Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Austin B-cycle Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction \n",
    "\n",
    "We are analyzing the bike sharing data from the Austin B-Cycle program from December 2013 to July 2018. The analysis looks to draw trends related to the demand of the service.  \n",
    "\n",
    "* The first part of the analysis will look at the demand by year, month, day, and hour. \n",
    "\n",
    "* The second part will rank stations and membership. \n",
    "\n",
    "* The last part will look at the unique and temporary Austin B-cycle population peeks such as SXSW, ACL, and Uber Ban for (May 9, 2016 - May 29, 2017).  \n",
    "\n",
    "\n",
    "### Purpose: \n",
    "\n",
    "This analysis suggests a more efficient allocation of resources in order to meet peak demand times and locations. The implications of this analysis could also help the program determine the low demand times to schedule maintenance and restocking.\n",
    "\n",
    "### Scope: \n",
    "\n",
    "\n",
    "Data is only from December 2013 through July 2018. \n",
    "\n",
    "\n",
    "### Observable Trends \n",
    "\n",
    "* Demand for the service continues to increase with the current year having the most users. \n",
    "* The most popular month is March due to SXSW. \n",
    "* The most popular hour is 5 p.m. \n",
    "* Busiest day of the week is Saturday. \n",
    "* Most popular station: Speedway & 21st by PCL\n",
    "* Most popular membership type: Walk up \n",
    "* Walk up memberships riders that the longest trip durations\n",
    "* Austin’s bike share system surpasses previous overall use record from 2015. 2018 saw a nearly 50% increase in bike share usage compared to 2017 and set an overall system record for the festival\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Dependencies and CSV\n",
    "\n",
    "The depencies include google maps. If needed install in terminal with the following command: \n",
    "\n",
    "> pip install gmaps\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Include graphs next to code\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To install gmaps in library within the notebook, uncomment the following command\n",
    "#!pip install gmaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies\n",
    "import requests\n",
    "import json\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import gmaps\n",
    "\n",
    "# Ignore Warnings as we are rewrititng values \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Google developer API key\n",
    "from config import gkey\n",
    "\n",
    "# Access maps with unique API key\n",
    "gmaps.configure(api_key= gkey)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Trip ID</th>\n",
       "      <th>Membership Type</th>\n",
       "      <th>Checkout Date</th>\n",
       "      <th>Checkout Time</th>\n",
       "      <th>Checkout Station</th>\n",
       "      <th>Return Station</th>\n",
       "      <th>Trip Duration Minutes</th>\n",
       "      <th>Trip Month</th>\n",
       "      <th>Trip Year</th>\n",
       "      <th>Trip Date</th>\n",
       "      <th>Trip Day of Week</th>\n",
       "      <th>Trip Hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>990480</th>\n",
       "      <td>18122340</td>\n",
       "      <td>student</td>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>23:42:50</td>\n",
       "      <td>Dean Keeton &amp; Whitis</td>\n",
       "      <td>Nueces &amp; 26th</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2018</td>\n",
       "      <td>31</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990481</th>\n",
       "      <td>18122349</td>\n",
       "      <td>student</td>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>23:44:32</td>\n",
       "      <td>Rio Grande &amp; 28th</td>\n",
       "      <td>Rio Grande &amp; 28th</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2018</td>\n",
       "      <td>31</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Trip ID Membership Type Checkout Date Checkout Time  \\\n",
       "990480  18122340         student    2018-07-31      23:42:50   \n",
       "990481  18122349         student    2018-07-31      23:44:32   \n",
       "\n",
       "            Checkout Station     Return Station  Trip Duration Minutes  \\\n",
       "990480  Dean Keeton & Whitis      Nueces & 26th                      2   \n",
       "990481     Rio Grande & 28th  Rio Grande & 28th                      4   \n",
       "\n",
       "        Trip Month  Trip Year  Trip Date Trip Day of Week  Trip Hour  \n",
       "990480           7       2018         31          Tuesday         23  \n",
       "990481           7       2018         31          Tuesday         23  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the CSV file using pandas and create a dataframe \n",
    "df_bike_cl = pd.read_csv(\"Clean_Data/out.csv\")\n",
    "# Display the top rows of the dataframe \n",
    "df_bike_cl.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  I. Austin B-Cycle Utilization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Yearly Count\n",
    "\n",
    "The following graph has a year by year breakdown of ridership. We are basing the analysis on the attributes Trip Year and Trip ID. We find that Austin B-Cycle was initially highly popular, then it reached a plateau. However, in the current year, 2018, demand has exploded since the offering of free 18 month memberships to U.T. students. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Year  Total Number of Rides \n",
      "0  2013                    1457\n",
      "1  2014                  155415\n",
      "2  2015                  184207\n",
      "3  2016                  184505\n",
      "4  2017                  193488\n",
      "5  2018                  271410\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the total number of rides by calcualting the count of trip Id's grouped by Trip Year and save the results in a dataframe \n",
    "# Plot using X-Axis as Trip Year and Y-Axis as Total rides with markers indicating Total rides for that year\n",
    "df_bike_year = df_bike_cl.groupby('Trip Year')\n",
    "number_of_rides_per_year = df_bike_year['Trip ID'].count()\n",
    "rides_per_year_df = pd.DataFrame(data = number_of_rides_per_year,index = None).reset_index()\n",
    "rides_per_year_df = rides_per_year_df.rename(columns = {\"Trip Year\":\"Year\",\"Trip ID\":\"Total Number of Rides \"})\n",
    "print(rides_per_year_df)\n",
    "\n",
    "# Plot the total number of rides per year using line graph\n",
    "Total_rides_per_year_chart = number_of_rides_per_year.plot(kind = \"line\",grid=True,marker='^',title = \"Change in the Total Bike Rides per Year\",figsize = (10,5))                                                                                                             \n",
    "Total_rides_per_year_chart.set_xlabel(\"Trip Year\")\n",
    "Total_rides_per_year_chart.set_ylabel(\"Total Rides per Year\")\n",
    "plt.text(2018.5,250000,\"There is an increase in the usage of bikes as time progresses. \",bbox=dict(facecolor='lightblue',alpha =0.75))\n",
    "plt.savefig(\"Images/Rides_per_Year.png\",bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Monthly count\n",
    "\n",
    "\n",
    "The following graph has a month by month breakdown ridership. We find that Austin B-Cycle is in high demand during the months of March and October which correspond to SXSW and ACL. However, the years 2013 and 2018 have been excluded for this section, since there is only partial data available and it skews the results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3     111774\n",
      "10     90177\n",
      "5      69564\n",
      "7      61475\n",
      "6      60186\n",
      "9      57748\n",
      "4      53314\n",
      "11     52268\n",
      "8      49125\n",
      "1      43736\n",
      "2      42018\n",
      "12     26230\n",
      "Name: Trip Month, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  For the Month by Month breakdown, Analysis is based on Trip Month and leveraging Pandas Value_Counts functionality  \n",
    "df_month = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] >= 2014) & (df_bike_cl[\"Trip Year\"] <= 2017),:]\n",
    "df_bike_month = df_month['Trip Month'].value_counts()\n",
    "print(df_bike_month)\n",
    "\n",
    "# Plot the graph with months on X-axis and total number of rides on Y-axis\n",
    "# For a clear presentation, we are importing python library Calendar to convert from month index to a month name\n",
    "import calendar\n",
    "mn=[calendar.month_name[int(x)] for x in df_bike_month.index.values.tolist()]\n",
    "bike_chart = df_bike_month.plot.bar(title = \"Total Rides per Month \",width = 0.75,figsize = (10,5),rot = 30,alpha = 0.75 )\n",
    "bike_chart.set_xticklabels(mn)\n",
    "bike_chart.set_xlabel(\"Trip Months\")\n",
    "bike_chart.set_ylabel(\"Total Ride Count\")\n",
    "plt.text(12,100000,\"The bars indicate a high utilization through \\n March and October.\",bbox=dict(facecolor='lightblue',alpha =0.5))\n",
    "plt.savefig(\"Images/Rides_per_Month.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Daily Count\n",
    "\n",
    "The most popular time of the week to ride with Austin B-Cycle is the weekend. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saturday     196583\n",
      "Sunday       162296\n",
      "Friday       154650\n",
      "Monday       125486\n",
      "Thursday     124186\n",
      "Wednesday    114329\n",
      "Tuesday      112952\n",
      "Name: Trip Day of Week, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For this section of the analysis, Data is being sliced by day of the week to find out the most popular day.\n",
    "# Checkout Day is the attribute from base data that is used to extract day of the week. \n",
    "busy_day = df_bike_cl['Trip Day of Week'].value_counts()\n",
    "print(busy_day)\n",
    "\n",
    "# Analysis is plotted with Day of the Week on X-axis and Total rides per day on Y-axis.\n",
    "plt.xlabel = \"Days of a week \"\n",
    "plt.ylabel = \"Ride count\"\n",
    "week_chart = busy_day.plot(kind = \"bar\" , title = \" Busiest Day of the Week  \",width = 0.75,figsize = (10,5),rot = 30,alpha = 0.75)\n",
    "plt.text(6.9,190000,\"Saturday is the most popular day of the week.\",bbox=dict(facecolor='lightblue',alpha =0.5))\n",
    "plt.savefig(\"Images/Busy_Week.png\",bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hourly Count\n",
    "\n",
    "In this section we see that that the afternoon hours are the most popular from 2 p.m. through 5 p.m. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17    84076\n",
      "16    83470\n",
      "15    83337\n",
      "13    82408\n",
      "14    81684\n",
      "12    76837\n",
      "18    71035\n",
      "11    63669\n",
      "19    56549\n",
      "10    48313\n",
      "20    43194\n",
      "9     39589\n",
      "21    35189\n",
      "8     33557\n",
      "22    27511\n",
      "23    18739\n",
      "7     16761\n",
      "0     13662\n",
      "1     10068\n",
      "2      8794\n",
      "6      5704\n",
      "3      2820\n",
      "5      1955\n",
      "4      1561\n",
      "Name: Trip Hour, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To get best hour of the day , pandas value_counts() function is used,\n",
    "# which takes unique values from Trip hour column  and counts the corresponding Trip ID'S\n",
    "busy_hour = df_bike_cl['Trip Hour'].value_counts()\n",
    "print(busy_hour)\n",
    "# plot the graph using Trip Hour on x_axis and Total ride count on Y_axis\n",
    "hour_chart = busy_hour.plot(kind = \"bar\" , title = \"Busiest Hour of the Day\",width = 0.75,figsize = (13,5),rot = 0,alpha = 0.75)\n",
    "hour_chart.set_xlabel(\"Hour of the Day\")\n",
    "hour_chart.set_ylabel(\"Ride Count\")\n",
    "plt.text(24,80000,\"2:00PM to 5:00PM are the peak hours.\",bbox=dict(facecolor='Lightblue',alpha =0.5))\n",
    "plt.savefig(\"Images/Busy_Hour.png\",bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Daily and Houly Count Combined\n",
    "\n",
    "Finally, we have included a heatmap by week day and hour that combines the previous findings. We can clearly see from this graphic that the weekends in the afternoon is the most popular time to ride with Austin B-Cycle. This may be the result of the tourism that comes to Austin during the weekends. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an array of the days of the week \n",
    "days_arr = [\"Monday\",\"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"]\n",
    "\n",
    "# Create function that returns the correct day of the week \n",
    "def get_day_of_week(x):\n",
    "    return days_arr[x.weekday()]\n",
    "\n",
    "# Use seaborn to create heat map \n",
    "df_heat = df_bike_cl.groupby([\"Trip Hour\", \"Trip Day of Week\"])[\"Trip ID\"].size().reset_index()\n",
    "df_heat2 = df_heat.pivot(\"Trip Hour\", \"Trip Day of Week\", \"Trip ID\")\n",
    "fig, ax = plt.subplots(figsize=(10,8))       \n",
    "sns.heatmap(df_heat2[days_arr] , cmap=\"YlOrRd\")\n",
    "plt.title(\"Total Number of Checkouts by the Hour and Day of the Week.\")\n",
    "\n",
    "# Save heatmap image\n",
    "plt.savefig(\"Images/Hours-day.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II.  Membership and Station Demand\n",
    "\n",
    "The following graph displays the station count and takes the top six more popular stations. The most popular stations are located within or in very close proximity to the university. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In case the google map is not displaying in the jupyter notebook try the command below in terminal to enable gmaps. \n",
    ">jupyter nbextension enable --py gmaps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Station Demand (by location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v2bF1a8G01JTEwsVL7PeytvWrV+XZs3t33jMubbtUks65pv3Mnnf879JN69bk3rlta+GLO926QJIKFC6cIkl7du1KePneW09f/svCSjLzndu2FVrxy08FY/ud+c24yksW/li5Y7N6nSQpZe/e3Mt/WVh8144dearXbbCgcLHi+ySpSo3aC2OPzercjqlTf2HBIkVTJGnxj3MrnnnZVZMlqVbjU9cXKlps849Tv8/076p/+h7GKlW+wsrKx9fcKkmlj6y0etWSX4vO/eHb3dl5D80Ub0l6jixTX7n4l+TH3/74fUnKnSdvWvHSZfZI0uCnHzvpp5nTjpGkHdu2Fv559oziDZqfuToxKSml1VXX/ypJZSpUWpuvQKHdufPkTavbtPnaV+7t+vuft0rHHLfoiPIVdkvSMbXrLZj5zbiKp5xzQYazxMWPKLPuoasvurB241MXtG7XcUFOnBsA5BSCGXJEYlJSavrrhMQE37s3NSEtNdVy5c2zO305SUZuffrlb6Z+Pean70aNqNr92ks63Pli3zdi27z+ZPeTCxYtuuP/3vqwd2pqil1Zu/KDWdXk8r/cG5NgkUniXLlz/16vJZin7NubUOOkUzY8M3x03zHD3qr6fu8XW8z8dvyiO3r2Hv+XjmPup7OEBJe7bn36laGxy9S+GDrk9x8SM6rnxDNbLf3ms48abl6/bnO7e7t/Neu7CdU/f+f14ypWqx43GObKnWff732664QTG8+897VBY6PbfNjvlWNkcW4NykadTc+7aPnrT3YvvnLxovw/z5pe/YaHHp8QHGsvjPi6f/oPodkV71pnVHc8efPnT40cm+BJSbn+6MvM9+3dmyD/63WVIsG5So1ag74a/m7Vvo/c1+anmdO+u/quB2dlNlZiUlJK+nJKd1exUkes7T128oB4bXPnzZcaHONpaWlxVx8MfPzh5lVr1l7S6dFnhi6cObXo4x2vbhfbJt73f/Ry3y+GDkleNHf20c9/MnZA4WLF93U5s1G7Pbt2JUlSQmJCWkJCZOiExERPS/2jDkvI/NawzPrdn+uQbtAT3Vq1uOTK79vccNPCL4YOSf54wGvNMi1gP3ha/Pf43Zefqblj29b8r37xfZ88+fKlXdPw2K67dm7/679r7mp63kUTOwRLNdO99uCdJ2UQRv4ks3PLky/fvj9axq8zMTEpzaO+T1NS9v1e4z95D+OJ/jOSkJDgqakpCdl9D/MXKrxr++ZNedO/XrN8ab48+fLv3O8iMhB7PqPeGnT00oU/Vnrh08jfK51bnHT9np07I9/bCYlRf28keFLw90hCYuKf/ryZYv4+NvOk3LnSov9e2Ld3T1JiYmKaJD3/8dg3vxr+TvIPX44+5rbWpzXtM27qa7FLqAEgLCxlxAFTvHSZPYWLFt889JXnjpOktLQ0Tf5ydOnYdj9OnVSs/mlnrL3l6Ze+PeLICqt+mTurZMEiRfbs3bM7d3qbXTt25ClSouS2xKQkf/uFp2qm/5AT2y5axarHLB311qAaUuSH0Lz5C+xM/1/aeJYsmFeoYJFi+6664/7ZLS658rtVi38pG6/dd6M+OUGSvhr+bsXcefLuLlmm3J7KJ9Rc9GG/V05MS0uTJE36YuRfltNkVE+VE2pt3bVzR/5N69aWOLZew01HVT9h2XejRjQ6tl7DpRle3MCJZ7RcvGD65ONWLPq5gBT5QeqnWdOLNGzRcvnKRT9XWjB9StH07ZKUnToTEhJUrVa9+a/c2/Ws4keUXl+mYvIuSapQrfqiQU90a5jeLt6SofwFC+3Zs3tX3PcjO3VndVw8x9ZruHT6hK+P37d3r61cvCj/yl9/rlTv1BYrf5o1vUi5oyrvuP6Bx6bXa9ZixpKFP5aVJEtISN2za1eWf/edcGKjDbt37Cgw7uP3j5QiszPTx48tldkxsee/Z9fOPCXKlNsqSSNe71s73jHxvv+j92/fsilP3vz5dxUuVnzfrG/Hl1y7cvmRWdVetuJRy4e98vwJkjS8z0txH6KT3X6zex327t6dp3SFSlslafzH79XKqsZ4SpYptydP3ny7xr7/TkVJGjlkYK2K1Y5dUrx0mT0FChfZOrzPS9Ulaef2bYlbN23MtXPb1jwFChfZkSdfvrTP330jecfWLUUkqUjxknv27dnz+/tQu8lpi6aM/bzO5vXrckvSrz/OLbRi0c8F6jRptnTB9CnHbt+yOWnTujW5f5kzs1q8urJ7bkcfV3PpxE8/qiFJcyZ9U2L75k1Fjm/YaH255KM3r1m+tExqSor9MndW4bUrl8ddMhktO+9h7HlmJLvv4fENT14y45txJ+zeuSNRkt7v9WLtClWPWZJV/9lR/uiqSwb16FZfijxQZuOa1Xm2b9mcJ2/+ArsKFimaMn382FLrV63M9hNv0y1d+GPldatW5N26aWOuhTOnH1Or8anLjzy62vad27YVWrtyed4dW7cm/TRzetX0cRfPn1v4rCuuXXz3y/3H7N65o8D2LZtyZTUGABwszJjhgLr1mVeG93roztZjhg5pmpaWmnh8w0ZzG7Y4e010m/dee+GkZT/NP8osIa1EmbLrWra97ufExCR/54Wn0jo0qdOpbtPmM9t0+N+UF+7sclmn5g2PT65+/OKkXLn2SVK9U1usiW5XpWad35er3dj9qXFP3XT9+R2a1O6clCv3vo7dn/gos1rnTv7+iI/6tT1TZp6QkJh67T0PfxavXb4ChXZ1Pv3E9ukP/5CkLj16jn/65vZn39C0bmfJrXCxEptPOrPV29HHZVZP2YpHrUjzyP8C12zUZNn4T95v0ejsc+POmEWr0+S0dWdf2e6r7tdderXcLSEhMbXtHfePPPW8i1ZcetPtI57reuNl7m75Chbc8fKoiW9mp05JOvWCi+c9f1unG85v3/n3Gm99+uVRL9zZpVWHJrU7p6WlJVSoUm3piWe0/DT6uKbnXrhkzLC3TunQtE6n9Id/7E/d1WrV3ZLVOce68MZb5v80c/qRnZo36CyZt7q6/ZiK1apvf+OZx2o91eW6xgkJiam58uTZe8tTL30oSbUaNZ3W+fQTOx9xZIXf4t1nli5v/gKpnR97dtjgpx9tOeTZx/OkpaUlnNLqvEl1Tz19XUbHxJ7/udfd+O3rT3Zv89UH75581LE1Fsc7Jt73f/T+s69s98v4j4fX79CkdudipUqvP6J8hb/cdxirY/cnR71wV5eLJoz44MTjG548P16b7Pab3evQ8qrrxvXpds+lQ57rsfXIo6uu2Lx+XbGs6oznhod7fDToiW6t33nxqVxFSpTcdM+rgz6SpJue6PlB74fvOXfUkIGnJSQkpt72fK/3zm//vzmPtr/8io7N6nc84sgKq4sUL7leijxBtuxRlZe3P6XW/yofX+vn+/u8MWbZT/NL3nHBGe0lKVee3HtveeqlD04+q/Vvk74YOffms0/pVKhY8S3lK1eN+2cuu+fW7t7uUx6/8erW7U+p3TkhISGt7R33f5S/YKHUZm0uXfbF0Dc33XBqvc4ly5ZbW7JMub88YCRWdt7D2POs3/zMn+O1y+57eEH7//20aM6ssl3ObNzREsyLlii18bbne/3+Z7xD0zqdsloB8cCV559/3nWdfohdOnzLky+O7Hnn/85tf0rtegkJCWnX3P3Qp62uav/zxE8/qtehSZ1OxUuXWV+q/JErs7ouscodVXnZ4x2vumjLhvXFajZqMjt93Matzp9490VndyxcvMSmkmXLrZOkfXv3JPS8o/NF+/bsyePudvLZrb/Jaik2ABxM5s4MPuKrfEKt158ZPnpJ2HUAABBrYI+H66789ZcjHur/9uic7Peui85OXjR3Vruc7BMAsoOljAAAAAAQMpYyAgCAw8719z86PewaACAnMWMGAAAAACEjmAEAAABAyAhmAAAAABAy7jFDhnZs3bLsrovOTg67DgAADpYdW7dk+atKAOBA4HH5AAAAABAyljICAAAAQMgIZgAAAAAQMoIZAAAAAISMYAYAAAAAISOYAQAAAEDICGYAAAAAEDKCGQAAAACEjGAGAAAAACEjmAEAAABAyAhmAAAAABAyghkAAAAAhIxgBgAAAAAhI5gBAAAAQMgIZgAAAAAQMoIZAAA5yMxWm9kpOdxnJzP7Mif7PFSYWXszGxF2HQAQNoIZAOCQY2bboz7SzGxX1NdtD8B4Lc1sppntMLNlZnZ+Jm2LmdnLZrY8qOcXM3vWzIrndF0Hmpk9aWb9s2jTzMwmmdkWM9tgZhPNrFawb78Co5lVN7OU6G3uPsDdz/17ZwAA/x4EMwDAIcfdC6Z/SFom6dyobW/l5FhmVlvSIEl3SiosqZ6k2Rm0zSdpnKTKkloE7U+RtFNS3Zys61BgZiUkfSzpGUnFJFWQ1EPSvjDrAoB/I4IZAOCwY2b5zOxVM/vNzFaY2TNmlivYd3Ywi/WImW00s1/N7JJMuntY0svu/qW7p7r7OndfnEHb9pJKSLrI3Re6e5q7r3b3h909euaogZnNDWaZ3jKz3FG1tzGz2Wa2OZh9Oi5qX7KZfWxm64OP5+KcuwUzdl+bWUEzSwzOdZmZrTGzgWZWKPpaxBy/2sxOMbMLJN0u6dpg5m9ynPM9VtIudx8enOtOdx/l7j+aWR1JL0hqFhy/Our8ZpnZVjNbamb3R/U3QVJi1OxnndhZNzM71cymB9dukpk1iNo3ycy6BZ+3mtlIMysW7CtgZu8G7/lmM/shfR8AHA4IZgCAw9EjkmpKqqHIDFczSXdH7U+WlFtSGUkdJQ02s6My6OskSUlmNi8IeoPNrEgGbVtI+szdd2VR38WSTpdURdKJkq6UJDM7SdJrkq5TJOC9KekjM0sKguUoSfMlVVRkdmp4dKdmlijp9eD8Wrr7dkk3SrpUUhNJVSUdIen5LOqTu38UtBsczEQ2jNNsvqR8ZjbAzM4ys6JRx8+Q1FXSuOD4MsGurcH5FpXURtKdZnZ2sK+ppNSo2c8ZMed3hKQRkp4Mrk9vSSNj3o8rJbWVVDYY49ZgewdJSZLKSyop6SZJe7O6DgBwqCCYAQAOR20ldXP39e6+RtL/Sbo6an+KpEfcfW8wk/WlImHpT4KgU0bSVZLOlXSMpOLKONiUkPRbNurr6e5r3H2dpJGSagfbb5T0irtPC2bn+krKo0i4PEWRpZH3BzNTu9z9u6g+80h6X5HAeaG77466Fs+4+1J33yrpAUltzcyyUWem3H2DpMaScimy3HOdmX1gZiUzOWasu88LZtimSxom6dRsDnm+pJnuPszdU9z9dUkrJLWMatPP3Re5+w5Frkf6td0nqZSkysGxU4I2AHBYIJgBAA4rQeAoI2lp1OalisyUpFsXFVzS95eL7cvdUxWZVenv7r8GweZJSa0yGH6DIjM1WVkd9XqnpILB60qS7g+W2m02s82KhInyisyQLXb3tAz6PFbS2ZIedffoe7zK6a/XIp8iAfMfc/e57n6Nu5dTJARVVuSes7jMrLGZjTezdWa2RVI7RWawsiP2XKS/vrcZXdsBksZLej9Y3tojCN4AcFggmAEADivu7or8cF4panNFSSujvi5pZnlj9q/KoMs5kjybw38pqVVM3/tjuaSH3b1o1Ed+d/8g2JdsZhn92zxTUmdJn5tZ5ajtq/TXa7FL0kZJOyTlT98RLJeMDmzZPe9IY/d5iiy/PCGT44dJGiqpgrsXUWTppWXSPlrsuUh/fW8zqm1PcK9fdUWWTF4i6fKsjgOAQwXBDABwOHpHUjczKxHcl/SApCFR+3NJesjMcptZc0lnKOZ+rSiDJHUws4pmVkDSXZI+zaDtAEUCz3tmVi14EEep4IEUp2ej7r6Sbjaz+sGxBc3sPDPLL+kbSdskPWZm+S3ygJNG0QcHS/v+T9JYM0sPMO8och9XxeChH/8n6e0gwM6XVNzMTg9C2SP687/9ayQdldGyRzOrYWZdzax88HWypMskTYo6voL98eAVU2QGa4O77w7qj37wylpFHv5RMYPr84mkOmZ2cXDf3TWKBLPRGbSPrrWFmR0XBNutiixnTc3qOAA4VBDMAACHo4cl/ShpniIzSd9Kejpq/xJFfjBfLWmgpOvc/dcM+uqlSGibIWmxpE2S7ojXMHjoRzOuZUWfAAAgAElEQVRFltd9pUiQ+l5SAUnTsyra3b+VdIukPpI2S/pJkYdZeLA8sZWkWorcV7VM0oVx+ugr6TlJX5nZkUH9H0j6TtIiRYLj7UHb9Yo8HOOtoM/VktZHdfeuIjNqG80s+n62dFsVufdtipntUCQ8TpZ0b7B/tCLXeq2ZrQjCYCdJz5rZNkUeyPJeVO2bFHmfpgVLOWsrSnC/4HmKBO0NijzAo7W7b45/Rf+kvCKP9t8maa4i9/YNy8ZxAHBIsMjfoQAA/DsETwB8xd2rhF0LAADZxYwZAAAAAISMYAYAAAAAIWMpIwAAAACEjBkzAAAAAAgZwQwAAAAAQpYUdgE4NJUsWdKTk5PDLgMAAAA4rE2bNm29u5fKqh3BDHElJydr6tSpYZcBAAAAHNbMbGl22rGUEQAAAABCRjADAAAAgJARzAAAAAAgZAQzAAAAAAgZwQwAAAAAQkYwAwAAAICQEcwAAAAAIGQEMwAAAAAIGcEMAAAAAEKWFHYBODRtWrVSwx65L+wyAADAIeLSbk+EXQLwr8aMGQAAAACEjGAGAAAAACEjmAEAAABAyAhmAAAAABAyghkAAAAAhIxgBgAAAAAhI5gBAAAAQMgIZgAAAAAQMoIZAAAAAISMYAYAAAAAISOYAQAAAEDI9iuYmVmqmc00s7lmNsLMimbjmO/+fnnZqukkM/shqGu+mXUPtjczs0ZZHNs8OI85Zva9mXU1s8RM2jc0swlmttDMFphZfzPLn8OnlFm9/c3suIM1HgAAAICDY39nzHa5e213P0HSRkldsjrA3TMNRzlgsKSO7l5b0gmShgXbm0nKcGwz6yzpbkn3uXsNSS0k5Zf0rplZnPalJb0n6R53P0bSsZJGSyqUc6eSOXfv4O4/HqzxAAAAABwc/2Qp4/eSykuSmRU0s7FmNj2YfTo/vZGZbQ8+NzOzcWb2fjDb9FZ6ADKzemY23symmdnnZlbWzCqb2fSofqqa2bQ4dRwh6TdJcvdUd//RzJIldZJ0WzCT1iT6ADOrKulSSa3dfW5w7A537yFpgaSL44zTRdJgd/8+aO/u/r67rzGz7mZ2Z1T/c80sOfhIn1mbG5xzCzP71sx+NrOGQfvuZjbYzL4wsyVmdqGZPR1cy9FmlitoN87M6qdfVzN73MxmmdmkIDgquG6TzGyKmT0adf3LBrN96TOeTQQAAADgkPC3glmw3O90SZ8Em3ZLauPudSWdJum5eLNOkupI6irpOElHS2ochI6XJV3s7vUkDZT0uLsvkrTFzGoHx14n6fU4ffaUtNDMPjSzG80sr7svkdRbUs9ghm9izDHXSeohKc3MXg0CYXcze1HS85KuijPOCZLiBcOsVJH0oqSakqpLulLSKZLulHR/VLvKks6RdL6kIZK+DmbydgXbYxWQNMnda0maIOmGYPuLkl509waSVkW1v1LS58HMYi1JM//GuQAAAAA4APY3mOUzs5mSNkgqLmlMsN0k9TCz2ZK+VGQmrXSc4ye7+wp3T1MkGCRLOkaR0DMm6PtBSUcG7ftLui4IgpdJeju2Q3d/VFJ9SV8oEj5GZ+M8akmaJOlcSfuCQLhVUhF336ScXZ642N3nBOc8T9JYd3dJcxQ5/3Sj3H1fsD1Rf5xHbLt0eyV9GryeFtXmZEWWXEp/vl5TFLmW3SXVcPdtsR2aWUczm2pmU7fu3Lk/5wgAAADgH/hb95hJqiQpt/64x6ytpFKS6gX710jKG+f4PVGvUyUlKRLq5gUzW7XdvYa7nxm0GS6ppaTWkqa5+4Z4Rbn7InfvpcgsXi0zK5HFeVgwfnX9EYBGSZKZ5YmpM908SfUy6C9Ff76W0ece3Vda1Ndpipz/n9oFAW5fEN7itUsX3SY1gza/c/cJkppKWinpTTO7Jk6bvu5e393rF85/0J5pAgAAAPzn/a2ljO6+RdItku4MliIWkbTW3feZ2WmKBLfsWiiplJmdLElmlsvMjg/G2S3pc0m9JA2Kd7CZnRO1bLKqIiFls6Rtynjma44iM0sLJaWHwLMUCWz3SHo/zjGvSLrWzE6MGvsqMysjaYmkusG2upKOyvKsD5xJki4KXl+evtHMKinyHvWTNEBBvQAAAADC97cf/uHuMyTNUuSH/7ck1TezqYrMni3Yj372KvKwjafMbJYiSxyjn6b4liRXZKliPFcrco/ZTElvSmrr7qmSRkhqE+/hH4o8ybGHpM8UWZ45TVJRScdL2q7IfW6xda4JzvXZ4HH58yU1UWQJ5HBJxYMaOkv6KbvnfwB0lXS7mU2WVFbSlmB7M0kzzWyGIsHtxXDKAwAAABDL/lgNd2gKnnZYxN0fOgD9nizpNndfZmb5JF0oaYK7L8/JsQ6m4Peq7XJ3N7PLJV3h7udndVysyuXK+hM3tsvx+gAAwOHp0m5PhF0CcFgys2nuXj+rdpnelxQ2M/tQkacVNs/pvt39WTNrJamfmR2hyMzSO4rcg3U4qyfplWB552ZJ14dcDwAAAIAsHNLBzN3bHOD+R0oaeSDHONiCXw1QK+w6AAAAAGTfP/kF0wAAAACAHEAwAwAAAICQEcwAAAAAIGQEMwAAAAAIGcEMAAAAAEJGMAMAAACAkBHMAAAAACBkBDMAAAAACBnBDAAAAABClhR2ATg0FStXXpd2eyLsMgAAAID/BGbMAAAAACBkBDMAAAAACBnBDAAAAABCRjADAAAAgJARzAAAAAAgZAQzAAAAAAgZwQwAAAAAQkYwAwAAAICQEcwAAAAAIGQEMwAAAAAIWVLYBeDQtHnNTn30/PSwywAAAIepC26vG3YJwGGFGTMAAAAACBnBDAAAAABCRjADAAAAgJARzAAAAAAgZAQzAAAAAAgZwQwAAAAAQkYwAwAAAICQEcwAAAAAIGQEMwAAAAAIGcEMAAAAAEJGMAMAAACAkBHMAAAAACBkBLMYZrbEzOaY2Uwzmxq1vZ2ZlYtpVzIb/Z1kZrOCPgdn0q6ZmW0Jxp1tZl+a2RH/8Fzqm9lL/6QPAAAAAAcewSy+09y9trvXj9rWTlK5DNpn5nFJXd29hqTuWbSdGIxbU9IUSV3+xni/c/ep7n7LP+kDAAAAwIFHMMsGM7tYUn1JbwUzWvmCXTeb2fRgNqx6BofvlXSkJLn74myOZ5IKSdoUfF3czD4KZtImmVnNYPscMytqERvM7Jpg+5tm1iKYhfs02FbAzAaa2RQzm2Fm5//NywEAAAAghxHM/solfWFm08ysoyS5+/uSpkpqG8xo7Qrarnf3upJ6Sbozg/4WSXrCzOpnsD9aEzObKWmZpBaSBgbbH5E0I5hJu1/SG8H2byU1lnS8pF8lNQm2nyRpUkzfD0j6yt0bSDpN0jNmViAbNQEAAAA4wAhmf9U4CFstJXUxs6aZtP0g+DxNUnLszmBWqkjQ19tmVtXMSpnZlAz6S1/KWEHSIElPB9tPkfSmJLn7V5JKmFkRSRMlNQ0+ekmqYWblJW109+0xfZ8p6d4g+I2TlFdSxZh6O5rZVDObunXHpkxOGwAAAEBOIpjFcPdVwee1kj6U1DCT5nuCz6mSkuLsP0vSWHefI6m9pI8l3SRpaDZK+USRwCVJFq9USRMUmSVrokjYWifpYkUCWyyTdFEQ/Gq7e0V3n/+nDt37unt9d69fuECxbJQIAAAAICcQzKIE92EVSn+tyCzT3GD3NkXu+9ofMyRdZmZ53X2iIkHvAUnvZOPYUxRZBilFAljboK5miiyh3OruyyWVlFTV3X+V9I0iSyrjBbPPFbknzoJ+6uznuQAAAAA4QOLN8vyXlZb0YZBdkiS97e6jg32vS+ptZrsknZzN/gZIqippppltlzRbkeD0vpmd7u47Y9qn32NmkrZI6hBs7y5pkJnNlrRT0rVRx/wgKTF4PVHSE4oEtFiPSXpB0uwgnC2R1Dqb5wEAAADgADJ3D7sGHIKqVDjOn71tSNhlAACAw9QFt9cNuwTgkGBm02J+DVdcLGUEAAAAgJARzAAAAAAgZAQzAAAAAAgZwQwAAAAAQkYwAwAAAICQEcwAAAAAIGQEMwAAAAAIGcEMAAAAAEJGMAMAAACAkBHMAAAAACBkBDMAAAAACFlS2AXg0FS0dH5dcHvdsMsAAAAA/hOYMQMAAACAkBHMAAAAACBkBDMAAAAACBnBDAAAAABCRjADAAAAgJARzAAAAAAgZAQzAAAAAAgZwQwAAAAAQkYwAwAAAICQEcwAAAAAIGRJYReAQ9PexUu09Jprwy4DAAD8R1V6Y3DYJQAHFTNmAAAAABAyghkAAAAAhIxgBgAAAAAhI5gBAAAAQMgIZgAAAAAQMoIZAAAAAISMYAYAAAAAISOYAQAAAEDICGYAAAAAEDKCGQAAAACEjGAGAAAAACE77IOZmVUws6/NbL6ZzTOzW/9GH+PMrH6c7WZmfc3sRzObY2YnZ9JHLjN70sx+NrO5ZjbZzFruby0AAAAA/nuSwi4gB6RIusPdp5tZIUnTzGyMu/+YA32fIqmqpOMl5ZNUKJO2j0kqK+kEd99jZqUlnZoDNcRlZonunnqg+gcAAABw8Bz2M2bu/pu7Tw9eb5M0X1J56feZsKeC2aufzKxJsD2fmb1rZrPNbKgioSuevZJKS8rl7jvdfU28RmaWX9INkm529z1BLWvcfViw/4pgxm2umT0Vddx2M3vczGaZ2aQgzMnMKgdfTzGzR81se7C9WTA7+LakOcG2q4Lzm2lmfcwsMfh4PRhvjpndFrS9JZj9m21m7/6Dyw4AAAAgBx32wSyamSVLqiPph6jNSe7eUFJXSd2CbZ0l7XT3mpIel1Qvgy7XSCos6XUzs0yGriJpmbtvjVNTOUlPSWouqbakBmZ2QbC7gKRJ7l5L0gRFwp0kvSjpRXdvIGlVTJcNJT3g7seZ2bGSLpPU2N1rS0qV1DYYp7y7n+DuNSQNCo69V1Kd4Lw7ZXI+AAAAAA6if00wM7OCkoZL6hoTkD4IPk+TlBy8bippiCS5+2xJszPo9n1Jp0vaKalnMM5rZnbOfpTWQNI4d1/n7imS3grGlyIzcp/Gqe9kSe8Fr9+O6W+yuy8OXp+uSKicYmYzg6+PlvSrpKPN7GUzO1tS+vWYLektM7tKkSWgf2JmHc1sqplN3bhn936cIgAAAIB/4l8RzMwslyKh7C13/yBm957gc6r+fE+dZ9HnEZJKuvtCSTdKSjazbpLqSxoX0/wXSRWDe9z+0lUmw+xz9/Q6YuvLyI6Yvge7e+3g4xh37+7umyTVCursIql/0P4cSa8qEuammdmfxnP3vu5e393rF8+TNxulAAAAAMgJh30wC5YYDpA0392fz+ZhExRZ8iczO0FSzTht1gXdnxY8ZKOjpFslTXf36HAkd98Z1PCSmeUO+i0bzEz9IOlUMytpZomSrpA0Pov6Jkm6KHh9eSbtxkq6OAiRMrPiZlbJzEpKSnD34ZIeklTXzBIkVXD3ryXdLamopIJZ1AEAAADgIDjsg5mkxpKultQ8eADGTDNrlcUxvSQVNLPZioSUybENgpmsiyQ9HiwT/EjSTZJOMrOL4/T5oCJh7kczmxu0X+fuv0m6T9LXkmYpEuw+zqK+rpJuN7PJijzpcUu8RsGTJx+U9EVwLmOC9uUljQvqfj0YP1HSEDObI2mGpJ7uvjmLOgAAAAAcBPbHSjocKoKnPO5ydzezyyVd4e7nH8waapYo6SPO2Z9b6QAAAHJOpTcGh10CkCPMbJq7/+V3Jsf6N/wes3+jepJeCZZpbpZ0fcj1AAAAADiACGaHIHefqMjDOwAAAAD8B/wb7jEDAAAAgMMawQwAAAAAQkYwAwAAAICQEcwAAAAAIGQEMwAAAAAIGcEMAAAAAEJGMAMAAACAkBHMAAAAACBkBDMAAAAACFlS2AXg0JT7qGRVemNw2GUAAAAA/wnMmAEAAABAyAhmAAAAABAyghkAAAAAhIxgBgAAAAAhI5gBAAAAQMgIZgAAAAAQMoIZAAAAAISMYAYAAAAAISOYAQAAAEDICGYAAAAAELKksAvAoWnJ1iW6/vPrwy4DAAAgRww8a2DYJQCZYsYMAAAAAEJGMAMAAACAkBHMAAAAACBkBDMAAAAACBnBDAAAAABCRjADAAAAgJARzAAAAAAgZAQzAAAAAAgZwQwAAAAAQkYwAwAAAICQEcwAAAAAIGQEMwAAAAAIWZbBzMzKmNm7ZrbIzH40s5FmVs3MypnZ+0Gb2mbWan8GNrN2ZvZKzLZxZlY/i+N+b2NmS8ysZAbt7jOztlmNeagws5Jm9rWZzTazyWZWMIN2FYJ2881snpnd+jfGyvI6AwAAADh4Mg1mZmaSPpQ0zt0ru/txku6XVNrdV7n7xUHT2pL2K5gdBGdK+iLsIvZDZ0kT3L2mpAsk7c2gXYqkO9z9WEknSepiZscdpBoBAAAAHABZzZidJmmfu/dO3+DuM919opklm9lcM8st6VFJl5nZTDO7zMx+NrNSkmRmCWb2S0YzWxkxs15mNjWYFXpkP48tLCm3u6/7u2OZWUszGxbVppmZjcisNjNrYGbfmdmsYNarUHCdJprZ9OCjUQZl7JV0pCQFoTduMHP339x9evB6m6T5ksoH448zs6eCsX8ysybB9nzBrOdsMxsqKV92rgsAAACAgyMpi/0nSJqWWQN332tmD0uq7+43SZKZVZfUVtILklpImuXu6+McfpmZnRL1dZWo1w+4+0YzS5Q01sxquvvsLOpN10LS2Gy2jTuWpDGS+phZAXffIekySUMzab8g2H+Zu08JwuEuSWslneHuu82sqqR3JMVbRrhI0n1mNiU6CGfGzJIl1ZH0Q9TmJHdvGCwt7RZci86Sdrp7zaDW6dm+MgAAAAAOuAP18I+Bkq4JXl8vaVAG7Ya6e+30D0lTo/ZdambTJc2QdLyk/Vmud7akUfvR/i9juXuKpNGSzjWzJEnnSPo4k9qOkfSbu0+RJHffGvSRS1I/M5sj6b1452Fm5SU9EPTRwcwuCrbPDgLeXwT3oA2X1NXdt0bt+iD4PE1ScvC6qaQhQV2zJcUNuGbWMZgJnLp7y+4MLxYAAACAnJXVjNk8SRdn0eYv3H25ma0xs+aSTlRk9izbzOwoSXdKauDum8zsdUl596OLhorMEv3TsYZK6iJpo6Qp7r4tk/YmyeMMcZukNZJqKRKE4yWexorMKq4xs3MUmYUrLWlJTOhKrzmXIqHsLXf/IGb3nuBzqv78/sar7U/cva+kvpJUslrJLNsDAAAAyBlZzZh9JSmPmd2QviG4j+rUmHbbJBWK2dZfkVmaYe6eup91FZa0Q9KWIKC0zO6BZna8pAX7MWZmY42TVFfSDfpjGWNG7RdIKmdmDYI6CgUzbUUUmUlLk3S1pMQ4NcyWdJqZlXP3NYqEuVclvR3n/EzSAEnz3f35bJ7jBAXh2MxOkFQzm8cBAAAAOAgyDWbu7pLaSDrDIo/Lnyepu6RVMU2/lnRc+sM/gm2fSCqojJcxZjbuLEWWCc5TZFnkt/txeEtFliBmpJ2ZrUj/kLQho7GCcPdp0OenmdUWPKzjMkkvm9ksRe5RyyvpNUnXmtkkSdUUCXWx57tAkaWMnwdLJG+XdLmkJ8ysWkzzxooEvObB9Z5pWf+qgl6SCprZbEl3S5qcRXsAAAAAB5FFstcB6Djye7J6unuTAzJAxuOOkXSNu/92MMf9tylZraSf9/J5YZcBAACQIwaeNTDsEvAfZWbT3D3L3yGc1T1mf3fwexW5x2u/7i3LCe5+xsEeEwAAAAD+iQPyVEZ3f9LdK7n7NweifwAAAAD4NzlQj8sHAAAAAGQTwQwAAAAAQkYwAwAAAICQEcwAAAAAIGQEMwAAAAAIGcEMAAAAAEJGMAMAAACAkBHMAAAAACBkBDMAAAAACFlS2AXg0JRcOFkDzxoYdhkAAADAfwIzZgAAAAAQMoIZAAAAAISMYAYAAAAAISOYAQAAAEDICGYAAAAAEDKCGQAAAACEjGAGAAAAACEjmAEAAABAyAhmAAAAABAyghkAAAAAhCwp7AJwiNrwi/R667CrAAAA+Pdr92nYFeAQwIwZAAAAAISMYAYAAAAAISOYAQAAAEDICGYAAAAAEDKCGQAAAACEjGAGAAAAACEjmAEAAABAyAhmAAAAABAyghkAAAAAhIxgBgAAAAAhI5gBAAAAQMgIZgAAAAAQsgMSzMws1cxmmtlcMxthZkWD7eXM7P0DMWbU2CPTx4vZ3t3M7vwb/TUPzmGOmX1vZl3NLDGDtvnN7K2g7Vwz+8bMCmbR/+tmdvH+1gUAAADg3+NAzZjtcvfa7n6CpI2SukiSu69y938cQswsKaN97t7K3Tf/0zGCcTpLulvSfe5eQ1ILSfklvWtmFueQWyWtcfcawbm3l7QvJ2rJaZldQwAAAAAH18FYyvi9pPKSZGbJZjY3eP2DmR2f3sjMxplZPTMrYGYDzWyKmc0ws/OD/e3M7D0zGyHpCzMra2YTombmmgTtlphZyeD1A2a20My+lHRM1FiVzWy0mU0zs4lmVj22aDOrKulSSa3dfa4kufsOd+8haYGkeAGzrKSV6V+4+0J337O/F8zMCprZWDObHsy+pV+DZDNbYGb9g3N+y8xamNm3ZvazmTUM2jU0s++C6/edmR0T7xrub10AAAAADowDOmsSLPk7XdKAOLvfVST4dDOzspLKufs0M+sh6St3vz5Ykjg5CFaSdLKkmu6+0czukPS5uz8ejJM/Zux6ki6XVEeR85wuaVqwu6+kTu7+s5mdKOk1Sc1j6rtOUg9JaWb2qqSTJI2QVExSd0mvS3ov5piBioTGiyWNlTTY3X/OzrWKsVtSG3ffGoTMSWb2SbCviqRLJHWUNEXSlZJOkXSepPslXaBIcGzq7ilm1iI4j4uC43+/hrGDmlnHoF9VLJHvb5QNAAAA4O84UMEsn5nNlJSsSBgaE6fNsGB7N0UCWnrIOVPSeVH3g+WVVDF4PSYqUEyRNNDMckn6yN1nxvTfRNKH7r5TktKDTXDPVyNJ70WtRswTp75akp6QdK6kfe5ez8xul5Ts7pvMrFDsAe4+08yODs6hhaQpZnayu8+P039mTFIPM2sqKU2RGcfSwb7F7j4nOJd5ksa6u5vZHEWutyQVkTQ4mPVzSbmi+h4TL5QF9fdVJLSq/lFFfT9rBgAAAPA3HdB7zCRVkpRbwT1m0dx9paQNZlZT0mWKzKBJkVByUXCPWm13rxgVbHZEHT9BUlNFlg6+aWbXxKkjXrhIkLQ5qv/a7n5snHYmKVVSdUmjg22jJMnM8kiKu0TR3be7+wfu/j9JQyS1itcuC20llZJUL7iOaxQJqIoZNy3q6zT9EbQfk/R1cJ/buVHHSlHXEAAAAMCh4YDeY+buWyTdIunOYGYr1ruKPFyjSPoskKTPJd2c/nANM6sTr28zqyRprbv3U2SpZN2YJhMktTGzfMHs1rlBTVslLTazS4J+zMxqxRlijiLL/hYqMgMmSWcpEtjukfSXp0uaWWMzKxa8zi3pOElL49WfhSLBue0zs9MUCbj7e3z6vW7t/sb4AADg/9u792C7yvKO498fF4HhIqDoIFDuVZCxEaOFQa1Sioq0qGBlagt4mYyiLV5QQaZK61irtngpeEFFsHJT0NZBHWAEjFoKJCSEYIgExBKhIF4QtMUKT/9Yb8z2uPc5OWHnrHDy/cys2Wu/611rv+vJu2bnOe+71pakGbTOH/5RVYuAG+ju95roolb+hYGy99BNvVvSHhTynhGHfh6wOMkiuvunPjLhc68HLgQWAxcD3xrY/ErgNUluAG4Cjhhy/HPo7s36Kt3UzIXAtsBTgQfo7iebaE/gm21a4SJgQfvsVY/xf9KIc/lkkpVtuRo4F5ibZEFr680j9hvlA8D7knwHGPpof0mSJEnrj1R5K9Eo7T63A4E3V9V/JdkCeBkwv6ru6Ld169bc3betBe9+dt/NkCRJmv2Ou6TvFmgdSrKwquZOVc/fsppEVf1TksOATyV5AnAfcD4Dj8SXJEmSpEfKxGwKVfU14Gt9t0OSJEnS7DUTPzAtSZIkSZqEiZkkSZIk9czETJIkSZJ6ZmImSZIkST0zMZMkSZKknpmYSZIkSVLPTMwkSZIkqWcmZpIkSZLUM39gWsM9bi847pK+WyFJkiRtEBwxkyRJkqSemZhJkiRJUs9MzCRJkiSpZyZmkiRJktQzEzNJkiRJ6pmJmSRJkiT1zMRMkiRJknpmYiZJkiRJPTMxkyRJkqSemZhJkiRJUs826bsBWj/d9qNfcPSZV/fdDEmSJGnaLph3YN9NmDZHzCRJkiSpZyZmkiRJktQzEzNJkiRJ6pmJmSRJkiT1zMRMkiRJknpmYiZJkiRJPTMxkyRJkqSemZhJkiRJUs9MzCRJkiSpZyZmkiRJktQzEzNJkiRJ6pmJmSRJkiT1bI0SsyS7JLkyybIkNyU5YWDby1vZw0nmTnGcbZMcP8n2x7fPWZLk2iRbTVL3lPa5S5IsTvKHa3IuayvJVVOd3xg/a6Mkr03y7SQ3JLk8yeET6pyd5Pvt3K9PcuDAthOT3Jxkadv/mJk+B0mSJElrbk1HzH4NvLWq9gEOAN6QZN+2bSnwMmD+GhxnW2BkYga8HphfVU8DXgL8alilloQcDuzf6h4C3LEmJ7K+SxLgXGA/4Miq+gPgOOAvBxPi5m1VNQc4Cfhk2/91wJ8Az6qq/YDnApmh5kuSJElaC2uUmFXVXVV1fVu/H1gG7NTeL6uq5RP3SfLUNuq1uI1q7Q38I7BnK/vgkI/6FbBzO+6dVTU0MQN2BO6tqgdb3Xur6s72ubcneX/77GuT7NXKd0hycZLr2nJQK98yyVmtbFGSI1r5FkkuaG2/ENiilf95ktPa+glJbmvreyb5dlt/Vzve0iRnprNnkusH4rN3koVDzu1Y4AdV9aaqurud3w+BvwAOT7LTkH3mA3u19cbiXbYAAAtpSURBVHcCx1fVz9u+91XVOSPiKEmSJGk9MO17zJLsBjwduGaKqq8DPtJGdOYCK+lGdm6tqjlV9bYh+9wKHNlGfSZzGbBLku8l+ViSP5qw/edV9SzgdODDrewjwIeq6pnAkcCnW/kpwBWt/PnAB5NsSTd698s2Ivde4Bmt/nzgOW39OcCPW7L0bOBbrfz0qnpmG7HaAji8qm4F7ksyp9V5FXD2kHM7BviHlkh+Lcl/tCT25cAZwCuG7POnwI1Jtga2bp8lSZIk6VFiWolZu+frYuBNq0ZkJnE18M4k7wB2rar/meLYO9ElSU8GXpvkyFa+JMk2g3Wr6gG6RGke8CPgwiTHDVQ5f+B11b1XhwCnJ1kMfAXYpiUyhwIntfKrgM2B36ObAvj59nlLgCVt/b+Brdq+uwDntbrPYXVi9vwk1yS5ETgYeGor/zTwqiQb0yVY5w0JxSYttu8EzmzH3YsuwVsO7DlQ94Ot3fOA19BNWawhx1wjSeYlWZBkwYMP/HRtDyNJkiRpmjZZ04pJNqVLys6tqi9NVb+qzktyDfBi4NIkrwVum2SXg4AbquruJC8GvpHkicDtw5LAqnqILpG6qiVAx7J6BGowOVm1vhFw4MQEsd3TdeTE6Zhd8cgk52q6Ea/ldMnYq+kSwLcm2Rz4GDC3qu5Icipdsgdd/N4NXAEsrKofDzn2Q+31KcDJVfVQksta2ROAewbqvq2qLprQ7l8k2aOqJov1UFV1Jl0yyPa77rPWCZ4kSZKk6VnTpzIG+AywrKpOW8N99gBuq6qP0o1QPQ24H9h6xC5L6EaantTurXoz3dS93xlVSvLkds/aKnOAHwy8f8XA69Vt/TLgjQPHWDWl8FLgr9s5kuTprXw+8MpWtl9rPwPbTmyvi+imQD5YVfexOgm7t40wHrVqp6r63/Z5Hwc+OyIOtNG45cChSTaie5jH5sBbgQtH7de8Dzhj1Shjkm2SzJtiH0mSJEk9WtOpjAcBfwUc3B7csTjJYQBJXppkJd2I0VeTXNr2eQWwtE21ewrwuTZC9J32UIzfevhHVd1MN5Xx0vaQjLcARwPvS/L7E9qzFXBOku8mWQLsC5w6sH2zNlp3Al2CB/A3wNw2NfK7dPfAAbwH2BRYkmRpew9d8rRVO/7bgWsHjv8tummM89vI3R3At9t5/Az4FHAj8G/AdRPafi7dSNxlDHc+8Pd0Cdbx7bi3tFic0eI0mY8DVwLXtfP5JvDLge1fTbKyLV+c4liSJEmSZkCqZteMtSS3000jvLfvtgyT5ETgsVX1tyO2b0Q35XExcFpV3Z9kB7qfJPhMVf16Jtq5/a771KGnnDUTHyVJkiSN1QXzDpy60gxJsrCqpvwt4TW+x0yPXJIv0z284+BRdarq4SRH0Y2WXdruWbuTLkmbkaRMkiRJ0syadYlZVe3WdxtGqaqXrmG9h4B/aYskSZKkWW7av2MmSZIkSRovEzNJkiRJ6pmJmSRJkiT1zMRMkiRJknpmYiZJkiRJPTMxkyRJkqSemZhJkiRJUs9MzCRJkiSpZyZmkiRJktSzTfpugNZPe+ywJRfMO7DvZkiSJEkbBEfMJEmSJKlnJmaSJEmS1DMTM0mSJEnqmYmZJEmSJPXMxEySJEmSemZiJkmSJEk9MzGTJEmSpJ6ZmEmSJElSz0zMJEmSJKlnJmaSJEmS1LNUVd9t0Hooyf3A8r7bMQs8Hri370bMAsZxPIzjeBjH8TCO42Ecx8M4jodxHG7XqtphqkqbzERL9Ki0vKrm9t2IR7skC4zjI2ccx8M4jodxHA/jOB7GcTyM43gYx0fGqYySJEmS1DMTM0mSJEnqmYmZRjmz7wbMEsZxPIzjeBjH8TCO42Ecx8M4jodxHA/j+Aj48A9JkiRJ6pkjZpIkSZLUMxMz/Y4kL0yyPMmKJCf13Z71TZLbk9yYZHGSBa1s+ySXJ7mlvW7XypPkoy2WS5LsP3CcY1v9W5Ic29f5zKQkZyW5J8nSgbKxxS7JM9q/zYq2b2b2DNe9ETE8NckPW59cnOSwgW0nt3gsT/KCgfKh13mS3ZNc02J7YZLHzNzZzZwkuyS5MsmyJDclOaGV2x+nYZI42ienIcnmSa5NckOL49+18qHnnmSz9n5F277bwLGmFd/ZZJI4np3k+wP9cU4r97qeRJKNkyxKckl7b39c16rKxeU3C7AxcCuwB/AY4AZg377btT4twO3A4yeUfQA4qa2fBLy/rR8GfB0IcABwTSvfHritvW7X1rfr+9xmIHbPBfYHlq6L2AHXAge2fb4OvKjvc56hGJ4KnDik7r7tGt4M2L1d2xtPdp0DXwCObuufAF7f9zmvozjuCOzf1rcGvtfiZX8cTxztk9OLY4Ct2vqmwDWtnw09d+B44BNt/WjgwrWN72xaJonj2cBRQ+p7XU8ez7cA5wGXtPf2x3W8OGKmiZ4FrKiq26rqV8AFwBE9t+nR4AjgnLZ+DvCSgfLPVec/gW2T7Ai8ALi8qn5SVT8FLgdeONONnmlVNR/4yYTiscSubdumqq6u7hvhcwPHmjVGxHCUI4ALqurBqvo+sILuGh96nbe//B4MXNT2H/z3mFWq6q6qur6t3w8sA3bC/jgtk8RxFPvkEK1fPdDebtqWYvS5D/bTi4A/brGaVnzX8WnNuEniOIrX9QhJdgZeDHy6vZ/sWrQ/jomJmSbaCbhj4P1KJv+S3RAVcFmShUnmtbInVtVd0P1HBXhCKx8VT+O82rhit1Nbn1i+oXhjm4pzVtr0O6Yfw8cBP6uqX08on9XatJun0/113f64libEEeyT09KmjS0G7qFLBG5l9Ln/Jl5t+310sdrgv3MmxrGqVvXH97b++KEkm7Uyr+vRPgy8HXi4vZ/sWrQ/jomJmSYaNlfaR3f+toOqan/gRcAbkjx3krqj4mmcpzbd2G3IMf04sCcwB7gL+OdWbgynkGQr4GLgTVX188mqDikzls2QONonp6mqHqqqOcDOdCMK+wyr1l6N4wgT45hkP+Bk4CnAM+mmJ76jVTeOQyQ5HLinqhYOFg+pan8cMxMzTbQS2GXg/c7AnT21Zb1UVXe213uAL9N9gd7dpjjQXu9p1UfF0zivNq7YrWzrE8tnvaq6u/1n5GHgU3R9EqYfw3vppvJsMqF8VkqyKV0ycW5VfakV2x+naVgc7ZNrr6p+BlxFd8/TqHP/Tbza9sfSTXH2O6cZiOML25TbqqoHgc+y9v1xQ7muDwL+LMntdNMMD6YbQbM/rmMmZproOmDv9uSdx9DdxPmVntu03kiyZZKtV60DhwJL6WK06qlNxwL/3ta/AhzTnvx0AHBfmx51KXBoku3aFJ9DW9mGaCyxa9vuT3JAm9t+zMCxZrVViUTzUro+CV0Mj25PzNod2JvuxvWh13m7Z+JK4Ki2/+C/x6zS+shngGVVddrAJvvjNIyKo31yepLskGTbtr4FcAjd/Xqjzn2wnx4FXNFiNa34rvszm1kj4njzwB9bQndf1GB/9LqeoKpOrqqdq2o3ur5yRVW9EvvjulfrwRNIXNavhe4pRd+jm99+St/tWZ8WuicI3dCWm1bFh24u9TeAW9rr9q08wBktljcCcweO9Wq6G2FXAK/q+9xmKH7n001r+j+6v5i9ZpyxA+bSfeHeCpwOpO9znqEY/muL0RK6L7cdB+qf0uKxnIGnh426zlsfv7bF9ovAZn2f8zqK47Ppps4sARa35TD749jiaJ+cXhyfBixq8VoKvGuycwc2b+9XtO17rG18Z9MySRyvaP1xKfB5Vj+50et66pg+j9VPZbQ/ruMlLTiSJEmSpJ44lVGSJEmSemZiJkmSJEk9MzGTJEmSpJ6ZmEmSJElSz0zMJEmSJKlnJmaSJEmS1DMTM0mSJEnqmYmZJEmSJPXs/wE3+gac9gllSgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The focus of this analysis, is bike usage by check out station\n",
    "# Again, value_counts() is leveraged to find unique row count by Check out station\n",
    "popular_checkout_station = df_bike_cl['Checkout Station'].value_counts()\n",
    "popular_checkout_station.sort_values(ascending = False).head(6)\n",
    "\n",
    "# Plot covers the top 6 check out stations that potray more usage than other stations\n",
    "pop_station_chart = popular_checkout_station[0:6].plot(kind = \"barh\",width = 0.75,figsize = (13,5),alpha = 0.75)\n",
    "plt.title(\"Top 6 Checkout Stations\")\n",
    "plt.text(300,6.5,\"The stations below receive the most rentals and are all located around the U.T. campus\",bbox=dict(facecolor='Lightblue',alpha =0.5) )\n",
    "plt.savefig(\"Images/Pop_Station.png\",bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "facfb03a1e58488fabc7d581593ad942",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create container of coordinates and stations\n",
    "b_cycle_stations = [\n",
    "   {'name': '21st & Speedway @PCL ', 'location': (30.283, -97.7375)},\n",
    "   {'name': 'Riverside @ S. Lamar', 'location': (30.26446, -97.75665)},\n",
    "   {'name': 'City Hall / Lavaca & 2nd', 'location': (30.26476, -97.74678)},\n",
    "   {'name': '2nd & Congress', 'location': (30.26408, -97.74355)},\n",
    "   {'name': '5th & Bowie', 'location': (30.2696, -97.75332)},\n",
    "   {'name': 'Rainey St @ Cummings', 'location': (30.25579, -97.73982)}\n",
    "]\n",
    "\n",
    "# List comprehension of station locations\n",
    "station_locations = [station['location'] for station in b_cycle_stations]\n",
    "info_box_template = \"\"\"\n",
    "<dl>\n",
    "<dt>Name</dt><dd>{name}</dd>\n",
    "</dl>\n",
    "\"\"\"\n",
    "\n",
    "# List comprehension of station information with unpacking\n",
    "station_info = [info_box_template.format(**station) for station in b_cycle_stations]\n",
    "\n",
    "# Use google maps to indicate location of the most popular stations\n",
    "marker_layer = gmaps.marker_layer(station_locations, info_box_content=station_info)\n",
    "fig = gmaps.figure()\n",
    "fig.add_layer(marker_layer)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Membership Demand (by category)\n",
    "\n",
    "Membership names have been changing over the years. Membership types have been categorized to simplify the changes and output more meaningful data. \n",
    "\n",
    "The categories are: \n",
    "\n",
    "* Walk up \n",
    "* Student \n",
    "* Day \n",
    "* Month \n",
    "* Weekend \n",
    "* Week \n",
    "* Year \n",
    "* 3 Year \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "walk up    374340\n",
      "year       216566\n",
      "student    161804\n",
      "day        125192\n",
      "month       55605\n",
      "weekend     44780\n",
      "3 year       6319\n",
      "week         5876\n",
      "Name: Membership Type, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analysis by Membership Types helps determine which programs are popular. \n",
    "popular_membership_type = df_bike_cl['Membership Type'].value_counts() \n",
    "print(popular_membership_type.head(10))\n",
    "\n",
    "# plot the graph for the popular mebership types \n",
    "explode = (0.1,0,0,0,0,0)\n",
    "colors = [\"gold\", \"lightskyblue\", \"lightcoral\",\"lightgreen\",\"orange\",\"plum\"]\n",
    "popular_membership_type[0:6].plot.pie(colors = colors , explode = explode, shadow=True )\n",
    "plt.title(\"Top 6  Membership Types\")\n",
    "plt.savefig(\"Images/mem_type.png\",bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Trip Duration (by Membership Category)\n",
    "\n",
    "* 2013 - 2018 is being considered for this analysis\n",
    "* We group by membership type and sort, to find out the top 6 membership types. \n",
    "* The data is illustrated as a bar chart which indicates average by membership type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For each membership type in the Top 6, prepare a data series with trip durations\n",
    "# Find the average for each membership type\n",
    "subscriber_type = df_bike_cl['Membership Type'].value_counts().index\n",
    "top_subscriber_type = subscriber_type[0:6]\n",
    "\n",
    "trip_minutes = []\n",
    "for element in top_subscriber_type:\n",
    "  time_min = df_bike_cl[df_bike_cl['Membership Type'] == element]['Trip Duration Minutes'].values\n",
    "  trip_minutes.append(np.mean(time_min))\n",
    "\n",
    "x_axis  =  np.arange(0, len(top_subscriber_type))\n",
    "tick_locations = []\n",
    "for x in x_axis:\n",
    "  tick_locations.append(x)\n",
    "\n",
    "trip_minutes.sort(reverse=True)\n",
    "plt.xticks(tick_locations, [\"walk up\",\"day\",\"weekend\",\"student\",\"month\",\"year\"], rotation=90)\n",
    "colors = [\"gold\", \"lightgreen\",\"plum\",\"lightcoral\",\"orange\",\"lightskyblue\"]\n",
    "plt.bar(height= trip_minutes, x= tick_locations, color=colors,alpha = 0.75)\n",
    "plt.title('Average Trip Duration by Top Membership Type')\n",
    "plt.savefig(\"Images/avg_duration_mem_type.png\",bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Trip Duration Over the Years (by Membership Category)\n",
    "\n",
    "In this section we calculate the average trip duration and display the trip duration by membership type through the years. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For each year, filter the data into a data frame.\n",
    "# For that data frame, for the above selected top 6 membership types \n",
    "# Calculate the average duration of trips\n",
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "title_font = {'fontname':'Arial', 'size':'20', 'color':'black', 'weight':'normal',\n",
    "              'verticalalignment':'bottom'} # Bottom vertical alignment for more space\n",
    "axis_font = {'fontname':'Arial', 'size':'20'}\n",
    "w = 0.2\n",
    "subscriber_type = df_bike_cl['Membership Type'].value_counts().index\n",
    "top_subscriber_type = subscriber_type[0:6]\n",
    "\n",
    "## For 2018\n",
    "df_bike_2018 = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] ==2018)]\n",
    "\n",
    "trip_minutes_2018 = []\n",
    "for element in top_subscriber_type:\n",
    "   time_min = df_bike_2018[df_bike_2018['Membership Type'] == element]['Trip Duration Minutes'].values\n",
    "   trip_minutes_2018.append(np.mean(time_min))\n",
    "\n",
    "## For 2017\n",
    "\n",
    "df_bike_2017 = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] ==2017)]\n",
    "\n",
    "trip_minutes_2017 = []\n",
    "for element in top_subscriber_type:\n",
    "   time_min = df_bike_2017[df_bike_2017['Membership Type'] == element]['Trip Duration Minutes'].values\n",
    "   trip_minutes_2017.append(np.mean(time_min))\n",
    "\n",
    "## For 2016\n",
    "\n",
    "df_bike_2016 = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] ==2016)]\n",
    "\n",
    "trip_minutes_2016 = []\n",
    "for element in top_subscriber_type:\n",
    "   time_min = df_bike_2016[df_bike_2016['Membership Type'] == element]['Trip Duration Minutes'].values\n",
    "   trip_minutes_2016.append(np.mean(time_min))\n",
    "\n",
    "## For 2015\n",
    "\n",
    "df_bike_2015 = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] ==2015)]\n",
    "\n",
    "trip_minutes_2015 = []\n",
    "for element in top_subscriber_type:\n",
    "   time_min = df_bike_2015[df_bike_2015['Membership Type'] == element]['Trip Duration Minutes'].values\n",
    "   trip_minutes_2015.append(np.mean(time_min))\n",
    "\n",
    "\n",
    "## For 2014\n",
    "\n",
    "df_bike_2014 = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] ==2014)]\n",
    "\n",
    "trip_minutes_2014 = []\n",
    "for element in top_subscriber_type:\n",
    "   time_min = df_bike_2014[df_bike_2014['Membership Type'] == element]['Trip Duration Minutes'].values\n",
    "   trip_minutes_2014.append(np.mean(time_min))\n",
    "\n",
    "## For 2013\n",
    "\n",
    "df_bike_2013 = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] ==2013)]\n",
    "\n",
    "trip_minutes_2013 = []\n",
    "for element in top_subscriber_type:\n",
    "   time_min = df_bike_2013[df_bike_2013['Membership Type'] == element]['Trip Duration Minutes'].values\n",
    "   trip_minutes_2013.append(np.mean(time_min))\n",
    "\n",
    "x_axis  =  np.arange(0, len(top_subscriber_type))\n",
    "tick_locations = []\n",
    "for x in x_axis:\n",
    "   tick_locations.append(x)\n",
    "\n",
    "# Plot with membership types on X-axis and Minutes on Y-axis and stacked by year.\n",
    "plt.setp(ax, xticks= tick_locations, xticklabels= top_subscriber_type)\n",
    "plt.setp(ax.get_xticklabels(), rotation=70, fontsize= 20)\n",
    "ax.bar(x_axis-0.5*w, trip_minutes_2018,w,color='r',label='2018',alpha = 0.75)\n",
    "ax.bar(x_axis, trip_minutes_2017,w,color='g',label='2017',alpha = 0.75)\n",
    "ax.bar(x_axis+0.5*w, trip_minutes_2016,w,color='b',label='2016',alpha = 0.75)\n",
    "ax.bar(x_axis+1*w, trip_minutes_2015,w,color='y',label='2015',alpha = 0.75)\n",
    "ax.bar(x_axis+1.5*w, trip_minutes_2014,w,color='c',label='2014',alpha = 0.75)\n",
    "ax.bar(x_axis+2*w, trip_minutes_2013,w,color='m',label='2013',alpha = 0.75)\n",
    "\n",
    "ax.set_title('Average Trip Duration by Top Membership Type (over the years)', **title_font)\n",
    "ax.set_ylabel('Minutes', **axis_font)\n",
    "ax.legend(loc=1, prop={'size': 16})\n",
    "plt.savefig(\"Images/avg_duration_mem_type_year.png\",bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III. Special Cases Analysis\n",
    "\n",
    "Since the inception of the Austin B-Cycle program there have been unique situations that have changed ridership. Below we explore:\n",
    "\n",
    "* Free U.T. Austin student membership\n",
    "* SXSW and ACL music festivals \n",
    "* Uber Ban "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To identify the top 6 membership types, apply group by and sort in descending fashion.\n",
    "# Just for the above identified Top 6 membership types, group by year to count the total rides using Trip ID.\n",
    "# Analysis plot has Trip Years on X-Axis, Total rides on Y-axis and pivoted by membership type.\n",
    "\n",
    "mem_type_df = df_bike_cl.groupby([\"Membership Type\"],as_index=True).size()\n",
    "mem_type_df = mem_type_df.sort_values(ascending=False)\n",
    "\n",
    "# Select the data for the top 6 memberships \n",
    "mem_type_year = []\n",
    "\n",
    "for memtype in mem_type_df[:6].to_dict():\n",
    "    df_temp = df_bike_cl.loc[df_bike_cl[\"Membership Type\"]==memtype]\n",
    "    mem_type_year.append(df_temp)\n",
    "    \n",
    "mem_type_year_df = pd.concat(mem_type_year)\n",
    "\n",
    "# Now plot the data, since we have the top 6 membership types now group by Year and display\n",
    "fig, ax = plt.subplots(figsize=(13,8))\n",
    "# Without unstack, the rows are displayed seperately. \n",
    "mem_type_year_df.groupby(['Trip Year','Membership Type']).count()['Trip ID'].unstack().plot.bar(ax=ax,width = 0.75, alpha = 0.75)\n",
    "plt.text(-0.45,180000,\"Effect of B-Cycle's Pilot Program for U.T. Campus Students in 2018\",fontsize = 15)\n",
    "plt.title(\"Trends in Membership Types Over the Years\")\n",
    "plt.savefig(\"Images/mem_type_year.png\",bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation between Annual Events and Bike Rides . \n",
    "#### The Annual events being considered are SXSW and ACL. \n",
    "- Analysis during SXSW \n",
    "    - 2013 and 2018 are excluded here as:\n",
    "        - SXSW was in March 2013 and B-Cycle data starts from December 2013\n",
    "\n",
    "    - SXSW Dates \n",
    "        - SXSW 2018 - Mar  9, 2018 - Mar 18 2018\n",
    "        - SXSW 2017 - Mar 10, 2017 – Mar 19, 2017\n",
    "        - SXSW 2016 - Mar 11, 2016 – Mar 20, 2016\n",
    "        - SXSW 2015 - Mar 13, 2015 – Mar 23, 2015\n",
    "        - SXSW 2014 - Mar  7, 2014 – Mar 17, 2014\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total rides during SXSW for the year 2018 : 23370\n",
      "Total rides during SXSW for the year 2017 : 14998\n",
      "Total rides during SXSW for the year 2016 : 16929\n",
      "Total rides during SXSW for the year 2015 : 21445\n",
      "Total rides during SXSW for the year 2014 : 11891\n"
     ]
    }
   ],
   "source": [
    "# For retrieving the data, apply filters on Trip Year,Trip Month and Trip Dates \n",
    "# Apply Group by on Trip ID within the filtered data frame to get the total rides\n",
    "\n",
    "#calculate total number of rides during SXSW for year 2018\n",
    "df_bike_2018 = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] ==2018), : ]\n",
    "df_bike_2018_march = df_bike_2018.loc[(df_bike_2018[\"Trip Month\"] == 3), : ]\n",
    "df_bike_2018_march_SXSW = df_bike_2018_march.loc[(df_bike_2018_march[\"Trip Date\"] >= 9)& (df_bike_2018_march[\"Trip Date\"] <= 18), : ]\n",
    "number_of_rides_SXSW_2018 = df_bike_2018_march_SXSW['Trip ID'].count()\n",
    "\n",
    "# calculate total number of rides during SXSW for year 2017\n",
    "df_bike_2017 = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] ==2017), : ]\n",
    "df_bike_2017_march = df_bike_2017.loc[(df_bike_2017[\"Trip Month\"] == 3), : ]\n",
    "df_bike_2017_march_SXSW = df_bike_2017_march.loc[(df_bike_2017_march[\"Trip Date\"] >= 10)& (df_bike_2017_march[\"Trip Date\"] <= 19), : ]\n",
    "number_of_rides_SXSW_2017 = df_bike_2017_march_SXSW['Trip ID'].count()\n",
    "\n",
    "# calculate total number of rides during SXSW for year 2016\n",
    "df_bike_2016 = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] ==2016)]\n",
    "df_bike_2016_march = df_bike_2016.loc[(df_bike_2016[\"Trip Month\"] == 3), : ]\n",
    "df_bike_2016_march_SXSW = df_bike_2016_march.loc[(df_bike_2016_march[\"Trip Date\"] >= 11)& (df_bike_2016_march[\"Trip Date\"] <= 20), : ]\n",
    "number_of_rides_SXSW_2016 = df_bike_2016_march_SXSW['Trip ID'].count()\n",
    "\n",
    "# calculate total number of rides during SXSW for year 2015\n",
    "df_bike_2015 = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] ==2015)]\n",
    "df_bike_2015_march = df_bike_2015.loc[(df_bike_2015[\"Trip Month\"] == 3), : ]\n",
    "df_bike_2015_march_SXSW = df_bike_2015_march.loc[(df_bike_2015_march[\"Trip Date\"] >= 13)& (df_bike_2015_march[\"Trip Date\"] <= 23), : ]\n",
    "number_of_rides_SXSW_2015 = df_bike_2015_march_SXSW['Trip ID'].count()\n",
    "\n",
    "# calculate total number of rides during SXSW for year 2014\n",
    "df_bike_2014 = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] ==2014)]\n",
    "df_bike_2014_march = df_bike_2014.loc[(df_bike_2014[\"Trip Month\"] == 3), : ]\n",
    "df_bike_2014_march_SXSW = df_bike_2014_march.loc[(df_bike_2014_march[\"Trip Date\"] >= 13)& (df_bike_2014_march[\"Trip Date\"] <= 23), : ]\n",
    "number_of_rides_SXSW_2014 = df_bike_2014_march_SXSW['Trip ID'].count()\n",
    "\n",
    "print(f\"Total rides during SXSW for the year 2018 : {number_of_rides_SXSW_2018 }\")\n",
    "print(f\"Total rides during SXSW for the year 2017 : {number_of_rides_SXSW_2017 }\")\n",
    "print(f\"Total rides during SXSW for the year 2016 : {number_of_rides_SXSW_2016 }\")\n",
    "print(f\"Total rides during SXSW for the year 2015 : {number_of_rides_SXSW_2015 }\")\n",
    "print(f\"Total rides during SXSW for the year 2014 : {number_of_rides_SXSW_2014 }\")        \n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACL Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 2013 and 2018 are excluded here as:\n",
    "    - ACL was in October 2013 and B-Cycle data starts from December 2013\n",
    "    - ACL has not yet happened in 2018\n",
    "- ACL Dates \n",
    "    - ACL 2017 - Oct 6, 2017 – Oct 15, 2017\n",
    "    - ACL 2016 -  Sep 30, 2016 – Oct 9, 2016\n",
    "    - ACL 2015 - Oct 2, 2015 – Oct 11, 2015\n",
    "    - ACL 2014 - 10/3-5 and 10/10-12, 2014\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rides during ACL for the year 2017 : 9944\n",
      "Number of rides during ACL for the year 2016 : 12537\n",
      "Number of rides during ACL for the year 2015 : 12440\n",
      "Number of rides during ACL for the year 2014 : 9239\n"
     ]
    }
   ],
   "source": [
    "# For retrieving the data, apply filters on Trip Year,Trip Month and Trip Dates \n",
    "# Apply Group by on Trip ID within the filtered data frame to get the total rides\n",
    "\n",
    "# Calculate number of bike rides during ACL 2017 \n",
    "df_bike_2017 = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] ==2017), : ]\n",
    "df_bike_2017_oct = df_bike_2017.loc[(df_bike_2017[\"Trip Month\"] == 10), : ]\n",
    "df_bike_2017_oct_acl = df_bike_2017_oct.loc[(df_bike_2017_oct[\"Trip Date\"] >= 6)& (df_bike_2017_oct[\"Trip Date\"] <= 15), : ]\n",
    "number_of_rides_acl_2017 = df_bike_2017_oct_acl['Trip ID'].count()\n",
    "\n",
    "\n",
    "# Calculate number of bike rides during ACL 2016\n",
    "df_bike_2016 = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] ==2016), : ]\n",
    "df_bike_2016_oct = df_bike_2016.loc[(df_bike_2016[\"Trip Month\"] == 10), : ]\n",
    "df_bike_2016_oct_acl = df_bike_2016_oct.loc[ (df_bike_2016_oct[\"Trip Date\"] <= 9), : ]\n",
    "number_of_rides_oct_acl_2016 = df_bike_2016_oct_acl['Trip ID'].count()\n",
    "\n",
    "df_bike_2016 = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] ==2016), : ]\n",
    "df_bike_2016_sept = df_bike_2016.loc[(df_bike_2016[\"Trip Month\"] == 9), : ]\n",
    "df_bike_2016_sept_acl = df_bike_2016_sept.loc[ (df_bike_2016_sept[\"Trip Date\"] >= 30), : ]\n",
    "number_of_rides_sept_acl_2016 = df_bike_2016_sept_acl['Trip ID'].count()\n",
    "\n",
    "number_of_rides_acl_2016 = number_of_rides_oct_acl_2016 + number_of_rides_sept_acl_2016\n",
    "\n",
    "\n",
    "# Calculate number of bike rides during ACL 2015\n",
    "df_bike_2015 = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] ==2015), : ]\n",
    "df_bike_2015_oct = df_bike_2015.loc[(df_bike_2015[\"Trip Month\"] == 10), : ]\n",
    "df_bike_2015_oct_acl = df_bike_2015_oct.loc[(df_bike_2015_oct[\"Trip Date\"] >= 2)& (df_bike_2015_oct[\"Trip Date\"] <= 11), : ]\n",
    "number_of_rides_acl_2015 = df_bike_2015_oct_acl['Trip ID'].count()\n",
    "\n",
    "\n",
    "# Calculate number of bike rides during ACL 2014\n",
    "df_bike_2014 = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] ==2014), : ]\n",
    "df_bike_2014_oct = df_bike_2014.loc[(df_bike_2014[\"Trip Month\"] == 10), : ]\n",
    "df_bike_2014_oct_acl = df_bike_2014_oct.loc[((df_bike_2014_oct[\"Trip Date\"] >= 3)& (df_bike_2014_oct[\"Trip Date\"] <= 5)) | ((df_bike_2014_oct[\"Trip Date\"] >= 10)& (df_bike_2014_oct[\"Trip Date\"] <= 12)), : ]\n",
    "number_of_rides_acl_2014 = df_bike_2014_oct_acl['Trip ID'].count()\n",
    "\n",
    "\n",
    "\n",
    "print(f\"Number of rides during ACL for the year 2017 : {number_of_rides_acl_2017}\")\n",
    "print(f\"Number of rides during ACL for the year 2016 : {number_of_rides_acl_2016}\")\n",
    "print(f\"Number of rides during ACL for the year 2015 : {number_of_rides_acl_2015}\")\n",
    "print(f\"Number of rides during ACL for the year 2014 : {number_of_rides_acl_2014}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7M239tl4M3E9VbdSb6vcl2thufud+gOo36iftbDKZuftxq1BN8tES9zPAbq36Ie8qlV1vZubJmbkZ1YWcA+i4Wrz2d78fVT8Pqt+/k9vo6/y29qV08iX/heo2tR6lQqnWJGDNiOjRKo7O9isXtg/U4uESW208/VttUxvPn4ANoxoL8zPMuS1vMlU/ZKOa92uVzKy9bXF+se4GnF/6I0+Wtvsjor1+yOXA/hGxLdXn+KfS/jTwWKvPbuXM/GRZ35nv8exYs/LDzHwfVWXZ1sw9AdPmwEPzeW3SEslElLqNUjFzNTX3hpdy6Geo/kG8bBnjZqNFPNXeEbFzGe/mdODuzHyaqiJrk4g4NKoBOZePiO3KD2pn4n+a6p7z70U1oOFWVNUoCzX7TFQDgn6SKuHQnplUHamVgF9GxDKZOZmq1PsHEdE7IpaJiI1q7n+f3+t4DLgZ+EYbq/9G1Zn4SkQsFxGforpVrMXFwBciYoeorBQRH42IlSNi04jYLarBKd+gqlR7e95TzK3cQrYt1dWzl6hKqKEa3+tDUQ3kuQrwrc68vs7IzKeoxuY6JSJWiIgd6SAhGNUgnS2vc5lyW90WzKlSOo3qNsDLSjXTUVTjPLQMCP/f5bu2QklaHUd1Be2fJZ7xVJV/x1ESUaXDfXdp62h8qN9S3ZbwiZKsfIWqU7QR7Xf8DgX+RTWOwzblsQlVCf6B5dxfB/47qnGtWr5nO0fERTXHifLfwuxHB3FKkpYcVwLDoxqIfC2qoQxaJuyYAqzVRuXHtMx8NaqxNRdl4opLgI1qq4prXAN8qvQzVqS6ZfCdmvU/B86MargEImKtiNinPP9wqXhZhuq3cBYd90O+EtVkMGtQVexfXdovohpOYmDp6/SKiI+3unjVKZn5DtVYTZ9qY/V4qmTQGaUv9H6qyuTO9iunAO2N/9QZNzL3bWnXA++LiP1LH/AwqkTUnwFK9fTvgPOBFShDBpRKp0uB8yJijfKebRARH1mAWAYwpz+yQ2nbgzkJprlk5uPAP6j6jFeXyiwo47tGxFdLv2S5iNiqvLewgN/jiBhUvgfLUV0gfYu5v1O7UFVNSUsdE1Hqbk6jSqrUOpJqgMgXqP5xf+cinuMKquqrF6luQTsYZg+GvTvVVZxJVFcDv091339nHUj1YzmJ6sd4eGbesgD7t8yq9yrVAIdTqcYPalf5cf0UVYnzpaWDdRhVJ+AfVMmb61iw26jOBo4qnc+2zvXZctz/pEp0tKwfS/V5/aSsH1+2hep9PJOqguvZEu9JHcTwjYiYTvU5XQ7cB3ygpSy7vK9XU3XS7qNKJC5OBwM7Un3vzijnerOdbV+hei0TqBJIZwHHZOZfI2IgVZXZUS0bZ+ZfSrw/ammi6iw9T/Xd+Qjw0XLraIvbqEr/a2e8u53qfWw3EZWZf6MaH2E41WdyM1Xncj/gyoh4Xxu7DQV+ltXsf7MfVB38oeW411F9/p8rMbeMIfKHmuN8gCrhOPsRCzGLkiSp4U6m6kP8nerCzx1Uv21QXcy4HngqqtubVqMa0+nzpf/yU+YkbRZYuRXtJ8B/t7HuAeB4qn7NRKrf3dpbzc6iqjL639KHuJPqdj+oKu7/QDVuz6NUv4XXdBDKVcBoqrE6HynHJquZZ79CdTvWy1QXbg5iISuQMvORzBzXRntSjYn1Xqp+09XAiZl5e+tt2/EdqsHIX46IYxcirjupLihtXZanUM1g/F9UfaNjgY9lZu2tZ1dQVa9fVZJsLb5K1VcYS3Ur5Z+Ze/zW+cUypaYv0jL8xtQOqsGhGk/rP5hzW15LUmxvqv7JU1T97AuYc3vign6P+wCXUX0PHi/HPB8gqlm5+zPvAPTSUiEyF7WqUpK0qCLiaqpy7vnOeChJkpZe5Raw/bOarKTbioiPAwdlZke3MC6RImJ3qotqnU54Lebz/xS4LzMvbcb5pUVlIkqSmiAitqOqxnqCqlLu98CO5WqsJEnqokxELd3K8Bu/BW7LzLPmt72keXlrniQ1xzrAGKrplc+nutXOJJQkSdISKiK2Yc6Mfz9tcjjSUsuKKEmSJEmSJDWEFVGSJEmSJElqCBNRkiRJkiRJaohuN831GmuskQMGDGh2GJIkaRHdd999z2fmms2OY0liP0eSJDVLZ/tm3S4RNWDAAMaOHdvsMCRJ0iKKiKeaHcOSxn6OJElqls72zbw1T5IkSZIkSQ1hIkqSJEmSJEkNYSJKkiRJkiRJDWEiSpIkSZIkSQ1hIkqSJEmSJEkN0e1mzZMkaVG98sorPPfcc8ycObPZoXRZyy+/PGuttRa9e/dudiiSJElajExESZK0AF555RWmTJlC37596dGjBxHR7JC6nMxkxowZPPPMMwAmoyRJkroQb82TJGkBPPfcc/Tt25eePXuahKqTiKBnz5707duX5557rtnhSJIkaTEyESVJ0gKYOXMmPXr0aHYY3UKPHj28/VGSJKmLMRElSdICshKqMXyfJUmSuh4TUZIkSZIkSWoIE1GSJEmSJElqCGfNkyRpMRg8uLHnGz16wfeZOnUqw4cP58Ybb2Ty5Mn06dOHLbfckmHDhrHTTjuxzTbbsMcee/DjH/949j5Tpkxhiy224MQTT+Sb3/wmAL///e/5/ve/z7hx45g1axbrr78+O++8M5dccgmvvvoqq666KpdddhkHH3zw7OMcccQRXHrppYwdO5Ztt912dvtOO+3ERhttxOWXX77wb4YkSZKWGlZESZLUTey3337cc889jBgxgn/961/88Y9/ZK+99uKFF16gZ8+ejBw5kp///OeMGjVq9j5HHnkkm2yyCSeccAIAo0aN4oADDmCfffbhrrvu4oEHHuDss88mMwHo1asX2223HaNbZcrGjBlDv3795mp//fXXuffeexnc6CyeJEmSmsaKKEmSuoGXX36Z22+/nVtuuYUhQ4YA0L9/f7bbbrvZ2+y4444cf/zxHH744TzyyCP85je/YdSoUTz00EMsu+yyANxwww3ssMMOnHTSSbP323jjjdlnn31mLw8ePJirrrpq9vKECROYOHEiZ599NjfffPPspNZf//pXZs6cyW677VbX1y5JkqQlhxVRkiR1A7169aJXr15cf/31vPHGG+1ud9ppp7HKKqtw6KGH8rWvfY2zzz6b97znPbPXr7POOjz22GM89NBD7R5j8ODBPP7440yYMAGA0aNHs/3227PXXntx++23M2vWrNntG264If37919Mr1KSJElLOhNRkiR1A8sttxyXXXYZv/rVr+jTpw877rgjJ5xwAnffffdc262wwgr8+Mc/5oYbbmDrrbfmmGOOmWv9l7/8ZXbYYQe22WYbNthgA/bff39+/vOf8+qrr87eZqeddmKFFVaYfRve6NGj2XXXXdl4441ZeeWVue+++2a3Ww0lSZLUvZiIkiSpm9hvv/2YNGkSN9xwA3vttRd33nkngwYN4rvf/e5c240YMYKePXvy2GOP8fzzz8+1bqWVVuJPf/oT48ePZ/jw4fTp04dvfetbbLHFFkyZMgWAHj16sMMOO8xORI0ZM4Zdd90VgF122YXRo0fz6quvct9995mIkiRJ6mZMREmS1I28613v4iMf+Qgnn3wyd955J0cccQSnnHIKb731FgC/+93vuOaaa7j11lvZYIMN+MIXvtDmcTbaaCM+//nPc8kll3D//fczadIkLrjggtnrd9ttN8aMGcMTTzzB5MmT+cAHPgBUiagxY8bMvkXPgcolSZK6Fwcrl6ROWBr+rdxqkjKpU9773vcya9Ys3njjDaZNm8bRRx/NySefzMCBAxk5ciTbbrstV155JQceeGC7xxgwYAA9e/ac6/a8wYMHc+qppzJixAi23357evToAcCuu+7K8ccfz+abb85mm23GuuuuW/fXKC3J9tgDynBqTdOvH9x8c3NjkCR1HyaiJEnqBl544QUOOOAAPve5z7HVVlux8sorM3bsWM466yyGDBlC7969+dSnPsWGG27IsGHDANhyyy059dRTOfbYYxk8eDDrrLMOp5xyCq+//jp77703/fv35+WXX+b888/n1Vdf5eMf//js8w0aNIgePXpw/vnnc9xxx81u33TTTVl55ZUZMWIEhxxySMPfB2lJM2ECrLNO82OQJKlRvDVPkqRuoFevXgwaNIjzzjuPXXbZhS222IKTTjqJgw46iKuvvppf/vKX3HTTTYwcOZJll1129n4nnngim2yyCUcffTRQ3Vr3xBNPMHToUDbffHP22GMPnnzySa6//no+9KEPzd5vxRVXZMcdd2T69Omzx4dqscsuuzB9+nTHh5IkSeqGIjObHUNDDRw4MMeOHdvsMCQtZbw1Ty3GjRvH5ptv3uwwuo2O3u+IuC8zBzY4pCWa/Zylz+abN78i6tlnYdy45sYgSVr6dbZvZkWUJEmSJEmSGsJElCRJkiRJkhrCRJQkSZIkSZIawkSUJEmSJEmSGsJElCRJkiRJkhrCRJQkSZIkSZIawkSUJEmSJEmSGsJElCRJkiRJkhrCRJQkSZIkSZIawkSUJEmSJEmSGmK5ZgcgSVKXMHhwY883evRC7fbAAw8wcOBABg0axB133DHP+jFjxnDOOedw11138dprr9G/f38+8pGPcPzxxzNgwADGjBnD4MGDmTp1KmusscaivgpJkiR1M1ZESZLUjVx88cV88Ytf5NFHH2XcuHFzrbvwwgsZMmQIq6++Otdeey3jxo1jxIgRvPPOO5xxxhlNiliSJEldiRVRkiR1EzNmzOCKK67gtttu4/XXX2fEiBGcc845AEycOJGvfOUrfOlLX+L888+fvc+AAQPYaaedePnll5sVtiRJkroQK6IkSeomrrvuOvr3789WW23FoYceyuWXX87MmTMBuPbaa3nrrbcYNmxYm/v26dOnkaFKkiSpizIRJUlSN3HJJZdw6KGHArDLLrvQs2dPrr/+egD+/e9/07t3b9Zbb71mhihJkqQuzkSUJEndwPjx47njjjs46KCDAIgIDj74YC655BIAMpOIaGaIkiRJ6gYcI0qSpG7gkksu4e2336Zfv36z2zITgKeffppNNtmEadOmMWnSJKuiJEmSVDdWREmS1MXNmjWLkSNH8r3vfY8HH3xw9uOhhx5iq6224he/+AX7778/K6ywAmeeeWabx3CwckmSJC0OVkRJktTF/elPf+L555/nyCOPZPXVV59r3Wc+8xkuuOACvv3tb3Puuedy7LHHMm3aNA4//HA23HBDJk2axBVXXMEbb7zBxRdfPHu/Rx99dJ4BzLfaaiuWWcZrXJIkSWqfiShJkrq4ESNGMHjw4HmSUAAHHHAAw4YN4y9/+Qtf/OIX2XTTTfnBD37Afvvtx2uvvUb//v3Zfffd+fa3vz3XfoMHD57nWNOnT6dXr151ex2SJEla+tUtERURGwCXA+sA7wAXZeZ5EbEacDUwAHgS+HRmvhTVCKnnAXsDrwOfzcz7y7GGAi094DMyc2Rp3xa4DOgB3Agcly0DXkiS1EijRzc7gna1zIzXlne/+93U/nQOGTKEIUOGtLv9rrvuij+1kiRJWlj1rJ+fBRyfmZsDg4AvRcR7gWHAqMzcGBhVlgH2AjYuj6OACwBK4mo4sAOwPTA8IlYt+1xQtm3Zb886vh5JkiRJkiQtgrolojJzcktFU2ZOB8YBfYF9gZFls5HAJ8rzfYHLs3IX0Cci1gX2AG7JzBcz8yXgFmDPsq53Zv6tVEFdXnMsSZIkSZIkLWEaMqJoRAwA3gfcDaydmZOhSlYBa5XN+gJP1+w2sbR11D6xjXZJkiRJkiQtgeqeiIqIXsBvgK9m5isdbdpGWy5Ee1sxHBURYyNi7NSpU+cXsiRJkiRJkuqgromoiFieKgn168z8bWmeUm6ro/x9rrRPBDao2X19YNJ82tdvo30emXlRZg7MzIFrrrnmor0oSVK352DdjeH7LEmS1PXULRFVZsEbAYzLzB/WrLoeGFqeDwX+UNN+WFQGAdPKrXs3A7tHxKplkPLdgZvLuukRMaic67CaY0mSVBfLL788M2bMaHYY3cKMGTNYfvnlmx2GJEmSFqPl6njsnYBDgUci4sHSdhJwJnBNRBwBTAAOKOtuBPYGxgOvA4cDZOaLEXFlE+RZAAAgAElEQVQ6cG/Z7rTMfLE8Pwa4DOgB3FQekiTVzVprrcUzzzxD37596dGjB9W1EC1OmcmMGTN45plnWHvttZsdjiRJkhajuiWiMvOvtD2OE8CQNrZP4EvtHOtS4NI22scCWy5CmJIkLZDevXsDMGnSJGbOnNnkaLqu5ZdfnrXXXnv2+y1JkqSuoZ4VUZIkdUm9e/c2QSJJkiQthLrPmidJkiRJkiSBiShJkiRJkiQ1iIkoSZIkSZIkNYSJKEmSJEmSJDWEiShJkiRJkiQ1hIkoSZIkSZIkNYSJKEmSpHZExKUR8VxEPFrTtlpE3BIR/y5/Vy3tERHnR8T4iHg4It5fs8/Qsv2/I2JoTfu2EfFI2ef8iIiOziFJkrS0MxElSZLUvsuAPVu1DQNGZebGwKiyDLAXsHF5HAVcAFVSCRgO7ABsDwyvSSxdULZt2W/P+ZxDkiRpqWYiSpIkqR2ZeRvwYqvmfYGR5flI4BM17Zdn5S6gT0SsC+wB3JKZL2bmS8AtwJ5lXe/M/FtmJnB5q2O1dQ5JkqSlmokoSZKkBbN2Zk4GKH/XKu19gadrtptY2jpqn9hGe0fnmEdEHBURYyNi7NSpUxf6RUmSJDWCiShJkqTFI9poy4VoXyCZeVFmDszMgWuuueaC7i5JktRQJqIkSZIWzJRyWx3l73OlfSKwQc126wOT5tO+fhvtHZ1DkiRpqWYiSpIkacFcD7TMfDcU+ENN+2Fl9rxBwLRyW93NwO4RsWoZpHx34OaybnpEDCqz5R3W6lhtnUOSJGmptlyzA5AkSVpSRcSVwK7AGhExkWr2uzOBayLiCGACcEDZ/EZgb2A88DpwOEBmvhgRpwP3lu1Oy8yWAdCPoZqZrwdwU3nQwTkkSZKWaiaiJEmS2pGZB7azakgb2ybwpXaOcylwaRvtY4Et22h/oa1zSJIkLe28NU+SJEmSJEkNYSJKkiRJkiRJDWEiSpIkSZIkSQ1hIkqSJEmSJEkNYSJKkiRJkiRJDWEiSpIkSZIkSQ1hIkqSJEmSJEkNYSJKkiRJkiRJDWEiSpIkSZIkSQ1hIkqSJEmSJEkNYSJKkiRJkiRJDWEiSpIkSZIkSQ1hIkqSJEmSJEkNYSJKkiRJkiRJDWEiSpIkSZIkSQ1hIkqSJEmSJEkNYSJKkiRJkiRJDWEiSpIkSZIkSQ1hIkqSJEmSJEkNYSJKkiRJkiRJDWEiSpIkSZIkSQ1hIkqSJEmSJEkNYSJKkiRJkiRJDWEiSpIkSZIkSQ1hIkqSJEmSJEkNYSJKkiRJkiRJDWEiSpIkSZIkSQ1hIkqSJEmSJEkNYSJKkiRJkiRJDWEiSpIkSZIkSQ1hIkqSJEmSJEkNsVyzA5AkSZIkSUuIPfaACROaHQX06wc339zsKFQHJqIkSZIkSVJlwgRYZ51mR7FkJMNUF96aJ0mSJEmSpIYwESVJkiRJkqSGMBElSZIkSZKkhjARJUmSJEmSpIYwESVJkiRJkqSGcNY8aSENHtzsCOZv9OhmRyBJkiRJ0hxWREmSJEmSJKkhTERJkiRJkiSpIUxESZIkSZIkqSFMREmSJEmSJKkhTERJkiRJkiSpIUxESZIkSZIkqSFMREmSJEmSJKkhTERJkiRJkiSpIUxESZIkSZIkqSFMREmSJEmSJKkhTERJkiRJkiSpIUxESZIkSZIkqSFMREmSJEmSJKkhTERJkiRJkiSpIUxESZIkSZIkqSFMREmSJEmSJKkhTERJkiRJkiSpIUxESZIkSZIkqSFMREmSJEmSJKkhTERJkiRJkiSpIUxESZIkSZIkqSFMREmSJEmSJKkhTERJkiRJkiSpIUxESZIkSZIkqSFMREmSJEmSJKkhTERJkiRJkiSpIUxESZIkLYSI+FpE/D0iHo2IKyPiXRGxYUTcHRH/joirI2KFsu2KZXl8WT+g5jjfKu3/jIg9atr3LG3jI2JY41+hJEnS4mciSpIkaQFFRF/gK8DAzNwSWBb4DPB94NzM3Bh4CTii7HIE8FJmvgc4t2xHRLy37LcFsCfws4hYNiKWBX4K7AW8FziwbCtJkrRUMxElSZK0cJYDekTEckBPYDKwG3BdWT8S+ER5vm9ZpqwfEhFR2q/KzDcz8wlgPLB9eYzPzMcz8y3gqrKtJEnSUs1ElCRJ0gLKzGeAc4AJVAmoacB9wMuZOatsNhHoW573BZ4u+84q269e295qn/ba5xERR0XE2IgYO3Xq1EV/cZIkSXVkIkqSJGkBRcSqVBVKGwLrAStR3UbXWrbs0s66BW2ftzHzoswcmJkD11xzzfmFLkmS1FQmoiRJkhbch4EnMnNqZs4Efgt8AOhTbtUDWB+YVJ5PBDYAKOtXAV6sbW+1T3vtkiRJS7W6JaIi4tKIeC4iHq1pOyUinomIB8tj75p1CzRjTHuz0kiSJDXABGBQRPQsYz0NAf4BjAb2L9sMBf5Qnl9flinr/zczs7R/psyqtyGwMXAPcC+wcenvrEA1oPn1DXhdkiRJdVXPiqjLqGZ/ae3czNymPG6EhZ4xpr1ZaSRJkuoqM++mGnT8fuARqj7VRcA3ga9HxHiqMaBGlF1GAKuX9q8Dw8px/g5cQ5XE+jPwpcx8u4wjdSxwMzAOuKZsK0mStFRbbv6bLJzMvC0iBnRy89kzxgBPlE7a9mXd+Mx8HCAirgL2jYhxVLPSHFS2GQmcAlyweKKXJEnqWGYOB4a3an6cOX2Y2m3fAA5o5zjfAb7TRvuNwI2LHqkkSdKSoxljRB0bEQ+XW/dWLW0LOmPM6rQ/K40kSZIkSZKWQHWriGrHBcDpVLO+nA78APgc7c8M01aibIFmkoFqWmPgKIB+/fotWMSdNHhwXQ672I0e3ewIJEmSJElSd9XQiqjMnFLGPXgHuJg5pesLOmPM87Q/K01b53VaY0mSJEmSpCZraCIqItatWfwk0DKj3gLNGFNmmWlvVhpJkiRJkiQtgep2a15EXAnsCqwREROpBvPcNSK2obqN7kngaKhmjImIlhljZlFmjCnHaZkxZlng0poZY74JXBURZwAPMGdWGkmSJEmSJC2B6jlr3oFtNLebLFrQGWPKTHrzzEojSZIkSZKkJdN8b82LiE0iYlREPFqWt4qIb9c/NEmSJEmSJHUlnRkj6mLgW8BMgMx8mGqsJkmSJEmSJKnTOpOI6pmZ97Rqm1WPYCRJkiRJktR1dSYR9XxEbEQ1wDgRsT8wua5RSZIkSZIkqcvpzGDlXwIuAjaLiGeAJ4BD6hqVJEmSJEmSupz5JqLK7HQfjoiVgGUyc3r9w5IkSZIkSVJX024iKiK+3k47AJn5wzrFJEmSJEmSpC6oo4qolcvfTYHtgOvL8j7AbfUMSpIkSZIkSV1Pu4mozDwVICL+B3h/yy15EXEKcG1DopMkSZIkSVKX0ZlZ8/oBb9UsvwUMqEs0kiRJkiRJ6rI6M2veL4F7IuJ3QAKfBC6va1SSJEmSJEnqcjoza953IuLPwM6l6fDMfKC+YUmSJEmSJKmr6UxFFJl5X0Q8DbwLICL6ZeaEukYmSZIkSZKkLmW+Y0RFxMcj4t/AE8Ct5e9N9Q5MkiRJkiRJXUtnBis/HRgE/CszNwQ+DNxR16gkSZIkSZLU5XQmETUzM18AlomIZTJzNLBNneOSJEmSJElSF9OZMaJejohewG3AryPiOWBWfcOSJEmSJElSV9OZiqh9gRnA14A/A/8H7FPPoCRJkiRJktT1zLciKjNfq1kcWcdYJEmSJEmS1IW1m4iKiOlA1jaV5QAyM3vXOTZJkiRJkiR1IR1VRI0C1gF+C1yVmRMaE5IkSZIkSZK6onbHiMrMTwB7AFOBiyPi1oj4YkSs1rDoJEmSJEmS1GV0OFh5Zk7LzF8AewE/B04DPtuAuCRJkiRJktTFdDhYeUR8ADgQ+CDwV+CTmXl7IwKTJEmSJElS19LRYOVPAi8DVwFHAbNK+/sBMvP+BsQnSZIkSZKkLqKjiqgnqWbJ2wPYnWq2vBYJ7Fa/sCRJkiRJktTVtJuIysxdGxiHJEmSJEmSurgOx4iSJEmSpKXF4JGDmx0Co4eObnYIkrRE63DWPEmSJEmSJGlxMRElSZIkSZKkhphvIioidoqIlcrzQyLihxHRv/6hSZIkSZIkqSvpTEXUBcDrEbE18A3gKeDyukYlSZIkSZKkLqcziahZmZnAvsB5mXkesHJ9w5IkSZIkSVJX05lZ86ZHxLeAQ4EPRsSywPL1DUuSJEmSJEldTWcqov4TeBP4XGY+C/QFzq5rVJIkSZIkSepy5puIKsmn3wArlqbngd/VMyhJkiRJkiR1PZ2ZNe9I4DrgwtLUF/h9PYOSJEmSJElS19OZW/O+BOwEvAKQmf8G1qpnUJIkSZIkSep6OpOIejMz32pZiIjlgKxfSJIkSZIkSeqKOpOIujUiTgJ6RMRHgGuBG+obliRJkiRJkrqaziSihgFTgUeAo4EbgW/XMyhJkiRJkiR1PZ2ZNe+dzLw4Mw/IzP3Lc2/NkyRJS42I+H5n2iRJklRfy7W3IiIeoYOxoDJzq7pEJEmStPh9BPhmq7a92miTJElSHbWbiAI+Vv5+qfz9Zfl7MPB63SKStPgMHtzsCDpn9OhmRyCpi4qIY4AvAu+OiIdrVq0M3NGcqCRJkrqvdhNRmfkUQETslJk71awaFhF3AKfVOzhJkqRFdAVwE/A9qnEvW0zPzBebE5IkSVL31VFFVIuVImLnzPwrQER8AFipvmFJkhaYFXDSPDJzGjANODAilgXWpur/9IqIXpk5oakBSpIkdTOdSUQdAVwaEauU5ZeBz9UvJEmSpMUrIo4FTgGmAO+U5gQc81KSJKmB5puIysz7gK0jojcQ5cqiJEnS0uSrwKaZ+UKzA5EkSerOOpo175DM/FVEfL1VOwCZ+cM6xyZJkrS4PE11i54kSZKaqKOKqJZxoFZuRCCSJEl19DgwJiL+BLzZ0uiFNUmSpMbqaNa8C8vfU1uviwgHK5ckSUuTCeWxQnlIkiSpCTocIyoi+gLrAg9n5lsRsRbVGAufBdarf3iSJEmLrq0La5IkSWq8jsaI+irwX8B4YMWIOA/4IXA5sG1jwpMkSVp0ETGaapa8uWTmbk0IR5IkqdvqqCLqKKrZZV6MiH5UCakPZeZdjQlNkiRpsTmh5vm7gP2AWU2KRZIkqdvqKBH1Rma+CJCZEyLiXyahJEnS0igz72vVdEdE3NqUYCRJkrqxjhJR60fE+TXLa9UuZ+ZX6heWJEnNM3hwsyPonNGjmx3B0iMiVqtZXIZqmIF1mhSOJElSt9VRIurEVsutryRKkiQtLe6jGiMqqG7JewI4oqkRSZIkdUPtJqIyc2QjA5EkSaqXzNyw2TFI0tJm8Mglo0R49FBLgKWupKOKKEmSpC4hIpYHjgE+VJrGABdm5symBSVJktQNLdPsACRJkhrgAqpxoX5WHtuWtoUWEX0i4rqIeCwixkXEjhGxWkTcEhH/Ln9XLdtGRJwfEeMj4uGIeH/NcYaW7f8dEUNr2reNiEfKPudHRCxKvJIkSUsCE1GSJKk72C4zh2bm/5bH4cB2i3jM84A/Z+ZmwNbAOGAYMCozNwZGlWWAvYCNy+MoShKsDKI+HNgB2B4Y3pK8KtscVbPfnosYryRJUtPNNxEVEZtExKiIeLQsbxUR365/aJIkSYvN2xGxUctCRLwbeHthDxYRvalu8xsBkJlvZebLwL5AyzibI4FPlOf7Apdn5S6gT0SsC+wB3JKZL2bmS8AtwJ5lXe/M/FtmJnB5zbEkSZKWWp0ZI+piqhn0LgTIzIcj4grgjHoGJkmStBidCIyOiMepZs7rDxy+CMd7NzAV+EVEbE01K99xwNqZORkgMydHxFpl+77A0zX7TyxtHbVPbKNdUhfgIOCSurPOJKJ6ZuY9rYYlmFWneCRJkha7zBwVERsDm1Iloh7LzDcX4ZDLAe8HvpyZd0fEecy5Da8tbY3vlAvRPu+BI46iuoWPfv36dRSzJElS03VmjKjnSyl7AkTE/sDkukYlSZK0GETEIRFxKEBmvpmZD2fmQ8BhEXHQIhx6IjAxM+8uy9dRJaamlNvqKH+fq9l+g5r91wcmzad9/Tba55GZF2XmwMwcuOaaay7CS5IkSaq/ziSivkR1W95mEfEM8FWq6Y8lSZKWdMcDv2+j/eqybqFk5rPA0xGxaWkaAvwDuB5omfluKPCH8vx6quRXRMQgYFq5he9mYPeIWLUMUr47cHNZNz0iBpXZ8g6rOZYkSdJSqzO35j2TmR+OiJWAZTJzepnhRZIkaUm3bGZOb92Yma9ExPKLeOwvA7+OiBWAx6nGnFoGuCYijgAmAAeUbW8E9gbGA6+XbcnMFyPidODest1pmflieX4McBnQA7ipPCRJkpZqnUlE/TYi9s3M1wAiYh3gT8C2dY1MkiRp0S0fESu19GNaRMTKwAqLcuDMfBAY2MaqIW1sm1RV5m0d51Lg0jbaxwJbLkqMkiRJS5rO3Jr3e+C6iFg2IgYA/wN8q55BSZIkLSYjqPoxA1oayvOryjpJkiQ10HwrojLz4lJy/ntgAHB0Zt5Z78AkSZIWVWaeExGvArdGRC+qyVdeA87MzAuaG50kSVL3024iKiK+XrtINaPLg8CgiBiUmT+sd3CSJEmLKjN/Dvy8JKKirTGjJEmS1BgdVUSt3Gr5d+20S5IkLfEy89VmxyBJ6r4Gjxzc7BAAGD10dLNDUDfXbiIqM09tZCCSJEmSJEnq2jq6Ne9HmfnViLiBajyFuWTmx+samSRJkiRJkrqUjm7N+2X5e04jApEkSaqXiOgJHA/0y8wjI2JjYNPM/GOTQ5MkSepWOro1777y99bGhSNJklQXvwDuA3YsyxOBawETUZIkSQ20THsrImLjiLgsIn4YEetHxE0R8WpEPBQRAxsZpCRJ0iLaKDPPAmYCZOYMqlmBJUmS1EDtJqKorhzeCUwC7gYuBdYATgB+Wv/QJEmSFpu3IqIHZdzLiNgIeLO5IUmSJHU/HSWiemXmRZl5DjAjM6/NzDcy8xZgxQbFJ0mStDgMB/4MbBARvwZGAd9obkiSJEndT0eDlb9T8/yVDtZJkiQt0TLzloi4HxhEdUvecZn5fJPDkiRJ6nY6SkRtFhEPU3XWNirPKcvvrntkkiRJiygi3t+qaXL52y8i+mXm/Y2OSZIkqTvrKBG1ecOikCRJqo8flL/vAgYCD1FdVNuKagzMnZsUlyRJUrfUbiIqM59qZCCSJEmLW2YOBoiIq4CjMvORsrwl1QQskiRJaqCOBiuXJEnqKjZrSUIBZOajwDZNjEeSJKlb6ujWPEmSpK5iXERcAvwKSOAQYFxzQ5IkSep+2q2IiohR5e/3GxeOJElSXRwO/B04Dvgq8I/SJkmSpAbqqCJq3YjYBfh4GVchalc6y4wkSVpaZOYbwLnlIUmSpCbpKBF1MjAMWB/4Yat1CexWr6AkSZIWh4i4JjM/HRGPUPVf5pKZWzUhLGmpM3jk4GaHwOiho5sdgiRpMeho1rzrgOsi4r8z8/QFPXBEXAp8DHguM7csbasBVwMDgCeBT2fmSxERwHnA3sDrwGdbKq4iYijw7XLYMzJzZGnfFrgM6AHcCByXmfN0MCVJUrd2XPn7saZGIUmSJKATs+Zl5ukR8fGIOKc8OtuRuwzYs1XbMGBUZm4MjCrLAHsBG5fHUcAFMDtxNRzYAdgeGB4Rq5Z9LijbtuzX+lySJKmby8zJ5e9TtQ9gIrBzc6OTJEnqfuabiIqI71FdTfxHeRxX2jqUmbcBL7Zq3hcYWZ6PBD5R0355Vu4C+kTEusAewC2Z+WJmvgTcAuxZ1vXOzL+VKqjLa44lSZIEQET0johvRcRPImL3qHwZeBz4dLPjkyRJ6m46GiOqxUeBbTLzHYCIGAk8AHxrIc63ds2VyckRsVZp7ws8XbPdxNLWUfvENtolSZJq/RJ4Cfgb8HngRGAFYN/MfLCZgUmSpIW3JIxdB45ftzA6k4gC6MOc6qZV6hBHtNGWC9He9sEjjqK6jY9+/fotTHySJGnp9O7M/A+AiLgEeB7ol5nTmxuWJElS99SZRNT3gAciYjRVAuhDLFw1FMCUiFi3VEOtCzxX2icCG9Rstz4wqbTv2qp9TGlfv43t25SZFwEXAQwcOLB7D2g+eMnIGndotBllSdJiM7PlSWa+HRFPmISSJElqns4MVn4lMAj4bXnsmJlXLeT5rgeGludDgT/UtB9Wxm0YBEwrt/DdDOweEauWQcp3B24u66ZHxKAy495hNceSJElqsXVEvFIe04GtWp5HxCvNDk6SJKm76dSteSXxc/2CHDgirqSqZlojIiZSzX53JnBNRBwBTAAOKJvfCOwNjAdeBw4v530xIk4H7i3bnZaZLbcIHkM1M18P4KbykCRJmi3z/9m77zDJqmoN4++aIUhOIpKTGAgqSFARJEgSlCAoCIqo4EVQucoVRBHBrGCOgAgqiogKqEiQJAoiGSQHEUaQLCBIGFj3j72LOQzdPT0z3edU97y/55mnq06dqVlVp7qmzld7r50Tu65BkiRJUwy3R9R0y8ydBrlp4wH2TWCvQe7nKOCoAbZfDKw6MzVKkiRJkiSpPdOcmidJkiRJkiSNhCGDqIiYEBF/a6sYSZIkSZIkjV9DBlGZ+TRwRUQs01I9kiRJkiRJGqeG0yNqceDqiPgr8EhvY2a+edSqkiRJkiRJ0rgznCDq4FGvQpIkSZIkSePeNIOozDw3IpYFVsrMP0TE3IBLIUuSJEmSJGm6TDOIiojdgT2AhYEVgSWB7wEbj25pkiRJkiQNbcNjNuy6BADO3vXsrkuQxoQhm5VXewHrAg8BZOaNwAtGsyhJkiRJkiSNP8MJoh7PzCd6VyJiNiBHryRJkiRJkiSNR8NpVn5uRBwAzBURmwDvB34zumVJkqRp2rA/piIM6WynKUiSJGmK4YyI2h+4B7gKeB9wCvCJ0SxKkiRJkiRJ489wVs17OiKOAS6kTMm7PjOdmidJkiRJkqTpMpxV87akrJJ3MxDA8hHxvsz8/WgXJ0mSJEmSpPFjOD2iDgM2zMybACJiReB3gEGUJEmSJEmShm04PaLu7oVQ1S3A3aNUjyRJkiRJksapQUdERcR29eLVEXEKcDylR9QOwEUt1CZJkiRJkqRxZKipeW9qXL4LeH29fA+w0KhVJEmSJEmSpHFp0CAqM3drsxBJkiRJkiSNb8NZNW954APAcs39M/PNo1eWJEmSJEmSxpvhrJp3IvAD4DfA06NbjiRJkiRJksar4QRRj2XmN0a9EkmSJEmSJI1rwwmivh4RBwGnA4/3NmbmpaNWlSRJkiRJksad4QRRqwHvADZiytS8rNclSZIkSZKkYRlOELUtsEJmPjHaxUiSJEmSJGn8mjCMfa4AFhztQiRJkiRJkjS+DWdE1GLAdRFxEc/uEfXmUatKkiRJkiRJ485wgqiDRr0KSZIkjQkbHrNh1yVw9q5nd12CJEmaQdMMojLz3DYKkSRJkiRJ0vg2zSAqIh6mrJIHMAcwO/BIZs4/moVJkiRJkiRpfBnOiKj5mtcjYhtg7VGrSJIkSZIkSePScFbNe5bMPBHYaBRqkSRJkiRJ0jg2nKl52zWuTgDWZMpUPUmSJEmSJGlYhrNq3psalycDtwJbj0o1kiRJ0ghwdT9JkvrTcHpE7dZGIZIkSZIkSRrfBg2iIuKTQ/y9zMxPj0I9kiRJkiRJGqeGGhH1yADb5gHeAywCGERJkiRJkiRp2AZdNS8zD+v9AQ4H5gJ2A44DVmipPkmSpL4VERMj4rKI+G29vnxEXBgRN0bEzyNijrp9znr9pnr7co37+Fjdfn1EbNbYvnnddlNE7N/2Y5MkSRoNgwZRABGxcER8BriSMnpqjczcLzPvbqU6SZKk/vYh4NrG9S8CX83MlYAHKCPJqT8fyMwXAV+t+xERKwM7AqsAmwPfqeHWRODbwBbAysBOdV9JkqQxbdAgKiK+DFwEPAyslpmfyswHWqtMkiSpj0XEUsCWwJH1egAbASfUXY4BtqmXt67XqbdvXPffGjguMx/PzL8DNwFr1z83ZeYtmfkEZUS6qxZLkqQxb6gRUR8BlgA+AdwREQ/VPw9HxEPtlCdJktS3vgZ8FHi6Xl8E+HdmTq7XJwFL1stLArcD1NsfrPs/s32qvzPYdkmSpDFt0GblmTnktD1JkqRZVURsBdydmZdExAa9zQPsmtO4bbDtA30OywG2ERF7AHsALLPMMkNULUmS1D3DJkmSpOm3LvDmiLiVMm1uI8oIqQUjovdF31LAHfXyJGBpgHr7AsD9ze1T/Z3Btj9HZh6emWtm5pqLLrrozD8ySZKkUWQQJUmSNJ0y82OZuVRmLkdpNn5WZu4MnA1sX3fbFTipXj65XqfeflZmZt2+Y11Vb3lgJeCvlD6dK9VV+Oao/8bJLTw0SZKkUTXo1DxJkiRNt/2A4+qqw5cBP6jbfwD8OCJuooyE2hEgM6+OiOOBa4DJwF6Z+RRAROwNnAZMBI7KzKtbfSSSJEmjwCBKkiRpJmTmOcA59fItlBXvpt7nMWCHQf7+Z4HPDrD9FOCUESxVkiSpc07NkyRJkiRJUisMoiRJkiRJktQKgyhJkiRJkiS1wiBKkiRJkiRJrTCIkiRJkiRJUisMoiRJkiRJktQKgyhJkiRJkiS1wiBKkiRJkiRJrTCIkiRJkiRJUisMoiRJkiRJktQKgyhJkiRJkiS1wiBKkiRJkiRJrTCIkiRJkiRJUisMoiRJkiRJktQKgyhJkiRJkiS1wiBKkiRJkiRJrTCIkiRJkiRJUisMoiRJkiRJktQKgyhJkiRJkiS1wiBKkiRJkiRJrTCIkiRJkiRJUisMoiRJkiRJktQKgyhJkiRJkiS1wiBKkiRJkiRJrTCIkiRJkiRJUisMoiRJkiRJktQKgyhJkiRJkiS1wiBKksu4W+0AACAASURBVCRJkiRJrTCIkiRJkiRJUisMoiRJkiRJktQKgyhJkiRJkiS1wiBKkiRJkiRJrTCIkiRJkiRJUisMoiRJkiRJktQKgyhJkiRJkiS1wiBKkiRJkiRJrTCIkiRJkiRJUisMoiRJkiRJktQKgyhJkiRJkiS1wiBKkiRJkiRJrTCIkiRJkiRJUisMoiRJkiRJktQKgyhJkiRJkiS1opMgKiJujYirIuLyiLi4bls4Is6IiBvrz4Xq9oiIb0TETRFxZUSs0bifXev+N0bErl08FkmSJEmSJA1PlyOiNszMV2bmmvX6/sCZmbkScGa9DrAFsFL9swfwXSjBFXAQsA6wNnBQL7ySJEmSJElS/+mnqXlbA8fUy8cA2zS2/yiLvwALRsTiwGbAGZl5f2Y+AJwBbN520ZIkSZIkSRqe2Tr6dxM4PSIS+H5mHg4slpl3AmTmnRHxgrrvksDtjb87qW4bbPtzRMQelNFULLPMMiP5OCRJkiRJkga04TEbdl0CAGfvenbXJTyjqyBq3cy8o4ZNZ0TEdUPsGwNsyyG2P3djCboOB1hzzTUH3EeSJEmSJEmjq5OpeZl5R/15N/BrSo+nu+qUO+rPu+vuk4ClG399KeCOIbZLkiRJkiSpD7UeREXEPBExX+8ysCnwN+BkoLfy3a7ASfXyycA76+p5rwYerFP4TgM2jYiFapPyTes2SZIkSZIk9aEupuYtBvw6Inr//k8z89SIuAg4PiLeA9wG7FD3PwV4I3AT8CiwG0Bm3h8RnwYuqvsdkpn3t/cwJEmSJEmSND1aD6Iy8xbgFQNsvw/YeIDtCew1yH0dBRw10jVKkiRJkiRp5HXSI0qSJEmSJEmzHoMoSZIkSZIktcIgSpIkSZIkSa0wiJIkSZIkSVIrDKIkSZIkSZLUCoMoSZIkSZIktcIgSpIkSZIkSa0wiJIkSZIkSVIrDKIkSZIkSZLUCoMoSZIkSZIktcIgSpIkSZIkSa0wiJIkSZIkSVIrDKIkSZIkSZLUCoMoSZKk6RQRS0fE2RFxbURcHREfqtsXjogzIuLG+nOhuj0i4hsRcVNEXBkRazTua9e6/40RsWtj+6si4qr6d74REdH+I5UkSRpZBlGSJEnTbzLwkcx8GfBqYK+IWBnYHzgzM1cCzqzXAbYAVqp/9gC+CyW4Ag4C1gHWBg7qhVd1nz0af2/zFh6XJEnSqDKIkiRJmk6ZeWdmXlovPwxcCywJbA0cU3c7BtimXt4a+FEWfwEWjIjFgc2AMzLz/sx8ADgD2LzeNn9mXpCZCfyocV+SJEljlkGUJEnSTIiI5YDVgQuBxTLzTihhFfCCutuSwO2Nvzapbhtq+6QBtkuSJI1pBlGSJEkzKCLmBX4J7JOZDw216wDbcga2D1TDHhFxcURcfM8990yrZEmSpE4ZREmSJM2AiJidEkIdm5m/qpvvqtPqqD/vrtsnAUs3/vpSwB3T2L7UANufIzMPz8w1M3PNRRdddOYelCRJ0igziJIkSZpOdQW7HwDXZuZXGjedDPRWvtsVOKmx/Z119bxXAw/WqXunAZtGxEK1SfmmwGn1tocj4tX133pn474kSZLGrNm6LkCSJGkMWhd4B3BVRFxetx0AfAE4PiLeA9wG7FBvOwV4I3AT8CiwG0Bm3h8RnwYuqvsdkpn318t7AkcDcwG/r38kSZLGNIMoSZKk6ZSZf2LgPk4AGw+wfwJ7DXJfRwFHDbD9YmDVmShTkiSp7zg1T5IkSZIkSa0wiJIkSZIkSVIrDKIkSZIkSZLUCoMoSZIkSZIktcIgSpIkSZIkSa0wiJIkSZIkSVIrDKIkSZIkSZLUCoMoSZIkSZIktcIgSpIkSZIkSa0wiJIkSZIkSVIrDKIkSZIkSZLUCoMoSZIkSZIktcIgSpIkSZIkSa0wiJIkSZIkSVIrDKIkSZIkSZLUCoMoSZIkSZIktcIgSpIkSZIkSa0wiJIkSZIkSVIrDKIkSZIkSZLUCoMoSZIkSZIktcIgSpIkSZIkSa0wiJIkSZIkSVIrDKIkSZIkSZLUCoMoSZIkSZIktcIgSpIkSZIkSa0wiJIkSZIkSVIrDKIkSZIkSZLUCoMoSZIkSZIktcIgSpIkSZIkSa0wiJIkSZIkSVIrDKIkSZIkSZLUCoMoSZIkSZIktcIgSpIkSZIkSa0wiJIkSZIkSVIrDKIkSZIkSZLUCoMoSZIkSZIktcIgSpIkSZIkSa0wiJIkSZIkSVIrDKIkSZIkSZLUCoMoSZIkSZIktcIgSpIkSZIkSa0wiJIkSZIkSVIrDKIkSZIkSZLUCoMoSZIkSZIktcIgSpIkSZIkSa0wiJIkSZIkSVIrDKIkSZIkSZLUCoMoSZIkSZIktcIgSpIkSZIkSa0wiJIkSZIkSVIrDKIkSZIkSZLUCoMoSZIkSZIktcIgSpIkSZIkSa0wiJIkSZIkSVIrDKIkSZIkSZLUCoMoSZIkSZIktcIgSpIkSZIkSa0wiJIkSZIkSVIrDKIkSZIkSZLUCoMoSZIkSZIktcIgSpIkSZIkSa0wiJIkSZIkSVIrDKIkSZIkSZLUCoMoSZIkSZIktcIgSpIkSZIkSa0Y80FURGweEddHxE0RsX/X9UiSJI0UP+dIkqTxZkwHURExEfg2sAWwMrBTRKzcbVWSJEkzz885kiRpPBrTQRSwNnBTZt6SmU8AxwFbd1yTJEnSSPBzjiRJGnfGehC1JHB74/qkuk2SJGms83OOJEkadyIzu65hhkXEDsBmmfneev0dwNqZ+YGp9tsD2KNefQlwfauFjn/PB+7tugi1xuM9a/F4z1rG2vFeNjMX7bqI0TKOP+eMpdfZWKnVOkeWdY4s6xxZ1jmyrHNkDeuz2WxtVDKKJgFLN64vBdwx9U6ZeThweFtFzWoi4uLMXLPrOtQOj/esxeM9a/F4951x+TlnLL3Oxkqt1jmyrHNkWefIss6RZZ3dGOtT8y4CVoqI5SNiDmBH4OSOa5IkSRoJfs6RJEnjzpgeEZWZkyNib+A0YCJwVGZe3XFZkiRJM83POZIkaTwa00EUQGaeApzSdR2zuDEzHUAjwuM9a/F4z1o83n1mnH7OGUuvs7FSq3WOLOscWdY5sqxzZFlnB8Z0s3JJkiRJkiSNHWO9R5QkSZLGqIiIrmuQJEntMojSc0TE0hFxdkRcGxFXR8SH6vaFI+KMiLix/lyobn9pRFwQEY9HxL6N+3lJRFze+PNQROzT1ePSwEbqeNfbFoyIEyLiunp/r+niMWlwI3y8b42Iq+rv98VdPB5N20ge83r7xIi4LCJ+2/Zj0fiTDs2XJGmW49Q8PUdELA4snpmXRsR8wCXANsC7gPsz8wsRsT+wUGbuFxEvAJat+zyQmYcOcJ8TgX8C62TmP9p6LJq2kTzeEXEMcF5mHllXeJo7M//d9mPS4Eb4eN8KrJmZ97b9ODR8I/2eHhEfBtYE5s/Mrdp8LBofIiIyM+uXFddl5gO9bV3XptHjMZ51NH7HtwImZuZJXdc0vXy9jpyx+FzW85iJmfnfrmsZylipcyCOiNJzZOadmXlpvfwwcC2wJLA1cEzd7RjKSQqZeXdmXgQ8OcTdbgzcbAjVf0bqeEfE/MD6wA/qfk8YQvWfUfr9Vh8byWMeEUsBWwJHtlC6xqnGCclWwO4RMWGsnKRExDz1p5+hhyEiFouIt0XEnM1j3O9TMiPi+V3XMJDe6y4i5o6I10fE+hGxSOP2vnheG8d6dmCniNi4d1u/1DiQZm1j6D0p6s/FIuJ9XdfT1Hif3DYi3txpMcPUqHkn4N11W9+9ZsdKnUPxP1ENKSKWA1YHLgQWy8w7oZzYAC+YjrvaEfjZSNenkTWTx3sF4B7gh3XazpG9D+zqTyPw+53A6RFxSUTsMVp1auSMwDH/GvBR4OlRKlGzlt8AGwFHRMQyXRczlMYH/F0i4mWZ2Te/A40T0Yk1LO4nKwKfBy6JiO9FxLrQ3yf5Ndg5ISJ2qiP6++kEr/e8fZfy5cE5wGsBImKuPnxeT6HUuF9E7Ar9fezrKK6IiF9ExJy97X10/AfSq207YAl45r2g85ob75M7AjdB3z+XzZpfDVxWt/Xda3as1DkUgygNKiLmBX4J7JOZD83E/cwBvBn4xUjVppE3Asd7NmAN4LuZuTrwCLD/CJaoETRCv9/rZuYawBbAXhGx/ogVqBE3s8e8TrG4OzMvGfHiNEvKzL9k5uaUE5T3R8QKXdc0mHqCOpEymvC3EfHexuiUTk+sam0LAd8Gjo6IiyPiUxGxbJd11drOz8wVgB2Ah4HDI+LSiPhkRLys4/Keo04huo/yXL4J2BT64wSvMd1tKeBlmfm/wMXA+RExF3B8RKzYbZXPGrU1b2Y+npnfA74ArB8RH6gj6PtuVGFErBoRuwOHAE9k5uONm2evU9v7TiOQeCEwVw0kn+r6NdsIyBcBHgQ2iYiFuq5rOKK0KJgf2DsiVuvXL9bHSp2D6as3APWPiJidcsJybGb+qm6+K0qvkV7PkbuHeXdbAJdm5l0jX6lGwggd70nApMy8sF4/gRJMqc+M1O93Zt5Rf94N/BpYe3Qq1swaoWO+LvDmKL3BjgM2ioifjFLJGsciYkJEvCciDo6IzYA5KSPtDo+I1Toub1D15O6TwFuBlwOb1O2dnVjVL/sAdqWMhtgBOABYHvhTRPyuw9pWjYjdImI24B+Z+X+ZuQrw4VrfXyLiFV3VN5DesczMX1BGgB4SEQdGxILwTM/TTmsD1gL+EBHbAXfW4GxhSi/Am7uqr6cRjHw8Is6PiM9Rfme2Bf4H2HOq/frFv4DlgA8Ba0fEhyLiJfW2vSgj+/pSlKmkSwAbAgdGxI4RsUr9v78Tjdfr6sAylGB314jYJCJe1Bxx1ocWBu4A5gDeR6l744hYotuynmOs1Dkggyg9R02wfwBcm5lfadx0MuWDDvXncBsP7oTT8vrWSB3vzPwXcHvjP+2NgWtGuFzNpJE63hExT+/bwfoNzKbA30a+Ys2sEfwd/1hmLpWZy1GG2Z+VmbuMQskapxojINajTAWdj3KCej2wL3Ap8O2IWKubCocWEUtGxHJ1VODfgR9FxCER8bwOy9o3Il4HPA84KjMfyMzTM3NXYDXK6I6uPEoZsfMGyjTu/4uINTLznMzcjbJAwhUd1veMxuiNBSLilRHxDuA/wNeBvSkBCpn5VHdVPuMk4CHgIOCndcTJgcAfoNuwrP6OLBYRLwT+AuxH+WzwDUpw+xFgjToibrau6hxIZt6bmR+nvBe9n/Lly+8j4jTKzI4ToC9HckWWRWP+F3gvZeTh2pTwrPP/ozPzD5TekvsDSwE7A58AVu6yrqFk5nXAJ4G3AecBK1Feu6t0WdfUxkqdg3HVPD1H/UBzHnAVU/qAHEDpKXI8JdW+DdghM++v/9lcTBka+DTlP+6VM/OhiJgbuB1YITMfbPeRaDhG+Hi/ktLEeA7gFmC3zHygzcejoY3U8QaeTxkFBWVa5k8z87NtPQ4N30j+jjfucwNg33TVPE2HxtSi04BDM/OMAfY5hDKyY/f2K3yuRs1LU07sXk4J0H5FCWSfoPx+HZKZj7Vc2wLAlyiji+amnIAeDFwHPNwnoQkRsQ6lxo2AFwP/oISOP87M+7usbWoR8SFKnX+njDz6HWWkydqUUaM798Pn2YhYlTJyZ3XgVspzelhm3tF7zXZU1/cpx/pk4C+ZefEA+6wHfK+OjusLETExM5+qIzQnZ+aZve3A6yhT9S7otMgh1M/fuwDnUvpyrUB5zf49M89v+zURZQGKp+vInNcC7wC+lZlnRJlauiHwm+yjRY0aNa9KWcyl94Xbh+rtywN3Zeaj1jkyDKIkSZI0qhqBziLANzJz58Zts1E+kz4ZEVsDc2bm8Z0V29D40H8QMBk4FHgRpWn0dcBLgM8BZ2Tmd7qqkTJ6Y0fKFwW3AhfUmv7eUU29470q8PnMfFPdvghldMkOwPsy8/Yu6htKL5Sol+fvhfAR8XPg15l5XAc19V6HS1PCp4nAaZQvgmbrl0CvBjebUaZhvQy4EziLEtbeQekfuhDly40/dlXnYCLiSuBdmXlpROxFCSMPy8yrOi7tORqviS2Bd1G+UHpLZi4XEYtSwrNOQtNGbUdTvtjamBKKfTgi1gRuyJnoPzwaGmHkUZTX67LAgpm5T0S8FbhloGC1bWOlzuHoq6GFkiRJGn8a38a/CtgsIk6oJyRk5uTMfLJePqlfQih4Vh+bNYCvZWm+fHVmXpOZT2fmtZQRCKu3XVtjitAHKaNP9qIEPOcC21PCqa70poetTFk1DYDMvC8zf5iZb+yXECqmNNdeKsoS89+IiLdExBxTnSz/kxI+tq7xOjyNMtVxa+D3wBeBrSJi9q6njPVOkDPzlMzckxJGnQhsABwBfKbefm8/hVCNaZmrAf+uIdR7Kc/xRcC7Ykovtn60PfB94ErKKGcoU7U+2VVBNYSaQJke/D1gLuDn9eYP0+1704BquBOUmo+m1NhbaGt7yhcQnRsrdQ5HX83NlSRJ0rh2HaXfzkaUprqPUKaNfr9fRnX0NEb1rAwsQpmic9rU+wBnUKbqtVlbb8TBhsDmmfm1GgTcSzk5ObrLYCIzJ9eLBwEvqyN5Pp2lsXa/OhT4K+VE7g3A5yPiYuBLmXk5JUhp/TXaeB2uBVyTmR+M0jx9aeD1lMDkxK5HmDRGke1LCR7+DJyamT+PiOUo/WueNeKsHzRC8tmBSVEa/D9I6Z24LPD2zHyiq/oG0wh7HqGMNvs40JvSvCElBHzmvaKDEhelvF+uDTydmRdG6af3ykad/WYCcCyl31Zm5p/r6LKXAL/ptLJnGyt1DskgSpIkSW25IzNPAE6IiGUp4c4mlB5HfRVENU5Qe6tKfjIiNgIuAy7IzH/UfW7torz68y3UE07KKKSn6qieVTPzcx3UBUw5+c3MVWqfun2AmyPiJkp/oCO7qm1q9YR+bspUsrdTRhVsRHlu30s5/pd3GJQG5XivC9wUEXPW3jr/jojrgB91HUL11GDkX5TRGm8Htq7H/CJqM/V+CqGa6kioIylB5PmZeWdEfJHaD7PfAjR45rV7FKU/63LAg3U019LUhaI6CqHIzLsiYhJwOuV1uw7lNXFhZj7SRU3TUkcb/QX4CrBaRHwdmBc4r59qHit1Tos9oiRJkjRqGj0t3kwZwbEB5STpJ1lWXO1rUZZAn0D5Zn9tSoP/JYFPZWanq4VGxO6U0Tv7A7dn5uSI+BlwWmYe3VFNvRE8L6SsjjgPZSTcv4E9KSHZ+7tsqj21iHgtZfXXIyh9oNau9e+Xmf/bbXXPBDw/pzR+vowSPPw+Mx/vtLBqoJAmIlYBtqKsQPeLzNy3k+KGMNVos02An/X6qkXE4sC7gW9n5r/76fU6tRrqf5SyIulvgXMz87QOR0P16poALEZ5HjejvG7PysxJXdU0lDoF88WUabiLU0Ybngtclpn/7bK2prFS57QYREmSJGnURcS1lEbVJwL/paxAdwOwXWbe0GVtU2tMfZsfWJWyCtXvMvOBOgVibeCULk5MG7UtAjwJfJbSFPpByrLdq1Km63W2alKdgnMiMCdl6uXclObpP2/s0/mJfUQsmpn31MsLUmaLHEIZwbMWsHBm7tjVCX0jKHleZj4WZaXEdwA7UU5AT8nMvduuazAR8QnKiJczGtsOBO7MzCO7DkYGExGvAT5DaaR+C2V602nAQ13+Hg2k8ZpYgDJqbzvK7/5Xs+Mm1bWv2hMR8QbKqMJXUVYW/V5m/q7L2gYTEbNnWShjF2AbynvAiyhTSz/Re3/o2lipc3rYrFySJEmjotEIeEvgb5Tpd//OzDUpvaLuBPrxA3QvIDmGMsrgQODyiDiR0n/l1A5DlN6/+wPKCJkvU6blLQRcC+zd1clzoy/VtsBtmbkh8HXK9Jz/iYjNe/v2QQj1auD8iPhKRGxXS7qXEqC9idJ351Mdlth8jvaugc4awA8zc11gS8ooiN5qdZ2IiPUiYvs6cvB+YIeI+HVEfLKONNsO+FPdvS9HQGTmBZm5MWVU1DmUUOpKYPku6xpI4zXxQcriBF8GLgF+GBGHdVYY0OildTDwMGWU1jHA+yJi+84KG0LWhTKAnSnN3nehvOffRxnN98z/Y10aK3VOD4MoSZIkjYrGSdNkyrSndZgSPD0A/LmOMuqbD9CNEQdLAy+kBCkPU6aWTAYOB9bsqr5a23z16umZeVtmfprSVPubmXllV7UxJWhYF7gZIDNvzrIS4gWUqZn9csK0HKUJ/YqUY/vjiDgU+FdmbpeZn8nM66C7PjvwrL5L81JOPj8XER8E5s3MX9T6uuxdtDjwHco0oU0pQc7XgUcpQd6JmXldP4yAa4opqyW+OCI2ioiFsqzq+C1gfeBUSrDbVxph7uzAJzPz3Mz8CmWU5ooRsX5Hdb0mIvasUxr/mpmfz8wrsvQEPBT4YES8oIvaBhMRG0fEZRHxbuB64A+Z+R/KNOLDgB0j4kVdv27HSp3Ty2blkiRJGlWZeRpARCwEvDkivgBsTjlBgSkNmTvX+DD/ekq/lZdSpuhcFxFfBS7KzAs7K7BYhTJd8NTaqPb8zHyg45p6Idk8lKl4B0bEq4CTKCHU0pSTJihfhnfa+Dkzj6vNlN9BCRx+QhlhdmA9YT4kM8/sqr5e36Uagv2kbuv1XfoIZcn2i7qqr6eGjMdHxIspIzO+VG86GtglM+/uqrahNMLFLSjThNeN0lj9qrrtvjoFtm8CtPocHxERD1KCvgUi4srMfDwz/1t7Rd1X92277rkor8vPAhkR92XmZ+pt9wAT++m1UMPwPwG/BN5H+XJh/YjYA7iUsmLi1Zl5U3dVjp06Z4Q9oiRJkjTiGiOLtqGMOPlaZl5fT6Y3okzVO6dfTvKmVkcdLUBpTL4HZZTHB4AHMvOjXfa7iYiXACtTTkoWBp6mrOx3TGbe2kVNta5nehpRlml/EWWa2xbA74BvZub5XdU3tToq5rWUE7zrgW9RRh69mvLavLfrIKLf+y7V5zCbz1FEbECZOrYecB6wR5322HciYi5KL6PXAktQmmsvDfxfZl7Q9fM7kIhYnTIt702UfnsXU4LdGzLzmx3X9nzgXZSp14sBVwC3AUdm5lkdljakGuLtTal9HsqI3Xf3vkTpF2OlzuEwiJIkSdKoqSdNH6UEE/cB36Q0WX64307yGkHKosCCmXlj3X4wJZz4J/DZzLy569pjymqEKwCvoUzNOTQzb++ono9Rpi6uDFxI6Vv1IuBWSlPleykjpc7KzC8NcjetiIi1Kf2MJlOCxsmUsPEeSj+wpzo+tutRTuJPAnan/O4sSlkx7w/At4Gd+m3KWwy8et6XgJsy8/COynqOmNLw/62U0VA/zcxroixOMD9wV6MnT98Y5PldH3gbNfzJzB+3/ZpovG/2ntfZsqzguQIlkHwnsEpm3tlWTdPSqHkBYD7g4cx8sN62OrAXcGDXNY+VOmeEQZQkSZJGXR15sC3wcUp/nrUy85pOixpEPUH9OHANZSTPzylTSx7rsKZe8PRW4GWUqY0XAidk5p+6DCQiYg3KqIwjKD2C1qKM0DoAOIUSRh1JCXwey8x/dVEnQEQsTAnF7qOMcrufsprjBMq0rK9mx8vL12P8rXr1fEpz4jsoI+A2pfRWO7ifQqimOp1oYg0j9gMmZ2anjbQHEhEHAG+nBMz/oayUd1Y/TnNqhDxrAO+mTM27jbJq2uWUljuRZdW6toOoXm07U1Z0ewx4HDgpM3/TVh3To/F++jnKe+ndlOfxCsoIxFs6LbAaK3XOCIMoSZIkjajGickSwJOU6QNP1W9230T5dvwL3VY5uNrnaFnKKKiNgKWAG4HPZOY/Oq7tMspUssMoS82/hjJFY6fM/GNHNc1OWc1pP8pS7V+PiPcD62fmjhExV2b+t4vaphZlhbltgQ9Rem19GfhKZj7eaWEDaPRd6q04djTwjV6vnX4NoprqSI7H+uX5bbw3vQo4iNLT6AnKyKgPArcDZ2fm1zss8zkadR9LeT+9HngBsCClx95vM/PUDuubSFlp8COUEGoB4D3AEZl5cj++Vuu00ospqyUuD2wMrEQZffipzLysw/KeMVbqnF42K5ckSdKIakxr+jJlOtapwB8j4h/AhpRvdfvqRLpZS2Y+AlwTETdQav8/ShjVSUPwxknoBsAkysideTJz14jYhbLK19Vd1AbPLC1+dERcQlkd662UkSYH110e75djXac2nQCcEBGLUE6Wb4uIa4FjM/OIrmtt9F26AdgH2KfRd+nqiOjrvktNvWlE/aLx3rQlpclzb+GBy2potjjwqojYqJ96GtXf/4mU96D9MvOROrpvNcoqlf+CTt9T1wf+3gzDImI24O0R8buppxT2iXmAkylB6cXAxXXk7vqU99h+MVbqnC4Tui5AkiRJ40udmgOlf8WPKKN2vgV8D1gd+HVHpQ0lACJiv4h4d0TMl5mTM/MO4GzKSetDjcfWmsbJ85OU6WTrAL2RWf8GnsjM+9quqyeqzLwK+CEl3Hkt8AiU+vshhJpaZt6XmV/KzMUoPaI2jIitu661+XzV8IHMPCczt8vMRYGbgO26rHEc+CWwRUTsGREL1m2rU6a63QC8rrPKptJ7DVBGwDxIWaVwqcy8PzPPzczPZebl8KxVP9usL4BLgAUj4rMRsVK9aQIwZ7+FUDXohdK7agPg1oj4fES8PDP/m6Xxd+c1j5U6Z5RT8yRJkjRiGqN35qMs6T1nZt5eR58sQwl0nui2yoHVb/A/RlnlbVHKil9/AD5MmRL1o2i5SXkdpfEa4E+Z+Z+6bXZKw+pHKSt+HZuZ32urpuGIiN0pUxu/VgMqzYSx0ndpLIiI52XmYxGxOWWa5irA7JRm9dtRfuf3zcy/dljmMxoNq/9I6Q+0EuV361bgF8ChlNdDV6t4zkOZIrgosAuwECUs/VRB3wAAEN5JREFUvxH4emae00VdQ6n/H11K6Q/3gvpzHcoI3q2yw9VHm8ZKnTPCIEqSJEkjLiLOpDStvY3yLf5llL4mVzLVcu9da4Rnb6PUdnyUFZ/eSWms/lvglx2NNvgEcAil6fe1lJPk0ymh3psovUP+2tVJ6GBqWLYncE1m/qHr6W7jSb/1XRorGoHOzpQVyC6gjCh5FFiC8ru0BPDhzNy7u0qfKyKeDxyTmVs2tr0d+ARlmuafOqhpocx8ICL2BFbLzPdHxEuBeYE5gCvqNOe+0Xiv3xDYLjM/0LhtIeD1mXlidxU+U8uYqHNmGERJkiRpRDRO9DagNK19N2WlrxcDS1NGSO2bfdK4uqdR9weA2/vpA35ErEdpAn4DZYTBGpSRB6dTVvi6zpBHGp46sux/Ke9JT1F6rl1NCclvpkzRXShrQ/iuNd6b3gB8irKC4m+zD1ZLi4h3Au8FXkR5X/9p47Y3Av/IzM561w0lIr5Mafr9C+D3wKR+7Lk2VuqcEQZRkiRJGhGNb3EPBO7PzG83bluFcoL3p34MTiJiccoqdGcB78jM+zsu6RkRsSawL2U1qh9TpmWsR5mes3dmXtFhedKYExHzU6a8bg7sAByXmft2W9XgImJbygqeC1Km5E2qP//U5aijiHgLZTGH5wP/pARlx1N6be2cmRd1VdtQIuLlwMrAZsDTlPf+G4CT+2mk4Vipc0YYREmSJGnE1Ma6vwS2An4CHJ6Z53db1dAiYmJmPlWn5r2T8g30X4DvZ+bPuq2uqH1Y3ko5ET2CEkqtmJnXdVqYNAY0RhZtA1yWmf9o3HYQ8HBmfqX3XtBdpUOrgf4GwLLAIsBHu1iooPGe+XJKpnBFlBU830kZ+frnzNy/7bqG0viiZAKlMf2TwN8oz+dGwFyZ+ZEOSwTGTp0zyyBKkiRJIy4iXgNsDbye0i/kpMw8pNuqpq1O3ZkdeAfweeA7mfmpjmrZD7gLuJcyJW92SnPly4A9e83LJU1bRMwLfJcS5t4FnAmcAJwGHJSZ5/XTaM1G2PMmYBNKT7i3ZuZFtU/Y8r3V8jqs8Vjgc80peBExJ/B0Zj7ZXWWDi4hfUaZjfgx4Ra/2iJgtMyd3WlzDWKlzRs3WdQGSJEkaPyJibmDBzLwAuCAinkeZVrBwvb3VVeeG0vjm+UXAAZQa7wX+DhyamT+oTbdbrzsiXkcJwgA+C/wOWAv4IXBNW3VI40ENmP4TEe8DXgGsSBlhcjDwu8w8D8pKBd1V+WyNkVlfpLyHbgLMVcPyN1J6BrWuMbpsI2C5zLy6joQFWIDyvJ7cRW2DabzXbwBMAL4ObFJrX4bS0/BjQKcBz1ipcyQYREmSJGmmNE5MFgcOBDaOiEeZstrcSb19+yWEgmfVsidwP3BMvb4N8KU6IunxqfZtq7Y/1ZWytgM+A6wLfDEzT2uzDmmsi4iPAZMjYmXKlNvZKM3KTwFuBy6v+/XTaKjee+rGlBGQ/wUeysw/1iBqX+DUjsqbQGn0vhKlyTvAhMx8MiLWAbbPzF91VNuAGu/f61KmjG9JWTURSg+mlTPz0S5qaxordY6ECV0XIEmSpDFv9vpzD+A24APAY5QTlUsj4rtdFTaYiJgnIn5cl3JfGvhyZp6bmecCB1F6c6zb5YlpZt6fmUdm5gspYdm2EXFXRJwbEct2VZc0VkTEGpQRhS+irDT5EmA+YDdgV2BO4ELou9FQvVpupwQRnwCOrdu2B/6VmQ/UPkJt1/ZURMwF/AB4NCLeDiweEatSVko9t+2apsP3KV80fJkyuhTgfcBPB/0b3TicMhXzi/R3nTPMEVGSJEmaKZn5RL24FfB24OPAfvXb+zspfS6e6XnSUZlTm5syEuI1lGk6p9fV/n6fmQ/VE7y+WY0uM68H/ici9gJeBzzccUnSWHAVJRzZD7gmMz8cEe+nTMfbKSLm6XLVuaHU96C7gRdSQrNzI2I2YAvgm73dWq7pZZS+f2+jhHg3U97zX04J9G9jSnDSFxq9tlagrDR4BKXR+ykRcRVwHX0Q8DRGwS2QmfdExAmUY9+r83r6oM6RYrNySZIkzbDay2iJzDw+Il6WmddGxI+BK4GjKEt5vyczb+2nqS8AtX/VgpSRW5tRAqlXAgmcmJkH9FvNkqZfRKwGfBA4o/48ODPP6KeedT01iHgwIrYHnp+Z36shyp7AE8BRmXlzR7WdSQn3fgIsRVlhdEfgBsr7fN+u4hkRXwfWBN6ZmTdHxBLAkpl5UcelAc/qD/VFSlD6x7r9hcAizYbw44FBlCRJkmZYRLyV8u3805SG2l8DHgF2BxYDls7MTfs10KmrT81Bma4DsAqwOeVk75J+PFGVNDy1nxJ1pMlrKdNuNwFel5nnd1rcIGq4vzplqvNRmfnVxm3LAg9n5v0d1LUW8P3MXGOq7YtSmmp/MzMv6Nf3eoCI2B1YGzgyMy/sup6pRcQLgHMoDfWfouQ1T9Upplf160qEM8IeUZIkSZphmXl8Zi4GbEjpC3Um8GdKs9WfZuamdddWp5AMpbfCU0S8ETgUuBjYJTNvofSR+XBmXgL91Vxd0vTJql4+PzM3o/TaeU8dJdVXanB2H7AEpa/VshGxf0S8NSKWBo4GVuiovA0p7+1ExPMiYraImCMz76nbd4P+6rUFU8LI6ieUWr8XEXs1VvvrF68BLsnMJzPz6RpCrQh8YjyFUGAQJUmSpJkQERPqN+DXZebeNZR6G/AP4GcR8auIWLTPAp1eLR8HvgScDfy7bvsfYJ0uipLUiqMp/d8Wg+cEFZ2qudm1wOeBTYHjgOcB61NGc82RmRd3VN6vgaUi4sWZ+VhmTm70B5yXKe+rfaPRd2nXiFif0gB8EqUJ+P6Uxu/95BrKc7xnXYUW4K2UXmHjis3KJUmSNMOaAVOvKWxmngecV7d9CdiWsgpQX6gnJosADwB3AC8F9qk370RZLr2vlnOXNDLqyJJvNK734+/4AsBalPeiw4C5gIWAe2BKP6GWa/o7pdn3dyLiJOCvtb4VKF8+7N5yPdNU3+vnA94CPER5DGsBjwN/oS6k0S8y88aI+AKwJbBEDc/upayaOK7YI0qSJEkjqo4wmJiZkyNiP2ByZh7WdV1NETEH8F5gPWDOzNwuIlYHfpiZr+y2Okmzmkaz6jWB71CmDK9F6V/3Y+AL/bDqaETsQBk1+gJKL6tbgTMy8xtD/b1+UlcfnC0zH+uDWnrHfVFKqJeU0XrzUJ7bWzPzXx2WOCoMoiRJkjRqajPwxzLz8a5rAYiIXSi9q57u9d6gLOX9T8ry4ydk5tG90V1d1ipp1tGYRnYA8ERmHlq3rwEcAHw2My/rtMiqjjJajDKyiMy8vduKBhcR+1JWy1ubMorr0A6nNw4qIn5K6aW4CfCKzPxnRMyemU+Ox9G59oiSJEnSqMnMB/sohNqRssT40xHxfOC/lJ5Qh1NW/NsV+BGAIZSkNjWChgWB+SNi4YiYMzMvpYySeR30R0+rzHw4M2/KzNv7MYSKiAn15wbAdsCngQ2A24BD6uijzjXq3AiYOzN3Av5ZQ6hlgR9FxLzjLYQCe0RJkiRp1rEl8JV6eV/gycw8EPhtdyVJUlFHab4emJPS/PuiiFiCMvLoWOjbnlb9phfWvRk4LTN7vaA+Wnsw7Q58rpPKGhp9vlYGzoyIfYBz6rZVgYUy8z9d1DbaHBElSZKkcS8iFgZ2pjSrhdJ75eh621wdlSVJwDO9gm7OzHUoK6XND/wfsDdlpbfn1ylxmobGiNZfA8tHxDKNm5ehLFLRT04EFgX2BH5bR0PtBpzQaVWjyBFRkiRJmhU8SAmifhIRLwRmpzSCJTP/22FdkgQwISLeAGwBXJeZH64h+euBHYBfAvsBp3RYY9+LiNUoOceVmXleRGwJXBYR1wBXUHpbHddxjb1+YLMDT2fmpIg4mzIK6t2URuVXAcd0Wedoslm5JEmSZil1dNQ7gI8BNwHHZOYR47EhrKT+1lsYISJ2AzaiBORrZ+ZmEbEK8K/MvC8ilgbuy8xHu6y330XE/sCylAUoLgXOBJ6iTM2eDJzbD9Pd6iqtuwBvBO4Evg+cRBkZ9URm3tVheaPOIEqSJEmzrIhYCTgY+HlmntR1PZJmTRHxB+D9lP5Fd2bmVyJiP4DM/GKnxY0hdUTUi4GXAotT+mtNAk4FJmXmwx2WB0BEvBL4ImWE1tHA5pSpeM8HdsvMU7urrh0GUZIkSZIktawxRWsi8BnKqp1HAa+t288FDs7Ms3ojpzoteIyJiFcBGwMLA1sB52Tm3t1WBRHxTeCuzPzMVNt3o9S5Y2Y+2UlxLbFHlCRJkiRJ7XtLRPyd0g/oDOB04AlgnYh4MfC8zDwLntWAW4OIiHcDcwNzANsCFwKvBuYFHgEe6q66Z1kDWA8gIuakrPI3mdK76o3AukxZPW9cckSUJEmSJEktiogAbgC2yszr67bNgY9TmlZ/H/hZZl7haKhpi4j1gHPr1X0ood48wN+ApTLz5q5qa4qIdYHzgA9SpoTfM9XtlwDvyczLu6ivLRO6LkCSJEmSpFnM24C/Zeb1EbFwROxNCZ/OAX4CfC0zrwBHQw1HZp5HafT9PuAA4KvASpn5eL+EUACZ+WfKyn1zAn+LiAsi4l0AEbE48NB4D6HAIEqSJEmSpLa9BLi4Xn4L8DrgE8AhlPP03Tuqa8zKzPsy84jMfCFlVNTrI+LuiPhjRCzbdX09mXlPZh6WmYsBuwJrR8SdlKbqZ3ZbXTucmidJkiRJUosiYi3gMMr0vDcBHwWOz8z/RsT3gSsz89sRMSEzn+6y1rGsNoJ/HXBVZt7fdT2DqXWuBVyfmQ90Xc9oM4iSJEmSJKlldVW3NYHJmfmDum1h4I/ABpl5b5f1SaPFIEqSJEmSpI5FxLzA9sD6mfluR0NpvDKIkiRJkiSpY3V61kJAZuZ9BlEarwyiJEmSJEmS1ApXzZMkSZIkSVIrDKIkSZIkSZLUCoMoSZIkSZIktcIgSpIkSZLGuIjIiPhx4/psEXFPRPx2Bu9vwYh4f+P6BjN6X5LUZBAlSZIkSWPfI8CqETFXvb4J8M+ZuL8FgfdPcy9Jmk4GUZIkSZI0Pvwe2LJe3gn4We+GiFg4Ik6MiCsj4i8R8fK6/VMRcVREnBMRt0TEB+tf+QKwYkRcHhFfrtvmjYgTIuK6iDg2IqLexxci4pp634e281AljVWzdV2AJEmSJGlEHAd8sk6hezlwFLBeve1g4LLM3CYiNgJ+BLyy3vZSYENgPuD6iPgusD+wama+EsrUPGB1YBXgDuDPwLoRcQ2wLfDSzMyIWHD0H6akscwRUZIkSZI0DmTmlcBylNFQp0x18+uAH9f9zgIWiYgF6m2/y8zHM/Ne4G5gsUH+ib9m5qTMfBq4vP5bDwGPAUdGxHbAoyP3iCSNRwZRkiRJkjR+nAwcSmNaXhUD7Jv15+ONbU8x+MyZ5+yXmZOBtYFfAtsAp05vwZJmLQZRkiRJkjR+HAUckplXTbX9j8DO8Mw0u3sz86Eh7udhylS9IUXEvMACmXkKsA9TpvtJ0oDsESX9fzt3TINQDIVh9P4bCxrRgAMsoAANDEjAAEEC69NQhq6Ql0ByBzhnbdN0/tJbAAD4EWOMR1UdXywdquqU5FZzfG63cs6S5JrkXvMT9MubrduqOifZ1Hx1tf/07sB/yBhjfRcAAAAAfMloHgAAAAAthCgAAAAAWghRAAAAALQQogAAAABoIUQBAAAA0EKIAgAAAKCFEAUAAABACyEKAAAAgBZPFhQoFJMpZAMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot the total rides during  ACL and SXSW from 2014 to 2017\n",
    "\n",
    "data_SXSW = [number_of_rides_SXSW_2017, number_of_rides_SXSW_2016, number_of_rides_SXSW_2015\n",
    "           , number_of_rides_SXSW_2014]\n",
    "data_ACL = [number_of_rides_acl_2017, number_of_rides_acl_2016, number_of_rides_acl_2015\n",
    "          , number_of_rides_acl_2014]\n",
    "\n",
    "x_axis1 = np.arange(len(data_SXSW))\n",
    "x_axis2 = np.arange(len(data_ACL))\n",
    "\n",
    "#Calculate optimal width\n",
    "width = np.min(np.diff(x_axis1))/3\n",
    "df_month = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] >= 2014) & (df_bike_cl[\"Trip Year\"] <= 2017),:]\n",
    "df_bike_month = df_month['Trip Month'].value_counts()\n",
    "monthly_bike = df_bike_month.tolist()\n",
    "months = df_bike_month.index\n",
    "\n",
    "import matplotlib.gridspec as Gridspec\n",
    "fig1 = plt.figure(figsize=[20,8])\n",
    "gs = Gridspec.GridSpec(100,100)\n",
    "ax1 = fig1.add_subplot(gs[:,0:45])\n",
    "ax2 = fig1.add_subplot(gs[:,51:100])\n",
    "ax1.bar(x_axis1-width, data_SXSW,width,color='blue',label='SXSW', alpha = 0.75)\n",
    "ax1.bar(x_axis2, data_ACL,width,color='red',label='ACL', alpha = 0.75)\n",
    "plt.setp(ax1, xticks=[0, 1, 2,3], xticklabels=[\"2017\", \"2016\", \"2015\", \"2014\"])\n",
    "ax1.set_title('Number of Bike Rides During SXSW & ACL')\n",
    "ax1.set_ylabel(\"Number of Bike Rides Made\")\n",
    "ax1.legend(loc=1, prop={'size': 14})\n",
    "\n",
    "\n",
    "months_bar = ax2.bar(months, monthly_bike,color='green',alpha = 0.75)\n",
    "months_bar[0].set_color('b')\n",
    "months_bar[1].set_color('r')\n",
    "ax2.set_xlabel(\"Months\")\n",
    "ax2.set_title('Total Rides per Month (over 4 years)')\n",
    "plt.setp(ax2, xticks=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], xticklabels=['January', 'February', 'March', 'April', 'May', 'June', 'July',\n",
    "             'August', 'September', 'October', 'November', 'December'])\n",
    "\n",
    "ax2.set_ylabel(\"Ride Count\")\n",
    "plt.setp( ax2.xaxis.get_majorticklabels(), rotation=70 )\n",
    "plt.savefig(\"Images/SXSW-ACL.png\",bbox_inches = \"tight\")\n",
    "fig1.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation between Uber Ban and Bike Rides \n",
    "- Austin did not have services such as Uber from: \n",
    "    - May-9-2016 / May-29-2017 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Total number of rides during Uber Ban : 206052\n"
     ]
    }
   ],
   "source": [
    "# For retrieving the data, apply filters on Trip Year,Trip Month and Trip Dates \n",
    "# Apply Group by on Trip ID within the filtered data frame to get the total rides\n",
    "\n",
    "# Number of bike rides during May 1st to May 29th 2017\n",
    "df_bike_2017 = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] ==2017), : ]\n",
    "df_bike_2017_may = df_bike_2017.loc[(df_bike_2017[\"Trip Month\"] == 5), : ]\n",
    "df_bike_2017_may_uber_ban = df_bike_2017_may.loc[(df_bike_2017_may[\"Trip Date\"] <= 29), : ]\n",
    "number_of_rides_uber_ban_2017 = df_bike_2017_may_uber_ban['Trip ID'].count()\n",
    "\n",
    "# Number of bike rides from Jan 1st to 30th April 2017\n",
    "df_bike_2017 = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] ==2017), : ]\n",
    "df_bike_2017_till_april_uber_ban = df_bike_2017.loc[(df_bike_2017[\"Trip Month\"] < 5), : ]\n",
    "number_of_rides_uber_ban_2017_till_april = df_bike_2017_till_april_uber_ban['Trip ID'].count()\n",
    "\n",
    "# Number of bike rides during June 1st to Dec 31st 2016\n",
    "df_bike_2016 = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] ==2016), : ]\n",
    "df_bike_2016_till_june_uber_ban = df_bike_2016.loc[(df_bike_2016[\"Trip Month\"] >= 6) & (df_bike_2016[\"Trip Month\"] <= 12), : ]\n",
    "number_of_rides_uber_ban_2016_till_august = df_bike_2016_till_june_uber_ban['Trip ID'].count()\n",
    "\n",
    "# To calculate the number of rides from May 9th to May 31st 2016\n",
    "df_bike_2016 = df_bike_cl.loc[(df_bike_cl[\"Trip Year\"] ==2016), : ]\n",
    "df_bike_2016_may = df_bike_2016.loc[(df_bike_2016[\"Trip Month\"] == 5), : ]\n",
    "df_bike_2016_may_uber_ban = df_bike_2016_may.loc[(df_bike_2016_may[\"Trip Date\"] >= 9), : ]\n",
    "number_of_rides_uber_ban_2016 = df_bike_2016_may_uber_ban['Trip ID'].count()\n",
    "\n",
    "number_of_rides_uber_ban = number_of_rides_uber_ban_2016 + number_of_rides_uber_ban_2017 + number_of_rides_uber_ban_2016_till_august+ number_of_rides_uber_ban_2017_till_april  \n",
    "\n",
    "print(f\" Total number of rides during Uber Ban : {number_of_rides_uber_ban}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot illustrates the comparison between Average number of rides per year and Total rides during Uber ban.\n",
    "number_of_rides_per_year_list = number_of_rides_per_year.tolist()\n",
    "axis_font = {'fontname':'Arial', 'size':'14'}\n",
    "title_font = {'fontname':'Arial', 'size':'15', 'color':'black', 'weight':'normal',\n",
    "              'verticalalignment':'bottom'} # Bottom vertical alignment for more space\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "y_value1 = number_of_rides_per_year_list\n",
    "x_axis1 = np.arange(len(number_of_rides_per_year_list))\n",
    "x_axis2 = [6]\n",
    "bars1 = ax.bar(x_axis1, y_value1, color='g', alpha=0.75, align=\"center\")\n",
    "bars2 = ax.bar(x_axis2, number_of_rides_uber_ban, color='r', alpha=0.75, align=\"center\")\n",
    "plt.setp(ax, xticks=[0, 1, 2, 3, 4, 5, 6], xticklabels=[\"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"Uber Ban\"])\n",
    "ax.set_ylabel(\"Number of bike rides\", **axis_font)\n",
    "ax.text(7,220000,s=\"Uber Ban Dates:\\n05/09/2016 - 05/29/2017\", fontsize=15)\n",
    "ax.set_title('Comparison During Uber Ban', **title_font)\n",
    "plt.setp( ax.xaxis.get_majorticklabels(), rotation=70 )\n",
    "plt.savefig(\"Images/Uberban.png\",bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:PythonData]",
   "language": "python",
   "name": "conda-env-PythonData-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}