{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will \n",
    "  * Compare distributions using probability mass functions\n",
    "  * Use kernel density estimation to compare distributions across different categorical groups\n",
    "  \n",
    "Again, we will examining [Austin Bikeshare](https://www.kaggle.com/jboysen/austin-bike) data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read in data and do some cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bikeid</th>\n",
       "      <th>checkout_time</th>\n",
       "      <th>duration_minutes</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>month</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_time</th>\n",
       "      <th>subscriber_type</th>\n",
       "      <th>trip_id</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.0</td>\n",
       "      <td>19:12:00</td>\n",
       "      <td>41</td>\n",
       "      <td>2565.0</td>\n",
       "      <td>Trinity &amp; 6th Street</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2536.0</td>\n",
       "      <td>Waller &amp; 6th St.</td>\n",
       "      <td>2015-03-19 19:12:00</td>\n",
       "      <td>Walk Up</td>\n",
       "      <td>9900082882</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141.0</td>\n",
       "      <td>2:06:04</td>\n",
       "      <td>6</td>\n",
       "      <td>2570.0</td>\n",
       "      <td>South Congress &amp; Academy</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2494.0</td>\n",
       "      <td>2nd &amp; Congress</td>\n",
       "      <td>2016-10-30 02:06:04</td>\n",
       "      <td>Local365</td>\n",
       "      <td>12617682</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>578.0</td>\n",
       "      <td>16:28:27</td>\n",
       "      <td>13</td>\n",
       "      <td>2498.0</td>\n",
       "      <td>Convention Center / 4th St. @ MetroRail</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2538.0</td>\n",
       "      <td>Bullock Museum @ Congress &amp; MLK</td>\n",
       "      <td>2016-03-11 16:28:27</td>\n",
       "      <td>Local365</td>\n",
       "      <td>9075366</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>555.0</td>\n",
       "      <td>15:12:00</td>\n",
       "      <td>80</td>\n",
       "      <td>2712.0</td>\n",
       "      <td>Toomey Rd @ South Lamar</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2497.0</td>\n",
       "      <td>Capitol Station / Congress &amp; 11th</td>\n",
       "      <td>2014-11-23 15:12:00</td>\n",
       "      <td>24-Hour Kiosk (Austin B-cycle)</td>\n",
       "      <td>9900319298</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>86.0</td>\n",
       "      <td>15:39:13</td>\n",
       "      <td>25</td>\n",
       "      <td>3377.0</td>\n",
       "      <td>MoPac Pedestrian Bridge @ Veterans Drive</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2707.0</td>\n",
       "      <td>Rainey St @ Cummings</td>\n",
       "      <td>2017-04-16 15:39:13</td>\n",
       "      <td>Walk Up</td>\n",
       "      <td>14468597</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bikeid checkout_time  duration_minutes  end_station_id  \\\n",
       "0     8.0      19:12:00                41          2565.0   \n",
       "1   141.0       2:06:04                 6          2570.0   \n",
       "2   578.0      16:28:27                13          2498.0   \n",
       "3   555.0      15:12:00                80          2712.0   \n",
       "4    86.0      15:39:13                25          3377.0   \n",
       "\n",
       "                           end_station_name  month  start_station_id  \\\n",
       "0                      Trinity & 6th Street    3.0            2536.0   \n",
       "1                  South Congress & Academy   10.0            2494.0   \n",
       "2   Convention Center / 4th St. @ MetroRail    3.0            2538.0   \n",
       "3                   Toomey Rd @ South Lamar   11.0            2497.0   \n",
       "4  MoPac Pedestrian Bridge @ Veterans Drive    4.0            2707.0   \n",
       "\n",
       "                  start_station_name           start_time  \\\n",
       "0                   Waller & 6th St.  2015-03-19 19:12:00   \n",
       "1                     2nd & Congress  2016-10-30 02:06:04   \n",
       "2    Bullock Museum @ Congress & MLK  2016-03-11 16:28:27   \n",
       "3  Capitol Station / Congress & 11th  2014-11-23 15:12:00   \n",
       "4               Rainey St @ Cummings  2017-04-16 15:39:13   \n",
       "\n",
       "                  subscriber_type     trip_id    year  \n",
       "0                         Walk Up  9900082882  2015.0  \n",
       "1                        Local365    12617682  2016.0  \n",
       "2                        Local365     9075366  2016.0  \n",
       "3  24-Hour Kiosk (Austin B-cycle)  9900319298  2014.0  \n",
       "4                         Walk Up    14468597  2017.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in data from a csv file\n",
    "df = pd.read_csv('../Data/austin_bikeshare_trips.csv')\n",
    "\n",
    "# Remove any trip with 0 minute duration\n",
    "df = df[df['duration_minutes'] > 0]\n",
    "\n",
    "# Reset the integer based index of the DataFrame\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "greater_than_2_hours = df[df['duration_minutes'] > 120]\n",
    "frac_of_dataset = 100 * len(greater_than_2_hours) / len(df)\n",
    "\n",
    "# Remove any trip lasting longer than 2 hours\n",
    "df = df[df['duration_minutes'] <= 120]\n",
    "\n",
    "# Reset the integer based index of the DataFrame\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seaborn comes built in with a number of different color palettes. This page contains information about [seaborn color palettes](http://seaborn.pydata.org/tutorial/color_palettes.html) and resources to learn about good techniques for using color in visualizations.\n",
    "\n",
    "Here, we pick out a color palette and visualize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['#66c2a5', '#fc8d62', '#8da0cb', '#e78ac3', '#a6d854', '#ffd92f', '#e5c494', '#b3b3b3', '#66c2a5', '#fc8d62']\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABECAYAAACF4e8fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAeNJREFUeJzt26GNlUEARtF5UAB2QwFg8eyWgeBZBAJHslWQrEOsQGBA\nIHkdLDgEFgogBIkiIctPBYCaN8nlnAo+Nzczmd22bdsAAAi7sXoAAMBsggcAyBM8AECe4AEA8gQP\nAJD31+D5+ev6WDsAAKbZ/etb+uOrV8facnSXZ/txffFo9Yxpbj59MS5eflg9Y4r9tx/j5Px0fH32\nbvWUKU7OT8frTw9Wz5jm4d03Y3y+t3rGPHc+ji/vn69eMc3t+0/G4XBYPWOKt7e+j8uzffbs+x/O\nvT/xpAUA5AkeACBP8AAAeYIHAMgTPABAnuABAPIEDwCQJ3gAgDzBAwDkCR4AIE/wAAB5ggcAyBM8\nAECe4AEA8gQPAJAneACAPMEDAOQJHgAgT/AAAHmCBwDIEzwAQJ7gAQDyBA8AkCd4AIA8wQMA5Ake\nACBP8AAAeYIHAMgTPABAnuABAPIEDwCQJ3gAgDzBAwDkCR4AIE/wAAB5ggcAyBM8AECe4AEA8gQP\nAJAneACAPMEDAOQJHgAgT/AAAHmCBwDIEzwAQJ7gAQDyBA8AkCd4AIA8wQMA5AkeACBP8AAAeYIH\nAMgTPABA3m7btm31CACAmdzwAAB5ggcAyBM8AECe4AEA8gQPAJAneACAvN+1UjBwuuqQMwAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ecd70b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hex_colors = sns.color_palette(\"Set2\", 10).as_hex()\n",
    "print(hex_colors)\n",
    "\n",
    "sns.palplot(hex_colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Last week we examined a histogram of the number of rides by hour over the Austin bikeshare dataset. Here is that plot again for our convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# convert start_time column to datetime type\n",
    "df['start_time'] = pd.to_datetime(df['start_time'])\n",
    "\n",
    "# Pull out hour of day into a seperate column\n",
    "df['start_hour'] = df['start_time'].apply(lambda t: t.hour)\n",
    "\n",
    "# Count the number of trips that start by hour\n",
    "hourly_counts = df['start_hour'].value_counts()\n",
    "\n",
    "# reorder by hour of day\n",
    "hourly_counts.sort_index(inplace=True)\n",
    "\n",
    "# Return a DataFrame with the index as an additional column\n",
    "hourly_counts_df = hourly_counts.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1v96hQ4f86U9/yqJFi5pP20uSurq6VZataezy5ctTX1+8G126tE99fd1nvHcA8Pmy3nod\nq91C1Xuodv3W0EO168PnUdkCU/fu3bPpppumpqYm3bt3T8eOHfPee+81v7548eJ06tQpS5YsyeLF\ni5uXNzU1paGhYaVlaxq7prCUJAsWvP8Z7hUAfD69/fbCardQ9R6qXb819FDt+tBaremPCWW7S96N\nN96Ys88+O0kyb968fPDBB2nfvn1ef/31lEqlPPTQQ+nZs2d22mmnzJgxI0ny5JNPZosttkhDQ0Pa\ntGnTorEAAADlUrYZpkGDBmX06NEZOnRoampqcuaZZ6a2tjannHJKGhsb06dPn2y//fbZdttt8/DD\nD2fIkCEplUo588wzkySnn356i8cCAACUQ9kCU9u2bXPeeeetsvz6669f6XltbW1+9rOfrTJuhx12\naPFYAACAcvDFtQAAAAUEJgAAgAICEwAAQAGBCQAAoIDABAAAUEBgAgAAKFC224oDwOfB2Y9NrUid\nUT0HVqQOAJ8tM0wAAAAFBCYAAIACAhMAAEABgQkAAKCAwAQAAFBAYAIAACggMAEAABQQmAAAAAoI\nTAAAAAUEJgAAgAICEwAAQAGBCQAAoIDABAAAUEBgAgAAKCAwAQAAFBCYAAAACghMAAAABQQmAACA\nAgITAABAAYEJAACggMAEAABQQGACAAAoUF/tBgAAqL6zH5takTqjeg6sSB34rJhhAgAAKCAwAQAA\nFBCYAAAACghMAAAABQQmAACAAgITAABAAYEJAACggMAEAABQQGACAAAoIDABAAAUEJgAAAAKCEwA\nAAAFBCYAAIACAhMAAEABgQkAAKCAwAQAAFBAYAIAACggMAEAABQQmAAAAAoITAAAAAUEJgAAgAIC\nEwAAQAGBCQAAoIDABAAAUEBgAgAAKCAwAQAAFBCYAAAACghMAAAABQQmAACAAgITAABAAYEJAACg\ngMAEAABQQGACAAAoIDABAAAUEJgAAAAKCEwAAAAFBCYAAIACAhMAAECBsgam+fPnZ/fdd8/LL7+c\n1157LUOHDs2wYcMyYcKENDU1JUkuuOCCDBo0KEOGDMnTTz+dJJ9qLAAAQLmULTAtW7Ys48ePT7t2\n7ZIkZ511Vk488cRcffXVKZVKmTZtWubMmZNHH300N9xwQyZPnpzTTz/9U48FAAAol7IFpnPOOSdD\nhgzJ+uuvnySZM2dOdtlllyRJ3759M3PmzDz++OPp06dPampqsuGGG6axsTHvvvvupxoLAABQLvXl\n2OjNN9+crl27Zrfddsull16aJCmVSqmpqUmSdOjQIQsXLsyiRYvSuXPn5vVWLP80Y7t27brGXrp0\naZ/6+rrPehcB4FNZb72OX+r6raGHatdvDT1Uu35r6QE+jbIEpptuuik1NTWZNWtWnn/++YwcOXKl\n2aDFixenU6dOaWhoyOLFi1da3rFjx9TW1rZ47N+zYMH7n9FeAcA/7u23F36p67eGHqpdvzX0UO36\nraUH+KQ1BfmyBKarrrqq+fHw4cNz2mmn5dxzz83s2bPTq1evzJgxI7vuums22WSTnHvuuTn22GPz\n5z//OU1NTenatWu22WabFo8F4PPr7MemVqTOqJ4DK1IHgC+esgSm1Rk5cmTGjRuXyZMnp0ePHunf\nv3/q6urSs2fPDB48OE1NTRk/fvynHgsAAFAuZQ9MV1xxRfPjK6+8cpXXR4wYkREjRqy0rHv37i0e\nCwAAUC6+uBYAAKCAwAQAAFBAYAIAACggMAEAABQQmAAAAAoITAAAAAUEJgAAgAICEwAAQAGBCQAA\noIDABAAAUEBgAgAAKCAwAQAAFBCYAAAACghMAAAABQQmAACAAgITAABAAYEJAACggMAEAABQQGAC\nAAAoIDABAAAUEJgAAAAKCEwAAAAFBCYAAIACAhMAAEABgQkAAKCAwAQAAFBAYAIAACggMAEAABQQ\nmAAAAAoITAAAAAUEJgAAgAL11W4AAACS5OzHplakzqieAytShy8GM0wAAAAFBCYAAIACAhMAAEAB\ngQkAAKCAwAQAAFBAYAIAACggMAEAABQQmAAAAAoITAAAAAUEJgAAgAICEwAAQAGBCQAAoIDABAAA\nUEBgAgAAKCAwAQAAFBCYAAAACghMAAAABQQmAACAAgITAABAAYEJAACggMAEAABQQGACAAAoIDAB\nAAAUEJgAAAAKCEwAAAAFBCYAAIACLQpM//qv/5r/+I//yLJly8rdDwAAQKvRosD03e9+Nw8++GD6\n9++f008/PU8//XS5+wIAAKi6+pYM2nnnnbPzzjtnyZIlufvuu3PCCSekoaEhgwYNyrBhw9K2bdty\n9wkAAFBxLQpMSTJ79uzceuutefjhh9O3b98ccMABmTlzZo4//vhcfvnl5ewRAACgKloUmPbcc89s\nvPHG+da3vpXx48enXbt2SZJevXrlW9/6VlkbBAAAqJYWBabf/OY36dChQ9Zdd90sWbIkr732Wjbd\ndNPU1tZm6tSp5e4RAACgKlp004fp06fnf//v/50kmT9/fo477rhcd911ZW0MAACg2loUmK6//vpc\nddVVSZKNNtooN998c6688sqyNgYAAFBtLQpMy5YtW+lOeG3atClbQwAAAK1Fi65h2mefffKd73wn\n+++/f5Lk3nvvzV577VXWxgAAAKqtRYHpJz/5Se6+++78/ve/T319fY488sjss88+a1ynsbExY8eO\nzSuvvJKampqcfvrpWWuttTJq1KjU1NRk8803z4QJE1JbW5sLLrgg06dPT319fcaMGZPtttsur732\nWovHAgAAlEOLv4fpq1/9ar7yla+kVColSX7/+99n5513Lhz/wAMPJEmuvfbazJ49O7/85S9TKpVy\n4oknplevXhk/fnymTZuWDTfcMI8++mhuuOGGzJ07NyNGjMhNN92Us846q8VjAQAAyqFFgen000/P\nAw88kG7dujUvq6mpyW9/+9vCdfbZZ5/sscceSZK33nornTp1ysyZM7PLLrskSfr27ZuHH3443bt3\nT58+fVJTU5MNN9wwjY2NeffddzNnzpwWj+3ates/uv8AAACFWhSYHn744dx9993NX1jb4o3X12fk\nyJG577778m//9m95+OGHU1NTkyTp0KFDFi5cmEWLFqVz587N66xYXiqVWjx2TYGpS5f2qa+v+1R9\nA/DFst56HavdQtV7qHb91tBDteu3hh6qXV8PfB61KDB169at+VS8T+ucc87JKaecksMPPzwffvhh\n8/LFixenU6dOaWhoyOLFi1da3rFjx9TW1rZ47JosWPD+P9Q3AF8cb7+9sNotVL2HatdvDT1Uu35r\n6KHa9fVAa7WmEN2iwLTOOuvkm9/8ZnbccceVbi9+1llnFa5zyy23ZN68efne976XtddeOzU1Nfn6\n17+e2bNnp1evXpkxY0Z23XXXbLLJJjn33HNz7LHH5s9//nOamprStWvXbLPNNi0eC8A/5uzHplak\nzqieAytSBwA+ay0KTLvttlt22223T7Xhfv36ZfTo0TniiCOyfPnyjBkzJl/96lczbty4TJ48OT16\n9Ej//v1TV1eXnj17ZvDgwWlqasr48eOTJCNHjmzxWAAAgHJoUWAaOHBg3njjjbz00kvp06dP5s6d\nu9INIFanffv2Of/881dZfuWVV66ybMSIERkxYsRKy7p3797isQAAAOVQ+/eHJHfddVeOP/74TJo0\nKX/9618zZMiQ3HrrreXuDQAAoKpaFJguu+yyXHPNNenQoUPWXXfdTJ06NZdeemm5ewMAAKiqFgWm\n2traNDQ0ND9ff/31V7qLHQAAwBdRi65h2nzzzXPllVdm+fLlef7553P11Vdnq622KndvAAAAVdWi\naaLx48dn3rx5WWuttTJmzJg0NDRkwoQJ5e4NAACgqlo0w9S+ffucfPLJOfnkk8vdDwAAQKvRosC0\n1VZbpaamZqVl6623XmbMmFGWpgAAAFqDFgWmF154ofnxsmXLcv/99+fJJ58sW1MAAACtwae+1V2b\nNm2y//7755FHHilHPwAAAK1Gi2aYbrnllubHpVIpL774Ytq0aVO2pgAAAFqDFgWm2bNnr/S8S5cu\n+eUvf1mWhgAAAFqLFgWms846q9x9AAAAtDotCkx77bXXKnfJSz46Pa+mpibTpk37zBsDAACothYF\npgMPPDBt2rTJ4Ycfnvr6+tx+++155plnctJJJ5W7PwAAgKppUWB68MEHc/PNNzc//853vpNDDz00\nG220UdkaAwAAqLYW31Z85syZzY8feOCBdOjQoSwNAQAAtBYtmmH62c9+lpEjR+add95JkvTo0SPn\nnHNOWRsDAACothYFpq9//eu588478+6772attdYyuwQAAHwptOiUvDfffDNHH310hgwZkvfffz9H\nHnlk3njjjXL3BgAAUFUtCkzjx4/Psccem/bt2+crX/lKBgwYkJEjR5a7NwAAgKpqUWBasGBB+vTp\nkySpqanJ4YcfnkWLFpW1MQAAgGprUWBq165d/vznPzd/ee1jjz2Wtm3blrUxAACAamvRTR9Gjx6d\n733ve3n99ddz8MEH569//WvOP//8cvcGAABQVS0KTPPnz8+NN96YV199NY2NjenRo4cZJgAA4Auv\nRafknXvuuWnTpk0233zzbLXVVsISAADwpdCiGaZu3bpl9OjR2X777dOuXbvm5YccckjZGgMAgEo6\n+7GpFakzqufAitThs7HGwDRv3rxssMEG6dKlS5LkqaeeWul1gQkAAPgiW2NgOu644zJ16tScddZZ\n+b//9//mmGOOqVRfAAAAVbfGa5hKpVLz49tvv73szQAAALQmawxMK753KVk5PAEAAHwZtOguecnK\n4QkAAODLYI3XML344ovZe++9k3x0A4gVj0ulUmpqajJt2rTydwgAAFAlawxM99xzT6X6AAAAaHXW\nGJg22mijSvUBAADQ6rT4GiYAAIAvG4EJAACgwBpPyQOgfM5+bGpF6ozqObAidQDgi8gMEwAAQAGB\nCQAAoIDABAAAUEBgAgAAKCAwAQAAFBCYAAAACghMAAAABQQmAACAAgITAABAAYEJAACggMAEAABQ\nQGACAAAoIDABAAAUEJgAAAAKCEwAAAAFBCYAAIACAhMAAEABgQkAAKCAwAQAAFBAYAIAACggMAEA\nABQQmAAAAAoITAAAAAUEJgAAgAICEwAAQAGBCQAAoIDABAAAUEBgAgAAKCAwAQAAFKivdgMA1XD2\nY1MrVmtUz4EVqwUAfLbMMAEAABQoywzTsmXLMmbMmLz55ptZunRpjj/++Gy22WYZNWpUampqsvnm\nm2fChAmpra3NBRdckOnTp6e+vj5jxozJdtttl9dee63FYwEA4IuiUmdAOPuh5coSmG677bZ07tw5\n5557bt57770ccsgh2WqrrXLiiSemV69eGT9+fKZNm5YNN9wwjz76aG644YbMnTs3I0aMyE033ZSz\nzjqrxWMBAADKpSyBab/99kv//v2TJKVSKXV1dZkzZ0522WWXJEnfvn3z8MMPp3v37unTp09qamqy\n4YYbprGxMe++++6nGtu1a9dy7AIAAEB5AlOHDh2SJIsWLcoJJ5yQE088Meecc05qamqaX1+4cGEW\nLVqUzp07r7TewoULUyqVWjz27wWmLl3ap76+7rPeRYAWW2+9jl/q+q2hh2rXbw09VLt+a+ih2vVb\nQw/Vrq+H1lG/tfTweVG2u+TNnTs3P/jBDzJs2LAceOCBOffcc5tfW7x4cTp16pSGhoYsXrx4peUd\nO3ZMbW1ti8f+PQsWvP8Z7RHAP+bttxd+qeu3hh6qXb819FDt+q2hh2rXbw09VLu+HlpH/dbSQ2uy\npgBZlrvkvfPOOznmmGPyk5/8JIMGDUqSbLPNNpk9e3aSZMaMGenZs2d22mmnPPTQQ2lqaspbb72V\npqamdO3a9VONBQAAKJeyzDBdfPHF+dvf/pYLL7wwF154YZLk1FNPzcSJEzN58uT06NEj/fv3T11d\nXXr27JnBgwenqakp48ePT5KMHDky48aNa9FYAACAcilLYBo7dmzGjh27yvIrr7xylWUjRozIiBEj\nVlrWvXv3Fo8FAAAoF19cCwAAUEBgAgAAKCAwAQAAFBCYAAAACghMAAAABQQmAACAAgITAABAAYEJ\nAACggMCaYwyWAAAU/0lEQVQEAABQQGACAAAoIDABAAAUEJgAAAAKCEwAAAAFBCYAAIACAhMAAEAB\ngQkAAKCAwAQAAFBAYAIAACggMAEAABQQmAAAAAoITAAAAAUEJgAAgAICEwAAQAGBCQAAoIDABAAA\nUEBgAgAAKCAwAQAAFBCYAAAACghMAAAABQQmAACAAvXVbgAAAGgdzn5sakXqjOo5sCJ1PgtmmAAA\nAAoITAAAAAUEJgAAgAICEwAAQAGBCQAAoIDABAAAUEBgAgAAKCAwAQAAFBCYAAAACghMAAAABQQm\nAACAAgITAABAAYEJAACggMAEAABQQGACAAAoIDABAAAUEJgAAAAKCEwAAAAFBCYAAIACAhMAAEAB\ngQkAAKCAwAQAAFBAYAIAACggMAEAABQQmAAAAAoITAAAAAUEJgAAgAICEwAAQAGBCQAAoIDABAAA\nUEBgAgAAKCAwAQAAFBCYAAAACghMAAAABQQmAACAAgITAABAAYEJAACgQH05N/7UU0/lF7/4Ra64\n4oq89tprGTVqVGpqarL55ptnwoQJqa2tzQUXXJDp06envr4+Y8aMyXbbbfepxgKfT2c/NrUidUb1\nHFiROgDAF1PZZpguu+yyjB07Nh9++GGS5KyzzsqJJ56Yq6++OqVSKdOmTcucOXPy6KOP5oYbbsjk\nyZNz+umnf+qxAAAA5VK2wLTJJptkypQpzc/nzJmTXXbZJUnSt2/fzJw5M48//nj69OmTmpqabLjh\nhmlsbMy77777qcYCAACUS9lOyevfv3/eeOON5uelUik1NTVJkg4dOmThwoVZtGhROnfu3DxmxfJP\nM7Zr165r7KNLl/apr6/7LHcN+BxZb72O1W6h6j1Uu35r6KHa9VtDD9Wu3xp6qHb91tBDtevroXXU\nbw09rKn+yf/x24r0cN7+R7ZoXFmvYfq42tr/nsxavHhxOnXqlIaGhixevHil5R07dvxUY/+eBQve\n/4z2APg8evvthdVuoeo9VLt+a+ih2vVbQw/Vrt8aeqh2/dbQQ7Xr66F11G8NPVS7/id7WFOAq9hd\n8rbZZpvMnj07STJjxoz07NkzO+20Ux566KE0NTXlrbfeSlNTU7p27fqpxgIAAJRLxWaYRo4cmXHj\nxmXy5Mnp0aNH+vfvn7q6uvTs2TODBw9OU1NTxo8f/6nHAgAAlEtZA9PGG2+c66+/PknSvXv3XHnl\nlauMGTFiREaMGLHSsk8zFgAAoFx8cS0AAEABgQkAAKCAwAQAAFBAYAIAACggMAEAABQQmAAAAAoI\nTAAAAAUEJgAAgAICEwAAQAGBCQAAoIDABAAAUEBgAgAAKCAwAQAAFBCYAAAACghMAAAABQQmAACA\nAvXVbqCSzn5sakXqjOo5sCJ1AACA8jLDBAAAUEBgAgAAKCAwAQAAFBCYAAAACghMAAAABQQmAACA\nAgITAABAAYEJAACggMAEAABQQGACAAAoIDABAAAUEJgAAAAKCEwAAAAFBCYAAIACAhMAAEABgQkA\nAKBAfbUb+LI5+7GpFakzqufAVlkfAAA+T8wwAQAAFBCYAAAACghMAAAABQQmAACAAgITAABAAYEJ\nAACggMAEAABQQGACAAAoIDABAAAUEJgAAAAKCEwAAAAFBCYAAIAC9dVuAKi8sx+bWpE6o3oOrEgd\nAIByMcMEAABQQGACAAAoIDABAAAUEJgAAAAKCEwAAAAFBCYAAIACAhMAAEABgQkAAKCAwAQAAFBA\nYAIAACggMAEAABQQmAAAAAoITAAAAAUEJgAAgAICEwAAQAGBCQAAoEB9tRvgy+fsx6ZWpM6ongMr\nUgcAgC8ugQmqQGgEAPh8cEoeAABAATNMfOmY3QEAoKXMMAEAABT43M0wNTU15bTTTssf/vCHtG3b\nNhMnTsymm25a7bYAAIAvoM/dDNP999+fpUuX5rrrrsvJJ5+cs88+u9otAQAAX1Cfu8D0+OOPZ7fd\ndkuS7LDDDnn22Wer3BEAAPBFVVMqlUrVbuLTOPXUU9OvX7/svvvuSZI99tgj999/f+rrP3dnFwIA\nAK3c526GqaGhIYsXL25+3tTUJCwBAABl8bkLTDvttFNmzJiRJHnyySezxRZbVLkjAADgi+pzd0re\nirvk/fGPf0ypVMqZZ56Zr371q9VuCwAA+AL63AUmAACASvncnZIHAABQKQITAABAAYGpQFNTU8aP\nH5/Bgwdn+PDhee2116rSx1NPPZXhw4dXpfayZcvyk5/8JMOGDcugQYMybdq0itZvbGzM6NGjM2TI\nkAwdOjR//OMfK1r/4+bPn5/dd989L7/8clXqDxw4MMOHD8/w4cMzevToite/5JJLMnjw4Bx66KG5\n4YYbKl7/5ptvbt7/ww8/PNtuu23+9re/Vaz+smXLcvLJJ2fIkCEZNmxYVf4dLF26NCeffHIOP/zw\nHHPMMXn11VcrVvvjx6HXXnstQ4cOzbBhwzJhwoQ0NTVVvIcVzjzzzFxzzTUVr//8889n2LBhGT58\neI499ti88847Fe/hpZdeytChQzNkyJCMGjUqy5cvr2j9FW6//fYMHjy47LVX18Nzzz2X3XbbrfnY\ncNddd1W0/vz583P88cfniCOOyJAhQ/L666+Xvf4nezjppJOa93+vvfbKSSedVPEenn/++Rx++OEZ\nOnRoRo8eXZFjwsfrz5kzJ4MGDcqwYcNyxhlnlL3+6j4bVfK4uKbPZpU6Jq6uh0oeF1dXv+zHxBKr\ndc8995RGjhxZKpVKpf/6r/8qHXfccRXv4dJLLy0NGDCgdNhhh1W8dqlUKt14442liRMnlkqlUmnB\nggWl3XffvaL177vvvtKoUaNKpVKp9Mgjj1TlZ1AqlUpLly4tff/73y/169ev9NJLL1W8/pIlS0oH\nH3xwxeuu8Mgjj5S+973vlRobG0uLFi0q/du//VvVeimVSqXTTjutdO2111a05n333Vc64YQTSqVS\nqfTQQw+VfvjDH1a0fqlUKl1xxRWlsWPHlkqlUunll18uHXPMMRWp+8nj0Pe+973SI488UiqVSqVx\n48aV7r333or3MH/+/NKxxx5b2nvvvUtXX311xesfccQRpeeee65UKpVK11xzTenMM8+seA/HH398\n6dFHHy2VSqXSyJEjy/5zWN3vozlz5pSOPPLIiv2O+mQP119/fenyyy+vSO3V1R85cmTpzjvvLJVK\npdKsWbNKDzzwQMV7WOG9994rHXTQQaV58+ZVvIfvf//7penTp5dKpVLpxz/+cWnatGkVrT9w4MDS\n448/XiqVSqXJkyeXbrnllrLWX91no0oeF1dXv9LHxNX1UMnj4urql/uYaIapwOOPP57ddtstSbLD\nDjvk2WefrXgPm2yySaZMmVLxuivst99++dGPfpQkKZVKqaurq2j9ffbZJ2eccUaS5K233kqnTp0q\nWn+Fc845J0OGDMn6669flfovvPBCPvjggxxzzDE58sgj8+STT1a0/kMPPZQtttgiP/jBD3Lcccdl\njz32qGj9j3vmmWfy0ksvVfQv2knSvXv3NDY2pqmpKYsWLarKd7+99NJL6du3b5KkR48eFZvl+uRx\naM6cOdlll12SJH379s3MmTMr3sPixYszYsSIHHzwwWWvvbr6kydPztZbb53ko5nwtdZaq+I9TJky\nJTvvvHOWLl2at99+Ow0NDRWtv2DBgkyePDljxowpa9019fDss89m+vTpOeKIIzJmzJgsWrSoovWf\neOKJzJs3L0cddVRuv/325v8XlexhhSlTpuTb3/52RX5PfbKHrbfeOu+9915KpVIWL15c9uPjJ+vP\nmzcvO+20U5KPvnrm8ccfL2v91X02quRxcXX1K31MXF0PlTwurq5+uY+JAlOBRYsWrfRm19XVVeSU\nh4/r379/Vb+Ut0OHDmloaMiiRYtywgkn5MQTT6x4D/X19Rk5cmTOOOOMHHjggRWvf/PNN6dr167N\n4bka2rVrl2OPPTaXX355Tj/99JxyyikV/be4YMGCPPvsszn//POb65eqdHPNSy65JD/4wQ8qXrd9\n+/Z58803s//++2fcuHFVOU126623zgMPPJBSqZQnn3wy8+bNS2NjY9nrfvI4VCqVUlNTk+SjY8TC\nhQsr3kO3bt2y/fbbl71uUf0VH0qfeOKJXHnllTnqqKMq3kNdXV3efPPNDBgwIAsWLMhWW21VsfqN\njY059dRTM3r06HTo0KGsdYt6SJLtttsuP/3pT3PVVVelW7du+dWvflXR+m+++WY6deqUf//3f88/\n/dM/5bLLLitr/dX1kHx0auCsWbNy6KGHlr3+6nr453/+50yaNCn7779/5s+fn169elW0frdu3fLo\no48mSR544IF88MEHZa2/us9GlTwurq5+pY+Jq+uhksfF1dUv9zFRYCrQ0NCQxYsXNz9vamqqanip\nlrlz5+bII4/MwQcfXJXAknw0w3PPPfdk3Lhxef/99yta+6abbsrMmTMzfPjwPP/88xk5cmTefvvt\nivbQvXv3HHTQQampqUn37t3TuXPnivbQuXPn9OnTJ23btk2PHj2y1lpr5d13361Y/RX+9re/5ZVX\nXsmuu+5a8dr//u//nj59+uSee+7JrbfemlGjRuXDDz+saA/f+ta30tDQkGHDhuW+++7L1772tYrP\n+iZJbe1//9pYvHhx1WZ+q+2uu+7KhAkTcumll6Zr165V6WGjjTbKvffem6FDh+bss8+uWN05c+bk\ntddey2mnnZYf//jHeemllzJp0qSK1V9h3333zde//vXmx88991xF63fu3Dl77bVXkmSvvfaqypko\nSXL33XdnwIABVTkeJMmkSZNy1VVX5e67784hhxxS0X+LyUfX7VxyySX5zne+k3XXXTddunQpe81P\nfjaq9HGxNXw2W10PlTwurq5+OY+JAlOBnXbaKTNmzEiSPPnkk9liiy2q3FHlvfPOOznmmGPyk5/8\nJIMGDap4/VtuuSWXXHJJkmTttddOTU3NSgelSrjqqqty5ZVX5oorrsjWW2+dc845J+utt15Fe7jx\nxhub/+PPmzcvixYtqmgP3/jGN/Lggw+mVCpl3rx5+eCDD9K5c+eK1V/h97//fXr37l3xuknSqVOn\ndOzYMUmyzjrrZPny5RWZ3fm4Z555Jr17984111yT/fbbL926dato/RW22WabzJ49O0kyY8aM9OzZ\nsyp9VNOtt97afFyo1s/huOOOa77xR4cOHSp6bNxuu+1y55135oorrsjkyZOz2Wab5dRTT61Y/RWO\nPfbYPP3000mSWbNm5Wtf+1pF63/jG9/I7373uyQfHZ8222yzitZfYdasWc2n61bDOuus03xGzvrr\nr1/RG/Ikye9+97v84he/yG9+85u89957+Zd/+Zey1lvdZ6NKHher/dmsqIdKHhdXV7/cx8Qv35RJ\nC+277755+OGHM2TIkJRKpZx55pnVbqniLr744vztb3/LhRdemAsvvDBJctlll6Vdu3YVqd+vX7+M\nHj06RxxxRJYvX54xY8ZUrHZrMmjQoIwePTpDhw5NTU1NzjzzzIrOdu655575/e9/n0GDBqVUKmX8\n+PFV+UvmK6+8ko033rjidZPkqKOOypgxYzJs2LAsW7YsJ510Utq3b1/RHjbddNOcf/75ufjii9Ox\nY8eq/EU/SUaOHJlx48Zl8uTJ6dGjR/r371+VPqqlsbExkyZNyj/90z9lxIgRSZKdd945J5xwQkX7\n+O53v5tRo0alTZs2WXvttTNx4sSK1m8NTjvttJxxxhlp06ZNvvKVrzRf81opI0eOzNixY3Pttdem\noaEh5513XkXrr/DKK69ULbgnycSJE3PSSSelvr4+bdq0qfjPYdNNN81RRx2VtddeO7169cruu+9e\n1nqr+2x06qmnZuLEiRU5Llb7s9nqemhsbMyLL76YDTfcsCLHxdW9ByeeeGJZj4k1pWpdjAAAANDK\nOSUPAACggMAEAABQQGACAAAoIDABAAAUEJgAAAAKCEwAVNUbb7zR/AWgH7fllltWtI/Ro0enf//+\nueOOOwrHTJkyJVOmTKlgVwBUm+9hAoAkU6dOzdNPP522bdtWuxUAWhEzTAC0ak1NTZk4cWK++c1v\nZsCAAbn00kuTJLNnz87w4cObx40aNSo333xz3njjjey3334ZOnRojjrqqBZt67jjjkupVMphhx2W\n+fPnr7TOr3/96/Tr1y+DBw/O008/3bz8yiuvzGGHHZYBAwbkwAMPzMsvv5xZs2ZlyJAhzWOmTp2a\nCRMmfNZvCQAVZIYJgKr7y1/+koMPPni1r11zzTWZO3dubrvttixdujTDhw/PFltskbXXXrtwe6+8\n8kp+/etfZ+ONN27Rti6++OJsueWWufXWW1ca/8wzz+Smm27K1KlTU1NTk8GDB2e77bbLokWLcv/9\n9+eKK65Iu3btcv755+fqq6/O2LFjM3bs2Lz++uvZZJNNMnXq1Jx88sn/8zcIgKoRmACouvXXX3+V\nsLLiGqbZs2dn4MCBqaury9prr50DDzwws2bNWu11Tyusu+66q4SlNW1rjz32WO12Hn300ey+++7p\n0KFDkmS//fZLU1NTGhoact555+XOO+/Mq6++mgcffDBbb711ampqMnDgwNx222059NBDM3/+/Gy/\n/fb/4LsCQGvglDwAWrWmpqaVnpdKpTQ2NqampialUql5+bJly5oft2vX7lNtq0hNTc1K69TXf/R3\nxrlz52bw4MFZuHBh+vbtm4EDBzb3MnDgwNx555254447CmfNAPj8EJgAaNV23XXX3HLLLWlsbMwH\nH3yQ22+/Pb169UqXLl3ypz/9KR9++GHee++9PP744//wtor07t0706dPz8KFC/Phhx/mvvvuS/LR\nqXqbbrppjjrqqGy//faZMWNGc/DaaKON8r/+1//KtddeKzABfAE4JQ+AVm3w4MF59dVXc/DBB2fZ\nsmU56KCDsu+++yZJdt9993zzm9/MRhttlG984xv/o22tztZbb53vfOc7GTRoUDp16pQNN9wwSfIv\n//Ivueaaa3LAAQekbdu22W677fLiiy82r3fAAQfk3nvvzQYbbPA/3HsAqq2m9PHzGQCA/5Hly5fn\npz/9afbbb7/069ev2u0A8D/klDwA+IyUSqXstttuqampyT777FPtdgD4DJhhAgAAKGCGCQAAoIDA\nBAAAUEBgAgAAKCAwAQAAFBCYAAAACghMAAAABf4/R0Q7GnEmyu0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116d169b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x='index', y='start_hour', data=hourly_counts_df, color=hex_colors[0])\n",
    "\n",
    "ax.figure.set_size_inches(14,8)\n",
    "ax.set_title('Number of trips by hour of the day')\n",
    "ax.set_xlabel('Hour of day')\n",
    "ax.set_ylabel('Frequency')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also noticed that there were different patterns of ridership for weekday rides versus weekend rides. \n",
    "\n",
    "Let's examine this difference a bit more closely by using the concept of probability mass functions to compare hourly rides for weekday versus weekend rides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Pull out start day of the week into a seperate column where Monday=0, Sunday=6\n",
    "df['start_day_of_week'] = df['start_time'].apply(lambda t: t.dayofweek)\n",
    "\n",
    "weekday_rides = df[df['start_day_of_week'] < 5]\n",
    "weekend_rides = df[df['start_day_of_week'] >= 5]\n",
    "\n",
    "# Count the number of trips that start by hour\n",
    "weekday_hourly_counts = weekday_rides['start_hour'].value_counts()\n",
    "weekend_hourly_counts = weekend_rides['start_hour'].value_counts()\n",
    "\n",
    "# reorder by hour of day\n",
    "weekday_hourly_counts.sort_index(inplace=True)\n",
    "weekend_hourly_counts.sort_index(inplace=True)\n",
    "\n",
    "# Return a DataFrame with the index as an additional column\n",
    "weekday_hourly_counts_df = weekday_hourly_counts.reset_index()\n",
    "weekend_hourly_counts_df = weekend_hourly_counts.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VlZUpPz9fOTk5LtcGBga6azcAoN6ZNRk1bmOLmuWGkQAAAFw83BZ2JcnLy0tJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109bfa3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(16,6))\n",
    "\n",
    "sns.barplot(x='index', y='start_hour', data=weekday_hourly_counts_df, color=hex_colors[1], ax=ax[0])\n",
    "sns.barplot(x='index', y='start_hour', data=weekend_hourly_counts_df, color=hex_colors[2], ax=ax[1])\n",
    "\n",
    "ax[0].set_title('Weekday number of trips by hour of the day')\n",
    "ax[1].set_title('Weekend number of trips by hour of the day')\n",
    "\n",
    "for i in range(2):\n",
    "    ax[i].set_xlabel('Hour of day')\n",
    "    ax[i].set_ylabel('Frequency')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's notice a few things about our histograms. First, our y scales are different. This makes it difficult to accurately compare the hourly trip counts for weekday versus weekend rides. We can improve on this by normalizing by the number of trips in each DataFrame, therbey creating probability mass functions, and plotting these. Also, let's ensure that both plots use the same y axis values.\n",
    "\n",
    "Let's do that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekday_hourly_counts_df['normalized'] = weekday_hourly_counts_df['start_hour'] / weekday_hourly_counts_df['start_hour'].sum()\n",
    "weekend_hourly_counts_df['normalized'] = weekend_hourly_counts_df['start_hour'] / weekend_hourly_counts_df['start_hour'].sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XegAAAAAAC3PbacYRERHavn27YmJiZIxRQkKClixZoho1aig8PFxxcXGKjY2V\nMUYjRoxQ2bJlNXHiRE2dOlV2u13GGE2aNMld6QEAAAAALMxtxay3t7emTp2a77k6deo4/h0dHa3o\n6Oh82++880795z//cVdKAAAAAIBSwm2nGQMAAAAA4C4UswAAAAAAy3HbacYAAAAAcLOtTtrrdJse\nkQ3dkAncjWIWgFPMmmkutfOKmljMmQAAAOD3jNOMAQAAAACWQzELAAAAALAcTjMGYEmunO7Mqc4A\nAAClByuzAAAAAADLoZgFAAAAAFgOxSwAAAAAwHIoZgEAAAAAlkMxCwAAAACwHIpZAAAAAIDlUMwC\nAAAAACyHYhYAAAAAYDm2kk4AwM1j1kxzqZ1X1MRizgQAAAC4MazMAgAAAAAsh5VZADcdK8QAAAC4\nUazMAgAAAAAsh2IWAAAAAGA5FLMAAAAAAMvhmlkAAADABauT9rrUrkdkw2LOBPh9YmUWAAAAAGA5\nFLMAAAAAAMuhmAUAAAAAWA7FLAAAAADAcihmAQAAAACWQzELAAAAALAcilkAAAAAgOVQzAIAAAAA\nLIdiFgAAAABgORSzAAAAAADLoZgFAAAAAFgOxSwAAAAAwHIoZgEAAAAAlkMxCwAAAACwHIpZAAAA\nAIDlUMwCAAAAACyHYhYAAAAAYDkUswAAAAAAy6GYBQAAAABYDsUsAAAAAMBybCWdAAAAAAB4ktVJ\ne51u0yOyoRsyQWFYmQUAAAAAWE6RitkBAwbo3XffVU5OjrvzAQAAHoQ5AADAUxWpmB04cKC2bdum\nyMhIPf3009q3b5+78wIAAB6AOQAAwFMV6ZrZxo0bq3Hjxrpw4YLee+89DR8+XP7+/oqKilJsbKx8\nfX3dnScAACgBzAEAAJ6qyDeA2r17t9544w1t375doaGhat++vXbs2KEhQ4Zo8eLF7swRAACUIOYA\nAABPVKRiNiwsTNWqVVP37t01adIklStXTpLUpEkTde/e3a0JAgCuz6yZ5nQbr6iJbsgEpQ1zAACA\npypSMbto0SIFBgbmey4lJUVBQUH673//65bEAABAyWMOAADwVIUWs8nJybLb7ZowYYJmzJghY4wk\nKTc3V1OmTFFSUtJNSRIAANxczAEAAJ6u0GJ2x44d+vjjj/Xzzz9r3rx5vzWy2dSzZ89CA9vtdk2Z\nMkVffvmlfH19NX36dNWsWdOxPTExUStXrpTNZtOQIUMUFhamc+fOacqUKfrhhx+Uk5OjiRMnqkGD\nBjd4iADAY4vwAAAgAElEQVQAwFk3MgcAAOBmKLSYHTZsmCRp3bp16tKli1OBN2zYoOzsbK1atUop\nKSmaOXOmFixYIElKS0vT0qVLtXbtWmVlZSk2NlYtWrTQ4sWL9Ze//EWzZ8/WoUOHdOjQIYpZAABK\nwI3MAQAAuBkKLWbnz5+vYcOGaffu3dq9e/dV25955plrtk1OTlZISIgkKSgoSAcOHHBs27dvn+69\n9175+vrK19dXNWrU0KFDh/TRRx+pXbt26t+/v/z8/DR58mRXjwsodVy5wY/ETX4AuOZG5gAAANwM\nhRazd999tyTp/vvvdzpwRkaG/P39HY99fHyUm5srm82mjIwMBQQEOLb5+fkpIyNDp0+f1tmzZ7V4\n8WKtW7dOs2bN0uzZswvdzy23VJDN5uN0foDV/Oxiu6pVf/t/7fce4/L2xLg6BnC5G5kDMDYDhSuO\nz9/SEsMTcvCkGHBOocVs3bp1deLECTVp0sTpwP7+/srMzHQ8ttvtstlsBW7LzMxUQECAKleurNat\nW0u6+FMAL7/88nX3c/r0OadzA35P0tLSieFBOZS2GKUNE5Hf3MgcgLEZKJynfIZ7QgxPyMGTYuBq\nhY3NhRazDz/8sLy8vBx3MLycl5eXNm7ceM22wcHB2rx5s9q3b6+UlJR8t/Vv0KCBnnvuOWVlZSk7\nO1tHjhxRYGCgGjVqpC1btqh+/fras2eP7rzzzqIcHwAAKGY3MgcAAOBmKLSY3bRpk8uBIyIitH37\ndsXExMgYo4SEBC1ZskQ1atRQeHi44uLiFBsbK2OMRowYobJly2rQoEGaMGGCevbsKZvNplmzZrm8\nfwAA4LobmQMAAHAzFOkGUOPGjStwe2E3f/D29tbUqVPzPVenTh3Hv6OjoxUdHZ1ve+XKlfXCCy9c\nN2kAAOBeNzIHAADgZnDbDaAAAIB1MQcAAHi6QovZSzdj6tq1q06ePKm9e/fKZrOpQYMGqly58k1J\nEAAA3HzMAQAAns67KC9699131blzZ61bt06JiYnq0qWLtm7d6u7cAABACWMOAADwVIWuzF6yYMEC\nvf7667rtttskScePH9eQIUMUGhrq1uQAAEDJYg4AAPBURVqZtdlsqlq1quPxHXfc4fjNWAAAUHox\nBwAAeKpCR6N169ZJkqpVq6bBgwerS5custlsevvtt3XXXXfdlAQBAMDNxxwAAODpCi1md+/eLUny\n8/OTn5+f4xqZChUquD8zAABQYpgDAAA8XaHFbGG/IXfhwoViTwYAAHgG5gAAAE9XpItekpKS9OKL\nL+rcuXMyxshut+vChQvauXOnu/MDAAAliDkAAMBTFamY/cc//qHp06dryZIlGjx4sD766COdPn3a\n3bkBAIASxhwAAOCpinQ344oVK6pp06Zq2LCh0tPTNWzYMKWkpLg7NwAAUMKYAwAAPFWRitly5crp\n22+/VZ06dfTxxx8rOztb6enp7s4NAACUMOYAAABPVaRi9oknntBzzz2nsLAw7dy5Uy1atNCDDz7o\n7twAAEAJYw4AAPBURbpm9v7779f9998vSVq7dq3OnDmjSpUquTUxAABQ8pgDAAA8VZFWZn/66ScN\nHTpU999/v1q0aKFp06bp1KlT7s4NAACUMOYAAABPVaRidvz48WrevLk2bdqkpKQk1a9fX+PGjXN3\nbgAAoIQxBwAAeKoiFbOnTp1SbGys/P395e/vr759++qnn35yd24AAKCEMQcAAHiqIhWzDRo00Pr1\n6x2PN2/erPr167stKQAA4BmYAwAAPFWhN4CqW7euvLy8ZIxRYmKinnrqKXl7e+vcuXOqVKmSZsyY\ncbPyBAAANxFzAACApyu0mD106NDNygMAAHgQ5gAAAE9XpJ/mOX/+vF544QXt3LlTeXl5atq0qR5/\n/HFVqFDB3fkBAIASxBwAAOCpinTN7NSpU3X+/HklJCRo1qxZysnJ0eTJk92dGwAAKGHMAQAAnqpI\nK7Off/653nzzTcfjSZMmqX379m5LCgAAeAbmAAAAT1WklVljjM6ePet4fPbsWfn4+LgtKQAA4BmY\nAwAAPFWRVmb79u2rHj16KCwsTJK0adMmDRw40K2JAQCAksccAKXV6qS9LrXrEdmwmDMB4KoiFbNh\nYWG65557tGfPHtntds2fP1933XWXu3MDAAAljDkAALjGlS9M+LLEOUUqZnv37q13331XgYGB7s4H\nAAB4EOYAAABPVaRitm7dulq3bp0aNGigcuXKOZ7/n//5H7clBgAASh5zAACApypSMbt3717t27dP\nxhjHc15eXtq4caPbEgMAACWPOQAAwFMVWsympqZq2rRpqlChgoKDgzVq1ChVrFjxZuUGAABKCHMA\nAICnK/SnecaPH6/atWtrzJgxysnJ0TPPPHOz8gIAACWIOQAAwNNdd2V28eLFkqRmzZqpS5cuNyUp\nAABQspgDAAA8XaErs2XKlMn378sfAwCA0os5AADA0xXpBlCXeHl5uSsPwGOZNdNcaucVNbFYYwBA\nSWIOAADwNIUWs19//bXCw8Mdj1NTUxUeHi5jDHcyBACgFGMOAADwdIUWs0lJSTcrDwAA4EGYAwAA\nPF2hxewdd9xxs/IA3MaVU3w5vRfA7x1zAACApyv0BlAAAAAAAHgip24ABQAonbhJGQAAsBpWZgEA\nAAAAlkMxCwAAAACwHIpZAAAAAIDlUMwCAAAAACyHYhYAAAAAYDkUswAAAAAAy6GYBQAAAABYDsUs\nAAAAAMByKGYBAAAAAJZDMQsAAAAAsByKWQAAAACA5VDMAgAAAAAsx23FrN1u16RJk9SzZ0/FxcXp\n6NGj+bYnJiaqW7duio6O1ubNm/Nt+/jjj9WyZUt3pQYAAAAAsDibuwJv2LBB2dnZWrVqlVJSUjRz\n5kwtWLBAkpSWlqalS5dq7dq1ysrKUmxsrFq0aCFfX1/9+OOPWrJkiXJzc92VGgAAAADA4ty2Mpuc\nnKyQkBBJUlBQkA4cOODYtm/fPt17773y9fVVQECAatSooUOHDikrK0uTJ0/WlClT3JUWAAAAAKAU\ncNvKbEZGhvz9/R2PfXx8lJubK5vNpoyMDAUEBDi2+fn5KSMjQ1OnTlW/fv30xz/+scj7ueWWCrLZ\nfIo1d5QuP7vQpmrV3/qnK+2J4ZkxLm9PjOL/mwCXMDajNCuOzz1ieFYOpS3G74nbill/f39lZmY6\nHtvtdtlstgK3ZWZmqkyZMvrkk0907Ngxvfjiizpz5oxGjBihf/7zn4Xu5/Tpc+45APyupaWlE6MU\nxvCEHIjh2ZhEFA/GZpRmnvLZWVpieEIOpS1GaVPY2Oy2YjY4OFibN29W+/btlZKSosDAQMe2Bg0a\n6LnnnlNWVpays7N15MgRNWjQQElJSY7XtGjR4rqFLAAAAADg98ltxWxERIS2b9+umJgYGWOUkJCg\nJUuWqEaNGgoPD1dcXJxiY2NljNGIESNUtmxZd6UCAAAAAChl3FbMent7a+rUqfmeq1OnjuPf0dHR\nio6Ovmb77du3uys1AAAAAIDFue1uxgAAAAAAuAvFLAAAAADActx2mjEAAAAAwDWrk/a61K5HZMNi\nzsRzsTILAAAAALAcilkAAAAAgOVQzAIAAAAALIdiFgAAAABgORSzAAAAAADLoZgFAAAAAFgOxSwA\nAAAAwHL4nVkAQLEwa6a51M4ramIxZwIAAH4PWJkFAAAAAFgOxSwAAAAAwHIoZgEAAAAAlkMxCwAA\nAACwHIpZAAAAAIDlUMwCAAAAACyHYhYAAAAAYDkUswAAAAAAy6GYBQAAAABYDsUsAAAAAMByKGYB\nAAAAAJZDMQsAAAAAsBxbSScAAAAAACh+q5P2utSuR2TDYs7EPViZBQAAAABYDsUsAAAAAMByKGYB\nAAAAAJZDMQsAAAAAsBxuAAUAAABLKe03tQFQNKzMAgAAAAAsh2IWAAAAAGA5FLMAAAAAAMuhmAUA\nAAAAWA7FLAAAAADAcihmAQAAAACWw0/zAAA8hlkzzaV2XlETizkTAADg6ViZBQAAAABYDsUsAAAA\nAMByKGYBAAAAAJZDMQsAAAAAsByKWQAAAACA5XA3Y3g0V+5syl1NAQAAgNKPlVkAAAAAgOVQzAIA\nAAAALIdiFgAAAABgORSzAAAAAADL4QZQAAAAAIACrU7a61K7HpENizmTq7EyCwAAAACwHIpZAAAA\nAIDlUMwCAAAAACyHYhYAAAAAYDluuwGU3W7XlClT9OWXX8rX11fTp09XzZo1HdsTExO1cuVK2Ww2\nDRkyRGFhYTpx4oTGjx+vvLw8GWM0depU1a5d210pAgAAAAAsym0rsxs2bFB2drZWrVqlkSNHaubM\nmY5taWlpWrp0qVauXKnFixdr7ty5ys7O1rx58/Twww9r6dKlGjRokObOneuu9AAAAAAAFua2ldnk\n5GSFhIRIkoKCgnTgwAHHtn379unee++Vr6+vfH19VaNGDR06dEjx8fEKCAiQJOXl5als2bLuSg8A\nAAAAYGFuK2YzMjLk7+/veOzj46Pc3FzZbDZlZGQ4ilZJ8vPzU0ZGhqpUqSJJ+uabbzRr1iy9+OKL\n193PLbdUkM3mU/wHAI/wswttqlYNyPf4RmO40p4YnhmjuPtGaYph5b/rlTFQ8hib4amK47OCGMUb\nwxNyIIbnxrgetxWz/v7+yszMdDy22+2y2WwFbsvMzHQUt7t27dLTTz+t2bNnF+l62dOnzxVz5rC6\ntLR0YhDDY3MghufGkCiKiwtjMzyVp3zeEMOzciCG58aQCh+b3XbNbHBwsLZu3SpJSklJUWBgoGNb\ngwYNlJycrKysLKWnp+vIkSMKDAzUrl27NGPGDP3rX//SPffc467UAAAAAAAW57aV2YiICG3fvl0x\nMTEyxighIUFLlixRjRo1FB4erri4OMXGxsoYoxEjRqhs2bJKSEhQTk6Oxo4dK0mqVauWpk6d6q4U\nAQAAAAAW5bZi1tvb+6pCtE6dOo5/R0dHKzo6Ot/2N998013pAAAAAABKEbedZgwAAAAAgLtQzAIA\nAAAALIdiFgAAAABgORSzAAAAAADLcdsNoAAAKAlmzTSX2nlFTSzmTAAAgDuxMgsAAAAAsByKWQAA\nAACA5VDMAgAAAAAsh2IWAAAAAGA53AAKAAAAAOA2q5P2utSuR2TDQrdTzAIAgGLnrokLAACXcJox\nAAAAAMByKGYBAAAAAJZDMQsAAAAAsByumQUAAMBN5co11VxPDeBKFLMAAKDU4kZUAFB6cZoxAAAA\nAMByKGYBAAAAAJZDMQsAAAAAsByumQUAAB6J610BAIVhZRYAAAAAYDkUswAAAAAAy6GYBQAAAABY\nDsUsAAAAAMByKGYBAAAAAJZTKu5mbNZMc6mdV9TEYs4EAAAAAHAzsDILAAAAALAcilkAAAAAgOVQ\nzAIAAAAALIdiFgAAAABgORSzAAAAAADLoZgFAAAAAFhOqfhpHngmV34yiZ9LAgAAAFAUrMwCAAAA\nACyHYhYAAAAAYDkUswAAAAAAy6GYBQAAAABYDjeA+n+u3KxI4oZFAAAAAFASWJkFAAAAAFgOK7MA\nAAAostVJe51u0yOyoRsyAfB7RzFbjDhVGQAAAABuDk4zBgAAAABYDsUsAAAAAMByKGYBAAAAAJZD\nMQsAAAAAsByKWQAAAACA5XA3YwAAgEK48lM0Ej9HAwDuxsosAAAAAMByKGYBAAAAAJbDacYokFkz\nzek2XlET3ZAJAAAoLq6cMs3p0gA8FSuzAAAAAADLcdvKrN1u15QpU/Tll1/K19dX06dPV82aNR3b\nExMTtXLlStlsNg0ZMkRhYWE6deqURo0apQsXLui2227TM888o/Lly7srRY/kyoqolH9VlFVVAAA8\nS3GsiLKqCgD5uW1ldsOGDcrOztaqVas0cuRIzZw507EtLS1NS5cu1cqVK7V48WLNnTtX2dnZeuml\nl9ShQwctX75cf/3rX7Vq1Sp3pQcAAAAAsDC3FbPJyckKCQmRJAUFBenAgQOObfv27dO9994rX19f\nBQQEqEaNGjp06FC+NqGhodqxY4e70gMAAAAAWJiXMca4I/BTTz2lNm3aqGXLlpKkVq1aacOGDbLZ\nbHrjjTf01VdfafTo0ZKkMWPGqEuXLpo8ebLeeustlStXTt9//73GjBmjFStWuCM9AAAAAICFuW1l\n1t/fX5mZmY7HdrtdNputwG2ZmZkKCAjI93xmZqYqVqzorvQAAAAAABbmtmI2ODhYW7dulSSlpKQo\nMDDQsa1BgwZKTk5WVlaW0tPTdeTIEQUGBio4OFhbtmyRJG3dulWNGjVyV3oAAAAAAAtz22nGl+5m\n/NVXX8kYo4SEBG3dulU1atRQeHi4EhMTtWrVKhljNGjQIEVGRuqXX35RfHy8MjMzdcstt2jOnDmq\nUKGCO9IDAAAAAFiY24pZAAAAAADcxW2nGQMAAAAA4C4UswAAAAAAyym1xazdbtekSZPUs2dPxcXF\n6ejRoy7F2bt3r+Li4lxqm5OTo9GjRys2NlZRUVHauHGj0zHy8vI0btw4xcTEqFevXvrqq69cykWS\nTp48qZYtW+rIkSMute/atavi4uIUFxencePGuRRj0aJF6tmzp7p166bVq1c73f7111935BAdHa17\n7rlHZ8+edSpGTk6ORo4cqZiYGMXGxrr0fmRnZ2vkyJGKjo5Wv3799N133znV/vJ+dfToUfXq1Uux\nsbGaPHmy7Ha70zEuSUhIKPLPWV3e/uDBg4qNjVVcXJz69++vX375xekYhw8fVq9evRQTE6OxY8cq\nNzfX5eN466231LNnzyK1vzLGF198oZCQEEc/eeedd5yOcfLkSQ0ZMkS9e/dWTEyMjh075nSMESNG\nOHJo3bq1RowY4XSMgwcPKjo6Wr169dK4ceNc6huff/65oqKiFBsbq2nTpl03RkGfW8720cI++4ra\nRwuK4Ww/LSiGq/0UxYOx+WqMzRcxNl/dnrGZsfkSxuZCmFIqKSnJxMfHG2OM+eyzz8zgwYOdjvHy\nyy+bDh06mB49eriUw5o1a8z06dONMcacPn3atGzZ0ukYH3zwgRk7dqwxxphdu3a5dBzGGJOdnW0e\nffRR06ZNG3P48GGn21+4cMF07tzZpX1fsmvXLjNo0CCTl5dnMjIyzPPPP39D8aZMmWJWrlzpdLsP\nPvjADB8+3BhjzEcffWSGDh3qdIylS5eaCRMmGGOMOXLkiOnXr1+R217ZrwYNGmR27dpljDFm4sSJ\n5v3333c6xsmTJ03//v1NeHi4Wb58udPte/fubb744gtjjDErVqwwCQkJTscYMmSI+fjjj40xxsTH\nx7t0HMYY8/nnn5s+ffoU+f+7K2MkJiaaxYsXF6nttWLEx8eb9evXG2OM2blzp9m8ebNLx2KMMb/+\n+qvp1KmTSU1NdTrGo48+aj788ENjjDFPPvmk2bhxo9MxunbtapKTk40xxsydO9esW7eu0PYFfW45\n20cLiuFsHy0ohrP9tKAYrvRTFB/G5vwYm3/D2MzYfL0YjM2MzQUptSuzycnJCgkJkSQFBQXpwIED\nTseoUaOG5s+f73IObdu21eOPPy5JMsbIx8fH6RgPPvigpk2bJkk6ceKEy7+9O2vWLMXExOi2225z\nqf2hQ4d0/vx59evXT3369FFKSorTMT766CMFBgbqscce0+DBg9WqVSuXcpGk/fv36/Dhw059Q3hJ\nrVq1lJeXJ7vdroyMDMfvHzvj8OHDCg0NlSTVrl3bqW+Qr+xXn3/+ue6//35JUmhoqHbs2OF0jMzM\nTA0bNkydO3d2KYe5c+eqXr16ki6uOJQtW9bpGPPnz1fjxo2VnZ2ttLQ0+fv7Ox3j9OnTmjt3rsaP\nH1+k4ygoxoEDB/Thhx+qd+/eGj9+vDIyMpyO8emnnyo1NVV9+/bVW2+95fj7OBPjkvnz5+vhhx8u\n0v97V8aoV6+efv31VxljlJmZWaS+emWM1NRUBQcHS7r4k2nJycmFti/oc8vZPlpQDGf7aEExnO2n\nBcVwpZ+i+DA258fY/BvGZsbm68VgbGZsLkipLWYzMjLyvRE+Pj5On04WGRnp0ofpJX5+fvL391dG\nRoaGDx+uJ554wqU4NptN8fHxmjZtmjp27Oh0+9dff11VqlRxTCBcUa5cOfXv31+LFy/W008/rVGj\nRjn9fp4+fVoHDhzQvHnzHDGMizfTXrRokR577DGX2laoUEHHjx9Xu3btNHHiRJdOVatXr542b94s\nY4xSUlKUmpqqvLy8IrW9sl8ZY+Tl5SXpYp9JT093Okb16tXVsGHDIud/ZftLH+affvqpXnvtNfXt\n29fpGD4+Pjp+/Lg6dOig06dPq27duk7FyMvL01NPPaVx48bJz8/P5WNp0KCBxowZo2XLlql69ep6\n8cUXnY5x/PhxVaxYUa+++qpuv/12vfLKK07HkC6eErVz505169bNpWP585//rBkzZqhdu3Y6efKk\nmjRp4nSM6tWr6+OPP5Ykbd68WefPny+0fUGfW8720YJiONtHC4rhbD8tKIYr/RTFh7H5N4zN+TE2\nMzZfLwZjM2NzQUptMevv76/MzEzHY7vdfkODn6t+/PFH9enTR507d3ZpsLtk1qxZSkpK0sSJE3Xu\n3Dmn2q5du1Y7duxQXFycDh48qPj4eKWlpTkVo1atWurUqZO8vLxUq1YtVa5c2ekYlStX1gMPPCBf\nX1/Vrl1bZcuW1alTp5yKIUlnz57Vt99+q6ZNmzrdVpJeffVVPfDAA0pKStIbb7yhsWPHKisry6kY\n3bt3l7+/v2JjY/XBBx/o7rvvdunbfUny9v7tf8PMzEyXv+G/Ue+8844mT56sl19+WVWqVHEpxh13\n3KH3339fvXr10syZM51q+/nnn+vo0aOaMmWKnnzySR0+fFgzZsxwOoeIiAjVr1/f8e8vvvjC6RiV\nK1dW69atJUmtW7d2afVIkt577z116NDB5b4xY8YMLVu2TO+99566dOni9HsqXbwOZtGiRfrb3/6m\nW2+9Vbfccst121z5ueVKHy2Oz76CYjjbTwuKcSP9FDeGsfk3jM35MTYXjLH5N4zNjM0FKbXFbHBw\nsLZu3SpJSklJUWBg4E3P4ZdfflG/fv00evRoRUVFuRRj3bp1WrRokSSpfPny8vLyytd5i2LZsmV6\n7bXXtHTpUtWrV0+zZs1S1apVnYqxZs0aR8dKTU1VRkaG0zEaNWqkbdu2yRij1NRUnT9/XpUrV3Yq\nhiTt2bNHzZo1c7rdJRUrVlRAQIAkqVKlSsrNzS3yN7eX7N+/X82aNdOKFSvUtm1bVa9e3eV8/vrX\nv2r37t2SpK1bt+q+++5zOZar3njjDUcfcfVYBg8e7LjZhp+fn9P9tEGDBlq/fr2WLl2quXPn6s47\n79RTTz3ldB79+/fXvn37JEk7d+7U3Xff7XSMRo0aacuWLZIu9rc777zT6RiX9n/plDdXVKpUybGK\nddtttzl9QxVJ2rJli5599ln9+9//1q+//qoWLVoU+vqCPrec7aPF8dlXUAxn+2lBMW60n+LGMDb/\nhrE5P8bmqzE258fYzNhckJv/dehNEhERoe3btysmJkbGGCUkJNz0HBYuXKizZ8/qpZde0ksvvSRJ\neuWVV1SuXLkix2jTpo3GjRun3r17Kzc3V+PHj3eqfXGJiorSuHHj1KtXL3l5eSkhIcHpb9PDwsK0\nZ88eRUVFyRijSZMmufSt2Lfffqtq1ao53e6Svn37avz48YqNjVVOTo5GjBihChUqOBWjZs2amjdv\nnhYuXKiAgACXvqW8JD4+XhMnTtTcuXNVu3ZtRUZGuhzLFXl5eZoxY4Zuv/12DRs2TJLUuHFjDR8+\n3Kk4AwcO/L/27h6kjT+O4/jnqAkGRSgtrWgxW4OLDg62SI2D1WBiJVKJHcSMjpVCazGgYFYHcZHi\nppgsNhqTpe1QdBALXewoRbGCUBAEBfEhuf9QGmrV1qe/ydX3a8rD7358cxz55Hv3O6Kenh7ZbDY5\nHA6Fw+H/o9y/6u/v18DAgGw2m27fvp25r+0sXr16pVAopGg0qsLCQg0ODp6rluXl5Qv9mAqHw+ru\n7lZeXp5sNtu5PovT6VQwGJTD4VB1dbXcbvcfxx/3vdXb26twOHzqY/Qyvvt+nyOVSmlpaUklJSWn\nPk6Pq+P58+c5cZxeV2Tz5SKbDyObjyKbjyKb/61sNszz3hgBAAAAAECWsL4KAAAAAGA5NLMAAAAA\nAMuhmQUAAAAAWA7NLAAAAADAcmhmAQAAAACWQzMLZMHa2lrmj79/5XK5rrSO169fq7GxUYlE4sQx\nw8PDGh4evsKqAAC4emQzYD3/7P/MAvi7WCymxcVF2e32bJcCAABENgNnwZVZIAel02mFw2F5vV75\nfD69efNGkrSwsKCOjo7MuJ6eHr19+1Zra2vyeDx69uyZgsHgqebq6uqSaZpqa2vTxsbGoW1GR0fV\n0NCgQCCgxcXFzOvj4+Nqa2uTz+dTc3Ozvn79qvn5ebW3t2fGxGIx9fX1XfYuAQAgq8hmIPdwZRbI\nku/fv6ulpeXY9yKRiNbX1xWPx7W3t6eOjg7dv39fDofjxPmWl5c1Ojqqe/funWqukZERuVwuTU9P\nHxr/5csXTU5OKhaLyTAMBQIBVVRUaHt7Wx8+fNDY2Jjy8/M1NDSkiYkJhUIhhUIhra6uqqysTLFY\nTC9evLj4DgIA4IqRzYC10MwCWXLnzp0jYfXzvpyFhQX5/X7duHFDDodDzc3Nmp+fP/Zenp9u3bp1\nJCz/NFddXd2x83z69Elut1sFBQWSJI/Ho3Q6rcLCQg0ODiqZTGplZUVzc3MqLy+XYRjy+/2Kx+Nq\nbW3VxsaGKisrz7lXAADIHrIZsBaWGQM5KJ1OH3pumqZSqZQMw5BpmpnX9/f3M4/z8/PPNNdJDMM4\ntE1e3o9zXuvr6woEAtra2lJtba38fn+mFr/fr2QyqUQiceIZbQAArIxsBnIPzSyQgx48eKCpqSml\nUsovEsIAAAE+SURBVCnt7OxoZmZG1dXVunnzpr59+6bd3V1tbm7q8+fP557rJA8fPtTHjx+1tbWl\n3d1dvX//XtKPJU5Op1PBYFCVlZWanZ3NBG9paamKi4sVjUYJTADAP4lsBnIPy4yBHBQIBLSysqKW\nlhbt7+/ryZMnevz4sSTJ7XbL6/WqtLRUVVVVF5rrOOXl5ers7NTTp09VVFSkkpISSVJNTY0ikYia\nmppkt9tVUVGhpaWlzHZNTU169+6d7t69e8FPDwBA7iGbgdxjmL+uiwCAczg4ONDLly/l8XjU0NCQ\n7XIAALj2yGZcBywzBnAhpmnq0aNHMgxD9fX12S4HAIBrj2zGdcGVWQAAAACA5XBlFgAAAABgOTSz\nAAAAAADLoZkFAAAAAFgOzSwAAAAAwHJoZgEAAAAAlkMzCwAAAACwnP8Ae8aZZN5920gAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109ca4358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Note the sharey boolean parameter here to ensure the y axis is shared\n",
    "fig, ax = plt.subplots(1, 2, figsize=(16,6), sharey=True)\n",
    "\n",
    "sns.barplot(x='index', y='normalized', data=weekday_hourly_counts_df, color=hex_colors[1], ax=ax[0])\n",
    "sns.barplot(x='index', y='normalized', data=weekend_hourly_counts_df, color=hex_colors[2], ax=ax[1])\n",
    "\n",
    "ax[0].set_title('Weekday number of trips by hour of the day')\n",
    "ax[1].set_title('Weekend number of trips by hour of the day')\n",
    "\n",
    "for i in range(2):\n",
    "    ax[i].set_xlabel('Hour of day')\n",
    "    ax[i].set_ylabel('Probability')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is better!\n",
    "\n",
    "Notice the different shapes of the distributions. More trips occur during the early morning hours (midnight-3 am) on weekends than during weekdays, which makes plenty of sense. Also, notice how peaked the weeekend distribution is around 1-3 pm. This peak doesn't exist on weekdays, instead the trips are much more uniformly spread with the most trips occurring at 5 pm, indicating after work commute periods.\n",
    "\n",
    "We can dig even further into these differences by treating the start time variables as a continuous quantity and using kernel density estimation to estimate a probability density function for each day of the week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hpzN236ug+0+tEV3HGphDXNFYVD0Hr/UmomoyjsD7mENLyiHqk6BFsW5ZROwX\nT2DZvYJIfG0K+9yJt8f1+NsNpqjPnWiuRGybfyDm2+cw5B2onDiFEJXu7rvHsGHDBgDC4TB9+/bk\nww/fK7k+duwodu7cftw2gsEgV145qMxjS0vbx9ixFZ/ULbf0rKqqTJo0qdR7jRo1OqLcDz/8UPK1\nzWbjpZdeKq+QhBBCCCFOSA8VEc38BRSFBNc3hM2diBhbnHZ7UUN9fLb6+GzXo0YPYgkvwRRZj896\nIyjl9rlQlaNYE1FjGxNx7yLi3kFi5Bq81hsJWAaCYjp+ZT2Ay/dfrKF5aEoshY6JhE0dCJp7El/4\nT1zeyRQYphE11K+QsQAYD27HseozDO5MNLMdb6crCTbpAuofj4xEqjfE/Y/7sa/+AuvOX4j57nn8\nbfoTOO8iUM+dv3shBHTo0JlVq1YxaFAD1q9fS6dOXVi27GeuvfZ6gsEgmZmHadz43HqkS9aKCSGE\nEEL8RteiRA4tBi2MK3YHJmMB+dZTWBVxApqhNn7DNfi5pszaPJuo1dqjefZT5OuEzXIAl/957MGZ\neG23EjRdfNTkjCGaTox3PMboHsKG8yh0TkJTawAQNTSiyDGOGO8EYjyPUBAzDV1xlO8YPHnYV32O\nOX0DOgqBJl3xtx2AbnUevYLJgu+CqwjXaYlj2Uzsa7/GlLEZb7cRaK5q5RqrEKLq6NixMx999A6D\nBg1j2bKfGTTocl599SU8Hg87dmyjbdt2rFu3hmnTpmIwGKhdO5kHHniEUCjEpEnjKSoqIjk5paS9\nsWNH0aRJM/bs2Y3P5+Hxx5+hZs1azJ49k/nzv0dRFC6+uA/Dhl3D4sU/MGPGuxiNRqpVS+Kxx54k\nLy+PSZPGo+s6CQmJJe0uWrSAzz77hEgkgqIoPPnks8ya9QHVqiUxdOhVFBYWcs89t/PWWzPO+J5I\nMkIIIYQQ4jda9ioI5mF0unCZFxM09SRiPLc+qSpPisGCoVp7opnLyPbehDMuDac2hxjvJCKGD/Ha\nRhEyXgCKAoA59BMu7xOoePFbrsBjGwuKuVSbQfNF+CJbsQdn4vJOptDxRLmtOFECHmLmvoDqLySc\n1ABfp6FEE1JOXBEIJ5+He9CD2H/9BEvaOmK/noKv/eXFqyl+G68QomKs3ppF2uGy3buhXk0X7VOr\nH/N606bN2LNnD7qus379WkaPvuO31RK/snv3Ljp1uoBnnnmCV199k/j4BN5441W+/fYrPB4PDRo0\nYvToO9iRS0zvAAAgAElEQVS8eRNr1qwqaTM1tQV33/1vXn/9FebP/57u3S9k4cL5TJ36JgD/+tcd\ndO58AfPnf8+1146kd+9L+O67r/F6vbz33nQuuaQvgwdfwcKF85gzZzYA6en7+e9/X8RqtTJlyhOs\nWLGMgQMvY+LERxg69Crmz59Lnz79yuSeyfowIYQQQghAK9qH5t4O5jjiHN+go5zaXhHipCgxjVFi\nGqEH3RRlJZAVnYzf1BdDdDexngeI9dyJKbweh+81Yr0PoxCh0D4ej/3eIxIRv/PaRhMytscSXoo9\n8N5Ry5QF+8pPUf2F+Fv1oajvXSediPidbnHg7XEDnu4j0VUDjl8/xr7yM9C1copYCFFVqKpK8+bN\nWb78FxISEjGbzVxwQVc2blzPhg3r6NChE7m5OTz66EOMHTuKFSuWc/jwIdLT93PeecWPCrZo0bLU\nCZVNmzYDoEaNGoRCQfbs2U1m5mHuvnsMd989BrfbTXp6Onfe+S9Wr17F2LGj2LRpA6qqkJ6+n9TU\n4nZbtWpT0mZ8fAKTJ0/gyScfY/fuXUQiEZKTU7DbHezdu4f58+fSr9/AMrknsjJCCCGEEOc8PVxE\nNHMZKEZs1WyYw9sJmC8lamhQ2aH97SiKgrFmNzRnHaKZvxLN202+pS3mpMG4tA+xhH/G7CnexDyi\nplDoeJyosfEJGjVS6JhIfNGt2ANvETE2I2TqUqZxm9LWYdm3lki1evhb9zvmaga9IIC+NQeCEZSO\nySiOv+yHoSiEGrQnUr0hzh+mYd3+E0rQh7fbtaX2mxBClJ/2qdWPu4qhvHTr1o3333+bSy7pC0Dr\n1m15++03UBSF2Ng4qlevztNPP4/T6WTp0sXYbHZ2797Jpk0b6dGjFzt2bCMSiZS0p/zl+1DduvWo\nX78hzz33EoqiMGvWBzRq1IQvv5zDLbeMIj4+gSlTnmDJkh+pX78hmzdvoEmTpmzdugUAj8fD9Omv\n8+mnXwPFKyt0XQdg8ODLeeedN0lKqk5cXFyZ3A9JRgghhBDinKbrOtHDv4AWxlCjC67oJHQMxRtM\ninKjOuui2GoQzV6FXribUEYB+YnDMcdcgzPwHpoah8d+L7pyjL0Y/kJX4yh0PEFc0e24vJPId72B\nZji1lQvHoviLcPz6CbrBhKfrtUdsPqnrOmQUoW3NhoOeP94/5EHtXgelTuwRbWqOeIr63Ilz0TQs\n+1ajhP14LrwRjEdf/SGEOPt17dqV8ePH8+ijxQc9mEwmXC4XjRs3RVVV7r77Pu6//250Xcdud/Do\no4/RsmVrJk+ewJgxt1CvXn1MpmNv+NukSVM6dOjI7bffQigUJjW1BUlJSaSmtuCBB+7Bbndgs9no\n2rU7Xbp0Z9Kk8SxYMI/atZMBcDgctGrVhn/+8yYMBiMul4ucnGwALrywN//73xQeffTxMrsfiv57\nquMsda6d01qVnIvn5Iqzj8xTUdXJHK180fytaNkrUZx1sSeGiPU9jt88AI9jXGWHVmWU9zzVPBlE\ns5ZDxAfmeIw1u6JYE09c8Sgswe+I8T1JRG1IfsyroNjPLDhdx7nkHcz71+NrfxmB83r/cSkcRd+V\nV7wSojBU/GYNB+p5Sej+MPqKg6DpKKnVUNrXQjEe5QnpcBDnkrcxH9xGuHpDPL1vRTefYcznKPl+\nKqq6s3mOBgIBxo4dxbRp76CewmlASUmuY16TPSOEEEIIcc7SQ260nDVgsGBI6oAj8DY6RlkVUcFU\nZwrGeoNRYptAKJ/I/m+J5qxF18Kc6udmQUt//JYhGLU9uLzPwBl+7mZOW4t5/3rC1RsSaN4TAL0o\niLbiANrHW9B/PQieMErjeNRBTTH0b4xSLxa1eTXUQU0g1oK+NQftm53oBYEjOzBZ8PS6lWD98zFl\n7cE17/9Q/IVnFLMQQpSljRvXM2rUDYwYcf0pJSJORB7TEEIIIcQ5Sdc1ood/Bj2KoXp3rNpijFoG\nfvNlaIZalR3eOUcxmDHW6ILmrEc0cxla3ka0vI2AAqoJVCOoJhTFWPI1qhHVXgs1tkmptjy2sRgj\nO7GGfyASbI7fOvz0YvIXYv91NrrBhLfLcHQU9J/T0XfmFRewGVFaVkdplohiLf6xOjfkZbc3m7ax\nKZjjbaiDmqKvOIC+Iw/t650onWujNE4o/ay3wYi3+0h0swPrjqXEzH2RokvGyNGfQogqoVWrNrz3\n3qwyb1eSEUIIIYQ4J2n5m9EDOSiuBqjO2jgK70PHjM92fWWHdk5THbVR6g9Gy92AHswDLYKuRUAP\nQzSIrnlAj5aUjxbtAxTU2D9tcqmYcDsfJ77wZhz+NwmY/4GuxpxaILqOY/nHqCEf3o5D0GKS0Fcd\nLE5ExFtRWlVHqReLYij+lDAv5OPH3B2sc2egobOh8CAjUzphMRpRutZBr+1C+zkd/eeM4n0luqSg\nmP+0YaWi4us0FN3qwLbhe2K+f4mii/9JNL72ad9LIYSoyiQZIYQQQohzjh7MR8tZDwYbhuqdsAa/\nwqAdxmcZhqZW/A7rojRFNWFIan/M67qugRaBcBGRjPnF+01Y4lCtf6wk0NVE/NbhOP2vYA19h996\n9SnFYN67GnPGJsI1GhFs1h1tVx76pmyIsaD2a4xiKU4kFIR9/Ji7kzUF6WjoJJmdxJps7PJm8076\ncq6v0xmbwYRSPw410Y62JA19bwF6tg+1Z12UJMefBq7gb9MfzWzHsWoOrnkv4+k9ikh1OdVFCPH3\nI3tGCCGEEOKcoutRIoeXAhqGml1R1TCOwFto2PFZR1Z2eOIkKIqKYjCjWBMx1OoBukb04I/oEX+p\ncgFzf3TMWIOfg66dfPs+N/aVn6IbzcWPZ2T70X/JALMB9eIGKBYDhWE/Xx3eyP/2LGJVwX4SzHaG\n1TqfOxv04uoa7WkRqEdhlsY721fiDhTHpbjMqP0bo7SuDp4Q2re70HblHdF/MLUnnm7XoYSDuBZM\nxbR//RndLyGEqIpkZYQQQgghzila7gYI5qPENkF1JGP3v46qu/Fab0NX4ys7PHGKVEcyeuL5aLlr\niR5agiHlUhSl+PM2XY0laL4Ia2gupshqwqaOJ25Q13Esn4Ua8uPtdCVRJQbth52g66i96uFx6CzJ\n3MTKgjQiuka8yc5F1ZrSOiYZBYVd+92s25FDJGyhJjUgB77Yl4bLbiIhxkp8jJX4ZBeJ8VYsyw6g\nL01H80dQWiaV2kci1LADutmGc8m7uBa/jT+1F/52g0A1HDt2IYQ4i0gyQgghhBDnDM2fg5a3CYxO\nDEkdULVMbIGPiSpJ+KxXVXZ44jSpCS3Rg7nonv1o2asxVP8j6eC3XIE1NBdb8POTSkaY96zAfGAL\n4ZpNCTTojPbdHghEUDons8aez9e7NxLWNeKMNnpVa8r5sSkYFJXMPB8rt2SRXxjEaFBo27QaVrOB\ntVkHyS8Mogd0PL4I+w97SvpKjDXRM1/HuvoQnjwf1i4pmM1//HgeTmmBe8C9uBa/hW3rjxhz0vBc\neAO6Pa5sb6AQoty9/PL/2Lt3J4cPZxIIBKhdO5l9+/bQvn1HHnvsqTLvb/nyX1i4cB6PPDKxzNsu\nK5KMEEIIIcQ5QdciRDN/BnQMNbuiqCYc3jdRCOG13QaKtbJDFKdJURQMNbsR2e9GK9iKYk1EjWkI\nQMSQStjQFHN4KaqWddw9QRRvAfaVc9BNFjwXXI229ADkB1CaJbIrJcoXBzZgNRjpX60F7eLqYlRU\nvP4wq7cdJu1QEQANk2M4v1kS9t9O12hcJ5b5OdtYkrOLBJz0j21FxKeQXxQkvzDIXE2nV0GIuL1u\n9h0sYnuyk8QEO9XjbVRPsOGIq4l7wL9xLJ+JZd9aYr9+Fk+PkURqNSv3+yqEKDt33vkvkpJcvPvu\nh6Sl7WPMmDtZs2YVX3zxaWWHVmkkGSGEEEKIc4KWuw5CbtS4VFR7TYyRHVhC3xMxNCZo7lPZ4Ykz\npKgmjLV7Edn/LdHMZSjmWBRrIigKAcsVuHzPYA1+ic9269Eb0HUcy2eihgN4L7ia6M4Q7HdDTQfZ\nbV3MSv8FVVEYmdKZOrZ4IlGNDXty2LQ7j6imkxhrpWOL6iTF2UrHpSj0SUrFrBhYkLOdL31rubnO\nBbSyJAIQDEXJyfLg/fUg9YtCWDM8LCkMsmN/AQB2q5GkeBvVkgdRO6YpdTbNwbXgNfxt+hFodSko\nsgWcEGez9PR0/v3vu8jPz6Nbtx7ccstoxo4dxf33P0y9evX5/PPZ5ObmMmDAIB588F/ExMTSpUs3\nbDY73333Naqqkpp6Hvfccz/79u3lqacmYbXasNmsuFzFpwh9+uksFi9ehN/vJy4ujieffJYnnphI\nnz796dq1O/v27eWVV17gv/99sULHLskIIYQQQvztab5MtPwtYIpBrXZ+8S+e/qko6Hhsd4Aiz+H/\nHSjmWAw1uxM9uIjIwcUY6w1AMVgJmC/B4X8Fa/ArfNYbQTnyR2DrpvmYD24jVLs5frUZ+oZ0cJnx\n9ajFeweWEdQiXF27HSnWOPYfLmLV1my8/jBWs4HOzZNomBxTas+Hv+pVrSkm1cB3WVt4c/8v3FSn\nCzWtMVjMBpJTYtFrutAWp1EzvZAhYdiXmsAhb4jsfD9ph4pIO1TEahJQE26hWjSXmrsOknT4C1wd\ne+OMiz1u30KI0rSVB9H3FZRpm0r9ONSOp34UbygU4qmnnkXTNIYO/Qe33DL6mGXz8nKZPn0GJpOJ\nW2+9nn//+0FSU1swZ85sIpEIU6e+yK23jqZjxwuYMeMd0tL2oWkabrebF16Yiqqq3HvvWLZu3czg\nwVcwZ85sunbtzjfffMnAgZedwehPjyQjhBBCCPG3pWtRdP9holm/AsVL+RXViDm8HHNkNSFjZ8Km\nDpUdpihDqrMOekJrtLwNRA/9hCH5YhTFSsDcH3vwE8zhnwiZe5eqY8rYjG3dd0TtcXiaDUVflAEm\nFa13XT7IXoM74ufias1oFZPMuh05bNyVi6rAeQ3iadU4EbPp5JJZ3RIaYVIMfJm5ken7f+Hmul2o\nZY0FQDGqqL3roy/PwLgjj8abcmjapyG4zHj8YXLyA+S4/WTnB8gpTCTLmggRYFkmFkMm1as56Xhe\ndRw2U9neUCFEuWrYsBFmsxkAg+HIX891/Y+va9WqjclU/G/84Yf/w0cfzeDQoRdp0aIVAPv37yc1\ntSUArVq1JS1tH6qqYjKZmDjxEWw2G1lZWUQiEc4/vz3/+98U8vPzWbFiOaNH31HOIz2SJCOEEEII\n8beiR3zo3gNongx03yHQIwCoCa1QbUmgR3D4XkFHxWO/vZKjFeVBTWyDHsxD92ag5azFkNSegOVy\n7MFPsAXnlEpGqIXZOJa+DwYjRZ1vIvpzFkR16FWPT/1byQgU0DYmhV6JTdixv4CNu3Jx2U307pBC\nrNN8yrF1iq+PUTUw59A6ZmSsYEz9C3EaLQAoqgJdUsBmQl+fifbNTtRLG+KqZsdlN9MguXjJdTSq\nkVcYwL1lPblZuRw21iA9E3IKAlzUIZmEWNn/RIjjUTvWhtNYxVAejraoyWy2kJubQ7169dmxYxvV\nqiX9VvaPx7K+/PJz7rtvHBaLhXvvHcvGjetp0KABmzZt4IILurJt22YAdu3ayZIlP/LGG+8SCAS4\n5ZbrfmtLoW/fAbzwwn/p1OkCjMaKTw1IMkIIIYQQZzVd1yGYh+bNQPdkoAdz/7hojkV1JKM46qDY\nijcutIa+xajtw28eSNTQsJKiFuWpeEPL7kT2f4uWv7l47whXfULG9pgjqzFE9xE11IdwAOeP01HD\nAYo6jyC8Ngj+CEqHWiy0HmJT7iHq2RK4vGZrDmZ7WbE5E4vZwEUdU4hxnHoi4nftYutQGA6wIGcb\nHx5Yyc11umD87chORVFQzq+JZjOi/3oAbe5ulC4pKA3jSh7FMBhUkuLtJHXrQvND23EseYd1xuYs\n5QK+X76fHufXJqW6syxupRCiEgwbdjXPPfc0NWrULElE/FWjRo25447bsNvtJCUlcd55LRk79l9M\nnjyBjz56n7i4OMxmCykpdbDZbIwZczMAiYnVyMnJBmDAgEEMGfIP3n13ZoWN7c8UXf/zwo+zT3Z2\nUWWHcM5KSnLJ/RdVnsxTUdXJHD19uhZFc28v3gsi4vvtXQXFXgPFkYLqSEExx/ylko8E93BU3Ude\n7EdoarUKj/tsdLbOUz1YQGT/twAY61+GRV9JrPdRfJaheG134Vz8Dub0Dfga9cab3bz45IwmCaxr\nofPZ4fUkmOyMrtedgFdn3vL96Dpc2rkOSfG2E/R8ErHpOh8fXMPGooO0i63DFTXbHLHvg57mRvtp\nP0Q0qBuD2iUF5SiPYZjSN+H68U12xLZlnrkrmqbT8bzqNKsff8Zxnk3O1nkqzh1VbY5mZ2cxefIE\nXnzx1XLrIynJdcxrsjJCCCGEEGcVXdfRPfuI5qyFsAdUE0pMw+Lkg702iuHYn1jbAzMx6Hl4rTdJ\nIuIcoFjiMFTvSDRzGdGcNYRqdieqVMManEt0d2PM6RsIJLbEe6AJeAMozRNJa2nli4zlWFUTI1M6\no4dUFq1KIxLV6dmudpkkIqB4BcQVtdqQG/ayxp1OTUsMXRNKr9RR6sWiJjRFW5oO+wvRMrejdE5G\naRBXKnERrtOSQGpPmm5djLVuHN+FW7JiSxZFvjDtUpNQZXNLIcRfLF78A9Onv859942rtBjkLCAh\nhBBCnDU032Gi+78leugnCPtQ41IxNrgCY83uqK76x01EqFoO9sBHRJUEfNZrKjBqUZmUmMYolkT0\nor1ogTwClsGoeLHnzSRoqU+RuzN4wyjn1yTv/Fg+PLgKHbg2uQMxqo0fVmXgD0bpkFqdujWP/Qnf\n6TCrRkYkd8RpsPBd1mZ2erOOjN9lQe3XCKVzbYjq6Ev2o/2Yhu4PlyrnO38QkcS61N3/I4NT8ol1\nmtm6L58law4SiWplGrcQ4uzXs+dFvPfeLFq3bltpMUgyQgghhBBVnh4sIHLgB6IZ89CDuSjOepjr\ntMEZswNX4AUswe9RtOMvfbX7p6MQwGe7BRR7BUUuylNUD5Ef3k5IKzxmGUVRUKt3Ki6ftYJgsDO6\nBkq9AgqDvSCkoXRNIdAynvczVuLXwlxWszX1rAksXn0AtydEav14UhuUzyMPsSYbI1I6oioqsw6s\nISfkOfoYUpNQBzeFGg5Ic6N9vh1t75+OJjQY8fS4Ac1kpeaaTxhwnpWaiXbSMz3MW56OPxgpl/iF\nEOJ0GSZOnDixsoM4Ez5fqLJDOGc5HBa5/6LKk3kqqjqZo8enR3xEs1ehZS6HcCGqxYQrIYN488c4\nIp9ijqzCFN2JJbwEW3Ampsh6FN2PplZDVxwl7Riie3D5niWq1sNjvx8U+TzmVFTFeVoYSWNd0fOk\nBeeSFphLVmgNPi0LXdexqHGoyh9PIysmB3qoCHwHsezaglFzY0jKJph1HuFOrVmVUMinh9aRH/bR\nI6Ex3RMa8fP6wxzI9lK3ppMurWoesZ9DWYo12Ygz2dhYdJBd3mzaxqSUbGj5Z4rFiNI4HiwGOFAE\newvQ8wMoNR0oJgO6xU40JgnL3tVYsndTq8cl+IIaB7K9pB0uolY1B1bL3/cp7ao4T4X4s3Nxjjoc\nlmNek2SEOG3n4j8mcfaReSqqOpmjx6Z5MoimfwuBXAwGL/GuRcTaF2BRdqIpdkLmnvgsw/FZR6Kp\nNVF0D+boBiyRZdiDszCHl6PobnQlFodvKkYtnSLHw0QN9Sp7aGedqjRPdV0jLTCXTd7XCemF1DB3\nxqzE4ImmURDZyeHQctICc8kLbyGg5aFgwKzGolqroeVtIWKzEklrhb3OCvKTdJ4NWNhcdJiIrtE1\noSF9klJZtz2HnelukuKs9GqfjEE9ueSVrmuEdQ8qplNOXtSyxhLSomzzZHIo6KZ1TPJR21AUBSXJ\ngdIgDj3XBwc96LvyUBJtKC4LWmxNlKAX84EtGIIearS/AEVVSM/0sPdgITUT7ditR26C+XdQleap\nEEdzLs7R4yUj/r6pUSGEEEKctXQtjJa5EHSNWOcv2Kx7iZha4zUNImTqSFRtWOpw9oixOT7b9aha\nJubQUizhJZgi6zH5t4L/dQBCxnaEjBdU1pBEGQhEc9nkfZOCyHbMSiznOW6mmrkVUPzIRkFkJ3nh\nLeSFt1AQ2UlBZAd7+ByDYiOlMIX6mdXRa5nwuVzooUSqW1di93ahc2ILOsfXx2m0sCOtgM178nDZ\nTfTqkIzR8Eciwh/Nxhs9RFB3E9QKCGkFBLUCgpqboF5ASHOjE8VhSKa9637MasyxhnJUfZJSyQoW\nscObxbzsrfSrft4xyyoxFtR+jdG35qCvPoS2cC9qn0YoNRz42g/GmL0X667lRGo0pnXjDjhtJn5e\nf4glaw8ysHt9zKYjV14IIURFkqM9xWmrakfTCHE0Mk9FVSdz9Oii2SvR8rfisG9BrzGUsLE1KMf+\ndOVoFM2NObwMS3gJhuheCp2TiRoalVPEf29VYZ4eDi5nm+99IrqfJNP5pDpuxKwee0PJsOYhL7KN\nvPAWcvwrCapeFE2lc24XTBjZYjtEH9c3uK2jCNlGApCeWcTi1Qcxmw3071IXl8NMWPOQGVrJoeAv\nuKO7j9qXggGLGodZiUVVDBREduI01KW96wFM6qntTxKIhnk9bSnZIQ9DarWlXWydE9bR0wvRftgL\nJgNq/0Yo8TbUwmxiv30WdB33P+5Di6nOuh05bNyVS71aLnq0rVWuj55UhqowT4U4nmDQzaBBg2na\ntFnJe+3bd+Smm24reT1hwjjGj5+EyfTHCqbly39h4cJ5PPLIxIoMt0zI0Z5CCCGEOGvooUK0/K0Y\nVA/G+Ab4TB1Prx01lqClH0FLvzKOUFSksOZju28Gh0PLMWAh1XEjtc09TviLtC9qICtQG3VHPt02\nD+ZwrQL2NlnFzphttCxqRT0tlqhuwhH6ipB1BHsOevhlwyFUVaFX+5oETJvZW/QL2eH16EQAhURT\nS+KMTbCocViUeMxqLBY17v/Zu+/4OKpz8f+fmdnetCtpVaxuufduY3AhBAwYCOQmtIRUSgyEhHy5\nyb03Pwg3ISHlJkBCIHQIhAQS04LBFBtswLj3Jjf1uirby+zunN8fa8sYyXLBsmU879dLr7V2Z8+c\nWZ8de54553kwSnak/XlIhBDsjD5LY+J9NoUfYKLzRyjHEEizKEa+VjyVv9R8yKstm/EYrVTY+i5D\nK5W4kM4pRXxQh/b2PuSLh6C5vESmX4Xjw7/iWP40wYtuZ9yQHFo6otQ2hyjMtTG0xH3U/dLpdCdG\neXkFDz746GFf/9//vfck9ubU0oMROp1Op9PpBpS0bw0ATvsaYtZfn+Le6E6lzuROtkUeJ6F14lIG\nM8ZxAzYlv8d2aaHRHA9SH+ukLtZFXayTQDLOvHqZmU0FCAxoCSv5ntvJsQUIJz7CqdqpS1RSYdlJ\ndesfWLH1fMzOAENG1bCDtSTDmaoWdqWIQtNMCs0zMMtHrqghSRIjbF8nJaK0qqvZHH6I8Y7vH5JQ\n80hyTQ6uLprMX+tX8XTdSi4pGMtUd9+5TuRKD1o8hVjT1B2QUCsmEW/djWX3x9jWvkx0+pWcM6GQ\nRR/UsGZ7G3keK1mOY5txpNPpTqz169fy8MN/wmg0ctllV/D443/hb3/7F83NTdx778+xWKxYrRac\nzsyyr4ULX2DZsveIxWK43W5+9av/45e/vJsLLriImTPPoaammj//+X5+97sHTvGRHZkejNDpdDqd\nTjdgaOEGRKQRk7EJ2VGEJve88NR9/kXSzdTE3qBZXYGExGDrlyi3XIIsHZrnIJiM8+/WzeyJ+EgK\nrfv5XM3MLbvzyOuyIJFAHm2geOo5+18tQBTmk6z9N0p0JsK8i1LT2+ybsg0hSXQARpyUmL9Iofls\nnErpMS9nkCSZ0fbrSYkYHcktbIs8zhj7jd2zJ47GELuXb5ZM54WmdbzaspnGmJ9L8sf0WmXjAHm0\nFy2RQmxuQ3unGvnCSqJTrsDgq8GyawWp/KE4yicyY2wByzc08cGGZi6aWYqi6NVldGeW3dEXaVXX\nnNA2801TGWq78ojb1dRUc+utN3b/ftllV6CqKo899gwAjz/+FwAeeugBrr/+JqZOncFzzz1NbW0N\nmqYRCAS4//6HkGWZH/3oVnbs2MZll13Byy//i5kzz2HRote45JIvndBj6y96MEKn0+l0Ot2AILT0\n/lkRgizHCiKW/z3VXdIdBU0kiWudxLR24ukO4lo7BsmG1zSx11kMfQmk9lITexNfcgMgsMuFjHJ8\nhyxDz1wfeyPtvNi0jkhaJc/kpNyWTYnVw2C/BceKFkhoGGlGmZZNctT4Q94rmd3I7uFI/p00ds2l\nOHspl0oN7JUnY7BcRY5p7DHNZOiNLBkY57iZDaE/0KquxiBZGWH7xjEFNirtXhaUzeb5xjWsDdTR\nmghxTdFkXEbrYd8jTSyAeAqxqzOT1PL8wYRnf4usN36PbfW/UAeNpKzQydD2LHbXB1i308e00XrQ\nT6c7WT69TGP9+rWUlvac+VRXV8fIkWMAGDt2ArW1NciyjNFo5O67f4rVaqWtrY1UKsXEiZO5777f\n0tXVxerVK7nppltO2vF8FnowQqfT6XQ63YCg+bdDMoTNuh3J7CFpmHCqu6T7lHZ1E/7UHuJax/7g\nQzsJEQB65kPfHXsRh1JMnmkKecZJ2JXeS1UKIehMbaMm9gZdqZ0AuJQKyq0X4zVO7DGbQBOC5R27\nWdJehYTE/LzRzPBUQFog1jcjtjcBGnZpHdrUoaijpvXYZ1oTrG7MZ6xlDwZtKF2SF7f4F+O0j1DV\nGGHl+6QNQz7z56VIZsY7fsC60G9pTCzDINkYavvqMbXhMdm4oexsXm3ZzKZgIw/VfMDVRZMpt+X0\nur0kSTCjGJFIQ20AbXktzC0nNvqL2Da9gXXHe8TGX8SUUXm0dcWoqvVTmGunJN/xmY9XpztdDLVd\ned14D8AAACAASURBVFSzGE4WWe55bqyoqGDr1s3MmDGTnTu3AbBnz26WL3+fxx57hng8zne/+3Ug\n872fN+9i7r//d0ybNgOD4fS4zD89eqnT6XQ6ne5zTSQjaB1bkGSBy7aWiPkHh5TuHChCqTiRlEq+\n2XnKKhEIIUiJCAktgCoCaCJJtnEUsmQ88puPU1KLsTX8GC3qx93PSchY5Gw8ynAscg5WJReLnItF\nziGu+WhT19GR3M6+2Cvsi72CTc7PBCZMk3EqZYCgTV1LTfwNQuk6AHKMYyizXIzHMLzXzzeaVvlX\n0wZ2RdpwGSxcXTSZUms2oiuGtrwOuuLIchgXS0mNn4I6ak7PY0lpLN/QRJMvjqNgMCMdu4jHJ9Dl\nvQx77EHMyY/xhL5L3HQpEev1CPmzJXk0yjYmOX/E2uC91MbfxCjZKbdefExtmGQDXymcSJHFzeK2\n7TxZ9zHz88cwzV3W6+ckyRLy7FK0d6uhLoj4uIHY1NlYqpZj2f4+8eGzMFgczJowiDdX1LJiczOX\nzirHZum/MaTT6Y7Nrbfezj33/Iy///1Z3G43JpOZ4uISrFYrCxZ8B4CcnFza230AXHzxpXz5y/N5\n5pl/nMpuHxO9tKfuuOnlk3SnA32c6gY6fYxmpJqXI0I1uJxrsVn30JH1EkiWU92tbk3xACs697El\n2EgawUhHPpfmj+1zuvxnFUrV4UtuIKH5M4EHLUBCZB4F6UO2tckFjLBfR7ZxZL/0Y3vsEULJZlxK\nBUNsX8Em52GS3T1yOHxaSsRoVzfRpq6jPbkFDRUAi5yDhExM8wES+aaplFkuwmU4fJLGhlgXf29c\nRyAVY4jdy1cLJ2I3mNH2dSE+rAdNYLI24EosJTFiJtGpX+4R0Eok07y3tgFfV5wir51ZEwugcTEk\nOlFKLkK2ejEmV+GI/gmDVosmOYhavkPMfAV8xmUb8XQHa0L3ktA6GWH7BsWWucfVzr5IO/9oWkc0\nrTIpq4RL88diPEweCaGm0d7aCx0xpLF5WO17sK9ZSHzEHKJTrwCgqraL1dvayM+28sXpJcgDMAh4\ntPTzqW6g688x6vO1cc89P+OBBx7ul/aPV1+lPfVghO646Sd83elAH6e6gU4fo6BFW0g3vI1sMpDv\nepiY5WoitlO/3lUTgp3hFlZ07qMm1okipRjtqsKsxGhPWEmkPUxwTWSaeyIG+cTdUfYnd1MTX0R7\ncvMhz0sYMMtZmCQXZtmNaf+fVRGgMbEcEBSYzmKY7SpMsusz90MIQUNiKbuiLyBIUWa5kErrl487\nl0JaJOhIbqVNXYdP3YhGikHmcyizzOszt4QQglX+Gt5s3YaG4Nzc4czNGYosSWh7OhEf1YNBxu7a\nhs2/kkTFZCJnfw0+tbwjGk+xZHU9/rBKxSAnM8cVIssSWrSVdMNbSOYclJILkGQjiBTWxMvY4k8i\nizApuYyY+ctIaEgimvkhdvDPIvPntJxPyH7nYQMXkXQza4P3khQRxthvosDccwnJ0fAnozzfuJam\neIAii5triqbgPkxgTMSSaG/uhWACaUoBnl2PIMcCBL70UzRHNkIIlq1vor41zPhhuYwb0vvyj9OB\nfj7VDXT9NUaXLVvKE088wh13/Dfjxg2sJY56MELXL/QTvu50oI9T3UB3po9RITRSta+D6sedvRGr\nspZO1z/QlEGnrE/xdJL1gXo+7qqmKxkFYLjDTKn9fZKipcf2QshYlULchlIcSjEOpRinoRSznHXU\n+xRC0JncRnX8dfypXQC4DUMptVyAXSnEJGVhkGyHXRoSTFWzI/JXQulaDJKNIdavUGSefUzVGz4p\nqYXZHnkKX3IDRsnBzEG3YYx99hwKB2giiUBDkfouK5nQUrzSvIktoSZsiokrB01iiN2baWN3B+Kj\nBjApOHK3Ym1diVo0mvDc78CnZgp0BuK8v66RSDzF8DI3U0flHfJZppo/QISqwWBD8U5BcmSWP0ia\nH3v8CSyJ15DQ6ItAQkIQtn6fmOXwa9GDqVrWhX6LJlQmOm8n2zjqSB9Xr5JamldbNrMx2ECuyc73\nymZhUXoPiomwirZoN8RTmCckcW1+jkTlNCIzrwUgoaZ5/cMaYokUF0wvIS/bdlx9OtXO9POpbuA7\nE8eoHozQ9Ysz8cukO/3o41Q30J3pYzTdtRPNtxrFmUO+5dckjOcQdNx7SvriT0ZZ0VnNukAdCS2F\nQZKZ4CpmbFaahsTTJEWYYvMXqLBeQiTdTIdazc7IVpKiBbshhCIdunTCIueSZagky1CJ21CJQynp\nMatACI225HpqYosIpWsByDGOpdwyH49x2DH1XwiNhsR77IkuJE2cLEMlI2zX4TSUHuPnsJstkUdI\naJ14DCMY47iB4vzSkz5Od4fbWNS2lXY1QqnVw1WDJpO1/+6/VtWB+LgBzArW0gYc+xaTzKskdN5N\nYDAd0k51U5CPN7eQ1gQThuUypjK7R1BHaCm0zq1oXVtBaEi2QpS8aUimTEBJSVdjSO1ESFaEZDv4\nyME/SyJKdvBaEBqdWc8j5OzDHltncicbQn9AloxMcf4XTkPJcX1GQgjebNvOiq59jHIUcE3RlMMG\nrERTCO3tfZBlxq38G0OwicAlP0FzFwDQ2hnlnZX12CwG5s8qx2zsewnOQHSmn091A9+ZOEb1YISu\nX5yJXybd6Ucfp7qB7kweoyIVJ1XzCiBw5+3Fln4Lv+M+ksYpJ7Ufqpbig469fNC5h5TQcBrMTHeX\nM9VdRldqBVXR5wEYbvtar+v8d4Ra+HfrJpKig0KLygSXGUluJZjaS1JEureTMeIylHcHKFIiSk3s\nTaJaC5m8CVMos1zcZ96Eo5HQutgVfYFWdTUSMiWW8xls/RKGI+TgEEKjJv4G+2KvIBAMtn6JCssl\nSJJ8UsdpczzA4rbt7I22IwEzPYO5IG8kyv5ZHtrOdsTKRrAYME4xkvXxw2iObILz70CYDi5V0IRg\nY1U72/Z1YjTInD2+8IgVI4QaJN22GhFtAmRkz0jknHGZpRtHwRJ/CWfsPmKm+YTt/9Xnti2JVWyN\nPIJZ8jDV9VMsyuGDF31JC42n6lZSE+tgnncks3IOP4NFW9WI2NGOUiqR3fgkasm4zEyS/Tbtamfz\nng6K8uzMnVx02uWPOJPPp7rTw5k4RvVghK5fnIlfJt3pRx+nuoHuTB6jqdaPEYHdKLmjyJP+k7Rc\nRJfr2ZNWRUMIwdZQM4vbthFIxXEazHwxdwTjs4qR0aiKPk9j4n2MkoNxjlvwGIcftq14Osk7vp2s\n9tcAMM1dzgXeEaToIJDa2/0TTjfwyTKYEgqFprMos16EXSk8ocfXoW5lZ/RZYpoPs+TB0ePu+6H/\nBUxofsLpesyyhzH2mw6ZmXEyxmlXMsoSXxWbgg0IYKjdywXekRRaDi530bb7EKubwGJAOa+IrA8e\nQI76Cc27jZS34uCxqGk+2NhEc3sUl93I3MlFZDn6XhJygBACEakn3bYGUpH9SzemIjlKj1xBRaTw\nhL6LIb2PLucjpAx9L8GojS1md+xF7EoRU5z/jVE+vuUR4VSCP9csJ5yK862Ss6i05x6mexraa7sg\nmMDu2YAtuJHAhT8k7S0HQNMES9Y00NIRZUS5h6mj8o6rP6fKmXw+1Z0ezsQxqgcjdP3iTPwy6U4/\n+jjVDXRn6hjV4u2k694AkxtXXgeOxJOErLcTt3z5pOy/OR5gUes2amIdKJLM2Z7BzMkZilkxoGpB\nNocfxp+qwqEUM95xG1al94u7T6uLdvJyyyZ8aphCs4tri6biMR28wEyJOMFUNYHUPjSRpMg8+7jv\niB+NtFCpiS2iNv4mGqkjbu81TmSk/duY5ENnEPTnOI2lkyzr2M3KrmpSQqPQ7GJe3qju3BAHaFvb\nEGubwWpAnleJY+u/MFevJTZuHrHxF3Vv1xVKsGxdI6FokiKvnXMmFGI6jiUHmaUbW9C6tvW6dONw\njMkNuMO3kVRG4nf+pUcizUP2IQS7os9Tn1iCxzCCic7bj7tEa120k8frVmBVjNxcPrt7SUuPffoi\naG/sAYtETuJZ0gXlhM6/uTsIqCbTLP64jkBYZdroPIaXeY6rP6fCmXo+1Z0+zsQxekqCEZqmcffd\nd1NVVYXJZOKee+6hrOzgtMMXX3yRf/zjHxgMBhYsWMC5555LU1MTP/7xjxFCkJWVxe9//3us1r5L\nZp1pf5kDyZn4ZdKdfvRxqhvozsQxKrQ0qbpFoPpRir5AbvJWJBGj0/0yQjr2O8OaEEhw5LvWQDSt\n8q5vJ2v8tQhghCOfi/JGk2OyAxBK1bMp/CfiWjte42RGO757xOUNn5bS0ixq28Yafy02xchVgyZT\n+akL65NNE6ke5UB7klAkU6+vHM04DaXi1EY7qY110pYIYTeYcRksh/w4DRacRgsGSSalpVnlr+H9\n9t3EtCRZBgtf9I5gvKu4x/IAbUsbYl0z2IzIF1Zi7tiG48O/ksopJXjhD7oTVta1hPhoUzOptGBM\nZTYThuUe1bjoy6FLNyQkexGyqxLJXox0mJKazvDPsCSXErT9Fwnz/L7bFxqbww/hS64n3zSdMfYb\njjvx6Mqual5v3UqJxcN3y2ZiOEw72vpmxOY2jPY23PFFBM9bQGrQwZk/oajKmyvqUNU0504poiiv\n7+UtA8WZeD7VnV5efvnvvP/+B6TTKSRJ4pZbfsiIEb2XZH711ZeYP/8yDIZjr2B02WXzeO21tz5r\nd0+IvoIRn61gch/effddVFXlhRdeYOPGjfz617/m4YczNU99Ph/PPvssCxcuJJFIcO2113L22Wfz\n9NNPc9FFF/G1r32N++67j3/9619cd911/dVFnU6n0+l0p4DWvg5UP3LWMKzGKhS1g6j5K0cViEik\nU7QkgjQnAjTHg7QkArQmQshIZBmtuI1WXIbMY5bB2v2c02BmfaCeJb4qYlqSXJOd+XljGOo4OA29\nTV3HtvDjpEnsz5dw6XFdFBpkhS8VjGOQJYvXW7bwdP1KLswbxUzP4M98YXy8MokzT9x/+4QQtKth\namOd3QGIzv2VR46GXckEPSJpFYtsYJ53JDM8FRh7ubjXNrUiNrSA3Yg8rxJFiWJb/U+EYiJ8znUg\nKwgh2LS7gy17OjAoErMnDqKs8PD/AT4WksmFUnQeIlxPunMzItJAOtIAsgnZWYHkqkSy5Bzydxux\n3Yw5sAJH7BFU0xyEdPiLeUmSGeO4kfWh/6NVXYVFzmao7avH1dfp7nLqY11sCjbyZus2Li0Y2/s+\nx+cjGkIkO/NIyCXYNrxOsHBo9ywOp83EuZOLeGdVPcs3NHHhWaV4XMcWlNPpdIeqrt7H0qVL+eMf\nH0WSJHbvruKee+7mmWf+3uv2zz77FBdeOP+4ghGni347snXr1jFr1iwAJkyYwNatW7tf27x5MxMn\nTsRkMmEymSgtLWXnzp2MHDmSlpZMyaxwOExBQUF/dU+n0+l0Ot0poIUb0Pw7wZSF7J2CNXwbAHHz\nFb1uXxPtoCbaQXMiSEs8SEcycsjriiSTZ3IggEAyhk8N97l/s2zgorxRTPdUdN81FkJjX+w1quOv\nIWNirONm8k2fPYnmVHcZ+WYnf29cy5tt22mMB7i8YBwm+fT9j2V1tIMVnXupjXURTavdz1tkI8Pt\neZTasimzZlNoziKmqYRSCYLJGMFU/JCfUCpOLJ1kpmcwc3OHYlN6zsgQmkCsa0Zs84HDhDyvEslh\nwP7O88hqjMiMq9BcXtJpjeUbm2loDeOwGZk7qQiP6+jyQxwtSZKQnKXIzlJEogstuBctuA8tUAWB\nqsx4dg1GdlYiGW1ocj5Ry3XY449hiz1NxHZrn+0rkonxjttYG/wltfE3scjZlFjOO65+fqlgHC2J\nIKv8NRRb3UzM6lmpQ1Jk5FmlaP/eRUiaQ3bHPzHWbSZZNqF7G6/HytnjC1m+oYmlaxu5aGYZNsvp\nO3Z1ulPN4XDQ1NTEokWvMn36TIYOHc5jjz3Dhg3reOqpx9A0jVgsxs9+dg+bN2+gs7ODu+/+H776\n1Wt49dWF/O//ZipNHZj18Mtf3k0gECAYDPCb3/yBhx76I9XV+ygqKkZVM+fnffv28Kc/3Yemafj9\nfu6447+IxWK89trL3HPPbwBYsOA7/OIXvyE39+TP4Ou3M0o4HMbhOBgFVhSFVCqFwWAgHA7jdB6M\nVtvt9u7gw+9//3tef/11VFXl1lv7PnEDeDw2DIbTr/TQ50Vf0250uoFCH6e6ge5MGaNpNYKv+mOQ\nZLyjLsSotEJgC9jOJrtgdI/tlzXv5vm6Nd2/2w0mhmflU+LwUGL3UOLwUGB1ocgHZy/E00m6ElG6\nElE6ExE641E61Sid8QgFNheXlI7B9YmKC8l0lI9bHqIxvha70cusQT/GYyk/YcfsxUllvpdHdnzI\n5mAjXeko3xs1i1zL6THt/QAhBBsTDSys24hAkG22MSa7kCFZeQxxeSm0ZZ3QygvpQBz/61Ukm0Io\nHgvZV45BcVnQ1r6FaN0NlRNwnnU+DgFvLN9HQ2uY0kIn8+dUYjX39wWzEyhFiDkkuuqItlUR76xG\na9+A1r4Bs7uUrMo5GHKuh9o3sSUWYiu4CsyDj9huludO3q77KVXR5/G6CylxTj+uHt7imsOvNrzF\na61bGFVYSLG9l7wPXieR2QlC79cQks7GtflNlEkzD1l64vU60SSJD9c38uGmZq6cNxzjAC/5eaac\nT3Wfke8PEHrnxLbpPB+8Pzrsy16vk4cffpjnnnuOZ555AovFwu233057ezv33/8H8vPz+ctf/sLq\n1R+wYMECnn32Kf785z+xceNGzGZj99iWZQmv14nFYmTChHP41re+xeLFi5EkjZdfXkhTUxMXXHAB\nXq+TNWuaufPOnzJ8+HD+/e9/s3TpYn7xi1/wpz/9HpNJo62tDa83l5Ejj3R+6h/9drZ2OBxEIgfv\nXmia1j3F5NOvRSIRnE4nd911F/feey+zZs3i/fff5yc/+QmPPvpon/vp6jr6KYG6E0tfl6c7Hejj\nVDfQnSljVAhBunEJIhlD9k7FHzXjiPwVKxCQv4T6qc9gX6Sdf9SvxaaYuLxgHIMsbrIMlkOXOUSh\nM3roTAkAAzJeHHgVB9jJ/OyXCKTwkdlXJN3MptCDRLVmsg0jGWNfQCrkwBc68X8f3yicxuvKVtb6\n6/jl+jcHRB6Jo6VqKd7s2s4aXy1Og5mrBk2m3JZzcIModET7npFyLERtAO2jelDTSBVuxFnFdCaS\nKFU1uD5ciLA4CUz6CpovxJrtbeyq9ZOXbeWccQWEgzFOXE+ORg7kzMTgnowWqkUE95Dw1+Hb+gZK\nyYWYTbeQlfxv1MZfEXD84SgqxdgYb7+NdcHfsqLpASY5/xO38fClOg9HRuI/Cibwt8Y1/HnLMhaU\nz8aq9EyMKUpdkG9HbS0j0VlLeuUSEkPPOmSb8nw7zcVZ7G0I8MqS3cyZNOiULTc6kjPlfKr77OxR\nFbN2YlMnJqIqkT7GX0NDPdnZDn70o/8BYOfO7dxxx23ccssPueuuu7Fabfh8bYwdOx6fL0Q6reHz\nhfD7oyQSye6xfeD5eDxJdnY+Pl+IbduqGDx4OD5fCKPRSV5e5nmTycl99/0Rs9lMNBrFbrfT3h7m\nvPPm8Y9/LKSpqZELLpjfr9+bU5IzYtKkSbz33ntcfPHFbNy4kWHDDpaHGjduHPfffz+JRAJVVdm7\ndy/Dhg3D5XJ1z5jIy8sjGAz2V/d0Op1Op9OdRJp/ByLahGQrQnaPQNL8WNR3ScnFqIZD7/52qBH+\n3rgWCbi2aMqhF74niE/dyNbIY6RFjFLLPIZYv4Is9d8dX4OscHnBeIos7u48EvO8o5jqLsOsDNyp\n7x1qhOcb19CaCFFq9XBN0RSchv7JHSBSGmJtE2JnBygS0sxipKHZmQvfdBL7h88iaWnCM69BWBxs\n3dNBVa0ftzOT30BRji/p44kgKWYU9zBwDyPV8hEiuBetfR2q92xUw3RMqVWYkh+gmmYfsS2XoYKx\njgVsCv+RjeH7KLdcQonlCyjSsS09GeksYE7OEJZ17GFh8wauLZraY/aKJEvI55SgvVpFODWDrI1L\nSFRMBsPBZTOSJDF9TD7hWJL61jDrd/qYPPL0Kvmp031axHYLEW45qfvcu3c3Dz74Gr/4xe8wGo2U\nlJTicDh54IHf89JLr2Oz2bnnnp91by9JMkIITCYzHR0dALS0NBMMBg7ZBqC8fDBLlrwFXEN7uw+f\nzwfAAw/8jrvuuofy8gqeeOIRmpubAJg//zJ+/vM7icdjfO97R16N0F/67V+/888/n48++oirr74a\nIQS/+tWveOqppygtLeW8887juuuu49prr0UIwe23347ZbObOO+/k5z//OZqmIYTgrrvu6q/u6XQ6\nnU6nO0lEvBOtfT0oFpSCmUiShDX+GhJqJleEdOgyi+caVhPTklxeMP6EByKE0KiOv86+2CvIGBlj\nv5EC84wTuo++THWXkW9y8nzjWhb7trPYtx2rbMRjtOE2Wvc/2vAYrXhMNtwG2ykLVuwKt/Ji0wbi\nWpK5hUOZ6xp22OoMn5UIJNCW1UBnHNwW5DllSJ6DQQ/b+tcxBFqIDzuHZNEodtf72birHbvVwHlT\nS46rdGd/UfKmkYq3o/l3IlnzCdtuwxP8Jo7Yg3Qap8NRBBVyTeMYZf8uVdHn2BP7J3XxtyizXESx\nZe4xBSXOyx1BQ8zPznAryzv2MDd3aI9tJKcZaVoRYkUDkch4zFUfkRh97qHHJEvMmTSIxSvq2F7d\nhdNuYlip+6j7odPpYM6cL+DzNXH99d/AZrOiaYKbb/4Bmzat5+abb8BqteDx5NDengkkjB8/gTvu\nuI37738Ih8PBDTd8k/LyCgoLi3q0PWvWHNasWcUNN3yTgoJC3O7M9/OCCy7izjt/gtPpwuvNIxDw\nA+D15mGz2Rg9euwpTZDZb6U9TxZ9Ktapo0+F050O9HGqG+g+72NUaKn9ZTwDKEXnIduLkLQusgPX\ngKTQmfVCd6UBTQiea1jNrkgbMz2DuTi/Zx6JzyIlYmwLP4EvuR6LnMM4x624DGVHfmM/CCbjfNC5\nh3Y1jD8ZoysZJSW0Xre1KyZyTHZyjHayTXZyTZnHHKMdSy9T7z8rTQiWdexmaXsViiRzWcE45g0Z\n1W/jVNvbifi4EVIa0rBspGlFSIaDQQ9DUxWuJQ+TduURmH8H9R0qy9Y1YjIqzDurlCxH7+VITyWR\n8JOqewMkCUPpJThSz2JL/J2I5TtErd8+6naSWoS6+NvUxd8hTRyT5KLcejFF5rmHLcP6aZFUgj/X\nLCeSVvnh4HPxGHtWrRFCIN7Zi2iKYDOuQ/2PKxG95DXpLvmZTPOFKcUM8tp7bHMqfd7Pp7rT30Aa\noz/+8Q+57bb/R3FxzyS3J9IpWaah0+l0Op1Op/nWghpAdo9Etmfu5tjjTyMTIWz5wSElD9/27WBX\npI2hdi/z8nqvu96bhOZHoCGhIKEgSwoScvfvkiQRTbeyKfwnIukmPIYRjHV8D5PsOuHHe7RcRgvz\n88d0/y6EIJJW6UpG8SejdCVj+x+jdKgRGmJ+6mJdPdo5EKiosOUyJasUj+nI5VH7Ek8nWdi8gR3h\nVrIMVq4tnkKRpX/ugItkGrGyEbG3C4wy0pxS5IpDEy1KiQiOFc8jJJnwOdfRFkrzwYYmZEXi3KlF\nAzIQASCZ3Sj500m3fESqeRmR4q9jVt/CFn+OuOkiNOXoKsYZZTuVtisotZxPbfxt6uPvsCv6D2pi\nb1JunU+ReQ6K1HdAym4wc753BAubN/Je+y6+XDihxzaSJME5ZYiXthJNjsf2wdskzv9yj+2cNhNz\nP1Hy8/xpJeS49ZKfOt3pJJGIs2DB9UyePLXfAxFHos+M0B23gRTZ0+kORx+nuoHu8zxGtXAd6ab3\nwezBUHIxkqygpOvwBL9BWi6ky/VX2H8htT5Qz0vNG8k12bmpbFavyfY+KZ7upEVdRYv6MeF0Q5/b\nSsgIBCAoMZ/PUNuV/Zofoj+khYY/GaNDDdOhRulIRuhUI3SoEbqSUTQEEjDE7mWKu4wRjnyUY1hS\nkRIa9bFOXm3ZTLsaYbAtl6sGTcJuyCwJON5xKoSAaBKCKiKYgFAi8xhUIZSAtIAcK/LcMiSn+dNv\nxrHsSUz1W4hOmE9L+WzeWllHMqVx7uQiivIGfkWSVMsKRHAPctZwbO4uXNFfkjDOJej4xXG1p2oh\n6uJvUR9fQpoEZslDuXU+xea53WvHe6MJwYPVy/CpIW6rmIvX3PudStEQQHt3HxIpjNNtpEf2Pjup\ntjnEBxuaMJkU5s0oIctxYkupHq/P8/lU9/lwJo5RfWaETqfT6XS6k0oko6RbVoCkYCiY1V0u0B57\nGIk0EeuC7kBEXTRzEWyVjXy9eNphAxFJLUyrupYWdSX+1C4AJBRyjGMxSnYEaTSRRrD/R6TRSCPI\nLH8oNp9LofmsXtse6BRJzizVMPWcFq9qKbaFmlnjr2V3xMfuiA+HYmaSu4QpWaVk9/KelNBoivnZ\nF+2gOtpOXayLpEgDcE52Jed7RxxTMANAaAK64ojWMLRFEP7EwYDDpxllyLIgFTuRxucj9ZJ80rJ9\nKab6LSTzh9AxeDZLVjWgJjVmjis4LQIR8In8EYEqYtZZWJUxmJPvY0xuImkcf8ztmWQnQ2xfodRy\nAbX7gxJV0efoSu5ktOOGw86SkCWJL3qH83zjWpa0V3F10ZRet5OKszBMcZNaGyC5OoKUF0TK6TmD\nqKzQiZrKZ+WWVt5d3cCFZ5Vit574JUM6ne7zTQ9G6HQ6nU6nO6GE0Ei3fAiaipw3HcmcmeZvTG7A\nnPwQ1TAe1TgLAH8yyt8a1yCE4KriyeSaDr3ITAuV9uQmWhIraU9uRpC5YHYbhlFgmkG+aQpG+fS4\nMO0vJtnAxKwSJmaV0JoIsdZfy4ZAA8s79rC8Yw+VtlymustwGSy9Bh8A8kxOKmw5jHIWHHXJaIl5\nXgAAIABJREFUUaEJ6IghWsOIlgi0RUA92CZGGdwWJKcJXGZwmpFcZnCZwGLoszykoXkX1g2vo1mz\n6JhxHUvWNRGNp5g0PJfK4qzj/qxONkk2YBg0h1TtItJtKwkWXU9O+ofYYw/hN/zlKEp99s4kuxhq\n+yqllgvYEv4Lbcm1JEJ+xjtuw3SY78NIRwHFFjdbQ800xf0MOszyGzGmAnPL+yQaPLB4F1w+Dsne\ncznM0BI3alJj/U4f766u54IZpVjN+qWFTqc7evoyDd1xOxOnGelOP/o41Q10n7cxKrQ0Wvt6NP8O\nJHsJyqC5mYtOoeEO3YAxvYsu52OkDCNQtRSP1X5EcyLIJfljmOGpOKStT5bfBHAoxRSYZlBgmo5F\nOfHlPj9PklqaraEm1vrrqI119nj9QPChwpZDuS0Hh6HvafYHxqlQ04id7YiWMLRFIfWJpJtOE1K+\nHfIdSAV2cJj6DDgcjhTxk7Xo/5CSMbrOu4XF1QZ8XTFGlnuYPNJ7XG2ealpwXyZAZ87Gk70Ga2op\nAfvPUU3nHvnNR2pbJNkWeZJWdRU2OZ8JztuxKb2X3twb8fFU/UqG2r18s6SPKjLpJKZ/vkQsPgLJ\nLiNdOhLJ0nugYf1OH9v2dZLtMnP+9FNb2eTzdj7Vff6ciWNUX6ah0+l0Op2u32nRZtKtqyAZBIMD\npeCs7gtHs/o2xvQu4qbzSRlGoAnBwuaNNCeCTHWXMd1dfkhbca2LbZEnECJNmeUiCk1n4TAUn4Kj\nOj0ZZaV7tkRbIsT6QD0pLU35UQYfPk0Igajxo61qhFgq82SWGanAAfl2pHx7r3fPj1k6hXP5U8iJ\nMP7J/8HbtUZ8XVHKBzlP20AEgOwajBZtQQT3EIjMxmJehiP2CJ3Gc7qXKx1325KRMfYbsMjZ1Mbf\nZG3wV4x3/oAsQ0WPbSvtXgbbctkd8VEd7aDicKVzFSPpc6dgXbySWGQM4p29yBcOQeol0DBxeC5q\nMs3u+gDvrWvkvKnFGHpZdqPT6XSfpp8pdDqdTqfTfSYiFSPV/CHphncgGUJ2j8BQdgmSsj/Lvkhg\njz2GwETEciMAyzp2sy3UTIUth0vyxxxykSmExvbwk6REhGG2qxhq+6oeiPgM8sxOLswbxSUFYxnj\nGnTsgYiwiv/lHWjv10IijTQhH/mqUShXjEA+qxh5sOfEBCIA27pXMLTXEi6fyuJgGa0dUUryHcwc\nV3jaBiIOUPKmgcmNFmwkkL4SRWvEmnj1hLQtSTJDbV9lhO06VBFiXfA3+NSNvW57vncEAO/4dtDX\nBOl0fiXKcBMWaRd0xNGW1iBSPcvPSpLEtDH5lBU4aeuMsXxDE5p2Wk+81un6zaOPPsoPfnAzt956\nI9///k3s3LnjuNuqra3h1ltvPIG9O/n0YIROp9PpdLrjIoQg7d9FquZVRGgfkjkHQ+nFKHnTkJSD\nF6e2+Asooo2Y5atoSgG7I20sba8iy2Dl6kGTeyRKbEi8R2dqGznGsRSZ557ko9IdIDSBts2H9koV\nib2dUGBH/tJw5AkFSP2QrNC0by2Wqg+Juov5t3I2rZ0xSgsczJ44CEU+vQMRcDB/BJKBaJcbNVWA\nLf40kgifsH0UW85lvOP7AGwK/4mG+Hs9timxehjpyKcu1sWuSFuf7cUmXYrVvh2TVAfNYbTldZlc\nIZ8iSxJnTyikMNdGY1uEFZub+wx06HRnourqfSxdupT77/8zDz74KLfd9iPuvffnp7pbp5QejNDp\ndDqdTnfMRLyTdP2baG0rAYGcNw2l9CIky6HTviWtE2v8OTTJTdRyHV3JKP9sWo8syVxTNKW7dOQB\nkXQzu6MvYpQcjLJ/+7S/G366Eh1RtEW7EWuaQJFwXTQUeV4lUlb/lHBUupqwr3yBuNHBq54raPMn\nKCt0MmvCIOTPQSDiAMmUhZJ/FogUneFLQYtgi//thO7Da5rAZNdPMElOdkafZXf0nwhx6IyGL3pH\nIAHv+Hai9RU0MFmIzfgKLul9DMYuqAsgPm7oNdCgyBJzJhXhdVuobgqxZnubHpDQ6T7B4XDQ1NTE\nokWv4vO1MXTocB577BluvfVGamtrAHjllX/xxBOP0NzcxE03fZs77/wvvvOdr/N//3cvAO3t7dx2\n2/f4/vdv4sknH+1u+7333uX737+JBQu+y803X4/f7+eRR/7MwoUvAhAMBvnOd75+0o/5SPScETqd\nTqfT6Y6a0JJo7RvR/DsBgeQsR/FOQTLYet3eHnsSmRghywKSwsI/GlcQTSe5LH8sxdZDs/lrIsXW\n8KNoJBljvxGz3Hu2/1NBxJKIfX7Evi4Iq1DgyJSlLHIh2T4/JQ1FMo3Y2IrY7gMBUqUHaeogbCUe\nIv2UdE1SoziWPYmqwcuDvkZbMEV5oZOzxxd+rgIRB8iuCkS8Dc1fhT/0Rdy8SMx8OZqcf8L2kWWo\nYIrrf9gYuo/a+JvEtU5G27+DvD8/Rb7ZxThXEZuCjWwLNTHWVXTYtpLFo1ErxpFVvYgu+9VouzvB\npMCUnktnjAaZc6cW8/bKOqpq/ZiNCuOH5Z6w49LpTpS0by1aqPaEtik7y1C8vZfNBfB683j44Yd5\n/PGnePLJx7BYLNx4482H3b6+vo777nsQs9nClVd+iY6Odv761yf44hfncdllV7Bkydu8/PK/urf9\n3e8ewGKx8Nvf/pLVqz/mkku+xN13/5T/+I8reeedxVxwwYUn9HhPBD0YodPpdDqd7qgILUmqbjGo\nXWB0ouRNR7YPOuz2Sroai/pvUnIZcfOlLGrdRmPcz8SsEqa6y3psXx17jVC6lkLT2eSZJvfnoRwV\nkdIQ9QHE3i5oDIEAJMBqhNoAojaAAMi2ZIISxU7w2pFOwwtoEU0iavyIbT6IJMFpQj6rGGnQ4bOg\nn5gda9g/+hupcJCX8r9GW1SmfJCTs8d9PgMRB8jeKYh4J/E4RA1DsZufIGT/nxO6D5uSxxTXT9kU\n/iOt6irMchbDbFd3v/6F3OFsCTbxrq+KUc7CHsulPik65ctkNVXhTr5Cp/PqzDjRBEwb1CMgYTYq\nnDe1hLdW1rF5Twc5bgvFeWd2+V2dDqChoZ7sbAf/8z8/A2Dnzu3cccdt5OQcDNh9cjJRUVExNpsd\ngJycXFRVpb6+jksvvQKAsWPHdwcjPJ5s7rnnZ9hsNmpraxgzZlz3+6ur9/HOO4v59a//cJKO9Ojp\nwQidTqfT6XRHJIQg3foxqF1IrkqUvBlIct8l/OzRh5DQiFgXsD7YzBp/LYVmF5flj+1xAeNP7qE6\nvgiLnMNw2zX9eSh9EkJAWwSxpwtR44fk/untOVakIR6kCg+YFQgmEA0hRGMQWiKIzjbEljYwKUiD\nHFDkQipxHbYc4kAg4qlMQKXaDy378xZIII3LQxqXj2To/9W8lq1LEI27eSnnK7QlrQwucnHWuALk\nz/nyHElSUAbNIVX7OsHIDHKMi1DMe0gbhpzQ/ZhkB5Oc/4+VgZ9RF3+HfNP07iobOSY7U9ylrPbX\nsiFQz5ReAoQHCKuT6JTLcax4HqdjFSFlJmJHO8SScE5pj7FisxiYO6mINz6qYeWWFi6dXYH5FJb8\n1Ok+TfFO6XMWQ3/Yu3c3Dz74Gr/4xe8wGo2UlJTicDhxubLo6GinrKycXbt2kpvrBeh1mWJ5+WC2\nbdvM0KHD2LFjOwDhcJgnnniEhQtfB+D222/pXiJ12WWX8/TTj+P15uF2D5zZhgcM3H8hdTqdTqfT\nDRiafwciVINk8aLkz0CS+r6wMCbXYk6tRDVMojY9mtdaPsIiG7mmaArGTwUxUiLOtsjjAIy2X49B\n7n3JR38SyTRiRztiV2dmGQaAzYg0IjezVMFtOfQNWRakLAuM9iKSaWgJZ4ITDUFETQBqAggJKHQg\nlbuRSrP6JTAhNAFqOnM7zaQgHaGkokimEXX7AxAHZnsA5NmQKjxI5Vn9kpyyN4amKqRN7/KS+3La\nRBaVRS5mnAGBiAMkgw2lcDbphrfpCp6H2/QYIfdvTvh+FMnMSPs3WR/6HTsiTzHNdReylBmLc3OG\nsSFQz9L2XYx3Fff4bn6SOngqyep1WJo3k5w2lvg+D6ImgIjtQ/5CBZL50Pd6XGbGDc1l46521m5v\n4+zxhSf82HS608mcOV/A52vi+uu/gc1mRdMEN9/8A4xGA7///a/Jzy/oDkQczje/+V1+/vP/j3ff\nfZtBgzLLq+x2O2PHjud73/s2imLA6XTS3u4DYPbsc7nvvt9y552/6PfjOx6SOM0zy/j6af2i7si8\nXqf++esGPH2c6ga602GMatGWTNlOxYKhbP5h80N0E2k8oe+ipPfRYn+YB+pb6EpG+XrxNEY4eq6L\n3xF5hsbEMsosFzHU9tV+OorDdFUTiF0diI2tEE+BQUYqz0Kq9GTyQhzjhbEQAgIJRH0QUeuH9ljm\nBYlMe2VZmZ8+LviFEBBLQTQJkSQinsr0LZ6CWOrQ3xOpgwEFAIOcWc9vUjIzOEwK0v7fRTQJDUFI\n739DjhWpwo1U7kZy9F2a84SOUy2NZdtStC3Lec0xD5/BS2VxFmeNzT8jE5amO7ehta/DZGhBKZ5L\nyjStX/azI/I0jYnlDLZezmDrZd3Pv9W2nQ8693JR3ijOzq7ssw053EHWa79BKAb8F/8n6bUBqA2A\n24J8/mAk+6HjWtMEiz+uoyMQZ+7kIkry+3e5xulwPtWd2U72GI3H49x66408+ujTyPKpqV3h9R5+\nuZ8+M0Kn0+l0Ot1hiWSUdPNyAJRBs/sORAgNQ3oHlsQbGNJ7iRkv5O+tYbqSUebmDO01EOFTN9KY\nWIZDKabSenl/HUbPrgqBqPYjNrRASM0EIcbnI432Zi7ej5MkSeC2ZGZSjM1DhFRErR9RG4DmMKI5\njFjVCPl2pJKszJuiSYiomWBBJJn5/Ui3ikwKWAzgMmceJTIzJNQ0JNKZNvzxzLF+8n1Z5kwAosLT\nb5Ux+qJ01KF+/DofqwXsyLqGtKQwpCSLGWPOzEAEgOwZhRSrRo2Ate1dKJoCfeRvOF5DrFfSrm6m\nOvY6eabJOJTMXdVZOUNY7a9lWccepmSVYVYOf3mgOXKITroU+5qFuJY/SeCLtyCsBsTODrQ3dmcC\nEp+YRSTLEjPHFbDoo1pWbW0hz1OB+TN8v3Q63dHbsmUTv/vdr/j2t284ZYGII9FnRuiOmx591p0O\n9HGqG+gG8hgVIk26/i1EvB3ZOxXFM7KXjeKYkmsxJT/EnPwYWXQCoEnZLEr9N6+3+xhi9/KN4uk9\npt+rWpCVgbtIiijTXXfhMBQfU//kQCum2o0Ym3YijGY0mwfN7kFz7H+0edBsWfCJiyshBDSG0NY1\nQVcCJFAGCUwFERQtCOkkyAaELINsAFlByMr+x8zvmt1NKq8SjnXWRFjN5Gio9UNbtOcGB5Jj2o2Z\nJSL7H7EakawGMBvAagDzkZdjdB/rgQCFmgZFzgQjjuOi/7OOU6HGCaxbztZWQbWpHACHVWHU4ByG\nlbrP2EDEAUJLImqeIZ0yYc51oWX3T2CuTd3A5vCfyDJUMsX530j7gx7vte9iSXsVX8gdzhdyhx2h\nswLbyhew7FmJWjKW0OxvIba2I9a3gElBPq8c6VMzILbu7WBDVTvlgzLlWvvLQD6f6nRwZo5RfWaE\nTqfT6XS6Y6a1rUXE25GcFcjuEd3PS1oH5uQKTMmPMCXXIJHJsaBJHmKm+ajGc9ihlrGoZSNZBitf\nLZzYIxAhhGBH5BlUEWSo9aqjDkTIwTZMtRsx1W7E0NWUaUuSkA5zb0UgIayuTAAh4SAWKieVzgPA\nLO3FLq1HaQlDy+H3KYA2JZc6Ywl1pmJCska5speyUUNwlQ0+6gtpyWFCGu3N5JmIJBHNISSjDDZT\nJgBhMZzQShySJGUCGOZT9989TRM07NjFjuoO2uQyMIHXDqOGD6I433HG5Ic4Ekk2IhWei9bwHon2\nTgy2ViTLiSv1eUCeaSL5pqm0qmuoTyyh1HI+ADM9g1nZVc2HnXuYlFWM29jHDChJIjrtKyjhDkz1\nW7BtWERs8mVoNiPio3q0t/YhzylFKjuYLG9URTb1rWFqmkKUFYQoLejnKi06ne60oAcjdDqdTqfT\n9aAF96IFqsDk3p+wMnPRaI8+jC3xfPd2Kbkc1XQ2CeM5pJSRICl0qlFeaF6OLMlcUzQFu6HncoAm\n9QN8yQ14DCO6L4gORw76PhGAaARAyApq8RjUsgmoxWNAAjniR4l0Ie//kUJ+RCBFOmwg1eYmKTIB\nD4OpHUt2C1KWgmo5C83iQFicaFYnQjEiaWnC8RTNAY3GEDSHJeLpgxfNRlJsIotN21Nk79jA4NJs\nyiqLsR1DgkrJbkQakn3U259u1GSafTU+duxpIyzMIGVTbg4xfPww8nJdp7p7A5O1Emv2u0Q7TGhN\nbyGXXY2k9J3L43gMs11LR3I7e6Mv4TVOxKrkYlYMXOAdycstm3i5eRPfLJnRd6BIMRCe/W1ci+/H\nun0pmstLYuhZCIsB7f1atPdqkWakkEdkShYeWK7x+oe1rNzaSp7HiuUUBsl0Ot3AoJ8FdDqdTqfT\nHULEO0i3rgTZiGHQXCQ5k5TOEl+ILfE8KbmYuPlyEsaz0ZRDZzTURTv5W+Maoukkl+WPpdjas5RY\nJN1MVeR5DJKV0fbvdk8V/zSlvQ77qhcxdDZk+iUrqEWjUcsnkCwegzBZD/ZZCNK4SUVN0O5EtOdA\nV/zQhAm5NuTJhYjC8cR62Z/PH6O6MUhze5RgBCDTL5vFQGWhjcJcO4U5NoxGhZbd+6je20itlsva\n2gTravcwyGOmoiyHknwHhqNYRvF5k0xpNLSFqW0O0tQWJi0kFKEwhhqGTRiBvWjEkRs5w6U812KP\n/ZJIdBSGhj9jz/YjlHzSciGaUkBaLiAt54N0/BVnzHIWw2xXsz3yBDsizzDR+SMkSWJSVgnbQ81U\nRdpY469luqe8z3aE2UboCzfievM+bKv+SdqRTap4OPKFlWjvViNWNqLJEvKwHACyHGYmDstl3U4f\nq7e3MXti/y3X0Ol0pwc9GKHT6XQ6na6bSCdINS8DkUYpnINkytzFNiZX4Yj9EU3yEHDch6YU9Hjv\nen89r7ZuRgjBJfljmNbLxYwmkmwJP4KGymj7AixKTq/9kP0tOJf8BSkZQy0ahVo2gWTJGITp4EWY\n0ATUBdB2dYIvAkntYAOKBF4bktcOuTYkrw3sxl6XVISjSdZX+ahtzqzjNSgSRV47hV47g3JtuOym\nHu8rGl5J0bDBnFWzjYatW6jSCmnsyqexqxmjIlFW6GJ4uZtsl6XH/j5Pkint/2fvvsPrqs5E/3/X\n3qc39S5LrnITtpE72NgYCC2hJYFMBkhhkpAJCZnJ3NzcydwZZjKZJPeXMkOGVCBDQhgSJgkQErAB\nG4NtcMNVbrIsS1Y/aqe3vff6/XGMQahYuICM1ud59KicvdZZR1pn65x3r/W+tAdjHO8I09Ydw7Sy\n0Z98o5+ZmWNMn1EOtVfCKCUjlTdJzY8suhZ7x07SqUK03jA+z/N47D2DjrNEDqY2iZTjUtL2VZj6\npHd0P2WOS+hKb6U3s5+O9BbKnZcihOCmsvncf+wlnus+wHRvEQUO76j9WP5Coqvvwv/8A/g2/oLw\ntV/GKixFu2461jMNyK1tyGLvqaSWs6bk0dIVobkjQnNphOoytV1DUSYylcBSOWMTMQGLcuFR81QZ\n78bTHJXSwmxbj4y3o+XPQy9cAIBuNpEb/jyCDAP++zFscwe1s6RkbfcBNvcfw63Z+VjFQqZ5h6+V\nfjj235xIPU+58zLmeD857DFatI/A2v9Ai4eIXvJx0tMGlzqUKQPZ0Ic82JOtPgEQcGYDDkUeRKEH\n8t2nzb+Qzpjsb+zj4PF+LEtSkONiQU0hJQUe9HeSu8GycDTtILnnFY5YZRx0zSSqZRP4lRd5qZ2a\nT3G++4JO0vjWeWqYbwQgIrR2RzFPlgrNtULUJBuYkWnCXz2NxEVXYfkL38thX7CkkcRsewGZyiaE\n1V0OHL40TkcXNtmBZnWhW20ITAAMfSop+2pSjtWY2uQxJVdNmD28Fvq/CGFjec6/4tSy1V32hNt4\nov11qt353FV1yZjyejiO7cC3+VFMXwHha/8G6fIhjw9gvdQMeS60D844lXQ1HE3zzKbj2GwaH1o5\nGfc53K4xns6nijKciThHR0tgqd933333vXtDOffi8fR7PYQJy+t1qt+/Mu6peaqMd+Npjlq9e5Dh\nowhvBXrJcoQQCGuA3OiX0WUfEe/XydiXDWqTNDM81rad3eE2ihw+PlW1nEp33rD996T3ciTxGB6t\njPn+e9DE0DchIhkl8Px/okf7iNfdQGrmilO3yVASubsTuekEtEZAgqjJR1tZhXZxKaI6B1HoQXiG\nXwFx6nFakoYTIV56vZ2Onjhup42lc0tYPKeYgNfxzpMqCoGZX4GoWUyl1s+i1mcoTbURsQVoTzhp\nbAvT3hPDadeHXWVxIfB6nfQPJKhv7OOV3e0cawsTiqbxixTzEvtZHd3I0sxeiqbPwFpxK+mpi5DO\nM99KMNEJzYbImYFwF4OZwEqEMOKQTFWQcl1H0v9pkq6PYOjVgIXdOIjD2Ik79Qec6RfRZC9S+LFE\n/oiBCbvmQRcugpnXSVl9lDgWA1Di8NOdjtAQC+LUbFR5Tp/bxMwrBySOE/uwBZtIT1mIyPdmS8y2\nRcCwEBXZVVZOh45N1zjRFSWayFBd6j9nz4nxdD5VlOFMxDnq9Y5cRlqtjFDO2ESM7CkXHjVPlfFu\nvMxRmY5gHH8SbF5s1dcjdCfINDnRv8Vh7CHm+iRx912D2vSko/y6dTvBdJQabzG3ltfh0u3D9p+y\nBngt9E8YMsGSwD/gt1UNPSiTJPD8A9h6T5CYu4ZE3Q3Z8pQdUawDwWwAArLbLWYXImbkI97hVdX2\nYIwdB7sJRdPYdEHttAJmT8k7pzkeRDqO6+BGnIdepssKsN2zkCZ7NQABr4O5U/OZUhF4Z6sv3kOJ\nlMHxrhi7D3ZhmBKnDWZrbcwOvkqREcTy5JKcvZrUjGVgf39vS3mvyFQ/Zn89MnwcsEB3oeXOQsut\nQeguhIzhyGzBmd6II/MaghQAhjaFkP/7WNrwK1SktNgR+TYh4yjzfPdQ7KgDIGakuL/pJVKWwV9P\nvoxi5xi2U0iJd9OjOI/vJDW5jtiKO5CGhfXHBgin0K6aciogIaVk3Wsn6O5PsGJBGVPKz01S0/Fy\nPlWUkUzEOapWRijnxUSM7CkXHjVPlfFuvMxRq2cXMtWLXrwEzV0EUuKPfwdnZjNJ+xpini8PusJ6\nNBbkkRNbCRlJVuRP4+ayBdhHyAsgpcXe6APErDZqPLedesMziGng3/Ag9mATqWlLiS/+MLIngfV8\nE7I+COE0FHvQFpcjlleilfgQtrEHEAYiKTbv7WRvQy+ptMn0yhxWLaygstiHdq6DArodo3QGyZoV\n+Gwmczo3MjN+AENz0m7lcKI7RmNrCOQbV4nFuR/DORBLZNh9pIctezpp64ritAmWaI1c2/U7pkUP\n4szJJbHwRuLLb8Msngq6SkV2vgibG81XhRaYDkIgkz3IeDvWwCGQFngqMW3TSTnWEHd9FEOvQZDB\nYe5FyARpx6XD9ysEubZptKVepj9zkHLnZejCjkOzUeDwsifcRmuyn7qcSadfMSQEmco52DqP4mg/\nCIBRXoMo9iCP9iPbIohpeQi7jhCC4nw3R1tDdPTEmFaZg/0dPJ9HMl7Op4oykok4R0dbGaGCEcoZ\nm4hPJuXCo+apMt6NhzkqM3HMrs1g950q4+lOPYYn9TgZfTZh37dAZFc8SCl5rf84v+vYhYXk5rL5\nrCyYPuoy6+bkc7SlN1Jon0eN5+NDj7UsfJt+iaPtAOnKWmIrbkf2JbHWNUIig5iai3bpJLT5pYhc\n15iXdFuW5ERXlG313bx+KEgknqG0wMOqheXUVOWekzc/o9JtGMVTSc5cgdNho6ZrM3Oju0FodJBP\nW0+Cw80D7Gvso6ElREtXlM7eGL2hJNFEhnTGBMm7HqwIx9K8fijIq/s66RlI4nEIVria+UDbb5gU\nO4pVPJnYso+RqLsBM78CRqiGopx7QrejecvRcmeB7kKm+pGxVmSiE+Epy5YCFXZMfTIp+2qc6Q04\njJ2kHFcgT+aEeDuHlr1q2ZPZTcLsotixCCEERU4/fek4DbEgutCY4hk+2ewgmk6mshZHyx4cJ/Zh\n5pRglVdlE8q2hJGhJGJKLkIInA4duy27XSOeMqguPftkluPhfKooo5mIc3S0YIQKYSuKoijKBGf1\n14O00PNrEULDkX4Zb+KnmKKYsO/fQGRfSEgpebb7AFv6j+HTnXy8YtFp95OHjCYaE7/HIXKY4/30\n0ECClHi2/Q+Olj1kiqcRXXknsj+Fte5Ydp/5yiq0qcPnoBhJImXQcCJEQ8sA8aQBQHG+m7lT8qko\n9r77ORtsDlKzV5GquRRn41Yu3f8iS/q2U++aRa+9mLDmI5zw0ZP0EBzhjb1TGHg0E7dN4nEI3A4N\nl8uO2+3E7XXjCgTweV1nFLQwTYv+SIreUJLO3jgnOqNIIODWqKORuW3PY5MmRsEkwguuxyibOaYE\nicr5IzQ7et4ctMB0zK5XkdFmjOZn0MtWoHkrTh5kI+b+K3Ji/4gn8SAR3z+P2N9k17X0Zerpzuzk\nWOJppnluAuD6krkci/ewoecIs3wllLmGD2i8lXT5iKz5DDl//gHe1x7HyK9Ezi1CtkegNYI81IOY\nnU1wW1Ody7G2MMfbI0yvjFFWOHr1DkVR3l9UzgjljE3EPU/KhUfNU2W8e6/nqDSSGE2/A92JbcrN\n2M1GciNfAAT9/gcwbTNOHft88CAbe49S7PBx56Sl5NpHT1BoyARbQ/9MwgpS5/8K+fY5Q45x7/4z\n7n3rMPIqiHzgHqwoWGsbIWVmAxHTxhaIkFLS3Z/gSPMALZ0RrJMrCqZW5FBTnUuef+TPF/y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vG21Q7twRjPbz1BJmOx/KJS5k4dvXqHoihnR9hcJ6trwEDkSlzJ36NZXe+oD5eez0L/V6lyXU3c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T1hdVGEUmDiETh0C5iGATgRqTWPHr6KofV5tOKvxRf22ISzAZMurzXYifU1858HM6tSmlONnz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\nHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1121f3b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['hour_fractional'] = df['start_time'].apply(lambda t: t.hour + (t.minute/60.0))\n",
    "\n",
    "days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(18, 5))\n",
    "\n",
    "for i in range(7):\n",
    "    day_i = df[df['start_day_of_week'] == i]\n",
    "    sns.distplot(day_i['hour_fractional'], hist=False, label=days[i], ax=ax, color=hex_colors[i])\n",
    "    plt.legend()\n",
    "    \n",
    "ax.set_title('Distribution of daily bike rides')\n",
    "ax.set_xlabel('Hour of day')\n",
    "ax.set_ylabel('Density')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Really interesting. You can almost see the ridership patterns morph throughout the week. \n",
    "\n",
    "Notice how Friday seems a bit different from both Monday-Thursday and Saturday-Sunday. You can see the weekend coming in this plot.\n",
    "\n",
    "This plot is also helpful because you can directly compare the distributions for each day since they are plotted on the same figure.\n",
    "\n",
    "Let's partition the trip durations into weekday and weekend trips as well and compare these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Select the bin intervals\n",
    "bins = np.arange(0,125,5)\n",
    "\n",
    "# Return indices of half-open bins to which each value of column belongs.\n",
    "weekday_duration_min_intervals = pd.cut(weekday_rides['duration_minutes'], bins=bins, include_lowest=True)\n",
    "weekend_duration_min_intervals = pd.cut(weekend_rides['duration_minutes'], bins=bins, include_lowest=True)\n",
    "\n",
    "weekday_five_minute_counts = pd.DataFrame(weekday_duration_min_intervals.value_counts(sort=False))\n",
    "weekday_five_minute_counts = weekday_five_minute_counts.rename(columns={'duration_minutes': 'weekday_duration'})\n",
    "weekday_five_minute_counts['weekday_duration'] = weekday_five_minute_counts['weekday_duration'] / weekday_five_minute_counts['weekday_duration'].sum()\n",
    "\n",
    "weekend_five_minute_counts = pd.DataFrame(weekend_duration_min_intervals.value_counts(sort=False))\n",
    "weekend_five_minute_counts = weekend_five_minute_counts.rename(columns={'duration_minutes': 'weekend_duration'})\n",
    "weekend_five_minute_counts['weekend_duration'] = weekend_five_minute_counts['weekend_duration'] / weekend_five_minute_counts['weekend_duration'].sum()\n",
    "\n",
    "duration_df = weekday_five_minute_counts.join(weekend_five_minute_counts)\n",
    "\n",
    "duration_df = duration_df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AW7kUZpcvX674+HiFhIR4uh4AAOBF2AcAAHgrl65mXL58eZUuXdrTtQAAAC/D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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1126b6da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(16,6), sharey=True)\n",
    "\n",
    "sns.barplot(x='index', y='weekday_duration', data=duration_df, color=hex_colors[1], ax=ax[0])\n",
    "sns.barplot(x='index', y='weekend_duration', data=duration_df, color=hex_colors[2], ax=ax[1])\n",
    "\n",
    "ax[0].set_title('Number of trips by duration - Weekday')\n",
    "ax[1].set_title('Number of trips by duration - Weekend')\n",
    "\n",
    "for i in range(2):\n",
    "    ax[i].set_xlabel('Counts')\n",
    "    ax[i].set_ylabel('Minutes')\n",
    "    ax[i].set_xticklabels(ax[i].get_xticklabels(), rotation=90)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}