diff --git a/fa23-team-d/EDA_Notebooks/extension.ipynb b/fa23-team-d/EDA_Notebooks/extension.ipynb new file mode 100644 index 0000000..20062da --- /dev/null +++ b/fa23-team-d/EDA_Notebooks/extension.ipynb @@ -0,0 +1,1630 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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service_dateroute_iddirection_idhalf_trip_idstop_idtime_point_idtime_point_orderpoint_typestandard_typescheduledactualscheduled_headwayheadway
02023-01-0101Inbound58061899.0110hhgat1StartpointSchedule1900-01-01T06:05:00Z1900-01-01T06:05:04ZNaNNaN
12023-01-0101Inbound58061899.067maput2MidpointSchedule1900-01-01T06:09:00Z1900-01-01T06:06:28ZNaNNaN
22023-01-0101Inbound58061899.072cntsq3MidpointSchedule1900-01-01T06:12:00Z1900-01-01T06:08:57ZNaNNaN
32023-01-0101Inbound58061899.075mit4MidpointSchedule1900-01-01T06:15:00Z1900-01-01T06:12:41ZNaNNaN
42023-01-0101Inbound58061899.079hynes5MidpointSchedule1900-01-01T06:19:00Z1900-01-01T06:16:35ZNaNNaN
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" + ], + "text/plain": [ + " service_date route_id direction_id half_trip_id stop_id time_point_id \\\n", + "0 2023-01-01 01 Inbound 58061899.0 110 hhgat \n", + "1 2023-01-01 01 Inbound 58061899.0 67 maput \n", + "2 2023-01-01 01 Inbound 58061899.0 72 cntsq \n", + "3 2023-01-01 01 Inbound 58061899.0 75 mit \n", + "4 2023-01-01 01 Inbound 58061899.0 79 hynes \n", + "\n", + " time_point_order point_type standard_type scheduled \\\n", + "0 1 Startpoint Schedule 1900-01-01T06:05:00Z \n", + "1 2 Midpoint Schedule 1900-01-01T06:09:00Z \n", + "2 3 Midpoint Schedule 1900-01-01T06:12:00Z \n", + "3 4 Midpoint Schedule 1900-01-01T06:15:00Z \n", + "4 5 Midpoint Schedule 1900-01-01T06:19:00Z \n", + "\n", + " actual scheduled_headway headway \n", + "0 1900-01-01T06:05:04Z NaN NaN \n", + "1 1900-01-01T06:06:28Z NaN NaN \n", + "2 1900-01-01T06:08:57Z NaN NaN \n", + "3 1900-01-01T06:12:41Z NaN NaN \n", + "4 1900-01-01T06:16:35Z NaN NaN " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import pandas as pd\n", + "dfs = [] # Create an empty list to store dataframes\n", + "\n", + "# Example: Read multiple CSV files\n", + "arr_dep_dir = '../data/MBTA_Website/MBTA_Bus_Arrival_Departure_Times_2023/'\n", + "csv_files = os.listdir(arr_dep_dir)\n", + "csv_files = [os.path.join(arr_dep_dir, i) for i in csv_files][:6] \n", + "for f in csv_files:\n", + " df = pd.read_csv(f)\n", + " dfs.append(df)\n", + "\n", + "df = pd.concat(dfs, axis=0, ignore_index=True)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading sheet: Age\n", + "Reading sheet: Household Type\n", + "Reading sheet: Race\n", + "Reading sheet: Group Quarters Population\n", + "Reading sheet: Nativity\n", + "Reading sheet: Geographic Mobility\n", + "Reading sheet: Educational Attainment\n", + "Reading sheet: School Enrollment\n", + "Reading sheet: Means of Commuting\n", + "Reading sheet: Travel Time to Work\n", + "Reading sheet: Place of Work\n", + "Reading sheet: Per Capita Income\n", + "Reading sheet: Occupation\n", + "Reading sheet: Industries\n", + "Reading sheet: Labor Force\n", + "Reading sheet: Household Income\n", + "Reading sheet: Family Income\n", + "Reading sheet: Housing Tenure\n", + "Reading sheet: Bedrooms\n", + "Reading sheet: Vacancy Rates\n", + "Reading sheet: Vehicles per Household\n", + "Reading sheet: Poverty Rates\n", + "Reading sheet: Poverty Rates by Age\n" + ] + } + ], + "source": [ + "file_path = '../data/2015-2019_neighborhood_tables_2021.12.21.xlsm'\n", + "\n", + "# Get the sheet names in the Excel file\n", + "xl = pd.ExcelFile(file_path)\n", + "sheet_names = xl.sheet_names\n", + "\n", + "# Create a dictionary to store data frames for each sheet\n", + "dfs = {}\n", + "\n", + "# Loop through each sheet and read it into a data frame\n", + "for sheet_name in sheet_names:\n", + " print(f'Reading sheet: {sheet_name}')\n", + " df2 = xl.parse(sheet_name) # You can use parse with sheet_name or parse with sheet index\n", + " dfs[sheet_name] = df2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "neighborhood_data = {\n", + " 'Place': ['Allston', 'Back Bay', 'Beacon Hill', 'Brighton', 'Charlestown', 'Chinatown', 'Dorchester', 'Downtown',\n", + " 'East Boston', 'Fenway', 'Hyde Park', 'Jamaica Plain', 'Longwood', 'Mattapan', 'Mission Hill', 'North End',\n", + " 'Roslindale', 'Roxbury', 'South Boston', 'South Boston Waterfront', 'South End', 'West End', 'West Roxbury'],\n", + " 'Latitude': [42.355537, 42.350707, 42.358708, 42.3489, 42.3787, 42.3492, 42.2995, 42.3555, 42.375097,\n", + " 42.345187, 42.2557, 42.311605, 42.3389, 42.272321, 42.333265, 42.365097, 42.291209, 42.3126,\n", + " 42.333431, 42.351938, 42.341310, 42.363919, 42.279265],\n", + " 'Longitude': [-71.132749, -71.079730, -71.067829, -71.1605, -71.0616, -71.0621, -71.0649, -71.0565, -71.039217,\n", + " -71.104599, -71.1256, -71.114384, -71.1072, -71.086995, -71.102029, -71.054495, -71.124497, -71.0899,\n", + " -71.049495, -71.049883, -71.077230, -71.063899, -71.149497],\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PlaceLatitudeLongitude
0Allston42.355537-71.132749
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" + ], + "text/plain": [ + " Place Latitude Longitude\n", + "0 Allston 42.355537 -71.132749\n", + "1 Back Bay 42.350707 -71.079730\n", + "2 Beacon Hill 42.358708 -71.067829\n", + "3 Brighton 42.348900 -71.160500\n", + "4 Charlestown 42.378700 -71.061600" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "neighborhood_data_df = pd.DataFrame(neighborhood_data)\n", + "\n", + "neighborhood_data_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "bus_stops_data = json.load(open('/Users/xavierohan/Documents/GitHub/ds-boston-transit-performance/fa23-team-d/data/stops.json'))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Place Latitude Longitude num_stops\n", + "0 Allston 42.355537 -71.132749 507\n", + "1 Back Bay 42.350707 -71.079730 49\n", + "2 Beacon Hill 42.358708 -71.067829 7\n", + "3 Brighton 42.348900 -71.160500 730\n", + "4 Charlestown 42.378700 -71.061600 876\n", + "5 Chinatown 42.349200 -71.062100 30\n", + "6 Dorchester 42.299500 -71.064900 487\n", + "7 Downtown 42.355500 -71.056500 26\n", + "8 East Boston 42.375097 -71.039217 1229\n", + "9 Fenway 42.345187 -71.104599 66\n", + "10 Hyde Park 42.255700 -71.125600 375\n", + "11 Jamaica Plain 42.311605 -71.114384 66\n", + "12 Longwood 42.338900 -71.107200 40\n", + "13 Mattapan 42.272321 -71.086995 715\n", + "14 Mission Hill 42.333265 -71.102029 78\n", + "15 North End 42.365097 -71.054495 12\n", + "16 Roslindale 42.291209 -71.124497 104\n", + "17 Roxbury 42.312600 -71.089900 130\n", + "18 South Boston 42.333431 -71.049495 144\n", + "19 South Boston Waterfront 42.351938 -71.049883 28\n", + "20 South End 42.341310 -71.077230 85\n", + "21 West End 42.363919 -71.063899 12\n", + "22 West Roxbury 42.279265 -71.149497 234\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from geopy.distance import geodesic\n", + "\n", + "# Given data\n", + "neighborhood_data = {\n", + " 'Place': ['Allston', 'Back Bay', 'Beacon Hill', 'Brighton', 'Charlestown', 'Chinatown', 'Dorchester', 'Downtown',\n", + " 'East Boston', 'Fenway', 'Hyde Park', 'Jamaica Plain', 'Longwood', 'Mattapan', 'Mission Hill', 'North End',\n", + " 'Roslindale', 'Roxbury', 'South Boston', 'South Boston Waterfront', 'South End', 'West End', 'West Roxbury'],\n", + " 'Latitude': [42.355537, 42.350707, 42.358708, 42.3489, 42.3787, 42.3492, 42.2995, 42.3555, 42.375097,\n", + " 42.345187, 42.2557, 42.311605, 42.3389, 42.272321, 42.333265, 42.365097, 42.291209, 42.3126,\n", + " 42.333431, 42.351938, 42.341310, 42.363919, 42.279265],\n", + " 'Longitude': [-71.132749, -71.079730, -71.067829, -71.1605, -71.0616, -71.0621, -71.0649, -71.0565, -71.039217,\n", + " -71.104599, -71.1256, -71.114384, -71.1072, -71.086995, -71.102029, -71.054495, -71.124497, -71.0899,\n", + " -71.049495, -71.049883, -71.077230, -71.063899, -71.149497],\n", + "}\n", + "\n", + "\n", + "# Assign neighborhood to each bus stop and count stops for each neighborhood\n", + "neighborhood_counts = {neighborhood: 0 for neighborhood in neighborhood_data['Place']}\n", + "for stop, stop_data in bus_stops_data.items():\n", + " min_distance = float('inf')\n", + " closest_neighborhood = None\n", + "\n", + " for i, neighborhood in enumerate(neighborhood_data['Place']):\n", + " distance = geodesic((stop_data['latitude'], stop_data['longitude']), (neighborhood_data['Latitude'][i], neighborhood_data['Longitude'][i])).meters\n", + "\n", + " if distance < min_distance:\n", + " min_distance = distance\n", + " closest_neighborhood = neighborhood\n", + "\n", + " bus_stops_data[stop]['neighbourhood'] = closest_neighborhood\n", + " neighborhood_counts[closest_neighborhood] += 1\n", + "\n", + "\n", + "# Add a new column \"num_stops\" to neighborhood_data_df\n", + "neighborhood_data_df['num_stops'] = neighborhood_data_df['Place'].map(neighborhood_counts)\n", + "\n", + "# Display the updated DataFrame\n", + "print(neighborhood_data_df)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NeighborhoodTotal Public TransitBusSubway or Train
0Boston33.2%13.5%19.7%
1Allston38.2%18.8%19.4%
2Back Bay24.8%2.9%21.9%
3Beacon Hill20.3%2.2%18.1%
4Brighton33.1%14.5%18.7%
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" + ], + "text/plain": [ + " Neighborhood Total Public Transit Bus Subway or Train\n", + "0 Boston 33.2% 13.5% 19.7%\n", + "1 Allston 38.2% 18.8% 19.4%\n", + "2 Back Bay 24.8% 2.9% 21.9%\n", + "3 Beacon Hill 20.3% 2.2% 18.1%\n", + "4 Brighton 33.1% 14.5% 18.7%" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "means_of_comm = {\n", + " 'Boston': {'Total Public Transit': '33.2%', 'Bus': '13.5%', 'Subway or Train': '19.7%'},\n", + " 'Allston': {'Total Public Transit': '38.2%', 'Bus': '18.8%', 'Subway or Train': '19.4%'},\n", + " 'Back Bay': {'Total Public Transit': '24.8%', 'Bus': '2.9%', 'Subway or Train': '21.9%'},\n", + " 'Beacon Hill': {'Total Public Transit': '20.3%', 'Bus': '2.2%', 'Subway or Train': '18.1%'},\n", + " 'Brighton': {'Total Public Transit': '33.1%', 'Bus': '14.5%', 'Subway or Train': '18.7%'},\n", + " 'Charlestown': {'Total Public Transit': '25.3%', 'Bus': '12.3%', 'Subway or Train': '12.9%'},\n", + " 'Dorchester': {'Total Public Transit': '36.5%', 'Bus': '19.6%', 'Subway or Train': '16.8%'},\n", + " 'Downtown': {'Total Public Transit': '20.1%', 'Bus': '2.6%', 'Subway or Train': '17.5%'},\n", + " 'East Boston': {'Total Public Transit': '53.6%', 'Bus': '4.7%', 'Subway or Train': '49.0%'},\n", + " 'Fenway': {'Total Public Transit': '26.3%', 'Bus': '11.0%', 'Subway or Train': '15.2%'},\n", + " 'Hyde Park': {'Total Public Transit': '24.6%', 'Bus': '12.1%', 'Subway or Train': '12.4%'},\n", + " 'Jamaica Plain': {'Total Public Transit': '41.8%', 'Bus': '9.9%', 'Subway or Train': '31.9%'},\n", + " 'Longwood': {'Total Public Transit': '14.4%', 'Bus': '6.5%', 'Subway or Train': '7.9%'},\n", + " 'Mattapan': {'Total Public Transit': '33.4%', 'Bus': '20.0%', 'Subway or Train': '13.4%'},\n", + " 'Mission Hill': {'Total Public Transit': '38.6%', 'Bus': '10.4%', 'Subway or Train': '28.2%'},\n", + " 'North End': {'Total Public Transit': '23.9%', 'Bus': '0.9%', 'Subway or Train': '23.1%'},\n", + " 'Roslindale': {'Total Public Transit': '29.4%', 'Bus': '9.4%', 'Subway or Train': '20.0%'},\n", + " 'Roxbury': {'Total Public Transit': '41.9%', 'Bus': '31.2%', 'Subway or Train': '10.7%'},\n", + " 'South Boston': {'Total Public Transit': '36.6%', 'Bus': '22.6%', 'Subway or Train': '14.0%'},\n", + " 'South Boston Waterfront': {'Total Public Transit': '18.3%', 'Bus': '6.9%', 'Subway or Train': '11.4%'},\n", + " 'South End': {'Total Public Transit': '25.8%', 'Bus': '8.5%', 'Subway or Train': '17.3%'},\n", + " 'West End': {'Total Public Transit': '22.0%', 'Bus': '3.0%', 'Subway or Train': '19.0%'},\n", + " 'West Roxbury': {'Total Public Transit': '18.6%', 'Bus': '6.7%', 'Subway or Train': '11.9%'}\n", + "}\n", + "# data from https://data.boston.gov/dataset/neighborhood-demographics/resource/d8c23c6a-b868-4ba4-8a3b-b9615a21be07\n", + "\n", + "meanas_of_comm_df = pd.DataFrame(means_of_comm).transpose()\n", + "means_of_comm_df = meanas_of_comm_df.reset_index()\n", + "means_of_comm_df.columns = ['Neighborhood', 'Total Public Transit', 'Bus', 'Subway or Train']\n", + "means_of_comm_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-------------------------+-----------+------+\n", + "| Place | num_stops | Bus |\n", + "+-------------------------+-----------+------+\n", + "| Roxbury | 130.0 | 31.2 |\n", + "| South Boston | 144.0 | 22.6 |\n", + "| Mattapan | 715.0 | 20.0 |\n", + "| Dorchester | 487.0 | 19.6 |\n", + "| Allston | 507.0 | 18.8 |\n", + "| Brighton | 730.0 | 14.5 |\n", + "| Charlestown | 876.0 | 12.3 |\n", + "| Hyde Park | 375.0 | 12.1 |\n", + "| Fenway | 66.0 | 11.0 |\n", + "| Mission Hill | 78.0 | 10.4 |\n", + "| Jamaica Plain | 66.0 | 9.9 |\n", + "| Roslindale | 104.0 | 9.4 |\n", + "| South End | 85.0 | 8.5 |\n", + "| South Boston Waterfront | 28.0 | 6.9 |\n", + "| West Roxbury | 234.0 | 6.7 |\n", + "| Longwood | 40.0 | 6.5 |\n", + "| East Boston | 1229.0 | 4.7 |\n", + "| West End | 12.0 | 3.0 |\n", + "| Back Bay | 49.0 | 2.9 |\n", + "| Downtown | 26.0 | 2.6 |\n", + "| Beacon Hill | 7.0 | 2.2 |\n", + "| North End | 12.0 | 0.9 |\n", + "+-------------------------+-----------+------+\n" + ] + } + ], + "source": [ + "combined_df = pd.merge(neighborhood_data_df, means_of_comm_df, left_on='Place', right_on='Neighborhood', how='outer')\n", + "\n", + "# Drop the redundant 'Neighborhood' column\n", + "combined_df = combined_df.drop(columns='Neighborhood')\n", + "\n", + "combined_df['Bus'] = pd.to_numeric(combined_df['Bus'].str.rstrip('%'))\n", + "# Display the combined dataframe\n", + "# print(combined_df[['Place', 'num_stops', 'Bus']].dropna().sort_values(by='Bus', ascending=False))\n", + "\n", + "from tabulate import tabulate\n", + "\n", + "# Assuming 'combined_df' is the combined dataframe\n", + "table = tabulate(combined_df[['Place', 'num_stops', 'Bus']].dropna().sort_values(by='Bus', ascending=False),\n", + " headers='keys', tablefmt='pretty', showindex=False)\n", + "\n", + "print(table)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " stop_id Neighborhood\n", + "0 1 South End\n", + "1 2 South End\n", + "2 3 South End\n", + "3 4 South End\n", + "4 5 South End\n", + "... ... ...\n", + "5798 109901 East Boston\n", + "5799 109912 East Boston\n", + "5800 869411 Brighton\n", + "5801 869451 Brighton\n", + "5802 883321 Brighton\n", + "\n", + "[5803 rows x 2 columns]\n" + ] + } + ], + "source": [ + "df_neighborhoods = pd.DataFrame(neighborhood_data)\n", + "stop_data = pd.read_csv('/Users/xavierohan/Documents/GitHub/ds-boston-transit-performance/fa23-team-d/data/stops_locations.csv') \n", + "# Function to calculate the distance between two points using Haversine formula\n", + "def haversine(coord1, coord2):\n", + " return geodesic(coord1, coord2).miles\n", + "\n", + "# Function to find the closest neighborhood to a stop\n", + "def find_neighborhood(stop_coord):\n", + " distances = df_neighborhoods.apply(lambda row: haversine(stop_coord, (row['Latitude'], row['Longitude'])), axis=1)\n", + " closest_neighborhood = df_neighborhoods.loc[distances.idxmin(), 'Place']\n", + " return closest_neighborhood\n", + "\n", + "# Assuming that the column 'route_id' represents the routes passing through stops\n", + "# Create a new column 'Neighborhood' in df_stops to store the assigned neighborhood for each stop\n", + "stop_data['Neighborhood'] = stop_data.apply(lambda row: find_neighborhood((row['X'], row['Y'])), axis=1)\n", + "\n", + "\n", + "# Group stops by route_id and aggregate the list of unique neighborhoods for each route_id\n", + "neighborhoods_by_stop = stop_data.groupby('stop_id')['Neighborhood'].unique().reset_index()\n", + "neighborhoods_by_stop['Neighborhood'] = neighborhoods_by_stop['Neighborhood'].apply(lambda x: x[0] if x else '')\n", + "# Now, neighborhoods_by_route contains the routes and associated neighborhoods\n", + "print(neighborhoods_by_stop)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "47: ['South End' 'Fenway' 'Chinatown' 'Longwood' 'Mission Hill']\n", + "65: ['Fenway' 'Brighton' 'Allston' 'Longwood']\n", + "76: ['Allston' 'Brighton']\n", + "44: ['Roxbury' 'Mission Hill' 'South End']\n", + "CT2: ['Fenway' 'Longwood' 'Mission Hill' 'Charlestown' 'Back Bay' 'South End']\n", + "{'47': ['South End', 'Fenway', 'Chinatown', 'Longwood', 'Mission Hill'], '65': ['Fenway', 'Brighton', 'Allston', 'Longwood'], '76': ['Allston', 'Brighton'], '44': ['Roxbury', 'Mission Hill', 'South End'], 'CT2': ['Fenway', 'Longwood', 'Mission Hill', 'Charlestown', 'Back Bay', 'South End']}\n" + ] + } + ], + "source": [ + "route_ranking = pd.read_csv('/Users/xavierohan/Documents/GitHub/ds-boston-transit-performance/fa23-team-d/data/route_ranking.csv')\n", + "\n", + "stop_ids_by_route = df.groupby('route_id').agg({'stop_id': 'unique', 'time_point_order': 'unique'}).reset_index()\n", + "# Define the list of route_ids you want to visualize\n", + "route_ids = route_ranking[-5:]['route_id'].values \n", + "\n", + "top_10_most_late_routes_neighbourhoods = []\n", + "unique_neighborhoods_by_route = {}\n", + "for route_id in route_ids:\n", + " # Get the list of stop_ids for the route\n", + " stop_ids = stop_ids_by_route.loc[stop_ids_by_route['route_id'] == route_id, 'stop_id'].values[0]\n", + " # Get the list of neighborhoods for the route\n", + " neighborhoods = neighborhoods_by_stop[neighborhoods_by_stop['stop_id'].isin(stop_ids)]['Neighborhood'].unique()\n", + " for neighborhood in neighborhoods:\n", + " if neighborhood not in top_10_most_late_routes_neighbourhoods:\n", + " top_10_most_late_routes_neighbourhoods.append(neighborhood)\n", + " # Print the route_id and associated neighborhoods\n", + " print(f'{route_id}: {neighborhoods}')\n", + " if route_id not in unique_neighborhoods_by_route:\n", + " unique_neighborhoods_by_route[route_id] = neighborhoods.tolist()\n", + " else:\n", + " unique_neighborhoods_by_route[route_id].extend(neighborhoods.tolist())\n", + "print(unique_neighborhoods_by_route)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['South End',\n", + " 'Fenway',\n", + " 'Chinatown',\n", + " 'Longwood',\n", + " 'Mission Hill',\n", + " 'Brighton',\n", + " 'Allston',\n", + " 'Roxbury',\n", + " 'Charlestown',\n", + " 'Back Bay']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top_10_most_late_routes_neighbourhoods" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "746_: ['Downtown' 'South Boston Waterfront']\n", + "171: ['South Boston' 'South End' 'East Boston']\n", + "SL2: ['South Boston Waterfront' 'Downtown']\n", + "SL1: ['Downtown' 'East Boston' 'South Boston Waterfront']\n", + "351: ['Brighton']\n", + "{'746_': ['Downtown', 'South Boston Waterfront'], '171': ['South Boston', 'South End', 'East Boston'], 'SL2': ['South Boston Waterfront', 'Downtown'], 'SL1': ['Downtown', 'East Boston', 'South Boston Waterfront'], '351': ['Brighton']}\n" + ] + } + ], + "source": [ + "route_ranking = pd.read_csv('/Users/xavierohan/Documents/GitHub/ds-boston-transit-performance/fa23-team-d/data/route_ranking.csv')\n", + "\n", + "stop_ids_by_route = df.groupby('route_id').agg({'stop_id': 'unique', 'time_point_order': 'unique'}).reset_index()\n", + "# Define the list of route_ids you want to visualize\n", + "route_ids = route_ranking[:5]['route_id'].values \n", + "\n", + "top_10_on_time_routes_neighbourhoods = []\n", + "unique_neighborhoods_by_route = {}\n", + "for route_id in route_ids:\n", + " # Get the list of stop_ids for the route\n", + " stop_ids = stop_ids_by_route.loc[stop_ids_by_route['route_id'] == route_id, 'stop_id'].values[0]\n", + " # Get the list of neighborhoods for the route\n", + " neighborhoods = neighborhoods_by_stop[neighborhoods_by_stop['stop_id'].isin(stop_ids)]['Neighborhood'].unique()\n", + " for neighborhood in neighborhoods:\n", + " if neighborhood not in top_10_on_time_routes_neighbourhoods:\n", + " top_10_on_time_routes_neighbourhoods.append(neighborhood)\n", + " # Print the route_id and associated neighborhoods\n", + " print(f'{route_id}: {neighborhoods}')\n", + " if route_id not in unique_neighborhoods_by_route:\n", + " unique_neighborhoods_by_route[route_id] = neighborhoods.tolist()\n", + " else:\n", + " unique_neighborhoods_by_route[route_id].extend(neighborhoods.tolist())\n", + "print(unique_neighborhoods_by_route)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Downtown',\n", + " 'South Boston Waterfront',\n", + " 'South Boston',\n", + " 'South End',\n", + " 'East Boston',\n", + " 'Brighton']" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top_10_on_time_routes_neighbourhoods" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "age_data = {\n", + " 'Allston': {'Population': 19261, 'Median Age': 27.5},\n", + " 'Brighton': {'Population': 55297, 'Median Age': 30.8},\n", + " 'Charlestown': {'Population': 19890, 'Median Age': 35.7},\n", + " 'Dorchester': {'Population': 126909, 'Median Age': 33.4},\n", + " 'Downtown': {'Population': 18306, 'Median Age': 33.5},\n", + " 'East Boston': {'Population': 47263, 'Median Age': 30.6},\n", + " 'Fenway': {'Population': 33489, 'Median Age': 26.3},\n", + " 'Hyde Park': {'Population': 38924, 'Median Age': 39.4},\n", + " 'Jamaica Plain': {'Population': 40867, 'Median Age': 34.8},\n", + " 'Longwood': {'Population': 5351, 'Median Age': 20.2},\n", + " 'Mattapan': {'Population': 26659, 'Median Age': 36.7},\n", + " 'Mission Hill': {'Population': 17386, 'Median Age': 30.1},\n", + " 'North End': {'Population': 8749, 'Median Age': 31.1},\n", + " 'Roslindale': {'Population': 30021, 'Median Age': 39.8},\n", + " 'Roxbury': {'Population': 54161, 'Median Age': 32.5},\n", + " 'South Boston': {'Population': 36772, 'Median Age': 31.9},\n", + " 'South Boston Waterfront': {'Population': 4403, 'Median Age': 34.5},\n", + " 'South End': {'Population': 32571, 'Median Age': 37.1},\n", + " 'West End': {'Population': 6619, 'Median Age': 37.8},\n", + " 'West Roxbury': {'Population': 33526, 'Median Age': 42.8}\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-------------------------+-----------+------------+\n", + "| Place | num_stops | Median Age |\n", + "+-------------------------+-----------+------------+\n", + "| West Roxbury | 234 | 42.8 |\n", + "| Roslindale | 104 | 39.8 |\n", + "| Hyde Park | 375 | 39.4 |\n", + "| West End | 12 | 37.8 |\n", + "| South End | 85 | 37.1 |\n", + "| Mattapan | 715 | 36.7 |\n", + "| Charlestown | 876 | 35.7 |\n", + "| Jamaica Plain | 66 | 34.8 |\n", + "| South Boston Waterfront | 28 | 34.5 |\n", + "| Downtown | 26 | 33.5 |\n", + "| Dorchester | 487 | 33.4 |\n", + "| Roxbury | 130 | 32.5 |\n", + "| South Boston | 144 | 31.9 |\n", + "| North End | 12 | 31.1 |\n", + "| Brighton | 730 | 30.8 |\n", + "| East Boston | 1229 | 30.6 |\n", + "| Mission Hill | 78 | 30.1 |\n", + "| Allston | 507 | 27.5 |\n", + "| Fenway | 66 | 26.3 |\n", + "| Longwood | 40 | 20.2 |\n", + "+-------------------------+-----------+------------+\n" + ] + } + ], + "source": [ + "age_data_df = pd.DataFrame(age_data).transpose()\n", + "age_data_df = age_data_df.reset_index()\n", + "age_data_df.columns = ['Neighborhood', 'Population', 'Median Age']\n", + "\n", + "combined_df = pd.merge(neighborhood_data_df, age_data_df, left_on='Place', right_on='Neighborhood', how='outer')\n", + "\n", + "combined_df['Median Age'] = pd.to_numeric(combined_df['Median Age'])\n", + "# Display the combined dataframe\n", + "# print(combined_df[['Place', 'num_stops', 'Bus']].dropna().sort_values(by='Bus', ascending=False))\n", + "combined_df = combined_df[['Place', 'num_stops', 'Median Age']].dropna().sort_values(by='Median Age', ascending=False)\n", + "from tabulate import tabulate\n", + "\n", + "# Assuming 'combined_df' is the combined dataframe\n", + "table = tabulate(combined_df,headers='keys', tablefmt='pretty', showindex=False)\n", + "\n", + "print(table)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['West Roxbury' 'Roslindale' 'Hyde Park' 'West End' 'South End' 'Mattapan'\n", + " 'Charlestown' 'Jamaica Plain' 'South Boston Waterfront' 'Downtown'\n", + " 'Dorchester' 'Roxbury' 'South Boston' 'North End' 'Brighton'\n", + " 'East Boston' 'Mission Hill' 'Allston' 'Fenway' 'Longwood']\n", + "['South End', 'Fenway', 'Longwood', 'Mission Hill', 'Brighton', 'Allston', 'Roxbury', 'Charlestown']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "print(combined_df['Place'].values)\n", + "top_10_most_late_routes_neighbourhoods = [i for i in top_10_most_late_routes_neighbourhoods if i in combined_df['Place'].values]\n", + "print(top_10_most_late_routes_neighbourhoods)\n", + "# top 10 most late routes against median age\n", + "median_age_list = []\n", + "for i in top_10_most_late_routes_neighbourhoods:\n", + " if i in combined_df['Place'].values:\n", + " median_age_list.append(combined_df[combined_df['Place'] == i]['Median Age'].values[0])\n", + "\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.bar(top_10_most_late_routes_neighbourhoods, median_age_list, color='skyblue')\n", + "plt.xlabel('Neighborhoods')\n", + "plt.ylabel('Median Age')\n", + "plt.title('Median Age of Neighborhoods with top 5 most late routes')\n", + "plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n", + "plt.tight_layout()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['West Roxbury' 'Roslindale' 'Hyde Park' 'West End' 'South End' 'Mattapan'\n", + " 'Charlestown' 'Jamaica Plain' 'South Boston Waterfront' 'Downtown'\n", + " 'Dorchester' 'Roxbury' 'South Boston' 'North End' 'Brighton'\n", + " 'East Boston' 'Mission Hill' 'Allston' 'Fenway' 'Longwood']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "print(combined_df['Place'].values)\n", + "top_10_on_time_routes_neighbourhoods = [i for i in top_10_on_time_routes_neighbourhoods if i in combined_df['Place'].values]\n", + "# top 10 most late routes against median age\n", + "median_age_list = []\n", + "for i in top_10_on_time_routes_neighbourhoods:\n", + " if i in combined_df['Place'].values:\n", + " median_age_list.append(combined_df[combined_df['Place'] == i]['Median Age'].values[0])\n", + "\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.bar(top_10_on_time_routes_neighbourhoods, median_age_list, color='skyblue')\n", + "plt.xlabel('Neighborhoods')\n", + "plt.ylabel('Median Age')\n", + "plt.title('Median Age of Neighborhoods with top 5 on time routes')\n", + "plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n", + "plt.tight_layout()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-------------------------+-----------+------+\n", + "| Place | num_stops | Bus |\n", + "+-------------------------+-----------+------+\n", + "| Roxbury | 130.0 | 31.2 |\n", + "| South Boston | 144.0 | 22.6 |\n", + "| Mattapan | 715.0 | 20.0 |\n", + "| Dorchester | 487.0 | 19.6 |\n", + "| Allston | 507.0 | 18.8 |\n", + "| Brighton | 730.0 | 14.5 |\n", + "| Charlestown | 876.0 | 12.3 |\n", + "| Hyde Park | 375.0 | 12.1 |\n", + "| Fenway | 66.0 | 11.0 |\n", + "| Mission Hill | 78.0 | 10.4 |\n", + "| Jamaica Plain | 66.0 | 9.9 |\n", + "| Roslindale | 104.0 | 9.4 |\n", + "| South End | 85.0 | 8.5 |\n", + "| South Boston Waterfront | 28.0 | 6.9 |\n", + "| West Roxbury | 234.0 | 6.7 |\n", + "| Longwood | 40.0 | 6.5 |\n", + "| East Boston | 1229.0 | 4.7 |\n", + "| West End | 12.0 | 3.0 |\n", + "| Back Bay | 49.0 | 2.9 |\n", + "| Downtown | 26.0 | 2.6 |\n", + "| Beacon Hill | 7.0 | 2.2 |\n", + "| North End | 12.0 | 0.9 |\n", + "+-------------------------+-----------+------+\n" + ] + } + ], + "source": [ + "combined_df = pd.merge(neighborhood_data_df, means_of_comm_df, left_on='Place', right_on='Neighborhood', how='outer')\n", + "\n", + "# Drop the redundant 'Neighborhood' column\n", + "combined_df = combined_df.drop(columns='Neighborhood')\n", + "\n", + "combined_df['Bus'] = pd.to_numeric(combined_df['Bus'].str.rstrip('%'))\n", + "# Display the combined dataframe\n", + "# print(combined_df[['Place', 'num_stops', 'Bus']].dropna().sort_values(by='Bus', ascending=False))\n", + "\n", + "from tabulate import tabulate\n", + "combined_df = combined_df[['Place', 'num_stops', 'Bus']].dropna().sort_values(by='Bus', ascending=False)\n", + "# Assuming 'combined_df' is the combined dataframe\n", + "table = tabulate(combined_df,\n", + " headers='keys', tablefmt='pretty', showindex=False)\n", + "\n", + "print(table)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Roxbury' 'South Boston' 'Mattapan' 'Dorchester' 'Allston' 'Brighton'\n", + " 'Charlestown' 'Hyde Park' 'Fenway' 'Mission Hill' 'Jamaica Plain'\n", + " 'Roslindale' 'South End' 'South Boston Waterfront' 'West Roxbury'\n", + " 'Longwood' 'East Boston' 'West End' 'Back Bay' 'Downtown' 'Beacon Hill'\n", + " 'North End']\n", + "['South End', 'Fenway', 'Longwood', 'Mission Hill', 'Brighton', 'Allston', 'Roxbury', 'Charlestown']\n" + ] + }, + { + "data": { + "image/png": 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5c2ratGkqWLCgChQooOnTpz/09f7880/t3r1b7du3V5o0aZzTixUrplq1aumHH3546PNjIjAwUL1799Z//vOf+17WKt29ouDKlStq3bq1y3rx9vZWuXLlHrpefvzxR0lytuRF6tmz5wOf06VLF5fHlSpV0uXLl52XkEYqX768SpUq5XycI0cONWnSRMuXL1d4eLjCw8O1YsUKNW3aVHny5HEulzlzZj3//PPatGlTtNfs1KmTvL29o9WUIkUKl6tDfH19VbZsWR07dsw57YcfflCmTJnUunVr57QkSZKoV69eCg0N1fr1653LeXt7q1evXi6/o1+/fjIz5+gCkdv33uXu1wlWqlSp9PPPP+uPP/6INu9hKlWqpJ07dzpb9Tdt2qT69eurRIkS2rhxo6S7rd8Oh0NBQUGxeu2oYrL+YuPfbNtXXnlFSZIkcT7u2rWrfHx8/nE/atmypWbNmqUXX3xRTZs21dtvv63ly5fr8uXLGjlyZIzqLVy4sMqXL+98HNkKX716deXIkSPa9Mj1EptjwL99D8SGn5+fswU+PDxcly9fVooUKVSgQIGH3soQaffu3Tp8+LCef/55Xb582XksuXbtmmrUqKENGzYoIiLiH1+nXbt2Lsf/X375RRcuXFC3bt2UNGlS5/QGDRqoYMGC+v777//FX3t/f/31l9asWaOWLVvq6tWrzr/h8uXLqlOnjg4fPuy8pD0mn2MAPIfQDSDOlC1bVjVr1lTNmjXVpk0bff/99ypcuLB69OihW7duxeq1/Pz8NHbsWC1btkwZM2ZU5cqV9e6778bZsDlRL/EcMWKErly5oieeeEJFixbVgAEDtHfvXpfl//rrL7366qvKmDGjkiVLpvTp0zt7F44ckuvChQu6ceOG8uXLF+333Tvt8OHDMjPlz59f6dOnd/n5/fffnZ2VPUyKFClUrlw5Zc+eXZI0a9YsnTt3Tm+88YZWrVqlAQMGaMyYMXr33XfVr1+/hwbWkydPSpIKFCgQbV6hQoWcJ6v/q1dffdV52e39RJ4wVq9ePdp6WbFixUPXy8mTJ+Xl5RWt1+f7bY9IUUOIJOd98vd+0XO/S3ufeOIJXb9+XRcvXtTFixd1/fr1B66/iIiIaPfq31tnpGzZskW7BDl16tQuNZ08eVL58+ePds9+oUKFnPMj/82SJYtSpkz5j8t5eXk5L6eOdL+/591339W+ffuUPXt2lS1bVsOGDYtRoK1UqZLu3LmjLVu26ODBg7pw4YIqVaqkypUru4TuwoULu4S+2IrJ+ouNf7Nt732/pEiRQpkzZ/5X/VEEBQWpXLlyWrVqVYyWv/c9HTl6QeRx4t7pkeslNseAf/seiI2IiAhNnDhR+fPnl5+fn9KlS6f06dNr7969MRoGMfJY0q5du2jHko8//lhhYWExep1799OHraeCBQs658eFI0eOyMz05ptvRvsbhg4dKum/HVvG5HMMgOdwTzcAt/Hy8lK1atU0efJkHT58WE8++eQD72e8X4dBvXv3VqNGjbRkyRItX75cb775pkaPHq01a9aoZMmSD/y9SZMmVVhYmMws2u8zM928edOlhaJy5co6evSoli5dqhUrVujjjz/WxIkTNX36dGfv3y1bttTmzZs1YMAAlShRQilSpFBERITq1q0bo9aSe0VERMjhcGjZsmUPbO2MjZCQEA0ePFjjx4+Xv7+/vvzyS5ehj5o3b64vvvhC1apVi3WtcSmytXvYsGH3be2OXJeff/65MmXKFG1+XPdWfr91LylaJ2PuELX1LCpP1hQTLVu2VKVKlbR48WKtWLFC48aN09ixY/XNN9889J7pyM4FN2zYoBw5cihDhgx64oknVKlSJX344YcKCwvTxo0bnfeo/lsJff39G9mzZ9fBgwdjtOyD/v64XC//9j0QG6NGjdKbb76pl156SW+//bbSpEkjLy8v9e7dO0bH3Mhlxo0b98ChxGJynH3QfhoTDofjvus3ph3kRf4N/fv3V506de67TOSXijH5HAPgOYRuAG51584dSVJoaKik/7YkXrlyxWW5B7UO5M2bV/369VO/fv10+PBhlShRQhMmTNDcuXMf+Dtz5sypO3fu6OjRo9FaOY8cOaLw8HDlzJnTZXqaNGnUoUMHdejQQaGhoapcubKGDRumjh076u+//9bq1as1fPhwvfXWW87n3HsZX4YMGZQ0aVIdOXIkWk33TsubN6/MTLlz59YTTzzxwL8lpiLH9W3Tpo2ku2PXRv1iIkuWLNq9e/cDnx+5Pu53Yn/gwAGlS5cuzoa06t27tyZNmqThw4dH63AosqU1Q4YMqlmzZqxeN2fOnIqIiNDx48ddWhrvtz1i636XbB46dEjJkyd3dpKXPHnyB64/Ly+vaC2N/4ucOXNq7969ioiIcGntPnDggHN+5L+rVq3S1atXXVq777dcRESEjh496tKC96CglzlzZnXr1k3dunXThQsX9NRTT2nkyJEPDVyRl3lv3LhROXLkcHZ6ValSJYWFhemLL77Q+fPn/7EDwJh0RPZv3e+106dPH+tte/jwYZcvuEJDQ/Xnn3+qfv36/6quY8eO3bczxrgU22PAP70H/tfttGjRIlWrVk2ffPKJy/QrV64oXbp0zscP+j2Rx5KAgIBYH0seJup6ql69usu8gwcPuny2pE6d+r5XANz7efegvyHydoYkSZLE6G942OcYAM/i8nIAbnP79m2tWLFCvr6+zstZc+bMKW9v72jDLX344Ycuj69fv66bN2+6TMubN69SpkypsLCwh/7eyJO+Dz74INq8qVOnuiwjKdqwXylSpFC+fPmcvyeyhejeFotJkya5PPb29lbNmjW1ZMkSl3sdjxw54rx3NlKzZs3k7e2t4cOHR3tdM7vvUGQPcujQIX3wwQeaPHmy8+QtY8aMzmAlSb///vt9W44jZc6cWSVKlNBnn33m8oXIvn37tGLFin8dFu4nsrV76dKl0b4IqFOnjgICAjRq1Cjdvn072nMvXrz4wNeNbAm6970U2av7/2LLli0u95GePn1aS5cuVe3ateXt7S1vb2/Vrl1bS5cudbmE+Pz585o3b56CgoIUEBDwP9cRqX79+jp37pwWLFjgnHbnzh1NmTJFKVKkcA7rVr9+fYWHh0fbFyZOnCiHw+HcDyL/ff/9912Wu/c9Hh4eHu2S3AwZMihLliz/uF9KdwP2zz//rLVr1zpDd7p06VSoUCFnj/GR0x8kMvjd+8VdXPD394/2uv9m286cOdPl/Ttt2jTduXPnH1uB7/f+/uGHH7Rjxw7VrVs39n9QLMT0GBDT94C/v3+MLt9+EG9v72jHxoULF0YblutB74dSpUopb968Gj9+vPNL36gedix5mNKlSytDhgyaPn26y9+7bNky/f777y49/ufNm1cHDhxw+V179uyJNmRZ8uTJ7/s3ZMiQQVWrVtWMGTP0559/PvRv+KfPMQCeRUs3gDizbNkyZ9C7cOGC5s2bp8OHD+uNN95wnpQGBgaqRYsWmjJlihwOh/Lmzavvvvsu2r26hw4dUo0aNdSyZUsVLlxYPj4+Wrx4sc6fP69WrVo9tI4SJUqoY8eOzsvaow779cMPP6hjx44qXry4c/nChQuratWqKlWqlNKkSaNffvnFORyOdLelJPKe8tu3bytr1qxasWKFjh8/Hu13Dxs2TCtWrFDFihXVtWtXZ+ApUqSIS8DMmzev3nnnHQ0cOFAnTpxQ06ZNlTJlSh0/flyLFy/WK6+8ov79+8dovffp00fPPfecy/A3zZs3V5MmTTRo0CBJ0rfffqvvvvvuoa8zbtw41atXT+XLl9fLL7/sHC4oMDAwxuNrx9Srr76qiRMnas+ePS6tZwEBAZo2bZpeeOEFPfXUU2rVqpXSp0+vU6dO6fvvv1fFihXv+2WKdPck+9lnn9WkSZN0+fJl55Bhhw4dkvS/tbwVKVJEderUcRkyTLo7rm6kd955RytXrlRQUJC6desmHx8fzZgxQ2FhYTEaJzo2XnnlFc2YMUPt27fXjh07lCtXLi1atEg//fSTJk2a5GzVbtSokapVq6bBgwfrxIkTKl68uFasWKGlS5eqd+/eztbAEiVKqHXr1vrwww8VHBysChUqaPXq1dGuErh69aqyZcum5s2bq3jx4kqRIoVWrVql7du3a8KECf9Yd6VKlTRy5EidPn3aJVxXrlxZM2bMUK5cuaKNWX6vvHnzKlWqVJo+fbpSpkwpf39/lStX7oH3yMdGqVKltGrVKr333nvKkiWLcufOrXLlysV62966dct5/Dp48KA+/PBDBQUFOYdLe5AKFSqoZMmSKl26tAIDA7Vz5059+umnyp49u3NfdqeYHANi+h4oVaqUFixYoL59+6pMmTJKkSKFGjVqFONaGjZsqBEjRqhDhw6qUKGCfv31V33xxRcundlJD38/fPzxx6pXr56efPJJdejQQVmzZtXZs2e1du1aBQQE6Ntvv431OkqSJInGjh2rDh06qEqVKmrdurVzyLBcuXKpT58+zmVfeuklvffee6pTp45efvllXbhwQdOnT9eTTz7p0vlesmTJVLhwYS1YsEBPPPGE0qRJoyJFiqhIkSKaOnWqgoKCVLRoUXXq1El58uTR+fPntWXLFp05c8Y5bvk/fY4B8DAP9JgO4BFzvyHDkiZNaiVKlLBp06a5DNdidnd4lGeffdaSJ09uqVOnts6dO9u+fftchn25dOmSde/e3QoWLGj+/v4WGBho5cqVs6+++ipGNYWHh9vkyZOtePHiljRpUkuaNKkVL17c3n///WjD0LzzzjtWtmxZS5UqlSVLlswKFixoI0eOdBny58yZM/bMM89YqlSpLDAw0Fq0aGF//PHHfYd5Wb16tZUsWdJ8fX0tb9689vHHH1u/fv0sadKk0er8+uuvLSgoyPz9/c3f398KFixo3bt3t4MHD8bo7/z+++8tRYoU9scff0SbN3r0aMuSJYtlzpzZxo4dG6PXW7VqlVWsWNGSJUtmAQEB1qhRI/vtt99clvm3Q4bdK3LYrvsN77Z27VqrU6eOBQYGWtKkSS1v3rzWvn17l2G77h0yzMzs2rVr1r17d0uTJo2lSJHCmjZtagcPHjRJNmbMmGjPvXjxosvzI9/Lx48fd06TZN27d7e5c+da/vz5zc/Pz0qWLBlt2Duzu0Ne1alTx1KkSGHJkye3atWquQzfFPV3bN++Pdrzq1SpYk8++WS06fcOMWRmdv78eevQoYOlS5fOfH19rWjRovcdRuvq1avWp08fy5IliyVJksTy589v48aNi7Zf3rhxw3r16mVp06Y1f39/a9SokZ0+fdrlPR4WFmYDBgyw4sWLW8qUKc3f39+KFy9uH374YbTfez8hISHm7e1tKVOmdBkube7cuSbJXnjhhfuuk3uHXVq6dKkVLlzYORRf5N8dm/V3PwcOHLDKlStbsmTJTJLLEFax2bbr16+3V155xVKnTm0pUqSwNm3auAzD9SCDBw+2EiVKWGBgoCVJksRy5MhhXbt2tXPnzv3jc83uDrt1v2GjIt/DUUUO03jvsIz/dAyI6XsgNDTUnn/+eUuVKpVJ+sf1f78hw/r162eZM2e2ZMmSWcWKFW3Lli2xej+Yme3atcuaNWtmadOmNT8/P8uZM6e1bNnSVq9e/dB6/uk4t2DBAitZsqT5+flZmjRprE2bNnbmzJloy82dO9fy5Mljvr6+VqJECVu+fPl934+bN2+2UqVKma+vb7TPlaNHj9qLL75omTJlsiRJkljWrFmtYcOGtmjRIucyMfkcA+A5DrNE3LMIACQSTZs2ZTgXD9m9e7dKliypuXPnOu95B9xh9uzZ6tChg7Zv367SpUt7uhwAQALBPd0AEMdu3Ljh8vjw4cP64YcfVLVqVc8U9Bi5d91Ld+9L9vLy+scOugAAANyBe7oBII7lyZNH7du3V548eXTy5ElNmzZNvr6+eu211zxd2iPv3Xff1Y4dO1StWjX5+Pho2bJlWrZsmV555ZU47T0cAAAgpgjdABDH6tatqy+//FLnzp2Tn5+fypcvr1GjRrkMYwX3qFChglauXKm3335boaGhypEjh4YNG6bBgwd7ujQAAPCY4p5uAAAAAADchHu6AQAAAABwE0I3AAAAAABu8sjf0x0REaE//vhDKVOmlMPh8HQ5AAAAAIBHgJnp6tWrypIli7y8Htye/ciH7j/++IMeawEAAAAAbnH69Glly5btgfMf+dCdMmVKSXdXREBAgIerAQAAAAA8CkJCQpQ9e3Zn5nyQRz50R15SHhAQQOgGAAAAAMSpf7qNmY7UAAAAAABwE0I3AAAAAABuQugGAAAAAMBNCN0AAAAAALgJoRsAAAAAADchdAMAAAAA4CaEbgAAAAAA3ITQDQAAAACAmxC6AQAAAABwE0I3AAAAAABuQugGAAAAAMBNCN0AAAAAALgJoRsAAAAAADchdAMAAAAA4CaEbgAAAAAA3ITQDQAAAACAmxC6AQAAAABwE0I3AAAAAABuQugGAAAAAMBNfDxdAAAAAID/GrPrkqdLeCS9UTKdp0vAY4qWbgAAAAAA3ITQDQAAAACAmxC6AQAAAABwE0I3AAAAAABuQugGAAAAAMBNCN0AAAAAALgJoRsAAAAAADchdAMAAAAA4CaEbgAAAAAA3ITQDQAAAACAmxC6AQAAAABwE4+G7mnTpqlYsWIKCAhQQECAypcvr2XLljnn37x5U927d1fatGmVIkUKPfvsszp//rwHKwYAAAAAIOY8GrqzZcumMWPGaMeOHfrll19UvXp1NWnSRPv375ck9enTR99++60WLlyo9evX648//lCzZs08WTIAAAAAADHmMDPzdBFRpUmTRuPGjVPz5s2VPn16zZs3T82bN5ckHThwQIUKFdKWLVv09NNPx+j1QkJCFBgYqODgYAUEBLizdAAAAOB/NmbXJU+X8Eh6o2Q6T5eAR0xMs2aCuac7PDxc8+fP17Vr11S+fHnt2LFDt2/fVs2aNZ3LFCxYUDly5NCWLVse+DphYWEKCQlx+QEAAAAAwBM8Hrp//fVXpUiRQn5+furSpYsWL16swoUL69y5c/L19VWqVKlcls+YMaPOnTv3wNcbPXq0AgMDnT/Zs2d3818AAAAAAMD9eTx0FyhQQLt379bPP/+srl27ql27dvrtt9/+9esNHDhQwcHBzp/Tp0/HYbUAAAAAAMScj6cL8PX1Vb58+SRJpUqV0vbt2zV58mQ999xzunXrlq5cueLS2n3+/HllypTpga/n5+cnPz8/d5cNAAAAAMA/8nhL970iIiIUFhamUqVKKUmSJFq9erVz3sGDB3Xq1CmVL1/egxUCAAAAABAzHm3pHjhwoOrVq6ccOXLo6tWrmjdvntatW6fly5crMDBQL7/8svr27as0adIoICBAPXv2VPny5WPcczkAAAAAAJ7k0dB94cIFvfjii/rzzz8VGBioYsWKafny5apVq5YkaeLEifLy8tKzzz6rsLAw1alTRx9++KEnSwYAAAAAIMYS3DjdcY1xugEAAJCYME63ezBON+JaohunGwAAAACARw2hGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuIlHQ/fo0aNVpkwZpUyZUhkyZFDTpk118OBBl2WqVq0qh8Ph8tOlSxcPVQwAAAAAQMx5NHSvX79e3bt319atW7Vy5Urdvn1btWvX1rVr11yW69Spk/7880/nz7vvvuuhigEAAAAAiDkfT/7yH3/80eXx7NmzlSFDBu3YsUOVK1d2Tk+ePLkyZcoU3+UBAAAAAPA/SVD3dAcHB0uS0qRJ4zL9iy++ULp06VSkSBENHDhQ169ff+BrhIWFKSQkxOUHAAAAAABP8GhLd1QRERHq3bu3KlasqCJFijinP//888qZM6eyZMmivXv36vXXX9fBgwf1zTff3Pd1Ro8ereHDh8dX2QAAAAAAPJDDzMzTRUhS165dtWzZMm3atEnZsmV74HJr1qxRjRo1dOTIEeXNmzfa/LCwMIWFhTkfh4SEKHv27AoODlZAQIBbagcAAADiyphdlzxdwiPpjZLpPF0CHjEhISEKDAz8x6yZIFq6e/Tooe+++04bNmx4aOCWpHLlyknSA0O3n5+f/Pz83FInAAAAAACx4dHQbWbq2bOnFi9erHXr1il37tz/+Jzdu3dLkjJnzuzm6gAAAAAA+N94NHR3795d8+bN09KlS5UyZUqdO3dOkhQYGKhkyZLp6NGjmjdvnurXr6+0adNq79696tOnjypXrqxixYp5snQAAAAAAP6RR0P3tGnTJElVq1Z1mT5r1iy1b99evr6+WrVqlSZNmqRr164pe/bsevbZZzVkyBAPVAsAAAAAQOx4/PLyh8mePbvWr18fT9UAAAAAABC3EtQ43QAAAAAAPEoI3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE08GrpHjx6tMmXKKGXKlMqQIYOaNm2qgwcPuixz8+ZNde/eXWnTplWKFCn07LPP6vz58x6qGAAAAACAmPNo6F6/fr26d++urVu3auXKlbp9+7Zq166ta9euOZfp06ePvv32Wy1cuFDr16/XH3/8oWbNmnmwagAAAAAAYsZhZubpIiJdvHhRGTJk0Pr161W5cmUFBwcrffr0mjdvnpo3by5JOnDggAoVKqQtW7bo6aef/sfXDAkJUWBgoIKDgxUQEODuPwEAAAD4n4zZdcnTJTyS3iiZztMl4BET06yZoO7pDg4OliSlSZNGkrRjxw7dvn1bNWvWdC5TsGBB5ciRQ1u2bPFIjQAAAAAAxJSPpwuIFBERod69e6tixYoqUqSIJOncuXPy9fVVqlSpXJbNmDGjzp07d9/XCQsLU1hYmPNxSEiI22oGAAAAAOBhEkxLd/fu3bVv3z7Nnz//f3qd0aNHKzAw0PmTPXv2OKoQAAAAAIDYSRChu0ePHvruu++0du1aZcuWzTk9U6ZMunXrlq5cueKy/Pnz55UpU6b7vtbAgQMVHBzs/Dl9+rQ7SwcAAAAA4IE8GrrNTD169NDixYu1Zs0a5c6d22V+qVKllCRJEq1evdo57eDBgzp16pTKly9/39f08/NTQECAyw8AAAAAAJ7g0Xu6u3fvrnnz5mnp0qVKmTKl8z7twMBAJUuWTIGBgXr55ZfVt29fpUmTRgEBAerZs6fKly8fo57LAQAAAADwJI+G7mnTpkmSqlat6jJ91qxZat++vSRp4sSJ8vLy0rPPPquwsDDVqVNHH374YTxXCgAAAABA7P3r0H316lWNGDFC69atU3h4uCpWrKihQ4cqXbqYj38XkyHCkyZNqqlTp2rq1Kn/tlQAAAAAADziX9/T3alTJ126dEnDhw/X0KFDdezYMbVp0yYuawMAAAAAIFGLcUv3xIkT1bt3bzkcDknS9u3bdejQIXl7e0uSChQowH3WAAAAAABEEePQffToUZUrV04zZsxQyZIlVatWLTVo0EBNmzbV7du39fnnn6tOnTrurBUAAAAAgEQlxqH7gw8+0NatW/XSSy+pWrVqGj16tObOnauVK1cqPDxcLVq0UI8ePdxZKwAAAAAAiUqsOlJ7+umntX37do0dO1bly5fXuHHj9PXXX7urNgAAAAAAErVYd6Tm4+OjwYMH69tvv9WkSZPUvHlz5/jaAAAAAADgv2Icuvfs2aMyZcooZcqUqlixoiIiIrR69Wo1aNBAFSpUcI65DQAAAAAA7opx6H7ppZdUqVIlbd++XS1atFCXLl0kSR06dNDPP/+sn376SeXLl3dboQAAAAAAJDYxvqf70KFDWrBggfLly6f8+fNr0qRJznnp06fX3LlztWLFCnfUCAAAAABAohTj0F21alW98soratWqldasWaOKFStGW6Z27dpxWhwAAAAAAIlZjEP3nDlzNHLkSC1dulTFixfXG2+84c66AAAAEIfG7Lrk6RIeSW+UTOfpEgAkcDEO3alTp9b48ePdWQsAAAAAAI+UWA8ZBgAAAAAAYobQDQAAAACAmxC6AQAAAABwE0I3AAAAAABu8q9C9507d7Rq1SrNmDFDV69elST98ccfCg0NjdPiAAAAAABIzGLce3mkkydPqm7dujp16pTCwsJUq1YtpUyZUmPHjlVYWJimT5/ujjoBAAAAAEh0Yt3S/eqrr6p06dL6+++/lSxZMuf0Z555RqtXr47T4gAAAAAASMxi3dK9ceNGbd68Wb6+vi7Tc+XKpbNnz8ZZYQAAAAAAJHaxbumOiIhQeHh4tOlnzpxRypQp46QoAAAAAAAeBbEO3bVr19akSZOcjx0Oh0JDQzV06FDVr18/LmsDAAAAACBRi/Xl5RMmTFCdOnVUuHBh3bx5U88//7wOHz6sdOnS6csvv3RHjQAAAAAAJEqxDt3ZsmXTnj17NH/+fO3du1ehoaF6+eWX1aZNG5eO1QAAAAAAeNzFOnRLko+Pj9q2bRvXtQAAAAAA8EiJdej+z3/+c9/pDodDSZMmVb58+ZQ7d+7/uTAAAAAAABK7WIfupk2byuFwyMxcpkdOczgcCgoK0pIlS5Q6deo4KxQAAAAAgMQm1r2Xr1y5UmXKlNHKlSsVHBys4OBgrVy5UuXKldN3332nDRs26PLly+rfv7876gUAAAAAINGIdUv3q6++qpkzZ6pChQrOaTVq1FDSpEn1yiuvaP/+/Zo0aZJeeumlOC0UAAAAAIDEJtYt3UePHlVAQEC06QEBATp27JgkKX/+/Lp06dL/Xh0AAAAAAIlYrEN3qVKlNGDAAF28eNE57eLFi3rttddUpkwZSdLhw4eVPXv2uKsSAAAAAIBEKNaXl3/yySdq0qSJsmXL5gzWp0+fVp48ebR06VJJUmhoqIYMGRK3lQIAAAAAkMjEOnQXKFBAv/32m1asWKFDhw45p9WqVUteXncbzps2bRqnRQIAAAAAkBjFOnRLkpeXl+rWrau6devGdT0AAAAAADwy/lXovnbtmtavX69Tp07p1q1bLvN69eoVJ4UBAAAAAJDYxTp079q1S/Xr19f169d17do1pUmTRpcuXVLy5MmVIUMGQjcAAAAAAP8v1r2X9+nTR40aNdLff/+tZMmSaevWrTp58qRKlSql8ePHu6NGAAAAAAASpViH7t27d6tfv37y8vKSt7e3wsLClD17dr377rsaNGiQO2oEAAAAACBRinXoTpIkibOX8gwZMujUqVOSpMDAQJ0+fTpuqwMAAAAAIBGL9T3dJUuW1Pbt25U/f35VqVJFb731li5duqTPP/9cRYoUcUeNAAAAAAAkSrFu6R41apQyZ84sSRo5cqRSp06trl276uLFi5o5c2acFwgAAAAAQGIV65bu0qVLO/+fIUMG/fjjj3FaEAAAAAAAj4pYt3TfuHFD169fdz4+efKkJk2apBUrVsRpYQAAAAAAJHaxDt1NmjTRnDlzJElXrlxR2bJlNWHCBDVp0kTTpk2L8wIBAAAAAEisYh26d+7cqUqVKkmSFi1apEyZMunkyZOaM2eO3n///TgvEAAAAACAxCrWofv69etKmTKlJGnFihVq1qyZvLy89PTTT+vkyZNxXiAAAAAAAIlVrEN3vnz5tGTJEp0+fVrLly9X7dq1JUkXLlxQQEBAnBcIAAAAAEBiFevQ/dZbb6l///7KlSuXypUrp/Lly0u62+pdsmTJOC8QAAAAAIDEKtZDhjVv3lxBQUH6888/Vbx4cef0GjVq6JlnnonT4gAAAAAASMxiHbolKVOmTMqUKZPLtLJly8ZJQQAAAAAAPCpiHLpLliwph8MRbXpgYKCeeOIJ9e7dW4UKFYrT4gAAAAAASMxiHLqbNm163+lXrlzRzp07VaJECa1Zs0YVK1aMq9oAAAAAAEjUYhy6hw4d+tD5gwcP1ltvvaXVq1f/z0UBAAAAAPAoiHXv5Q/y/PPP69dff42rlwMAAAAAINGLs9Dt7e2tiIiIuHo5AAAAAAASvTgL3d98840KFy4cVy8HAAAAAECiF+N7ut9///37Tg8ODtaOHTv0/fffa9myZXFWGAAAAAAAiV2MQ/fEiRPvOz0gIEAFChTQhg0bVL58+Vj98g0bNmjcuHHasWOH/vzzTy1evNill/T27dvrs88+c3lOnTp19OOPP8bq9wAAAAAA4AkxDt3Hjx+P819+7do1FS9eXC+99JKaNWt232Xq1q2rWbNmOR/7+fnFeR0AAAAAALhDjEO3O9SrV0/16tV76DJ+fn7KlClTPFUEAAAAAEDcibOO1Nxl3bp1ypAhgwoUKKCuXbvq8uXLni4JAAAAAIAY8WhL9z+pW7eumjVrpty5c+vo0aMaNGiQ6tWrpy1btsjb2/u+zwkLC1NYWJjzcUhISHyVCwAAAACAiwQdulu1auX8f9GiRVWsWDHlzZtX69atU40aNe77nNGjR2v48OHxVSIAAAAAAA+U4C8vjypPnjxKly6djhw58sBlBg4cqODgYOfP6dOn47FCAAAAAAD+K9ah+8cff9SmTZucj6dOnaoSJUro+eef199//x2nxd3rzJkzunz5sjJnzvzAZfz8/BQQEODyAwAAAACAJ8Q6dA8YMMB5n/Svv/6qfv36qX79+jp+/Lj69u0bq9cKDQ3V7t27tXv3bkl3hyXbvXu3Tp06pdDQUA0YMEBbt27ViRMntHr1ajVp0kT58uVTnTp1Yls2AAAAAADxLtb3dB8/flyFCxeWJH399ddq2LChRo0apZ07d6p+/fqxeq1ffvlF1apVcz6ODO3t2rXTtGnTtHfvXn322We6cuWKsmTJotq1a+vtt99mrG4AAAAAQKIQ69Dt6+ur69evS5JWrVqlF198UZKUJk2aWPcUXrVqVZnZA+cvX748tuUBAAAAAJBgxDp0BwUFqW/fvqpYsaK2bdumBQsWSJIOHTqkbNmyxXmBAAAAAAAkVrEO3R988IG6deumRYsWadq0acqaNaskadmyZapbt26cFwgAABKmMbsuebqER9IbJdN5ugQAQByKdejOkSOHvvvuu2jTJ06cGCcFAQAAAADwqIh16D516tRD5+fIkeNfFwMAAAAAwKMk1qE7V65ccjgcD5wfHh7+PxUEAAAAAMCjItahe9euXS6Pb9++rV27dum9997TyJEj46wwAAAAAAASu1iH7uLFi0ebVrp0aWXJkkXjxo1Ts2bN4qQwAAAAAAASO6+4eqECBQpo+/btcfVyAAAAAAAkerFu6Q4JCXF5bGb6888/NWzYMOXPnz/OCgMAAAAAILGLdehOlSpVtI7UzEzZs2fX/Pnz46wwAAAAAAASu1iH7rVr17o89vLyUvr06ZUvXz75+MT65QAAAAAAeGTFOiVXqVLFHXUAAAAAAPDIiXXovnz5stKmTStJOn36tD766CPduHFDjRo1UuXKleO8QAAAAAAAEqsY917+66+/KleuXMqQIYMKFiyo3bt3q0yZMpo4caJmzpyp6tWra8mSJW4sFQAAAACAxCXGofu1115T0aJFtWHDBlWtWlUNGzZUgwYNFBwcrL///ludO3fWmDFj3FkrAAAAAACJSowvL9++fbvWrFmjYsWKqXjx4po5c6a6desmL6+7ub1nz556+umn3VYoAAAAAACJTYxbuv/66y9lypRJkpQiRQr5+/srderUzvmpU6fW1atX475CAAAAAAASqRiHbknRxue+9zEAAAAAAPivWPVe3r59e/n5+UmSbt68qS5dusjf31+SFBYWFvfVAQAAAACQiMU4dLdr187lcdu2baMt8+KLL/7vFQEAAAAA8IiIceieNWuWO+sAAAAAAOCRE6t7ugEAAAAAQMwRugEAAAAAcBNCNwAAAAAAbkLoBgAAAADATQjdAAAAAAC4CaEbAAAAAAA3IXQDAAAAAOAmhG4AAAAAANyE0A0AAAAAgJsQugEAAAAAcBMfTxcAAAAAAInVmF2XPF3CI+mNkuk8XUKcoaUbAAAAAAA3IXQDAAAAAOAmhG4AAAAAANyE0A0AAAAAgJsQugEAAAAAcBNCNwAAAAAAbkLoBgAAAADATQjdAAAAAAC4CaEbAAAAAAA3IXQDAAAAAOAmhG4AAAAAANyE0A0AAAAAgJsQugEAAAAAcBNCNwAAAAAAbkLoBgAAAADATQjdAAAAAAC4iY+nCwASozG7Lnm6hEfWGyXTeboEAAAAIM7Q0g0AAAAAgJsQugEAAAAAcBNCNwAAAAAAbkLoBgAAAADATQjdAAAAAAC4CaEbAAAAAAA3IXQDAAAAAOAmhG4AAAAAANzEx9MFAAAQ1ZhdlzxdwiPpjZLpPF0CAACPJY+2dG/YsEGNGjVSlixZ5HA4tGTJEpf5Zqa33npLmTNnVrJkyVSzZk0dPnzYM8UCAAAAABBLHg3d165dU/HixTV16tT7zn/33Xf1/vvva/r06fr555/l7++vOnXq6ObNm/FcKQAAAAAAsefRy8vr1aunevXq3XeemWnSpEkaMmSImjRpIkmaM2eOMmbMqCVLlqhVq1bxWSoAAAAAALGWYDtSO378uM6dO6eaNWs6pwUGBqpcuXLasmXLA58XFhamkJAQlx8AAAAAADwhwYbuc+fOSZIyZszoMj1jxozOefczevRoBQYGOn+yZ8/u1joBAAAAAHiQBBu6/62BAwcqODjY+XP69GlPlwQAAAAAeEwl2NCdKVMmSdL58+ddpp8/f9457378/PwUEBDg8gMAAAAAgCck2NCdO3duZcqUSatXr3ZOCwkJ0c8//6zy5ct7sDIAAAAAAGLGo72Xh4aG6siRI87Hx48f1+7du5UmTRrlyJFDvXv31jvvvKP8+fMrd+7cevPNN5UlSxY1bdrUc0UDAAAAABBDHg3dv/zyi6pVq+Z83LdvX0lSu3btNHv2bL322mu6du2aXnnlFV25ckVBQUH68ccflTRpUk+VDAAAAABAjHk0dFetWlVm9sD5DodDI0aM0IgRI+KxKgAAAAAA4kaCvacbAAAAAIDEjtANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuImPpwvAf43ZdcnTJTyS3iiZztMlAAAAAHhM0dINAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATQjcAAAAAAG5C6AYAAAAAwE0I3QAAAAAAuAmhGwAAAAAANyF0AwAAAADgJoRuAAAAAADchNANAAAAAICbELoBAAAAAHATH08XAADuNmbXJU+X8Eh6o2Q6T5cAAACQ4NHSDQAAAACAmxC6AQAAAABwE0I3AAAAAABuQugGAAAAAMBNCN0AAAAAALgJoRsAAAAAADchdAMAAAAA4CaEbgAAAAAA3ITQDQAAAACAmxC6AQAAAABwE0I3AAAAAABuQugGAAAAAMBNCN0AAAAAALgJoRsAAAAAADchdAMAAAAA4CYJOnQPGzZMDofD5adgwYKeLgsAAAAAgBjx8XQB/+TJJ5/UqlWrnI99fBJ8yQAAAAAASEoEodvHx0eZMmXydBkAAAAAAMRagr68XJIOHz6sLFmyKE+ePGrTpo1OnTr10OXDwsIUEhLi8gMAAAAAgCck6NBdrlw5zZ49Wz/++KOmTZum48ePq1KlSrp69eoDnzN69GgFBgY6f7Jnzx6PFQMAAAAA8F8JOnTXq1dPLVq0ULFixVSnTh398MMPunLlir766qsHPmfgwIEKDg52/pw+fToeKwYAAAAA4L8S/D3dUaVKlUpPPPGEjhw58sBl/Pz85OfnF49VAQAAAABwfwm6pfteoaGhOnr0qDJnzuzpUgAAAAAA+EcJOnT3799f69ev14kTJ7R582Y988wz8vb2VuvWrT1dGgAAAAAA/yhBX15+5swZtW7dWpcvX1b69OkVFBSkrVu3Kn369J4uDQAAAACAf5SgQ/f8+fM9XQIAAAAAAP9agr68HAAAAACAxIzQDQAAAACAmxC6AQAAAABwE0I3AAAAAABuQugGAAAAAMBNCN0AAAAAALgJoRsAAAAAADchdAMAAAAA4CaEbgAAAAAA3ITQDQAAAACAmxC6AQAAAABwE0I3AAAAAABuQugGAAAAAMBNCN0AAAAAALgJoRsAAAAAADchdAMAAAAA4CaEbgAAAAAA3ITQDQAAAACAmxC6AQAAAABwE0I3AAAAAABuQugGAAAAAMBNCN0AAAAAALgJoRsAAAAAADchdAMAAAAA4CaEbgAAAAAA3ITQDQAAAACAmxC6AQAAAABwE0I3AAAAAABuQugGAAAAAMBNCN0AAAAAALgJoRsAAAAAADchdAMAAAAA4CaEbgAAAAAA3ITQDQAAAACAmxC6AQAAAABwE0I3AAAAAABuQugGAAAAAMBNCN0AAAAAALgJoRsAAAAAADchdAMAAAAA4CaEbgAAAAAA3ITQDQAAAACAmxC6AQAAAABwE0I3AAAAAABuQugGAAAAAMBNCN0AAAAAALgJoRsAAAAAADchdAMAAAAA4CaEbgAAAAAA3ITQDQAAAACAmxC6AQAAAABwE0I3AAAAAABuQugGAAAAAMBNCN0AAAAAALgJoRsAAAAAADchdAMAAAAA4CaEbgAAAAAA3CRRhO6pU6cqV65cSpo0qcqVK6dt27Z5uiQAAAAAAP5Rgg/dCxYsUN++fTV06FDt3LlTxYsXV506dXThwgVPlwYAAAAAwEMl+ND93nvvqVOnTurQoYMKFy6s6dOnK3ny5Pr00089XRoAAAAAAA+VoEP3rVu3tGPHDtWsWdM5zcvLSzVr1tSWLVs8WBkAAAAAAP/Mx9MFPMylS5cUHh6ujBkzukzPmDGjDhw4cN/nhIWFKSwszPk4ODhYkhQSEuK+QuPIzdCrni7hkRQS4hvnr8m2ch+2V+Lhjm0lsb3chX0r8WDfSlzYtxIP9q3ExV3bKy5FZkwze+hyCTp0/xujR4/W8OHDo03Pnj27B6pBQhD93YCEjO2VeLCtEhe2V+LBtkpc2F6JB9sqcUlM2+vq1asKDAx84PwEHbrTpUsnb29vnT9/3mX6+fPnlSlTpvs+Z+DAgerbt6/zcUREhP766y+lTZtWDofDrfU+LkJCQpQ9e3adPn1aAQEBni4HD8G2SlzYXokL2yvxYFslLmyvxINtlbiwveKemenq1avKkiXLQ5dL0KHb19dXpUqV0urVq9W0aVNJd0P06tWr1aNHj/s+x8/PT35+fi7TUqVK5eZKH08BAQHssIkE2ypxYXslLmyvxINtlbiwvRIPtlXiwvaKWw9r4Y6UoEO3JPXt21ft2rVT6dKlVbZsWU2aNEnXrl1Thw4dPF0aAAAAAAAPleBD93PPPaeLFy/qrbfe0rlz51SiRAn9+OOP0TpXAwAAAAAgoUnwoVuSevTo8cDLyRH//Pz8NHTo0GiX8SPhYVslLmyvxIXtlXiwrRIXtlfiwbZKXNhenuOwf+rfHAAAAAAA/Cteni4AAAAAAIBHFaEbAAAAAAA3IXQDAAAAAOAmhG5IkubOnat58+Z5ugzgkUX3GYnDjh07FBoa6ukyAADAI4TQDf3555/6/PPPNXXqVC1evNjT5QCPlG3btkmSHA6HhyvBw5iZNmzYoDJlymjWrFm6du2ap0sCAACPCEI3lDlzZo0YMUJZs2bV+++/r6+++srTJQGPhOHDh6tbt25asmSJp0vBP3A4HKpcubIGDhyoAQMGaPbs2QRvAAAQJwjdj7mIiAhFRESoXLly6tSpkzJlyqSJEyfq+++/93RpQKLXqFEjpUmTRtOnT9c333zj6XLwAAsXLtSqVaskSSNHjtRrr72mV199leCdgEVERHi6BMRQ5K01Z86c0aVLl3T58mVJbMOELOrtUBwDE7bIbRUaGqrg4GAPV4OHIXQ/5hwOh7y8vLR06VJ99tlnOn78uLZv364333yTS80TgbfffltTpkzxdBm4j9u3b+upp57SBx98IIfDoVmzZmnp0qWeLgtRRERE6OLFi+rZs6cmTJigDRs2SJJGjBihQYMGEbwTqIiICHl53T192bhxo5YuXaqLFy/qzp07kug/IaFxOBxavHixqlSpokqVKqlx48batm2bvLy8CN4JVOTtUJMnT9bcuXMlSeHh4Z4sCQ/gcDi0dOlSNWzYUEFBQXrrrbd09uxZT5eF+yB0P+YcDod++uknNW/eXEFBQfroo4+0dOlSBQYGauLEiVwWm8CFhoZq9uzZOn/+vKdLQRQRERFKkiSJpLt9JuTLl0+bN2/W6NGjuYokAbl+/brSp0+vVatW6ezZsxo3bpzWr18vieCdkEUG7gEDBujZZ59Vhw4dVK5cOX3yySe6cuWKHA4HwTsBiNwGJ06cUMeOHdWnTx/16tVLOXLkUJUqVbRp0yaCdwK3a9cujR8/XmYmb29vT5eD/xf1+LZ582Z16NBBpUqVUpMmTTR+/Hj169dP+/fv92CFuB9CN5ydB3Xp0kVFixZVgwYN9M477ygiIkLDhw/XsmXLPF0iHqB27dq6ceOGDh06JIlvohOKyFDw2muvqXXr1sqQIYNeeeUVnTp1ShMmTKDFOwEYP368Jk+erFu3bqlIkSKaN2+ejh49qvHjxxO8E6ioJ5pr167V+vXrtWjRIv3++++qWbOmPvjgA3388cf6+++/Cd4JgMPh0IYNG7Rx40Z169ZNPXr0UNeuXTVu3Di1bNlSNWrUcAZvPrs8L+r+EvlFyGuvvaZUqVI5+/phn0oYIq9EOH78uPbt26fXX39dEyZM0DvvvKONGzdq3bp1GjFihH777TcPVwoXhsfetGnT7Mknn7Rz5865TF+0aJH5+fnZk08+ad98842HqoOZWUREhPP/t27dcpnXqFEjq1SpUnyXhH+wb98+y549uy1fvtw5befOnVaxYkWrWLGiff/99x6sDm+++aYdOHDAzMxu3LhhZmZ79uyxQoUKWcOGDW3dunUuyyZNmtTGjRtn165d80i9+K+5c+da7969bcCAAS7Te/bsaUWKFLHx48fbX3/95aHqECkkJMQaN25sDofDWrZs6TLv9OnT9uKLL5q/v7+tWbPGQxUiUtRzjKiuX79uderUsWbNmsVzRXiY27dv2/nz583hcFiSJEls8ODBLvO3bdtmGTJksNatW9uePXs8VCXuReh+zNzvwPrjjz9aQECAffrppxYeHu6cvnHjRitfvrx16dLFTp48GZ9l4gGmTp1qQ4cOtf379zunbdq0yUqWLOk8cXnQhyfi14kTJyxbtmz23XffmZk59609e/aYv7+/Va9e3b744gtPlvjYiXp8i7Rp0yYbMmSI/fnnn2Zmtnfv3vsG7759+1ratGkJcwlAzZo1zeFwWN26de327dsu83r16mXFixe3oUOHWkhIiIcqRKQtW7ZYy5YtLUWKFPbbb7+Z2X8/o86cOWPPPPOMZciQwa5fv85nl4dEPS4uWLDAXnrpJbt8+bJdv37dzMy2b99u6dKlsyVLlniqRNwjNDTUzMy+//57S5o0qdWrV8/OnDljZv/dv3755Rfz9va2Dh06WFhYmMdqxX9xefljxMzkcDj0yy+/6Ouvv9a3334rSapTp466dOmiLl266NNPP9WJEycUHh6uH374QU8++aRGjRqlHDlyeLh6SHfvr9q0aZPKlCmjIUOGaPny5apYsaKSJEmi//znP5IYD9oT7P8vubN7Lr3z8fHRr7/+6pxnZipWrJiKFy+u3377TXv37o33Wh9nXl5eOnPmjLOnckn65ptv9NVXX2nmzJm6cOGCihYtqgULFjgvNY/sXG3ChAn6/ffflTp1ak+V/1i6d5+SpJUrV6pt27bav3+/vvjiC924ccM5b/LkySpevLiOHj2qFClSxGepj73IbRUREaHbt29Lkp5++mkNHTpUFStWVK1atfTbb785L/3PmjWrpk6dql27dilZsmR8dnmAmTlvh5o5c6bWrFmjvXv3qnTp0urXr5/Wr1+vwoULq1q1atqxY4ckep33tD179ih37tw6deqU6tevr8WLF2v58uUaOXKkzp0759y/SpUqpe3bt+uNN96Qr6+vp8uGxOXlj5uFCxdaYGCg5c6d27Jnz25169Z1znvjjTcsTZo0lidPHitevLj5+/vb7t27PVjt4+1+rXJmd7/h/Oyzz6xhw4aWPXt269ixo/Xo0cMCAwNtx44d8Vwlom6nK1eu2O3bt523AEyZMsW8vLzs888/dy5z/fp1e+GFF2zhwoUP3MZwjxs3bljbtm2tZMmStmzZMuf0119/3Z566il766237Pz582Z2t8W7aNGiVqlSJdu0aZOZcRVJfIu6f5w5c8YuXLhgFy9edE5r2rSpFSlSxObOneu8ReDe57LN4kfkev7xxx+tTZs2VrVqVevTp4/zHOL333+3+vXrW7Zs2ez33393eQ48I+r6HzNmjBUoUMB5DjFlyhR74YUXLEmSJPbGG29YhQoVLFOmTHbixAlPlfvYizymnT171qpUqWJ9+/Z1tnj/8MMP5uXlZV27dnXeKsr+lfAQuh8DkTvq9evXrWnTpjZnzhw7c+aM/ec//7G8efNauXLlnMuuXbvWvvzyS/vwww/tyJEjnir5sRf1ZHPt2rW2fPly52XKkS5evGh79uyxevXqWdmyZc3hcNjEiROjPR/uE3U9jxkzxqpVq2ZPP/20NW7c2I4fP25mZm+99ZY5HA5r166d9e7d26pUqWIlSpRwPvfOnTueKP2xtWrVKnvmmWesWrVqtnTpUuf0/v37RwveO3futHLlytmpU6c8Ve5jK+oJ49ChQ61cuXKWMWNGq1Gjhk2dOtU5r0mTJla0aFGbN2+e83LYSBwH49fSpUvN19fXXn75ZevTp4/lypXLKleubAsXLjQzs19//dWaNGliyZMnt4MHD3q4WkT65Zdf7MUXX7Qffvgh2rwVK1bYK6+8Yk8//bQ5HA4bPHiw3blzh0AXjyLXdeQXjhERETZ58mSrWLGibdu2zbncDz/8YH5+fta2bVvnZxgSFkL3I+h+J/Pr16+3mjVr2nPPPWenT592LrdhwwbLkyePlS1b1iO1IrqoH2YDBw60XLlyWaFChSx16tTWpUsXu3Llisvy4eHhduzYMevZs6dlyJDBLl26FN8lP/aGDBliadOmtffff9/efPNNq1KliqVNm9Z+/vlnMzObP3++PfPMM1a7dm1r06aNsyWcUBB/oq7rDRs22HPPPWeVK1d2afGODN7Dhw933uPNvXCeNXz4cEuTJo0tXLjQZsyYYX379rUkSZLYqFGjnMs8++yzliFDBpdOCxF/IiIi7NKlS/b000/bmDFjnNPPnTtnjRo1ssqVKztbt3/++Wdr1aqVHTp0yFPlIoovv/zSnnrqKStQoIBzG0X2kxB5LhIaGmrnzp2zNm3aWPHixfmi2APWrVtn/v7+NnXqVLt06ZKFh4db1apVrXbt2i7LLVmyxNKmTev8/ELCQuh+xESeWO7du9fee+89u3r1qoWHh9v8+fMtV65cliFDBrt586bL8hs2bLAnnnjCChYs6KmycR+jR4+2jBkz2pYtW8zsbkuqw+Gwtm3bOoN31IB+/vx5K1GiBD3Nx7PTp09bkSJFnK05Znd77X3uuecsXbp0zi9Bou53ZhatAyjErchjYdTQHLXn/44dO1ry5MmtatWq9p///Mc5/fXXX7fcuXPbqFGjaNHxsL/++suqVKlin3zyiXNacHCwTZ482fz9/V32uUGDBhEG4klERIRFRES4fJF148YNe/LJJ23GjBlm9t997fz585Y1a1YbMmSIc9l7j4WIP/cez/bt22d169Z1js5wv+Ui/3/jxg3LmDGjzZ49O36KhdPMmTPN4XBY7ty5rXfv3jZnzhw7ffq0FShQwMaPH++ybOQl50h46EjtERIRESEvLy/t2bNHxYsX19WrV5UiRQp5eXmpUaNGGjdunMxMrVq1cj7Hy8tLFStW1LRp05Q0aVKdOHHCc38AnE6cOKHdu3dr+vTpevrpp7V06VKNGTNG/fv313fffaeePXvqr7/+cul4JkOGDLp+/brOnz/vwcofP6GhoTpx4oSyZs0q6e5+mDJlSk2aNEmZM2fWp59+Kkny9vZ2PsfM5OPj45F6HxdeXl46fvy4GjZsqB07dig8PFxJkiSRJI0dO1ZLlizR8OHDlTp1ak2cONHZseSYMWPUtm1btWrVSt7e3nTu5EFhYWH69ddfFRIS4pwWEBCgtm3bqmrVqvrll1+c4zuPHDlS3t7ejPfsRvb/HaWFhobK4XDIy8tLP//8s3bs2KGIiAiZmQ4fPizp7vHu9u3bypAhg2rWrKmDBw86X8fPz88j9eO/Ha0uXLhQR48e1ZNPPqmpU6eqRo0aWrx4sebPn+9cLnJ7OxwOhYeHK2nSpMqdOzdjdceDyHV869YtSVKnTp306quvKlu2bMqcObO+/PJLPffccwoKCtKqVat04MAB53OTJ0/ukZoRAx4M/IhDkd8479q1y5IlSxZtzD6zu/d0f/XVV5Y9e3Zr0aJFtOffez8cPOf69ev22Wef2d9//21btmyxHDly2AcffGBmdy9ldjgc1rBhQ5dvNJcvX26+vr7OYVkQ9x7U6lm6dGnr1q2bS+t1WFiYlStXzgYNGhRf5eEeISEhlipVKitfvrxzrNJ3333XUqdObStWrDCzu5ftNWnSxGrVqmWLFi3yZLmPtd9//905HNugQYOc9/y2a9fOWrZs6ewjIVLr1q2jfY7B/c6dO2e5cuWy1atX2/Lly83Pz8/Wrl1rZnfHT/fy8nK5MsHMrHHjxtajRw8PVIv7+fXXX6148eJWv35953518OBBq1evnlWvXt3mz5/vXDbqZ963335rDofDDhw4EN8lP5ZWrlxpr7zyivNe+23bttlLL71kS5YssUuXLlmrVq0sS5YsLv35IGGjpfsRYP8/5MOvv/6qKlWqqF+/fnrnnXec8ydPnqxNmzYpWbJkatiwocaPH6+ff/5ZrVu3di7j5eWlZMmSeaJ8RDFy5EhNmzZNyZIl03PPPadUqVJp+fLlKl26tF588UVJUqpUqdSyZUuZmcs2y5o1qw4dOqRChQp5qvxHWkREhLOV4NatW7p69apzXrNmzbRjxw5NmTLF5Tne3t4KDAyM1zpxV3h4uFKmTKlTp07pwoUL6tGjh/r06aOxY8dq4cKFqlWrliSpSpUq6t+/v+7cuaM5c+YoNDSUlpx4tmfPHlWtWlXz589X9+7dNXr0aOeQU9WrV9evv/6qTz/9VMePH5d0t6X1jz/+UO7cuT1Z9mPp5s2baty4sZ555hk1btxYX375papWrSozU+PGjfXmm2+qY8eO6tGjh8aOHauePXtqzZo16tq1q6dLf2zdezwrUqSI+vTpo5s3b6pXr146ceKEnnjiCU2cOFFJkybVJ598olmzZklyHYK0SpUqOnLkiAoUKBCv9T+ukiRJol27dmnMmDHq06ePihYtKl9fX3333XdKmzatvvzyS02cOFGtW7dW3bp1PV0uYsKjkR9xIiIiwv7++29zOBwWFBTkcp/V2LFjzeFwOL+JNrvbirpw4ULz9/e39u3be6BiRLq35XTYsGGWPn16Z2v1nTt3rHnz5latWjUzu7vtGjdu7HJPFfcxul/UfWr06NFWv359y5kzp/Xp08c2b95st27dsl69elmxYsWsatWqNnjwYKtYsaI9+eST3LvtQZHrPjg42AoXLmwOh8OmT5/unB91u27evNnZySTi3+DBgy116tSWLFkyW79+vcu89957z4oUKWIlSpSwxo0bW7ly5di3PGjx4sXmcDgsWbJkzs7rIj/Lrl27Zl999ZWVKVPGnn76aatTp47zKhN41r3D6s2ZM8eqVKlijRo1cg4FdvDgQStbtqz16tXLEyU+1qKeD0Z+Np0+fdo+++wzy5Ejh1WrVs2mTZtmSZIksffff9+5LJ19Jh4OM77Sf1QMHjxYEydO1IQJE9S1a1eNGzdOY8aM0fz581WrVi2ZmfNby9u3b+uHH35Q4cKFlT9/fg9X/niKuj0inT59Wq+++qry5s2rt956SylTptS6detUu3ZtFStWTNevX5ePj4927tzJPcEeMGTIEE2fPl0DBgzQ7du3tWjRIqVOnVr9+vVT3bp19fXXX2vRokW6deuWsmTJoilTpsjHx0fh4eEu93Qj/ty5c0c+Pj66du2aSpUqpVSpUmn69OkqUaKEJLFtPCxy/S9YsEBdunRRsmTJNGTIEDVv3lwZMmRwLrdy5Urt27dPe/bsUd68eTVw4ED5+Pg4ty/cK/Lz6ubNmzp27JgOHDign376SR9//LFmz56tZ555xnk1kMPh0O3bt5UkSRJdv36de0wTgM8++0wrVqzQlClTlCZNGuf0OXPm6L333lPevHk1efJkZcuWTadOnVK2bNnk5cXFsPElcv/auHGjNm/erJMnT+rZZ59VmTJlFBAQoBs3buiFF17Q7du3tX37dvn7+2vRokUqXry4p0tHbHgy8SNuRG2tefPNNy1JkiTWsGFDS58+va1evdrMot+Xc+zYsXivE/c3fPhw69y5s3Nc9E8//dQKFChgu3fvNrO723fTpk326quv2ttvv+1s3aGFO34dPHjQChUq5DLE1L59++z555+3atWquexTUbcNrXGeF7kNQkJCLG/evFa6dGnn/gXPuHe4vEuXLllwcLANHDjQcubMaRMmTLALFy5Eex77VvyLPH9YtmyZ9evXz/bt22dmZseOHbPu3btbQECAy5j333zzjf3yyy8uz4VnDR061MqUKWNdu3Z19p0QqVu3bubn52dBQUF29uxZ53SGtIxfixYtsuTJk1vNmjWtbNmyliJFCuvevbvt3LnTuczXX39tzZs3t8DAQJdthcSB0P2IiHpwfOedd8zhcFj37t2jnZQMHDjQUqVKZadOnYrvEnEf586ds4IFC5rD4bCePXvaiBEj7M6dO9a2bVsrU6bMA5/Hyab73XvCcfLkScuSJYtzeKnIk8nffvvN0qZNe99hVDjhTDiiBu+CBQtavnz57Ndff/VwVdi8ebNt3brVeXmrmVnfvn0tZ86cNnnyZLt48aKZmbVq1cqOHj3qqTIfO/ceu77++msLCAiwgQMHOju5MzM7ceKEdevWzVKkSGETJ060N954w1KkSBGt4zvEn/uF5fDwcBs3bpyVK1fOOnfu7BzK0sxs6tSpVrNmTRs0aBBB20OOHj1qefPmtY8++si5782ZM8dKlChhvXr1cvkCMiwszHlcROLCtSOPCC8vL0VEREi6e5n5sGHDNGPGDM2YMUPXrl2TJA0dOlSTJ0/WihUrlD17dk+W+9iye+7myJgxo0aNGqWkSZMqefLkOnv2rIoXL64GDRro4sWLeu+99+77OlxO6X6Rl9bt379fZqbw8HD5+Pg4h76JHCKnUKFCKlq0qPbv3x/tNRhuKn7cu1/db1rkpcgpU6bUzz//rICAAPn7+8dXidDd2zNmz57tfNyvXz+1bNlSNWvW1CuvvKIZM2ZIkiZMmKDmzZtr8uTJ6tWrl6pWrarVq1fzuRWPHA6H7ty5I0navXu3unbtqkmTJmnUqFF64oknJEkXL15UtmzZ9O6776pnz5567733tGrVKq1bt065cuXyYPWPrzt37jg/u9atW6c1a9Zo48aN8vLyUr9+/fTcc89pz549ev3113X69GnduHFD69evV5MmTfTOO++4nEvCfe79fLp586Zu376twoULO88bXnjhBfXp00effPKJjh075lzW19dX6dKli9d6EUc8mfgR9+691Nzb29tmzZplAwcOND8/P+clX/Cs+fPn25w5c5ytb6+//rq1b9/ezp8/bwMHDrTixYtb6tSprUCBAs7LzhG/IiIibPXq1eZwOJytcOPHjzcfHx9buHChc7lr165ZiRIlGLLDQyJbBZYvX24dO3a0Tp06OW+ruZ/IfY6rEOLX0aNHrWbNmla5cmX7+uuvbceOHVa4cGHbvHmz/fDDD/byyy/bU089Ze+9957zOe+++6516tTJ2rVrx2018eSDDz6wGjVquEz78ccfrWLFinb16lW7evWqzZo1y2rWrGlFihSxjh072pUrV8zM7MKFC3b58mVPlP3Ya9OmjX3xxRfOx3369LE0adJYjhw5zNfX155//nnnucSUKVOsfPnyljx5citWrJgVLFiQ42I8iDw/j3qefuDAAfvrr79s7969liZNGmcnklGH8H3yySdt6NCh8Vor3IPQ/Qi493KgqI+HDh1qDofDfH19bceOHfFdGu4RERFh165ds+rVq1tQUJA1bNjQLl26ZKtWrbJ27drZpk2bzMxsxYoV1qlTJ6tatSqXe8Wj+63rmjVrWs2aNZ0fgq+99po5HA7r0KGD9ezZ02rUqGFFihThkn8P+uGHHyxZsmTWuHFjq1SpkjkcDps5c+ZDn8PJZfz75Zdf7LnnnrPatWtb586dbfDgwc55hw8fth49eljJkiVdvsCK2jMv+5j7RERE2O3bt23u3LmWNWtWlzHQlyxZYt7e3jZ48GArXry4NWrUyHr27GmjRo2y3LlzP/RLLrjfhQsXrE2bNpYqVSpbunSpnT592vLmzWtbt26148eP26ZNmyxbtmzWoEEDO3funJmZ7d2712bMmGHTp0/nC614dPz4catVq5aZmf3nP/+xPHnyOEeradGihWXLls3li6ubN29auXLlXEbdQOJF6E5E7ty54zxRDAkJcRn+4WHB+4MPPrC9e/fGT5GI5n5BLjg42JYtW2ZBQUGWLVs2++ijj6xatWr2zDPPOJcJCQlxbm+Cd/zatWuX3bx508zM1q9fb0FBQfbpp58658+bN8+aNWtmDRo0sM6dO9utW7fMjJMWT7h8+bLNmDHDpk2bZmZmV69etdGjR5u3t7dzGjwr6n6xdetWe+655yx9+vT24osvuix35MgR69mzp5UuXdpGjhzpMo8vSdwrcri80NBQW7RokeXMmdPl82jkyJHWuHFj69u3r7MvhFu3blmJEiWcw4bBc44fP249evSwwMBA6969u3Xp0sXM/rvf7N+/31KnTm19+vS57/P57Iof69ats4IFC1rhwoXNy8vL5s+f75x38OBBq1SpkmXJksW+//57W7ZsmQ0ePNjSpEljhw8f9mDViCuE7kTgm2++cXm8dOlSK1OmjAUFBVmnTp0e+DwOop4X9UTx66+/tg8++MC+++47l2Vee+01q1evntWqVcscDoe9/fbbD3wNuN+8efPM4XBYt27dbPHixWZm1r17d6tevbpdu3bNudy9Y2PSChf/fvvtN3M4HFagQAH76quvnNPDw8NtzJgx5uXlZTNmzPBghYgq8svD7du3W7NmzSx79uw2b948l2WOHDlibdu2tfbt23PsiydLly41h8NhGzZsMLO7wXvhwoWWM2dOa9y4sXO5v//+2+V5gwcPtrx58zK+vQdF/dw5deqU9erVy5InT261a9c2s7v7XOQXyNOnT7fs2bPb+fPnOT/0oFGjRpnD4bD8+fM7p0Ue644ePWpt2rSxTJkyWb58+ax48eIuvZcjcSN0J3DHjx83h8NhzZo1M7O7l+f5+/vba6+9Zq+//rplzpzZqlSp4rynCglH1BPGN954w5InT26lSpVyBrpDhw45569YscKGDRtmDofD5bI+uN+9J/aLFy+2VKlSWatWrax58+bWtWtXO3v2rKVOndrlvqqoJy2EA8+4fPmy9e7d23x8fGzq1Klm9t9gFxERYe+++645HA6XqxQQf6JeofPll19avXr1nFeFbN++3Vq0aGGVK1e2BQsWuDzvzJkzLtsR7nX+/Hlr3bq1BQQE2MaNG83MNXg3bdrUuWx4eLh9+umn1rFjR0uXLh2BIIHYv3+/md1tLX311VfN4XBE269mzZplRYsWteDgYE+U+FiLetXiokWLbNiwYVamTBkrW7as89a1qOcUx44ds1OnTrn0Mo/Ej9CdwEVERNjy5cstY8aM1rp1a1u1apWNHj3aOW/fvn2WL18+q1y5MgfSBGrfvn1WtWpV27Ztm0VERNiPP/5ogYGB1rFjR5ehV8zufqlChyaeEXWc7S5dulj+/Plt7969VrlyZWvatKnVqVPHMmbMyP2LCcxff/1lPXr0MB8fH+dVJJH7TkREhE2aNMl5zxziT9TAvXr1auvUqZN5e3tbp06dnMF7y5Yt1rJlS6tcubJL54T3ew3EvaifMRcvXrS2bdta8uTJ7xu8I7/4NzP7+OOP7ZlnnnEGPXjW7Nmz7YknnnA+PnnypHXp0sV8fHxszpw5dubMGTt37pzVrl3batWqxblFPItc3+vWrbPPP//ceV/9qlWrrGTJkla2bFmXK+e2bNlCh4SPKEJ3Ahb1xHHFihWWLl06S5o0qQ0cONBluX379lnevHmtevXqtHgnMKNGjbKWLVta27ZtnSeaZmbLli2zVKlSWadOne57rw6XKsevL7/80ipUqGDDhw83M7MbN25Yy5YtnfcEjxs3zho3bmwOh8OGDRvmyVIfW5HHw507d9qSJUts1qxZdu7cOQsPD7fbt29bly5dLEmSJNGCNzyrb9++VqpUKevcubOVLVvWMmXKZK1bt3YeD7du3WqtWrWyQoUK8YVWPLn3y4zIfeX8+fMPDd5Rr8IKDQ2Nv4LxUBs2bLB8+fJZcHCwc1uePHnSunbtat7e3pY+fXrr1auXVa5c2bnf8YVW/IjcHl9//bUFBgbaoEGDnL3I375921avXm3Fixe30qVL29GjR23w4MFWoEABZzDHo4XQncBEHgij3jt66NAhi4iIsJUrV1revHldhvO4t5OMBg0acLKZgMycOdMcDoflzZvXTp48aWb/3WY//vijpU2b1po3b25nzpzxZJmPnXv3kb1799rbb79t2bJls1q1atmaNWts3Lhx1qNHDzt//ryZ3b3VY8aMGXwh4kELFy60VKlSWZkyZczPz89KlChhY8eOtVu3btnt27eta9euljx58mj9YCD+RD2Zj/yyePPmzc557733npUoUcKef/55ZwDYuHGjvfnmm9xnGo9+//13GzRokJ04ccJlvZ8/f97atGkTLXh//fXXljJlSmvTpo2Z8aWWp9xvvf/++++WNGnSaB3mHjt2zF5//XVzOBz29ddfO6fzGRa/NmzYYIGBgTZ79uz7zt+6dauVLFnSMmbMaLly5bJt27bFc4WIL4TuBOjkyZPWrl0727t3r3399dfmcDjst99+szt37tiKFSssbdq01rJlS+fykQfh33//nR4OPehB3xx/+eWX5nA4bMCAAdEuGVqyZInVqVOHb53jUdR1PXr0aPvggw+c044dO2ZBQUFWu3Zta9q0qeXIkcNl3OBInLTEj6gnmHv27LEMGTLYJ598YiEhIRYaGmpdu3a1oKAgGz9+vEVERNiVK1esffv2lj59elri4lmDBg1s3759LtPmzp1rWbJkcbkvMSQkxN566y1Lnjy5vfzyy9F6/id4u9+tW7esTJkyzs6c+vfv73L/b2hoqLVq1coleF+9etWWLl3KOUYCMWXKFOvUqZPNmzfPPv30U6tataqtWrXK5Yo6s7tfFk+YMIHb1jxowoQJzg4Jr127ZsuXL7eWLVtamzZtnP2NXL9+3davX29nz571ZKlwM4eZmZCgrFu3Tr1795a/v7927typjz76SG3btpUkmZlWrVql1q1bq0aNGlqwYIFzusPh8GTZj7WIiAh5eXlJkjZs2KC//vpLDodDderUUdKkSTVr1iy9/PLLeuONN9S/f3+lSZPmoa8B94i6ji9evKgWLVpo3759eu+999SsWTOlSJFCERERmjp1qnbs2KE5c+ZIkjZv3qynn37ak6U/VmbPnq2goCDly5fPOW3JkiV67bXX9NNPPyl9+vSSpODgYA0YMEA7d+7UunXrlCJFCgUHB+vGjRvKlCmTp8p/7Bw/flwffvihRo4cKV9fX+f0jRs36pVXXtH777+vWrVqOaefPn1a5cuXl7+/v4KCgjRz5kx5e3t7ovTH1rhx4+Tj46MiRYrop59+0vvvv6/69esrKChInTt3VkhIiPr3768FCxZoyZIlql69OucZCcTNmzf18ssv6/r16zpz5owuXryoU6dOKVeuXCpWrJhKlSqlTJkyKWfOnKpSpYr8/PwkSXfu3JGPj4+Hq3/8vPnmm/riiy80evRozZ07V+Hh4YqIiFDq1Kl16NAhffXVV8qbN6+ny0R88GjkxwONGzfOHA6HlSpVyn755ReXeZH3eGfKlMnq1q3roQoRKeo3x6+99poVKFDAChUqZJUqVbI8efLYhQsXzMxszpw55nA4bMiQIXbx4kVPlQu7e59phQoVrGXLlpY/f35LliyZffLJJy6dEf755582bNgwq1SpEq1v8Wjbtm1Wt25dO378uMv0xYsXW65cuezEiRNmZs4WnUuXLpmXlxeXlCcQEyZMsJ9++snMzM6dO2dPPfWUNWzY0Dm2s9ndYXGaN29ub7/9tpUsWdI2bdrkqXIfW2vXrrWAgADbvn27mZn98ccfNmzYMEuaNKmVL1/eZs6caRs3brQXX3zRsmbNatevX6eV1EPudyVc5Lb4+++/7c8//7QKFSpYoUKFbMyYMVatWjXLnj27NWzYkKvo4lnUvpgiO0e7ceOG1ahRw5544glr166ds++KzZs3W5EiRZy3HuLRR+hOQKJ+oH311Vc2fPhwCwoKsmbNmtm6deuiLfvdd99Znjx5uB/Yg6J2djF16lRLly6d836cyZMnm8PhsKVLlzqXmT17tjkcDps+fXq814q75s+fbwEBAbZz504LDQ21sLAw69atm/n5+TkvXY4qcr8keMefyNswdu7c6ezh/+zZsxYQEGDdunVzWfaPP/6w4sWL2/r16+O9TrjeanH8+HFr3LixpU6d2rZu3Wpmd/sbyZo1q9WuXdsmTJhgq1atspo1a1rr1q3tr7/+spQpU973Fg64X//+/a1NmzZ248YNMzN77rnnrGDBgvbiiy9a1apVLUmSJDZw4EDG4fagqKF52bJlNmfOHFuwYEG0oPbuu+86G2Ei90mG3Ytfket52bJl9sILL1iZMmVswIABzl7+//zzT5flBw0aZGXKlKER5jFC6E5gNm/ebK+++qrzBP+HH36w8uXLW7NmzWzDhg3O5SJPaKJ2uIb4NWnSJMuePbvzfsXu3bvb+PHjzexuq1zKlClt5syZZmYWHBzs/Nbz+++/555gD5o6daqVKVPGbty44XJC07FjRwsMDLQ5c+a43A8cERHBSUs8ibo9zpw5Y0FBQVa/fn3nkF9Lly61ZMmSWefOnW3fvn128uRJGzJkiGXJkoVg4GEjRoywjz/+2LZt22atW7e2DBkyODtQO3DggDVv3twKFixoefPmtSpVqjjHpi1fvrzNnz/fk6U/thYuXGjly5e38PBwe/nlly1jxozO+/J///13mzJlSrT79OEZ/fv3t/Tp01upUqXM19fXqlSpYh999JFz/syZMy1TpkwWGhrqchylpTt+LVmyxPz9/a1v37726aefWv78+a1UqVIuw+stXbrU+vXrZ4GBgbZr1y7PFYt4R+hOQMLDw23MmDFWsGBB69q1q/PyyWXLllmFChXs2Weftfnz59vw4cPN4XA4e1VG/Js+fbr5+fm5dD7TpEkTGzlypH3//feWIkUK+/DDD83s7nadMmWKTZkyxeUDkODtGe+//74FBgbazZs3zcycrTw///yzeXl5Wdq0aZ09vXLCEr/u/XJjxowZVqNGDWvevLmzxfv777+39OnTW/bs2S1PnjyWM2dO27FjhyfKfaxF3Te++eYbCwwMtD179pjZ3dbtFi1auATvq1ev2uXLl11a6AYOHGhZsmSJdisB4k/lypXNy8vLsmTJYrt37/Z0ObiPL774wjJlymTbtm2z27dv24kTJ+z555+3qlWr2ty5c83M7Ndff7UsWbLYqVOnPFzt4yPqlQQRERF24cIFe/rpp23SpElmdvccL3369NanTx/nc27cuGHdu3e3ihUrRuttHo8+QncCc/XqVRs/fryVKVPGOnfu7AzeK1assDp16lihQoUsb968zvuwEP9mzpxpvr6+tnjxYpfpgwcPturVq1vKlClt6tSpzukXL160+vXr29ixY+O50sfbg77tDw4OtmLFilmDBg2cVx+Y3e0d+7XXXrOOHTtaunTp+FIrnkUG7rVr19rHH3/snD579myrXLmytWjRwhm8z507Z2vXrrXVq1dze42HffHFFzZp0iTnVT6R9u3bZy1atLCMGTPazz//7DJv165d1qhRI8uSJYvt3LkzPsvF/4vc377//nt74oknnJ9nXNWT8AwePNiqVatmZv/dPseOHbM6depYs2bNzMzsyJEjVr9+fW6Diicff/yxzZkzx+Uc4sqVK1a6dGn7888/7dixY5YlSxbr1KmTc/7atWvt2rVrdv36dZcRHfD4IHQnAEePHnV5HBoaamPHjrWyZctaly5dnMH7+PHjdvDgwWj3hSD+rF271hwOhw0fPtxlevfu3a1z585WpEgRy5cvn23ZssVCQ0PtxIkTVq9ePStbtiwt2/Eo6onj9OnTrUOHDjZq1CjnCf6SJUvsqaeessqVK9uuXbts8+bNVrduXWvTpo2dO3fO0qZN+8AxNRH3IrfX119/benSpbPOnTvboUOHnPNnzZpllSpVsubNmzsvNYdnhIWFOW9runr1qmXOnNkcDod179492rL79++3Vq1amcPhsN9//91l3tSpU+3AgQPxUjMe7Ny5c5YvXz4bMmSIp0uBma1Zs8aGDx9uQ4cOteXLl5uZ2fDhw61cuXLOgBcZrFesWGFeXl7R9iOCt3tFRERYUFCQFSlSxBYuXOjcLmfPnrVcuXI5Lyvv1KmTc1scOXLEnnnmGVu5cqUnS4eHEbo97MCBA1a6dGkbNGiQy/SQkBAbOnSoZc6c2fr16xdt7EV4xqFDh6xSpUrWuHFj59UGzZo1s3z58tnNmzft1KlT9sQTT1jRokUtffr0Vr58eStXrly0sWjhPlED9/Dhwy0gIMCee+45y5gxo9WuXduWLFliZmarV6+2ihUrWvLkyS1nzpzOL0b++usve+KJJ+zHH3/01J/wWFq/fr2lSJHCZs2add/5X375pVWrVs1q165tR44cid/iYGZmixYtsmbNmlnJkiVtxIgRZmZ26tQpK1++vOXPn/++IXr37t02ZMgQjn0J2Oeff27+/v7RrkhA/Proo48sffr0VrNmTcuRI4dlz57dVq1aZbt27TKHw2EffPCBy/Jr16614sWLc7VPPIo8v7h9+7Y1atTIihUrZgsWLHD2UTFkyBDz9vaONrLQ4MGDrXjx4vQ98pgjdHvYhQsXrEuXLlaxYkXnSUykK1euWJ48eSxVqlT26quveqZARHPo0CGrW7euNWjQwIKCguypp55yuSfxwoULtnr1apsxY4atWbPGebJJS3f82rlzp7Vr1842btxoZnfveWvSpIlVrVrVZXipbdu22ZEjR5yXoA8cONAKFizIh2M8ioiIsNGjR1ubNm3M7O4wOCtWrLA2bdrYc889Z8uWLTOzu/d4N2zYkJNMD5g+fboFBARYnz59rHfv3ubl5WXTpk0zM7PTp09boUKFrHTp0g/dbwjeCdOZM2esatWqHPM86KOPPjJfX19buHChmd1t8Q4ICLAXX3zRzMzGjh1rPj4+9s4779iOHTvs+PHjVq9ePatWrRp9j8SzyJbt8PBwe+6556xYsWI2f/58u337th07dsxat25tGTNmtOnTp9vMmTOtR48eljJlSvpMAKE7vt3vfqkLFy5Y3759rUyZMi7B+9KlS/b888/bqFGj6BwjgTl06JDVrFnTAgMD7auvvnJOf1Cw5mQzfs2ZM8cqV65sTz/9tMu92Xv27LEmTZpY9erV7YsvvnB5zi+//GJdu3a1VKlS0aNoPLj3WDho0CDz9/e3jRs3WqNGjaxOnTrWuHFjq1ixohUvXtx5SfOVK1c8Ue5j7aOPPrIkSZK49GPRunVre//99523O506dcpKlixpZcqUIbwlQpEdSiL+Pei2tSxZsljFihUtODjYQkJCbMmSJZY6dWrLnj275c6d2+UqOoJ3/Prss8/s9ddft/DwcGvQoIEVL17c+YXJwYMH7Y033rAsWbJY6dKlrWnTpnSaBjMzc5iZCfHCzORwOPTTTz/pp59+0l9//aUaNWqoVq1aCg4O1ttvv60NGzaobNmy6tKli+bOnatt27Zp4cKFSps2rafLxz2OHj2q7t27y8vLS4MGDVJQUJCk/25neM7KlSs1aNAgHTlyRLNnz1aTJk2c83799VcNGzZMR44c0YQJE1SzZk1J0s6dO7Vy5Uo1adJEBQsW9FTpj4XIfWTjxo06evSo2rdvr6tXr+q5557Tjh07VLt2bbVv3141atTQ/v371bx5c3377bfKly+fp0t/7Kxbt07Vq1fXsGHD9NZbbzmnlyhRQhERETpx4oSKFi2qbt26qXLlymrYsKGuXr2qrVu3KkOGDB6sHEgcDh8+rJdfflmpU6fWm2++qdKlS6tZs2b67rvvnOeHAQEBatWqlfz9/ZUpUyYlT55cxYoVk7e3t+7cuSMfHx9P/xmPjWPHjqlOnTrq2rWr+vbtqzt37qhp06Y6ffq03nrrLTVp0kQ+Pj66dOmS0qZNq5s3bypZsmSeLhsJAKE7nn399ddq3769SpUqpZs3b2rbtm3q3bu3Bg8eLB8fH02fPl2ffPKJQkNDlTRpUi1atEhPPfWUp8vGAxw+fFi9evWSJA0ZMkQVK1b0cEWPn4iICHl5eUWbvmnTJr322mtKmzatevfurRo1ajjn7dy5UwsXLtQ777wjb29v5/Tbt28rSZIk8VL34yoycH/zzTfq1q2bmjZtqr59++qJJ56QdPeEJk+ePM7lX3/9da1fv17Lli1T6tSpPVX2Y+t+geDZZ5/V3r17NXLkSAUEBKh///6KiIjQihUrFB4eroEDB+rzzz932bcAPFjkuYS3t7eCg4N1/fp1zZo1SwULFtSmTZt08OBBjR07VtevX1fr1q01efJkSVJ4eDj7WTzavXu3vvjiC125ckXTp0/XnTt35Ofn5wzeZ86c0eDBg9WwYUNn0KYhBk4ea2N/DB05csRy5MhhH330kfPSyi+//NLSpk1r/fv3N7O7l3idO3fOtmzZYufOnfNkuYihQ4cOWYMGDax06dLOcWoRP6JeUrdq1SpbuHCh/ec//3GOwb127VqrUKGCNW3a1FavXn3f1+DS//i3du1a8/f3t08++eSBy6xcudJeffVVLvdPAKL2Y1GxYsVo/Vjs2LHDHA6Hs5PCSOxbQMxFvW1twYIF0eZfuXLF1q1bx37lIcHBwdaiRQtLly6d1apVyzk98nzj9u3b1qRJE8uZM2e0IWUBMzOuR3GDyJa3e1vgbt68KR8fH5UpU8Y5rVWrVoqIiNALL7ygpk2bqmLFikqaNKkyZszoidLxL+TPn1/jxo3Txx9/rCJFini6nMdK5P41YMAAffXVVy7TvvvuO1WtWlVvv/22hg4dqg8//FBhYWGqV6+ey2vQSuBeH374oTJlyqRmzZrJ7vYjotWrV6tVq1Z66aWXdOXKFe3atUtz587VzZs31b17d2XPnl0bN27Ujh07tGHDBhUtWtTTf8ZjLX/+/Hr//ffVrVs37du3Tx999JFy5cqliIgIZwtOoUKFot0Gxb4FxFz+/Pk1ffp0de/eXbNnz1aWLFmct63duXNHgYGBqlKliiRauD0hICBAAwYMkMPh0PLly/X555/rhRdekJ+fn27duiVfX18tWrRIbdu2VbFixTxdLhIgLi+PY5FB+8SJE1qxYoWeeuoplS5dWpK0Y8cOlS9fXhs3blS5cuUUFhYmPz8/SVLRokXVvn179evXz5PlIw486HJnuMesWbPUv39//fjjj8qWLZv+/vtvDRgwQDt37tTWrVuVM2dOrV27Vl26dFGzZs00evRoT5f82Lh06ZJeeeUVjRs3Tnnz5nVOHzhwoGbMmKHvvvtOEyZMUGhoqHx9fXX27FmZmX766SfduHFDkujPIgGJ2o/FwIEDValSJUlSo0aNFBoaqtWrV3PsA/5H3LaWMNj/XxZu91wevnPnTo0ZM0Z//vmnevbsqZYtW0qSyzk9cD+E7jgUGbZ+/fVXNW/eXE8++aQ6duyo+vXrO5dp2bKl9u/fr2+//dZ53+KtW7dUoUIFde7cWZ06dfJU+UCiNGjQIJ08eVJffPGFc1pISIgaNWqkiIgIrVmzRkmSJNHu3btVtGhRWgfiQdSTlBs3bihZsmTatm2bjh49qtatW+vkyZPq1q2bNm7cqMaNG+uFF15QnTp1tHfvXj3//PNavny5smbN6uG/AvcTGQgig/fEiRO1b98+7du3T0mSJOFLRyAOHD58WH369NH58+f1ySef0HIazyI/wzZs2KDly5fr6tWrKl++vFq0aCEfHx/9/PPPeu+99/Tnn3+qV69eat68uadLRiLAJ2Mc8vLy0oEDB1SlShU1a9ZMH3zwgUvglqS+ffsqa9asql+/vtasWaMNGzZoxIgROnnypEtHTwBi5u+//9bu3budj8PDwxUQEKCXX35ZFy9e1KVLlyTd7W3Z29tb4eHhHqr08RB5yfHFixf1999/K1myZLp69arefPNNTZ48Wd98841y5syp77//Xr/88ovmzp2rOnXqSJLmzJmjwMBApUyZ0sN/BR4k8lJzh8Oh6tWra//+/c7AfefOHQI3EAcib1urXLkyt615QGRnnw0bNtTp06d16NAhffDBB+rRo4du376tcuXKqW/fvsqWLZtGjBihJUuWeLpkJAYeupf8kXTjxg1r0aKFde/e3WX6rVu37NSpU3b06NH/a+/eo2rO+j+Av083lUKNoZCichSDWrl2eRhZYRArkiGG0EKSS4XGNadRjfvQzGDktlrMUEZyyUTjuIYJo0S6TExYFLoMxdm/P/z6Ps7DPA9Tx3F5v9Y6a3X23mfvz7fWWZ3P2fu7txBCiCtXrohhw4YJIyMj0aZNG9GuXTtx/vx5bYRM9M64e/fuS8tTU1NFu3btxIoVK9TOSd+/f79wcHDgGfdacO3aNdGqVSsRGBgo7ty5I4QQ4vLly2LAgAGiV69eIiEhQa29UqkUU6dOFWZmZiIzM1MbIdNrys7OFlOnTpXec8+/94iobvEc7jfr1KlTwsbGRqxfv14I8WyTOzMzM9GsWTMxYsQI6Xx0pVIpxo4dKwoKCrQZLr0j+JV0HdLT08OtW7fUzvg9ePAgwsLC0L59e/Tq1Qv9+vWDXC7Hzp07cfbsWaSmpuLo0aNwcnLSYuREb7djx45h6NCh+PXXX6Uy8f93xri4uKBHjx7Ys2cPFAoFHjx4gPz8fKxevRo2NjZo0aKFtsL+IKlUKmzduhUFBQXIzc1FZGQkiouL4ejoiNjYWNSrVw8bNmyQNr7Lz89HcnIyzp07h/T0dHTs2FHLV0Cvom3btli9ejX09PR4TjCRhnEFyZuVm5sLNzc3jB8/HgUFBejbty+8vb0xffp0pKamYsqUKaiqqoKrqyvWrVsHa2trbYdM7wDe012HHj58iK5du8Ld3R0zZ87E7t27sXnzZrRv3x4eHh4wMTFBVFQUBg0ahOXLl/PsPqJXlJOTg8DAQJiYmGDOnDnSxjI1O7jeuXMHCoUCv/zyC65evQq5XA4DAwOcOnWK95lqQWZmJnr16gUXFxfo6uqibdu2mD17NiwsLHDlyhVMnz4d1dXVmDJlCoYMGYLi4mLo6+ujcePG2g6diIgIFy9ehKOjIz777DNYWloiPj4e5eXlcHJyQlFREfz8/BAfH8/P8vTKmHTXsbS0NHh5eaF58+YoKSlBbGwsevfuDTs7O1RXV2PAgAHSm5eIXl3NBk5CCMybN09KvKurq6Gvr4+qqipUVVUhLi4Onp6e6NChA3R1dTkLp2HPf+AQQkClUkFXVxfz589HZWUljI2NkZKSAnd3d4SHh0uJd2hoKP78808sWLAAgwYN0vJVEBHRh6jmS/lHjx4BAAwNDaW63NxcDBo0CHFxcfjXv/6F27dvIzg4GK6urhgyZAisrKy0FTa9gzj1U8c+/fRT5OXlYdeuXcjLy0NgYCDs7OwAPDuztGHDhrCyspLOqyWiV/P8Bk6RkZFQKpUAAH19fQghcPfuXfj6+iI/Px9OTk7SpmlMuDWnZtO0kpIS3LlzBzKZTFpRYG1tDaVSibCwMPj7+0OpVCI6Olq6BWfp0qVo3bo1l5MTEdEbdfLkSZSWlgJ4tnT/559/hp+fH3r37o34+HipzsjICE+ePMG+fftw7949rFmzBsXFxRgxYgQTbnptnOl+Q6qqqhAZGYkffvgBR48ehb29vbZDInonvWzG+/bt2/D19cXNmzeRnZ0NfX19bYf5wbh27Rr69esHQ0NDREVFQS6XQy6XA3j2JWTnzp0RHR2NJUuWIDk5GW5ubpgxYwaaNWsmrVIgIiLSNCEEzp07hy5duiAyMhJhYWE4ffo0+vXrh5EjR6KiogIJCQkIDg7G9OnT0axZMygUCsTHx6OqqgpPnz7Fvn374OzsrO1LoXcQk+43YNu2bcjIyMCOHTuwf/9+bppGVEs1ibdMJsOkSZOwZs0a3LhxAxcuXJCOLuIMt+apVCrpKDBTU1NYWlrCzs4OjRs3RnR0NH788UcolUp8++23MDAwgEKhwJYtW+Dj44PIyEjo6OjwXjgiItK452+FWrNmDUJCQhAbGwuZTAaZTIaQkBAAwM6dOzFx4kSMHj0aixYtgpGREbKzs1FYWAhnZ2e0bNlSi1dB7zJ+KtWwnJwcbNy4EWZmZjhy5AgcHBy0HRLRO69mqXlISAi8vb3Rtm1bJtxaoKOjg6CgIFRUVKCwsBDm5uYYMWIE5syZg1GjRqGiogJpaWlwd3fH2LFjERERgXr16mHo0KHQ1dXVdvhERPQBqLlv+9atW7hx4wb8/Pxgbm4Of39/NG/eHLNmzZLa+vr6QgiBCRMmQFdXFzNmzICTkxMnzKjW+MlUw+RyOXbs2IF69eqhYcOG2g6H6L1hb2+PZcuWwdbWFsuXL+fRRVpiaWmJsLAwREVF4bfffkNubi4yMjKQkpKCtLQ0pKWlwdTUVGr//IcbIiIiTapJuLOysjBx4kQYGxvDxMQEu3fvRmVlJQIDA3Hp0iWUlpbCzMwMADB8+HDo6urC19cXRkZGWLRoEW+Folrj8nIiei8w4dau4uJiREVF4eTJkxg1apS0VC8vLw+tW7fWbnBERPTBqVlSfvnyZbi5uWHy5MkIDAyEpaWllESvW7cOQUFBiIqKwqRJk9QmyBITE+Ho6CjtU0JUG0y6iYioTty6dQsKhQJnzpyBt7c35s6dC+Df56kTERG9SSUlJfD29oazszNWrVollT//RX3N7WoKhQJTpkxBgwYNtBUuvcc4LURERHXCwsICERERUCgUSElJwePHj7Fo0SIm3EREpBW3bt1CcXExfHx8pKXmAKCnpycde1mzMev06dNRUVGBsLAwJt5U53hONxER1ZmaxNve3h4nTpzAvXv3tB0SERF9oDIzM1FYWAh3d3fo6OhApVJJdTUnaFRWVsLX1xffffcd1q5di+rqai1GTO8rLi8nIqI6d/v2bQBA06ZNtRwJERF9qE6cOIHevXtj27Zt8PHxeWmbVatWYd++fTh06BBKSkpgbm7+hqOkDwFnuomIqM41bdqUCTcREWmVtbU1GjRogC1btqCwsFAqf37OsaioCJ06dYJKpZJ2MCeqa0y6iYiIiIjovdO8eXPExcXh4MGDmDdvHrKysgBAWlY+d+5c/PTTTxg/fry03JxIE7i8nIiIiIiI3ksqlQrr169HUFAQ7Ozs0L17dxgaGuLmzZs4deoUDhw4ACcnJ22HSe85Jt1ERERERPReO3PmDGJjY5GbmwtTU1P06NEDAQEBsLe313Zo9AFg0k1ERERERO+9p0+f8hhL0gre001ERERERO+9mnO6AfXN1Ig0jTPdRERERERERBrCmW4iIiIiIiIiDWHSTURERERERKQhTLqJiIiIiIiINIRJNxEREREREZGGMOkmIiIiIiIi0hAm3UREREREREQawqSbiIiIiIiISEOYdBMREb0lbGxssHLlylduX1BQAJlMhszMzL9tEx8fj0aNGtU6tpdZuHAhOnXqpJG+/5eePXsiJCREK2MTERG9DibdREREtfTFF19AJpNh6dKlauVJSUmQyWSv3E9GRgYmTpxY1+ERERGRFjHpJiIiqgOGhoaIjo5GaWnpP+7j448/hrGxcR1GpTnV1dXaDoGIiOidwKSbiIioDnh6esLCwgJfffXV37ZRKpVwd3eHkZERrKysEBwcjIqKCqn+P5eXX7lyBW5ubjA0NISjoyMOHz4MmUyGpKQktX7z8vLQq1cvGBsbo2PHjjh58uQLYyclJcHe3h6Ghobw8vJCUVGRWn1cXBxsbW1hYGAAuVyOrVu3qtXLZDLExcVh0KBBqF+/PhQKhVS3detW2NjYoGHDhvDz80NZWZlU9/jxYwQHB6NJkyYwNDSEm5sbMjIy1PpOT09Hly5dUK9ePVhaWmL27Nl48uSJVF9RUYHRo0fDxMQElpaWWLZs2QvXt27dOun6mjZtiqFDh77kL0BERPTmMekmIiKqA7q6uoiKisKaNWtw48aNF+qvX7+Ovn37wsfHBxcvXsSOHTugVCoRFBT00v6ePn2KwYMHw9jYGKdPn8b333+PiIiIl7aNiIjArFmzkJmZiTZt2mDEiBFqSWtlZSUUCgW2bNmC48eP4/79+/Dz85PqExMTMW3aNMycORO///47AgMDMXbsWBw5ckRtnIULF2LIkCG4dOkSxo0bJ11XUlISkpOTkZycjPT0dLVl9mFhYdi1axc2b96M8+fPw87ODl5eXigpKQEA3Lx5E/3790fnzp1x4cIFxMXFYePGjViyZInUR2hoKNLT07Fnzx4cOnQIR48exfnz56X6s2fPIjg4GIsXL0ZOTg4OHDgADw+Pv/1bERERvVGCiIiIamXMmDHC29tbCCFEt27dxLhx44QQQiQmJoqaf7UBAQFi4sSJaq87duyY0NHREX/99ZcQQghra2uxYsUKIYQQ+/fvF3p6eqK4uFhqn5qaKgCIxMREIYQQ+fn5AoDYsGGD1Oby5csCgMjOzhZCCLFp0yYBQJw6dUpqk52dLQCI06dPCyGE6NGjh5gwYYJabMOGDRP9+/eXngMQISEham0WLFggjI2NxcOHD6Wy0NBQ0bVrVyGEEOXl5UJfX19s375dqq+qqhLNmjUTMTExQggh5s6dK+RyuVCpVFKbtWvXChMTE/H06VNRVlYmDAwMxM6dO6X6e/fuCSMjIzFt2jQhhBC7du0SDRo0UIuDiIjobcGZbiIiojoUHR2NzZs3Izs7W638woULiI+Ph4mJifTw8vKCSqVCfn7+C/3k5OTAysoKFhYWUlmXLl1eOmaHDh2kny0tLQEAd+7ckcr09PTQuXNn6Xnbtm3RqFEjKcbs7Gy4urqq9enq6vrCNbi4uLwwto2NDUxNTdXGrxn7+vXrqK6uVutbX18fXbp0URu7e/fuahvOubq6ory8HDdu3MD169dRVVWFrl27SvXm5uaQy+XS8z59+sDa2hqtW7eGv78/tm/fjsrKypf+roiIiN40Jt1ERER1yMPDA15eXpgzZ45aeXl5OQIDA5GZmSk9Lly4gGvXrsHW1rZWY+rr60s/1ySvKpWqVn2+TP369f/r2DXja2Ls/8bU1BTnz59HQkICLC0tMX/+fHTs2BH3799/o3EQERG9DJNuIiKiOrZ06VLs3btXbUMzZ2dnZGVlwc7O7oWHgYHBC33I5XIUFRXh9u3bUtl/bkD2qp48eYKzZ89Kz3NycnD//n04ODgAABwcHHD8+HG11xw/fhyOjo7/aLwaNRuzPd93dXU1MjIypL4dHBxw8uRJCCHUxjY1NUWLFi1ga2sLfX19nD59WqovLS3F1atX1cbS09ODp6cnYmJicPHiRRQUFCAtLa1W8RMREdUFPW0HQERE9L755JNPMHLkSKxevVoqCw8PR7du3RAUFITx48ejfv36yMrKQmpqKr755psX+ujTpw9sbW0xZswYxMTEoKysDF9++SUAvNbZ38Cz2eipU6di9erV0NPTQ1BQELp16yYtVw8NDYWvry+cnJzg6emJvXv3Yvfu3Th8+HAtfgvPZsYnTZqE0NBQmJubo2XLloiJiUFlZSUCAgIAAJMnT8bKlSsxdepUBAUFIScnBwsWLMCMGTOgo6MDExMTBAQEIDQ0FB999BGaNGmCiIgI6Oj8e94gOTkZeXl58PDwgJmZGVJSUqBSqdSWoBMREWkLk24iIiINWLx4MXbs2CE979ChA9LT0xEREQF3d3cIIWBra4vhw4e/9PW6urpISkrC+PHj0blzZ7Ru3RqxsbEYOHAgDA0NXysWY2NjhIeH4/PPP8fNmzfh7u6OjRs3SvWDBw/GqlWr8PXXX2PatGlo1aoVNm3ahJ49e/6ja3/e0qVLoVKp4O/vj7KyMri4uODgwYMwMzMDADRv3hwpKSkIDQ1Fx44dYW5ujoCAAOkLBgCIjY1FeXk5Bg4cCFNTU8ycORMPHjyQ6hs1aoTdu3dj4cKFePToEezt7ZGQkIB27drVOn4iIqLakonn13MRERHRW+v48eNwc3NDbm5ure8DJyIiojeDSTcREdFbKjExESYmJrC3t0dubi6mTZsGMzMzKJVKbYdGREREr4jLy4mIiN5SZWVlCA8Pxx9//IHGjRvD09MTy5Yt03ZYRERE9Bo4001ERERERESkITwyjIiIiIiIiEhDmHQTERERERERaQiTbiIiIiIiIiINYdJNREREREREpCFMuomIiIiIiIg0hEk3ERERERERkYYw6SYiIiIiIiLSECbdRERERERERBrCpJuIiIiIiIhIQ/4PY3/EqmcNeV0AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "print(combined_df['Place'].values)\n", + "top_10_most_late_routes_neighbourhoods = [i for i in top_10_most_late_routes_neighbourhoods if i in combined_df['Place'].values]\n", + "print(top_10_most_late_routes_neighbourhoods)\n", + "# top 10 most late routes against median age\n", + "median_age_list = []\n", + "for i in top_10_most_late_routes_neighbourhoods:\n", + " if i in combined_df['Place'].values:\n", + " median_age_list.append(combined_df[combined_df['Place'] == i]['Bus'].values[0])\n", + "\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.bar(top_10_most_late_routes_neighbourhoods, median_age_list, color='skyblue')\n", + "plt.xlabel('Neighborhoods')\n", + "plt.ylabel('Bus Usage %')\n", + "plt.title('Bus Usage % of Neighborhoods with top 5 most late routes')\n", + "plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n", + "plt.tight_layout()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Roxbury' 'South Boston' 'Mattapan' 'Dorchester' 'Allston' 'Brighton'\n", + " 'Charlestown' 'Hyde Park' 'Fenway' 'Mission Hill' 'Jamaica Plain'\n", + " 'Roslindale' 'South End' 'South Boston Waterfront' 'West Roxbury'\n", + " 'Longwood' 'East Boston' 'West End' 'Back Bay' 'Downtown' 'Beacon Hill'\n", + " 'North End']\n", + "['Downtown', 'South Boston Waterfront', 'South Boston', 'South End', 'East Boston', 'Brighton']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "print(combined_df['Place'].values)\n", + "top_10_on_time_routes_neighbourhoods = [i for i in top_10_on_time_routes_neighbourhoods if i in combined_df['Place'].values]\n", + "print(top_10_on_time_routes_neighbourhoods)\n", + "# top 10 most late routes against median age\n", + "median_age_list = []\n", + "for i in top_10_on_time_routes_neighbourhoods:\n", + " if i in combined_df['Place'].values:\n", + " median_age_list.append(combined_df[combined_df['Place'] == i]['Bus'].values[0])\n", + "\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.bar(top_10_on_time_routes_neighbourhoods, median_age_list, color='skyblue')\n", + "plt.xlabel('Neighborhoods')\n", + "plt.ylabel('Bus Usage %')\n", + "plt.title('Bus Usage % of Neighborhoods with top 5 on time routes')\n", + "plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n", + "plt.tight_layout()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "income_data = {\n", + " 'Allston': 34149,\n", + " 'Back Bay': 110677,\n", + " 'Beacon Hill': 100005,\n", + " 'Brighton': 41261,\n", + " 'Charlestown': 75339,\n", + " 'Dorchester': 29767,\n", + " 'Downtown': 80057,\n", + " 'East Boston': 31473,\n", + " 'Fenway': 28021,\n", + " 'Hyde Park': 32744,\n", + " 'Jamaica Plain': 51655,\n", + " 'Longwood': 7975,\n", + " 'Mattapan': 28356,\n", + " 'Mission Hill': 23446,\n", + " 'North End': 89696,\n", + " 'Roslindale': 41252,\n", + " 'Roxbury': 20978,\n", + " 'South Boston': 64745,\n", + " 'South Boston Waterfront': 129651,\n", + " 'South End': 83609,\n", + " 'West End': 77069,\n", + " 'West Roxbury': 47836\n", + "}\n", + "\n", + "income_data_df = pd.DataFrame(income_data, index=[0]).transpose()\n", + "income_data_df = income_data_df.reset_index()\n", + "income_data_df.columns = ['Neighborhood', 'Income']" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-------------------------+-----------+----------+\n", + "| Place | num_stops | Income |\n", + "+-------------------------+-----------+----------+\n", + "| South Boston Waterfront | 28 | 129651.0 |\n", + "| Back Bay | 49 | 110677.0 |\n", + "| Beacon Hill | 7 | 100005.0 |\n", + "| North End | 12 | 89696.0 |\n", + "| South End | 85 | 83609.0 |\n", + "| Downtown | 26 | 80057.0 |\n", + "| West End | 12 | 77069.0 |\n", + "| Charlestown | 876 | 75339.0 |\n", + "| South Boston | 144 | 64745.0 |\n", + "| Jamaica Plain | 66 | 51655.0 |\n", + "| West Roxbury | 234 | 47836.0 |\n", + "| Brighton | 730 | 41261.0 |\n", + "| Roslindale | 104 | 41252.0 |\n", + "| Allston | 507 | 34149.0 |\n", + "| Hyde Park | 375 | 32744.0 |\n", + "| East Boston | 1229 | 31473.0 |\n", + "| Dorchester | 487 | 29767.0 |\n", + "| Mattapan | 715 | 28356.0 |\n", + "| Fenway | 66 | 28021.0 |\n", + "| Mission Hill | 78 | 23446.0 |\n", + "| Roxbury | 130 | 20978.0 |\n", + "| Longwood | 40 | 7975.0 |\n", + "+-------------------------+-----------+----------+\n" + ] + } + ], + "source": [ + "combined_df = pd.merge(neighborhood_data_df, income_data_df, left_on='Place', right_on='Neighborhood', how='outer')\n", + "\n", + "# Drop the redundant 'Neighborhood' column\n", + "combined_df = combined_df.drop(columns='Neighborhood')\n", + "\n", + "combined_df['Income'] = pd.to_numeric(combined_df['Income'])\n", + "# Display the combined dataframe\n", + "# print(combined_df[['Place', 'num_stops', 'Bus']].dropna().sort_values(by='Bus', ascending=False))\n", + "\n", + "from tabulate import tabulate\n", + "combined_df = combined_df[['Place', 'num_stops', 'Income']].dropna().sort_values(by='Income', ascending=False)\n", + "# Assuming 'combined_df' is the combined dataframe\n", + "table = tabulate(combined_df,\n", + " headers='keys', tablefmt='pretty', showindex=False)\n", + "\n", + "print(table)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['South Boston Waterfront' 'Back Bay' 'Beacon Hill' 'North End'\n", + " 'South End' 'Downtown' 'West End' 'Charlestown' 'South Boston'\n", + " 'Jamaica Plain' 'West Roxbury' 'Brighton' 'Roslindale' 'Allston'\n", + " 'Hyde Park' 'East Boston' 'Dorchester' 'Mattapan' 'Fenway' 'Mission Hill'\n", + " 'Roxbury' 'Longwood']\n", + "['South End', 'Fenway', 'Longwood', 'Mission Hill', 'Brighton', 'Allston', 'Roxbury', 'Charlestown']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "print(combined_df['Place'].values)\n", + "top_10_most_late_routes_neighbourhoods = [i for i in top_10_most_late_routes_neighbourhoods if i in combined_df['Place'].values]\n", + "print(top_10_most_late_routes_neighbourhoods)\n", + "# top 10 most late routes against median age\n", + "median_age_list = []\n", + "for i in top_10_most_late_routes_neighbourhoods:\n", + " if i in combined_df['Place'].values:\n", + " median_age_list.append(combined_df[combined_df['Place'] == i]['Income'].values[0])\n", + "\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.bar(top_10_most_late_routes_neighbourhoods, median_age_list, color='skyblue')\n", + "plt.xlabel('Neighborhoods')\n", + "plt.ylabel('Per Capita Income')\n", + "plt.title('Per Capita Income of Neighborhoods with top 5 most late routes')\n", + "plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n", + "plt.tight_layout()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['South Boston Waterfront' 'Back Bay' 'Beacon Hill' 'North End'\n", + " 'South End' 'Downtown' 'West End' 'Charlestown' 'South Boston'\n", + " 'Jamaica Plain' 'West Roxbury' 'Brighton' 'Roslindale' 'Allston'\n", + " 'Hyde Park' 'East Boston' 'Dorchester' 'Mattapan' 'Fenway' 'Mission Hill'\n", + " 'Roxbury' 'Longwood']\n", + "['Downtown', 'South Boston Waterfront', 'South Boston', 'South End', 'East Boston', 'Brighton']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "print(combined_df['Place'].values)\n", + "top_10_on_time_routes_neighbourhoods = [i for i in top_10_on_time_routes_neighbourhoods if i in combined_df['Place'].values]\n", + "print(top_10_on_time_routes_neighbourhoods)\n", + "# top 10 most late routes against median age\n", + "median_age_list = []\n", + "for i in top_10_on_time_routes_neighbourhoods:\n", + " if i in combined_df['Place'].values:\n", + " median_age_list.append(combined_df[combined_df['Place'] == i]['Income'].values[0])\n", + "\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.bar(top_10_on_time_routes_neighbourhoods, median_age_list, color='skyblue')\n", + "plt.xlabel('Neighborhoods')\n", + "plt.ylabel('Per Capita Income')\n", + "plt.title('Per Capita Income of Neighborhoods with top 5 on time routes')\n", + "plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n", + "plt.tight_layout()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "poverty_rate_data = poverty_rate = {\n", + " 'Allston': 27.9,\n", + " 'Back Bay': 11.9,\n", + " 'Beacon Hill': 6.4,\n", + " 'Brighton': 17.2,\n", + " 'Charlestown': 14.4,\n", + " 'Dorchester': 22.2,\n", + " 'Downtown': 21.1,\n", + " 'East Boston': 16.0,\n", + " 'Fenway': 39.2,\n", + " 'Hyde Park': 14.9,\n", + " 'Jamaica Plain': 12.8,\n", + " 'Longwood': 29.7,\n", + " 'Mattapan': 20.1,\n", + " 'Mission Hill': 37.0,\n", + " 'North End': 8.0,\n", + " 'Roslindale': 9.8,\n", + " 'Roxbury': 31.7,\n", + " 'South Boston': 14.1,\n", + " 'South Boston Waterfront': 5.9,\n", + " 'South End': 19.9,\n", + " 'West End': 14.0,\n", + " 'West Roxbury': 6.3\n", + "}\n", + "\n", + "poverty_rate_data_df = pd.DataFrame(poverty_rate_data, index=[0]).transpose()\n", + "poverty_rate_data_df = poverty_rate_data_df.reset_index()\n", + "poverty_rate_data_df.columns = ['Neighborhood', 'Poverty Rate']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "combined_df = pd.merge(neighborhood_data_df, poverty_rate_data_df, left_on='Place', right_on='Neighborhood', how='outer')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-------------------------+-----------+--------------+\n", + "| Place | num_stops | Poverty Rate |\n", + "+-------------------------+-----------+--------------+\n", + "| Fenway | 66 | 39.2 |\n", + "| Mission Hill | 78 | 37.0 |\n", + "| Roxbury | 130 | 31.7 |\n", + "| Longwood | 40 | 29.7 |\n", + "| Allston | 507 | 27.9 |\n", + "| Dorchester | 487 | 22.2 |\n", + "| Downtown | 26 | 21.1 |\n", + "| Mattapan | 715 | 20.1 |\n", + "| South End | 85 | 19.9 |\n", + "| Brighton | 730 | 17.2 |\n", + "| East Boston | 1229 | 16.0 |\n", + "| Hyde Park | 375 | 14.9 |\n", + "| Charlestown | 876 | 14.4 |\n", + "| South Boston | 144 | 14.1 |\n", + "| West End | 12 | 14.0 |\n", + "| Jamaica Plain | 66 | 12.8 |\n", + "| Back Bay | 49 | 11.9 |\n", + "| Roslindale | 104 | 9.8 |\n", + "| North End | 12 | 8.0 |\n", + "| Beacon Hill | 7 | 6.4 |\n", + "| West Roxbury | 234 | 6.3 |\n", + "| South Boston Waterfront | 28 | 5.9 |\n", + "+-------------------------+-----------+--------------+\n" + ] + } + ], + "source": [ + "# Drop the redundant 'Neighborhood' column\n", + "combined_df = combined_df.drop(columns='Neighborhood')\n", + "\n", + "combined_df['Income'] = pd.to_numeric(combined_df['Poverty Rate'])\n", + "# Display the combined dataframe\n", + "# print(combined_df[['Place', 'num_stops', 'Bus']].dropna().sort_values(by='Bus', ascending=False))\n", + "\n", + "from tabulate import tabulate\n", + "combined_df = combined_df[['Place', 'num_stops', 'Poverty Rate']].dropna().sort_values(by='Poverty Rate', ascending=False)\n", + "# Assuming 'combined_df' is the combined dataframe\n", + "table = tabulate(combined_df,\n", + " headers='keys', tablefmt='pretty', showindex=False)\n", + "\n", + "print(table)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Fenway' 'Mission Hill' 'Roxbury' 'Longwood' 'Allston' 'Dorchester'\n", + " 'Downtown' 'Mattapan' 'South End' 'Brighton' 'East Boston' 'Hyde Park'\n", + " 'Charlestown' 'South Boston' 'West End' 'Jamaica Plain' 'Back Bay'\n", + " 'Roslindale' 'North End' 'Beacon Hill' 'West Roxbury'\n", + " 'South Boston Waterfront']\n", + "['South End', 'Fenway', 'Longwood', 'Mission Hill', 'Brighton', 'Allston', 'Roxbury', 'Charlestown']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "print(combined_df['Place'].values)\n", + "top_10_most_late_routes_neighbourhoods = [i for i in top_10_most_late_routes_neighbourhoods if i in combined_df['Place'].values]\n", + "print(top_10_most_late_routes_neighbourhoods)\n", + "# top 10 most late routes against median age\n", + "median_age_list = []\n", + "for i in top_10_most_late_routes_neighbourhoods:\n", + " if i in combined_df['Place'].values:\n", + " median_age_list.append(combined_df[combined_df['Place'] == i]['Poverty Rate'].values[0])\n", + "\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.bar(top_10_most_late_routes_neighbourhoods, median_age_list, color='skyblue')\n", + "plt.xlabel('Neighborhoods')\n", + "plt.ylabel(' Poverty Rate')\n", + "plt.title('Poverty Rate of Neighborhoods with top 5 most late routes')\n", + "plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n", + "plt.tight_layout()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Fenway' 'Mission Hill' 'Roxbury' 'Longwood' 'Allston' 'Dorchester'\n", + " 'Downtown' 'Mattapan' 'South End' 'Brighton' 'East Boston' 'Hyde Park'\n", + " 'Charlestown' 'South Boston' 'West End' 'Jamaica Plain' 'Back Bay'\n", + " 'Roslindale' 'North End' 'Beacon Hill' 'West Roxbury'\n", + " 'South Boston Waterfront']\n", + "['Downtown', 'South Boston Waterfront', 'South Boston', 'South End', 'East Boston', 'Brighton']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "print(combined_df['Place'].values)\n", + "top_10_on_time_routes_neighbourhoods = [i for i in top_10_on_time_routes_neighbourhoods if i in combined_df['Place'].values]\n", + "print(top_10_on_time_routes_neighbourhoods)\n", + "# top 10 most late routes against median age\n", + "median_age_list = []\n", + "for i in top_10_on_time_routes_neighbourhoods:\n", + " if i in combined_df['Place'].values:\n", + " median_age_list.append(combined_df[combined_df['Place'] == i]['Poverty Rate'].values[0])\n", + "\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.bar(top_10_on_time_routes_neighbourhoods, median_age_list, color='skyblue')\n", + "plt.xlabel('Neighborhoods')\n", + "plt.ylabel('Poverty Rate')\n", + "plt.title('Poverty Rate of Neighborhoods with top 5 on time routes')\n", + "plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n", + "plt.tight_layout()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mlproject", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.18" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/fa23-team-d/README.md b/fa23-team-d/README.md index 598b011..555f53d 100644 --- a/fa23-team-d/README.md +++ b/fa23-team-d/README.md @@ -1,3 +1,5 @@ +# City of Boston: Transit and Performance - Team D + ## Team D: - Xavier Thomas, xthomas@bu.edu, XThomasBU, Class of 2025 @@ -5,3 +7,28 @@ - James Xiao, jamxiao@bu.edu, jamxiao2025, Class of 2025 - Ketan Suhaas Saichandran, ketanss@bu.edu, KetanAI, Class of 2025 - Haoxiang Huo, stevehuo@bu.edu, ioshuoyuhao, Class of 2024 + + +## Description +Problem Statement + +Public transportation is a pivotal element in shaping the daily experiences and quality of life for residents across Massachusetts and the Greater Boston area. Yet, the assurance of equal and fair access to quality service across all reachable areas remains a question that demands investigation. Given its profound impact on the day-to-day lives of residents, it becomes imperative to quantify the equity and fairness embedded in Boston's public transportation system and to discern the varying perceptions of service quality among different neighborhoods. +Our analysis leverages a combination of data science methodologies, encompassing the extraction and examination of public transportation data, and demographic information. We explored key questions such as: How does the quality of public transportation services vary across different neighborhoods? Are there discernible disparities in service frequency, reliability, and accessibility? How do factors such as income levels, population density, and geographic location correlate with the perceived quality of public transportation? +Our analysis provides a comprehensive overview of the existing state of public transportation equity. The goal is to pinpoint areas where improvements can be made, ensuring that the benefits of an efficient and reliable transportation network are shared equitably among diverse communities. + +Extension Project: Understand how Demographic Factors play a role in Bus Ridership/Accessibility. + +## Files +Please find the Notebooks under 'fa23-team-d/EDA_Notebooks' + + +## Datasets +Collected Data from the https://mbta-massdot.opendata.arcgis.com/ website. +The following Datasets were used for analysis: +1. PATI Bus Stops +2. MBTA Bus Arrival Departure Times +3. Bus Reliability +4. Bus Ridership by Time Period, Season, Route Line, and Stop +5. Wheelchair/Accessibility +6. 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