2SGrTLCO3f+ytY2QEUFDyTV\nDqCnssTh15OOVgQiWywIgpAcTq0DEm0nkCjx1nvr+YCo0v7huN1uVq58wLbu5+dvIC/vN6RmB262\ntepRm6lK1bI37kPi3e/Ym4pvAZYAtzNo0JDga5xbFPQd6f4uDXQkbVLoF9gX5chocHd3tyUVwt5s\ndNiwXXR2TgVg2LCldHZ+iVCPmsEOx09PKlysna2ysltobPyYlpaQxG9+/gZbGiMkVnieaHpKQ8Nh\nW6qG1/swFRU7aWryxDRcLtdQJk26jrq6icCLwHzjGZWn3x/ozdz7/iShLAiC0N9xSrGrqZnq2Cwa\n7JGnUPp8KVZJfDON3rr22/vDqnry5uZ3KS4ez5YtPzeyT35gO18y5RD2dd9Z5MyJyBKAOmpq7raI\nhtwXjGbFuw+JZuuSKTNI/H5nL+GponV1qm69sbEN6+eRn78hOCe9jaRwRiLOm9APCWBdNGAjN9xw\nFYBjD5qXX36YRYtUr5olS8r4znf+nebmGcHnS0tLUo7GRFtI4y0mbrebLVtmUVv7Fq2tqmasrGxG\n1HrAeIt3ck6mD7Uofwocp7n5y3z3u6v5+tfHUlw83tEAjByZxbJliyPy9EeNegK/f5xj/71wY6KK\nwmf1qPee49XEmOt09v2z0l/V1gRBEPobTs7CwoVVFhVHgjXoVll+cy1XTtMe/P7PkJW1wxAsUet4\nqB5uH7m5LXzta7/nyJH7gM3AbDyeaezdW0V7uwb8gJ5s0Dqt+07CaU73BIWF2VxxRRXjxl1NWVlJ\n0jbE5/OxevVenn/+kFEH746oa0vXpmLIbkbWyoP6PJVQS0hZevr0S1M6Xzo2XqUOPZKknTdN0y5F\n5atlAUOBH+i6fkjTtHyUIsMfgd26ri+JcRhBsGG/CR8EfAu1aADMYvhw9Xe0Beyll+YHj+XU7NG+\nu7eP3NxDFBc/FHNMsZwG+2Ki5IW/+92fkpen4XINDS5Sc+cWMndu4vOQ6g6Tz+fD7/czevQTnDhx\nFapAehOq3xo0Nuo0Nt6Kx/NGWAuFkGGIbDLqY9CgEREF0hDaOTWPpVIsPoPLtSftkapYC3eyBk1a\nAAiCIPQNdll++1pu3ohb655UpOouVGpfGV7vbQQCz1FU9ByNjU8Fj9PevgBYkdAY4tWCxbIPiUQY\nT516hbIy53NH22y0H3caqvb8CzQ3X0Zt7VvMnVsIJLbR63Se3Nzn8fuvxOfz2RxBVSu/IlgrH7nZ\nHdmiIBkkYnbhSFqwRNO0xcDHuq6v0jRtDPCGrus3apr2DlCi67pX07TtQIWu6+/EOFTGFmOnQiYU\nclrlgrdt+yQoopGKDLHTfJ09e9Ym3xtLjMPeIDOyYDdUzGuXF05GtCNcWjjUMHw+VgGWeEXCzu0E\nvkCkWMoe4BuOx8vKcrFiRQPr1r2D1/sE4La0MEh/Q9NkiCbaEp6CGo90i5tkwm8ynYhgSdJkrI3M\n9N/WQLv+aA2qlVMTeiwkyx9dBCPceVuw4BLgNtt7ioqeobGxArt9+xnQgdq4VGl+mzfbs13C7wFy\nc1cwZ86VlJXdAuBoHyC0WekkMBZq15O6kIhzU/GdwG3k5q5g//774jqSTvcztbV7DZuuInl5eS9H\ntBpwGk8iYi+JbIQmK3oS7Xuf7tYU/ZHeaBXwPNBp/O0CzmualgUM1XXdazy+C7gFiOW8CYIN665S\nWZkv7TVHqiYsMq2itHRixCLn8bxCYWF21GPZ0w5COeMq1XNP3LB+uNrT0qUrDfGPIuAVYCQqsD0h\n7nU5txNYgXLe4uPz+Zg5cwv795cBM4IGDcIjcbF3TtONaRzOnTvHqFFVnDy5wHhmIx7PfE6dSlzV\nEiT1QhAE4UIQLQMi/DEwZfnLgfhp7qWlE1mzZjle7222x1WzbXtpBWSjarQXAzdEpPn5fD6jabZz\nXXhhYVaEfait3UpTU0fQQVIiJD5CtjZ5YqdTmiUPflT5iAuv9+HgfUr45nIse+Z2u3G5XJbr9dHS\nMoyWlshWA+HjiZfR0pOImmpPkFzmi9ShRxLTedM07X5C6gUm5bqu/6emaVegFA3moSTq2i2v6QCu\nSudAhcyit2qOzp07Z1mEZqC+0nfQ3HwZI0e+Q17ey7S0qLzH8ALqwsJsRo78BY2Nzk5SLLUm+8K7\n26LaCFAObANuJTu7iuLi2cH3JZ72dxWRxs25Hq2u7oDhuIUMmsuljIXV0OblrTWM5g6c+sKkk8ho\n4iuondVfAAuBLHG+BEEQ+gnRnIDwx5K5CXe73TQ23s+4cUvp7DR3Epdx7FgH4aUVsBr4DLAIGBxM\n8zNtZnPzu3i9X3M4yyU0N5dzxRVVEc+0tr5n26z0eh82MlKewCy/WLLkfj78cH0wUygVYY/S0ols\n3foyLS3DANPeV2P2hfX7/RHOUmFhlvPBorLXOHZim5fR7sHMtgrNzV/B2lPX6Vjh6ZvZ2cvweObj\n8biTTqGUOvQwAoFA0v/GjBlz3ZgxY/5rzJgxU4z/zx4zZsyvLc/PGzNmzCNxjiMIvcr58+cDEya8\nHIBO49/awF/9VWUA2gMQMP51BuDxAHQFoCswYcLLgVWr6gNr1jQFzp8/Hzh//nxg0qT1tucnTHjR\ndkz4JABrA6tW1Uecf82apsCaNU2BVavqjWMEAuCx/G2O4bEAnA9AZ2DNmqbg+63nnjRpfeDMmTOB\nVavqA9dcsyw4BnPM6p/H+Be6hnDWrGmKOL/1nOZ41dx1Gf/UdU6atN7xmD3FaUzQZPmvfZyJEJo/\nNU8XauxCVFKyNxn8TxAyHmUL2o11v8mwr1sibJ6yw58EwBO4+urHA2fOnAmcOXMmcPXVCwKwNACb\nA7AyANZ7gPVBO7t8+eYI+7BqVaRtXr58s3Fu8x7gxcBNN/00AA0BaAhMmPBi8F7BtPeJ2Bmnc4HH\nGEd9xHOrVnni2jO7zYs8fqL2M/p9QGj+Vq3yOF6v+d7S0mcj7rWSsd8ZQFL2IRXBkmtR1aMzdV0/\nBqDrerumaV2apl0FeFGdFhfHO9ZAyunuawZaDnxfE22+vvnNz3LwoNlq4F7+z/8ZjL0RJ0Ae5u7U\nwYOzmT5dRaE6OvxUV++2RakOHpzN4sWb+MIXqvB4rgVGAAeBWfh8ezh+/AMWLqymu7ubU6dGcPjw\nAwDk56+3RPW6UGmODxvnfxV4HLVT9g06OnycPt1hOXc3sJf9+7O5/vp/5eTJhYCP3NxFzJlzPWVl\nxVF3s06f7qCubjsQitxNmzaeSZNq2b9f1QoUFFQzbVpJcP5mzJhIdfVuDh4sx5qaadaddXT46ehI\nb0+Yjo5YfVxUH7/wcSZCbe10y67v9B6NXX6TyTFyZLI7xUKmfr8y/beVydcffu3KFpjCGaDs5SWU\nl38xGF0rLS020iLX4PX+gPfem8b111cRCMD77y813leDShD7LFAJnAfGAW8CH+Lz5UTYB4DXX7dH\n1Xy+Szl+fD6he4DLsdaXHzzYxYoV9trwjRvjR5l8vu6Ix0pKjrFy5QOOatk+X3dC9sx8jdISWG/T\nEkjEftqzYHagMoNCitSwg/Hjf8drrwUMhcrI650xYyIdHT7q6uzXb97bmGT69z4ZUql5ewZVjLNK\n0zSAs7qulwAPAK8BQ4Bduq4fSeHYgnBBcbmGEl78nJvbjNer+sSNGLGUtrYfRn2/UypkdfWv2L37\n+5w6tdOiZvkMN998LzfeWE1Hx2PGK9cDTcBQDh26m8rKHcyYsY3a2n0cOzYZ5USG+teB3yHN0YcS\nSLkCcHHy5AiUM5eF17sEl2tbVAEWIGqe+ptv3s3q1cnlkxcUXHvB8s6jtSHIz9/A9OmXGmmdiadc\niMqkIAjCwKO0dCL19SGnAzaSl9dJWdks2zpeV3fAVtN+8uTfYBft+jawg+HDdzJsWIAzZ8YBpm19\nJer5A4FulG02/45PvNrwkDibqm1zuYZSXDye7dvtm6imKFc0lcp4qYRWu1dWdgtlZYn1rbNiL/Fw\nchl+zRVXdNrUP53SKC9UW59MJWnnTdf14iiPtwAFPR6RIFxAnBaQmpqHgtL5xcVzmT37jRgLTGQP\nuhMnvkZDw2FqaqYGd/683mlMmvQkn376JCHjcR/KCNwK1HDuHAwf/hna2j4BDgFXA/cDMHToE3zz\nm8N57rm5Nvnfn/70Xzh58q9RDijGWHZiGiG/v4vq6t0WxU51vK1bX+byy0/T3PxVwvPUVb59bMem\ntxdea4GyvQ2Bc6+8WIhcsSAIwsDE7XazefMMamvraW09bvRRuz3l9fvcuamcO3cMsIp8leP3b7LZ\nifr69Vx22e9oaQk5JS0tXRQV1VNQYK0F/y2DBtkjWuPGXY3H43x+Z3XoQjyeN9ixYxZr10Y6V5H2\nMIu6ugMxNyKj2b2e1Y1Nxn7/8yrwPYYMWRn3nSI6kl6SbhWQRjJWBjkVMjmcnAqx5iuRXi7RnlfS\nt7cCZhrDBGAfy5apHTlnud/bLf+/B7Nvyuc+N48zZ24kVKD8HErI9RzwFOCmoOAVo5faYQD273+H\n7dsXhZ3jWWAso0e/zRe+8Oe0tv4V8F/A91EtB3yoRdY8Ty1mdK+y0q6mFUs+f6BGr5KVK04U+U0m\nh7QKSJqMtZGZ/tvK5OtP9dp9Ph+TJm0I9ixT0bQAatMUVPZGN3AXStxkAfEk/+HHEa9btixS+RGI\nkuVSjippeMYoaZjs2ILIbOOzZs0eZsyYGPMaE213ky67Fy7TP378S3z00fucOHETcDMFBXWObSFS\n2SDN8O/9BW8VIAgDmnipBrGeVykcr0WkcJSWznLMS1cRtanB15o9aADOnPkiVvUneARlLCqxph9M\nmWL2W4PPf/4/Is7w+c//Xz7+eBonTkzjxIkq4GZUZK7GOJ9dZcrMUy8o+AjITkg+fyA5bk7pooIg\nCMLFi9vtZteuuw17mQ/cRV5eLbfe+iovvPA2bW1TgG+Sn/863d2f4ciRUAQpP39DlGjZl1GbnaFU\nxmjpik6qmrW1W1m37n283qepqFBtEpJXiQwR2R6glHnzVlJQcG3a7LKTrbdHzEqDY4E9UdtC9Od7\nhIsBibwNEDJ5RyIVLuR8mc0vQykctwQbW9p3/l4FCoG1gMbgwb/gT39aDIDb/S/4fDeg+rqFdsbG\njp3P0aMriR69a2fYsCo6O/8ZgNGjl3PixHexNvW2RvdUgTGEN+w2xUZqa/dSUTGDWLtziTQDTdSx\nu9BOoNNY07UrGI78JpNDIm9Jk7E2MtN/W5l8/T299mhNp8M39Gpr36K19T3GjbuGsrLJALYIk32z\n9c2geEgydsMp+lVZaYqZWM8zi4KCOvbt+7ZNcCR83PaonT2jxskuJ9vYOpnI3oUgw7/3SdlHcd4G\nCJn8pU6Fvpqvs2fPGo1A84Gbyc9/3RDYGMrUqV9h0aI6AJYsKWXOnN1GX5fQ7t/GjdNsjkZu7vN4\nvQ9id84aKCnRKSi4Fr+/i4qKO4lMwVDO23XXfd8QQzmLucjn5j7P/v0qcjhz5uaIMWzePMNm8Jqb\n38XjmU8oBTNkyABHZ8lM87Q6aL1hGKKlioSnuaTjnPKbTA5x3pImY21kpv+2Mvn6+/LarUIijY1t\nth6vqdiqaPaouHh8UIX6hhtyGT78sxQXj2fv3l/R0eGjtHSioZ75uiHCohqE3333CF57TTdSFrtQ\nte6xN16TsXsXqsQgUczPfiBl+qQLSZsUhD4kJyeH/fsfsaQU2AU2Xnop1PN+y5ZZxu5flbH7NyMi\n/aC4+G6mTHkhLJp3JwUFQygvvxWfz2dppO1j2LDH6Oy8GfiI3NznmDHjJoYP/4iWljJgB7m5h9i1\n6yHcbjfV1bsNad9ulMPnZ/r0S4OOW8jRKkKlYN4J1ANleDzTOHXqFQoLI9MurWme9fXrg86r39+V\nUIrmhUAafAqCIAixsNqJsjJfj9MAnYS+iovNTBClQn369CvU1NxsPBYSS/nww/c4efJZQg3C/4nK\nyjUowXcYMeJp2tqmEnK0Yl/PQEEExhJDIm8DhEzeiUuFi2m+wqN5BQV1EekRtbVvGbn1apfO7V6K\nz/co4CY/P+RAWXexYu2yOT3nVLztXOQdLtKyE7iN3NwVEVHEdO/qpZIqkioX03esN5DIW9JkrI3M\n9N9WJl//xXbtsVMfIXGxlAbCyx9ycxfh9S4B0mPretN+muezzs1f/uVIfvzjrX0a/esrJPImCBcZ\nkdE8+2LqdrtxuYbaetz4fBWY6ZOHDt3HHXdELn7Jyv+PHftbjh4Nf2wUe/dW0d6+wBhLJT7fd8Pe\neQngwut9OMLYpLvdgBROC4IgCP2FRKJf3d1O/eP+GqtYChwm1KhcMWfO9bhc9vYBxcXjHcsWEh1r\nb9lPpwjbvn3fjvMuwUScN0EYAFyI9AfrQp2V5WbatNBC7dwP73Fmz7Y/5nJl0d4+D+Uogs/3CLm5\nzwQdtHCFTdPYqHNcGMMwEFNFBEEQhIuf4uLxLF0a2vDMyqqkq6sLWAQ8DriBtXz+87/m449vBv6N\nYcN+TmfnYqw91goKqikrU5ufISfIx9KlK2lvXwiklnLYW/YzUjmznOrqPdLMO0HEeROEi4DwBS87\nexnt7d8HumIufuZCHZ6qEm0HrqZmKgsXVgFQVVVu7PC5Ce0IdgUdtHPnzrF8+Qd0dAwGusjOXsbM\nmbPJyckB1M5bdfXu4PglQiYIgiBczDQ0HLZseHbR0fH/8eabZsPunwBXAi4+/lg1vs7NXUFj48Ps\n3HmQc+dc/OIXzzBkyBCqqsqDteshJ2i34bglVlceTRikLwVDJHsmMcR5E4SLgEihk9k0NKhoWKqL\nn+nYRSpwqULrU6eUsmT4LllZWUnQqHR0PIoZlWtv/z4NDXuCx5SiZEEQBCHzMDc8twOh6BPMI7zW\nzet9mJ07lWKyspkVgLK/mzalHpGKZoPBVKH+IgBbt25my5ZZAGl16JwibOXlqlWCZM/ER5w3QbhI\nCF/w0rH4hS/wKm2jG3DT3FxOQ8O2YDNSs2dO2KiwRuVMnFImekt5UhAEQRD6ArvT4o943qm2HKLb\nTPvxJpCdHUrJjJV143S82tp6IGC0D7oNgJaWGjZs2MmuXb7gfcDWrS9TVDQCl8uVsiMXLcJm7XMn\nREecN0EQohK+wKt8e7OPXIimpg6amx/D44GmJrWDJ7nrgiAIghDC6rT4/V1s27aeQ4dU39VJk15l\n7drI2vLS0pJg1CvW8cDMukkt5XDdune47jo3UIHV5ns88zl6dCVmc/CWlmG0tMwAepY1IxG21JFW\nAQOEi00+90Ij85Uc0ebLuWXADuC2oIywk/RxZWU9c+dOi5lTn6okcX9p4CnfseSQVgFJk7E2MtN/\nW5l8/Zl27VZ79r3vTaOjw+9o49It4+/z+Zg0aUNED9miohU0Nlqdty6Kip6xPLYduJULIeWfaZ+9\nFWkVIAhC2giPnuXnbzB6xm2Luau3bt07lJVNjrqzlmpRstTKCYIgCBcLVhtppg062c10C3m43W7m\nzLmSioqdKFfgbmAweXkaH30Uigbm52/guefu5/Tp6KmeQu8jkbcBQibvSKSCzFdyxJqveJGuaDt4\ny5btSXtKRKzG4r2NfMeSQyJvSZOxNjLTf1uZfP1y7b137dGieRApThISLuti27ZPgs5dOht5Z/hn\nL5E3QRDSR7y89Gg7eIIgCIIg9E9iRfOcIn/mY2VlPsf39JeShkxAnDdBEHpMWdktNDV5Lrg4iYig\nCIIgCEJ6SEU0xOk9UtLQu4jzJghCj+mtxprSwFMQBEEQ+hfS/qd3EedNEIS00FuyvyIvLAiCIAhC\npiKFKYIgCIIgCIIgpERp6UQKCl5BtRPqMkoaJvb1sC5aJPImCIIgCIIgCEJSWEVKamqmptwgXEgO\ncd4EQRAEQRAEQUgYESnpOyRtUhAEQRAEQRCEhLGLlLgMkZIDfT2sjCDpyJumaZ8BXgdyUMmts3Vd\n/52mafnAT4A/Art1XV+S1pEKgiAIgiAIgiBkMKlE3uYAR3RdnwS8CiwwHn8RuEvX9a8DeZqmXZ+m\nMQqCIAiCIAiC0E8QkZK+I+nIm67rKzVNM52+UcAZTdOygKG6rnuNx3cBtwDvpGeYgiAIgiAIgiD0\nB6Tvat8R03nTNO1+YH7Yw+W6rv+npml7gb8BbgUuBdotr+kArkrnQAVBEARBEARB6B9I39W+YVAg\nEEj5zZqmacB24KvAIV3Xv2w8Pg+4RNf15THenvqJBUEQhIHGoL4ewABDbKQgCEJmkJR9TEWw5HHg\nN7qu1wKfAn/Udb1D07QuTdOuAryoaNzieMc6fboj2dNnLCNHZsl8JYHMV3LIfCWPzFlyjByZ1ddD\nGHBk6vcr039bmXz9cu2Zee2Q2defrH1Mpc/beqBG07T7gCHAPxiPPwC8Zjy2S9f1IykcWxAEQRAE\nQRAEQXAgFcGSj4CpDo+3AAXpGJQgCIIgCIIgCIJgR5p0C4IgCIIgCIIgDADEeRMEQRAEQRAEQRgA\niPMmCIIgCIIgCIIwABDnTRAEQRAEQRAEYQAgzpsgCIIgCIIgCMJ1LsLiAAAGG0lEQVQAQJw3QRAE\nQRAEQRCEAYA4b4IgCIIgCIIgCAMAcd4EQRAEQRAEQRAGAOK8CYIgCIIgCIIgDADEeRMEQRAEQRAE\nQRgAiPMmCIIgCIIgCIIwABDnTRAEQRAEQRAEYQAgzpsgCIIgCIIgCMIAQJw3QRAEQRAEQRCEAYA4\nb4IgCIIgCIIgCAMAcd4EQRAEQRAEQRAGAOK8CYIgCIIgCIIgDADEeRMEQRAEQRAEQRgAiPMmCIIg\nCIIgCIIwABDnTRAEQRAEQRAEYQAgzpsgCIIgCIIgCMIAQJw3QRAEQRAEQRCEAYA4b4IgCIIgCIIg\nCAOAS1J9o6Zpfw0cAi7Tdb1L07R84CfAH4Hduq4vSdMYBUEQBEEQBEEQMp6UIm+apmUDywGf5eE1\nwF26rn8dyNM07fo0jE8QBEEQBEEQBEEgBedN07RBwEvA48B547FsYJiu617jZbuAW9I1SEEQBEEQ\nBEEQhEwnZtqkpmn3A/PDHj4J1Om6/itN0wAGAdlAu+U1HcBVaRynIAiCIAiCIAhCRjMoEAgk9QZN\n044DvzH+Nx9oAaYDh3Rd/7LxmnnAJbquL0/jWAVBEARBEARBEDKWpJ03K5qmeQHNECz5JTAD8AJN\nwGJd14+kZ5iCIAiCIAiCIAiZTcpqkwZWz+8B4DVgCLBLHDdBEARBEARBEIT00aPImyAIgiAIgiAI\ngtA7SJNuQRAEQRAEQRCEAYA4b4IgCIIgCIIgCAMAcd4EQRAEQRAEQRAGAOK8CYIgCIIgCIIgDAB6\nqjaZMJqm5QE/0nX97zVNuxqoBv4E/BfwkK7ropxiIWy+vgpsA44bT6/RdX1z342uf6FpmgvYAIwC\nhgFLgf9GvmOORJmv36BafPxv42XyHTPQNG0IsBYYg1LYfQDoRL5fjkSZr6HI9yshNE37BfCJ8b//\no+v6/X05nt4g0+8PMtXeZ7LtznQ7nMl2NR02slecN03TFgD3An8wHloBPKHr+gFN09YAtwMNvTGW\ngYDDfN0IrNB1fUXfjapfcw9wWtf1Mk3TPgccBX6JfMei4TRf/wIsl++YI4XAn3Rd/7qmaZOAZ4zH\n5fvlTPh8VaJuRuX7FQdN09wAuq7/fV+PpbfI9PuDDLf3mWy7M90OZ7Jd7bGN7K20yfeAO4BBxv/f\noOv6AePvncAtvTSOgUL4fN0ITNM0bb+maes0Tfts3w2tX7IFWGT8PRjwI9+xWDjNl3zHoqDr+s+A\nfzT+dzRwBrhRvl/OOMzXWeT7lShjgeGapu3SNG2vEZG52Mn0+4NMtveZbLsz2g5nsl1Nh43sFedN\n1/V64I+WhwZZ/v4DcGlvjGOg4DBfLcCjuq5PAv4H+Oc+GVg/Rdf1T3Vd/4OmaVmoBfFJ7N9t+Y5Z\ncJivCuAw8h2Liq7r3ZqmVQMrgdeQNSwmDvMl36/E+BT4sa7rU1CpNK9pmnZR16Zn+v1BJtv7TLbd\nYocz26721Eb2lVH4k+XvLJTXKUTHo+v6L42/G4Cv9uVg+iOapv0lsA/YqOv6G8h3LCZh81WHfMfi\nout6OaAB6wC35Sn5fjlgma+1wG75fiXE/0YZcnRdPw60AX/epyPqfTJ97c6otTiTbbfY4cy2qz2x\nkX3lvP3SyPMEmAociPVigTc1Tfua8fdkoLUvB9Pf0DTtcmA3sEDX9WrjYfmORSHKfMl3LAqappVp\nmva48b/ngW6gVb5fzjjM15+Aevl+JcQ/AMsBNE37CyAb+LBPR9T7ZPranTFrcSbb7ky3w5lsV9Nh\nI3tNbdLAVI15BFiradpQ4F3g33p5HAMFc74eAH6qaZofZci/03dD6pc8gQqvL9I0zcwhnwesku+Y\nI07zNR94Xr5jjvwbUK1p2n7Ahfpu/S9kDYuG03y9j6xhibAeeEXTNPOm5R90Xf9TrDdcRGT6/UEm\n2vtMtt2Zbocz2a722EYOCgQuOhVOQRAEQRAEQRCEi46LuhBaEARBEARBEAThYkGcN0EQBEEQBEEQ\nhAGAOG+CIAiCIAiCIAgDAHHeBEEQBEEQBEEQBgDivAmCIAiCIAiCIAwAxHkTBEEQBEEQBEEYAIjz\nJgiCIAiCIAiCMAD4f+4ZcAcajAdMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fa7a0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(15, 5), ncols=2, sharey=True)\n",
    "axes[0].scatter(res_basic.fittedvalues, res_basic.resid)\n",
    "axes[1].scatter(res_exchangable.fittedvalues, res_exchangable.resid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Scikit-Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-Learn's algorithms all deal with numpy arrays. My workflow is typically\n",
    "\n",
    "- data munging in pandas\n",
    "- pass numpy array to an Estimator\n",
    "- wrap result in a DataFrame or Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import california_housing\n",
    "data = california_housing.fetch_california_housing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = pd.DataFrame(data.data, columns=data.feature_names)\n",
    "y = pd.Series(data.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252        41  6.984127   1.023810         322  2.555556     37.88   \n",
       "1  8.3014        21  6.238137   0.971880        2401  2.109842     37.86   \n",
       "2  7.2574        52  8.288136   1.073446         496  2.802260     37.85   \n",
       "3  5.6431        52  5.817352   1.073059         558  2.547945     37.85   \n",
       "4  3.8462        52  6.281853   1.081081         565  2.181467     37.85   \n",
       "\n",
       "   Longitude  \n",
       "0    -122.23  \n",
       "1    -122.22  \n",
       "2    -122.24  \n",
       "3    -122.25  \n",
       "4    -122.25  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.526\n",
       "1    3.585\n",
       "2    3.521\n",
       "3    3.413\n",
       "4    3.422\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.grid_search import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.58 s, sys: 137 ms, total: 1.71 s\n",
      "Wall time: 14.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "param_grid = dict(\n",
    "    max_features=np.arange(2, 8),\n",
    "    max_depth=[2, 4],\n",
    "    min_samples_split=[5, 10, 15, 20],\n",
    ")\n",
    "rfc = RandomForestRegressor(n_estimators=10)\n",
    "gs = GridSearchCV(rfc, param_grid, cv=5, n_jobs=-1)\n",
    "gs.fit(X.values, y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[mean: 0.29760, std: 0.05526, params: {'max_depth': 2, 'max_features': 2, 'min_samples_split': 5},\n",
       " mean: 0.21563, std: 0.05313, params: {'max_depth': 2, 'max_features': 2, 'min_samples_split': 10},\n",
       " mean: 0.26315, std: 0.07534, params: {'max_depth': 2, 'max_features': 2, 'min_samples_split': 15},\n",
       " mean: 0.27852, std: 0.08764, params: {'max_depth': 2, 'max_features': 2, 'min_samples_split': 20},\n",
       " mean: 0.32477, std: 0.07437, params: {'max_depth': 2, 'max_features': 3, 'min_samples_split': 5},\n",
       " mean: 0.35445, std: 0.06549, params: {'max_depth': 2, 'max_features': 3, 'min_samples_split': 10},\n",
       " mean: 0.30673, std: 0.08844, params: {'max_depth': 2, 'max_features': 3, 'min_samples_split': 15},\n",
       " mean: 0.29626, std: 0.08799, params: {'max_depth': 2, 'max_features': 3, 'min_samples_split': 20},\n",
       " mean: 0.34954, std: 0.06641, params: {'max_depth': 2, 'max_features': 4, 'min_samples_split': 5},\n",
       " mean: 0.34526, std: 0.06700, params: {'max_depth': 2, 'max_features': 4, 'min_samples_split': 10}]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = gs.grid_scores_\n",
    "scores[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def unpack_grid_scores(scores):\n",
    "    rows = []\n",
    "    params = sorted(scores[0].parameters)\n",
    "    for row in scores:\n",
    "        mean = row.mean_validation_score\n",
    "        std = row.cv_validation_scores.std()\n",
    "        rows.append([mean, std] + [row.parameters[k] for k in params])\n",
    "    return pd.DataFrame(rows, columns=['mean_', 'std_'] + params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_</th>\n",
       "      <th>std_</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>max_features</th>\n",
       "      <th>min_samples_split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.297598</td>\n",
       "      <td>0.055257</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.215631</td>\n",
       "      <td>0.053126</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.263148</td>\n",
       "      <td>0.075339</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.278520</td>\n",
       "      <td>0.087642</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.324767</td>\n",
       "      <td>0.074375</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mean_      std_  max_depth  max_features  min_samples_split\n",
       "0  0.297598  0.055257          2             2                  5\n",
       "1  0.215631  0.053126          2             2                 10\n",
       "2  0.263148  0.075339          2             2                 15\n",
       "3  0.278520  0.087642          2             2                 20\n",
       "4  0.324767  0.074375          2             3                  5"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = unpack_grid_scores(gs.grid_scores_)\n",
    "scores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10e8b8390>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+fNL1JZqGm+H0uFCUWoAzi3ZiY+66qPX3DLKm8G8KJTWby44B6yAGfCHV5NeA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\nlyKLXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e8b8278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='max_features', y='mean_', hue='max_depth', col='min_samples_split', data=scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111236780>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8NiJm5NtYHBHjJAmYD6wBXgJW5/M/CZwGbADuiYg5Vd1LMzPrU63O6ewCfDQi9gUeBz4S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\nM0vmvwHhxXbBFyzLdwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1116614a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(gs.best_estimator_.feature_importances_,\n",
    "          index=X.columns).order().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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