LCC_Footfall.ipynb

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    "# Predictive Data Analytics for Urban Footfall\n",
    "\n",
    "Authors: [Nick Malleson](http://nickmalleson.co.uk/) ([School of Geographym, University of Leeds](http://geog.leeds.ac.uk/)), Molly Asher ([Leeds Institute for Data Analytics](http://lida.leeds.ac.uk/)) and Simon Brereton ([Leeds City Council](http://leeds.gov.uk/)).\n",
    "\n",
    "_This document summarises the work of the 'Predictive Data Analytics for Urban Footfall' project_\n",
    "\n",
    "## Project Summary\n",
    "\n",
    "An understanding of what attracts people to city centres, and what discourages them, is vital for city councils and other planning agencies in order to attract a larger number of visitors to their city centres. Understanding the factors that drive footfall is also of substantial academic interest, particularly around questions of mobility, inclusivity, and of accessibility of opportunities.\n",
    "\n",
    "To better quantify footfall, Leeds City Council has installed some CCTV cameras that count the number of people who walk past them in hourly intervals. Data from these cameras are available dating back to 2008, and therefore give a reasonable picture of how the number of visitors to the city centre has changed over time. There is great potential to leverage these data as both an explanatory tool for understanding what has been driving city-centre attendance and as a predictive tool for forecasting future footfall under different scenarios. \n",
    "\n",
    "**This report documents one of the major aspects of the project: the development of a machine learning algorithm to predict footfall based on relevant exogonous factors.**\n",
    "\n",
    "## Table of Contents\n",
    "\n",
    " - [Introduction](#intro)\n",
    " - [Background / Context](#background)\n",
    " - [Data](#data)\n",
    " - [Method - background](#methodbackground)\n",
    " - [Method - machine learning for footfall](#method)\n",
    " - [Results](#results)\n",
    " - [Conclusions](#conclusions)\n",
    "\n",
    "\n",
    "## TODO\n",
    "\n",
    " - Tidy code so that the `FOOTFALL_FILE` isn't read more than once\n",
    " - Work out why some of the errors in the tests for LinearRegression explode. This happens with both mse and r2.\n",
    " \n",
    "\n",
    "## Required Packages\n",
    "\n",
    " - pandas\n",
    " - numpy\n",
    " - scikit-learn\n",
    " - xgboost"
   ]
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   "source": [
    "## Code Initialisation"
   ]
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   "source": [
    "Start by importing the packages required throughout and defining some global variables"
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    "## Load the required packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import re\n",
    "import copy\n",
    "import datetime\n",
    "import time as thetime # (I know it's messy to rename as 'thetime', but 'time' is used elsewhere)\n",
    "import operator # So that it is possible to sort a dictionary on the values\n",
    "\n",
    "import multiprocessing\n",
    "from multiprocessing import Pool # To run models in multiple threads simultaneously\n",
    "\n",
    "# To display tables in HTML output\n",
    "from IPython.display import HTML, display\n",
    "\n",
    "# Tell the notebook to do inline charts\n",
    "%matplotlib inline\n",
    "\n",
    "# The location of the data\n",
    "FOOTFALL_FILE = \"footfall_data/footfall.csv\""
   ]
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   "metadata": {},
   "source": [
    "<a id=\"intro\"></a>\n",
    "## Introduction\n",
    "\n",
    "XXXX"
   ]
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   "source": [
    "<a id=\"background\"></a>\n",
    "## Background\n",
    "\n",
    "### Footfall\n",
    "\n",
    "XXXX\n",
    "\n",
    "### Machine Learning for Footfall Estimation\n",
    "\n",
    "XXXX\n"
   ]
  },
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   "metadata": {},
   "source": [
    "<a id=\"data\"></a>\n",
    "## Data\n",
    "\n",
    "XXXX Information about the data sources\n",
    "\n",
    "### Weather\n",
    "\n",
    "XXXX\n",
    "\n",
    "### School, University, and Public Holidays\n",
    "\n",
    "XXXX"
   ]
  },
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   "source": [
    "### Functions to analyse the data\n",
    "\n",
    "Begin the data analysis by defining some useful python functions"
   ]
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    "\"\"\" Define function for splitting dataframe into time segments.\n",
    "    For each it is grouped by that time fragment and then sorted\n",
    "    to be in ascending order. \"\"\"\n",
    "    \n",
    "def segment_time (file):\n",
    "    #file = total_results.loc[total_results['LocationName'] == file]\n",
    "    ################### Group by year\n",
    "    year = pd.DataFrame(file, columns = ['Year', 'InCount'])\n",
    "    year = year.groupby('Year', as_index=False).sum()    \n",
    "\n",
    "    ################### Group by month\n",
    "    month = pd.DataFrame (file, columns = ['Month', 'Year', 'InCount', 'Month_yr'])\n",
    "    month = month.groupby(['Year', 'Month', 'Month_yr'], as_index=False).sum()  \n",
    "    # Sort\n",
    "    month = month.sort_values(by=[\"Year\",\"Month\"], ascending=[True, True])\n",
    "    month = pd.DataFrame(month, columns = (['Month_yr','InCount']))\n",
    "\n",
    "    ################### Group by week\n",
    "    week = pd.DataFrame(file, columns = ['BRCWeek', 'Year',  'InCount', 'Week_yr'])\n",
    "    week = week.groupby(['Week_yr','Year'], as_index=False).sum() \n",
    "    # Sort\n",
    "    week = week.sort_values(by=['Year', 'Week_yr'],ascending=[True, True] ) \n",
    "      # Keep only relevant fields\n",
    "    week = pd.DataFrame(week, columns = (['Week_yr','InCount']))\n",
    "    \n",
    "     ################### Group by week type 2\n",
    "    week_custom = pd.DataFrame(file, columns = ['week_no', 'Year',  'InCount', 'Week_yr'])\n",
    "    week_custom = week_custom.groupby(['week_no','Year'], as_index=False).sum() \n",
    "    # Sort\n",
    "    week_custom = week_custom.sort_values(by=['Year', 'week_no'],ascending=[True, True] ) \n",
    "      # Keep only relevant fields\n",
    "    week_custom = pd.DataFrame(week_custom, columns = (['week_no','InCount']))\n",
    "    \n",
    "    ################### Group by day\n",
    "    day = pd.DataFrame(file, columns = ['Day', 'Month','Year', 'InCount', 'NewDate'])\n",
    "    day = day.groupby(['Day','Month', 'Year', 'NewDate'], as_index=False).sum()    \n",
    "    # Sort\n",
    "    day = day.sort_values(by = ['Year', 'Month', 'Day'], ascending=[True, True, True])\n",
    "    # Keep only relevant fields\n",
    "    day = pd.DataFrame (day, columns = ['NewDate','InCount'])\n",
    "    \n",
    "     ################### Group by hour\n",
    "    hour = pd.DataFrame(file, columns = ['InCount','Hour_yr', 'Hour', 'NewDate'])\n",
    "    # Group by hour\n",
    "    hour = hour.groupby(['NewDate', 'Hour'], as_index=False).sum()\n",
    "    # Sort\n",
    "    hour = hour.sort_values(by = ['NewDate', 'Hour'], ascending=[True, True])\n",
    "    # reoder to hour comes first\n",
    "    hour = hour[['Hour', 'InCount', 'NewDate']]\n",
    "    \n",
    "    # Store the results in a dictionary\n",
    "    time_dico = {'Year': year, 'Month': month, 'Week': week, 'Day': day, 'week_custom': week_custom, 'Hour': hour}\n",
    "\n",
    "    return time_dico\n",
    "\n",
    "# Function to process date and recreate it in a different format\n",
    "def create_date_format (col, old_time_format, new_time_format):\n",
    "    col = pd.to_datetime(col, format = old_time_format )\n",
    "    if type(col) == pd.core.series.Series:\n",
    "        col = col.apply(lambda x: x.strftime(new_time_format))\n",
    "    elif type(col) ==  pd.tslib.Timestamp:\n",
    "        col = col.strftime(new_time_format)\n",
    "    return col  \n",
    "\n",
    "\n",
    "\"\"\" Final functions calling all other functions and producing dictionaries of dataframes \"\"\"\n",
    "\n",
    "# Creates a dictionary containing for each time segment and location a dataframe with dates and counts\n",
    "def create_dico_of_results (f, time_of_day):\n",
    "    # overall_counts  = pd.read_csv(\"Footfall Data/Cleaned Footfall Data/sb_results.csv\")\n",
    "    overall_counts  = pd.read_csv(f)\n",
    "    # Create splits based on hours of day\n",
    "    if time_of_day == 'day':\n",
    "         overall_counts = overall_counts.loc[overall_counts['Hour'].isin(list(range(8,18)))]\n",
    "    elif time_of_day == 'evening':\n",
    "         overall_counts = overall_counts.loc[overall_counts['Hour'].isin(list(range(18,21)))] \n",
    "    elif time_of_day == 'night':\n",
    "         overall_counts = overall_counts.loc[overall_counts['Hour'].isin([21,22,23,24,0,1,2,3,4,5,6,7])] \n",
    "    locations = list(overall_counts.LocationName.unique())\n",
    "    final_dico = {}\n",
    "    for Location in locations:\n",
    "        location_breakdown = overall_counts.loc[overall_counts['LocationName'] == Location]\n",
    "        results = segment_time (location_breakdown)\n",
    "        for (k,v) in results.items():   \n",
    "            # Save the results to an overall springboard results dictionary\n",
    "            name = k + '-' + Location\n",
    "            final_dico[name] = v                  \n",
    "    return final_dico    \n",
    "\n",
    "# Creates a dictionary containing for each time segment a dataframe with dates and total counts across all locations\n",
    "def create_totals_dico(f):\n",
    "    file = pd.read_csv(f)\n",
    "    total = segment_time(file)\n",
    "    times = ['Hour', 'Day', 'Week', 'week_custom', 'Month', 'Year']    \n",
    "    totals_dico = {}\n",
    "    for Time in times:\n",
    "        total_time_split = total[Time]\n",
    "        totals_dico[Time] = total_time_split\n",
    "    return totals_dico        \n",
    "\n",
    "def create_aggregated_dico(input_dico):   \n",
    "    aggregated_dico = copy.deepcopy(input_dico)\n",
    "    aggregated_counts_dico = {}\n",
    "    for (k,v) in aggregated_dico.items():\n",
    "        timeframe_column =v.columns[0] \n",
    "        if k == 'Day': \n",
    "                 v[timeframe_column] = create_date_format(v[timeframe_column], '%d-%b-%Y', '%A' ) \n",
    "        elif k == 'Year' or k == 'Hour' or  k == 'week_custom':      \n",
    "                 v[timeframe_column] = v[timeframe_column]  \n",
    "        else:                         \n",
    "            v[timeframe_column] = v[timeframe_column].apply(lambda x: x.split('-')[0])   \n",
    "        grouped = v.groupby([timeframe_column], as_index = False).mean()\n",
    "        sort = grouped.sort_values(by=['InCount'], ascending = [False])\n",
    "        aggregated_counts_dico[k] = sort\n",
    "    return aggregated_counts_dico\n",
    "\n",
    "def prepare_to_plot (file, dico):\n",
    "    location = dico[file]\n",
    "    location = location.reset_index (drop = True)\n",
    "    location.count = location['InCount']\n",
    "    return location\n",
    "\n",
    "def make_ticks (file):\n",
    "    t = file.iloc[:,0]\n",
    "    ticks = list(t.values.flatten())\n",
    "    if len(t) <= 10: \n",
    "           ticks = ticks\n",
    "    else: \n",
    "           ticks = [ticks[i] for i in range(0,len(t),int(len(t)/10))]\n",
    "    return (ticks)    \n",
    "\n",
    "\n",
    "\n",
    "# Save values above and below normal\n",
    "def find_strange_points (data):\n",
    "    above_twostd = data.loc[data['InCount']> twostd_above_mean]\n",
    "    below_twostd = data.loc[data['InCount'] < twostd_below_mean]\n",
    "    anomolies[location + '_above'] = above_twostd \n",
    "    anomolies[location + '_below'] = below_twostd  "
   ]
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    "### Temporal Variation in Footfall\n",
    "\n",
    "_Taken from:_\n",
    "\n",
    " - `Exploring temporal variation in footfall.docx`\n",
    " - `subplots.py` (this file also has lots of graphs for the different cameras)\n",
    "\n",
    "Examination of historical records from Leeds City Centre demonstrate that the footfall level is variable at a number of temporal scales. One of the most prominent patterns that is observed is the change in footfall levels taking place within the scale of a week. A gradual increase is seen from Monday through Friday, with footfall levels then jumping markedly on Saturday before falling to a level significantly lower than the mean on Sunday. "
   ]
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9gN9yqus+ucpUnOMW3f3d3cq+gG9G8cwsuP25Oek5nOrW87y5m9PiabTeWzAt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\nL1JDY19BNey8OTOf3kXsT6WmP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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118ea2b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "''' Make bar charts showing averages over the life history of the cameras.\n",
    "    E.g. the average footfall of all of the Mondays, Tuesdays, Wednesdays ever.'''\n",
    "\n",
    "# Check that the footfall file is available\n",
    "\n",
    "\n",
    "# Create the dictionary of results for each location and time\n",
    "try:\n",
    "    totals_dico = create_totals_dico(FOOTFALL_FILE)  \n",
    "    aggregated_counts_dico = create_aggregated_dico(totals_dico)  \n",
    "except FileNotFoundError as e:\n",
    "    raise FileNotFoundError(\"Error: could not find the footfall file '{}'.\\nHave you uncompressed the zip file?\".format(FOOTFALL_FILE))\n",
    "\n",
    "# Put in chronological order for plotting\n",
    "aggregated_counts_sorted_dico = {}\n",
    "for (k,v) in aggregated_counts_dico.items():\n",
    "    if k == 'Day':\n",
    "        v = v.reset_index (drop = True)\n",
    "        v = v.reindex([5,4,3,2,1,0,6])\n",
    "    elif k == 'Week':   \n",
    "        v = v.sort_values(by = 'Week_yr', ascending = True)\n",
    "    elif k == 'week_custom':   \n",
    "        v = v.sort_values(by = 'week_no', ascending = True)\n",
    "    elif k ==  'Month':     \n",
    "        v = v.reindex([4,3,7,0,8,6,5,1,11,10,9,2])\n",
    "        v = v.reset_index (drop = True)                              \n",
    "    elif k ==  'Year': \n",
    "        v = v.sort_values(by = 'Year', ascending = True)\n",
    "        v = v.reset_index (drop = True)   \n",
    "    elif k ==  'Hour': \n",
    "        v['Hour'] = v['Hour'].astype(int)\n",
    "        v = v.sort_values(by = 'Hour', ascending = True)\n",
    "        v = v.reset_index (drop = True)\n",
    "    aggregated_counts_sorted_dico [k] = v\n",
    "del (aggregated_counts_dico)       \n",
    "\n",
    "# Create a figure with 6 subplots\n",
    "fig, ax = plt.subplots(3, 2, figsize=(10, 10))\n",
    "subset = [k for k,v in aggregated_counts_sorted_dico.items()]\n",
    "for time,index in zip(subset, list([(i,j) for i in range(4) for j in range(2)])):\n",
    "    # Prepare the data for plotting\n",
    "    processed_data = prepare_to_plot(time, aggregated_counts_sorted_dico)\n",
    "    if processed_data.columns[0] == 'Day_yr':\n",
    "         processed_data['Day_yr'] = processed_data['Day_yr'].apply(lambda x: x.split('/')[0])                            \n",
    "    ticks = make_ticks(processed_data)\n",
    "    # Calculate mean and std statistics\n",
    "    mean = processed_data['InCount'].mean()\n",
    "    std = processed_data['InCount'].std()\n",
    "    onestd_above_mean = mean + std\n",
    "    twostd_above_mean = mean + std + std\n",
    "    onestd_below_mean = mean - std\n",
    "    twostd_below_mean = mean - std - std\n",
    "    # Plot\n",
    "    ax[index].bar(height = processed_data.InCount, left = processed_data.index, color = 'black')\n",
    "    title =  time.split('-')[0]\n",
    "    ax[index].set_title(title)\n",
    "    ax[index].ticklabel_format(style='sci',scilimits=(-3,4),axis='both')  \n",
    "    ax[index].set_xticks(processed_data.index)  \n",
    "    ax[index].set_xticklabels(ticks, rotation=70)\n",
    "    ax[index].locator_params(nbins=12, axis='x') \n",
    "    # Add mean and standard deviation lines\n",
    "    ax[index].axhline(mean, color='b', linestyle='dashed', linewidth=1,label='Mean')\n",
    "    ax[index].axhline(onestd_above_mean, color='r', linestyle='dashed', linewidth=1,label='1 Std above mean')\n",
    "    ax[index].axhline(onestd_below_mean, color='r', linestyle='dashed', linewidth=1, label='1 Std below mean')\n",
    "    ax[index].axhline(twostd_above_mean, color='r', linestyle='dashed', linewidth=0.5,label='2 Std above mean')\n",
    "    ax[index].axhline(twostd_below_mean, color='r', linestyle='dashed', linewidth=0.5, label='2 Std below mean')\n",
    "    #ax[index].legend()\n",
    "# Set the title\n",
    "st = fig.suptitle('Average footfall counts (over camera history)', fontsize=42)\n",
    "# Adjust the positioning of the title\n",
    "st.set_y(0.95)\n",
    " # Set tight layout \n",
    "fig.tight_layout()\n",
    "# Adjust level of title\n",
    "fig.subplots_adjust(top= 0.85)\n",
    "# Delet the 6th plot\n",
    "#fig.delaxes(ax.flatten()[5])\n",
    "# Save the figure\n",
    "#fig.savefig('AverageHistoricalFootfall' + '.png', bbox_inches='tight')\n",
    "#plt.clf()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Throughout the year footfall levels also vary, and this can be observed by looking at either the average monthly or average weekly footfall levels. Looking at months we can see that footfall levels are at their lowest point in the first two weeks of the year. They are maintained at a fairly stable level between March and September (aside from in July when they dip) and then increase slightly in October and November, before reaching their peak in December.\n",
    "\n",
    "Breaking down footfall by week shows a more nuanced picture of the change in footfall levels over time. Week numbers were assigned starting with 1st January always being the first day of Week 1, this meant that in different years the same date would always be placed in the same Week(NOTE: realised this isn't quite true as leap years through it out). This shows variation hidden by the months plot, for instance although December has the highest footfall rate, we see here that in fact week 51 has a very high footfall count, and week 52 a much lower one. It’s also easier to observe fine scale differences, such as spikes in week 22 (around the May Bank Holiday weekend) and in week 39 (around when the University term starts). \n",
    "\n",
    "The effect of time on footfall is not linear; this is particularly apparent on significant days such as Christmas Day, New Year’s Day and Easter Sunday. These three days have consistently the lowest footfall levels (On Christmas Days levels are consistently below 10,000, compared to values around 150,000 - 200,000 normally). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Impact of later shopping hours\n",
    "\n",
    "_Taken from_:\n",
    "\n",
    " - `Impact of later shopping hours.docx`\n",
    " - `Exploratory Data Analysis/proportional_footfall_different_locations.py`\n",
    "\n",
    "To examine how footfall levels have varied annually the total footfall level for each year was compared. This shows that between 2009 and 2016 the annual footfall levels have been variable. Between 2009 and 2012 there was a decrease in footfall, this was followed by several years where levels remained fairly constant before rising again in 2016. \n",
    "\n",
    "The opening of the Trinity Centre in Leeds in March 2013 meant that there was an increase in the number of shops in the city centre offering later shopping hours, with Trinity open until 8pm daily. It is thought that this new development with its associated later opening hours, and an encouragement of a switch from retail to leisure might promote an increase in the footfall levels later in the evening. \n",
    "\n",
    "To examine this, the percentage of total daily footfall found in the daytime (8am to 6pm), the evening (6pm to 9pm) and the night time (9pm to 8am) was plotted. This showed that the proportion of footfall coming from the daytime hours has an overall downward trend and conversely the footfall count in the evening has been increasing. A step change can be observed at around the 2013 mark. There has also been a slight increase in the proportion of footfall coming from the night time hours, but this rate of increase is much smaller."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions that are useful for drawing the charts that show the change in footfall over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Keep only certain hours in the day\n",
    "def split_day(day, hours_wanted):\n",
    "    time_split = day.loc[day['Hour'].isin(hours_wanted)]\n",
    "    return time_split\n",
    "\n",
    "def make_file (hours_wanted, file):\n",
    "    #file = file[file.LocationName.str.contains(LocationName) == True]\n",
    "    day = pd.DataFrame(file, columns = ['Day', 'Month','Year', 'Hour', 'InCount', 'Day_yr'])\n",
    "    day = split_day(day, hours_wanted)\n",
    "    del day['Hour']\n",
    "    day = day.groupby(['Day','Month', 'Year', 'Day_yr'], as_index=False).sum()    \n",
    "    day['Day_yr'] = day['Day_yr'].apply(lambda x: x.split('/')[1]) \n",
    "    day = pd.DataFrame(day, columns = ['Day_yr', 'InCount'])\n",
    "    day['datetime'] = pd.to_datetime(day['Day_yr'], format = '%d-%b-%Y')\n",
    "    day = day.sort_values(by = 'datetime', ascending=[1])\n",
    "    del day['datetime']\n",
    "    day = day.reset_index(drop = True)\n",
    "    return(day)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Read the cleaned camera data file\n",
    "f  = pd.read_csv(FOOTFALL_FILE)\n",
    "\n",
    "# Produce dataframes with the incount for the wholeday and for each of the three time segments\n",
    "wholeday = make_file(list(range(0,24)), f)\n",
    "evening = make_file(list(range(18,21)), f)\n",
    "daytime = make_file(list(range(8,18)), f)\n",
    "nighttime = make_file([21,22,23,24,0,1,2,3,4,5,6,7], f)\n",
    "\n",
    "# Filter out columns of interest\n",
    "wholeday = pd.DataFrame(wholeday, columns = ['Day_yr', 'InCount'])\n",
    "evening = pd.DataFrame(evening, columns = ['InCount'])\n",
    "daytime = pd.DataFrame(daytime, columns = ['InCount'])\n",
    "nighttime =pd.DataFrame(nighttime, columns = ['InCount'])\n",
    "\n",
    "# Join together to produce a dataframe with percentages of total daily footfall\n",
    "# found in each of the time segments\n",
    "result = pd.concat([wholeday, daytime, evening, nighttime], axis=1, join='inner')\n",
    "result.columns = ['Day_yr', 'total', 'daytime', 'evening', 'nighttime']\n",
    "result['eve_percentage'] = result['evening']/result['total'] * 100\n",
    "result['nighttime_percentage'] = result['nighttime']/result['total'] * 100\n",
    "result['day_percentage'] = result['daytime']/result['total'] * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw the charts that show the change in footfall over time.\n",
    "\n",
    "#### Footfall proportion - 6-9pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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WtbkjzPKmADMm1Nma2JaOCJG4UQIk3BHh+DnzKfG6h62idT4CA6CG7pyK4tL3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JAAAg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\nhjSkIe9JGgajIQ1pSEMa8p7k/wEaUpw7TlQwKwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118e04048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = result.index\n",
    "y = result.eve_percentage\n",
    "fit = np.polyfit(x, y, deg=1)\n",
    "ticks = make_ticks(result)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(x,y, s=6)\n",
    "fig.subplots_adjust(top=0.85)\n",
    "ax.plot(x, fit[0] * x + fit[1], color='red')\n",
    "ax.set_title('Percentage of footfall in evening (6-9pm) over time')\n",
    "ax.set_ylabel('Percentage of total footfall in the evening')\n",
    "ax.set_xticks(result.index)  \n",
    "ax.set_xticklabels(ticks, rotation=70)\n",
    "ax.locator_params(nbins=10, axis='x') \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Footfall proportion - 8am-6pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5lWl4NAlLzPh2qo8lup7eV0Iox4akTlYyxUg8xQEz5tASitFQ6dsknjlvrWyx\nVI/NwRhvrWxh30mjh/SZhcCeFma4vc1MTUNTRxQJjKz08fx5U4uCuRY7CnG5vQ+YCCyiV8UsgbxM\nQwixA4ph7AHEgf8JIZ4z9r0ipbxKCPEbVNr1Swb8BnngJGqGYylOu3c+Y2v8vHzhtKx4jkx4NEEi\nJS3CnEjlVppEDMLbbmM4SR0EEhek2TI0SGMY0GvsjUfSPYfCsRRn3p+dVDATbpfknAcW0twVS9vf\nYUgk/YFHE9RXejl72tb89bllRBIp1ndGmP6P2cSSOrUVbso9gkgCEinJafe+h88tiCWV4f60e1V6\nkpGVPp44ay+Ove3NNFUAKOmnttzLG8tb+MtzH/XZJgH85uBvcN1LnxJL6oSiSZqCkUFZrToZHu0F\noMysAjUVbqJx3ZJcNQGaIZWaTgz29ixtCrK2I4IE1nZENkkcwKI1waztYmAaU7dpQDO8z4bb28zv\n1Xj5gumOqtIS8qMQSWN3YHspZX/pziTgbSllD4AQYg5wJKp87HTjnHuB1xgipjG5sZrGmjLH1fmG\nrqjlUurRXGmShR3JDAkhj7dqTkjIItqJIQhcC8V0QrFY1v7+PsrUpqVSOte+9Alx46XjujL4AzQH\ns4v7xJPZT9oQinHoTXMJxbKZrQvJkTPfoKOn70JBVT6NQJmHGS99SlKXg57B1alCoD2iP5ZI0RVN\ncNmTS+iJ9TL1lIRUUsfrFtQFPEVhsDxuylbc+OqKtO1igN+r0VDpMaQ5z7ATab9XY3JjdVHlDNsc\nUEhq9CXAQJYpS4CpQogRQohy4BBgS2CUlHIdgPF3pNPFQoifCSEWCCEWtLQMrLaD36vxyoXTuevk\nb1LjTx+gKSkJ9iRorPZTW+HJcYf+E9xCUOEppNsHF+4cdhUT5uHqMg2Jkh42hBKWlKL1cb15j3KH\nd2vvSZJwcPFN6EpFlXks81EuAVcfszNt3coJQSC57JBvZAUa5ktO2VcSyHwVAs97+H1Ouftdzrz/\nfdq7E8RSMmvixJMSZHYnTW6sprG2DJ/bRWNt2SbRmY+t9fPGJfvyqwO25Y1L9mVs7fCnuQclda0P\nxknqsD4Yz0rUualgHyv2BIrDldh0c0NOSUMI8QyKZlYCHwkh3gWsZayU8rB8N5ZSLhNCXA28BISB\nxUAy3zUZ198G3Aaw++67D5h2R+IpfvvEEiJxnYDPRSwpSaQkUsKp97xLTbknK/32QJGpgnI8R8A/\njt2Fs+7eqmgUAAAgAElEQVRfaDGkM/YexyPvfUlnZGgG6+gqH7qE9lDM8QP4NMFfj9iRsTVl/PIh\nZzVYSma75mb2mg702GwjprtxnpCQtHtU+VzowkU4mt5Kt0vwp6eXWNJgUleJCxtrytlz4giArOSU\ndQEP1xy1Ez6PxoT6QEGRyE4VAqOJlKX3znzXTLSGs12iM9Ugmwp1FV72nDiCdcEIdRXeYV/VFwsy\ng0i7jO+9PhjloBvmEoomiyaYrlg94PKpp/6xsTc3Uo3cCSCEuAL4ElgvhBgjpVwnhBgDbNjY5+RC\n7NEneHTm40zz1bKusp6mqgY2VNUj/BXKdpCSbAgNXg3lTEJS4dXotq1aNAF/Pmwy3/q/EYyq9loq\nnjvfWE1tuYfMqAQnJuQCTt5zK579sJnWcLwgSejnUydwzQufOjMMwwNpQkMFAF0RZ74ujGenUCnK\nK30asYSkO4cBHSg4d5TXrWwCXTGdzDcWQE25l2Ak3VtJAqfdO58tav1cfdRObOiKWUwloqdY25Gy\nnCBq/G5iSdlnoFSmb3xjtZ//fthUUB/3VVf9/IcXbTJjeD4vteHEhPoADZU+Onvi1JR7mVC/6VV5\nmUGk1X430URKBY6GlJNGMQTTbZYR4VLKOQBCiKullGk2B0OCmNPXzYUQI6WUG4QQWwFHAXsC/wec\nDFxl/H1q4M3Pj87n/sfps+/HlTHtu7zlNFfWs66qnqbKepor62mqqmddZQPrjP093o0X6WOJFAGv\nC5/HTSiqfPr/9t+P+dfrn3HO9G353VNLAEVc27qzXTidyLEO3Pv2F1xy0HZc/fwnBRG0ma+tpLbC\nQ7IrlZYDSwDXH7cLlz+3jBPueIfaCg81fg+tGW1xu9Tq3mRpiRS09wyeVOT3aEgpHVVYdRUerjpq\nR37z+Idpkdgm1gcj/PLBhZbdxSWybVSdkSQuQZ92ELtvfGO1n0P/+XqW8d7tgoDPTaeNubpdcMWR\nO3LozmMdJ/bC1R2b1Bi+tCmY00ttuNAejnPIjXPp7I6RlNAVTXD4zHmbnBhmLgyuOnInTrt3PjHD\nvX6w3bgHis09IvwAsg3VBzvsc8JjRjR5AjhbStkhhLgKmCWE+CnwBfDD/jS4P1jzxyvZu/5gRoXb\nGR1qpbGrlTGhFkaH2mjsUn+33/AZDd2dWdcGfRUWA1lXafyqGnr/r6wn4i3L+/ykhHBcJxyPo7lA\n13tTtDfW+Ab8XrqEK5/PndAvE+3dcQI+d1bSRAlc/OgiwjE1YZqDMTwuCPhcRBO6ZfTvj/FfE3Da\nXuPZc2I9v3tqiaMTQiZiiRTVRtxIpnTS1p3gzPsXUhfw0GBk/7U3J6mTFjPhdgmqyjQSKVcaYdcl\nnPqdcZz+3YkALFkbdBT7Td/4JWuDtGR4oZkE5a+HTebiRz+gtTuBAEZWlTEhB5GJxFNcMOv9TVbX\nrz0c58z/vJfGgIc7NUYknuKgG+aywcb0o4mhD8x0QmbQHEBthduS+gfTjXtjYMaZtYZk0ThYmMhn\n0zgTOAuYIIT4wHaoElXytU9IKb/rsK8N2K+f7RwQJjdW0zCiki81D19Wj0o75nULy9vHm0wwMtxG\nY6iVMV0tjAm1MSbUQmNXK6NDrezYvIL6nmyddGdZwMZQ6mmqbLBJLeoX8yjmYHr1moQn5uBpNJgw\nXYVB2RSCUWe1UyiWzhESOiRi/eASGZDAfW9/wdMfrOOuk6fwo9vfpivHs01Ek3rOKHuBipNpD8Xx\ne7Us6UtmnKvyUyVw8jWYOecznlzchEuItEj/TAJhZiYu92pp7s+n7TWepxev4+wHFlFT4eYP358E\nCO584/O0bAP2+61sCaep/IYygCwST/G9G+ak5ezyaAJXrujSTYSlTcE0hmEinkxlxbVsCpgLg0g8\nxcLV7Wzo6u2vrkhi0Ny4NxbCcAkRWa4hw4t8ksYDwPPAlahYChMhKWX7kLZqkOD3ajx7znfZ79pX\n6cgwMpdpLmr9GutDceJuD1/WjObLmtxOYr5knFGhNhpDLYzrbmNEZwtjQq2WBLPzuk8ZEenKuq7d\nX2UwkBGsq2pghz22Z5tx23P+dQsY562lubKemHtwB2iV14XP66Y9HM+bisMOTajV+MayMl0aTKAr\nxvdvmpd2rKbMlVUpMBdcQGWZRoXPQ3u3SvoYy2FvMTGqymfFqCR0ZeD3ebQ0ptUSiuESwkpBsrQp\naKWh/6w1TCyR4sJHFrG+q7fOiNslSEm4641VvXaTzhR/eW4Z0OsUkLlyjsRTRBMp6it9tHRFqShz\n8/gQrmKdCmslUpLWIlBPOTlOaC7BZ61h6gKbvn5FJJ5i/+vm0ByMpOeQS+nDwsgysbIlTFtYjfti\nybxrIp9NI4hKTPijTdecwUdTMELYIUagK5bC59G45YRduezJJXR1J/K6dsXcXr6oHcMXtWN4O8c5\nvkSMMaFWS1IZ09VqbLfSGGrlm2s/pvb9/wJwh+261vJqZWNJk1pMW0sD6wMjiLuz3YIFsMsW1bz/\nZboU1BXXIe5s4NeEszeTREV/x/K5Om0EhICefkhXOipXl0By2l7/xx3zPsubmdftUpLAFTa1XbnP\nzaO/+A4/vv0tK8FgdZkbl8tFKJqkLuDh/IcX0RrKTkBpQhNw1G5jefz9ppyxPKrvXGlqILshs6bC\nTXW5l1A0yfG3vzVkevyJDQEaqnxZdphYUsc/jDmVTLfjlq4YAZ8LXQqCEeWGPVz1K95a2cbazkjW\nfq87O0BzOLC5JyzcrDGxIUBNhZcWBy+plnCctnCccDRZuC9wHsQ8PlbVjWVVnXNqBAFMrID7D9qS\ni2Y8y6hgiyWpjA61skVwPXusWUJ1rDu7reU1rKsyGInN1tLZPIot/HWsD9SR0HLHmwBUeAQuTSPk\noC7SpcpL5XWpVfpgsw4pnYP/MpG5Iu2M6sycsxJXHxJ6SoebZ69I2xeOJTnlnnd54Iw9efDd1cxa\nsMYy8teWe/jdIdtzXkYZ3kwkdXho/pd5n+0SRioRW8vthkw9bOT9GuIEi6Z771srWzjv4UWEjASZ\nEjhwxhzmX3bAsBBDv1fjmbO/y8E3zLGYt4lNtYq2u69G4inOz+FaXuNXZQRy2bw2FTbrhIWbO/xe\njRfOm8ZBN8ylsydOmSbUStzAjJc+yUs0BgumjWFtXOMt9wjeGb8z8RyamvJ4REkoNklln4oYoeWf\nM66jiW9/8SFVGYxFR9BaUUOTwVjsBvumKmVrWR+oI6nlfle/R6MuoOJWWrpilLkF0aTMG2cxWHDh\nHDlvwu0SfH/HMTyxqClt/xl7j+ffb39BLKnTlZFFOJGSbAhGOeC6OVm2kI6eREGpWfp69T/+YHuu\neeETIokUbaG4ldjRHmFeWaZZ3nGxAejx++uvf9lTSyyGYSIlGdZcT03BCJ2R9HQ2m8pTKbMmSiIp\n6crQPvjcqsDXFUfuaJURGE5XV3s26GLDV55pROIpmoIRnjhrL47457w0IyFAW09uGaPG56KzQKNw\npc+VZVS2Q7fVy5gyvi4tu21mPEbU62fliC1ZOWJLa9+NALv0nlMR67EYypiuVhptXmET2tay16pF\nVMbTxW8dwYZArSWtZLoan3jMd9hqh6058Z73SEnodsh14hKFx1/0B3318oiAj7krsjMD7DlxBE8v\nbmJ9jnibMo9GLDUYcmQ2BLDtKMUc1ndGiKd0LnlsMVc+v4zHz9yLq47cURXp6olbfaZLWLYuyF4F\n5l3K5a/vlJFX2TTirOvMNjoDTBnG2tf2BIEAY2rKuPH4oS+iBZl1wPU0s7IQSi1bVeYGBGfevzAt\nu3Uu9+ihCLyzF3g79J+vF12cjYlCEhYeBVyNSvchjJ+UUlYNcds2GvYJFyjTshhGPoyo8NCeEa+Q\nL+I7H8MA8LgEVx61I9+bPJqVLWG8msvyGKr0CYKxXkpcCE3u9pWzwrcVK+qd8wq5BFRHu6nvas3y\nCtuqp51tW79g2ucLqUjY9N/3g3S5mBOoY21ghOENpgz466vq6R41ht+cfgAHz1qO7uodwCd9e0vu\ne3tNAa0eGLya4IL9t+EPTy1N2z+muow/PP0R7d3O37XG72bmj3fjxxmZjgcTPo/GPafswf7XzTFq\nfEjWd8WYes2ruG3p9e24YNZi5vy6sHTluRIpZladPHzmPFq6Yvi9uVPUrAtGhi2liKk6W7i6nbWd\nEfafNHqTqcqUitpNpDNFIiXTCqMh1TdrcSgLHUvq/PLB97npR+nMbSgC78yAzJauGOVejXAsWVRx\nNnYUImlcAxwqpVw21I0ZbNgnXL7stE4oNNiuUMRTOlPG11kriYYqH+s7IyQlZGYP2diFvNsFN/1o\nV85+4H06fBUsbxiHJpQ3Undcp8qv0RZOgpRUxboNaaWFrbrbGR9pp6p1PaNDLUxq+Zz9Vs7Hn7St\nXP8FnwoX6wMjLBvLN5om4W2SrLWpxForatIYixNqy1x05PGm0lwCr6ZUGPtPGs1Ns1ek1UzviiY4\n54GFOdVnmiArNX4h8LsFkT7sL26XYFRVGRPqA+x73eysb2YmMhSAx0VajEwo6lx3wwmZRaAaq/1Z\njGTu8hZaumKW67JLqEVDpt0+lid6f1MgEk/xq0cWE4wkuWn2iqwSy/bzBnsVb5/+AnAZAatmXrVc\nY2hdUFWItJcXHorAO5UNWS3gMrNc1wdyZxoYDhTCNNZvjgwD0gsxDSQ77WBCl/CDm14nmtCpr/Ty\n0Bl7ctjNr9PWnSjIQDwy4GGDw2rIRK1fIxhNGW6zgqoyb5o0U+51Wbmt2sIqQlpH0FUWoKsswCcN\n4xlR4aE7lkqPmZCS6miYxlALEyLt7B+I8+XSlTQa3mHbr19J44Pv8Ptk+mo/4dIUY6msp7mqntaa\nBrbebRJPtLlY4a1hXWUDXa4astMTKvjcLu46eXeqy1U6j6ZgJI3ItHf3XbQpn+oxH342bQI3vboy\nrxouqUtSus6ydUE6HZ7jEsoTZ0TAy5lTJ/I7m5RUWeYuiAg4FYE6fOY8njpr7zTPmqnbNFDh06zv\npkv4y2GTueGV5ZZ0rarSOUshuVRdubbNa/pKK26/DuDgG+ey3nCJzldiebBX8SpWpnfupDKMZ/l8\nLMw4IXt54cz+HwyCnouhu11w3n5bb/T9BxOFMI0FQoiHgSdJT1j4+JC1apCQqxBTobj04G/wjxc+\nzoqktkPQm2ajL9Jvrx44f1U73Q6uwE7wuFR0uZvcGR/tcShCSj5vDVvxAfGUzFKf2Qmi2wW1FV6e\nPGtvjrrlDaL2SGghCPorCforWcYEngOY+p309gnJlnqEsg3NjAm1ML6nnbqODZbNZcd1yxnz6Vv4\n3noMe7RnQnPTHBhheYPZbS3do8awvXtHyuprOfCGuVlE5P5XVhXUdwPBDa+spNoLDtnf09DWHWdt\nZ8Q5eaOEeFJn1s++w6rW9NTbVx21Q79UU+lFoOI0BSNpKdsXrWnPUqU21pTxwvnTOOj612jvSVBV\npvHBmiBdkURWcSwnVVeu7RfPn6YivK9/jQ1hFRHvpHPPvO+MY3dJiyFxuwSdkURWZPjGruKdGJzp\niryuM+K4eOxrzWbOb9P7zez/wZSGcjF0ieCPT3/ETbNXFE3+qUKYRhXQAxxo2yeBomcaG4s7Xv+M\n2gpfWjSr26U+pMclqC738PiZe9HRE6e23GtJDiY0AVV+j8UsTJR7XUwZX6cGckckbdAes2sjr33a\nkpb/KSXJIgr5kJTwu6eW0hDwcuGB2/aZciSpg1fTqKvw8vy5UzlgxmuO6rlcSCH4TJTDqAl8NGoC\nGpmpFwEpqYt0MaarhcZQKxOi7dS1b2CkYXPZbe3HjA614dVtbPHWc9C9Xu4vr6Opsp4N1Q2Ev3iW\n2Lgt+WJBJ5PLlSTT7q/KWVPXLXoz9PZH2OyLYYBKOTG5sTrnYkGX8MaKVuoHoLs3AwOry91Egqo3\nBekpQc57+P2smAwTZ9y3kLm/3gdN00jqCdp7UmmFrkZW+vjfeVNpCkYs1VZLV4y5y1uyVF/27aVN\nQc66/z1L6pU469wziT9gqWTjOoSNRJ4+TeD3uvB7NJasDdJY7c9KGrlglYol7stonktKsdefv23u\n56SM0kA+tyCpK3tjrowEkl41nwBqKzxEE6rtTszMjDLvr91mQn0gbfFRV+7m+CnjuPvNVUWXf6qQ\nGuGnboqGDBU2plB8R0+cm360K396eqnlX15Z5uHRX3yHSCJlrTJM4+LT53yXH9w4lw5TJylEVnCZ\nS0BPXOf429/ioTP25Mh/vp6mdnr2w2b8Xhc+t0q6Z65yBoKWcDwvw7Drcu1pvV+6YDqH3DiXllCs\nT3dbt+Era29iimw9PkLQXl5Ne3k1S0c7i9tC6ozoCbJFdxsjO1vYqqeNn4738Mk7S6ltW8/ua5dR\nf9s8RCLBzbbrom4v66vqabKkloYsD7HOssrcxdr7CQFUlytV3kl3vZP33Bkvf0o8w572m8eX8L/z\n6hz1+dBL/Fq6YmlVJb1uF9cftwt+r+aYG8uOlC55eP4XrAs6M5UNIaUeeuzMvUgYYmc8pbPj2Oos\n1Ze9OFVXJJnleJBwiKK2B6fVBTzEEimuOnJH3vm8Pa1AVFyXxLoT7HfdHMo8LhoqfZYasr9eRLmk\nlPZwnKl/n22VQDA9ABMpyTPn7M3Spi6ueeHjrEqamRBAIqk7powx1XXnPLjQKrWruZYUXAe9KRjB\n51bqZI8L3JrGXW98TmIICo5tLPLlnrpYSnmNEOImHDQvUspzh7Rlg4TM6n2aS6Ah02Ik6iu8BKOJ\nrCyrSR3OeXARt5+0O7/4zwLiKUlHT4IT7nib2RftkyWOH3/7W4Rj9uyngo6eOM+fN42Xl60nntT5\n23+XWYN6/qp2S23kNmqJm8ZMzcjKWlmmpaVv92iCM6dNZObsFX2K1Zmo8oItzQ4NlT5Lx2xP610X\n8DL7on1YuLo9p2rPJL8jq8qIxFNZ0tRAbK5SuGitqKWtohZGbs3YWj+/umAae6MIQm1DgKc+bOJv\nd7+WFcdiJqD81poljA614ZbpDYi4fUYql/RsxvbI+y5fhSNjGVHh4fIjduCcBxeR0lUIX088RTyp\no+u6o3rKRHt3nFRGTfGObmd9vgkzS21mBHptucfKWzWxIUCV301rHjvXlrXlOY+BUq+9uHSdRUx1\nCcf9601uOH5XfB7NWtk/ddbeKkNtT4LT/z0/y9aTWea2PawklIfO2JN1wQi/fPB9TrjzXSN9fHoA\nqr0eaDTRK+2YXoattmDAvryIMmNjgj0JIvFUWm1y8z3Nvz+5611C0URarFaZEbOR6catg2Ujsnuy\nLW0Kcv7Di9hgqIJN9KcOuqlCs9f5iCZ1ytwuy+uyGFRTkF/SMI3fCzZFQ4YKZvW+havb+aQ5RCIl\nuWXOijQPhfaeOG6XcIxBSOmSps4ItRVei8B29iSyBu/C1R00d0bTiGU8qXPuQ+9bCfLqAh5GGJla\nzXgNcyWpS6gt99ISVs/waGqwTBlfl2bw1aUq3/nwgjVWezwuOOU747nnrdUkUjLLG8TjggqfZlSW\nU+9d5nZx8493s85xEv2ry70898u9s3JIuV3w24MnEShzs/+k0UQSKfa++tWCvb5GV/sQiJyrYIli\njlcftaPVJrOvp243ivMDdbQE6vhgzLaO17v0FPXdnTTacoONCRkux10tfGf1YkaF29EyGEu3p8yS\nUDZU17PNNydRtc0Exkzemi82rKY23kOrVgZCEDc8oxqqyrj31D045tY3CUUTVHjdackhq8rcuDVX\n2rumJFn6/MZqP3OXtzBlfB3nPfx+FsOoD3j577lT075RX1HyFz/2Qd7jiZTkL8+m+7i0dic49Z75\n1JR7mHHsruw2rpbPWsO0d8fTCKILJW2Zjh3mgqM9HGfKFS9bdcBvP2l3690l0BbKr/ZMpHQuffxD\nrn3pE8vgbEoaDZW+nNHapi3joTP25KiZ89gQinPine8wttbPwz/b06pNnolQLJkV3Bs16tyb8Htc\nRDJWQSMCSnV24PVzWB+M5gwQ7is2xh6bMeNYFYg1ptrP0beonLD1ld6iYhiQP/fUM8bfezddc4YO\nFz22OGfQky5VdlSPyzlw7ZY5K7nr5CkcevM8JdbqMk0cbw/HOemud7Kulag0CWBkag3DfT/dgzKP\nxsSGACtbwniMwezVXFx/3M6WS6I5WFa2hC1VFYBAsi4Y4fEz9+Kwm+fRHUsST+ncPm+VcVzpjjd0\nxaz2+D2urKqAdmNoJszaB8FIkiq/G19GTqqkDpf/dxlet8tynZx5wq78atbitMp9mTCLUH1jTBUT\n6gMsWtPORY9+QDiSSJP8BKo2+yWPf5imjojEU3zWGma0LTGhE3SXxobKEWyoHAFs59wWPUVDuMMW\nINlik1pa2XbVIkYueRVhSAnboFZPYa/fciteX13PXtN3pf7xFUxf1c7H7hq6R46iC6/FQP9y+GT+\n+NSSNEauCaz6H7FkCgEWoRUohpmJtnCcZeu62G1cLX6vxsqWcJ92rkKYuNM5ZnGyE+98hzE1ZehS\nZhFFHRU4ee0Pd6I1HCcST+H3ammr+pQuWbSmI+26FM7JC0FJUtGEnmbwt1c9rC33csCMOfTEUzk9\nrip8Ljp7EmnSSUdPnPm/3d+S9i9/7iPLhhEvQPer6zItRsurCa4/bhc+aw3nZRheF8xf1U5dxei0\nMWwyic9aw1buM7PiZE2Fm2RSEowkqC4vjjTtmfjKR4RDr66zLyT19AHtdgmSuqQtHOPEO3uZgscl\nLBHanCi53DNr/G48bs2SLuyE2i6S1ld62W1cHbMv2ifNK8MUuU2DZ0rHkl66Y0nKfRoyhsVU3Jrg\nwv235Q9PLbUmRpdD4OGMY3fO6SZpd42UUlJT7iCqS6VO2BCMsu8/XqUz2rcnmARumfMZ7d2mnlt3\nJHweTViV1MyIXJMwOKltnJBPZQSQcmk0Vyl34PczjrmEUrnUemF0T5Cy5ia2ibdz0hZu5r++mNp2\n5Rm23crVjPzgZYSUzLBdH/KpIl9ttSNpfrmBH5XVZhX5ShhFvnQJx932lkVoJTgSIQmccve71AWU\ns4I5LpoLsdgPECoqOpqzI1tDUU6/9z10wCU+ZMFlBzB1mwZrVe8SMLGhwkpHYyLg0+iOpbIcE/50\n6PZc+fzHAJYRfGVLmMmNKo357n97yZpn9mhtuy0jkrB5EdIrnXzWGub6Vz6lpStKSjrY3Bxgd5u+\n+MDtOP/hxYZhXDKm2s8x/3ojf94yiSU1vXj+NECVJd4QjJLQJZ6M4M+IniLS2dv+llCcl5c15yzu\nNVwQUhayHhle7L777nLBgoFrySLxFPtc+2qfE8ynqQI+Ld296gW/R1PR5Dai6RLgc6s8TTcctytj\nqv1phraacg/ReIrqcg/PGyqFXO55hQQyReIpnlm8lt8/tZRYUsfnVu55MUPnWe13W3aPsbV+nj5b\nuUnmWgU1Vpfxyq+mOz5vydogx9zypsVwRlf5uPaHu1jVzTYG9na7RW5Xx4aA1zJKmsbPlS1hfnjr\nW2lEAdSqr9rvKbj0bSFwCvYyJYAKg+DFU5Iyt4tHT5+C/uVa/nLrC5aNZWyolS172tlRdsGaNX0W\n+VpfWa8CIwss8jW6ysfsi/YB4M55K/nHi8sH6c37h+oyFRtk4tx9t+bM6VsTiad4edl6rnvpE0eJ\nsMyjinxlwmTW1eUeHjj925xwx9uW1P3Lfbbm4sc+TDvf9ADzezX2n/EazZ3RtO9WWebmf+dN5fjb\n38orEeRDhddFuddDh1Fx0P6u/5r7WdacqC5z0xVNz7Hl1QSXH7EDExoCnHjHO2meWua4Skm1SM28\nn8/tYmSVb8DutkKI96SUu/f7wjz4Wkgafq/GzT/6Jj++/e28A6e63Mu5+25jlWEFOGv6RHbbqoYT\n73zXGggezUUkYdShNgJ+Xjx/Kj+89U06ehJ09iQYWenj+XOnWsbBXMY7syBMX+0/dOex3DR7heWN\nIugtJPTUWXvzmRELMKE+YAXCvbysOWuied2CG3+0a84IXFP6Mb1lrv3hLkwaU8VIY1+ma2Iu7y4n\nt1tT6moNZd/HREPAy1nTJ3LF8x+TSEkrC6rdG6fK76IllECXirjfc+oePPjuFzy68EuiCd3Z5bcf\nyJU6Pp6SxHuSlpNCfaWXiY21ROqrWLjllzmlTU8qwahQG2PDrYzq6s1qbKZ32aF5JQ092YwlV5Gv\nttqRrHmnjvpvTOTfb65Kf5ZtBS2AO0/enVgyxaWPf5ClohwIqnyqfHFXNInbbVaNV/jX3M94YtFa\nXjx/GhMaKmjJKLx04Paj+P6OY7jwkcVW+6r9vaVzzVos9CQ4xphLoIzO9QFvFjPfEIqxzz9m8/hZ\nezkWKoondeavaqc1FO+TYYyu9nHFETtw4azFaRUfu+M63fFsxvfP2Sscx4lTsbN4SnLJYx8yqtqH\n36uljf2GgIeLvvcNvjmujvdWdzDj5U9pDkYtWjPUmZEHgnzeU45eUyYK8Z4SQlwAnG7c50PgVOBW\nYBqqVgfAKVLKRf1o84AwubGaUdVllhuj0wSfcezOjK8PoD2tBqfmEpzwrXH4vRqNtWVpBNv0xjI/\n6odrg3THU1aHBQe5AphTmUq7hFIXqEtz1TRjSLao9dPcGUFHidoNlb60ynFOvu1m4Nj5Dy/i9H8v\nsBjT3OUtXPLYB9ZqyKsJavxux0j1+kovQghrpTmy0sfzxqrwmcVrueyJD9PUA2aWUc3l4s8246xZ\nFMd8/4Wr2znnwfet76chOezmeWkTOJM0jqz0cPjOYy27D2xc4kWP5uLPh01mQkMFAJ+1hvPeS3o8\nnPvTA9hr6wbuf3s1t72+MovR+pJxRobbabTZVsbYDPhZRb7uvxSA/2UU+bIzmXWV9TSv72TmO2vp\nsRmNvJrgVwdsy1X/+yRrRXzynuO4963VOYms2+2iK5okltTRe3Rq/B7CsQQpW/DbwtXtXPDw+1lE\n9VLywhIAACAASURBVMO1QeZ82mvz8GgqH9s5D/Se6/doVPhcaV5hAZ/GH57+SJXy9WloLpcliQaj\nSQ64bg5ejyvreXUVXhoCXmorPGlqn0zccsJuTN9uJEubgmkMIx/6K7RIesspmxJVld+NR9P4/ZNL\nLJtGld/NzBN25fdPLrFitYqlMJSJfJLGRnlNCSHGAucC20spI0KIWcDxxuFfSykf3Zj79xd2oltb\n7uXoW96w0jN4NEVMJ42p5vCZ83C7BNVlbp4557sW0X/5gulpLnZ2qaXa77b82e2eHoPpV+2kxspc\neaxsCffmIOqKcfQtb/Dfc6daPu9OcQG5fNvLPMoOYzdKfm/yaP7+4sfWO46sKuPps/dm0Zp2fvXI\nYjp7lFhuZxCZqSYi8RQ3vLrcIpouoYhHTbmX6ww1mB0CmVbd7cJHFqe593rdmhUo5gS3gNZwgvve\n/iKtCp+GJIlSRZV5NAJlHtrDsT6LUAmUS/KNry63JL2rjtwx7zWjqvzsP2k0P7j59Zw102NuL2tq\nRrMmX/XIRIwtutuZOa2Big3NzHriTUaaNVlCrezW9Al1mdUjb4MDM4p8NVfVs7XYkW99kWCtrchX\nPCV55oN1Wa7ndrTbVLcJXXmBNQS8eN2aZau6YNZix4VEprdcPCW59PEP0TQXKUNl+b3JDcz+uDXt\nvB9+cwvufnM1sZRExHWqytKlCp1sg7bHpQzxP713Ab4+9ClmfZnPWrr7tIVtLBK68ly86uid2KLW\nz4l3vGONuWhSJxqKc9kTHxKyZYvQBEVRGMpEPu+pwfCacgN+IUQCKAea+jh/0JGZI8cktI/ZvI+q\nyz08fbYKKDLTNrjiOh09cStwz65Gskst1eUe/muooeyeHplZMTcm5UCh+XgmNgSUG6Sxug9GkjQF\nI1a7nQZdrgphufZfc9ROfN7azf/VByxPnpFVfqIJFb/gc7uYecJu1rMy00qvbAnTHk5YbrVm6dWu\nSBKfx5Vm9Adl+Deru2WWM3UJ5aKZD6YeOprU8WmCn0+baOiie2MTErEU5V6Nu06ZQms4Zhk8neB1\nuzh/v23449MfWQzV59FoqHQu9OV2wQ3HK0+bXAyjUMQ8PjrHbkXr7rvRMKaKh8ITs2pvlyWiNIba\naAy3MSrYYkgqSmrZIrieKV8upSYahjmwn+26zCJfzUYMyzrD1pKvyFcwkuCBM75JmUfFRpx6T/60\nPaYeP56SaSv7pIQnFzVnnX/765+R0NU1gTItKyWOSrudfk25101nj1JL9VGinhkvf8qNry5nQzDS\nb4bhcSmpwy3ArblISsXA3EIxHyf+W1Pu4XuTRxOJp9Jyhploz8hllpJsHpKGEOIZ8qunDst3Yynl\nWiHEP4AvgAjwopTyRSHEj4G/CSH+ALwC/EZKmaU0FEL8DPgZwFZbOaf/7gtmuuHMiFKAo255gzYj\nslX2JPisVXlpFJKILFdVLb9XyyKSg5GALV8+nkyG9Py5U9MCx+zlR50YV753yVSH5erLTAZjers4\nJb2zp4nItM1Mbqy2GO/H67r463PL0mok2+0t5T6NSFy5Z3pdUOZ1EzIMkPUVHs747kSufuHjNLVR\nIiWZtWCNo0G/oyfBBbMW094dy65lLZSqIxxTrp57bd1AtV9NHbPdL5w3zQqAi6d0pFSTp67Cx4T6\ngGVz2li0dSc44c53qK/w4hLpLa3xe4hogvaqrTh++n7c/eaqNP24ifJ4hNGhNltQZK+r8VYd6/pd\n5Ku5uoGte7bBN25L9v/PgrySChjSnduVFSmfC6YaU9m4Eoyq8qFLSW2Fm3Omb8NNry7P8u5zsi3k\nQnt3HF3X+x2Q6tME5+y7Df+cvYJoUiduBHoKFAO06khg1JoXapzef/q3ATh85jzC0WSf0k2xSRo5\nvaeEENPyXSilnJP3xkLUAo8BxwGdwCPAoyhG0Qx4gduAlVLKv+S710C9p5asDXL0LW9aRMLndvHY\nmd8h2JPglLvftdIn2JOuARslFTi1wfT68Xs0HvnFnv02aOUrxFNogZ6NZVy5+tKJeUXiqTTG5ZQE\nz1SVmRG7U7dpSJsUmfYZ06nAznzMGhLlPo0eIzuvJlTBpmAkkcUcvMbqtr8YVenl+fOmWWo+87lm\ninZT4rK3zYxBicR16gIerjlqJy6ctShnsajBgp0A1QdUXXKzH/rjIFAR61FFvUIttgDJdFtLZpEv\nKQQbKupYVznCirRfX13Pl4FeV+MNgTpwu5G6HHCpAaXSVCrl64/bhR/d/nafjCofGgJekrrMympQ\nSDteumAax9/+VpqUaTfYV3pdlHk9dHbHMGuaCeAPtoqP+ZArIWSh2KTeU30xhQKwP/C5lLIFQAjx\nOPAdKeV/jOMxIcTdwEUb+ZycyKwW1lDpo7bcy6E3zUvj7GYQnrmCH0wvBXtqA7MeQn+RSxrIJYFk\nemQNRv5/p760S2LmMzPjPJyS3pkqMzP1txMzs6evCBopwc3jZtufOmtvDrphLh3dMWuVmJIqoCuT\nhAig0u+mLU/ajVxwa5r13CVrg7SG4kSTOrFQnNPunZ9Wa8G0ef3m8SVWDEpTR4pT75nvyLCqfC5c\nLo1QJLFRHl8m7E8I9iSoq+iNsenP/bt95az0lbOyfsusY9VlbrpjScqiPVZQ5LiedsZH2qhsXc/o\nrla2aVvD1FXvE8isHilcbAjU0lRZn1bkq7c0cT2tFbWk8tRi0SWWBApK2ssVuFsIrj9uF8bXB/pM\nte/UjqNvecPw9uvN8Wb/zKG4TijD+0oCf372IzSXSAvctaMh4OXqo3fKG4Q7XCikct82wJXA9oDl\nOC6lnNDHpV8A3xZClKPUU/uh0qyPkVKuE0II4AhgSb6bbAzMamF2O8MLS5uzCMpQ1irOR/z6e59M\nQp/L7pCJfOcVam9x6kun8zPtDnYngczn98XMmoIRgpFkzuPL1nU5MgjTXmJffWouOHva1vzlufS0\nGR4NavzevHEe7d29zzb78v/ZO+8wqcrrj3/OzFZYdgFZEFBU7IKKiib2rtHEEmOLsUWjsZeY2GNi\nTPGXmNhiCcYYTTS2qNi7Yo2KiooVCxbq0hYWtu/5/XHunb0zOzM7u9w7O7O83+fZB+bemXve297z\nnvY98+qbaG3XpF4LQ6tKaW+ni5WTqWAPLJC/IE2BY28CsoJRZ/kuueHVFVx2wDiOuzU8JqDqihKW\nN7XRBjSUD2BG7VrMqF0LsGsuBM7Va/IVtFT8/6++bAEb181kt8/fYEBr8qTaJjGah6/OktVG8HZH\nVUpr4s4mX42ttkC44geb09zWzswFK7j6mU+Sakd8ZEoNHzW4gi3XMpfysKrSrFxe6bCksS1JYfQE\ncYHLDhjPNc/OYMEyW1ReflBhKoogcqnTuAX4FXAlsCuWNtstXaiqviYi9wJvYYRHb2PuqMdEpNY7\nxjTgpN4NPTekxhl2Wr82Kd1yZE1Fl3aOYaO7ya+3yGSB5Pq9nrqt0sVsUhGMOwSTBNLJ707pdafs\nzr777bQWxYiack7cYSw3TPksQTDX3gF/f/kLBgfqAgCqyko4ZKs1uPnlmRmLF2sqO5sm+YuAfa6e\nkkQk2dzWkXW1m2nSyuR776nCOGO39Th2u3UAePrDeYweXMmWaw3hrS8X9fBI2bE0S6ygtd34pspi\nGC2MdDb5+qR27fQ/8hSLn2q8RsNC1mlcyPeGtDNkwTw2eX8Ge3z6GhWZmnz929xeS4etzqLqYXy7\nbLCnWGpZMLAGlZjR4qe59sOqynj4tB0TrMENObAaZEJPlbxgSn2PjUckUreDsa8ouheGhVyURqWq\nPiMioqpfAr8WkRcxRZIVqvqrNN/brRfjDAWNLe3Mrm/kxXN34+VPFyRerKhvSq4WQW+QS3Fgpu9F\n0bYyW2A93bF9kja/diRIRpdNKVo3ts4JzE+nram04Pplj3yIYgWFja0dNLd1sHh5K9cdMYETbnsz\n4UJY2tTO9VM+tzGWxhPcP77lMXxQeYIo0H+Rm1rbWdpkNTllXrpwuhhKEJOOmshFD0xPSjutrSph\nQUPvugum4lvrDE0wD+y3+ajEtequNUBcYJcNa3nmo7pey66pKDF3XVsHpTHhxJ3W5YYpn6WNM/hK\n3acPDyqWj4avw2oDSlm4opXfAKttVUr9Dq20dSiDm5Z1KYr0/7/p3BmM+uRVytuTrYSWWAnzBqVv\n8jW7upaFQ4Yze8kKhno9Smqry9NWjpeIVbG3t3d0ac3soycKY3BlCX85dALlJTH2v+4lFja0UDMg\nTnNrB4u9zCmfdSKs7oVhIhel0SQiMWCGiJwGzAKGRzus8BFFG8lckatFEDVSVy9RKbPuFJmfBn3W\nXdOSqtrTxTcyHSto0VSWxVjRYpPW0qY2OlQTL/GKlnaGDixLBOW3XbeWNy7akxdm1LF4eQuXPtzZ\nmOiEHddhpw1qk1Z86TrcpbIVTz5lBz6cU89Jt7+VyPlPRXlJjGt/uEVSfc+ChmS6iQGlworWrtPP\n4IoYS5utIFUwRuNbUirBP57bwHn3vZcYk3/9RtZUZi1kbFeY9tXiXtcnVFeUsKKljQ6EihIrTrvn\nzW+6KAy/Ot13vTS2tPP3Fz/nby98lhQDWBhwbSbiTyIsqaxmSWU1H4zI4BX3m3wlFUV20udvMftj\nVl/2MuXtyfdHry+jZeQoytYaw3Oj12DuoGHc9EULswbVJgokF1VW09ZhtCSNy5N/HyQyzBUxgYsf\nmE5dQzNt7fas+o22fHQoBdd8yUcuSuMsrMbiDOAyzEV1TJSDigJRrKp7glwtgpVFJrM2k9LMpzIL\nKou6Zc0Jsrb5S5t5+sO5Pbo//tjfn13PGXe+zZIVTYnOdgBzliQXIAZTXivL4qwxpJLLH++Mbwhw\n5xtfc9OLX6RdVASfn1S2YoBz73s3o8IA+GLBCn6w1RpJFPupk3SmXvH1TZ2kdgr869WZXb7zh8c/\nTExAwV4PB93wcreV77n2Ul+9upxFy1tobe9Uyp3uKqWq3Ar8ggZXTUWcX+y9MftuOjIpO66yLM6P\nvr0WN075LCfZ3SLY5GvEuum/oh0MXWGKZc2GBfx8k4H874VpVC+Yx5pfL2SzL79i1OxZXNqabLH4\nTb7mVq3GNyE0+bI6jM5rno7rzEeu/eTziWx1Gv9S1aOwjKc3gAYsnlGUiNJFlAn59ktms6ZyzbTK\nduxMyiiXcwyy1AaZPcGKoa56ZkbS6t1Px8127MqyOBWlcRYvb00UFl5z+BaMHVaV5NcHEopqtaoy\nYiIJpQUWbxhUUZoolEuntNLVovhj8jOqsmHU4HIA/nLI5pz2n7fTpndmmjhSNwd1S0kc2tstniAk\nJ3WkJiX0FAPLYiwP0I98b7ORjB89GAF+fu87XayJ+jQUHC3tMGHM4C41BosaWtj/ry/2Ou02G6x/\njDUyCh5fJcbCgYNZOHAw8weNY+6hE7is441Es6N7T96OUYPK+dHvJ1M6Z3aSpTJ6mfGGfevr9xnR\nsJDSjmTLIFOTr86ssMxNvqrK42mD92BURoWGbJbGViKyFnCciNxGSvBbVcONsEWMvlhV59sdls2a\nSm2/2dTanuiB0Ntz6ck5+mNr9poXlXuujKWNbYl4Q+rqPdcq+OBkPnZYVRc31/uz65m12Kp9Zy9p\nSqQ5+uMYPKC0S8ZX6qIi2/OTSl+fDjMXrOCSB6ewsKGFwQNLGB4vZ1FDJ3Nq3Kso7imT8KCyOAPK\nyxIUHn7qr+9+rK0uZ359U0Z6lExuqd8dMJ7/e/z9pG23vDwTNLfUXRGo8JigU581Py27J33ofdSU\nZe7fLtIZY7rjJ9/mwOtf7mL91VTEKS+1QtCf/3daolartUMtHf+Gl6mLD6J6/XGc9YPNmb2kkYsn\nJ18Hv8lXsP+KH19ZfdkCtvvyXYY3dO0eGWzylbBQhg7ni4qhlhlWXUtT5UAGlMWob7Lfzq1vSlDA\nFwqyKY0bgceBscCbJCsN9bYXFfLlIoK+cYdls6aC7pyz7pqWts9xT8+lJ+eYqrSuPmyLLhN8utV7\nd8dOncyDrVL936WiprKUZU1tXcYRzPjKZNlkGsPTZ+/CDc9/mtT/OojfPPJhYoLuWNbBgLJ4ksXQ\nrqBtHZTFhUEVcURiLG5ooYPkST21D8SSxnb+esSm1Awoy1rtP2RAGd+//uUEtYYPBS757sZc+9yn\nSdbPRZO7ZsL3pH6uPB7jon035ppnZ3DUza8nKbTeWEBxgUHlMZY0ZVaqqpbuu2RFCwff2FVhAKxo\n7aCpzZIWWpe2ExOhHSMQfPnTBcxe3OTVbbUmqPyDKIsLAweUMz9mTb7eydDkK9bRTu3yxYxaasrk\n0i1rePKJN6heOJ9Ry+rYYebbDF++uEv3yJbKgXw5YGgS8WT53sOhGJSGql4DXCMiN6jqyXkcU0Fg\nZV1LqUV9QwaUpW1TGSa6s6Z8d06QiDAXZZYrP1WmVpzZxtbd6j0Xd2KwsPDMu95OtGJdrarzd6sP\n7mx29chpO3Uhb1xZK7SyLM5hW4/JqDTAqx+J2cTWnMaV04HtW7g8fYxheFUpZ+y+AVc+/UnSKr28\nNPNiyLc4Pqtr4PEz7bznL21Kqt2oqijh4dN3ZP+/vtir1X8qyktiDKkq5c9PfZxQRLMDbQQmn7JD\nEh3M8qa2rESRAtx09ETOuDO1XVZXqEJzm9Lclv4aDiyLU1lWwpz6Ji/2YnJb2jsYVlXeeRzg7Lvf\nYcmKZLPmmG3XYtt1h3HOPdMSmU7p0BGLM2+Q9UqZBvz46G1Z64c/4Zh/vNZpXXa0M7xhUaDXfR37\nDG5n3vszGLVsARss+JLhDYtpWFFYIeRcAuFd3tZAvKNfIgzXUrCob8mKVnb603OUxWORu6q6s6Yy\nTcbZlGQu/FQ+vUa2a5ZubNlW7z2dyINkiOUlMa46bELCjRaXGDER4hLr0Th6gsUrWpKoSlIza4YN\nLKVd6TFdBcDQASXE43F+/dD7SYHmYVXpub58pHuW162tYvQQI9xsV/jVgx8wbFAZk46ayA9ufLWL\n7OqKEhpb2rpwM3XpRR8Xhgwo5fKDNuP8+97rcp6+9Te7vrEL4/SCZc0Zm3KVl8RY0NBMY/PK18w3\ntXVQWtLRtfhToLqyhNFDKqlb1pywRlPTb296aWYSxX4uUOCjOUu5fspnydZlLG7V8NW1gPGb/fi0\nHTg00NCtKtbBsHdjPLZnbq7kfKCr/dUV44IfRKQE2Cqa4RQG0rldegO/qK+prYP2Dl3p44UBfzK+\n56Rtu8QmDrnxVfa6agqNaajG/Uk1kyLwGYJ7c46NLe1Mn1XfRW4mmZngK8TK0jjDqzv7hnxW18DC\nhpYk8sMosG5tFUOrOplgO7CMo5KYTe5XH74lK5pzy1QaWlmCHwMV4Ff7jWduYnWcjGz3L92z7LvT\n/njw5pR5DcXm1zdxSBqFAZYh5SuMIZVxLvjORlzy3U0YXp3cWbC13fibfnb3tC4rdPFICn0qHd8C\nOvymV1m8vIV27fR/V5WXcMfx2+C3Sm9u6+BPT3zcIxqUkgyPTFNrB8sa2xg6oPM++YV240bV8NTZ\nO/Pfk7fj8TN3YtigsrQuqlwxZEApZXEhHhMufej9rCzHMYGHTtuB0UMqeePCPThnzw2oLI3T0BFj\n3or2Pp0zUpHxiojIBSKyDNhMRJaKyDLv8zxgct5G2AcITj4rk2nlH6eiJEY8JkkvTV8idTIOQ0n2\n9prlorByRTqFuDJj6438n+2R7ONuaVfisRgVpXHWHjYwaaVZURqjpjL97LaspT2RKqvArx+cnjZg\n3dDc3oXbK3j/Mp17ZVmcvcetntg3sKIkp0ympY3tXP7ER/zmkQ+Yt7SJE3dcO2l/q0d3nrpC71Bz\nAS31qHR8y8hvRaB0xm4amts44ubXiXmZRgqJyv64wCk7j6XCm8wFGDqgNKFgfBy5TVdm7JjXcbG2\nupzHztyZe0/altuP34Z7Tto2QQjovxs+i8E/jplIbY7ssuUpt7KhqZWK0jhx0awMukMGlPLU2Tsz\np76RqTMXUVkW5yc7js3LM9sbdNsjXET+oKoX5Gk8abGyPcJ7g7DSZf3j+Ga4X2SW7yrPbOcTVqZX\nb65ZGCzAUY2tN8evLI2z15VTaNfOql7/3C7df5POPu8erfZ6w6s4+fa3ko6VLqMpE0NvTODFc3fj\n8JteTcsiHKxkT9eIKzju3f/SyVH635O25dQ73mLJCgsaZ5olfnvAeH45Ob1CywT/Pq9bW2Vp2Fmy\nu9KhujzO0hRXVSaaFh8+I21ja3vOz8CihpaEe3lQRRxV0vKE9QZB115FSYxBFSUJxTh6cCVP/ywc\nxu0+6RGuqheIyP7ATt6m51X14TAHUYgIK9PKP870WfWR8E/lgu6UQljpyNmuWaZJO1/1M+nGFubC\nwKdxb+3QROfHe366Hcf+8/VExthfnv4kkVLb3K5c9/ynrFZVxsiaCuqWNVFTWcqfDt6cSx58P5Ga\n295OItNreVNbl3akqjAnJUYQpKX377U/QQefAeiclHzF0djaTkVJjK8XN/LoGTvxwow6LrjvvbQU\n3jGBXTcazmrPlCXar2ZDkBrDv+ZPnrUzT7w/l/PufSdnxZGqMMAURrbGR7/ZfzyjBlfmbEWnsjVL\ns/Cv47ehubU9Y51NJqSjpPfdcaVxobIsllAYALOWNCbSbAupEtxHLiy3fwC2AW73Np0pItv3tfVR\nbAiDIr23yCU1Nsp05GxKq68oVsKso/Gvr9+BrR2IeQ2i/N7qtVXlXVrZNrV2sGBpMwPLS4iJIN6k\n6neRDLIBr1tbxewljex55ZSkCm8Flja2JhTDLlc8l0RLny49ev7SZl79rI4L75+e1PPEfz5bO5Tz\n//tuos+8vz21w9x1R2zBnPrGnBQGmItqUEUJk0/ZIen+7z1udf781MfULW1mYJmwaEV7ryhNMgXS\nfeWWer8z9XKB9GzNfoysvDR9wZ1430tV7APKYjS0dLXWFKipLGPJiq5El0sbWyLPtuwtcsme+i4w\nQdUSikXkVoyx1imNHiCYTbUyFOm9QV9UwwfRndLKZ/1MrmPqCYILgtYOTWTJjaqpZL/rXkziq0oN\nhra2K4u8yWlBQytH3vxal6Y740fXsKihhSNuejUtJcj5973HlF8MSzvR+fd6VE0l1RVxmlrbaW7r\n4Ce3vZk41tz6Jj6cU8+TZ+3MQ+/M4uIHpqftM19ZGmePv0xJdKLbbI0hPDF9TtJYSiR58q6pKGF5\nS1vCdVTf2NqlC53/bvhV/GsPG8hzH83n6mc+YckKC5KXxWNUlcdYtKIt6RrEsCB7JiNFMLfU4hUt\nSff7rS8XcfQtb9DeYcy8b1y4R9KY0rE1V5YZG+7iNCnRfv8L0KRrC9ZTA2Cf8SN4bPq8pN/VNaRn\nRv7JbW8WNWEhwGDArwAvPHupSBAVRXp36KvVvI++VlpRjyk19di3Et6fXZ8oFpu7pJl/H78NYPn/\nSxtbkzoO+lCsR0Tw2Ui4SjJQlSxraku4pvye6TGB/568fSKmccD1LyW5VFLb4B59yxu88ItdufrZ\nGUmxk2Cf+emz6qnwXFhl8RgHXf9SlzEFFUZZSYzK8jjLAhljw6rKu1zrxpZ29rvuxS6thH+w1Rq8\n9eWiRE/6jUdWM+3rRZxzzzssWeGRPaYojHhMKIlJUodJP44RvN+zljQm0lrbO5QXZtRx4Baj097T\ndHT+/n31lUV5SSxBGDmippxTd16PrxYtZ9KLMxPHTFUYqfATtdo6ip+w8A/A2yLyHKa4d8JZGb1C\nX06efbGaD8ouBJbflRlTLjxY/vXN1Mu5vNT6kUz5xa4JBbPfdS8yPyWVNrUrYqYKaj9GMLSqlPlL\nGznq5tcSE2hZPMbiFS2MHlKZsKpaPH6q0hiJQL0vt71Due+tb1gUaEKU2pws+PxWV5awpBu/fktb\nB4saWij16FHKS2JcffiELtfPH58/9/tKc1RNZcIaiAmMqK5gQYAZtkvTJ4zK/oqDN0tM4D6NCSQX\ncDa2tBOPTU9YGjutX9tl/JnqeYLNyHw2gSROteWw0chq0rUdysYmnC6Qn47Spq+RSyD8PyLyPLA1\nds7nqercqAfWH9HdRNUXBIf5kteXSisTch1Tb+MfY4dVEYuJTXoxYeywri+/IIgIw6pKM7b3TPR6\nWNJIm9pqesiAMu4/ZXvm1Ddy2n/e4rhb30w67uABpWknez+4vrTRMoIWrWhLTJwHbbkGd7/5dReq\nF/8Zgc7+J2OHVbHfdS9m5dsCs2JiiRW0pr0GwdU7dCrNJ96fm7AGOpSkXiTlJTGGDCxhbgoR1aLl\nLdQMKOOps9NT5vj3u7IszhsX7pExppENlWWdzch8upsgl9nQqlIjyFza9drcfvw2PDp9LndP/Yru\nssuryksyUtr0JXJ1T21NZ/ZUB/BQNMPp/8g0UeWb4LAv+4sUG3ob/5hd30ippzRKY2JxgbI4e145\nhbplzQyqKGFpYyst7UpdQwsX3v8ez/1817QFlJNP2YE9r3yehctb6WhXljW1sXhFC82t7Z0NjTwM\nSeHPCi5WmlrbOerm12lq60Cahdt+vDV1DS2JidPnJwOSuMGGVllzq2C/jqfP3oW3vlzEqbe/xZIM\ntPAlnjXQDolrkDpBp1u9f1bXwNZrDyXuXT9/ze6v0n/53Y3TUrZUlsYSxYPdUeYMrSpLckn1Buk4\n1QCO/PtrSdlglaVx/n38Npx19zSLfeVQjjT51O17pMzyhVyypy7HlIafPXWGiGyXS/aUiJwN/AS7\n1+9h1OojgTuBoVgr2KNUNbf0i36MfBMc9nV/kbCQD2upt27FUTWVSSyqo2oqkxh3m1Oyjuob2zLe\nh9n1jTQEJuaWtnaGDCjj2Fte6/Jdv0gtdZvPzxU8ly3XGtrlu2ffPS3hglqaiMHZeQSJIMePrqFm\nQFnadFwwt0TtoHJiIkmU9+ngr95Tm13ddNSWLGhoYfv1ajls0qvULWumdlA5G42sZmljV/dYAhum\nqwAAIABJREFUfWMb+1/3Ek+dvXNe3MHpvAeNLe3UDCilaWmnMm/v6OCVzxYmZdmlw4hBZRy29RgO\n23oMo4f0bRFwJuRiaexLL7KnRGQ01rhpE1VtFJG7gcO9412pqneKyI3A8cANK3EO/QL5jncUYnC6\np8iXtdTbmEw6SyMTUuMHqVi3torBgQZOMYGXP61LannrY/Hy1pxZgVN5qp54v7MZlqoy2KPbSLU0\ngq6v4LjAeLKuOGRCwtUGuRepBRczsxe3c/LtbzO8upz9Nh/NU2fvnOQqC3ZubGhuTzSHCiYS5COW\nluo9qCyL89gZO/Gdq55nvhcjamlXrnvuU1o7NEHNn4qyuHDdj7YqKBr0dIg6e6oEqBSRVqz73xys\nR/gR3v5bgV/jlEbeg8WFGJzuKfJpLfUmJuPHIlIV86gAWWBZPNalD0Ym+fedvD07/t+zdGD06Fc+\nPYNhg8oTabzxmCTSfXNhBQ4itUDRp68IVpdD18nfnyD9yumaAaU8dsZODK0qS7ICc712/mLGDyyn\nWjbB4wQz1oLZV35MJEortLtjD60q4/ojJya19/WbPf3mgPFc8eTHiaZfkMx/lcvx+xKRZU+p6iwR\nuQL4CmgEnsT6cixRVX959A2Q1qkoIicCJwKMGdOVR6Y/It/B4kIMTvcEhW4tZVLMT5+9S0Zaj2xY\nvKKF0sAqddHyFm7/ybcS+8cOq+rR8YIIFihWlMT4w0Gbsve41aksiyf51YNpwP55Da0q47mf75qI\nSQRJMHtqBfrXLNhD3s+AWtTQknR+wec3NSaS2oM+TCs013MbO6yKoQPLWLKiNal+Z/v1htHS1sGf\nnvgw0bFPgf87aFMqy+IJ+pK+ohzqDtnavW6vqi8D9wHP08PsKREZAhwArAMsAe4B9knz1bQZaKo6\nCZgExj3VnTyHVQ/FYC1lStvsLj03HXwl6WcsrVZV1sU66W3gNFUB+wojHdJNmgBn3Pl2IuZwzeFb\n9NoK9OMbwQyoI//+WtLEm44KJxgTCabAhm2F5mLh+rUxvvV138nbs3hFC0MGlLFTgPrch2Ap2an0\nJYUYb8xmaVyDUaC/qqpbAg/28Nh7AF+oah2AiNwHbAcMFpESz9pYA5jd82E7OBiK3VrqCSrL4jx0\n6o585+oXqG9sTbDAhnXsXBVwukmzfkVLwk02e0kTdcuaVtoKDGZA+cHjxo7sSijBnBtIgQ3bCs3F\nwg1abtJomW7jR9fwwNuzuigMsKp2P2usu9bDfY1sSqNVRG4BRovINak7VfWMbo79FfBtERmAuad2\nB6YCzwEHYxlUx9DPadYdVm2E7ZueXd/Isqa2pN4gYSnNXBVwuknzoXdmJX3nogem8/TZu/TYXZZ6\nvTJRtGRLGEhNgc0WK+oNclGwmRTL1hmC3GXxWOJapaMvKSRkUxrfw6yF3bBYRI+gqq+JyL1YWm0b\nlnE1CXgEuFNEfuttu7mnx3ZwKAZEkd2VbZWbr+Bpuklzj41XJybvJehJGls6EvQjuSLT9UpH0ZIt\nYaAvMqZyHcfiFS0JNuHyuFBVUcoKLw06yPxbyC7XbD3CF2CT+4eq+k5vDq6qvwJ+lbL5c4w118Gh\nX6Mn2V25TviZJpV8F2umTppDq8p48dzd2P+vLyVNgj1BpuvV0xhQsCYllSm2r1kQUpV+au+TTL8r\nJORCI9IrheHgsKoj1+yunk746SaVQijW9IkGn/5wHsOqynh/dn2PXENhZsNlCtb3NQtCOqVfWRYv\naMsiFbnWaTg4FBUKIc89V1dDGBN+IaQf+2y1fnaXQBea92zIZkX19F6mu6ZAnytWSFb6xUjn45SG\nQ79DIb2IubgawpjwC8EX7k/UPtLRvHeH1OvV23uZ6Zr2tWJNRSFYiD1FtjqNn2X7oar+JfzhODis\nPIrtRQxrwu9rX3hqHYnPPbUyk3Nv72Wma9rXijUVhWAh9hTZLI1BeRuFg0OIKMYXMdcJvxDcbpkQ\nZKttbm2nvDS+0umuK3MvuyusLAQUgoXYU4hq4RdbT5w4UadOndrXw3AoIhTy5Oqjp2MsJLdbKvqS\n56k/IKpzFJE3VXViaAckN2r0CoyJdhxQ4W9X1ePCHIiDA4T38hTaijIVvVEAhep2i1qZFfq9XFkU\n8mIgHWI5fOdfwOrA3sAUjPpjWZSDclg14b88h9z4KntdNYXG7lqbFTEyZfdkg++q8RloC8Xt1ptz\ncehEsV2/XLKn1lPVQ0TkAFW9VUTuAJ6IemAOqx4KdSUdBXrjqy9U/7cx3ZayYJkytKq0YJRZsaDY\nYnC5KA2fPWuJiIwH5gJrRzYih1UWxfbyrAx6qwAK1VUjXkNWITwSxVUFhboYyIRclMYkj+b8Yozp\ntgr4ZaSjclglUWwvD6xcDKZQFUBP8VldAwsbWiIhUSwURB2ML6ZnIRel8YyqLgZeAMYCiMg6kY7K\nYZVFMb08xRbAjAr93UJ09zkZuQTC/5tm271hD8TBodhQbAHMqOBbiPectG1RTqg+sWGmxIso7nN3\nMgsZ2SrCN8LSbGtE5KDArmoCqbcODqsq+vsKuycoJgsxiMaWdva8ckqi42A6nqyw73OxWy7Z3FMb\nYj01BgP7BbYvA06IclAODsWAYozBOCTj/dn1zFrciAKzFjfy/ux6JqY0Sgr7Phd7lmC2fhqTgcki\nsq2qvprHMTn0IVaF6tswUawrbIeeIcz7XOwWai6B8K9F5H5ge4y48iXgTFX9JtKROeQdxW42Ozj0\nFONG1TBqSEXimR83KvoFQLFbqLkojVuAO4BDvM9Hetv2zPYjEdkQuCuwaSxwCebuOgGo87ZfqKqP\n9mDMDhGh2M1mB4eewidZzPcE7vc/L0bFkYvSGK6qtwQ+/1NEzuruR6r6MTABQETiwCzgfuDHwJWq\nekUvxusQIYrdbHZw6A36wsVYzFZ9LkqjTkSOBP7jff4hsLCHcnYHPlPVL0VcxWihotjNZgeHYkEx\nW/W51GkcBxyK0YfMAQ7GrIWe4HA6lQ7AaSLyroj8w6s27wIROVFEporI1Lq6unRfcYgA/qrLKQwH\nh+iQC/lkodZydNtPQ0S2V9WXu9uW5fdlwGxgnKrOE5ERwAIsqH4ZMLI7mnXXT8PBwaG/IVumYlju\nqyj6aeRiaVyb47ZM2Ad4S1XnAajqPFVtV9UO4CZgmx4cq0coVE1dqONycHDIH7JZ9YXMNpCtInxb\nYDugNqVfeDXQE5X3QwKuKREZqapzvI/fB6b34Fg5o1ADTYU6LgcHh8JBISelZAuEl2GMtiUk9wtf\nisU1uoWIDMBSc38a2PxHEZmAuadmpuwLDYUaaCrUcTk4RA1XOJo7CjkpJVtF+BRgioj8U1W/7M3B\nVXUFsFrKtqN6c6yeolA1daGOy8EhbASVBOAs7B6iUNkGuk257a3C6GsUqqYu1HE5OISJVDfslYdO\ncBZ2P0EugfCiRaGmjxbquByKA8WQSJHqhgUKsr+5Q8+RLRD+f6p6nogcoqr35HNQDg4O6VEsiRSp\nbthxo2qchd1PkM09ta+IXAxcADil4eAQAlY2GFwsiRSZ3LCFOFaHniGb0ngcK8IbKCJLAcEyngRQ\nVa3Ow/gcHPoNwrASiimRolADuQ4rh2zZU78AfiEik1X1gDyOycGhX8G3Lpo862BlrASXSOHQ18gl\ne+oAj/pja2/Ta6rqyKAcHHJA0LoYWlXKalVlLGxYOSuhmFfwrlaj+NGt0hCRQ4ArgOcx19S1IvIL\nVb034rE5OBQ9gjGIRQ3wr+O3oaI0vkpOmj1xz4WpXJyiChe5UKNfDGytqvMBRKQWeBpwSsPBoRuk\nyyJaVSeuXIP4YWaIFUu2WTEhF6UR8xWGh4X08/oOB4ew4GIQncg1iB9mhlixZJsVE3JRGo+LyBN0\nkg4eBrj2rA4OOaKYYxBhIlcFGmaGWDFlmxULuu2nASAiBwE7YDGNF1T1/qgHFoTrp1GYcL5ih6jg\nYhrhIIp+GrlYGqjqfcB9YQp2KG44X7FDlAjTOnOWXrhwsQmHXqGQm8Q4ODhEB6c0HHqFXHocO/Qd\nioHU0KE4kZN7SkQqgTGq+nHE43EoErisoMKFcx06RIluLQ0R2Q+YhnFRISITROTBqAfmUPhwFO+F\nCec6LA4UqzWYi3vq18A2wBIAVZ0GrN3dj0RkQxGZFvhbKiJnichQEXlKRGZ4/w5ZmRNwcHBIRk9d\nh8U6eRUzfGvwkBtfZa+rphTVtc/FPdWmqvUi0qMDe66sCQAiEgdmAfcD5wPPqOrlInK+9/m8Hh3c\nwcEhI3riOnSurL5BMRcd5mJpTBeRI4C4iKwvItcCr/RQzu7AZ17r2AOAW73ttwIH9vBYDg4O3SBX\n12GYrixnseSOYk4kycXSOB24CGjGqsKfAC7roZzD6awoH6GqcwBUdY6IDE/3AxE5ETgRYMyYMT0U\n5+DgkAvCqph2FkvPUMyJJDlVhK+UAJEyYDYwTlXnicgSVR0c2L9YVbPGNVxFuINDdAijYnr6rHoO\nufFVGlvbqSyNc89J2xaNu6U/o08qwkXkIaxjXxD1wFTgb6ra1M0h9gHeUtV53ud5IjLSszJGAvOz\n/NbBwaEbrOykH0bFtON4WnWQi3vqc6CWZMLCecAGwE3AUd38/oeB3wI8CBwDXO79O7kH43VwcAig\nUNxCxexucegZclEaW6jqToHPD4nIC6q6k4i8n+2HIjIA2BP4aWDz5cDdInI88BVwSE8H7eDgYCik\nLBzH8bRqIBelUSsiY1T1KwARGQMM8/a1ZPuhqq4AVkvZthDLpnJwcFhJOLeQQ76Ri9I4B3hJRD7D\nqNHXAU4RkYF0ps46ODj0AZxbyCHf6FZpqOqjIrI+sBGmND4KBL+vinJwDg4O3cO5hRzyiZwIC4H1\ngQ2BCmAzEUFVb4tuWA4ODg4OhYhcUm5/BewCbIK1ed0HeAlwSsPBwcFhFUMuNCIHY4Hruar6Y2Bz\noDzSUTk4ODg4FCRyURqNqtoBtIlINVaMNzbaYTk4ODg4FCJyURpTRWQwVsj3JvAW8Hqko3JwcFgp\nOPJAh6iQS/bUKd5/bxSRx4FqVX032mE5ODj0FoVSJe7QP5FL575n/P+r6kxVfTe4zcFhVUWhruZd\n5z6HKJHR0hCRCmAAMMzrrud3YaoGRuVhbA4OBYtCXs27KnGHKJHNPfVT4CxMQbxJp9JYClwX8bjy\njjDooR36Hvm6j4XE+ZQKVyXuECUyKg1VvRq4WkROV9Vr8zimvKOQV40OuSOf97HQV/OuStwhKuQS\nCL9WRLYD1g5+vz9VhBfqqtFZPz1DPu+jW807rKrIpSL8X8C6wDTAj/gp/agivBBXjc766TnyfR/d\nat5hVUQu3FMTgU006r6wfYhCXDUWqvVTyCjE++jg0N+QS3HfdGD1qAfS1/BXjYUy0fir5srSeMFY\nP8WAQruPDg79DblYGsOAD0TkdaDZ36iq+0c2Kge3anZwcChI5KI0ft3bg3v0I38HxmNxkOOAvYET\ngDrvaxeq6qO9ldGf4XzmDg4OhYZcsqemiMhawPqq+rTX9zvXZe/VwOOqerCIlGHFgnsDV6rqFb0e\ntUMkcNlaDg4O3SGX7KkTgBOBoVgW1WjgRrrp8+0x4u4EHAugqi1Ai4hk+5lDH8Flazk4OOSCXALh\npwLbY5XgqOoMYHgOvxuLuaBuEZG3ReTvXl9xgNNE5F0R+YdHUdIFInKiiEwVkal1dXXpvuIQIhxf\nkYODQy7IRWk0e1YCACJSgsUnukMJsCVwg6puASwHzgduwCyWCcAc4M/pfqyqk1R1oqpOrK2tzUGc\nw8rAZWs5ODjkglwC4VNE5EKgUkT2BE4BHsrhd98A36jqa97ne4HzVXWe/wURuQl4uIdjdogALlvL\nwcEhF+RiaZyPuZnew0gMHwUu7u5HqjoX+FpENvQ27Y6l7o4MfO37WB2IQwHA1Tg4ODh0h1wsjUrg\nH6p6E4CIxL1tK3L47enA7V7m1OfAj4FrRGQC5uKaiSkiBwcHB4ciQC5K4xlgD8CPjFYCTwLbdfdD\nVZ2G0ZAEcVRPBujg4ODgUDjIxT1VoaqJVBrv/wOiG5KDg4ODQ6EiF6WxXES29D+IyFZAY3RDcnBw\ncHAoVOTinjoTuEdEZnufRwKHRTckBwcHB4dCRValISIxoAzYCNgQa/n6kaq25mFs/RaOrsPBwaFY\nkVVpqGqHiPxZVbfFpcaGAkfX4eDgUMzIJabxpIj8QBxpVChwdB0ODg7FjFxiGj8DBgLtItKIuahU\nVasjHVk/RSG2lnVwcHDIFblQow/Kx0BWFTi6DgcHh2JGt+4pMRwpIr/0Pq8pIttEP7T+C0fX4eDg\nUKzIJaZxPbAtcIT3uQG4LrIROTg4ODgULHKJaXxLVbcUkbcBVHWxxyXl4ODg4LCKIRdLo9UjKVQA\nEakFOiIdlYODg4NDQSIXpXENcD8wXER+B7wE/D7SUTk4ODg4FCRyyZ66XUTexPphCHCgqn4Y+cgc\nHAJwVfQODoWBjEpDRCqAk4D1sAZMf1PVtnwNzMHBh6uid3AoHGRzT92K9cJ4D9gHuCIvI3JwSIGr\nondwKBxkc09toqqbAojIzcDrPT24iAwG/g6MxwLpxwEfA3cBa2Od+w5V1cU9PbbDqgNXRe/gUDjI\npjQSTLaq2tZL6qmrgcdV9WAvTXcAcCHwjKpeLiLnYz3Iz+vNwR1WDbgqegeHwkE2pbG5iCz1/i9A\npfc5J+4pEakGdgKOxX7QArSIyAHALt7XbgWexykNhyxwQXAHh8JBRqWhqiv7do4F6oBbRGRz4E2s\nodMIVZ3jyZgjIsNXUo5DP4YLgjs4FBZyqdPoLUqALYEbVHULYDnmisoJInKiiEwVkal1dXVRjdGh\nwOGC4A4OhYUolcY3wDeq+pr3+V5MicwTkZEA3r/z0/1YVSep6kRVnVhbWxvhMB0KGX4QvLI07oLg\nDg4FgFy4p3oFVZ0rIl+LyIaq+jFWHPiB93cMcLn37+SoxuBQ/HBBcAeHwkJkSsPD6cDtXubU58CP\nMevmbhE5HvgKOCTiMTgUOXwqeQcHh75HpEpDVadhBYKp2D1KuQ4ODg4O0SDKmIaDg4ODQz+DUxoO\nDg4ODjnDKQ0HBwcHh5zhlIaDg4ODQ84QVe3rMXQLEakDvuzlz4cBC0IcTjHIdufc/+X2pWx3zsWD\ntVQ11EK3olAaKwMRmaqq6TK4+q1sd879X25fynbnvGrDuaccHBwcHHKGUxoODg4ODjljVVAak1ZB\n2e6c+7/cvpTtznkVRr+PaTg4ODg4hIdVwdJwcHBwcAgJTmk4ODg4OOQMpzQcHBwcHHKGUxqAiEhf\njyGfEENe731fXWMRia1q97ev4F3reOCzu+79EKuc0hCREhGpFJEJIrIugK4i2QAiso2IjFZDh7ct\nL89A8BrnazIRkZiqdqwq9xdARIaLyCYiMjSPMseLSLV3rdv97f39uovIYBEZ0NfjyDdWuewpEfkN\nsC7WjnYL4GvgaeB+VW2KWHYMWA3rl7428LWqLotSpid3N+AnQCuwPdAA3ALcpKor8iD/YGAm8KGq\nLg9sj/nKKwKZOwBHAVsBV6rq7SIi+ZjIRKQSWAvYG3gfeAain0RF5NvAecBY4DXgfGAAsCbwlqo2\nRyR3GjAYeAV4QFXvFpF9PZlzo77uIvI94LGgwvK2R/Z8ece/EmhQ1V96n8uASgBVrc/X85ZvrFJK\nQ0Q2wHqV7wHUAidjL1UD8JSqPhSx/NOB3YBSoAXrZvgq8HBUL7Qn9wFMMU5S1RYR+T7wU6AauEBV\np0Qo+1DgKuCfmLL8BHgPmKGq7Z5CuT/1hQ9B7nvAxcBwTHlcrqqPevtKVbU1THkpsi8Gvo21Nl4N\n+LuqvhqVvIDcO4BnVPVmEfkPEMcarc0HHoni+RaRcuCXGDfTS8CPgNWxhdnxqnpP2DJT5B8AHKqq\nP/Is2MHAzsDTqtoQsezpwIGq+qmIHIl1Jp0LTAWuj/Kd7lOo6irzBxyMTZz+522wyWxH4E1g8whl\nl2PtbUuxld/vgEeBG4ELAcFT4iHLLcUUxj5p9v0QuBYYHOF5X4sVRu0DXIIpkD9jSutXwOwIZO6N\nTZ7+513wJnDv833AmAjP+V1s0hwInAa8DKzh7dsf2DYCmWXAp8BQ7/N87zrUAscBD2HkdVE9238D\nfuJ9PgsjGH0DswAiuc6erDuBg73/HwP8A7N4lgBnRSh3TeAh7/+DgHeAcd79fQr4BRCP8tz76q/P\nB5DXk7Ub/Tbwd+Bo4Fbg596+i4ELI5S9PfBo4HMZ8BiwOfAwsHOEsncC/g3slbJdgBnAsAhlb+RP\n0J68cdiK7BxvYvGvf0mIMn8CnB08LnCFN4FvCkyN8Hy/ja1yg9v+D/iN9/8pwBYRyK0FTvT+Pwiz\nIIP7pwGjIpAb8/4d5V3jGuBm4DRv+ybev6FPoJiXYAFwqif/ZbzFkXef/wGMiPBeXw3cDpwCXBPY\nvnHwXe9vf6uUewpARNYADgL2BW4DHlTVBhF5Afidqj4RhS9URGqwh6wOmIM9WMtV9SwRORzYT1V/\nFKZMT24Mc1Eci03UA4G7MbfYHtgK+LsRxxe6uIO8AGIDMFpV54Tp/xWR4cBI4D1V7fDcFhsBPweq\ngDdU9YowZKWRvQGwAzAZWKSqKiKbARdgk9ilqrpdFLKzjGl/4BRV/U4Ex45h4RoVkR8BRwDbAWur\nan3Y8lJk+67HamzxtbaqTvDut2Cr/91UtS7CMfwCW4xuB/wLU86HAfWqekFUcvsSJX09gHxBRKqx\n4OQKLEj4N/V8jiIyHligqk8ARDF5qgXGLsRM6DWAtzA3CZib6OGwZXpyO7D4ySRgkohs7Y1hL8yM\nnuZ9NbKMpqDCCGROVWBWxpywFZaqzsfcM/5nBT4UkSbMyvl5WLLSyP4Ei9sAiWDsuyLyPvAEcGZU\nsgMyS4B2byKPA7tjsbzQEbxvaskGizGXVH3UgWhVnS8i12KT9kZAs7ddPUX5tarWRbQILFHVNuAO\nzP3Z7I1jS+Az4K9hyiskrDKWhoj8E9gMu6HfYKvcd4D7vNVoTVQPuvfifgtoBOYBTaq6yNs3HHOT\nHaIhB+5EZGMsZhDpii+D7CHAWFV9M2V71Jk0A7Hr2yWwLiKDsODsVVHJD8iKY/OXn9o8AngRcxHO\nDFnWZphb6ENVXZCybyCmoBeH+VyLSAW22HlHVd9K2TdAVVfkO3tIROJqyRVlmEvwRVW9z9+eB/kj\nVXVO1HL6GquE0vDcP6eq6o6ee2ojzOc5DgtOX66qLRHKvwtTGBMxpfEW5vN8LiqZntzPvP++Adyp\nqg8E9m0ONKvqRxHJ/ivm630ZeMSTP9PbNwA4VlWvD1nmKOAuzO34ChYzWQ6J1edqqrowTJkp8s8A\nvlTVyYFtiYkzqpW3iLyDBd9nYOc8HfjUWwRthLlKQp3MRORMLPvwCaADW4C94llaeBl6kyN0eW4B\nfBW8n56i7vDvNZ57MGS5I7GYzS3YO7w8Zf8IVZ0XpsxCw6pS3NeMvVCo6jeq+rSqXomZkNsCu0Yl\n2Jucx6vqsao6HnNPLAH+IiJPeBNdFHK3wCbMvTF33KkiMk1Ervcmkr8CURaA1WIZU7/GlOWzIvKk\n5zb4GXbdwy70OxpYB9gauA7L0joMGO1NIk95K+TQISKjgT8Bp3vX+ToR2S6gME7EMqrClrs+Fvh+\nCHO9bg4c6Y1jd+BZLAkjbOwE3I/Fxz7HFmHnisj5IjIJuCgQT4oCU4A6EXlFRI4BUFXfJbcTsGZE\nVs7xmNfgRGCaiNwsIsH5Y5Jn6fRbrCqWxmrY6qAey1h6K7Aiug0zsf8chTktInti6XenAjNT/Pu/\nAz5R1VvDlOkdeyzma71PVZeISCnmb90bcysMUdXVw5bryS71ZH+hqp962yqwoOX3ge8Ae6rqM2Gu\nvr1A7DeqOkVExniy9gTasHqJJaq6Xxiy0shOJDOIyDbAgVhNTjs2wZ2MJR0sz3KY3sgdik3YL3qT\n9NqYkh6LWdK7qOpaYcr05K4PtKjql9JZzLgRtlj4HZa9dnsUriERGYclleyPKcgTgfWB54HLvH0X\nagT1RyJyFjZfPOclPfwY2A9zd7cCS1X1u2HLLSSsEkrDh4ichK1EY9jqrAp7uQ7SCCtXvQyLUVgw\n8lPsZVssIpdgGR/HhS2zm/FcjaVKnh4I6OVL9hDgXVVdM4JjDwTKVHVxyvY1sOt+iEZUwOnFSzYA\n3lePWcA713WxFO+ZqnpgFLKzjOlOoE1Vj8yjzHUx99igqJ4rz6rbDgu4NwS2HY1lCLZFuCAaDAxU\n1Vkp2zfCaoH2U9VHopBdKOj32VPei/sDzD31FJa5VAsoVrl6WZQKw8OVmKVxMRbTmC4iEzCFdVrY\nwjzf7qZAE3aeDcDcwIovhrlvwPzRYcvfAqvEXg1YCvwvEKDdEKO6IIJV6ApguIg0aHKK71zg2agU\nBoAaHcybKdsWA1NFpA5LdggVXmxoBJY6/UmalfVzwP8ikLsmcACWlfegqs4N7G7FXFNtUQWgvQn7\nnjTb/iCWOTbSG2foCyLPam9Pc26fYtZev1YY0M8tDRFZD8uN/wBzzSwEXsdoO97Ig/wRWMbWephb\n7EvMF7wxpjy+Ss0uCknuXzBuq/WwAOVMrKjxEY2Y2kBEvgucgSmrVzB6iVKsLuTfngslCpfF77CV\n/kJsIn0foyd505tch4edtRSQPQZz+SnwIaakPvWsyUrgx2EH/T2512PXdzamOIZi1uxVqvp52PI8\nmdtjVf0PYc/yWtg5/1NV78v225DkD8T4xNqwjLHZwEeq2ixWM3IucI+qfhZ24oGInIqlL3+KPdMf\nYYuRGSIyDIujvB2WvEJFf1cavwZqVPVs7/M44FAsOPo/rII2kqwpMU6eF7HsoQrM71mPBaBvC9u3\nHZBbjfE6jfA+T8RoUrbDCvtOV9XPshxiZeU/A9ygqveKyOrYqm8jLI7xgqreHIHM/TFNbCXBAAAg\nAElEQVS3xDnepqHY+a6FZW09EbbMFPnvYZP1aKzSvwmzbP8ZVbaW93xNB76lnenbm2IZa2sC56jq\nxxHIvQ7LEPuj93kQnXGFxzXigjZPUY7GnuV3MUX9DUaU+EWEcg/EEjj+iNGmDMbiKHGMmmhGVLIL\nDloAZelR/QFnA//BVt0lge2CWSDbRyj7aGylG9y2G5Ztcg0R8dJgE/TjWMZWcPsALEB5TsTX/Dy6\nUlj4dSpP4tFKhCzzEqyaPyhvGDaZPQFMjPB8dwReCnyuwFb9k7CaoEj4zLBYySPAlmn2nQLcgMV3\nwpZ7IcaXNiJlexxzGe0Y4bWuBuYHPm+I8cldjlnym0Yo+3Lg3JT7vDHGn/YCsEFUsgvtr7+n3F6P\nuYSOBrYRkZEiUqt217fAVithp336aAaWiUiVv0FVn8VYQKuxrIvQoVZ38ThwoYj8QLzeCmoU6K9i\nGSdRnTMYfcZ3ReROEfmpiGyslgr5GjAe4woKG7cAO4rIpWIFVu2qukBV/41RtmwRgUwfS4AOETnG\nu85NaindJ2JcTN+PQqiatfhv4PcicoIEmh9hz/yGGo0VfTVmSZ0nItuJSJmIDFJzN44HFkNkz9cg\n4GUR2RlAVT9W1XuxiXsK8KOU6xAm7gSOFJHTRWSYd58/VNVLseu9ZURyCw790j0VDGqLVVyfjqUh\nfo35vLfAMpj2j3gcf8JWIzer6v2B7fdipvzfI5R9FLYKbsAoNbbGlNXNqnpnVEHKFPmbYVlj62Mv\n1lJVPT5sX7MnbzssrXU45ud+GaPzuAPYSSPy8Xuy98JSbD/0/uarUYdchxVQ/ixC2d/DeK02wQLf\n07G6o5tV9Z8RyRyOJXYcgL1P07CsxFZVPSwKmQHZxwOHYMzNbwMfqFHR7IMF4HeIMAtyL8y9LVhR\n8HQsdvYsZmFF5vYtJPRLpQGJNMutsNz8KV6gajcsc+hTLJvomygmsMAYyjGL4jTMXfIEFtfYBiNS\nC7UBkpc5sjE2UT+DTaC7Yo1hlmHnHEnvDLHajA0xS+ZutR4D62CKqhLz9b+mXsAyrGvurWhL/VW1\niGyFKcjvYVbN3er10YgC/gQlIjti516NVf/vgimQszU5uygMmTVY0L9ZVd/1tg3DsgQFcwPODPO5\n9lbw4zFX73z1+oOIyC5YRuLr3vYlUb5TnsztsXqjQVgG6FbAIuBWVb0nokSLUlVtFauL2RVbCG2P\nualuUdU7wpRXyOiXSsNbEZyPBciWY1TV/82j/LHYQzUn8FKPxQrNZmDFQaEHSEXk31hgULHCul+F\nLSOL7POxamQwC+OSqK+5l059ivf3EfArVX0psD9qnqvNMSr0EdjKfpYY+WUlZlm1R3Sfb8IWB02Y\nVXW+qs5O+U6o5+5Zjkdj79My7J0KPY04i/wq7FqvicUFY5j3IOb9zfPftZDl1mDMvcdi1/oiVf3A\nc8kti1pBFiT6OqgSxR/GGPsDLC3ucGwV9C1v3wbA9yOUPd6T/zrwIEacVp6Hc94Bq4itxWo0HgJ+\nHdh/MDAgQvn/A7by/r8j5irZyPv8PWBCBDIvxJpoDcWq7u/CC/5ilk1k1x2r8Xkd+ANwKebrD60n\nSBa5W+L1A8Em0JuBnwX2f48Immph6dP+/T0Acw9t6H3+FrB3xOf9EBbcvxureYn8Wntyz/Nkjsdi\nJ9cF9pXmYwyF9tfvAuHeimRNrFq0VVXvxLI6Tve+8jNsYo0qWHcyMEVVt8HaYG6KuUsQkREiEgmN\nBdYj5FFVrVPV97CHfaKIDBarVj1VI+oH7qV6tqjVRMRU9UUsMHmA95VfEs01PxBrq7lIVf+E5e5f\n5u07BlPYUeEErJjrAqzie3VMgSAiA0Tk9xEFZQ/Fri2q+jWWEXekiKznuU5+o6pLwhQoVodShfnv\nUSNkfJtOivnziDCpxHNHDcLe4ROwON11gf0nBBNOQsbBwB9UdTrwF2AdEfHP+1gR+WNEcgsW/U5p\nYCvMGzHF4eNGoFyMyGwi8EC6H4aEbbBVNqr6jifrXG/fj7G4ShSYDnwmhlJV/QArajwU8/9OhYRv\nOmwsA24QkWrtNNXvAvbyAtRNqvoUJHpbrDQ819RLeOfsbT4J2FtEarEmW8+GISsDxmGxA3/yvgDY\nVowF9fsYz1QUiQZP4vVe8RT051hflqOx838py297i3rMmhoS2HYZVn1/MDAGs6pDu78p+C7whKq2\nqdH8/x4YJSJjRGRD4KcaQT9wL+DvswejVvV/GnCUF8P7LlaLtWqhr02dqP/wTEjMTbUYK7gCr01l\nyLLKsQljrZTtj9OZ8RFlzUB5yuf1MVr0OXh55BB+H3LvuCWp1xRzH3VgVC0QYm0KtuBZG6v0Thwb\ne6n/Bbwe4XUWLND97eCzhFmxF2CulO9FKH9gyljKMSW9iJT6nCifM6wKvh6rQI/knfKO+z3vTwL3\n+XzvWp+DWQKhPl/e8UoxT8GolPt8EXAt1gEy0mtdiH99PoDQTyjDg4tlObwNnOB9jqq4LhZ4qXyF\ntTWWSfNKRDIl3f+9z2djBIGRKYws4yrB+L7Wy3ZvQpY5GpjlT2R5Ok9/IhuD0ba8n0fZFd6/J2HZ\nafm8v0OxwrY9g9chIlllKZ9Xw6znpUS8IArI9BOHNvaU5R/zeb0L5a9fZk9BUirk+sCBaj7vvhjH\nQFVdLiK3Yhw5f4hYnn/epwP/UdUFIrKOqn6Rh9oMPy1xH2wyu19E1lRz30QOERmvqtPF+K+mq+qX\neZCZlKUk1ohprKqeFbHcmBqP1wlYzc/XElEDIPH6gGOTZocYNc22qnpt2LK6GUfqtT4YOFlVd486\nUy4gc6iqLhJjzH5WvRYLqxL6s9LwJ7ArsA5ev4960kwzhirgJFW9Qow0r02j7RDon/PqwFOqumlU\nsroZxwtYlsldeZDlt/g8AthHVY+KWmaGcdwLHKmqTXmcwFbDfOqbaR7o7cVjjfXO9VFV/Ue+Uk69\nuJWvtH4F3KSqs0VktFqqcxQFo/4CzD/vI7AssWPClFNs6I+BcAC0kxp7fTpplPOST+2tzMDqBzbx\nxrMiCoURzFYJnPN3MLdQVIHvJNkiMt57oXwCu+l5Uhg7Yn5usASDJ73teaH89++zWGvTEk9hlEah\nMPxzEpH9vAAtWNLF896EFvo5i8gWYp0eDxKjZ/EVk2LBd/KhMDw56imMkcBx6tWlqNfXIopxeApj\nbOC898Tiknl7xgoR/ebEAyvO9YEJ2GT9sKr6aZ/kafV3FHC797EW61cdRe8IIPFgl2MP8yMYSeI/\ng0MKW2ZQtvffoRjf1Dy1bnyXQDTFdYHV3zZYHcru3vnfQWfWWj5W3RsBPovsaIygEKLpT1ICbCzW\nRvRarAIaVX1MRPystCjOuQSLD+0M7CMiX2NMA0docr+SSBC41/sDr6pqHVYTMsnbH8k7FZC7M0ZU\nuK33jD2DPWd5ecYKFf3OPSUi72DBuQasEKoJS0e9UjsbAYUtcyRGE7IvsJeq7p6yPxITPuDX3gor\nPBqK5cvPwlaC92nIOfsZxlGBpXwejdF2XBOhLH9xcA5WrHhZYF/ChRGRbP9674plhO0QlB3VosSz\nLI7w/qqxWoXFGB1OO/BbrL1pFBNoNda3Ymssk2gMRpMyD/ibqs4PW2aK/BhWbzMYS7X9BqhS61US\nFceUf59/hiVvXJFuf9hyiwX9wtIIrAzWwnpJnO7FEIZh5IT7YdQOUWEkxl57LPCwiHwLI+f7UER+\ngD3wofeRCODXwE0Yi207Vkx3JnCEiPxRVZ8MW2DgmpeqtTedJCKvAceINau5VVUbwn6xAxPjeE/W\nt7D+HY94cvKxCvoRRrmfSHQAdvbO9bmwhanqfBG5BnMnj8N6rbdjNQQTgdFhK4zAxDgCo2G/X1Xv\nE5GNMbfYphilSKTwJu8rsfTtizBl/aW3L5J7HVAI62PP2LrAHWpFq3lzyRUq+oWlEVh9now90Jeq\n16XNW6lUaETV0IExjMA61lVgbqnFmIXzC+A8DbDchix3MOaW2U5VG73V9kDM8piNsY9eEpXFISKn\nYEr5Naxnx5HY5HYz8FtVbYxCrid7I6wSfndsgTAFODNqN6SIXIu5Pp8IbLsHSz6YlPmXvZLlK+cK\njP7mP9JJvrk1tvJ+UlU/DFNuQP6BWNp2I0YVc5e3GCrNk4sqIUdELsR4zSZhlDkaoXVXginM8Zh7\nbiPM1fs/VY2SaaDg0S+Uhg8RuRwr+1+K+R7vVtWvIpbpK6xdMGrol8XYXXfD2D8/UdX/RCTbn1Au\nwhr//E5Vnxbr0T0J46N6HStCC33y9nzsh2K9ucdgPbLjmJtsX4xl9tKwZYu1/BytgXRHsWr/dTQP\nJHoisjvWy+IWrJCvBSsonBj24iTwfJ0KjFPVU8I8fg7yy7D7OR5Tzrth9/t4jZBu3pM9CLOYN8MW\nBCOxor7ngV+qx7Qbofwx/vwhlpG4F1Z7FaXXoODRr5SGDxHZG6Ps+BbwBfAdja6tqz9xPw78JQpX\nUDfyR2CT80+xCt2xWBrmZG/7GaoaKt9V0OWUyb/rrYY/AMZ47qtQZIrxK/0Ji1fVAI8C/8W650W+\n8g2MZ0OsbfD3sQ59/1bVyOhpROR/wGmqOlVEKj2r8mSMzfjxkGV1CTB7FrvP/zSc/Fh0W2Fte5dh\nrXtfoLNj3lFYh8jQzj3wjA3BukFujSnMR4FrNQ91P8WAoo9pBIJWPtf/MuA9VT3ci2tsr6otUQWv\nvIdsA2zl66d8lnq7r8QI5CIJForIKMwN9TbGQnorFiitx9wJx2KEdqEioDB+jvFLNWN8SK9ilCVL\nsM6FZ4WhMDzEMD/+scBcLBjsB9yfwdKqo24ANBCjgxmHkSP+TlV/I14ef4Ryy7FGR+thDLe+5XYE\nRpsSKnyFIUa1vwiriXgPqBeR+VhwWKMM/nvjeBOzXlPxmPduH4xR9ISF4DPWir1bv8ViOl+IyNOq\nuleI8ooSRa80PIVRiVFTz8JM6Ke8h/sVbJUQdfBKgM9FZHNVfUetwG481rM4yuyScmw1PxqzrFqx\nl+wdNc7/u70gbWgIKOmtsaZDJ2Oug1aM5fU/WC/lZXjpiWGJ9v79HuayOBH4k1rV+SIsmycSBFbe\nP8Z6hizDCAkbxKqj/Qy9sOXGVLVDrXHVncC5nqXVit37MjVSzDBlVmEW3GwsvfcY4L8iMg+7z/tj\nHFtg9yTslGr/+Vobm7x/iKWTTwZeCCxCXsSamkWB72OLkjMwXqsHReT/sGy1VR5FrTQCL/MRGOfP\nH7FU0/sxGuPh2MMWKVT1YxF5HjhLRF7BXrrNsBVwlHK/AK4WkdHY6vcErJvYed7+KLNbDsd6WayP\n0ZWcLiKXASPVaFPCzpryV/KXATOxCWuCiLyKxW5CX3EHZPuumh8C+3hj8LOkDsSUSOhKw5s8N8bO\ndSFmSa6HuSAXA8eHLROz1sZhVuMrfgxFRPbFMqaOVdW3/PFFIN9fHFyIcUvdg1kUewMDReQ8Vb1N\nI+hA6cWO4sBVWIJBNTDSC4pPwCjwV3kUtdIIvMxbYivcE4A7VXWyWO3Eap4ZHVWdxNoYcVoJllnS\njGVObYJZOKH7uAMrsQnYqvffalWxs8ToO24H3gpbro/AdfwQmzh/iCUegNHS+9Tcoa1CReRsjATw\nSfVSWsX6b1+EuQBb1KjgI4PncnwMo8PeUVXP9HbtRmevljDl7YitePfB+lF/jHUDfFJV3whbXgDv\nYr1B9gX2FZHPMOv1fxph21wfgXd6a4zB9nasEvsbrOK/DcKvlRAryv1MVV/Bq3YXkRuBs7C05oGq\nOiMsecWMog2Ee9o/5sUrRmMrsQMxt8WV2Gr7l6r6XBRKw/Pnb42l4n2EvdifAA+oVa5GCm/l92ss\nW+kN7OUai1Xr7h2Fv9nLyloYzEgTkXFYYLoV64q4o4ZYROlN1guwIOwczFXxN1V9xQu2lwANGkE/\nBU9+4tnxVv03YH0l/omlGO+lqjtHIPdJrEfFJCwYuw2msDbC2rtGnTk0ANgJ68K4DrYweAfjnIo0\nIOxlKh2BpW3fj7k7p4rIA8DRqro06wF6Lq8ciwM2Y42mXsKs57e9xZkAX4f5XBczillpnIHl5j+M\ncR2t8NIDz8J6CX+lETGNinUyexQ4RC1nfV1P5mHY6vo4jaA3dIaxbIm1tj0EWyVepaovpcuACUHW\nm5gVdTu26nvU8+uvhzW9WqSq74SppL3FwUFYSvGn2IT9Q+w6/wO4LcpJTEQEc02UYskFldg12Arz\n+9+qKf25Q5C5JvCcqq6XZt/F2Ht7WddfhiI7Rkr9g5f6uj9mVY0FzlbVaRHI3kEDPd69bSdh9z6G\n8XvtH4GVUYLxxG2DLcCqsNqjBsyD8Yiqzg1LXrGjmJXG7liNwPpYNs0ULCXv6+CqMyIr45fAMFU9\nMzg5ixVg3YC5jEKPZ0hKoaJ4ze3DlpNBdhyz4s7BsqMUc829CPxdvWrZqDJqRGRzLCj7gqo+ICI7\nYGmRRJXR4gX7D/f+pmPW5GyslfD/opDpyd0Rq0f4AWbB+c2HmkVkLDaR7aghp5EHFYZ01ofciWUA\nfuB95zbgY1UNNStPjM3hC8yafAq4OfBMHYBN4J+p6swoFkSenP0xKpxHsCD7t4HjgMXaR+zJhYii\nVRo+xLhx9sNM91FYNtEzqvrfCGVeBNSo6rnSyXZZ7gWAL8SCwVH4uf+NEeJtiBXttWAB91ewAOkU\nDbkiOc0YNsAy1F7FXvJTsSrwJmCriBRGFVZQtgaW7PClqp4Xtpw0ch/HUjr/gWUrbYvFGLbCXESR\ntZMVkb8Cn6vqX1K2H4FZuN8PWV6XCm8vLvigqm4d2HYA8KGG3EfCUxq/w+oxFmBB90HYtb9BIy7S\nDYxjPeyZ/kBVb/K2DdCIGSWKCUWrNLxVUVIGhxix21FYWmYF8CONoGrVcx/cgHXueiFl37OYi+jB\nkGWuhmVjfYzVJ4wG6jC/918x19SLqvpNhIF/v1/Ht7HstGdU9ZfevvVU9dMwV4FijaT2BV7Gkh1m\nYSv9c7C027s0ovoIEVkDmKyqW6XZ92egXlV/E4VsT8bOWG/7RZjiehjLjDsUSwN9LGR5kzFf/lXa\nSduxA1blflVUq/s04zgYUxZ3YXVX52Hv9BWqem4E8o7EaEJewp6xGBbbOAbrPX5f2DKLHUWrNHwE\nzOhrsIrsmd72f2EpgzeELE8wd8FJwLnAexitwdtYtst4Vd01TJkB2Rth6YCLsNXQdsDPVXXXqBSF\nJ3dzzFWyFFsJPou92Ndjuez/9DK6QnVNichdmBX5EEbZsTrmvvgCi58siNAddh6wtaoenDphilWD\n34A15Im0Ct2bRPfCFMYTGOdVqBaOF5N73jv+60FrNarrm2EcMSx2dBr2vP3MdwOKyGqqujBs5SUi\nL2Hv0dPAb7A4aRWWrfWFqn6Zz2tQDCh6pQHgZU89qKpb+TdYrA3myxphKqaI1GJWzZ7YhHYHttr/\nOOsPV05mDLgAe7n2xugN7ohyJSgiv8Xy5v+FpX2Oxla/7VjCwdSI5A4EdsESDEZgqcRTMXLEeVFO\n2GIdH9fB4mRfYBbeXFWt91xEB6nqwRHKj2PxhQ7xWuaKSIVG0BlQRC7FLPP/Yu6gD7DMw4/zMWGK\nyIl0pmiviaVy748FwH+oVo0elewa7B0+HuO2egCzMCOLWRU7ilJppD7IYlxT26vqJQEXSqSc9ykB\n8JGqOicqWQGZfo1GDeaeOQ+jObhKo2WT3RB7gScC8zGupTlYpsmS/2/v7GO2LMsw/juBmQ7FzFcQ\nhSV+IObSkZ8oyngVdSUaqS2dH5Q6df1hrvrHMGk2s/mxnB9ImR+JoljMFOcUwjZsCVq2qaHgTKeY\nlIkiJjno6I/junvvnh7obdzXg8/rdWzPeLkf9p7383Dd13le53mcx6nmpEI2dw974lPHJExBfVzS\n9zLa2w5TTifhjexdYCV2IjdgBd9GNZ82cy+L8Zz7Rqmmtd//JKayrgirJn8bO+mrJGXvgo6IN7Ay\n88045bon7g3ZgIkPWdhxbfaRcbjr/3jcc3SPpO/nsN3N6CqnkQqigyW9W7tWbaQdG4ySosCQx2x+\nEZgq6dxO3EN9oYf7Bi7HlMC7ctpN9qrIfyJOU60BXgZuzuW00slqUPquPyXp7YiYgBs3F+Sw2eYe\nRuL00OH4c4+WdHgGO/soNZBF31zqLwNnSZqaY32l0/LFkmbUTumj8Loaj2tX8zLWjgLXraYke7/B\nJIvlHXJYgesY1fNcfe9TgBGS5uS+h25DtzmNWVjN9Q5cKFtUi/Z3ACY2XSBssV8/XVQP2CPAXSlF\n1AmnUTnJGVieezywXh79mW1CIP/N3d8eyzvsKumqpm22uYchuDckW8TdxmbdYe0h0z33wxMD2wnp\nbYmtbfFmeV09AIiIIwFkyf1cVNOROD20uqV2cxZ+pi5o2uYm7mM3XOifhOsKK3GT3ZKMNv9NqElr\n7G4s+56lWXQgoNucxq/wf+quOM89GHO6r05/Hyvpohx52PBM6ioimilpYXVd0rImbbWxPQTne/+Z\nHNVoXMMZn9Num/toy91P7zX2nVdFz/Rz5STPAM6U9PnIrCrb5n4G427ooyStyWTjHCyL8jLuB5mp\nhpsGN2H3Ftz1/TwOwn5ci7YD+HRyljmJFu2CkrE4QHxCFqXsRG3la3iN9UaHhkx1IwZt7RvoL1Ik\n9hOcirlS0oHAeTgieQy4FphX/fMMt3Albmq7AZgcEV+PiKfxmM9hGewBfWkKSRtrD81nSRpP4S74\njiA5jJHAXmohGDToMLYFvpuizjqleglJiJHOjHStUhfgYOF5eS51Lr22L+Fi7BlYnuSS8FyHbIiI\n07Fa7/5YDPHciJhUc8iHYOJDbpXourMYFRG3SVoh6ZtKEy+bdBgRMSQiTo6Im8KU2wrLMOED3A9V\n0AZd4zQSFgNjIuKQiNhG0pOSzsf0vNVKYnYZ8r6jcH7zR5LuxZIDG3FHci9ubmscYSrkhxExJyLq\ng5T+gKmukATcOog98WmP6Jsb0iTOx4Ob3oiIXSLilIh4HEed6+A/RO2yorZRvY6bCiFDQBKWpdlP\n0hJJb2Pq53Dg8Yg4qUqhZMCpWNsJSQ/jwGtmuqeJwKW5o/tkW/R9r9NJApiZ1hf4eb0Yi24eFBGH\nphPXMfiU17E11o3omvRURNyONYB+ixfYBky/nIs37jGSZuXI+4bVbC/DJ50jgXMkHZDeG4ujtKOb\nPs6G+yNm4YLz7un1O9xw1dOkrTa2P4kppy8Da1tSBzlTFbNwr8DtEXEzphY/iLW1Kl2vXNTiY3Fn\n/SJgVa7ifhu7Pbgn5JGW6xdh0sGdangiZHJEF+Hv+qla6vF+vJ6PA16RdF2mZ2ob3OF/PG4SXZGu\nfwtYLOn3GWt0i4EfSno0Ih7GwfN8nKZ7UxmaCAcSukIaPSKm4+hzfCpE7oIVVQ/Eju+OKm2QaUN5\nFSvZzgZ+DjwREUPleRW9wMoc+U9Z/O8CYDJwE9b3X4IzJ2twx+q8zf2OLcCVOCL7GfBkmJb5ZioQ\nDo2IqZKaHLJU4WHgK+HGvsDU1leBhxLpYCLWGcuBGzHV8kRgRaqhPSc3eJ0AHKo8XeA9wI4ppz4I\nWCrp2RQE9eAIuFGnkepEs3AqDPpSRFfjE/TemC2W65m6BAeBfwd2SgHCcXi0wevVPTZtNKxo+wrw\nXiJzHAkcmP6P7wMeiIgJyqwi3M3oCqeBU0HPAEhaDiwPK64eAcyMiFVVYToHUpR9dVrY63E39osR\n8Qx+yK/PZRs7q5Owsug1EbERz0jeH/dM5Ir8R+MHO3AaYzrwXETMxZ3vo4B7mi5QSlqQUiP3Ym2t\n4yLiViwpsTvOOzeOVJd6ApiBN/FpOAW4IaxBdTaW6m7a7sFYyn8VloIZBhwSES8B10u6IlfdKq2Z\ndbWfkbQsItYBf5b0TsZT5TQ8h+QlfLLbL93LjIi4vKplNA1Z9PE+7ITvxUPalN5bm+pIz+SwPVDQ\nFempsKbUApxbvhsL872V3puDu7Bn52BYRMTFWAjxHtVGa4aFzU7ChfnGO8DTJjYUy3X0YDnwA7DK\n7WFN22uxPQTTHp9VGlcbnptxKtbnmQr0Svp1k5tKRByGU2JDsbLsZ7BT/iVOR66VdOmmf8MW2x8D\nvKfa3ISIOALrEJ0N7KSGGxkjYjbwenIOO+CmurH4O/4AmKEOi+WFZ2nsJGlVptTUQTg9dGyqW/wR\nmCzrpp2IHcolykCtTk56ZxwUjcAqwqfhWsY7eIRuUbTdDLrCaVQIS4McgDdSsBM5GThG0l8yOY3V\nWNZgLxwJPQTcL8s6TMHqto03mUXET/EGvQx4H3/m0cCNKRr/hKR/NG032d4GO6qhWI66Lgo5DtMg\nG62ppIf5GtwR/DR+mIfh+RVPY9mS9zPWUvbH6c7VapG1TySECyV9IYPdU7BC83dUUxUIC1Teibv9\nFzVtd2siOY3hcm/ROOAwSXem9/bBM1ImZLB7MC72v4Wfqx1JczrSn0uBhYmMULAJdEt6qsJtOOrf\nF0ekuwPTMjqMffEY1/OwNHYvng741Yh4ET/sRzdpM9mdDuxRq+GMwJ93NyyhQS6HkXA9/lzPYcba\n7Ojrjdgep+doOAo9HxdEr0inrN1wxD0Zp+IWZHQYn8PsqA+BjRFxNi4Sf5DW1Jvkm0G+ENdQZkfE\nU6SOaFmcbxyOfgcUlBoj0/p5AadgK0zHUi1Nry/wGltUW2Mj8CTEyVgWZ36O2uSAg6Ty2swLC7lt\n23JtGN5Yl2eyWUlB169tD5yA8+7HZ/y8p+MNGuwUlwKTau8f1fp9NGT3FBwUjGy53oOL48dm/Mw3\nYUVVMMliLg5GwI2kp3dgnfXi5r5bMUPuMTyIKKvdTr9wADCpzfXBOFB4ENg7XYsOrbFd8Pz3bGts\nIL26rU+j45C0XrU8djrRrMU59rmZzD4KHB0R88N9Cj2S1skCea9g/aN681mT+JLfrKsAAAHFSURB\nVF/c/W8oj0DhQlx0nx0Rl0VEb0RsJ9cX9iVvxD0eD7JCnu8+D/eFgCe3NZ4qaYUsd34t1nyahif3\nXZjb7lbADZhAsTQifpDSgsgnig3A3UqaU0o7eoPY1Br7K54AOuBOdTnQVTWNjxLCk8beVsZxq52u\n4UT/uPuvSro2R4E03UMv3qTH4M38b3iE77lN20r2BmPa5Z8kvVa7/gu8yZyGZ5YURs0WIhX6l2CS\nw3CsMzUBeA8z084EHpCpxtlkQzq9xgYaitP4CCNtaK01nDnyhLxcw4cGYUG+ddGn+3QoNe6+pKwR\nWVhKZGecsujBLK7cw44qB1l95n1wyuJdtZneV/D/I62tsZihtipd2xkz9U7D+nHDlXG4Vu1eOr7G\nBgq6rRD+sUKK5F9Lr9b3sjxQ2rrc/eoe1uO+BUiyDrlRnZqSwxgsaWVYmHF1J+x/HJDWzAstl9dI\nml8x9pLDyK4WvTXW2EBBOWkU9Au5ufsfRUSbOfQFeRAepCZJj31c1le3ojiNgoKCgoJ+o7CnCgoK\nCgr6jeI0CgoKCgr6jeI0CgoKCgr6jeI0CgoKCgr6jeI0CgoKCgr6jeI0CgoKCgr6jX8BzP2i7zoP\ncycAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116163908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = result.index\n",
    "y = result.day_percentage\n",
    "fit = np.polyfit(x, y, deg=1)\n",
    "ticks = make_ticks(result)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(x,y,s=6)\n",
    "fig.subplots_adjust(top=0.85)\n",
    "ax.plot(x, fit[0] * x + fit[1], color='red')\n",
    "ax.set_title('Percentage of footfall during the day (8am - 6pm) over time')\n",
    "ax.set_ylabel('Percentage of total footfall in the day')\n",
    "ax.set_xticks(result.index)  \n",
    "ax.set_xticklabels(ticks, rotation=70)\n",
    "ax.locator_params(nbins=10, axis='x') \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Footfall proportion - 9pm-8am"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1MlbURIOhVEyOQOmMJS1qwKghETke+AXwPCDAjSJykVLqTwPs9wfgEKBRRD4E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\nls64M6pOZBQtXX1OvIbqMLFk1hHY7md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m3ffmJ1/WZR7cTluFFkgtXQmnfkrC5RSMdyb4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bEUw+9cEK1MRHZV1SgbzwR4HNhfrJhMsDTNr4BHXWB+C4xXygf+jqUN7gJ84mJSU9WKin4CbKp+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1228677b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = result.index\n",
    "y = result.nighttime_percentage\n",
    "fit = np.polyfit(x, y, deg=1)\n",
    "ticks = make_ticks(result)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(x,y, s= 6)\n",
    "fig.subplots_adjust(top=0.85)\n",
    "ax.plot(x, fit[0] * x + fit[1], color='red')\n",
    "ax.set_title('Percentage of footfall at night (9pm-8am) over time')\n",
    "ax.set_ylabel('Percentage of total footfall at night')\n",
    "ax.set_xticks(result.index)  \n",
    "ax.set_xticklabels(ticks, rotation=70)\n",
    "ax.locator_params(nbins=10, axis='x') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In summary, it looks like the proportion of footfall in the evening has gone up after the openning of the Trinity Centre."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"methodbackground\"></a>\n",
    "## Method (Background)\n",
    "\n",
    "Resources:\n",
    "\n",
    " - Code: `machine_learning_master.py`\n",
    " - Documentation: \n",
    "   - `Machine Learning Overall Notes.docx`\n",
    "   - `machine-learning-tweaking.docx`\n",
    "   \n",
    "\n",
    "### Machine Learning Introduction\n",
    "\n",
    "XXXX Outline the different methods etc.\n",
    " \n",
    "\n",
    "### Machine Learning Workflow\n",
    "\n",
    "  1.\tSplit the data into training, validation and testing: 33.333/33.333/33.333\n",
    "  1.\tSelect a model using the training data:\n",
    "        1. Perform K-fold cross validation using each model\n",
    "        For each union of (k-1) folds:\n",
    "            - Scale the three weather based columns (in both test and training – using scaling parameters from training data on testing data)\n",
    "            - Fit a model\n",
    "            - Evaluate its performance on the test fold \n",
    "          \n",
    "        1. Compare the negative MSE score for each model – select the one with lowest score\n",
    "        \n",
    "  1. Tune the hyperparameters using the validation data:\n",
    "        1. Specify the possible hyper-parameter values in a grid\n",
    "        1. Perform K-fold cross validation using each parameter value:\n",
    "        For each union of (k-1) folds:\n",
    "            - Scale the three weather based columns (in both test and training – using scaling parameters from training data on testing data)\n",
    "            - Fit a model using those hyperparameters \n",
    "            - Evaluate its performance on the test fold \n",
    "\n",
    "  1. Evaluate the performance of the selected model and selected hyperparameters on unseen data\n",
    "      1. Construct overall training data from the union of the training and validation data \n",
    "      1. Scale this overall training data, and then train the test data using the parameters from the training \n",
    "      1. Fit the model to this training data and evaluate its performance on the test data\n",
    "\n",
    "  1. Extract information about feature importance\n",
    "\n",
    "### Feature Scaling\n",
    "\n",
    "XXXX see `Standardising Variables.docx`\n",
    "\n",
    "### Removing Outliers\n",
    "\n",
    "XXXX See `Removing Outliers.docx`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"method\"></a>\n",
    "## Running the Machine Learning Models\n",
    "\n",
    "This section will document the construction of the machine learning models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preamble\n",
    "\n",
    "Begin by loading the extra libraries required for machine learning and by defining functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nick/anaconda/envs/py35/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#import seaborn as sns\n",
    "#import statsmodels.formula.api as smf\n",
    "\n",
    "from pprint import pprint\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Packages needed for regression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.dummy import DummyRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import ElasticNetCV\n",
    "from sklearn.linear_model import PassiveAggressiveRegressor\n",
    "from sklearn.linear_model import RANSACRegressor\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.linear_model import TheilSenRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "import xgboost as xgb\n",
    "\n",
    "# From now on, make the plots look like they've come from ggplot\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "\n",
    "def solve(s):\n",
    "    \"\"\"Remove suffix (i.e. rd, st) from dates\"\"\"\n",
    "    return re.sub(r'(\\d)(st|nd|rd|th)', r'\\1', s)                \n",
    "                 \n",
    "def normalize(value):\n",
    "    \"\"\"Normalise date so it is all expressed on a range of 0 to 1 \n",
    "    The temperature data contains values which are negative, this is corrected for\n",
    "    by adding to all values the value of the most negative number, so that everything is \n",
    "    above zero\"\"\"\n",
    "    # http://people.revoledu.com/kardi/tutorial/Similarity/Normalization.html \n",
    "    # this is to do with normalising negative data - which applies to temperature\n",
    "    most_neg = - np.amin(value, axis = 0)\n",
    "    value = value + most_neg\n",
    "    min_value = np.amin(value, axis = 0)\n",
    "    max_value = np.amax(value, axis = 0)\n",
    "    normalized = (value -min_value) / (max_value - min_value)\n",
    "    return normalized\n",
    "\n",
    "def split_day(day, hours_wanted):\n",
    "    \"\"\"Keep only certain hours in the day\"\"\"\n",
    "    time_split = day.loc[day['Hour'].isin(hours_wanted)]\n",
    "    return time_split\n",
    "\n",
    "\n",
    "\n",
    "def make_processed_file (hours_wanted, footfall, bank_hols, school_holidays, \n",
    "                         uni_holidays, weather, easter_sundays ):\n",
    "    \"\"\" This is the main function which will be used in other files to produce \n",
    "    the processed file.\n",
    "    \n",
    "    PARAMETRS:    \n",
    "     - hours_wanted: used to focus on particular time segments. \n",
    "     - footfall: the footfall data (as a DataFrame)\n",
    "     - bank_hols: times of bank holidays\n",
    "     - school_holidays: school holidays\n",
    "     - uni_holidays: university holidays\n",
    "     - weather: the weather\n",
    "     - easter_sundays: times of easter sundays\n",
    "     \"\"\"\n",
    "    \n",
    "    # Import the file and reduce it to only the columns we are interested in and aggregate\n",
    "    # it at the level of one day \n",
    "\n",
    "    # Filter only the needed columns\n",
    "    day = pd.DataFrame(f, columns = ['Day', 'Month','Year', 'Hour', 'InCount', 'Day_yr', 'week_no'])\n",
    "    \n",
    "    # Split the day file to keep only some of the hours to represent different segments of the economy\n",
    "    day = split_day(day, hours_wanted)\n",
    "    del day['Hour']\n",
    "    \n",
    "    # Aggregate count data by day\n",
    "    day = day.groupby(['Day','Month', 'Year', 'Day_yr', 'week_no'], as_index=False).sum()    \n",
    "    # Create a column for the day of the week\n",
    "    day['day_week'] = day['Day_yr'].apply(lambda x: x.split('/')[0])  \n",
    "    # Recreate date column without the day of the week\n",
    "    day['Day_yr'] = day['Day_yr'].apply(lambda x: x.split('/')[1]) \n",
    "    \n",
    "    ######  Bring in external information and join to the original dataframe. ###### \n",
    "    # Festive bank holidays = Christmas Day, New Years Day and Easter Sunday\n",
    "    bank_hols['UK BANK HOLIDAYS'] = create_date_format(bank_hols['UK BANK HOLIDAYS'], '%d-%b-%y','%d-%b-%Y')\n",
    "    bank_hols.rename(columns={'UK BANK HOLIDAYS':'Day_yr'}, inplace=True)\n",
    "    bank_hols['bank_hols'] = 1\n",
    "    \n",
    "    # Bring in school holiday information\n",
    "    school_holidays.rename(columns={'holidays':'Day_yr'}, inplace=True)\n",
    "    school_holidays['school_holiday'] = 1\n",
    "    \n",
    "    # Bring in uni holiday information\n",
    "    uni_holidays.rename(columns={'holidays':'Day_yr'}, inplace=True)\n",
    "    uni_holidays['uni_holiday'] = 1\n",
    "    \n",
    "    # Bring in weather info\n",
    "    weather = weather.rename(columns = {\"date\": 'Day_yr'})\n",
    "    weather['Day_yr'] = create_date_format(weather['Day_yr'], '%d/%m/%Y','%d-%b-%Y')\n",
    "    weather = weather.drop (['abnormal_rain', 'high_temp', 'high_wind', 'low_temp', 'wind_speed'], axis = 1)\n",
    "    \n",
    "    easter_sundays['easter_Sunday'] = easter_sundays['easter_Sunday'].apply(solve)\n",
    "    easter_sundays['easter_Sunday'] = create_date_format(easter_sundays['easter_Sunday'], '%d %B %Y','%d-%b-%Y')\n",
    "    easter_sundays.rename(columns={'easter_Sunday':'Day_yr'}, inplace=True)\n",
    "    easter_sundays['easter_sunday'] = 1\n",
    "                  \n",
    "    # Join the holiday and weather information\n",
    "    day = day.merge(school_holidays,how='left', on='Day_yr')\n",
    "    day = day.merge(uni_holidays,how='left', on='Day_yr')\n",
    "    day = day.merge(bank_hols, how='left', on='Day_yr')\n",
    "    day = day.merge(easter_sundays, how='left', on='Day_yr')\n",
    "    # Replace NaNs with 0s (before joining weather data which has lots of NaNs)\n",
    "    day = day.fillna(0)\n",
    "    # Join weather\n",
    "    day = day.merge(weather,how='left', on='Day_yr')\n",
    "    \n",
    "    # Remove rows where we have no weather data\n",
    "    day = day.dropna()\n",
    "    \n",
    "    # Create columns for before or after Trinity opened\n",
    "    trinity_opened= \"13-Mar-2013\"\n",
    "    trinity_opened = datetime.datetime.strptime(\"13-Mar-2013\", '%d-%b-%Y')\n",
    "    day['after_trinity_opened'] = day['Day_yr']#\n",
    "    day['after_trinity_opened'] = pd.to_datetime(day['after_trinity_opened'], format = '%d-%b-%Y')\n",
    "    day['after_trinity_opened'] = day['after_trinity_opened'] > trinity_opened\n",
    "    day['after_trinity_opened'] = day['after_trinity_opened'].astype(int)\n",
    "    \n",
    "    ######  For categorical variables create a dataframe containing dummy variables. \n",
    "    ###### E.g. for each variable we create a column which is coded as 0 or 1. \n",
    "    ###### Join these dataframes on to the original dataframe, and delete the original columns\n",
    "    months = pd.get_dummies(day['Month'], drop_first = True)\n",
    "    months.columns = ['February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']\n",
    "    days_dayweek = pd.get_dummies(day['day_week'], drop_first = True)\n",
    "    week_no = pd.get_dummies(day['week_no'])\n",
    "    del week_no[33]\n",
    "    days_years = pd.get_dummies(day['Year'], drop_first = False)\n",
    "    del days_years [2015]\n",
    "    \n",
    "    # pd.get_dummies creates columns with names which start with digits. This is not allowed\n",
    "    # in Python's variable naming conventions. So, we convert all the names to strings and append\n",
    "    # and underscore and use these to rename the columns\n",
    "\n",
    "    days_years_names = list(days_years.columns.values)\n",
    "    days_years_names =[str(i) for i in days_years_names]\n",
    "    days_years_names_new = ['_' + x for x in days_years_names]\n",
    "    days_years.columns= [days_years_names_new]\n",
    "   \n",
    "    # Join the dummy variables onto the main file\n",
    "    day= pd.concat([day, days_dayweek, days_years, months, week_no], axis=1)\n",
    "    \n",
    "    ######  Final processing and cleaning. ###### \n",
    "    # Remove all unneeded columns\n",
    "    day = day.drop (['Day', 'Month', 'Year', 'day_week', 'week_no'], axis = 1)\n",
    "    \n",
    "    # Reset index so there are no missing entries\n",
    "    day_new = day.reset_index(drop = True)\n",
    "    \n",
    "    day_new['week_before_xmas'] = np.where(day_new['Day_yr'].str.contains(\"24-Dec|23-Dec|22-Dec|21-Dec\") == True, 1,0 )\n",
    "    day_new['week_after_xmas'] = np.where(day_new['Day_yr'].str.contains(\"29-Dec|30-Dec|31-Dec|01-Jan\") == True, 1,0 )    \n",
    "\n",
    "    day_new = day_new.reset_index(drop = True)\n",
    "    \n",
    "    return day_new\n",
    "\n",
    "\n",
    "# Specify a scaler which will only scale certain columns   \n",
    "def MinMaxScaler_keepcolnames (scaler, data_to_be_scaled):\n",
    "    scaled = scaler.transform(data_to_be_scaled)\n",
    "    X = pd.DataFrame(scaled, columns = data_to_be_scaled.columns)\n",
    "    return (X)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation\n",
    "\n",
    "Seperate the data into:\n",
    "\n",
    "  - Y : the response variable\n",
    "  - X : the predictor variables\n",
    "  \n",
    "The following reads in all the data, creating a big data frame with loads of information about each day. Many of the explanatory variables are binary (e.g. is it a Tuesday? is it a bank holiday?) but some are continuous (e.g. the average daily temperature). This leads to some challenges with scaling, which we'll come on to.\n",
    "\n",
    "#### Read in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Day_yr  InCount  school_holiday  uni_holiday  bank_hols  \\\n",
      "0  01-Jan-2011    47767             1.0          1.0        0.0   \n",
      "1  01-Jan-2012    32486             1.0          1.0        0.0   \n",
      "2  01-Jan-2013    45364             1.0          1.0        1.0   \n",
      "3  01-Jan-2014    34198             1.0          1.0        1.0   \n",
      "4  01-Jan-2015    46056             1.0          1.0        1.0   \n",
      "\n",
      "   easter_sunday  mean_temp  rain  after_trinity_opened  Monday  \\\n",
      "0            0.0        0.0   0.0                     0       0   \n",
      "1            0.0        8.6   2.4                     0       0   \n",
      "2            0.0        6.2   0.0                     0       0   \n",
      "3            0.0        6.9   1.9                     1       0   \n",
      "4            0.0       11.8   0.6                     1       0   \n",
      "\n",
      "        ...         45  46  47  48  49  50  51  52  week_before_xmas  \\\n",
      "0       ...          0   0   0   0   0   0   0   0                 0   \n",
      "1       ...          0   0   0   0   0   0   0   0                 0   \n",
      "2       ...          0   0   0   0   0   0   0   0                 0   \n",
      "3       ...          0   0   0   0   0   0   0   0                 0   \n",
      "4       ...          0   0   0   0   0   0   0   0                 0   \n",
      "\n",
      "   week_after_xmas  \n",
      "0                1  \n",
      "1                1  \n",
      "2                1  \n",
      "3                1  \n",
      "4                1  \n",
      "\n",
      "[5 rows x 84 columns]\n"
     ]
    }
   ],
   "source": [
    "# Read in data\n",
    "footfall_f = pd.read_csv(FOOTFALL_FILE)\n",
    "holidays_f = pd.read_csv(\"holiday_data/UKbankholidays.csv\") # Bank holidays\n",
    "school_hols_f = pd.read_csv(\"holiday_data/school_hols_long.csv\")\n",
    "uni_holidays_f = pd.read_csv(\"holiday_data/uni_hols_long.csv\")\n",
    "easter_sundays_f = pd.read_csv(\"holiday_data/Easter-Sundays.csv\")\n",
    "weather_f = pd.read_csv(\"weather_data/overall_weather.csv\")\n",
    "\n",
    "original = make_processed_file( \\\n",
    "                               hours_wanted=list(range(0,24)),  \\\n",
    "                               footfall=footfall_f,  \\\n",
    "                               bank_hols=holidays_f, \\\n",
    "                               school_holidays=school_hols_f, \\\n",
    "                               uni_holidays=uni_holidays_f,  \\\n",
    "                               weather=weather_f, \\\n",
    "                               easter_sundays=easter_sundays_f \\\n",
    "                              )\n",
    "\n",
    "print(original.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove outliers\n",
    "\n",
    "The model should predict _normal_ footfall. Therefore any days that have extremely high or low footfall should be taken out of the training data. We don't actually want the model to try to predict footfall on unusual days, because the things that make the day unusual (like errors in the camera counters, or the presence of special events) are not captured in the input data.\n",
    "\n",
    "Outliers are detected using the Median Absolute Deviation method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I found 57 outliers from 2179 days in total. Removing them leaves us with 2122 events\n"
     ]
    }
   ],
   "source": [
    "def doubleMADsfromMedian(y,thresh=3.5):\n",
    "    \"\"\"Find outliers using the Median Average Distance.\n",
    "    \n",
    "    VALUE: return a list of true/false denoting whether the element in y is an outlier or not\n",
    "    \n",
    "    PARAMETERS:\n",
    "      - y is a pandas Series, or something like that.\n",
    "    \n",
    "    warning: this function does not check for NAs\n",
    "    nor does it address issues when \n",
    "    more than 50% of your data have identical values\n",
    "    \"\"\"\n",
    "    # Calculate the upper and lower limits\n",
    "    m = np.median(y) # The median\n",
    "    abs_dev = np.abs(y - m) # The absolute difference between each y and the median\n",
    "    # The upper and lower limits are the median of the difference\n",
    "    # of each data point from the median of the data\n",
    "    left_mad = np.median(abs_dev[y <= m]) # The left limit (median of lower half)\n",
    "    right_mad = np.median(abs_dev[y >= m]) # The right limit (median of upper half)\n",
    "    \n",
    "    # Now create an array where each value has left_mad if it is in the lower half of the data,\n",
    "    # or right_mad if it is in the upper half\n",
    "    y_mad = left_mad * np.ones(len(y)) # Initially every value is 'left_mad'\n",
    "    y_mad[y > m] = right_mad # Now larger values are right_mad\n",
    "\n",
    "    # Calculate the z scores for each element\n",
    "    modified_z_score = 0.6745 * abs_dev / y_mad\n",
    "    modified_z_score[y == m] = 0\n",
    "    \n",
    "    # Return boolean list showing whether each y is an outlier\n",
    "    return modified_z_score > thresh\n",
    "\n",
    "# Make a list of true/false for whether the footfall is an outlier\n",
    "no_outliers = pd.DataFrame(doubleMADsfromMedian(original['InCount']))\n",
    "no_outliers.columns = ['outlier'] # Rename the column to 'outlier'\n",
    "\n",
    "# Join to the original footfall data to the list of outliers, then select a few useful columns\n",
    "join = pd.concat([original, no_outliers], axis = 1)\n",
    "join = pd.DataFrame(join, columns = ['Day_yr', 'outlier', 'InCount'])\n",
    "\n",
    "# Choose just the outliers\n",
    "outliers = join[join['outlier'] == True]\n",
    "outliers_list = list(outliers['Day_yr']) # A list of the days that are outliers\n",
    "\n",
    "# Now remove all outliers from the original data\n",
    "df = original.loc[~original['Day_yr'].isin(outliers_list)]\n",
    "df = df.reset_index(drop = True)\n",
    "\n",
    "# Check that the lengths all make sense\n",
    "assert(len(df) == len(original)-len(outliers_list))\n",
    "\n",
    "print(\"I found {} outliers from {} days in total. Removing them leaves us with {} events\".format(\\\n",
    "    len(outliers_list), len(join), len(df) ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set up the data in the correct format\n",
    "\n",
    "Scikit-learn requires input data to be represented as a list of lists. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The predictor variables\n",
    "Xfull = df.drop(['InCount'], axis =1)\n",
    "\n",
    "# The variable to be predicted\n",
    "Yfull = df['InCount'].values\n",
    "\n",
    "# Store the predictors themselves in a list for future reference\n",
    "predictors = list(df.drop(['InCount', 'Day_yr'], axis=1).columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Validation Data Sets\n",
    "\n",
    "The following data sets are created:\n",
    "\n",
    " - training (**`train`**) (66%): Used for finding the best models from a set of all possible models\n",
    " \n",
    " - testing (33%). This split is actually divided again (50:50) and used for:\n",
    " \n",
    "   - hyperparameter tuning (**`validate`**) on the models chosen from step 1.\n",
    "   \n",
    "   - testing the final model on totally new data (**`finaltest`**)\n",
    "  \n",
    " - training the final model: the `validate` and `train` data are combined for the final training, before the model is tested on `finaltest`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Split data into training and test sets\n",
    "X_train, X_temp, Y_train, Y_temp = \\\n",
    "    train_test_split(Xfull, Yfull, test_size=0.6666, random_state=123)\n",
    "    \n",
    "# Split off another training set now to train the hyperparameters using the two best models from above\n",
    "X_validate, X_finaltest, Y_validate, Y_finaltest = \\\n",
    "    train_test_split(X_temp, Y_temp, test_size=0.5, random_state=123)\n",
    "\n",
    "# Save copy of the X files with dates and then delete\n",
    "X_finaltest_date = copy.deepcopy(X_finaltest)\n",
    "\n",
    "# Clean up\n",
    "del X_temp, Y_temp, X_finaltest['Day_yr'], X_validate['Day_yr'], X_train['Day_yr']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find the best model\n",
    "\n",
    "Use k-fold cross validation to evaluate a range of regression algorithms on the training data. \n",
    "Use a pipeline for evaluation which first scales the (weather) data. \n",
    "Print the results and assess which models perform best.\n",
    "\n",
    "The following models were trialled: \n",
    "  - Decision Tree\n",
    "  - Random Forest\n",
    "  - Extra Trees\n",
    "  - Dummy Regressor\n",
    "  - Elastic Net CV\n",
    "  - Passive Aggressive\n",
    "  - RANSAC\n",
    "  - SGD\n",
    "  - TheilSen (_dropped in code below because it takes too long_)\n",
    "  - K Neighbours\n",
    "  - LinearRegression\n",
    "  - XGBoos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><thead><td>Name</td><td>Median R2</td><td>Median MSE</td><td>runtime (sec)</td></thead><tr><td>RandomForest</td><td>0.7340005920078179</td><td>-378591739.6379578</td><td>0</td></tr><tr><td>XGBoost</td><td>0.7462969523449481</td><td>-378694633.1382051</td><td>2</td></tr><tr><td>ExtraTrees</td><td>0.7211988588678552</td><td>-472166948.4711811</td><td>1</td></tr><tr><td>LinearRegression</td><td>0.7037563092954271</td><td>-512696142.9362885</td><td>0</td></tr><tr><td>DecisionTree</td><td>0.6372122581043411</td><td>-572538613.3732395</td><td>0</td></tr><tr><td>KN</td><td>0.47136621704493603</td><td>-894956745.5104225</td><td>0</td></tr><tr><td>SGD</td><td>0.4636683734500078</td><td>-934851175.4437718</td><td>0</td></tr><tr><td>ElasticNetCV</td><td>0.019836323075337614</td><td>-1577692292.0104644</td><td>3</td></tr><tr><td>DummyRegressor</td><td>-0.001232023795954662</td><td>-1608469454.9979224</td><td>0</td></tr><tr><td>PassiveAggressive</td><td>-16.824496120573784</td><td>-29107081384.49559</td><td>0</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define a list of all the models to use\n",
    "Models = {'DecisionTree' : DecisionTreeRegressor, 'RandomForest': RandomForestRegressor, \n",
    "          'ExtraTrees' : ExtraTreesRegressor,'DummyRegressor' :DummyRegressor, \n",
    "          'ElasticNetCV' : ElasticNetCV, 'PassiveAggressive' : PassiveAggressiveRegressor,\n",
    "          # RANSAC': RANSACRegressor, # This one is terrible too\n",
    "          'SGD': SGDRegressor, \n",
    "          #'TheilSen': TheilSenRegressor, # Drop this - it isn't great and takes too long\n",
    "          'KN': KNeighborsRegressor, 'LinearRegression': LinearRegression,\n",
    "          'XGBoost': xgb.XGBRegressor}\n",
    "\n",
    "# Now just run each model, but do this in multiple processes simultaneously to save time.\n",
    "\n",
    "# This function runs a model, returning a tuple containing:\n",
    "#  (name, model, r2 (all), median r2, mse (all), median mse, runtime).\n",
    "# It expects a tuple with (name,model_type)\n",
    "def run_model( x ):\n",
    "    name, model_type = x # Unpack the tuple\n",
    "    # See how long it takes to run this model\n",
    "    start = thetime.time()\n",
    "    # Use a pipeline to first scale the inputs (especially the weather)\n",
    "    model = Pipeline (\n",
    "        [ ('standardize', MinMaxScaler(feature_range = (0,1))), \n",
    "         (name, model_type())]\n",
    "    )\n",
    "    # Evaluate the pipeline (run the model)\n",
    "    kfold = KFold(n_splits=10, random_state=7)\n",
    "    mse = cross_val_score(model, X_train, Y_train, cv=kfold, scoring = 'neg_mean_squared_error')\n",
    "    r2 = cross_val_score(model, X_train, Y_train, cv=kfold, scoring = 'r2')\n",
    "    \n",
    "    # See how long it took (in seconds)\n",
    "    runtime = int(thetime.time() - start)\n",
    "    # Return the results, taking the median of the errors\n",
    "    return (name, model, r2, np.median(r2), mse, np.median(mse), runtime)\n",
    "\n",
    "    \n",
    "# Now call that function simultaneously for each model\n",
    "p = Pool(processes=None) # A pool of processes (one for each core)\n",
    "results = p.map(run_model, [(name, model_type) for name, model_type in Models.items()])\n",
    "\n",
    "# Sort the results by median mse (that's item 5 in the tuple)\n",
    "results.sort(key=lambda x: x[5], reverse=True)\n",
    "\n",
    "# Put the results in a nice dictionary and print them\n",
    "results_dict = {}\n",
    "txt = \"<table><thead><td>Name</td><td>Median R2</td><td>Median MSE</td><td>runtime (sec)</td></thead>\"\n",
    "for name, model, all_r2, r2, all_mse, mse, runtime in results:\n",
    "    txt += \"<tr><td>{}</td><td>{}</td><td>{}</td><td>{}</td></tr>\".format(name, r2, mse, runtime)\n",
    "    results_dict[name] = (model, all_r2, r2, all_mse, mse, runtime)\n",
    "txt +=\"</table>\"\n",
    "\n",
    "\n",
    "display(HTML(txt)) # print as html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# I THINK THIS IS REDUNDANT NOW THAT WE HAVE THE TABLE ABOVE\n",
    "## Find the errors associated with each model that we just calculated\n",
    "#r2_result = [] # Store the R2 results\n",
    "#mse_result = [] # And the mean square error\n",
    "#for name, model, r2, mse, runtime in results:\n",
    "#    mse_result.append([name, mse])\n",
    "#    r2_result.append([name, r2])\n",
    "# \n",
    "#\n",
    "## Sort and print the results \n",
    "#mse_result.sort(key=lambda x: x[1], reverse=True)\n",
    "#r2_result.sort(key=lambda x: x[1], reverse=True)\n",
    "#\n",
    "#txt = \"<table><thead><td>Model</td><td>R2</td><td>MSE</td><td>runtime(s)</td></thead>\"\n",
    "#\n",
    "#print(\"MSE RESULTS\")\n",
    "#pprint(mse_result)\n",
    "#print(\"R2 RESULTS\")\n",
    "#pprint(r2_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A bar chart showing the success of each model. Note that especially bad values are surpressed, and for the Mean Square Error the graph actually shows the error for each module minus the worst error so that the numbers are positive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DnmXDhg0aPny4vvzlL3fpv5NPFRQUqLm5Wf/85z919913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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c29dc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_mse = min([mse for (name, model, all_r2, r2, all_mse, mse, runtime) in results])\n",
    "               \n",
    "x =  [ name for (name, model, all_r2, r2, all_mse, mse, runtime) in results]\n",
    "y1 = [ mse-min_mse   for (name, model, all_r2, r2, all_mse, mse, runtime) in results]\n",
    "y2 = [ r2 if r2 > 0 else 0 for (name, model, all_r2, r2, all_mse, mse, runtime) in results]\n",
    "\n",
    "fig, (ax1,ax2) = plt.subplots(1, 2, figsize=(15, 7))\n",
    "\n",
    "ax1.set_title(\"MSE\")\n",
    "#ax1.invert_yaxis()\n",
    "ax1.bar(range(len(x)), y1)\n",
    "ax1.set_xticks(range(len(x)))\n",
    "ax1.set_xticklabels(x, rotation=90)\n",
    "ax1.set_ylim([27000000000, 29000000000])\n",
    "\n",
    "ax2.set_title(\"R^2\")\n",
    "ax2.bar(range(len(x)), y2)\n",
    "ax2.set_xticks(range(len(x)))\n",
    "ax2.set_xticklabels(x, rotation=90)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#del x,y1, y2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tune the Best Models\n",
    "\n",
    "It looks like *RandomForest* and *XGBoost* are the most accurate predictors, although *LinearRegression* does surprisingly well. \n",
    "\n",
    "Now tune their internal parameters ('hyperparameters') to try to improve the predictions further (this isn't relevant to linear regression as that doesn't have tuneable parameters).\n",
    "\n",
    "Use GridSearchCV to evalaute the performance of a range of hyperparameter values and to identify the best performing one, and the resulting MSE score.\n",
    "\n",
    "Again, place each model in a pipeline with the scaler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hyperparameter results for RandomForest\n",
      "\tBest Score: 0.7367937849711531\n",
      "\tBest params: {'randomforestregressor__max_depth': None, 'randomforestregressor__max_features': 'auto'}\n",
      "Hyperparameter results for XGBoost\n",
      "\tBest Score: 0.7973302696546442\n",
      "\tBest params: {'xgbregressor__learning_rate': 0.1, 'xgbregressor__max_depth': 5, 'xgbregressor__n_estimators': 150}\n",
      "Hyperparameter results for LinearRegression\n",
      "\tBest Score: -3.7584551302556895e+21\n",
      "\tBest params: {}\n"
     ]
    }
   ],
   "source": [
    "## Set up a dictionary containing the hyperparameters we want to tune\n",
    "hyperparameters_rf = { 'randomforestregressor__max_features' : ['auto', 'sqrt', 'log2'],\n",
    "                  'randomforestregressor__max_depth': [None, 5, 3, 1]}\n",
    "hyperparameters_xgb = {'xgbregressor__max_depth': range(1, 11, 2),\n",
    "                   'xgbregressor__n_estimators' : range(50, 400, 50),\n",
    "                   'xgbregressor__learning_rate' : [0.0001, 0.001, 0.01, 0.1, 0.2, 0.3]}\n",
    "hyperparameters_lr = {}\n",
    "\n",
    "# Set up the pipeline containing the scalers\n",
    "pipeline_rf = make_pipeline(MinMaxScaler(feature_range = (0,1)), \n",
    "                         RandomForestRegressor(n_estimators=100))\n",
    "pipeline_xgb = make_pipeline(MinMaxScaler(feature_range = (0,1)),\n",
    "                         xgb.XGBRegressor(n_estimators=100))\n",
    "pipeline_lr = make_pipeline(MinMaxScaler(feature_range = (0,1)),\n",
    "                         LinearRegression())\n",
    "\n",
    "# Store the scores in a results dictionary (and print them)\n",
    "final_results = {}\n",
    "for model_values in [(pipeline_rf,  hyperparameters_rf,  'RandomForest'),\n",
    "                     (pipeline_xgb, hyperparameters_xgb, 'XGBoost'),\n",
    "                     (pipeline_lr,  hyperparameters_lr,  'LinearRegression')]:\n",
    "    \n",
    "    clf = GridSearchCV(model_values[0], model_values[1], \n",
    "                       #cv = None, # Cross-validation method. None means default (3-fold)\n",
    "                       cv = 10, # positive intiger means k-fold (e.g. 10-fold)\n",
    "                       #scoring  = 'neg_mean_squared_error', # MSE to calculate score\n",
    "                       scoring  = 'r2', # MSE to calculate score\n",
    "                       n_jobs=multiprocessing.cpu_count()) # Run on multiple cores\n",
    "    \n",
    "    #clf = GridSearchCV(model_values[0], model_values[1], cv = 10, scoring  = 'r2')\n",
    "    clf.fit(X_validate, Y_validate)\n",
    "    name = model_values[2]\n",
    "    final_results[name] = clf\n",
    "    print (\"Hyperparameter results for {}\".format(name))\n",
    "    print (\"\\tBest Score: {}\".format(clf.best_score_))\n",
    "    print (\"\\tBest params: {}\".format(clf.best_params_))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chart the results of the final models. Note, see: [GridSearchCV docs](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) for information about the things that you can get from the results (e.g. the `cv_results_` member gives lots of information about the scores for each validation step)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression diagnostics:\n",
      "{'mean_fit_time': array([ 0.02600694]),\n",
      " 'mean_score_time': array([ 0.00133996]),\n",
      " 'mean_test_score': array([ -3.75845513e+21]),\n",
      " 'mean_train_score': array([ 0.78938896]),\n",
      " 'params': ({},),\n",
      " 'rank_test_score': array([1], dtype=int32),\n",
      " 'split0_test_score': array([ 0.73025631]),\n",
      " 'split0_train_score': array([ 0.78944174]),\n",
      " 'split1_test_score': array([ -2.79290731e+22]),\n",
      " 'split1_train_score': array([ 0.78862725]),\n",
      " 'split2_test_score': array([ -9.49667027e+21]),\n",
      " 'split2_train_score': array([ 0.78705718]),\n",
      " 'split3_test_score': array([ 0.72292301]),\n",
      " 'split3_train_score': array([ 0.78981013]),\n",
      " 'split4_test_score': array([ 0.58634133]),\n",
      " 'split4_train_score': array([ 0.79330258]),\n",
      " 'split5_test_score': array([ 0.63760364]),\n",
      " 'split5_train_score': array([ 0.79832617]),\n",
      " 'split6_test_score': array([ 0.76129813]),\n",
      " 'split6_train_score': array([ 0.78548659]),\n",
      " 'split7_test_score': array([ 0.77993879]),\n",
      " 'split7_train_score': array([ 0.78580165]),\n",
      " 'split8_test_score': array([ 0.75782019]),\n",
      " 'split8_train_score': array([ 0.78398919]),\n",
      " 'split9_test_score': array([ 0.68872676]),\n",
      " 'split9_train_score': array([ 0.79204709]),\n",
      " 'std_fit_time': array([ 0.00467028]),\n",
      " 'std_score_time': array([ 0.0001855]),\n",
      " 'std_test_score': array([  8.55951288e+21]),\n",
      " 'std_train_score': array([ 0.00407072])}\n"
     ]
    },
    {
     "data": {
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EFwDAOIQXAMA4hBcAwDiEFwDAOIQXAMA4hBcAwDiEFwDAOIQXAMA4hBcAwDiE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\nCwBgHMILAGAcwgsAYJz/BR0gcKQahUwqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c29dac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LinearRegression is sometimes very bad, and I'm not sure why. These explosive errors\n",
    "# drag up the error. Ignore it from the bar chart below.\n",
    "# E.g. see the different split scores below:\n",
    "print(\"Linear Regression diagnostics:\")\n",
    "pprint(final_results['LinearRegression'].cv_results_)\n",
    "\n",
    "x = [ name for name in final_results if name != 'LinearRegression']\n",
    "y = [ final_results[name].best_score_ for name in x]\n",
    "\n",
    "fig, ax1 = plt.subplots(1, 1, figsize=(7, 7))\n",
    "\n",
    "ax1.set_title(\"Final Results After Hyperparameter Tuning\")\n",
    "#ax1.invert_yaxis()\n",
    "ax1.bar(range(len(x)), y)\n",
    "ax1.set_xticks(range(len(x)))\n",
    "ax1.set_xticklabels(x, rotation=90)\n",
    "\n",
    "del x, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Choose the best model\n",
    "\n",
    "  - Choose the model which (with optimised hyperparameters) performed best on the validation data.\n",
    "  - Fit this model to the whole of the training set. \n",
    "  - Scale the data again. \n",
    "  - Test the models performance on the final held out testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final scores for XGBoost: MSE: 382796400.1814735 R2: 0.7533400228808265.\n"
     ]
    }
   ],
   "source": [
    "# We have selected XGBoost as being the most appropriate model and discovered the optimum \n",
    "# hyper parameters. We can now test the performance of our final model on the untouched \n",
    "# testing data to verify its predictive power\n",
    "\n",
    "# Build the union of the two training sets to make a final training set\n",
    "X = X_train.append(X_validate)\n",
    "Y = np.hstack((Y_train,Y_validate))\n",
    "\n",
    "# Scale the data, using the same scaling constraints from the training data on the test data\n",
    "scaler = MinMaxScaler(feature_range = (0,1), copy = False).fit(X) \n",
    "X = MinMaxScaler_keepcolnames(scaler, X) \n",
    "X_finaltest = MinMaxScaler_keepcolnames(scaler, X_finaltest)\n",
    "\n",
    "# Fit an XGBRegressor model to the training data. \n",
    "xgb_model = xgb.XGBRegressor(n_estimators = 200, max_depth = 5, learning_rate = 0.1)\n",
    "xgb_model = xgb_model.fit(X,Y)\n",
    "# Use the fitted model to predict the values from the test data\n",
    "Y_pred = xgb_model.predict(X_finaltest)\n",
    "# Evaluate the performance of the model at correctly predicting the values of the test data\n",
    "score = mean_squared_error(Y_finaltest, Y_pred)\n",
    "score_r2 = r2_score(Y_finaltest, Y_pred)\n",
    "print(\"Final scores for XGBoost: MSE: {} R2: {}.\".format(score,score_r2))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extricate information about feature importance and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_temp : 1142\n",
      "rain : 383\n",
      "Monday : 131\n",
      "_2013 : 131\n",
      "Saturday : 130\n",
      "_2016 : 130\n",
      "after_trinity_opened : 123\n",
      "Thursday : 122\n",
      "Tuesday : 116\n",
      "school_holiday : 115\n",
      "Sunday : 105\n",
      "_2011 : 97\n",
      "_2012 : 93\n",
      "Wednesday : 77\n",
      "_2014 : 76\n",
      "uni_holiday : 74\n",
      "December : 69\n",
      "48 : 57\n",
      "bank_hols : 46\n",
      "November : 45\n",
      "October : 42\n"
     ]
    },
    {
     "data": {
      "image/png": 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CBAYPHszYsWOPsRT7Jhp+p/xEo0wQnXIdikzOvexTho4lIhIC3lLVG912HNadZKaqti7o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\nSo0aNahbty516tThhRdeSOlZr8/Kw4FrCg9R1OWOVA6Q0HEKbBWbBqC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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c33c828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# It's a bit of a pain to get the feature importances. I adapted the code here: \n",
    "# https://github.com/dmlc/xgboost/issues/757\n",
    "\n",
    "feature_importances = xgb_model.booster().get_fscore() # A dictionary of features->scores\n",
    "sorted_features = sorted(feature_importances.items(), key=operator.itemgetter(1),reverse=True) # Convert into a list of tuples\n",
    "for (i, (feature,importance)) in enumerate(sorted_features):\n",
    "    if i > 20: # Only print the first 20 features\n",
    "        break\n",
    "    print(\"{} : {}\".format(feature,importance))\n",
    "\n",
    "\n",
    "\n",
    "# Graphing them is much easier:\n",
    "fig = xgb.plot_importance(xgb_model, importance_type = 'weight')\n",
    "fig = fig.get_figure()\n",
    "#fig.savefig(\"Plots/weekno_cat_noxmas-eastsun-nyday.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check difference in result if Random Forest is applied"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final scores for RandomForest: MSE: 437514784.4651792 R2: 0.7180815005723133.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>RF-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>0.339144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>0.195933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>December</td>\n",
       "      <td>0.073648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mean_temp</td>\n",
       "      <td>0.060966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>rain</td>\n",
       "      <td>0.053617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Monday</td>\n",
       "      <td>0.022535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>_2016</td>\n",
       "      <td>0.020811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>school_holiday</td>\n",
       "      <td>0.018272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>0.011935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>0.010601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>after_trinity_opened</td>\n",
       "      <td>0.010500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>November</td>\n",
       "      <td>0.010409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>48</td>\n",
       "      <td>0.009104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>_2011</td>\n",
       "      <td>0.008995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>uni_holiday</td>\n",
       "      <td>0.008851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>51</td>\n",
       "      <td>0.008816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>_2013</td>\n",
       "      <td>0.008474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>October</td>\n",
       "      <td>0.007721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3</td>\n",
       "      <td>0.007037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>0.005722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>_2014</td>\n",
       "      <td>0.005187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>39</td>\n",
       "      <td>0.004898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>44</td>\n",
       "      <td>0.004785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2</td>\n",
       "      <td>0.004274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bank_hols</td>\n",
       "      <td>0.004209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>27</td>\n",
       "      <td>0.003983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>47</td>\n",
       "      <td>0.003286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>April</td>\n",
       "      <td>0.003071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>0.002962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>_2012</td>\n",
       "      <td>0.002782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>16</td>\n",
       "      <td>0.001332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>23</td>\n",
       "      <td>0.001314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>42</td>\n",
       "      <td>0.001282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>40</td>\n",
       "      <td>0.001272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>9</td>\n",
       "      <td>0.001173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>week_before_xmas</td>\n",
       "      <td>0.001165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15</td>\n",
       "      <td>0.001142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>21</td>\n",
       "      <td>0.001128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>17</td>\n",
       "      <td>0.001068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>19</td>\n",
       "      <td>0.000849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>32</td>\n",
       "      <td>0.000841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>easter_sunday</td>\n",
       "      <td>0.000823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>49</td>\n",
       "      <td>0.000820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>31</td>\n",
       "      <td>0.000683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>34</td>\n",
       "      <td>0.000671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>18</td>\n",
       "      <td>0.000664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>28</td>\n",
       "      <td>0.000618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>26</td>\n",
       "      <td>0.000546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>30</td>\n",
       "      <td>0.000544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>20</td>\n",
       "      <td>0.000518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>11</td>\n",
       "      <td>0.000506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>36</td>\n",
       "      <td>0.000500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>24</td>\n",
       "      <td>0.000497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>10</td>\n",
       "      <td>0.000478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>37</td>\n",
       "      <td>0.000379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>25</td>\n",
       "      <td>0.000337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>29</td>\n",
       "      <td>0.000311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                features  RF-score\n",
       "8               Saturday  0.339144\n",
       "9                 Sunday  0.195933\n",
       "28              December  0.073648\n",
       "4              mean_temp  0.060966\n",
       "5                   rain  0.053617\n",
       "7                 Monday  0.022535\n",
       "17                 _2016  0.020811\n",
       "0         school_holiday  0.018272\n",
       "11               Tuesday  0.011935\n",
       "12             Wednesday  0.010601\n",
       "6   after_trinity_opened  0.010500\n",
       "27              November  0.010409\n",
       "75                    48  0.009104\n",
       "13                 _2011  0.008995\n",
       "1            uni_holiday  0.008851\n",
       "78                    51  0.008816\n",
       "15                 _2013  0.008474\n",
       "26               October  0.007721\n",
       "31                     3  0.007037\n",
       "10              Thursday  0.005722\n",
       "16                 _2014  0.005187\n",
       "66                    39  0.004898\n",
       "71                    44  0.004785\n",
       "30                     2  0.004274\n",
       "2              bank_hols  0.004209\n",
       "55                    27  0.003983\n",
       "74                    47  0.003286\n",
       "20                 April  0.003071\n",
       "29                     1  0.002962\n",
       "14                 _2012  0.002782\n",
       "..                   ...       ...\n",
       "44                    16  0.001332\n",
       "51                    23  0.001314\n",
       "69                    42  0.001282\n",
       "67                    40  0.001272\n",
       "37                     9  0.001173\n",
       "80      week_before_xmas  0.001165\n",
       "43                    15  0.001142\n",
       "49                    21  0.001128\n",
       "45                    17  0.001068\n",
       "33                     5  0.000963\n",
       "47                    19  0.000849\n",
       "60                    32  0.000841\n",
       "3          easter_sunday  0.000823\n",
       "76                    49  0.000820\n",
       "59                    31  0.000683\n",
       "61                    34  0.000671\n",
       "46                    18  0.000664\n",
       "56                    28  0.000618\n",
       "54                    26  0.000546\n",
       "58                    30  0.000544\n",
       "48                    20  0.000518\n",
       "34                     6  0.000513\n",
       "39                    11  0.000506\n",
       "63                    36  0.000500\n",
       "52                    24  0.000497\n",
       "38                    10  0.000478\n",
       "77                    50  0.000436\n",
       "64                    37  0.000379\n",
       "53                    25  0.000337\n",
       "57                    29  0.000311\n",
       "\n",
       "[82 rows x 2 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model = RandomForestRegressor(n_estimators = 200, max_depth = None, max_features= 'auto' )\n",
    "rf_model = rf_model.fit(X,Y)\n",
    "Y_pred = rf_model.predict(X_finaltest)\n",
    "score = mean_squared_error(Y_finaltest, Y_pred)\n",
    "score_r2 = r2_score(Y_finaltest, Y_pred)\n",
    "print(\"Final scores for RandomForest: MSE: {} R2: {}.\".format(score,score_r2))\n",
    "\n",
    "# Also get the feature importances (this is a bit easier than with XGBoost)\n",
    "\n",
    "importances = rf_model.feature_importances_\n",
    "# Finds the standard deviation of the importance from the mean\n",
    "std = np.std([tree.feature_importances_ for tree in rf_model.estimators_],axis=0)\n",
    "# The index values of the variables that would return a list of them in descending size order\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Print, in descending order, the variable plus its importance value and store these results in a table\n",
    "#for f in range(X.shape[1]):\n",
    "#    print(\"%d. %s (%f)\" % (f + 1, predictors[indices[f]], importances[indices[f]]))\n",
    "#randforestfeats = [[predictors[indices[f]], importances[indices[f]]] for f in range(X.shape[1])]\n",
    "randforestfeats = pd.DataFrame(list(zip(predictors, importances)), columns = ['features', 'RF-score'])\n",
    "randforestfeats['RF-score'] = randforestfeats['RF-score'].abs()\n",
    "randforestfeats.sort_values(by = 'RF-score', ascending = False, inplace = True)\n",
    "randforestfeats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Join results back to original data and check known events\n",
    "\n",
    "Join the dates on to the X final test file, so we have the date predicted count and actual count. So can look at particular dates and see how good predictions are.\n",
    "\n",
    "Begin by preparing a dataset that contains the original data for a load of dates that we're interested in looking at."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Will look at the following events: \n",
      "                      Event       Day_yr\n",
      "319     TourdeFrance Depart  05-Jul-2014\n",
      "697        xmas_lights_2011  10-Nov-2011\n",
      "554        xmas_lights_2012  08-Nov-2012\n",
      "483        xmas_lights_2013  07-Nov-2013\n",
      "916        xmas_lights_2014  13-Nov-2014\n",
      "845        xmas_lights_2015  12-Nov-2015\n",
      "702        xmas_lights_2016  10-Nov-2016\n",
      "408      light_night_2016_1  06-Oct-2016\n",
      "480      light_night_2016_2  07-Oct-2016\n",
      "623        light_night_2015  09-Oct-2015\n",
      "197        light_night_2014  03-Oct-2016\n",
      "264        light_night_2013  04-Oct-2013\n",
      "333        light_night_2012  05-Oct-2012\n",
      "475        light_night_2011  07-Oct-2011\n",
      "522           marathon_2016  08-May-2016\n",
      "665           marathon_2015  10-May-2015\n",
      "736           marathon_2014  11-May-2014\n",
      "807           marathon_2013  12-May-2013\n",
      "878           marathon_2012  13-May-2012\n",
      "517           marathon_2011  08-May-2011\n",
      "1416     John Lewis Opening  20-Oct-2016\n",
      "1443        Trinity Opening  21-Mar-2013\n",
      "1474  leeds_fest_2014_thurs  21-Aug-2014\n",
      "1690    leeds_fest_2014_sun  24-Aug-2014\n",
      "1762    leeds_fest_2014_mon  25-Aug-2014\n",
      "1907  leeds_fest_2015_thurs  27-Aug-2015\n",
      "2113    leeds_fest_2015_sun  30-Aug-2015\n",
      "1764  leeds_fest_2016_thurs  25-Aug-2016\n",
      "1980    leeds_fest_2016_sun  28-Aug-2016\n",
      "2048    leeds_fest_2016_mon  29-Aug-2016\n"
     ]
    }
   ],
   "source": [
    "# Create a dataframe that has all the events in it, they're original footfall, and eventually\n",
    "# the predicted footfall\n",
    "events = pd.DataFrame()\n",
    "\n",
    "# Define all the events to check\n",
    "events_to_check = {'TourdeFrance Depart' : '05-Jul-2014',\n",
    "                   'xmas_lights_2011' : \"10-Nov-2011\",'xmas_lights_2012' : \"08-Nov-2012\",'xmas_lights_2013' : \"07-Nov-2013\",\n",
    "                   'xmas_lights_2014' : \"13-Nov-2014\",'xmas_lights_2015' : \"12-Nov-2015\",'xmas_lights_2016' : \"10-Nov-2016\",\n",
    "                   'light_night_2016_1' : \"06-Oct-2016\", 'light_night_2016_2' : \"07-Oct-2016\", 'light_night_2015' : \"09-Oct-2015\",\n",
    "                   'light_night_2014' : \"03-Oct-2016\", 'light_night_2013' : \"04-Oct-2013\", 'light_night_2012' : \"05-Oct-2012\",\n",
    "                   'light_night_2011' : \"07-Oct-2011\", 'marathon_2016' : \"08-May-2016\", 'marathon_2015' : \"10-May-2015\",\n",
    "                   'marathon_2014' : \"11-May-2014\", 'marathon_2013' : \"12-May-2013\", 'marathon_2012' : \"13-May-2012\",\n",
    "                   'marathon_2011' : \"08-May-2011\", 'John Lewis Opening' : \"20-Oct-2016\", 'Trinity Opening' : \"21-Mar-2013\",\n",
    "                   'leeds_fest_2014_thurs' : \"21-Aug-2014\", 'leeds_fest_2014_sun' : \"24-Aug-2014\", 'leeds_fest_2014_mon' : \"25-Aug-2014\",\n",
    "                   'leeds_fest_2015_thurs' : \"27-Aug-2015\", 'leeds_fest_2015_sun' : \"30-Aug-2015\", 'leeds_fest_2015_mon' : \"31-Aug-2015\",\n",
    "                   'leeds_fest_2016_thurs' : \"25-Aug-2016\", 'leeds_fest_2016_sun' : \"28-Aug-2016\", 'leeds_fest_2016_mon' : \"29-Aug-2016\", }\n",
    "\n",
    "# Loop over each event name and data, finding the original footfall data and adding it to\n",
    "# the 'events' dataframe\n",
    "for (event_name,event_date) in events_to_check.items():\n",
    "    # Find the row in the original data that match the day we're after for this event\n",
    "    event_impact = original.loc[original['Day_yr'] == event_date]\n",
    "    event_impact.insert(0,'Event', event_name) # Add the name of the event\n",
    "    events = events.append(event_impact) # Add this row of data to the 'events' dataframe\n",
    "\n",
    "print(\"Will look at the following events: \\n\"+str(events[ ['Event','Day_yr'] ]))\n",
    "    \n",
    "# Extract the event names from the dataframe of all event information (not sure why Molly does this)\n",
    "event_names = pd.DataFrame(events, columns = ['Event'])\n",
    "event_names = event_names.reset_index(drop = True)\n",
    "del events['Event']    \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Define a function that makes it easy to do predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_prediction (f, real_vals, model):\n",
    "    \"\"\"Make a prediction using a given model. \n",
    "    \n",
    "    PARAMETERS\n",
    "      - f:         A dataframe with all the original data\n",
    "      - real_vals: A dataframe with the year and (actual) footfall\n",
    "      - model:     The model that will be used to make the prediction\n",
    "    \n",
    "    VALUE\n",
    "      - A pandas datagrame with the day of the prediction, the real value, the predicted value, \n",
    "      and the differences (both absolute and percentage)\n",
    "    \"\"\"\n",
    "    # Prepare a dataframe that will be used to make the prediction. It will need to include \n",
    "    # everything except the date and footfall count (obviously these aren't used to make the prediction)\n",
    "    everything = copy.deepcopy(df)\n",
    "    everything = everything.drop(['Day_yr', 'InCount'],axis = 1).reset_index(drop = True)\n",
    "    \n",
    "    #Make a precit\n",
    "    prediction = pd.DataFrame(  model.predict(everything)  )\n",
    "    \n",
    "    # Compare predictions to real values\n",
    "    comparison_xgb = pd.concat([real_vals,prediction], axis =1, ignore_index = True)\n",
    "    comparison_xgb.columns = ['Day_yr', 'real_value', 'xgb']\n",
    "    # Add difference and percentage difference columns\n",
    "    comparison_xgb['xgb_difference']= comparison_xgb['xgb'] - comparison_xgb['real_value'] \n",
    "    comparison_xgb['xgb_%difference']= comparison_xgb['xgb_difference']/ comparison_xgb['real_value'] * 100\n",
    "    #comparison_xgb = pd.concat([comparison_xgb,everything], axis =1)\n",
    "    return comparison_xgb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11b775470>]], dtype=object)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b7c9f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For convenience save the date and original footfall for comparison shortly\n",
    "# (Note: 'df' is the original data minus the outliers )\n",
    "real_vals = pd.DataFrame(df, columns = ['Day_yr', 'InCount']).reset_index(drop = True)\n",
    "\n",
    "# Run the model on the original data:\n",
    "predict = make_prediction (df, real_vals, xgb_model)\n",
    "\n",
    "# Now run it just on the events\n",
    "predict_events = make_prediction (events, real_vals, xgb_model)\n",
    "\n",
    "XXXX HERE - NEED TO CONCATENATE THE COLUMNS CORRECTLY\n",
    "#predict_events = pd.concat([predict_events,event_names], axis = 1)\n",
    "\n",
    "# Create histograms comparing the error distribution\n",
    "histo_xgb = pd.DataFrame(predict, columns = ['xgb_%difference'])\n",
    "histo_xgb = histo_xgb.loc[histo_xgb['xgb_%difference'] < 100]\n",
    "histo_xgb = histo_xgb.loc[histo_xgb['xgb_%difference'] > -100]\n",
    "pd.DataFrame.hist(histo_xgb, bins = 60)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"results\"></a>\n",
    "## Results\n",
    "\n",
    "XXXX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"conclusions\"></a>\n",
    "## Conclusions\n",
    "\n",
    "XXXX"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}