05-Data_Quality.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.setup import *\n",
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stdout err\n",
    "%%R \n",
    "library(gridExtra)\n",
    "library(tidyverse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Name</th>\n",
       "      <th>Favorite_Color</th>\n",
       "      <th>Favorite_Flower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>Lisa</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Daisy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>F</td>\n",
       "      <td>Karen</td>\n",
       "      <td>Green</td>\n",
       "      <td>Rose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>M</td>\n",
       "      <td>Michael</td>\n",
       "      <td>Purple</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73</td>\n",
       "      <td>F</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>Red</td>\n",
       "      <td>Orchid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6338</th>\n",
       "      <td>11</td>\n",
       "      <td>M</td>\n",
       "      <td>Jacob</td>\n",
       "      <td>Red</td>\n",
       "      <td>Lily</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6339</th>\n",
       "      <td>20</td>\n",
       "      <td>M</td>\n",
       "      <td>Jacob</td>\n",
       "      <td>Green</td>\n",
       "      <td>Rose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6340</th>\n",
       "      <td>72</td>\n",
       "      <td>M</td>\n",
       "      <td>Robert</td>\n",
       "      <td>Blue</td>\n",
       "      <td>Lily</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6341</th>\n",
       "      <td>64</td>\n",
       "      <td>F</td>\n",
       "      <td>Debra</td>\n",
       "      <td>Purple</td>\n",
       "      <td>Rose</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6342 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Gender      Name Favorite_Color Favorite_Flower\n",
       "0      48      F      Lisa         Yellow           Daisy\n",
       "1      62      F     Karen          Green            Rose\n",
       "2      26      M   Michael         Purple            None\n",
       "3      73      F  Patricia            Red          Orchid\n",
       "...   ...    ...       ...            ...             ...\n",
       "6338   11      M     Jacob            Red            Lily\n",
       "6339   20      M     Jacob          Green            Rose\n",
       "6340   72      M    Robert           Blue            Lily\n",
       "6341   64      F     Debra         Purple            Rose\n",
       "\n",
       "[6342 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_parquet('data/usa_names.parq') \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Age Gender     Name Favorite_Color Favorite_Flower\n",
      "count   6342.000000   6342     6342           5599            5574\n",
      "unique          NaN      2       69              6               5\n",
      "top             NaN      F  Michael         Yellow          Orchid\n",
      "freq            NaN   3190      535            965            1356\n",
      "mean      42.458846    NaN      NaN            NaN             NaN\n",
      "std       27.312662    NaN      NaN            NaN             NaN\n",
      "min        2.000000    NaN      NaN            NaN             NaN\n",
      "25%       19.000000    NaN      NaN            NaN             NaN\n",
      "50%       39.000000    NaN      NaN            NaN             NaN\n",
      "75%       63.000000    NaN      NaN            NaN             NaN\n",
      "max      101.000000    NaN      NaN            NaN             NaN\n"
     ]
    }
   ],
   "source": [
    "with show_more_rows():\n",
    "    print(df.describe(include='all'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age Group'] = df.Age//10 * 10\n",
    "fig, ax = plt.subplots(figsize=(12, 4.5))\n",
    "sns.countplot(x=\"Age Group\", hue=\"Favorite_Flower\", \n",
    "              ax=ax, palette='gray', data=df)\n",
    "ax.set_title(\"Distribution of flower preference by age\")\n",
    "plt.savefig(\"img/(Ch05)Distribution of flower preference by age.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 4.5))\n",
    "sns.countplot(x=\"Age Group\", hue=\"Favorite_Color\", \n",
    "              ax=ax, palette='gray', data=df)\n",
    "ax.set_title(\"Distribution of color preference by age\")\n",
    "plt.savefig(\"img/(Ch05)Distribution of color preference by age.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Biasing trends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding Bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Birth_Month</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Home</th>\n",
       "      <th>Age Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>June</td>\n",
       "      <td>Matthew</td>\n",
       "      <td>M</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>September</td>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>January</td>\n",
       "      <td>Liam</td>\n",
       "      <td>M</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>96</td>\n",
       "      <td>March</td>\n",
       "      <td>William</td>\n",
       "      <td>M</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6338</th>\n",
       "      <td>29</td>\n",
       "      <td>August</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>F</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6339</th>\n",
       "      <td>51</td>\n",
       "      <td>April</td>\n",
       "      <td>Michael</td>\n",
       "      <td>M</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6340</th>\n",
       "      <td>29</td>\n",
       "      <td>May</td>\n",
       "      <td>Christopher</td>\n",
       "      <td>M</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6341</th>\n",
       "      <td>62</td>\n",
       "      <td>November</td>\n",
       "      <td>James</td>\n",
       "      <td>M</td>\n",
       "      <td>Texas</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6342 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Birth_Month         Name Gender            Home  Age Group\n",
       "0      17        June      Matthew      M          Hawaii         10\n",
       "1       5   September         Emma      F   West Virginia          0\n",
       "2       4     January         Liam      M          Alaska          0\n",
       "3      96       March      William      M        Arkansas         90\n",
       "...   ...         ...          ...    ...             ...        ...\n",
       "6338   29      August      Jessica      F   Massachusetts         20\n",
       "6339   51       April      Michael      M         Wyoming         50\n",
       "6340   29         May  Christopher      M  North Carolina         20\n",
       "6341   62    November        James      M           Texas         60\n",
       "\n",
       "[6342 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = pd.read_parquet('data/usa_names_states.parq')\n",
    "names['Age Group'] = names.Age//10 * 10\n",
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 4.5))\n",
    "somenames = ['Michael', 'James', 'Mary', 'Ashley']\n",
    "popular = names[names.Name.isin(somenames)]\n",
    "sns.countplot(x=\"Age Group\", hue=\"Name\", \n",
    "              ax=ax, palette='gray', data=popular)\n",
    "ax.set_title(\"Distribution of name frequency by age\")\n",
    "plt.savefig(\"img/(Ch05)Distribution of name frequency by age.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Detecting Bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qgIiYCpBSGlBsJA0XET8jS2B7pZTqsx5CY88znez2gDs11znKzrcJWXI+OqVU25cRktTiOFIuSZK0hsivud6cbNR/XHMk5Plt1vqSXfrQj2wKf8Xla2McTXZLwg/Jro8/J388oTnOKUnNwaRckiRpzfEo2SJ9U4ALmukcOwL/Szb1fHRK6fZmOs/HZHesOBHoSraw5lTgBymlhq5eL0mFcfq6JEmSJEkF8ZZokiRJkiQVxKRckiRJkqSCmJRLkiRJklSQ1X6ht4gIYANgftGxSJIkSZLWGJ2At9IKFnJb7ZNysoT8jaKDkCRJkiStcb4AvLm8CmtCUj4f4PXXX6dz585FxyJJkiRJWs19+OGH9OrVC+oxY3tNSMoB6Ny5s0m5JEmSJKlFcaE3SZIkSZIKUmhSHhFtIuLiiHg5Ij6JiP+LiAsjolVJnYiIURHxVl5nakRsU2TckiRJkiRVQtEj5ecCw4FTgK2Ac4CzgVNL6pwDnJHX2QmYA9wXEZ1WbqiSJEmSJFVW0deU7wL8KaV0V77/SkQcDfSH6tuZnQ5cklK6LS8bBMwFjgHGrfSIJUmSJKnClixZwuLFi4sOQ/XUtm1bWrduXZG2ik7KHwKGR8TmKaV/RcR2wG5kiTjAJkAP4N6qJ6SUFkbEA8Cu1JKUR0Q7oF1JkSPqkiRJklqklBJz5szh3//+d9GhqIG6du1Kjx49yMaSG6/opPwnQBfgnxGxBGgN/CCldHN+vEf+c27Z8+YCG9fR5vnAyEoHKkmSJEmVVpWQd+/enQ4dOjQ5wVPzSymxYMEC5s2bB0DPnj2b1F7RSflRwLFkU9GfBbYHxkTEWymlySX1UtnzopayKpcBV5bsdwLeqEi0kiRJklQhS5YsqU7I11133aLDUQO0b98egHnz5tG9e/cmTWUvOin/KfDjlNIt+f7TEbEx2Wj3ZLJF3SAbMZ9d8rzuLDt6DmTT24GFVft+0yRJkiSpJaq6hrxDhw4FR6LGqHrfFi9e3KSkvOjV1zsAS8vKlvCfuF4mS8wHVh2MiLWAPYBHVkaAkiRJktScHEhcNVXqfSt6pPxO4AcR8RrZ9PUdyG5/NhEgpZQiYgwwIiJeAF4ARgALgN8WErEkSZIkSRVS9Ej5qcB/A2OBWcAVZCuq/7CkzuXAmLzOdGBDYO+U0vyVGqkkSZIkrWJeeeUVIoKZM2c26vkRwe23317RmIowatQott9++6LDqFWhSXlKaX5K6fSU0sYppfYppS+mlC5IKS0qqZNSSqNSSj1TSmunlPZIKT1TZNySJEmS1BIMHjyYiKje1l13Xfbdd1+eeuopAHr16sXs2bPp27fvcttpjqS16guBNm3a8Oabb9Y4Nnv2bNq0aUNE8Morr1T0vKvaFwlFj5RLkiRJkppg3333Zfbs2cyePZv777+fNm3acOCBBwLQunVrevToQZs2tV+5nFLis88+a9b4NthgA37zm9/UKJs8eTIbbrhhs553VWFSLq3AjBkzmrxJkiRJzaVdu3b06NGDHj16sP3223Puuefy+uuv8/bbby8zfX3q1KlEBH/5y1/o378/7dq144YbbmD06NE8+eST1SPukyZNqm7/nXfe4dBDD6VDhw5sttlm3HHHHQ2Kb9CgQVx//fU1yiZNmsSgQYOWqfvAAw+w8847065dO3r27Ml5551X40uDAQMGcNppp3HOOefwuc99jh49ejBq1Kjq47179wbg0EMPJSKq96vccMMN9O7dmy5duvCtb32L+fOLvyrapFySJEmSVhMfffQRN910E3369Fnuvc/POeccLrvsMmbNmsXee+/NmWeeyTbbbFM94n7UUUdV1x09ejRHHnkkTz31FPvvvz/f/va3ee+99+od00EHHcT777/PQw89BMBDDz3Ee++9xze+8Y0a9d588032339/dtppJ5588kmuueYaJkyYwMUXX1yj3uTJk+nYsSOPP/44l19+ORdddBH33XcfANOmTQPg+uuvZ/bs2dX7AC+99BK33347U6ZMYcqUKTzwwAP8+Mc/rvfraC4m5ZIkSZK0CpsyZQrrrLMO66yzDp06deKOO+7gd7/7Ha1a1Z3uXXTRRQwcOJAvfvGLbLjhhqyzzjq0adOmesS9ffv21XUHDx7M0UcfTZ8+fbj00kv5+OOPeeKJJ+odX9u2bTn22GOZOHEiABMnTuTYY4+lbdu2NeqNHTuWXr168atf/Yott9ySQw45hNGjR/Ozn/2MpUv/cyftbbfdlpEjR7LZZptx/PHH079/f+6//34A1l9/fQC6du1Kjx49qvcBli5dyqRJk+jbty+77747xx13XPXzimRSLkmSJEmrsD333JOZM2cyc+ZMHn/8cfbee2/2228/Xn311Tqf079//3q3v+2221Y/7tixI506dWLevHkNivHEE0/k1ltvZc6cOdx6660MGTJkmTqzZs1il112qXH/769+9at89NFHvPHGG7XGA9CzZ896xdO7d286derU4Oc1N5NySZIkSVqFdezYkT59+tCnTx923nlnJkyYwMcff8x111233OfUV/mIdkTUGLmuj759+7Llllty9NFHs9VWW9W6GnxKqUZCXlVWdc6mxlOJ19EcTMolSZIkaTUSEbRq1YpPPvmk3s9Za621WLJkSTNGBUOGDGHq1Km1jpIDbL311jzyyCPViTjAI488QqdOnRq0Unvbtm2b/bVUkkm5JEmSJK3CFi5cyJw5c5gzZw6zZs3i1FNP5aOPPlpmIbXl6d27Ny+//DIzZ87knXfeYeHChRWP87vf/S5vv/023/nOd2o9fvLJJ/P6669z6qmn8s9//pM//elPjBw5kjPOOGO518eX6927N/fffz9z5szh/fffr1T4zcakXJIkSZJWYffccw89e/akZ8+efPnLX2batGnceuutDBgwoN5tHHbYYey7777sueeerL/++tx8880Vj7NNmzast956dd4zfcMNN+TPf/4zTzzxBNtttx3Dhw/nxBNP5IILLmjQeX72s59x33330atXL3bYYYdKhN6sonRqwOooIjoDH3zwwQd07ty56HCaXSXuid2vX78KRLL6WF36dNiwYU1uY9y4cRWIRJIkSQCffvopL7/8Mptssglrr7120eGogZb3/n344Yd06dIFoEtK6cPlteNIuSRJkiRJBTEplyRJkiQ12PDhw6vvj16+DR8+vOjwVhm1T+aXJEmSJGk5LrroIs4666xaj60Jlw5Xikm5JEmSJKnBunfvTvfu3YsOY5Xn9HVJkiRJkgpiUi5JkiRJUkFMyiVJkiRJKohJuSRJkiRJBTEplyRJkiSpICblkiRJkiQVxFuiSZIkSVIL079//5V6vunTpzeo/uDBg/n3v//N7bff3jwBrUEcKZckSZIkrZYWL15cdAgrZFIuSZIkSWq0e+65h912242uXbuy7rrrcuCBB/LSSy9VH3/llVeICG677Tb23HNPOnTowHbbbcejjz5aXWfUqFFsv/32NdodM2YMvXv3rt6fNm0aAwcOZL311qNLly7sscce/P3vf6/xnIjg2muv5eCDD6Zjx45cfPHF9OnThyuuuKJGvWeeeYZWrVrViLMoJuWSJEmSpEb7+OOPOeOMM5g2bRr3338/rVq14tBDD2Xp0qU16v3gBz/grLPOYubMmWy++eYcffTRfPbZZ/U+z/z58xk0aBAPPvggjz32GJttthn7778/8+fPr1Fv5MiRHHzwwTz99NMMGTKEIUOGcP3119eoM3HiRHbffXe++MUvNv6FV4jXlEuSJEmSGu2www6rsT9hwgS6d+/Oc889R9++favLzzrrLA444AAARo8ezTbbbMOLL77IlltuWa/zfO1rX6uxP27cOLp168YDDzzAgQceWF1+zDHHMGTIkOr9E044gQsvvJAnnniCnXfemcWLF3PjjTfy05/+tMGvtTk4Ui5JkiRJarSXXnqJY445hk033ZTOnTuzySabAPDaa6/VqLfttttWP+7ZsycA8+bNq/d55s2bx/Dhw9l8883p0qULXbp04aOPPlrmPOWL5PXs2ZMDDjiAiRMnAjBlyhQ+/fRTjjjiiPq/yGbkSLlWa+PHj29yG/369atAJKoyY8aMJrfheyJJktRyfOMb36BXr15cd911bLDBBixdupS+ffuyaNGiGvXatm1b/TgiAKqnuLdq1YqUUo365Yu0DR48mLfffpsxY8aw8cYb065dO3bZZZdlztOxY8dlYvzOd77Dcccdx1VXXcX111/PUUcdRYcOHRr/oivIpFySJEmS1Cjvvvsus2bNYty4cey+++4APPTQQw1uZ/3112fOnDmklKoT9pkzZ9ao8+CDDzJ27Fj2339/AF5//XXeeeederW///7707FjR6655hruvvtu/va3vzU4xuZiUi5JkiRJapRu3bqx7rrrMn78eHr27Mlrr73Geeed1+B2BgwYwNtvv83ll1/O4Ycfzj333MPdd99N586dq+v06dOHG264gf79+/Phhx9y9tln0759+3q137p1awYPHsz5559Pnz592GWXXRocY3PxmnJJkiRJUoMsXbqUNm3a0KpVK2655RZmzJhB3759+X//7/81agG1rbbairFjx3L11Vez3Xbb8cQTT3DWWWfVqDNx4kTef/99dthhB4477jhOO+00unfvXu9znHjiiSxatKjGInAtQaEj5RHxCrBxLYfGppS+F9m8hZHAUKAb8DjwvZTSsysvShVl2LBhTW7Da48lSZK0Kpo+fXrRISzXvHnz6NOnDwB77bUXzz33XI3jpdeH9+7de5nrxbt27bpM2fDhwxk+fHiNshEjRlQ/3mGHHZg2bVqN44cffnid5y03e/Zs2rRpw/HHH19nnSIUPVK+E9CzZBuYl9+a/zwHOAM4Ja87B7gvIjqt5DglSZIkaY33/vvvc9dddzF16lT22muvosOpl4ULF/Liiy/ywx/+kCOPPJLPf/7zRYdUQ6FJeUrp7ZTSnKoNOBB4CXggHyU/HbgkpXRbSukZYBDQATimsKAlSZIkaQ01ZMgQhg0bxplnnsnBBx9cdDj1cvPNN7PFFlvwwQcfcPnllxcdzjJazEJvEbEWcCxwZUopRcSmQA/g3qo6KaWFEfEAsCswro522gHtSoocVZckSZKkCvjjH/9YdAgNNnjwYAYPHlx0GHUqevp6qUOArsCkfL9H/nNuWb25Jcdqcz7wQcn2RqUClCRJkiSpklpSUn4icHdK6a2y8vIr9aOWslKXAV1Kti9ULEJJkiRJkiqoRUxfj4iNgb2Ab5YUz8l/9gBml5R3Z9nR82oppYXAwpK2KxeoJEmSJEkV1FJGyk8A5gF3lZS9TJaYV63IXnXd+R7AIys1OkmSJEmSmkHhI+UR0YosKZ+cUvqsqjxf7G0MMCIiXgBeAEYAC4DfFhGrJEmSJEmVVHhSTjZtfSNgYi3HLgfaA2OBbsDjwN4ppfkrLzxJkiRJkppH4dPXU0r3ppQipfSvWo6llNKolFLPlNLaKaU98vuVS5IkSZLWYBHB7bffXnQYTdYSRsolSZIkSSWGDRu2Us83bty4BtUfPHgwkydPZtiwYVx77bU1jp188slcc801DBo0iEmTJlUwytVT4SPlkiRJkqRVT69evbjlllv45JNPqss+/fRTbr75ZjbaaKMmtb148eKmhrfKMCmXJEmSJDXYjjvuyEYbbcRtt91WXXbbbbfRq1cvdthhh+qye+65h912242uXbuy7rrrcuCBB/LSSy9VH3/llVeICH7/+98zYMAA1l57bW688UYAJk6cyDbbbEO7du3o2bMnp5xySo0Y3nnnHQ499FA6dOjAZpttxh133NHMr7ryTMolSZIkSY1ywgkncP3111fvT5w4kSFDhtSo8/HHH3PGGWcwbdo07r//flq1asWhhx7K0qVLa9Q799xzOe2005g1axb77LMP11xzDd/73vcYOnQoTz/9NHfccQd9+vSp8ZzRo0dz5JFH8tRTT7H//vvz7W9/m/fee6/5XnAz8JpySZIkSVKjHHfccZx//vnVo90PP/wwt9xyC1OnTq2uc9hhh9V4zoQJE+jevTvPPfccffv2rS4//fTT+eY3v1m9f/HFF3PmmWfy/e9/v7psp512qtHW4MGDOfroowG49NJL+eUvf8kTTzzBvvvuW8mX2axMyiVJkiRJjbLeeutxwAEHMHnyZFJKHHDAAay33no16rz00kv88Ic/5LHHHuOdd96pHiF/7bXXaiTl/fv3r348b9483nrrLb7+9a8v9/zbbrtt9eOOHTvSqVMn5s2bV4mXttKYlLcg48ePb3Ib/fr1q0AkkiRJklQ/Q4YMqb7W++qrr17m+De+8Q169erFddddxwYbbMDSpUvp27cvixYtqlGvY8eO1Y/bt29fr3O3bdu2xn5ELDMtvqXzmnJJkiRJUqPtu+++LFq0iEWLFrHPPvvUOPbuu+8ya9YsLrjgAr7+9a+z1VZb8f7776+wzU6dOtG7d2/uv//+5gq7xXCkXJIkSZLUaK1bt2bWrFnVj0t169aNddddl/Hjx9OzZ09ee+01zjvvvHq1O2rUKIYPH0737t3Zb7/9mD9/Pg8//DCnnnpqxV9DkUzKJUmSJElN0rlz51rLW7VqxS233MJpp51G37592WKLLfjFL37BgAEDVtjmoEGD+PTTT7nqqqs466yzWG+99Tj88MMrHHnxTMolqZGGDRvW5DbGjRtXgUgk1UfpAkKNNX369ApEIkkr1tL/Rpg0adJyj99+++3Vj/faay+ee+65GsdTStWPe/fuXWO/1LBhw+r8m6u25/z73/9eblwtkdeUS5IkSZJUEJNySZIkSZIKYlIuSZIkSVJBTMolSZIkSSqISbkkSZIkSQUxKZckSZIkqSAm5ZIkSZIkFcT7lEvSKm7GjBlNbqNfv34ViESSJEkN5Ui5JEmSJEkFMSmXJEmSJK00EcHtt99edBgthtPXJUmSJKmFGT9+/Eo939ChQxtUf/DgwUyePHmZ8hdeeIE+ffpUKqw1gkm5JEmSJKnB9t13X66//voaZeuvv36znGvx4sW0bdt2lWu7Ppy+LkmSJElqsHbt2tGjR48aW+vWrbnzzjvp168fa6+9NptuuimjR4/ms88+q/Hc2bNns99++9G+fXs22WQTbr311upjr7zyChHB73//ewYMGMDaa6/NjTfeyKhRo9h+++1rtDNmzBh69+5dvT9t2jQGDhzIeuutR5cuXdhjjz34+9//XuM5EcG1117LwQcfTMeOHbn44ovp06cPV1xxRY16zzzzDK1ateKll16qTIfVwaRckiRJklQRf/nLXzj22GM57bTTeO655xg3bhyTJk3ikksuqVHvhz/8IYcddhhPPvkkxx57LEcffTSzZs2qUefcc8/ltNNOY9asWeyzzz71Ov/8+fMZNGgQDz74II899hibbbYZ+++/P/Pnz69Rb+TIkRx88ME8/fTTDBkyhCFDhiwz6j9x4kR23313vvjFLzaiJ+rP6euSJEmSpAabMmUK66yzTvX+fvvtx9y5cznvvPMYNGgQAJtuuik/+tGPOOeccxg5cmR13SOOOILvfOc7APzoRz/ivvvu45e//CVjx46trnP66afzzW9+s0Exfe1rX6uxP27cOLp168YDDzzAgQceWF1+zDHHMGTIkOr9E044gQsvvJAnnniCnXfemcWLF3PjjTfy05/+tEHnbwyTckmSJElSg+25555cc8011fsdO3akT58+TJs2rcbI+JIlS/j0009ZsGABHTp0AGCXXXap0dYuu+zCzJkza5T179+/wTHNmzePCy+8kL/+9a/MnTuXJUuWsGDBAl577bXltt2zZ08OOOAAJk6cyM4778yUKVP49NNPOeKIIxocQ0OZlEuSVhuVWKm2oavPtmTDhg1rchvjxo2rQCSSpNVRVRJeaunSpYwePbrWEe611157ue1FxDLtl2rVqhUppRplixcvrrE/ePBg3n77bcaMGcPGG29Mu3bt2GWXXVi0aNFy2wb4zne+w3HHHcdVV13F9ddfz1FHHVX9JUJzMimXJEmSJFXEjjvuyPPPP7/C26I99thjHH/88TX2d9hhh+U+Z/3112fOnDmklKoT+PLR9QcffJCxY8ey//77A/D666/zzjvv1Cv2/fffn44dO3LNNddw991387e//a1ez2sqk3JJkiRJUkVceOGFHHjggfTq1YsjjjiCVq1a8dRTT/H0009z8cUXV9e79dZb6d+/P7vtths33XQTTzzxBBMmTFhu2wMGDODtt9/m8ssv5/DDD+eee+7h7rvvpnPnztV1+vTpww033ED//v358MMPOfvss2nfvn29Ym/dujWDBw/m/PPPp0+fPstMsW8uJuWSJEmS1MKsqpdT7bPPPkyZMoWLLrqIyy+/nLZt27LllltWL+pWZfTo0dxyyy2cfPLJ9OjRg5tuuomtt956uW1vtdVWjB07lksvvZQf/ehHHHbYYZx11lk1Ll+bOHEiQ4cOZYcddmCjjTbi0ksv5ayzzqp3/CeeeCKXXnppjUXgmlvhSXlEbAj8BNgPaA/8CzgxpTQjPx7ASGAo0A14HPheSunZYiKWJEmSpDXbpEmT6jy2zz77LPcWZlXXhZ988sm1Hu/du/cy145XGT58OMOHD69RNmLEiOrHO+ywA9OmTatx/PDDD6/1/LWZPXs2bdq0qTG1vrkVep/yiOgGPAwsJkvKtwbOBP5dUu0c4AzgFGAnYA5wX0R0WqnBSpIkSZJWSwsXLuTFF1/khz/8IUceeSSf//znV9q5C03KgXOB11NKJ6SUnkgpvZJSuj+l9BJUj5KfDlySUrotpfQMMAjoABxTWNSSJEmSpNXGzTffzBZbbMEHH3zA5ZdfvlLPXXRSfhAwPSJujYh5EfGPiPhuyfFNgB7AvVUFKaWFwAPArrU1GBHtIqJz1QY4oi5JkiRJqtPgwYNZsmQJM2bMYMMNN1yp5y76mvJNgZOAK4FLgZ2BX0TEwpTSb8gScoC5Zc+bC2xcR5vnk12DLqnCKnEP6H79+lUgEkmSJGn1UPRIeSvg7ymlESmlf6SUxgHXkSXqpcqvxI9ayqpcBnQp2b5QwXglSZIkSaqYopPy2cBzZWWzgI3yx3Pynz3K6nRn2dFzIJvenlL6sGoD5lcqWEmSJEmSKqnopPxhYIuyss2BV/PHL5Ml5gOrDkbEWsAewCMrI0BJkiRJkppL0deUXwU8EhEjgN+TXVM+NN9IKaWIGAOMiIgXgBeAEcAC4LeFRFyHYcOGNbkNr7WVJEmSpDVLoUl5SmlaRBxKdh34hWQj46enlG4qqXY50B4YC3QDHgf2Tik5LV2SJEmStEorevo6KaUpKaUvpZTWTiltlVK6rux4SimNSin1zOvskd+vXJIkSZK0mhk8eDCHHHJI9f6AAQM4/fTTC4unuRU9fV2SJEmSVGbGjBkr9XyNvZT2kUceYffdd2fgwIHcc889FYnl5z//OSnVdbOt1U/hI+WSJEmSpFXTxIkTOfXUU3nooYd47bXXmtTWkiVLWLp0KV26dKFr166VCXAVYFIuSZIkSWqwjz/+mN///vecdNJJHHjggUyaNKn62NSpU4kI7rrrLrbbbjvWXnttvvzlL/P0009X15k0aRJdu3ZlypQpbL311rRr145XX311menrqzuTckmSJElSg/3ud79jiy22YIsttuDYY4/l+uuvX2ba+dlnn80VV1zBtGnT6N69OwcddBCLFy+uPr5gwQIuu+wyfv3rX/Pss8/SvXv3lf0yCmdSLkmSJElqsAkTJnDssccCsO+++/LRRx9x//3316gzcuRIBg4cyJe+9CUmT57M3Llz+eMf/1h9fPHixYwdO5Zdd92VLbbYgo4dO67U19ASuNCbJEmSGqx///5NbmP69OkViGT1YZ9qVfL888/zxBNPcNtttwHQpk0bjjrqKCZOnMhee+1VXW+XXXapfvy5z32OLbbYglmzZlWXrbXWWmy77bYrL/AWyKRckiRJktQgEyZM4LPPPmPDDTesLksp0bZtW95///3lPjciqh+3b9++xv6ayOnrkiRJkqR6++yzz/jNb37Dz372M2bOnFm9Pfnkk2y88cbcdNNN1XUfe+yx6sfvv/8+//rXv9hyyy2LCLvFcqRckiRJklRvU6ZM4f333+fEE0+kS5cuNY4dfvjhTJgwgauuugqAiy66iHXXXZfPf/7z/OAHP2C99dZbo1ZWrw+Tckkq0Pjx45vcRr9+/SoQSdN5LWRlVaI/W8pnQ5LUcC353/AJEyaw1157LZOQAxx22GFceuml/P3vfwfgxz/+Md///vd54YUX2G677bjjjjtYa621VnbILZpJuSRJkiSp3u688846j+24446klJg6dSoAu+22G88880ytdQcPHszgwYOXKS+93zlQ3dbqymvKJUmSJEkqiEm5JEmSJEkFcfq6JEmSJKmiBgwYQEqp6DBWCSblkiSpWc2YMaPJbbTkBY8ktUwuQKpVhdPXJUmSJKlAjiivmir1vpmUS5IkSVIB2rZtC8CCBQsKjkSNUfW+Vb2PjeX0dUmSJEkqQOvWrenatSvz5s0DoEOHDkREwVFpRVJKLFiwgHnz5tG1a1dat27dpPZMyiWtkSpxnZnXuEqSpKbq0aMHQHVirlVH165dq9+/pjAplyRJkqSCRAQ9e/ake/fuLF68uOhwVE9t27Zt8gh5FZNySZIkSSpY69atK5bkadXiQm+SJEmSJBXEpFySJEmSpIKYlEuSJEmSVBCTckmSJEmSCmJSLkmSJElSQVx9XVpFNPW+2t5TW5IkSWp5HCmXJEmSJKkgJuWSJEmSJBXEpFySJEmSpIIUmpRHxKiISGXbnJLjkdd5KyI+iYipEbFNkTFLkiRJklQpLWGk/FmgZ8n2pZJj5wBnAKcAOwFzgPsiotPKDlKSJEmSpEprCauvf5ZSmlNeGBEBnA5cklK6LS8bBMwFjgHGrcwgJUmSJEmqtJYwUr5ZPj395Yi4JSI2zcs3AXoA91ZVTCktBB4Adq2rsYhoFxGdqzbAUXVJkiRJUotUdFL+OHA8sA/wXbIk/JGIWDd/DNnIeKm5Jcdqcz7wQcn2RiUDliRJkiSpUgqdvp5Surtk9+mIeBR4CRgEPFZVrexpUUtZqcuAK0v2O2FiLkmSJElqgYoeKa8hpfQx8DSwGdmibrDsqHh3lh09L21jYUrpw6oNmN8swUqSJEmS1EQtKimPiHbAVsBs4GWyxHxgyfG1gD2ARwoJUJIkSZKkCip0+npEXAHcCbxGNgJ+AdAZmJxSShExBhgRES8ALwAjgAXAb4uJWJK0upsxY0aT2+jXr18FIpEkSWuCom+J9gXgZmA94G2y68i/klJ6NT9+OdAeGAt0I1sYbu+UklPSJUmSJEmrvKIXevvWCo4nYFS+SZIkSZK0WmlR15RLkiRJkrQmMSmXJEmSJKkgRV9TLklStWHDhjXp+S6wJkmSVjWOlEuSJEmSVBCTckmSJEmSCmJSLkmSJElSQbymXJIkaSUaP358k54/dOjQCkUiSWoJHCmXJEmSJKkgJuWSJEmSJBXEpFySJEmSpIKYlEuSJEmSVBCTckmSJEmSCmJSLkmSJElSQUzKJUmSJEkqSKOS8oj4a0R0raW8c0T8tclRSZIkSZK0BmjsSPkAYK1aytcGdm90NJIkSZIkrUHaNKRyRGxbsrt1RPQo2W8N7Au8WYnAJEmSJEla3TUoKQdmAinfapum/glwahNjkiRJkiRpjdDQpHwTIID/A3YG3i45tgiYl1JaUqHYJEmSJElarTUoKU8pvZo/dNV2SZIkSZKaqKEj5dUiYnOyBd+6U5akp5QualpYkiRJkiSt/hqVlEfEd4FrgHeAOWTXmFdJgEm5JEmSJEkr0NiR8guAH6SUflLJYCRJUssyfvz4JrfRr1+/CkSiKjNmzGhyG6vTe1KJz+jQoUMrEIkkNU5jrw3vBtxayUAkSZIkSVrTNDYpvxXYu5KBSJIkSZK0pmns9PUXgR9FxFeAp4HFpQdTSr9oamCSJEmSJK3uGpuUDwU+AvbIt1IJMCmXJEmSJGkFGpWUp5Q2qXQgkiRJkiStaRp7TbkkSZIkSWqixt6nfOLyjqeUhjQuHEmSJEmS1hyNvaa8W9l+W6Av0BX4a1MCkiRJkiRpTdHYa8oPLS+LiFbAWOD/GtNmRJwPXAr8PKV0el4WwEiyheW6AY8D30spPduYc0iSJDXFsGHDmtxGv379KhCJJGl1UbFrylNKS4GrgP/X0OdGxE5kifdTZYfOAc4ATgF2AuYA90VEp6ZFK0mSJElS8Sq90NsXaeDoe0SsA9wEfBd4v6Q8gNOBS1JKt6WUngEGAR2AYyoVsCRJkiRJRWnsQm9XlhcBPYEDgMkNbO5q4K6U0v9ExAUl5ZsAPYB7qwpSSgsj4gFgV2BcHbG1A9qVFDmqLkmSJElqkRq70NsOZftLgbeBM4HlrsxeKiK+BexINjW9XI/859yy8rnAxstp9nyy69AlSZIkSWrRGrvQ255NPXFE9AJ+DuydUvp0eacrf2otZaUuA0pH8jsBbzQqSEmSJEmSmlFjR8oBiIj1gS3IkuR/pZTebsDT+wHdgRnZ5eMAtAb+KyJOyduFbMR8dsnzurPs6Hm1lNJCYGFJjA0ISZIkSZKkladRC71FRMeImEiWLP8NeBB4KyImRESHejZzP/AlYPuSbTrZom/bk91abQ4wsOS8awF7AI80Jm5JkiRJklqSxo6UX0mWHH8DeDgv2w34BfAz4KQVNZBSmg88U1oWER8D7+YrrRMRY4AREfEC8AIwAlgA/LaRcUuSJKmF8L7vLdP48eOb3MbQoUMrEIm0ZmhsUn4YcHhKaWpJ2Z8j4hPg99QjKa+ny4H2wFigG/A42TXo8yvUviRJkiRJhWlsUt6B2q/rnpcfa5SU0oCy/QSMyjdJkiRJklYrjbqmHHgUGB0Ra1cVRER7sluRPVqJwCRJkiRJWt01dqT8dOBu4I2IeJJs9fXtyVY937sikUmSJEmStJpr7H3Kn46IzYBjgS3J7h1+C3BTSumTCsYnSZIkSdJqq1FJeUScD8xNKV1XVj4kItZPKf2kItFJkiRJkrQaa+w15cOAf9ZS/iwwvPHhSJIkSZK05mjsNeU9gNm1lL8N9Gx8OFpd9O/fv8lteN9RSZIkSau7xo6Uvw58tZbyrwJvNT4cSZIkSZLWHI0dKf81MCYi2gJ/zcu+DlwO/KwSgUmSJEmStLprbFJ+OfA5YCywVl72KfCTlNJllQhMkiRJkqTVXWNviZaAcyPiR8BWwCfACymlhZUMTpIkSZKk1VljR8oBSCl9BEyrUCySJEmSJK1RGrvQmyRJkiRJaiKTckmSJEmSCmJSLkmSJElSQUzKJUmSJEkqiEm5JEmSJEkFMSmXJEmSJKkgJuWSJEmSJBXEpFySJEmSpIKYlEuSJEmSVBCTckmSJEmSCmJSLkmSJElSQdoUHYAkSZJUpBkzZjS5jX79+lUgEklrIkfKJUmSJEkqiEm5JEmSJEkFMSmXJEmSJKkgJuWSJEmSJBXEpFySJEmSpIKYlEuSJEmSVBCTckmSJEmSClLofcoj4iTgJKB3XvQscFFK6e78eAAjgaFAN+Bx4HsppWdXfrSSJEmS1iTDhg1rchvjxo2rQCRanRU9Uv4GcB7QP9/+CvwpIrbJj58DnAGcAuwEzAHui4hOBcQqSZIkSVJFFZqUp5TuTCn9OaX0r3z7AfAR8JV8lPx04JKU0m0ppWeAQUAH4JjiopYkSZIkqTKKHimvFhGtI+JbQEfgUWAToAdwb1WdlNJC4AFg1+W00y4iOldtgKPqkiRJkqQWqfCkPCK+FBEfAQuBa4FDU0rPkSXkAHPLnjK35Fhtzgc+KNneqGzEkiRJkiRVRuFJOfA8sD3wFeAaYHJEbF1yPJXVj1rKSl0GdCnZvlCxSCVJkiRJqqBCV18HSCktAl7Md6dHxE7A94Gf5GU9gNklT+nOsqPnpe0tJBt1ByC7NF2SJEmSpJanJYyUlwugHfAy2WrrA6sPRKwF7AE8UkxokiRJkiRVTtH3Kb8UuBt4nWxBtm8BA4B9U0opIsYAIyLiBeAFYASwAPhtIQFLkiRJklRBRU9f/zxwA9CTbFG2p8gS8vvy45cD7YGxQDfgcWDvlNL8AmKVJEmSJKmiCk3KU0onruB4AkblmyRJkiRJq5WWeE25JEmSJElrBJNySZIkSZIKUvQ15ZIkSZIqZNiwYU1uo1+/fhWIRFJ9OVIuSZIkSVJBTMolSZIkSSqISbkkSZIkSQUxKZckSZIkqSAm5ZIkSZIkFcSkXJIkSZKkgpiUS5IkSZJUEJNySZIkSZIKYlIuSZIkSVJBTMolSZIkSSqISbkkSZIkSQUxKZckSZIkqSAm5ZIkSZIkFcSkXJIkSZKkgpiUS5IkSZJUkDZFByBJkiRJqtuMGTOa3Ea/fv0qEImagyPlkiRJkiQVxKRckiRJkqSCmJRLkiRJklQQk3JJkiRJkgpiUi5JkiRJUkFMyiVJkiRJKohJuSRJkiRJBTEplyRJkiSpICblkiRJkiQVxKRckiRJkqSCmJRLkiRJklQQk3JJkiRJkgrSpsiTR8T5wDeBLYFPgEeAc1NKz5fUCWAkMBToBjwOfC+l9OzKj1iSJEnSisyYMaNJz+/Xr1+FIpFavqJHyvcArga+Agwk+5Lg3ojoWFLnHOAM4BRgJ2AOcF9EdFrJsUqSJEmSVFGFjpSnlPYt3Y+IE4B5QD/gb/ko+enAJSml2/I6g4C5wDHAuJUasCRJkiRJFVT0SHm5LvnP9/KfmwA9gHurKqSUFgIPALvW1kBEtIuIzlUb4Ii6JEmSJKlFKnSkvFQ+Kn4l8FBK6Zm8uEf+c25Z9bnAxnU0dT7ZNej11r9//4ZUr5XXvUiSJEmSGqoljZT/CtgWOLqWY6lsP2opq3IZ2Yh71faFSgUoSZIkSVIltYiR8oj4JXAQ8F8ppTdKDs3Jf/YAZpeUd2fZ0XOgenr7wpK2KxusJEmSJEkVUuhIeWR+RXZbtK+llF4uq/IyWWI+sOQ5a5Gt2v7ISgtUkiRJkqRmUPRI+dVkq6gfDMyPiKpryD9IKX2SUkoRMQYYEREvAC8AI4AFwG+LCFiSJEmSpEopOik/Kf85taz8BGBS/vhyoD0wFugGPA7snVKavxLikyRJkiSp2RR9n/IVXvCdUkrAqHyTJEmSJGm10ZJWX5ckSZIkaY1iUi5JkiRJUkFMyiVJkiRJKohJuSRJkiRJBTEplyRJkiSpIEXfEk2SJEmSVlvjx49vchv9+vWrQCRqqRwplyRJkiSpICblkiRJkiQVxKRckiRJkqSCmJRLkiRJklQQk3JJkiRJkgpiUi5JkiRJUkFMyiVJkiRJKohJuSRJkiRJBTEplyRJkiSpICblkiRJkiQVxKRckiRJkqSCmJRLkiRJklQQk3JJkiRJkgpiUi5JkiRJUkFMyiVJkiRJKohJuSRJkiRJBTEplyRJkiSpICblkiRJkiQVxKRckiRJkqSCmJRLkiRJklQQk3JJkiRJkgpiUi5JkiRJUkFMyiVJkiRJKohJuSRJkiRJBSk0KY+I/4qIOyPirYhIEXFI2fGIiFH58U8iYmpEbFNQuJIkSZIkVVTRI+UdgSeBU+o4fg5wRn58J2AOcF9EdFo54UmSJEmS1HzaFHnylNLdwN0AEVHjWGQFpwOXpJRuy8sGAXOBY4BxKzNWSZIkSZIqreiR8uXZBOgB3FtVkFJaCDwA7FrXkyKiXUR0rtoAR9UlSZIkSS1SoSPlK9Aj/zm3rHwusPFynnc+MLJZIpIkSZKkNVT//v2b3Mb06dMrEMnqpSWPlFdJZftRS1mpy4AuJdsXmikuSZIkSZKapCWPlM/Jf/YAZpeUd2fZ0fNq+RT3hVX75deqS5IkSZLUUrTkkfKXyRLzgVUFEbEWsAfwSFFBSZIkSZJUKYWOlEfEOkCfkqJNImJ74L2U0msRMQYYEREvAC8AI4AFwG9XdqySJEmSJFVa0dPX+wP/W7J/Zf5zMjAYuBxoD4wFugGPA3unlOavxBglSZIkSWoWRd+nfCrZwm11HU/AqHyTJEmSJGm10pKvKZckSZIkabVmUi5JkiRJUkFMyiVJkiRJKohJuSRJkiRJBTEplyRJkiSpICblkiRJkiQVpOj7lEuSJEmSVG/jx49vchtDhw6tQCSV4Ui5JEmSJEkFMSmXJEmSJKkgJuWSJEmSJBXEpFySJEmSpIKYlEuSJEmSVBCTckmSJEmSCmJSLkmSJElSQUzKJUmSJEkqiEm5JEmSJEkFMSmXJEmSJKkgJuWSJEmSJBWkTdEBSJIkSZLWDMOGDWtyG/369atAJC2HI+WSJEmSJBXEpFySJEmSpIKYlEuSJEmSVBCTckmSJEmSCmJSLkmSJElSQUzKJUmSJEkqiEm5JEmSJEkF8T7lkiRJkqQ1yowZM5rcRqXul+5IuSRJkiRJBTEplyRJkiSpICblkiRJkiQVxKRckiRJkqSCrBJJeUScHBEvR8SnETEjInYvOiZJkiRJkpqqxSflEXEUMAa4BNgBeBC4OyI2KjIuSZIkSZKaqsUn5cAZwISU0q9TSrNSSqcDrwMnFRuWJEmSJElN06LvUx4RawH9gB+XHboX2LWO57QD2pUUdQL48MMP6zzPkiVLmhQnwKJFi5rcxieffNLkNj766KMmt7G8vqov+7SmltCn9mdNfkZrWl361P6syc9oTatLn9qfNfkZrWl16VP7syY/ozWtCn3akP6OlFKTg2kuEbEB8Cbw1ZTSIyXlI4BBKaUtannOKGDkSgtSkiRJkqTafSGl9ObyKrTokfIS5d8cRC1lVS4Driwr+xzwXqWDqrBOwBvAF4D5BceyurBPK8v+rDz7tLLsz8qzTyvPPq0s+7Py7NPKsj8rb1Xq007AWyuq1NKT8neAJUCPsvLuwNzanpBSWggsLCtu+lyNZhYRVQ/np5RafLyrAvu0suzPyrNPK8v+rDz7tPLs08qyPyvPPq0s+7PyVrE+rVd8LXqht5TSImAGMLDs0EDgkWWfIUmSJEnSqqOlj5RDNhX9hoiYDjwKDAU2Aq4tNCpJkiRJkpqoxSflKaXfRcS6wIVAT+AZYP+U0qvFRlZxC4HRLDv1Xo1nn1aW/Vl59mll2Z+VZ59Wnn1aWfZn5dmnlWV/Vt5q16ctevV1SZIkSZJWZy36mnJJkiRJklZnJuWSJEmSJBXEpFySJEmSpIKYlEuSJEmSVBCT8hYiIk6OiJcj4tOImBERuxcd06oiIv4rIu6MiLciIkXEIWXHIyJG5cc/iYipEbFNQeG2eBFxfkRMi4j5ETEvIm6PiC3K6tin9RQRJ0XEUxHxYb49GhH7lRy3L5sg/7ymiBhTUmafNkDeV6lsm1Ny3P5shIjYMCJujIh3I2JBRMyMiH4lx+3XeoqIV2r5jKaIuDo/bl82UES0iYiL8789P4mI/4uICyOiVUkd+7UBIqJTRIyJiFfz/nokInYqOW5/LkdU4O/5iGgXEb+MiHci4uOIuCMivrBSX0gjmZS3ABFxFDAGuATYAXgQuDsiNioyrlVIR+BJ4JQ6jp8DnJEf3wmYA9wXEZ1WTnirnD2Aq4GvAAPJbp14b0R0LKljn9bfG8B5QP98+yvwp5L/SOzLRsr/2BkKPFV2yD5tuGfJbjtatX2p5Jj92UAR0Q14GFgM7AdsDZwJ/Lukmv1afztR8/M5MC+/Nf9pXzbcucBwsj7biqwPzwZOLaljvzbMr8k+m8eR/Rt6L/A/EbFhftz+XL5K/D0/BjgU+BawG7AOMCUiWjdTzJWTUnIreAMeB64pK5sFXFZ0bKvaBiTgkJL9AGYD55aUtSP7w2hY0fGuChuwft6v/2WfVqxP3wNOtC+b1IfrAP8C9gKmAmPycvu04X05CphZxzH7s3F9+mPgweUct1+b1r9jgBfzfrQvG9eHU4AJZWV/AG7IH9uvDevP9sBnwAFl5TOBi+3PBvdng/+eB7oAi4CjSupsACwB9in6Na1oc6S8YBGxFtCP7Nu0UvcCu678iFY7mwA9KOnflNJC4AHs3/rqkv98L/9pnzZSRLSOiG+RfRv8KPZlU1wN3JVS+p+ycvu0cTbLpwS+HBG3RMSmebn92TgHAdMj4tbILgP6R0R8t+S4/dpI+d9NxwITU/ZXt33ZOA8BX4+IzQEiYjuykcU/58ft14ZpA7QGPi0r/4SsX+3PpqlP//UD2pbVeQt4hlWgj03Ki7ce2S/x3LLyuWQfPjVNVR/av40QEQFcCTyUUnomL7ZPGygivhQRHwELgWuBQ1NKz2FfNkr+xcaOwPm1HLZPG+5x4HhgH+C7ZP30SESsi/3ZWJsCJwEvkPXrtcAvIuL4/Lj92niHAF2BSfm+fdk4PwFuBv4ZEYuBf5DNOLo5P26/NkBKaT7Zl+0/jIgN8i/hjwW+THbJhf3ZNPXpvx7AopTS+8up02K1KToAVUtl+1FLmRrP/m2cXwHbkn3LW84+rb/nge3J/pA8DJgcEXuUHLcv6ykiegE/B/ZOKZWPSJSyT+sppXR3ye7TEfEo8BIwCHisqlrZ0+zP5WsFTE8pjcj3/5GvI3ES8JuSevZrw50I3J2PgJWyLxvmKLIZB8eQrSmxPTAmIt5KKU0uqWe/1t9xwETgTbIp038Hfkv2JXIV+7NpGtN/q0QfO1JevHfIfnHLv8HpzrLfBqnhqlYQtn8bKCJ+STYFc8+U0hslh+zTBkopLUopvZhSmp5SOp9sIZPvY182Rj+y/pkREZ9FxGdkixOelj+u6jf7tJFSSh8DTwOb4We0sWYDz5WVzQKqFnC1XxshIjYmW0fi1yXF9mXj/BT4cUrplpTS0ymlG4Cr+M8MJPu1gVJKL6WU9iBb86RXSmlnsunUL2N/NlV9+m8OsFa+0GZddVosk/KCpZQWATP4z0qiVQYCj6z8iFY7Vf8QVvdvfj3aHti/tcpvOfEr4JvA11JKL5dVsU+bLsgWKLEvG+5+slVtty/ZpgM35Y//D/u0SSKiHdlqzLPxM9pYDwNblJVtDryaP7ZfG+cEYB5wV0mZfdk4HYClZWVL+E9uYL82Ukrp45TS7Dw53Af4E/ZnU9Wn/2aQ3fGitE5PoC+rQB87fb1luBK4ISKmk12PMpTs2/RrC41qFRER6wB9Soo2iYjtgfdSSq9Fdv/iERHxAtn1fSOABWRTirSsq8mmsx0MzI+Iqm8lP0gpfZJSSvZp/UXEpcDdwOtAJ7LbdAwA9rUvGy6/bu+Z0rKI+Bh4t2rdA/u0YSLiCuBO4DWyEYULgM7AZD+jjXYV2XX5I4DfAzuT/d8+FMB+bbjI7p99Atnn8rOqcvuy0e4EfhARr5FNX9+B7HZTE8F+bYyI2IfsS/fnyf4u/Wn++Hr7c8Wa+vd8SumDiJgA/Cwi3iVboPgKsplf5YvCtjxFL//ulm3AycArZAtBzSC//ZRbvfpuANm1IuXbpPx4kN3yZzbZqpgPAH2LjrulbnX0ZQIGl9SxT+vfnxNKfrfnkf3HMNC+rGgfTyW/JZp92qj+uwV4i+xWMm+S3RZpa/uzyf16INkfg5+STV3/btlx+7Vh/bl3/n/R5rUcsy8b3p+dyG4t9yrZCuEvkd26ay37tdF9emTejwvzPvsV0MX+rHf/Dajj789J9e0/YG3gl8C7ZAn7nWSXEhT++la0Rf4CJEmSJEnSSuY15ZIkSZIkFcSkXJIkSZKkgpiUS5IkSZJUEJNySZIkSZIKYlIuSZIkSVJBTMolSZIkSSqISbkkSZIkSQUxKZckSZIkqSAm5ZIktUARsWtELImIe1byedeKiLMj4u8R8XFEfBART0bExRGxwcqMRZKkNUGklIqOQZIklYmIXwMfAd8Btk4pvbYSztkOuBfYFhgJPAx8AHwROAT4d0rp/Dqeu1ZKaVFzxyhJ0urGkXJJklqYiOgIHAlcA0wBBtdS56CIeCEiPomI/42IQRGRIqJrSZ1dI+JveZ3XI+IXedt1+X/AbsDXUkq/SCnNSCm9mFL6S0rpJGBESdtTI+JXEXFlRLwD3JeX7xERT0TEwoiYHRE/jog2Jc97JSJOL3stMyNiVMl+ioiTIuLuPPaXI+KIBnShJEmrDJNySZJanqOA51NKzwM3AidERFQdjIjewH8DtwPbA+OAS0obiIgvAX8BbiMb+T6KLOH+1XLOezRwX0rpH7UdTMtOrxsEfAZ8FRgWERsCfwamAdsBJwEnAhes4PXW5kfAH/J2bgRujoitGtGOJEktmkm5JEktz4lkiSjAPcA6wNdLjg8nS9rPTik9n1K6BZhU1sbZwG9TSmNSSi+klB4BTgOOj4i16zjv5sDzpQUR8ceI+CjfHimr/2JK6Zw8hn8CJwOvA6eklP6ZUrqdbBr8mRHR0L85bk0p/Tql9K+U0g+B6cCpDWxDkqQWz6RckqQWJCK2AHYGbgFIKX0G/A4YUlJtC7LR6FJPlO33AwaXJNQfkY2ctwI2WU4I5aPhJ5ONxk8EOpQdm162vxXwaNmI+sNkXyp8YTnnrM2jtew7Ui5JWu20WXEVSZK0Ep1I9v/zm6Uz1oHFEdEtpfR+vl+ePEfZfiuyae2/qOUcdS0a9wKwZWlBSmk2QES8V0v9j2uJoa64qsqX1hJr2zriKefqtJKk1Y4j5ZIktRD5gmjHA2eSjU5XbdsBrwLfzqv+E9ip7On9y/b/DmyTL9RWvtW1SvrNwMCI2KGRL+E5YNfS69+BXYH5wJv5/ttAz6qDEdGZ2kfuv1LL/j8bGZckSS2WSbkkSS3HgUA3YEJK6ZnSjWxhtxPzeuOALSPiJxGxeUQcyX9WaK8aTf4JsEtEXB0R20fEZvmK7b9czvmvIpsm/teI+H5E7BgRm0TEPsB+wJIVxD8W6AX8MiK2jIiDgdHAlSmlpXmdvwLHRcTuEdEXmFxHu0dExJD89Y0mm9K/vEXqJElaJZmUS5LUcpwI/E9K6YNajv0B2D4idkwpvQwcDnwTeIpslfOq1dcXAqSUngL2ADYDHgT+Qbai+ey6Tp5S+pRsQbkfAycADwGzgDFk14YfsrzgU0pvAvuTJdBPAtcCE4CLS6pdBvyN7FZvfyZbQf6lWpobCXwrf32DgG+nlJ5b3vklSVoVxbJ3N5EkSauaiPgBMDyl1KvoWJoqIhJwaL56uyRJqzUXepMkaRUUESeTrcD+Ltl9ws/G6d2SJK1yTMolSVo1bQZcAHyObDX1n5FNDZckSasQp69LkiRJklQQF3qTJEmSJKkgJuWSJEmSJBXEpFySJEmSpIKYlEuSJEmSVBCTckmSJEmSCmJSLkmSJElSQUzKJUmSJEkqiEm5JEmSJEkFMSmXJEmSJKkg/x8X/6R5bifvOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 4.5))\n",
    "months = ['January', 'February', 'March', 'April']\n",
    "popular = names[names.Birth_Month.isin(months)]\n",
    "sns.countplot(x=\"Age Group\", hue=\"Birth_Month\", \n",
    "              ax=ax, palette='gray', data=popular)\n",
    "ax.set_title(\"Distribution of birth month frequency by age\")\n",
    "plt.savefig(\"img/(Ch05)Distribution of birth month frequency by age.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Birth_Month</th>\n",
       "      <th>January</th>\n",
       "      <th>February</th>\n",
       "      <th>March</th>\n",
       "      <th>April</th>\n",
       "      <th>May</th>\n",
       "      <th>June</th>\n",
       "      <th>July</th>\n",
       "      <th>August</th>\n",
       "      <th>September</th>\n",
       "      <th>October</th>\n",
       "      <th>November</th>\n",
       "      <th>December</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age Group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>67</td>\n",
       "      <td>59</td>\n",
       "      <td>76</td>\n",
       "      <td>66</td>\n",
       "      <td>77</td>\n",
       "      <td>71</td>\n",
       "      <td>65</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>56</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>37</td>\n",
       "      <td>72</td>\n",
       "      <td>71</td>\n",
       "      <td>78</td>\n",
       "      <td>70</td>\n",
       "      <td>73</td>\n",
       "      <td>82</td>\n",
       "      <td>81</td>\n",
       "      <td>83</td>\n",
       "      <td>79</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>52</td>\n",
       "      <td>60</td>\n",
       "      <td>76</td>\n",
       "      <td>72</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>71</td>\n",
       "      <td>66</td>\n",
       "      <td>68</td>\n",
       "      <td>75</td>\n",
       "      <td>76</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>54</td>\n",
       "      <td>56</td>\n",
       "      <td>66</td>\n",
       "      <td>64</td>\n",
       "      <td>73</td>\n",
       "      <td>58</td>\n",
       "      <td>87</td>\n",
       "      <td>82</td>\n",
       "      <td>66</td>\n",
       "      <td>65</td>\n",
       "      <td>57</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>57</td>\n",
       "      <td>43</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "      <td>39</td>\n",
       "      <td>36</td>\n",
       "      <td>45</td>\n",
       "      <td>34</td>\n",
       "      <td>38</td>\n",
       "      <td>30</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>57</td>\n",
       "      <td>39</td>\n",
       "      <td>28</td>\n",
       "      <td>21</td>\n",
       "      <td>31</td>\n",
       "      <td>37</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>27</td>\n",
       "      <td>31</td>\n",
       "      <td>34</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>55</td>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Birth_Month  January  February  March  April  May  June  July  August  \\\n",
       "Age Group                                                               \n",
       "0                 20        67     59     76   66    77    71      65   \n",
       "10                37        72     71     78   70    73    82      81   \n",
       "20                52        60     76     72   65    65    71      66   \n",
       "30                54        56     66     64   73    58    87      82   \n",
       "...              ...       ...    ...    ...  ...   ...   ...     ...   \n",
       "70                57        43     39     33   39    36    45      34   \n",
       "80                57        39     28     21   31    37    23      28   \n",
       "90                55        17     31     24   21    23    30      29   \n",
       "100               10         7      4      2    6     2     4       6   \n",
       "\n",
       "Birth_Month  September  October  November  December  \n",
       "Age Group                                            \n",
       "0                   67       67        56        63  \n",
       "10                  83       79        70        79  \n",
       "20                  68       75        76        71  \n",
       "30                  66       65        57        58  \n",
       "...                ...      ...       ...       ...  \n",
       "70                  38       30        37        37  \n",
       "80                  27       31        34        37  \n",
       "90                  33       25        28        20  \n",
       "100                  5        5         7         7  \n",
       "\n",
       "[11 rows x 12 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_month = (names\n",
    "    .groupby('Age Group')\n",
    "    .Birth_Month\n",
    "    .value_counts()\n",
    "    .unstack())\n",
    "\n",
    "by_month = by_month[month_names]\n",
    "by_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age Group\n",
      "0      0.289808\n",
      "10     0.172563\n",
      "20     0.061524\n",
      "30     0.138908\n",
      "40     0.077120\n",
      "50     0.059772\n",
      "60     0.169321\n",
      "70     0.104118\n",
      "80     0.227215\n",
      "90     0.284632\n",
      "100    0.079604\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "with show_more_rows():\n",
    "    print(by_month.var(axis=1) / by_month.sum(axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison to Baselines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Population_2019</th>\n",
       "      <th>Population_2010</th>\n",
       "      <th>House_Seats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California</td>\n",
       "      <td>39512223</td>\n",
       "      <td>37254523</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Texas</td>\n",
       "      <td>28995881</td>\n",
       "      <td>25145561</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Florida</td>\n",
       "      <td>21477737</td>\n",
       "      <td>18801310</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New York</td>\n",
       "      <td>19453561</td>\n",
       "      <td>19378102</td>\n",
       "      <td>27.0</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>52</th>\n",
       "      <td>Guam</td>\n",
       "      <td>165718</td>\n",
       "      <td>159358</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>U.S. Virgin Isl</td>\n",
       "      <td>104914</td>\n",
       "      <td>106405</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>55641</td>\n",
       "      <td>55519</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>N. Mariana Isl</td>\n",
       "      <td>55194</td>\n",
       "      <td>53883</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>56 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              State  Population_2019  Population_2010  House_Seats\n",
       "0        California         39512223         37254523         53.0\n",
       "1             Texas         28995881         25145561         36.0\n",
       "2           Florida         21477737         18801310         27.0\n",
       "3          New York         19453561         19378102         27.0\n",
       "..              ...              ...              ...          ...\n",
       "52             Guam           165718           159358          0.5\n",
       "53  U.S. Virgin Isl           104914           106405          0.5\n",
       "54   American Samoa            55641            55519          0.5\n",
       "55   N. Mariana Isl            55194            53883          0.5\n",
       "\n",
       "[56 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states = pd.read_fwf('data/state-population.fwf')\n",
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(names\n",
    "    .Home\n",
    "    .value_counts()\n",
    "    .sort_index()\n",
    "    .plot(kind='bar', figsize=(12, 3), \n",
    "          title=\"Distribution of sample by home state\")\n",
    ")\n",
    "plt.savefig(\"img/(Ch05)Distribution of sample by home state.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(states\n",
    "    .sort_values('State')\n",
    "    [['State', 'Population_2019']]\n",
    "    .set_index('State')\n",
    "    .plot(kind='bar', figsize=(12, 3),\n",
    "          title=\"2019 Population of U.S. states and territories\")\n",
    ")\n",
    "plt.savefig(\"img/(Ch05)2019 Population of U.S. states and territories.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Benford's Law"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>POP2019</th>\n",
       "      <th>AREA_KM2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New York City</td>\n",
       "      <td>8336817</td>\n",
       "      <td>780.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>3979576</td>\n",
       "      <td>1213.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>2693976</td>\n",
       "      <td>588.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Houston</td>\n",
       "      <td>2320268</td>\n",
       "      <td>1651.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>Vacaville</td>\n",
       "      <td>100670</td>\n",
       "      <td>75.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>100471</td>\n",
       "      <td>72.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>Bend</td>\n",
       "      <td>100421</td>\n",
       "      <td>85.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>Woodbridge</td>\n",
       "      <td>100145</td>\n",
       "      <td>60.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>317 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              NAME  POP2019  AREA_KM2\n",
       "0    New York City  8336817     780.9\n",
       "1      Los Angeles  3979576    1213.9\n",
       "2          Chicago  2693976     588.7\n",
       "3          Houston  2320268    1651.1\n",
       "..             ...      ...       ...\n",
       "313      Vacaville   100670      75.1\n",
       "314        Clinton   100471      72.8\n",
       "315           Bend   100421      85.7\n",
       "316     Woodbridge   100145      60.3\n",
       "\n",
       "[317 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities = pd.read_fwf('data/us-cities.fwf')\n",
    "cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    206\n",
      "2     53\n",
      "3     20\n",
      "4     10\n",
      "6      9\n",
      "5      8\n",
      "8      5\n",
      "7      3\n",
      "9      3\n",
      "Name: POP2019, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "pop_digits =  cities.POP2019.astype(str).str[0].value_counts()\n",
    "with show_more_rows():\n",
    "    print(pop_digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    118\n",
      "2     47\n",
      "3     31\n",
      "4     23\n",
      "8     21\n",
      "9     21\n",
      "7     20\n",
      "6     20\n",
      "5     16\n",
      "Name: AREA_KM2, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "area_digits =  cities.AREA_KM2.astype(str).str[0].value_counts()\n",
    "with show_more_rows():\n",
    "    print(area_digits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  10280 200\n",
      "      2 206\n",
      "    398 301\n",
      "   1680 304\n",
      "    181 403\n",
      "    901 404\n",
      "      9 500\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "zcat data/gnosis/*.log.gz | \n",
    "    cut -d' ' -f9 | \n",
    "    sort | \n",
    "    uniq -c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "162.158.238.207 - - [30/Mar/2020:00:00:00 -0400]\n",
      "\"GET /TPiP/024.code HTTP/1.1\" 200 75\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "zcat data/gnosis/20200330.log.gz | head -1 | fmt -w50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def apache_log_to_df(fname):\n",
    "    # Read one log file.  Treat is as a space separated file\n",
    "    # There is no explicit header, so we assign columns\n",
    "    cols = ['ip_address', 'ident', 'userid', 'timestamp', \n",
    "            'tz', 'request', 'status', 'size']\n",
    "    df = pd.read_csv(fname, sep=' ', header=None, names=cols)\n",
    "    \n",
    "    # The first pass gets something workable, but refine it\n",
    "    # Datetime has superfluous '[', but fmt matches that\n",
    "    fmt = \"[%d/%b/%Y:%H:%M:%S\"\n",
    "    df['timestamp'] = pd.to_datetime(df.timestamp, format=fmt)\n",
    "    \n",
    "    # Convert timezone to an integer\n",
    "    # Not general, I know these logs use integral timezone\n",
    "    # E.g. India Standard Time (GMT+5:30) would break this\n",
    "    df['tz'] = df.tz.str[:3].astype(int)\n",
    "    \n",
    "    # Break up the quoted request into sub-components\n",
    "    df[['method', 'resource', 'protocol']] = (\n",
    "                df.request.str.split(' ', expand=True))\n",
    "    \n",
    "    # Break the IP address into each quad\n",
    "    df[['quad1', 'quad2', 'quad3', 'quad4']] = (\n",
    "                df.ip_address.str.split('.', expand=True))\n",
    "    \n",
    "    # Pandas lets us pull components from datetime\n",
    "    df['hour'] = df.timestamp.dt.hour\n",
    "    df['minute'] = df.timestamp.dt.minute\n",
    "    \n",
    "    # Split resource into the path/directory vs. actual page\n",
    "    df[['path', 'page']] = (\n",
    "                df.resource.str.rsplit('/', n=1, expand=True))\n",
    "    # Only care about some fields for current purposes\n",
    "    cols = ['hour', 'minute', \n",
    "            'quad1', 'quad2', 'quad3', 'quad4', \n",
    "            'method', 'path', 'page', 'status']\n",
    "    return df[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>quad1</th>\n",
       "      <th>quad2</th>\n",
       "      <th>method</th>\n",
       "      <th>path</th>\n",
       "      <th>page</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "      <td>158</td>\n",
       "      <td>GET</td>\n",
       "      <td>/download/pywikipedia/cache</td>\n",
       "      <td>DuMont%20Television%20Network</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>172</td>\n",
       "      <td>68</td>\n",
       "      <td>GET</td>\n",
       "      <td>/TPiP</td>\n",
       "      <td>053.code</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>162</td>\n",
       "      <td>158</td>\n",
       "      <td>GET</td>\n",
       "      <td>/download/pywikipedia/archive</td>\n",
       "      <td>?C=N;O=A</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>162</td>\n",
       "      <td>158</td>\n",
       "      <td>GET</td>\n",
       "      <td>/juvenilia</td>\n",
       "      <td>History%20of%20Mathematics.pdf</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13447</th>\n",
       "      <td>23</td>\n",
       "      <td>52</td>\n",
       "      <td>162</td>\n",
       "      <td>158</td>\n",
       "      <td>GET</td>\n",
       "      <td>/download/gnosis/util</td>\n",
       "      <td>hashcash.py</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13448</th>\n",
       "      <td>23</td>\n",
       "      <td>52</td>\n",
       "      <td>172</td>\n",
       "      <td>69</td>\n",
       "      <td>GET</td>\n",
       "      <td></td>\n",
       "      <td>favicon.ico</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13449</th>\n",
       "      <td>23</td>\n",
       "      <td>52</td>\n",
       "      <td>162</td>\n",
       "      <td>158</td>\n",
       "      <td>GET</td>\n",
       "      <td>/publish/resumes</td>\n",
       "      <td></td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13450</th>\n",
       "      <td>23</td>\n",
       "      <td>56</td>\n",
       "      <td>162</td>\n",
       "      <td>158</td>\n",
       "      <td>GET</td>\n",
       "      <td>/download/pywikipedia/cache</td>\n",
       "      <td>Joan%20of%20Lancaster</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13451 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       hour  minute quad1 quad2 method                           path  \\\n",
       "0         0       0   162   158    GET    /download/pywikipedia/cache   \n",
       "1         0       3   172    68    GET                          /TPiP   \n",
       "2         0       7   162   158    GET  /download/pywikipedia/archive   \n",
       "3         0       7   162   158    GET                     /juvenilia   \n",
       "...     ...     ...   ...   ...    ...                            ...   \n",
       "13447    23      52   162   158    GET          /download/gnosis/util   \n",
       "13448    23      52   172    69    GET                                  \n",
       "13449    23      52   162   158    GET               /publish/resumes   \n",
       "13450    23      56   162   158    GET    /download/pywikipedia/cache   \n",
       "\n",
       "                                 page  status  \n",
       "0       DuMont%20Television%20Network     200  \n",
       "1                            053.code     200  \n",
       "2                            ?C=N;O=A     200  \n",
       "3      History%20of%20Mathematics.pdf     200  \n",
       "...                               ...     ...  \n",
       "13447                     hashcash.py     200  \n",
       "13448                     favicon.ico     304  \n",
       "13449                                     200  \n",
       "13450           Joan%20of%20Lancaster     200  \n",
       "\n",
       "[13451 rows x 8 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reqs = pd.concat(map(apache_log_to_df, \n",
    "                 glob('data/gnosis/*.log.gz')))\n",
    "# Each file has index from 0, so dups occur in raw version\n",
    "reqs = reqs.reset_index().drop('index', axis=1)\n",
    "# The /16 subnetwork is too random for this purpose\n",
    "reqs.drop(['quad3', 'quad4'], axis=1, inplace=True)\n",
    "reqs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x900 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 2, figsize=(12, 9))\n",
    "\n",
    "# Which factors should we analyze for class balance?\n",
    "factors = ['hour', 'minute', 'quad1', 'quad2', 'method', 'status']\n",
    "\n",
    "# Loop through the axis subplots and the factors\n",
    "for col, ax in zip(factors, axes.flatten()):\n",
    "    # Minute is categorical but too many so quantize\n",
    "    if col == 'minute':\n",
    "        data = (reqs[col] // 5 * 5).value_counts()\n",
    "    else:\n",
    "        data = reqs[col].value_counts()\n",
    "    data.plot(kind='bar', ax=ax)\n",
    "    ax.set_title(f\"{col} distibution\")\n",
    "\n",
    "# Matplotlib trick to improve spacing of subplots\n",
    "fig.tight_layout()\n",
    "plt.savefig(\"img/(Ch05)web-traffic feature distributions.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalization and scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature_1</th>\n",
       "      <th>Feature_2</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.112999</td>\n",
       "      <td>19247.756104</td>\n",
       "      <td>11.407035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.204178</td>\n",
       "      <td>23432.270613</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.173678</td>\n",
       "      <td>19179.445753</td>\n",
       "      <td>17.336683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.161411</td>\n",
       "      <td>17579.625264</td>\n",
       "      <td>16.633166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>0.137692</td>\n",
       "      <td>20934.654450</td>\n",
       "      <td>13.316583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>0.184393</td>\n",
       "      <td>18855.241195</td>\n",
       "      <td>18.241206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>0.177846</td>\n",
       "      <td>19760.314890</td>\n",
       "      <td>17.839196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>0.145229</td>\n",
       "      <td>20497.722353</td>\n",
       "      <td>14.371859</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Feature_1     Feature_2     Target\n",
       "0     0.112999  19247.756104  11.407035\n",
       "1     0.204178  23432.270613  20.000000\n",
       "2     0.173678  19179.445753  17.336683\n",
       "3     0.161411  17579.625264  16.633166\n",
       "..         ...           ...        ...\n",
       "196   0.137692  20934.654450  13.316583\n",
       "197   0.184393  18855.241195  18.241206\n",
       "198   0.177846  19760.314890  17.839196\n",
       "199   0.145229  20497.722353  14.371859\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unscaled = make_unscaled_features()\n",
    "unscaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature_1</th>\n",
       "      <th>Feature_2</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Feature_1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.272963</td>\n",
       "      <td>0.992514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature_2</th>\n",
       "      <td>-0.272963</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.269406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target</th>\n",
       "      <td>0.992514</td>\n",
       "      <td>-0.269406</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Feature_1  Feature_2    Target\n",
       "Feature_1   1.000000  -0.272963  0.992514\n",
       "Feature_2  -0.272963   1.000000 -0.269406\n",
       "Target      0.992514  -0.269406  1.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unscaled.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_univariate_trends(unscaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying a Machine Learning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = unscaled.drop('Target', axis=1)\n",
    "y = unscaled['Target']\n",
    "\n",
    "X_train, X_test, y_train, y_test = (\n",
    "    train_test_split(X, y, random_state=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.027756186064182953"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "knn = KNeighborsRegressor()\n",
    "knn.fit(X_train, y_train).score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9743878175626131"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "X_new = MinMaxScaler().fit_transform(X)\n",
    "\n",
    "X_train, X_test, y_train, y_test = (\n",
    "    train_test_split(X_new, y, random_state=1))\n",
    "\n",
    "knn2 = KNeighborsRegressor()\n",
    "knn2.fit(X_train, y_train).score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling Techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] TRUE\n"
     ]
    }
   ],
   "source": [
    "%%R -i X,X_new\n",
    "# Import the data frame/array from Python\n",
    "py_raw_data <- X$Feature_1  # only feature 1\n",
    "py_scaled <- X_new[,1]      # scaled column 1\n",
    "\n",
    "# Utility function to scale as [0, 1]\n",
    "normalize <- function(x) {\n",
    "    floor <- min(x)  # Only find min once\n",
    "    return ((x - floor) / (max(x) - floor))\n",
    "}\n",
    "\n",
    "# Scale the raw data\n",
    "r_scaled <- normalize(py_raw_data)\n",
    "\n",
    "# Near equality of elements from normalize() and MinMaxScaler\n",
    "all.equal(py_scaled, r_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"A few 'equalities':\"\n",
      "[1] 0.1776148 1.0000000 0.7249096 0.6142706 0.8920478\n",
      "[1] 0.1776148 1.0000000 0.7249096 0.6142706 0.8920478\n",
      "[1] \"Exactly equal?\"\n",
      " [1]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE\n",
      "[1] \"Mean absolute difference:\"\n",
      "[1] 6.130513e-17\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "print(\"A few 'equalities':\")\n",
    "print(py_scaled[1:5])\n",
    "print(r_scaled[1:5])\n",
    "\n",
    "print(\"Exactly equal?\")\n",
    "print((py_scaled == r_scaled)[1:10])\n",
    "\n",
    "print(\"Mean absolute difference:\")\n",
    "print(mean(abs(py_scaled - r_scaled)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "X_new2 = StandardScaler().fit_transform(X)\n",
    "\n",
    "# Second column for example (both were scaled)\n",
    "plt.hist(X_new2[:, 1], bins=30)\n",
    "plt.title(\"Value distribution after StandardScaler\")\n",
    "plt.savefig(\"img/(Ch05)Value distribution after StandardScaler.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Mean relative difference: 0.002503133\"\n"
     ]
    }
   ],
   "source": [
    "%%R -i X,X_new2\n",
    "# Import the data frame/array from Python\n",
    "py_raw_data <- X$Feature_2  # Only feature 2\n",
    "py_scaled <- X_new2[, 2]    # scaled column 2\n",
    "\n",
    "r_scaled = (py_raw_data - mean(py_raw_data)) / \n",
    "            sd(py_raw_data)\n",
    "\n",
    "all.equal(py_scaled, r_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Mean from R scaling:\"\n",
      "[1] 6.591949e-17\n",
      "[1] \"Standard deviation:\"\n",
      "[1] 1\n",
      "[1] \"Almost equal with tolerance 0.005\"\n",
      "[1] TRUE\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "print(\"Mean from R scaling:\")\n",
    "print(mean(r_scaled))\n",
    "\n",
    "print(\"Standard deviation:\")\n",
    "print(sd(r_scaled))\n",
    "\n",
    "print(\"Almost equal with tolerance 0.005\")\n",
    "all.equal(py_scaled, r_scaled, tolerance = 0.005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X['Feature_3'] = X.Feature_1\n",
    "X.loc[0, 'Feature_3'] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "X_new3 = RobustScaler().fit_transform(X)\n",
    "\n",
    "# Third column for example (all were scaled)\n",
    "plt.hist(X_new3[:, 2], bins=30)\n",
    "plt.title(\"Value distribution after RobustScaler\")\n",
    "plt.savefig(\"img/(Ch05)Value distribution after RobustScaler.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import QuantileTransformer\n",
    "# Ten quantiles is also called \"decile\"\n",
    "deciles = QuantileTransformer(n_quantiles=10)\n",
    "X_new4 = deciles.fit_transform(X)\n",
    "\n",
    "# Third column for example (all were scaled)\n",
    "plt.hist(X_new4[:, 2], bins=30)\n",
    "plt.title(\"Value distribution after QuantileTransformer\")\n",
    "plt.savefig(\"img/(Ch05)Value distribution after QuantileTransformer.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Factor and Sample Weighting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>quad1</th>\n",
       "      <th>quad2</th>\n",
       "      <th>method</th>\n",
       "      <th>path</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3347</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>172</td>\n",
       "      <td>69</td>\n",
       "      <td>GET</td>\n",
       "      <td>/publish/programming</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2729</th>\n",
       "      <td>9</td>\n",
       "      <td>43</td>\n",
       "      <td>172</td>\n",
       "      <td>69</td>\n",
       "      <td>GET</td>\n",
       "      <td>/TPiP</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8102</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>172</td>\n",
       "      <td>69</td>\n",
       "      <td>GET</td>\n",
       "      <td>/member/images</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9347</th>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>162</td>\n",
       "      <td>158</td>\n",
       "      <td>GET</td>\n",
       "      <td>/publish/images</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6323</th>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>162</td>\n",
       "      <td>158</td>\n",
       "      <td>GET</td>\n",
       "      <td>/download/pywikipedia/cache</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>162</td>\n",
       "      <td>158</td>\n",
       "      <td>GET</td>\n",
       "      <td>/download/gnosis/xml/pickle/test</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12728</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "      <td>158</td>\n",
       "      <td>GET</td>\n",
       "      <td>/download/relax</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12235</th>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>172</td>\n",
       "      <td>69</td>\n",
       "      <td>GET</td>\n",
       "      <td>/dede2</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       hour  minute quad1 quad2 method                              path  \\\n",
       "3347      0       4   172    69    GET              /publish/programming   \n",
       "2729      9      43   172    69    GET                             /TPiP   \n",
       "8102      4      16   172    69    GET                    /member/images   \n",
       "9347      0      48   162   158    GET                   /publish/images   \n",
       "6323     21      30   162   158    GET       /download/pywikipedia/cache   \n",
       "2352      0      35   162   158    GET  /download/gnosis/xml/pickle/test   \n",
       "12728     9       0   162   158    GET                   /download/relax   \n",
       "12235    19       3   172    69    GET                            /dede2   \n",
       "\n",
       "       status  \n",
       "3347      200  \n",
       "2729      200  \n",
       "8102      404  \n",
       "9347      304  \n",
       "6323      200  \n",
       "2352      200  \n",
       "12728     200  \n",
       "12235     404  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reqs.sample(8, random_state=72).drop('page', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GET     13294\n",
       "HEAD      109\n",
       "POST       48\n",
       "Name: method, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reqs.method.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>quad1</th>\n",
       "      <th>quad2</th>\n",
       "      <th>method</th>\n",
       "      <th>path</th>\n",
       "      <th>status</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3347</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>172</td>\n",
       "      <td>69</td>\n",
       "      <td>GET</td>\n",
       "      <td>/publish/programming</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2729</th>\n",
       "      <td>9</td>\n",
       "      <td>43</td>\n",
       "      <td>172</td>\n",
       "      <td>69</td>\n",
       "      <td>GET</td>\n",
       "      <td>/TPiP</td>\n",
       "      <td>200</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8102</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>172</td>\n",
       "      <td>69</td>\n",
       "      <td>GET</td>\n",
       "      <td>/member/images</td>\n",
       "      <td>404</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9347</th>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>162</td>\n",
       "      <td>158</td>\n",
       "      <td>GET</td>\n",
       "      <td>/publish/images</td>\n",
       "      <td>304</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      hour  minute quad1 quad2 method                  path  status  weight\n",
       "3347     0       4   172    69    GET  /publish/programming     200       1\n",
       "2729     9      43   172    69    GET                 /TPiP     200       5\n",
       "8102     4      16   172    69    GET        /member/images     404       1\n",
       "9347     0      48   162   158    GET       /publish/images     304       1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The row index positions for rows to overweight\n",
    "tpip_rows = reqs[reqs.path == '/TPiP'].index\n",
    "head_rows = reqs[reqs.method == 'HEAD'].index\n",
    "post_rows = reqs[reqs.method == 'POST'].index\n",
    "\n",
    "# Configure the weights in a copy of data frame\n",
    "reqs_weighted = reqs.copy()\n",
    "reqs_weighted['weight'] = 1  # Default weight of one\n",
    "reqs_weighted.loc[tpip_rows, 'weight'] = 5\n",
    "reqs_weighted.loc[head_rows, 'weight'] = 122\n",
    "reqs_weighted.loc[post_rows, 'weight'] = 277\n",
    "\n",
    "# Do not use column page in the model\n",
    "reqs_weighted.drop('page', axis=1, inplace=True)\n",
    "\n",
    "# View the configured weights\n",
    "reqs_weighted.sample(4, random_state=72)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{200: 1, 304: 1, 403: 1, 404: 10, 301: 1, 500: 1, 206: 1}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_weight = {code:1 for code in reqs.status.unique()}\n",
    "target_weight[404] = 10\n",
    "target_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8183169788878977"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "# Create the model object with target weights\n",
    "rfc = RandomForestClassifier(class_weight=target_weight,\n",
    "                             random_state=0)\n",
    "\n",
    "# Select and encode the features and target\n",
    "X = reqs_weighted[['hour', 'minute', \n",
    "                   'quad1', 'quad2',\n",
    "                   'method', 'path']]\n",
    "\n",
    "# Encode strings as ordinal integers\n",
    "X = OrdinalEncoder().fit_transform(X)\n",
    "y = reqs_weighted['status']\n",
    "weight = reqs_weighted.weight\n",
    "\n",
    "# Perform the train/test split, including weights\n",
    "X_train, X_test, y_train, y_test, weights_train, _ = (\n",
    "     train_test_split(X, y, weight, random_state=1))\n",
    "\n",
    "# Fit the model on the training data and score it\n",
    "rfc.fit(X_train, y_train, sample_weight=weights_train)\n",
    "rfc.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cyclicity and autocorrelation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start: 2003-07-25 16:04:00\n",
      "  End: 2004-07-16 15:28:00\n",
      " Days: 358\n"
     ]
    }
   ],
   "source": [
    "thermo = read_glarp()\n",
    "start, end = thermo.timestamp.min(), thermo.timestamp.max()\n",
    "print(\"Start:\", start)\n",
    "print(\"  End:\", end)\n",
    "# Fencepost counting includes ends\n",
    "print(\" Days:\", 1 + (end.date() - start.date()).days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m# A tibble: 171,349 x 5\u001b[39m\n",
      "   timestamp           basement   lab livingroom outside\n",
      "   \u001b[3m\u001b[90m<dttm>\u001b[39m\u001b[23m                 \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m      \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
      "\u001b[90m 1\u001b[39m 2003-07-25 \u001b[90m16:04:00\u001b[39m     24    25.2       29.8    27.5\n",
      "\u001b[90m 2\u001b[39m 2003-07-25 \u001b[90m16:07:00\u001b[39m     24    25.2       29.8    27.3\n",
      "\u001b[90m 3\u001b[39m 2003-07-25 \u001b[90m16:10:00\u001b[39m     24    25.2       29.8    27.3\n",
      "\u001b[90m 4\u001b[39m 2003-07-25 \u001b[90m16:13:00\u001b[39m     24.1  25.2       29.8    27.4\n",
      "\u001b[90m 5\u001b[39m 2003-07-25 \u001b[90m16:16:00\u001b[39m     24.1  25.2       29.8    27.8\n",
      "\u001b[90m 6\u001b[39m 2003-07-25 \u001b[90m16:19:00\u001b[39m     24.1  25.2       29.8    27.5\n",
      "\u001b[90m 7\u001b[39m 2003-07-25 \u001b[90m16:22:00\u001b[39m     24.1  25.2       29.8    27.6\n",
      "\u001b[90m 8\u001b[39m 2003-07-25 \u001b[90m16:25:00\u001b[39m     24.1  25.2       29.8    27.6\n",
      "\u001b[90m 9\u001b[39m 2003-07-25 \u001b[90m16:28:00\u001b[39m     24.1  25.2       29.8    27.7\n",
      "\u001b[90m10\u001b[39m 2003-07-25 \u001b[90m16:31:00\u001b[39m     24.1  25.2       29.8    27.6\n",
      "\u001b[90m# … with 171,339 more rows\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "%%R -i thermo\n",
    "glarp <- as.tibble(thermo)\n",
    "glarp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "clean_theme <- theme(\n",
    "    panel.background = element_blank(),\n",
    "    panel.grid.major.y = element_line(\n",
    "        color = \"gray\", size = 0.25, linetype = \"dotted\"),\n",
    "    axis.title.x = element_blank())\n",
    "\n",
    "no_xlabel <- clean_theme + theme(\n",
    "    axis.title.x = element_blank(),\n",
    "    axis.text.x = element_blank(),\n",
    "    axis.ticks.x = element_blank(),\n",
    "    plot.margin = unit(c(0, -1, -1, 0), \"cm\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "im <- ggplot(glarp, aes(x=timestamp, y=outside)) +\n",
    "  geom_line() + clean_theme +\n",
    "  ggtitle(\"Outside temperature over recording interval\")\n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stdout err\n",
    "%%R\n",
    "ggsave(\"img/(Ch05)Outside temperature over recording interval.png\", dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "im <- ggplot(glarp, aes(x=timestamp, y=basement)) +\n",
    "  geom_line() + clean_theme +\n",
    "  ggtitle(\"Basement temperature over recording interval\")\n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stdout err\n",
    "%%R\n",
    "ggsave(\"img/(Ch05)Basement temperature over recording interval.png\", dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Domain Knowledge Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "# Model the data as a second order polynomial\n",
    "year.model <- lm(outside ~ poly(timestamp, 2), data = glarp)\n",
    "\n",
    "# Display the regression and the data\n",
    "im <- ggplot(glarp, aes(x=timestamp)) + clean_theme +\n",
    "  geom_line(aes(y = outside), color = \"gray\") +\n",
    "  geom_line(aes(y = predict(year.model)), \n",
    "            color = \"darkred\", size = 2) +\n",
    "  ggtitle(\"Outside temperature versus polynomial fit\") \n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stdout err\n",
    "%%R    \n",
    "ggsave(\"img/(Ch05)Outside temperature versus polynomial fit.png\", dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m# A tibble: 171,349 x 3\u001b[39m\n",
      "   timestamp           outside no_seasonal\n",
      "   \u001b[3m\u001b[90m<dttm>\u001b[39m\u001b[23m                \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
      "\u001b[90m 1\u001b[39m 2003-07-25 \u001b[90m16:04:00\u001b[39m    27.5        1.99\n",
      "\u001b[90m 2\u001b[39m 2003-07-25 \u001b[90m16:07:00\u001b[39m    27.3        1.79\n",
      "\u001b[90m 3\u001b[39m 2003-07-25 \u001b[90m16:10:00\u001b[39m    27.3        1.79\n",
      "\u001b[90m 4\u001b[39m 2003-07-25 \u001b[90m16:13:00\u001b[39m    27.4        1.89\n",
      "\u001b[90m 5\u001b[39m 2003-07-25 \u001b[90m16:16:00\u001b[39m    27.8        2.29\n",
      "\u001b[90m 6\u001b[39m 2003-07-25 \u001b[90m16:19:00\u001b[39m    27.5        1.99\n",
      "\u001b[90m 7\u001b[39m 2003-07-25 \u001b[90m16:22:00\u001b[39m    27.6        2.10\n",
      "\u001b[90m 8\u001b[39m 2003-07-25 \u001b[90m16:25:00\u001b[39m    27.6        2.10\n",
      "\u001b[90m 9\u001b[39m 2003-07-25 \u001b[90m16:28:00\u001b[39m    27.7        2.20\n",
      "\u001b[90m10\u001b[39m 2003-07-25 \u001b[90m16:31:00\u001b[39m    27.6        2.07\n",
      "\u001b[90m# … with 171,339 more rows\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "outside <- glarp[, c(\"timestamp\", \"outside\")] %>%\n",
    "    add_column(no_seasonal = glarp$outside - predict(year.model))\n",
    "outside"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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udhmS7SAbKnAwmSCAr7gPPAQXOF7eJbDPBw882DwFgIvDCwl8Ed6858AeEphtbHs3gUNxgWOugb0/eOBB5il1+1znwB8J4AvwZliJt7xhanIAJhL4VDAZZwCOr87oBDwQF/i5/EQXa67m2fPVN1tP7xLYUtZaN0afFVbgocXe9pYpyRb4mgZ8MzCYjKW9eZ6J0i8swDVdAB8pb/mJKykKALc3T6kb1Hs5Amc0ABd621umpFV2Ab5/14Bv+OvAJyltQ0g8dcbUh6vZADfsOgAXOFzSFXBVEgWBA22BC4b7VV9ffONbJ5It8K8KcBAD7iGAR7ArTxx1xtSHq9oBx/PiwMWjOHA785S6/nbAg/OF+1V5vn/jWyeSLfA5BF66kd2N/Xw14GO0rocJOIktcANf8v8B/Jl5Sm3/3suswIMchkFYqUc5zrM1UrYQVuIM3KEfAHuRMQL4AK3L/o2tzugA7COBQ12suSrp8XqbztO7BG5pnlLLvxcHzqACD3SICpdghNaoKRKB7YKqnMOKTAC+hsDdcWN/ROBY6oxm4Oen4N8G3hJ4oIs1VyXdXn/z3y1wM/OUmsQW2CGuSQyyU77M812kbCF1C3i0AN5nBU5sAj6QEP5twG/B5FCx/z/gHsvwmSi9AdghKoIKvCVStpA6AJ/FYqsO37D8tAK8F4ENDdoBOPkX+vsf48K/DTwlsF05qExVbC7ur5DeK+AaEjjtew98SQceJYD3IHAMdcZEDDiwnv5eAIv2KgejPHDYdeOUagy4CLxII4DPw5sBDsDB7wz4lC3w7vCB9yFwfQG8PYoCbxElWC2a/ZzR+AnLOC41AJ+DN/2z2H9PENkuX+aOdOD5qoUA7suBhwngnQisBuBhwFUMwHsNwOtdAlf4YIGn1+F/WzQ7ZOpryYCX2AA7xDUJINqIEpEHvFwAD/1cmXgKC547QEBcP0KG0s64sT9YgeO9BnBZ8uWb2IF3ADzNEbgK6TGrMLwwAPdzBF4nX0Ye8LJc/K9L4E4CuA77N0hdOtbhj0lAXf29ACYSuL+LNZclnd/EDrxL4KYH0xg/KUu6zswPL1KLQ4DjcvX7yP57FOBc7wK4vQl4ix1wZQPwHiNw4agOPM8EXJy0n4nlkqkMwKY7tUxvFXiICfgMpe0E8BABvN4OuKINcD0JnDeqA8+xAcaiwZTuAa+VLyMPeIEAHmwCPkg3FGDAVy3AgezztufEjLEOV7ABrqsB9xPTbz2xrvnDAv7zsf56qgvgdjMxU5NCBf7GHPZCJAU4Z6QBz3UEbgWjDlz11YHXIXAAbM4OMSMAe6nAWBUhgdfk/UZMrz/HuuYPC7juXP21BG5uA9x6Bh5QA3Df1Pbf+XaARTmpC+DBAnitFTgmAyYqsBcM9WUFrm0zYHRZ0ulN7MBbAq45U38tgZtagIuRz6djrPvkKvDXqey/8+0AZ+Z/zcAHEPhWfm8zsD81AJc3AhOoAZPAq/P2FdPtgd/I6H5vCbiGm8DNp+IBNQB/9Q6B/2wvsvChat0mA96PwGcJAx4EjSnhhKS1JbAsgQkxAN8/uYfArUoH/pq9e9npZVQFbmIGLko+mWIAxkhafd4h8CySgb/oZwFubgWuyv71g83ZJmaM8Z0KvPjj3eQ/9reO2LtVuQH4EUO3Ay5DOryJPXiHwA0amYELm4Fx7OXeDuHv/dghFSnSgGfK0IRm4B9pMwk80AKcXQIHe8ZXgBdW5sA1JHAuKLQF4FofPHB1e2BT9qkwaTIFnon2JhSHYD9M7RU+cI7Nb3Rr9WQPfBIqf+sw4NMAPMAA7Ms+TyOBA0lME/AjCnUqPCUP6UOjCnA1O+AGs03AeUn9KXBAZYsdDtzDAdjzrQCLZzQrcC0GfAriEAyA1rJG4DaiEWCALXAVuXukN/2ggQue017aAtc3AQ+OI4Blgw4CYxHR7g7A5K0Ai5UbgE84AFeRwL4L+YwBJMQlMPRdAOBSHyZwWj3QalU74LrjjMBFCKlnBMYoNN0cAneqwJts53hyznYyfWo/2SbNlHGv+qVXGmYI4A4M2EMH/pZ+zP71pgZgf0LqyMUWVt5lA/wbm1Y0igKPMQIXtADvhMldTZ3mZVKAs22wnWNzYfslM7jdjFoHDq6gTz0BP71apD0H7q8BV2b/elH6rwtgGDD3YwPwFTYtagFP0YBHGx+ACtgDdzF1mpcJDqlIWeyBNzi0x0x6Ipwt19IMCfyNAvxiIQdux4H7bcptBI4ZRwL7E18VuNIuHI3RPeDS5AvrxIindwFcm/+tX2eUETgfAMOTU3oDcCdH4OXypQb8l2GODQ7tMd0HnmYHfJxw4C8EcHu+saslsHewDuxjAN6JwBUNwJcYcGFb4NbubqWrFKnAB7WXVZU90IBrjzDSMeCaU+CqnU4egB9gsjPwUvlSA878izrHeofWXBEBFpHrVOCjBFrYAfBJ2MgGdsAzRsKcCFxTLhd1gM/8in/sgcel1ICHG7oBsKckUnMynNQa8HqY3NEReLF8mWW9eJHGEEB/vcMgrgD83K3h46fJsEgq8BEEriGBE2Q3AXv4+S7shzUFCFxdLmcPfBGAp1tX/T4Dd+GF6ApwFR24d6AErjXY0EocgGsYgVfC5A6OwIvkSyfgzPZLJj3O6FqFvx8wFrvI4X0TZAJum1ACy8SBPRmwtwNwnokwllvvEBPwPw7An1snRjxFCnBnAXxAm6ICB+jAhhZq0Bq6xmR47jQCf2GIa6InQhbKlwx4IfaHNwKvcwBOwoDHNXVnT3RgDyPw7lLEDLyKVuLAxNtHAPsBcDW53EKSAob6qksMwL8y4EI2wKU+XODK/IUFODUAwwGNMHDmdXR0c/wKI7BDcz0EbuzOnkyJAPAKDdjDSwP2JqSyXG4hSQ4jQZmALzDgAh8a8Nf4xwHYX1TQMOBAdak/EwIwXI/T6ceMugLWYnnqwAfVOdY6NPZ5FWBiAi4JwMeswB4ATBZwYF8AriSXW0gSOQDnj1LAngK4Xq3BvupSSdgjR41JACxbzRIMUtLWEViLYZK56FEEvpjCLeDEbgNPej1gHwAuRf/g27GQJHAAzmcLbOle+yrpHQATDXiQt7pUfCKBZYsdMhamt3EEnidfZiaLEDgzMQKbR6kSCYEbubMnE2Xkur4K8GEN+BcVeLkGzK4t/bC2HoGL07PQFGvR1oUkPgDXcQ+4JGnhzhaGl94+cC+SDMNf0Xo1Q9VRPGkcDTi2Abi1I/Bc+VICpzMBpzEvw1Oi43RLZbeAJzgB70Lgn1XgZQhMjMBeAHwaHgfbDlpI4gJwJTvgly/Nq/5ggXsQUgtfMGAPtdg/NgJDnKmQVwVOawRe7Qzc3f9NAB9SgZdwujAE/kYAeyIwPA4CcCwYZqSiEfg8eUjzTg/FXiwFzh8XY+YdDHuvgXn3MkdgPl5snZqhRGlNK4AhDJEGPBqmOwNrjU0B+BNqAV7lAtgnosDltYmH7IEXczojMEHgmBSAJ5EQ8iel5e2Ae2G8hjSHm43j6wj8owT51J0tDC9FDjCPn2MDfOCZCjwQW7DIFAuAJxqAR9DlF+nnEQd+OSAM/qxyaM0FwJ4N3dmTcIB/UoHnc+C7ZuBi9DT0Cm87aAIJgrF3TMDnyAMG3FMCj+XrCLhe7H0G5vUgafdrU6qIXETaI9DXkgPXNgLv14Fj6MAVF9PPHIFny5dm4H8JdhVY4QR8zH1gsXIz8E4G3JbuV4Hn0Arw5xYC99WBi9JT5DkAjyP+AFzWDPwXzTu+JxaspTn0gQDz8Cpp92lTGPBxiD2a5rAOXKvmAAX4pQd0qqw+AQKJacBDaYXFtJVbwKM+oVbglPZLJjrWMYV7wCNJXP7ia1JGmwjn7c4ShGQesU8Fns7PzVthJuB8Engs8YGBHcqYge/QvGN6ouYHA8zvb2m3hIkJndJOp0tLUyNwzRoq8FPc46oT2BPFkyB5AIbQcgw4oe1K2HGcJV9nEsBp3AVuEOAusKgP+ZroozcB8A8M2LPmXhV4Kge+/sIEnFcH9gTgEmbgazTvoA8MmJc2p/EffYtPyOsznS7RgXnxe82q/cktbRkOXBmAk3nIAzCIlhv7T8uIA/8TEeDdXZz3RAHWR45wAJ5sBfY2AWNQ6eJm4PM0b6gGPIavI+D3ogTL5ujW1xsfK3KA+SmahniK36M9cJV+pIC2zA0BzA5oTO0AMGD/8READjECL0tpv30JDcALyjrviQbcxwQcnNQCPMkBOOXCua6Bz9G8A83APpclcLF19HWSE/CQO/RpaPcpr/alHSUwf855wYBD6WIzcA0GrLWpuR5iBxxKy/mMaeEIrJX/ZCQLEdjXCLzYMFJkfu1USLispr+HDlyGOqbhErg3ybBSTsS8czADLvWNCjyOA197bgImxBuA2wyC4bCuWYDPMuDcA3uiZppDTQWwpwZcdC19nWQPfLdL1Tt0y2o69uwrfekXBuAnsV/k9flGAB8CYF6BVlUFvuzPgcfFVp6SyABaznu0m8AQlEkCP+TAiwwDCca8LF8lDIylAM8r7bwnwyRwF6LnshCY5RM8AlRf8qUjsBcHHmELfAaAB/RsfnM9HJ4mFuBCkQEc9rT/HTr6Et3Gvvze9evPI/ilravgHwH8D3ma16ePCTjfDQTW2tRc9sM9rjQulgrcn5bzGv2pI/BU+Tq9CfgBL0FZaGhwqwD7ByvAc1yMy6QBdw4fmJzXged/7RYwhBs9wYBz9uvZ/NsSBuBLRQmvDikYGcCUDrhDB96i+5dSOrZUqSPsFunuf3+wfxtXwdepAfgGAnv3oZNKs9dpNiHwDZpgN61Sth80K+cL/ugpgEOgw4cOXNxrVKEEtisCYPkagOuwF74QeOcG3Ncew/QFSdRFQi7L1wl9g/whK8/fjinmvDvDSEz+AoHFxIOwaQzXw98IfJoDP2P/jP+qI8ypAN9oMwhG3/md3iimL9GOzXOC7KE5v+zZfFZxeiPNodpj+Fo89xeF84C9zr3W/YNv+O9heMCjr9IdGx0+dkzPvdmTz2f8DGbAoyhcL5/mYcCLDGdwgrO0CjuDtYbRotAPzuBn+gH4mpbzHByUwHZNLwnRsgjptDOYF57d52fwAsMTdIh+BhMGrJ/BRZ13Z6jdGYzAgVbg4wj8O2z//D54BnsZzmAYLek36CSrJSi+0s7gIsYzuIi4k+Vb7bx5biQXwBvX0DFXIvp9T6FZWSsOnEoCP87jGR7wQQE8ygDck5YjPf3tgV8YgUfaARsu7ipwoAI8+1WBiZ8R+BgC/4bAvR2Ar9oAn6Y5v+HAqQ81GcHXAcC8pUCeSAN+3K/rbIdPnRMH/hhf68C5LcDBHFgrSxTAFUfEpI+NwJ2JI/Bk+VoC+wjge9C4jdL5jsD+CvCsVwL2sQIfLQf/XuXA2C3BABzqBHyK5uj75SccuP5Qvg6PS4UFcO7IAX7VxIF5M6SUGnAOj94CeK8ADuTAWjZ3vwQOejFIPQDlSDvjOBdaYsAT5GsrMNa6GYFjqMB+ep+wWQ4dXCANleGBOxO9KAuBPWyAy+rAPVVgDx34igEYAgYDcNavJXCN3nwdHhcLvcfALTlwMhvgTQI4gANrJ+c+CexzWzlk3Rnwp87A4+TrdGS+AObF3+EAxyd+fnqHg5kFqGNSgbXM9gEH4CMRBoZolMcYcOavvqzNgasrwLwGOvN7CNyiIr5OpAOzJz4FuKoGrLV836sB31IOWRcGXMs4kImWXoj2AJAY8AgA9ub9EcMDjkN8/Uht+W6GQ+NaSEMiAvwTAl9xCXzZAvwzOUkz9/mylhU4Jb56H4Gb8ao1Fbg7XQSXOBW4MgPWmlXusQXuxIDzGyP0akkFTkvmjoB2zt5kF76/x0dFsQd+WTiY+PqIZiUsTbe/x2PSgDu5A1wKgZ8iMI444iU/YsCtbYAh3OjP5AQHLgjAlSRwwxDR3i/Tewr8T25b4I06cCUVeDff4wojvFXgjgw4DQm0XdML/u2Y0pLZCOzFezPRv/nQ0kbgYN4UjD4n3sTTR+9wMJUHaZ+4jZrS1ccMWDTdZsAl5HRH4JLw7+UnCIw1pp7yo2eUfo7AlyDUjAH4sAG4Ih/PLoykk4FrM62kr5MiBbgpA/7TWwf+L3zgHzTgPw3AZUlSN4DTaMA8QtXf7EC+MAMHKcAePnp79ClBp+FP03HUlMqvYsCiXaAtsK8d8CUE7mEBbmEDDPVYh0nCp5n6fFmTA1fgQ628JAmJ2O9MK9w68E4pUoAbM+DrbgEHyOW22wK3cQH8HBp8iJSGzBTAPELV3+QirbeWzg+4qyygA3sZgCfzMA1NC483raHM8ggCl1CAscWcCtwsdBgHLmIEPkTIL2n6dKougLvjKq6ROO8p8BNolFKvHKVXvNnTiA7cTQCv1YErVu5HtJbv2/gelx3hpQK3YsDx3AKeMaIJNQJXXkjnE62y5K+uCrAnPMZWlB9N4g9rTeO1Ma2BAQ9WgIvL6Qfk5oULrFVsq8CF9SUgGiV76EpP+nSoBsAxDlXgQ61g9Bb+68+04qmlSW0E0psHvk5pnbI0bA35jwFDL1kD8BrajZAqlPoz4Ar9iNby/Tu+x0WswHEdgP8yAg+Honkvwm+kCLyAAZ+Wc5xIagbWmsFOFMBxbIE9+Gu3gIuZgbWPGHAjBL5oAIbuDww4FenNgQN/EsDeEL1FAreaRV89vXlgln+tU4ZeIORRAkKGUwHcxQx8EoE95XJb+B4XsgLHdgC+ogCnJtOHQTMhHfhXDqz19FaAn5qAx7sEFgeoE7SOFElramcP/NgeuIENMJRnHgDgXu0rcuDy3WjYXQAOlsAZVzQcEjEDQ3rzwGcQ+BwHhoZ2CNyZLoJY7jrwvhYxGbC2+s18jwsYgVsy4Jj6jdqQLhuApw2Dokcvwgcx/Jtc4MDH5BzHk9LAS3ITPaEOXgMeGVHgH+Xm+dgB/wrAY7phCw0j8FBbYPZzSUlq1ysHwAEAvL0YAAfowA1eZ4y7Nwe8Dh89n8BJU6eUCTg7+YIuysZep1lMu3Lg3lmJDXB+I/GnvXYAACAASURBVPCntAyJgfE9tbRC3pAuGYCnDqtPVeBztPJ8BqxFdDieRAH2AOBsc8VHEji2DXB/uYUdofmrSG4Aj0LgMCvwBQwmJFM7/m0pWZ6t3GoJvLkA7IgCXN9Fo7Fw05sDrogBFZ6QIwy4JAD/qwH/k520pYsgnkKa2RKYHeTy/aAfAE8b+R7nMwI3Z8CBRmCvP8ULFTgVA4bBTVTgSgB8VM5xTAN+NACA2X/ZxEcjEHhazljmkAlllsHIwPw1Ay4ip7sN/NIIPITgSFAK8GeU3smEwKTsMgQuJ4G9NeAF7w/w3v42wDeLkfvZ2QPPIujqx4ErUeongF+IpTfwPc5rBQ5wAP7VADxlGFQe6MDsKWz2pkKwMTz9koQGcOBfgwjP3MrhmIYh8Mck2NwflwH3kj/BDuIHMfFMeMAXdOAX2kdPJfAZA3BL6HtGCPSmLLnECEyI2O+M894f4HlVGfBhSmuXuDFOAz4TSP5CYGgBlxqBy0Pfdw4sOycJ4DwjPM3APibgm+LFBQPwZAPwXwA8abgvaSLn+FkHlg1GZBH0UAEcYA/M2ysxYIzoUmZpuMD/sX9GIvBzK/ApJ+AS85lrwIDSCrAfrjnj3PcJ+GFb8hMAL2Xb/G8cCXwageEwppxJv2SXI6iaJ6ScDfBwC7CXATjMQ3S/MwFPGArRijwF8G12plQaz4ABZRzE/FGAZUsqCTxAAPvaA/MOkO0FcMl5Ws1X+MB60wUN+CTGezMBJwDg6QUgaEs6BZgXEmSYWP91Bm94k8AbShcdFQjN3moXB+AzsRH4dAA5lZ205sDJZkC9TFm+B2UZsOzasI7vcU4jcKL7pdn1VAV+SeR4hxf4QximFBowH0gYgEuMEcBFoWGxAiwb2kjg7ghcmfiYe9Qz4J5yC9uL+V0CYyHkeZfALAua3wIMPTmKT80D17WU7yXwUyhfq7B4YHBQDA5cbElyQjyDJfDJ7ORzEzAU4hXsqAOv5XscI4MBmFwtTYgB+LkE/jepCRgqDzwJH0j4NjlNCzgByyoCGaHlSwSuRDzNgZUYMHume4SvbYE9iSH9hHTnAHioE/BxjBYlUwsOHIDA6QE4BQPOL4B9cM0ZxtV958C3IDoQAw70DlKAodkSAz7lD8CfCeBp8ERZVi/EeyS+Yq2205dcAj+RwHeIAXj80KoUgHnodwDOP2a4D2akTMAXJLAcUE0CWzpcl3YANvRnsACfBeBQEzC7E9VHYPbcltsCDKn4lLg68Es8PF4YTTnD8FqvM3jDGwA+2O06+R2B2cUmEIJ41yi2OCkH/koCt6KLoHoz2VTIrpTRnxCtwIawCAjsp6wsPGAeEe0WAg/zxrNUAvvgaMQ6sAgVQrtIYHNYFg7MI+KpwHtcAz8C4K7NcWMV4NoI/LMTcMEpcSBnIoD5NKzRTD285jsGXl7mDAM+mE0A76O0erHF8BP0x5bdp3zIiewkqN1CAcwOVWkdWAYU1IEPvwawhxjt7hbLreYbzYDhLHUbWBkmFFPxpTrwF4QH9w8f+AwH/oTNrDcfBOCBA10B550SAsDJu9LN+eTzFQKnGl7tHQJ3eiCBi5J5ABwAjWaqF1sE2+eL9WEcmDRRgAtZgddoO33QJfBjW+CEpPWn0JLTADzKDIzDTevAMr4DB65oBS6IwH/j67ZifreBoQXRvypwrfCAYyBwJzNwyrr5Xidw9GsCx/kVgE8z4AJkLAD7k90MuCgCe2B9mBm4HXSo1Mp4/hFfpAMvcB94mJwaSDy8oVWrh4gRjsBDhyjAh3RgWUUg22R3dgHcVYwbAcA4vxvAwDoQgf/RPvpPAc5qC5xnciAAJ2tlBmbPI66BH2x39enrAp+jdJkAHj6AAXtDjOfqReeIzebAxwVwmAR+mk/u4kPxRTqw1vsbgYs6Ad82AhNvqKzyEDHCb7HnzXxDByvABxnwKfzsvHy40YGhC5MjMG+n2YbwNtxOwHBL+gn36sADBIb2Bw9V4Jr2wPKClWdSAAKXVoEvsV9t0vCAt+Vx9elrAsc+zYBLc+ABHPgHAJ4lNrsTAv+CwIlvpZTAD7RdlMCrtSmGHl1XC7oFzO723iUoAK/C938A8JDBXgZgb161pAHLRvccuIJr4FaEN3JkwD/lJdbkpQHnusf++aorlKHpwOxOVMMeeJcVOC+9L4APs6teEkLauzT4PjKBYzHgOaV/YcD5yFcAjM3equWYKTb7CwbsSX5G4ERXY9BkkyG7ktsEfPTQNS3Iu7vAt1RgPw4c5iGCwN8gJwCYEIghqAHzgulzElg2uu+EwGWV0OwiFbAHXuVtA+yjAWcFYFIMsuQ6MNvN6v048NMstsA5Jnl1YsBJStGNealoeFhNALuOfPx9Llefvi4wu+zNLH2EdKdpSVcNuGqaGWKzW+MwYT8hcEIAngTAOU3Afbod0Xfa0FTRAXhFFytwcRjAhCzD9wCclwMXeFQUgtICcEfIL12qItuyJqL8wbgjApdUQrOLpAK3JLzlFgCbyjcwwY2dn9ocOLcR+K4OXEZZnAHvEC+zT2S/IF+SsATdmPRjUdhT4HBid4AdRqbg6TWBYwJw/iMkJgPOlApvoACccprY7M/5WKwAPE8At30t4P8QeFJVCsdgqJwqgC/LKP/XGHCewYMA2PNOEWhMdzDJU2+yh73Y4SPLWBL8zYuKOHAxpfORSAWmPtaAP1WAbXyxL9pPuXTg7NDARN/NOwA8AIELKUt9qrU2JB9NQOAEhcI2+geIMEMZfkzkCvhl8cFQUL41m/3HPL0J4IxrGXAazGcI4GSjDMA/MuBGcxJeDRbAOUzAvZ2BC5iA/0XgiRXpTQJBeEQC4GIK8AlynOYepALvixXiwYG9JXD8O7y9FQcuCk9vxlQgZDwCV/87fOBcGnBmBM4GDUz+jgjwOASOS77b6C/P8Qz7QrA7vAMwex67QemRzyMdmNHGhM7AOnBSCfyZBlxvTnwAngjA2UzAvdwHfki2hdFrn9sAFzEA/0JzacDQ4HkPZM45sPxuHRge4KzAz/IHJAkB4FjsUt5cAJeYR1faAVfRgFNw4AZs5rsRAM44jq3Ahx3ANTpwyn3BlAY7Av+FHSXGxc1i/zFPrwt8EoAJA04lN3oHA04yUrz5+DcJXGdOPAacdCI8cGS5bwTu2VUpvjIAn89vAj5IPE/R4aQCyyjD0MwiAXBhbOSxBN+fIEdproEI7HGnCPRw2RPoCNxBAps6oeUKDICTvRwAr85E+AA7TsB72b3kYE78WgTOWp8LKMDffO0KOCUHDlCBk7oGvoV7NNbHRd+qNwI8wwi8lQF7aEpdAHgfA641JzYAT6CfsavYHW0XsR9vjy+VqJ4G4DP5jMB/sEO+nQ6zAfYoDONbiHE6jgHwABV4d4AA3qb1F4p3h1fmt5fA+Y37JobnSjko5iW47PBctxMwhG84iMOvxEHgjPDMpQPfZsCxvV0BJxzLVsCBtVxC4nCB1zNgrUjONr0u8AkIe8+Ai2t1o6vVwflIZwDeC8CzyQUEZg8cmXTgTNAipkeXba6Albjw19jbbXQoqXB/iAkYLrC/SuBfNOAwcqcIRILZ6S+AV2jAcQVwOwmc17hvAjgO8b8Ilx0BPMcFcDZ4FRuB41mB8QgZgZsrwGM0YCKBE+0LCgd4Hb3WTO9kbZdeEzjkODQcZ8AfaRvtcd4J+HwQTToWHjgUYNL4AQPu/L0+wVCLbg/8DSm/z88MnA+B+TAOP5MjNAcAZ3jJgKF7xQ5fAbxM+24J3EYCm8ZJE8CxiH/BMwCMIXtKzHq5wsob0xuAD+ATbizcuXjQgkgHvkVpNW+XwLEB2Jv4G4D3MuAgbBdglxjwWvZjl6MO2KfXBT5GtzeDPcyob/UxC/AeBlxzNjnHgEebgaEzR49mW/X3Xspn5LQDcDlb4Asyyj8AZ+7PgNNI4G2eFuDYt03AptyoDhy4E4CxH3qJ5ENsgBfGPlNLAseA7B8JgggqOvA1dt8SwEqTLBU4JgB7cWCZErgG/hOAh0Q6cGtoURSiAB9VRl/UgWvMImcl8Ecm4G5N9aJoE3AeO+BeAniQnKwDz8X3RyVwagCG/jPb4KzYTVVgr484cBMJbBonTQDHJH6BO1uDHEwsEecrG+BFCc7UY8CZ8Wuv4yQA1nfzkglYNBtqpAOH2ADH3xWE5ewOwH8A8GAiA+Lap9cF/kUAawOGEnZsHYBPB9FE9sCNFWCehRRVDqdz2wB/HcCAfczAeSF4ughfeYQcppn7MeAUAAxt8/AmD73Dl+pr8ufA9cMD9vXe+RlsEqW3aYlYfWyAFyc8U58BZ8I3VzVgPRrFRQbsqQCLaI31deAYAAyzrNHv8XF/cAl8HYD7sVyCK6FIAVaGnVCBjwfRBKNpC5b1UYHZvne1AotxOU7nsgHuwJ5d9nkrwH3hrM+DwHyUUwDOBEWDKV6QOwWhwAvvATupAdgH2qNL4MJOwP7E12Mn+1VCZj7WnyVi2AEvSXROB75sA/wrPFoowIn55DomYJhlzfyIAse2/5inNwAMw+GEaGNZMeDflIahOvBMchiBm1uBv2z4rfmQSeCc9sBlDcC48twQ4I5MwgkcuD87kAw4LwB/5wxcD4Hz6i3hRcog52LAbKOJVxgNvFY8Rq/lNsA/vQRgntO8ZAN8QQeGm5mH+O7aCvBoBgwv1szRloq7gwM7jM6hA//mLPSawDEYMFxuQtIqwJ8ou86BWe6jxgxyICjMG4EzmIEbrCamJIBPZbUCfw/Ae+FEDxVT0wrgszy8J/SaP8SBEwFwvaMK8GILcA0clyGX3o5WJIHgzYB/AGA2tx8A63dxHRiuA2R/RhOwHo3iHAMmHBiK/Dz4uQ7VG/IBEYCxncuaWdpScbYFfE/ZA/wn9gf/NwDuy47VRYeY+JBeH7gVxA4NVoGbWoBZ7qP6dLLf9ykZRRl/ehX4gi1wfP7nhCMwfDpQTJXAZyDwEqRDEjgOAMPEzRJ4ob4Obx6TthqOy5BLb2Ypkg5MOPAT6nuteJAd8FIV+Cz+C03EHIE9M/PJ1XXgYA14prZUHPb0eJsBO4yjeQmAv2LA53zC7OegrwH8BOtlGXALBNZGo2PATQzAJ1nulf1gq00j+8lDMtICfJrSLvUtwBvsgX8H4C8k8AAxVQHm/acB+CMAjoXA3UTPiZ3UAEwIDhheFR+AcolWdXrSgD0F8H8MuJhHuMBnNGC9ibcJuIUArqYDB4YK4G+NwLecgX8F4G4ATJxjALwy8AqMwyaBA1OFB1wVgO9z4HQq8AlKO9bHGhr1AUmU05+AR0sffa2/AXBdQkojcH8xFVeei9LTPG6NAhzzuQBeK4ENTb4QuAoG4sqlt+ERSQDDvYADPwBgEi7wKQ34pgpcRQGeogNrRTwJIMMAwDO0pUK2ugQ+B8CfMuCzkQG8FGNhSeCAhPreHrICs9xH1alkBgI3NQEfZMDxcLRCNXirAP7ZBHxVABdF4K/FVFw6B1ZcWYFTBkC8sTUSeL6yEpINgsBrwKZBeEQjE8gb7UDge9R3X0HSazGxJAbcgH09Bz7hGjgV+3dqVv5zrqrFroBcR4JnBuDPSAwB3ITaprOwYgYcKzKBf6YtggE4WBFTgTsBsC/7DVSZCoeIA6dVgfdg1z0H4IM2wN9pwLJLhwLMO+IdYj+bj+q3QOAgDCj3rS0wPIRDGBDqAhjSNgT+m/owHTvgZQi8hQMf1YD/cAKelpOXuVfVYlcAsMcTA/AODuxvbZEvEgI3R+AX9nPQSAHeZwGG3swI/BcHTq0C75bAgW2Vqf/xXNaBLB4W4M0MuMhe/uU8IXB2CqvibcQRGEq1Q55DWwt2Wq9wBi4pgVMY91ABrouhj+5S7+QMeJEjcIbwgY9y4NzsUSI/zrRezhKL5eIMwDs1YIdR+BC4GQI7x+R/TeDgo/RTqGn1VoD3WoHZ5ehjqHS6Q0b0jesEHFeNcfdfcgHspQLfgLF3N1iAk+vAfMy1Q+RHBuyNwAF4Wi93Bi6BRUHZRYWvnhRg3hLwL+qdmAEb7uI8LefA6U3AN7QZzlqBy6zBmdaZgVdL4D0kmF2/rzkDn4HnMwYc8yx/3rNNrwkcdJQ29zIDN1Z2vSMOogzAk9jf22QYPEOlVIGrPrABDnrBged/ZADGwP9rGHChPRbgbFDPrwHvu+IP2aMYEniZK+DYLMOSWVQX6Sk9MaU7HHiuIzAv7TniJvBGnEnrsxMkgJcowOz6fZEBO4zhdQZqRwH4dOQCEzNwPWXX2+MYu+xQl56IwEMBOIUKzC5f7fFvPAU4IUcj+NvQgX8A4G/rEFJQAv8JLd2TwZuslB6H2HgC+DCuNegp3Ou6iAIsJ+BYlCbxdht4thV4hRW4igH4jBF4hg0wS9AvkcyVwHtJMHt6P+8MfFwAh5yMPODAo9Ow3NwAXFvZ5laUX7FIUQC+yYGTRQCYHXh9nPAfIEu7igEXkMDrixiBmwrg3YcQOOCxCrzNBnjHtuIw8EYij/CBb1PveIT0tAFehcCbzcBDtBlMwDPtgbGp2tzp8jiS4E3s3GfADewJjgFw00gDLsuBe+DGeCnN1berwBWpaE9XZAKBipahcIM2Ap8RwPGVUOAGYC1aGgdeAcAYnVYCQ19VAD7mAMxy20tglg3so3kGmUu0Vw8n4BTElG5R77gugXlpjw6st4A+bQLOw4A32QPPmSze7UPgE/5Y6eQI3CTSgLEVcUBoDcverlYnlZfAhQH4sh3wSejNQozAQx2AYcoyBpx/F7zqaAL+xQLs+xjy0h0UYOMdlAPHoDQBA06s7N5dbJBsTM7A3yLwBt6ylDcgtADzOtSDMNABmVmAA1cxAmNjnzmTxLsfEfiYP9FDW+sJhoQ/Bg1YAPho5AH7p4ll2dvlKnAZOWBOIQC+yIGThgt8RwNmPh7aSrEbgA7M4NYVlsAZoSGHKBM4RHb9hEUU3o/FfC6BvZsisCyy77aUHsxlA/wn9WZ723OWeTqBPo0MeL1bwHA/UYDVQlp8+J8qgQ+Q4A3slPdTYuzpqd9g/D3Ppw1ZTvKoYaDtNwQ8EQO/+aeOScxpWXXlTWkD8Hky0Ap8FHqzsJTAFtjXAryY3QLy47ncXgXOgMCNBPDOgwjsJYGxdGK9Bfgi7dU9KRRxwGO3jC7dZDzdk9kG+Cb1DnIEbkTIWmfgUxDlBZIALqgBq7XL2MRn8kTxjgMf8bEUkkPq1kcA12fAh7VACW8QeCQGfvNPYejtiWmuUt9PSslh0AsC8FnSH4HVgTfIVpfAfhbghQw4HxbhmoFZfo6PpfIT+cEEDGEE7YETEWgTYQOc2LxjDJg98/ecaZ4ORcLQsHaNS2B+VDjwLAZc1gqMv3sN+BACH/IR3d6swD8DMHtmjPFT5AH7JTCFEoKtr6i8KSaD7xYA4JkcOIlapMGAP8e/KvBfGjDk3ozA8xlwHqz+A2DojoDnGgeuKYA38X63HJjNFwp/bYGhH1CgAbjxGLonky0wPPJ3n26eDq1XIaP4rTPwSRvgzTiT2nwAa58mGIEDPYhdu9jOHLjcBXa/Cj6gBUpwAv5YpAgAD0Ngn/jWvpTTVeCiEjj/eAIP830R2BAqaXN/HnsuoQFY5mHhMyPwvFqE5MaGS2bgIxL4INnIgT0RuB2l0LPPBvgU7QUPeF5G4FH2wH84Aq9H4FWpBQwkd4HVxzasnKgkRwhnwCIHJoGv3daORCcOjI8UQfvCBd4mUoSBvawnMBluC5xvHP7pDsCJb6rzr23B/yZSgO9qwHAL0OpKsH1Lv5rswo2/Kwa8FoATwNU4PTTk0IF5UGcNeIBwMAEfo22xob1bwDcQuIsN8AYEXpFKwEDSgaEhjwZ8wAw8T/kaPCxx5LujJEiczDJ7315rJ0zb9sELFllR0y1gSp//tHPnThdDYVuBsZm4l01n6FAVuIiM7Jh3LP6xAxZFm4kV4L8ZMG/+bQXuVVPOBcAFEbizGXjDLpxDA+4Pf9fZAH+Gfw3AjThwPPOOLUSxDo7Ay1PhGzMwjCJ9wgg8zx4YS740YD2elMzefxGqHf62vXF3yXIA3q6FOnEGbpU7qExc16ECTMDYgsnL0IqZJxOwiAuX2xF4pQBOouStATgYq0zhCqpVhiFwRw24nQb8NQIfksAHyAbeJIQDf0GhbRoH1pu0QfoFetIQASzHT2o0EoFDiCmlQ7H208zTCQxqy4CXpcI3/JKlA8PF4wQGASESeEFhDlzZuD3Yojq2fKcDyw1TgNv05iO2ArD/NjeA04d9cfh3c5CKNwIsQpYExMU/NsDLGvG/ZuAsUMiBLYi1yjDMO3cQwD7Q+3htAQTuL4Fx7PGwWWQDf770kMB94a8dcEv8i6erHEKv0RB74DQoFsPw3BDkCSAceGlKnMRvDjow3P6PUT48ONmPwAuLuAF8XJsugdvIRmj0csNefKBqLHRY4wZwxhej5lGX0TxMaWgSGIPKy4NYkgl4t+FDDnxNnbS0If9rBE5JsiMwFKRowNj8oY0ATgy9jwUwyyQH14KINTjm7mV2u+XdtI3Aay3AP0tg+PnJIfScgJMpuSaZMoVcZP9uYsAeZAnP+fNLlg4MT2gMuDS+4cCLGHC5zRg4W90ePCxayZEV+PNe8kDU9+jBgbHQ4VvyIFzgrmWOZOiZOyLAuAs2vmRABeWNHXCuRAbgJQ343yT39Gl/07QM2Efs71MDcGsDcP7HcxkwVDanAmCopYPWaGvEyAgI3FYAr3EEDgNg2YBcAMcw71gSm93NHAKtZBlwB0+y2AzMZx9NMESlAbg4B65AT5ZVvswIfFKbLi8trXrKA1GTSOBq7J/VvBeuS2B6jq7teyWiwGHEJvULD7hFot/VSYttgT9iwNAjEPIcMqg0b98igTNCt8q1ec/H04AP6MCrs/GZrsA/DBg6X2MQLRfAsTTg39raAtvsbhYE3syAvclCDsxjlcIjJ58FMh8m4MVlCCm/BaKyGvJ8RuAz2nQJ3LK7PBA1DMAryL1wgf9bO2vmzJkRAB4Mu/CS2KRvXAKzHFVLI/BCG+B7DLhHESjkgLoVBG6xQrRubi3KuhsjcFYEhpytDnyBAYtgRdjOq40AXmUBPkr5M1oY/I4Y8FUEDl3tmcZN4KwSuKM36YMlamQn/qsDQwGABrwXi90Wl/EkFcIDPqtNl8CfdtWBu/PxmxB4mRvAVcp80a5duwgAD4Qcgj1weeVNYTNwIwvwfAROniHJfaI9dAHwxt4ADM8CCFxjhgBuIYCbQJ+dtZkYcHwC5cMpYYSaKgJ4lQDGTGBrAbzCAnwYWiWyFAaHNSY9hUUKDQeshia0FuDENrvLgbew4+FLxIMdH0CmkivgJWV8SYXvoCrGAAyNgnXgc9oNQWYOmmnA1UnLQ+MRGJpbuwPsYhRs2zQTB/97QWzSN6WVN4W0SF88AXCrhL+pk+ZhC5AaA5P+l1gbaYoDwyGGp3ksaa0kgT8RwE0R+KPzcRkwVOGlhEduBD7Pnr0EMOZzCv/MgZdZgH8SwC9hVTHpMSxSaNjfHjjRU+vOZpPAw7Rt5w9oJeyB92Cx21IA/t4CfBX+0WpvbIC1M7AqaTqnGgJDNfMyETHVFXC1vyIGPAp/o8+JTeprBP7B8CEAf5bwqjppXh34t0ZoMgxJyNN9Bry5N+RjARjziOUlcDMDcNpzsRkwNLlK7ghMejsB7zMBY5FCw86TAdhSjZLosXkKIdlD4C7/HaWjtW3n7SR1YCiE/5lSHFyY7EbgZWX8SIXtcAOzAmuZdx1YdgCurw06UIU0nV0VnseWQS3kUjeAi8erXrt2bcfZHICfEZvUt5TyJp8Wyo0nAG6d4Ko6SQInhzEZRbpPM5EtCAyP+0bg7AK4GfS6W5sUgeGhKCmUqSDwCXY1FsD8IPVyAu79Fy9lEcBHsZN3w8BEOnCgPrMdcPEQ2JetlI7RZtSAxQkPwEcl8C4YspMsm1eEVNgBNzDD5mCOUAM+bwGuqQDXml0FygiXAHAJiOITDvAGTO4Dj0Rgm2uWG8BtElxRJynA2s5xYLhaGYA3wWfBor75EwBenFADTqIBH2HZGANwt3s4IIoVmGxUgEMEcH2/eADMxZSB6BLYAJeQwGMFsJcOLAK+Q4HyUYyVSCTwCtqMVI84cDUNuDKpPrsyAM/nGZ7wgcMWtPxivfu+dAQCPyE2KVzgtiZgbMNVMzSFAfiL+Bz4F2IBrsbnaQXAi4IQGDouJILBFD62B+alS0vcBY5rCxxgA1wq5lWCwOPEFd3HAgzPAUflkIU7EXg1A67zg9vAsgt/FS3cTgVSfVYlAE7HCwhvhQvcs8z8qYUHOs5mSROcgb8uqbzJK4BlW1AAbpfgojo/VP8RUssETPNtGQgZyl+YkT3wZwgccFYH3qMDf20HvNhNYF8E5jdVZThn/3+JJZWKe5Ug8ETRrtSX8DK04uEB10VgQwMv7Fms5e3Oa+VmEriyBlyeVJ9Z4Ud9yZtmHwtwqqfsgpvJcTZLmgQNJh2ASyhv8or+kZvEewDubQSeiwUXtQYxYK0cFoEHw8PpLyyfxIEnSWDRYoQD+54BYOhbZgDuYgTmpUsQdtbUZM4OuB5BYJ4tVoA9bYGxwyOlU8WP05fwNho6MBTCKMAQ8UICF3IFfEHLtkngSp/qwFVmlIsQcHr2rPksnfvAk3GAOlvgr5SrGskj+kfKTnQA/JUReDZmmuoCsFZDx4AL7xgChT7H2EUPC+LKjxRDWErgjhAobpE3AkPNeEJ45JbALbOoq+jGH8ZdAMMFVgLXZcApKeVV3UX1ee2AyyDwdkqna8C8cxoCJ4RKcOiUtd8I/C1tzoB94haihgZe4QGXl8Bb05LyM8pGCDi0yKwF5bs6zmZJ05yBe6uhviSw7AbbvSEhI+L/qs4vgVMagW+//B1KcY8TP/4QAMD4iCmBB5iAE8AjtwRubAc8zwq8gQO/gPx7kAYcWwdWMhR2EImchQAAIABJREFUwGXjQqHNDkpniedXXzHoRE4IfZwEGuRAG671EvgHrJj8lj2d1d0de30uIzAeFg1YG6JNKyTXgJsTkqtKaQX4j3CB6ZrPms2LwFjxAHyzgE2ugz2TFNRCtQEwP3d3ivfdG5BPj8c/r84vgAezY9pTTuO1I/BUdJL4YwcTBMb8iwF4ocep2DQugS5cCFxZADeyAgfGnm0FXmcAPoTAtUkAAPOdmKDP62EDXC6eAJ7DczukjhcHzgqhjxEY7voa8Da8DX1LW5C6B2Ovz0wNLfjOwT/a49ZF7aFRApdTgD1JqYgBU/rCud2HPfC5OP9Z99gBWBZYMuA55+KfU+cXwENT8hAcmHTgswz4ohFYVEci8DxyPDaNg8Dx6U5eQgjAtYzAu9g/SarPsgKv4ZXRL+BsCaIHBLAPAPMZTujzejwgllQuAWzzD5Se4k1DyVS/+Vi8kkEDhqARs3RgONPX0tYAvDGdERirFzRWHVjWggjgTQ8aQ+aixD59yfCBtzX8J1282e4DT2f7dtYRWG8GIIHlSCUc+Kw6/2R8rq07PBWlWnXDQwU4gAOX04BFZcYAiAQ4h/wMwPBBfCg0k8A1jcBwAclWcxbfcDWtVIADNWCiA59UZv6bWFK5BFBLDxHWxPApCMzOUp+wB2NIUshwQX/iFBL4ewReL4BT0Bnql2HwB431knYuS+CyHDjnjjoAXDxCwNnOLWv9IG2EgMuejWNzzWLA+fVKA3Yr4l3YRetoAJ57LuRLdf6x+NhTbwTLuGpxpP/RgC+RYAgj5gy8F4Dh5hxPBa6aWV0FAlezA17OgZ9D0zt/eiDYAqwVmwdoYQfUVC4B9PKGIJhiDxEYGrnfPhKXJIW8AWS6klIRhPQ7LOLeQNuQuj/F3pTcCpxcOzl04BBx0Etz4Ow78MGyqDKG4g3qlCRwZtpyPY3AYxK7tJQ+E+cfYpN65VMixubgxYuiVzQ71Az4vHF+DtzAAfiyBC5rBh6oAEP2Oh48ciPwYXaIM6qr4MC1ZlqBlynAfnS/AizqBq/LOScmJ3eIJXHgvfxXBWmq31xCoI0KAsOtAzo2asBbEHgjbUvqHoprBoarhdbgjFzR6pVi8GM+NGFz/JtlB85UJELAH7dO/E9/86gULhLL/ZU6E0cfVkRJvfISrWaF5HhYBv/KAb661Sfz7IFHplZGseDZATj5GbCHBozd4YNFdWQoBAibQ/bEvBcbgeMAcEUBXMYIDDUeTeyAl3LgZwDsI4Dh/EgpqkLLaG30GbDe7LOgvANwYBi44xd87znVbw7UZLJj/lMQSQbNbqAiJKkcF5oDb6ZtPeqfjrcpiREY7vd618wrWqmAAK5EOHDGbQhcSBmg6zp1ShL44eQjtNef7gOzvSh+OrZNroOQnrmVoN45xBOR7AsNwBeM849B4IYArP1gODAEmLzsFcMTR5UsO0ICl+PzhJJmsB27AvwCCRSXxYFGlxXYnL+y/S9hBW6BwKY2kUsEMPwkPf/jwJDpSymqQodrZ+2kFApwsTziRfmEEvgYvveempAdGog2cp1ds5NDZgPa6CeVwwZvgQDfAFz2u/udtyQ2/t7gIlBXe6cDBxuB423FbGnBnfqS4QNHtF30PHYPOBVbH3tBST1zKvFybIDn2wI3MgDz3nLw8HzFWwIPF8GQTMDf+7LfEwDGhkIz6Ja+ydsCDD8AW+DFAhhvKjN+tACP0IFTE71DhgZcIRFMhPENeXY723dJGHBn9up3lutKIYGT6MBQpPIdbQtVu1vId5OJkuCa20B7dzWufBUkgT/BvzG/wwfF/BECjmi76HkIrDSy0VOPHMroZRJYBpyxAR6KMeAajUmrAD/WgX1ieJ6EN2WGiWBIwfyiT4Zx4C0+DHgXAeDvYQgNDlzcDWA4lxYKYMwWTliMwJCrTynqukdqQb0npdPvx87Aq2lSBgyZyCsSGBrwJpGDjnLg7yXwivHqZsI1t6H27qpW7BPA5n1aI6yiBN6EuZC8SjVO+MA27aKfhnaf4rgce7gr4gBcKbsKLJqO/SmyhK6BX8oFFWDfGJ7HEXiw6EwbLBoUjLIDhovQRpYRLWYFblV7hgG4BozoMk8AQygQUtPTAjxKB05PtEowz+IMGNreJGibGE5xAOYB7hgwe9Luzp5izh9g3wLAkJPWgDdjnfc2+gUAbyXLDMBQWKBHcv3NAHyXvBTAz/3X4xHIEyFgm3bRW1bTsWedllvqDJwupQKcTbTuvSX6GjHgBSbgQRjcoNEYGLBZltNxYLgGXfUN8cJRJcuECuAgI/AGAIbffmy4Z5dxBh5pAu4Jl+LZHPipFxQhfczvdnDLSCEaMxiAtdyhd3F2wYXGGB0WYGvuAxrwtzQpM+vJsplnARhKdCDrrwBD/uQH2gGulrvJEgPwLvZ/C+2dEfgv8rIcB35AOHCuCAHbtIsefYluW0vpvevXbcJsLXMGJvoQsbE8s0jglOJQW4H7Yt/3RmMgXLOsL+TAcE9iwL6/IPBAeh0LhoNK8nlmFm4MBVNrARjmjAVlKumeOwPXmWYEhrZgM1XgShwYrigSeKwKfFq+9imRj538BICTSuD7GCKYAZO4aXuTdAicCoDhGT0xRlojAAyN83Zz4INkwTh1M2Er2+CrdF9YgV8UhV80PEuuw+OQM0LANu2iB96i+5dSOrZUqSOU3jD9t5wBn4xpU7QDqZwcgLVnQCZRyyCB29Qn403APbDve+PxGdnXSuAruBbIU131jxHvF1hjmYE3duFnHqJ71rgJDW8w4FVeDBjOkljYjf82vbHRkwGnVVfRDTo1DaozhX3PMG1iT7gaDuP3vCceAFwWsjM3MFxKUgl8Ss4+OCNpLl/7VMjHiyBbLkgKMxyArcVS7W//TEpSfvIVGUt2AjBUm0EH0UT0hgSGmBT7aIv2bJGDZJ4BGJ6YeYlnLvbcdy2BnO7Ltuo2+T0fSfcrW+oeWVcSpmbfri953QLEMjQG4MeTvvx9s9F+9FW6Y6PTDwOAT8TOQGzTLmiuD3nAwYGZRAnHbRGUyOYM7o5ly02mwhksmxQ+0c/gkJCUR8QZvM2w3LxJDeAMXuRFvHBc+Jj43HybncGeljMYlhxVdzL7mqnaRASewIGh1iSQlOFnsAFYO4Mnf6TfefJOzMdLqDouTApXMTiDeT/FNewMTteiH5lCTgIw3LXhDE5oOIM998kzeN5YdTNhTh6vJDcDvq6F+YF4yrfIi3w81ioDxmLPbAbg8M7gzxpnu1n8G8NHG9fQMVcsC4i0AoBjWSOwYNoFDzIwDNjgwAyigEcBXnjROPeXmCdsMg2A5RcqwDFDUv4Ib0oP1EPvYhLA87yIJ++yORaAb/FaYzvg+kbgr6FB5TgODIXqQaQkAndJhCK8KnSsVjw5WSn8/JxqwMkA+CBsIMYpWEuTMeAh7AZ9UALD5gRSiuPekZVwBnsJ4JNk3iiiJLgO9cFXeTZ7m4D/JM/z8rHAGTBeIbMov/fwgTOFFaBPjOFWH/fr6lz7wB7uihx3CQwPEgxYjEp4W5zs3epZgL/Akik7YChN/5sBQ1EvLTWQbjQsN29SPShRm+nJsqz4M+oPRZv2wFBkMqqBEbgf5GfG8OdODlwYgfEpLAHlFSk68BSl+gKBIXBPl4WpjcDrGHD6FsM8/k58gAGnAWBohhIggRcDsPePHPgcmS0HeSSgCXVPGGuC5N0cl9xwAP5TAGcaqG9Q+MDZnxag/0SgsoE9kRb5OaY1xA7WFO75mt0+oGhucGB6AXxHB150ybhEWyy4aDrdHvhhrJCUMN4RLTmAGofuEMATYJ1YTtgPDqU9MFRpjWowzggM3ZJG87iLoBlMcmORghFYy2ZMyaZ/3+c0Py+C7Lo4NRT2/KQApybpZ2/bTZMDcFoFOAcuuQiAfQ7SjgB8nUzRgOEyBvfgwfgu3+Z4CrDHE4g9fYFd5IvvhVdrMUeeVNnB8IGnVErRL1Oo42yWxB5vixyxA4Zs0h44SDB8jA78SBxwAL5sXKINdrBzAv6XAUNtHC3e3wJcCyo98CKHeXUJDLeyYobsAQduOMIIDPmZERwYqsViqMDxBfA47UFhSnb9+z6HqgPYmm6L00BxLQLj3XMdTQNVwZSm2g/A19jtA64r/pTy38d8APY7TDsD8B9kynD5lZARAWAe/jA/A/4jpba+R8B6FoY3qInAfJBEZQfdyEX/2LvbJvd9IZx8vk9iWnp38J4XCFyL3bMGB6UT1aTPxOCGAHzFuERrDjwDgOVGP1WA46dB4GL9DUHDGPAUBB4BrzGv2w9OXQlsiDTZGe7OoxoNMQJDkNtBvOwXqsVikBz4QGIAHq+VxlqAYTXdlqR/RAQwBmRcR9Ny4I9+ZDmIdNeIN/nOEy+yWflGU292V35Bu3VC4EmD5VcCMNQcT8F3BbbEJzcNwH+Qs3CPKIDAWeSRlskN4Ai26GDAyYkdMGSeEXjUTvZzDEor6rSeiYFkAPiqcYnPEfiTGVntgeNMzrIT3hTtZxp7e960WjC2Lf7kOTAc8kt2wB0RuPEgK/A32OqeQBlpUpIN73YIHA/G12Zpgg6cU9lkAIYWM92WZgzzMgJnlMC7GPANAPY2ALPZf6O0D4Smv2UCZg9UATzqToHt8cmfqbT1PYLLOQLnAeq1mN9Tu9aEDxzRFh0MOBkJUWLMygR9ZBF4vBFYZFFcAOewAENr5n/jzs6GwIW/4cDalUkAY66EA0Mu9KIKXEvUW3aEy+SoxqFG4I7snz4cGBhTkawYuRiB41qAp+bRluTAUMPPgKEPgA68ngFnhHeZ9u0iJP1dAcy+l2fC50BTr2uU7vpeB8ZTEQoDGG4MBTi1tr5H0JrpjAC+QZrhQ74a4Ch84Ii26ODA6igLIsF1ZR9l+aZ5DHh4UFpRafmMl9CQbnWtwHhE7YCh+5wO3JcDa9GN5k/P8DsD7gWvsYypJ5Tg/cqBi2Jo31HiF9gB7s6jWhqB+yMwr3+FO202kkUF5q1VdOBpSmNRBIbcRbdlH0HgfhU4Ex9/KdO+HYRkuM8YvvPBiMj8EjaHKhy3yHgALjWI/QPFZ/M04IIArJfVPATg4wCcGwc8w4J9NcBR+MARbdHhCJyeXaN/hIgjSxnwzaA02NrOgzwX16JudchiE/BnpTlwTva1MoYQB/6L/YYfxZ2T/QcE7sEDTmohJBdMJyvodH6lwlZ8MESoPXAbKBMb1QaG4ZmiA3cCYN6CAvLK2SHsiwSOI2q2FGCluXdraGEFwL2XfwSjIyFwBw6chWTiwNsJyXifxPQAYC8NOMwAPA5sK8NTfDUOHMLbZBZiwLf1BwEEPpZZAw7gBzUiwBFt0XERgQ2jdfOUuS8AVyCNnjPgf4LSiMKJ5yI4NgD/ZlxCAM+0AkOZ0uO487LvgNcFunJg7elgIQfG/cRKSSMwDGs8WgC3hnN/TLu+FFvsS2Cote3Kgf8SwF468H0zsNLHoTUtwoH7rnAE3sWBzyf6LkAD9iBzXliBPwZgqMKKaQTOqq3vAQwJ9gvkU3PxgUnMye0WHc79TE3pMjwQ2QJ/A7VnFUhbusuD/BuckjfJUoGXmIBbIfCns3JZgdl160m85TkROP/nCJxSjksV4+hMHRgfrfFBNPQQAmdPDU8to8X3CWAIUqO3c0bgTryrItQYlCeZMTQ1Ase2AM9Q+jggMBSu9F2RCQawOwQbCF9HNrLMFAaYyrxlGwN+QJ4n2hqAIa+Bx5PMgVpI2QbyNgKXPQrAok+sAC68kwHn0taHwIfhjM6Oh96S3MpFQ3K7bwM+6vjZRMnK8g0UzQLwbi/yKDiZu8AtZ9sAs+vWk5QS+DMEzizbTuegsxAYEzamQeAC43n/ByzUHSMqZFpDGdGYdp2twO05MAQrO0nYSRYmuuLHEsATNeBZxfRNRmAoZe+7IosG3BU+2SKBc67fIoC/D3QCvoPADeghDw8f0dguhA+9V/hqC/JXbm19d2HMt30C2FSUj+nNA/9msxZM2QzAMZOKQQhfOAOnw9ulEfiZBvw05QoOnL2FGXgOWS6BsUCRA48TwJDnGS/GuGwNxYpj2ktgD8yfcOBqcjNC/vLKhFETETimBNaanVmAofTsm9XZNGCMWLyFZuPD1BZcu9kWGIq4deCxoTDkxiGfk/Fria3gwEXY0+5dPdt+Cw74trcK/IfNWjBx4CoMeI8X+S9WUjHwkwtglnz8SavZucMB/hSBs6jAyyQwljdhRKT848RYU5CFmiyBYcmxXTohcC7iiRvTvwv7p50GXJb6MIMmLzlwjPCBofSs3zodGOvut9DsHLjwSpZzz/hyBE0sgIHHk8y2Az7sQ+OL1pQCuCi7s/+tA9+ESyaOu5Xt+R9vB9i2RTSk7P2h7L0qAAeQ57EF8MdhQ/nH3WqTpb9bFlqSibSal98B+Nuc2xG4uQtg1GgJ92MNGPjmqsCLu7ZbtZMxtCGemJVG4KpV5fNGHeoDzyWPaXF4FyB6WejAs83AJ0ixBEPW54BhoHXgrTQHBy62ciMOM6ABA0+DTDMZcBUZvvseGTtAACcQrSlD+LhqxWyAN8PmZVtX1tTqGNObB6Y2xdD8uG8F4DrkC7o3MXkeNwk/2o+oBK5pC5yZtJqvAovwoyzn8TTVhlxbFeCssuWbARh/by2hT1D+sQIYbonLJDCcF8u6ftFmEAJ7YY8gBC74McnEmycwYChZ+JcipL8E1toBzlX6GbamReGePazAkPW52HnIgfEJ9nsJXHzlOj4iceLtgRi0HoAn15jxH9HD0j0gYziwL00oGttpwLfIIx34DxgseHMq9irT6uKGjl0iRQJwbJvVQMq5DZ4a6pJ2dG9S8jx+YgksWlJ0q2ELnI202gBRJxOJCQK4MQNOvSnXd/A62ycInE0HnkuWCmBvLDguK4D5aHEqcAs4+Mu7tkHgjsQbu/kjMPEiWXgz3PrUVwH2FcCTNOD5Sviv1rQEA/bWgA/DBuJDnAZcYuVaAbwDCnQFcK3pjxyARUTWmLx3cTF2h3icT1vf7/Bcugm6S6d5VeCIxuigcW1WAynndvhBA/C+VL4v4ifGo+3xn6wo6MqAr1kWWpqDtNoMwKLEWgK3UIGbUfh12wLHxGgwmaD0Lt8YUemkAn8CDeSWd2vzOQAvqeSDwAO68A8F8GHqC8WsAthLtGNWgJUAfgpwbg0Yx94C4OzwrtQKBpwJgaGKrO5TG+CHCjDvxuoZhwOXZMDPjmrru4rAUMSTYEVhPcShnsIHjmiMDprWZjWQJHB7+uj7v2gCDszubGG86NQeeFke8tm+slbgzgC8JS+2JcpWHy+C2XXgeTowtq9B4JwSuDuYCuA6CNyzdSsEri2ARRc4AXzZBMxL6XTgBQpwG4iZc9JnWMEhGxH4KGwghm74nuYkGCq97DIGDA9MSXbg2Mr3YNQsBvyP7DpJoX/paCNwkh/iSWCW/9a613tcgfibm6CQNt6KAqeJNYUPHNEYHTSfzWog5doJwO1i8Eb0CRLzYQeeyLBTDHjZdctCywqQzyiMQy+bxQjgrgC8Iz8HrouPONllH4kczHupKHnkQytnhD5ROSQwdCbXgEF0RagAbuoA7A/XwH84sKcE1nKTFuBTPsMKDdmUm97FOGcCeAfNDZ37GfCS1TimMc2xA+879+D6z4AfEj248yMBfMyXJoGCN5LsvAb8OOFLLUiV52UEhkZ4rwwc0Rgd0KTBNuU+hFeshgI4ET/aT2WL2K7VyLIbloVWFYDg3lbgHmzJtEcKcODqmE9VgBcyYF7ymAyB00OXmawSuDdRgOF3sGKIAG7pg/HlLMAxoBT0gRF4sga8ygBcFoCHFxpy/3sotNaB9+vAqyAEDaW5d2Lh6r1U8G21pqnAj8mofjDU03FfehErxZI/6MOBS+Hnso2010UYQn4jRsSdVOAUsabwgSMao0PE9bKmPAK4gwDm996nMtvdtSpZbn2GdgJmu//0x2caMLRvySGBc7Jb8nzKhwlLht0Q0gBwegkMse00YPgdrBz6uQTGDO9gCcxDQlylyeAIPsASCZbt5cBTZB/37ncV4LZQXXbKd3hRaELwrxE4L5QXU1pusQA+8wyBt8L+T6019SHRR9B4TEZ+hcB+YtyflCKrJoBvi2acPheg8nIjPmIEvCJwRGN00H9tapIk8BGW/5XAM3VgDwF807LQqoL2wP1xSQNwThV4JuXjkiQL80DgdDlJKgkMIcBX5BDAEw3Avgg8pKsJuFcAAvPaDAE8VQKvpwbgOoSc9htedKgCjJWZB9i9C/sPlJu7EsLAQcLBUTGXPLXO5AcK8BMysjcCBwjgVGIMPlKazyCaifudh3KHjbw9UwE15oD7wBGOF63HlTQBH0bgJgI4kAfNeqYAr7ACry4IIX8VYPFTG4jABbEtUdbqULVOchmBedFyBWgIQ1KScttICgOwqMMt3IwDtxTAC2yAf6N9YJfuG4GnSeANCnCG1bQeIWf8RhQfRuFOSrDrxbdG4JkrDMBQkOE5rd7k+0QfaDBcYNFEzf8sAK/n18D8fV8JOMLxop2A8xqAZe08AkPVRNeKZKULYNk01Qb4o2pm4CUacFP8rScl5beR2GPEExmMZrNCDdm1cehnHLiVHwfuzqdn4cNGXqN94Bd4TwCHCWAZb1UFbgzB0shZ/xElhiOTAnyQZU4Q+OOJywzA0HLEywrcC8bTtAKLiltR2BCAwKs4cA67ox4+cETjRctQjpYEwD9bgJ8DMJxlnSuQVVbgNWbgMAMwBklNawvMO281wxUFkArbSEwJDBVEK5QqPrJpmABu77eJ/SaKD73Gh9TJWl0cI2xzLoGfCWA5TsMmqo810hgHGz0XMBKBn+LQhGbgquMZcH4dGCoTvKfVn3xPAX5GRvaE0Z84cMYYOrDoqR2P5IHys6DTkJNbxiu3bXuThA8c0XjRlNq0x4KU74gd8Au43MCTsKeHHfDagmJsdhfAaapRaGOaWwLnYsdihgbMh0uoqAAPsgK3YsDjyNJOfiyfsvrsNT6YkgT+gwPfFaOOPpXAnhJYH3OzCQKfDx5ZcgQyrcON3cSBC3LgauOWSGDctpoI3HDS38oQFEbgkdkAeKkKHJ9UhptL8CkFOM2rAUc0XrQZWDuSArhpR5xJNr/QgFlaLYH12uRwgNcL4GEqcG4GPN0MvEsHHuIEvKyz3znoyEslMK9rv8mBb4gf5RPemGG6aA3JgCtr39QUgS8EjywFwM95xwYJXJJgL9xaYxdjI1cVOMfWhhPtgE8HQGXjqGwQIdMAnFwAH1eA9aZ4EQKOaLxoc8R7rYV+fg78SRecSTbtfQnAviZgby3Uw4YyjsDs7l1wDaXFbjNgqLDILR+y8iDwOAHMewFU2ktiGICVGiCyebgDMK9r/5MDXxXl4f9JYJEV2yxGeIb0CaXsuP8aY3SpURQGrlCAD0nguqMXQ7BRzlSQt9hYSxtCVxhtlCAO3IGeC6F0tz8H5g1WyvEZ0pKqABzrGISnWcqBkxOb5HZlQwSSsV+SAZjlOVp0NQCHwfzlTcA+ZLA4wzbUhaFvXAHHusiAwSyPDryUXUBNwH8n8B8tmk/DDXulEbglAx5mBeZVsbc58GUbYG8vI3BzCiPi/RpTAv+kAS94QUtz4MajF0C9PQduwts9M+DRfynAz8mIrgB8AaKNJmbAaSRweT5DOlINgOP8ArXTS3muR+tUGtnAxuokB+BUOlgcUt8E7EeGFuevvpPAWUVbQaoCF2bAMS+mqoY5zaI68HIySQPmK6pMs/iOho5xPlZgnyPDWzDgvlbgRgL4K/hzAQM6G4FjbbUFjjW6NAfGugaM6nUUIv1C8QZt1iiDBM4AwBUQuNFIE/CXAHy/jQ7MxxOuoAFDH9e4hwB4Cd8i23rayAfWukEWOIrALbsZgCkAiwAy30rgYDJcAG+vpwGXCbICf8vu+L+mqmoEzktXkHGU96/9hBc/kY9pbp/RUKQUxH9yctxLSJnoCA68qof/BT5IFm8jnbWxAMYhaM8L4EcSuBsD3gfAVU3AF+OOLjeawuhgP6vAFfnDUXPom1UUP8jo0ZTg5WsdbQzAWsCEF2T4l7zHoBm4Ip9WhFQH4MR7ENhXHLW3BGysLxytAh+zBxZHe42MRhSbjChlAs5GxkImQg7qHorARRiG/8+pKuMozwbgGnQMvv5UXNs/piU4cDz+k1vZTt/CzHRkDQRe0yvgCh/mjjcyz4rl/OQOxabb52h8FXgGZU/LsfdBWxw9/g0HvpRgYoXReAxP4MYe8UHgyhz40wI6cAp28S+DwE1G3FaAw2yAV6jAxUiN5uxdsh1QO72EZ1JtH08jA9h4LxgfKC6uBTlwBw6Mefrg2AAWt8xXJuCUOnB90k4F9jIDP/XYkZIDF1eBSwnglpT3DKtCS3iNhpMgAY+csLKjvoVZ6MgyLUNtgLNJYGypdVQAP+TAM+2APxXA8yqMwWN4km9tBgT+WADH1YAz9Wd54NIasLd2CMPIsC9Jw7M6cFoTcElSAyocUuygLPO9mAPbFjCdiwTgRIY1TA4WTZsA+Dilnfk4ipjpL/0DAMcbPsgEnJ2MLM2fMnc0Irz2KTsZZwUusYr+R7YkLY3X0FIqcAkNOIcALsmAl2H/Fmils7aTAbg0A/6abO0daADO+4kAxqvQVhGV/L4E7sGBv2PAsi9BC+wrejnB/EqT8RieUYFr8KffFnCvLIHT8/T/hwcWNwNTTwbcW7xOMjobSSeBK/FpZThwagSezQ+wHiZSSQciHzgGqY7PtYU4cA8OjC0Ay+4C4PjDRe+VtRI4JxlZNhFWkfwggXOTcWktwCVXMuC1SYvgMN4K8CpSmPJ7fysBXBWBl2L3Bwbssb+LCjyqJAJvH2AEnsCB7/IMmwS+y4Hn0F4sF7ufAxcQuYiWlLJLw9UE86tjIDEPHfhnGBgUgVsCMI5mTPMCcAkEbjr8FvZ/ksAEqIjlAAAgAElEQVRDdeDkNsCNSE0ATrsNgCdyYJu26DxQ15sGVjqYs9NwRQj5z1cCn9CAMVpDOQEs2t2tFfE9a+chI8vVmQcvdzYRwHnIOGgl7SvWMYgDr6D/koVJcyFwOQmcj65hz5kjBDBvf1BNAqdA4PF8PCwd+FMEHgXAazXgqZ8KYNHulWcuLnDg+fAVcQ/AsAytSbEOGvD/2rsPuCiOLgDgD5BiL9hQESsSY8HeW+y9xG4UNWpEjS0BEVQEW6yIFY0VjbFEYwW7iSbGxJaiKeYzYm+xx17mmzezuzd7twu31APv/fwht7fL7u3/bm+nvaFL4wpEd2LAjn9oAA/OwVdE4LBHvCaIAk9VA08aDcHS714UuLQM3Iov6wOdsC9w2T3Yq2AOp7WY4hYXTE1ZYPrW+i6XDHwK8DtJAj7PgXFEiMcMM+DLVSlwF3aSD/lJtxpVOXAWc+BHsLQQB24mAteQgL+X+h+0Jw0dGXBtNpp6PhHyjJUjs+r7IfDBWdnjODAfJrJ4kATM2pZ3SD07j8nA42XgIKj/iQL8GQMehtNdEhcJ+B1Wfgjm9VeDswvA//GMFjvIgCk3lHcvDaeJAvCc8jiyWAXcF97H+vKKOwm9+Z5lOT01C1zs8CKlgW8dgacMuDYHjlpqAm7BgIvMXCAA4yjZaibgvgpwJA7slVOcT2a3nI020jfwAgm4uQxcXQC+JSXrbU8aOczGNvMNLAnOfDGBfzkyp2ofCtzo6awc11XA/irg9lz2O/5fNJlAgY+pgQdy4ILR/ChdpFucsgz4N28G7I/FGT7PUd3wx/RwGfDASWrgUAF4LgPm1XBtBGDHVVW3YedrPWB25X5CdCLxwEVUu7jzqs9rDnwa5LtKwgdKObT8Br9Ti8ySgW/xtx0CN1OAed11NS3gxhvJTejv7ovnWgYuVqk62UGROfBtKd12Bwo8i6yBTAdZEpwFZK3pIMuTOW5eFNiPzMtxA1gDgQr4Hu/+s1Aq32/mwGuxNsT9FAdu+Cl/7iOC7R5xhdbwo3T9i///Lrv7IH1Y9cYQE/B74U940iEEvo75OkzAIxRgn8jy2M12qwjcjwGT6ttwqNBMHeAILHE/JjqRHMCOzg44PIplYYQ6FsBt/vgWgT1VwB4MeFbbLmyoxrd9YaQILE8zMpk1UzBgbB/Epu7mvJpkZIPqZLcAzLtfdyTvwSz61VqLNMIkOAvVwPQmigFvUgNHceAHUg9N6TtuCT+dX5CZ4OD+B/amC4JGHNiRAs9wgEuF1/KjzHxOAcYScR9WOBqCFTaDTMCVGfBH4ddZHhAZeIIJuCwFLisDt1UB196Kg/3GWU4wzyIWK8l0028kHlhJ8gOZnB0ZMCuC1z0tNY8qwD2IGngbAntR4Oowq11X1gfp8IfAGydq6ABfZ8B4y9QCf3VkwHso8HQJ+EcFeBJZRYGbawDT/Y/7obAf2ZNTDcwn83kiNcVKIR3sF/Rt4Zr3T5zR6pf3G3NgZwo80wkuF5GAs/ytAJ81AWN5lRfum056gn16Edh/4jXMeqcAjx8BIQJwOQvgzgNokaLOVpy1fqy2Lwd+RHQiOYAblHdmwDll4LPKSjjKtCf5Fot+JWet57eB2xGYlnav1NQExgZteaqvKTLwVQaML7ElAmeFUY1qaAB3oiXH8W8iKHBr/BpeKHVhk4AXAYSsBT+yF4GxCZI/G8XP3DM18DwZOBJyFfiTTawS1pj3AXEZzIGLf6EGLscrHAbUxwdDsaVsBFvebPIT3kawgwyhwIVN5zBTiA5wO77sQ+g8kN51c+AxoB2x2I75kOhE4oFNSX52DM5sAq5HgX9XVrrIgI/gfUXpWYQ3vG6/rQDPfr8bO8lHBugCv2HA2JW6ogk4G4xqXIvshSrSiJh/FeDmMCYWJ31sg2msFonAFbDqmQEfyHVbBF7yjw/9VE5+owZm9991KPDnUKDAeWh/kZDtY0zAs7LA5dIScFYJ2JcDD+PArgLwU/qSGPCwiVehiAj8sQl4XgX6JpSAeQGa+COwK6n/NVYo6AEfQOAHyQ9sanjeMTirHvB1BnxCAu5kAdylG9Z1BT36SAKuyYF9VMBNNrC0BRWfYJ1iF/yT2RnwX/SEcOC7hGctfp+0hJHbsBWnPc5XuUg0Y8BjoqEv+Sb3XTzZRLrdXoo9ofEIVcCsFeMIBb41zLPAVekl8eYnV39CZmenwOv4UWa9wP/3hcbYL2YEAx6HN7ej2XIGnJsBfzzxigjsEuKvAJcXgTvyZTvKWgt8n+hE4oFNXUd2DM5pAq6vAsar8QfkDJ4+79kyMCbEoFtfVYC/I59k5s2LdSESx9FWkzZXgLG4VYF9oQ7DerAcDPgfelPCge9J6bg704vEkK1YB9wRE60vtgAeFg39yDHMcy0B/4nADTmwKg3XHA5MDZd6FbgmVfbyRJI5hhAyJzdc8d7IjzLbZf6/L7BZTQIb4s+J+H0UyJY3R2C8QdlJgS+Dp+kcuow1AftyYD7Up5O00K8LAjfYgsCBaQXsdGZwHjZ/pAJsyhOPwL3Jn670Vx8ZeAcCl1IDvxrFa+xuZEbg7DOlzady4HUsD2QF1gd6FALngtEU+AK8owDHsf+7kG4w4GsE7oSz3XFg6eazIt4lD0HgE7kfK8AXELgpB94mnrap4OoJ38OXhJwLL/jImZeE+N5y0iLdvPxw3XsTP8ojUveElhw4nPV5jcC7cF7b03zKcwfWeWWXObBrkD+Mk35H4ArSBKuZZOD+XT9G4M3kDz6aThO4dcoAK737spHBH6mBTW0bHPgcApedQ3jSsR2Yb4HeK1+bV3gOB/6ekE/G8w2yzivDJoLjwYGbrWS1S56YaivrEiwxuUNAEwQuKQ07vkeuSsDdoc9mHH3ZGWKxxxwCS80vvgg8eDUFPpXnKX6aGPAVbE/QAp4E7vM4MDlTkGTnwPyWLif9ap3nYQKWYxkHns+AWcV2EFvedgrJzNoIEPiSCOwWNEgBrjafAWNeXMiiAHcP4MC/s6lcNAtKB9qxM6ATiQdW0idnI/4BDJhVETQ4jY3mciBwHw5cPoLwLh07EdgbuzFWm9OlhwQcJ13mLIAJA1amBm/KisQeEnBR3sRHX94dVgnflQJ334TAPWAfAh/PBo5BMvAyCryKAv/s8RRPNgO+hhNHNuPA28XTFg7ui+AnBnxWAZ6lAEcVcdIDXsiAI03AvafQEqSzJnDgQAW41gI28HS/Cti/x1h6x9lwMzkL8DGfyNQSGHuF6qZHSjywkj45GxkyRg18TlkJgf3I3xJwdwvgtT8isNAWkgOBveRHCrCSl5EDe0JA01r0Ft2TN9IzYDyD3bD9ZT3OzvwBTmGyBPOsZ+LjzR18yfcS8M2hz9ik3fj5vi4Aq6bMDIO8i+BnBnx3JMnBXxLv1pBnJCGrPOs/escMeDktrtFYzHqts2IW/96Z8DnhreW7yPAJF11Gm7ZAYOnSxYArmQOP6hGS/xsEPsOyynyTmsBK8tXsZGgQu0TkdpCA/1ZWus2BsfKGAvOW9V0CMG8xFlozcyOwt/xoKktc1XwlURpBm+J7ZnZFCKTAl+gNKQe+T56wa2A34get12E7bG9MAr8U2x8lYBdfvNMehMCE0O9ECfiWAKzKYz0R8i2Gs7CeH0gO/pIkYHrHv5p+DsttVp8RCTia9XllFSWh8lMy8IhxF7MJW7gHDAQ5zX5jClxZAs4qA4/uGVqImIAPg0akFLCSQSI7GcaB840sw4H/p6zEgf+HrUOVI8iHCnCBcdgRGYHXmwHP9xGAp7GW4eYrHyvVsM0R+JwvjKHAN+jFg3cieEBeMODupB80XYvDa/tgnrKleJQScF4GPJADE1dXBN7AMqAxYLzGqLL/BIPHSnoLJgHn5i8pQgVc06ybsQzMukQisItSQ8z72ewiI9XAQQEDlDdB0wW+MrBDTrmHOgX2koH96d2oJnCHlAGuIQCPrYsZvAvvqm8OjDfMfcn/MM92VRk4hgLX+pMBR3DgY6a/mmf+uwLwZ6z7Q4uVptldGPAVXwiiwDchqwT8kBDH/BJwg2gE7gen8PaJHqUzA3YwA3aTgOkBLsfudAisyh0yFoqthzgZeB8f7jiXdYp3l4HNEmxLwJuZDgKbmhWyKsBx2YUtxn5qAYyXEady8kDe0T0nesnA9B7zB9PxmQZ3HsAixh09puQB5hXmRWIaOTDg88pKmAChH4lD4JpzyQAFuPY57GlePYK3f/5k+qt5F5QD1wryIw7cUgV8D8rcr06Ba9PLgwiMnSF7kP5QayUC98cEtBS4CTizG2zn/L44+ZwCnBWBt4PDPey0oQUcBKU2wCUZWIq5bFiLO/0WjabA3X5SPUm2cuA3LF39QhUw70QeQ0abAQ+zAMa0TWVXyCuM+YABf4XAg0yT9ILYufLgRz7uKQFcUwvYCRqcxc7jcmCaif7k0Qf014Yq4Ncz4CapM9cCuCgFrqPc83PgVotNMzM3J//BY9IAgpvVJo8hiwnYCT9cPelFovJyHD89AKswlmEvZRc2vWSWQpUQ+MOVEnCefQSBMz8TgO+LqTeHQpkNTjdhg+o1c+BCn3Jg89jNgXkgsKndKLcMHKICDh7WXwFutpABv6Bf1hHKCkG9Ebhx9O3f6JsT6BVFCdPAkoP+U7ulMPDHHNgz5r1M8N45EfgVA2bRNFICjr2LlcUH6Z1RnUg2fYvUcZxFyYXlpL6IGNPZh6D1IhNwC3o79ZhM8AhpVZu8yOxC+MwjjygZnvtedBflPsfRl4NwxmIOfAfo5ydHocqEnqR+K/nhuHog8E7I9lwAVnUU7Qs+G1wsgFkvh6KBesDCCD5t4E/GXsghbBFcGQTgqmxMhCM2ksgR1HuqF32q9fscWLjG9FGu0Qev3+2VWsCNM0HT82zeMjkcHeV+v80j+bwkDLgO+YveGdWP5PWDp0zrawG3UwG/+fQ1fckITE7DGxMwtk/3psDeOPs8GSYBtwCXu1iplbuwCrivDwLvhWwvELiTBnAvCpzbHPh31vDkowO8Xx84rwxc9lxOYYvgYibgFotqysCmOUHH9tlQjL7axu048E3T8Z1UDvYgoYXClAUezutTvWKaUOA4FbBTTpBuGCjwRwLwffgPP9Ssi8pp0/qlFlZLAJhFq5DWtXE+yDdhCrAnA/4Uii1C4NGYQnoZVh+6PsHpM/IVrYJJzCkw72XxJV5N90L2lwjcQwI2JWjGeXx9NuS9CRvVL5pNF1hNB/iQCLzKEtgplnwCp1XAnibgNlEc2M0SuHmVthz4X1NV1ikO7MSAe6cocA4ZuFhsE2daUG0hAjvncpWAW89TAT/L/pIBbzEDXtQP6imPprNz1H6RaeptCbg1A/4dXivAuTw7eEAfEgAeC7C0HAK36irAOwG6dKuCxSAKPNC0s72Qi36HrCQ9JWAxc1AN8NmU/yaY1WUowJs0gI+IwJjpPovyiAHPvUeBf8olbBHiAY7yXEZtomqxUU3uInAwA27h3fo3lmv1nqnXDgK7QrZ2DLhPSgArUxjkJDsPsSXFdzdxoed2Oc4qIkf2XM2l+Q/bLRCA6Yf0GdEEXtwP6iuPOHAnAVjqMNwupHVdnFdAAv6PkIJFQ9+hN1CBWIVMD2IcPL/Dgd1eZIoB2L6kCtZX+6mAD0Ce1yKwUjCgURWBn2sAO0EtCvyjBvAxEfi4JfAfhL7/jqiA88NYuZkAgXG4aUEReFyfrxDYo8lvbOLZ+ybg0/mxlr3AS+xrQm8tdcfvJwuwFKV2t3Cj5/YLTK0oR/a8X0u/dVkuAe++C9KHFIEPe1gAN1AeRXLghSZgqT9ph3EI/BdcmygDF2qzuZLrAhIM2efi6EsKfJeWcEkrepHPcbIkbF+lAXwQ8r/GjpMysJC0HyqBz1cFSWEzYFpOce3tR4H/rGR5RlTAJ82Bs2B1ZwBsUwHngzny752iujBgDzXwzuL4Li1PSwD9AB6Yer0z4EweL3GOGzI8dYC997dwc25OvhSBcw6SWx7uvJCB78nAzeYRdi0TgaNE4HkM+P0F0qj+yg1l4B7jWtfDgusZBXjRI1KlCDbJu85G4Anw+qECfKocbN9Qhdx2sAAuRBw4MCuyvicAV6TAhYiv2fSrcQCld/cP1D4jP4qn+VcVcD5acB1OP6tjYG1uYYsQd4iUf+8SFcCAi6qB95TGqSuyScCmqXQQOGt2BD5C6B1HCgArCWwU4MH/tHBre5xsgMumtXL/LWwiAd9XAX/PBqvJUSpqqAC8yIEDS3OlDRkOrfkTPSe0q4dZySVgXiXIgR1mInAovH6KwO0oXa7TvrD96ypYrO6zQg1cmN60RpMD2ThwYwG4FAOuZgZ8Fft/DdABPiF+E/5jDszKv2NhjRnwYvn3LlEhWsD7KPD7kOVXLLjBIw1ghyOYETAFgJW3u3Bb2DqzH1EDfyL2BuODOfeogX/ivYmlKLVEBF6cGYG7zz/F9zR0hAI8sUM9nLXkTKgAXIMCh7H5OxnwMwTuSK8BHqdrwpkd1bDoogY+BCWIMwWm5SN2wysCu4PP5hKk1m71i76KlYKDdID/EFtlWdcx0yvPLgOvVgHngaXy7wfOhzNgLzXwQQrcBRx/xYKbCrgAvdWYQoGdvk8h4CbyroQvlY49jyGw3mhVPrRn7wMZuDUCnwZpfC2LUkuGOTRQHkW744egV+R3rM8ajA6Qe4T3CuvZAKv1fuXAvF9/zaIMOIQDv3kGq/DNn5kU/rk+XNpVHSuLLYHddYBzg8+WEuTaK/UruIHAg8dov7yLIjCmm85jeliYAwfDSndhi5CsJmAc7o7JrFTAE/scYsAOv7Bcef9lUwH3OuXxCta/wPeNUIemjsQDL9EA7oSNm/rAyxVgfqPcej4xB35nyTCXlsqjN1sQuPfc76AtjhMMDZI7DPcK79kQkwie5sA8b00tL5YTYDRWaIYCeQWr6Zc1pSv6yyQKXBOPzBL4fwhcQAEuK/djyE6BfYh5xAt8Sezb+NJBBewlA0flFbYYnwmE7IKTWMoHL+yoIAcF9mbAP7Ou5P8pFZROEvBr1j9qQkoA79AAfh+BN+oCR7Ny+t5n8sBZDWDfpZ8IwOQbBO4TcQQ6YdbXiWNlYD8G/BROmQOvot/UEjBxWos54zOToy8vwhUE/gqBB5n+OgI/ReDCCnBdeZKOLOCz1TI7HAP2D9J+eZdUvZOdcH48JYrJwIvyCeuMdwRhvsjJDLi4CBzmx4HhKHZEgydKz5ZMp2lxqtfpQq9Z95npyjwfFpECwFv0gVkxbu8LBzNgU0d54rsixE0A/g6B/eYc4sAzgpUu/5MQ+IXDyQkCcJ0SrA2/H5aWJzrQO/jNCIwtWZfg3zuxeGRq4JMOZSjwGhyGYwGcGXx2WALfROCh4yyWs7isAs5kBszqoINhYX5hnVDHPEJb+BQ94K6sL0cWgGcKcObThRG4MAdeLNYtqSPxwDs1gDtz4Ks6m0SzYtxe+tobsMet8ctG1QuTVFoZkquV6eH3yOM3Zz90wrki526tLgPPQmCS5SQfAMznk27AsxD1kIFzbUdgvNO5xG9CzIHJhAoqYHpbUU8A/rG3xSu4jcDDxlssZ6EGdlYBF+f3oiEwXxi5QiY5iLMJzpGBlyiLwvtKwAfwiBxeK8BZJeA3DHip0L5jFokH3qUB3B1f+te614s1mdTAbRH4ZzXwuhCvnqaHP8nAnTHr6wISqQZ2/yFEAG7Ec5h0QOAwBN6LAzDxxCrAvZdLSTF5yMCevESDwB1NwBqv4Db2nRimM/XBVRWwqwq4C/+ynQATROBpcko7FhGWwJP9D5VhwHuxYtJF6Hx4hQF7EgcE/jxlgHmDlZcIjB2MYvSBK86RgFnfcMIyILwcrgLeFzLqjemhBLwHOmPL8mKyoD1f/mFEL/wTHodZ8jIpb817JVkuoxYIHE6BCxxGYCyWSMDbNICfwVose8rA9aUJquiL0wX+/YL2y7ulAnZTAfflw8MmgldBYZ3pZsDYzLK8uKMIfJgD78HcX27KtEP0OoLAP3sSRwReliLAFbDtzAe+NgeOxXmcNGNNTczZsU8B7sDKAytVwPtDRglbsKr5vrN3Q5dXDvjdFCUBX384CjunFtsnAjcuiRX+mA2jJb34EeJzBMfnIfBlDrwLui+XcibyoMBvHNbinasC3E0B1prB4n48vZ/IHRVwdpxGXAl/fp8QBnk8hHWmg/B1RCJ5O1rFnNHKosn+p30ZcCwHVtoIH+DwXQR2wv6eJxyE6n91JAG4Blb0h6mAeyHwHl3gdbUwZ8d++vXEgTsy4LViEiAz4JN4jvrOjoWu2E76OVnaQX4mCJPBldg3VgBuWppNGl5LBu5JX/sgETgGupkBVyTEYQMC45UCO9/V5/3GwMFVE/gBOOmO4yN3VYM4Swn9uwkZwr9GJgOIwDPkihsWSxxY9UDFDabM+5P9CS3j93VxjMFe725SkjbAPiyeFPh8ZeLEOsC5CnVL6kgKMI7UCYetQtWMHwLv1wX+utYFCZjPSdCVAW8TOsqTmkdUwKexUNF3VgwFzoTd477sIT8zHu+1vXeLwM282aThlbFJYrKUS+0jVhhVgLuqgUMpsNMGxGDAbSiwNFN5/kw6wLpnEj/eIvA7LL+3HMMzsyqTGebAHYVHL8NY9YCvMH/VzpX4c6CXK5vE1c2UN4MD0+cyMWA33UxoSQGewoC3CcD9EfiALvDWWjihlDnwAaGjPLlPLIH7zdwB3RB4ldShDYMBl1UBN+fA74rAg9XAXZbDEOHPI3AWBsxeBL3Bqi+lgS/AJz0yjzer4jkj/6mA31UBjyjE/lsE4vBgCtxV/AOzGXAliwnKhni5bubAUqpZxwrP1cAHdKfbSCrwJNguAA/ASdTjAa5NjnFgPidBV1bTfgjEBgkSIvT8J78gcP/a2ymwM6a8MUUofnuViw16D6ueeIViS29s0cGBi63IVBnYGcudCnDnZVIyEOmPIPBGQkqbgKVx9AWBz1BnJJ6ogKsAZgeWYzTvKfo5iCNXzIHn6wAP9XJlHauzyaPusbd4SRFYP5IKPAV2WAA76Q1121YHAUzAg1in1CNmwOLkPr9joaJ/SQSegcPtTTEZv718Y4MC8TaLv39beWOLDhRC4GkS8KjSePGSgGOhw+fSoGweCJx9Iw6kYy+iEwUOTi7gqirgMXxEbPzAC9mFrZJZCxYDZgeVVwWckwOfN19bHYkHjqkRnQtgOsQKNa4DEfh7d71NLixBgP20iPiesPRYPMAvsG/5gLyB0BMrRMQecFMQuHLsmEAsCfNFHbyxnd05LwJPl4ADWCd6CXgvtF8K4jcAAufeiHP7MmBaRKq/QQEuZ9VpEOKJKplRbcDconLMrMn+WyYMvMKlauBFrCmlkll/evouLWYCZs0N2Jm4dAmcWoA4pxjw0VaYaXcu/CV8LTHgYwV0t2G5DffTIqIIfBxUV5ndFnnnB0Cx3AspcO7DwkIOHBMYOF0B7sKAc2ZH4BkS8BhWmjUBLwHxGyC0CiF5NuHs3ApwLAculAjgp6Zk/TQ6OIrAL+PYf8tVX8zmwFEsNUd1C+AxFWRgL56KBYF/6MOBdUvAPBIPTLADCiwUu2+QQQj8Y3zAf2NCIjXwSUjgTTiQZdb+hY30U0IBvlBbBu7qg7kTC7iKwONYdm494Jn1VcCdKfCexAMT1VvzWR0RWIoVqus2Be4hPrucAdewAA7t5KYAs4SGLBviIAbsEhf/MSUV+HMN4IJ66xM81RQ4M4hVsL/oV8PwGMg66Z1VDyNhwNVjAseQPnJ66e4+OA1CUQfMG60As7RztxTgKBUwDgTkwOx7hgOzglKRxACrow5mBzaL1QDiBJFmwCtYNY4lcFg3eo9VlgMvy2YCxkEaLrpVHDySBBwIsEwF7I/AP3norU/YgOEDJIs86QSLa1YA10dgVR/lqQhcIyYwiPixMaaEA7/E5BAILCUMDGeVQ4858Alz4PX0OPKqgBvsc5gjAZeP/6gSjLqmQXRKRGMnTlPMYh9CJSTgGPOtZvagXzs1wW0lKQbLcsrA/gxYv4qDR5KAx9CiqQr4YwQ+bhD4oRXADehnP5Oqh6MJeLAI/AqLwRR4gQS8gVX2PpG6tEA7NfDFr8yBG+53ZMCewCcRTULU06jNXsNHnssx2zrgB3G5oMSf2C5WHJa5Y2pBXDqEAfvqV52ySBJwCBS4qgKevZL+OO4Vzya3sKO2OfCF+HczEFjVZlHVeOtZHDhgLH0nS8A9GXAlBrzQlS+TgflpAN8oEG/SMfJtwmHiDLgrA46QgCvHf1QJRj2NqpL1WBVvijmgapH8Due6JE2OWP6tXFDqPHYZKAnLijuCM2vcHMrH0SUQSQKeAEX/tay7O255b2GK2+BwyAz4cULAgziw1xZx4VIsUtTomXssfSfLwO8gMI64aEMWScDH2aCLJ1JvKXBcbAGc/ysFuBtAowMcuGjSgetrAD9viv3UlZgjpclWhVallDt4/4NdBrxhWVk3cGZNI6kAHKYJfGdGPJvcBqdDJJsK+I1jAjeCw/l8ul5bxYXL8HJWow6MJSNk4A8YME7k0pYsdhVXloHdHCyBC27GWicZuPEBJwZcjE/+m5QYqFXZ2R3Esl4EfGbd3yoNZS4gcBlY5uvtk4111/w4ScBTb5PnkwIW6TwrxSQoeie+2neNuA3Oh0gOFTDJlADwCD7VW3EV8HJsV6pRiwJ/Is+C0PsdnMSoKQOOMgPmlWuZYZEFcBEGzCpnEPhQ1ghs7a9gysaW2BihdZvWA8QLcARMt+5vVQIfDpxledWB23Oy7j9JAb4zqu1tEruZRPyh+bQck8DLKPAdcLEAdr6ktzaPEXwmsHe3iQtNwEFykvze5RG4NQNeYgbMKxEzwwILYK/NWK3Imm6703fSodwR2KWyTdKBR0JFy4X9VMDzYKblKlpRGd69gF1nyuRZWW3Q3nLsRm14ErfBFXIAAA7rSURBVIDfPJ94m8w+T/bRD829K1deaq6ErZuGge9D5m9xljtxmUsCf2IUT5BdTlWPcBarPWrWhBAyVgbuUxGBO8UDnAXmWwCX2ILDChlwD/pO+iYfBc4HbZMDWOM+fAB8JzxabBrYEH9UhnIXsDImpuHqjh8d4g0XI5J0iQ67TcJvkqP0LEY0anSCkKta/8LA6yxc1nxK79+fkHULyQvNxYUup+Pfagzm6r9Kyq2xeKpyRRhOgikwe9inLFakYDKupmSJi7jmE6wNfYSf4M8osPqPlFyGWSbz4u8MOG8E1mM1Y8UkQy+N/7uh/D4SmyvMVxiCcwkoDz+DKOv+LAPOS39pHTl08NERbOEI6BbvVg/1gFcP3cGAZ8eRAxYNG6qIdCh2J55Jt7TiPmQ9TIHFjgxkju6MEtKbDbOM0k+wRfmQ1CxPP8HB8ifYj32CcZKc9mSJm7jei1J8F1lgssUn+J0tOG6Uf4IzU+ACEVjN0R6gppHXpRFj1dVUPPY6iR3WooT+dfEGBY5jx9h6dYDcYyHJn+CdX5M5F+LdeL5bcaPADyDbYeKhBk4opoEr2pbfY/FMzVIUOFSe5sIPL4mYjAY6kJVuFisTTJUx3gK4LANm3dE/KAjNjnDgbvLMkomP5538NZb+JT6IgnXW/a0qUDkOsGW7zZrkA34a+slynWelWORW/J5uJ2jteAAeR0lhY8Cf8Sz4FfZaPEOBx9EPuAQ8kAF/zICjNYELQogFcOV9CvC1AGh+tEAkpkr1Tzow6TYyoTW+UVV7xBNVERjPwuk7QTLwyJQvB0dlLf5Qf9CEZjyEoj+QIsaAZ3DgypbAtVTA/gz4Ewa8JovFygSHqARZAH/zCNNR8AElE6H5sYKLILs3vk3qWW5vLPrqdJAXYsUt6/5UNQTmvX5CZeBRKQ+8xKP6Q93+OdrxEPY/Mwp8LgcDjrFMmVurJIw3AY/EUfcQzID/1ajvQ+AxFsAY7UzAPxZhwMPB2DFqxeCEga2N6lAlDsqwXyfLwKNTAbgBBbthaJNH2H3U0+DJK6zXwlyrOAUOl4FDsHLRcSIoUx5Y/iEI0ARuLwGHZW7xoycFroLAnQ0do0YMm5jUv6BEdagWJ/VFORYrLfs0VYCfl9Odkkkz/ksMcBG92u3anhR4Mkhl3hDMQpRpWjzARegFXAu4kwycpcVPnjugQFUE7m7oGDVir/6UkUbjfQQ2G3cekPLASxsY3uR5/pfGgb312t5rF2HA0g3VeAY8J17g0ZrAXWTgIj1PeF6EY1VhBE6iZTvxCKpfVFWREFsFZuGZtY2h9X0q6DxBgSeYgLGHFXFeEC/wKE3grhLw1IHkJAU+xYAXGDrGlI1nCHxKvSwgU38rtkwTYN9axoAr6LW911UBT2PAnwOoxguI4akD3A34qN1pA8mZkgx4FEACZcRUjTdQ+4EFcLcTVmyZJOBlDRK33baxbQ2t30ivabZJYRF4OtYe58HZLvvorF9MB7iXBHyC3sD8S4GrIfAKjRXTLBzrEjHbFEbAWO1V1ZEk4BUNE7nh8m6GVu+vB9wMgafKOSFnYJIaz/XxAJeCkZrAH4Aw7v4inK2GpemVho4xhcOpHhGmhGRhy8BPjJWtBjXSeaJZQQgl0xRgTLlQaku8wIM0gf3EGSMR2CHAxoAzNSTmvYs3fGXNhkkCXtU44XWSIwa/p/NE8zwUeLqccIwD74wHuBcM1ATu76QC/r1avgBwTr5CTjKEGwU21jArRZKAVzdJeJ3kCH9d4FwUeIaccGwmAnvvjQd4KwyATzWWD3ETgC9T4BIBYg4kG4gsFNhYpbAU6QNY70rRPDsFnikDz8LmgXePUmCdNDgU+ENN4I/dhK6+Nxiwg20B56HAxiqFpUgS8NpUAh7SUueJFtkYsDQNQiQC38RUAF/orK8HPEIEvgXnYnvbGnDepmkB/EXThNdJjhjVSecJBrxEzpa5lDfwuQFYthzz0AMOzFrI9OA2/ENGBGbSHSOZJuHRPCMDB+gCZ6HAX8rAyzlwDtVETKrQAw7OI4zavQ0XyIgxznk11ku78GhBDDbrSJEk4HWpBazXsNPCBSZSYCkNjAScF97Va2XdCb0gQGN5sLuQL4UDu9kWcKF2xMHKtmN1JAn4y1QCDu6q80RLoMAbQMoxsIKnOC2sGsSvit3Qw0rgsVlsC9izHXnnUcKrWUaSgA9YU9udDDHZT+cJBnyziwS8mte7FAfdrjIIrJXsOdhdGNR5G+LIqGAbA/Zqn8gNkwScWjFV743UCiCMkGUScDRvhCyjytKgCgQO0VgenFfImfQvXCGjxtkYcDG9BrKEIl0AT9MDbs+Ao9XA7+oDn4TuoNXNIriRabIx8pACh8/On09jvbSLEontX5IugGd/qPMEBQ4nZI1UotnBx2K+q/k1y+IsdNEGFofp/sd6AtsYcHWNPtZWRboAvq2XxIMCT6GlNfXVNF7g92GqxnIV8GNbBO7XK5Ebpgtg3egOMI2W1gwAdzLNYyOECvgpSweQP7/GemkXg/omcsP0Dfwh4ADbL60G/p0Ca/XEGS8Cv2qGg+0KaExtloYxPrEFlvQNPAhwgO3GJAOHWeZ2LxBfnoLUjwkDE15HM9I38EcAMwjZqgauqFnUZYHAWj3iwy2BS5VO8tElZ0zQrbxJINI98ExCdhZWLawKOtPecOBfNJZrAPvaFvAMvRbQhCJ9Aw9hwLvUX5fV4gc+o7FcC9gyyVWaxpuEV9GM9A38MQPeYw6sOybod+jYVetMhVt2AalsY8CJjfQNHMSBi6oWVlcm1baIRzk6DNdabge20QgFmGUBXEsfmBTWAfazWGQHtoXQBG6aVX9Un/XAVbRm1UmHkb6B5zPgvWrgV9/q590q3E6zuBFuWU9kB7aF2MyADxdNeE0pCrfTbCvWAK5uB7aB2AYwm5CL1jeGF2mr2ZSoAVzXDmwDQYHnGNqgSNvRWos1gBtqTZuUDiN9A+8wDqw1coWEWw6lbmQHtoGINQzcRhN4hh3YNoMCRxjaoEgbrW7RZJYlcBM7sA3EHsPAra0Fbqo1s106jPQNvC8FgZM6ZYONRPoG/tEwsLvVwJUSd0i2Fukb+DfDwJpDk0ikZX+JpkmdssFG4q0D1uzO89Rybho7sC3EH8kErBF2YFuIi8aBdftrmYUd2BaCAs81tIH1wK3swDYQhoE9rQZuZwe2gUhB4PZ2YBuIy8aBdXtcmoUd2BbiKlg78ZAUnhBs5Zp2YFsIChxlaAM7cPoKCmzlxENSFLUDp6ugwMsMbWA9cIekTj5qI5G+ga/ZgROKdA9sLC97MTtwugoKvMbQBiWsBu5oB7aBuA5gVVZsJUpoZlHSik52YBsICrzL0AZ24PQVFPigoQ28rQbubAe2gbgJlfQyLGlHGauBu9qBbSBuwkZjG5SzA6erMAxcUX/0v1l0rW74aGwy3jLgmlYDd6tr+GhsMtI38G3YZGwDO3D6ijtgOSt4vFEznvQO6uhhB7aBuAN/G9vAeuBedmAbiHvGga2dlfsDO7ANxEM7cEJhB9aJT+3ANhCGgeviFABWRZgd2AbCMHB9mGTlmuF2YBuIFASOtAPbQNiBE4z0DfzYOPBnVq4ZWc/w0dhkpG9gYhS4AWaItyrswDYRxoHnW7mmHdgmAv5nbP0GmnNyaMV8O7AthFHghnbg9BXGgRcmvBKLKDuwLQT8Y2x9O3A6C7hgbH07cDqLFARuaPRYbDO0gZ9NGzvp6fNJAYtS+WgMB+hn79eMhlYPGF/ayPDB2GRoA2/fQTbExG4mEX+k8uEYDbhkbP1GVgOvzdDAP98mWzfPPk/2bSXk3pUrL1P5oKwPuGxsfeuBN2VoYEK+878XfpMc/ZKQiEaNThBy1Tb/wWXrV35EfzbFumir1t/0XuIO6UaanxP530M94NVDd7yOnHKfzI4jB3am3FsrWcLoJ7gFLLVyzaNDDR+MTYb2J3j3Svpj59dkzoXUPJZEhHHgLSlzIDYb2sDTRwQG7nsa+omx4fNpEMaBt6bMgdhspPNycP/HxtZvAdtS5kBsNtI5sNGwA2fwaAnb0/oQUjneOuAdaX0IqRx24AwebxlwKztwxo7WduCMHa3hm7Q+hFSOtwy4IxxO60NI5XjLgDvBd2l9CKkcbxvwR8/T+hBSOd42YGt77GSYeMuAu6xM6yNI7XjLgG88SesjSO14y4DfvrADZ/CwA2fwsANn8LADZ/CwA2fwsANn8LADZ/CwA2fwsANn8LADZ/CwA2fwsANn8LADZ/CwA2fwsANn8LADZ/CwA2fwsAHg66nTF/23M6mxl20GBywnKv750fp1bQD4SL9U2U2ktYmEkxT1rqXCTr4Ksn5dO3Dyhh3YMm7/lCq7OWcwd3ji4uDTVNjJ5V+tX9cGgO2RkmEHzuCRFsBHhw0b9TP77dqvYuJT9uPumKCIN8mymweB4iOz3RBydkqy7GVaRGrsRX4xMYeM7iMNgK8Ou0uu9L+Pv55cJyY+ZT++OEpm/ZUs+1EDm+2GvBk3OTl28jRgyCv59zcpthcVsLF9pAHw2t30x+p9D8LGLggfcllIfMp+nHlMwg2mkNWJB4H3Q0Mnvdg2Zf74J8R8N+TQxmQ59d9uWnCCrPts/KSnMZPmpthe8MVsOUzW/IrAxvaRBsBz8fIcs3nFYbLi0DpcICc+5dlPH44e9ixZ9vMg8O8zZGbctvVk9UmL3TwP+zdZTv3kq8cjybr1ZMuumFUkxfaiAja2jzQAjt5Pf3x+JPQWv0SbEp+yHzffkHXJkcvqJ3J/TFz4zBHntx0mm36w2M3G43eT49Q//iB0nN+rdcfJz9Hs3KfMXtiLocCrGbCxfaQB8MURD8jVIc8X/ULmHV4nJj5lP6bEkY27k2EvAc//mbzwNzJRBjbbzYzJE/vEJH0vBzbQK9LpdV+TLQcYcMrshb2Y2H1kEgM2to+0uIv+duiwwP+RW+NDv7gx+IqQ+JT9OB8wbnpyjML/dlTAnycCIiOjJWCz3dCLXnJ8tsLo7cLRBevCwie/YsApsxf2Ym6NiwxkwMb2YS8HJ0OsO5nWR6AfduBkCDuwPdIs7MAZPOzAGTzswBk87MAZPP4PAI7/zmhTTGAAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "im <- ggplot(outside, aes(x=timestamp)) +\n",
    "  geom_line(aes(y = no_seasonal)) + clean_theme +\n",
    "  ggtitle(\"Outside temperature with removed seasonal expectation\")\n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stdout err\n",
    "%%R\n",
    "ggsave(\"img/(Ch05)Outside temperature with removed seasonal expectation.png\", dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Call:\n",
      "lm(formula = no_seasonal ~ sin(x) + cos(x), data = outside)\n",
      "\n",
      "Coefficients:\n",
      "(Intercept)       sin(x)       cos(x)  \n",
      "  0.0002343   -0.5914551    3.6214463  \n",
      "\n",
      "\u001b[90m# A tibble: 171,349 x 5\u001b[39m\n",
      "   timestamp           outside no_seasonal day_model no_daily\n",
      "   \u001b[3m\u001b[90m<dttm>\u001b[39m\u001b[23m                \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
      "\u001b[90m 1\u001b[39m 2003-07-25 \u001b[90m16:04:00\u001b[39m    27.5        1.99      3.61    -\u001b[31m1\u001b[39m\u001b[31m.\u001b[39m\u001b[31m62\u001b[39m\n",
      "\u001b[90m 2\u001b[39m 2003-07-25 \u001b[90m16:07:00\u001b[39m    27.3        1.79      3.60    -\u001b[31m1\u001b[39m\u001b[31m.\u001b[39m\u001b[31m81\u001b[39m\n",
      "\u001b[90m 3\u001b[39m 2003-07-25 \u001b[90m16:10:00\u001b[39m    27.3        1.79      3.60    -\u001b[31m1\u001b[39m\u001b[31m.\u001b[39m\u001b[31m80\u001b[39m\n",
      "\u001b[90m 4\u001b[39m 2003-07-25 \u001b[90m16:13:00\u001b[39m    27.4        1.89      3.59    -\u001b[31m1\u001b[39m\u001b[31m.\u001b[39m\u001b[31m69\u001b[39m\n",
      "\u001b[90m 5\u001b[39m 2003-07-25 \u001b[90m16:16:00\u001b[39m    27.8        2.29      3.58    -\u001b[31m1\u001b[39m\u001b[31m.\u001b[39m\u001b[31m28\u001b[39m\n",
      "\u001b[90m 6\u001b[39m 2003-07-25 \u001b[90m16:19:00\u001b[39m    27.5        1.99      3.56    -\u001b[31m1\u001b[39m\u001b[31m.\u001b[39m\u001b[31m57\u001b[39m\n",
      "\u001b[90m 7\u001b[39m 2003-07-25 \u001b[90m16:22:00\u001b[39m    27.6        2.10      3.55    -\u001b[31m1\u001b[39m\u001b[31m.\u001b[39m\u001b[31m46\u001b[39m\n",
      "\u001b[90m 8\u001b[39m 2003-07-25 \u001b[90m16:25:00\u001b[39m    27.6        2.10      3.54    -\u001b[31m1\u001b[39m\u001b[31m.\u001b[39m\u001b[31m44\u001b[39m\n",
      "\u001b[90m 9\u001b[39m 2003-07-25 \u001b[90m16:28:00\u001b[39m    27.7        2.20      3.53    -\u001b[31m1\u001b[39m\u001b[31m.\u001b[39m\u001b[31m33\u001b[39m\n",
      "\u001b[90m10\u001b[39m 2003-07-25 \u001b[90m16:31:00\u001b[39m    27.6        2.07      3.51    -\u001b[31m1\u001b[39m\u001b[31m.\u001b[39m\u001b[31m44\u001b[39m\n",
      "\u001b[90m# … with 171,339 more rows\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "# Make one day add up to 2*pi radians\n",
    "x <- 1:nrow(outside) * 2*pi / (24*60/3)\n",
    "    \n",
    "# Model the data as a first order trigometric regression\n",
    "day_model <- lm(no_seasonal ~ sin(x) + cos(x), \n",
    "                data = outside)\n",
    "print(day_model)\n",
    "\n",
    "# Create a new tibble the holds the regression \n",
    "# and its removal from the annually detrended data\n",
    "outside2 <- add_column(outside, \n",
    "                day_model = predict(day_model),\n",
    "                no_daily = outside$no_seasonal - day_model)\n",
    "outside2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "week <- outside2[5000:8360,]\n",
    "p1 <- ggplot(week, aes(x = timestamp)) +\n",
    "  no_xlabel + ylim(-8, +8) + \n",
    "  geom_line(aes(y = no_seasonal))\n",
    "p2 <- ggplot(week, aes(x = timestamp)) +\n",
    "  no_xlabel + ylim(-8, +8) + \n",
    "  geom_line(aes(y = day_model), color = \"lightblue\", size = 3)\n",
    "p3 <- ggplot(week, aes(x = timestamp)) +\n",
    "   clean_theme + ylim(-8, +8) +\n",
    "  geom_line(aes(y = no_daily), color = \"darkred\")\n",
    "grid.arrange(p1, p2, p3,\n",
    "            top = \"Annual detrended; daily regression; daily detrended\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stdout err\n",
    "%%R\n",
    "ggsave(\"img/(Ch05)Annual detrended.png\", plot=p1, dpi=600)\n",
    "ggsave(\"img/(Ch05)Daily regression.png\", plot=p2, dpi=600)\n",
    "ggsave(\"img/(Ch05)Daily detrended.png\", plot=p3, dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discovered Cycles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "not_summer <- filter(glarp, \n",
    "                     timestamp >= as.Date(\"2003-08-15\"), \n",
    "                     timestamp <= as.Date(\"2004-06-15\")) \n",
    "\n",
    "# Plot only the non-summer days\n",
    "im <- ggplot(not_summer, aes(x=timestamp, y=basement)) +\n",
    "  geom_line() + clean_theme +\n",
    "  ggtitle(\"Basement temperature over non-summer days\")\n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stdout err\n",
    "%%R\n",
    "ggsave(\"img/(Ch05)Basement temperature over non-summer days.png\", plot=im, dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "per_day <- 24*60/3\n",
    "basement.temps <- ts(not_summer$basement, frequency = per_day)\n",
    "auto <- acf(basement.temps, lag.max = 10*per_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stdout err\n",
    "%%R\n",
    "png(\"img/(Ch05)basement.temps ACF.png\", width=1200, height=800)\n",
    "plot(auto)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bespoke validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collation Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO]\u001b[0m 159688 duplicated records removed\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "cd data/prokaryotes\n",
    "zcat 16S.fasta.gz | \n",
    "  seqkit rmdup --by-seq --ignore-case \\\n",
    "               -o clean.fasta.gz \\\n",
    "               -d duplicated.fasta.gz \\\n",
    "               -D duplicated.detail.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1384\tJN175331_1|Lactobacillus, MN464257_1|Lactobacillus, MN4\n",
      "1383\tMN438326_1|Lactobacillus, MN438327_1|Lactobacillus, MN4\n",
      "1330\tAB100791_1|Lactococcus, AB100792_1|Lactococcus, AB10079\n",
      "1004\tCP014153_1|Bordetella, CP014153_2|Bordetella, CP014153_\n",
      "934\tMN439952_1|Lactobacillus, MN439953_1|Lactobacillus, MN43\n",
      "912\tCP003166_2|Staphylococcus, CP003166_3|Staphylococcus, CP\n",
      "908\tCP010838_1|Bordetella, CP010838_2|Bordetella, CP010838_3\n",
      "793\tMN434189_1|Enterococcus, MN434190_1|Enterococcus, MN4341\n",
      "683\tCP007266_3|Salmonella, CP007266_5|Salmonella, CP007266_6\n",
      "609\tMN440886_1|Leuconostoc, MN440887_1|Leuconostoc, MN440888\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "cut -c-60 data/prokaryotes/duplicated.detail.txt | head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def matched_rna(dupfile):\n",
    "    \"\"\"Count of distinct organisms per sequence match\n",
    "    \n",
    "    Return a mapping from line number in the duplicates\n",
    "    to Counters of occurrences of species names\n",
    "    \"\"\"\n",
    "    counts = dict()\n",
    "    for line in open(dupfile):\n",
    "        line = line.rstrip()\n",
    "        _, match_line = line.split('\\t')\n",
    "        matches = match_line.split(', ')\n",
    "        first_id = matches[0].split('|')[0]\n",
    "        names = [match.split('|')[1] for match in matches]\n",
    "        count = Counter(names)\n",
    "        counts[first_id] = count\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "dupfile = 'data/prokaryotes/duplicated.detail.txt'\n",
    "counts = matched_rna(dupfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'Mannheimia': 246, 'Pasteurellaceae': 1})\n",
      "Counter({'Microbacterium': 62, 'Microbacteriaceae': 17})\n"
     ]
    }
   ],
   "source": [
    "print(counts['CP004752_1'])\n",
    "print(counts['AB729796_1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Proteobacteria': 1, 'proteobacterium': 2, 'Phyllobacteriaceae': 8})"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts['AB851397_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Shigella': 11, 'Escherichia': 153})"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts['CP020753_6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Aster': 1,\n",
       "         \"'Elaeis\": 1,\n",
       "         \"'Tilia\": 1,\n",
       "         \"'Prunus\": 2,\n",
       "         \"'Brassica\": 3,\n",
       "         'Papaya': 1,\n",
       "         \"'Phalaris\": 1,\n",
       "         \"'Eucalyptus\": 1,\n",
       "         \"'Melochia\": 1,\n",
       "         'Chinaberry': 1,\n",
       "         \"'Catharanthus\": 4,\n",
       "         \"'Sonchus\": 1,\n",
       "         \"'Sesamum\": 1,\n",
       "         'Periwinkle': 1,\n",
       "         'Candidatus': 1})"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts['FJ537133_1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transcription Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sequence = namedtuple(\"FASTA\", \"recno ID name locus bp\")\n",
    "\n",
    "def get_sequence(fname):\n",
    "    fasta = gzip.open(fname)\n",
    "    pat = re.compile(r'n+')\n",
    "    sequence = []\n",
    "    recno = 0\n",
    "    for line in fasta:\n",
    "        line = line.decode('ASCII').strip()\n",
    "        if line.startswith('>'):\n",
    "            # Modify base pairs to contain single '-' \n",
    "            # rather than strings of 'n's \n",
    "            bp = \"\".join(sequence)\n",
    "            bp = re.sub(pat, '-', bp)\n",
    "            if recno > 0:\n",
    "                yield Sequence(recno, ID, name, locus, bp)\n",
    "            ID, name, locus = line[1:].split('|')\n",
    "            sequence = []\n",
    "            recno += 1\n",
    "        else:\n",
    "            sequence.append(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 AB000106_1 Sphingomonas sp. 16S ribosomal RNA\n",
      "ggaatctgcccttgggttcggaataacgtctggaaacggacgctaataccggatgatgac\n",
      "gtaagtccaaagatttatcgcccagggatgagcccgcgtaggattagctagttggtgagg\n",
      "taaaggctcaccaaggcgacgatccttagctggtctgagaggatgatcagccacactggg\n",
      "actgagacacggcccagactcctacgggaggcagcagtagggaatattggacaatgggcg\n",
      "aaagcctgatccagcaatgccgcgtgagtgatgaaggccttagggttgtaaagctctttt\n",
      "acccgggatgataatgacagtaccgggagaataagccccggctaactccgtgccagcagc\n",
      "cgcggtaatacggagggggctagcgttgttcggaattactgggcgtaaagcgcacgtagg\n",
      "cggcgatttaagtcagaggtgaaagcccggggctcaaccccggaatagcctttgagactg\n",
      "gattgcttgaatccgggagaggtgagtggaattccgagtgtagaggtgaaattcgtagat\n",
      "attcggaagaacaccagtggcgaaggcggatcactggaccggcattgacgctgaggtgcg\n",
      "aaagcgtggggagcaaacaggattagataccctggtagtccacgccgtaaacgatgataa\n",
      "ctagctgctggggctcatggagtttcagtggcgcagctaacgcattaagttatccgcctg\n",
      "gggagtacggtcgcaagattaaaactcaaaggaattgacgggggcctgcacaagcggtgg\n",
      "agcatgtggtttaattcgaagcaacgcgcagaaccttaccaacgtttgacatccctagta\n",
      "tggttaccagagatggtttccttcagttcggctggctaggtgacaggtgctgcatggctg\n",
      "tcgtcagctcgtgtcgtgagatgttgggttaagtcccgcaacgagcgcaaccctcgcctt\n",
      "tagttgccatcattcagttgggtactctaaaggaaccgccggtgataagccggaggaagg\n",
      "tggggatgacgtcaagtcctcatggcccttacgcgttgggctacacacgtgctacaatgg\n",
      "cgactacagtgggcagctatctcgcgagagtgcgctaatctccaaaagtcgtctcagttc\n",
      "ggatcgttctctgcaactcgagagcgtgaaggcggaatcgctagtaatcgcggatcagca\n",
      "tgccgcggtgaatacgtccccaggtcttgtacacaccgcccgtcacaccatgggagttgg\n",
      "tttcacccgaaggcgctgcgctaactcgcaagagaggcaggcgaccacggtgggatcagc\n",
      "gactgggtgagtcgtacaggtgc\n"
     ]
    }
   ],
   "source": [
    "fname = 'data/prokaryotes/16S.fasta.gz'\n",
    "prokaryotes = get_sequence(fname)\n",
    "rec = next(prokaryotes)\n",
    "\n",
    "print(rec.recno, rec.ID, rec.name, rec.locus)\n",
    "print(fill(rec.bp, width=60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_dup_subseq(bp, minlen=45):\n",
    "    count = Counter()\n",
    "    for i in range(len(bp)-minlen):\n",
    "        count[bp[i:i+minlen]] += 1\n",
    "    return {seq: n for seq, n in count.items() if n > 1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2180 AB051695_1 Pseudomonas sp. LAB-16\n",
      "{'gtcgagctagagtatggtagagggtggtggaatttcctgtgtagc': 2,\n",
      " 'tcgagctagagtatggtagagggtggtggaatttcctgtgtagcg': 2}\n",
      "2534 AB062283_1 Acinetobacter sp. ST-550\n",
      "{'aaaggcctaccaaggcgacgatctgtagcgggtctgagaggatga': 2,\n",
      " 'aaggcctaccaaggcgacgatctgtagcgggtctgagaggatgat': 2,\n",
      " 'accaaggcgacgatctgtagcgggtctgagaggatgatccgccac': 2,\n",
      " 'aggcctaccaaggcgacgatctgtagcgggtctgagaggatgatc': 2,\n",
      " 'ccaaggcgacgatctgtagcgggtctgagaggatgatccgccaca': 2,\n",
      " 'cctaccaaggcgacgatctgtagcgggtctgagaggatgatccgc': 2,\n",
      " 'ctaccaaggcgacgatctgtagcgggtctgagaggatgatccgcc': 2,\n",
      " 'gcctaccaaggcgacgatctgtagcgggtctgagaggatgatccg': 2,\n",
      " 'ggcctaccaaggcgacgatctgtagcgggtctgagaggatgatcc': 2,\n",
      " 'ggggtaaaggcctaccaaggcgacgatctgtagcgggtctgagag': 2,\n",
      " 'gggtaaaggcctaccaaggcgacgatctgtagcgggtctgagagg': 2,\n",
      " 'ggtaaaggcctaccaaggcgacgatctgtagcgggtctgagagga': 2,\n",
      " 'ggtggggtaaaggcctaccaaggcgacgatctgtagcgggtctga': 2,\n",
      " 'gtaaaggcctaccaaggcgacgatctgtagcgggtctgagaggat': 2,\n",
      " 'gtggggtaaaggcctaccaaggcgacgatctgtagcgggtctgag': 2,\n",
      " 'taaaggcctaccaaggcgacgatctgtagcgggtctgagaggatg': 2,\n",
      " 'taccaaggcgacgatctgtagcgggtctgagaggatgatccgcca': 2,\n",
      " 'tggggtaaaggcctaccaaggcgacgatctgtagcgggtctgaga': 2,\n",
      " 'tggtggggtaaaggcctaccaaggcgacgatctgtagcgggtctg': 2,\n",
      " 'ttggtggggtaaaggcctaccaaggcgacgatctgtagcgggtct': 2}\n"
     ]
    }
   ],
   "source": [
    "for seq in islice(get_sequence(fname), 2800):\n",
    "    dup = find_dup_subseq(seq.bp)\n",
    "    if dup:\n",
    "        print(seq.recno, seq.ID, seq.name)\n",
    "        pprint(dup)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Characterization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2003 07 26 19 28 25.200000\n",
      "2003 07 26 19 31 25.200000\n",
      "2003 07 26 19 34 25.300000\n",
      "2003 07 26 19 37 25.300000\n",
      "2003 07 26 19 40 25.400000\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "zcat data/glarp/lab.gz | head -5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/glarp/basement.gz: 169516\n",
      "data/glarp/lab.gz: 168965\n",
      "data/glarp/livingroom.gz: 169516\n",
      "data/glarp/outside.gz: 169513\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "for f in data/glarp/*.gz; do \n",
    "    echo -n \"$f: \"\n",
    "    zcat $f | wc -l \n",
    "done"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Oversampled Polls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['New technology makes people closer to their friends and family',\n",
       " 'New technology makes people more isolated',\n",
       " '(VOL) Both equally',\n",
       " \"(VOL) Don't know/Refused\",\n",
       " '(VOL) Neither equally']"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pew = pd.read_csv('data/pew-survey.csv')\n",
    "list(pew.q23a.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Very happy', 'Pretty happy', 'Not too happy', \"(VOL) Don't know/Refused\"]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(pew.q1.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Eastern', 'Central', 'Mountain', 'Pacific']"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(pew.timezone.unique())"
   ]
  }
 ],
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