deviation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deviation\n",
    "Emphasise variations (+/-) from a fixed reference point. Typically the reference point is zero but it can also be a target or a long-term average. Can also be used to show sentiment (positive/neutral/negative)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "#ggplot equivalent: plotnine\n",
    "from plotnine import *\n",
    "#scales package equivalent: mizani\n",
    "from mizani.breaks import *\n",
    "from mizani.formatters import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diverging Bar\n",
    "A simple standard bar chart that can handle both negative and positive magnitude values"
   ]
  },
  {
   "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></th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Profit Ratio</th>\n",
       "      <th>new_sub</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Furnishings</td>\n",
       "      <td>-26</td>\n",
       "      <td>00_Furnishings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Appliances</td>\n",
       "      <td>-11</td>\n",
       "      <td>01_Appliances</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tables</td>\n",
       "      <td>-9</td>\n",
       "      <td>02_Tables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bookcases</td>\n",
       "      <td>-8</td>\n",
       "      <td>03_Bookcases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Supplies</td>\n",
       "      <td>-7</td>\n",
       "      <td>04_Supplies</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sub-Category  Profit Ratio         new_sub\n",
       "0  Furnishings           -26  00_Furnishings\n",
       "1   Appliances           -11   01_Appliances\n",
       "2       Tables            -9       02_Tables\n",
       "3    Bookcases            -8    03_Bookcases\n",
       "4     Supplies            -7     04_Supplies"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/diverging-bar.csv')\n",
    "df['rnk'] = df['Profit Ratio'].argsort().map(lambda x: str(x).zfill(2)+'_')\n",
    "df['new_sub'] = df['rnk'] + df['Sub-Category'] \n",
    "df.drop('rnk',1,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223363304363571214)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = (ggplot(df, aes(x='new_sub',y='Profit Ratio',fill='Profit Ratio')) + #baseplot\n",
    "    geom_col() + #type of plot\n",
    "    coord_flip() + #flip coordinates\n",
    "    theme_minimal() + #theme\n",
    "    theme(legend_position = 'none') + #wher to put legend\n",
    "    scale_y_continuous(breaks=range(-30,60,10), limits=(-30,50)) +#handle y ticks\n",
    "    scale_x_discrete(labels=list(df['Sub-Category'].unique())) + #change x-axis labels\n",
    "    labs(x='Sub-Category') #change label title\n",
    "    ) \n",
    "g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ~~Diverging~~ Stacked Bar\n",
    "Perfect for presenting survey results which involve sentiment (eg disagree, neutral, agreed). The diverging aspect is more of a gimmick than to show real difference between features so we do not implement it."
   ]
  },
  {
   "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>type</th>\n",
       "      <th>question</th>\n",
       "      <th>percentage</th>\n",
       "      <th>_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Not at all</td>\n",
       "      <td>Has grace under pressure</td>\n",
       "      <td>0.021531</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Not at all</td>\n",
       "      <td>Makes good coffee</td>\n",
       "      <td>0.021480</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Not at all</td>\n",
       "      <td>Likes the Beatles</td>\n",
       "      <td>0.019139</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Not at all</td>\n",
       "      <td>Is Kind to animals</td>\n",
       "      <td>0.028986</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Not at all</td>\n",
       "      <td>Can Play Jazz</td>\n",
       "      <td>0.028777</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         type                  question  percentage  _1\n",
       "0  Not at all  Has grace under pressure    0.021531   0\n",
       "1  Not at all         Makes good coffee    0.021480   0\n",
       "2  Not at all         Likes the Beatles    0.019139   0\n",
       "3  Not at all        Is Kind to animals    0.028986   0\n",
       "4  Not at all             Can Play Jazz    0.028777   0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/diverging-stacked-bar.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>type</th>\n",
       "      <th>question</th>\n",
       "      <th>percentage</th>\n",
       "      <th>_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Not at all</td>\n",
       "      <td>Can Play Jazz</td>\n",
       "      <td>0.028777</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Small degree</td>\n",
       "      <td>Can Play Jazz</td>\n",
       "      <td>0.059952</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Moderate Degree</td>\n",
       "      <td>Can Play Jazz</td>\n",
       "      <td>0.249400</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>High Degree</td>\n",
       "      <td>Can Play Jazz</td>\n",
       "      <td>0.249400</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Very High Degree</td>\n",
       "      <td>Can Play Jazz</td>\n",
       "      <td>0.412470</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                type       question  percentage  _1\n",
       "4         Not at all  Can Play Jazz    0.028777   0\n",
       "15      Small degree  Can Play Jazz    0.059952   1\n",
       "23   Moderate Degree  Can Play Jazz    0.249400   2\n",
       "30       High Degree  Can Play Jazz    0.249400   3\n",
       "36  Very High Degree  Can Play Jazz    0.412470   4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.question=='Can Play Jazz']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8732491045289)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = (ggplot(df, aes(x='question',y='percentage', fill='_1')) + #baseplot\n",
    "    geom_col() + #type of plot\n",
    "    coord_flip() + #flip coordinates\n",
    "    theme_minimal() + #theme\n",
    "     \n",
    "    #legend customizaation\n",
    "    guides(fill = guide_legend()) + #use guide_legend instead of guide_colorbar\n",
    "    labs(fill='Satisfaction Level') + #legend title\n",
    "    scale_fill_continuous(labels=list(df.type.unique())) + #\n",
    "     \n",
    "    #tick customization\n",
    "    scale_y_continuous(breaks=[i/100 for i in range(0,110,20)],   #breaks\n",
    "                       limits=(0,1),                              #limits\n",
    "                       labels=[f'{i}%' for i in range(0,110,20)]) #labels\n",
    "    ) \n",
    "g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spine\n",
    "Splits a single value into 2 contrasting components (eg Male/Female)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "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>Gender</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Women</td>\n",
       "      <td>UAE</td>\n",
       "      <td>-0.331337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Women</td>\n",
       "      <td>Saudi Arabian</td>\n",
       "      <td>-0.429000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Women</td>\n",
       "      <td>Malaysian</td>\n",
       "      <td>-0.460717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Women</td>\n",
       "      <td>Vietnamese</td>\n",
       "      <td>-0.469591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Women</td>\n",
       "      <td>Indonesian</td>\n",
       "      <td>-0.484211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender    Nationality  Response\n",
       "0  Women            UAE -0.331337\n",
       "1  Women  Saudi Arabian -0.429000\n",
       "2  Women      Malaysian -0.460717\n",
       "3  Women     Vietnamese -0.469591\n",
       "4  Women     Indonesian -0.484211"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/spine.csv')\n",
    "#make women negative and men positive\n",
    "df['Response'] = df.apply(lambda x: -x['Response'] if x['Gender']=='Women' else x['Response'], 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Men</td>\n",
       "      <td>German</td>\n",
       "      <td>0.486275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Men</td>\n",
       "      <td>British</td>\n",
       "      <td>0.484152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Men</td>\n",
       "      <td>Swedish</td>\n",
       "      <td>0.483712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Men</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>0.480357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Men</td>\n",
       "      <td>French</td>\n",
       "      <td>0.477833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender Nationality  Response\n",
       "35    Men      German  0.486275\n",
       "36    Men     British  0.484152\n",
       "37    Men     Swedish  0.483712\n",
       "38    Men   Hong Kong  0.480357\n",
       "39    Men      French  0.477833"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Nationality_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UAE</td>\n",
       "      <td>19_UAE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Saudi Arabian</td>\n",
       "      <td>18_Saudi Arabian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Malaysian</td>\n",
       "      <td>17_Malaysian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vietnamese</td>\n",
       "      <td>16_Vietnamese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Indonesian</td>\n",
       "      <td>15_Indonesian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Indian</td>\n",
       "      <td>14_Indian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Egyptian</td>\n",
       "      <td>13_Egyptian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Filipino</td>\n",
       "      <td>12_Filipino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Norwegian</td>\n",
       "      <td>11_Norwegian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Finnish</td>\n",
       "      <td>10_Finnish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Danish</td>\n",
       "      <td>09_Danish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Australian</td>\n",
       "      <td>08_Australian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>American</td>\n",
       "      <td>07_American</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Thai</td>\n",
       "      <td>06_Thai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Singaporean</td>\n",
       "      <td>05_Singaporean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>German</td>\n",
       "      <td>04_German</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>British</td>\n",
       "      <td>03_British</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Swedish</td>\n",
       "      <td>02_Swedish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>01_Hong Kong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>French</td>\n",
       "      <td>00_French</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Nationality      Nationality_\n",
       "0             UAE            19_UAE\n",
       "1   Saudi Arabian  18_Saudi Arabian\n",
       "2       Malaysian      17_Malaysian\n",
       "3      Vietnamese     16_Vietnamese\n",
       "4      Indonesian     15_Indonesian\n",
       "5          Indian         14_Indian\n",
       "6        Egyptian       13_Egyptian\n",
       "7        Filipino       12_Filipino\n",
       "8       Norwegian      11_Norwegian\n",
       "9         Finnish        10_Finnish\n",
       "10         Danish         09_Danish\n",
       "11     Australian     08_Australian\n",
       "12       American       07_American\n",
       "13           Thai           06_Thai\n",
       "14    Singaporean    05_Singaporean\n",
       "15         German         04_German\n",
       "16        British        03_British\n",
       "17        Swedish        02_Swedish\n",
       "18      Hong Kong      01_Hong Kong\n",
       "19         French         00_French"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#order countries by women's responses\n",
    "women_df = df[df.Gender=='Women'].copy()\n",
    "women_df['rnk'] = women_df['Response'].argsort().map(lambda x: str(x).zfill(2)+'_')\n",
    "women_df['Nationality_'] = women_df['rnk'] + women_df['Nationality'] \n",
    "women_df.drop(['rnk','Gender','Response'],1,inplace=True)\n",
    "women_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Gender</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Response</th>\n",
       "      <th>Nationality_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Women</td>\n",
       "      <td>UAE</td>\n",
       "      <td>-0.331337</td>\n",
       "      <td>19_UAE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Men</td>\n",
       "      <td>UAE</td>\n",
       "      <td>0.668663</td>\n",
       "      <td>19_UAE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Women</td>\n",
       "      <td>Saudi Arabian</td>\n",
       "      <td>-0.429000</td>\n",
       "      <td>18_Saudi Arabian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Men</td>\n",
       "      <td>Saudi Arabian</td>\n",
       "      <td>0.571000</td>\n",
       "      <td>18_Saudi Arabian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Women</td>\n",
       "      <td>Malaysian</td>\n",
       "      <td>-0.460717</td>\n",
       "      <td>17_Malaysian</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender    Nationality  Response      Nationality_\n",
       "0  Women            UAE -0.331337            19_UAE\n",
       "1    Men            UAE  0.668663            19_UAE\n",
       "2  Women  Saudi Arabian -0.429000  18_Saudi Arabian\n",
       "3    Men  Saudi Arabian  0.571000  18_Saudi Arabian\n",
       "4  Women      Malaysian -0.460717      17_Malaysian"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge new nation labels to main df\n",
    "df = df.merge(women_df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yDKvilhn3xLnt9XkVt6Hjikj9acy9iIiIiEiLUM+9SANZzww5i6uouq+KefMVt4HjZrPl9FWxKkJVn1dx2zduYYYcEWks6rkXEREREWkRLZfcm9mRZjav3u0QERERERlrTTcsx8yuA+5w96+XHN8R+Asw2d0fG2Fd84AV7v6tmjdURERERGSMNWPP/QXAfmbWWXL8IOBXI03sRURERERaTdP13AM/B34E/DvwKwAzexkwHTjYzGYDb3L3aVnZO4C5wPbACuAod7/KzL4A7AdEM/sicJu772ZmC4GbgPcCbwf+Bkx39wez+uYCnwI2Bv4X+Kq7/y4rmw28lTSF2KeAx4EDgS2Bk4Fu4Ax3P67wYcxsf+Bb2Tl3ADPd/a6s7JvAl4CJwGPAl939V9kquIcDBwObAH8AvuDuy2vw+xURERGRJtV0PffZQlGXkZLmgo8DAbi6+Fwz2wK4DjiNlATPAM4zs+3c/cfAfGCuu2/k7rsVXbo/8N/Aq4BHge8WlS0GdiYl9/OBn5rZhkXlewJXZeWXZm39EOnLxXuBI83sLVn79srq3jeLdQnwSzPbwMy2Bb4IvNPdJ2Z1FCYu+CLpy8y/A5sBtwGXj+DXJyIiIiItrBl77iENzbnJzCa5ex8p0b/E3fvNrPi8/YFfu/vPs/0/mtnPgE8DxzG0ee5+N4CZXVp8rrvPLzrvNDM7GtiOF6Ybu9Xdr86uXQB8GzjB3dcAd5vZ34C3AXcBhwDfc/e/Zdeea2bfAHYFcsA44C1m1lt4cpA5BPiauy/L4swGvm5mW7n7Q8N8rrLlcrlxWTtqbULhZy6XK+vC7u7u8Z2dnQwMDIzv7e2d2OpxRUQaUbbY1Yvo/tw4tABZ+2rK5N7d/2JmS4H/MLMrST337xjk1K2Bvc0sX3TsZcDF6wmxomh7DbBRYcfMvgZ8DugBImnIzCZF5z9Wcu1gxwr1bQ2camYnFZVvAGzp7ovM7CvAd4CfmNmvgcPd/eHsusvNbF3RdeuAyUBNk3tgFnBMjessVvZQonz+X/85F7VDXBGRBtVXekD354aidQjaVFMm95kLSD3244A7i3q/iz0ELHD3GUPUMdyCQS9hZu8DjgR2z2KuM7Mnqfwv0EPAKe4+b7BCd78UuNTMJgI/JL078OnsupnuvrDCuOU4KYtbaxNIN9LJwKpyLuzu7t6xs7Nz0cDAwG75fH5Ji8V9yT+WIiINalLpgRa/P4s0hWZO7i8hvaS6JTBnmHMWZ2Pbf0V6x+BtwFPufg+pR/31ZcScADwP/BN4mZl9ndRzX6kfAd8zs7+SXtwdT/risJD0ZGBL4BbgWeAZXvjv9SPgBDM7wN3/YWYbAx92959U0ZZB9fT0rAXW1rreokefq8p9dNjf378aoKOjY3W51zZ83MpXNxURGVOD3cta+v4s0iSa7oXaAnd/HLgW2IL04upg5ywnDdn5CimRf5TUE10YQ34+8EYze9LMfjuCsP+P9NLuvcCDQD/wcBWf4RfAscCFQJ40+87+WfG4rK3/zNq+JVCY2/8s0gu0V5vZU8AS4COVtkNEREREWkOIsayRKSI1kb2I1QdMqqCHZmfSC8y7dHV13dZKcafduVR/IaUsx885ut5NkDY1Zd6ClwxJbeX7s0izaNqeexEREREReTEl9yIiIiIiLULJvYiIiIhIi1ByLyIiIiLSIpp5KkyRlrNg+ylt/4Ka4pYZ98S57fV5Fbeh44pI/Sm5F6mTCmbGWVxFuL4q5tBX3AaOm82W01fFCglVfV7FbZ+4g82OIyKNR8NyRERERERahJJ7EREREZEWoeQeMLM3mVldFw8ys1+Z2X/Vsw0iIiIi0txaasy9mS0EFrj7OfVuS7nc/aP1boOIiIiINDf13IuIiIiItIiW6rkvMLOpwALgBOBIoBM41d1PzcpfDvwA+CTwT+D7JddvAZwN7AasAs4BTnH3aGYzgJnAb7KfzwLfcPf52bUbAMcC+wIbAdcD/+3uT2Zx/wf4WNamfwB7u/vy4qcOZvY64DxgJyACNwCHuvuTWYxlwFnAdOCNwM3AfoVyEREREWlPrdxzvwnQA7wW2BM4wcymZGXfBt4KbAu8B9iv5NpLgTywFbAHcChwQFH5LkAO2Az4EnCumU3Myk4C3ga8E3gN8BzpiwTAgUB3dvxVwOeA1YO0PQDfy9q/HbAl8N2Sc6YD+2TndANfHeZ3ISIiIiJtoCV77jPrgO+4ez/wJzO7l9QTvpTUq36Yuz8OYGYnAz/PticD7wc+4e5rgL+b2enA/sCFWd2PuPvZ2fZVZjYP2MbMFgMHA+8oqvvbwD/M7ECgn5TUv9HdlwB/Hazh7n4/cH+222tmc3lpcn+Wuz+cxbgC+EAlv6T1yeVy44Bxo1D1hMLPXC5X1oXd3d3jOzs7GRgYGN/b2ztx/Vc0d1wRkUaQLW41LN2fG4cWIGtfrZzcr8wS+4I1pGEykHq7Hyoqe7Boe0ugr2SIy7LseMGKkliFujcFxgO/N7Pi8nWkXv6LgcnApWb2SuAyYJa7ry0+2cxeDZwJvI900+kgDQ8qVtyG4s9Wa7OAY0apboDl5V6Qz+cLm4vaIa6ISIPoW98Juj83FC061qZaObkfTo405GZJtr9VUdkjwCQzm+TuhRvZ1tnx9fkn8Aywk7svG+Kc44DjzGwr4BrSuPuzS845iZTQ7+DuT5jZ3sC5I4g/Gk4C5o5CvRNIN9LJvPSLy7C6u7t37OzsXDQwMLBbPp9fsv4rGjbuev+hFBFpIJPWd0IL3Z9Fmla7JveXA0ea2R+y/W8WCrKXWxcBp5jZV0jj4w8jvSQ7LHdfZ2bnAqeb2aHu/mjWC/9v7v4LM9ud9AXgbuBp4HlgYJCqJpDG4veZWQ9wRMWftEo9PT1rgbXrPbFMRY8+V5X76LC/v381QEdHx+pyr22ouCurWFBeRGSMjeS+1zL3Z5Em1sov1A7nOOAe4D7gVtLwmGLTScNolpNmu/kf4KIR1v0t4G/AzWa2Kqv/nVnZ5sAVpB7be4A/AOcPUsexwA6kl3qvJXsfQERERERkOCHGui7MKm0qezGrD5hUQQ/NzsBiYJeurq7bmjXutDuX6i+fVO34OUfXuwnSJqbMW7DeMdytcn8WaWbt2nMvIiIiItJylNyLiIiIiLQIJfciIiIiIi2iXWfLEam7BdtPGdEcxO02hlVxy4x74tz2+ryK29BxRaT+1HMvIiIiItIi1HMvUgcVzpSzuIqQfVXMq6+4DRw3my2nr4pVE6r6vIrb+nFHMkuOiDQO9dyLiIiIiLQIJfd1YmZvMrNYtP8rM/uverZJRERERJqbhuVUycwWAgvc/Zxq6nH3j9amRSIiIiLSrtRzLyIiIiLSItRzXyNmNhVYAJwAHAl0Aqe6+6lZ+cuBHwCfBP4JfL/k+oVkTwDM7HXAecBOQARuAA519yezc5cBZwHTgTcCNwP7FcpFREREpD2p5762NgF6gNcCewInmNmUrOzbwFuBbYH3APsNU08AvpfVtR2wJfDdknOmA/tk53QDX63NRxARERGRZqWe+9paB3zH3fuBP5nZvaTe96XAvsBh7v44gJmdDPx8sErc/X7g/my318zm8tLk/ix3fzir6wrgA7X+MAC5XG4cMG4Uqp5Q+JnL5cq6sLu7e3xnZycDAwPje3t7J7Z6XBGResoWtRoR3Z8bhxYga19K7mtrZZbYF6wBNsq2e4CHisoeHKoSM3s1cCbwPtJNpwNYVXLaiiHi1Nos4JhRqhtgebkX5PP5wuaidogrIlJnfSM9UffnhqL1CdqUkvuxkwO2ApZk+1sNc+5JpIR+B3d/wsz2Bs4d5fYN15a5o1DvBNKNdDIv/eIyrO7u7h07OzsXDQwM7JbP55es/4qGjDvifyxFROps0khPbJH7s0hTU3I/di4HjjSzP2T73xzm3AnAaqDPzHqAI0a7cUPp6elZC6ytdb1Fjz5XlfvosL+/fzVAR0fH6nKvbZi4la9iKiIypsq537XE/VmkyemF2rFzHHAPcB9wK3DZMOceC+wA5IFrGWJsvoiIiIhIMfXcV8ndpxbtbl5StmvR9jPAf2Z/Cn4wWD3ufhewS0mo04rKty6Jcw5Q1SJaIiIiItL81HMvIiIiItIilNyLiIiIiLQIJfciIiIiIi1CY+5F6mDB9lNGPP9wf3//zsBiYJeurq7byomTLT7TB0yqYPYJxW2GuCfOba/Pq7gNHVdE6k899yIiIiIiLUI99yJ1MO3OpbGCyxZXEbKvirn1FbeB4x4/52iAvipWTqjq8ypu68edMm+BVjoVaSLquRcRERERaRFK7huEmR1pZvPq3Q4RERERaV5tPyzHzBYCuwLPFx3+u7uXLiJVy5gzgJkli1ydOFrxRERERKQ9tH1yn/lKtsqriIiIiEjTUnI/DDPbFzgB2Bg4H3gvcA5wKfAo8FF3/1N27iuAFcD7gTzwAPB54BhgHHAecDSwbVZHl5k9nYXaDPg68CZ3n5bVdxkwFdgQ+BtwqLvfkZXNA9YAmwMfzmId4O63j85vQkRERESagcbcD8HMtiUl9J8DXg30ArsAuPtzwGXA/kWX7AMsK0mwPwq8Gfg3YBpwoLvfA8wE/uzuG2V/Vg/ShOtJXwReTZrt4LKS8n2BOUA3cCNwZuWfVkRERERagXruk7lmdnLR/pXAg8C17n4jgJmdCnyt6JwLgOvM7HB37wcOAC4sqXe2u68CVpnZ94H9suvWy93/dZ6ZzQYOM7NXufsT2eFfuPutWflFpC8hNZfL5caRnjzU2oTCz1wuV9aF3d3d4zs7OxkYGBjf29s7sdXjiojUU7ao1Yjo/tw4tABZ+1JynxxeOubezM4BHi7su/s6M3ukaH+xmT0KfNTM/gTsDswoqfehou0HgS1H0hgz6yQNB/o0sCmwLivaBCgk9yuKLlkDjB9J3RWYRRpaNFqWl3tBPp8vbC5qh7giInXWN9ITdX9uKFqfoE0puR9aDtihsGNmHbw0OZ9HGprzRuDX7v5YSflWwB1F24UvB+tbwGg68AleGE8/kTSOvx5/UU8C5o5CvRNIN9LJwKpyLuzu7t6xs7Nz0cDAwG75fH5Jk8Yd8T+WIiJ1NmmkJ7bI/VmkqSm5H9pPgW+Z2VTgZuCrpBdri10CHEv6EnD0IHUcY2YHkXrfDwMK010+BmxpZuPcfe0g100A1pJ66V8BHF/dR6lcT0/P2qwtNVX06HNVuY8O+/v7VwN0dHSsLvfaholb+SqmIiJjqpz7XUvcn0WanJL75Awzm1O0n3f3yWb2edIY+W7Sy7V/oyjRdffHzexGYDfAB6n3OuBu4OXZ9YVx9DcCtwOPDvFE4CLgI6Se/ieAb1f38URERESkHbR9cu/uU4cpmw/MBzCzl5HGuT9cctoDQG6IHvhL3P28Qep9Dtir5PDsovKngb1Lyi8qKp9RUt+9aGydiIiISNtr++R+OGa2F/BbYIA07GYN8Kei8h7SDDgfqksDRURERESKaJ774X2c1FP/GGlxqr2zXnfM7BjgPuB/3L3cl3dERERERGpOPffDcPeZpAWnBis7lvQy7WBly9AwGREREREZY+q5FxERERFpEeq5F6mDBdtPGfGTnf7+/p2BxcAuXV1dt5UTJ1tZsg+YVMHUcorbDHFPnNten1dxGzquiNSfknuROph259L1LWQ2mMVVhOyrYm59xW3guMfPORqgr4qVE6r6vIrb+nGnzFugYaYiTUTDckREREREWoSSexERERGRFqHkvkpmdpeZfSjbnm1mC7LtrczsaTMbN8J6njazbUazrSIiIiLS2jTmfoTMbCGwK/B80eHPuvtbBjvf3R8CNhpp/e4+4nNFRERERAaj5L48X3H3c+rdCBERERGRwSi5r5KZLQNmuvt1Jce3Bh4AXuHuz2Y9/78nrXT7VuBPwAx3fzg7PwLbufu9ZjYPWANsDnw4q+cAd789O3cb4IfALsDjwEnuPm9UP6iIiIiINDyNuR9b/wl8EdgUWApcMsy5+wJzgG7gRuBMADPrAq4GbgU2Aw4EzjCz949es0VERESkGajnvjxzzezkbLvP3V9b5vWXuPttAGb2TWClmU129+WDnPsLd781O/ci4HPZ8XcBrwSOdffHq2xOAAAgAElEQVQB4A9ZT//+wO/KbM965XK5ccCIXgou04TCz1wuV9aF3d3d4zs7OxkYGBjf29s7sdXjiojUU7ao1Yjo/tw4tABZ+1JyX57Dqxxz/1Bhw93zZvYUsCUwWHK/omh7DTA+294SeDhL7AuWAR+pol3DmQUcM0p1w+CffVj5fL6wuagd4oqI1FnfSE/U/bmhaPGxNqXkfmxtVdgws0nAROCRMut4BJhsZp1FCf7WFdQzUicBc0eh3gmkG+lkYFU5F3Z3d+/Y2dm5aGBgYLd8Pr+kSeOO+B9LEZE6mzTSE1vk/izS1JTcj639zOxi4F7gZOCWIYbkDOePQB6YZWanADuRxt1/oqYtzfT09KwF1ta63qJHn6vKfXTY39+/GqCjo2N1udc2TNyVVSwmLyIyhsq537XE/VmkyemF2rE1jzTLTS+wLbBfuRW4ez+wF/AB0kw5lwBHuPtva9dMEREREWlG6rkfIXefOsTxrYu2ZxdtL+Ol492Wufu3hqgnFG3PKCm7t7iubP8DI2y6iIiIiLSJinruQwh/CSHMDCGMeByeiIiIiIiMrkqH5dxHeskyF0K4KISgOdZFREREROqsomE5McbpWa/9fsBBwG9DCPcD5wMXxhhbZ6LYGhlqWI+IiIiISK1UPOY+xthHejn0hyGEHUirrx4OHBdCuI6U6HuMcV1NWirSQhZsP2XE8w/39/fvDCwGdunq6rqtnDjZ4jN9wKQKZp9Q3GaIe+Lc9vq8itvQcUWk/mo1W85y4AHgMaATeCNwJfC/IYRdaxRDRERERESGUdVsOSGEj5B67I208MMlwKdjjPeEEN4AnA38X+DN1TZUpFVMu3NprPDSxVWE7atibn3FbeC4x885GqCvipUTqvq8itv6cafMW6CVTkWaSEXJfQjhOGAGsCWwkDTu/qoY43OFc2KM/wghHAvcXH0zRURERERkfSodlvN5YD6wTYzxgzHGBcWJfZG/k3r2ZT3M7C4z+1C92yEiIiIizavSYTmviTE+v76TYowrgQsrjFERM1sIvAfYzt2XZsfeBNxTvFBUo3H3t9S7DSIiIiLS3CrtuV8bQnjnYAUhhF1CCANVtKkWngKOq7YSM+uqQVtERERE2kYI4aMhhGtDCL0hhP4QwmMhhF+GED4eQhiTjtYQwiYhhBhCmDEW8RpJpT33w/2H6QLqndyfBXzNzHZy99uLC8xsInAG8HGgH1gAHOnuz5nZ1Gz/FOAIYImZ5YD73f0EM9sIeBI42d2/bWYbAHngje7+iJm9g7S41/bACuAod78qi/tK0vSgHwAeJA1rOsTdt87KlwEz3f06M3s7cCbpReRngauAr7r72uzcCBxMmnp0C+CXwH+6+2BDo0RERETGRAjhRGAW8DPgi8CjwGbAPoCTJmG5pm4NbAMj7rkPIWweQtg5hLBzdmjbwn7Rn3cDnyMlr/W0gpQcnzBI2ZlAD7At8E5Ssj2rqHwT4LXA64FPkF4Y3j0rey/wUNH+u4BHssR+C+A64LSsjhnAeWa2XXbuWdnPLYG9gQOHaf8A6cvFJsCuWbwvlZzzCeDdpGlH/w347DD1iYiIiIyqEMLHSTnVsTHGT8QYL48xLoox/jTGuB8pp1lR31aWL4Twinq3oRzl9NwfDBwDxOzPvEHOCaTE9NCqW1a9U4D7zex9QC+AmXUA+wLvdPc8kDez44A5wLHZdQGY5e7PZtf8Fjgn66V/P2l6z2+Y2YbAVFLyD7A/8Gt3/3m2/0cz+xnwaTM7Afg0sLO7Pw08bWY/JCXwL+Hufy3aXWZm5wIfzNpZcLK7r8zaeA2wM2na0ZrK5XLjgHG1rheYUPiZy5W3oHF3d/f4zs5OBgYGxvf29k5strhlXiciUlfZolYj0uz353LjNrI6LUB2OKmn/vjBCmOMfy7ez74MfAfYAXgauAI4Isa4OiufCvwW+Aip03QvYCVwdozxlJK6Pg8cBbwa+H22/RLZMJ3DgW2AJ0j57DGFd0mz8gtIHajHZT/nAf89kl9AIygnuZ9HSmQDcCPpQ95dcs5zwH0xxidq0bhquHufmX0POIn0NAFgU2ADYFnRqctIvekFT7j7mqJ6lpvZClIv/VTgEFKS/x5Sj/r52albA3ubWb6orpcBF2dxu4CHi8qKt1/EzLYhDe95O7BhVs+SktOKv/muATYfqr4qzSJ9qRsty8u9IJ//1694UTvEFRGps76Rnqj7c0MZ00lEQggvI+VGV4xk0pUQwqeAy0mJ9DGkYcYnAxsD00pO/xEpn/o/pJEL3wsh/C3GeF1W157Aj0m56gJS/rRgkJiHkzp/Twe+BmxHGuXRCXyr5PT5WZ0nAs+s7/M0khEn9zHGB8mG24QQdgduizGuGq2G1chZwGHAntn+P0lfQF4L/C07tjXwSNE16wapZyFpjP7rs+sWAv9Oery0f3bOQ8ACd59RerGZdZLG97+GF26Srxmm3T8C7gCmu/tTZnYY6YlDPZxE+qJRaxNIN9LJpAXQRqy7u3vHzs7ORQMDA7vl8/nSLz3NELfV/gERkdY2aaQntsD9udHzmkb2KtKT/hd1XmYv0HYWHVpHGgEyB7g8xvi5onMfA64OIXw3xnhX0TVXxhhnZ+fcSMrrPkUaDg1wNHBTjPGgbP//hRDGUzTsOoQwgTRK45QY45HZ4RtCCM8Dc0IIp5Z0Tv8oxnhqub+ERlDRC7Uxxt/VuiGjwd2fyYbdnJDtD5jZAuAEM/ssqVf8aNLKusNZCPwA+K27r8um21xINt4+O+cSYLGZ7QX8ivQ+w9uAp9z9HjO7EjjWzA4k/QU4ZJh4E0gz/qzKevFnUkbPSS319PSsBdbWut6iR5+ryn102N/fvxqgo6NjdbnXNkLccq4TEam3cu53zX5/rtNQllZReFJQugr7J4GfFu2fTep8fS3wlazHv+B32fVvB4qT++sLGzHGdSGEe0lfxgghdAK7AN8oiXsFL36n8t3ARsBPS2LeCLyCNBlKcX577aCfsgmU80LtUyGEXbLtVdn+UH/qkogO4XzSDDcFXyaNsbqPtCz3IlLv9HB+C0zkhfH1S0jvFhT2cfflpN79rwCPkcacncQL49W/SPoy9QhpdpvLGDppPgL4DCkRPJ8X/6UQERERaTT/JOU1k0uO/wZ4R/bn0ezYJtnPn5FGNhT+PE3KTUtHN+RL9p8DXp5tb0rKrx4vOeexkv1CzNtKYt6THS+NWVpf0yin5/40XviPchov/WbWENx9asn+86QZZQr7faSXMga7diGDjF3PEvdQtL8OeOUg5/2F9OLrYHU/QZolBwAz+ypFj64KU2Jm24uAN5VU8Z2i8heNo3P30nFiIiIiImMmxvh8COEW4IMhhM4Y40B2/EngLwAhhMKU3Suzn18E/jhIdeW82dwLPE96kbbYZiX7hZifYPD3Hh8o2W/IPHckyhlzf2zR9uxRaU0LM7NtScOAbic9+jmM9OKIiIiISCuYC1wNHAl8d5jz7iW95/D6GOPZ1QSMMQ6EEG4jvWx7elHRp0pOvZU0AcnkGOPPqonZ6CpdxErKN5705vZk0qOri4Hz6toiERERkRqJMV4TQjgZOC6EsBNpNpxHSS9lv480OmJVjDFmM9dcmr34eg2wmjQO/+PAkTHG+8oIfQLwixDCBbwwW870krb1hRC+A5wSQphMGnK9jjRZyt7AJ2OMa2gBI07uQwhnllFvjDEeVkF7Wpa730aaU1VERESkJcUYZ4UQbiZNmf5DUmK/kvSe43+STVEZY/xpCCFPmo++sBDnMtIMOKXj5dcX00MIM7O6ppGG+uxL6q0vPu+0EMIjpHnuv0Qac/8P0tOG52gR5fTc71XGuZE07ERESizYfkpZcw/39/fvTLop7tLV1XVbOddmi8/0AZMqmH1CcZsh7olz2+vzKm5DxxWB1INP6o1f33k3ADcMU76QQebrjzHuOcixc4FzSw4Pdu0CBpkDv6h8HoMv1No0yhlz/7rRbIiIiIiIiFRHY+5FxsjhK9cA9LFyaaVVLK4ivOK2aNzj5xwN0Fdx1Co/r+K2btwNTxyN9QtFZLRVldyHEF5OehHh5aVlMcayHgOKiIiIiEh1KkruQwgbkF6S2H+YOjqHON5SzOwu4DB3/3UN6noa2Nndh3xD3MymAgvc/SXz8YuIiIiMtaUzpt0LbFDreqfMW/D6WtfZDirtuT8G+AhpMaj5pDeiV5Pedn4D6Q3klmJmC4FdSQslFHzW3d9SqxjuvlGt6hIREREZI69jFJJ7qUylyf2ngdnAT0jJ/Z9ijIuBi0II80gz61xbiwY2mK+4+zn1boSIiIiIyGAqTe4nA/dlq4I9C2xcVDYfuAw4pNrGNQMzWwbMdPfrzGw28FbSfK6fIS2JPLMwZCfr/b8JeC9pgYW/AdPd/cGsPALbufu9ZvYxYA7wGmAV8AN3P7Eo7pdIK8B1Aqe6+6mj/2lFREREpJF1VHjdo0B3tv0AMLWorN0XatqTtBjCK4Gzgf9bUr4/aRjTq0i/x6GWZz4fONjdJwBvAa4vKtsE6CGt5LYncIKZTanVBxARERGR5lRpz/1C0jLCvwT+B5gTQtiOtLrXPsClNWld45lrZidn233u/tpBzvm9u/8CwMwuzK7ZxN3/mZXPc/e7s/JLgeOGiNUPvNnMlrj7k8BfisrWAd9x937gT2Z2L7ATUMVsaYPL5XLjgHG1rheYUPiZy+XKurC7u3t8Z2cnAwMD43t7eyc2WVwRkWbRbvfnsuM2Mi1A1r4qTe6PIvUeE2M8I4QQgE8BrwDOZOiEtdkdPoIx9yuKttdkPzcC/jlE+VAv0X6S9Hs+2czuBI5095uyspVZYj+Seqo1i/QC9WhZXu4F+Xy+sLmoCeOKiDSLdrs/lx23wZW1GnqzCCEsBN4PvDfGeEvR8e8DXwYOiTG29fuRFSX3McYVFCWpMcbTgdNr1SgBd/8zsI+ZvYz0/sJVwKZ1aMpJwGisZDKBdCOdTHqnYMS6u7t37OzsXDQwMLBbPp9f0mRxRUSaRbvdn8uOK3VzH3AgcAtACKEL+A9GYQRDM9IKtQ3IzDYgzUh0tbv3ZfPfD9SjLT09PWuBtbWut+jR56pyHx329/evBujo6Fhd7rUNEFdEpFm02/257LhSN/OBL4cQDosxPkN6/3AJRcOIQwh7ACeQpmi/HzgsxnhTVraQQSY4iTE+OJYfYrRU9EJtCOEVIYQTQwj3hRDWhBAGSv48v/5aZD32Bx4ws6eALwLT6tweERERkUbwOHAr6T1PSOsuzSsUhhB2JH0BOIw0wcm3gZ+HEDYpqmOkE5w0nUp77s8GpgM/BS4gvUjb0tx96hDHty7anl1S9ixFY95K63D364Di64vHx+0xRLyFwOYlx3Ydru0iIiIiLWYe8IUQwq+Bd5OG5RyclR0MnBdjvDnbvyaEcDvwMeCiwvUxxrsBQgjDTXDSdCpN7vcCvh5jPKuWjRERERERGYFfAj8EvglcFWN8Ns3vAqSO06khhIOLzu8CbijaH+kEJ02n0uR+APh7LRsiIiIiIjISMcb+EMJPgMOB95QUPwScEmOcPeYNawCVLmJ1DmmskoiIiIhIPRwPfCjG+PuS4z8mDdl5bwihI3tXdPcQwuQ6tHHMVdpzvxp4Xwjh96RHHPmS8phNjykimbmv3BBgUgWzQOwMLAZ26erquq2ca3O53ESgT3FbOO6Jc9vr8yruWMYVaWgxxseBGwc5flsI4UBgDrAtaWHQPwGHjm0L66PS5P572c+tgHcNUh7RvPciIiIiUkMxxqkjKYsx3sCLx9gPWUeM8UUTnDS7ShexqnQ4j0jbOnzlGoA+Vla8xsbiKsIrbovGPX7O0QB9VazcUtXnVdzWjbvhiaOxfqGIjDYl6SIiIiIiLaLiFWpDCK8iTf7/PtICASuBRcAPY4xP1KZ57cPMtgLuBl7l7kOuCGtmM4CZmtteREREREpVlNyHEN5ASuQ3Ia0Qdh9pYaWjgZkhhN1ijP+oWSsbhJktBHYlvZixDlgGOHCKu6+qpm53f4gWmmNVRERERMZepcNyTiO9Sf/GGOPuMcZ9Y4y7A1NIM+fMqVUDG9BX3H0CsDFpueN3AbeY2YZ1bZWIiIiItL1Kh+XsDvxXjPGh4oMxxodDCLOB86ptWKNz93XAX83sU6QnFweZ2bWkz74TacagG4BD3f1JADNbBpwFTAfeCNwM7OfuT5rZ1sADwCvc/VkzOxA4BtgUeAI41t0vKMQ3sxOAmcCzwDfcff7of2oRERERaWSVJvcdwPNDlD1PG72o6+5PmdkNwG7Ar0jThP4OmAhcCXwX+GLRJdOBfYAngeuBrwLfKa7TzMaTvgS8w93/bmabAa8uOmUX4GJgM8CAi8zsl+5e1jzIIiIiItWaMm/BuHq3QV5QaXJ/C/DtEMKiGOPKwsEQwsbAUaQe6XaSA3Z29/uB+7NjvWY2l5TcFzvL3R8GMLMrgA8MUec6YHsze8jdHwMeKyp7xN3PzravMrN5wDbAX6r/KCIiIiLSrCpN7r8G3AQ8GEK4EVhB6kX+IPAccGBtmtc0eoCVZvZq4EzSDEITSE8wSl+0XVG0vYZBXqJ199Vm9hngCOB8M/sDcIS73zlIHUPWUwu5XG4cMBrfyCcUfuZyubIu7O7uHt/Z2cnAwMD43t7eiU0WV0SkWbTb/bnsuI2s3FWNpXVUuojVXSGEHYDDgfcCbyZNhflj4PQY4/LaNbGxmdlE4EPA8cBJpIR+B3d/wsz2Bs6tpF53vx643sxeDhwLXAC8ozatLsss0tj/0VL2/yv5fL6wuagJ44qINIt2uz+3Wu4SxirQtDuXrgU2qHW9C7afMmafoZVUPM99lsAfXsO2NBUzC8AOwMmkF14vAN4PrAb6zKyH1PNeSd2bkWbh+Q3wDKlnfqAGza7EScBoLFM4gXQjncxLn24Mq7u7e8fOzs5FAwMDu+Xz+SVNFldEpFm02/257Lgijaji5L6NnWFmp5LGxD8I/BKY5u5rzOxY4CLSdKBLSS+9fr2CGB2koU8Xk2bduQP4Qg3aXraenp61wJCLalWq6NHnqnIfHfb3968G6OjoWF3utQ0QV0SkWbTb/bnsuCKNaMTJfQjhb8D0GOOdIYQ7SEnnUGKMcceqW9dg3H3qesrvIs1kU+y0ovKtS84/Bzgn217GC4/QHiU9BRgsxjxgXsmxzYdvuYiIiIi0g3J67heThpwUtodL7kVEREREZIyNOLmPMR5UtD1jVFojIiIiIiIVq2ixqRDCd0IIPUOUbRFC+M5gZSIiIiIilQohzAoh/Lbk2M0hhHtKjl0RQjh9bFvXGCpdSfYY0lvlg+lhdKdOFBEREZH2tBDYNYQwDiCE8HJgO2B8CGGzovPeB/xu7JtXf5XOlhMYesz9FqTZYkSkyNxXbggwqYJZIHYmveeyS1dX123lXJvL5SYCfYrbwnFPnNten1dxxzKuSCP6M2l68HeSFlT9N9L/648BuwE/DSFsB2wKLAohbAGcnZWtIk1kckqMMYYQZgAzSV8YPk+aHfC/gSeBH5I6rBcAh8QYI0AIYQ/gBOANwP3AYTHGm7KyhVmb3gu8HShMRvPgqP02BlHObDn7AvtmuxE4LYRQmsS/nPRhbqlN80REREREkhjj8yGEW0mzCt5EStoXkZL79wM/zY7dEWNcmQ3heQDYCngNcD2wArgwq3IX0lpFrwZmAOcBNwDvAV4B/BVw4NoQwo7AfGBv4Fbgo8DPQwjbxhj/mdW3P/Ax0pTolwLfBQ4Yjd/FUMrpud+AF5ZoDsB4Xrqw0nOked5Pqb5pIq3j8JVrCr1gfaxcWmk1i6toguK2aNzj5xwN0Fdx1Co/r+K2XtwNT5w7qfJwImNiIbA7cDwpoZ9NSu6/nJW/H1gYQpicbX8ixrgG+Hs2Dn9/Xkjul8cYzwUIIVxGSu5/FGN8EngyhPA7YGfgWuBg4LwY483ZtdeEEG4nJfMXZcfmxRjvzuq7FDiu9h9/eOXMlnMh2S8i+xZ0SIzx3tFqmIiIiIjIIH4HHBVCGA/sBPwxxrg2hLBJCGET0nj7w4Atgb4sUS9Ylh0veKywEWNcE0J40TFgDbBRtr01MDWEcHBReRepp79gxRDXjpmKXqiNMe6uxL46ZrbMzPbIto80s3l1bpKIiIhIM/hT9vMQ4M4Y49ps/2bS0JotSUN1HgEmhRCKn0ZtnR2vxEOk8frdRX/GxxhPrrC+UVHpC7WEEDqADwDbkMbaF4sxxoaZfsjMfgHsAWzh7iurrGsGMNPdd61F2wDc/cRa1SUiIiLSymKM/SGE3wNHkIbRFCwCjgLuKoyBDyEsAk4JIXyFNOb+MODYCkP/GLg6hPBr0pj7ccCuwP/GGJdXWGfNVZTchxA2Jz0SeSPp5dqQFRXPoNMQyb2ZvZr0wsPTwHTgB2MQs8vd+0c7joiIiEibWgh8kJTQFywCziS9VFswnTTzzXLSbDk/5oXx8WWJMd4WQjgQmANsC/STniIcWkl9o6XSnvu5wD9JPfcPA+8ijU/6LOmN4I/XpHW18Vngf0lTGR1Eltxnw2BWuPu3sv03Afe4e8j2DyTN178p8ATpW94fSFModZnZ01n9mwFfB3YAngL2AU42s8tJ3yZ3In3puQE41N2Lx32RxZoNvMndp2X7lwFTgQ1J0ygd6u53FLV7DbA58GHSG+AHuPvt1f6iRERERJpBjPF40gu1xceW8EKHc+FYjpSbDVbHPGBeybHS62eU7N/Ai8fYF5dNLdm/jjQMaExVuojVbsBpwKPZfogxPhRjPBG4mDHoHS/DQaRpiy4BdjazHdZ3gZmNB84CPuruE0hfXv7i7veQ5kP9s7tvlP1ZnV22F3Ad8Erg+6T/ub5HmiN1O9L4r++OsM3Xk74Rvpo008FlJeX7kr41dgM3kr6lioiIiEibq7TnfhLQG2NcF0J4ipSEFvwe+FbVLasBM3s78BZgvrs/aGa3kJL9r47g8nXA9mb2kLs/xovfnB7MYndfkG0/Q1rY4P5sv9fM5jLC5N7dLyj6DLOBw8zsVe7+RHb4F+5+a1Z+EfC5kdRbiVwuN440pqzWCtOqTsjlcmVd2N3dPb6zs5OBgYHxvb29E5skrohIs2i3+3PFcRtZuQufSeuoNLl/gLQSLcBdpPlCr872/w9Q1UurNXQQcLO7F1YGuxg43sy+MdxF7r7azD5DelHjfDP7A3CEu985zGUPFe9kY/3PJE3HNIH0lGTV+hpsZp2klc8+TRoStC4r2oQ0PAheOs3S+PXVW4VZpOFJo6XsF1Dy+X+tnbZouPMaNK6ISKNbXvJzxJr8/twwL0TWSFj/KdKKKk3urwH+HfgJabzTL0IIj5NeLNgc+GZtmlc5MxtHGr4yzswKyfDLgFcBe5JesN2w6JLNi6939+uB683s5aTx9hcA7+DFLw0XW1eyfxIpod/B3Z8ws72Bc0fQ9OnAJ3hhPP1EIE/9/pKeRHrHotYmkG6kkxnBl55i3d3dO3Z2di4aGBjYLZ/PL2mSuFrKXUSaxWTa6/5ccVyRRlRRch9jnFW0/asQwntIPfYvB26IMf6qRu2rxj6kz7c9aZhMwRmkHv1fALPM7LuklXb/9YXEzDYjjbP/TXbtGl5YjfcxYEszG+fuaxnaBGA10GdmPaSnACMxAVhL6qV/BSUvi4y1np6etVl7aqro0eeqch8d9vf3rwbo6OhYXe619YpbxaqlIiJjrZDgtsX9uZq4Io2o4nnui8UY/wz8uRZ11dBBwMXu/kDxQTM7nbTIwedJs/0sBXKk3uk9stM6gK+RhvFE4A7gC1nZjf+/vTuPk6Mq9z/+OTMMUUKSAYLgGBY14gaCBBRRMXB/KpuPKCrIZfUqoAJu6BWMbCaAghE0XBYXghgIiwiPgIiiIXIB0XBZIpsRQoAhMIAzhABhmNTvj3MaimYymZ7pnu6p/r5fr3lNd1Wd85zqma5++tSpU8BtwKNm1sIr73KWdzxxqqXuFON84qw6q/NL4KPEGyw8CXx3EGVERERE6uVxYM16N0KikGWrGmVStmEIy1j1kJRyWZZlE1a/mTSrzs7O8cShKhOG0EOzNXEWoSltbW23joa4ey9cNNj3jkhFpp86rd5NkIJZ68SZE2ii4/Nw4oo0okp67n/I6pP7DxBvKKBERkRERERkhA06uc+y7LhVrUtj7o8jJvZ3ACcMt2EiIiIiIlKZod7ECoAQwgdDCH8kTj21HvDJLMu2yrLssqq0TkREREREBm1IF9SGEHYg9tRPBW4FPpFlmVevWSLFMnPdtTSGVXFrE/fEmc21v4o7UnFFZJSqqOc+hDA1hPBnYB7xxkkfy7JsGyX2IiIiIiL1N+ie+xDCPOLdVv8K7Jpl2TW1apRIkZTNlNMzjDnvFwyjGYpb0LhptpyeYdxJYVj7q7jFi5tmyxGRUaqSYTk7pN9bABeHMOANUzUVpoiIiIjICKskuT++Zq1oEmb2O+BSd/95vdsiIiIiIsVTyVSYhUnuzewK4t1oX+/uT41UXHffZaRiiYiIiEjzGdZUmKORmb0O2AVYDuwzQjFbzazpXmsRERERGVlDmgpzlNsX+CcwFzgImAVgZscRryfoAT4FPA4cALwBOBloB05z95du0GVm+wHfTtvcCRzq7v9I6xYDZwF7AW8HNjWzucBcdz8rbbM/8C1gE+Ax4DB3v8bMdgFmAJNTe2YDx7h7ZmabAg8A+xNvFtYOzHb3r1X3ZRIRERGR0aYZe5MPAuYAvwK2NrN35dbtDlwGrANcAFwI/D9gc+ADwNFm9k4AM/sY8D3gs8QbeP0K+K2ZrZmrb19gT2A80JVvhJntAfwAODit3xF4MK1eDhxITNx3S9vsWbYfO6V2TQEOMrOdKn4lRERERKRQmqrn3sy2Ad4JzHH3B83sf4nJfqnX+0Z3vzJtOxf4LjDD3Z8F7jKzO4B3A/8AviE8Lq4AACAASURBVAh8393vSGXPNrNvAdsR79gLMMvd78/FzzfnUOBUd78xPX+otMLd5+e2u8PMLgQ+BFyaW368uy8H7jez+cDWwJ8qflFWo7Ozcwwwptr1AuNKvzs7Oysq2N7ePra1tZW+vr6xXV1dld5spV5xRURGi2Y7Pg85biOr9MZnUhxNldwTE/kb3L3UQ34+MD0l5RCHxpQ8u4pla6fHmwKnmNlJufVrEofolCwZoC0bA/1OXWxm7yUOBdo81TkG+E3ZZktX0a5qOwo4tkZ1AzxcaYHu7u7Sw/kDbdegcUVEGt3DZb8HbZQfnyuO2+AGnLNciqtpknszG0McQjPGzEqJ8RrEITW7D6HKJcAP3H32ANusXE35yatYdwFwJrCruz9nZj8CXj+ENlbDScDMGtQ7jnggnQQsq6Rge3v7lq2trfP7+vp26O7uvn0UxO2pJI6ISJ1NonmOz8OKK9KImia5B/Yg7u/mwHO55acRe/RvrbC+M4Hvm9n/AXcAY4nj5ue5+2AODucAZ5jZDcS7/r4BWNvd7yEeaP6dEvttiLP6/LnC9lVFR0fHCmBFtevNnfpcVumpw97e3uUALS0tyystW5e4Q79jqYhIPZQ+w4p/fB5mXJFG1EzJ/UHA+e7+QH5h6hW/AXikksrc/QozWws4D3gjcWjMX4B5gyx/mZmtA5wLbAR0AocD9wBfAn5oZqcB1wOXABMraZ+IiIiINJ+QZVm92yBNqLOzczxxuMqEIfTQbA0sAKa0tbVVdMalHnH3XrhIbzKpmemnTqt3E6Rg1jpx5gSa5Pg83LgijagZp8IUERERESkkJfciIiIiIgWh5F5EREREpCCa6YJakbqYu/nkMBrHkiruKIl74szm2l/FHam4IjJKqedeRERERKQg1HMvUkP9zJTTM4x57xcMoymKW9C4abacnmHcTWFY+6u4xYo7efZc3dVUZJRTz72IiIiISEGo575BmNmBwKHuvt0Qym4M3AWs5+5Vv5usiIiIiIwOSu5HiJk9k3v6WqAXeDE9P5F4h9ohcfclwNpDb52IiIiIFIGS+xHi7i8l32Z2M3CWu8/OLTuwDs0SERERkQJRct9gzGwGcCjwPPAtd5+Tlu8CzAAmE6dGmw0c4+6ZmW0KPAC81t2fr0e7RURERKT+dEFtY5lCHJ6zAXA4cLaZleYbXg4cCLQDuwEHA3vWoY0iIiIi0qDUc99YHnH3M9Ljy8xsNrAZ8Hd3n5/b7g4zuxD4EHBpLRvU2dk5BhhTg6rHlX53dlZ2uUF7e/vY1tZW+vr6xnZ1dVV6s5V6xRURaXjpBlbNdnwectxGVumNz6Q4lNw3lqVlz58lXShrZu8FTgY2B9YkJty/GYE2HQUcW8P6H660QHd3d+nh/IG2a9C4IiKNrCf3uNmOzxXHbXC6Z0GTUnI/elwAnAns6u7PmdmPgNePQNyTgJk1qHcc8UA6CVhWScH29vYtW1tb5/f19e3Q3d19e4PH7Vn9JiIiDWMCzXN8HnZckUak5H70GAf8OyX22wD7AH+uddCOjo4VQNXnzs+d+lxW6anD3t7e5QAtLS3LKy074nGHfrdSEZER19HR8XTTHJ+rEFekEemC2tHjS8AxZrYMOA64pL7NEREREZFGo577OujvLrRpzvvZZcs2zD2+lFVcPOvui9HYOhEREZGmp557EREREZGCUHIvIiIiIlIQSu5FRERERApCyb2IiIiISEHoglqRGpq7+eQAL931sQeYMIQp3rYGFgBT2trabq2krOI2QdwTZzbX/ipuzeOKyOim5F6khvZeuCgrW9QzjLnvFwyjKYpb0LjTT50G0DOMOyoMa38Vt1hxJ8+eq5nXREY5DcsRERERESkIJfciIiIiIgUxqpN7M/ugmf2r3u0QEREREWkEo2LMvZltD3wf2CItWgQc4+5XA2+uW8NERERERBpIwyf3ZjYeuAr4KjAHaAPeC/TVs11DZWYBaHX3F+vdFhEREREploZP7oHNgDZ3Py89fxGYB2BmU4G57r5her4Y+AmwD/AW4AbgP93932n9Z4EZwDrAz4EPAGe5+2wzeyPwM2ArIAP+AHwpV3YxcHaqe2Pgj8B/uXt3Wr8rcDKwCXA3cIS735LWzQNuArYHtgV2MrNbgeOBzwJrA9cCX87FuxCYCqwF3JHacmdaNxt4FtgQ+DDwALC/u9825FdZREREREa90TDm/j5ghZnNMbPdzWz91Wy/D7AH0AG0A18DMLO3EhP6zwOvA7qAKblygTj0pwN4O/AG4HtldR8IfByYBIwBTk91vwW4FDgKWC/F+Z2ZrZMrewDwFWAc8H/AScC7gfcAGwEvALNy218LvDW1dQFwYVlbPgucmvbxT8CPV/O6iIiIiEjBNXzPvbs/bWYfAL4JnAFMMrPrgS+soshP3P0hADO7FNgpLd8LuNrd/5TWnQJ8IxfnfuD+9LTLzGby6uR+VtoOM/sOcIuZHZTq/r27X5W2+6mZfRnYDfhVWvbLUs+6ma0EDgG2dffH07LvAv8yswPc/UV3P7cU1MyOA75iZuu5+5Np8RXufmNa/0vil5aq6+zsHEP8IlNt40q/Ozs7KyrY3t4+trW1lb6+vrFdXV3jR0lcEZGGl26c1WzH5yHHbWSV3vhMiqPhk3sAd78b+ByAmW1KHB5zPnB0P5svzT1+ljjkBWKP/EO5Olea2SOl52b2OmLv9weJb/QWYFlZ3Utyjx8E1gTWJ/byLy7bdnFa3l/Z9YGxwE1mli+zEtjAzJYShw99Om27Mq2fCJSS+/L9HEttHAUcW6O6AR6utEB3d3fp4fxRGFdEpJH15B432/G54rgNTjcka1KjIrnPc/fFZvYTXj1MZXU6gXeVnphZC69Mvk8iJvTvcvcnzezjxC8ReRuXPe4FngAeAbYu23ZT4lCdkpW5x08AzwFbufvi8oaa2X7AJ3l5PP14oJv6vFFPAmbWoN5xxAPpJF79JWpA7e3tW7a2ts7v6+vbobu7+/YGj9uz+k1ERBrGBJrn+DzsuCKNqOGTezN7G/Ax4CJiz/v6xCEoN1VY1SXAt9NFuDcQx+Lnx8SPA5YDPWbWARzZTx1fMrMricn5dOAid+8zs4uBo8zso8B1wP7E5P/q/hqSzhqcDfzIzL7k7o+mMwfvc/crUltWEHvpX5ti1UVHR8eK1Jaqyp36XFbpqcPe3t7lAC0tLcsrLTvicZ8axk3jRURGWEdHx9NNc3yuQlyRRjQaLqhdBmwD3Jge3wY8Q7xAddDS0J4vAOcSL6bdgDgLTSlxPZ7Ys99NTMov76eaXwJO/IbfR7xAFne/D9ibeIHrk8ChwG7u/tQATfp2in+DmS1L+/eeXJz7iWcE7gb+Vsm+ioiIiEhzClmW1bsNdWFmaxDHre/h7jcMYvvFwKHufk2t29YM0kVbPcCEIfTQbE2cQWhKW1vbrY0cd++Fi5rzDSYjZvqp0+rdBCmQybPnhmY5PlcjrkgjavhhOdVkZh8D/kzsdZ9GvBD1lro2SkRERESkSkbDsJxq2o04bv8x4EPAx939hfo2SURERESkOpqq597dDyWOhx9K2U2r2xoRERERkepqquReZKTN3XxygNE5llRxR0ncE2c21/4qbs3jisjo1mzDckRERERECks99yI1soqZcnqGMff9gmE0R3ELGjfNltMzjDsqDGt/FbcYcSfPnqu7mYoUhHruRUREREQKQsm9iIiIiEhBaFjOKpjZp4CvE+9auwJ4EJgL/Njdn69n20RERERE+qOe+36Y2VeBM4EfAx3uvh6wL7AJsFGFdbVVv4UiIiIiIq+mnvsyZjYBmA4c5O6XlJa7+13Al9M2gdirfwgwEbgZONjdH07rM+Dw9PN6YLyZLQbOAD4LvBW4DjgA+AHwGWApsJ+735Lq2B/4b2BjoAv4obufkdZNJZ5FmAEcDbQCp7j7KbV4TURERERkdFDP/au9D3gNcPkA2xwG7AN8BNgAuBW4qGybTwEfTOtLPgPsDnQAbyJ+KbgCWDeVn5Xb9gng48B44CDgFDPbNrd+Yqpnk1TnDDObPNidFBEREZHiUc/9q00EnnD33tICM/sdMelfk9hb/0XgG+6+OK0/DvimmW3s7ktSsZPd/fGyume5e2cqczWwrbtfmZ7PBb5tZi3uvtLdr86Vu97MrgV2AP6Wlq0EjkntvMXM7gG2AoYxY9qrdXZ2jgHGVLPOZFzpd2dnZ0UF29vbx7a2ttLX1ze2q6tr/CiJKyLSsNINs0qa7fg85LiNrNIbn0lxKLl/tSeAiWbWVkrw3X0XADO7mTgEZlPgIjNbmSu3EpgElJL7JbzaY7nHz/bzvI34BeJ5M9sFOBbYjHiGZS3g7tz2T+W/gKTyaw9+NwftqNSOWnm40gLd3d2lh/NHYVwRkUbU08+yZjs+Vxy3weneBU1Kyf2r3UScHWcP4JJVbLMEONTd5w1Qz8oB1g3IzMYAvwY+B/za3XvN7DfU5416EjCzBvWOIx5IJwHLKinY3t6+ZWtr6/y+vr4duru7b2/guP19WIqINKIJucfNcHyuSlyRRqTkvoy795jZMcAZZtYC/I74Zt8MWD9tdiZxjPv+7v4vM1sH+LC7X1ylZqxJHArTBbxoZh8hju+/t0r1D1pHR8cK4pedqsqd+lxW6anD3t7e5QAtLS3LKy07onGHfqdSEZERlT+mNcXxuUpxRRqRLqjth7v/EDgC+BrwKDHJvgD4H2Jv/k+IF8BeaWZPA7cDH61i/GUp/gXAv4H9gN9Wq34RERERKaaQZVm92yBNKF281QNMGEIPzdbAAmBKW1vbrY0ad++Fi/Tmkpqbfuq0ejdBCmDy7LkvDftshuNzteKKNCL13IuIiIiIFISSexERERGRglByLyIiIiJSEJotR6RG5m4+WWNYFbf2cU+c2Vz7q7g1jysio5t67kVERERECkI99yI1MMBMOT3DmP9+wVALKm5x46bZcnqGcVeFYe2v4hYjbn62HBEZ3dRzLyIiIiJSEEruR4iZ/aeZ/WmA9Rub2TNmNmY19RxoZjdXv4UiIiIiMtppWM4Qmdk8YDvgRSAD7gW+4e7X97e9u88B5uTKZ8Db3f2etH4JsHaNmy0iIiIiBaae++H5qruvDUwAzgYuN7O28o36WyYiIiIiUm3qua8Cd19pZnOAc4DXm9nngHcBTwN7ACeb2VLgUHffzszmp6ILUg/+14FrgQeA17r782Z2AHAssD7wJHC8u59bimlmM4BDgeeBb6UzAyIiIiLSxNRzXwVm1gocACwBOtPijwHXAOsCp+e3d/cd0sMp7r62u59TVt9Y4CfALu4+Dngv8PfcJlNSnA2Aw4GzzWx8VXdKREREREYd9dwPz0wzOxl4LbASOMDdXzQzgAXuPjdt91xaVomVwOZmtsTdHwMey617xN3PSI8vM7PZwGa88gtAVXR2do4BBrzId4jGlX53dnYOuGG59vb2sa2trfT19Y3t6uqq9EtNveKKiDSsdMOskmY7Pg85biOr9MZnUhxK7ofn6+5+lpm1ANsDV5nZv9K6JUOt1N2Xm9lngCOBn6fZcY5094Vpk6VlRZ6ldhfjHkUcHlQrD1daoLu7u/Rw/kDbNWhcEZFG1NPPsmY7Plcct8Hp3gVNSsl9Fbj7SuAGM7sP2DEtXjnMOq8FrjWz1wDHA+cC2w6roUNzEjCzBvWOIx5IJwHLKinY3t6+ZWtr6/y+vr4duru7b2/QuP19UIqINKoJucdFPz5XLa5II1JyXyVmth3wDmAhcYrM1XkMeDNwTz91bUAcZ38d8ByxZ76vao2tQEdHxwpgRbXrzZ36XFbpqcPe3t7lAC0tLcsrLTticYd+l1IRkRGXP6YV/vhcxbgijUjJ/fCcZmanpsdLgWnufk1K9FfnWOBnZvZa4JvAH3LrWoBvAOcT59C/Ezi4es0WERERkSJScj9E7j51gHXH9bNsNjA79/xs4tz4eaXxcY8CH1pF3a+oJy3bcHXtFREREZHi01SYIiIiIiIFoeReRERERKQglNyLiIiIiBSEknsRERERkYLQBbUiNTB388mvuHlIuvtjDzBhCFO8bQ0sAKa0tbXdWklZxW2CuCfObK79VdyaxxWR0U3JvUgN7L1wUbaKVT3DmAN/wVALKm5x404/dRpAzzDurDCs/VXc0R938uy5upOpSIFoWI6IiIiISEEouRcRERERKQgl93VmZoea2bzc82fMbLPVlJlqZktr3jgRERERGVU05r6MmW0PfB/YIi1aBBzj7lePRHx3X3sk4oiIiIhI8Si5zzGz8cBVwFeBOUAb8F6gr57tEhEREREZDCX3r7QZ0Obu56XnLwLzAMzsOuAX7j7HzN4G3A183t1/bmaTgHuBdnfvNbOdgRnAm4H7ga+4+19SPRsD5wLvAe4A5ucbYGYZ8HZ3v8fMdgVOBTYClgGz3P3E3LaHA0cDrcAp7n5K1V8RERERERk1NOb+le4DVpjZHDPb3czWz62bB+yYHk8lJu2l5zsCN6bEfktir/9XgHWB7wKXm9nEtO0FxC8C6wNHAP81QHt+Dhzi7uOAdwLX5tZNBDqATYDdgRlmNrniPRYRERGRwlDPfY67P21mHwC+CZwBTDKz64EvEJP7c9OmHyL2zB+Xnk9N6wEOAX7m7jek51eZ2W3ArunC2e2B3dz9eWCBmc0B3r2KJvUC7zCz293938Dfc+tWEq8F6AVuMbN7gK2I1whUTWdn5xhgTDXrTMaVfnd2dlZUsL29fWxrayt9fX1ju7q6xo+SuCIiDSndLCuv2Y7PQ47byCq98ZkUh5L7Mu5+N/A5ADPbFDgbOJ+YwHeY2UbAB4EvA18xs7cQe+5/karYFJhqZofkqm0D/kDsae9x957cugdZdXK/J/Ad4GQzWwgcXRreAzyVEvuSZ4FaXIx7FHBsDeotebjSAt3d3aWH8wfarkHjiog0mp5VLG+243PFcRucbk7WpJTcD8DdF5vZT4AL3f0FM7uJ2DP/hLs/lXri9wM2AG5JxZYAP3D348rrS+PtJ5jZeHcvfaPeeID4fwP2MLM1gC8ClxGH84ykk4CZNah3HPFAOol4PcGgtbe3b9na2jq/r69vh+7u7tsbNO6qPixFRBrNhLLnRT8+Vy2uSCNScp+TLpT9GHAR8BAxkf48cFPaZB5wJC8Pz5kHzCaNt0/LzgGuNLM/AjcSh7RsB/zT3ZeY2c3AiWb2DeAdwH8SL84tb8uawKeBK929x8yeoQ6z9nR0dKwAVlS73typz2WVnjrs7e1dDtDS0rK80rIjFvepqo6OEhGpmfLjWeGPz1WMK9KIdEHtKy0DtiEm5cuA24BngAPS+j8D43l5fP31xG/8pee4+61p+1OBJ4nDbr7By6/1PsSLY58AZvHycJ7+7Ac8YGZPA4cBew9j30RERESk4NRzn+PujwB7DbD+BnJj2Nz9Kfr5guTufyCOse+vjsW8PMtOf+vzY+R2XsU284ANy5Ztt6o6RURERKQ5qOdeRERERKQglNyLiIiIiBSEknsRERERkYLQmHuRGpi7+eRXzC+cbhLTA0wYwiwQWwMLgCltbW23VlJWcZsg7okzm2t/FbfmcUVkdFPPvYiIiIhIQajnXqTK9l64KBtgdc8w5sBfMNSCilvcuNNPnQbQM4w7KwxrfxV3dMedPHuu7mIqUjDquRcRERERKQgl9yIiIiIiBVGY5N7M5pnZoWXL3mZmAw2RGLH2mNnWZva4mR1Rj/aIiIiISPFpzP0IMLP3A78FjnT3X9S7PSIiIiJSTE2V3JvZeOA0YDegF5gLHO3uL5jZ1PR8BnA00Aqc4u6npLKvAWYBewJPpHpmufuAFyOZ2YeBS4BD3P2i3PLNgP8BpgCPAye5++y07jhgC+Ap4DNAF3Cou/8xrd8YmA1sC9wOzAe2d/epQ31tRERERGT0K8ywnEH6MdABvBV4D7ATcFRu/cS0fhNgd2CGmU1O674LvBPYDNge2GsQ8XYhJvb7lSX2bcCVwI3ABsABwGlm9qFc2d3TNusCZwD5Hv8LgbuA9YHDgIMG0RYRERERKbii9dzPNLOTc89f+vJiZi3AZ4H3uHs30G1mJwCnAsenzVYCx7h7L3CLmd0DbAUsSmWPcPeuVN8PgA+upj07AvcD88qWv5eYtB/v7n3AzWY2G9gPuD5tc5O7X5FinZf2bSKwFvA+YBd3fx64zczmANus7sUZis7OzjHAmBpUPa70u7Ozs6KC7e3tY1tbW+nr6xvb1dU1fpTEFRFpOOlGWeWa7fg85LiNrNIbn0lxFC25/7q7n1V6YmZvA+5OT9cH1gQW57ZfDLwh9/yplNiXPAusnR53AA/l1uUfr8qxxB7435nZzu7+TFr+BuChlNjn2/LR3POlZe0gtWVDoMfd82/ah6hRck88s3FsjeoGeLjSAt3d3aWH80dhXBGRRtIzwLpmOz5XHLfB6R4GTapoyf1AngBeIA65uSMt2xR4ZJDlO4GNiGPcSY9X5zngY8BVwNVmtou7L08xJ5lZay7BH2xbOoEJZjbO3ZdV0JahOgmYWYN6xxEPpJOAZavZ9hXa29u3bG1tnd/X17dDd3f37asvMeJxB/qwFBFpJBP6WVbk43NV44o0oqZJ7t29z8zmEsfR70sc3jIN+NUgq7gIOMrMbk7Pjxxk3GfNbHfgauAqM9sV+CvQner7AXHozwHAJwdR35LUhhlm9k3i9QP7APcNcj8q0tHRsQJYUe16c6c+l1V66rC3t3c5QEtLy/JKy45I3KHfoVREZET1dywr9PG5ynFFGlGzXVB7BPAkMRFeQDx9d9Igy56Qyv0TuAm4nHgmYLVSb/1uxBl4rgTaiD36OxFnyvkVcZrMPw+yLfsA7yKejTgTmEMNEnARERERGV1CltXlHk+jnpl9gjhV5uTVblz7tpwOjHX3z9e7LYOVLuLqASYMoYdma+KXsyltbW23NlrcvRcu0ptKRsz0U6fVuwkyik2ePfdV47KLfHyudlyRRtQ0w3KGy8w2JE6DeQNxjPt3gUvr1JYpwNPEWXzeTxzSs3c92iIiIiIijUPJ/eCtQZxv/k3EC24uB75Xp7ZsAFxGnAHoUeA4d7+mTm0RERERkQah5H6Q3P1h4l1j687drybO+iMiIiIi8hIl9yJVNnfzyRrDqrgjF/fEmc21v4pb87giMro122w5IiIiIiKFpZ57kSoaxEw5PcOYB3/BUAsqbnHjptlyeoZxd4Vh7a/iju64/c2WIyKjm3ruRUREREQKQsn9KGRm88zs0Hq3Q0REREQai4blVMjM5gHbAS/mFu/r7pfXp0UiIiIiIpGS+6H5qruftaqVZtYKZO6+cgTbJCIiIiJNTsl9lZjZYuAsYC/g7cCmZvYscAqwG/G1vhQ40t2fN7OpwFxgBnA00Aqc4u6npPpagK8BhwCvB5YA+7l7aUqzN5jZn4FtgDuAfdz9wRHYVRERERFpUBpzX137AnsC44Eu4FxgDPAO4G3AW4Dv5rafCHQQb0i1OzDDzCandUcAXwA+mer7BPBkrux+wJeB9Yh3qa3X3XJFREREpEGo535oZprZyelxj7uX7hY7y93vBzCz1wEGrOvuy9Ky6cB5wHfS9iuBY9y9F7jFzO4BtgIWAYcC09x9Ydr2vrI2zHb3u1K9FwAnVHsnATo7O8cQv6BU27jS787OzooKtre3j21tbaWvr29sV1fX+FESV0Sk4aQbZZVrtuPzkOM2skpvfCbFoeR+aL6+ijH3S3KPNyUOtXnIzErLQlpW8lRK7EueBdZOjzcmJvmrsnQV5artKODYGtUN8HClBbq7u0sP54/CuCIijaRngHXNdnyuOG6D0z0MmpSS++rKX0C7hDijzuvc/YUh1LUEmAzcVo2GDcNJwMwa1DuOeCCdBCyrpGB7e/uWra2t8/v6+nbo7u6+vcHiDvRBKSLSaCb0s6yox+eqxxVpRErua8Tdl5rZVcDpZnY00E08cLzT3a8ZRBXnACeY2V3A3cRE/4WRvmi2o6NjBbCi2vXmTn0uq/TUYW9v73KAlpaW5ZWWrXncod+dVERkxPV3LCvs8bkGcUUakS6ora0DiL33txF7dH8PbDbIsqcDvwR+CzwNXAasW4M2ioiIiEhBhCzL6t0GaULpIq4eYMIQemi2BhYAU9ra2m5d3fYjGXfvhYv0hpIRNf3UafVugoxik2fPfdW47KIen2sRV6QRqedeRERERKQglNyLiIiIiBSEknsRERERkYLQbDkiVTR388n9zis8GseSKu4oiXvizObaX8WteVwRGd3Ucy8iIiIiUhBK7kVERERECkJTYYqIiIiIFIR67kVERERECkLJvYiIiIhIQSi5FxEREREpCCX3IiIiIiIFoeReRERERKQglNyLiIiIiBSEknsRERERkYJQci8iIiIiUhBK7kVERERECkLJvYiIiIhIQSi5FxEREREpCCX3IiIiIiIFoeReRERERKQglNyLiIiIiBSEknsRERERkYJQci8iIiIiUhBK7kVERERECkLJvYiIiIhIQSi5FxEREREpCCX3IiIiIiIFsUa9GyDFYGbPlC16DXC1u1tavznwM+BdwP3AF939L2ndfwA/B8YAX3X3i9LyNuAGYE93f3g18fcEjgfeCDwBfM3dL6tVXDObDewDvJBb/A53X5LWb5Tqfj+wFPimu1+W1m0JXAisD5zo7j/K1XsN8B13XzDQ/qZt1wPuBRa5+3ZpWc1eZzM7B9gNGAc8BZzj7ifWMq6ZjQHOAP4DmAgsSa/ZnBHY38OAA4EtgN+4+965dTX9fy5rRztwDrAL8DQww93/J/2PXQK8FfiFu38jV+ZnwJXufnkFceqyv3X+G4/4/3Qu9ki/f2dTx2NWHY7Rdf1MEqkn9dxLVbj72qUfYALwGHAxvHRAdOA3wDrAycAVZrZOKv4T4AvAh4Ezzaw1Lf8mMHcQeGDxTwAADfxJREFUH5I7AacBhxI/pLcFbqt1XGBmfr9LH5LJhcQP7ompXeeZ2WZp3UnAj4gfKt8xsw3TfuwD3DuYxD75IXBn7nWo9f6eBkx29/HAB4F9zezTNY67BtBJTPwmAIcA/2Nm7xuB/e0EpgM/zS8cgbjlZhFfhw5gd+B7ZrYjcBRwHbAx8HEz2ya1b3tgYiWJfVKv/a3n37ge/9MlI/3+hTods+pxjK7nZ5JIvSm5l1rYGVgb+HV6PhVYCzjF3Ve4+6+AB4BPpvVvBK5394XACmA9M3sjsUftx4OIdwJwgrvf4O4r3f1xd79/BOL2y8zeArwH+K67P+fufwCuBfYri/so8E9gYzObAHwdmDbIGFOBNwO/zC2eSg33193vcvfncotWAm+pZVx3X+7ux7j7/elvewPwv8D2I7C/l6UE+YmyVTWNm2dmY4FPA9PcfZm7/x8wG/hcLs4y4G/Am8xsDWLSeEQlceq5v3X+G4/4/zTU5/27mvbU+phV72P0SH8midSVhuVILRxE7N0ofWhuDtzp7itz29yWlgMsBP7DzB4ifrg+QTwl+jV37xsoUOpReQ9wlZktIh6w/wB8tZZxk4PN7GDgIeB0d/9Fbn8fdPfusrhb5+LuZGbLgE2BfxF7xk5OidqA0jCGWcBngSm5VbXeX8zsJGLiuBbwIPArYM9ax83FHwtsA5w+Evu7CiMZdzMguPtdZbG+DvyR+H90M/H/YEZa/uuyHtnhGtHXeaT/xiP9P13P9y91OGbV+RhdMmKfSSKNQMm9VJWZTQQ+BuyQW7w20FO2aTfxVCnE05+nE8dE7k/sPVkCLDWzK4B2YJa7X9JPyA2ANuAzxN6YZ4ALiKeAF9Uw7o+BI4F/E0/nX2pmPe7+6wH2d1x6fCRwJnAwMRl7M7ARcJ2ZXQBMAi5291n9xIU4HOP37n6nmeWTg1q+zgC4+1FmdjQx+fp42v+axwUwswCcC9xC7FV8z0jE7ceI7G8u1tP9xBpHTK7OBP6Sfi8D9gA+bGZnAu8E5rv7oM4GraYNI7K/9fgb1+F/ul7v33ods+p1jAbq8pkkUndK7qXa/pN4gdhfc8ueAcaXbTeBmIzg7rcBHwIws3HAn4njb39KHCN5JbDQzK5z96fK6nk2/Z7laRykmc0ALgdOrFVcd78193SemZ1BHD7x60Hs74PAriluK3A98fT3UcA/iBc13mpmfyrrsS2dPt8P2JJXq+XrnN/3DPibme0MHAc8XOu4Kek7C3gD8BF3zyxeMFfz/e3HSMZdZaxUdq/SQjO7HPgGcADx2P4h4Foz29ndr6lg/wbdBqje/tbzbzxS/9P1fP/W65hFnY7ROSP9mSRSdxpzL9V2ELHnLW8hsIWZ5f/ftkrLy00HTnX3HuKsHX9Ljx8GJpdvnE4jPwRk/dRVs7j9WAmEXNxN05jU1cU9gjiryQO5uC8QL7TbvJ/t309Mfhab2RPEC7+mpMf3j+D+QkwgJ1Pj1zklfWcA7wZ2cffladVI/n3zRjLufUBmZm8fKJaZfQJY6u435eJkwN+JF0EOR833t4H+xrX+n26k9++IHLMa4Bg9op9JIo1APfdSNWa2NXEowPllq+YBzwPfMLMfE8ezvok4U0G+/LuBt7j7V9KiB4jjPHuIF7mtahzxz4DDzOxqYDnwbeJMCDWLa2afAX6X4m0PHAYcDuDu/zSzvwEnmNm30/qPAkeX1fEG4qnq0uniB4AdzexG4hCBH/azrxcTx1qXfJrYg2bE2SBqtb8TUowriL1e7wO+CHyP2v99ZwHbAf/h7vkhKjWNa/Hi1NJPi5m9Bugbgf19ibsvN7NLiTPkHJTiHEj8vynFGUv83/pwLs5UMzuPmEwO6gLAOu/viP+N6/Q/XZf3bypXr2MW1OEYncrV6zNJpK7Ucy/VdBBwlbs/ll/o7r3ED69PEcc1fgfYo+x0dQtxDObhuaJHEXuJ/kGcW3npKuKeSJx7+C7ihV6lOZRrGfcwYm9UD3A2cZaJubn1ewPvAJ4knso90N3vK6vjNOJc0r3p+UnATqled/e/lwd192fd/eHSD3H87AvpeS33NyMmlQ+mff458YN8Vi3jmtkmwJeIr+VDZvZM+jl6BP6vpgHPpXo/nR7/dATilvsy8fV/lJicHePuf8qtPwE4zV++GPJs4nSGjxN7F1+RsAygLvtbx7/xiP9P1/H9C3U6ZiX1OEZD/T6TROoqZFl/Z8pERERERGS0Uc+9iIiIiEhBKLkXERERESkIJfciIiIiIgWh5F5EREREpCCU3IuIiIiIFISSexERERGRglByLyIiIiJSEEruRUREREQKQsm9iDSdEMJxIYQs9/NECOGGEMKu9W5bowshTC177Z4JIdwZQvhKCKF1CPVtlf4ea60izjbVa72ISPEpuReRZvUc8L70czCwJvDbEML2dW3V6HEQ8bXbE7gNOA345hDq2Qo4FlirbPmtqf67h9FGEZGms0a9GyAiUicrsyy7ufQkhHAT8AhwAHBj3Vo1eizMsuzvACGEa4EpxIT/5GpUnmXZ08DNq91QREReQT33IiJAlmWPAl3AxvnlIToyhHBfCGFFCOH+EMLXyraZFEK4OITwWAjh+RDCAyGEH+XWH5eGr2wbQrglbXN3CGH38naEEA5O61aEEJaEEKaHENbIrT8wDVfZOoTwuxDC8hDCP0MI+5fV8/4QwvwQQk8IYVkaOnNA2Ta7hRD+GkJ4LoTQFUI4M4QwdgivXQbc2c9rt1sI4Q8hhMdDCE+nWDvn9wU4Nz3tSvu1OK171bCcEMJrQgg/DCE8kl6fO0MI+1TaXhGRIlNyLyIChBDWBtYF/lW26nTgBOA8YDdgNvD9EMKhuW1+CbwLOALYmTjMpHz8eRtwUarnk8Ai4DchhM1zbTgcOBv4E2DAWcC30rJyvwKuBfYAbgdmhxDekeoZD1wFPA18Nm1zDtCei/UpwIlJ+SdSnE8CP1/Va7QaG/Pq1+6NwG+B/YjDd/4XuDqEMDWtvwqYnh7vTByG84kBYswBvgTMJL4+fwfmhBD2G2KbRUSKJ8sy/ehHP/ppqh/gOOAZ4tDENYCNiInjk8Bbctu9GVgJHFxW/gfAo0BLev4McPhq4mXA53LLWoEHgAtyz7uAi8vKHpXa8Kb0/MBU15dy24wjXkMwLT3fJm2zxSraE4DFpdi55bumWO8cYF+mprrfm167dYGvpXKfGqBcS9r+9/m4uf2ZuIo426Tn7yrf77T898Diev9P6Uc/+tFPo/yo515EmtVYoDf9LAH2AvbLsuyfuW3+X/r96xDCGqUf4DpgQ+KXAogXfx4ZQvhiCGHyADF/U3qQZVkfsed8u7TobcBEYu9+3oXEZPz9ZcuvzdW1DHgImJQW/YvYa39mCOEzIYT1y8puBmwCXFy2X9eTEuoB9qHkZuJr9ySxJ/37WZZdmt8gDVc6L4TwCPBi2v4jKX6lPph+9/f6bBJC2AgREVFyLyJN6zlgW2IP9L7EnvjzQwivz20zkZhYP8HLXwR6gWvS+lJCuRcx4Z8B/DOEcE8I4ZNl8XqzLPt32bLHgVK8ddLvpWXblJ6vW7a8u+z5C8BrAFKcDwPLgPOBpSGEeSGELXL7BfHLRn6/niF+LgwmUd6f+PrtBvwF+O+y8fQtxC8vHwCOAXZM2/+u1M4KrQO8mGXZk2XLV/X6iIg0Jc2WIyLNamWWZnsBbgkh3APcQkxEv5iWP0Xsyf4AMXkudy+8dDHu50IInyfOGjMNuCiE8NYsy+5P27aFENYpS/BfR/xSUYoFsEFZjA3L1g9KlmW3ALuEEF5LTKxPBS4nDjUq1XUY8Nd+incOIsTd2cuz5cwH7gF+GEL4fZZlGTAZeDewR5ZlV5QKpfYMxVPAGiGEdbMsy78WQ3p9RESKSj33IiJAlmULiEM8DgohlBLG69Lv9bIs+3s/P8vK6liZZdnfiMn9GsQEN++li0XTDZ+Ml5Pre4lj7j9TVmYv4heMG4a4X89lWXY1cCbwxhDCa4iJ+MPEcfz97ddgkvt8jGeI1xW8g3jxLkApiX/pS1EIYRNePbyotH51vfml/e/v9Xkwy7KHKmiyiEhhqedeRORl3yPOLvNV4NtZlt0XQjiDOFznFGIi3kYcM75jlmV7hBAmEC/qPJ+YoLcRZ83pJo7FL3kBmJaS6weIs75MAk6COAY/hHAC8JMQQhdxlpmtgeOBc7Mse2CwOxFC2A34L+KwmyXE3u3Dgf/Nsuz5tM3XgQvS1JdXAcuJ4/B3A47Osuy+wb9sQJwF6DvAf6e4pS8QJ6cvMmPTvjxSVq50k6ovhxAuB57NsuzO8sqzLLsjhPBrYGaId7P9BzHR35k4REhERFByLyLykizL7g0hXAh8MYRwUpZlPcRE/V7gEOKQneXp+cWp2PPE6SQPJ04H+RxxisaPZFn2RK76XuIXhzOALYgJ/p5Zlt2Riz8rhNBLnH3mEOAx4BRir3glFhFnr5lBHObzBPEC3KNysS4JIXQTE/J90+LFxOsJHqswHlmW9YYQZgA/DSFMzbJsXrru4AzgEuIFv9OBnchdsJtl2f+FEI4DPk+cjvMhYNNVhNk37dORxOsG7gP2zbJsTqXtFREpqhCHRoqISK2k5PXILMvWrndbRESk2DTmXkRERESkIJTci4iIiIgUhIbliIiIiIgUhHruRUREREQKQsm9iIiIiEhBKLkXERERESkIJfciIiIiIgWh5F5EREREpCCU3IuIiIiIFISSexERERGRglByLyIiIiJSEEruRUREREQK4v8D5n3BMMZWsfsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8755573863779)>"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = (ggplot(df, aes(x='Nationality_',y='Response', fill='Gender')) + #baseplot\n",
    "    geom_col() + #type of plot\n",
    "    coord_flip() + #flip coordinates\n",
    "    theme_minimal()  +#theme\n",
    "    scale_x_discrete(labels=list(df.Nationality.unique())[::-1]) + #cusotmize x-axis labels\n",
    "    scale_y_continuous(breaks=[i/100 for i in range(-70,80,10)],   #breaks\n",
    "                       limits=(-0.7,0.8),                              #limits\n",
    "                       labels=[f'{abs(i)}%' for i in range(-70,80,10)]) + #labels\n",
    "    labs(x='Nationality', y ='Response Ratio')\n",
    "    ) \n",
    "g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Surplus/deficit filled line\n",
    "The shaded area of these charts allows a balance to be shown; either against a baseline or between two series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "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>Date</th>\n",
       "      <th>MonthlyAvg</th>\n",
       "      <th>surplus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>-33621.456256</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>-43338.459256</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>7832.657744</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>-19563.006256</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>-24210.064256</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date    MonthlyAvg  surplus\n",
       "0  2015-01-01 -33621.456256    False\n",
       "1  2015-02-01 -43338.459256    False\n",
       "2  2015-03-01   7832.657744     True\n",
       "3  2015-04-01 -19563.006256    False\n",
       "4  2015-05-01 -24210.064256    False"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/surplus-deficit-filled-line.csv')\n",
    "df['surplus'] = df.MonthlyAvg.map(lambda x: True if x>0 else False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223363281282218692)>"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = (ggplot(df, aes(x='Date',y='MonthlyAvg',fill='surplus')) +\n",
    "    geom_col() + theme_minimal() + theme(legend_position='none') +\n",
    "    scale_x_date(breaks = date_breaks('6 month'), labels = date_format(\"%y-%m\")) + #date formating\n",
    "    scale_y_continuous(breaks = range(-40000,80000,10000), \n",
    "                      labels = [f'{int(i/1000)}k' for i in range(-40000,80000,10000)]) #format y-axis\n",
    "    ) \n",
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223363281280887338)>"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = (ggplot(df, aes(x='Date',y='MonthlyAvg',group=1)) +\n",
    "    geom_line() + #use line instead\n",
    "    theme_minimal() + theme(legend_position='none') +\n",
    "    geom_hline(aes(yintercept=0), color='red') + #horizontal line\n",
    "    scale_x_date(breaks = date_breaks('6 month'), labels = date_format(\"%y-%m\")) + #date formating\n",
    "    scale_y_continuous(breaks = range(-40000,80000,10000), \n",
    "                      labels = [f'{int(i/1000)}k' for i in range(-40000,80000,10000)]) #format y-axis\n",
    "    ) \n",
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}