{ "metadata": { "celltoolbar": "Slideshow", "name": "", "signature": "sha256:628f568b8e2368dd1cc6e13bacf7702a760bb9a9c63d25d8d6315c151b3b8767" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Routes and Trips" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "How many routes have how many trips?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "The first thing that I wanted to do was to see which routes have more trips, which would give us a rough estimate of which routes are most busy. Of course, this has a few issues (some routes with more trips may simply be shorter in length) but it's a good start. We don't need `Stops` yet, technically, but it's nice to get our class definitions out of the way." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Let's get our imports out of the way, and define what routes, trips, and stops are." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import csv, math, pickle\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from classes import Route, Trip, Stop # just classes to store the data\n", "from pandas.stats.api import ols\n", "import pymongo\n", "from pymongo import MongoClient, ASCENDING, DESCENDING\n", "\n", "client = MongoClient()\n", "db = client.datasummative # my name for the database" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 39 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Download the data from [here](http://www1.toronto.ca/wps/portal/contentonly?vgnextoid=96f236899e02b210VgnVCM1000003dd60f89RCRD&vgnextchannel=1a66e03bb8d1e310VgnVCM10000071d60f89RCRD), extract, and place the files in a folder called \"schedules\". In it, you should see the files:\n", "\n", " $ ls schedules/\n", " agency.txt routes.txt stops.txt trips.txt\n", " calendar.txt shapes.txt stop_times.txt\n", "\n", "We don't use all of these files, but it's good to have them nonetheless." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Let's start by opening and parsing the `routes.txt` file. This should give us a list of all the routes in the TTC, which we can use for later analysis." ] }, { "cell_type": "code", "collapsed": false, "input": [ "routes_list = {}\n", "with open('schedules/routes.txt') as routes_file: \n", " routes_csv = csv.reader(routes_file)\n", " next(routes_csv) # skip header\n", " for route in routes_csv:\n", " route_id =int(route[0])\n", " route_short_name = route[2]\n", " route_long_name = route[3]\n", " route_type = int(route[5])\n", " routes_list[route_id] = Route(route_id, route_short_name,\n", " route_long_name, route_type)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Next the `trips.txt` file. This'll tell us what trips each route has. For each trip we find, we'll attach it to a list associated with the route it belongs to.\n", "\n", "A trip is one vehicle departing from its home station at one point specific point in time. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "with open('schedules/trips.txt') as trips_file:\n", " trips_csv = csv.reader(trips_file)\n", " next(trips_file)\n", " for trip in trips_csv:\n", " route_id = int(trip[0])\n", " service_id =int(trip[1])\n", " trip_id = int(trip[2])\n", " trip_headsign = trip[3]\n", " route = routes_list[route_id]\n", " route.trips[trip_id] = Trip(trip_id, service_id, trip_headsign)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Now let's go ahead and analyze the data we have. We'll start by putting together tuples of routes and the number of trips they have, then plotting them to see their frequencies. In other words, we'll count the number of trips each route has." ] }, { "cell_type": "code", "collapsed": false, "input": [ "## Distribution of trips vs routes\n", "trips_freq = []\n", "for route in routes_list.values():\n", " trips_freq.append((route.route_short_name, len(route.trips)))\n", "with open('trips_routes.pkl', 'wb') as trips_routes_file:\n", " pickle.dump(trips_freq, trips_routes_file)\n", "trips_series = pd.Series([x[1] for x in trips_freq])\n", "print(trips_series.describe())" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "count 182.000000\n", "mean 689.664835\n", "std 622.333137\n", "min 13.000000\n", "25% 275.750000\n", "50% 554.000000\n", "75% 945.000000\n", "max 4696.000000\n", "dtype: float64\n" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "From this data, we see that there is a total of 182 routes, the mean route has ~690 trips, the route with the fewest trips has 13 trips, the route with the most trips has 4696 trips and the median route has 554 trips, all over a six week period.\n", "\n", "That's pretty interesting, but still doesn't give us very much. Let's add in the definition of Pearson's Index, interquartile range, range, and find out whether we have any outliers, according to the formulae we learned in class." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def pearsons_index(series):\n", " return 3 * (series.mean() - series.median()) / series.std()\n", "def data_range(series):\n", " return series.quantile(1) - series.quantile(0)\n", "def interquartile_range(series):\n", " return series.quantile(.75) - series.quantile(.25)\n", "def outlier_range(series):\n", " iqr = interquartile_range(series)\n", " return (series.quantile(.25) - 1.5 * iqr, series.quantile(.75) + 1.5 * iqr)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "print(\"Pearson's: %s\" % pearsons_index(trips_series))\n", "print(\"Range: %s\" % data_range(trips_series))\n", "print(\"IQR: %s\" % interquartile_range(trips_series))\n", "print(\"Outliers lie outside: (%s,%s)\" % outlier_range(trips_series))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Pearson's: 0.653981736266\n", "Range: 4683\n", "IQR: 669.25\n", "Outliers lie outside: (-728.125,1948.875)\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "According to the Pearson's Index, it would appear that the distribution of trips over routes is right-skewed, but not very much. Let's see the plot to make sure." ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots(1,1)\n", "n, bins, patches = ax.hist([x[1] for x in trips_freq], bins=25,\n", " align='mid', range=(0, 5000))\n", "plt.xlabel('Number of Trips')\n", "plt.ylabel('Number of Routes')\n", "plt.title('Distribution of trips over routes')\n", "plt.xticks(rotation=70)\n", "ax.set_xticks(bins[:-1])\n", "plt.xticks(bins[:-1],['%.1f' % (x-0.5) for x in bins[:-1]])\n", "plt.subplots_adjust(bottom=0.2)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Oxrlz54RuHAA4d+6cykZ9fHxw/vx5pKSk4J133sHChQshkUgQHh4OkUiEJk2a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\n1avUoEEDcnV1pbFjx9Lo0aOFdQ4ODpS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"text": [ "" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "There appears to be a few outliers, and it also appears to be obviously skewed. Let's remove these outliers." ] }, { "cell_type": "code", "collapsed": false, "input": [ "outlier_r = outlier_range(trips_series)\n", "outliers = filter(lambda x: x[1] < outlier_r[0] or x[1] > outlier_r[1], trips_freq)\n", "print(list(outliers))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[('29', 2224), ('32', 2011), ('501', 2705), ('504', 2025), ('509', 2059), ('510', 4696), ('52', 2043), ('BLR', 2181), ('YUS', 2267)]\n" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Of the outlier routes, two are subways, four are streetcars and three are buses. Let's see what effect removing these outliers from our data has." ] }, { "cell_type": "code", "collapsed": false, "input": [ "trips_series = pd.Series([x[1] for x in trips_freq if x[1] >= outlier_r[0] and x[1] <= outlier_r[1]])\n", "print(trips_series.describe())\n", "print(\"Pearson's: %s\" % pearsons_index(trips_series))\n", "print(\"IQR: %s\" % interquartile_range(trips_series))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "count 173.000000\n", "mean 597.156069\n", "std 445.952944\n", "min 13.000000\n", "25% 261.000000\n", "50% 525.000000\n", "75% 881.000000\n", "max 1814.000000\n", "dtype: float64\n", "Pearson's: 0.485405940789\n", "IQR: 620.0\n" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "As expected, Pearson's index and the IQR decreased as well when we removed the outliers. Let's check the plot:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots(1,1)\n", "n, bins, patches = ax.hist(trips_series, bins=25,\n", " align='mid', range=(0, 2000))\n", "plt.xlabel('Number of Trips')\n", "plt.ylabel('Number of Routes')\n", "plt.title('Distribution of trips over routes')\n", "plt.xticks(rotation=70)\n", "plt.xticks(bins[:-1],['%.1f' % (x-0.5) for x in bins[:-1]])\n", "plt.subplots_adjust(bottom=0.2)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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K/mtirGgKPGb/5s0beHp6Yu/evRg3bhzu3LmDatWqqSs3xhhjxaTAPftjx47B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\nOHLkCHr37g1BEJCRkSHmCQAWFhZo0KC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"text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Despite the large mode on the left of the histogram, the Pearson's Index indicates that the data is not as skewed as it seems. As well, since only 9 of the 182 routes are outliers, the data can be considered non-skewed. For us, this means that while routes vary by up up to 1801 trips over six weeks, most routes are fairly close to the median of 554 trips over a six week period." ] }, { "cell_type": "heading", "level": 1, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Ridership and Costs" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "How is the daily cost of a route related to its ridership?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ridership_collection = db.ridership\n", "rts = []\n", "riders = []\n", "costs = []\n", "for ridership_row in ridership_collection.find():\n", " rts.append(ridership_row['route'])\n", " riders.append(ridership_row['ridership'])\n", " costs.append(ridership_row['cost'])\n", "riders_costs_df = pd.DataFrame({'route': rts, 'ridership': riders, 'cost': costs},\n", " index=rts)\n", "del rts\n", "del costs\n", "del riders" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "print(ols(x=riders_costs_df['ridership'], y=riders_costs_df['cost']))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "-------------------------Summary of Regression Analysis-------------------------\n", "\n", "Formula: Y ~ + \n", "\n", "Number of Observations: 138\n", "Number of Degrees of Freedom: 2\n", "\n", "R-squared: 0.9276\n", "Adj R-squared: 0.9270\n", "\n", "Rmse: 7045.0030\n", "\n", "F-stat (1, 136): 1741.2502, p-value: 0.0000\n", "\n", "Degrees of Freedom: model 1, resid 136\n", "\n", "-----------------------Summary of Estimated Coefficients------------------------\n", " Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5%\n", "--------------------------------------------------------------------------------\n", " x 2.0056 0.0481 41.73 0.0000 1.9114 2.0998\n", " intercept 2478.4971 815.2188 3.04 0.0028 880.6683 4076.3258\n", "---------------------------------End of Summary---------------------------------\n", "\n" ] } ], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "# define a convenient way to graph some stuff, find its regression, and graph that too\n", "def plot_with_reg(x, y):\n", " plt.hold(True)\n", " plt.plot(x,y, 'b.')\n", " res = ols(y=y, x=x)\n", " m = res.beta['x']\n", " b = res.beta['intercept']\n", " x_fit = np.linspace(min(x), max(x))\n", " y_fit = np.poly1d((m,b))\n", " plt.plot(x_fit, y_fit(x_fit), '-k')\n", " plt.hold(False)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "plot_with_reg(x=riders_costs_df['ridership'], y=riders_costs_df['cost'])\n", "t = plt.title(\"Cost of a Route vs. Number of Riders\")\n", "t = plt.xlabel(\"Number of Customers per Weekday\")\n", "t = plt.ylabel(\"Cost per Weekday ($)\")" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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fp+7du5OhoSEtWLBAKM/NzSVHR0cyMjKifv36UUxMjPDarl27yMjIiIyMjGj3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\nC+h0wPXrwKlTQln3kXPdkw930zHGBhruputFebm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"text": [ "" ] } ], "prompt_number": 47 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "The regression here indicates (expectedly) that the cost of a route is strongly positively correlated with the number of daily riders it serves. We can interpret the coefficients as indicating that every additional rider adds a cost of \\$2 to the route, with a fixed cost of ~\\$2500." ] }, { "cell_type": "heading", "level": 2, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Types of Vehicles" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "How are routes, trips, and types of vehicles related?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "The data also tells us what type of vehicles each route has. For the TTC, this means either streetcar, subway or bus." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# from https://developers.google.com/transit/gtfs/reference#routes_fields\n", "TYPE_SUBWAY = 1\n", "TYPE_STREETCAR = 0\n", "TYPE_BUS = 3\n", "\n", "buses = 0\n", "subways = 0\n", "streetcars = 0\n", "for route in routes_list.values():\n", " if route.route_type == TYPE_SUBWAY:\n", " subways += 1\n", " elif route.route_type == TYPE_STREETCAR:\n", " streetcars += 1\n", " elif route.route_type == TYPE_BUS:\n", " buses += 1\n", "print(\"Bus routes: %s\" % buses)\n", "print(\"Subway routes: %s\" % subways)\n", "print(\"Streetcar routes: %s\" % streetcars)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Bus routes: 166\n", "Subway routes: 4\n", "Streetcar routes: 12\n" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "This would be most useful as a pie chart." ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.figure(figsize = (6,6))\n", "p = plt.pie((buses, streetcars, subways), labels=(\"Bus Routes\", \"Streetcar Routes\", \"Subway Routes\"),\n", " startangle=90, autopct=\"%1.1f%%\")\n", "t = plt.title(\"Vehicles in the TTC by Number of Routes\")" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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uw9AhQxnz4hhCQ0OLdr+SQ5JFtxhJS0tj8+bNfPHFYv744xhCDMJgGA40xVF7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\nQ1FkT1eSpJycp0aN2mqHyBOnKLo1a1b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"text": [ "" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "It would also be useful to look at the vehicles by trips." ] }, { "cell_type": "code", "collapsed": false, "input": [ "subways_trips = 0\n", "buses_trips = 0\n", "streetcars_trips = 0\n", "\n", "trips_map = dict(trips_freq)\n", "for route in routes_list:\n", " route_type = routes_list[route].route_type\n", " route_name = routes_list[route].route_short_name\n", " if route_type == TYPE_SUBWAY:\n", " subways_trips += trips_map[route_name]\n", " elif route_type == TYPE_BUS:\n", " buses_trips += trips_map[route_name]\n", " elif route_type == TYPE_STREETCAR:\n", " streetcars_trips += trips_map[route_name]\n", "print(\"Bus trips: %s\" % buses_trips)\n", "print(\"Subway trips: %s\" % subways_trips)\n", "print(\"Streetcar trips: %s\" % streetcars_trips)\n", "plt.figure(figsize=(6,6))\n", "p = plt.pie((buses_trips, streetcars_trips, subways_trips), labels=(\"Bus Trips\", \"Streetcar Trips\",\n", " \"Subway Trips\"),\n", " startangle=90, autopct=\"%1.1f%%\")\n", "t = plt.title(\"Vehicles in the TTC by number of trips\")" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Bus trips: 101427\n", "Subway trips: 7586\n", "Streetcar trips: 16506\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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QAQEBbN26lfj4eH7++Wd69+7NO++8w6BBg67ZVq/X8+CDDwLkF95mzZoBkJ2d\nTVhYGH/88Qd79uwhLi4OsP98Xf66oE2bNtG+fXtq1qwJQMWKFQG4cOECAwcO5NChQyiKckU/cefO\nnfO3ux5FUXj44YcLvfRdURQeeeQRAOrUqUNkZCT79u0jLi6OiRMncuLECXr16kWdOnWK3L8028rQ\n+fPnadv2HlauTCcrawNSlL1H/qBfWV7p5yhBkNkvk7XJa2naqinHjx936O51Oh3t27dn/PjxzJgx\ng6+//rrQ7fz8/K4odIMGDSIpKYmkpCT27t3LG2/YW/V33313/u27d+9m9uzZ1+xLUZRC/wIYO3Ys\nHTt2ZNeuXaxYseKKwcKS/EIqmK80v8B0Oh19+/ZlxYoV+Pv707Vr12Jb5lKYy8hff/1FdHQcO3ZE\nYzItxZ1XpBaltw4w3ej0nlowQPb92RyKOEST2CZs2rTJIbs9cOAABw8ezP8+KSmJWrVqARAUFMSl\nS5cKfVzHjh1ZunQpZ8+eBexzxyQnJ9OqVSt+++03Dh8+DEBmZmb+/gvur2XLlvzyyy/5c4acP38e\ngEuXLhERYR8y/uyzz0r9fIrq7il4u6qqLFmyBFVVOXz4MEeOHKF+/focOXKE2rVr89xzz9GjR49i\np2uVwlwGNm/eTNOmbThx4mlyct4HZK02b/OBXs+l+qp7/YQp9lPqzt99no5dOjJ33tyb3mVGRgaD\nBw/mtttuo0mTJuzbt4/x48cD8OSTT9KlS5f8wbqCrdGGDRsyYcIEOnfuTJMmTejcuXP+QOLcuXPp\n27cvTZo0IS4ujv3791+zv9DQUGbNmkWvXr2Ijo6mT58+ALzyyiuMGjWKpk2bYrVa84+pKEqJZuQr\nuM3VXxfcV40aNWjRogVdu3bl448/ply5cixZsoSoqChiYmLYvXs3AwcOLPo4crqcYy1btoz+/Z8k\nK2sO0F3rOB7LlU+XOw3UBrJfACpoHOZGnQHj10aG9BvC++++LwvBlsJjjz1Gt27d6NWr1w3vw51+\nn7u8yZOn8uijz5KV9QNSlL3Xp5ev9HPXogxQBbIGZTFn5Rzu63kfOTk5WifyKtJidgBVVRk+/BXm\nzFlFVtYqoKbWkTyeq7aYbcAtCpzuBjTVOo0DWMB/uT9xt8Sx8tuV+PrKqjnOIC3mm/RPUV5HVlY8\nUpS923ogS6dAtNZJHMQHTD1N/J7yO/fcfw/Z2dlaJ/IKUphvgqqqvPTS68yZs4asrDVAsNaRhMam\nXb7Sz5MERptrAAAgAElEQVR+svRg6m5ic+pmOt3biaysLK0TeTxP+vg43ahR45g5cyVZWWuRy6vF\n38AaV77S72bowdTNxNZLW7nrnrvIzMzUOpFHk8J8g8aO/TfTp3+dV5Qrax1HuAD7lX46KNnFY+5H\nbz/XeUf2Djp26ejwmdzEP6Qw34Dx4yfy3nuLyMpaD1TROo5wAflX+rV1gyv9boYOsrtmszNzp5yt\nUYakMJfShAn/4d135+cV5apaxxEu4mcg05MG/YqjA9P9Jjad2sSDvR/EarVqncjjSGEuhf/8ZwqT\nJn2SV5TDtY4jXMg0d7zS72bowdTDxPq96+k/uL+siOJg3vIxumkLFnzBv/89Pa8o36J1HOFCzgA/\nWa3QWeskTmaArAez+C7hO4Y9M6xMpg31VlKYSyAhIYEnnxyRN7l9da3jCBfzqaLgE6z33EG/4pSD\nrIez+GLVF0yYNEHrNB5Drvy7jmPHjhEd3ZoLF2YD92kdR+RxlSv/bEB1ReHU/SrEahxGSxfBf64/\n3yz8hi5dumidxu1Ji7kY6enp3HVXN9LTX0aKsijMBiBDB8RoHERrFexXCD7S75H8KTnFjZPCXASr\n1UrPnv04ebIVVusIreMIFzVNryfdmwb9ilMTMttkcvd9d5ORkaF1GrcmH6cijBjxKps2ZWI2/w/X\nWklTuIqzwGqrFdUTr/S7QbZYGynlU+jdv7cMBt4EKcyFmD17Dp9++h1ZWUsBg9ZxhIv6TFEwVNJD\nJa2TuBAFsjtns3HHRhkMvAlSmK+yceNGhg9/nays75FJidzLBeAhoCHQCChscaTngbpAEyAp77az\nQFugMbC8wLY9sU96XxgV+ADIaCMXV1zDAJkPZDJp8iRWr16tdRq3JIW5gJMnT9KjRx9MpoVAPa3j\niFIaDnQF9gI7sRfoglYBh4CDwCzgqbzbFwFPA5uBqXm3rcA+nXJRy+duIG/QzxPmXC4LMhh4U6Qw\n57FarTzwQH8yM58GOmodR5TSRSAeeDzvex+uXUDkO2BQ3tctsbewTwPlgEwgG/vqjFbsreFXijne\nNL2e9Hoy6FesmpAZJ4OBN0I+Vnn+/e9J7N4NFsvrWkcRN+AvIBR4DHsj9gng6lmDT3Ll5UHVgFNA\nP+xdGJ2B0cD/gIGAXxHHOgv8YLWietuVfjfA1kwGA2+EFGYgPj6ed9+dQVbWAmRFa/dkAbZh75LY\nBgQA7xSyXWGloTzwPZCIfQ6i74EHsRf3h7m2r3qeomCopJNBv5KQwcAb4vWF+cKFC/Tq1R+T6RNk\nDgz3VS3vX/O87x/CXqALugU4XuD7E1z7jr8FjAEWAncA84DxBe5XsfdDZ8TJpD0ldnkw8N1JbNt2\n9bsiCuP1hXnIkGdJT78fuF/rKOImhGHvpjiQ9/1a4LartukOzM/7ehP2qS0KTtx6EHvXxh2AiX/O\nXi84HfxGIF2Hd19+fSMqgOlOE30G9CE3N1frNC7PqwvzkiVLWL06EbP5v1pHEQ4wHXgU+6lwO4FR\nwMd5/8B+xkYkUAf4F/DhVY8fA0zM+7ov8BHQAih43ec0vZ70ujLod0OawEnbSf47WX7ersdrJzFK\nSUmhfv1o0tNXYP/xE+5Ei0mMUrG3yrOfQ5Z4vFHnwf8zf3Zs2UHdunW1TuOyvPL3vqqq9OkzBJNp\nGFKURUnNUxR8KuqkKN+MSmCOM/Po4EflLI1ieGVhXrx4MVu3nsJiGaN1FOEm8gf92sig382ytbCx\n59QeZs2epXUUl+V1XRnp6enUrNmQ8+cXA220jiNukLO7Mn4B7tcrpI+W/mWHOA0BXwZwYPcBIiK0\nnlXb9XjdR2z06H9jMnVCirIojQ/0ejJk0M9xwiAnOofH//X49bf1Ql71MduzZw+ffDKX7Oz/aB1F\nuJFzwCqZ3tPhctvkEr81nm+++UbrKC7Hawqzqqo8/vhzmM1jufLsVSGKN09RMMign+MZIKtLFkOG\nDeH8+fNap3EpXlOYv/rqK/78MxWb7Wmtowg3cnnQL721DPqViZpgutXEcy8+p3USl+IVhTkjI4On\nn36JzMwZ2OcdE6JkfgUuKPxzrbdwOPOdZr5Z8Q0bN27UOorL8IrCPHbsW5hMHYB2WkcRbma6DPqV\nPT8wtTfx/EvPy7nNeTz+47Z3715mzpyDySSXgYrSOQeskOk9nSMKDp86zE8//aR1Epfg8YV5xIjR\n5OS8RtFrUQhRuPmKgqGCDPo5hQ4yW2cyctRIaTXj4YV5586dxMcnyICfKDUZ9NPAbXD0zFF++OEH\nrZNozqML86uv/huz+SXAqHUU4WZ+A84ryFQqzqSzL0UlrWYPPkVh165dbNz4KzbbPK2jCDc0Xa8n\no47VMU2Xb7FP9hyAfYkVgPXA/ryvjdiX5L56kcKLwDLsCxKCfQ7oVnlfr8G+smwY8EDebTuwTx7d\nCvfVEI4nHOf777+nW7duWqfRjMe2mEeNmoDZPBL7T4MQJZcGfOfIQb8YoP9Vt7XBvkz3U0AD7Mtu\nX00H3AM8AwzFvvbVWeyrxqbkPVYP/A3kAttx/xa+tJoBDy3Me/bsYf36DdhsT11/YyGuMh8cO+hX\nk2tXdvUt8HUOhfe2BQHhBbavDKRjX1rFhr0jPBd7cf4d+9LfnvAT3QBS0lNYvny51kk04wlv4zVG\njXqLnJwXgECtowg3owJTFcU5g37rgPewt3TbXmfb88Bp7IsU+gJ1sS/NEpT3/UnsLW9PoIOMuAxe\nev0lbDbvHHz1uMK8d+9efvppHVbrM1pHEW7odyDNWYN+HYEXsS/N/WMx25mBr4Au/NPSbgMMAzoD\nPwMdgK3AEuxzlLq7+vB31t8sW7ZM6ySa8LjC/PrrE8jNHYG9KSFE6UzX68io4+Qr/Rpjb/EWxoq9\nKN8ONCzk/pS8/0OAPcDD2DvJzzk4o7Mp3t1q9qjCfOjQIVav/gmr9Vmtowg3dB5YbrU5Z3rPgoVz\nP//0JRekAsuBUKB1Efu53Fq25m0P9j5oi2NiaqoepOamsnTpUq2TOJ1HFeYZM2ZhtT4GlNc6inBD\n+YN+oQ7e8VJgDvbVXN8DtgFrsS/T/RFwFHuXBMAl4Iu8r5OxL/f9FzAz79/BAvvdB0Rg/+PQH/up\ncx9iL9KeMLOtAhktMxj/9nitkzidxywtZTabCQ2tTnr6b9hHRoQnc/TSUioQqSgc7awW3ToVzmcD\n44dGflvzG9HR0VqncRqPaTF/++232DvrpCiL0ksAUhXsp5wJ16ED8+1mPvjfB1oncSqPKcyTJ39M\nevqTWscQbmq6XkfWrTK9pyuyRltZvHgxGRkZWkdxGo/4GB44cIDdu3fzz7WpQpTceeBbqw2bTO/p\nmsqDrpaORYsWaZ3EaTyiMP/vf7OxWAYB5bSOItzQAsBQvgwG/YTDZN6eyeRpk7WO4TRuX5jNZjOf\nfTaP3NwntI4i3JAKvK8opLfyvnNl3UY6KH8rHPjzAGvWrNE6jVO4fWFetmwZqiqDfuLGbALOKrj3\njGyeyArsA/0CPUwFXXwtFKUzy5at0jqZU7j96XLNm9/Fli3DgEe0jiKcyFGny/XT61lc24rt6tnf\nhDbOgG6bDluSDZ3OiM3UG3gb+0naBwkKasu5cycwGAwaBy1bbt1iPnLkCH/++Sf2yWyFKJ0LwDKr\nVQb9tJYNbAHdRzqYraAmNgPzamymTOBT/lkWri5wq1esC+jWhfmrr5YCvZBBP3Ej8gf9qmidxAvZ\ngL9Av1QPk0G3tiq2v9+A3CxU6x/YJ6K+Vnr6QD78cL5To2rBrbsyGjRowf79bwOdtI4inOxmuzJU\noI6icKSTap+pTTjHBVC261C3qCi5PqjmrsA7lHzO0jR8fWtz5sxxypf33KkX3LbFfOzYMY4dOwK0\n1zqKcEN/AGdk0M85coFdoPtMDzNA+b0+ZMxHNWdjX3erNBNJB+Pr25K1a9eWTVYX4baF+euvv0FR\nugOePQggysZ0vZ6sSNW++odwPBU4BbrvdfBf0K2qgO3YU2BJw5azB/taWzdWfi5duo+lS1c6Mq3L\ncduujCZN2rFz52vAfVpHERq4ma6Mi9hn2TQ9hWfMwuZKMoFdoGzWoWaAYm2Han0biHPgQQ5RoUI7\n0tJOotO5bduyWG65SnZqair79u3AvgSEEKWzADAE6TFVtWodxTNYgcOg26rHdtiKTl8dm3kE8Dxq\nmZSYOlit5UlKSiI2NrYM9q89tyzMq1evxtf3LnJyrl7hUojiXb7S71JLKco3LRV0STps22woqh+2\n7AeBt7FZapT5oc3m+/juu5UeW5jd8u+AxYtXkp5+v9YxhBtKBP5WZM7lG2YGkkD3sR5mKqh/NAHT\nctRsE/a/Rcq+KAPk5t7HV195bj+z2/Ux5+bmUrFiVbKy/gSHTZMu3M2N9jH31+v5spYV64AyieWZ\nVCAZ9Nv0WHdb0RsqYzU9AYwBjBqFysHXtwrHju2nalXPGyhwuxZzQkICPj61kaIsSusi8I3VilWu\n9CuZS6DEKyjvKygLfbDu7AyWHVhNZ7FfJq1VUQYoh8HQiR9++EHDDGXH7Qrzxo3xZGV10DqGcENf\nYB/0kzMximEB9oBunh4+ACX+VtRLs1DNZlBXYV+u2zVkZNzH4sWe2Z3hdoN/P/74OxbLEK1jCDej\nAu/rZNCvSKfzBvKSbOj0gdhM/YC3sFld+Xr1e9mw4QUsFgs+Pm5XyorlVs/GZrORlJQAfKJ1FOFm\ntgCnkUG/K5iwn3OcqEO9AKqtFVgnYMNd/iINw2AIZ/fu3TRp0kTrMA7lVoV5//79KEpF7JcHCFFy\nM/R6TDWtcqXf5cmDtuixHrSi84nAlv0sMBLVDScDs1qbs3nzZo8rzG7Vx5yQkIBjryAS3uASsMTb\nB/3Og7JegckKyhJfrHsfAMshbNkngVG46wyNWVkt+OWXRK1jOJxbtZjXrv2dzEwpzKJ07IN+Okxh\nXrZ8VA6wF3SJemynrSjKbai5Y1DprXUyB2rOb795XtemWxXm+PjfgWe0jiHcSP6gXwsvKcoqcBJ0\n2/TYdlnR+VTCZnoMGIcNT5wmswnHjx8gKysLo1HL0/ccy20Kc1paGn//fRxorHUU4Ua2Aimq6vk9\nYBmg7FRgswJZCjZLB7C9jS23udbJypgfRmMjkpKSaNPGcybWdpvCvGnTJvz9W5Cb6zaRhQvw6EE/\nK3AwbyDvLys6n5pYzSOBp3Gz4aObYja3IDEx0aMKs9u8e/HxCdK/LEolHfjKEwf9zoLux7x5jpcb\nsR4aANaTWM1/Ac/iRj/WDmE2N2f9+s1ax3Aot2l+/vbbdqzWx7WOIdzIF4Ah0EMG/bKB3aDbrMOW\npqKqsWB5Exv3ap3MBbRg8+a3tQ7hUG5TmA8fPgjU0zqGcCNT3X3QzwYcy5s8aK8VnU8VbNlPAa+h\nIlPe/qMBaWmnSUtLIzg4WOswDuEWhdlisXDmzFEgUusowk1sBU6obrrQ6kVQtiuoiaDk+mDNuRfU\nd7BZGmqdzEXpMRpvZ9euXbRv7xlrgLpFYU5OTsbXtwoWi7/WUYSbmKHXYa6hgt5NZrXNBfbnnXN8\n0oqiq4+aMwr1JtbG8yYWS22OHj0qhdmZDh06hI9PXa1jCDeRDiy22rDcrXWSEkgB3TYdth02dPry\n2EwDgTex4Rl/kjtLVlZtDh/+S+sYDuMWhfngwYOYzVKYRcksJG/QL8JF+5ezgJ15kwelg2ptC9aJ\n2GirdTK3paq12LMnXusYDuMWhXn37oNkZ9fROoZwE1N1Cpeau1hRtgGH8845PmxF51MNW/YIYHgZ\nLVjqbWpz8OB8rUM4jFt8InbuPAhuMxWh0NJW4Liq4jKNz3OgJOlQt9oXLLVmPwBMwmapqXUyD1Ob\nEyekK8Op7KfKSVeGuL7/ucKgnxn7KiCJemxnbChKY9Tccag8oF0mj1eNCxdOk5OTQ7ly7jlTXkEu\nX5gtFgupqcnIqXLiejLQcNBPBY7nnXP8pxW9ISRvwdLR2AjUIJC3MeDvH8Hx48e59dZbtQ5z01y+\nMB8/fjzvVDk5oV4U70vAJ0AHzhz0SwdlhwKbAbMea04nUN/BavGsidvdgY+P/ZQ5KcxOcO7cOXx8\nQrWOIdzAe84a9LMAB0C3RY8t2Yqij8RmfgUYipxzrJ3c3Fr89Zdn9DO7fGG+cOECUFHrGMLFbQOS\ny3rQ7++8c47zFyztC0zAZnHlBUu9h8lUm8OHj2odwyFcvjCfP38eVa2kdQzh4j7U6zBXV8HHwYN+\nJuDPvMmDLqiotpZgfQsbHR17HHHTVDWUU6d2ah3DIVy+MF+4cAGLRVrMomgZwCJHDvpdXrB0qx7r\nASt6n3Cs2c8AL7vlgqXeowLnzl3QOoRDuEVhzsmRwiyKthgwBOjhFuvN7eh83uRBW0CxGrBmdwMm\nYbXIqZruoSJpaRe1DuEQLl+Yz527gMUiXRmiaO/rdFxsdoNFOZd/FixNsaIojVBzR6PS16EZhTNU\n5OJFaTE7xd9/nwcitI4hXNR24Khqg3aleJAKnMpbsHSnFZ1PxbwFS8d76IKl3qKCFGZnSU2VszJE\n0Wbo8q70K8mgX4EFS9UsBdXSPm/B0pZlH1Q4QRAmU4bWIRzC5QuzvTNfCrO4ViawyHadQT8rcChv\n8qAjVhSfGtjMI4FnUOWcYw/jT06OSesQDuHyhfnChfOA9DGLaxU76HcWdEk6bNts6BQjVtPDwNvY\nrNIt5rn8pDA7i8mUBRi1jiFc0IcKXIwtUJQvL1iaqMN2TkVVY8Dyb2x01SyjcCZ/cnNNqKqKoiha\nh7kpLl+YhSjKRbAP+h3NG8jbc3nB0n8Br8uCpV7HB0XRYbFYMBgMWoe5KW5QmN37N58oGzZUcv2A\naQpKjg+2nHtBfRub5TatowkN6XQGcnJypDA7h5ssqCmcJkcph2q5BUzjUBmATB4kQMVqNePr66t1\nkJvm8oXZ3fuKRNmwqWb7xSFC5LOgKDp8fFy+rF2Xyzcz7IXZxdZvE0K4oGwMBs8YV3D5wlyunC+Q\no3UMIYTLk8LsNPb+omytYwghXJ4UZqfx8/PFvrqlEEIUJxtfX3+tQziEGxRmP6TFLIS4PhPlykmL\n2Sn8/aUwCyFKIjuvIef+XL4wV60aDJzTOoYQwuVl4+srhdkpatUKQ1FOax1DCOHypMXsNBER4fj7\nS2EWQlxPKqGhwVqHcAiXL8xhYWH4+KRoHUMI4fJOULduda1DOITLF+bw8HDpyhBCXJev73Fq166m\ndQyHcPnCHBYWRm6utJiFEMXz9T1O9erSYnaKqlWrkp19FpkvQwhRHEU5QbVq0mJ2inLlyuHvXx45\nZU4IUZycHGkxO1VISDgg3RlCiKKYyck5T5UqVbQO4hBuUZirVAkDZABQCFGUk1SsGIFer9c6iEO4\nRWG2j7Qe1zqGEMJlHSc83DP6l8FNCnPz5o0oV+5PrWMIIVzWcWrW9Iz+ZXCTwty4cWP8/XdpHUMI\n4aIU5Sj16klhdqrGjRuTkyOFWQhRuICAHcTGNtE6hsO4RWGOiIhAUXKBv7WOIoRwSUnExMRoHcJh\n3KIwK4pCvXq3A9JqFkJc7SJmcwr169fXOojDuEVhBmjevDFSmIUQ19pOnTq3e8ypcuBGhblZs8YY\njVKYhRBXS6JVK8/pxgA3KsyNGzfGx0cKsxDiSgEBScTFNdU6hkO5TWGOiorCZNoLWLWOIoRwITrd\nNo8a+AM3KsxBQUFUqBAKHNY6ihDCZZgwmQ4RFRWldRCHcpvCDNC6dWvgV61jCCFcxi6qV6+Pr6+v\n1kEcyq0Kc/fudxEQsE7rGEIIl5FEs2ae1Y0BblaY77rrLmy29YCqdRQhhAsICIinU6fWWsdwOLcq\nzJGRkQQG+gH7tI4ihNCcis22jo4dO2odxOHcqjADdOx4FyDdGUKIvQQE+BIZGal1EIdzu8LcrVtH\ngoLWax1DCKG5dXTu3AlFUbQO4nBuV5g7dOhATs4G5HxmIbxbUNA67r/f87oxwA0Lc3h4OKGhYcB2\nraMIITSTg9m8wSP7l8ENCzPAvfd2RFGkn1kI7/UrkZH1PWbx1au5aWG+S/qZhfBiBsMqHn64q9Yx\nyoxbFuYOHTqQnf07kK51FCGEBnx9V9KtmxRml1KxYkVatGgLfK91FCGE0x1BUdKIjY3VOkiZccvC\nDPD44w8TGLhU6xhCCCdTlG/o1q0bOp3blq/rUlRVdcvrm9PS0oiIqI3ZfBII1DqOEMJJAgNvZ8WK\nadx5551aRykzbvsrJzg4mNjY1sBKraMIIZxmB76+F7njjju0DlKm3LYwAwwd2pvAwEVaxxBCOInB\n8DmPP97fo7sxwI27MgAuXrxI1ao1MJuPACFaxxFClCkr/v7V2bZtPQ0aNNA6TJly6187FSpUoFOn\nLsASraMIIcrcOmrUuMXjizK4eWEGGDZsAEFBn2sdQwhRxozG+Tz11ACtYziFW3dlAOTm5hIScgvp\n6ZsAz5v+TwgBkIGvbzWSkw947GXYBbl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"text": [ "" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Evidently, the order remains, but buses retain a smaller majority and both subways and streetcars take a higher portion of the pie.\n", "\n", "Next, we can see which vehicles average more trips per route. This is best done in a bar chart:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "streetcar_trip_route = streetcars_trips / streetcars\n", "buses_trip_route = buses_trips / buses\n", "subway_trip_route = subways_trips / subways\n", "print(\"Streetcars: %s trips per route\" % streetcar_trip_route)\n", "print(\"Buses: %s trips per route\" % buses_trip_route)\n", "print(\"Subways: %s trips per route\" % subway_trip_route)\n", "index = np.arange(3)\n", "bar_width = .35\n", "rect_buses = plt.bar(index, (buses_trip_route, streetcar_trip_route, subway_trip_route), bar_width)\n", "plt.xlabel(\"Vehicle Type\")\n", "plt.ylabel(\"Average Trips per Route\")\n", "plt.title(\"Average Trips per Route by Vehicle Type\")\n", "ticks = plt.xticks(index + bar_width / 2, (\"Buses\", \"Streetcars\", \"Subways\"))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Streetcars: 1375.5 trips per route\n", "Buses: 611.0060240963855 trips per route\n", "Subways: 1896.5 trips per route\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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IO3fuwMzMDABgZmaGO3fuAACSk5OhUqmk6VQqFZKSksoMVyqVSEpKqvLyiYio\ncip8o9y3336L999/H25ubgCAefPm4b///S+mT59e7YUrFIpyH+NRFX5+ftJnV1dXuLq61ljZRETP\ngtDQUISGhlZq2goTxMcff4zIyEjpDXL37t1D3759q5wgzMzMcPv2bZibmyMlJQWmpqYAis8Mbt26\nJU2XmJgIlUoFpVKJxMTEUsOVSqVs2doJgoiIynr04Hn58uXlTlthE5OxsTFatWolfW/VqhWMjY2r\nHNzIkSPh7+8PAPD398fo0aOl4YGBgcjLy8ONGzcQGxsLjUYDc3NzGBoaIjw8HEII7NixQ5qHiIhq\nT4UP65s0aRKuXLmCUaNGASi+mOzg4AAHBwcoFAosWLCg3Hl9fHxw8uRJpKamwszMDO+//z5GjRoF\nb29vJCQkwNraGnv27EGbNm0AACtXrsTWrVuhq6uLdevWYfDgwQCKu7lOmTIFOTk5GDZsGNavX192\nRfiwPpLBh7KRHNYLrdKq8zTXkmabkmsFQohS1w3ee++9GgqzepggSA53BCSH9UKrND7umxor7ghI\nDuuFVmmP2XeWe5F6/vz5WLduHby8vGQLPHjwYI0FSEREDU+5CaLkXoc333yzTHapya6pRETUMD22\niamgoACTJ0/Grl276jKmKmETE8lhUwLJYb3QKq2qb5TT1dVFQkICHj58WGPBEBHR06HcJqaEhARY\nWVmhY8eOGDBgAEaOHIkWLVoAQIXdW4mI6OlXboIYPXo0Ll68iM6dO6Nz584oKipCZmZmmW6uRET0\nbCr3GoRarUZkZGRdx1NlvAZBctjWTHJYL7RKq8p9EKamphg/frzsjAqFQvZu5vrEBEFyuCMgOawX\nWqVV5T6I5s2bw9nZuUyTEpuYiIgaBzYx0TONR4okh/VCq7SqdHPV19evsQCIiOjpw2cx0TONR4ok\nh/VCq7Sq3ihHRESNFxMEERHJqlSCOHXqFLZt2wYAuHv3Lm7cuFGrQRERUf2r1AuDLly4gOvXryMm\nJgZJSUnw9vZGWFhYXcVYKbwGQXLY1kxyWC+0SqvONYgffvgBBw4cQMuWLQEASqUSGRkZNRYcERE1\nTBUmCH19fejo/P/JsrKyajUgIiJqGCpMEOPGjcO//vUvpKenY9OmTXB3d8eMGTPqIjYiIqpHlboP\nIiQkBCEhIQCAwYMHw8PDo9YDe1K8BkFy2NZMclgvtEqrysP6njZMECSHOwKSw3qhVVp1LlIbGBiU\n+VOpVBjvSlkVAAAU00lEQVQzZgz++uuvGguSiIgalnKf5lpi/vz5sLS0hI+PDwAgMDAQcXFxUKvV\nmDZtGkJDQ2s7RiIiqgcVNjE5ODjg999/LzXMyckJly5dgqOjI6Kiomo1wMpiExPJYVMCyWG90Cqt\nOk1MLVq0QFBQEIqKilBUVIQ9e/agWbNmUsFERPRsqvAMIi4uDvPnz8fZs2cBAH369MHatWuhVCpx\n4cIFDBgwoE4CrQjPIEgOjxRJDuuFVmnsxUSNFXcEJIf1Qqu0qrxytEROTg62bNmC6Oho5ObmSsO3\nbt1aYwESEVHDU+E1iEmTJuHOnTsIDg6Gi4sLbt26hVatWtVFbEREVI8qbGIq6bFU0pspPz8fAwYM\nQHh4eF3FWClsYiI5bEogOawXWqVVpxdT06ZNAQCtW7fG5cuXkZ6ejrt379ZYcERE1DBVeA1i5syZ\nSEtLw4cffoiRI0ciMzMTH3zwQV3ERkRE9eixCaKoqAgGBgYwMjKCi4sL3yRHRNSIVHgNwtnZGRcu\nXKireKqM1yBIDtuaSQ7rhVZp1bkPYunSpTA2NsYrr7wivVUOAIyMjGoswJrABEFyuCMgOawXWqVV\nJ0FYW1vLPlKjoTU3MUGQHO4ISA7rhVZpvJOaGivuCEgO64VWadXp5pqVlYUPPvgAr732GgAgNjYW\nhw4dqrHgiIioYaowQUydOhVNmzbF6dOnAQDt27fHO++8U+uBVYVCoXgq/wwNG9b1HCIioBIJIi4u\nDkuWLJFumNO+UN3wiKfyLyPjfq1sDSKi6qgwQejr6yMnJ0f6HhcXB319/VoNioiI6l+Fd1L7+flh\nyJAhSExMxIQJExAWFobt27fXQWhERFSfKtWLKTU1VXphUO/evWFiYlLrgT0p9kogOawXJIf1Qqu0\n6rwPwsvLCz4+Phg1alQDv/5AREQ1qcJrEAsXLsSpU6dgb2+Pl19+Gfv27Sv14iAiIno2VfpGuYKC\nApw4cQKbN29GcHAwHjx4UNuxPRGeMpIc1guSw3qhVVp1bpQDil87+t133+Hrr7/GuXPn4OvrW+2g\nrK2t4eDgALVaDY1GAwBIS0uDh4cHnnvuOXh6eiI9PV2aftWqVbC1tYWdnR1CQkKqvXwiInq8Cs8g\nvL29ER4ejiFDhmD8+PFwcXGBjk6l8spjdezYERcuXCj10L/FixfD2NgYixcvxkcffYT79+9j9erV\niI6OxoQJE3Du3DkkJSVh0KBBiImJKRUHjwhIDusFyWG90CqtOmcQ06ZNw19//YVvvvkGbm5uCAsL\nw9y5c2sksEeDOnjwoHR24uvri/379wMADhw4AB8fH+jp6cHa2ho2NjaIiIiokRiIiEhehQliyJAh\niIqKwqJFi9ChQwcsW7YMdnZ21V6wQqHAoEGD8Pzzz2Pz5s0AgDt37sDMzAwAYGZmhjt37gAAkpOT\noVKppHlVKhWSkpKqHQMREZWv3G6u169fx+7duxEUFAQTExOMGzcOQgiEhobWyILDwsJgYWGBu3fv\nwsPDo0zSKXlOUXnkx/lpfXb9vz8iIioRGhpa6f14uQmia9euGDFiBI4cOQIrKysAwGeffVYjAQKA\nhYUFAMDExARjxoxBREQEzMzMcPv2bZibmyMlJQWmpqYAAKVSiVu3bknzJiYmQqlUypTqV2PxERE9\ni1xdXeHq6ip9X758ebnTltvE9P3336N58+Z44YUXMGvWLBw7dqzGLoxkZ2cjIyMDQPHjxENCQtCj\nRw+MHDkS/v7+AAB/f3+MHj0aADBy5EgEBgYiLy8PN27cQGxsrNTziYiIakeFvZgyMzNx4MAB7N69\nGydOnMDkyZMxZswYeHp6VnmhN27cwJgxYwAU318xceJEvPXWW0hLS4O3tzcSEhJgbW2NPXv2oE2b\nNgCAlStXYuvWrdDV1cW6deswePDg0ivCXgkkg/WC5LBeaJVWU2+US0tLw759+xAYGIjjx4/XWIA1\ngT84yWG9IDmsF1qlNZZXjvIHp0exXpAc1gut0qp7JzURETU+TBBERCSLCYKIiGQxQRARkSwmCCIi\nksUEQUREspggiIhIFhMEERHJYoIgIiJZTBBERCSLCYKIiGQxQRARkSwmCCIiksUEQUREspggiIhI\nFhMEERHJYoIgIiJZTBBERCSLCYKIiGQxQRARkSwmCCIiksUEQUREspggiIhIFhMEERHJYoIgIiJZ\nTBBERCSLCYKIiGQxQRARkSwmCCIiksUEQUREspggiIhIFhMEERHJYoIgIiJZTBBERCSLCYKIiGQx\nQRARkSwmCCIiksUEQUREspggiIhIFhMEERHJYoIgIiJZTBBERCSLCYKIiGQxQRARkSwmCCIikvXU\nJIjg4GDY2dnB1tYWH330UX2HQ0T0zHsqEkRhYSHmzZuH4OBgREdHY/fu3bh69WodRxFax8ujp0No\nfQdADU5ofQdQY56KBBEREQEbGxtYW1tDT08P48ePx4EDB+o4itA6Xh49HULrOwBqcELrO4Aa81Qk\niKSkJFhaWkrfVSoVkpKS6jEiIqJn31ORIBQKRX2HQETU6OjWdwCVoVQqcevWLen7rVu3oFKpSk3j\n6OiIqKjaTiTLa61kJsHaxHpBcmpz29ZenQBqtl44OjqWvxwhhKixJdWSgoICdOnSBceOHUP79u2h\n0Wiwe/dudO3atb5DIyJ6Zj0VZxC6urrYsGEDBg8ejMLCQkyfPp3JgYiolj0VZxBERFT3noqL1DWl\nSZMmUKvVcHJygrOzM86cOVPfIVEtW7FiBbp37w5HR0eo1WpERERg3bp1yMnJqbFlrF27tkbLo7ol\nV0fK4+fnh08//bQOo6tfT0UTU01p0aIFIiMjAQAhISF46623EBoaWr9BUa05c+YMDh8+jMjISOjp\n6SEtLQ25ublYu3YtXn31VTRv3rzMPEVFRdDRebLjpnXr1mHSpEmy5T2JgoIC6Oo2qn/JeidXRx4+\nfFju9I2t00CjOoPQ9s8//8DIyAgAEBoaCi8vL2ncvHnz4O/vDwBYunQpunXrBkdHRyxatAgAcPfu\nXbz88svQaDTQaDQ4ffo0AODkyZNQq9VQq9Xo2bMnMjMz63itSNvt27dhbGwMPT09AICRkRH27duH\n5ORkuLm5wd3dHQDQqlUrvPnmm3BycsKZM2ewc+dO9O7dG2q1GrNmzUJRURGA4oOKfv36wdnZGd7e\n3sjKysL69evLlBccHAxnZ2c4OTnBw8MDQPHNnv369UPPnj3Rv39/xMTEAAC2b9+OkSNHwt3dHR4e\nHrh9+zZeeOEFqNVq9OjRA7/99ltdb7ZGRa6OWFhYwNraGmlpaQCA8+fPw83NTZonKioK/fr1w3PP\nPYdvv/0WADB37lz8+OOPAIAxY8Zg+vTpAICtW7fi3XfflYY///zz6N69OzZv3iyNf+ONN6SyN2/e\njAULFiA7OxvDhw+Hk5MTevTogT179tTyliiHaESaNGkinJychJ2dnWjdurW4ePGiEEKIEydOiBEj\nRkjTzZs3T/j7+4t79+6JLl26SMP/+ecfIYQQPj4+4rfffhNCCBEfHy+6du0qhBDCy8tLnD59Wggh\nRFZWligoKKiT9SJ5mZmZwsn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"text": [ "" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Clearly, subways run the most trips per route, followed by streetcars, followed by buses. This is a loose indication of how busy each type of vehicle is in the TTC. This makes sense, since subways are worked pretty much continuously over the day, and buses are 'resting' the most over a day." ] }, { "cell_type": "heading", "level": 2, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Geographic Distribution of Bus Stops" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "The TTC has over 10,000 stop locations. Where are they?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "stops_list = {}\n", "\n", "with open('schedules/stops.txt') as stops_file:\n", " stops_csv = csv.reader(stops_file)\n", " next(stops_file)\n", " for stop in stops_csv:\n", " stop_id = int(stop[0])\n", " stop_name = stop[2]\n", " stop_lat = float(stop[4])\n", " stop_lon = float(stop[5])\n", " stops_list[stop_id] = Stop(stop_id, stop_name, stop_lat, stop_lon)\n", "\n", "lats = [x.stop_lat for x in stops_list.values()]\n", "longs = [x.stop_lon for x in stops_list.values()]\n", "plt.figure(figsize=(8,6))\n", "plt.scatter(longs, lats, marker='.', c='b')\n", "plt.xlabel('Longitude')\n", "plt.ylabel('Latitude')\n", "t = plt.title('Geographic Distribution of All Stops')" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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kZIHrGDx4MHp9GrCX0NCwmy4HrSglhUoSFEXBw8MDnS4LnW47Op0DtWsns3nz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\ndeo0ISYmSzvMCzGZvLz55pv8+uuvbN26VTvyrxExsXHjxkrXUFoAHkS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VKQe4CJHLOO+8\nkSfcb9euXVx55ZWkpORqXX0PSoPeTbgKwMrixYt54YUXyMmpS8+e/U86sW/btg216roXERdPPfVU\naFswGGTRoruoW7e+nqwdiFh46qmnuOWWW2jfvisGt0D1JXCjojJz9eRqAIxcRJKx2VwnfQ6BgFE9\nYMPI96ua9wRUpMWByLWYTC5++uknQAG1F154AYCEhFwU2c5L/frH172oUaOUcA7ecPweVKvjy6lV\nqzFqhfo94SiKetZeb5wWbVKOd/z4iaxfv56yspaUlbUKpUj27t3L8uXLtaCR0dCoOyIe3n33Xc0V\nSEDkR0RMEaTHTp066W3nIBITSof8/PPPJCQk67Gk6POW6G6ZAVT6ph4qJeFBxEFOTjHz5l1Phw5d\n9H0G9H0ks3r1ag4fPsz8+fN190mTPvdfEIkhLa2QgwcP8uOPP+L3ZyDionPnXmzcuJG1a9dWuYpm\n586dmExGq2s/tWuXVQJvu3bt0n0xPISrRwagSi/t+o9q3W21xtGtW49Kzb8OHz5MgwaNteO/DJGF\nmvQYhwHAFT9mpX52VyCSplMKTs45pxsTJkwgK8vQSvBpJUkHKsJhDYFaBazdKEDm58Ybb612VVHU\noiChWhYFCWfPNm/ejNudgtl8ATVrNj4rY5g6dRomkwer1Rsh9FJRUcFXX33Ft99+S1xctp7kDXCw\nUk+eX+ufDgyd/uqKMqlJNBeRouP2J/n8888JqxXaCK/AvIgs1E7tRsLcABX6nz59Ok2alOljffxx\nJVpeXs4bb7zBVVddRXx8nr6PQtSK2Ee4G6Jy1D169KBx4xYn7NzXrl1HDUxuweE4fvvtLVu2YDYb\n97EEqzWG2NgUVM4+i4sumkx6eg2UYmBAEwaNBlV21Kr2LURcOBwJ2gGr8r+6dZswevRo/Qx8qFB9\nOqrMUokl/fTTT7pzpU87ZldEwyWlPulAVZp4aNSoKS5XmnZKSfrc9+p93kLETMOGLRHxYTK5sFic\nlJU1i+B0vPOOoRD6vHaagohfg9MC/e89GOWXIi42bdoU8dz+9re/0bOnkaZJpl+/YVX+vjZu3KjP\n/QQihSxcuDBie61aTQm36b5Wf18p+luvgUGoHDNmzAm/7aKi2vpZD9b/T4yKiLtQUZGB+rxKXtts\njqFp07YcPXo0gliqFDatiMzEZPJSUlIHo8KjQYOWBINB7rvvPv1+b0PE+W8RgfpftChIqIZFQcLZ\ntYceeojs7NrUqdP8tDgB27dvZ/bs2WzYsOG0x7Br167Q6njTpk106tRdN1/y4Xb79GS+BBVWdqC6\nByrHlZtbgsrRzsNqrR6vAiAnp0A7wgDFxfVCnQBHjBhF48YtCK+2m6B4DbGIvKAnUwUaPJ5UGjc2\n5H0fQ8TKkSNHdM25DSOk361bb+6//35q1GiAy5WIAjYeVCTFgwIf4zBC/VarjzZt2vH000+f8j5u\nueUW7Qji+KOegGGzZs3BZuuISAwm0yisVh/Lly/n5ptvZunSpaEV4a+//kowGKSiokKTDjeiVuqG\nsqSKbCheSl9EBuB0Gj0sJqLy+uNQUQkFMu666+7QOEaNOl+/y2akpak01w8//MC3336rtSjCIMLI\n1Yu8gmLxN9L36WHkSCXFfeTIERYvXqzPOTiibO/IkSMalLQPHafy+i30O01BJAanM4FZs2ZVAgjb\nt2/X1RAeVKRiFXFxedX6xpQOQzYifho3bhqxLTW1gHCJZiyNGjWjTp06+t+LUamwZIySS7s9gebN\nW/HFF1/w9ttvY7PFY6QXwiB6pn6GfTDA7dVXX82IESN47rnnTlitcPjwYRyOACKTcDpjdLqkLyKj\ncDjiadKkLSZTHGazl9jYHG666abjnidqp7YoSKiGRUHC2bXZs6/Dah2CyFR69676CglUSD4xMQuz\neQIuV2yVWPnHs48++gi3OwmHI06r512oJ0ZDUdF7DDBQSnwDBigi2KpVq/SE2gURX5VCnwcOHKBe\nvebY7W5iYtJQhL9BpKbma8KWGVXxUE+DkjcJE+HcmM2xXHvtLF544YXQPf/444+YzW69jzBxouo4\nmJFRhMoB1yXc+e9c1KquN2ql11A7RDdGiLi6KpZhYSErIv7jduecP/9GTKY4FLHPjkg/XK6Ek5a/\n3nLLnZjNVlJT8xk8eBjhPguxnHNOD3JyjNLFAKqSQykNqmoOuwY+Vp577rnQOVWpYHP93D3Mmzdf\nv1sbLpcfpQnwAcd2KRSx4HQm4nB4sNvjqFOnaUT76L/85S8oMHkVDkdk6/D33nsPi8WOApNePaZM\nRGw4nclMmTIlgixaXl7Otm3bGDhwsAY5SYg8oB2vi9tuu61a7+baa6/V7/VmRNwRehfvvPMOTZq0\nJykpnS5duoV4C0OHjiCsezAYBc7m67+n6e/MqkHEI3p7vH5mgxFxkJubx/jx4yvpYJzMPv/8c2rV\nqofLlcLIkaP1/bcjnApZh4jztBqqRS1sUZBQDYuChLNrq1atwu3OweMpZfr06okiVVRUYLE4MGRr\n161bd1pjcDqTUCvPQj35Xawnwlj8/kwee+wxna92ovL/SzCZ4ti+fTuHDx/Gble9BaqqHPnUU0/h\n8bRCkdSMKoRY/bOOdt4DCUsO10TEhdPpY8+ePScEImH52/EaEBj5YBtqVehCrVzLUEQxEyaTn5o1\n6+lGRy69zXvc9s0nM0XuM6FSMAnH7Rmxa9cuzGYnIkZYfwZud9op2elHjhwJ3XNRUU0U96FY35dD\nj/kmVBg/hrVr1+p+EUZZnpM6dRpw+PBh9u/fz2WXXX4M2HDrlXoequTURJgA6sZisXHllVeGQu1q\nVT4GkaahjrOguBCBQDJGWeaLL74Y2rZ161ZcrmREHsFmczN48GDatWtH3boN8fmyadCgEVarT5cB\n+rBYjH4MThSgU2mUzp17nnAVHgwG2bx583E7J9577736/8gYRHzceuvtlfb59NNPsdsDmEz2UNO0\nYDDImjVrdDmnRwObWFSKJ0sDKA+qysGvtzfBYnGECI3VNSXw5EbxPAyZ7WUahNgQ6YzF4g1F/qJ2\nehYFCdWwKEg4+/byyy/z5JNPnpZoimrw8wIi9Rg8eMgJ93vnnXc499zzsNniKSoqjdCPV0TEc1E6\nA1Y9QReSnV0rRNIKBoO0atWeMGPdjdOZxLp16/TvdiFiPWXnut27d9O6dRftmBKPcXT7UCtf5aB8\nvjSys+tQUFConeLdmM0Jxz3nu+++y4ABg1D5bRtq1ekk3C3TgSrTdCAixMQk0717L3bv3h0CNYcO\nHSLc6MhzWi3T4+NzMVQEj9foCeDCCy/B5QpQUlKKyeQjPj6nWrLYTz/9tHZMdVDaExcRVqr08vnn\nn4f2zc4uQkWD/oLiDRipBDsKBF6C6mppSD876dNnKPHxqfr53VqpoiQxMR9VBVJInz59IrYlJRUh\n8igiw6hfv0nEtgULbqVJk06sWbOGsWMvREWGYlCrcC8qCmJwH8yIfKXfZRGKOOipJK/922+/hTgV\ntWuXIZKA1eqrROYNBoMkJKSjqmSGM2HCxZWea48ePVARp1XExFSW3963bx9TpkyhoKA2KSl5WK1u\njDbaCjiX4HTGU6NGw4h3UB3bs2cPCxYs0Ofsr/9/FBJud30nIi6++eab0zp/1MIWBQnVsChIOPu2\nadMmrrhi6mnxCmrWbIQKc3qYMmVKxLYnnniCrl2707p1B+0IirUzKeDcc88N7de790CMTnqjR4/G\nYlGOzuFIqpRbf//993Wp3CREzqVjxy66Q6IiF55qgpw5czZW6yDtDO7TqzI/BqFt7ty5rFy5MrRy\nHj7c0DlQVQegIiiLFy+mRo36uqWxWz+DJMJsdEPYxq+PtSHix2TynXBsTmeKdlaZtGvXrlrvAdRq\n1GZTNfWn6j6pRHlWItKCOXPmVPkaSk3Sjcp3Gw2ODEnlZJ5++mm+//57Vq5cqSWaXdrZuFB5eSPK\nYpTrqYjLsmXL2LFjB8FgkCVLluhr9EDEHXF9JVxlQ+k9OCJWtMOHn4+REpgx49oT3kPDhs1R+gle\nVCWBGyU7naKBg+r9YLMZ1RwpiKiI1vfff89NN93E6tWrtXS3jZYtO6GA63OItObmm2+udM1t27ZR\no0YjSkqaHlf9cN68eRgAsWXLDqd8Dz/++CNffPEFq1evJhDIJiEhly1btpzyuOON68MPP+S5554j\n3IUzA1WpU4jigbQNPYe0tKhM87/DoiChGhYFCWfXFFnJh0Hq+qPAzKls8eLFWK0dEXmA4uImTJ48\nmdWrV+sOi15UOZsVFb68HrVibI3dHgitltUqTBGwnn32WaxWLyIf4nTWPO6KWqnsZSASS69efahf\nv6kGIK1o0qR1pf2PtRUrVuB252O1ZmKxxOvJz6HBQj8aN448/pNPPtHjvxgRky4NM5xHPURu1WM3\ncuxGNEFo0qQ5mzdv1qx/O0oR0RUh5XusqXB5DiL9iI3NrNZ7ABg5cqSe2Fdgs8WddF+nM0E7bTfZ\n2dUrf1WyvQ7tPM1YLDH6XE4cDr92mF5UeD0GpTPQVR+TzDnndKV79z7aoZcgEqB79+6h8+/evRub\nzeBoxHDVVdNC2z777DN9nvmIOCu1Kn/22We55JJLWLDgxgjOAqiV/zXXXIPHk4DRxrp+/fokJCSh\nAF4pFksMK1eu5P333+fo0aO43akYqZWrr74atzsBQy9CheA3IeIgM1M1+bLZYo+bcjiVBYNB7r33\nXubMmcPevXtPuf/GjRsZM+bCakecKioqWLFiBZdeeik9e/ZHcWySKCoqIdzue6G+Pzfp6fkUFtbj\n8ssvZ968edFyx3+TRUFCNSwKEs6uHTp0SIf7H0FkKLNmVY+X4PH4tRNN013+fIj4WLQlvbmOAAAg\nAElEQVRoESp8/ldUqNmoszfK11SYf/HixZjNLlT1ggen0zhHJlarP+QENmzYQM2atTGbEwjXvate\nCfXrN0ax1ZtTu/bJSzmDwSBr167lkUce4bPPPuPTTz+lpKQUlerIJzMzH1DkxocffpiysrZ6woxH\nkQ+9hFXvYvV1A1gssTrc79SrL2+osdP+/fv171V1xq233nrcsSk56JqIXITfX32QoOSTXYiUkp1d\n+6T7KkBhRDiciJiJj8/ghhvUKnjfvn00a9aWvLwSXnvtNRYsWBC6H9Ucy6HvRwirMvbQ53pKj2MR\nYY0JL0lJqRHCS0qwpwMiy7FYIuWLMzIKEJmAyK0UFjaI2DZt2nQSE/No2LBxSLHRsKVLl2I2FyFS\nl6ZNW0YQQIcMGU1YKMqDz6cEm6655hqU8mA9RHx899137Nu3j+LiRrpqwxnapr7hjwinhtIoLm7I\nP//5T957772IzpfVtUOHDlFS0hgRc4RC47H2/fff89133+mKDTcWS+CUxMQ9e/Zw7bXX0rfvAMKp\nndYo4DoCkUUUFJRiRHYcjmTKyprx2muvnfa9RO3kFgUJ1bAoSDj7NmbMeMzmGByOAB9++OFJ962o\nqKBx4yYEAok6j+rUTtMIHa9HJJe+ffvqSSkWs9nDvffey4EDB5g9+wbCgj5X07JlW0wmg/Wfpyex\nDETuwuNRq2HV7dHIwT6EYoqP0k7Kw8yZM/X2Goj4j6s2eDK744479LmvQMRCfLwhbmPX4CBGX+su\nDGKhiBur1U+LFq15/PHHOXr0KEeOHNH7LkKkNEKuuHbtuiii2RISEgqPO44nn3wSRT5rjcXiq/Z9\nBINB0tLUM3S5ko+7It2xYwc7duzQ+gGGxkRA328sFouDAwcOMHLk+ajV9XmEO1q6yM+vgdWaSLic\nMI9wa2Gjb0MDrFYfOTl1ueiiybpz5g2INOK66677w/0qwSmLJRAhEnTXXXdhtJD+Y3fPn3/+GZPJ\ni0giZnOAX3/9lV9++YWWLTvoFIAfxXmI1wBG6Veo6pMYwg2mhIqKCk36NEh6bi666CLGjh2LioK1\n1ABCAdumTdtoLkc2mzdv5rXXXqv2ezqRvf3225hMuShiqZ21a9fy6aefMm3aNLZu3UrPngNQlSNG\n87CfETHxww8/RJynvLycdevWhbpQqnRcK/0cHtLPQGl9mEwxmExeXnrpJX766afT5jNErXoWBQnV\nsChIOPv2008/0blzF84///zjCrZs2bIFuz0eszlA3br19KQ5CCP/q9jPNtLSClAhWBfx8elcccVU\nVBvlO+neXZEa+/YdoidupXMwatQoTWjrrx1JAmZzAKczjrvvXgbA+vXrCdfc9yNcLliGSACHI0ZP\nfJ8hYuKzzz6r0n2vXr2aHj36UlTUiLAErxslkPShdnxNCUvkGqF05wnBlN0eg5E6yc8P529nzTKq\nF2KwWDyVJnZQKz4jiiLi4q233qrSfRj266+/6jE+jEgDbr/9do4cOcKHH37Ib7/9xmWXXYES0/Ey\na9Ysve86VPmoqvJITy+kvLycCRMu0vdhdCq8A5VysSCymnB54z362VsQCZCUlMWSJUvYs2dPaFyq\nskBJV48bNy5izIWFJSgSYS3atesUse3gwYOVyIIAH3zwgR7TIu3wbRoAJGKUGSpNA9HfTSNUxMdI\nE32OSDHJyZmUlDTC4VBltSpl1ByLxc+cOXP0+UoIN7ey88gjj1TrnVTH9u3bhwJGcYjEU1RUrN/L\nAA0M/ChRqJ7ExCRhMtlp3boLoADi/fffz8yZM2nYsJV+b05effVVPfYV+h0ovozZHGDx4sV89NFH\nVWr0FbV/r0VBQjUsChLOvhUW1kOF7gsYPHgYc+fOpaCglEaNWrF37178/kRUTnmknrQKUUQ1o2dB\nPUTczJgxQ5O9BqAqBJSErs3m54EHVF212ezAaD4j4ubyyy/XLas9iPho2LAJr732WkTu8+jRoyQm\n5uhJ36KdQAYqd6rCs2ryVzoKq1atOu59btq0iRtvvJGWLTsSqev/mL6PL/W4nKgSRbWybNasDS+8\n8II+Riki/rFfgWHFxbW0s3kTqzUyhG4yOfVkncG0adMqHRsMBo8BIyplU1ULBoM8+OCD+HzpGmQ4\niI9P1T0hEomNTdc8hJmIzKF1686YTHZUukHJJz/wwAMhvQClpphNWBbaCG97CLe29qBW7A6aNGly\nwrEtX74co4LgWFlpgAYNWiDSGZFskpMrp1gqKip4//33Q6AjGAwydep0DMClvsmU0DcnMpZwU6YM\nVE+PK7XDN0orYxBxU1hYAxUp6Kfffzf9R1VUTJs2jZ49e+sUwG2IXEly8v9n77vDo6q29tf0kmnJ\npPeEFEIKJARCJ0DoSJcqvSMIAipFpagol6JSFBRsoKLXgqJXRQT9VECuoigXREQEywVFkSIISeb9\n/bHWnslxkgC/736C3qznyZPknDPn7LPPmb3XXutd7xtceVDdM/nxxx8rJbaqyhjbkAB20BTosx+I\nLIiKSgZjKezYvXs39uzZgwkTJiAiIknuOUS+p27pUxPCw5PQv/8gsANXCwsWLMDOnTsvuT019n9j\nV8RJUICUuXNZbOfw4cOXXWt9JazGSbiyduDAARgMdhks0xEIP18PomYoKemAkBAPOJTfFJyH7yrH\nqFXWVBA50bt3b0ybNlNCwUPk5wE0ahQIu7du3QW8UmKWuO7d++LLL78UZj9mP3zppZeC2lm/fhEY\nWLUIAZIZhUTPEPDlQhC18Csl/vzzz9i6dStGjRovzHROmfgcYHxBO9nWRwZYHZRCY7t2HbFz507N\nSparKoaBqD86dOhQaX9OnjwZAQY9q6a80OGIksE/Dk5n5eWUHJqfBaIxaNGi9UWf37Fjx7Bw4UI0\na9YcSrqbn1Gy9E97MIDQhaKiltLvVrzwwgv4+9//Ls97mEZQCQBq1coGc0WoKgQzjEYbJkyYgJiY\nWDRs2EQ+mwGibjAaqwZK7tq1C+z8bIBO58HevXv9+5iK2QFm9rNpSlh9Ph+Sk3OhwJFffPGF4AdU\nuN0NJexFZEPDho3QsmVrFBY2kHtXlSdOsIOpojyDpN0GsJPZXlbq6p2yaib2W2+9DSp61bJl+yrv\n85///Cf69u2L8PDECuWeepjNrkvWOHnmmWfATq9VHJanQBSKtm274NSpU7jrrrvQo0cPTJw4EQEO\njigQzQVHZB6C0eiW+z0Jsznyf62vUmP/ebsiTsKYMWMwbtw4ZGYyzelPP/2E+vXr/8cb8p+2Gifh\nj7f33nsPs2bNQmhoDAJEQkqx0CkOQUsQJSEkJAy9eyvudwsUer9evQKMGDFCiIyagKgRoqMTkJyc\nC4MhFGZzEYjssNlisWTJUgCcKy0u7iQThlWcCA/q1lWh4CUg6ojp06cHtXnLli0iu+vAlClTcO+9\n96KwsCWUXG6rVu1hNNaCTheK22+/HRMnTpX2KqBjRxl8v0YgpM+5+Hbt2iMhIUW2p1ZZWTBmzDgo\nUSebzVtpLvqTTz6RY1JBZNJMNqzSFyL97agUcFarVm2Z3EIRFhYbtB8A7r77bni9EfB44mA2u8Cr\nTSsCDlRdMD+BAyo9Ex6egt9++w2bNm3yM/6dPn0aeXn14fGE+0GJym6++WaZfJRSaLr0mRMBjQBV\nFTIUer27ireNbcKEGxAamoJx4yYH7ePUxXoQhWLNmjX+7T///LO8K6cRwKQosq07pS0ZILJh/vz5\nOHHiBFavXo34+CT5XFM5ZqA4B9FgZ7gDiLwwGMyYNm06evfujz179gjbpg16vQs7duzQtHHt2rVo\n1aoVhg0bFlQ1sWXLFqGTViWhhSC6BcwhEQOiYZdcZsq4FhWlYUczJ6cuGjRoDpPJIw54gXxHO4Oj\nJDYwENEBiyUKzz77LJo04WiZTufCLbfcdknXrrE/zq6Ik1CvXj3NbwDIy8v7jzfkP201TsL/vR04\ncAAvvfQScnMbCkhQAdY6y99fgVfYSh5W1fhngVf9JjAn/xoQNYNOF+af/K65pgeUqBEP5G1gtRZj\nxIgR2LFjh6akkhXklFNhB1dUWFBQ0BJTptwEgyEU0dEZmnx2dVZWVobHH38cDz74oADd7OBoghkG\nQ4zcw1IEWAGZKrhhw2LMmjVLk9YYPny4TCANQRRS6fU+//xzmXR2g0iPlStXVnpcbGwtqPC8imoA\nCoCp0P9RlVY59OzZW5wE1mBQTH5LlixBcnKmqP3ZwKtGlWN/Qe59ivw2g50nO0pKOuPll1+uNLc/\nbdo0aWcHeDxJmn379+8XBUMVgdGB6DG5LrMrms3MyWAweLB69Zqg81+qLVy4CA5HIgoLm2kiLz6f\nDwkJtWXCjJB3cC50OpfgCHRgHEoY9HojAuJSLeXvHOmjGfJ+G6HTefzORsuW7TTtaNmyNdgpuhlx\ncVmafePGTQCXPhYiN1ervxAXlyoOSWewY3ZthWfISo5GYygOHTp00b7gaiP1vhZDpeH49z/FcRgC\nom6SvrLA6YzAwIEDNXTPjzzyCMzmRiDqAK83saZ08SqzK+IkNGzYEGVlZX4n4YcfftA4DFer1TgJ\n/3k7cuQItm/fjieeeEIoZp0I5GSjwMQxo8UpUGF2hVCvDw653ljBiVBlbErpMMSvTTB06FBwHfm7\n4JWaAyZTKKZNm4bFixdrEPa8MnSDmfZcsNki0bHjNThy5Ai2bduGqKg4uN2xQava31t5eTlWr16N\nBg2aCmjRIG20gWimtDNEJuTpILKgTp061fLNM+bAAqIFILJXOqD7fD5hmOSQfXh4YqXnYjZHdraS\nkrQ5bE45sOJgenrw95OJhFLADpm+wqRmAfMw6MCgvLVgh4snPIOBr8e0y16ZSKpf3bOGQgqIHkRI\nSHTQ/gMHDmDlypXYtm0b0tMZ8OZ2xyMmpjbmzJlb7bkrs99++w3t23dGfn6RH3mvrLi4A3Q6N4zG\nME14nNtoBa/KeVKsW7ce1q9fL8+hIrYkX/r2SXEKeoMoFhERyejWrTfeeecdKUc1gVlCrRrxrIED\nB0uf6mCxRGgYSNu37yLfjWiYTKGaSZd5O2zyWT1CQxMwdOhQrFmzRhyt7SDKrBSHomz58uWoXVvh\nYExyb3Pl+X4v73ht6HRKabQuoqNTqyy5PHbsmLyr02E0xv9/U6bX2P+NXREnYe3atbjmmmsQGxuL\nGTNmID09vUpw1dVkNU7Cf9beeecdGAycm+a86CQwO10HMJDrTiiWu6SkLFitHvAKukQG4WdAZEJI\nSDgSEurIoNRUBt9xUGqF0dGshLdp0yYZIONBZIfF4oHR2ABEoTAau6JRoxJN+9auXSvXuRFEVjz2\n2GOCVrfLwB4KhyOm0nv79NNPRTjIBHZ6hoJXbDNkELXIQG3D2rVrYbU6ZWAfgvDw5Gr77ejRoxWc\nDHulrHgAMGzYCKj0hU5nq5TSmEmaloFoGvR6m2YfO1XtQLQJJlOEf4V/6tQpfPjhh+jevReUI8Zt\nV2F2C1gm2QhFS22zOfGPf/xDc/7p06eDV7DhsFi0wMnfW1lZGTp06IbExJyLTiLl5eVYtGgRbrrp\nZjz00EOYMePWyxahat++IzhS0wUuV5xmHztDt4KoBYYNG+bfzukbs7wzOvldIu+jAby6V3LMDhAR\nDAavOMeDQBSLrl17+c9XWloqctnsRI4eHdB/OH78uJzzBxCF4P777/fv+/bbb+Uzj4IoRqONMGvW\nLHnvXgSRSeNAZGQUgKMvNrRt2xEAO5tffPEF3nzzTXi9iXA4FHZCL/2zVv6/AyrF53bHYcCAgdi4\ncSNstmgQ3Y2wMG0fKjt48CDef/99WCwRYMpmLzp37nlZz6rG/m/tilU37N27F8uWLcOyZcs04KCr\n2WqchMu38+fPY+fOnZg+fTpat24PiyUKRUWtcO7cOcyePUcGliPgfHyeDFJGITtyQKeLwfjx43Hd\ndaME5GUFkQsJCSkwGMJhMNiwYMES7Nu3TwarQQgA4uJkIPX49Rd4cG4Azg+bQDQNHF24CzqdB5s3\nb/a3/fvvv5f2rZLB3yEr71gQfQYiI2JjM3Ds2DGsXLkSGRm1QeTwS+RyJGQQOD9+kzgJTni9qYiK\nSpRzG/Dkk0/ixhunyIQaASLzRVHdzNzIq/xhw0ZVesy5c+dkol4JIktQDT/Aab4ASZFFM2kcOXJE\n+pTJpDp27IiUlEzw6lCHANmNARxedvqPjYvLwowZM/3nWr/+GeTmFiEsLAmpqTnIz2+Jrl27Q4W4\nzWZHUNsq2rFjx9CuXU/Uq9cMGRk56NOnX5Ai5LFjx/Dss88iL68RFC8FCzkNQ2Fhsf+4jRs3Ys6c\nOTh9+jQOHz6Mdu16wOGIgdsdhXfffRcAkJ6eCS4p7QYilya3X1jYXPolCw5HDDZv3oyFCxcKTsAp\n77EK4W+Ue1SltAYQhaBJk+bYunUrfD4ftm/fLjgBGyIjUzXpljFjxoBz+lYYjeH+5+Pz+cS54FTO\n4MGDNX3B7UgHkVuDMXj33XfBjl0nGAwujaIkc0Go98WMTZs2oW3bzlA03cydkQTGkbSS88TI/v4g\nike7dtq0yMyZt0rUxY5Zs27HgQMHsHTpUixbtgwbNmyA3e6F1ZogxE+pIOqLxo2156ixK2t/qJPw\n008/aX6OHz+O48eP+/+/2q3GSbg8Ky0thcUSLoOlWwbWASAqwrJly3DgwAFYrczsFxOThmnTpuHe\ne+/FiRMn0LFjDxnks4SxrRM4d50C5jpwwGjMB1EuwsKScP78eSlZVKFdpWFwDAyoYiY8HowYIBgV\nFY/ExFzRBsgHUSfMnj1b034e4ONk8DwABgsq+WK1YlQrwzRwNcUCxMRk+B0LIh30ejcGDx6O9957\nD6WlpVL+Nw9EU9CsWQm++eYbucYsMKjLW62IVWxsFnilOACNGjWr8jivN176IwMZGflB+z/44ANp\nYyJ0Oov/mqWlpXjvvfckwmMHK/mp0rVosEiVHgHpaCfM5jAsXbo0iBjptddek36fJZN3NBgwFwqd\nrgt0uhCkpqbj1VdfrfI+Jk2aBoNhELhsMAaKojosLAF5eQ2xb98+KaHMkHZ+ABZ2soBoJoxGL4jM\nyMioJ/eQjsjIWujUqQ84PdAIRKGIj68DQKkhWvz3/MYbb/jbEiBPqifPOF7+7yTv1o3yHqjqG5tE\nTRxg8F4O7rrrLv/5zpw5I5P6chBl4MEHH/TvW7FihbwX34LIqkktjRo1HgZDOPR6bSQBAMaNGwfm\nGVgDt1sLbi0oaCgTckcUFrbwP+9XX31V2twTRFbodDHgdN4zYEd3vLwHisGRq36YcVIHouSgUtr5\n8+eDK4vyodc7YLNxukWnaw+73Q2zuR+I1sBkioBeHwaXK+GiSp819sfaH+okJCUlITk5GUlJSdDp\ndAgLC0NYWBh0Oh2Sk6sPsV4NVuMkXJ5xaN4MVi1UCOgU6HSxeP755wEAq1atRmFha6xfH0g3zZ07\nF9HRidDr48HAOReYElflU5UYkR5EL0Ov92Dfvn1ISMiUwS8GKoLAf6dBie9wZUB/cGrDDqu1MXiV\nlwSdzo233nrL345AJCEVivs/JCQaLVq0lwEyB4zeHiiTU4r/HkeNmoDi4mJwlGKv//rKJk2aLOew\no6TkGgBAnz59pM0LQKTHgQMHquxbLgnkPPe0adOqPI5TBg1AtA46nSdoP0cLLOCVoR4dO3ZE//4D\npP+UpG8zcHmbSyY6J4jC4HYnoKCgOdavX69hIPy91avXVO5LhbrDwdGYZBDdJte/GXq9HadOnQLA\nfBPbt2/3CwHl5TWU49MRAH0mVpjgzdL3j8k7YgSRE253FJKS0qTthxGQd2Z+h86de4GdWAOIEhAV\nxaRSZWVlkgrrDaIRqFu3ER5//HHk5BRCTfzsCDjB7JZdwJGjWCjcgU7nxKFDh/yOV1FRM7n/WNSt\nG+Bq8Pl8cLsTwCBGBzZs2ODf9+uvv0pbOb01ZMhQ/76ysjIkJGRBOU0Vo2Br1qyRZ1UIIqcGD9Co\nUXOwg1MfERFJiI7mcmK9PkruK0/6dJOkAVwgsqKwsBmcTq+853dAp7Pj8cefwJIlS8DOXwFMJrfG\nuf3yyy/FCX8SZnMDmM0KJ/MmjEYb0tLqwu2Oxssvv4zPPvusUoK0GruydkXSDSNHjtSsGv7xj39g\n1KjKQ6ZXk9U4CZdnv/32m4REFV9+PIj0WLFiBXw+H/bt2wez2QmitdDrbWjdulsFPfvrQOSExxON\nfv36YcCAYejQobsMfO+AVyZWcDjYjtWrV6Ndu87giEBn8Co/VwY9hbJ3I7AKUqF9r6Q2dCgp6aYZ\npHw+n+SEFZGPIvNR9etdwSV2PEg7HFEoKmqGdevWoby8HK1bt0aAlc6l6Rue5DNAtBJGI4fay8rK\nZOVuBVEKBgy4rsq+ffvtt6VPrweRVRM2rmichnFBVRf8nvRoypQpUAyGgckvFswnESm/Oa2g1zvg\ndiegW7du2Lx5M7755husW7cOW7duxfvvvx9UbqesU6c+0OkagMgJk8mK0NA46HTJcq0U6d/lILLg\nxRdfhNebJO+MGVwapySb9fL8a8v/TjBmIlX+ZlGqrKwCmEyx4DLVEOh0WQhEQxTz5ksgSkBOTo5M\niNtA5NKMSy1bloCdwygEFCSVYFM42HHi9JfZHI7Ro0dLesAIpvJ24qOPPvKf78EHH5R2PwAiC77/\n/nv/vnfeeUfOFQqLJVxTsup2x4GrB5oiOlobFTAYXGCcQ7aGIZKBt075XBzGjBmDsrIy3HTTzWjT\npq1cK0/ez0RwCuEl6PUl/n7W66149NEnsHv3bvz73/8WYGYYVDXKoEEBhyU7WwEZU9Gjh1bL4ZVX\nXkFcXG106nQtxoyZBI8nHpGR6f5y4xq7uu2KOAnZ2dmXtO1qs/92J8Hn8+Hnn3+Gz+fDDz/8gM8/\n/xzt23dG797Xonv3XkhMzAwKGZ8+fVrCszwAElkxfvx4HDhwAA5HuAz62TJh3ysDeCSI7gNRCGy2\neNhsWahfvxk2bdokoLEYEDngdkdCpyuAyRSNF198EUuWLIHB0BhMquOEAoMxmDECjMCOAZdLfiYD\nvQMjR17vdw58Ph/Onj2Lo0ePIiKilkwuIeCVHJfgcRsbyf8tYLG4g3jkuQLBDEXo5HTGa/Y///zz\nMilngcjmF4fKySkCVwTU9VdlVGZc4mgC0WrodCFVqjUCEInoPBC5kJZWGytXrkTv3kp2mOTZjABX\nGVjAYDsuP9XpXGjduh3uuece7NmzB/PmzcOhQ4fQu/dgBEpJOSUTEhJdqaNw4sQJhIXFgugWeT5u\neeb1YDTWhV5vQ1hYKmJj02XCsoGdk1R5D0aCHZXt0u963HbbbRWAqA753RsGgwfTp0+HyxUPjjp0\nkOekJjG3nKsQRDY0bqxKXJnkaOzYsXjooYdgNkdC6TFwW+xgFUUrOGVmk/cnG82atfbfd2lpqSiD\ndgHRCDRqFJDRPnTokPQV63ZUBHJu2LBBnufPILJqHAh25CxQrKEV0zlDhoyEwgzk5BRoMCVeb2qF\nturkd5a8X06w+JMeARInMzIzC/DII4/g5ZdfDnqOtWrlg/lJusBi0Yo15eQUgB3m6RcF3tbYn8uu\niJPQtm1b3HHHHTh06BC++uor3HnnnUGAl6vR/pudhPLyciQmZoPIhcjIOBksjQhwxVtANAhmc0Sl\nn+/ff6gcOwRETsyePRs2WzcQvQ63Ow5Wq1qdp4AoEnp9KPLzC2AwDAQjyfNhNrtgs4XJYGdC7dq5\n0OsdIAqFyxUDo1GVE3aU3/ngcLlC2Y+Q7aqU0g6iSLRq1QFnzpxBUVExVBojMTFVJpKVcn/fyoDq\nQUlJCYqLSxDAPhjRuHFjREWloFGjYpw8eRJz585FoGbfiIMHD2Lbtm348ssvASi1RTNYZCoGaWlZ\nOHfuHE6ePAmTSa127f7jK7NFixYhLCwFbdq0xYULF6o87pprrpEJbSACkZSuYMfJI/ehtCxMcLsT\nsHr1ao3Dd/ToUYmqpMFgcElVxBgwwj0DRHeByKIhGKpo06bdCpstFjqdXiapu0DkgccTh+nTpyMq\nKkH68mUwzsEi75inwjPjaMjIkSP9573//vuldM8Cs1mJO3kREL0Kl2d/K1QaKiYmVZ5lAoisklZQ\noMM4+WxvcJTCAdbpsIHIhrS0XIwaNQpJSRlgUqDWaNy4peZeMzPzwEqdBbBawzUTd3x8Mpg1cwZS\nU+v6t5eXl6NOnYYgIoSGRmlKKxmvYgNXxrg0wlOfffaZ9NUKEJmxYcMGjBw5Gu3adUDbtu3lXvLk\nXmuDeT+G+L+3FosXu3fvxpIlS4KkrJWdPHkSsbGZ4GhSKoxGjyYlAqhIiFv63lqjyvgXsiviJBw/\nfhwTJ05EvXr1UK9ePdxwww01wMWrzPbs2YPw8GTo9Q6MGDFOVm1R4DCtHZxfTgOvzm+WwXQGrNao\nSs/HZVkOMNNeOO644w4BmhkQGZmEhx56CEVFTWG1KorejdDrzUhMzJYQ/DQZKEfIANe9wgSSLedO\nBXP2D5EJ5QYQtUPjxk3w9NNPo1+/gdiyZYu0P1cmhHoIENuEyrn2I7BKTkGAl8GK9evXA1Bsc2ql\nSXJ8MxC1QNu2nQU4VhtqhRYW5vVPfI899hgAIDExDQFK3kz06tUHv/32m5z3XRClYdmyZVU+o+uu\nU6RKddGmTTD1cmlpKdLT8xFIk0yHUhkk+rtsd8okq+7BrCG6AYDGjdtIm+JBtBPsaDik3YPBq3Uj\niBxVajj4fD7s2bMHo0aNqtDPReAVvnp2tgrtc6Nx4zZ47rnnsHz5cmRl1ZX2snDRhQsX8Nprr6FX\nrz4oLGyJGTNmIuAIdkIAUGoQoJ7i3zgu96ocXCUfHgl2TpaDHSilp6BKZi247bbb/BP+iRMnxGli\n/Ypdu3b57/Whhx6SzzcGkVUDxOvdu6+0Iw02W7gmf88gRXZQfl9ay9GJMBBFIk9NIicAACAASURB\nVCMjIK3NESWD/93n+w4DOwZGOd9EuScPzGYvnM44PP7449i+ffslYQCY0dIGxtYY8M4771R6HBMm\n7QRREh5++OGLnrfG/hxWI/B0GfZXdhI+/vhjjB8/HjZbBPT6UEEh2/0Dw/vvvy+DYjMYjWpyVBMp\nD5aZmfX8JWSV2YoVKySHynXfBoMDRDvAAMI4ZGc3wNSpU2G3h8FiYaEevb4ezOYwZGYWSJnYfJlk\nWsrgaAVL4xpBZIfTGY7bbrsNc+fOq9BGK+x2Fzp16gKHQ7EbuhHIwb8Gjhro5NxOpKbWxb333ouJ\nEyeiqKiFTCIh/gkegEQxWoMdDTUpqftTZYVzQPQ32T4XRAuQlpaHX375Rdjq1kKh6PV6JzZu3IiC\ngsZQ2IeYmMwq+7Nx41bgEG9HeL3xmDp1KqKj05CRkY9vv/0WO3fuhE4XB6LPpX8Uo6MdTmcixowZ\ngxdeeAH79++XNr8IoiT/KrC0tBTNmysmvdegwvoREQr1niETbQx4sp+LJk0uruEwf/58WCxumcwV\nffIbYMeNpK/uAFERbLZQvPbaa1KdMABE7dG9e0/h/A+TPv6b6Bk4wOmIWoiMTMBPP/3k50c4fvw4\nXK4YeSe6giMJJWDgpEf6uxmYYMuLW265Bffddx9MpjCwk2BCixZaHg3uS2YArai4eOrUKbmnLiBq\niSFDhvv3fffdd9LmcSCK0xAksdZDBIjGQ6/XAl1bt24LdoDmwWj0YtasucLwqDAy+WCeAVWeWEeq\nfZTT5UKdOnWqBZhWZk899ZQ4kVwV5HTGVOlYTJ06HQaDA3XqNKw2slVjfy67Ik5CcXFx0E+rVq0u\n9rErbn81J+HUqVMoKmol9d1W8MrHBc4BK3rVLBC5YTJ58eGHH2LJkiU4fvw49u/fjx07dsjnrCCK\nQvfufS96TR7sV4BoEhIT02GzeWA0xoGZ2vQwGBJRt24hhg0bBpstXdqwGUQm2GwehIenyqScCSIS\nwiIG3mVk5GnypEyIczM4dKxYDePAzsEtUOWGvM2K9PR05Obm46677tIAxzyeFDDYrA/atAlMFGlp\neeCVdHOZdBqA8+YeBBT+FLmQYhy0gsiECRMmiGTwaATy221hNrtx0003gaM0j+H33AXKTpw4gRtu\nuAHs8KiUgXKe+qFRo1aiismIe53OBa83BaNGjapU+nno0JGwWCLRunVnlJWV4cMPP0R4uJrAWc5Y\nr3fg1KlT+OKLL+R+30dAqImjFStWPHBJ796xY8cQG5sIdl5MUBoVTmek/K0EuupCr/fIhMfXqFMn\nXz6nyl0nw2RyYsqUKbDbXWjSpEml5aO//PILzGauPmHWx3CZXO0y0RaCyKjBA1x//fXyvu2CwaCt\nDmnbtjt0OhfMZnsQURSXwBaBKBHp6bX928vKykTAqwOI7OjfPwBOPX/+PJzOWPnOheCee+7x72PM\ngh1Kw8NszganLZLAjmgtMBEZO3KRkSlYvfoR1K5dINtCkZV1+fo4LVoUgyMRi2GzxeDnn3+u9viy\nsjKsXbsWzz77bA298l/EroiT8M9//tP/8+6772Ly5MnVlnFdLfZXcBIOHz6MwYOHY+HCRRg9eixY\neW+4TDSvy0DcAHq9E/n59WVQehZEesyZMyfofLVq1QGvmCYjJibjote/+eZboISH7HYvfv31VzRr\n1h68Em8EZgcsQrt23dGv32AYDA7odG1BxCBFIgP0ehv0+hA0bFiMefPmgVMPL4AoFPv37/dfa/Pm\nzcLC6AHRBjCgUTEgqmiIDRyqNVbZ5sGDh0KVzb3yyiv+7Vzi6ZQJK0z+NviPJXJg69atco0bwBTP\nTWU/K/hZLC5kZ9eTgbwZiBqgVSul+BgLIg9mz56NU6dO4bnnnkN+fpMKTI5eufYLco2R4NV4K3Ts\n2B0AOxPvvvvuZUkAnz17VsrWxoGjBBmwWFjZUFmXLt3lHuuDyCh5ff1lTUTl5eV4/vnn0bp1O7CD\n1QhmswcrV65EcnJyhWcUIv31LIgGS0TLBq6AscJu9+KVV16RbTeBiOWJfT5fkA7EyZMn/cJR3Id9\nwQBHRZVtw8yZARIoBqAqHowQDVbg+PHj4ugNA5ENhw8f9u9bsUKBdQeCyI4jR47497Vs2QocQSmC\nxaJVpPR4ksBOdAFSUtJgNociKqoW8vKaSH+/DKI46PXh8j4rLRJFeEQaLgVOe9UB0WfQ66snrKrM\nHn/8cbl3O0aNGnfR48eOvQGcZovBsGEjLvt6NXb12VWTbigsLLzkY5XuQ5cuXQAAt956K/Ly8lC3\nbl20bt1a84VU9vnnn/sxEPXq1YPL5fITkMyePRtxcXH+fa+99lql1/2zOgnffvstZs+eg1deeQUJ\nCXXAYUuvSPamg8FMDhC5EROTgubNm+PTTz+VOuw6Mki3A5E5qG85pO0Br+xt1RLiABVR/8tAZMQv\nv/wCAPjiiy9Qu3YD6PXRMBhKYLGEwm5PRMeOvXDddYNlBWgF0RfQ6ZSQj0uEf1R434ycHK1Q2OTJ\nU2QfpyMSEpLRt6/KC98Ddk5Gw2arHHB57tw5GAwhUMh/JQK1aNEi2O0x0qZvoZgGA07AcyBKxtq1\na+FyRYBz1BEIVA+0kkmuLnr27Cmr9q5Q1RN2eyyYbKke9HoD9HoVLeguzsMGqNUoR0o4QmGxeHDt\ntX2r5Mm/mP3888+YMGGCf2IgMsPh8FRKcDNkyHC4XEkoLm6FQBTFUS0JVEV78cUXsXjxYtx+++3y\nfMLl3YhFQUEzuaf98o4qvIIVzZu3kLJA9RyduPvuu6VvF4EoBMOGDRMwrA7Nm7cMunZ5ebk4HjkI\ngBxZmjs6OlFzHOsKRIGoJdq27eTfx4BCO4jWgcikGTf27t0rk/YTIDLhww8/9O979NFHK3wHzPj4\n449x/PhxdO3aC6GhikdApa6Wg6gvUlOzpQ1FILIiLS0TQ4cOxx133CFMiQozYsftt9/uv9aOHTuE\nyEqPVq0Cbb8c27dvH7Zt23ZJkQFmu5wCoh7Q6+010YS/gF0RJ6Ei6+KPP/6I1157DRkZF1+FKlu8\neDEGDBiAa65hEhpFwAIAS5cuxYgR1Xuw5eXliI6O9k94c+bMweLFiy963T+rk5CT0wgGwzDYbFGS\nWpgJokR07doTgwcPRWZmDhYvXhw0uG/atAmrVq2SAZzVGDt1Ch5oeGX7JojSMXBg1bX9APDvf/8b\ngTy2XaO8WF5ejlWrHsLQocMQEpILokdhMjnw8ccf45tvvsGAASNgsYSCV+sqbaDAY3XAKQG3Ju9q\nMkWCy7YiEReXipMnT+L06dPy2TYgssPjiUTDhi0qdS45v2wG0SMgssBuD4fVqoSoFslAHocAuFHh\nDwphMrlw8OBBfP3115gwYQLWrFmDJk3ayr2rMH4shg4din379slqnFeDAXnjLHDqIQ7MhNcIvAoN\ng05nr0ATHYtOnTpf7quhsbvumg9Fk8xaE/fKxB2Na68dUOXneFWuuCdclcpKA8ybMXLkaIwbd70w\n8bHuQ3R0ktzr/f6J1Wz2CvEOgyRNJo9Mgl7cf//9ePHFF6HX28ApMsVFYZI2kPSvKlv1oHfv/kHt\nSUmpjQB3hkUmYQY0ViwzLCpqCY42JSIxsZbmHL169YMSSxo4cIhmX2RkqpxTh8aNA7TQ/E5ZwA6f\n+m7ppK8L5Zn/y/9+EIVg1apViImJkT74G2w2rRaCxRIt70cCUlPTNfuOHj2K3bt3X7LzduTIEaSm\n5iIhISuotLc6++GHH9CvXz95ht1AZKyyYqLG/jx2RZwExbyYnJyMtLQ0lJSUVAt4q2jffPMN2rRp\ngy1btvgjCRVt/vz5uOWWW6o9xxtvvIGmTZv6/58zZw4WLVp00Wv/WZ2EuLgMmWissgp3wWoNw549\nezB48FiEhHhx88234dy5c3jiiSdQt24RYmISZQANRaAM7A4QBa8Oxo+fCCUT63TGXRQcxYC9PSBK\nxvXXXx+0/+zZs8LSZwHRUJhMdni9qWjYsBi7d++u4BRsAZEDNpsCnvFk7XLF+YFTkZG1ZLKwa0Bi\nJlMomNY4AYy071dluuTmm2+ByRSFAF6jUPqkq/yeBaJmCAlRk3xTENkqHWC/+uorREUlS5s+BJEZ\noaFeeDxKqleV+62QexoGZohkKWSj0YHs7HysW7cO//73v1FU1ApMqNQV9es3uOi7UJ15vbXAEZAi\nuV44mIxnNIgsMBpDK0W2c6UHgZHtoX7J6aNHj+Knn35CTEw6jMYIJCSkghkqi+H1JoLLIOcKdbcd\nnPvm6EX//oPh8/nwzTff4MKFCxJJaQ+ifIwdOxYAMG3aTfKOuMBKlEqR8BcwZsIAZta8E+HhtYLa\nzdgPGxgQqbA1n4AoXVNVsm3bNnnnBoHI7WeBBLgEk8Grm0HkxunTp/37evToLc7FAeh0NgwcOFDe\nV+WYxIDLKJ0guh0cpp8p75QeRFYUFBTigQcY5/HEE09ARc3i47WAVq4aiQE7yiEacGF5eTlSU+uC\nSIfc3EYXXd03btxCntMg1KpVt9pjL1y4gPnz52P8+PFSrVTb30a3O74mkvAXsCviJCixnYttq8x6\n9+6NXbt24e2339Y4CTNnzkRCQgIyMzODuON/b8OGDcOKFSv8/8+ZMwdJSUnIy8vD8OHDq/z8n9VJ\nuPPOO2EwNAXRqyCKhcnUCzNn3o4HH3wQOp0JROuh0xng8SRAiRDxymwyuL5cVQkw693vJz+mL3aC\nGehq4dFHH622Pe3adQWRATqdA++//75m3+nTp/HUU0/hgw8+EHCZCvV2A1E2brjhBgGxOcGrKid2\n7dqFpKRkmaiyQBSKwYOH4YUXXoBKMxDZNCupRo0ag3P6KuTcATqdS5OP3r59OzyeJHg8CbKSzQVz\nMNQCUSQcDg8CaYYQFBQUITm5NkymEMyffw+qMg6NKyVJPQLAQyMY5R8CxgMo7n8XiOy45557ggbd\nyZMng1ebXAL3v7Fhw8ZA1bmPHj1aIiZZ0oYXQFQXjRoVV/rZ5ORcMF7Ehd27d6NRozZgOWgVNv+H\nTIytQdQLderUg8Fgh15vw+rVq5GcrEog7ZWWz8XEpIFTWk4MHx6IFM6ZMwcBPIaqyPFAr/egqKgR\nFJYgPj7YSTh48KD0+d/BAkpKMCkEL730kv+4U6dOQa93gfEQDg32gHkKbP5rKzzA8ePHxYGwyPN1\nys+N0sducBTkJtnvgc0WieTkPAEvPgSibsjODkh0c/QlFURfQaezau5l+/bt8t70AJFLw/TIHAYG\ncPmnXdP+3xsTn1nADmJL1K3bRLO/rKwM27dvx6ZNmxAamiTYFSsCDvNeEFmwfv36IDxIjf057Yo4\nCfn5wUIzlW37vW3cuBHjx7Nc6tatWyuNJNx9990YOnRo0HZl58+fR3h4OH744Qf/tmPHjsHn88Hn\n82HWrFkYPnx4pZ8lIsyePdv/s3Xr1ou2+Y+08vJynD59GqtXr8Z7772HCRMmIDu7AA888AAcjnDY\nbClITMzGtdcOwUcffSRf7gngHKdSAzwu26OhCFcWLlwo2zj0OWTIsKDrut3xMkhEoU2brtW2s2vX\n3iCKgl4fimeeeQa7d+/G5MmTsXXrVjid8SDKhdHoREhIKDjsvQDMxOfCnXfeie3bt8PrjQaRDdHR\ntfDrr7/i1KlTMJtdYEfmaRBZkJWl6Hg5GlJxgm3dukQG9wC7IDtFnPs2mUIRHZ0IomvBK143OJJi\nggLU6XSeCviGVBA5kJ6eB4slwh+ZOnfuHA4ePBg0uf/tb39D06ZNK0ycf5fJgmAy2RAVFYs333wT\nderUA2MO5iEsLCmoL5ne2Qom2rEFcRxcjvl8PuzcudOvGdG0aQkCIXvm9fd4YjWVH8q2bNkCvd4K\nnc6OdevWST/ug4pgEXWAXu9ESEgkIiJS8OWXX6KsrMy/4mWwZi6IZiAiIi3o/OHhaeB0RGvUqZOj\n2XfvvffC44lAcnI2Xn/9dezcuRPnz59HcXFrcFSnDyIjg50EAGjTpj04dROC2bPnwWYLBxOGpWrK\n+Hbv3o2YmBQYjeEYO1Yb/UpPryd9ZEVcXJpM8lZ5Vzzy3ijg4VRxEnTyuz2IDHj44Yf9/Tpv3h3y\njoVCp3P4KwpefPFFKO2SsLBE/N4yMrLkeY1DenqBf/uPP/4o1OORsFi8VZYnBkph7eCqIAu+/vpr\nfPfdd3j66afRqFEzcZbUoiEWnGZk5k/ljOfmNqr0/DX257CtW7dq5rk/1En4/vvv8eGHHyIzMxMf\nffQRPvzwQ3z00UfYunUrMjOrrgdXNmPGDMTHxyM5ORnR0dGw2+0YNGiQ5pjDhw9XS/G8YcMGtG/f\nvsr9hw4dQk5OTqX7ruZIwk8//YSoqFQZnBQyPBZEnaHXu/Cvf/0LWVkNYDA4UFzcFosWLZKJkVc1\nmzdvRm5uI7BioQcNGzbBjz/+6F995+c3BtdgvwGHIzro+m+88QbM5logKoHBYKuWpIX1HG4H0XWS\nhzeAKEfqsc0y8MShX7/roKoEPB4vRo8e52+P0eiR+4z0vwOsaGgDh8iVSmNtEHWAxaIFJjLWwiHX\nMggRTAtwnrgzVJSCB/kktGhRgvT0AnTr1kMGw60gysCIESPkPMpJsoKdGjPi4lTo1QaDIQRRUSma\nCgGfzweOfijJXQcWLFigaWePHj3ATk4GiEKCJuiACNXfQGTXiPxcrv3444/weOJBZMS11w5AaWkp\nZs6cibvuukv681EQxeGZZ57Bt99+iwceeADp6fVgs0XKfSSCwZhuOBzRIDJDr3eifv3GSEvL1OBP\nfm/jxl0vjkhX1KnTMGj/8OHD5V3NhU7nvqQwdsuWbcDRhyzExAQ7HgDLI7O2wyZYLGHgyML7ILLh\nf/7nf/zHvfnmmwjgJmz4/vvv8d1332HVqlWw25UOiIoWjACTGRXL/42k7SwTbbO58N133wlnSDh0\nOhc+/fRT/7VYdMsIllCP8xNULViwABxZGwKXKz7oXjp37ibtiEV+fpFm31dffYX777+/So0PAIiM\nTANXOikGRwtMJuV4W8HRGhs4ItlSvgcNode74XIlYcOGDRctkayxP5/9oU7Co48+iuLiYjgcDg1H\nwjXXXONXBbxUq5huqDjwLl26FNddVzV4rm/fvhpCHACauuglS5agf/9gkBNwdTsJLIxUCM4nPwCm\nvk0E0Sjo9W7RCqgDLnN0yIBsB9daj0RUVDJOnDiBs2fPVjoAb9++XUrPkkHk1pQCArxiZsU4Jom5\n7rohlbaTyWRUztUJXmkegCJriYiIB5ELERGpKC0txf79+yvVJeABfSyIbkRqasApfPLJJxEAEbaR\nv28EkVWD+GfxJpcMquEYNWqUoOGbgMv63OCQbwvYbB58/vnn/s/GxtYGr9hswpJnBdFRBGr+Z0tK\nxA6i3VAywDrd9bj11ts093HixAkEwIKWoFJFFnKyglfEjkpLGbn6IgVE8bj22j6V9vulGKv5tQSH\nwckfbfP5fLBaI+VZKa5/pcbZAlyKx/LL/PyZ1//RRx+tVlOiopWXlyM8PA5EOkREpATd52OPPSYT\nJFNnX8pkxJiAbBBlIS0tr9Jjnn76aSjWTp3OJWmlJiCyYcKEif7jvv76a5k8i0CkR3JyljwXmxxv\nBNEZ+f8a+Z4YYTAoOXF70Cqe78kBIkJ4eKr/e+fz+eS7pANRAuLj2cG57rrrwOmQ22AwhAbdy6lT\npxAbqwSz7Hj66aeDjvn555/9FUUVbdy4SfIdvBZEETAYPHJ/mWCmTju4UsMMIgPMZg/mzJmDuXPn\nVnq+Gvvr2BVJNzz33HP/64ts3brVX93Qq1cv5OTkoG5dLidTiNrvvvtOg8Y/c+YMvF6vphoCAAYN\nGoTc3Fzk5eWhW7dulZLNAFe3k8A1+6qmnMPD0dGJSEurh5deegnvvvuuDGCJMtD/W77wKeC8sxVE\ndtx5551YuHAhVq9eHbRq5RruiSDqgpyc3KA2sOQsa84nJFS+cmNNAx2ITkJx8Ste/oKCZrhw4QIO\nHjx4UbrY2rWzwfiJRFitAQ379evXw2hsCgY1xsu5eUVfscYdgJSGpYDIg7FjxwpgsgECAMJa0sbW\nqFUrEF269tprwemFtggJiRY9CS7JTEvLQOfOPfDGG29IPj4PHEkIhc0WVmWK6pdffqkSfW61hsm9\nWoMcXACoW7eJTKA2TJ06tdp+q84YFEr+eyGyYdKkSUhJyZR3xgYu3VTo9WFggJvSsTCLI2H2Mx1e\nqvEkbACTasXg7bff1uznqhjFDRGBpUsvriDIeXoLiBLhdMZWeswvv/wCh0OVpjqQk5MHZoK8G1Zr\nDA4fPozPP/8csbEZQo1sB0d1HOAQexQ4VRQCVSWRlZWHmTNvxenTp3HmzBnpzztBlO0vuwZUNMsK\nIq6gqZgCbd++i/RlMoisOHv2rLx39UHUGEZjWGW3I6Wzm0DUCV26XKPZ98gjj8j1LFiwYAE++OAD\nHD58GNu3b5cqmXogMiAkJAwGQxg4/eOFSi9ERqZj7Nhx2Lt3b6Uppxr7a9of6iQ88cQTALjGfPHi\nxf4f9f/VblezkwAAjz32BKKiakGvrw+zOVGD5mdNADMCZVU2eL2xUPz2TIg0C2azFzodM+2ZTGEw\nGiMwceINmDdvHlq0KJGJl0OQypn64IMP0LNnT+EsUCtMHYxGO3r3HqyZ/D755BO5JuMdbrzxRqxa\nteqyyH4AYPbsOTIwZyI9PYBn+eqrrxASEi6DW4YcMxxEObjppps054iNTZIJ4S5ERKRq+uqHH35A\nTk5dcQZqweGIhM/nw6FDhzB48GAo+uYAW9+NYPZFu6Ytixcvxtdff43PP/+8WsBYdcYRj1dBlIGe\nPXsG7X/44YehpLHt9uBU0FNPrUdSUi5GjpxYbRncDz/8IM92jvSZQuCrFXkUmLrZCgW0jIlJQpcu\n3fDGG2/Is+8JIutllc4BXNHCJY/8Xuzdu1eznyM/oeDVbH0UF7e56Dn5fVSAUEuVx0VFpYArF+5A\nXFyKHG9ARX4NBrcWg1MHj0OVTur1LoSFJaNfv/7yXreA0xnv7+fy8nLY7dFgnpEwvP766/7rnj9/\nHhERKSAKgdcbr3ESuKLCJo5CCDZv3ox77rlHno8H9es3CboPABg7diJUhCM/v6HG2WaF0TYI0FlH\nyjWM8v9KEJmxb98+tGrVEYoorH379n6cSo3999kf6iSsXLkSAJMXzZkzJ+jnarer3UkAOFpy3333\n4bnnnvOHL5944gkUFTWRQa8QRK/BYAjDoEGDwOHlrTLoORAWFieT/GkZQBbLAKKcAxs4lWHEbbfd\nhlOnTskKaxQUyp8HogIQpcJmiwka8Jk8xwwiB6ZPv/Wi96RApRXtwoULAjJLgMkUqgk/Hz16FO3b\ndxKyIzcYS2HD/PnzNecoLGwgkz1L9/7eGLehGBvNyMwsBFEITCYHOCKzSCaGaHD6Iabayej/1xgn\nEAOiTGRmBuNtePKIANM464P6iiW5NyIkJA3btm3T7Ks4iXDKyiTX8oCjBxniAIVBMR9GRkbjvvvu\nC4rIcYQoGkQezJgx47Lvs3nztmBMw1D07RucMszObijPyoGRI8de9HxcFlgCxp14Kk2j7d+/HyNH\njoYSqApUI7SXex8BBs+GgStb3NI/sejTpw9OnjwJAALWTAPRwyByagCkR44cQXFxMbzeWAwfPkrT\njnXr1sFoTAdRT6Sl1dO0jbEe40DUUSpnsqQtUzUS1BWttLRU7iEPRFasWrUKM2fOREGBkja3yXfV\nCi4bLQFHhIbA4fDiwQcfAsDfuc2bNwd9d2vsv8+uSLqhMk6ES+VJuJL2Z3ASfm+TJt0oA1sYmByH\nwY116hQK85tSQLTj8ccfxyeffAKvNwUBpb++4hzMEOdguPzfBsyLoOSGJyHAwd8bHJa2IDo6RVM7\nDgDdu3cH5zqTgwCFyvbt24fU1DzExqYhJCQMHk80Pv74Y/9+xjbYQHQYRCZN/jU3twi8ynPKYPgW\niBIwePBgzTUYCJYCzqm7/au/w4cP49NPP0VYWIxMjL0QoF/eU+E+eyDgOKWCQ8Eh/6lH57dJkyb5\nJzCbLTIoGvDll19CyWcT2bFz507N/oKCFggJaY2QEC+++eYbbNu2DTNnzoTZzJLKZnMoysrKcOzY\nMXEMFLbCAqczCm53AqZMmYIdO3ZU204Odf8NRM0xffr0y77PzMx8cOldGBo3bha0/8yZM8jKyobJ\nZMO11/a5KHhx165d0i+cJhg0aBDi4zOh04WiR4+e8t1QVQhWcBWLQ97toeCogRl6vR09evRA3779\nhX2xB4gi0K/fQP+1SktL/RUERDFo166jf9+FCxcktTUERNFYt26df9/SpUvBUZpRIHJpCL2KizvI\nO5UErzcGGRl1xUFJQr9+wU6Uz+dDw4YNwbiFO6FYVDlaFgIW+DKCIyGqQkFF9ax48MEHL+t51dh/\nh10RJ6FevXpB2y6lBPJK25/RScjPbyADXlsQmaHT2TBx4kT/KpAHx3+AKA033ngjAB5svv76a8ye\nPRtDhgwRkRijDDhR4NXcvQiguZ+SgVZx4Dv9g9CsWbNw9uxZDWDrvvvuQ4AlL0RT011eXo6BA0fB\nYvFKmwvAyo+3YsyYG/zH+Xw+hIUlQJUkjho1Cj17DsLrr78u7XwHRC2kasEBIg+ysrTEMAFa3VQQ\neVFY2BiRkUkIYDts4JXxMhCFwG4PB1EdGI1uPPnkkxgwYAD2798v9fZmMOmROWiS/t8a59ZNYGCc\nFQcPHtTs5yoJDzg33tNPNqTsl19+Qb9+/eBwqPp8i0w20WAVThMGDuQJr3nzNuCSzHSYzZXnvauy\na67pA17ButGjR6/Lvk9O42SBaFiQ8+jz+dCr10C5z8YgSvSTDFVnrN1hRYDaORFcoeAUJckZ4IiQ\nDUxkpGSzWfDJZArTaCEMHz4aSsvBYgnVpMlCQ5VT2RJGYwBYWFZWJmRQ+o/T/gAAIABJREFUk0GU\njGHDAiXEJ0+elGc7EERuDf01v58GcBrLCKORHXqdzqiJnP3666+YMWMG8vLy5N01SB+FyvNeIfdd\nJN9PrtjhfWug0zmxYMHFyeRq7L/T/lAnYdu2bVi0aBHi4uL8WIRFixZh9uzZyMurHH18Ndmf0Ul4\n6623wKtdVuubP38+fD4fjh49irKyMrRp0xG8eoyExRKBFStWSCSBc8OxsRn+levrr7+OuXPninyu\nIhKygqifDE7rwSHaGBmkOkGp0un1bmRlNUCfPn0krG0Fr8rjsH79en97d+3aBZstUSYvPYhCoNM5\nYLWGBVVUhIbGysB7l0x898FicaJ+/eYgsos4lBJT8iAxMQk+nw9ffvklli9fLrX3ivNeEUgVyWRx\nD3hVawUDuoxYuHAhXnjhBfz000+advBA75B7t112pc7F7Ny5czLJcIqmMickPFzxGVgxfPgInDt3\nDk8++SS83lQBVoYjkCJxgegDubcssNNnwFtvvSVofzuIXgGR6bKkhXv16gWuwrgJJlPlEaLqjMmC\n0kDUD3q9W7OPeRjCwKQ9Nmk3YciQ0ejU6Rpcd92QSuv/mcq4CVhfQclJd4HC1Sh8hcFghnImk5Mz\nZeXfCESxGgZXBliaxNGwa4CorHkRA6LnQWTTEMT16dNHJu0e0Om0QktDhoyR91CP8eMn+bczk6UN\nzAoaLe29BkQ2LF++HDfffLPIp7vAlUs2MKB0I4g8MJsjoMi4srMbYPbs2cL6mABeGFhhs6WhVatg\nvpkaqzFlf6iT8Pbbb2P27NmIjo7WYBEWL16sKWO8Wu3P6CSUlZXJavpWGRAdqFWrNvR6KyIjUyTk\nngouhdTLYNVZBpx/gigEer1Tw6LILH8x4Dx4XRDlwuEIhdebAoMhHLxaaSrncYBoFxTvPn/GjgD4\nzQGLxY3S0lJs27ZNRKdMYGS1B0TLYbU2wsKFC4PuLTIyGgGyJAuIZsJsduKLL74QEBzBbHbIuTKh\n19uQmckIbr6/R2QQXQROKRjBuWgbGM2dJKWMaSDqik6delfZz2FhySC6C3p9Jp566qlqn8m5c+ew\nYcMGzJp1O5o06RDk/FRmXBJ3N4hqo3t3LXixvLxclBSZc8FgsArttBtE08CpnTb+SYq3m5GZmYdA\nLj4RRA6sXbtW+vRaEFXPd/F7Y6yJHaoS47PPPqtUGKq6fuHPR4DIqsmH9+kzCMxh0V4mS7Uidsh7\nlochQ4ZozsclneYKzgGBVRSV3sc+GAwZGDDgOmzatEm2F4LIDpcrHpwys2pKqlnwySH9asPtt8/2\n77tw4YJM2FEgcmPMmDH+fUypHJDUrsiLwAJNBWDHzanhMsjKygdXFA2X9jsQYPlU+Im75R5z5HsV\ngujoFLz99tt4+umn8euvv/rPxyW3TnDVRDimT59ew4xYY9XaFUk3VAzf/Znsz+gkAMDo0aNlYOgG\nXi3bQfQgiKLQoEFLpKSoMOW/wCuWhjLQWcC0sbf667h37NghSnVG2c+gxr59OVy9c+dO4Q4wgyMI\nqobfJk7CCnBotUgGvOEgcqB58+YI5PmjwWjzENjtRbDZIoIAdwAwcuRIBEijQmA0xsBiqY3i4hLw\nyrkOAo6PSpU4wFwRkXItlZd1we0OR61adTB58o0ID0+HTtccBkM+DIZk2GwNcNNNVYMsmQwoFERJ\nqFevcuS5sqyshjKg20A0DjqdqdrqjvvuWw6j0Q4lQR0fXwsHDx7Evffei4yMXLjdsXK/dnAuWgFI\nJ0FVJeh0TtnWCER10LIl0yuPGDFKtueCyAidzoXo6BQYDFZ069bvUl4vv7322msISHGrScyMKVMu\nrSwzEJF5FUS1sW7dOpSWlmLVqlVISEiX9yJEnmNTcKQqDOw4FAUxsHL0pTM4R28ER0wUpXdrEOmQ\nlFQHr7/+upQQK+fBid69e8uxt4DIouHY6Nq1Fzh9kQqDwanBRjidqo2xcLm0ZZes//EkiIahb9++\n/u0cnVAU5Hbs27cPPp8Pb775pjBqqna5QJQv/8+RZ6uonu1wOGKxZs0arF27tlK8xt69ezFgwGB4\nvbHQ6bywWMJryI9q7KJ2RZyEY8eOYerUqejYsaOfUKlVq8rRuleT/VmdhPLycnTu3FMGmQEI5C2j\nkZTE9f88oCqgH+dwMzKyodIFnA9VmvXK2XgRRH2h01mDViMcOraDqDYsFi+ee+459OvXDwaDSwBe\nSTLYLZYJTvHfT4PiNeje/Vo8++yzVeb4T5w4IXz7oQjw1jdFdHQCAkqTHjApUntwWsQDTmHYkJub\nj3nz7sDKlSuRkVEbDRsWweNRYd3HQGSDxZKL4cNH4Iknnqi2Nrx//wHgaMwcmM1Vh9oPHz4sffqS\nTDRMcztv3rygY0+fPo1mzZohoBTohCITCpStDZXf46W/b/dPzkQEhyMCEyZMQFlZGbKz68sxebBa\nuY0+nw+JiZnyma9AlAyj0Y33338fp0+fRllZGZ5//vlKhZ0qex78jrwqE/pAEK2EXh+OJUuWXPTz\n999/P8LDo6FK+JzOSHg8MRXuV1U/NAIT/MSDyAq7PRwZGfmaFTiH6gOTPKe/XDAYHLDbQ6FSZfz+\nxMgxOhD9CCKnVISYoEpBK1ZfcUrGDEUuVHGiZVxFI3BEwq1x/vLzm8i9GTF37lzNvTOjpBXsSLuk\nskEnaQ+3vLvNQLRE+oOl0cPDo2C1utG1a9dquSlOnjwJk8kL5WhdjH2xxmpM2RVxEkpKSvDwww8j\nMzMTb7/9NoYOHRpUw3412p/VSVC2YcMG1KqVK2F6I4hi4XTGAFApBAuUwA+juj0yIF0HXh0myM88\n8GrOJA6Ew5+7HjduAgKI6Ugw46BOE7YuLS3F0qVLUaeOSimYZRAcLwOiGzfffHF0fEB9MAMqdcED\ntBUcQfgSRKEwGmuBnQgrJk2ahI8//lgj4sUYiy5yzG1yz2NhNjtx3333XVLI/cCBA3IeM7p1qxy0\nV15ejoiIRAQwIm4Q3QeiwYiJSQo6lmWxLWCVSaV2uEw+t1m2PYVAbt3q/3vz5s3YsmWLphKCxZBi\nQbRVIxB04cIFEVCyy/NVGA0X8vIKYDZnQ6fzBlFG/97KysrkGTRHgC/DAgbeWSqVkD5y5AjOnTuH\n5cuXy/Wj5LMqn24H00E3lr8TEQDMmkCkR9euwdwRTGJkkv6ygeguGI1enDlzBpmZDaEcXO7TJuIg\nuOS8KRXaHw2iEkRGBng0fv31V1gsbhCNhNns0oTz2Ql0g3EydTTO0enTp4XmmFNaH3/8sV/pUqcz\ny+eSEOAu+Vaehx6BCIgbMTExePbZZ3H27FkcPHhQcDdJsFojquTBiIpSGhp7QZSM1atXV/ssa6zG\nlF1Rgafc3ABrX/369f/jDflP25/dSQCAzZs3I4AJMMNodGHLli0YOnQo2HG4TfYXQ6H+edWWjEAu\n2IWA8M8NIGLHo0ePnjK4NpDBNRREdkRGJlXali1btoBDwY9WGKB7gWhGlYxyvzedzi0TfEOZFF6C\nUvIjMsBiicDrr7+ODz/8UDOYa89hAdEYqNJGi8WD/v37Y9euXdVe+8yZM3j77bexePFiOJ1xcg+d\nQRSCjRs3Bh3PpXAh4JWrEQaDKqWMhk7nxLfffostW7bg448/ht3uhVotciWJFewQhfo/Q2SB15uK\nTp26YPjw4UI2ZAFRBFq1Kgm6/m+//SZRHJ68K1ImswOTDI5uWBFYXYeAUyMEgyGYS+L3FhqaBC7n\ny0d2dl251m0gsuCjjz7ScCu0aNEeRCYYDE4B1OWBU0TdwZGcMP+Eyf3mlmdlAacLGoLof6qU+B46\ndCSMRi8cjgiEhqb4sSJLliyFwaA4A1R07IQ4CmawdkMysrOz5Ro3w+FI0JybMQxOEOkQGhoQW/L5\nfHC5EsD8A6Ho2TOAYzl9+rRcb7pci+S3QZwBHYgOQgFtidwwmUIFU5QIjro5NX3IUY1ksOy4Ffv3\n79e0s7y8XDgcLNKveoSFJV6y6m6N1dgVcRKKilh8pG3btti4cSM++ugjpKamXuRTV97+Ck5C3779\nwMqPSxFATFv8EySvyq3g1U44OPTvhE4XgoSEBARypw6wE6EY29RqtiG4HtyBuXPnYu7cuSgoKMaY\nMZOCVuTnz58XeWpWnsvLqwteQWYiNjYDJ0+evGgtvNUaDkZ8F4HIDIMhDElJ2Thz5gyOHDlySUJA\nJSVtwc5QfdSuXS9ooK1oe/bswfjx4xEVlS48DC7pixvk90oQJaBdu3aVfj40NA4cYYkSASKlFKgc\nN7dM5OkycajnYsOUKVPg8aSgceM2eO+994TWV9EB2+TnAIjS0LBhUdC1GVjnlP5yYPLkyUHPY/Xq\n1WjQoCG4euADKDVCVgelavsGANq27SLvlR3jxk3AokWLULt2Pho0aARWzgzBW2+9BQDQ6Twgeg9E\njdGhQwfExlaMCPEE3qtXb5hM4SDqAAbJXiOprwKoipS+fatWfa3Kjhw5gp07d+Lrr78WsTAPiGLE\n6bSBKFQUPb0gciIvr0ATfWK57wgQ/QAinYbf4JZbbgFHI2aAyINTp07h1VdfRVFRcQXnJF36NhUc\nQVDYGRZdM5tZ7rl37z7yfNW7ZkBBQYBD4vz589JmTiN16tRNc5/jx0+ETqck0Z2wWiNq0gw1dll2\nRZyEl19+GSdOnMCnn36Kli1bIj8/X6PffrXaX8FJYAS6IpjJAdFycN62pwxEb4hjoIfBEAKTyS6D\nuVMGtE5g8KGqOe8PLvlSQMdsEHVGYWFzAEB0dApYijesUt2B7t17yWfegskUikceeQRTpkxB/frN\nQGSA1Rru1+KozJhVLxREU0BkrPSYs2fPYuLEif7yz99bkyYtwMyT4xEZma7Zd+HCBXlPW6Nx4+b+\niZzLJAuk70xggKfa50CDBi2CrnPhwgWUlJQgwAJpBa+c28qEEwUut7TJypn5JnJz6wexGzKznhXM\nCKjK+UJkG+MuKjMlNkWUhLZt21Z6zIEDBxCg7XXITySIItCrV/X8B+Xl5ViwYAGWLVumCX1brVHg\nEPxNaNeOAYYFBaqShTUulAPB28aDyAKzWWkl3AOWTnYhLS0b7BS2BZG9Usrg48ePY86cOXj22Wer\nbS8ATJ06FZyGWSdtyQbRP2EwuNC1aw+591g0bx6Iznz77bdQDJRELjz88MP+fSzKpaJAZnE0VFqs\nUL5fb8h7uwCM8SHUrp0l37GB0OncQhimOEciwJibjTAaIzXt5woIBqa63bH+d3zZsuXSvgdB1BSN\nGzevqWSoscu2K+IkVGaXAmy60vZXcBI4pDwavNJRojwWGYRMsk3lp2+TfRtloJwIBW6sV69AQH4q\nF26DwxGKpKTaaNq0lX/V5XQmgelfe6Fdu3YYMWKEZlB/+eWXoRgLGzZk8GpgxbsFRM2wYMECTJx4\nE+Ljs9C2bRfccsutfgxEAJXuAFEo+vfXSoefP39eCJJqgSihUqT9jh07ZDXmRkREsn8g3b9/v6wy\nreAURhiYg78tONwdAQYHRqFFizbIzc2VSb8FrNZwAKzMt2zZMlx//fUICYlFQD9jBzi83BxE18Fq\nVf2/A0RR6Nq1K3bv3h3EyaCsVasOcq54mWjiwTl2O3hV6qhUnc/pjJQJMRTR0UlVvifseNwLonZS\nRloIonBYLI6gY30+Hz744INqnbn+/QeBI1IcwdDrmf+Cqwj0IPoIRG6UlJQgPDxe7mcs2KEaASIn\nDAYnrr/+enzyySeoUycfsbGplToBP//8s6zY3SAK1WiYVGasa+IAp9BUOoPf6YyMXHFYboTLpdXF\niIpKBKcV7kNSUg7uvfde1KqVKRgBmzgFrAbJERM7uDpBYSoUDsKNUaNGYe/evfJM64HIi5ycQnAU\nIQ9cisyRht9jXp555hlw5IcZJg8dOiTOlgdMJZ0Lnc6JV199tdp+qLEaq8yuGichPj5YH/1qsz+7\nk3D06FEohkIiNwoKCnHq1CmcOnUKzzzzDHbu3IlZs2ZBKcVxDtQiE4QNOp0HY8eO06gper2Kuc3m\nV/csLS316y0MHDgEgRCySyZXGxyOSMyYMQMrVqzA/v378corr2hWy4mJdUAUCr0+BFOn3iTXSAFR\nO1gsbXH77YFqgI4du4HBcVNBZPdfe9GiRYiMTJTPjgfRYJSUBOhylR06dEgmql9A5MC+ffuwZs0a\niaB4QbRTBu8CSTHoERYW9f/YO+8wKavrj9/pfWZntjd2l7a7LHXpsHSkF2lSbSCIqBgUVLBgMIKI\nDUFjQSmaiP5CURNLFDFINLaQKIolIYIimthBRWDn8/vj3DvvDrOgpCl55jwPz7Izb7nvfd99z7nn\nfM/3y+zZc/joo48SK2YB37kRhkgPeXkm2Mqq44T+mHBASvnw+7OIRhvwpz/9Cb8/F1ldNuKkk04i\nGMwkEslJYqQ0ZrPFkJR1BAGWhrG6Gt5FqZJ6s3OjRo1GMkB52Gzho5ZjRIo7C6VC+HwG63AdSnn4\n9NNP+ctf/sL8+fNp1qy9Zhq0YbMFyM9vSE5OPg888EDKMf/85z/z9ttv07RpG5S6GqnNx7RTDSLB\nl49f//rXSKllGkbdUnAOPtzuLvj9BbRr1wW3O4vGjVunBEMSeIYRfEo1nTt3rvcajVmZkyUoFdTk\nU/NRar7mkjBKpXaefPJJQAKjK64w+J1MvY3ROLkeyTJNQ7qJ3CgVw+2OUl3dmUAgF9FkOIWxYyck\njSUcztOBx0lYaq359O49qE47ZIThw8ck9vn888/1cyn3/+yzz9FAyJh+5twp3RRpS9v3tXSQcBx2\nIgcJ8Xhc0wpX6ZdZ6KjKm59//jlLliwhGi0lEMimWbNmPPXUU/Vu+/7773POOTMYNWoUc+bMYeBA\n0TOIRgsYNOhkLPzCo3q10zVxfnnhldGjR3/atKlBKRvZ2Y34+uuvOXz4MHfeeScWt35v7UD74PH0\nYf78qxNjeOWVV3QmIIrN5sftDtCrV18EfJePcACEUSqTtm27pVyDBE9BZGUX1IJJAYSrQVoM3e4s\nWrfuyieffHLUVsgXXnhBj7UNFk7DrA5baEdSQEZGA/r3H8wdd9yR2FdWx80xaerGjZvrYONKpk0T\n9ca66XuXy4fgRkRY64wzzmDnzp1EowbjEUg6vjGh+nUhFM6RhOjakdawYWM9Z9n6eB6kN9+DkUQ2\nXQNyL9cjAUspUgrw8vTTT9d77Jkzf1Ln/nuxWvryErwD48adqoOHABLQeHE6GyDp+KV6XMtQqk8S\nI+KmTZto1szgFUoxgM+bbro5sc3LL7/MXXfdxZdffsmcOZdocqTWKJVDRkYO48adqp+HgA4UT8Zg\nPzp37srEiWfqMZvn+FLtjGNIG6zBNZhshpff/e53CUbIF198UbNHZmG3B5LwDN2790FaHXtgwIt2\ne5h9+/ZxyinjEIDtBTgcFu0zQJMmzZGW0//T4zCBqYeTTur/vbA5aUtbfZYOEo7DTuQg4fe//71+\ncbVCqQgnn3zyv+3FUV3dDitN60KpLSjlwuPJQ/AKAkY0TIbyEszXTu72xItUqS9RysX8+fMBNPti\nMbLyldV3WVkll19+VRI6W8R1glhcAitwuTIw4jji1KpR6mFstki91yCp3C5IIBJEshZNUaoxHTt2\nPeb179ixg1Aovw6OYDYW8U8zlAoQDBZwySWX89xzzyWCjOeee47KyuZ65Wrooz0YpkqvN4bPF2PJ\nkiUEAlFCoSxefvllAHr27ImUQDqjlDeRhcnJaYqs+KdQXd0hZazxeFyzMYqk88yZM1O2ASMqZebT\niQRQEUy2RoCaefp3AzQ0zvFxlMrjrLPOOuqc/eIXv8Ai2DK8BWtRypW4t5dfbspd92KzeRg//gz8\n/gKczgF6XKIIaQKd++67T2s1jNBjDGHIujIzSwBD+CSZsqysAqzsSwZKdaJTp+5cddVVulxh9Dt6\nIHiRsD5ubyTg3aw/O007ZTc2W4xGjaqYN2+ePu46lHIksWp+/fXXegzTUSorKVjfunWrPm9rfe5+\nZGaWAVIWs/g/fDzzzDOJ/ZYuXYqVoXKj1JsoVUbXrt2Pye+RtrR9l/1Xg4RAIEAwGKz3n91u/7cP\n5N9tJ3KQcNddd+mX6lqUCvLb3/6WDRs2sHjx4n8ZzOR0hhDuhMX6pdkNmy3A2WefjVWDVyh1sXYI\nOdo5GMR2TJPGCECyqKicL7/8knA4F0vFTiSs66P5FRCfDUmzZ+F2Z9K5cw0SXPxCO7mAfpE7+fDD\nD1OOMXXqNO2oBiCgxCyUakI4nNx7/s0337B48WJGjBiFzebGZgvTpUtPve/r+iU9EaVysNtdTJ8+\nPamVcsuWLWRnF9RxYr30zxnIyjiGgDCDPPTQQ+zevZsJE6Yg2QkvdnuUcePGacfsxuhFGEcjzI/S\nTtqyZXW990tKSlkIviKYCBa/+OIL7r//fkaNGo+VCVmsnY8RvhqNVdpwEQhkM3XqdN566y3dXmtW\n10GKipry5z//+ajPjcPhRwK56/R9Po26VNDvv/8+DkcAwST4CQaz2LFjB8uWLWPEiNFEIvlMmiTK\nntLm59b/LsBi0rwOpbyJ0sqZZ56JlAHW6LFegnBVBBg+fAQOR4YOCmxYXAwGFPokEnCaLgzT7eNB\nqeY4HJGkkllhoen+qaCwsDzxeTweJxrNRQLXwUktlIDGZPj0sxhIUj8VLo7NKFVNVZXVQi7ZMA8S\neEhWLC+vyXFpb6QtbfXZjyaTcCLYiRwkfPPNN/h8efrFl4Hh+VeqCRUV7erdp7a2lhUrVtCqVXu6\ndx/ItGnTuPLKK1OQ9hIMyIqstLQp119/faJVbv/+/ezduxe7PYxgG7w0a9aCs846i5qaXtoBVNd5\nGUuL2IQJp2oH2ghZ9QZRykmnTj1Sxrlo0bU6LVxARkYBzz//vF51CeOjldYehFLepFY2Y4MGDarz\n4q+ioKARs2bN4ptvvmHr1q3cf//9FBdX6PEZToTHUOokAoGopj1upo/hRKks8vPLk85x4MABvdKd\npa/nFARsaACjBgTaD6Ucib7+Bx54UH/+ByTDYdfzEkFKIs24+OKL9VwsQuriwuNfnz344IN639YI\nCLU9OTmNdOtlU/1c3I5VYjAcDQ4Msc+8efPqbaU7ePAgPXr01XPhxG4PHXXbSZMm6XHUEAjkUlpa\nwT333JO0zfbt23G5vCh1F4FAkyR67htuuJmMjCLc7gw95jBSzgpqGWwTiP0CpWwMHjyCl156SZcX\nfPp7wYbY7X7+9Kc/6Xv3mP7ZHAmmTGmhD0r5KSwspX//gfTr108HJ4a7wptU4pk48VRk1S+MoHXJ\npAQb0gEjQ24sHo9TWtpQ7/MQSjmTMn6NGlVghL6U8iU+HzhwJBZjY1uKisrqvfdpS9vxWjpIOA77\nsQUJr7/+OgsWLOCll176XtuvXLkKr7dIO6UHtQN9BJstTG5u0xSw2YUXGhKlCJICj6JUR8Lh/CSa\nWoD169djs3mx2bzcfPPNHGnduvXUTnowTqek/Hft2oXbnaVfsLv1y3gBShWTkRFFSiMdsTAJj6NU\nBkOGDE96cQrZzC9RKoNLL7WYGjds2IDTmYl0V0zDZnNQUVHJa6+9ljK+Rx55BKvdzMUdd9zBF198\nwfXXX49VSnGi1LPaCedhuPZHjpzAc889x2233caZZ07D9PhffPElSeewZIFvx6y4HY4IeXnF3HPP\nPXzzzTdkZ5dgAIOrV68G4JxzzsECbvoQYFy+HmuENm1qEvS/TzzxBFa3h49333035VpF+MjwL3j0\nsachq+fX9H1uicW/4CIcjmDpdQR49NFHj/qciXPMQATC2iJgwOx6y1tXX301kydPTtJGONL69JHy\nSyxWkiDE2rt3r55DoxNRg8iDGwrniL5X5vmpIBAQdtHPP/+c3/zmN3qe7kWpAjZu3Mjhw4dp3rw9\nFkbCiWSHTJAUQqlymjZNzgoIiPQ6lOpD165Waeovf/mLHttfUaqU++67L/Hdtm3bMFocSvkS5FsS\n5DVAMkcZVFW1SZoLIU/yYgLfGTNmEApF9XxXYwjPvosILG1p+76WDhKOw35MQcLevXsJhbKw28/F\n74/Wuzquz5544gmys8uwRI+8yOpxKXZ7RtK2HTr0RbjiByOAsWKkfe5ClAqzfPnyxLY9evRHEOsL\nCAbzU867ceNG7bh6olROQlXy7rtX6rEU6/FMQKkoXm+Yyy+/kokTT9WOz4dhcFTKS9++QwF5ScvL\neyBKeZPGBDBnzhwMfbQ49YEJZ2Hs0KFDGqyosGrP+bjdmbRp00lf1yL9udSKvd4c5s+fz8MPP5xU\njojH49x0001UV3fhxhuTg6V4PK6xD0KL3bZt+5R5atOmo3Z8heTkyGqwuNiAAXvWcSyt9P/9STXt\nL7/8Equ1NcLy5cvZuXMnt99+Oz5fLnl5hqLa6His1A5mTCKwyMlpxJAhw/B6hXpYqZ9oRH4IyX4U\nHRNvcPDgQcLhHKyultYoZfunWP4++eQTfYwWeL0N+MMf/gCYgMvgCZzasdqR9t11GGpuyfDIdhdc\nMDtx3AMHDlBaKpmhpk3bJhF9xeNxLrjgJ3qeKlEqQFlZI6wSmSfpWtq27ajP1xWlAkkp/pycUj2u\nXIqKKpOuraKiGUqdiVLLKSio5B//+AcuV0BfwzSi0dKUwEo4GMJIScu0V3qRMmIuSrkTuJW0pe3f\nYekg4TjsxxIk3H///Tpt7UOpU/B4Iuzdu/d773/48GGefvpp9u7dyy9/+Uv94uuHUhG2bNmS2K6w\nsAkm3Sr1Y0OsczlKZVBUZLFkCnBKnFb79qlAv9raWk0bfBlKtUtybB9//DEbN24kL89oBjRHqYyk\nVfDq1au1sy9Hqb/gcAhZ0HPPPaedXDeUcqZgFuLxuJZZboOsji/F4Ygwa9aFDB06goyMQh0ceJEA\n6OcIgHEJStlYsGCBnmtJsV9zzTX1rs7rXqeIBs1BqcwkcBlAeXnDzs9IAAAgAElEQVQLHSRkk5GR\nTzweZ9++fTz22GMUFDTRTq8YoytQW1uLrPDHoFSUli1baqVBEywEcbt9SSUg0dxYgKmZi6MMIyyb\nrZFSRS+swMtLTk4xy5cvT0qJi0JkFkrlkpdXptU1q1DqYrze3KPOgfAOuPR53CjViNLSf45R9dxz\nz008V05ncs3fZrMj2bDOSHnDSzAoHQPl5a20c3Zx66238ve//z3l2KNHT8JuD1Nd3S0p0Nu6dasG\ndwb0uRtq1kefPo+DN998M7H9smXL9HP7O5SyJ93zCRMm6Of5ciKRkqTzT51q8AOZhMN5etsSDJvk\n8uXLmTBhAnPnzuWUUybqMYX0PWmA4EXK9TEGopSP6ur6cShpS9s/a+kg4TjsxxIktGrVDgFn3Y7b\nncuqVWv+pePFYvkoVYTbnZdoWxNilyCSxi/hiSeeYO3atVRXVyOrq7HY7cltWPPnz8cQ0UydOj3l\nPBs2bNC4iCDRaElKf/v+/ft1LVl0Im66aWnS9w8//LB2eC5GjxbSJOkdD2rH603BSwBEIka0KFBH\n38CpHcCZSIYhhmRMKjB6AZmZpXz99dcJLokvvviCP/zhD8cEen711Vd6/1koFWXjxo1J38+ePRvJ\nAixAqaAGyhlEupm/coQ6O6S1AAr0PmY7Q4l9K0JwVUBeXgk9evTWwkNuPYdt9XVv0/sIPqOiog2B\nQBESHAmrYn0lq3Xr1un9ilDKzc0336zn+WTqAh6PNMlmeBFAYJjs7PxE+9/x2tChIxB641to2LB1\n0ndynX0RGvAA5eWtGTVqFIGAKChaLKDhlPNLucKN6H44k7AQ3bv31IFHVM9xLkoFCIVi+t50Jxaz\ntBwkAM7QjrspFRXNE999/vnnNGrUEocjkwYNqpKCi4MHD2K3G4Ilk9WTslJWVhk+n3k2DO3yE/q+\nn17nfppnwqWDC09alyFt/1ZLBwnHYT+GIGH06IlYojTOehkEj9feeustTj11GsuW3ZZ48e/YsQO3\nO4xSZfh8FsL/T3/6k36xebHZfKxda+EYTjppsHZ0l+J2R1POI6tiO0IolMWSJUtSthk/fjymP99g\nF+raoUOHklgI27XrhKyuMjmSljkej3PhhRcSChXoF21dgNszSB27GVZrm/m+Dx5PZpIT3L9/PwUF\njQkEmtCpU59jzmfjxi208w1z6qnJugKWMJDJBrRBqY16DAb/YCivPWzZskVTaYf0tkZPYRRW54ih\nT+6kf2+HrCyn6/3sDBs2mrFjxyWVY+RcAryrqak58jJ48cUX9VikpfDFF1+ktLSpvvf+Y+ISgsFc\njHy1ISD6Zyw/vxRTLjj//POTvps7d66+7hhKOXA6I3oOAkiZzI1S21GqIGVfURB1YTIlhYVWpqOo\nqCkSlGUg5ZUMlOqFzxdEMjBTsNszkp6PwsLGSNlOnH3d76ZPPxehL7+Y5s07HTFPBUhwqlBqGMJz\nYLIYHgTYe6d+PgzXRlDf51NQqrmWE/diSLzqSlenLW3/qqWDhOOwHzpI+PTTT5EV8JsoVZgE0vt3\n2/79+8nKKsZmCzNr1qyk7844Y5p2cg1RKkiPHn15+umnNfjPQuobBsa6JmQ/FSjlSQJyGRs8eLA+\ndneU8rN169ZjjtNCoS9EqQjXX389o0ePoUWLzvTo0Uu/cPsj5YNeSEnB8Bl4yc4u5pprruH111/n\nhRdewOEIYrcXM3z4+KTzbN++HZ+vEKV2YbPZjikf3bhxa6SsMoLGjavqGbMDpXZiKS02IBotpm/f\nYVx++eVIINUZpSLMmzePAwcOUFbWHKuV1KWdQkNklT9J7/O8dlIGbzIUpVSijn+kSVZmZGIuHn/8\n8ZRtpOOhGqW8rFq1irKyVih1G0pNpkOHozMZVlS0wrS6lpXVr9L4fcwSpDoJvz85aBRp6pAOEk7S\n8/q4dqKmA6UBhn/iSDBf06aVSHr/cupmG4QjQjANDocXySTEdJkngJRRspKIqG699VYMIZRSoaQ5\nnzJliv68gqwswZns3LmTefPm6SyRlBwsiegWSBBoAloPkUiUoUNH8Nvf/pbKyhb6WRiHUs0JhUJ1\nMlJN6dlzwD8932lL25GWDhKOw37oIEFeXhGk1uxn+/bt/7FzySqtJUqtxeFIlm1ev369fnnt1E7u\nYpzOoFZkjGgnMpPTTz8z5bifffYZbrdQCttsgZROA7nGrginQoSamt7HHGcslq0DgX4oFcbrjeqX\nZ0S/cJtoJ9IUEQzK5B//+Af33HNPvdiC4cNPQSkbdnskaX4PHz7M4MGn4HR6mT37smOOSbIFIvzT\nokVqe6mh+FUqB58vyl133ZWUDpeWvtNQ6hRyc4to0aI9hg1SrmUlwrxn19edi1I+nM4oRUVNOe20\n07QTkdLO0dgy7777bmS1fRaCJUkVpRKgpZSQevToyeTJU7UzysDhiB0VC1NV1Vo7u4X4fEfHLwDs\n2bOHhQsX0qZN+5TOGK83hmBE2pOdnUq4lpFhSgrj9f3OwDBOWk7Xg1KdGTVqdNK+99xzj352RFdi\n37591NbWagyKCKFJe68Ti2QrhOBFQikBoJz7NpTqT7Nm1nfvvfcekUgOSjkoLW3KkiVLsNpdc3Vw\n8bn+3WTp/KxevVq3DndAKR+/+c1vKClpqoOUQn0f8vXPq/U1t6ewsOKY8522tB2PpYOE47AfOkiI\nxYr0i6Axfn/2f/RcUuv3o1TzBOObsdraWrKzG2ER7hjQ4ukIMtvw2IsgT93e9n379ukX99soFUyo\n5+3du5esrBJstqBmuytBqdsIBI7NxHngwAGNCLfh8WRqGeEHUSpEIBAjGMzF58ti3rx5bN68+ZgZ\nAEhmXhw2bNg/NXcbNmzQc/B3lHKmpH+FQncESk2jWzeRlP7mm2949tlnGThwGF5vSM9fUDsEP0rt\nQLIGdTkVbITDRcyfPz8ppS+pdD+mE+PKK6886lhHjhyFoQ92uzOTAHwAoVAhku6+gFgsX3M9ZKDU\nz1CqN+eee269x7333nsxIkYOR0YKfmHlypXk5zfQMsYms3MySvkoLm5MeXk73nzzTbZs2UIoFCMW\ny2H06NEsXrw4KaAS3IQHYX70sHnzZp3Cb4zFCSKCV4Z3wpgwHxpCqhi33norZ501XR/vWpTya8Cs\nsHjK310EkwFyOpPnKxIp1M9tAJ8vkwMHDrB48WJuv/12HSQYnYeQnr+fYoinJEOURVlZQ3bv3p3A\n68h9FtxMTo5pez0PyR45kZbVMFJa8uNwBJPYHdOWtn/V0kHCcdgPGSSICE0Ao2Fw2WXHXs3+O2z1\n6tVMmzatXnW/b7/9lrVr1zJgwBDatOlGZmYDBAQmYj+iWRBDevsDeDwR+vYdxP79+7W+g49gMI+R\nI0eRmVlGkyblSAbhdA1eFK783NzSBAfA0ay2tpa9e/cSj8dZu/ZBmjXrzNChI+qVEK5re/bsYcOG\nDYwZc0ZCIa+oqCEi3XsDLVsenY558+bNnH322XTu3I0VK1YkfffOO+/oVH4BXm92iuP929/+RkVF\nW8rL27Jx40bKyqqw2YwA1gTtCIZpJ/AORuHSaF0YwaEjz2vsm2++wQK8ZdCyZet6twMpo8hxu6OU\nSknJt25djVn12u1Bdu7cid8fRVbA0aOyOq5YsQIBuL6CUj42b97M2LHjiUSMFkRQBzwr6wQ9qxCM\nQAZKzaKsrKVG9BvgXiVKRWjdumPiPPv378dm8yBdKT4WL17Mhx9+qAG2hg1RMh9HZinatu2G4Dek\nq0Y4MSJIlqY54bDgUoTLoztKBTQRkw2l3kKpGE888UTieCtXrtQByeMo5aO0tBlW264bwZQ8j1JB\nTeHtJxbL0n8nLVHKy5133pk0xjZtqpESWYm+jiUo1RCHI4PJk8/G4QijVJhIJI9rrrnmqPc5bWn7\nZy0dJByH/ZBBwuTJkxMvM6X8xySf+SHs448/ZuLESbqmbnj1hQ9fevx7YaSVlQpq1TrDeDdEO0hR\neSwvb4vfn4lSs7DZcrjwwjnfef6XX36ZsrJWVFd3OyZnxMsvv8z1199AXl7jhPSuUjfgcvm58MKL\nuPvuu/F4svH7K7n44isS+7366quMHj2ajIwi8vPNCzsP4S4IpvSm33HHHbhcmYRCBQlq4sOHD7Nj\nxw4++ugjXUM2DJNtkNXsSYjWgKH7FSdXXFzJvHnzaNWqWjudJTgcoaTzPf3000yfPp1wuLCOE26P\nkVs+mgmltRfBUKTiEi699FL9fU+UCuv2zuH6s2YcDU3/u9/9Dosm2an/xRB8SCFCQR1C1BANWVEO\nFp6ij1aLDCJ4k/YotVo/U/4kBsf58+fjcMTIyMjj//7v/wgG87G4ICbrZ3F+UjfOQw89pM8zB6V6\nU1RUwqhRo7QzboRSIdatWwfUZYY8Q1NxhxA8SJiOHWt455136N27P1lZxRgsg1y3FyFZaoVFEy0E\nVffffz+7du0iEslHsgsRvN7sRJZk3bp1jB07VlOeG7ZSydDZ7f6k4CRtaftPWjpIOA77oYKEq6++\nWrMKBlAql+bNU4V7fkzWt+8g7UBzsBj9zkdWn7/QTiIDpTYhNXV5KZ933vlMmzadL7/8kpNOGq5f\nir/B6fR+Z1AkgLolKNWJVq2ScQDbt2/nrLPO5ZxzzsPvL8CS0L1PByrno5Qfm+1cMjOLeOyxxzjv\nvPNYs2aNJsoxDtsQPnm142mI4B+yuOWWW5LOWVRUgbSpzqG8vBUdOnTFgNA8ngztLHboOWmkHYgd\nmy1M48ZV2snMRikvmzdvBgyRTgBZ6YaYNGkSlZXVuv3OiwRbQSQo64gEXYMwrZT1mYD/3IkxHNkF\n8Pjjj9e5b6bd7n4kWLkJpaIsX76cAwcO8Ktf/YoePfpqlkujlzEHK2hsgaTYDY7Aq51/Byza57+g\nlJ927Try0Ucf0b17f5Sy4XKZNP0KlGqY0hnTrFlbDA5EjvOBvs8mOBmJx5MDSGAkGYrxKNUUuz1D\n4wQMVXOE1q2tLNKePXt0YCBqotGooWo2dN9epOW0b53z3Y+U3xTBYD4rVqygQYOG+m+imGi0gMLC\nCn3dUZTKY9iwk2nUqKk+bgbSbmkCnodRqoyampp6M3tpS9t/ytJBwnHYDxEk/OMf/9AvoYko5ad1\n69ZMn34Bc+Zc9qPth37llVf0mIeiVJBVq1bRuXNvCgoaIqsmv5YBNlwFviQpXxCAYyCQidvdl2Aw\nWi8ZTl2TtrVqjBLhz39+O61bd8Dni2i2QCFRstkuRKlLsNkKUMpJMBhj6NAROqW8GEtDwtDwjtIv\n7MZIqtgAy4z4TwVKtad58+TApG3bztphh/XxshGsw6N6jEEEee8nFsulX78BPProo9TW1upygRvh\nGfAmaunSPeLR43FirVhP0ed5WDvIHKwU/gyUsvGXv/zlqHMXjeYgXQ7VBIPZSYBOAfKFteNzY7O5\nsUof3VDKhd3u1g62VH93DRZ48Ap9vQqlAmRkNGD8+PG89tpr+P1+LMpvN5JWb4jfn51UYjI4koIC\nI6zkIDu7LJFNkFZNNyI+tRgJ3hw4nVH27NnDiBEjaNWqFW+//TaApmR2I0FUPkq5KC0tRTI5c/F6\nsxJB1aFDh3jzzTc1v8Y4JFBUehxz9fiv0p9PwOkMYGF1vLjdnsSxBg4cpK/xTv19FKU+RSkbzZq1\n1vd1kt5mEBbLpxHNyuCCCy445t9B2tL277Z0kHAc9kMECTt37sTimBeyFbu9Hx5PHxYtWvxfH8/3\ntSuvvBKXK4rNlsH48aLUd/jwYZ599ll27dpVJ5BYi1JNUkiHQF7mUrvtQ+PGR6+rAyxebNQKTS06\nhgQpw/X8zcXjyaKoqILi4kquuuoqnn/+eV544QUuueQSIhFDxzwEWVXfrJ14K+1IxNlnZhZx2WWX\ncejQIYYNG4FkRaQ17q233uKcc86jrKySdu3aaSdwoX7Bm66AbJQKaj0GQbcPGzY65XoyM4uRbEAO\nOTkNNcrdh6wu2yEcDy2QMoBQJtvtMfr3H8iVV17Jhx9+qOeuJUoFefXVV486dxdddJEOPrJQahqV\nlcl00Q5HFpLqH6hX0aZMFEICnxBSVjpXO83zsMB4IrzlcMQSJFT79u0jKytffxfECGPl5OQwefLk\nFJItY/K3oBBNCMkSDB8+hi1btuj7E0EClQB+fy579uyp9zh79uzBgHLrlnWM6uNpp53BeefN1Dgb\nIwZVqP9/JxbNt+GvCFNU1IROnXqxY8cO2rZti1Fi3LRpU+K8r79uVEKrdSAh/B6BQD4LFy7U82Gy\nISHc7myNz1mGUicRCsXqJQtLW9r+k5YOEo7DfqhyQ9u2NfqlU45kFGK4XFXccMONP8h4vo9Jm2QB\nSt2FUgG++OKLpO+F378IpSpwOAQQd6QtWLBAO+rV2O2hlO/rmhA1ScpeeCS8KDUQhyOXBg3KOeOM\n6dx3n4Vu37VrFzk5JfplPUA7jRjSDtgSo0bpcvlp1aodP//5z1MAiLt27dJO6zyUilFUVKTPexGy\noo9h0u1er1ndD0YpP2VlVlbFbg8C0qkxceIkCgoM379xXmGENdGOJd3sRUCsEwgEcuuVZBZQm/Ta\nd+jQ4agA0N27d+vxNkCpuWRnSzfLu+++q++BTZ/PrL6DKLVVO+QFiXMoFaasrJKePfuyceNGjU/J\nSjju3bt3A9C5c28ECNgBoXn2oFQLsrNLj3p/7777Hh30GFCrD6WmY7NFiMfjXHLJPNzuCOFwLAV8\n+eabb3LFFVfQrFlHxo2bzIEDB9i+fTstW7bGorf+JbJ6d9b5rBeysi9BAk4vgvHw6fseQakoTmfy\ns3nJJZcgwWB/IpHipO/kvguQMjOziMsvv5wPP/xQd/2YoDmH6dOn89JLL2mp7GyUCjFt2tlHnZ+0\npe0/Zekg4TjshwoS4vG4ZiKM6hfMNSjl+U70/g9pr776qn6ZD8FmC/PXv/41ZZuvv/6a2bNnk5dX\nwMSJE1O+f+2113A6I9hsOTRp0uw7V1FDhozGEAPJKtzPpEmT6l2Z3nzzzbjdExGGO3FUTmcEpzMT\nny+EtCiuwmaL1XMm+NWvfqVLJhG9v8kY+FBquUbcO7RTdWmgXADRT8ikcePG2jkuQSk3BQXlOq2f\nq4+5EFmlP6WP2QalQthsUSwypQCBQG4S1W9dc7uzkY4AU/KoH/AmIlkm3R/E5fJrR2kElMxPB1aH\nhXF2Mt9du3ZPCfQ+//xz7Uy7oJQ70ZrXsGFzJMPTV19HG5S6E5stIyUQ++yzz2jatAoJkLKRdlvh\nkFDKRXHx0TkBDh48yLRp5+txdkOpYtzuKhYvXszSpUvrAA1Nq6Sh6+6vA4ZKlOqB6GPE9HF6oFSJ\npkwOIQFhJIn0a8aMGVggxQAffPBB4jsp1wxDqfUoFU26Xr/fBJIxWrfuqHEtXVCqJQUFxQkFzLSl\n7b9pJ2SQcPjwYVq3bs2QIUMAuPzyy2nZsiWtWrWid+/eiRVLXXvzzTdp3bp14l84HGbpUtEG+OST\nT+jbty9NmjThpJNOOio6/ofsbojH45x88sn6RSZBQllZFc8///y/7RxXXbWQ0tKWnHXW2SxatIi/\n//3vfPnll0cFvX2XnXHGGdq5DCQaLUl8/swzz/DTn/6U/PwmWOj+CGPHjk85xlNPPYXDIUj9srIW\nxzyfODvz0jeSuiLgc6S9+uqruFzSo26z+fnZz37Gjh07iMfjdXgOclAqxLJlyxg/fjweTyZKebjg\ngllUVFQjaf952rmKnsGMGefRpEk1a9as0aQ8F2FIeVwug20IEYnE9L28VDuTSmTFbfrmDYhOHPTU\nqVMTz7VwF9yMUvMIh3OOOh/SVlqsnfqTSMbEybnnXpjYZtu2bZx55pmawMmcuwlCB9wZC5nfQl9v\ngI4duzFu3Dii0Tw97iAeT3Iwdfvtd2iH6ENKInkMHDhYC25lIRkGH+PGjdOBRxZKOZJYDOPxOAUF\nxUhmpxtWmSOIaQVu3jxVSRNg9eo1OBwGHBnU82xwACIHLuM6WQcsZVjlD8lYeL0RGjZsrOmey/X8\njNbBiluXgC5GqSijRo1JnFvKGUYIrTjRmmjRXAcxuIWFCxcm9pMOJpNZCeJwxDCAyFNOmXCsRz9t\nafuP2QkZJNxwww1MmDCBoUNFLrjuCvOWW25hypQpx9y/traWvLy8xEt3zpw5LF4s9f1rr72WSy65\npN79fmgyJYBp02ZgswU1Cc31VFV1+u6dvoft2bNHO81JSH17NHZ7FJvNSffuA+slITp48CC/+tWv\nePHFF+s95nnnnYe0bd6IwxGib98BjB5tVvt+ZLXfEFlNdyEnJ1Vi+o477kDqwb9BKW9S61t9VlnZ\nso5zHYhgOTJQKkLPnj0Tq7G//e1vBAL5KLUGv38Qq1evBkSg6ZNPPsHp9GK1rbmR1fjpKHUXdnuM\nOXMuw2436HnjcNxJLYTC/piBUnk0alRJ+/bttdMarB2RC2v16kCpp7WzbFfnuGUo5eX1119PHHfq\n1KlYQk+uJOXGuibtixGkg8DoPCgs1UuDzm+Dxdx3s74nk/R9Mq18oxHgnicBghRmx3yU+hNHkkZF\no6V67s/AkDUp5a1D9PSsdtjldZx2OcXFFoXzmDHj9Ln76HPLfc3IyEYAo8KUWF+mKCOjDKU2IKWC\nUdqxD0TAiWF9vo5INqNAX7uLFi1aYLdXIpmcMII3iek5MwRNAkgtLCxD8AWDcDqTZdazsor0vpPI\nzJQySklJpb7fxRhthrpkVBYA8w2krHIJSk0gEslJKdelLW3/LTvhgoT33nuPPn368PTTTycyCXVt\n4cKFR3Xyxp544gm6drVanMrLy/nwww8BYf4rLy+vd78fQ5AAIo/s9+fidrdAUtAeVqy4+1865v79\n+zV3QS/9IpurX4hf4vFkJsoF77zzDqeffjoVFe3weDJxuxvhcGQwZszEJHXEeDzO6tWrdRbAr491\nrn7xDtKOqDGiVBjGkMns2LEjaVzvv/++/l7Ag9dff/0xr6Oysr12dDOx6I/HIrXzCG53mIEDB9Oi\nRRcM3392dgnXX389N910Exb1bkS/pGuQjMLVGGnlsrLmxONxfve73/H666/jdIaRFV8DmjSxnFxx\ncRmyKr8OjyebO++8Ux9/FUrZtVDWBQhuIwNJLXu184xp5ygr32nTpiWO+9Zbb+mxnIdSPq677rp6\n50KUDh36uNlYOAfTgtpGn+c3WN0ICosx08GMGTMYMGAAVmZDsWrVKkCyEPJZY5SKcNFFs7ntttsY\nMGAIFhDQ3IPfo1SEHTt2MHbsqfr4GUhLo6HVFm4DhyPGKaeYDMNZWKDEm/R9tSPBk+hU/O1vf0u5\n9pychlggwMux2ZpquXOl74FpzZWy0JgxY4jH43z11VdaWtyLUo/p+9paj8V87kGpfK2eKqRZSgVZ\nv3594vyjRo3WY5yFUmHdSupBAhNhCFUqSJs2XYjH47zwwgvMnPkTPSYTtJSiVB69e6e1GNL2w9kJ\nFySMHj2aP/7xjzzzzDNJQcK8efMoLi6mvLz8mGQ6AGeeeSa33npr4veMDGsVEI/Hk36vaz+WIAHg\n2WefJS+vBKmdj8LhyOL++x/4rt2OaiJ360ZWOdIHbrPlYLOFKCpqSosW7aisrNZOwagX/h+yUmuH\nw5HPtddey7x588jPL9POyUgal+sX3wKUykwQKTVv3pLhw0/W292AUl4eeuihlLF1794HUw/2eo9O\nR/3cc89RVVWlzx3WL3CDoi9GMgGZyMo6rB1QvuagMB0M12CB0yIY8KDDEWLq1GncfvvtfP3110nn\nHTXKOLTZKBVk2bJlACxYcLU+xgAKCppy9tln15FyDpGZmY1V73fp9HIBsoo3gVULlMqmZcuWSed0\nu6NIarqQ5s1b8dlnnyUyCu+//z4rV66kpqY3VhumAR5GEfDjeO2EGqFUGI8nSDSaw3XXXYffX4gA\n6CZRWFiqyzh+hPApPxGUxONxvN5sfY+NVoZR3LxUz4lfj6EDSnmZNOlM9u7di9+fofcL6f1+rfdr\ng1KXaWEp8/wE6d+/P1ZWw4+0e0apqqq/62XLli00aFBJx469GDBgNHPnzqe2tpYXXniBTp06IZmE\nzShVkILTOO206djt2ShVjc3m1+JaUf28n4QEjXdi6UQMQyk/zZpZEtEvvfSSPkcVSoVxu420c0td\nhnIjREt2rDJKqT7mbIR91Em3br2SVE/Tlrb/tp1QQcIjjzzCjBkzAKHFrS+TsGjRIs4444yUz419\n++23ZGVlJfXdHxkURKOpMsfw4woSAM499ydYrWvXYbc7eeCBfz5QmD//Ks0f8B52u4t77rmHNm3a\n65fYIGS1F0Sp21EqgMPRBJvNOD3zM6y3eQmpV7fVL9KOKBWmbdsu1NbWJpUvZs26CK83jy5deqU4\nYIBrrrlGH1fa1V555ZXEd1u3buW0087QgYmQTYnjuVu/oC/FojI2rH4+JAU9WI+7EEn1G8lpw4HQ\nEEmL+74TlyGBz3yUipCZWcw999zDli1bNAjQOLzuei6fQ6lmFBQUaMdxLUplMGHCFGKxQoYOHcbj\njz9OQUEJApS7h2g0GfkvJYfcIxyNoQw2ug1+7dx+q52bUZA02RIXSoV0F4QfSd97OfnkEUi5Q+Z7\n3759lJe3RMogZVRVtaSysp0OEPx63hdoBz5HH38mplQTCmUggZkAQWWfJvq+lON2B3G5crSmRTZK\nNda8AX6UkvIaSDeJCD7FUCqDiorqpOxVffb111+zZs0amjdvpTUnQvp+S7BbVdUxpZR2+PBhNm7c\nyNKlSxOlFZmjoB6fkZh26WAnD6XGY7cnv0cikTyknNG3zt9QHhMmTMLqWjGEY8VIScdkYNrhcJya\nKIOmLW0/lJ1QQcLcuXMpKiqitLSUvLw8/H4/p556atI2u3btoqoqVZ7X2MaNG+nfv3/SZ+Xl5Qk1\nuw8++OCY5Yb58+cn/hkmvB/K4vE4K1euxO02q+aO2GyxpLH+gewAACAASURBVN7s47GDBw9SU9Mf\nm83GhRfOA4yccFvt4LpqZ2hHVn8u/a8xUqboj5DyNMAA0zyemN5+CEq5E4Q2R9rYsSJY43JFUoh/\nhDo4ivTqj2fIkCG0bVtDWVnTOs6sr36J/0OfrxqrVS4TaR0187QIqybfHgt34KWw0EhZV+l9ZSX7\n0EMP8de//pVvv/223oChV69e2mnX6GMXY+EA7tXfXaTHIk7qiiuu0GPqhlLhlLqzyA/7UaqU4uLk\nZ/K+++7T4z4NCQy6aydjnP8f9Thc+l5J3f+aaxYya9Ysfc/OwOPJ5sknn9TzuBalAlpHwp2YP6E5\nDiFMjwP0/M7TY79CO7ZSPR4vHk8GNTU92bhxI19++SUPP/wwVtumR8/pSJRqSSgUS1opr1mzhltv\nvZXa2lruvfdexo2bwPbt2xNz/tVXX/Hkk08etUYfj8fZt28f559/PuFwHobGWcZ8MhKcrECpwVRU\nSGfEK6+8wmmnTWHx4sVHFQATcbXGKJVJYWEZTmcOsVgB8+bN09dzEW53Moh0xIhT9H3phgQ2t2Gz\nhfnmm2+IRDL1fP5Uj9Ghn9lTsNvD2O1+gsHMY/JbpC1t/wnbvHlzkp87oYKEula33FDX8dxyyy1M\nmjTpqPuNHTs2UVM1NmfOHK699lpAMhE/ZuBifSbtdTFkRdqR888/P0nP/nit7otS2rD6oJSb4uLG\n/PWvf9UqewMQMpqIfglO0y87H15vFqNHj06s+FeuXEllZVt69uyZEFI60iy2ubKkDFE8Hufbb7+l\nX7+h2gkZ9PllOnD4DbJCM/LQCkmLK+2QTCATRqk1WJoGHiydgArtvCWgiEQK6dFjMJamgGHRE4Ig\nlysLjyebrKwCevU6iX379rFjxw4s0qQwsrJuh4XB8GG3ZzBo0Eieeuop9uzZozEDbiSwyOa+++5L\nmpPf//732sGNQikny5cvp7q6A3a7EUfyIRkCm3YwJtMj+IJmzdrSurVZkTdFKR/PP/88l112mXaW\n47Dbpe3w0kvnkpPTlBEjxmiHNQgre9NGX38usmp2I4BEkzYPIrTNDev929u7d69exQ9EgKxenM4I\n3bv3S1HIPNLi8TjV1e1RKkQ0WsLzzz+fAl7dt28fjz/+OJ06dddzE9T3rgxpOW2o/8X0NRnSIp/u\nODHBnJ9wOKveQMFmM9mDANdffz3PPvssV1xxBYsXL6Z//8HYbCEcjiArV65M7PP1119rhVIDgA1x\n1llnAWgxKYPXcVNUVKrneYSeI08CTJu2tP2QdsIGCZs3b050N4waNYrmzZvTqlUrRo4cmeA237Nn\nD4MGDUrss3//fjIzM1P67T/55BP69Onzo26B/C4755yZ+HwFFBSU4/NV4vFkpjidf8Y+/vhjBg06\nGbvdRU1Nf7799luaNGmjnUQnTHrU48lm5cqV9dL/xuNxgsE8DGd/fYFCIGBW2Nfj9crq9pZbbtGE\nSz6GDx+tHePL+pzn1HnJ+igoaKK58Q0Az6S8Q8hqzacDiUyUcjFy5EgAHnjgAUpLGyErzXYotQWl\ngowceQoWOVN7lNqonfE0pJ7fAmEK9KKUjx49eurtf6adiRe3O0uD26pRqjNNm7ZMmRcp13RAqQYp\nDlZW+EZe2CDyK5C6/xPakZsSix2lXHTp0pUFCxYkVudCMuVCqRdRKp9bb72VQ4cO4fNl6fOWMW/e\nvMQ5v/32W3JyGuvjTUdAlgawN1Ffu58OHbozZcpZvPLKKxpL0RmlfJx2mrBrvv322/Tt2w+323Sy\nxLQTHI/dHk0Ahb/Lpk2boe/hGpQSLgmbzc9NNy1lxozziMUKsNpez9XOP1Tn3hjtAy9Op0dzUYSQ\ntkcjSV6ir+1+lMpmwIChfPjhh8yZM4crrrhCS0r7ERxCHtOnT2f48DFI9iSbBg1KEf2PJUnZhIMH\nDyKB7O1IS6mDdevWMXLkKDZu3IgBwioVpHHjxgllSFPKOFJsK21p+yHshA0Sfgj7MQcJxnr3Hq6d\nXSlebwbvv//+v3xMrzeMUjsIBCrYunUrH3zwAR069CQczkZq5suJRI7OlieOKoCk8Eto1KhpyjZX\nXDEfa+VvlAODiIrfYL2SC2iH6ycYzKVnz35s376dL774gmeffRZLGOcF7dBN+t1N//4DkBVyNtLb\nHkpqK3zsscewxIEErNihQ2e9v2HhM0GJ6Q4Q6WILrBehuLicSZPO4KOPPuKzzz5jwIBhSDDVjDZt\nUoW5ROwpG6VyKS9vxtatWxkyZDglJRVYIL0xSFmhSDvbciyBpACdOnU5KpUxoLsNJNgwfBW5uY2Q\nro8CfL5kjoN4PK6lns05nLozIILJ1tTtQnn00Uf1tvNRykltbS3BYCEie52t52++vv/DKCurYt++\nfdxxxx08/fTTRx33N998o9UyI9qZmnZNQ1rlxcralOj7amij85Eymcl2uPR+E/Q+q7EyIzGs0oxp\nBTUlGqMUGkRKJH42b95Mw4YtEbzLBH19plvCm/ibE6pwHyZrIcf0IAGFVxNj3YAEN0Ypcw82W2Ei\n45C2tP3Qlg4SjsNOhCDhj3/8o8YNPIvDUUmDBhVH5TH4vjZo0Bjc7mJcrgzWrl2b+FzqVSEkdR3i\njTfeOOoxcnNLkdW/dAIcPHiQPXv2sGnTJjp37qXT0UGktFGp/1+jHWwTqqs76e6F1gwePDSFbbJ7\n997aKVyhx1REIJDFnXfemcgszZw5UzubC1AqA5crmpRalja1MMLbUIVSGSxevJh9+/bx7rvvsnHj\nRhwO03GQj99v0v5hJO2/FKczG7s9E4cjgt3uJiurBMO+53BEElLAxho0qEAAlJWYXn1LGClLO55y\nfc5clPqVvj4boiLp47333jvm/ZPyQnbC2X300Uca+OhCOj6cKdkzYUt0I0ySHhYsWECjRs1RStGm\nTU3StqLDYQI7v6ZyDmOBRoVUKTOziCFDRvDpp58yZMhYfL5++P15SWyFdU2eL4++bpO2Ny2chlPA\nqz8TPEtJSSNNfhRAeBIa6blbgQUUzMEocirl0PfRrsc/XZ/vESRT40epv+r5FjKpLVu2sHTpUn2M\nSqxWz4koVUlFRQXNmlXpz+16jJOxMBl3oZSHbt26YbWWuhDqZzt5eY2OSqGdtrT9ty0dJByHnQhB\nAsBtt92JxxPBbi9GqUW0a9f7Xzrerl27cLmCKHUzbncggSgXtcKQfrGOqLfbxNiaNWuQFVsLlFIM\nHDgcmy0DpzMHWWndrl+0pyLZCaEDHjt2LAsXLuTQoUMAdOrUB5utBUr5eOyxxxLHj8XytFPtgwAn\nZxIKFaSADPv0OamOE/bSr9+wpO8ffPBBrBXfMpRy0759R7Zt28ayZcuw28sRZcUgUj82K1ojVFSN\nAOQcKPW+djiZKPUZSik++eQT3nzzTaZMmUJ1dTsstcZz9P/v0PM0GwsL4UYCMa+evy7aQZWilJ+B\nAwcds/tC0uVhBLBZzAUXXMBnn32GBU70ctVVV6XsJ+RaksqPxYoA6q3XHz58WHctPIrgDjwITiVE\nQUEj5s6dmwAGG2vevDNKLcHv78T999+f9N0vf/lLamq6YWFG5unrv0X/9OmfQZxOPz5fTIN3i+oE\nB6YMU6HH48FuD1FYWEJursn8tNLHqtHPjA8B4JrshQFbNtT3VzpnbrvtNt599129/WV6bv0IC6RF\nDCUBylv6GLdjsmROZxZ9+gzUmJMwEiQ2QikHhYUV7Nu376j3Mm1p+29bOkg4DjtRggSAp59+Gr8/\nD7+/I61b15CX14TLLlvwTx3r448/1iWHYmy2MC+//HLiuw4demC1P4ZS8AZvvvkmV155JdnZTbCy\nA5X6hfkMFgDvTCxhnR4o5eWpp55KGYsAzX6NUpU0ayY98m+88YZ+secjXQXmhe5P6AUY+/bbbwkE\nsrUjzsdmiyYFGwB33323diyXYEiFlPIzatQoQqEsLDKeh/R1Z6BUT2y2Imy21ihVpIMqP1KKMDgJ\nUw83HReDkMAioIMDF5LaD9C2bVc2b97Mp59+SnZ2KRJ49CAazSYzs5hotAjBSogTbNKkdVJr6JHm\n85nWwQFkZzcEIBIpRsCuV9GwYWXS9ocPHyYQiOgxS9viZ599RosWnfB4gqxYsTJp+5KSptQF4il1\nH0rlMnPmzHrH89JLL9G+fR/OOOOcpOyKZHN8SAbJi6z6+2FlTwwLZN0A6nJ93ilI5qIdFjC1IXWB\nibJPmT7ug0jQVYYpNcViuViaGwYEOluPpQFKebnooos0WNiHdDx4saS7hyFZiHKsUkOhPn6UrCxh\nFRXqb1MuORul2hOJHJ0DJG1p+6EsHSQch51IQQIIOn7FihU4HF6U+gMOh5eOHbvxs5/97LiPNXHi\naZiWrZEjrbZTWaXaUOpLlIoya9asxHe//vWvsUh8ZiAp71OR1LwLu703NluA88+/gKlTp2paWg+y\nqvLTsWOXlHG0b98JI8hTUlLOtGnTqKpqh7APDsVuNzXkCErl0bhx85RjPPfcc1qcaThKTcbtjqX0\n25955pnaaRteg24o5cDnC1FRYRj4DP/BhWRnN+S006Zx2223sX79er766iuaNWuD1SFg6vselNqv\nf05AsgFmtdqS5s3bpqzWW7XqgEW04wHg5z//OVbd/HqkCyPMmDFj2Lt3L/v376e2tjaRgREK5UqU\n+ilZWRIkSFnAoneuy1Hx05/+tM79ugqlghqRLyBEpzNWZ9sFeDy5WORHJghqTdOmrb73MwYG9Z+D\nkCX5sTAiNXUCAwcSNG3Uc/IzTCDmcmWSl5enxxJBAgbJLMl+t2oHbgIPAYXu3r07IbZUUdEO6QYx\n3S0msyS00KLh4cDq+Oiqv2uUuMeiT2EyDHfo/a9FKR9VVW2x+BBmopSfjIy8JCGotKXtx2LpIOE4\n7EQLEkAQ1vn5DQkEemqnl41SGSxYcHyBwoYNG/D58vD7S7j55uVJ30WjDTB8BVu2bEl8PnLkaIQb\n4FasPnpBcE+bNo177703BeXeqFElZpXv8SSTWsXjcW644QbsdicWB0JfjVhvhVIldO3ak8LCIiQz\nsQC7PbPe67n44kuQVWBrlCqkS5eTkr5/5plnKC5ulhivqXtL62cYAdDZqanpR4sWXfjjH//IF198\nwcknj+Hkk0ezf/9+2rXrhqSv+6HUJmRFacR9wgQC+TRo0IgGDZogqeo22GyBlCBBOBGMQwon8Bgi\nMNQDwU8YB2icvtGv8BGNltKr1wAaNxbEv83m49FHH+XPf/6z3qYApUJJ/BqnnnoqkpmRrpM2bTpz\n5ZVXImn+LJRSnHPOORQWVug5mqvn5TEkO+JAKS/z56eWMY5l0hEQwDBBynVEEHZEOxZ+w5R4fPj9\nmbhcmZrfIYTgTpYhvB3dkKDUSEw7sduDFBY2xlLT9CeBWA8cOEBhYYmeS4N7UPoZrkQCE6NwaTo3\nApSWVnHjjTdywQUXUFhYrsc9Uc+XB6WuxAoWX0QCEScDBgw/rjlKW9r+m5YOEo7DTsQgAeDvf/87\na9eu1S+0S1DqInr06Pe99o3H48Tjcd577z1uueUWhg4dSosWLZLUJ4VmVlq5ysoaJT5/8skntT6B\nn6KiEqZMmcry5csx5DYtW6aKU0lrWAhJ28ZYu3Ytc+ZcisMRJhwu0sJWmToIyEOpB1DKy5w5c7jw\nwgv59ttvWb16NRbzo5cXXngh5Ty1tbWUlJTqQGEoLleUgwcPcvjwYX7600V4PDnICrAIC+TmRoSc\nzkEQ6nIdbrcBz/l1ENGJbt36MHLkWO3sjJhTgE6danjggQeSMheScXEgtXdfCigzHo9jt0eRgGss\nffvKvZO20h7aedmRtLgPARvOR/ABIUTzwHQHnI1SJ9GqlagnZmWVImJGk2nSxMq6WAGEG6WUJsUy\nNMQGRxHRDrIHgsUwcxBi6dKlSc/IJ598ws6dO1m+fPkxCYK+/PJLrABwgJ6/GfoemE4JA2A0baEe\npOzypH42pLXTyiZIluMPf/gDH374YSIIk2dTJKvHjJnARx99xMaNG3V7oymzbEBkqR3691E6OPOT\nldWQggJDQW7TVNIGxNgJyfBk0KJFJ6ZMmZJ4HqUToxCbLcAzzzxz1LlIW9p+DJYOEo7DTtQgwdiU\nKVMRQagwCxcuqneb119/naqqasLhLGpqeuByZeL1hjVJjQdJ3Urvu2G9k5X8QiTl70/KDnz00UdJ\n6PuqKkPAJKj/I5nzDh48qLkDLkJWsial/iSyig3rfYdpxxdh4sRUGu5QqBhZxRXRuLHFVrhz5056\n9epLixbVyEowgpEd7ty5Kw6HW19PB5TqgNsdSzi/vn37aUeQq8fmRVbNFdpZjUVwBt2oqqrm0KFD\nzJgxA6czoB3eDNq27Zoy1kOHDhEM5mJAgqeeOjllm0aNWiABTS5KuXC5spAyjwHPeenYsUbLIxuB\nqph2WFO1gx+NKcXY7Rk89thjtG3bQTvfCHZ7gNdee43p06cTDudrh2zaMEMoZbo//MjqfJP+PZes\nrBytnHk+Xm8mO3bsYNu2bdxzzz0sWrQEp9OPzRbC7R5DIJCZ6Dg50kRky7QtGn4IU7t3YSlYepCy\nxBQsUaom2GwZeDzZ9OzZk9mzTdC0AaUK+clPfpJ0rtGjx9c5rsErmFbJ5VhBkhcJwkJ4vVm89tpr\nTJ48mRYt2mIxfvbW+63Xcx1DqbZ4PFns2LGDNm3a6PF+jFI2fL6s780Vkba0/ZCWDhKOw070IAGg\nrKw5NtuF+Hx5bNu2LeX7vLyG+sU3SL8cL8JaDVcgKe2TUcqeUIYcNGgoFo9AlFgsv1754jfeeIO8\nvCbage1AKRtPPPEE8Xic886bSYcOnZkw4QystrY2WAyJgmovKmqE252F2x3EZgsTDObVmymoqmqN\npHnvRCkPS5feSn5+U7zePITBsAGSps9DApxm2kEsRGrWTXA4vJx11lT27dtHRUVzMjKyWL9+vZ6f\nS/Q1T0IIhgx1sRA71eURGDJkJAa4VlTUpN5OhMGDB2snvg6XyyLk+fTTT1m/fr2WnfbpIMGHBCTG\nYV+LUh4eeeQRADZt2sTKlStZt24dq1atIj+/CKtk4sdKlRsnaHFDyNzHkDKJ6Tb5pXZ8Br1vgIOC\n0Rg+XMorTz31FL17n8yyZT/n7bffxuUK4nA0wuPJQ5gx3Si1Dq83N0Xt01jDhs0QIKLgJURh0wQq\nI/X/85DAxoPgJbxEo7lMnz49hShNgryOKOUiGMwlHo+zadMm2rXriMNheB+eReiTfSj1lP7sVP27\nDaW8DB48mJqabrjd2VgljRBCNpWB6GM49N9KhEikgBEjRrB27VqdaWuKocx2OCLpDoa0nTCWDhKO\nw/4XgoTi4gqUugy/vzCpS8GYyxVDWvui+iU8CpstA683E9PBYFL5q1atSex3+uln6RdqM5RqwezZ\nc1KOPWTIWKR3vqt2Um4sytogUkLIQMSZIgi7og2l7HTq1IV169YljlVd3RnJXFxJ06ZtUs51//33\nY6XEs7RT26qvawZCKe2o4yDrdh1U62tsilJ+IhGzMpQ2OZ8vivS9GwKdDGS1LoqLNlsGrVu3Z/ly\nwW5Iq6hDO5IQa9asSRrr4cOHeeCBB6hL/DN06DDC4WKs1a1JwW9FMgr5WNiDEpTKpXXr+hURAUpK\nTKCXoefeBAmmhTOmr30M0pnQUM+VwWK4CYViWmzLi2hluOoN0MBkBKJIsGG0ICS7ccEFszlw4ADr\n169P4dYIhw12IIBgJYyWhgspLRnqbXNMISzq2rV7veMYPnw4FimSIcXy6fs/AEOBbJEdeWnZsgPT\npk1j3LhxuN1+LBbHLH3dS7GChDx9TNmmsLAoUUq66aab9Vw1R6mHsNszePLJJ4+qD5G2tP0YLR0k\nHIf9LwQJr732GpMmTeXuu1fW+7202y1DqUKKi8s499zz2bp1K/F4nF27drFy5Ur90v4NLlcyU59k\nARwolYXdHkmprc+dOx+HIx8LAT9A/zxFv4Bv1NS0dqyWwdY0aZLq/Pr2Hai/F82FI8sWQnlsBJp2\noJSXQKAXgUAm2dmN9HlnYQUSsxAQYw8EYOZEqVe0M3YiK8vLUCrChg0b6Nu3L40bNyaZbS+ErIJN\n212ImTNn8vLLL+NwBBDgopfMzBxatKhm0KBB9Os3AKWceqxGodHUrn+uj7OozvEl9V5SUsYjjzxC\no0blSKblphQVQmPbtm2jsrISqzxhwIA3I/gFp3Z2Rt7bg1JOcnJMO2kVSsXo3bsPAMFgjp73KE5n\namcIwKpVq/R5OuJyZWoSKj+RSCEHDhxgyJCxBAId8fszE4HC7t279fhGIxmT31O3q8DwbMRiJdTW\n1mq+gzBKuXE4rGexWbO2KJWB1xvD6czWz1MzlHoDK2NyV517b4ignJx//vl4vTm65FWu53waQtRl\nSm1NUSpMNFrM+eefr+nA81HqBuz2CIcPH2b8+In6fi7R+wQZM2ZCvfcnbWn7MVs6SDgO+18IEuqz\nP//5z+TmlpGf34jVq1dTUzOIyy77ab1pcQsA2RK7PZL03Z49e/B4AnrldCGdOyeDI2tra8nKKkOp\ne/QL+gn9Io0RDudRWFjJ+vXr+fTTT6mu7qid9iR8vpyU1dfWrVv1y38jSuUmVu033ngj7dp1ZcOG\nDZSVNcRivQswd+5cdu/ezZ49e7RjuBWlfDrFXY5BwJeWVtKwYQUmwzBwoAlIwng8IU3cE9bXaUoj\nI7EIfAJI22Q5Vv3cCATN1PsGsDgY7tfXGkOyHD/XzmtmwqmFQrnU1HTX4z4Xpbx88MEHmqQqrPcP\nJIS97rrrLqJRkxp3YjoYrJZJ075pMgWSSbn00kuTsgNVVVVId8aV+HzS49+pUw11RZ0WLUrFt2za\ntAmnsxGCB/Bit2eg1I1kZpYA6K6ItQQCHdiwYQMgbKEyJ6eiVBCbLUBhYRmzZ89m2LDhWPgAN/v3\n79dMknlIrV8xdKgRAXMi3RA+JHNlAg2hsbbZAgQCRXTt2oPRo0cjwM8u+p7YkRJSMyRY7okEfdJ+\n2r59V2666aYkbocbb7xRz98AlPLrLosMfd4pKOXhuuuWfK+/xbSl7cdm6SDhOOx/NUg4/fSzEQbC\nuUydet4xtz148CBt2nTBlApGjBif9P3tt9+O212C0zmGAQNGp+y/fv0Gjco3Ke72+P25KavRd955\nRzvW7ijlYsWKFUnf19bWahChpM0zMwt45pln9Mu6B3Z7hL59+2pnuACl/Nx9992J/adPP5dgsIjT\nT5/CoUOHyMjIxdTabbYIb7zxBh9//HGCKVAImAwYcLJ2Or/VDkbhdufSuXMX7RwysUCXYSRLEUTq\n6LP0Z8t1EGAcn6cO7kCCiqKihixbtoydO3cCZqXtQDISIR566CEALBS/ackL6Hnoqce7TX/fRX+f\njWEGrKnpznXXXZeirFj3fsr1FFFR0Urfw/X6eF1RylevENHhw4epqmqPpXMxFqV8TJ48DYCHH36E\noqJKhg0bn6AgHjJkOBJIlVJcnKzvsW3bNn19MZSKUFNTQ9u2bfU4RmApOzbR19ZHz+Xpes4MrbNi\n9+7dRxzX6HH018+KYGuczpjGNAQxjJf33nsvpaWNcbnC2O2ZRCLmWXZgARan67lpRiAQParyadrS\ndiJYOkg4DvtfDRLuu++X+Hy5+Hw5PPDAg9+5/YQJExBym+dxubISn5t2yV/84hcsWHB1Qomwrv3k\nJxdjIcjLUWoOSoVTgI6HDh3SpDVRlIoyePBgAD744APOOussVq1apVfWXkT8yMfVV1+NpMaXYbNF\n2LVrFxZvgC8l0KhrX331lR7TJpRqxR133JH0vcORhWAmGqKUTQtOBZEUdJj9+/frevp4lLoapSLk\n5ub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J999/v4wUWSicDzurVcticrLqdZFEzfnxUPRWqEMt2qE4DU4KLkR3+dpDBECbzSlf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\n/DF58mT85z//Qf/+/TF8+PBS1xYVFWHz5s245557sHn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"text": [ "" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "It's interesting how obvious the slant of Toronto's streets from GPS grid is. When we say we're going north on a street, we're likely going slightly to the west as well. There's also an obvious gap of stops along the Don River, which we can follow as it travels to (from?) Lake Ontario.\n", "\n", "Let's take the mean and median of these points to approximate the geographic centre of Toronto." ] }, { "cell_type": "code", "collapsed": false, "input": [ "longs_pd = pd.Series(longs)\n", "lats_pd = pd.Series(lats)\n", "fig, ax = plt.subplots(1,1)\n", "n, bins, patches = ax.hist(longs_pd, align='mid', bins=25)\n", "plt.xlabel('Longitude')\n", "plt.ylabel('Number of stops')\n", "plt.title('Longitude of Stops')\n", "plt.xticks(rotation=70)\n", "plt.xticks(bins[:-1],['%.3f' % (x-0.5) for x in bins[:-1]])\n", "plt.subplots_adjust(bottom=0.2)\n", "print(\"Longitudes: \", longs_pd.describe())" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Longitudes: count 10738.000000\n", "mean -79.399878\n", "std 0.110536\n", "min -79.650347\n", "25% -79.489651\n", "50% -79.400623\n", "75% -79.313075\n", "max -79.123046\n", "dtype: float64\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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A3t7eCAkJAQAcPHgQHh4eMDY2hlQqhbW19RMXIMYYY5qr9dDQzZs3sWPHDpw6\ndUpl6/zUqVO1Nq5QKNCvXz/Ex8fj7bffhr29PTIyMmBubg4AMDc3R0ZGBgAgNTUVAwcOFD5rZWWF\nlJQUrTv0LDM1bYeCgpyGToMx9oyptRDs27cPCQkJTzS+kIGBAa5du4a8vDyMHj1arXhIJJIazzdU\n956vr6/wu7OzM5ydnbXOrTEqLwLa7oYzxvRReHg4wsPDNYqttRD07t0bOTk5wlb8k2jdujXGjh2L\ny5cvw9zcHOnp6bCwsEBaWhrMzMwAAJaWlkhKShI+k5ycDEtLyyrbq1gIGGOMqau8kbxu3bpqY2s9\nR5CTkwMbGxuMGjUK48aNw7hx4+Dq6lprEllZWcjNzQUAFBcX46+//kLfvn3h6uqK4OBgAEBwcDAm\nTJgAAHB1dcXu3btRWlqKhIQExMbGwsnJqdb/wxhj7OnUukdQUxWpSVpaGry9vaFQKKBQKODl5YUR\nI0agb9++cHNzQ2BgoHD5KADY2dnBzc0NdnZ2MDIyQkBAAD8JjTHG6gHfWdyI6PZyUG3jxZSLruPF\nlIu28WLKRdt4vny0Lj3VncWtWrUStsxLS0tRVlaGVq1aIT8/v26zZIwx1iBqLQSFhYXC7wqFAqGh\nofj77791mhRjjLH680SHhhwcHHDt2jVd5FMrPjTUeHfbG2+8mHLRNl5MuWgbz4eG6tJTHRpSPpcA\nKN8juHz5Mpo3b1532THGGGtQtRaCis8lMDIyglQqxcGDB3WeGGOMsfrBVw01InxoqKHixZSLtvFi\nykXbeD40VJee6NBQdfcPKPcOPvroozpIjTHGWEOrthC0bNlS7YauR48eITAwEFlZWVwIGGPsGaHR\noaH8/Hz4+/sjMDAQbm5uWLp0qTBGUH3jQ0ONd7e98caLKRdt48WUi7bxfGioLj3xVUMPHz7EV199\nhV27dmHmzJm4cuUK2rZtq5MkGWOMNYxqC8GyZctw4MABzJ8/H9evX4eJiUl95sUYY6yeVHtoyMDA\nAE2aNIGxsbH6hySSBhtigg8NNd7d9sYbL6ZctI0XUy7axvOhobr0RIeGFAqFzhJijDEmHrU+j4Ax\nxhoHI+Gph5r8mJq2a+iERaPWO4sZY6xxkEGbQ0kFBfy8EyXeI2CMMT2n00KQlJSEYcOGwd7eHi+8\n8AL8/f0BANnZ2XBxccFzzz2HUaNGCY+0BIBNmzahV69esLGxwbFjx3SZHmOMMeh4rKH09HSkp6fD\nwcEBhYWaLS2CAAAgAElEQVSF6N+/P0JCQhAUFIQOHTpgxYoV2Lx5M3JycuDn54fo6GhMnz4dly5d\nQkpKCkaOHImYmBgYGPyvXvFVQ433io7GGy+mXLSNF1Mu2sbrPhd9WpfUtO7U6R6BhYUFHBwcAJQ/\n6czW1hYpKSkIDQ2Ft7c3AMDb2xshISEAgIMHD8LDwwPGxsaQSqWwtrZGZGSkLlNsUKam7bQ6ucUY\nY7pQb+cI7t27h6tXr+Kll15CRkYGzM3NAQDm5ubIyMgAAKSmpsLKykr4jJWVFVJSUuorxXpXUJCD\n8i0YTX8YY6zu1UshKCwsxOTJk7F161a1O5Rr29rlLWHGGNMtnV8+WlZWhsmTJ8PLywsTJkwAUL4X\nkJ6eDgsLC6SlpQkD2FlaWiIpKUn4bHJyMiwtLdXa9PX1FX53dnaGs7OzTvvAGGONTXh4OMLDwzWK\n1enJYiKCt7c32rdvj6+++kp4fcWKFWjfvj1WrlwJPz8/5ObmqpwsjoyMFE4Wx8XFqewVPEsni8V1\n8lfbeDHlout4MeWibbyYctE2nk8W16Wa1p06LQTnzp3DkCFD8OKLLwor802bNsHJyQlubm5ITEyE\nVCrF3r170aZNGwDAxo0bsX37dhgZGWHr1q0YPXq0xp1pbLgQNJZ4MeWibbyYctE2ngtBXWqwQqAL\nXAjEEi+mXHQdL6ZctI0XUy7axnMhqEsNdvkoY4wx8eNCwBhjeo4LAWOM6TkuBIwxpue4EDDGmJ7j\nQsAYY3qOCwFjTE/xE82U+AlljDE9xU80U+I9AsYY03NcCBhjTM9xIWCMMT3HhYAxxvQcFwLGGNNz\nXAgYY0zPcSFgjDE9x4WAMcb0HBcCxhjTyLN7J7JOC8GcOXNgbm6O3r17C69lZ2fDxcUFzz33HEaN\nGoXc3FzhvU2bNqFXr16wsbHBsWPHdJkaY4xpSXknsmY/BQU5DZSn9nRaCGbPno2wsDCV1/z8/ODi\n4oKYmBiMGDECfn5+AIDo6Gjs2bMH0dHRCAsLwzvvvAOFQqHL9BhjjEHHhWDw4MFo27atymuhoaHw\n9vYGAHh7eyMkJAQAcPDgQXh4eMDY2BhSqRTW1taIjIzUZXo6YWraTuNdR8YYE4N6P0eQkZEBc3Nz\nAIC5uTkyMjIAAKmpqbCyshLirKyskJKSUt/pPbXy3UFNdx8ZY6zhNejoo7VtGVf3nq+vr/C7s7Mz\nnJ2d6zgzxhhr3MLDwxEeHq5RbL0XAnNzc6Snp8PCwgJpaWkwMzMDAFhaWiIpKUmIS05OhqWlZZVt\nVCwEjDHG1FXeSF63bl21sfV+aMjV1RXBwcEAgODgYEyYMEF4fffu3SgtLUVCQgJiY2Ph5ORU3+kx\nxpje0ekegYeHB06fPo2srCx06dIFH3/8MXx8fODm5obAwEBIpVLs3bsXAGBnZwc3NzfY2dnByMgI\nAQEBfEKVMcbqgYSIGtVZS4lEAjGnXF68NM1Pm1ixxYspF13HiykXbePFlIu28WLK5cnixbSuqmnd\nyXcWM8aYnuNCwBhjeo4LAWOM6TkuBIwxpue4EDDGmJ7jQsAYYzqh+bDVDT1kdYMOMcEYY88u5bDV\ntSsoaNh7pniPgDHG9BwXAsYY03NcCBhjrME17GMw+RxBDeLi4vDvv/9qHN+sWTMdZsMYe3Zpfj4B\nqPtzCjzWUA1cXT1w/HgsjI2tag8GUFT0J2SyEujHWCpiykXX8WLKRdt4MeWibbyYctF1vO7HMapp\n3cl7BDWQyQjFxctQXDxNo/iWLbtAJkvWcVaMMVa3+BwBY4zpOS4EjDGm57gQMMaYnhNdIQgLC4ON\njQ169eqFzZs3N3Q6AKIbOgHGGNMpURUCuVyOhQsXIiwsDNHR0fj1119x69atBs6KCwFj7NkmqkIQ\nGRkJa2trSKVSGBsbY9q0aTh48GBDp8UYY880UV0+mpKSgi5dugh/W1lZISIiogEzAsr3CD7WKLK0\nNE+3qTDGmA6IqhCUP/i9Zn369NEorm6t1SiqrEz5mzb5adsXMcWLKRddx4spF23jxZSLtvFiykXX\n8dq1re16sE+fPtW+J6pCYGlpiaSkJOHvpKQkWFmp3tV77dq1+k6LMcaeaaI6R+Do6IjY2Fjcu3cP\npaWl2LNnD1xdXRs6LcYYe6aJao/AyMgI33zzDUaPHg25XI4333wTtra2DZ0WY4w90xrdoHMMICKN\njw9qE8vqFk8n8dP2e39Wp5OoDg2JUcU6qVAoah3xT9v4J6HNjKjrmVab/j1prC6+Q55OdR8rhumk\nbby237uYplNd4kJQC4lEgtjYWJSVlcHAwECYERQKhVbx1U3g3NxcnDhxAjKZTOX16tpftGgRrl69\nCqD8BryaZpydO3ciL0/9ktbqPnP79m3hvcoLVHUq9q+mOGXs1atXIZfLVV6v6nPK71Eul+tk4ePp\nVL3GMJ3qYvnTZhoB4ptOdYpYtSIiImjBggXk4eFBPXr0oDFjxtDhw4erjY+MjKTly5fTW2+9RdbW\n1uTm5kZ//fVXtfEnT54kNzc3Gj58OC1ZskRoozp//fUXtWnThlauXEnp6ek15n706FFydXUV/k5J\nSaHjx49TQUFBlfFhYWH01ltvqbx269atGtvv378/hYSEUE5OjvC68neFQqESf+HCBZo/fz5NnDiR\nPvvsMyIiunnzZpVtX758mRYvXkzTpk2jOXPm1JgHEVFeXh7l5uaSTCZTe69yHkQ8nYgax3TS5fKn\nzTQiEt90qmtcCGowZcoUWrduHWVkZFB6ejr5+flR7969aejQoVWuCGbMmEEbN26kwsJCOnjwIPXp\n04esra3J3d2dUlNT1eK9vLxoy5YtJJfLyd3dnebOnUv29vbUvn17+vHHH9XiZ86cSb6+vrR8+XKy\ntLSkgIAAKi4uJoVCobZwTZkyhQIDA4mI6KeffqKZM2fS888/T1KplDZt2qQ2Y3l5eQnxYWFhtGzZ\nMhoyZAg5OjrS0aNH1XI5fPgwNW3alOzt7UkqldKbb75JixcvpgkTJlT5XXp5edG6devo6tWrNGXK\nFHr33Xdp8ODBNGDAAAoLC1OLXbVqFUVERNDatWtp9uzZlJeXR0REDx8+pAcPHqjET548mcaMGUPB\nwcF069YtKiwspLKyMiIievDgASUnJ6vE83RqHNNJl8ufNtNImYuYplNd40JQDZlMRi+//DKdOXNG\n7b1vv/2WVq1aRcXFxcJrZWVl5ODgQJmZmcJrixcvpri4OFq9ejUFBAQQ0f8qe2lpKfXu3Zvu3btH\nRES2tra0d+9eIiI6ffo0zZ49W2VBKikpIQcHB+Hv0NBQmj17Nv32229V5v/JJ5/Qxx9/TEREr776\nKkVERBAR0Y0bN8jDw0NloXv8+DF17tyZ/vzzTyIiGj58OH3zzTeUmppKgYGBtGzZMpW+KvPfuXMn\nHTt2jDIyMujLL7+k1q1bk5WVFS1evJiSkpJUcre3tye5XE5ERD169KCtW7dSTk4OBQUF0cqVK6mk\npESIfe6554TPFhYWkoeHB/36669ERLRixQphAVPmbmVlRWvWrCF3d3caNmwYLVy4kPbv309lZWU0\nZ84c2rx5M0+nRjaddLn8aTuNxDaddIELQQ0OHTpE8+bNU9s1Li0tpV69elFaWprK659++im9/fbb\ndOfOHQoJCSEbGxsiIrp//z65uLhQbm6uEKtQKGjDhg305ptv0ocffkgdOnRQacvW1lZl5jpx4gS9\n/fbbwv8vLS2lHTt2UNeuXcnDw4Pu3Lmj8vn4+HiaPXs27d+/n1asWEE3btwQtnR69epFiYmJQuyd\nO3do+vTp9OGHH9LcuXOpd+/ewnv5+fnUt2/fKnefU1NT6fXXX6f9+/cTEVHPnj3p4sWLNGfOHJUt\nsKtXr9K6deuE70LZD2X7/fv3p4yMDCIiioqKovnz51N2drawtXj06FF6+eWXiYhowIABdP/+fZUc\nKm6V37lzh/z8/GjMmDE0ZswYatWqlbASrzid3nrrLa2m09y5czWeTu+88w4Rla8QGtN0unbtmjCd\nEhMTNZpOOTk5Gk2ntLQ02rZtm1bTSVfLn7bTiIjo7t275O3trdF0iomJIQ8PD62nU1pamkbTSRe4\nENQgJyeH1q5dSxYWFtSnTx/y9/enM2fO0IoVK2jYsGFq8Xfv3iVPT0+ysrKiJUuW0JEjR4iofLfP\n0dGxyvi1a9fSuXPnaP369bRmzRrKy8ujbdu20ciRI4U45dbp48ePiYiELTYiokePHtHbb79NW7du\nVYsPDQ2lIUOGkFQqJVdXVzpw4AAtXryYJk6cqJZLYmIihYaGkq+vLwUFBQmvHz9+nFxcXNTilTlk\nZGTQ8uXLaezYsTR16tRqv8vCwkLh96ysLOH3kydP0qhRo1TyfvjwodBXpeXLl1P//v1p/Pjxam0X\nFRVRUVGR2utr165V2fJTunfvHs2YMYMsLS1pyZIlwnHn6qZTQkIC+fj40JkzZ2jjxo3k6+tb43Sq\n6rhxVdOptLSUiIh+//13GjJkCHXv3p0mTpxY5XRStp2SkkJ//PEH+fr60vbt24X3q5tOSunp6bRs\n2TIaM2ZMjdPp0aNHwu8Vj1VXnE5KaWlpwh6CMr/qppNcLqeioiLh0FFF1U0nXSx/2k6jisLCwoTp\nNH78+BqXp/T0dGE6Vdwzqm46KeeF9PR0Wr58ea3Tqa5xIahCUVGRsJWjtGfPHpo8eTK5ubnRN998\nQ5cuXVJ5v3K8csVXVlZGq1atop07d6q8r9yaUC5sCQkJ9Nprr5GFhQUtXryYTp48qdL2w4cP6fz5\n85Sfn6+W7+XLl4V88vLy6PTp0yq7nn///TetXr2aVq9eTXv37qXo6GjhvdLSUrXcK64AZs6cqbLC\nUeZeXFwsbGEptwTPnTsntKmkUCiorKyMiouLKTY2VqWdoqIilWOvZWVllJ2dTZGRkWoLalxcHDVr\n1oyCg4OF13Jzc+n06dNVFgEioh9++EHle1coFPT48WOVlZ1yRVZWVkarV6+mXbt2VdmW0pUrV2jo\n0KFkYWFBixYtohMnTqi0L5PJqKSkhOLi4qr8rHI6PXz4UOVQCFH5luq7775Lq1evpv3796tMp6Ki\nIpXvVfmakpeXl8p0UvY1JSVFmNeOHz9Ozs7OVU4nJblcrrKhofw/kyZNop9++kmln3K5nOLj41Vi\n4+LiqHnz5irTKSsri/z8/NT+j3K+27Ztm8p00uXyp5wfi4qK1OZHItVpRFQ+r2dnZ9PFixeF186e\nPUs+Pj60evVq2rdvn8p0Us6T1Z0Q9/T0VJlOpaWlarGHDx+m4cOH1zid6hoXgip89NFH9P777xNR\n+Ywkk8mELdTKMxwR0bFjx8jd3Z1GjBhBn3/+udr7iYmJKgvXH3/8QZMmTaI33niDFixYQO+9956w\nVfro0SOVFRVR+cmpMWPG0Lhx42jPnj1UVlZG586dq3IXdvXq1TR//nwiKl+gkpOT6ciRIyq7rhV9\n++23NGjQIFq/fr3K8diEhASSyWQUHh6uMiMq+zpq1CiVLaeKxaMiZV/Hjx9P7777Lr333nv0888/\nU1paGsnlcjpx4oTQvrKfb7zxhko/b9++TUREu3fvFlbclftaWlpKSUlJtGPHDrpy5QoREWVmZqqc\nxNu3bx9NmjSJXnnlFQoJCSGFQkHx8fHCbndsbKzKdEpJSamyT0SkNo2IyrcYZ82aRW+++SbNmTOH\n3n//ffrPf/5DDx8+VIvduHEjSSQSmjhxotpKraqVSMV5UiaTCf1V5n769GmV6aTs65AhQ+jIkSOk\nUCgoMzNTWGlVPrlZVV/LyspIoVCQXC6nixcvCu0r+zlnzhyhn7t27aKsrCySy+W0Z88elemk7Ouk\nSZPo6tWrRFReCJQ5ZGVlqeSjy+WvpvmxKsp5cuzYsbRr1y6SyWQUHR1d5TQlUp0ni4uLKSkpiXbu\n3CnMk2fOnFGZTsrlb8OGDXT+/HnhdeX3VNW8oAt8Z3EVRowYgQ0bNmDgwIHYv38/duzYgRYtWqB7\n9+5YsmQJOnbsqBLft29frF+/HkSEL7/8Ev3798fnn38OAEhPT0fLli1hYmIixFtbWyMgIACtWrVC\nYWEhEhIScOfOHXTs2BEffPCBWj7K9po1a4Z169ahZcuWaNOmDQBg4cKF6Nu3rxA7ePBgfPHFF3By\ncsKaNWtw+/ZttG7dGhkZGVi4cCFGjx6t0vYHH3yA8PBwjB07FlFRUWjevDkyMzPRp08f+Pn5qeVS\nU19TU1PRpEkTdOjQoca+xsfHo2PHjli+fLnG/Vy8eDF69+6tcmdnVX1t164d0tPT8c4776j11dHR\nERs2bEBJSQl++eUXGBgYoHnz5pBKpVi0aBHat2+vEt+nTx+UlZXhtddeg6enJ/r16ye8Fx8fDyMj\nI3Tr1k147YUXXsDWrVthamqK/fv34/r167Czs4O9vT3mzJmjkvvw4cOxdu1anDlzBnfv3sWqVavQ\nq1cvoS2FQgEDg//d5lN5nty5cydMTEyE3Ct+5zX1tXv37li4cKHaPFxTX+Pi4tCuXTu0a9dOo35W\nVrmvq1evhrW1tVpcdX2ty+WvpvlxyZIlMDJSHXWn8jzZokULmJiYwMDAAIsXL1Yb0bOmeXLhwoVw\ncXFRidd2+dOZeik3jciVK1fI2tpa+PvFF1+kU6dO0YkTJ8jd3Z3WrFmjcvz6ypUrKseVHz9+TAMG\nDKBTp04REZG7uztdvnxZeD81NZUGDx6s8j8LCwvp4sWLNHr0aPrwww9V2o+PjxdOwBERmZiY0I0b\nN+jmzZu0fv16mj17tnCIprCwkObNm0enT58mIqKXX36Z7t+/T1lZWbR3716aPn26ylUVSitWrCBf\nX1+6ceMG7du3j0xNTWnYsGE0ZcoUlT2J6vqqPIw1bdo0rfq6evVqYcuxtn56e3ur7HXU1lcPDw+V\nvl66dEkldyMjIzp//jyFh4fTtGnTyNfXV+V7j4qKol69etH58+fp/fffJ0dHR3r11Vdpw4YNVFxc\nTG+88QadPXtWiL93755aX0ePHk1HjhwhJycn4UojovLr3ZW5ZGZmko+PD3Xr1o2+/vprKiwsVNta\nr22e/PDDD1W2wKvq67lz5+jUqVM0bdo0WrduncpWqSZ9Ve4tJiQk1NjP3bt3q7ynSV8r9vdJlr/+\n/fsLf9e0/NU2P1ZuOz4+ngYOHCj8XdOyp2yrtnmy8iW1RNUvf1OnTlW7pFZXuBBUcvLkSeratSsN\nGjSIhg0bRtOmTRPeu3fvHg0ZMkQlPiwsjFavXk1lZWXCTBQYGEiLFi2ixMREsre3V4lXKBS0dOlS\ncnR0pH379qms3FJSUuiVV15Ric/KyiJ3d3eytbWl2bNnq6wsHzx4QE5OTirxx44do1dffZWWLl1K\ns2bNEl5PS0tTOxGq3O1MSkqixYsXU3Z2NkVERJCDgwPduXNH5SSXsq+rVq2isrIyYcVTV33Vtp/a\n9vXo0aP05ZdfElH5ORPlVSNE5YcOKp98vH79On3++efCOZmcnBw6cOAAzZs3j7p3705t2rRRic/J\nySEPDw+aMWMG7d69m7799lvh5Orly5dp0qRJQux//vMf+uqrr9Ty8/T0pJ9//lmtnydOnKAuXbpo\nPE/+8ccfQl8jIiJq7WtUVJTGfdWmn0REu3bt0mlfjx49KsyTtS1/crmc3n//fRowYIBGy96DBw+E\neXLOnDlazZPLli2rdZ5UFuOkpCRasmRJrcufLvGhoWqcPXsWwcHBGDVqFNzc3AAAGzduRHp6Ovz9\n/YU4IsKDBw/QoUMHGBoaQiaToaCgAMuWLcPdu3cxdOhQ+Pr6qrUfHByMixcvQqFQoGPHjnj++ecR\nGRmJ3Nxc7Ny5UyU2Pz8fR44cQdeuXfHFF1+gW7duGD9+PI4fP4709HT89NNPQi4SiQRhYWEICQnB\ngQMH0K1bNyxcuBBXrlzB48eP8d1331XZ3927d+PkyZOIiIiAp6cnli9fjpKSEjRr1kwlLisrC6am\npmjSpAlkMhny8/OxfPnyGvsaFBSEyMhIyOXyGvuan5+Pw4cPC/2USqVV9rNyXw8cOICQkBBIpVIs\nWLCgxr4SqQ8atnHjRqSlpeHrr79Wi5fL5TA0NFR5beHChSgtLcW2bdvUvptt27bhwoULsLa2hre3\nN/r27QtfX1+kp6fj+++/V2u/rKwMRkZGkEgkOHLkCObNm4clS5aoHTYDgJMnT2LXrl0YPXp0jfNk\nTbnX1NfS0lI0adKk1r5mZWXhp59+wvnz59GzZ0/MmjULDg4ONfYTAGQymXDo5Y8//sDcuXOr7Wt4\neDh27txZ6/KnzKfiPFnb8rd9+3ZERESAiGpd9nJzc/Hnn3+iU6dO+Oqrr1SWvYyMDJV5Uumvv/7C\n3r17ERoaim7dumHBggW4evVqjcvfrl27EB4ejoiICHh5eVW7/OlMvZWcRk4ul5OTk1O1QwtU3sUN\nCgoiiUSidiJQ6dGjR3TlyhXav38/bd6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"text": [ "" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots(1,1)\n", "n, bins, patches = ax.hist(lats_pd, align='mid', bins=25)\n", "plt.xlabel('Latitude')\n", "plt.ylabel('Number of stops')\n", "plt.title('Latitude of Stops')\n", "plt.xticks(rotation=70)\n", "plt.xticks(bins[:-1],['%.3f' % (x-0.5) for x in bins[:-1]])\n", "plt.subplots_adjust(bottom=0.2)\n", "print(\"Latitudes: \", lats_pd.describe())" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Latitudes: count 10738.000000\n", "mean 43.725382\n", "std 0.060305\n", "min 43.591810\n", "25% 43.677031\n", "50% 43.721847\n", "75% 43.771064\n", "max 43.914362\n", "dtype: float64\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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CAAAKhQJisRgAIBaLoVAoAAAZGRmQSCTCZyQSCdLT06uqiewll5+fg+K9DO3/imMZqx2q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\nwxMnTqB3794AAFtbW4SEhODRo0fYt28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"text": [ "" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Interestingly, the standard deviation of the longitudes is significantly larger than the standard deviation of the latitudes. This indicates that Toronto is a city that is wider on the east-west axes than the north-south axes. Both are roughly mound-shaped and symmetric, however.\n", "\n", "For us, this means that as long as you don't live in the fringes of Toronto, such as very far north or very far east/west, there is an adquate supply of bus stops." ] }, { "cell_type": "heading", "level": 2, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Live Vehicle Location Data" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "What does real-time vehicle location reveal?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Thanks to NVAS, we can keep track of each bus or streetcar (but not subway) by simply querying a server. The service requires a route per request, so in order to collect data for the entire TTC, 178 requests (one \"meta-request\") had to be made. The data was saved into a Python pickle file. \n", "\n", "The 178 requests took approximately 44 seconds and was run every two minutes for three weeks. Currently, there are a total of 10,126 pickle files, each containing the complete information of a meta-request.\n", "\n", "The script to request the data and save it to a file is found in `busLocations.py`. For the sake of the filesystem, I've written a script to import this data into [MongoDB](http://www.mongodb.org/), which can be found in `busLocationsMongo.py`.\n", "\n", "Each data point in the database consists of:\n", "* a timestamp (in milliseconds since January 1st, 1970)\n", "* a latitude\n", "* a longitude\n", "* a direction tag (like northbound)\n", "* a vehicle ID (like 7945)\n", "* a heading (in compass degrees)\n", "* a route (like '25')\n", "\n", "Let's count how many we have, and look at one specific data point." ] }, { "cell_type": "code", "collapsed": false, "input": [ "collection = db.vehicles # the vehicles collection in the database\n", "print(\"Number of points: %s\" % collection.count())\n", "print(\"An arbitrarily selected point: %s\" % collection.find_one())" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Number of points: 9420806\n", "An arbitrarily selected point: {'routeTag': '191', '_id': ObjectId('536bedf4920c0e21c26c2e79'), 'dirTag': '191_1_191', 'heading': 71, 'vId': 7945, 'lat': 43.735985, 'timestamp': 1398694091674, 'long': -79.595016}\n" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Wow! Over 9.42 million data points! There's a tremendous amount we could do with this data. Let's start by aggregating the headings of the vehicles. It's hard to say that heading is a discrete variable, but in this case we'll treat it as one since there are only 360 possible options. Fortunately, MongoDB's aggregate function makes this easy." ] }, { "cell_type": "code", "collapsed": false, "input": [ "headings = collection.aggregate([{\"$group\":{ '_id': '$heading',\n", " 'count': {'$sum': 1}}},\n", " {\"$match\": {'_id': {'$gte': 0}}}])\n", "headings = headings['result']" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Let's plot this on a polar bar chart, which is the obvious way to visualize this." ] }, { "cell_type": "code", "collapsed": false, "input": [ "theta = []\n", "r = []\n", "for heading in sorted(headings, key=lambda x: x['_id']):\n", " theta.append(heading['_id'] * np.pi / 180)\n", " r.append(heading['count'])\n", "\n", "ax = plt.subplot(111, polar=True)\n", "ax.plot(theta, r)\n", "ax.grid(True)\n", "\n", "ax.set_theta_direction(-1)\n", "ax.set_theta_zero_location('N')\n", "plt.title(\"Headings of TTC vehicles.\")\n", "print(\"Maximum: %s\"% max(r))\n", "print(\"Values on two sides of maximum:\",r[r.index(max(r)) -1 ], r[r.index(max(r)) + 1])\n", "print(\"Angle of maximum (radians): \", theta[r.index(max(r))])\n", "print(\"Total count: \", sum(r))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Maximum: 830814\n", "Values on two sides of maximum: 3654 6356\n", "Angle of maximum (radians): 3.7699111843077517\n", "Total count: 8635317\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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UKOrTpw+vn6kpx44do8ePHyulr+pISUmRGasyv5u6RNnt+KdPn/LK1ooYPXq0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\ntsUslKs917yycl6Wm0wm7NGjh6QbEo8iG3H58ccfcf78+UR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6cUFEXL58OU6cONHu1p5O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\n", "text": [ "" ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "The strange spike at 216 degrees can probably be attributed to instrumentation error, since it was three orders of magnitude larger than the values at 215 and 217 degrees. Let's remove it from our dataset and try again." ] }, { "cell_type": "code", "collapsed": false, "input": [ "spike_idx = r.index(max(r))\n", "del r[spike_idx]\n", "del theta[spike_idx]\n", "\n", "ax = plt.subplot(111, polar=True)\n", "ax.plot(theta, r)\n", "ax.grid(True)\n", "\n", "ax.set_theta_direction(-1)\n", "ax.set_theta_zero_location('N')\n", "ti = plt.title(\"Headings of TTC vehicles.\")" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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4VOleAwMDWFlZwcrKCgMHDuTOv3jxApGRkbh16xZ+/fVXJCcno6CgAF27dsWgQYMwZMgQ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\nvfhOrVkiigtHtGvXDitWrEBeXh769++PUaNGYezYsfjpp5/E1SWWyc3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"text": [ "" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "As expected, there are spikes heading north, east, south and west but very very little in between. The slight offset is likely due to Toronto's streets having an inclination, as seen in the geographic scatter plot of the buses. Another thing that's interesting, but also expected, is that the graph is mostly symmetrical -- vehicles spend the same amount of time heading east as they do west, and heading north as they do south. This also means that on average, vehicles travel at the same speed heading east and west, and north and south.\n", "\n", "One item of note is that the east and west spikes are much larger than the north and south spikes. This indicates, like we saw earlier with the larger standard deviation in longitude, that Toronto's east-west axis is longer than Toronto's north-south axis.\n", "\n", "Let's load it into Pandas for easier analysis." ] }, { "cell_type": "code", "collapsed": false, "input": [ "headings_dataframe = pd.DataFrame(data={\"heading\":[x * 180 / np.pi for x in theta],\n", " \"count\": r})\n", "print(headings_dataframe[:10])" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " count heading\n", "0 8722 0\n", "1 6229 1\n", "2 5606 2\n", "3 7725 3\n", "4 10014 4\n", "5 5448 5\n", "6 8428 6\n", "7 7050 7\n", "8 4323 8\n", "9 4621 9\n" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Obviously, there are more rows in this data but I truncated it for brevity. To find the mean, median and mode, we will have to implement those methods ourselves." ] }, { "cell_type": "code", "collapsed": false, "input": [ "total_headings = headings_dataframe['count'].sum()\n", "\n", "# median\n", "median_idx = total_headings // 2\n", "current_idx = 0\n", "for heading in range(len(headings_dataframe)):\n", " current_idx += headings_dataframe.iloc[heading]['count']\n", " if current_idx >= median_idx:\n", " median = headings_dataframe.iloc[heading]['heading']\n", " break\n", "\n", "# mean\n", "s = 0\n", "for heading in range(len(headings_dataframe)):\n", " s += headings_dataframe.iloc[heading]['count'] * headings_dataframe.iloc[heading]['heading']\n", "mean = s / total_headings\n", "\n", "# standard deviation\n", "s = 0\n", "for heading in headings_dataframe.iterrows():\n", " n = heading[1]['count']\n", " h = heading[1]['heading']\n", " s += n * (h - mean) ** 2\n", "stdev = math.sqrt((1/total_headings) * s)\n", "\n", "# mode\n", "modes = []\n", "max_heading = max(headings_dataframe['count'])\n", "modes = [x[1]['heading'] for x in headings_dataframe.iterrows() if x[1]['count'] == max_heading]\n", "\n", "print(\"Mean: %.3f\"% mean)\n", "print(\"Standard deviation: %.3f\" % stdev)\n", "print(\"Median: %.0f\"% median)\n", "print(\"Modes: %.0f\" % modes[0])" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Mean: 197.525\n", "Standard deviation: 102.488\n", "Median: 179\n", "Modes: 253\n" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "As expected, the mean and median are around 180 degrees, since the entire circle encompasses 360 degrees and the points are equally spaced out. The mode is 253 degrees, which could be expected since it is the west spike. The east spike is likely the second-most-popular option. The standard deviation here is not particularly useful." ] }, { "cell_type": "heading", "level": 3, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Time Distribution" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "What happens to the number of active vehicles over time?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pickle\n", "from matplotlib.dates import HourLocator, DateFormatter, DayLocator\n", "from datetime import datetime\n", "from pytz import timezone\n", "import pytz\n", "tz = timezone('America/Toronto')" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "with open('time_dist.pickle', 'rb') as time_dist_pickle:\n", " time_dist = pickle.load(time_dist_pickle)[1:]\n", " times, dist = zip(*time_dist)\n", " # note that the timestamp from the pickle file already has timezone data\n", " date_times = [datetime.fromtimestamp(x) for x in times]\n", "fig, axes = plt.subplots()\n", "fig.set_size_inches(8,6)\n", "days = DayLocator()\n", "hours = HourLocator(interval=8)\n", "formatter = DateFormatter('%b %d')\n", "axes.plot_date(date_times,dist, 'b-')\n", "axes.xaxis.set_major_locator(days)\n", "axes.xaxis.set_minor_locator(hours)\n", "axes.xaxis.set_major_formatter(formatter)\n", "axes.set_xlabel('Date and Time')\n", "axes.set_ylabel('Number of vehicles on duty')\n", "axes.set_title('Number of TTC vehicles on duty from April 27th to May 4th')\n", "plt.show()" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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3bt2wa9cuw/EjR44gNjYW0dHRmDVrluj1bGgAduxo2l7SxYWISPv2JEuYklgS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"text": [ "" ] } ], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Note that the lables indicate the start of the day, so the data here starts the morning of Monday, April 28th and ends shortly after Monday, May 5th. Also note that the small gaps on April 30th can be attributed to a server error.\n", "\n", "Even without looking at the exact times, there are many more buses and streetcars on the road during rush hour on weekdays than there are on weekends, and much more than there are at night. Weekdays follow a bimodal distribution, whereas weekends follow a modal distribution. Also, there are consistently more vehicles active during morning rush hour than evening rush hour on weekdays.\n", "\n", "At the peak of Saturday service, there are only as just as many vehicles as there are during the least busy hours of weekdays. There are even fewer active vehicles during peak hours on Sundays." ] }, { "cell_type": "heading", "level": 3, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Velocity" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "What velocities do TTC vehicles travel at, and what are some factors that affect it?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#velocity_collection = db.velocity\n", "#velocities = []\n", "#for idx, velocity_point in enumerate(velocity_collection.find(sort=[('timestamp_orig', ASCENDING)])):\n", "# if idx % 1000 == 0:\n", "# print(idx, end='\\r')\n", "# timestamp = velocity_point['timestamp_orig']\n", "# v = math.sqrt(velocity_point['vx'] ** 2 + velocity_point['vy'] ** 2)\n", "# velocities.append((timestamp, v))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "with open('velocities.pkl', 'rb') as velocities_file:\n", " velocities = pickle.load(velocities_file)\n", "\n", "date_times, dist = zip(*velocities)\n", "del velocities" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "n, bins, patchs = plt.hist(dist, bins=25, range=(0,20))\n", "t = plt.title('Aggregated TTC Vehicles Velocities')\n", "plt.xlabel('Velocity (m/s)')\n", "plt.ylabel(\"Number of Vehicle-Time Points\")" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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A/Prrr+0aEBERdQ5GE1BMTAwee+wxlJaW4t1330VERARmzpxpidiIiKgDa9F9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\n", "text": [ "" ] } ], "prompt_number": 28 }, { "cell_type": "code", "collapsed": true, "input": [ "velocities_pd = pd.DataFrame({'date_times': date_times,\n", " 'velocity': dist})\n", "print(velocities_pd['velocity'].describe())\n", "print(\"IQR:\", interquartile_range(velocities_pd['velocity']))\n", "print(\"Pearson's Index: \", pearsons_index(velocities_pd['velocity']))\n", "print(\"Range: \", data_range(velocities_pd['velocity']))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "count 8735310.000000\n", "mean 5.363947\n", "std 19.412805\n", "min 0.000000\n", "25% 2.230395\n", "50% 4.529046\n", "75% 6.747218\n", "max 11713.119951\n", "dtype: float64\n", "IQR:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 4.51682350164\n", "Pearson's Index: " ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 0.129023176137\n", "Range: " ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 11713.1199507\n" ] } ], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Clearly, a vehicle with a velocity of 11,713 m/s makes no sense (unless the TTC has a space program. For comparison, the ISS orbits at 7,000 m/s). Like before, let's take out the outliers." ] }, { "cell_type": "code", "collapsed": false, "input": [ "outlier_b, outlier_t = outlier_range(velocities_pd['velocity'])\n", "velocities_series = pd.Series(\n", " [x for x in velocities_pd['velocity'] if x >= outlier_b and x <= outlier_t])\n", "\n", "n, bins, patchs = plt.hist(velocities_series, bins=25, range=(0,20))\n", "t = plt.title('Aggregated TTC Vehicles Velocities')\n", "plt.xlabel('Velocity (m/s)')\n", "plt.ylabel(\"Number of Vehicle-Time Points\")\n", "\n", "print(velocities_series.describe())\n", "print(\"IQR:\", interquartile_range(velocities_series))\n", "print(\"Pearson's Index: \", pearsons_index(velocities_series))\n", "print(\"Range: \", data_range(velocities_series))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "count 8617369.000000\n", "mean 4.550967\n", "std 3.079863\n", "min 0.000000\n", "25% 2.187578\n", "50% 4.475319\n", "75% 6.643428\n", "max 13.522408\n", "dtype: float64\n", "IQR:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 4.45584987475\n", "Pearson's Index: " ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 0.0736866892983\n", "Range: " ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 13.5224079069\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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jUCg6PDDGGGNdm9oEZGBgIAxfAwAPHz7s0IAYY4x1D2oTUEBAAF555RWUlpbi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\nQkICwsLCAABhYWGIi4vTRmgt0lT8Pj4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"text": [ "" ] } ], "prompt_number": 30 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "As Pearson's Index indicates, there is slight right-skewing, but not very much. The range of the data is surprisingly small (after outliers were removed), from 0m/s (0km/h) to 13.5m/s (48.6km/h). Overall, the data is mostly mound-shaped and normal, except for a strong mode at 0 (likely due to resting vehicles / drivers) and a slight right skew." ] }, { "cell_type": "heading", "level": 3, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Velocity and Time" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "How does velocity of TTC vehicles change over time?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "velocity_moving_average = ([],[])\n", "i = 0\n", "while i < len(velocities_pd):\n", " start_point = velocities_pd.iloc[i]\n", " t_start = start_point['date_times']\n", " t_end = t_start + 1000 * 60 * 15 # fifteen minute moving averages\n", " s = 0\n", " n = 0\n", " while i < len(velocities_pd) and velocities_pd['date_times'][i] < t_end:\n", " this_point = velocities_pd.iloc[i]\n", " v = this_point['velocity']\n", " if v <= outlier_t and v >= outlier_b:\n", " s += v\n", " n += 1\n", " i += 100 # sample every 100th point for speed\n", " if n != 0:\n", " avg_vel = s / n\n", " velocity_moving_average[0].append(t_start)\n", " velocity_moving_average[1].append(avg_vel)\n", " i += 1 " ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "date_times_gen = [datetime.fromtimestamp(x // 1000) for x in velocity_moving_average[0]]\n", "\n", "fig, axes = plt.subplots()\n", "fig.set_size_inches(8,6)\n", "days = DayLocator(interval=2)\n", "formatter = DateFormatter('%b %d')\n", "axes.plot_date(date_times_gen,velocity_moving_average[1],'b-')\n", "axes.xaxis.set_major_locator(days)\n", "axes.xaxis.set_major_formatter(formatter)\n", "axes.set_xlabel('Date')\n", "axes.set_ylabel('Average Velocity (m/s)')\n", "axes.set_title('Velocity of TTC Vehicles Measured Over Time')\n", "plt.show()" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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G3n/f7pxYD0V9E0TJhYLJ/qVp06Z47LHHsHr1aqxbtw4dOnTAwoUL0bakr4JO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"text": [ "" ] } ], "prompt_number": 32 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "This clearly shows that the moving average velocity of vehicles is periodic, but it's hard to see from this plot when. Let's look specifically at the time of day and ignore the date." ] }, { "cell_type": "code", "collapsed": false, "input": [ "times_gen = [x.time() for x in date_times_gen]\n", "fig, axes = plt.subplots()\n", "fig.set_size_inches(8,6)\n", "axes.plot(times_gen,velocity_moving_average[1], 'b.')\n", "axes.set_xlabel('Time')\n", "axes.set_ylabel('Average Velocity (m/s)')\n", "axes.set_title('Velocity of TTC Vehicles Measured Over a Day')\n", "plt.show()" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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HDq2z2hdCoKqqCllZWd4J1cCdAJOhuDgy+Q8eDLzyir4fAR0PCSHEv/jqNMA5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\nZuw8u61wGq7J98P/+HkRZoYKQA3pVgD8kEErFTg9BZEQv+L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"text": [ "" ] } ], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Interestingly, the TTC has its vehicles at the fastest very late at night. This is likely because there is little traffic and also much fewer other TTC vehicles, so they do not have to maintain as far of a spacing. As well, there are much fewer people at these hours, so vehicles do not have to make as frequent stops. However, there is also much higher variance, likely due to vehicles being ahead of schedule and making long stops in order to synchronize.\n", "\n", "Overall through the day, TTC vehicles appear to be mostly uniform in speed, with an overall median speed of 4.5 m/s, or approximately 16km/h. This is remarkably slower than expected, since most streets have a speed limit of between 40km/h and 80km/h. " ] }, { "cell_type": "heading", "level": 3, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Correlating Velocities and Busyness" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Is there a correlation between velocity and busyness? (Measured by number of active vehicles.)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "active_vehicles = db.active_vehicles\n", "\n", "bus_vel = ([], [])\n", "for i in range(len(velocity_moving_average[0])):\n", " ts = velocity_moving_average[0][i] // 1000\n", " vel = velocity_moving_average[1][i]\n", " number_active = active_vehicles.find_one({'timestamp':\n", " {'$gte': ts}},\n", " sort=[('timestamp',ASCENDING)])\n", " n = number_active['count']\n", " bus_vel[0].append(n)\n", " bus_vel[1].append(vel)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "p = plt.plot(bus_vel[0], bus_vel[1], 'b.')\n", "plt.xlabel(\"Number of Active Vehicles\")\n", "plt.ylabel(\"Average Velocity of Active Vehicles (m/s)\")\n", "t = plt.title(\"Average Velocity of Active Vehicles vs Number of Active Vehicles\")" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Gx3B6ngoRobKyEgEBumtRuowZcq0ZhmF8DTPoTt2Ryvnz53H33Xdj8ODBAICf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\nYYaK8SY8C59hGG9gBt3p3VmMjCo8C59hmOsVHqnUAjwLn2E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"text": [ "" ] } ], "prompt_number": 35 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "From the scatter plot, it looks like there is a weak negative correlation between the velocity of active vehicles and the number of active vehicles at a given time. Let's verify this by performing regression." ] }, { "cell_type": "code", "collapsed": false, "input": [ "velocity_active_df = pd.DataFrame({\"active\": bus_vel[0], \"vel\": bus_vel[1]})\n", "res = ols(y=velocity_active_df['vel'], x = velocity_active_df['active'])\n", "print(res)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "-------------------------Summary of Regression Analysis-------------------------\n", "\n", "Formula: Y ~ + \n", "\n", "Number of Observations: 1273\n", "Number of Degrees of Freedom: 2\n", "\n", "R-squared: 0.2501\n", "Adj R-squared: 0.2495\n", "\n", "Rmse: 0.6348\n", "\n", "F-stat (1, 1271): 423.9162, p-value: 0.0000\n", "\n", "Degrees of Freedom: model 1, resid 1271\n", "\n", "-----------------------Summary of Estimated Coefficients------------------------\n", " Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5%\n", "--------------------------------------------------------------------------------\n", " x -0.0007 0.0000 -20.59 0.0000 -0.0008 -0.0007\n", " intercept 5.4518 0.0381 143.14 0.0000 5.3771 5.5264\n", "---------------------------------End of Summary---------------------------------\n", "\n" ] } ], "prompt_number": 36 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "The coefficient of determination (R-squared) is 0.2501, which indicates a very weak fit. The coefficient in front of the x variable is negative, which indicates that it is a very weak negative correlation. This can also be interpreted as the velocities of the vehicles can be 25.01% explained by the number of active vehicles in the system.\n", "\n", "This is actually an example of a confounding variable. The correlation between the average velocities of the vehicles and the number of active vehicles is likely due to having more vehicles in the middle of the day, where traffic is likely higher." ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.hold(True)\n", "p = plt.plot(bus_vel[0], bus_vel[1], 'b.')\n", "plt.xlabel(\"Number of Active Vehicles\")\n", "plt.ylabel(\"Average Velocity of Active Vehicles (m/s)\")\n", "t = plt.title(\"Average Velocity of Active Vehicles vs Number of Active Vehicles\")\n", "m, b = -0.0007, 5.4518\n", "x = np.arange(0, 2000, 100)\n", "p = plt.plot(x, np.poly1d((m, b))(x), '-k')\n", "plt.hold(False)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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3bx7i4+PRsmVLzJs3z6yyGAwGg2E7KCqVtLQ09OnTBzdv3gQANGrUCHPmzDG5\nwrNnz+Kbb77BsWPHcOrUKfz444+4fPmyyeUxGAwGw3ZQVCp5eXl46qmn4OjoCABwdnY2y/R14cIF\ndOzYEfXq1YOjoyO6d++O77//3uTyGAwGg2E7KCoVT09P3LlzR/v+8OHDZjnqW7Zsif379+Pu3bt4\n8OABtmzZghs3bphcHoPBYDBsB8Upx6effooBAwbgypUr6Ny5M27fvo1169aZXGHTpk3x+uuvo0+f\nPvDw8EBiYiIcHPR1W3p6uvb/pKQkJCUlmVwng8FgPIxkZmYiMzOzrsXQQVX0V0VFBS5evAgAaNKk\nCZydnS0mwPTp0xEVFYVJkybVCGUDEQwMBoNhb9hC3ymrVNavX68VUCgov/Bx8ODBJleam5uLoKAg\nXL9+HX379sWRI0fg7e1dI5QNNAyDwWDYG7bQd8qavzZv3mxw5bw5SmXIkCG4c+cOnJ2d8Z///EdH\noTAYDAbDfmGbdDEYDMZDgi30nYrRX9OnT0dBQYH2fX5+Pt58802rCsVgMBgM+0RRqfz000/w9fXV\nvvfz88OWLVusKhSDwWAw7BNFpVJdXY3S0lLt+5KSEpSXl1tVKAaDwWDYJ4rrVJ5++mn07NkT48aN\nAxFh8eLFGD16dG3IxmAwGAw7Q5WjfuvWrdi5cyc0Gg169+6Nvn37WlcoG3A2MRgMhr1hC30ni/5i\nMBiMhwRb6DtlfSpdunQBwOX+8vLy0nmxdSUMBoPBkILNVBgMBuMhwRb6TlU57KuqqvDXX3+hsrJS\n+1lUVJTVhLIUEycCly4B7u7AypWAIDKawWAwGFZAUal88cUXeOeddxAUFKTdUwUAzpw5Y1XBLMGl\nS8Devdz/EycCa9bUrTwMBoPxsKOoVObOnYuLFy/C39+/NuSxKO7u3N927YAFC+pWFgaDwfg7oLj4\nMSoqym4d8ytXAkOHAj//zExfDAaDURvIOuo//fRTAMD58+dx4cIFPPHEE3BxceF+pNHglVdesZ5Q\nNuBsYjAYDHvDFvpOWfNXUVERNBoNoqKiEBkZifLycpaehcFgMBgGUR1SXFxcDA8PD2vLA8By2pZF\nfzEYjL8TtjBTUfSpHDp0CM2bN0fTpk0BAKdOncLzzz9vdcEsAR/9tXUrp2AYDAaDYV0UlcrLL7+M\nbdu2ISAgAADQunVr7OXjdE3kgw8+QIsWLRAfH4/U1FSUlZWZVZ4cLPqLwWAwahdFpQLoL3R0clK1\nZlKSrKxgJR5GAAAgAElEQVQsLFy4ECdOnMCZM2dQVVWFjIwMk8szBIv+YjAYjNpFUTtERUXh4MGD\nAIDy8nJ8/vnnaNasmckVent7w9nZGQ8ePICjoyMePHiA8PBwk8szhK8vt+CR+VYYDAajdpCdqVRU\nVAAA5s+fjy+//BLZ2dkIDw/Hr7/+ii+//NLkCuvXr4+pU6ciKioKYWFh8PX1Ra9evUwuzxATJwJJ\nScC6dcy3wmAwGLWB7EwlPDwcTz75JEaMGIEVK1bAwUGVpUyRy5cvY+7cucjKyoKPjw+GDh2K7777\nDk8//bTOcenp6dr/k5KSkJSUZHRdwjQtAJCYyHwrDAbj4SEzMxOZmZl1LYYOsiHFeXl5WLduHTIy\nMvD7779jyJAhGDFiBB555BGzKly9ejV+/vlnfPPNNwCA5cuX4/DhwzqzH0uFxSUnc7MTnpQUYONG\ns4tlMBgMm8SmQ4oDAgIwadIkZGZm4tixY4iNjcWUKVPQoEEDTJ8+3eQKmzZtisOHD6OkpAREhJ07\nd6J58+Yml2eIlSuBkBDu/3btgCVLrFINg8FgMP6H6sWPRUVF+P777/HZZ58hJycHubm5Jlc6e/Zs\nLF26FA4ODmjTpg2++eYbODs71whlQW1bUMD5URYsAF57jTnsGQzGw4stzFQMKpWSkhJs3rwZGRkZ\nOHjwIPr164cRI0agV69eZoUVKwplwYYRRn4VFgL/C2TD0KEsFT6DwXi4sAWlIqsZUlNT8fPPP6N7\n9+54+umn8d1338HNza02ZbMIQme90BTGHPYMBoNheWSVSt++ffH111/Dy8urNuWxKBMnAqdPc/8n\nJgIbNgCvvsopFGb6YjAYDMvzUO9Rn5RUM0thkV8MBuNhxxbMX5ZZfGKj8Lm/HByAAweAa9fqVh4G\ng8F42HmoZyoFBUBgIFBZyb13cQF69mSRXwwG4+HELmYqxcXFePfddzFhwgQAwO+//44ff/zR6oJZ\nAl/fGuXh4ACUl7NULQwGg2FNFJXK2LFj4eLigkOHDgEAwsLC8MYbb1hdMEswcSLQoAHg6gp06sR9\nxiK/GAwGw3ooKpXLly/j9ddf1+5PX1u7P1qCS5eAI0eAsjIgIIClwWcwGAxro7iC0dXVFSUlJdr3\nly9fhqurq1WFshTCTbqWLGHKhMFgMKyNolJJT09Hv379cOPGDaSmpuLgwYNYYidJtAIDuRkKUyYM\nBoNRO6iK/srLy8Phw4cBAB07dkRgYKB1hbLCOhVT0rKwzb0YDIY9YQvRX4ozlQEDBmDEiBFISUmx\nK3+KcDW9hweQn8+FGBujGIQpXiZOZLnCGAwGQwlFR/3UqVOxf/9+NG/eHEOGDMG6detQWlpaG7KZ\nxaVLnCIBgOJiYOdOoFkzTrGoReiTYRFjDAaDoYzqxY+VlZXYs2cPFi5ciG3btqGwsNB6QllgCsdv\n0OXjA9y7V/P5wIFcDjA1CNPmM9MXg8GwdWzB/KUqTUtJSQnWr1+Pr776CseOHcOYMWOsLZfZrFzJ\n+VFOnQIEW7XAmPb29eVMXkyhMBgMhjoUZyrDhg3DkSNH0K9fPwwfPhzdunWDo6OjyRVevHgRw4cP\n176/cuUK3n33Xbz00ks1QllY2/buzZm/EhKAPXuYkmAwGA8ntjBTUVQq27dvR69evcxSJHJUV1cj\nPDwcR48eRWRkZI1QFmoYPnrL2Zlz1ltirQqLCGMwGLaKLSgV2eivXbt2oWfPnrh//z5++OEH7edE\nBI1Gg8GDB5td+c6dO9GgQQMdhWJJhNFbsbGcP0WNMjCkOFhEGIPBYMgjq1T27duHnj17YvPmzdBo\nNHrfW0KpZGRkIDU11exy5BBGb7m6qlcGhhQHiwhjMBgMeRTNX1euXEFcXJziZ8ZSXl6O8PBwnD9/\nXm8xpaWmcGPGcBFgrVtz73fu5JSBUv4vPnJM6lgWEcZgMGwVWzB/KSqVNm3a4MSJEzqftW3bFv/9\n73/NqviHH37A/PnzsW3bNn2hNBrMmDFD+z4pKQlJSUlG1xEaCty6xf3v6Mj5Vlq3BurXN2wCY4qD\nwWDYA5mZmcjMzNS+f+edd2xXqfz22284f/48Xn31VXzyySdaX0phYSE+/vhjnDt3zqyKhw8fjv79\n+0uGJ1tK27q6cnuoSGFK2hYGg8GwZWxhpiLrU7l06RI2b96Me/fuYfPmzdrPvby8sHDhQrMqLS4u\nxs6dO80uRwl3d12l4uTE7QJpjD+ERXsxGAyGehTNX7/88gs68Ttc1RKW0rb8+hSAW1m/bx8wa5Zx\nZi25pJQTJwKbN3N7tbRtC6xdy5VpCSXEFBmDwTAFW5ipKK6onz9/PgoECbPy8/Mxbtw4qwplKcLC\nuH3pnZy4hY9RUfor5CdO5BRHcrJ0XjC5aK9Llzh/TX4+p7j4LYr5yDFzti22RBkMBoNRFygqldOn\nT8NX0Av7+fnpOe5tlWvXOPNXZSXXSUt10EodOJ/uRRwFxisbAEhMrFE4lgg5ZmHLDAbDXlFUKkSE\nu3fvat/fvXsXVVVVVhXKUsh1/FLHyHXgcvm/Vq4EUlK4BZW7d9d8L6eEjMESZTAYDEZdoLifytSp\nU9GpUycMGzYMRIS1a9fijTfeqA3ZzCYwkDN/EQHe3tLHrFxpWviwry+wcaP05+ZGlVmiDAaDwagL\nVKW+P3fuHHbv3g2NRoPHHnsMzZs3t65QVtj5EeBmFkFB5jnBmROdwWDYKnbhqAeAFi1a4MUXX0S/\nfv2wfv16tGjRwtpyWQSh+QsAKirkfShKDnseaznR1dbPYDAYtoyiUsnOzsZnn32G9u3bo2XLlqiq\nqkJGRkZtyGY2K1dykV88zs6Go7nUKAtrOdFZxBeDwXgYkFUqX3/9NZKSktC7d28UFBRg0aJFCA0N\nRXp6OuLj42tTRpPx9eVG/wAXUrxkiXI0l5KysJYTnUV8MRiMhwFZn4qzszP69euHWbNmofX/MjLG\nxsbi6tWr1hfKgnbBMWOAn37ilAq/QFGKus73Vdf1MxgM+8cWfCqySiUvLw9r165FRkYGcnNzMWTI\nECxevBg3btywvlBmNgzvTL98GSgs5F6Acfm+/g4O+b/DOTIYfydsWqkI+fPPP7F69WqsWrUKxcXF\nGDx4MN5//33rCWVmw4ijvgCuw7x61fz0LGqwl87anHNkMBi2hy0oFVXRX5GRkZg2bRr++9//YtOm\nTahXr5615TIL3j8h3AG5c2fjOndzfBz24nRnfhwGg2FpVM1UahtztS3vn8jN5Tr3xETdVe/GlGGK\nj8PQJl+2BPPjMBgPF7YwU3molIrY7MR/Vtudpq121vZilmMwGKZh00rl4MGD6NKlC0pLS2vd3GVq\nwxhKU2+JztTeO2Vh+8TGclmb7fVcGAyGPragVGR9Ki+99BIA1PpeKuZg7sJGJezFVyKHsH3Cwuz7\nXBgMhm0im1DSyckJEyZMQHZ2Nl566SUd7afRaPD555+bXGlBQQGeeeYZnDt3DhqNBosWLcIjjzxi\ncnk8UskhJ04ETp/m/pfLVKwWe3dsC9snNZX7jD8XW5uF2Zo8DAZDHbJK5ccff8SuXbuwY8cOtG3b\nVk+pmMPkyZORnJyMdevWobKyEsXFxWaVxyOV3ffSJW4jLYAz95jTOZma0dhWELaP+Fz4WRjAfV7X\n4cW2Jg+DwVCHoqP+5MmTSEhIsFiF9+7dQ2JiIq5cuSIvlBl2QfEINzXVPiKx6ho+Yi0gAGjShNsq\noC5nCPYSQcdg2BI27VPh8ff3x6BBgxAYGIjAwED84x//MGtV/dWrVxEYGIixY8eiTZs2mDBhAh48\neGByeWLEfg9jcnXZSqbgupCDb6cmTYCDB7n2a9Om7tqDbVTGYNgppEDPnj1p0aJFVF5eTuXl5bR4\n8WLq1auX0s9kOXbsGDk5OdHRo0eJiGjy5Mn01ltv6RyjQixZ+vcnAojatSPKzzd87IQJRN27c7/J\nz+f+57b0Iho6VH2d4nLUfieHqXKYw4QJRCEhRE5OXL2enkQ+PrUvB4PBMB1z+k5Lobjz4+3btzF2\n7Fjt+7S0NMyZM8dkJRYREYGIiAi0b98eADBkyBB8+OGHeselp6dr/09KSkISn25YAaGv4LXXDDt7\nxXZ7Ux3xwnIaNQLat6+pb/Nm4NYt7ruxY4ENG5TLq4uAgEuXauQEgPv3a/6318AEBuNhJzMzE5mZ\nmXUthg6KSsXf3x/Lly9HamoqiAgZGRkICAgwucKQkBBERkbi0qVLaNy4MXbu3Cm56ZdQqRiD0Bmt\n5OyV6rwnTgTc3Li959VGHgk3A8vL40xHaWncdsNlZTXfEamLahI70WsjEkq8oRkA+Ptzf5n5icGw\nTcQD7nfeeafuhOFRmspcvXqVnnjiCQoICKCAgAB68skn6dq1a2ZNj06ePEnt2rWjVq1a0aBBg6ig\noEDnexViqYI3hQUEEHXpom+Cys/nzDpis5Sx5qf8fKLAwJrfAEQDB3Lf9erFvU9IMM7EJjSbdemi\n/BtTzGzic+BNXwCRuzuRtzczfzEY9oSl+k6zZKhrAaSwVMPwSkNNpyzEFL9MQEBNHa1a1fxOrLjE\nZYuVAf/ez6+mvJAQZXnUKCslxcMrwMRE3Tbz8zNNUTEYjNqFKRUZLN0wxigJIvkZjBTCzjwsjJuh\nGPqduGyxMhC+52XOypKWR6gkeIUgVlYRETWzNCXlmp9PFBtLFBxcM2vx8eHqZzAYto8tKJWHKqGk\nFBMnAufPcxt2HT4MREdbpFitn+PcOc6PYup6CvF6DH5dTWIicOcOEBkpv2ZEmMsrJQVwcanxw0jt\nKePmBpSUcOWdPi3dFuLfDRwoHVzAVrwzGLaHLaxTUVRrlZWV1lZseqgQSzXmhOcaMhcJy42IMN08\nJJ65CN8ryW5oBsZ/x4cFt2tH1LGjfnnic+R/x5vBpEx04vNXaldz/T0MBkMdluw7TZZB6YDY2Fia\nNm0anTt3rjbkISLLNoyxpi8hvC9D6HiXK9caHaeS7IbMdPx3w4dz/p5evaRNZOK1KPn5RCkpRNHR\nhs1mxrSrtdbd8Gtr/Py4c2MKi/F3xy6Uyr179+jrr7+mTp06UYcOHeirr76ie/fuWVcoCzbM6NFc\nZFavXkSNGnGdaECAOj+B0FmekqL7nZJvRIwpSscY344UYqUxcKC8zH5+XFvxnbSvr+FAAWNkM0ex\nS52TVFQci1BjMOxEqQjZs2cPhYWFkZubG40ePZp+//136whlwYYRzjaEr7Aw5d+Kw4GJ5JWDUsdp\nymjd3NmPlNKQMnU5OnKfiztppUABtZirHOXOSXhthdeIwfi7YhdKpaKigjZu3EgpKSnUunVr+vTT\nTyknJ4fWrl1LjRo1so5QFmwY4WxDo6n5PzlZ+bdSnaGcclDqOE0ZrZujiCIiamYpfASXsBNOSeHk\nEIZCOzvX/B8frxzJVhcI2zEri5ORP5e/A8zkxzCEXSiV2NhYGjt2LB08eFDvuxdeeME6QlmwYaTW\nXrRsafzDKF6PIlQOamYUpozWzVVE/Cs6mvtcuLiR9xFFRNTMVvjvXF2NDyNWagO59TjCkGdLh3s/\njIivLzP5MYTYhVLZv3+/qs8sCQA6d+4cFRQUUHV1tVllCTshpQ7J1Ggv8YzCHLOV8LemmJ2E0Vv8\n7ENs1uKjuoikTV7874wZCSvNqpTW49RGB2mPUWiGovOsZfIztZ3ssX0fNuxCqSQmJup9lpCQYBVh\neABQ48aNycPDg9zd3alRo0bUvXt3Sk1NpWnTptGcOXNozZo1dODAAbp69SqVlpbKlqX2RpeKhBKi\nJnyX/05s9zdGiZkbKZWfT+TiUjP7OHWqRr6EhBqTFl+3q6u0UjFWSSrNqsTf8+/5VDDt2un7fCxN\nXfi1zEUsc36+9U1+pt6DdZFdm6GLLSgV2YSSv/zyCw4dOoTc3Fx89tln4OQFioqKUF1dbbV1MzwX\nL14EABQWFuLmzZvIzs7W/r1y5Qr279+v/ezWrVvw8fFBWFgYwsPDtX/Dw8Oxdm0YCgrCAYQhLS0Q\nGzfqbiHDL+I7fRq4d4/7zM9PPyuvoezH4gSQwuSMt27J71wolfDS3AzF/C6OXbsCBw5wCxyldqwU\n1i2HgwOwezfQsCFw5Aj3WXAw91vxwkmlXTHF3/PvHR2BXbu4zy5f5vZyEbaHMSgtyDSlbet6B0qx\nzL6+6jJdW7JOa/+O8XAhu6J+79692LNnD77++mtMmjRJ+7mXlxcGDBiARo0aWU8oI1eFVldX4/bt\n2zqKh/+7dOlNVFZmA7gJoBARESGIiKhRPj/+GI6rVzmlA3B/BwzwQmGhfOckXHU+dKh+R1NQADRr\nxikUfqW8VBp+qd0NCwpM37JY2KkGBgLXrtX8v2MHlzG5bVtg7dqalfumEBYGZGcbJ4/cqnthW4aE\n6LYZf7xcOfznly9zSu78+Zqto+Wui1Lbittw82auzIQEYM+e2s8cYM79UNt11oWsDF3sYkV9Vh0k\nflIhlmp4Rz33KqXk5Kt04MABWr16Nc2ZM4diY6cRkEpAdwIaEeBOgBcBTQh4jKKiRtLrr79On3/+\nOa1bt45++eUXSkq6RkA5eXrK+x3UrGMRHyNlajHG/CKsQ5g1WRjhxZvk4uLkTV4tW+pGyolfwsg5\ncTSS0ISlJpEnbwZzcODKkHLcy5lV3Nyk5TNnPYywLmG7iRe/Mhi2iCX7TlORnalMnjwZ8+bNw4AB\nA/S+02g02LRpk9UUnSX3qO/QAfj9d+47T0/g7Fld0w0/utq1C7h7F3BzI7RpU4iDB7PRuPFNvPxy\nNgoKuFkPPwO6cSMbN2/mAvADEA43tzD4+obDwyMML74YjoYNa2ZCAQEB0Gg0qvZcF8+AfH2BJUuA\nigruM7k8XDx8HZ6eXB6wu3e5+nx9gZ07dY91cgIqK/X/d3UFLl4EWrUCCgtrjvf05DbuatWKG7Xz\nMx8i3a2GAwOB27e5/+VmHsLr5OwMZGbW1K/RcGXybbBmjfx+9c7ONb8DuHxpUVFcm5k6UhbWxbeb\nqXnd1GLpPGosL9vfF5ueqRw/fpyIuAWP4ldmZqZVNZ0BsRQRj2qFzndDI+asLG6UnJVVk63XUKhr\nv36VBNykZs2OUdOmPxDwHwLepJiYsdS3b19q2bIl+fn5kYuLC0VHR1OHDp0pImIITZo0mT766CNa\nsWIF7dmzhy5evEj3798nIv39X4RrbAD9Vf1ixPu68KHBfOqVoKCakby/P/e/u7t0mCo/w/PxIYqM\n5PKGublxf8VyCaOR+N95enLlRkVJt6OwTj6wQG62IRe1V78+d2y9etzsic8I4OLCRa/5+OjOJI0N\n/R49uibFjTlZDZTqtHR+uofVYV7XQRP2gDl9p8VkUDqgqKhIJ6lkZWWlthO0mlBmNIw4ykhowjBm\nfYrSgylM/yLOqSXkwYMH9Mcff9C+ffto1apV9Omnn9Irr7xCw4cPp0cffZQaNGhA9erVI29vb2rS\npBkFBfWkwMDRBPyLgC8IWE/AYWrc+Drl5pZLyiqVAl9KdmGHKVaivCnJ0ZHrmDt25P5mZUmH/wrb\nNDm5Rnn06lWjsMRmOKEswut06lRNFJqnJ6f81FhdhecgvmbCV1CQugSdYiyRNFNNGeaksZEq35Jp\nceoaYzer+7tjF0qlQ4cOVFRUpH1fWFhInTp1MqvS6Ohoio+Pp4SEBGrfvr2+UGY0jHiWceoUl5Il\nOdm4B4xfFOjtLd3BCR9mcU4tY6murqa7d+/SmTNnaPv27RQfv4iAWRQQ8BwFB6eQr287CgkJJScn\nJwoJCaE2bdrQgAED6Nlnn6WZM2dS48bfELCVgNPUv38e1atXrSe7XKfHfy6nNIYO1Q//TUzk2lMY\n1iqVPsXBoeYzcTtKZWeWU0Bq4a+Z1Cs2tmaGJVynI24HYftIdc5qlEdsbM3CTj6HmlSdcm1hDFIy\n1uYCUWvPHqTuK2srS3vOWmAXSqV169aqPjOGmJgYunPnjrxQZjaMJab/cqMi/objV6dbegHahAlc\n3fwsQcj48RX0yCM3qFOno7RixQb68ssvafr06RQenkZAb6pXrwX5+PiSRuNKQCwBXSky8imaMmUK\nxcV9QsBKAvZSRMTv1LVrMUVE6G4ZLH45O9csGo2N5WYv9epxsz/xA8d3brzZS7h6X9gpCDtn8YNr\nyo6bws5MbiGneCdLKTOiOIWN3HWQu7eEskvJkZxsnQ64rjMMWDMDtTiDhTGLgc1pa/Egy56CNOxC\nqXTu3FnrXyEiOnbsGD3yyCNmVRoTE0N5eXnyQpnZMEqdk5oUIXJliG84f3/LdhKGHlKp78Sd34QJ\nRL6+xQT8To0a7aWFC1fSJ598QjExUwgYRp6eXcnVNZYAVwJ8CWhBQG8C0giYTsCXBGwgB4ejFB5+\ng7y9KyggQHcvFuGL76BHj9ZVJLyfRDhbEcot5ccxpoMUKgHevCXOJgDUZKRWuieEfqKBA9UpD2E5\nQtmFEW1qyrQ1jOmQhe1hyuJVNTO/iAjjyzanrcX3kZIv05awC6Vy9OhRiouLoy5dulCXLl0oLi6O\njh07ZlalsbGxlJCQQG3btqUFCxboC2Viw/A3aK9ehpMhqkkRItfBCW84Dw/d31tiJGrI5CKVd8xQ\n+hO+sxWfD1dHNWk0eQScIs509g0BM8nP71kCBpC7exsCgglwIiCUNJp2BKSQi8tzBMwiYBEB2ykk\n5CwFBd0loFpbr68vZ3aMiOD+SpktpDYDU0uTJtLKLT9fNymm+DtDCkuYI87QrElYjtz15o8Rhjzz\n/ia5a2sN85GhstWmJFLqkIXtoeZ34nrVKm9xuLxSe6kdWEqVk59fE9Rib9mv7UKpEBGVlZXRmTNn\n6MyZM1ReLu0sNoabN28SEVFubi61bt2a9u3bpysUQDNmzNC+9uzZo6pctQ+DVIoQteaWXr1q/Al8\nJyEVrWUoPYtS+dHR3D7xvGlIaE4Rj9rEMojXpEjt8MibEYSjfT8/OVNYOWk016lVq8PUocN6eued\nL8jB4V8EjCagJzk4NCPAmwA3AhoQ0I1cXUfQCy9Mpc8++4weeyyDfH33k5PTZXr00RJtm4wezc3y\n1DjlJ0zgOmhHR+43Xl76cvbuzR0rDlTgZ5NKiSvFSkcYiGFq1Ja4PevXV29OMxW1jm1D9Zrq6Ffz\nO3G9apS3sGxPT8P3tlq/klK717VZUS179uzR6SvtQqmUlZXR3LlzafDgwTR48GD6/PPPLaJYeNLT\n0+mTTz7RFcrEhpG7QcU3HX/DiG20xo7e+HL4UY3S7EeI2AQnVEhSixV5c0r9+rqmKD5IQNiB8JFU\nQpOElMIT/kbKByI14ifS3WemphMvIuAiAbsJWEGenh/RpEmTKSBgCAGdCYgmwIVcXPwpPj6e/Pz6\nETCOgLeoTZv5tGnTJjp+/Djl5OTobWEt3hNHamGmszPXloGBut8LZ5NSMzi5667U6UyYUGPi8/LS\nVRRNmhhePKrGnGYItfepIce2oXpN7VDV/E5cr9q6+OOkZnumKOaHKUJOiF0olXHjxtHo0aNp165d\ntHPnThozZgyNHz/e5AqLi4upsLCQiIju379PnTt3pu3bt+sKZWLDyN2gam86Q8fxkUX83iRChOss\nePOL0s0qF3GVmKg72k5M5OoTdlJ8fVLmJLFDU64e4ShR+PLzq1E2CQn6O0cS6Y7is7JqlKowfT6v\nqITmKHf3KurWLZf27TtJbdtuIWABhYWl05gxE+jxxx+nhIQECgwMJGdnZ4qIiKAOHTrQoEGDyNX1\nnwS8T8ASAn6mr746R0ABCU1uci9DCp/vnIXXj/eRGQoTl7p+vHNfLkiBfymNyNWgdpZhyLFtbL2W\nMtOZOwOQ+r0pCkJcjtzWDPa2JsYulEp8fLyqz9Ry5coVat26NbVu3ZpatGhB77//vr5QFm4YtTed\noeMMmRKEHW9ysrqHhq9LvDiTD31OSdH1Cwk7Kn9//Sy1Sj6gxETdxY/8KJEfzSYmcmWOHq3r+A8L\n0x+NS2XO5Wd+UuHE9erpr10x5PcqKyuj4cOzKCHhENWvv5ZcXecR8BoBIwnoQUBjAjz+92pEjo5J\nBKSSi8urBMwhYA0BB8jL6ypdvFiq52fh/SaGQqn9/XWDH+TCjYUK11B5gO6WCeZ0WkqzDKWFu6ZQ\nW0EGSn4gqVBf/px5k3FYmPHnL57h2euaGLtQKomJiTrbBv/xxx+S6fAtKpSFG8bYKbbUgy8cuYoj\nUcROXiFy+bz4jvvUKd3RvFykiVS0lLHnzs8whA/d8OGcuczXV99/Exiou1f90KGcaYdXcPysRsqW\nza92d3fXjb4S2sT59RxSa2fEylb6dY+A3wjYSS4uyygy8gMCXiRgMAGPUFBQJDk7O5ODQwABrQjo\nT2Fh4+m1196mrl2/Ig+PzQScIOAWAVVaU5nYbi/ucPi25NshPl7XuS+VccDLS3cELDxGuK5FTWdo\nrr9AiNrN1XiTLO+7Uzre2M3X1MgufgaEOegMzcjVIB4k8IMjpfO1NexCqezcuZMiIyOpW7du1K1b\nN4qKiqJdu3ZZV6g6aBglu7pwgaPwc2dn7v3AgdJhj1IPifgzoY9C7sbNz68xLzk5ccpITm65c5J6\n8MSjeKmZhnBULOzshb8VP7zC1e5CJcrLIF7PIfy9UE6+4/b31zUBCk1t8fHSa0Pc3Ii6dasiL69b\n/1Mem8nT82tq2PBtCgoaT0D//ymbAAKcCYgkP79HKCjoHwS8SOHhH9L8+cuoQ4dd/1NehZKKhjeZ\n8eYmKZNbeLi+MpFqB0OdodprrWS6E6KkgMShvUojeLH/y9iRvqFZmLjjDwqS/o6/T/j9hIQI20n4\nvA4fXnM/837LevV0g0LsYcZiF0qFiKikpIROnjxJp06dMrghlqWwdsNI2U+FHSTvO5C7waV8EXIj\nWuEInZ+yi1frq51JCaOJIiK4z4xZ1yIlt/Dl5MTVITS1+fjomqn4EaswZ5gx/iOhcpYLn+Y7Xh8f\nrpM8dlEAACAASURBVFOQ6qh79+ZmXJGR0lFv/O+F74WKkvej1JxrKQFXqUePA7R48Rpq3XoOvfji\nq5SamkpduyaRoyOXwdrBwYsaNeIyWHPmuNcJmEfAOnrssUN07do18vUt05NFrEx4c6OwHfiX2PEv\ndz3VtLESSqZh8fdKmSaklKaSHGp3Ox09Wn8AxCM0GQsVgUajayoTtpPwnhFuVieVi87Hh81UVMsg\n98W6deto/fr1On/5/9evX29doazUMFKmB7EyAGpMUHKdvdAXAXAzjNGj9R8o/rfCm1ccqWXM6EfY\nofMPtJqRnZQPhe9MPT25h0jKuezoyLWN0GzTvTvXmatVhkIlIZ6JSf1ebp2NsG19fKSj2sQRV3JO\nev4VFqafd8zQVgZDhlRTVlYBnT9/noCfiQsceJ+Af5JGM4jatu1A4eHhpNE4ExBEQAIBjxMwgYB0\nAhaQu/sW8vH5lbp1y6W7d6u1ZYt34HRz0ze7GAoWMTWaSeka8t/z7S02h4rhZ0mtWqnfnVJqDYpw\nFiF3raWU2oQJ0veyVF403l/o42M4swQ/iLEHbFqpjBkzhtLS0mRfVhXKxIZRsg+LlYd4a1u+Y5Fy\nzkqFJQ8cWJNI0dAITbzqWNjJik1mhvIOZWVxCqVjR/3waLFPgl/zwj/Y4oWhWVnKWwnLKV5jFKF4\nBG3oOgnDdIWjUaHfiu9QxdcyMZFo8OCa9w4ORPv3S5tjpAYPxuYdE5d16lTNefXsWUkODjcJOE7A\nDwTMJ+BNAsaSRtOXgJYE1CfAhSIjo6lz587k7DyEgMkEzCZgBQF7iAvRvq+Vx9BgRNz5i314pkY2\nSQ3EDCkvQ3LIlc+XLQz/NrQfkLOzvlmLR8rEK/R1Cp8XYXvyAxCx2Vd8r9g6Nq1U6hJTG0bc8Ykf\nHmEklHAUlZ+vG50kZcpSY//nFYU4qkl4I4s7WbHMSg554fdyjm41fhwi/Y5Co6kZ5fEmBE9PbkTH\nm5I8PPSVnaFOSmoELaeg5BQAH80UEsLZvrt311eIfn7658On/ufLFYdLS5k01ZprhPUEBuqfl1zn\npOs/eUABAZdpy5Z91LJlBgGfkkYzlYDh5OjYjYAGpNHUIy8vb2rWrBn5+/ciYDSFhPwfffTRF/T9\n999TSspheuSRP6lv3wq9+0vu2hszQDB0fxtSVsLzFN6nvLKRWp8ljE4U+oWEgTBKsx/+Ojo5cTNt\nV1duECYVOMAfy6ch4gdz4mvm6Kgua7YtYBdKJScnh8aNG0d9+/YlIqJz587RN998Y12hTGwYQ6kd\nhKGvUjclb1pwduZuMLkFiHL+FTU3vLAefsQtllmcvkQ84hMeL6fopDpIqc+E62E0Gt2oJ2dn+X1T\nDClWsaKTanO5DlyqvnbtuFBR/r1Q+at58ZFt/Mp9uT1SxHIamjGKlZ+vr/61lPLx8IpNKh8aXz/v\nU+D/3r1bk8F6/frtFBCwiKKi3qWoqOcoOTmFvLzaERBGgDPVqxdC3t5tCBhAgYGTaPr0mfTtt99S\nu3ZcBuuEhDy6e7dab+ZsaEZh6P4WB6wITWO8jzIhQfc+lWsX8boa4YwnKKgmICY/n4tA9PHhyuIH\nGR4eNYpE6Z4Qm7eF8iUny/9Oyuxoa9iFUunbty9lZGRo16aUl5dTixYtrCuUiQ0j7hiMGX1KReAI\nV6bzN7zY1iuXUVhNPWJFx0dJBQXVrFcRdmDR0br1qU1xYegzQ500f/78LEUurY0wGEHoHBfOFoUj\nRTlnLG/jdnTkTFf8MVKjWanOw8lJt35x521MUkfxCN3VtUbBSIU782Y6XmYpZ6+ckjHGJCUXfOHv\nX0Ht2t2g9u2PUocOG+jjj7kM1mPGjKEePXqTt3dz8vHxJVdXV/L0jCUnp64UHDyMwsKmEPAxASsp\nKWkv/f777/TgwQOD9434uktdB/5/fk0Sr1CEioe/Z8TbUgjbQuwXGzpUt/3VtrP4PhDWJdwSYfRo\nw9kQ+AAZW8UulErbtm2JiCghIUH7mbmp75WwVMOojaoiks4HJtX5yUWPWCI9hJKJil/7IVZIpmSH\nFcsjfrm710ReZWXVpL7nd38Ur00Rm6P46CApG7dcXjThjERowxaaP3hZlNaxSHU2KSm6M1KpFP5S\n7SJ2/EoFAAgjkaQSXgpfwk4rMpI7F+Fnhuz3cmlOxGl6hOcl7KRv3LhPHTr8TkAmASvJy+tjAl4m\nP79h1LFjF4qNjSVXV1fy9fWlFi1aUJ8+fSgtLY3eeOMN+vLLL2njxo109OhRys7Opry8Sp1BT8uW\nXIctVhxSqYTEikKIeAYk/L9Ll5rPNBrDs2mpl3gtmbCulBTlBaxyvhxbwS6USvfu3SkvL0+rVH75\n5Rfq1q2bdYWqg4YRmx/kOmfhQ23MegAeQ1vUGjJRCfNsGUrHb2wsvXg25O2tP/Nq0kR6ZiCsS+rh\nFo6kxdE1Uh2neEYi5Vw1tDCSl1EY2cO/vLw4U4lclI+wvgkTOAXn5MSNtIW/4U1dwgzEAFHbtjWK\nVknhyflbhJ2wUgZkuQAHKdOa3OxGbuBUXV1No0blUdu2p6hdu630+eff0MyZM+nZZ5+lAQMGUJs2\nbSgkJIScnZ0pNDSMfH3bU0hICo0f/zzNmjWLmjdfTMB2As5SfPxdbZQbr9CF95LU2iyhfLzsvr7y\n2y+4uOgrMvHL07Mm/FzKryIOmZZ7CRdc2iJ2oVSOHz9OnTp1Im9vb+rUqRM1bNiQTp48aV2hLNQw\n1t4UyZiZEI8hBaBktpKrz9DsR+2+7MLRuHiKL9VJiuviFZ7YRJafXzPLEXecYsSKQGkxntRLo+GO\nkeqAhBFFUiYOKcc2wC2CA3QXnRrKKSbnN1CjaPz99R3YcgMFucgs/sWv9Bf78QwNbKTKDAiQjibr\n27eczpy5TocPH6b169fTF198Qf/6179o+PDRFBDQkzw9m5KXlze5ublRgwYNyNu7GwHDCZhK3t6f\nUrt2GbRlyz66fPkylZSU6NyPUve8VHJQY15SEX78/clnwTZk+gK4e9SWsWml0r9/f1q+fDkVFRVR\nRUUFnTlzhk6fPk1lZWXWF8pCDaN2BG+O8jH2t6auJzCElJNZKgrHUBvwHaGDQ41DVPyd2NchJYOS\nyRCQ3z9FKCvvaxCjtICTf/GdPq8QhTNLPz9uZiEnkzjEXDhT4dtQyrzGX1M+m4AaOePjuY5q8GCu\n0xMrcL5MpYwP/EvY8fKzQXEkltjvJURcplSqeWNnxkVFRXTx4kUKCOAyWGs0H1F4+EuUkjKEOnXq\nRNHR0eTi4kL169en+Ph46tu3L40bN47efPNNmj9/Pv3www90/Phx8vS8SUClSQrFw0N+sa7SQIV/\n8dGEtoxNK5UNGzbQU089RQEBATR06FD6/vvva0WhEFmuYcQduFxEj9RKeDFy6R2MXchoyuzGEEqd\njdp9vbOydDtK4blIrY8xBv46SIVbSx3n5yf/8Obnq5sJ+Plx58531nxGZd4HxY/Evb31HcX5+VyH\nLA5iELahsGN2dJR2NhuST5ypQCp0lw/MkLvPxAo2LEzaRCoMpBCaicSKW+i0jo/n5DNUntqBEX+P\nik1UwnusqqqKcnNz6ddff6UtW7bQggULKD09nSZMmEDJycmUkJBAGk0gcZvGhZNG04GAgaTR/JOA\n94jPYA3IZ7Dmt0YQPvvCcxa+pMy9Go3lnltrYdNKhef+/fu0atUqSklJoaCgIEpLS9NLVW9xoSzU\nMOIOXG4NiPjhVDK7CKfRajttayE1alSymcthbBCBEKWd9NTIoPY44VoEXl6pjbvE14qPRhMqBDVO\ncQ8PzgQmVKru7rr1iEOpDY1+nZz0FXRNFBf3Eif35J3T4oV8fOfH57nizTlC34GUIhZmS+ARO63F\n10TtzqpilBYkEqlLbNmxI5GLSxlt3ZpF8fGHCFhLwFziM1h7evYgoAkBngS4E9CIAC6DNfAqubvX\nZLBOTr5KpaWlBq+T1C6izKeiQgZjDj558iS1bt2aHBwcrCUPEVmvYcRmDX7EInRWq8mBJBy9GdNp\nq8ES5jS1nbPc5mVK5y/1vTgqjg8hVput1pjzFvpp+KACqdmLt3eNXK6u+nnN5ExswnqkyhU7w52c\n9GcScmY6BwddcxofqdW9O9eRC8sRBwNIdWpq88EJ15sIowiFW/PKXWO59EZqr6Ow7uRkaYWkNGiR\nCzYQXs/kZGHQSSFxSUB3EbCMgA8pPPxFAv5BHh6PUHg4l8Ha2TmAgNbk5NSfgGfIxeVtAr4mb+/N\n5OlZk8Gar4P5VFTIoHRATk4OzZs3jzp16kQxMTH0+uuv25WjXmjuysrSN2vwo1el9SbmOujVYqy9\nWo0sasx+UhkIjKlHaGKRG/kZmuGo6bCEiGUfPlzayZqSwn0vjvoyZGKTOi9+diN1fl276qfikVII\nvKKTax9+AR/A5c4SzrL4lzjAQW0+OOH1k7pWhkLUxaN5Y7YLFtZtKPRdylQtt+hX+AwKTWoDB+qG\npUspZOE9XFVVRRcv3qJevf5L3367mdq2/Zpeeultio19hvz8hBmsXQiIIo3mEerf/x/04osv0ocf\nfkjLli2jXbt20W+//abdeLCusWml8vXXX1OPHj0oNDSUXnjhBTp48CBVV1fXjlAWahjxwyCXOdia\nG/IIV/8a04mZGsklhZLZTy4DgTHwD7nUHvH8iNzbW78d1DrxxRiSnX8Jw1X5ztfNTd//IYdwsDF4\nMFeG1GJRFxd1u23yx/JKzVDocViYvq2fjwwTXn/hFgPCa2Fooy7xtRJ35GIFr9YnJvRPSMlqTOSj\n2mwYwohBpaSQ4lme3F5H3buLZ6ilpNFcpSVLDtDq1atpzpw5NG3aNEpNTaXu3btTo0aNyN3dnTw9\nPalJkybUo0cPGjlyJL3++us0b948WrduHR06xGWwtrZf2qaVSlpaGu3YsUNvv3BLUVlZSQkJCfTE\nE0/oC2Vmw0jfGNyLD6UUdg7W9IsIOw41q3GFW/UaMhEIzRZKCE0xwo5W3PmYsu5GjNhk5O3NdURS\nZhqxbID6xH3iTkbY8SUn66cUkep8lZBb6Kq0Z4hShFpEBHedDa23GT685vvmzbk2FIYxS4Vky8ku\nN0AQX3/x4kRj95EX1im2BgjbRc1GX8JV7oaUmJIiMaRUlPKkiV+G7s3q6moqKCigc+fO0Y4dO2jJ\nkiX03nvv0T//+U8aOHAgdejAZbB2dnamoKAgSkxMpMcff5wmTpxI6enptHDhQjp69KjhBlaBTSsV\na/Ppp59SamoqDRgwQO87cxtGeGPIbewkVCSW9osIkTJPqJVd3BlI2ZHVdML5+VwnJJWbTFgfv/LZ\nnBX6QjmFJiZhO/D5mviULaYqdbX7cJiKlB8tIIDr3IXXQLyAT7zvh1RHLe68+M7R2ZmbFUkFEwij\n8ww5jNV2ykS6CpJXWkozEmPaS8oEJ2UZkAuDF9/fwrQ/UinujVEqhnLkSZk5lRS5GiorK+nmzZt0\n/Phx+uGHH2j+/Pn05ptv0rhx42ju3Llml/+3VSp//vkn9ezZk3bv3m2VmYrUSNHJqeZBs7YiEWLs\nCNmQ+Yt/KIUdjpobXc00X00WYbmyxEiNbIXtYEyiT0NIyWlKWne5Y6X8aFKLHg2NgMUzED7iSmxS\nkjOfOTnV3Dtqr7tUFJccQlOX2OdgLELfiSEfpZT/RHhuhgYZateUSL38/XXLk7rv5My4UrtI2iJ/\nW6UyZMgQOnHiBGVmZlpFqQijuRITdafiERHWVyRymNohixHmwlITUSXlBBc+nOI2MSe0WA2GNpsy\nBik51chnanAAkfSahsBA6faTW52tpEjFgyLe1Mmfm5JJSG5TNCnU3kuGMLRttVS7GvKf8LNbpbaR\nM3spZSlWe515GYWh6raeTJLIjpTKvn37aNGiRURElJubS1euXDG5ws2bN9Pzzz9PRER79uyRVSoz\nZszQvvbs2WN0PVLRLsYu1rLEaFeIOR2yKaYeQ1E7amZEhh5q8RbAxpjLLBUYIZZTyuyjZiW6MWY3\nuUAE4cyBl0uq4xN23nL7kfTqVTMjEpphoqLk/W08YnOmsW1oCnLhvmrbVc2iV7G8WVn6QRN8aHZU\nlPQ1atXK+PM01nxd2+zZs0enr7QLpTJjxgx64oknqFGjRkREdOPGDercubPJFf7f//0fRUREUExM\nDIWEhJC7uzuNGjVKVygLN4xU52OoEzTG/GOMojAnRYvYaaymA///9u49KKrrjgP4FxR8ESW1Bg1o\n1oDGIvuSV2uNgLqKTyRGBDOIJMrEtpmaIRrTpFOMSYitHUOjaTXRmGRGHhqNSQ2IsaDEVmwUWyam\ngggTRbRBEUXU5fHrH9u97i77Zu/uXfl9ZjJhH/fe3z2ePefe87rWRu04W5iY287RytLedDC3bL61\n75tr9rE2Z8OZvoOWFvNDfc31cRiu3Dx0aM8FDU1jM9e/Zdg3YfpcEnuG5rqDfiKofm00R/OWtbXI\nrDG8ozO8QzH375OQ4Fx6ODPAw5O8olJRKBTU1dVltPS9/tkqvSVW85cttgpBaz9Mw47NpCTHfsS9\nuSq0NLfAnuP1tuPd3tjsLcicGU1kzzlb63g1fCCVozPCTZlb18tcpWK6qKXp7H7TeJ2dX2LIFXce\njt55Gs48HzTIsW1N+1McuXNtaenZWW84SMDwPz8/5/K/GIvSiskrKpXo6Ggiuv88lba2NpdWKmKM\n/jLHMHPYGjpr7Ydp2KauL5gcuQsyF48jBbF+XL7+eSX2cEU/iCmxR14R9Zx4aKvSstbx6uhdpTXm\nngtirkPc8Epare6Z70zjtZTvDJvFHnvs/oQ/R/tBnH0QmC2GBbvpM39sxWNYIQUGmo/LWtyG84/0\nd4IzZljvz3KEGL8dMXlFpfL73/+esrKySCaT0bZt2yg2Npby8vLEDUqEhDHXtGDvD9IwU9vqLLU3\nEzqbWZ3pjxCjScTZOTPWWFo2xlWVlqvSwbQjfdgw4/0ZVgKjR99fy8vZ87A0X8beOT3m9uPoHZ89\n+1UoHJvrZHonammYtLW4LY0qNFep2FqWxxxPNCf2hldUKkREhw4douzsbMrOzqbS0lKxYxIlYVzV\nn6Ff9sPSPuw9jrPxOLOdK5pELMXhSHOcLa6+KnRkbTNHtLTcbwY117lsadirtWVqrFXKhmmt75x2\nJh/bm3ccTSfD7zuyrWHlbK0T3ZnflGnaGw7NtkasPOMuXlOpuJsYCeOK/gxX9pvYauqwZ+6EJ+nj\ncMUsfD1XXxWK1XRha604c/OkLC2bbu/Md8M7FHPD4l01XN2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