From c42203ac7a897a5e8fe4487ce244c805fd4e2e88 Mon Sep 17 00:00:00 2001 From: Shweta Adhikari Date: Tue, 27 Aug 2024 23:28:33 +0000 Subject: [PATCH] Ridership Dashboard Refactoring --- ahsc_grant/create_stop_freq.py | 173 --- ahsc_grant/create_stop_freq_06_27_2024.ipynb | 521 ------- ahsc_grant/create_stop_freq_refactor.ipynb | 1195 ++++------------- ahsc_grant/create_stop_freq_refactor.py | 2 +- .../join_analytical_file_refactor.ipynb | 1011 ++++++++++++++ ahsc_grant/process_metro_refactor.ipynb | 230 +--- ahsc_grant/process_mst_refactor.ipynb | 282 ++-- ahsc_grant/process_sbmtd_refactor.ipynb | 691 +++++++++- 8 files changed, 2196 insertions(+), 1909 deletions(-) delete mode 100644 ahsc_grant/create_stop_freq.py delete mode 100644 ahsc_grant/create_stop_freq_06_27_2024.ipynb create mode 100644 ahsc_grant/join_analytical_file_refactor.ipynb diff --git a/ahsc_grant/create_stop_freq.py b/ahsc_grant/create_stop_freq.py deleted file mode 100644 index 3eccaf9e3..000000000 --- a/ahsc_grant/create_stop_freq.py +++ /dev/null @@ -1,173 +0,0 @@ -import os -os.environ["CALITP_BQ_MAX_BYTES"] = str(800_000_000_000) - -import branca -import folium -from shared_utils import gtfs_utils_v2 - -from siuba import * -import pandas as pd -import geopandas as gpd - -import datetime as dt -import time - -import seaborn as sns -import matplotlib.pyplot as plt - - -#Function to fetch feeds, trips, stoptimes and stops_geo data from warehouse v2 -def get_feeds_trips_stops_data(selected_agencies, selected_date): - - trip_cols = ["name", "gtfs_dataset_key", "feed_key", "trip_id", "route_id", "route_type"] - stoptimes_cols = ["key", "_gtfs_key", "feed_key", "trip_id", "stop_id"] - stop_cols = ["feed_key", "stop_id", "geometry", "stop_name", "stop_code", "location_type", "stop_desc"] - - feeds = gtfs_utils_v2.schedule_daily_feed_to_gtfs_dataset_name(selected_date=selected_date) - - def select_by_agency(df, column, values): - if isinstance(values, str): - values = [values] # Convert single string to list of strings - - selected_df_list = [] - for value in values: - selected_df = df[df[column].str.contains(value, case=False)].copy() - selected_df_list.append(selected_df) - - selected_df_concat = pd.concat(selected_df_list, ignore_index=True) - return selected_df_concat - - feed_data = select_by_agency(feeds, 'name', selected_agencies) - - if feed_data.empty: - raise ValueError(f"No feeds data found for agencies '{selected_agencies}' on {selected_date}.") - - feed_key_list = feed_data['feed_key'].tolist() - - trips_data_list = [] - stoptimes_data_list = [] - stop_locations_gdf = gpd.GeoDataFrame() - - for feed_key in feed_key_list: - trips = gtfs_utils_v2.get_trips(selected_date=selected_date, operator_feeds=[feed_key])[trip_cols] - trips_data_list.append(trips) - - stoptimes = gtfs_utils_v2.get_stop_times(selected_date=selected_date, operator_feeds=[feed_key], - trip_df=trips, get_df=True)[stoptimes_cols] - stoptimes_data_list.append(stoptimes) - - stops_gdf = gtfs_utils_v2.get_stops(selected_date=selected_date, operator_feeds=[feed_key])[stop_cols] - stop_locations_gdf = pd.concat([stop_locations_gdf, stops_gdf], ignore_index=True) - - trips_data = pd.concat(trips_data_list, ignore_index=True) - stoptimes_data = pd.concat(stoptimes_data_list, ignore_index=True) - - return feed_data, trips_data, stoptimes_data, stop_locations_gdf - - -#Function to analyze data -def analyze_dataset(df): - # Number of rows and columns - num_rows, num_cols = df.shape - print(f"Number of rows: {num_rows}, Number of columns: {num_cols}") - print() - - # Print column names - column_names = df.columns.tolist() - print(f"Column names: \n{column_names}\n") - - # Print data types - print("Data types:") - print(df.dtypes) - print() - - # Check for duplicates - duplicate_rows = df[df.duplicated()] - if not duplicate_rows.empty: - print("Duplicate rows:") - print(duplicate_rows) - print() - else: - print("No duplicate rows found \n") - - # Print first 3 rows - print("First 3 rows:") - display(df.head(3)) - print() - -#Function to merge stops and trips and aggregate by route type, stop id, feed_key and agencies_name -def merge_and_aggregate_stops_and_trips(stoptimes_data, trips_data, agg_prefix=''): - on_cols = ["trip_id", "feed_key"] - how = 'left' - joined_df = pd.merge(stoptimes_data, trips_data, on=on_cols, how=how, suffixes=('_stops','_trips')) - - groupby_cols=['route_type', 'stop_id', 'feed_key', 'name'] - agg_cols={ - f'n_trips_{agg_prefix}': ('trip_id', 'nunique'), - f'n_routes_{agg_prefix}': ('route_id', 'nunique') -} - aggregated_data = joined_df.groupby(groupby_cols).agg(**agg_cols).reset_index() - return aggregated_data - - -#Function to merge stops_geo and stop_times -def merge_stops(stoptimes_data, stops_location_data, on_cols): - joined_df = pd.merge(stoptimes_data, stops_location_data, on=on_cols) - return joined_df - -#Function to merge stoptimes for weekday, saturday and sunday -def merge_stoptimes(stoptimes_weekday, stoptimes_sat, stoptimes_sun, merge_cols, final_cols): - merged_df = pd.merge(stoptimes_weekday, stoptimes_sat, on=merge_cols, how="outer") - merged_df = pd.merge(merged_df, stoptimes_sun, on=merge_cols, how="outer") - - merged_df=merged_df[final_cols] - return merged_df - -#Function to plot trips per stops and routes per stop -import matplotlib.pyplot as plt -def plot_histogram(data, column, title): - data.pivot(columns='name', values=column).plot.hist(grid=True, bins=100, rwidth=0.9, log=True, - title=title) - plt.xlabel('Trips per Stop') - plt.ylabel('Number of Stops') - plt.show() - - -analysis_dt = dt.date(2022,6,1) -analysis_sat = dt.date(2022,6,4) -analysis_sun = dt.date(2022,6,5) - -dates_labelled = {'weekday': analysis_dt, 'saturday': analysis_sat, 'sunday': analysis_sun} -selected_agencies = ['LA Metro', 'Salinas', 'SBMTD'] - -warehouse_data_by_date = {} -for daytype in dates_labelled.keys(): - print(daytype) - analysis_dt = dates_labelled[daytype] - # tuple ordered: feed_data, trips_data, stoptimes_data, stop_locations_gdf - warehouse_data_by_date[daytype] = get_feeds_trips_stops_data(selected_agencies, analysis_dt) - - stops_all = [] - -for daytype in dates_labelled.keys(): - print(daytype) - analysis_dt = dates_labelled[daytype] - trips = warehouse_data_by_date[daytype][1] - st = warehouse_data_by_date[daytype][2] - stops = warehouse_data_by_date[daytype][3] - st_merged = merge_and_aggregate_stops_and_trips(trips, st, agg_prefix=daytype) - stop_merged = merge_stops(st_merged, stops, ["stop_id", "feed_key"]) - stops_all.append(stop_merged) - -merge_cols = ["name","route_type", "stop_id","geometry", "stop_code", "stop_name", "location_type"] -final_cols = ["name","feed_key","location_type","route_type","stop_name","stop_id","stop_code","geometry","n_trips_weekday","n_trips_saturday","n_trips_sunday","n_routes_weekday","n_routes_saturday","n_routes_sunday", "stop_desc"] - -stoptimes_all = merge_stoptimes(*stops_all, merge_cols=merge_cols, final_cols=final_cols) -stoptimes_all_gdf = gpd.GeoDataFrame(stoptimes_all, geometry='geometry') -filtered_stoptimes_all_gdf = stoptimes_all_gdf[stoptimes_all_gdf['route_type'] == '3'] - -GCS_FILE_PATH = 'gs://calitp-analytics-data/data-analyses/ahsc_grant' -filtered_stoptimes_all_gdf.to_parquet(f"{GCS_FILE_PATH}/tbl1_trips_perstop_07_08_2024.parquet") - - - diff --git a/ahsc_grant/create_stop_freq_06_27_2024.ipynb b/ahsc_grant/create_stop_freq_06_27_2024.ipynb deleted file mode 100644 index 6be094019..000000000 --- a/ahsc_grant/create_stop_freq_06_27_2024.ipynb +++ /dev/null @@ -1,521 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 60, - "id": "8f31c0d0-f626-485b-a7c4-658f7ec1c5bd", - "metadata": {}, - "outputs": [], - "source": [ - "# %run create_stop_freq.py" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "eb73bba3-7736-4df6-a0b0-94e61e06c758", - "metadata": {}, - "outputs": [], - "source": [ - "from create_stop_freq import *" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e50217d6-fc21-4371-a874-befcb6df3431", - "metadata": {}, - "outputs": [], - "source": [ - "analysis_dt = dt.date(2022,6,1)\n", - "analysis_sat = dt.date(2022,6,4)\n", - "analysis_sun = dt.date(2022,6,5)\n", - "\n", - "selected_agencies = ['LA Metro Bus', 'Salinas', 'SBMTD']" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b1b39ecd-6dd4-4715-88f5-e1eea090a91c", - "metadata": {}, - "outputs": [], - "source": [ - "dates_labelled = {'weekday': analysis_dt, 'saturday': analysis_sat, 'sunday': analysis_sun}" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "5c182fc1-4b59-4c8a-860e-547d8ff73caf", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "weekday\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'GEOGRAPHY' of column 'pt_geom'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'GEOGRAPHY' of column 'pt_geom'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'GEOGRAPHY' of column 'pt_geom'\n", - " sqlalchemy.util.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "saturday\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'GEOGRAPHY' of column 'pt_geom'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'GEOGRAPHY' of column 'pt_geom'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'GEOGRAPHY' of column 'pt_geom'\n", - " sqlalchemy.util.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sunday\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'GEOGRAPHY' of column 'pt_geom'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'GEOGRAPHY' of column 'pt_geom'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'GEOGRAPHY' of column 'pt_geom'\n", - " sqlalchemy.util.warn(\n" - ] - } - ], - "source": [ - "warehouse_data_by_date = {}\n", - "\n", - "for daytype in dates_labelled.keys():\n", - " print(daytype)\n", - " analysis_dt = dates_labelled[daytype]\n", - " # tuple ordered: feed_data, trips_data, stoptimes_data, stop_locations_gdf\n", - " warehouse_data_by_date[daytype] = get_feeds_trips_stops_data(selected_agencies, analysis_dt)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "99cbe84a-ef5d-4896-aee4-da5808e938e9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['weekday', 'saturday', 'sunday'])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "warehouse_data_by_date.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "35fa6a83-c756-4a67-93e9-611f03887acf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tuple" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(warehouse_data_by_date['weekday'])" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "e63ab915-19ae-484c-9711-b1e54c75e0d7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(warehouse_data_by_date['weekday'])" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "ee91b452-f3d1-4155-a3ad-154072fe3624", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "weekday\n", - "saturday\n", - "sunday\n" - ] - } - ], - "source": [ - "stops_all = []\n", - "\n", - "for daytype in dates_labelled.keys():\n", - " print(daytype)\n", - " analysis_dt = dates_labelled[daytype]\n", - " trips = warehouse_data_by_date[daytype][1]\n", - " st = warehouse_data_by_date[daytype][2]\n", - " stops = warehouse_data_by_date[daytype][3]\n", - " st_merged = merge_and_aggregate_stops_and_trips(trips, st, agg_prefix=daytype)\n", - " stop_merged = merge_stops(st_merged, stops, [\"stop_id\", \"feed_key\"])\n", - " stops_all.append(stop_merged)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "414546fc-7d2d-4b73-9d2b-b4c5ac106cb7", - "metadata": {}, - "outputs": [], - "source": [ - "merge_cols = [\"feed_key\",\"route_type\", \"stop_id\",\"geometry\", \"stop_code\", \"stop_name\", \"location_type\"]\n", - "final_cols = [\"name\",\"feed_key\",\"location_type\",\"route_type\",\"stop_name\",\"stop_id\",\"stop_code\",\"geometry\",\"n_trips_weekday\",\"n_trips_saturday\",\"n_trips_sunday\",\"n_routes_weekday\",\"n_routes_saturday\",\"n_routes_sunday\"]" - ] - }, - { - "cell_type": "markdown", - "id": "439cc3d5-339f-4053-93ec-7ab4e6f41cb1", - "metadata": {}, - "source": [ - "https://www.geeksforgeeks.org/packing-and-unpacking-arguments-in-python/" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "adabf584-17eb-4cba-a3ca-e92af5eed597", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/data-analyses/ahsc_grant/create_stop_freq.py:120: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n", - " merged_df = pd.merge(stoptimes_weekday, stoptimes_sat, on=merge_cols, how=\"outer\")\n" - ] - } - ], - "source": [ - "stoptimes_all = merge_stoptimes(*stops_all, merge_cols=merge_cols, final_cols=final_cols)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "a89d8474-5caf-4b2a-bc11-95aa3cd80e56", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['3'], dtype=object)" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "stoptimes_all.route_type.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "fd0d6e8b-2c0f-4e54-a1c6-7129da674c35", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(stoptimes_all.route_type == '3').all()" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "de2496f1-45f7-462f-a0f2-a58bcb375ceb", - "metadata": {}, - "outputs": [], - "source": [ - "# stoptimes_all = (stoptimes_all\n", - "# >> filter(_.route_type==\"3\")\n", - "# )\n", - "\n", - "# check using assert instead of filter" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "8a4c2ba9-7f80-40fc-bbd2-42bd3482ff98", - "metadata": {}, - "outputs": [], - "source": [ - "assert (stoptimes_all.route_type == '3').all()" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "af844dc0-1e00-4ac6-909b-280015b773a8", - "metadata": {}, - "outputs": [], - "source": [ - "valid_weekday_data = stoptimes_all[pd.notnull(stoptimes_all['n_trips_weekday'])]\n", - "valid_saturday_data = stoptimes_all[pd.notnull(stoptimes_all['n_trips_saturday'])]\n", - "valid_sunday_data = stoptimes_all[pd.notnull(stoptimes_all['n_trips_sunday'])]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9e5e705a-8365-4256-8c2a-31ae159bccb9", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO export to GCS" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "2cf7f5ed-518a-4b09-a7c9-cf579d36bc43", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_histogram(valid_weekday_data, 'n_trips_weekday', 'Weekday Trips Per Stop')\n", - "plot_histogram(valid_weekday_data, 'n_routes_weekday', 'Weekday Routes Per Stop')\n", - "plot_histogram(valid_saturday_data, 'n_trips_saturday', 'Saturday Trips Per Stop')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "f75eb3cd-9b8e-41ae-a71c-d27fcc333cbe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.lmplot(x=\"n_routes_weekday\",y=\"n_trips_weekday\", hue=\"feed_key\", data=valid_weekday_data).set(title=\"weekday routes vs trips\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.9.13" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/ahsc_grant/create_stop_freq_refactor.ipynb b/ahsc_grant/create_stop_freq_refactor.ipynb index 87ba8c4d0..7cf2b945a 100644 --- a/ahsc_grant/create_stop_freq_refactor.ipynb +++ b/ahsc_grant/create_stop_freq_refactor.ipynb @@ -1,1063 +1,476 @@ { "cells": [ { - "cell_type": "markdown", - "id": "02063212-cde4-4ea3-ae8f-7013fb80ad1b", - "metadata": {}, - "source": [ - "# Transit Ridership Dashboard GTFS Refactor" - ] - }, - { - "cell_type": "markdown", - "id": "e080cabd-e085-41a2-8e65-a3b5cbf89dad", + "cell_type": "code", + "execution_count": 1, + "id": "8f31c0d0-f626-485b-a7c4-658f7ec1c5bd", "metadata": {}, + "outputs": [], "source": [ - "- Migrating the transit ridership dashboard created Fall 2022 to warehouse v2" + "# %run create_stop_freq.py" ] }, { "cell_type": "code", - "execution_count": 1, - "id": "b8bd1058-65b3-4743-9823-1fc2e846e582", + "execution_count": 2, + "id": "eb73bba3-7736-4df6-a0b0-94e61e06c758", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: shared_utils in /home/jovyan/data-analyses/_shared_utils (2.5)\n", - "Note: you may need to restart the kernel to use updated packages.\n" + "weekday\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'GEOGRAPHY' of column 'pt_geom'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'GEOGRAPHY' of column 'pt_geom'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'GEOGRAPHY' of column 'pt_geom'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'GEOGRAPHY' of column 'pt_geom'\n", + " sqlalchemy.util.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saturday\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'GEOGRAPHY' of column 'pt_geom'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'GEOGRAPHY' of column 'pt_geom'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'GEOGRAPHY' of column 'pt_geom'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'GEOGRAPHY' of column 'pt_geom'\n", + " sqlalchemy.util.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sunday\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: 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type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'GEOGRAPHY' of column 'pt_geom'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'GEOGRAPHY' of column 'pt_geom'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", + " sqlalchemy.util.warn(\n", + "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'GEOGRAPHY' of column 'pt_geom'\n", + " sqlalchemy.util.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "weekday\n", + "saturday\n", + "sunday\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jovyan/data-analyses/ahsc_grant/create_stop_freq_refactor.py:120: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n", + " merged_df = pd.merge(stoptimes_weekday, stoptimes_sat, on=merge_cols, how=\"outer\")\n" ] } ], "source": [ - "pip install shared_utils" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a22dd650-cef5-4471-9753-6a9f19f6d723", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.environ[\"CALITP_BQ_MAX_BYTES\"] = str(800_000_000_000)\n", - "\n", - "import branca\n", - "import folium\n", - "from shared_utils import gtfs_utils_v2\n", - "\n", - "from siuba import *\n", - "import pandas as pd\n", - "import geopandas as gpd \n", - "\n", - "import datetime as dt\n", - "import time" + "from create_stop_freq_refactor import *" ] }, { "cell_type": "code", "execution_count": 3, - "id": "793efb67-0391-40c6-8816-b4dd683a3bd4", + "id": "e50217d6-fc21-4371-a874-befcb6df3431", "metadata": {}, "outputs": [], "source": [ - "# Creating function for datacheck\n", - "def analyze_dataset(df):\n", - " #Number of rows and columns\n", - " num_rows, num_cols = df.shape \n", - " print(f\"Number of rows: {num_rows}, Number of columns: {num_cols}\")\n", - " print()\n", - " \n", - " # Print column names \n", - " column_names = df.columns.tolist()\n", - " print(f\"Column names: \\n{column_names}\\n\")\n", - " \n", - " #Print data type\n", - " print(\"Data type:\")\n", - " print(type(df))\n", - " print()\n", - " \n", - " # Print data types\n", - " print(\"Data types:\")\n", - " print(df.dtypes)\n", - " print()\n", - " \n", - " # Check for duplicates\n", - " duplicate_rows = df[df.duplicated()]\n", - " if not duplicate_rows.empty:\n", - " print(\"Duplicate rows:\")\n", - " print(duplicate_rows)\n", - " print()\n", - " else:\n", - " print(\"No duplicate rows found \\n\")\n", - " \n", - " # Print first 3 words \n", - " print(\"First 3 rows:\")\n", - " display(df.head(3))\n", - " print()\n", + "# analysis_dt = dt.date(2022,6,1)\n", + "# analysis_sat = dt.date(2022,6,4)\n", + "# analysis_sun = dt.date(2022,6,5)\n", "\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "8acd858f-8d0a-4244-b994-dfa7699c9228", - "metadata": {}, - "source": [ - "## Creating trips per weekday, saturday and sunday by stop" + "# selected_agencies = ['LA Metro Bus', 'Salinas', 'SBMTD']" ] }, { "cell_type": "code", "execution_count": 4, - "id": "e8c7aebf-8fca-46d0-b005-94fe554504d3", + "id": "b1b39ecd-6dd4-4715-88f5-e1eea090a91c", "metadata": {}, "outputs": [], "source": [ - "# using the 2022 data used in the previous dashboard.\n", - "analysis_dt = dt.date(2022,6,1)\n", - "analysis_sat = dt.date(2022,6,4)\n", - "analysis_sun = dt.date(2022,6,5)\n", - "\n", - "analysis_operator_list = [182,293,208]" - ] - }, - { - "cell_type": "markdown", - "id": "970f727a-be42-4f29-9494-b769f1651738", - "metadata": {}, - "source": [ - "### Extracting Feed Data for Weekdays " + "# dates_labelled = {'weekday': analysis_dt, 'saturday': analysis_sat, 'sunday': analysis_sun}" ] }, { "cell_type": "code", - "execution_count": 39, - "id": "2d54faa0-24b4-4a35-ac87-8ec966701f13", - "metadata": {}, + "execution_count": 5, + "id": "5c182fc1-4b59-4c8a-860e-547d8ff73caf", + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "feeds = gtfs_utils_v2.schedule_daily_feed_to_gtfs_dataset_name(selected_date=analysis_sat)" + "# warehouse_data_by_date = {}\n", + "\n", + "# for daytype in dates_labelled.keys():\n", + "# print(daytype)\n", + "# analysis_dt = dates_labelled[daytype]\n", + "# # tuple ordered: feed_data, trips_data, stoptimes_data, stop_locations_gdf\n", + "# warehouse_data_by_date[daytype] = get_feeds_trips_stops_data(selected_agencies, analysis_dt)" ] }, { "cell_type": "code", - "execution_count": 40, - "id": "2df018a1-51e4-42bd-a53a-436ca2bc9455", + "execution_count": 6, + "id": "99cbe84a-ef5d-4896-aee4-da5808e938e9", "metadata": {}, "outputs": [], "source": [ - "filtered_feeds = feeds[feeds['name'].str.contains('MTD')]" + "# warehouse_data_by_date.keys()" ] }, { "cell_type": "code", - "execution_count": 41, - "id": "d887ee31-c6c8-4e7d-bb83-dfc0e64bc35c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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keydatefeed_keyfeed_timezonebase64_urlgtfs_dataset_keynametyperegional_feed_type
19607cf74ca10f10d0c504828ccace9b5a92022-06-04339cdaba8e9729e2b21b45754b842646America/Los_AngelesaHR0cDovL3NibXRkLmdvdi9nb29nbGVfdHJhbnNpdC9mZW...fa0149a8b4dcc0fecc412387f0230fa7SBMTD SchedulescheduleNone
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keydatefeed_keyfeed_timezonebase64_urlgtfs_dataset_keynametyperegional_feed_type
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namegtfs_dataset_keyfeed_keytrip_idroute_idroute_type
0LA Metro Bus Schedulea09d454d421c1ef01e77b9e94aad0f5e06d1f3ac2b0ae5e74424edbbfefa19edDSE-HG-1650-DS-008DSE-HG3
1LA Metro Bus Schedulea09d454d421c1ef01e77b9e94aad0f5e06d1f3ac2b0ae5e74424edbbfefa19edDSE-HG-1650-DS-007DSE-HG3
2LA Metro Bus Schedulea09d454d421c1ef01e77b9e94aad0f5e06d1f3ac2b0ae5e74424edbbfefa19edDSE-HG-1650-DS-005DSE-HG3
\n", - "
" - ], "text/plain": [ - " name gtfs_dataset_key \\\n", - "0 LA Metro Bus Schedule a09d454d421c1ef01e77b9e94aad0f5e \n", - "1 LA Metro Bus Schedule a09d454d421c1ef01e77b9e94aad0f5e \n", - "2 LA Metro Bus Schedule a09d454d421c1ef01e77b9e94aad0f5e \n", - "\n", - " feed_key trip_id route_id route_type \n", - "0 06d1f3ac2b0ae5e74424edbbfefa19ed DSE-HG-1650-DS-008 DSE-HG 3 \n", - "1 06d1f3ac2b0ae5e74424edbbfefa19ed DSE-HG-1650-DS-007 DSE-HG 3 \n", - "2 06d1f3ac2b0ae5e74424edbbfefa19ed DSE-HG-1650-DS-005 DSE-HG 3 " + "False" ] }, + "execution_count": 21, "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "output_type": "execute_result" } ], "source": [ - "analyze_dataset(metro_trips)" + "(stoptimes_all.route_type == '3').all()" ] }, { - "cell_type": "markdown", - "id": "73f5d5fc-a1a6-47b6-bd7f-91bbbfcb26bd", + "cell_type": "code", + "execution_count": 14, + "id": "de2496f1-45f7-462f-a0f2-a58bcb375ceb", "metadata": {}, + "outputs": [], "source": [ - "### Getting Stop Times Data for Weekdays" + "# stoptimes_all = (stoptimes_all\n", + "# >> filter(_.route_type==\"3\")\n", + "# )\n", + "\n", + "# check using assert instead of filter" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "54634e9c-bd52-43fd-ad43-a2ec2002a6ac", + "execution_count": null, + "id": "8a4c2ba9-7f80-40fc-bbd2-42bd3482ff98", "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'arrival_time_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'departure_time_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'start_pickup_drop_off_window_interval'\n", - " sqlalchemy.util.warn(\n", - "/opt/conda/lib/python3.9/site-packages/sqlalchemy_bigquery/_types.py:101: SAWarning: Did not recognize type 'INTERVAL' of column 'end_pickup_drop_off_window_interval'\n", - " sqlalchemy.util.warn(\n" + "ename": "AssertionError", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[22], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m (stoptimes_all\u001b[38;5;241m.\u001b[39mroute_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m3\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39mall()\n", + "\u001b[0;31mAssertionError\u001b[0m: " ] } ], "source": [ - "metro_stops = gtfs_utils_v2.get_stop_times(selected_date=analysis_dt, operator_feeds=metrofeed_list, \n", - " trip_df = metro_trips, get_df= True)" + "# assert (stoptimes_all.route_type == '3').all()" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "a04eb1d1-bd30-46c2-91be-3ccc3ac6649a", + "execution_count": 16, + "id": "af844dc0-1e00-4ac6-909b-280015b773a8", "metadata": {}, "outputs": [], "source": [ - "stop_cols=[\"key\", \"_gtfs_key\", \"feed_key\", \"trip_id\", \"stop_id\"]" + "valid_weekday_data = stoptimes_all[pd.notnull(stoptimes_all['n_trips_weekday'])]\n", + "valid_saturday_data = stoptimes_all[pd.notnull(stoptimes_all['n_trips_saturday'])]\n", + "valid_sunday_data = stoptimes_all[pd.notnull(stoptimes_all['n_trips_sunday'])]" ] }, { "cell_type": "code", - "execution_count": 16, - "id": "74b3ff29-f009-430b-90a1-66e26613335f", + "execution_count": 17, + "id": "9e5e705a-8365-4256-8c2a-31ae159bccb9", "metadata": {}, "outputs": [], "source": [ - "metro_stops= metro_stops[stop_cols]" + "# TODO export to GCS" ] }, { "cell_type": "code", - "execution_count": 17, - "id": "e914b55e-9354-4699-914f-47af29d7c5fb", + "execution_count": 18, + "id": "2cf7f5ed-518a-4b09-a7c9-cf579d36bc43", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of rows: 793697, Number of columns: 5\n", - "\n", - "Column names: \n", - "['key', '_gtfs_key', 'feed_key', 'trip_id', 'stop_id']\n", - "\n", - "Data type:\n", - "\n", - "\n", - "Data types:\n", - "key object\n", - "_gtfs_key object\n", - "feed_key object\n", - "trip_id object\n", - "stop_id object\n", - "dtype: object\n", - "\n", - "No duplicate rows found \n", - "\n", - "First 3 rows:\n" - ] + "data": { + "image/png": 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"text/plain": [ - " key _gtfs_key \\\n", - "0 bd8866407ade3e81eddbbe7ebe6e6e86 8bda692f5f1c6b0fd99b05990845b189 \n", - "1 1449dbb7bbac7b5e9ae8356de963b096 6a13fb05e30ae71fa772fdfb445c11d6 \n", - "2 7add6f0ae1867b4c6a23e0c32fd3e4ce 881d52b8a1aa430179c64b3d52155d74 \n", - "\n", - " feed_key trip_id stop_id \n", - "0 bc633d97886566eba81d46f81b0573b6 56064321 80109 \n", - "1 bc633d97886566eba81d46f81b0573b6 55217353 80427 \n", - "2 bc633d97886566eba81d46f81b0573b6 55217353 80426 " + "
" ] }, "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "analyze_dataset(metro_stops)" - ] - }, - { - "cell_type": "markdown", - "id": "7ccdd44d-64b4-4451-b462-7222a71c8d58", - "metadata": {}, - "source": [ - "### Joining Stop Times and Trip Data " - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "940ed697-5e8c-4bf2-99f0-f39d1f676d8f", - "metadata": {}, - "outputs": [], - "source": [ - "metro_joined = pd.merge(\n", - " metro_stops, metro_trips,\n", - " on = [\"trip_id\", \"feed_key\"],\n", - " how = 'left'\n", - ") " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "46df0312-de02-4ef1-b111-dcd1d2d9d816", - "metadata": {}, - "outputs": [ { "data": { - "text/html": [ - "
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", "text/plain": [ - " key _gtfs_key \\\n", - "0 bd8866407ade3e81eddbbe7ebe6e6e86 8bda692f5f1c6b0fd99b05990845b189 \n", - "1 1449dbb7bbac7b5e9ae8356de963b096 6a13fb05e30ae71fa772fdfb445c11d6 \n", - "2 7add6f0ae1867b4c6a23e0c32fd3e4ce 881d52b8a1aa430179c64b3d52155d74 \n", - "\n", - " feed_key trip_id stop_id name \\\n", - "0 bc633d97886566eba81d46f81b0573b6 56064321 80109 LA Metro Rail Schedule \n", - "1 bc633d97886566eba81d46f81b0573b6 55217353 80427 LA Metro Rail Schedule \n", - "2 bc633d97886566eba81d46f81b0573b6 55217353 80426 LA Metro Rail Schedule \n", - "\n", - " gtfs_dataset_key route_id route_type \n", - "0 683682f3c501f1edd5954f0a1f2a4d12 801 0 \n", - "1 683682f3c501f1edd5954f0a1f2a4d12 804 0 \n", - "2 683682f3c501f1edd5954f0a1f2a4d12 804 0 " + "
" ] }, - "execution_count": 19, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "metro_joined.head(3)" - ] - }, - { - "cell_type": "markdown", - "id": "c36852a3-e91f-4e20-87ce-6f5c7cab1170", - "metadata": {}, - "source": [ - "### Finding Number of Trips on weekday per Stops" + "plot_histogram(valid_weekday_data, 'n_trips_weekday', 'Weekday Trips Per Stop')\n", + "plot_histogram(valid_weekday_data, 'n_routes_weekday', 'Weekday Routes Per Stop')\n", + "plot_histogram(valid_saturday_data, 'n_trips_saturday', 'Saturday Trips Per Stop')" ] }, { "cell_type": "code", - "execution_count": 21, - "id": "04af2b1e-33ed-4813-8341-ea743f27671a", - "metadata": {}, - "outputs": [], - "source": [ - "metrotrips_weekday = metro_joined.groupby(['route_type', 'stop_id']).agg(\n", - " ntrips_route = ('route_id', 'nunique'),\n", - " ntrips_trip = ('trip_id', 'nunique')).reset_index()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "f63e91d4-31b3-47ab-8d48-382d375d664c", + "execution_count": 20, + "id": "f75eb3cd-9b8e-41ae-a71c-d27fcc333cbe", "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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route_typestop_idntrips_weekdayntrips_route
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...............
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12251 rows × 4 columns

\n", - "
" - ], "text/plain": [ - " route_type stop_id ntrips_weekday ntrips_route\n", - "0 0 80101 197 1\n", - "1 0 80102 94 1\n", - "2 0 80105 197 1\n", - "3 0 80106 197 1\n", - "4 0 80107 197 1\n", - "... ... ... ... ...\n", - "12246 3 9992 99 1\n", - "12247 3 9993 99 1\n", - "12248 3 9994 99 1\n", - "12249 3 9996 99 1\n", - "12250 3 9997 116 2\n", - "\n", - "[12251 rows x 4 columns]" + "" ] }, - "execution_count": 28, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" - } - ], - "source": [ - "metrotrips_weekday" - ] - }, - { - "cell_type": "markdown", - "id": "8c73c76d-936f-404d-9d38-c9ef040f3a3e", - "metadata": {}, - "source": [ - "### Finding Number of Trips on Saturday per Stops" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "2eb4002c-8032-46ea-a29e-51b41e00cab1", - "metadata": {}, - "outputs": [ + }, { "data": { + "image/png": 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vvEKjRo08Jtydz+23387MmTOZOXMmoaGhxUaGx40bR3p6OgMGDKBRo0YcO3aMF198kc6dO+u5zCXx8fGhbdu2rF27lpiYGEJDQ2nfvr1HCbmyaNmyJf369WPChAnYbDaWLl1KvXr1eOihhwD3qO/VV1/N7bffTtu2bTGZTHz88cecPn1aL78mhBCiEtV0+QghCmVnZ2tGo1ELCAjwKDf1zjvvaIA2cuTIEq/bsmWLNnDgQC0oKEizWCxaixYttNGjR2u//vqrx3mHDx/W7r33Xi0yMlLz8vLSGjZsqA0dOlT78MMP9XOKliwr5HK5tLvuukszmUzahg0b9P0fffSRFhsbq5nNZq1t27ba+vXri5Wv0jRNi4uL01q1aqWZzWatTZs22sqVK7U5c+Zo5/vv99xzz2mA9vTTT5f6tWvatKk2ZMiQEo8dPnxYu/XWW7Xg4GDNYrFol112mbZx48YSz7vmmms0s9ms1a9fX3v00Ue1zZs3FytZlpubq919991acHBwsRJtdrtde/bZZ7V27dppZrNZCwkJ0bp166bNmzdPy8rK0jRN07799lvtxhtv1Bo0aKB5e3trDRo00O666y4tKSmp1M/bt29fDdDGjRtX7NiHH36oXXfddVpERITm7e2tNWnSRPv3v/+tJScnX7DdHTt2aN26ddO8vb09ypeNGjVK8/PzK/Ga85Us+89//qMtWrRIa9y4sWY2m7XLL79c27t3r35eWlqaNmnSJK1Nmzaan5+fFhQUpPXs2VP74IMPSv06CCGEKD1F0/5XH0kIUSssW7aMadOmcfToUZo0aVLT3RFldPToUaKjo/nPf/7DzJkza7o7Qggh/kdyeoWoRTRNIy4ujv79+0vAK4QQQlQiyekVohbIy8vj008/ZcuWLSQkJPDJJ5/UdJeEEEKIi4oEvULUAmfOnOHuu+8mODiYRx99lBtuuKGmuySEEEJcVCSnVwghhBBCXPQkp1cIIYQQQlz0JOgVQgghhBAXPQl6cc+Yz87ORjI9hBBCCCEuThL0Ajk5OQQFBZGTk1PTXRFCCCGEEFVAgl4hhBBCCHHRk6BXCCGEEEJc9CToFUIIIYQQFz0JeoUQQgghxEVPgl4hhBBCCHHRk6BXCCGEEEJc9CToFUIIIYQQFz0JeoUQQgghxEVPgl4hhBBCCHHRk6BXCCGEEEJc9CToFUIIIYQQFz0JeoUQQgghxEVPgl4hhBBCCHHRM9V0B4QQQoiK0FQV6779uDIyMIaEYGkbi2KQMR0hhCcJeoUQQtRZeT/9RNprr2M/cgTN4UDx8sI7Opqw++/Dr1evmu6eEKIWkV+FhRBC1El5P/1E8pw52A4exODriyk8HIOvL7akJJLnzCHvp59quotCiFpEgl4hhBB1jqaqpL32OmpuHqb69TFYLCgGAwaLBVNEBGpePmmvvY6mqjXdVSFELSFBrxCiyqiaSuLZRLaf3E7i2URUTQIQUTms+/ZjP3IEY3AwiqJ4HFMUBWNQEPYjR7Du219DPRRC1DaS0yuEqBLxyfHEJcRxJPsITtWJyWAiOjCasR3G0jOqZ013T9RxrowMdw6vt3eJxxVvb7SsLFwZGdXcMyFEbSUjvUKIShefHM/8nfNJykjC1+RLmE8YviZfkjKSmL9zPvHJ8TXdRVHHGUNCULy80Oz2Eo9rdjuKlxfGkJBq7pkQoraSoFcIUalUTSUuIY48Rx4RvhFYTBYMigGLyUKEbwR5jjziEuIk1UFUiKVtLN7R0biystA0zeOYpmm4srLwjo7G0ja2hnoohKhtJOgVQlSq/en7OZJ9hCBzUIm5lkHmII5kH2F/uuRaivJTDAbC7r8Pg58vztRUVKsVTVVRrVacqakY/PwIu/8+qdcrhNDJdwMhRKXKtGbiVJ14G0vOtfQ2euNUnWRaM6u3Y+Ki49erF1Hz5mGOiUHNz8d55gxqfj7mmBii5s2VOr1CCA8ykU0IUamCLcGYDCbsLjsWk6XYcbvLjslgItgSXP2dExcdv1698L3sMlmRTQhxQRL0CiEqVWxoLNGB0SRlJGE2mj1SHDRNI8uWRUxIDLGhkmspKodiMODTvl1Nd0MIUcvJr8JCiEplUAyM7TAWPy8/UvNTsTqtqJqK1WklNT8VPy8/xnYYi0GRbz9CCCGqj6KdO+31EpSdnU1QUBBZWVkEBgbWdHeEuChInV4hhBC1iQS9SNArRFVRNZX96fvJtGYSbAkmNjRWRniFEELUCMnpFUJUGYNioF09ybUUQghR82TIRQghhBBCXPQk6BVCCCGEEBc9CXqFEEIIIcRFT4JeIYQQQghx0avRoLdZs2YoilLsY9KkSQBYrVYmTZpEvXr18Pf3Z/jw4Zw+fdqjjePHjzNkyBB8fX2JiIjg//7v/3A6nTXxOEIIIYQQopaq0aD3l19+ITk5Wf/YvHkzALfddhsA06ZN47PPPmPdunV8//33nDp1iltuuUW/3uVyMWTIEOx2Ozt27GD16tWsWrWK2bNn18jzCCGEEEKI2qlW1emdOnUqGzdu5NChQ2RnZxMeHs57773HrbfeCsCBAweIjY1l586d9OrViy+++IKhQ4dy6tQp6tevD8CKFSt4+OGHOXPmDN7e3qW6r9TpFUIIIYS4uNWanF673c4777zDmDFjUBSFXbt24XA4uOaaa/Rz2rRpQ5MmTdi5cycAO3fupEOHDnrACzBw4ECys7NJTEw8771sNhvZ2dkeH0IIIYQQ4uJVa4LeDRs2kJmZyejRowFISUnB29ub4OBgj/Pq169PSkqKfk7RgLfweOGx81m4cCFBQUH6R+PGjSvvQYQQQgghRK1Ta4LeuLg4Bg8eTIMGDar8XrNmzSIrK0v/OHHiRJXfUwghhBBC1JxasQzxsWPH+Oabb1i/fr2+LzIyErvdTmZmpsdo7+nTp4mMjNTP+fnnnz3aKqzuUHhOScxmM2azuRKfQAghhBBC1Ga1YqR35cqVREREMGTIEH1ft27d8PLy4ttvv9X3HTx4kOPHj9O7d28AevfuTUJCAqmpqfo5mzdvJjAwkLZt21bfAwghhBBCiFqtxkd6VVVl5cqVjBo1CpPp7+4EBQUxduxYpk+fTmhoKIGBgTzwwAP07t2bXr16AXDdddfRtm1bRo4cyXPPPUdKSgqPP/44kyZNkpFcIYQQQgihq/Gg95tvvuH48eOMGTOm2LElS5ZgMBgYPnw4NpuNgQMH8sorr+jHjUYjGzduZMKECfTu3Rs/Pz9GjRrF/Pnzq/MRhBBCCCFELVer6vTWFKnTK4QQQghxcasVOb1CCCGEEEJUJQl6hRBCCCHERU+CXiGEEEIIcdGr8YlsQojSUzWV/en7ybRmEmwJJjY0FoMiv7sKIYQQFyJBrxB1RHxyPG8kvMGhjEM4VAdeBi9ahbRiXIdx9IzqWdPdE0IIIWo1CXqFqAPik+N5dNujpFvT0TQNNECBXad38WfWnzzd72kJfIUQQoh/IO+LClHLqZrK4l8Xk5afhqqqGA1GvIxeGA1GXKqLtPw0Fv+6GFVTa7qrQgghRK0lQa8Qtdy+s/v4I/MPALyN3hj+99/WgAFvozcAf2T+wb6z+2qsj0IIIURtJ0GvELXc72d+x6k5MRqMJR43Gow4NSe/n/m9mnsmhBBC1B0S9ApRRygoZdovhBBCiL9J0CtELdchvAMmxYRLc3HuquGapuHSXJgUEx3CO9RQD4UQQojaT4JeIWq5dvXa0TK4JRoaTs2JqqlomoaqqTg1JxoaLYNb0q5eu5ruqhBCCFFrSdArRC1nUAxM7z6dMEsYBsWAS3Ph1Jy4NBcGxUCYJYzp3afLIhVCCCHEP1C0c98vvQRlZ2cTFBREVlYWgYGBNd0dIUoUnxzPG7+/waHMIotTBLdiXEdZnEIIIYS4EAl6kaBX1B2yDLEQQghRPrIimxB1iEExSO6uEEIIUQ4yRCSEEEIIIS56EvQKIYQQQoiLngS9QgghhBDioidBrxBCCCGEuOhJ0CuEEEIIIS56EvQKIYQQQoiLngS9QgghhBDioidBrxBCCCGEuOhJ0CuEEEIIIS56EvQKIYQQQoiLngS9QgghhBDioidBrxBCCCGEuOhJ0CuEEEIIIS56EvQKIYQQQoiLngS9QgghhBDiomeq6Q4IIUpP1VT2p+8n05pJsCWY2NBYDIr87iqEEEJciAS9QtQR8cnxxCXEcST7CE7ViclgIjowmrEdxtIzqmdNd08IIYSo1WSISIg6ID45nvk755OUkYSvyZcwnzB8Tb4kZSQxf+d84pPja7qLQgghRK0mQa8QtZyqqcQlxJHnyCPCNwKLyYJBMWAxWYjwjSDPkUdcQhyqptZ0V4UQQohaS4JeIWq5/en7OZJ9hCBzEIqieBxTFIUgcxBHso+wP31/DfVQCCGEqP0k6BWilsu0ZuJUnXgbvUs87m30xqk6ybRmVm/HhBBCiDpEgl4harlgSzAmgwm7y17icbvLjslgItgSXL0dE0IIIeoQCXqFqOViQ2OJDowmy5aFpmkexzRNI8uWRXRgNLGhsTXUQyGEEKL2k6BXiCqgaiqJZxPZfnI7iWcTKzTJzKAYGNthLH5efqTmp2J1WlE1FavTSmp+Kn5efoztMFbq9QohhBD/QNHOHTq6BGVnZxMUFERWVhaBgYE13R1Rx1VVPV2p0yuEEEKUnwS9SNArKk9hPd08Rx5B5iC8jd7YXXaybFn4efkxu/fsCgWosiKbEEIIUT41/tPy5MmTjBgxgnr16uHj40OHDh349ddf9eOapjF79myioqLw8fHhmmuu4dChQx5tpKenc8899xAYGEhwcDBjx44lNze3uh9FXOKqo56uQTHQrl47+jbsS7t67epEwJtjz6npLgghhBA1G/RmZGTQt29fvLy8+OKLL9i3bx+LFi0iJCREP+e5557jhRdeYMWKFcTHx+Pn58fAgQOxWq36Offccw+JiYls3ryZjRs38sMPP3D//ffXxCOJS5jU0y3O4XJIKTUhhBC1gqkmb/7ss8/SuHFjVq5cqe+Ljo7W/61pGkuXLuXxxx/nxhtvBOCtt96ifv36bNiwgTvvvJP9+/fz5Zdf8ssvv9C9e3cAXnzxRa6//nqef/55GjRoUL0PJS5Zpamnm23PvqSCwLSCNDQu+QwqIYQQtUCNjvR++umndO/endtuu42IiAi6dOnC66+/rh8/cuQIKSkpXHPNNfq+oKAgevbsyc6dOwHYuXMnwcHBesALcM0112AwGIiPjy/xvjabjezsbI8PISrq3Hq6VqeVXHsuVqf7XYlLrZ5uli0Lm8tW090QQgghgBoOev/880+WL19Oq1at+Oqrr5gwYQJTpkxh9erVAKSkpABQv359j+vq16+vH0tJSSEiIsLjuMlkIjQ0VD/nXAsXLiQoKEj/aNy4cWU/mrgEFdbTTStI42jWUY7nHOdU7imO5xznaNZR0grSLpl6ug6Xg0xbZk13QwghhNDVaNCrqipdu3bl6aefpkuXLtx///3cd999rFixokrvO2vWLLKysvSPEydOVOn9xKXBoBjo06AP+Y58CpwFABgVIwAFzgLyHfn0adCnTkw+qwhN09xpDVIYRgghRC1Soz99o6KiaNu2rce+2NhYjh8/DkBkZCQAp0+f9jjn9OnT+rHIyEhSU1M9jjudTtLT0/VzzmU2mwkMDPT4EKKiVE1lx6kd+Jp88TH5AODSXAD4mHzwNfmy49SOClVvqAuy7dmS1iCEEKLWqdGgt2/fvhw8eNBjX1JSEk2bNgXck9oiIyP59ttv9ePZ2dnEx8fTu3dvAHr37k1mZia7du3Sz/nuu+9QVZWePaVgv6g+hdUbwnzDaBrYlMYBjWng34DGAY1pGtiUMN+wi756g91ll7QGIYQQtVKNVm+YNm0affr04emnn+b222/n559/5rXXXuO1114D3GWepk6dyoIFC2jVqhXR0dE88cQTNGjQgJtuuglwjwwPGjRIT4twOBxMnjyZO++8Uyo3iGpVtHqDoij6aG+hi716g6Q1CCGEqM1qNOjt0aMHH3/8MbNmzWL+/PlER0ezdOlS7rnnHv2chx56iLy8PO6//34yMzPp168fX375JRaLRT/n3XffZfLkyVx99dUYDAaGDx/OCy+8UBOPJC5hRas3WEwWrE6rvlywxWS56Ks3ZNoy9coVQgghRG0jyxAjyxCLyqFqKuM3jyfxbCJO1YldtYMGKOBt8MZkMNGuXjtWXLviopvMZnPZSMlLOe8ob7OgZtXbISGEEOIcF9dPXiFq0KVaveGf0hocqoOHf3iY9w+8L2kPQgghatTF9dNXiBp0qVZvyLRl4nA5Sjz27v532Ze+j6fjn2bSt5NwqCWfJ4QQQlS1Gs3pFeJiUrR6g9loxuqy4lJdGA1GLEYLNpdNr97Qrl67mu5upbC5bGTZsko8lnAmgfWH1uvbUX5ReBm8qqtrQgghhAcJeoWoJJda9YbCtIaS5NpzWbx7MRrulIZmgc2Y2WNmdXZPCCGE8CDpDUJUkqLVG0pysVVvyLBlnDetYfnvy/WA2KgYeeaKZ4r9EiCEEEJUJwl6xSVN1VQSzyay/eR2Es8mVijfNjY0lujAaLJsWcUmbWmaRpYti+jAaGJDYyva7RpndVrJtmWXeGzria388NcP+vaI2BEXTTqHEEKIukvSG8QlKz45nriEOI5kH9Hr6UYHRjO2w1h6RpV9NT+DYmBsh7HM3zmf1PxUgsxBeBu9sbvsZNmy8PPyY2yHsXW+eoOqqedNa0jNT2X53uX6drt67RgeM7y6uiaEEEKcV93+6StEOcUnxzN/53ySMpLwNfkS5hOGr8mXpIwk5u+cT3xyfLna7RnVk9m9Z9MquBVZtixO5Z4iy5ZFq+BWzO49u1zBdG2TYc3AqTqL7XdpLhbvWky+Mx8AX5Mv07tN18u2CSGEEDVJgl5xyVE1lbiEOPIceUT4RmAxWTAoBiwmCxG+EeQ58ohLiKtYaTHlAtt1VIGzgBx7TonH1h9aT+LZRH17QqcJRPhGVFfXhBBCiH8kQa+45BSWFgsyB6EontGooigEmYP00mJlVTiCfCjjEEHmIBr4NyDIHMShjEMVGkGuDVRN5WzB2RKPHco4xLv739W3r2h0BVc2vrKaeiaEEEJcmAS94pJTtLRYSbyN3jhVZ5lLixUdQQ73CUdDI9+Rj4ZGuE945Ywg16B0a3qJaQ1Wp5VFuxbpC3GE+YQxoeOE6u6eEEII8Y9kIpu45BQtLWYxWYodL29pscIRZG+jNydyT2Bz2dA0DUVRMBvNBHoH1tnFKfId+eTac0s8FvffOE7mngRAQWF6t+n4e/tXZ/eEEEKIC5KRXnHJqarSYpnWTPId+aQVpGF1WjFgwKSYMGDA6rSSVpBGviO/zi1OoWoqZ60lpzX8nPwzXx79Ut++pdUtdAjrUF1dE0IIIUpNgl5xySksLebn5UdqfipWpxVVU7E6raTmp5a7tFigOVBvy6SYMCgGFEXBoLiD38J7BJoDq+jJqka6NR2X6iq2P8OawQu/vaBvtwhqwT2x91Rn14QQQohSk6BXXJIKS4vFhMSQ73SPzuY784kJiSl3aTHlfyUaClMaPI4pij6qrNShUg7nS2vQNI0XfnuBLHsW4M6DntF9Bl4Gr+ruohBCCFEqktMrLlk9o3rSI7IH+9P3k2nNJNgSTGxobLkXj8iyZWExWShwFOBQHRgNRgwYUFFxqS6MihGLyUKWLauSn6RquFTXeas1fH7kc349/au+Pbb9WBoHNC7xXJNBvs0IIYSoefLTSFzSDIqh0iaVBVuC8fXyxc/Lj2x7NjaXzV2pQQGLyUKgdyAaWpknyNWUdGu6XpGhqOPZx3nzv2/q293rd2dws8EltqEoCmE+YVXWRyGEEKK0JOgVopIUTpBLykiiSUATbC6bvryx2WgmNT+VmJCYMk+Qqwl5jjzyHHnF9jtcDhbtWoRdtQMQ5B3ElC5TiqVzFAq1hJ63NJwQQghRnSSnV4hKcu4EOQBfL1+ACk2Qq24u1UV6QXqJx97Z/w5/Zv2pb0/pOoUQS0iJ5/p5+RHgHVAlfRRCCCHKqnb/9BWijqmKCXLV7az1bIlpDb+f+Z2P//hY3x4cPZjLIi8rsQ2TwUQ9n3pV1kchhBCirCS9QYhKVtkT5KpTrj2XfEd+ifuX7F6ChrsCRUP/hoxpN6bENhRFIcI3ok48rxBCiEuHBL2iUqmaWiXBXlW1K/7mVJ1kWDOK7dc0jVf2vkJaQRoAJsXEzO4zS1zNDiDEEiJ5vEIIIWodCXpFpYlPjicuIY4j2Uf0CVzRgdGM7TC2Qm/rV1W7VaWu9bfQ2YKS0xq2/rWVH0/+qG/fE3sPLYNbltiGr5cvgd51a/ENIYQQlwYZKhOVIj45nvk755OUkYSvyZcwnzB8Tb4kZSQxf+d84pPja1W7VaWu9bdQrj2XAmdBsf2n806zYu8KfbtdvXbc3OrmEtswGozUs0gerxBCiNpJgl5RYaqmEpcQR54jjwjfCCwmCwbFgMVkIcI3gjxHHnEJce6atbWg3apS1/pbyKk6SbcWr9bg0lws3r2YfKc7x9fP5Mf0btMxKsZi5xbm8RoNxY8JIYQQtYEEvaLC9qfv50j2EYLMQSUuvxtkDuJI9hH2p++vFe1WlbrW30JpBWklBuIfJn3IvrP79O3xncYT4RtRYhvB5mDMRnOV9VEIIYSoKAl6RYVlWjNxqs7zTl7yNnrjVJ1kWjNrRbtVpa71FyDHnoPVaS22PykjifcPvK9v92/UnysbX1liGz4mH4LMQVXVRSGEEKJSSNArKizYEozJYMLuspd43O6yYzKYyrz8blW1W1XqWn8dqqPEag0FzgIW/bpIn9QW7hPO+I7jS2zDaDDKMsNCCCHqBAl6RYUVLr+bZctC0zSPY5qmkWXLIjowuszL71ZVu1WlrvX3bMHZEtMa4hLiOJV3CgAFhendpuPv7V9iG+E+4ZLHK4QQok6QoFdU2LnL71qdVlRNxeq0Vmj53apqt6rUpf5m27NLTGv4Kfknvjr2lb59a8yttA9rX2Ibwebg89bqFUIIIWobRTt3SOoSlJ2dTVBQEFlZWQQGSo3R8pI6vW61vb8O1cGp3FPFRqMzrBlM/m4y2fZsAFoGt+S5K57Dy+BVrA2LyUKkX2S19FcIIYSoDBL0IkFvZZIV2dycqpMvjnxBcl4yUX5RDI4ejMlQO9aCSclLKTbKq2kac3fOZXfqbsA96W7ZlctoFNCo2PVGxUiUf1SteR4hhBCiNOSnlhCVrKSR3s8Of1YrRnqzbFklpjVs/HOjHvACjGs/rsSAF6CeTz0JeIUQQtQ58pNLVBpJb/h7RbY8Rx5B5iC8jd7YXXZ9RbbZvWfXWJ8dLgeZtsxi+49lH2NV4ip9+7LIyxjUbFCJbQSZg/D18q2iHgohhBBVp/a+PyzqFFmGuPavyJZWkFYsj9fhcrDo10XYVXeZtWBzMA90eaDY4hrgzuMNNgdXR1eFEEKISidBr6gwWYbYrTavyJZly8LmshXb//b+tzmSfUTffrDLgyUGtkbFXY+3pGBYCCGEqAsk6BUVJssQu9XWFdnsLnuJaQ17z+xlwx8b9O3ro6+ne2T3EtuQPF4hhBB1nQS9osJkGWK3c1dkszqt5Npz9YljNbEim6ZpJaY15NpzWbp7KRru/Y38G/Gvdv8qsY1Ac6Dk8QohhKjzZOhGVFjRYK+kxQoqYxniymy3qhSuyJZ4NhGn6nTnyWqAAt4Gb0wGE+3qtavWFdmybFnFlkXWNI2X975MWkEaACbFxMzuM0t8jc1GMyHmkGrpqxBCCFGVZKRXVJgsQ+xmUAz0adCHfEc+Bc4CwJ0LC1DgLCDfkU+fBn2qrb6w3WUny55VbP+WE1vYdnKbvj2i7QhaBLcodp5BMRDmK3m8QgghLg4S9IoKk2WI3VRNZcepHfiafPEx+QDg0lwA+Jh88DX5suPUjmqZeHe+tIaUvBRW/L5C3+4Q1oGbWt5UYhv1fOqVuBqbEEIIURfVjmhB1Hk9o3oyu/dsYkJiyHfmk1aQRr4zn5iQmArVpq2qdqtC4cS7MN8wmgY2pXFAYxr4N6BxQGOaBjYlzDes2ibeZdoyi6U1uFQXi3ct1keh/bz8mNZ1mj4aXZS/tz9+Xn5V3k8hhBCiutRoTu/cuXOZN2+ex77WrVtz4MABAKxWKzNmzGDNmjXYbDYGDhzIK6+8Qv369fXzjx8/zoQJE9iyZQv+/v6MGjWKhQsXYjJJunJ16xnVkx6RPSp9ueCqareyFZ14pyiKPtpbyNvoTbY9u8on3tlcNrJsxdMaPjz0oUfAPanTJMJ9w4ud5230pp6lXpX2UQghhKhuNR4ZtmvXjm+++UbfLhqsTps2jU2bNrFu3TqCgoKYPHkyt9xyC9u3bwfA5XIxZMgQIiMj2bFjB8nJydx77714eXnx9NNPV/uzCHdKQrt67epMu5WpNky8K0xrOFdSRhLvHXhP376y0ZVc3ujyYucZFAPhPuGSxyuEEOKiU+NBr8lkIjIystj+rKws4uLieO+99xgwYAAAK1euJDY2lp9++olevXrx9ddfs2/fPr755hvq169P586defLJJ3n44YeZO3cu3t4ll7oSoioUTrxLykjCbDR7BI6FE+9iQmKqdOJdhi0Dh8vhsa/AWcDzvz6v5xJH+EQwvtP4Eq8PtYTiZZQ8XiGEEBefGn9/+NChQzRo0IDmzZtzzz33cPz4cQB27dqFw+Hgmmuu0c9t06YNTZo0YefOnQDs3LmTDh06eKQ7DBw4kOzsbBITE897T5vNRnZ2tseHEBVV0xPvrE4r2bbiX8tvJLxBcl6yu48YmN5teon5uv7e/vh7+1dJ34QQQoiaVqNBb8+ePVm1ahVffvkly5cv58iRI1x++eXk5OSQkpKCt7c3wcHBHtfUr1+flJQUAFJSUjwC3sLjhcfOZ+HChQQFBekfjRs3rtwHE5esmpp4d760hp2ndvL1sa/17eExw2kXVjxNxMvoRagltEr6JoQQQtQGNZreMHjwYP3fHTt2pGfPnjRt2pQPPvgAHx+ff7iyYmbNmsX06dP17ezsbAl8L1Gqpl4UE+/Srek4VWexfS/ueVHfbhnckrvb3F3sWkVRCPcJr3UTA4UQQojKVOM5vUUFBwcTExPDH3/8wbXXXovdbiczM9NjtPf06dN6DnBkZCQ///yzRxunT5/Wj52P2WzGbDZX/gOIOiU+OZ64hDiOZB/BqToxGUxEB0YztsPYCo/IVufEuwJnATn2HI99mqaxbPcyfb/ZaGZGtxmYDMX/y4daQs+71LMQQghxsahVQzu5ubkcPnyYqKgounXrhpeXF99++61+/ODBgxw/fpzevXsD0Lt3bxISEkhNTdXP2bx5M4GBgbRt27ba+y/qjvjkeObvnE9SRhK+Jl/CfMLwNfmSlJHE/J3ziU+Or+kuloqqqZwtOFts/8Y/N7I7dbe+Pbb9WBoFNCp2np+XHwHeAVXaRyGEEKI2qNGR3pkzZzJs2DCaNm3KqVOnmDNnDkajkbvuuougoCDGjh3L9OnTCQ0NJTAwkAceeIDevXvTq1cvAK677jratm3LyJEjee6550hJSeHxxx9n0qRJMpJbQ6oiXaCyqZpKXEIceY48Inwj9CoLFpMFs9FMan4qcQlx9IjsUev6fq6S0hqOZR9jZeJKfbtnZE8GNRtU7FqTwUQ9H6nHK4QQ4tJQo0HvX3/9xV133cXZs2cJDw+nX79+/PTTT4SHuwvmL1myBIPBwPDhwz0WpyhkNBrZuHEjEyZMoHfv3vj5+TFq1Cjmz59fU490SavKdIHKVLhyWpA5CHCnB7hUF0aDEYvRQpA5SF85rTbXBi5wFpBrz/XY53A5eP7X53Go7rJlweZgHujyQLG6u4qiEO4rebxCCCEuHYqmaVpNd6KmZWdnExQURFZWFoGBgTXdnTqpMF0gz5FHkDkIb6M3dpedLFsWfl5+tWrJ4O0nt/P49sfxMfmQbk3H5rKhaRqKomA2mgm1hFLgLGBB3wX0bdi3prtbIlVTOZl7Epfq8tgf9984NvyxQd+e03sO3et3L3Z9iCVED/qFEEKIS4EM84gKOzddwGKyYFAMWEwWInwjyHPkEZcQpy+OUNOCLcG4NBfJeclYnVYMGDApJgwYsDqtJOcl49JcVbpyWkWlW9OLBbx7z+z1CHiHRg8tMeD19fKVgFcIIcQlR4JeUWFF0wVKehu9aLpAbdA6pDWqquJSXe5gVzGgKAoGxR38ulQXqqrSOqR1TXe1RPmO/GJpDTn2HJbsWqJvNw5ozOj2o4tdazQYqWeRPF4hhBCXHgl6RYVlWjNxqs7zlr3yNnrjVJ1kWjOrt2PncTDjIEaDEaNixKk5UXGPQKuoODUnRsWI0WDkYMbBGu5pcS7VxVmrZ7UGTdN4ec/L+n6TYmJGtxmYjZ6TORVFIcI3AqPBWG39FUIIIWoLCXpFhQVbgjEZTNhd9hKP2112TAZTrUkXyLRmYlAMRPlHYTFZUDUVp+pE1VQsJgtR/lEYFEOtCdKLKimt4bsT37H91HZ9e2TbkbQIblHs2mBzcLFAWAghhLhU1KrFKUTdFBsaS3RgNEkZSZiNZo8UB03TyLJlERMSQ2xobA328m+FQbqXwYumgU2xOq16tQmLyYLVacVhcNSaIL1QviOfPEeex76UvBRe/f1VfbtjWEduanlTsWt9TD6SxyuEEOKSJiO9osIMioGxHcbi5+VHan4qVqcVVVOxOq2k5qfi5+XH2A5ja015rMIgPcuWhaZpWEwW/L39sZgsepAeHRhda4J0+F9awzmLULhUF4t3LabAWQC4F5qY2nVqsdfZaDAS5hNWbX0VQgghaqPaEYWIOq9nVE9m955NTEgM+c580grSyHfmExMSU6vKlUHdC9Lhf2kNmmdaw7qkdR6TAyd1mkS4b3ixa8N8wiSPVwghxCVP6vQidXorU11Yka1QXVlMI8+Rx5n8Mx77DqYf5KEfH9LLwF3V+Cqmd5te7Npgc3CtS9MQQgghakK5gt45c+YwZswYmjZtWhV9qnYS9F66anuQ7lJdnMo95THKW+As4MEtD5KclwxAhE8ELwx4AT8vP49rLSYLkX6R1dpfIYQQorYq10/3Tz75hBYtWnD11Vfz3nvvYbPZKrtfQlQLg2KgXb129G3Yl3b12tWqgBfgrPVssbSGNxLe0ANeAwamd59eLOA1KpLHK4QQQhRVrp/we/bs4ZdffqFdu3Y8+OCDREZGMmHCBH755ZfK7p8Ql6xcey75jnyPfTtP7eTrY1/r27fG3Eq7eu2KXVvPpx4mgxRnEUIIIQqVe1irS5cuvPDCC5w6dYq4uDj++usv+vbtS8eOHVm2bBlZWVmV2U8hLilO1Um6Nd1j39mCs7y450V9u1VwK+5qc1exa4PMQfh6+VZ5H4UQQoi6pMLv5WqahsPhwG63o2kaISEhvPTSSzRu3Ji1a9dWRh+FqDKqppJ4NpHtJ7eTeDZRnxhW084WnPXoi6qpLPttGTn2HADMRjMzus8oNpprNpoJNgdXZ1eFEEKIOqHc73/u2rWLlStX8v7772M2m7n33nt5+eWXadmyJQAvvvgiU6ZM4Y477qi0zgpRmWpr9YYce45ee7fQxj838lvqb/r2fR3uo6F/Q49zjIqRcN9wj8VBhBBCCOFWruoNHTp04MCBA1x33XXcd999DBs2DKPRsw5oWloaERERqGrtGDn7J1K94dITnxzP/J3zyXPkEWQOwtvojd1lJ8uWhZ+XX43VFnaqTk7lnvIY5T2WfYxpW6fhUB0A9IzsyWM9HysW3Eb4RkhagxBCCHEe5Rrpvf322xkzZgwNGzY87zlhYWF1IuAVlau2lwADdx/jEuLIc+QR4RuhB48WkwWz0UxqfipxCXH0iOxR7X1PK0jzCHgdLgfP//q8HvCGmEN4oMsDxQLeAO8ACXjFJU21WgEwWCw13BMhRG1VrqD3iSeeqOx+iItAbU0XONf+9P0cyT5CkDmoWPCoKApB5iCOZB9hf/r+EisjVJVsezZWp9Vj31v73uJo9lF9+8GuDxJkDvI4x2w0E2oJrY4uClHraJqGKz0dV3Y2pvCImu6OEKIWK3dO719//cWnn37K8ePHsdvtHscWL15c4Y6JuuV86QJJGUnM3zm/Vi1FnGnNxKk68TZ6l3jc2+hNtj2bTGtmtfXJoTqK3W9P6h42HN6gbw9tPpRu9bt5nGNQDIT5hkker7gkqVYrzrQ0NIejprsihKgDyhX0fvvtt9xwww00b96cAwcO0L59e44ePYqmaXTt2rWy+yhqudqcLlCSYEswJoMJu8uOxVT8rVC7y47JYKrW5XvPrdaQbc9mye4l+naTgCaMbje62HX1fOrhZfCqji4KUWtomoYrIwOXlMYUQpRBuSKQWbNmMXPmTBISErBYLHz00UecOHGC/v37c9ttt1V2H0UtV5Z0gdogNjSW6MBosmxZnDuPU9M0smxZRAdGExsaWy39ybJleaQ1aJrGy3te1uv0mgwmZnafidlo9rjO39u/2EpsQlzsVKsVx8lTEvAKIcqsXEHv/v37uffeewEwmUwUFBTg7+/P/PnzefbZZyu1g6L2K5ouoGkaBc4Ccu25FDgL0DQNb6M3TtVZrekC/8SgGBjbYSx+Xn6k5qdidVpRNRWr00pqfip+Xn6M7TC2WkalHS4HmbZMj33fHv+WHad26Nv3xt5LdFC0xzneRm/qWepVef+EqC00TcOZno4jORnNYb/wBUIIcY5ypTf4+fnpebxRUVEcPnyYdu3cE37S0tIqr3eiTihMF8iyZZFtz8bmsqFpGoqiYDaaCfQOrPZ0gQvpGdWT2b1n6xPvsu3ZmAwmYkJiqnXiXVpBmsdoc0peCq8lvKZvdwzryI0tb/S4RlEUwn2kHq+4dLhzd89KsCuEqJByBb29evVi27ZtxMbGcv311zNjxgwSEhJYv349vXr1quw+ilouNjSWUHMoBzIOoKBgVIwoioKGhtVppcBZQJuQNtWWLlBaPaN60iOyR42VWMuyZWFz2fRtl+pi0a5F+sIU/l7+TOs2rVh/6lnq4WWUPF5x8ZPcXSFEZSpX0Lt48WJyc3MBmDdvHrm5uaxdu5ZWrVpJ5YZLmQYaGhhAwR30amqZ1z6pVgbFUK1lyQqVlNbwQdIHHEg/oG9P6jyJMJ8wj3P8vf3x9/avji4KUaNUmw3nmTQZ3RVCVJpyBb3NmzfX/+3n58eKFSsqrUPib3VhoQdwT2RLt6VT368+2bZsrC4rTpwoKPiYfAg0B5JuS69Q3du68lqUhqZpxdIaDqQfYM3BNfr2gMYD6Newn8d1XkYvqccrLnoyuiuEqCrlrtMrqlZdWegB/p7I5mPygXPTTBV39YECZ0G5J7LVpdeiNArzngvlO/JZvGuxXrKsvm99/t3x3x7XFObx1tVAX4jSkNFdIURVKnXQGxISUuqJM+np6eXukKhbCz2AeyKbqqkk5yajoWE0GDFgQMVdESE5N5lAc2C5JrLVtdfiQuwue7G0hjcS3iA5LxkAAwamd5tebEnhUEvoeRfTEKKuk9FdIUR1KPWw0dKlS1myZAlLlizh8ccfB2DgwIHMnTuXuXPnMnDgQECWKK6ocxd6sJgsGBQDFpOFCN8I8hx5xCXEeSxkUNNah7TGpbpwaS5MignD/76sDBgwKSZcmguX6qJ1SOsytVsXX4t/UlJaw45TO9h8fLO+fVvMbbSt19bjOj8vPwK8A6qtn0JUJ9Vmk7q7okyuvPJKpkyZwkMPPURoaCiRkZHMnTtXP7548WI6dOiAn58fjRs3ZuLEifo8JIBVq1YRHBzMxo0bad26Nb6+vtx6663k5+ezevVqmjVrRkhICFOmTMHlcunX2Ww2Zs6cScOGDfHz86Nnz55s3bq1Gp9cVFSpR3pHjRql/3v48OHMnz+fyZMn6/umTJnCSy+9xDfffMO0adMqt5eXkLIs9FATE7BKcjDjIAaDAaPBiFNzYsSoT2RzaS73yK/BwMGMg2Xqc118Lf5Jpi0Tu+vvt23PFpzlpd9e0rdjQmK4s82dHteYDCbq+Ug9XnHx0TQNV2YmrszMmu6KqINWr17N9OnTiY+PZ+fOnYwePZq+ffty7bXXYjAYeOGFF4iOjubPP/9k4sSJPPTQQ7zyyiv69fn5+bzwwgusWbOGnJwcbrnlFm6++WaCg4P5/PPP+fPPPxk+fDh9+/bljjvuAGDy5Mns27ePNWvW0KBBAz7++GMGDRpEQkICrVq1qqmXQpRBuRIEv/rqKwYNGlRs/6BBg/jmm28q3KlLWdGFHkpS2xZ6AHefjYqRKL8oLCYLKiouzYWKisVkIcovCqNiLHOfq+O1UDWVxLOJbD+5ncSziVU2amxz2ci2Z3vcd+nupeQ4cgCwGC3M6DYDk+Hv30MVRSHcV/J4xcVHH92VgFeUU8eOHZkzZw6tWrXi3nvvpXv37nz77bcATJ06lauuuopmzZoxYMAAFixYwAcffOBxvcPhYPny5XTp0oUrrriCW2+9lW3bthEXF0fbtm0ZOnQoV111FVu2bAHg+PHjrFy5knXr1nH55ZfTokULZs6cSb9+/Vi5cmW1P78on3JNZKtXrx6ffPIJM2bM8Nj/ySefUK+ejEpVROFCD3aXHYvJUuy43WWvdQs9FPbZy+BFk4AmWF1WXKp7hNditGBz2XAYHGXuc1W/FtU1Qa6ktIbPDn/GnjN79O1xHcbRwL+Bx3XB5uBiSw8LUZfJ6K6oLB07dvTYjoqKIjU1FYBvvvmGhQsXcuDAAbKzs3E6nVitVvLz8/H1dc+X8PX1pUWLFvr19evXp1mzZvj7+3vsK2wzISEBl8tFTEyMx31tNpvEPXVIuYLeefPmMW7cOLZu3UrPnu7gID4+ni+//JLXX3+9Ujt4qYkNjSU6MJqkjCTMRrPH2/qappFlyyImJKZWLfRQtM8RvhHuKg7/U5E+F23X2+BNjiMHu8uOt9GbAK+ACr0W1TlBLtOWicPl0LePZh1l9b7V+navqF5c1/Q6j2t8TD4EmYMq5f5C1AZSmUFUJi8vzwV6FEVBVVWOHj3K0KFDmTBhAk899RShoaFs27aNsWPHYrfb9aC3pOvP1yZAbm4uRqORXbt2YTQaPc4rGiiL2q1c75uOHj2a7du3ExgYyPr161m/fj2BgYFs27aN0aNHV3IXLy0GxcDYDmPx8/IjNT8Vq9OKqrmrIKTmp+Ln5cfYDmNr1VveVdXnwnZdmouDGQc5mXuSMwVnOJl7koMZB3FprnK1W50T5GwuG1m2vyfo2F12nt/1PA7VHQSHmEOY3Hmyxy83RoOx2KIUQtRVmqbhzMjAmZwsAa+ocrt27UJVVRYtWkSvXr2IiYnh1KlTFW63S5cuuFwuUlNTadmypcdHZGRkJfRcVIdy1+nt2bMn7777brH9BQUF+Pj4lHCFKK2eUT2Z3Xu2/tZ7tj0bk8FETEhMra1NW1V93n92Pzn2HPdKb0VoaOTYc9h/dn+Z266uCXKFaQ1Frd63mmPZx/TtqV2neozoFtbjNRo8RxKEqItUux3XmTOodgl2RfVo2bIlDoeDF198kWHDhrF9+/ZKWUArJiaGe+65h3vvvZdFixbRpUsXzpw5w7fffkvHjh0ZMmRIJfReVLVyBb1TpkzhhRdeKLY/Ly+PoUOH6onfovx6RvWkR2SPOrUKWWX32ak6eSPhDVRNxWwwoykaaIACiqZgV+28kfAGI9qO8JgAdiFFJ8hpmlYsB9nb6E22PbvCkwUzbBkeaQ27U3fz6eFP9e1hzYfRtX5Xj2uCzcEl5i8LUZcU5u6qWVkeuexCVLVOnTqxePFinn32WWbNmsUVV1zBwoULuffeeyvc9sqVK1mwYAEzZszg5MmThIWF0atXL4YOHVoJPRfVQdHK8R2pRYsWjBgxgnnz5un7cnNzGTx4MAA//vhj5fWwGmRnZxMUFERWVhaBgYE13R3xP58d/ozHtz+OQXHX+z2XU3OiaioL+i5gWIthpW438WwiU7dMRUHRV0fTNA1FUTAbzQR6B6KhsfSqpeUe6bU6raTkpejb2fZsHvjuAdKt7oVbmgQ0YfGViz0mqllMFiL95G0yUbfV5OiuKTwCo79ftd9XCFE3lGuk9+uvv+byyy8nJCSEqVOnkpOTw8CBAzGZTHzxxReV3UdxiUrOS0bTNIxKyW/1GzHi0lz6amalFRsaS6g5lAMZB1BQMCpGFMVdV9jqtFLgLKBNSJtyTxZUNdUjrUHTNF7e87Ie8JoMJmZ2n+kR8BoNRsJ9wst1PyFqAxndFULUduUKelu0aMGXX37JVVddhcFg4P3338dsNrNp0yb8/OS3bFE5ovyiUBQFFy5MJXypunChKApRflHlu4Hmzg3GgL6YhqZW/Id1hjUDp+rUt789/i07Tu3Qt0e1HUV0ULTHNWE+YZLHK+osyd0VQtQF5U4Q7dixIxs3buTRRx/F19eXL774QgLeSuZ0qny8+yQvfXeIj3efxOmsG8vtVpbB0YMJ8ArApbqKjRxpmoZLdRHgFcDg6MFland/+n7SbenU96uPj5cPqqbiVN2pEj5ePtT3q0+6LZ396fvL3OcCZwE59hx9OzkvmdcSXtO3O4V34oYWN3hcE2wO9ijzJkRdoVdmOHVKAl4hRK1X6pHeLl26FJvpDmA2mzl16hR9+/bV9+3evbtyencJe/2Hw7y89TA5BQ5U3L+dzNuYyKQrW3DfFS0udHmNUTW10iaymQwmxnUYx7Ldy7Crdo80B5fmwqgYGddhXJkmscHfE9nCfMIIsYRgdVr1xSksJouenlDWiWyqpnK24OzffVRdLPp1EQXOAgD8vfyZ2nWqx+thMVlq1UIjQpSWjO4KIeqaUkcLN910UxV2QxT1+g+HefbLg7hUDZNRwaSAqkFWvoNnvzwIUCsD36pY4Wx0+9Eczz7O+j/W49T+ThkwKkZubnkzo9uPLnOb5670dm61hPKu9JZuTfdIa1h7cC0HMw7q25M7T/aov2tUpB6vqHskd1cIUVeVOuidM2dOVfZD/I/TqfLy1sO4VA1vk6KPChoUMCgqdqfGy1sP868+0ZhMtad8WdEVznxMPpiM7i+tiq5wFp8cT3xKPEHeQZgMJlRNxaAYcKpO4lPiiU+OL3O7VbHqXb4jn1x7rr59IP0Aaw+u1bevbnI1fRv29bimnk+9Mo9SC1GTZHRXCFGXlTtqyszM5I033mDWrFmkp7tnpe/evZuTJ0+WuzPPPPMMiqIwdepUfZ/VamXSpEnUq1cPf39/hg8fzunTpz2uO378OEOGDMHX15eIiAj+7//+D6fTSV302e/J5BQ4MBmVYmkBBsWAyaiQU+Dgs9/LVrGgKhWucJZpy6TAWUBKfgopeSmk5KdQ4Cwg05ZZrhXOiq6cVt+vPmG+YUT4RRDmG0Z9v/rlXjnt3BXksmxZZNuyybJllWsFOVVTOWv9O60h35HPol8XoeLuV33f+tzf4X6PawLNgfh6+Zap30LUFMndFUJcDMoV9P7+++/ExMTw7LPP8vzzz5OZmQnA+vXrmTVrVrk68ssvv/Dqq6/SsWNHj/3Tpk3js88+Y926dXz//fecOnWKW265RT/ucrkYMmQIdrudHTt2sHr1alatWsXs2bPL1Y+adjIz353DWzx9Gv63X/vfebXF/vT9HMw4SL4jH6vT+ndVBM1drzbfkc/BjINlnhhWlpXTyqpnVE9ui7kNp+okOS+Zk7knSc5Lxqk6uS3mtjKNHqdb03GpLn379YTXScl31+g1YGBGtxkeAa7ZaCbEHFLmPgtRE1S7HeepU7gyMyWdQQhRp5Ur6J0+fTqjR4/m0KFDWCx/50Nef/31/PDDD2VuLzc3l3vuuYfXX3+dkJC/g4GsrCzi4uJYvHgxAwYMoFu3bqxcuZIdO3bw008/Ae6awfv27eOdd96hc+fODB48mCeffJKXX34Zex0ckWgY7IsBdw5vSVQNlP+dV1ukF6STY89xLxaBilNz4tJcHts59hzSC9LL1G7RldNK4m30xqk6y7VyWnxyPOuS1mFUjET5RdHQvyFRflEYFSPrktYRnxxfqnbOTWvYfnI73xz/Rt++vfXtxNb7O03CqBgJ8w0rcVKoELVJYe6ujO4KIS4W5Qp6f/nlF/79738X29+wYUNSUlJKuOKfTZo0iSFDhnDNNdd47N+1axcOh8Njf5s2bWjSpAk7d+4EYOfOnXTo0IH69evr5wwcOJDs7GwSExNLvJ/NZiM7O9vjo7YY1jGKAB8vnC6t2Nv2qqbidGkE+HgxrGM5a9NWgUxbpscErpI4VSeZtswytVt0wllJyjvh7Ny0iSBzEIHmQILMQWVKm3CpLo+0hrMFZ3lpz0v6dkxIDHe0vsPjmno+9fAyeJWpv0JUN81ux5mcjDMjQ0Z3hQBWrVpFcHCwvj137lw6d+5cpfds1qwZS5curfR2R48eXebiBIqisGHDhkrvS3UrV9BrNptLDBSTkpIIDy/bqlJr1qxh9+7dLFy4sNixlJQUvL29Pb7QAOrXr68H1ykpKR4Bb+HxwmMlWbhwIUFBQfpH48aNy9TnqmQyGZh0ZQuMBgW7U8Opqv+rI+uexGY0KEy6skWtmsTm7+XvTmf4Bxoa/l7+ZWq3cMJZlq34LPHCCWfRgdFlXjmtstImiqY1qJrK0t1LyXW4R30tRgszus3wmKgW4B0gebyi1nNlZuI4dQrVZqvprogKKG1g89dff+Ht7U379u1L3a6iKIwfP77YsUmTJqEoCqNHjy51P7du3YqiKHqaZGUrbL/wIzw8nOuvv56EhIQytXPHHXeQlJRUpms+/vhjevXqRVBQEAEBAbRr185jzpKofuWKnG644Qbmz5+Pw+EA3IHC8ePHefjhhxk+fHip2zlx4gQPPvgg7777rkeaRFWbNWsWWVlZ+seJEyeq7d6lcd8VLXh4UGuCfL1QVQ2HS0NVNYJ8vXh4UOtKKVemaiqJZxPZfnI7iWcTyzwZrKiiZbkq47xC5044szqtqJqK1Wkt14SzQpWRNpHvyCfPkadvf3r4U/ac2aNv39fhPhr4N/BoM9QSWqZ+ClGdNLsdx6lTMrpbRVRVI+GvLL5POkPCX1molbD6Y2VYtWoVt99+O9nZ2cTHly6tq3HjxqxZs4aCggJ9n9Vq5b333qNJkyZV0s+KpisePHiQ5ORkvvrqK2w2mz4XqLR8fHyIiIgo9fnffvstd9xxB8OHD+fnn39m165dPPXUU3rcJGpGuYLeRYsWkZubS0REBAUFBfTv35+WLVsSEBDAU089Vep2du3aRWpqKl27dsVkMmEymfj+++954YUXMJlM1K9fH7vdXuw3wNOnTxMZGQlAZGRksWoOhduF55zLbDYTGBjo8VHb3HdFC3599Bqev60z06+N4fnbOvPro9dUSsAbnxzP+M3jmbplKo9vf5ypW6YyfvP4UuexnkuhdPmppT2vqJ5RPZndezYxITHkO/NJK0gj35lPTEhMucugVTRtwqW6PBahOJJ1hNX7VuvbvaN6c23Ta/Vtg2Ig3Ddc8nhFrSWju1Vrxx9pjFr5M/9++1dmfrCXf7/9K6NW/syOP9JqtF+aprFy5UpGjhzJ3XffTVxcXKmu69q1K40bN2b9+vX6vvXr19OkSRO6dOnica6qqixcuJDo6Gh8fHzo1KkTH374IQBHjx7lqquuAiAkJMRjlPjKK69k8uTJTJ06lbCwMAYOHAjA999/z2WXXYbZbCYqKopHHnmkVNWaIiIiiIyMpGvXrkydOpUTJ05w4MAB/fjixYvp0KEDfn5+NG7cmIkTJ5Kb+/d8jXPTGy7ks88+o2/fvvzf//0frVu3JiYmhptuuomXX3652Hk9evTAYrEQFhbGzTff7HE8Pz+fMWPGEBAQQJMmTXjttdc8jp84cYLbb7+d4OBgQkNDufHGGzl69Kh+3OVyMX36dIKDg6lXrx4PPfRQsV9qS0qj6Ny5M3Pnzj3v813ovrVVuYLeoKAgNm/ezGeffcYLL7zA5MmT+fzzz/n+++/LtBTx1VdfTUJCAnv27NE/unfvzj333KP/28vLi2+//Va/5uDBgxw/fpzevXsD0Lt3bxISEkhNTdXP2bx5M4GBgbRt27Y8j1drmEwGbu7akMkDWnFz14aVktJQWE83KSMJX5MvYT5h+Jp89Xq65Ql8G/g3KBbQlrRddOSzLHpG9WTFtStYetVSFvRdwNKrlrLi2hXlXvCiomkT6dZ0XJo7rcHusrNo1yI9pznUEsqkzpM8AlzJ4xW1lYzuVr0df6Tx6McJ7E/Oxs9sIiLAjJ/ZxP7kHB79OKFGA98tW7aQn5/PNddcw4gRI1izZg15eXkXvhAYM2YMK1eu1LfffPNN/vWvfxU7b+HChbz11lusWLGCxMREpk2bxogRI/j+++9p3LgxH330EfD3SOyyZcv0a1evXo23tzfbt29nxYoVnDx5kuuvv54ePXqwd+9eli9fTlxcHAsWLCj1M2dlZbFmzRoAvL3/frfPYDDwwgsvkJiYyOrVq/nuu+946KGHSt3uuSIjI0lMTOS///3vec/ZtGkTN998M9dffz2//fYb3377LZdddpnHOYsWLaJ79+789ttvTJw4kQkTJnDwoPtdU4fDwcCBAwkICODHH39k+/bt+Pv7M2jQIH0Ue9GiRaxatYo333yTbdu2kZ6ezscff1zu5yrtfWurClXG79evH927d8dsNpdrFCsgIKBYHpGfnx/16tXT948dO5bp06cTGhpKYGAgDzzwAL1796ZXr14AXHfddbRt25aRI0fy3HPPkZKSwuOPP86kSZMwm80VebyLTtEJXBG+EfrnzGKyYDaaSc1PJS4hjh6RPcqUMjA4ejDP/vwsWfYsFBS0//0B9O1A70AGRw+ukucqq8K0ifk755Oan0qQOQhvozd2l50sW9Y/pk3kOfI80hpWJ67mWPYxffvBrg8SZA7St/29/fHzKv0vgkJUF1dmppQhq2KqqrH8+8Pk2pxEBlr+/p5rMBIZaCAl28by7w/Tq3k9DOerU1mF4uLiuPPOOzEajbRv357mzZuzbt26UuXkjhgxglmzZnHsmPv73/bt21mzZg1bt27Vz7HZbDz99NN88803+kBV8+bN2bZtG6+++ir9+/cnNNSd9hUREVFsJLVVq1Y899xz+vZjjz1G48aNeemll1AUhTZt2nDq1CkefvhhZs+ejcFw/p9bjRo1AtCD+htuuIE2bdrox4vm2jZr1owFCxYwfvx4XnnllQu+FiV54IEH+PHHH+nQoQNNmzalV69eXHfdddxzzz16bPLUU09x5513Mm/ePP26Tp06ebRz/fXXM3HiRAAefvhhlixZwpYtW2jdujVr165FVVXeeOMN/Wtr5cqVBAcHs3XrVq677jqWLl3KrFmz9FKvK1as4KuvvirXMxUqzX1rq3IFvaqq8tRTT7FixQpOnz5NUlISzZs354knnqBZs2aMHTu20jq4ZMkSDAYDw4cPx2azMXDgQI8vQqPRyMaNG5kwYQK9e/fGz8+PUaNGMX/+/Errw8WiLBO42tVrV+p2TQYT4zqMY9nuZbg0Fybl7y+rwu1xHcaVe/WxqljeuDBtorDdbHs2JoOJmJCY87brUl0eZdd2p+7m0z8/1bdvaH4DXSO6AqCicjz7OCbFRIhPCLGhsWXOPRaiKmh2O860NEllqAaJp7I5nJpLiK93id9zg329OJyaS+KpbDo0CjpPK1UjMzOT9evXs23bNn3fiBEjiIuLK1XQGx4ezpAhQ1i1ahWapjFkyBDCwjyXVf/jjz/Iz8/n2muv9dhvt9uLpUGUpFu3bh7b+/fvp3fv3h6vZd++fcnNzeWvv/76x3ziH3/8EV9fX3766SeefvppVqxY4XH8m2++YeHChRw4cIDs7GycTidWq5X8/Hx8fcs+AdnPz49NmzZx+PBhtmzZwk8//cSMGTNYtmwZO3fuxNfXlz179nDffff9YztF1y5QFIXIyEj9ne29e/fyxx9/EBAQ4HGN1Wrl8OHDZGVlkZycTM+ef/88M5lMdO/evUK/7F7ovrVZuaKQBQsWsHr1ap577jmPT1j79u1ZunRphYLeor8lAlgsFl5++eVieTBFNW3alM8//7zc97xUlGYCV7Y9u1x1b0e3Hw3AGwlvkOPIQdM0FEUh0DuQcR3G6cfLqujyxkVHZCu6vDG4A98ekT3Yn76fTGsmwZbgfwxOz1rP6mkNWbYslu3++224poFNGdVuFAB7z+zlw6QPSc5Ldgf9lRCkC1EZZHS3eqXn23G4NLyNJX9PMRsNZKka6fnV/5bwe++9h9Vq9QiINE1DVVWSkpKIiYm5YBtjxoxh8uTJACX+jC7Mid20aRMNGzb0OFaad2LLki55IdHR0QQHB9O6dWtSU1O544479HUFjh49ytChQ5kwYQJPPfUUoaGhbNu2jbFjx2K328sV9BZq0aIFLVq0YNy4cTz22GPExMSwdu1a/vWvf+Hj43PB6728PFPjFEVBVd0Tz3Nzc+nWrRvvvvtusevKUknLYDAU+57wTxPuKuu+NaFcQe9bb73Fa6+9xtVXX+1RtqRTp04eieGidik6gctiKl4to7x1bwuNbj+aEW1H8MWRL0jOSybKL4rB0YPLPcJbVekYRRkUQ6lGtXPtueQ73KvgaZrGy3teJt3qHvU1GUzM6DYDb6M3e8/s5eU9L2N1WgmxhFRqkC5Eecnobs0I9fXGy6hgd6lYDMZix20uFS+DQqhvyQMRVSkuLo4ZM2YUG9WdOHEib775Js8888wF2yjM4VQURZ9oVlTbtm0xm80cP36c/v37l9hGYV6ty+Uq8XhRsbGxfPTRR/qgCrjTKgICAvT0hdKYNGkSCxcu5OOPP+bmm29m165dqKrKokWL9BSJDz74oNTtlVazZs3w9fXVUyw6duzIt99+W2IudGl07dqVtWvXEhERcd4J+VFRUcTHx3PFFVcA4HQ62bVrF127dtXPCQ8PJzk5Wd/Ozs7myJEjFbpvbVWuSOHkyZO0bNmy2H5VVaUcRy1WVXVvizIZTAxrMYz7O97PsBbDyh3wQvF0DKvTSq49F6vTWuFliMvCqTrJsGbo25uPbWZn8k59e1TbUUQHRaOi8mHSh1idViL9IrGYLBgUAxaThQjfiFIveiFEZXJlZUllhhrSrkEgLSL8ych3lPg9NzPfQYsIf9o1qJrAISsry2Oi+J49ezhx4gR79uxh9+7djBs3jvbt23t83HXXXaxevbpUFRGMRiP79+9n3759GI3Fg/qAgABmzpzJtGnTWL16NYcPH2b37t28+OKLrF7trnjTtGlTFEVh48aNnDlzxqNiwrkmTpzIiRMneOCBBzhw4ACffPIJc+bMYfr06f+Yz3suX19f7rvvPubMmYOmabRs2RKHw8GLL77In3/+ydtvv10s/aGs5s6dy0MPPcTWrVs5cuQIv/32G2PGjMHhcOjpHnPmzOH9999nzpw57N+/n4SEBJ599tlS3+Oee+4hLCyMG2+8kR9//JEjR46wdetWpkyZwl9//QXAgw8+yDPPPMOGDRs4cOAAEydOLFYRa8CAAbz99tv8+OOPJCQkMGrUqBI/n2W5b21VrqC3bdu2/Pjjj8X2f/jhh6XK0xE1o6rq3laVwnQMh+rgWPYxjucc51TuKY7nHOdY9jEcqqPcyxCXxdmCv9MaTuWe4vWE1/VjncI7cUOLGwA4nHmYv3L/ItgcXKFFL4SoDJrdjiM5GWd6uqQz1BCDQWFC/xb4m42kZNsocLhQVY0Ch4uUbBv+ZiMT+reosklsW7dupUuXLh4f8+bNIy4ujrZt23pM5Cp08803k5qaWuqUwQuV/XzyySd54oknWLhwIbGxsQwaNIhNmzYRHR0NuFdynTdvHo888gj169fX0yVK0rBhQz7//HN+/vlnOnXqxPjx4xk7diyPP/54qfpa1OTJk9m/fz/r1q2jU6dOLF68mGeffZb27dvz7rvvlrhgVln079+fP//8k3vvvZc2bdowePBgUlJS+Prrr2ndujXgLsu2bt06Pv30Uzp37syAAQP4+eefS30PX19ffvjhB5o0acItt9xCbGwsY8eOxWq16p+TGTNmMHLkSEaNGkXv3r0JCAgoVhZt1qxZ9O/fn6FDhzJkyBBuuukmWrQ4f3nU0ty3tlK0cnw3/OSTTxg1ahSzZs1i/vz5zJs3j4MHD/LWW2+xcePGYknrtV12djZBQUFkZWXV+k9YZaiKiWFVIfFsIhO/mUi2LRsNDaPBiAEDKiou1YWCQqA5kFeueaVME+/KIseeo9fkdapOHv7xYZIy3Kvy+Hv589KAl6jnUw9wT2x7YfcLhPuGl/iLg6qppBWksaDvAvo27Fsl/RUC3KO7rkuwDJkpPAKjf+2rlrLjjzSWf3+Yw6m5OFQNL4NCiwh/JvRvQZ+WYRduQAhRKcr13vONN97IZ599xvz58/Hz82P27Nl07dqVzz77rM4FvJeisk7gqimtQ1rjUl24NBfeBm+99q8BA4qiYFftuFQXrUNaV8n9z01rWHtwrR7wAkzuPFkPeAEaBTTCy+hVZTnTQlyIZrfjPHsW1Wqt6a6IIvq0DKNX83oknsomPd9OqK837RoE1kiZMiEuZeVOuLz88svZvHlzZfZFVKPSTuCqSQczDmIwGDAajDg1J0aMet1fl+Zyj/waDBzMOFglz5JWkKbn3+4/u58PDv49seHqJld7jNb6efnRPbA70YHRJGUkYTZ61q4uzJmOCYmpUM60EOdzqY7u1hUGg1LtZcmEEJ7KNbQ3e/ZstmzZglVGE6qUqqkknk1k+8ntJJ5NrBMToCqzz5nWTIyKkSi/KCwmizutQXOhomIxWYjyi8KoGKskpzfHnoPV6f76znfks2jXIlTczxLpG8n9He7XzzUZTNTzqVfncqbFxUFyd4UQonTKNdK7c+dOFi9ejNPppEePHvTv358rr7ySvn37lqrunLiwqs67VTW10tMbKrvPhSXWvAxeNAlogtVlxaW6R3gtRgs2lw2HwVHp6QIO1eGR1vBawmuczj8NuEfIZ3Sfga+Xu26joigeObzlWfRCiPKS0V0hhCi9ck1kA3ett/j4eH744Qe+//57duzYgc1mo0ePHh4rvNQFtW0i2/kWZChcIreitV6rIqCuij6rmsr4zeNJykjyqNML7nSB1PxUYkJiWHHtikodPU3JS9FHebef3M4zv/xdr/Ku1ndxd+zd+naIJcRj2eGifa/tOdOi7tIcDnfdXXm3zUNtncgmhKgdyp3TazKZ6Nu3L+Hh4YSGhhIQEKDXgRPlV9ULMlTFCmdV1efCdIH5O+eTmp+KxWTRc3qtTmuVpAtk27P1gDetII2X9rykH2sd0po7Wt+hb/uYfEoMeAv7XttzpkXd5MrOxiWpDEIIUWblihZee+017r77bho2bEifPn348ssv6devH7/++itnzpyp7D5eUs5dkKGoitZ6PTc4razFE6qyzz2jenJbzG04VSfJucmczD1Jcm4yTtXJbTG3VWq6QNG0BlVTWbp7KbkOd6F0i9HCjO4zMP5vVSWjwUiYj5QaEtVHczjcubtnz0rAK4QQ5VCukd7x48cTHh7OjBkzmDhxIv7+/pXdr0tW4YIM3saSl6X0NnqTbc8u1+StsgSnZRmlLNpnVVXJdmTjcDnwMnoR6BVYoT7HJ8fz9r63yXO4l20sLFuW58jj7X1vE1svttIC37MFfwcTnx7+lL1n9urH7u94P1F+Ue4+KArhPuF6ACxEVXNlZ7tzd9XaP5lVCCFqq3KN9K5fv5577rmHNWvWEB4eTp8+fXj00Uf5+uuvyc/Pr+w+XlIKJ2/ZXXY0TaPAWUCuPZcCZwGaplWo1mtpAuryrHBW2OfU/FSSMpM4lXuKMwVnOJV7iqTMJFLzU8vVZ1VTWfzrYtKsaWho+qQ2k8GEhkaaNY3Fvy6ulKoWWbYsPa3hSNYRVu9brR/rHdWba5pc8/fzmoNLrMMrRGXTHA4cKSnu0V0JeIUQokLKFfTedNNNLF68mN27d5OSksKjjz7KyZMnGTp0KKGhoZXdx0tKbGgs0YHRpOWncSz7GCdyTnAy9yQnck5wLPsYaflpRAdGl6vWa1UF1LGhsZgNZtKt6cUCUFVTSbemYzaYy9znxLOJ/JH5BwoKJsWEQXEvSmFQDJgUEwoKf2T+QeLZxDK1ey6Hy0GmLRMAm8vG878+j1N1rzsfagllcpfJHnnK58vjFaIyubKzcZw6hVpQUNNdEUIUMXfuXDp37qxvjx49mptuuqnG+lMZzn2myrJ161YURSEzM7PU11Tl61nuGUBnz55l/fr1PPHEEzz66KO88847BAYGMnjw4Mrs3yXHoBjo06AP+c58CpzuH3YmxZ2FUuAsIN+ZT58Gfco1eauqAurC5XULKUX+FCq60ENpJZxJcC9KoRhRFAUVFVVTUVFRFAWj4l60IuFMQpnaPVdaQZqe1rA6cTXHc47rx6Z2nUqgt7uih9FgJNwnvEL3EuJCZHRXVIbRo0ejKArjx48vdmzSpEkoisLo0aMr9Z5VFThVpiNHjnD33XfToEEDLBYLjRo14sYbb6zQJPxly5axatWqyutkOVTFc12MyhX0dujQgfr16/Pvf/+bkydPct999/Hbb7+RlpbGxx9/XNl9vKSomsqOUzvw9fLFx+SueezSXIC7WoCvly87Tu0o11v6VRVQf3HkCwpcBfporFbkT+GobIGrgC+OfFHmPoP7NbGrduwuu8fflZXWYHPZANh9ejef/fmZfuyGFjfQJaKLvh3mEyZ5vKJKyejuRUxV4dRv8Mc37r+r4Reaxo0bs2bNGgqKfD1ZrVbee+89mjRpUuX3Ly+73V4l7TocDq699lqysrJYv349Bw8eZO3atXTo0KFMI5HnCgoKIjg4uNL6WVZV9VwXo3IFvePHj2fPnj2cOXOGjz76iAceeICOHTtWdt8uSYWTzcJ8wmgW1IwmAU2I8mtAk4AmNAtqRphPWIWqN1RFQJ2cl4ymaRgVI94Gb7yN3h5/GxUjmqaRnJdcpnY7hnfEoBhwak5UTfUYQVY1FafmxKAY6Bhevq+9omkNWbYslu5eqh9rGtiUUW1H6dtB5iD9NROissno7kXuz+/hnVtgzQjYMNH99zu3uPdXoa5du9K4cWPWr1+v71u/fj1NmjShS5cuHufabDamTJlCREQEFouFfv368csvv+jHC9+m/vbbb+nevTu+vr706dOHgwcPArBq1SrmzZvH3r17URQFRVH00c/MzEzGjRtHeHg4gYGBDBgwgL17/54oXDhC/MYbbxAdHY3FYrngdUePHsVgMPDrr796PMfSpUtp2rQpagn/jxITEzl8+DCvvPIKvXr1omnTpvTt25cFCxbQq1cv/byHH36YmJgYfH19ad68OU888QQOh+O8r/O5b8dfeeWVTJkyhYceeojQ0FAiIyOZO3euxzWLFy+mQ4cO+Pn50bhxYyZOnEhubq5+/NixYwwbNoyQkBD8/Pxo164dn3/+eYn3L+1z/fXXX9x1112Ehobi5+dH9+7diY+P92jr7bffplmzZgQFBXHnnXeSk5OjH1NVlYULFxIdHY2Pjw+dOnXiww8/9Lj+888/JyYmBh8fH6666iqOHj3qcbykdwOWLl1Ks2bNzvPqlu6+pVWuoHfSpEm0b9/+gucFBgby559/lucWl6xzJ5tZTBZ8vfz0iVPlnWwGJQfUDfwrHlBH+UWhKAou3AG0AQMGxYDhf19eLlwoiqJXPyitNqFtMBvM/3iO2WCmTWibMrUL7sUtCtMaNE3jpT0vkWFzlyszGUzM7DbT43MQYgkp8z2EKA1XTo6M7l7M/vweNk6F04ng7Qf+9d1/n05076/iwHfMmDGsXLlS337zzTf517/+Vey8hx56iI8++ojVq1eze/duWrZsycCBA0lPT/c477HHHmPRokX8+uuvmEwmxowZA8Add9zBjBkzaNeuHcnJySQnJ3PHHe665rfddhupqal88cUX7Nq1i65du3L11Vd7tP3HH3/w0UcfsX79evbs2XPB65o1a8Y111zj8WwAK1euZPTo0RgMxcOb8PBwDAYDH374IS6X67yvWUBAAKtWrWLfvn0sW7aM119/nSVLllzglfa0evVq/Pz8iI+P57nnnmP+/Pls3rxZP24wGHjhhRdITExk9erVfPfddzz00EP68UmTJmGz2fjhhx9ISEjg2WefPW+lrNI8V25uLv379+fkyZN8+umn7N27l4ceesjjl4PDhw+zYcMGNm7cyMaNG/n+++955pm/F2dauHAhb731FitWrCAxMZFp06YxYsQIvv/e/TV84sQJbrnlFoYNG8aePXsYN24cjzzySJlet5Jc6L5lUaVLREktybIrOtkMQD3nJayN1RsGRw8mwCsAl+oq9jnXNA2X6iLAK4DB0WXL9z6YcRBvk3ukGPBImwDcI8smbw5mHCxTu+CZ1vD1sa/5Kfkn/diotqNoFtRMv4fU4xVVQXM63aO7aWkyunuxUlXYtgRsuRAQBV4+oBjcfwdEufdvW1KlqQ4jRoxg27ZtHDt2jGPHjrF9+3ZGjBjhcU5eXh7Lly/nP//5D4MHD6Zt27a8/vrr+Pj4EBcX53HuU089Rf/+/Wnbti2PPPIIO3bswGq14uPjg7+/PyaTicjISCIjI/Hx8WHbtm38/PPPrFu3ju7du9OqVSuef/55goODPUbr7HY7b731Fl26dKFjx46lum7cuHG8//772Gz/S1HbvZuEhIQSg3qAhg0b8sILLzB79mxCQkIYMGAATz75ZLHBuccff5w+ffrQrFkzhg0bxsyZM/nggw/K9Lp37NiROXPm0KpVK+699166d+/Ot99+qx+fOnUqV111Fc2aNWPAgAEsWLDA4x7Hjx+nb9++dOjQgebNmzN06FCuuOKKcj/Xe++9x5kzZ9iwYQP9+vWjZcuW3H777fTu3Vs/R1VVVq1aRfv27bn88ssZOXKk3mebzcbTTz/Nm2++ycCBA2nevDmjR49mxIgRvPrqqwAsX76cFi1asGjRIlq3bs0999xT4bzx0ty3LGRd1FqmcLJZli1LH4UspGkaWbasCldvyLJlcSz7GMdzjnMq9xTHc45zLPsYWbascgXUJoOJcR3GYVSM2FU7Ts2Jpmk4NSd21Y5RMTKuwzhMhrKVhc60ZmJUjDTwb4CvyRejYsSgGDAqRnxNvjTwb4BRMZY5SLe77GTZswA4lXuK1xNe1491Du/MDS1u0Lfr+dQrc7+FuBBXTg6OkydldPdil7IX0g6BTwicUxsdRXHvTzvkPq+KhIeHM2TIEFatWsXKlSsZMmQIYWGev8gfPnwYh8NB37599X1eXl5cdtll7N/v+c5f0VTGqCj3u3epqannvf/evXvJzc2lXr16+Pv76x9Hjhzh8OHD+nlNmzYlPDy8TNfddNNNGI1GfS7RqlWr9EDyfCZNmkRKSgrvvvsuvXv3Zt26dbRr185jFHbt2rX07duXyMhI/P39efzxxzl+/Ph52yzJuSmfUVFRHq/TN998w9VXX03Dhg0JCAhg5MiRnD17Vi/7OmXKFBYsWEDfvn2ZM2cOv//++z/e70LPtWfPHrp06fKPFbaaNWtGQEBAiX3+448/yM/P59prr/X4fLz11lv652P//v307OlZN79oUF0epblvWchP81rm3KV3/UwBmE1mHE4HWbasCi29GxsaS6g5lAPp7tmcJqN74pmKSoGjgAJHAW1C25QroB7dfjQAbyS8QY4jB5fmTmkI9A5kXIdx+vGyKAzSvQxeNA1sitVlxaW6MBqMWIwWbC4bDoOjTEF60bQGp+pk0a5F+ohvgFcAU7tO1V/bQHMgvl6+Ze63EOejOZ0409Ik2L1U5J8F1QGm86RpmcxgzXSfV4XGjBnD5MmTAXj55Zcr1JaXl5f+78JSjiXlzxbKzc0lKiqKrVu3FjtWdPKXn59fma/z9vbm3nvvZeXKldxyyy289957LFu27ILPEBAQwLBhwxg2bBgLFixg4MCBLFiwgGuvvZadO3dyzz33MG/ePAYOHEhQUBBr1qxh0aJFF2y3qKKvE7hfq8LX6ejRowwdOpQJEybw1FNPERoayrZt2xg7dix2ux1fX1/GjRvHwIED2bRpE19//TULFy5k0aJFPPDAA+V6Lh+fC89J+ac+F+Ybb9q0iYYNG3qcZzb/cxpiUQaDodg7wv+UL11Z9y0kQW8t1DOqJ7N7z+aNhDgOZ/xJrjMHb6MXMSExjO0wtuIrkCnusmKqqurBqaIoetpAeY1uP5q7Y+/mzf++yYmcEzQOaMyY9mPOm05xIYWj3kkZSUT4RnhMJCsc9Y4JiSlTkJ5ly9JTR9YcWENSRpJ+bHKXydTzqQeA2WgmxCx5vKLyuHJycKWnSyrDpcS3Hhi8wGlzpzScy2lzH/etV6XdGDRoEHa7HUVRGDhwYLHjLVq0wNvbm+3bt9O0aVPAHYj88ssvTJ06tdT38fb2LpZT2rVrV1JSUjCZTP84Anuu0l43btw42rdvzyuvvILT6eSWW24p9T3AHdi1adOGHTt2ALBjxw6aNm3KY489pp9z7NixMrV5Ibt27UJVVRYtWqTnHpeUPtG4cWPGjx/P+PHjmTVrFq+//vo/Br1FnftcHTt25I033iA9Pb1c6ym0bdsWs9nM8ePH6d+/f4nnxMbG8umnn3rs++mnnzy2w8PDSUlJQdM0/Zemwhzu8t63LKo06D13qVtRej2jetIutAs7TvyOpuTSKDic2NDYco3wFtqfvp90Wzp+Jj9yHH/PyCyMdQO8Aki3pZd5GeJC8cnxxCXEcST7CE7ViclgYvfp3eUO1M8d9Q4yB+Ft9HanJ5Rj1NvmsulpDfvO7mNd0jr92LVNrqVPgz76fcN8w+TrV1QKzenEefYsqqxWeemJ7ARhrdyT1kwWzxQHTYOCDKjfzn1eFTIajXqagtFYvOyin58fEyZM4P/+7/8IDQ2lSZMmPPfcc+Tn5zN27NhS36dZs2YcOXKEPXv20KhRIwICArjmmmvo3bs3N910E8899xwxMTGcOnWKTZs2cfPNN9O9e/cS2yrtdbGxsfTq1YuHH36YMWPG/OOI5p49e5gzZw4jR46kbdu2eHt78/333/Pmm2/y8MMPA9CqVSuOHz/OmjVr6NGjB5s2bar0UqwtW7bE4XDw4osvMmzYMLZv386KFSs8zpk6dSqDBw8mJiaGjIwMtmzZQmxsyQM8pXmuu+66i6effpqbbrqJhQsXEhUVxW+//UaDBg1KlYIQEBDAzJkzmTZtGqqq0q9fP7Kysti+fTuBgYGMGjWK8ePHs2jRIv7v//6PcePGsWvXrmL1i6+88krOnDnDc889x6233sqXX37JF198QWBgYLnvWxYyka0WszuhZVAbLovsQ7t67SoU8II7Rzbblu0Z8BaR48gh25ZdrsoQ8cnxzN85n6SMJHxNvoT5hOFr8iUpI4n5O+cTnxx/4UZKUDjqHRMSQ74zn7SCNPKd+cSExDC79+xSB9NF0xryHfks2rUIFfeIW5RfFPd1vE8/N8wnDC+D1/maEqLU9NxdCXgvTQYD9JsGZn/ISQZHAWiq+++cZDAHuI+XUGmgsgUGBp43sAB45plnGD58OCNHjqRr16788ccffPXVV4SElP4dr+HDhzNo0CCuuuoqwsPDef/991EUhc8//5wrrriCf/3rX8TExHDnnXdy7Ngx6tevf962ynJdYVpAYSWJ82nUqBHNmjVj3rx59OzZk65du7Js2TLmzZunj+zecMMNTJs2jcmTJ9O5c2d27NjBE088UerXoDQ6derE4sWLefbZZ2nfvj3vvvsuCxcu9DjH5XIxadIkYmNjGTRoEDExMbzyyivlfi5vb2++/vprIiIiuP766+nQoQPPPPNMib8Enc+TTz7JE088wcKFC/V+bdq0iejoaACaNGnCRx99xIYNG+jUqRMrVqzg6aef9mgjNjaWV155hZdffplOnTrx888/M3PmzArdtywUrRIiU5fLRUJCAk2bNvX4D7Jt2zZ69OhRrryL6pSdnU1QUBBZWVn/+E2hup3MLMDmcBHi602IX/lSBIrae2YvIz8fqacxFF0xrei+t69/m07hpR95UDWV8ZvH62kIRUdINU0jNT+VmJAYVly7otyBu6qp7E/fT6Y1k2BLcJlHvTOsGWTZ3KO8S3Yt4bsT3wHuUd1nL39WL3sW4B2gpzgIUV4yulv9NFXFtv8APp064t24cU13x9Of37urNKQdcuf4GrzcI8D9pkHzir9le6l78sknWbdu3QUnewlRrvSGqVOn0qFDB8aOHYvL5aJ///7s2LEDX19fNm7cyJVXXglAv379KrOvlxRV1bA5zl9HsDyOZR8rMeAt3C4sB3Ys+1iZgt7C+r9B5qBiKQGKohBkDtLr/5YnbQLcwWl5r7W5bHrA++PJH/WAF+DO1nfqAa+30ZtQS9lznYQoSnJ3q5fmcpH3ww9kfPABjqPHCL7tVqKefLKmu+WpeX9odrm7SkP+WXcOb2SnahnhvZjl5uZy9OhRXnrpJRYsWFDT3RF1QLn+x3344Yd06uQOij777DOOHDnCgQMHmDZtmkfytyi/gkoOeAFS8lL0YFcr4Q+4g9+UvJQytVtV9X8rQ2FaA0BaQRov7/l75nLrkNbcHnM74A6qw33CJY9XlJvmdOI4fVrq7lYTze4g+4svOXHf/aQ+9x8cR92TjTI3fIIjuWyrP1YLgwEadIGW17j/loC3wiZPnky3bt248sorL5jaIASUc6Q3LS2NyMhIwL3k3G233UZMTAxjxowpVbkQcWHWKgh6o/yi9BHd81Eo+8ppRRfUKFw5rqiKLKhRURm2DBwuB6qmsmTXEvIceYB72eUZ3WdgNLjzmer51MPLKHm8onxcubm4ZAnhaqFareR88SWZH32E62zxUl8BV/ZH+4cSSOLisWrVqmITpYT4J+X6VbN+/frs27cPl8vFl19+ybXXXgtAfn5+mZKixflVxUjvwGYDLziSqSgKA5sVL2nzT85dUKOoii6oURFWp5VsWzYAG/7YwO9pf+d73d/hfj249/f2x8/Lr8Q2hPgn+ujumTMS8FYxV24uGe+v4fio0Zx97TXPgNdgwH/AVTRdu5ZGL76Id5MmNddRIUStVa6R3n/961/cfvvtREVFoSgK11xzDQDx8fG0adOmUjt4KXKpGnZn5f8APZR5CB+TD7mO3POe42Py4VDmoTLlzxYtLXY67zQ+Xj76iHKBowB/b/9yL6hRXkXTGv7M/JO3972tH+vToA9XN7kaAC+jF/UsMnFNlJ2M7lYPV2YmWRs2kPXZRrRzJwaaTARcdy3Bt96KV1QUpvCImumkEKJOKFfQO3fuXNq3b8+JEye47bbb9OoMRqORRx55pFI7eCmqilFecOfeGhSDexU2rfgP6sJj5cm97RnVk9tibuONhDdIzkvWC08HeAVwW8xtFV9Qo4zSrek4VSc2l43ndz2PU3MCEGoJZVLnSfqCHBE+EZLHK8pEKjNUD+eZNDI/+pCcL79Cs9k8jilmM4HXDybollswnbOkrhBCnE+5F6e49dZbi+0ra5FgUbICe9UEvYHmQKxOKwoKZoMZFVUPTg0YcGpOrE4rgeayl22LT45nXdI6TAYTDfwa6PsLnAWsS1pHbL3Yagt8rU4rOXZ3LeJVias4kXNCPzat6zQCvd3PF2oJlTxeUSau3Fx3ZQZX1fwfFeA4dYrMDz4g59vvwOn0OGbw8yPwhmEE3XgjxqCgGuqhEKKuKnfQ++2337JkyRJ9lZfY2FimTp2qpzqI8quKSWzwd5kyTdNQDApGjBStXKapmr5EcVmomkpcQhx5jrxidXoDvANIzU8lLiGOHpE9qjzFQdVUPa1h1+ldbPxzo37sxhY30jmiM+DO4w3wDqjSvoiLh3t0Nx01P6+mu3LRsh85QsYHH5D3w49wTsqIMTiYoJtvInDIUAx+vjXUQyFEXVeuoPeVV17hwQcf5NZbb+XBBx8E3OsrX3/99SxZsoRJkyZVaicvJU6XisNVNTmCWbYsLCYLBY4CHKoDo8GIAQMqKi7VhVExYjFZ9Jq2pVUddXpLqzCtIcuWxdLdS/X9zQKbcW/bewF3Hq/U4xWl5crNw5V+VkZ3q4j1wAEy135A/k8/FTtmDA8nePhwAgZeh8FSvDKMEEKURbmC3qeffpolS5YwefJkfd+UKVPo27cvTz/9tAS9FVBV+bzgLi3m6+WLn5cf2fZsbC6bO7dXAYvJQqB3IBpamUuLlaZOb7a9fMsbl0WBs4Bcey6apvHiby+SaXPfz8vgxczuM/E2eqMoCuE+4dU6qU7UTZrLhTPtrIzuVgFN07D+/juZa9ZSsGdPseNeDRsQdNvtBAy4CsVLUpDEpefo0aNER0fz22+/0blz50ptu1mzZkydOpWpU6eW6vytW7dy1VVXkZGRQXBwcKX2pbqV6yd/ZmYmgwYNKrb/uuuuIyurbKOEwtO5Qe/zXx3k4Y9+58v/Jlc47aGwtJjdZadJQBOaBDShgX8D/d92l71cpcWK1unVNE0PPgucBWiaVi11eoumNXx97GviU+L1Y6PbjaZpYFMAQiwh5w3OhSjkys3DcfKkBLyVTNM08uLjOTV9BsmPzCoW8HpHRxPxyMM0evVVAgdeJwFvBYwePZqbbrrpguf99ddfeHt70759+1K3qygK48ePL3Zs0iT3JOHRo0eXup9bt25FURQyMzNLfU1ZFLZf+OHj40O7du147bXXquR+hVwuF8888wxt2rTBx8eH0NBQevbsyRtvvFGl9xX/rFwjvTfccAMff/wx//d//+ex/5NPPmHo0KGV0rFLlc3xd2qD1eHiu4OpWB0qX+87zYoR3RjUPrLcbRctLZaSm4LJaNInsmW4MggwB5SrtFhhMJ2YlojNacOu2dHQUFDwVrwxm8y0C2tXoTq9qqayP30/mdZMgi3BxIbGevQz3ZqOS3VxMvckrye8ru/vEtGFoc3dX5O+Xr76JDYhSiKju1VDc7nI27aNzLUfYD9ypNhxc5s2BN95B76XXXbRVlO50PewmrJq1Spuv/12fvjhB+Lj4+nZ88ITjhs3bsyaNWtYsmQJPj4+AFitVt577z2aVFGNZLvdjrd3+QcsDh48SGBgIAUFBXz22WdMmDCBFi1acPXVV1diL/82b948Xn31VV566SW6d+9OdnY2v/76KxkZGVVyP1E65fof17ZtW5566imGDBnCggULWLBgAUOHDuWpp56iffv2vPDCC/qHKD3HOfm8P/2ZjvV/QbC/2cSVrcMrfI+eUT3pGdmTbEc2ZwrOkGZN40zBGbId2fSM7FmuCgsGxUCfBn3IdmRj02z6im8aGjbNRrYjmz4N+pT7G3x8cjzjN49n6papPL79caZumcr4zeOJT3aP5uY78sm15+JUnSz6dRE2l7u8UYB3AFO7TsWgGDAZTIT5SGkjcX4yulv5NIeD7K++5q9//5vUZ54tFvD6dO5M1MKnabB4EX49e160Ae+FvofVFE3TWLlyJSNHjuTuu+8mLi6uVNd17dqVxo0bs379en3f+vXradKkCV26dPE4V1VVFi5cSHR0ND4+PnTq1IkPP/wQcL+Ff9VVVwEQEhLiMUp85ZVXMnnyZKZOnUpYWBgDB7oXTfr++++57LLLMJvNREVF8cgjj+A8p8pHSSIiIoiMjCQ6OpopU6YQHR3N7t279ePNmjVj6dKlHtd07tyZuXPn6q/V3LlzadKkCWazmQYNGjBlypTz3u/TTz9l4sSJ3HbbbURHR9OpUyfGjh3LzJkzPV6b5557jpYtW2I2m2nSpAlPPfWURzt//vknV111Fb6+vnTq1ImdO3d6HN+2bRuXX345Pj4+NG7cmClTppCX9/f3sNTUVIYNG4aPjw/R0dG8++67HtcfPXoURVHYU+Rdl8zMTBRFYevWred9vgvdt7YqVxQSFxdHSEgI+/btIy4ujri4OBITEwkODiYuLo4lS5awZMmSYl9A4p+dm9qw9WCq/u9r29bH4lXx1e5W/XcVH//xMaqmYlJM+oeqqXz8x8es+u+qMrepaqrH4g8leXvf2yXWBr6Q+OR45u+cz8H0g+6JdkYLRsXIwfSDzN85n52ndnLW6l6Z6f0D73Mo85B+7QOdHyDUEurO4/WVPF5RMs3lwnE6FeeZVJmsVklUm42sTz7l+NhxpC1diuPkKY/jvr170WDJYqIWPo1P584XbbALf38PS8pIwtfkS5hPGL4mX5Iykpi/c36NBr5btmwhPz+fa665hhEjRrBmzZpSBy5jxoxh5cqV+vabb77Jv/71r2LnLVy4kLfeeosVK1aQmJjItGnTGDFiBN9//z2NGzfmo48+AtwjscnJySxbtky/dvXq1Xh7e7N9+3ZWrFjByZMnuf766+nRowd79+5l+fLlxMXFsWDBglI/s6ZpfPnllxw/frxUo9qFPvroI5YsWcKrr77KoUOH2LBhAx06dDjv+ZGRkXz33XecOXPmvOfMmjWLZ555hieeeIJ9+/bx3nvvUb9+fY9zHnvsMWbOnMmePXuIiYnhrrvu0oP8w4cPM2jQIIYPH87vv//O2rVr2bZtm8d8q9GjR3PixAm2bNnChx9+yCuvvEJqaioVUZr71lblSm84UsLbU6Liiubs5tud/HQkXd8e1imqwu07VSdvJLyBS3PhbfBGUzTQAAWMmhG7aueNhDcY0XYEJkPpvzT2ntlLasE//ydKLUhl75m9dIno8o/nFVVYCi3TmolTc5LtyNbTMbwN3jitTl7d+yqz+8xm/9n9fJj0oX7tdU2vo3eD3gAEm4MxG82lvq+4dEhlhsql5uWTvWkjWR9vwHVujqbBgN8VlxNy++14R0fXSP+q2/nKOVpMFsxGc7WWcyxJXFwcd955J0ajkfbt29O8eXPWrVtXqpzcESNGMGvWLI4dOwbA9u3bWbNmjcfooM1m4+mnn+abb76hd2/39+PmzZuzbds2Xn31Vfr3709oqLuSTkRERLFJUq1ateK5557Ttx977DEaN27MSy+9hKIotGnThlOnTvHwww8ze/ZsDIbzv4aNGjXS+6SqKvPnz+eKK64ozcsEwPHjx4mMjOSaa67By8uLJk2acNlll533/MWLF3PrrbcSGRlJu3bt6NOnDzfeeCODBw8GICcnh2XLlvHSSy/paxy0aNGCfv36ebQzc+ZMhgwZArhTJtq1a8cff/xBmzZtWLhwIffcc48+Ia1Vq1a88MIL9O/fn+XLl3P8+HG++OILfv75Z3r06AG4P+exseVPNQQueF9LLa60Uu46vaLyWe1/j4TuPHxWX4o4wGKiX8uKpzZ8ceQLchw5GBUjDtWByt/3M2DAqBjJceTwxZEvGNZiWKnbXbt/banPK0vQuz99PwcyDpDvykfTNIyKEUVxL29sc9mwYeNQ5iH+m/Zflu1epj9PlF8U4zqMA9zLKgeZpYi98KS5XO5V1erA23F1gSsri6xPPiH708+Kv6YmEwHXXE3wbbfh1aBByQ1cpGpTOcdzZWZmsn79erZt26bvGzFiBHFxcaUKesPDwxkyZAirVq1C0zSGDBlC2Dmr4/3xxx/k5+dz7bXXeuy32+3F0iBK0q1bN4/t/fv307t3b4/Xsm/fvuTm5vLXX3/9Yz7xjz/+SEBAADabjZ9//pnJkycTGhrKhAkTLtgPgNtuu42lS5fSvHlzBg0axPXXX8+wYcMwmUoOo9q2bct///tfdu3axfbt2/nhhx8YNmwYo0eP5o033mD//v3YbLYL5hR37NhR/3dUlHvwKzU1lTZt2rB3715+//13j5QFTdNQVZUjR46QlJSEyWTyeB3btGlT4QoMF7pvRYPqqlTqoHf69Ok8+eST+Pn5MX369H88d/HixaVqc/ny5SxfvpyjR48C0K5dO2bPnq3/JmS1WpkxYwZr1qzBZrMxcOBAXnnlFY/h/+PHjzNhwgS2bNmCv78/o0aNYuHChef9Qqyt7E4VZ5GC7FsO/v2WSIeGQRxMyaFdg0AMhvK/DVi4PLCT4vlPKiqqpqKgkJyXXKZ2i6YUVMZ5hdIL0vUSZCbFhKZoaJqmj0w7NSf5jnzWHlhLar57pNmgGJjRbQY+Jh+MBqPk8YpiZHS38jjT0shav57sz78ocanggEGDCB4+HFP4pfn/sLaUcyzJe++9h9Vq9XiLvzBwSUpKIiYm5oJtjBkzRn9L++WXXy52PDc3F4BNmzbRsGFDj2Nm84XfffPz87vgOaUVHR2tB3vt2rUjPj6ep556Sg96DQaD++dLEQ6HQ/9348aNOXjwIN988w2bN29m4sSJ/Oc//+H777/H6zxVRgwGAz169KBHjx5MnTqVd955h5EjR/LYY4/pEwAvpGjbhcG++r9YITc3l3//+98l5hY3adKEpKSkC7ZfODpe9NmLPndJLnTf2qzUkeFvv/2mvxC7d+8+bw5WWXKzGjVqxDPPPEOrVq3QNI3Vq1dz44038ttvv9GuXTumTZvGpk2bWLduHUFBQUyePJlbbrmF7du3A+6SIEOGDCEyMpIdO3aQnJzMvffei5eXF08//XSp+1EbFM3nzbU5iS+S2pBwMot/v/0rLSL8mdC/BX1alu8HSKRfpD7J7Hw0NCL9ylYhIs9ZutGy0p5XKNOWiUtzYcCAQ3Po/9HBvWpc4dLJv6f9ru+/q/VdtA5tDUC4TzhGQ8XzoMXFQUZ3K48jOZnMdR+Ss3lzsaWCFV9fgoYNJeimmzDW8ZqeFVW0nKPFVPwt3+oo53g+cXFxzJgxo9io7sSJE3nzzTd55plnLtjGoEGDsNvtKIqiTzQrqm3btpjNZo4fP07//v1LbKOwIoOrFL+ExsbG8tFHH+lpbuBOqwgICNDTF0rLaDRSUFCgb4eHh5Oc/PeAT3Z2drFUTh8fH4YNG8awYcOYNGkSbdq0ISEhga5du5bqnm3btgUgLy+PVq1a4ePjw7fffsu4cePK1PdCXbt2Zd++fbRs2bLE423atMHpdLJr1y49veHgwYMe5eHCw93vIicnJ+uj73tKqJ1dlvvWZqUOerds2aL/+59m9JXFsGGeb6E/9dRTLF++nJ9++olGjRoRFxfHe++9x4ABAwBYuXIlsbGx/PTTT/Tq1Yuvv/6affv28c0331C/fn06d+7Mk08+ycMPP8zcuXMrVN6kutmKBL3vxR/DpbqDU4PC/7N35/FRVefjxz/33tknyWQjJGENm+yIgoAb4r7hLuCvKipqtS5FW7W2FbdWq63aWv26K23ViisVXBEFN8QFRfY17IEkJDNZZ7v3/P64ySSTmSQzSSABzrsvKjP3zJkzQyBPzjznechLsyNQWFNUye/fWcED549oU+Cb504sLzjRcfWy7FnsrNqZ0LhkpNvTUVAIC/ObaqSNct3/dKL/kRySOYSLB10ceWy8bzLSocmoria8V+7utldw61a8c16navHimFbBaloanvPPJ+3ss9BSUjpphV1LfTnH9eXrsWv2qE0hIQS+gI9BGYPaVc6xJT6fLyaAycrKYu/evSxbtoxXXnmFwYMHR12/5JJLuO+++/jTn/7U6iemmqaxZs2ayO+bSk1N5be//S233HILhmFw7LHH4vP5+Oqrr0hLS2P69On06dMHRVGYP38+Z555Jk6nk5Rmvn5+9atf8fe//52bbrqJG2+8kXXr1nH33Xdz6623tpjPC2ZKgN/vj6Q3/Oc//+Giiy6KXD/xxBOZPXs2kydPJj09nVmzZkW9ptmzZ6PrOuPGjcPlcvHyyy/jdDrp06dP3Oe76KKLOOaYYzj66KPJzc2lsLCQO++8k0GDBjF48GAsFgt33HEHt99+OzabjWOOOYaSkhJWrVrFjBkzWnwt9e644w7Gjx/PjTfeyNVXX43b7Wb16tUsWLCAJ554gsMOO4zTTz+dX/7ylzz11FNYLBZmzpwZtcvsdDoZP348f/nLXygoKKC4uJg//vGP7XrerizpzPlQKITFYmHlypUduhBd1yMnRydMmMAPP/xAKBTi5JNPjowZPHgwvXv3jpTsWLJkCSNGjIhKdzjttNOoqKhg1apVzT5XIBCgoqIi6ldnq9/pNYTg/RW7I/e7nX5CtrUo9h10T7NSFdB5avEmDKPlHdt43t7wduuDkhhX75iex3TouHoZjoyYTw6a26l2WpzceuStaKrZSrkzdk6krkfoOqHiYkLFsjJDewTWr2f3/X9ix3XXU/XZZ1EBr5aVRdYvr6X3v2aTMW2qDHgbqa+N7ra6Ka4pxh/2YwgDf9hPcU0xbqu7TbXRE7Vo0SJGjx4d9evee+/lhRdeYOjQoTEBL8D5559PcXEx77//fkLPkZaWRlpa8/XP77//fu666y4efPBBhgwZwumnn857771HQd1hxh49enDvvffyu9/9ju7du7dYAaBHjx68//77fPvtt4waNYrrrruOGTNmtBqkARx22GHk5eUxYMAA7rjjDn75y1/yz3/+M3L9zjvvZOLEiZx99tmcddZZnHfeefTv3z9yPT09neeee45jjjmGkSNH8sknnzBv3jyysuJv5px22mnMmzePyZMnM2jQIKZPn87gwYP5+OOPIz9M3HXXXfzmN79h1qxZDBkyhKlTpyZVWWHkyJEsXryY9evXc9xxxzF69GhmzZpFfqPc+Zdeeon8/HwmTpzIBRdcwLXXXktOTk7UPC+++CLhcJgjjzySmTNntloNI5Hn7aoU0TSJJQH9+vXjnXfeYdSoUe1ewIoVK5gwYQJ+v5+UlBReffVVzjzzTF599VWuvPJKAk3yxI466igmTZrEQw89xLXXXsvWrVv56KOPItdrampwu928//77kdzgpu655x7uvffemPt9Pl+Lf3mb096i44Gwzs5y82OWZVu9/PbN5ZFr7p4vo7k3o2DBZuTjrD6FcHV/nrlsDCN6JndA64ZPbuDznZ+3Ou74Hsfz5Mmx+VnNCepBxr48NupgXFMqKt9d+l1S3dBWlK7gig+uIGyEI7u7zZl5xExO6n0SmqKRl5KXVPUJ6eAkd3fbRwiBf8VKvHPmUNuonmk9S24u6VOmkHrSSSi2rtE5zdItBy2l4/JAO8rSoqW8sOIFCisKCRthLKqFgrQCZoyY0aba6JIktU2bIoM//OEP/P73v+c///lPpNxIWx122GH89NNP+Hw+3nzzTaZPn87ixYvbNWdr7rzzzqjDeBUVFfTq1atNc3XEP2ZRVRs2l0Z+r2iVKO41CMVAoODXNhBKLUIN/oKympHxpmpRjiun9UFJjKtn02wc1+M4Fu9s/s/tuB7HJd3+tyJQgcPioDZcG6nxqwjFPHTXKMAemjmUE3uZKTDZrmwZ8B7iZO5u+wghqP3ue8rnvEZg9ZqY69Y+fciYOgX38cejxPlIW4o1Lm8cY3PHdsmObJJ0KGlTdPDEE0+wceNG8vPz6dOnT8wJy2VxdgWaY7PZIsnQRx55JN999x3/+Mc/mDp1KsFgEK/XG1VeY8+ePeTmmgetcnNz+fbbb6Pm27NnT+Rac+x2e0InR1tTX3S8OlSNx+7BptkI6sFI0fFZE2YlFPg2PsS2cqcv8ntL2oq6j/ctmAV1dXSlAuF5l3TntKTX2yclfu5RW8fVM4RB0AhiV+0EjEDMdbtqJ2gEMYSR1D/y6Y50XFYXbqsbX8BHQA/EBLwqKpcMuSRS/sdpSexErHRwkru7bSd0neqvl+CdM4fgpk0x1+2HDSJ92jSzVXAr+ZNSLFVR93tZMkmSorUp6D333HP3WQcdwzAIBAIceeSRWK1WFi5cyIUXXgiYpw63bdsWKXI9YcIE/vznP1NcXBzJUVmwYAFpaWmRU5L7SkcVHRdCRJpSlNcEWV9cFblmSV1Vd3ir4ZcgjLDuRrHvADKSWvPS4sQ6/ywtXsoVXJHwvPW1KHuk9kA1VHbW7iSsh7FoFno4e2CoRptqUTY+BJLnziOgByjzl1ETromMKfAUMLLbSBwWBxmO5N4P6eAhd3fbToTDVH22CO/rrxPasSPmumPUSDKmTsNx+KiDunOaJEkHvzYFvfW9qNvrzjvv5IwzzqB3795UVlby6quvsmjRIj766CM8Hg8zZszg1ltvJTMzk7S0NG666SYmTJjA+PHjATj11FMZOnQol112GQ8//DC7d+/mj3/8IzfccEOH7OS2pKOKjgfCBkZdWvXn60uoz7BWLF5Ux3bM82rmneazqKAarNy7gpE5zbdAjEdN8NxiouPq1dei3Fu7F1+wYac6FA6xsXIjHpsHq2ZNuhZl/SGQe76+h721e9FULSrgdVvcXDn8SqyKVdbjPYQZ1dWEy8oQ4dj601LzjECAygUL8L3xJuE4h2dcRx1F+rSpOLpwoXlJkqRktCno7devH999913MqUWv18sRRxzB5s2bE5qnuLiYyy+/nKKiIjweDyNHjuSjjz6KdG957LHHUFWVCy+8MKo5RT1N05g/fz7XX389EyZMwO12M336dO677762vKykdFTR8dpgw8ewjRtSWFJXoijRB7fMjsFKzP2JGtZtWEIH2YZ1S+4juHRHOlXBKvy6P+51X9CHQ2tbRYUjux/J9Ydfz6trXmX13tWR+x2ag9vG3saobqPIcmbJPN5DkNB19LIy9Kqq1gdLEUZNDRXvv4/v7XfQy8ujLyoK7uOOI33qFOz9+nXOAiVJkvaRNkUKW7ZsiVtIOhAIsCPOx2PNeeGFF1q87nA4ePLJJ+N2eqnXp0+fhEurdKSOKjpen89bWhVgxY7G+bzLqQ9zGwiEomMYGiO6JbfLC3DVsKt46qenEhqXjP6e/s0GvPX8up/+nv4tjomntLaUEVkjcFvdkeoNFsXCXyf+lb5pfUmzp+GyupKeVzqwyd3d5OkVFfjefZeK/72L0fQHBU0j5cQTSZ9yMbYki/xLkiQdKJIKet99993I7+tTEOrpus7ChQsjtfcOdh1RdNwwBIGweShr0bqSSEEuxeJDdewARQHRKNVAMcfq/u4UOFtvEdlUMu2CR2QnHlTPXjU74XHXjbou4XkrghX4w34+3PIh3+5uOLB45fAr6ZvWF7tmJ8Mu83gPJXJ3N3nhsjJ8b71NxfvvI/zRP5wqNltdq+ALsOQkV7VFkiTpQJNU0HveeecBZs7q9OnTo65ZrVb69u3LI4880mGL68rq803vW3IfxTXFUdUbfAFfQkXH/WE90u960brGOXUGQk9F0aqhce1boWDoKQSKz+C3b6/k6cuOTGrN3xd9n/C4ZILe7ZXbO3QcQMgI4fV72VG5g+dXPh+5f3TOaM7udzaqopLtypYHaw4hRk2NWZlB7u4mJLRnD7433qTy448RdS3k6ylOJ2lnn43n/POwZMgfHCVJOjQkFfQadV14CgoK+O6778jOPrQPD43LG8esCbMidXorghVYVAuDMgYlVKe3Pp93d4Wf1UWVkftFOIPArinYshah2neDooPQMAK5BPeegF4zgG1lyZ9Sn7d5XsLjrhxxZcLz9kptqHGsEBuE1qclNB7Xmr21ewnoAR754RGCehCAVFsqM4+YaQa8zmysatcoiC/tW8Iw0Pfulbu7CQpu24b39TdiOqcBqKmpeM47l7TJk9FSUztphZIkSZ2jTTm9hYWFCY0bMWIE77//fpsbPxwI2lN0vD6fd3GjA2xOq0ptyECvGUBtTT9Uxy4UrQahuzD8+dR3ju6dmXzXoT3Vezp0XL2rhl/F08ufRhc6AhEV+NYHvJqicdXwxHKF69Ma/rv2v2z0bozcf9PhN5HpyCTVlirzeA8Rcnc3cYGNG/G+Nofqr7+GJo02tcxMPBdcQNqZZ6A6ZS1r6dA1e/ZsZs6cidfr7dB5t2zZQkFBAT/++COHH354Qo+55557mDt3Lj/99FOHrkVq3j6tML5lyxZCTT5WOxjVFx0/pscxDMsallDAqxuCYF0+72eNUhvOG9W4d7WK4e+JXj0Iw9+Txn9cj16YfEe2fcWm2bhgwAWR26LR/+pdMOCChDqyhfQQ5f5yVpWu4s31b0buP7XPqUzIn4BNs5HpaF8XQKnrE4ZBuKSE0J49MuBtRe3KlRTddRc7b7qZ6q++igp4Lbm5ZN90I71feon0Cy+QAe8hpKSkhOuvv57evXtjt9vJzc3ltNNO46uvvoqM6du3L4qioCgKmqaRn5/PjBkzKG9U1WPRokUoikJGRgb+Jjnh3333XeTxAFdccUXkdrxfffv2BeCEE06I3Ge32+nRoweTJ0/m7bff7pDXJUnNkW11Okl9Q4qd5bWs39Pwse3Jw3IZkpvS4mNH9/LgciX/0f5Az8AOHdfYrKNnMbHHxLjXJvaYyKyjZyU0z17/XqqCVTz6w6ORoDnPncfVI65GVVS6ObvJPN6DnFFTQ2jnTpnO0AIhBDXff8+u226j6Lbbqf3+h6jr1t696Xbbb+n1/HOknXkmik2mAnU2YRjUrlxF1RdfUrtyFaJJ6klHu/DCC/nxxx/517/+xfr163n33Xc54YQT2Lt3b9S4++67j6KiIrZt28Yrr7zC559/zs033xwzX2pqKu+8807UfS+88AK9e/eO3P7HP/5BUVFR5BfASy+9FLn93XffRcZec801FBUVsWnTJt566y2GDh3KtGnTuPbaazvkdUlSPDLo7ST1QW/jXd6+WS4Kst08+Ysj6wJfA9WxA8293qzmgMHoXh7eueHYNj3nOQPP6dBxjS0tWsrykuVxry0vWc7Sota7wfkCPvxhP08vf5riWvN9URWV3475LU6Lk0xHJlZNfvM+WMnd3dYJw6Dqyy/ZefOv2X3XLPwrV0Vdtw0cQPc//pGeT/0fqSeeiKJpnbRSqbHqb75h29XXsOOmm9h1553suOkmtl19DdXffLNPns/r9fLFF1/w0EMPMWnSJPr06cNRRx3FnXfeyTnnRP/7npqaSm5uLj169GDSpElMnz6dZcuWxcw5ffp0Xnzxxcjt2tpaXnvttahD7R6Ph9zc3MgvgPT09Mjtbt26Rca6XC5yc3Pp2bMn48eP56GHHuKZZ57hueee45NPPmnX6/J6vfzyl7+ke/fuOBwOhg8fzvz586Pm+uijjxgyZAgpKSmcfvrpkSC93vPPP8+QIUNwOBwMHjw4qkcAwLfffsvo0aNxOByMGTOGH3/8Mer67NmzSU9Pj7pv7ty5rW7atPa8UvvIiv4dwBBG0jm99fm8ixrl804a3FAy6FdnKLyzaS4/lqxFN8JoqoXROUP45eHXtHmd/TMSq5Ob6Lh6hjC4f8n9eIPeuNe9QS/3L7mfd89/t9n3JaSH8Aa8fL7jcxbtWBS5/5LDLmFQxiBSbCmk2FreAZcOXDJ3t2UiHKZq8WK8c14ntD22Copj+HDSp03DecRo+UlIF1P9zTcU3X03RlU1Wno6is2GCAYJrF9P0d13k3fvvbjruox2lJSUFFJSUpg7dy7jx49PuEPpzp07mTdvHuPGxR7Cvuyyy/jrX//Ktm3b6N27N2+99RZ9+/bliCOO6LB1T58+nd/85je8/fbbnHzyyTHXE3ldhmFwxhlnUFlZycsvv0z//v1ZvXo1WqMfAGtqavjb3/7Gf/7zH1RV5dJLL+W3v/0tr7zyCgCvvPIKs2bN4oknnmD06NH8+OOPXHPNNZEGWFVVVZx99tmccsopvPzyyxQWFvLrX/+63a+/teeV2k8Gve20tGhppHpD2AhjUS0UpBW0WL2hPp93294aNpc2VGE4YZD5U/DPpd/zj5/vo9xfhoEBKujAsuLv+f0XhTxw3AOtVoaIpypYhVWxEhLN51lbFStVweQ+Vl5RuoKtlVtbHLO1cisrSlcwqtuouNdLa0vZU72H//up4afaIZlDuHjQxVg1K1mOrLiPkw5swjDMuruVla0PPgQZwSBVCxbgfeNNwntiD5g6x4whY+pUHMOT66Io7R/CMCh99jmMqmos3btHfiBRHA4Uu51wcTGlzz6H66ijUNSO++DVYrEwe/ZsrrnmGp5++mmOOOIIJk6cyLRp0xg5Mvo8yB133MEf//hHdF3H7/czbtw4Hn300Zg5c3JyOOOMM5g9ezazZs3ixRdf5Kqrkmtk1BpVVRk0aBBbtmxp8+v65JNP+Pbbb1mzZg2DBpn17Ps16S4YCoV4+umn6d/f3OC58cYbozq53n333TzyyCNccIF5VqWgoIDVq1fzzDPPMH36dF599VUMw+CFF17A4XAwbNgwduzYwfXXX9+u19/a80rtJ9Mb2mFp0VLuW3If68vX47K4yHZm47K4WF++nvuW3NfsR/r1u7yfNkptGJCTQq9MF4YwmL32SUr9JejoUYfCdHSKa4t59PtHMUTy+WAptpQWA16AkAglvaO6YMuCdo3zBXzUhGt47IfHqA6bPwQ4LU5uPfJWLJqFHGeO3L06CBm1tWburgx4Yxi1tXjfepvtV15F6RNPRge8ioL72GPp8c9/knf/fTLg7cL8q9cQLCw0d3ib/BumKAqax0OwsBD/6jUd/twXXnghu3bt4t133+X0009n0aJFHHHEEcyePTtq3G233cZPP/3Ezz//zMKFCwE466yz4nZdveqqq5g9ezabN29myZIl/OIXv+jwdQshWvz3vrXX9dNPP9GzZ89IwBuPy+WKBLwAeXl5FBeb34+rq6vZtGkTM2bMiOwsp6Sk8Kc//YlNmzYBsGbNGkaOHInD0dCNdcKECe152Qk9r9R+bd7pXbhwIQsXLqS4uDhSv7defd7PM888Q/fu3du3wi7KEAYvrHiB6lA1Oa6GoMxhcWDX7BTXFPPCihcYmzs25iP92qDZlCIqteEwc5d3o28thRXrW3zutWVrWbV3VVINJAAKfYmVmiv0FTa7IxvPzqqdbR4X1IN4A17mbpzLyr0rI/dfN/I6ct25Mo/3ICR3d5unV1ZSMW8evrn/w2j6/qhqQ6vgg7gM5MFELy9HhEIotviVaxSbDeHzoTeqltCRHA4Hp5xyCqeccgp33XUXV199NXfffTdXXHFFZEx2djYDBgwAYODAgfz9739nwoQJfPbZZzEpBmeccQbXXnstM2bMYPLkyWRldewncLqus2HDBsaOHdvm1+VMoEKJ1Rr9PUVRlEijqKq6A7TPPfdcTJqHlkSOvKqqkTnrtVTNqqOeV2pZm4Lee++9l/vuu48xY8aQl5fX7E9l/+///b92La4rW1O2hsKKQjx2T9yf4D12D4UVhawpW8OwrOidGH9Ip7C0mm1lNZH7TqgLeteVrzRTGlpgYLB8z/Kkg96VpStbH1Q37rwB5yU8r11LLF+s6TghBKW1pWwo38DLq1+O3H9sj2OZ1GsSbqubVJtZQL8tedNS12PU1hIuLZW5u02Ey8vxvTOXivnzEbW1UdcUq5XU007Fc9FFWA/STYSDlZaRgWK1IoJBlEa7gvVEMIhitaLtp654Q4cOZe7cuS2OqQ+wapt8HYKZXnD55Zfz8MMP88EHH3T4+v71r39RXl7OhRdemNTjGr+ukSNHsmPHDtavX9/ibm9zunfvTn5+Pps3b252J3vIkCH85z//we/3R3Z7v2lyKLFbt25UVlZSXV2N223W1W+pHm8izyu1X5uC3qeffprZs2dz2WWXdfR6Dhhev5ewEW629qxNs1ERrMDr90bdH9YNQrrBZ412eQfnppLnMX863Vq5NqHnX1W2qvVBTTgtidXoTHRcvWxHYp35mo7zBXxUBCp45PtHCAszCMpyZHHDqBuwWWxkOc1dhLbkTUtdi9zdjS9cXIz3zTep/OhjRDAYdU1xOkk760w855+PJVPWpj4QOYYOwVZQQGD9ehS7PWqDRAiB7vNhHzQIx9AhHfq8e/fu5eKLL+aqq65i5MiRpKam8v333/Pwww9z7rnnRo2trKxk9+7dCCHYvn07t99+O926dePoo4+OO/f999/Pbbfd1u5d3pqaGnbv3k04HGbHjh288847PPbYY1x//fVMmjSpza9r4sSJHH/88Vx44YU8+uijDBgwgLVr16IoCqeffnpCa7v33nu5+eab8Xg8nH766QQCAb7//nvKy8u59dZb+X//7//xhz/8gWuuuYY777yTLVu28Le//S1qjnHjxuFyufj973/PzTffzNKlS2NSS5J9Xqn92rRVFgwGm/0LcahId6RjUS2RFrn+sJ+qYBX+sFm8O6gHsagW0h3pUY+rDZmpDY1LldWnNgCkORLLp3VZku9IdkqfUyLd0pQ4/6u//5Q+pyQ1b15KXtLjgnoQX9DHS6teYkfVjshz33LkLaTaU+nm7IaqqG3Om5a6DqO2ltCuXTLgbSS4YwfFjz7KtqtmUDFvflTAq6akkHHpL+j9r9lkzZghA94DmKKqZF97DarbRbi4GMPvRxgGht9PuLgY1e0m+9prOvQQG5hVDsaNG8djjz3G8ccfz/Dhw7nrrru45ppreOKJJ6LGzpo1i7y8PPLz8zn77LNxu918/PHHzQa1NpuN7Ozsdp+zeO6558jLy6N///5ccMEFrF69mjlz5rRYoivR1/XWW28xduxYLrnkEoYOHcrtt98eN0e5OVdffTXPP/88L730EiNGjGDixInMnj2bgoKCyDrmzZvHihUrGD16NH/4wx946KGHoubIzMzk5Zdf5v3332fEiBH897//5Z577mnX80rtp4imSScJuOOOO0hJSeGuu+7aF2va7yoqKvB4PPh8PtLS0hJ6jCEMrltwHav2riJshAkaQRCAAjbVhkW1MCxrGE+f8nTUx/DFlX6WbS3nupcb6iC+ds04ctIcuGwWvi39mD98+YdWn//Px/6Zc/onV0/XEAbnvHNOVKUFBSWqc1qf1D4tlhaLZ0XpCi5///LIbm08FsXCv8/8NyOyRyCEoKi6iK93fs2939wbGXP+gPO5avhVZDozSbOlRd7j9eXro/KmwdwlKa4pZlDGoJj3WOoa5O5urMCmTXhff53qL76MbRWckdHQKtgl22y3haVbDlpK8i3a97Xqb76h9NnnCBYWmjm+Viu2ggKyr72mw8uVSZLUvDalN/j9fp599lk++eQTRo4cGZMUHq/cycFGVVSOzj+a73Z/hy50NFVDUzR0dGrDtWiKxtH5R8cEY/6gwWdrG3Z5h+enkZNm5gR5nFbOLDiTB755IFLFIB63xc2ZBWe2ac13TbiL3y7+Ld6AFyAq4E23p3PXhLuSDiCHZQ2jR0qPFsuW9UjpEclt9ga8FNcU848f/xG5XpBWwGVDLsNldZFmM3/waE/etNS5jNpas+7uIdCGPBH+1aspf20OtY06UtWz5OSQfvHFpJx6CmozB56kA5t7/HhcRx2Ff/Ua9PJytIwMHEOHdPgOryRJLWtT0Pvzzz9z+OGHA7ByZfThqEOltJQhDL7e9TUui4uwMHd6daGDYubEWhQLX+/6msuHXR4JIoNhg5Cus2h9Qz7vCYeZDSlsFhWnzTxAcN2o6/j7sr+b8zWhKRrXjboOi9q2whvj8sZxSu9TeHvD2+g0zK+hcUrvU/Z5jmxAD+D1e3l82eORwNum2vjNmN/gtDrJdjbk/bY1bzoZ8oBcxxKGgV5ejl5R0dlL6XRCCGp//BHva3Pwr1gRc93asyfpU6aQMukEFIssmX6wU1QVpywvJ0mdqk3/0n722WcdvY4DTv0uZLYrG7tmx6/70Q1zx9ehOQjogZhdSH9YZ01RJXsqAgAowMRBZpCX7moI7K4YfgXbKrbx9sa3owJfTdG4YMAFXDH8ijave/bK2by14a2YChE6Om9teIveab2Tnn/13tUUVRdF8oIb7x7X31dUXcSq0lVkOjP5cMuHfLenYcfrimFX0NfTl26ublEBZ+O8aYcl9uRzc3nTiZIH5DqW3N01CcOgZulSvK/NIbA+tvygrX9/0qdNxT1hgmwTLEmStB/J7YU2arwLqShKTMWDeLuQ/qAe1ZBiVK90slLsWFQVt63hm9/SoqUs3b2UNGsaFs0SKdYd1sMs3b2UpUVL2xSUhY0wT/70ZLMl0QwMnvzpSS4demlSO8k/l/xMWJhBo6Zo6EKPrLn+dliEWVq0lMGZg3l+5fORxx6RcwRn9zubdHt6TEmzIZlDKEgrYH35euxa7MlnX8DHoIxBDMlM/uRz/QG56lA1HrsHm2YjqAcjB+RmTZglA98Eyd1dk9B1qhZ/jvf11wltjU31sQ8dSsa0qTjHjDlkPhGTJEnqSmTQ20Zt2YWsDoZZvD62IYXHaY18E2zc9CI3JTfu4a3mml60Zv7m+fh1f4tj/Lqf+ZvnJ1Wnt/H6giLY0C1OgK7oqJhFuquCVTzywyORihdptjRmHjETl9WFx+6JmU9VVGaMmMF9S+6juKY4Kjj1BXy4rW5mjJiR9PvQnsYiUjTD7zfr7h7Cu7siGKJy4UK8b7xBuKgo5rrzyCNJnzoF54jk6mpLkiRJHUsGvW2U7C5kIKzz03Yve6vMgE9V4LiB2aiKQqqj4Y+h8eGtsBFms3czOjoaGv3S+7Xr8Nbi7YsTHpdM0Dui2whURSVsmNUb6lMawAwwDQw0RWNLxRY2ejdGrt08+mayXdlRebxNjcsbx6wJsyJpCBXBCiyqhUEZg9qchtD4PQaoDddGpabIA3Ktk7u7ZsBf+cGHeN96C33v3pjrrmOOJmPqVOwDB3bC6iRJkqSmZNDbRsnuQvqDBovWNuzyHtE7g3SXjTSnFVVtCBLr0yY2+zZHPZ+OzgbvBgCyndltOrxVGUysdFSi4+oNyRyCXbVHgt7GOb31NEXj0+2fRm6f1vc0xuWNI9uZjaa2nNc4Lm8cY3PHdtiBs/r3OGSE2F29m4AeQCBQULBrdrKcWYSNcLsOyB3MDvXdXb2qiop58/HNnYvRNOhXVVImnUD6xVOw9endKeuTJEmS4pNBbzskswtZGQjx+Ybo1AZFUUhzRP8RpDvSKa0tbfF5S2tL23R4a4BnAEt3t97MYYBnQFLzritfh91ipzZc22y+cMgIRYLhfHc+Vw+/mnR7esLd31RF7bBd13RHOrrQ2VW1K6ZCRk24hkBVgDR7WpsPyB2shBBm3d1DdHdX93rxzZ2Lb958RE1N9EWLhdRTTyX9ogux5iXWrEWSJEnav2TQ206J7EIKIfhmcxnlNebOmEVVOHZgNil2CxYtercyy55Ya8dExzW2o3JHh46r5/V7URWVHqk92Fu7F3/YHwlw6w+n1ecSa4rGb8b8hnRHeqcFlYdlHEYwHIwEvI3TMQQCXegEw0EOyzisU9bXFR3Ku7vhkhK8b71F5YcfIQKBqGuK3W62Cr7gAiztbMsqSZIk7Vsy6O0Are1CBsIGn67dE7k9pm8GqQ4rHqc1ZuxF8y5K6DkvmncRX17yZVLr/Knkpw4dV6/+UF/YCJsNjevzmwXohk5INARKlwy+hCGZQ1rM493X1patJWAEWhwTMAKsLVvL8Ozh+2lVXZMQwszd9fk6eyn7XWjnTrxvvEHlwk8hHN1tUE1JIW3yZDznnYuWYBdHSZIkqXPJoHc/qKgN8eWGhpSFSYfl4LJZsFlic1J9wcSCi0THNRY0gh06rt6QzCFk2jNZW7YWgUBTNRShYGBEBbxDModw0aCLyHJmtbm5Rkf4ueRnDGFgUSwYGA3VJjB/gFFRMYTBzyU/H9JBr7m7uxcRSu7r4UAXKCzEO+d1qr/4AozodB0tPR3P+eeTdtZZqG7ZKliSJOlAIoPeDtBaV68vNpRS4Td3iqyawtH9s+Lu8u5rWbYsdtS2nrqQZWvbx7Si7n/1B9oaU1C45chbyHBk4LJ2jWBBVdRI4IsAFCIBb+NA+FBzqO7u+tesxTtnDjVLY/PetW7dSL/oIlJPOxXVbo/zaEmSJKmrk0FvO7XW1cswBB+v3h0ZP64giwy3LdJyuCkLFsLEBo3xxiXLaXNCbYLjkrCmbA1FNUWoihq3dTKYub1BI0iGPSOpufeFEd1GYFEs6EJHQTF/QKnPyBBmTq9FsTCi26FXV/VQ290VQuBfvpzy1+bgX7485rq1R35dq+BJKNb9/4OqJEmS1HFk0NsOiXT1Oswzmi83Nk5t6NbiLq/b4sYXbn2HzW1xJ73eXVW7OnRcvbLaMqqCVQDYFBsGBmERHbiH9BDCEF2iE9WwrGEMSB/A2vK1hEUYDQ0FJXKITSAYkD7gkKrRe6jt7gohqFn6Ld45cwisXRtz3VZQQPrUKbiPPVa2CpYkSTpIyKC3jRLt6nVq1h+pDuh111SOGWBWbWhORTixclCJjmusaSDa3nH1vAFvZNdUIGIer6JiYFAZSq7+776iKiq3jrmV33/xe8oD5VG706qikmHP4NYxtx4y3diMQIBwSekhsbsrdJ3qL77E+/rrBAsLY67bhwwxWwWPHdslfkCTEqc6HCg2uRsvSVLzZNDbRo27ejX95qgoSqSr15vbNkTuH98vi1yPo9O+mWbYMtjt353QuGSk29PRFI2wEY5pTFGfRmBRLaTb05Oad18alzeOB457gOd/fp4N3g2EjBBW1crA9IFcPfLqNnV6O9AcSru7IhSi8tNP8b7+BuFdsZ9kOEcfTvq0aThGjJDB7gFCURQUpwvV7UJ1OuWOvCRJrZJBbxvVd/Wyaba4122aDW9tDT9u8UfumzQ4h1RHyzsRDs1Brd564q1DcyS3YKDAU5BQ0FvgKUhq3kxnJinWFLwBb0xzirAIo6KSaksl05mZ1Lz7Wkd3ejuQHCq7u4bfT+VHH5utgktKYq67JkwgfeoUHIfJmswHAkXTUJ1OVJcLxelEUQ/+v6uSJHUcGfS2UX1t2qAexGGJDUCDepBA5QACdZ/0u2waJw7OQVNb3kXqYevBxtqNrT5/D1uPpNfstieWB5zouHqD0gfhsXkoD5THvW5gkOvKZUjmkKTm3R86stPbgeBQ2d01qqvxzZ+P7525GE1fq6qScvzxpE+dgq1v305Zn5Q4xWJBdbnMQNfReZ+USZJ04JNBbxsNyRxCQVoB68vXY9fsUf8QCyHwBXzoFWdF7ju6fxbd01rfnd0e2J7Q8yc6rrFcV26Hjqu3t3YvO6t3tjimtdbKrWmtLJzUukNhd1f3+vD9by4V8+ZjVFdHX7RYSD35ZNIvvhhrvmwV3JUpVpuZtuByyRJxkiR1GBn0tpGqqMwYMYP7ltxHcU1xVPUGX8CHQ/Wws7xhN/b0YXlxm1E0Fa/GbXvGNbYvqjdUh6p5avlThIyW29MW1xazsnQlI7uNTHjueq2VhZNaJoRA93rRvd7OXso+Ey4txfvW21R+8EHcVsGpZ5xB+gUXYOnWeZ0ApZapdnvDjq4tftqYJElSe8igtx3G5Y1j1oRZPLnsGX4o2gDogMaReQMZ6bqctbq505Rit3DK0O4JzamQ2Ed3iY5rzFvr7dBxuqGzvHg5/9v0v4TGf7Tlo6SD3kTKwsnAt3kH++5uaFeR2Sr4k09iWwW73aRNPhvPueehpXs6aYVScxRFQXE4GgJdi/x2JEnSviX/lWmnaf8oRXA+qmMXilaD0F18vjGfz2n4aPX4Qdl4XImV0tmXQW8ih9iSGlezm4e/e7jZhhRNrStbl9C4eomWhRubO1amOjRxsO/uBrdswfv6G1QtXhzTKlj1ePCcfx6es89GdSdfz1radxRVRXE6UV1uVJc8iCZJ0v4lg952KPjde3UFulQMf89mx50zKj/hOT0OD6X+1vNfPY7kd640JbGSPomMqwpW8fzPz7PJtynh5xdCtD6okUTLwq0pW3NIHUZrzcG8u+tft95sFbxkScw1LTub9AsvIPX001EdyVc3kfYNRdMadnOdTnkQTZKkTiOD3jbaVuojkRAuxW5WbUjUmb3P5N/r/53QuGQ1raHb1nFhI8ziHYt5a8NbST1/N2e3pMYnUhauIliB1+9Nat6DVf3uruHzJf0DRlcmhMC/YgXe1+ZQ++OPMdcteXmkT7mY1JNOkq2CuwjFao0EuvIHEEmSugoZ9LbRxL992eiWEZXeYPh7QF36QVVAx2ZJvGj65qrNHTqusVCo5cNmiY7b6tvKI98/EgmOU6wpVIWqWp0325ncIaJEysJZVAvpjvSk5t0vDAN2L4eaveDKgtxRsA8/yjUCAfTSUozgwbO7K4Sg9rvvKZ/zGoHVa2KuW/v2IWPqVNzHHScbE3QBqs2G4nKhut2o8iCaJEldkAx626h+H01zbcSW9RmqfReoYUQohZrC26ANObdAQsFjMuMaC5Fg0NvCuMpgJY8ue5SSWrPQv6ZonNL7FN7Z9E6r8+alJFcmKpGycIMyBnW9+r+bF8OXj0HpBjBCoFoheyAcewv0m9ihT3Uw7u4KXaf666/xvjaH4ObYH+7shw0ifdo0XEcdJXNCO5na+CCa3GWXJKmLk0FvO2iujdh7vIqq1UTuC9f2B+q/EYdJ9i3u4ezBT/yU0LhkqSQWIDQ3LmyEeXvD23y+4/PIff9v8P/jmB7HJBT0DstOLu+2tbJwbqubGSNmdK1DbJsXw/yZEKgCZwZY7BAOwJ5V5v1n/73DAl8jGEQvKTlodndFOEzVZ5/hff0NQjt2xFx3jBpFxtSpOA4fJfNCO4ls/StJ0oGsU6OFBx98kLFjx5KamkpOTg7nnXce69ZFn/D3+/3ccMMNZGVlkZKSwoUXXsiePXuixmzbto2zzjoLl8tFTk4Ot912G+Fw8nVsk5FiMbDnzo0KeAHCFaMiv7ekrSDFYjR9aIuWFi/t0HGNtbcyxKrSVfzfT/8XuT00ayiXDr004cYTWyu2JjSusfqycIMyBlETrqG0tpSacA2DMgZ1vXJlhmHu8AaqIDUPrE5QVPO/qXnm/V8+FlNtIFlCCMLl5YR37TooAl4jEMA3bx7bZ8yg5NHHYgJe17hx5D/6KPl/eRDn6MNlwLufKZqGlpKCNScHa58+WLvnoKWkyIBXkqQDTqfu9C5evJgbbriBsWPHEg6H+f3vf8+pp57K6tWrcdeVGrrlllt47733eOONN/B4PNx4441ccMEFfPXVVwDous5ZZ51Fbm4uX3/9NUVFRVx++eVYrVYeeOCBfbb2GycrPLEmOtgzwm70mv6R29b0pdx40uFJzevX/R06rrH2HGQr95fz4LcPUhM2g3yXxcVtY26ju6s7e2r2xIyPJ9FxTY3LG8fY3LFdvyPb7uVmSoMzA5oGZopi3l+6wRyXP7pNT3Ew7e4a1TVUvP8evnfmopc3aWGtqriPO470qVOwFxR0zgIPYbL1ryRJB6NODXo//PDDqNuzZ88mJyeHH374geOPPx6fz8cLL7zAq6++yoknngjASy+9xJAhQ/jmm28YP348H3/8MatXr+aTTz6he/fuHH744dx///3ccccd3HPPPdj20YGKR774AHuTc1l65XDqN88Viw/VuZVHvviA68adnfC8Ds2RUL6uQ0v+RHR+aj6lgdZ3ZfNTo0ushYwQL6x4gVV7V0Xuu37U9QzPHo6mauS581AVFSFE3IBZQUFRFPLcbW/9qipq1y9LVrPXzOG1NNM21WIHv9ccl6SDKXdXr6jA9793qXj3XYyqJl/rFgupJ52I56KLsPVsvgyg1PFk619Jkg52XWqrzOfzAZCZmQnADz/8QCgU4uSTT46MGTx4ML1792ZJXZ3OJUuWMGLECLp3b+h4dtppp1FRUcGqVauIJxAIUFFREfUrWaoWiLkv1Di1IfVnFEXEHdeSIRmJHcpKdFxjw7OHt2nckl1LeHnNy5Hbx/c4nvMGnBepqHBGwRnYFFuzO8kCgU2xcUbBGUmvuV7YCDNv0zye/flZ5m2a16Y2zPucK8s8tBYOYCBYpYT5Sg2ySgljIMzcXtVqjkuCEQwS3rUL3es9oAPecFkZe597nm3Tr8D76qtRAa9is5F2zjn0fvEFus2cKQPe/US127FkZmLr0QNbzx5YMjJkwCtJ0kGryxxkMwyDmTNncswxxzB8uBl07d69G5vNRnp6etTY7t27s3v37siYxgFv/fX6a/E8+OCD3Hvvve1ar17bC2tGQ16tEU7FqO0buW1J+zkyLhl7/YntAiY6rrGeKYkFEo3H7anewwNLH4h0Xct2ZnPLkbfElglr7dPPdnw6OnvlbF74+Tkyt1WQWmtQ6VR5uPdfmDHyGq4YfkXbJ+5ouaMgeyBLy9bwgitEoWIQVgQWoVAgVGbU1jAue4g5LgEHy+5uaPduvG+8SeXHH8e0ClacTjyTz8Zz/vloTf6eSx3PPIjmNHdznU7Z+leSpENKl/kX74YbbmDlypV8+eWXrQ9upzvvvJNbb701cruiooJevZILTvtYu9E4QzVcMYJIaoO1DNWxPTIuGb5aX4eOayzNnpbUuJAe4pEfHmFn1U7ATFO4bcxtFHiicyw/KPyAoBFERcUg9pCWikrQCPJB4QdM7j85qTXPXjmbj996hBuWhMkvA4sOYc1gV2YZ7054BKDrBL6qytLhZ3LfT5uoJoRHKNhQCQqD9ejcl2Zj1vAzGZdAma2DIXc3uG2b2Sr4s89iWwWnpeE571zSJk9GS0nppBUeGmTrX0mSJFOXCHpvvPFG5s+fz+eff07PRh9r5ubmEgwG8Xq9Ubu9e/bsITc3NzLm22+/jZqvvrpD/Zim7HY79nZ+hLfDX0PjqpThypGR35upDQ3jklEcKO7QcY157Im1Lq4fN2/TPD4o/CBy/wUDL2BS70loavSp7aLqIoQQ2FQbiqKgCx0hBIqioCkaQgiCRpCi6qKk1hs2wnz6zj+4+sMwziBUOiDkBGsYepfA1R+Gma3+g0uHXopF7fwvZUMYvFD6PdWOVHJCAZRwAIwwDkXBbrFTbLXzQun3jBVXN3sI72DY3Q1s2ED5nDnUfL0EmrwGLSsLz4UXkHbGGbJT1z4kW/9KkiTF6tRIQQjBTTfdxDvvvMOiRYsoaHJK+8gjj8RqtbJw4UIuvPBCANatW8e2bduYMGECABMmTODPf/4zxcXF5OSY7X4XLFhAWloaQ4cO3WdrNxw7G34f8sRNbWg6LhEhkWADiQTHNfbDrh8SHtcnrQ+P/vBo5L5+nn78atSvcFqcMePz3HlmsIuOJjSz5Jli7gwLIdDR23SQ7b2N8zjzKz/OIOxNIZIiEbRCmQUyq+DMr/y8t3Ee5w46P6m594U1ZWsorCjE4+qGojkgVANGGFQLitWFR/dTWFHImrI1cQ/lHei7u7UrV5qtgn+I/Tqz5OaSfvHFpJ58MopNNjHYF2TrX0mSpJZ1atB7ww038Oqrr/K///2P1NTUSA6ux+PB6XTi8XiYMWMGt956K5mZmaSlpXHTTTcxYcIExo8fD8Cpp57K0KFDueyyy3j44YfZvXs3f/zjH7nhhhvavZvbsoadk8ZlyhRridmdLc64zvbmxjcTGvf6+tdZWb4SX9BMobCpNn4/7vfkuHPijj+j4Awe+vYhfEEfOnrUgTYFBYHAY/MkfZBt1ZL5HFtm7vDGvI0KVDkgvwy+XDK/SwS9Xr+XsBHGptnM9dpcUddtmo2KYAVevzfmsbrXe0AeVBNCUPvDD3hfm4M/zsFRa+/epE+dSsrE42Vd131AtdvN3VyXS7b+lSRJakWnBr1PPfUUACeccELU/S+99BJXXHEFAI899hiqqnLhhRcSCAQ47bTT+L//a2iQoGka8+fP5/rrr2fChAm43W6mT5/Offfdt0/XrikNwYnVswzNuYVw5UgUtSaqRGvjcQnNi4aOntC4ZNXX2G11nFHD93u+j9y+esTVHNn9yGbHW1QLp/Y5lTc2vBFTwaH+9ql9Tk06BcFRFcSimykN8YQskOI3x7WVIYwOq/+b7kjHoloI6sFIZYvGgnoQi2qJOgQogkHCpaUYgeSqfHQ2YRhmq+A5rxPcuDHmun3gQNKnTcU1frzMIe1AiqKgNG79Kw+iSZIkJazT0xta43A4ePLJJ3nyySebHdOnTx/ef//9jlxaq26cOIynV30Sua3ayrBlLYo7LhnxDoK1Z1x7HzOm+xguH3p5iwGrIQx2VO3AZXHhD/ujnkdFxWFxsKNqB4YwkgooB/cbR1j7HmvYTGloyhqGsGaOa4ulRUt5YcULFFYUEjbCWFQLBWkFzBgxo02d3oZkDqEgrYD15euxa/aoPEohBL6Aj0EZgxiSaZabOxB3d0U4TNWixXhff53Q9u0x1x0jRpA+bSrO0aNlHmkHka1/JUmSOobcJmijzBRX64OSGFevPV3TOprH5uGP4/+I2+ZucVx9LmuuOxe7ZqciWEFID2HVrKTZ0gjogRZzWZtzyinX8s7jT9OzWKfMQnSKgzB3eXfkaJx/yrVJv7alRUu5b8l9VAYqsWgWEKAbOmv3ruW+Jfe1qcWxqqjMGDGD+5bcR3FNMR67B5tmI6gH8QV8uK1uZoyYgRIKEzrAdneNYJCqBQvwvvEm4T2xnfWcY8eSMXUKjmFdvIHIAULRNNS60mKKyyV/gJAkSeoAMuhto5HdRmLXbAT05j9at2s2RnYb2ez1/c2u2gkYiQdavxnzG/p5+rU6LiqXNY6WcllbYrXYsFx+Mf7HXyOzyszhDVnMHd4UP/htYLn8YqyW5HIZDWHwwooXKK0pxa/H7kwH9AAvrHiBsbljk051GJc3jlkTZkV2kCuCFVhUC4MyBjFjxAzGuAYT2rXrgNndNWpqqHj/A3xvvx3bKlhRcB97DOlTpmIf0D/+BFLCGrf+VZ3N5PRIkiRJbSaD3jYamjWUPHceWyu3AtGVmeo3ZfLceQzN2ncVJJLVN7Uv63zrEhp7ZsGZTO4/OaEdpvpc1uKaYnwBX6SRBcBuZXdkxzOmoUUCLrjkbt4GjP+8SU5pmBS/mdKwq7sF9bKLuOCSu5Oec03ZGlaUrqBGj81xNjCo0WtYUboi6Z3peuPyxjE2d2xUrvDg1AEYe8sIl5UlPV9n0CsrqXj3XXxz/xfbKljTSJk0ifQpF2NLsr61FE212cxDaG63PIgmSZK0j8mgtx1qw7WR38eLDRtf7wr2BhLr4qaicsfYOxI+eDYkcwh21c7u6tgOeLrQKfOX0Se1TySXNVkXXHI3oYvv5LNPXsBbvJ30nF6ce/KMpHd465XWlFIdqm5xTHWomtKaUkiuY3CEqqiRgFmvqEDfVdQxu7tCENi4Cd3nQ/N4zB3WDvzoO1xWhu+dd6h4731EbfTXr2K1knr6aXguvBBrky6IUuLUxgfRrLJ8myRJ0v4ig942WlG6gpLakhbHlNSWsKJ0BaO6JdZ2dl8zRGIH2VwWF5nOzKTmLa0tbXFMaW1p0gfZGtM0Cz3GHo+7budU09r+pbumbE2rOdECwZqyNUzsNbHNzyNCIbMyg9/f5jkaq12+nPI5rxPasQMRDqNYLFh79iRj6hSco9r3NRbaswffm29S+dHHiFB0DWjF6STtrLPwnH8elszEvy4kU1TrX5dLHkSTJEnqJDLobaNPtn6SUOD0ydZPukzQW5BaQFmg9Y/XB3kGJTXvB4UfUB1uZec0XN2mNsTQ8VUW9ge9ogK9vBxhJF8xI57a5cspefxxjJpatNRUsFohFCK4ZQsljz9Ot5tvblPgG9y+vaFVsB5dKk9NTcVz7rmknTPZfE4pYbL1ryRJUtcjg942qg0llrqQ6Lj9oZenFz+Utt6VrXd676Tm3VGxo0PHNVZfZaE6VB1VDWF9+fo2V1nYl0QoRHjvXozaDvxzF4LyOa+bAW9WVkOetd2OZrOhl5VRPud1nCNHJpzqENi4Ce+cOVR/9VVsq+CMDDwXXEDaWWfKA1VJUCyWhooLsvWvJElSlyOD3jYa0W0Ec9bPSWhcV7HdF1tXtT3j6q0uW92h4+rVV1moDlWT48qJBBEOiwO7Zqe4prhNVRaGZA6JdIprjoKSdA6yXlmJXlbWYbu79QIbNxHasQMtNTUmkFIUBS0lhdCOHQQ2bsI+cECLc/lXraL8tTnUfv99zDVLTg7pF19MyqmnyENVCVKsNrN+rsuFuk87QEqSJEntJYPeNirwFHTouHoppFBFVULjkrWzemeHjqu3ozLBnd4Ex9Wrr//rsXviBnseu6dN9X+zXdm4rW6qQs2/z26rm2xXdkLziXDY3N2tSazjXbJ0nw8RDpspDfFYrYiqKnSfL/76hKB22Y94X3sN/8qVsQ/v1csMdiedIDt8JaC+9a/qcqHIHw4kSZIOGPI7XBtVBitbrXvrUB1UBiuTmreWBNMmEhzXWEBPrEZvouPqxava0J5x9fZV/d8hmUMYkT2C5cXL49bpdWgORmSPSGinV6+qQt+7t8N3dxvTPB4zGA2FIN5uYiiEYrGgeTxRdwvDoOabb/C+NofAhg0xD7MNGED61Cm4jz5a5py2QLb+lSRJOjjIf73byGP3tNrowW/48dg9LY5pSkdvfVAS4xozEgzMEh1Xz6okVnYp0XH16uv/BvUgDosj5npQD2JRLUnX/23cOa2+I5sQAkVRCOthUu2pzBgxo8WUiX29u9uYfUB/rD17EtyyBc1mi2lvrFdVYevbN9IgQug6VYs/N1sFb90aM59j2DCzVfCRR8q802YoqoricMrWv5IkSQcRGfS2UaI1eLtSrd6qcOtpE8mMqzcqZxSLdi5KaFwyhmQOoSCtgPXl67Fr9phgzxfwMShjUJvq/zbtnBY2wmiqxoD0Aa1WhdCrqszcXT35HzzaRFHImDqFkscfRy8rQ0tJiVRv0KuqUJ1OMqZOQYTCVH7yCd433iC8O3ZX3XnkkaRPnYJzRNfJM+9KFE1r2M2VB9EkSZIOOjLobaMXV76Y8LixuWP38WoSk+jucGul2JqaPnx6QkHv9OHTk5q38Y5scU1xVPUGX8CH2+pudUe2JfE6pw3JHNLsfELXCZfuxahpuTzbvuAcNYpuN9/cUKe3qgrFYsHWty+e888jsHkzxX/9G/reJg1IFAX30UeTPnUK9oED9/u6uzrFam1o/euI/TRBkiRJOnjIoLeNSipbbkyR7Lh9raRm362jte5myY5rrOmObEWwAotqYVDGoA6p09u4c1pL9Kpq9LK9+293Nw7nqFE4R46MdGRTrFb8q1ZR8rdHMCoqogerqtkq+OKLsfVJrgTdwU62/pUkSTo0yaC3jSrCTYKMxpujSgvjOoEQgt998buEx2c5kuu9u6ZsTcLj2tLhLNkd2Y4kdN3M3a3e/7u7cSkKlm7ZVH/5Bb5582NaBWOxkHrqKaRffDHW3NzOWWMXJFv/SpIkSTLobaPIiX9BbDKAqIt7FaIqA3SWf6/+N9/u/jbh8SOyk8v5VBpF+YoQ9N0tSKuBChdsyVUQdbmRjcclK9Ed2Y5kVFcT3tu5u7uNhUtK8L75FpUffogIBqOuKQ4HaWeeieeC87FkJfdDy8FItv6VJEmSmpJBbxtZVWv8gLeOABRRN64TbSjfwOPLHk/qMZcOuzSp8fkp+agoDCnUOe8bg/wysOgQ1mBXJswdr7KmQCM/JT+peTtLV9vdDe7Yge+NN6n89FMIh6OuqSkppJ1zDp5zz0FLS+ukFXYNiqrWHUJzyda/kiRJUgwZ9LbR+OxjeaPytRbHiLpxnSWkh7j989sJGuauoNvqTiivNqgHWx3T2Bl9TuN/L/+e//eRgTMIlQ4IOcEaht4lcO1HBq+ernLGL05r0+sAs+asf/Ua9PJytIwMHEOH7JOgxqiuJlxWZjaD6GSBwkK8r82h+ssvoUkZOS0jA8/555F25lmoblcnrbDzKRZLw26uwyErLkiSJEnNkkFvGy34MRfSExt3d/JprB3isR8eY6N3Y+T2pUMu5Zmfn+nw59F2r2DGl2EIwt4UIjnNQSuUWSCrCmZ8GUbbvQJ6Hpn0/NXffEPps88RLCxEhEIoViu2ggKyr70G9/jxHfIahGGg792LXpVcubZ9wb9mLd7XXqPm29iUFEtONzwXXUTqqacesm1vZetfSZIkqS1k0NtGvkBRh47raEuLlvLympcjt88fcD4fbf4oocf+5Zu/MO/CeQk/l3/lctLKBUUOhZi0XQWqHZBXLvCvXI4zyaC3+ptvKLr7boyqarT0dBSbDREMEli/nqK77ybv3nvbF/gaBkbhUsJFWxHWNOg2GDpht1AIgf+n5ZTPeQ3/8p9jrlt79CB9ysWknHjiIdkRTLb+lSRJktrr0Pvu2UE0RwWJfACuOfZ/9QZfwMedX9wZqbfbO7U3d467kwmvTEjo8durtif1fGG/Qq2hEGrmqylkgRq/QtifXDApDIPSZ5/DqKrG0r175KNrxeFAsdsJFxdT+uxzuI46qk2pDmLjZ+gfP4K+ezOIMCgWyOgLR14BvfZPbWVhGNR8+y3e114jsG59zHVbv36kT5tqtgo+hA5jyda/kiRJUkeT30nayJa+nXACPRxs6ckFkO0lhOCer++hpNasy6spGg8d/xBOi3OfPee27Ez8FtDCoMc5t6eFIaCZ45Kpv+BfvYZgYaG5w9tk91VRFDSPh2BhIf7Va3AOT66yg7F6AeG3fosI1IDdAxYbhIOwdwMs+jOc8Id9GvgKXaf6iy/wznmd4JYtMdftQ4aQMW0azrFjDpk8VUVV6youuOVBNEmSJKnDyaC3jWpZ2aHjOsq7m97lk22fRG5fP+p6hmcPB8CBg2paP8jmILnOVKW90ijKNA+tlWlg00EzQFchqEGKH7Z1A71XctUF9PJyM4e3mY+zFZsN4fOhl5cnPKcwDPTSUvSPHoNADbi7NaQzWO1g6QbVJfDDbOg5psNTHUQoROWnn+J9/Q3Cu3bFXHeOHk36tGk4Rgw/JIJd2fpXkiRJ2l9k0NsOCs2XLKu/vj/tqNzBA0sfiNw+vNvhXD3i6sjtsBJuecGNxyVhdflaFk9QuXGeQe/S6NctMOv1zp2gMrF8LRP7TEp4Xi0jA8VqRQSDKHFaxIpgEMVqRcvISGg+o7bWrLu782co32Lu8DYNshTFvL98C5SshZwhCa+3xef2+6n88EO8b72NXloac901YQLpU6fgOOywDnm+rky2/pUkSZI6gwx626MLRb1hI8wdX9xBTbgGMMuTPXz8w2hqQx6ooRgJBb2GklxDjdLa6CBOiEZvjdL8uNY4hg7BVlBAYP16FLs9ahdQCIHu82EfNAjH0JYDU2EY6OXl6PWtev1eM4fX0syBKIsNghXmuHbSq6qomP8evnfeid8qeOJE0qdOwdanT7ufqyuTrX8lSZKkziaD3nZS6qK7mC7E+3mb9/kVz/NzScOp/z+O+yN5KXlRY3JduQkdUst1Jde+1qk6OG+JgSbMNAZ7uCG9IWCBzCo4b4nBjtOT29VTVJXsa6+h6O67CRcXo3k8keoNus+H6naTfe01LeZ+RnZ3Q6GGOx3p5qG1cNBMaWgqHDSvO9KTWm9juteHb+5cfPPmIWpqoi9aLKSecgrpF12ENT8v/gQHAdn6V5IkSepKZNDbEeJU6tqfVpau5OnlT0dun973dM7uf3bMuLsn3M3VC66OuT/euGQML3fhKjObUqBAoEl8U+WA/DJIL0++iYJ7/Hjy7r23oU6vz4ditWIfNKjFOr1CCPSysobd3ca6DTarNOzdAFo3AmU6ut9Ac6jYMzUI+CBroDkuSeGSUrxv1bUKDgSiril2O6lnnEH6hRdgyc5Oeu66F0Zg4yZ0nw/N48E+oH+nlFiLx2z96zJr6Dqdh1S1CUmSJKnrk0HvAa4mVMPtn9+OLnQAuru6M2vCrLhjh2Q1SQMQjfanGwVOMeNakV6rYehmFzYAe6jRTq/VLFmW4jfHtYV7/HhcRx2VcEc2w+8nXFoavbvbmKLAkVdQ+9r9lH++l1CVijBAUcGaYpAx3Inz1CuSCiZDu3bhfeMNKj9ZGNsq2O0m7ZzJeM45Fy3dk/CcTdUuX075nNcJ7diBCIdRLBasPXuSMXUKzlGj2jxveyiahup0mru5Lpc8iCZJkiR1WTLoPcA9/N3DbK80UxYUFB46/iFSbalxx9786c3mb0T9/zUKUETdbcUcN/uM2QmvITUnn3KLSkqtQYrfbD9cn9Mbspg7vYZFxZOTn/wLrKOoaqtlyVrc3W2itsxGycpUjEofmsUAmwBdIVhpoWRlCt3KbDh7tb6u4JYteOe8TtXnn8e0ClY9Hjznn4fn7LNR3e7WJ2tpvcuXU/L44xg1tWipqWC1QihEcMsWSh5/nG4337zfAl/Z+leSJEk6EMmg9wD26bZPeWvDW5HbVw6/kiO7N9/xbGVpffm0JgEv0BCmKo3GJWbw+DP5PO1+uu2oQgCGCoYCigBbCLJCUNLTxeDxZyY1bzJa3d1tTAjK57yOERRo+QUoegCMMKgWNM2OXlZG+ZzXcY4c2exur3/dOrxz5lCz5JuYa1p2NukXXUjqaad1THWC+vXW1KJlZTUEmXY7ms2W0HrbS7b+lSRJkg50Mug9gN39dUPu7eDMwdx4+I0tjg8agYYd3bgUEMIclwRVUenmyAKqzM3iuukVhUgs3c2Rhap0fLMBIYRZmcHnS/gxgY2bCO3YgZaaagaQlobAVAG0lBRCO3YQ2LgJ+8ABUc/l//lnvHPmUPvjTzHzWvLzSZ9yMaknntihB7earlf4/QjDMJs5OBzNrre9ZOtfSZIk6WAig94OoAhB392QViuocCpsyQWxHz7y9Qa8ANg1Ow8f/zBWreVAy4GFWlrfCXUk+WXhX70Ge1WQQLcsdJ8XLayj1MXWulXD4knHXhVsU+e0lhiBAOGSUkQomNTjdJ8PEQ6bKQLxWK2IqqpIIC2EMFsFz3mdwJo1McNtffuSPnUq7uOO3SeHt+rXK8Lhht1sIUBRzDrF6emIcDipwL859fm5svWvJEmSdLCR39XaadgWg/OWGOSXgUWHsAa7Ms1mDKv67p82qreNuY0CT0Gr45yKk1rRetDrVJJrWVzfOc3VLQeR3Z1AtQ8jHEK1WHG5PShCEC4pSapzWkuEEOheL4bPhxAJFB5uQvN4zIAuFIJ4H9WHQmbeamoqVYsXm62CCwtjhtkPO4z0adNwjTtqn+a1ah4PGAbh4mIz2NU0UFUQAhEMEi4uRk1JMcclyay40CjQlRUXJEmSpIOUDHrbYdgWg2s/NHAGzXJdIad5iKt3CVz7ocGzp7PPA9/jex7PlMOmJDS2Vg2DUFpIcTB3D2vV5DqyNe6cpjocOFPSo64bgUBSndNaYgSD6CUlGMHkdncbsw/oj7VnT4JbtqDZbDFNL8KVlWhpaZQ8/DChOK2CHaNGkTF1Ko7DR+2XQ1y2fgUIXQddN3enG+ePqCqEQghdx9av9R98wDwUaAa6blSXs8U6x5IkSZJ0sJBBbxspQnDeEjPg3ZtCJIYMWqGsUUOG1X32XVCU4cjgvqPvSzjwUiKBbsOhtQYNt5Ukqw53VOe0lrR3dzeKopAxdQoljz+OXlaGlpJipjQEAuhlZebuqdcb8zDXuHGkT52KY0jy9XvbI7i50Nzd1TQz8G28G1t/W9MIbi5sNqdX0bSG3VynU1ZckCRJkg45Muhto767zYYL9Q0ZoigNDRn67t53a/jTMX8iy5mV8Pj6Wr6RlnHN1OmNjEtQdOe0PYTdDnSrihYysFT7Ud0prXZOa0lH7O425Rw1im4330z5a3Pwb9wIfr8ZQDYNqFUV93HHkT51CvaCxHZSO5ru86GoKlpODrrXG53Ta7ejpadj+P0xOb2K1RoJdDukioQkSZIkHcBk0NtGabUCS6OGDE3VN2RIq237rmTjA3J7PAq7M4kKTo/veXxS89mwEaBRZYZmdvtsJH9S3z1+PN6bp1H67LOk7t6Npgt0TaEyN5Xsa6cyoJnOaa3RvV4z0Gvv7m4ctStX4l+1ysztbcpiIfWkE0m/+GKsPXp0+HMnoz4HWbFYsOTlYVRVRZpTqCkpkRxkzeNBtdlQXC5UtxtVVlyQJEmSpAgZ9LZRhVMhrJk5vME4RQCsYfNQW4WzbR8jNz4gF7DAff9Pa3cNVpvFBnWbpS1VnLBZkg+WlhYt5b7Am9RMszFkbw88fgWfQ7Amy48r8CazioYyLm9cwvOJYJBwaSlGILnyaYkI791L8V8ewr+ymXrEioLnogvJmj69w5+7LepzkAPr1yMMwwzS63Z69cpKFE3DMXQoKROPlzV0JUmSJKkZMuhtoy25ZpWG3iVmDm/T9NgUP2zrZo5LVuMDchUO+PfJKt5U8wk0XdBnj2BzfvKpAr6gLzJ/SxUn6sclyhAGL6x4gepQNTkp3dmbqrC37lo3ISiuKeaFFS8wNndsQrV6dZ/PrAjRwbu7od278b7xJpUffxzTKjiKEFTMf4/MSy/tGtUMFAXXkUfg//lnMwXDYjHzeA0DamsRFgupJ0yUAa8kSZIktUAe224joSjMnaDit5mH1myhhg5kmVXgt5lBZLL1epsekFs6WOHnfg1/TOd8Y3DJYgOlDQFhmHAkoO5TAn4rlKeY/62vODFsi0GY5Ko3rClbQ2FFIR67BzUsGLF0N8d+uI0RS3ejhgUeu4fCikLWlMXWuG1MhEKEiooIl5XFBrxCENiwkZrvfyCwYWNs7m0Lglu3Ufy3v7F9xtVUvv9+ywFv/dNVVVG1aHHCz7FPCUHND8tQnE5w1uXTGIaZ01uXylD15VfmLrAkSZIkSXHJnd52WNVX5dnTieyapvjNXdNt3dpep7fxAbmyVHh3fMMcBUWCo1cLXMG2HZDbVxUnvH4vYSPMCZ8Vc/xiL/ZAQ32IM+YV8/nEdN49xoHX7212Dr2y0qycECdwq12+nPI5rxPasSOSy2rt2ZOMqVNwjhrV7JyB9espn/M6NV9/ndTrqRfc3Y5TiEIQ2LgJ3edD83iwD+ifdHqKYrGgulwEtmwlVFSEtXt3VIcDo7YWoetmRQanE8PvJ1hY2OHNPyRJkiTpYCKD3nZa2UdlVW+Fgj2QWiOodCkUdjd3gtuSgVt/QM7vgteP1wjYzFkcAcGUL3R0zUxJaMsBucNKbOSX1bZaceKwkuRyetMd6ZzyRSUnL6xGNUBXzXLAigCHH05e4KXWcJN+UnrMY0U4THjvXoyamrhz1y5fTsnjj2PU1KKlppp1akMhglu2UPL443S7+eaowFcIgX/lSryvzaF22bKY+Sy5uWjZ2QRi8nljaxcbZWVJvQ+N17z31VcJbd6MCOsoVivWfv3IumRai0E6gGK1obrrKi7UpSv4V6+BcDjSClh1Rp+eVGw2RF1KiCRJkiRJ8XVqesPnn3/O5MmTyc/PR1EU5s6dG3VdCMGsWbPIy8vD6XRy8skns2HDhqgxZWVl/OIXvyAtLY309HRmzJhBVVXV/nkBdXGnUBQ25yos76eyOVdpSGloQ0pq/QG5L4YrbO3eEISdt8Qgvbp9B+Sya+1mxYlmftQJWcyAOrs2udzQw1L6c/oXZsAb1kComBXRVPO2asDpX1RzWEr/qMfpVdWEdu1qNuBFCMrnvG4GvFlZZg1gVTXLdGVmYtT6KZ/zutmZTAhqvvuOXb+9jaLb74gJeK19+tDtttvo9fxzOA9vHHjW/yEpTW6DlplkMw09RO3Sr9h9/58I/rwCUVUNfj+ispLg8uXs/vMD1C5fHvMw1W7HkpGBrUcPbD17YMnIiMrPbdz8I+7bFAx2WPMPSZIkSTpYdWrQW11dzahRo3jyySfjXn/44Yd5/PHHefrpp1m6dClut5vTTjsNv98fGfOLX/yCVatWsWDBAubPn8/nn3/Otddeu1/W31pM25ZjWFty4bsB8PGRDX80h280GFUoIgfkdmW27YBcmSscqTgRT31AXeZKLqe36rVncQQadngbE4p5vyNgjgMQuk6ouJhwSbHZaawZgY2bCO3YgZaaGtNMQVEUtJQUgtu3433jTXbedDO7Z91NYPXqqHH2gQPpftcf6fl/T5J64iQUTUOJ/MGIRv8VTW7TaFwLhIBgFVTtAd9OSp56FlFdHX9oZSUlj/8ThLlba8nKwtarF9b8fLT09MhOblP1zT/0OI056pt/2AoK2tX8Q5IkSZIOdp2a3nDGGWdwxhlnxL0mhODvf/87f/zjHzn33HMB+Pe//0337t2ZO3cu06ZNY82aNXz44Yd89913jBkzBoB//vOfnHnmmfztb38jPz9/v72WjiIUhVdP1DBUM8jLqDTzcG0hM+Bt6wE5gO05WkPFCQ3Sa8xAN2QBr6uh4sT2nOQqFoR2bEcRAlVVMIDGWbkKoCqgGILQju0YNTWE9+5FJHCYTPf5zHHWuppwYT8YYVAtCM2O4fdjlJVR9tJLMY91jBxJ+tQpOEePjgmYrTkOEvmRxRzX3Iv2Q7AaQjWRQ3W1G7cQ3lPS4qzhXbsIeb2kJNgyGJo2/yg26/babIhgEN3nQ3W729X8Q5IkSZIOBV02p7ewsJDdu3dz8sknR+7zeDyMGzeOJUuWMG3aNJYsWUJ6enok4AU4+eSTUVWVpUuXcv7558edOxAIEGhU/7WiomLfvZA2CFrr2gEbgisW6OSVt/+AHIBqsTB3gspv3zLoVxx9LbMKqusCatWS3JeFtWcvhKJg1KXFNl6dAAwBKgpqehahPXsSnre+KQM1FRD2gR5CCIERUNADREfXdVxHjSV9yhQcw5o/0GW1C7AYEG7hfbQY5rjGjLAZ6AarzNJhTdR8+2NCr6t64UJSjhid0Nh67vHjybv3XkqffY5gYSHC50OxWrEPGkT2tdfgbmPzD0mSJEk6VHTZoHd33cn57t27R93fvXv3yLXdu3eTk5MTdd1isZCZmRkZE8+DDz7Ivffe28Erbr+F2xZG3RYKzJ2gxW0g0RY9U3oyfs1eXM1083UHYfwag29G90xq3pRp1+J/7Cns/roDbDRUbwDQDAjYwHly/B9CmmMf0B9rdirBzZtRHQIRVND9SuwmraLgPu5Y0qdMwd6/f9y5oubNceJI1/GXAUacwFc1cKTr2HOc5nOFqut2df2xYxuJaaSh1J3mq8/5qNsRbjaHuRXu8eNxHXUU/tVr0MvL0TIycAwdInd4JUmSJCkBh+R3yzvvvBOfzxf5tX379jbN01r4mUx4WlJTwj1f39NkAoXCPPOAXGGe0q6AFyBddXPSzy2POelnc1wy1lVt4sPj3AgFrLrAogu0uv9adYEAvhjjpLBmW9Jr9vSqRBiCsE9Br6VJwCtw9nTQ85mn6X7nnQkFvAC40skYGEBt5kc+1QIZA/ygquDbDtV7Wwx4VZsVzZOKe8xI87bVIK1PLT2O3svAybtR1OgovbUKDi1RVBXn8GGkHHcszuHDZMArSZIkSQnqsju9ubnmSa09e/aQl5cXuX/Pnj0cfvjhkTHFxdGf04fDYcrKyiKPj8dut2PvoO5VjXc0m96fKEMY3PXVXXgD3g5ZU3OOWbANrZX+BZphjuPsxOf1+r1szVWptYPLH9OcDr8NdnZTqQgknkYSLivD98rzVHxaggjHfzetHoW8owNgT7JahzMTVCuKImL/ABXq7reAxd5sEwzVbkN1OlFdDjMFo7YcT59qrJO8uLNrUBqlRbtz/VTtNPODFbebtLPPSm69kiRJkiS1W5cNegsKCsjNzWXhwoWRILeiooKlS5dy/fXXAzBhwgS8Xi8//PADRx55JACffvophmEwbty4/bbW9u2/wn/X/pevdn3VIWtpiX13YnVnEx1XL92SymlfVCNU2J2lkFoLWggMC1Q7wF0Lx31fQ9rFre8gh/bswffmm1R+9DEiFGpyNToADfkUij4Pk3eSN6n1kjmA8g1WhAHWlBAYCkIoZrCrCnS/RvlGG05Pn8hDFEVBcdhRnQ5Up8NsT1y1B1bNh82fwq4fUYVBSvfYp0vJrwt6NY1uN/wq6ZxpSZIkSZLar1O/+1ZVVbFx48bI7cLCQn766ScyMzPp3bs3M2fO5E9/+hMDBw6koKCAu+66i/z8fM477zwAhgwZwumnn84111zD008/TSgU4sYbb2TatGkHTOWGjeUbefT7R/fLc2XvTayIWqLj6hWsXk/VXoOABpkVYAnXbaCGzeoQ1Tbo7hX03LgFsg6LO0dw+3a8r79B1WefxTkk1tx6BLW7VXTFSTL1JgLLPiNUqaDZdLOygwWUhqLLaDaDUJVGYOW3OCecjOpyoDocZiqBdxv89Cls/gz2NG1w0SAc1KjaYadih5OaYgeqJ43sX15L1lVXJbFSSZIkSZI6SqcGvd9//z2TJk2K3L711lsBmD59OrNnz+b222+nurqaa6+9Fq/Xy7HHHsuHH36Iw9FQSuqVV17hxhtv5KSTTkJVVS688EIef/zx/f5a2iKoB/ndF78jaJgny7IcWez1741cV4Sg72467CBbrS2xxyY6rp5RXESKX6DrSuTcloEZ+GphSNVB0wTG3thyXoGNm/DOmUP1V1/FpBIodjsi0PLhMYCydz6n282Ht75QYUCwCn3nRoShgN1qRuaNg2pFQbFZELUCRQSwZmXA3o2w4lNzR3fvxmanx50D/U6A/iei5oyAz7/DWR4gLT+ftLPOlDu8kiRJktSJOvW78AknnBBTbL8xRVG47777uO+++5odk5mZyauvvrovlte65hJ6G19vwePLHmdd+brI7fuPuZ9fLfwVAMO2GJy3xCC/zOySFtbMphTtKVlWGU6sakCi48DsBmZoDtSwGfCGGzWoqH9rLAYoQkHNzI48zr9qFeWvzaH2++9j5rR07076xRfh+/h9Qus3t7qGQOGG5i8KIFwLgSqzzq8QZiMIFTMy16ygCPOPSgVFUTHCOq6sII7AMnj5TfDtaH5+T0/odyL0PxG6D8Oc2Dwhmn76yZDeq9X1S5IkSZK078mtp/Zq40m2b4q+4V+r/xW5fcngSziu53GAGfBe+6GBMwiVDgg5zTSB3iVw7YcGz55OmwLf1b1gZAIFFFYnGKfpPh96eTm4e5oHwITAqiiIRm+I2RhCmOW7PH2o+WEZ3tdew78yNjXA2qsX6VOnkHLCCSiaRuWC+fWz0PybLFBEnEYXesispxusBiP69J595Hisma8TLA1gSQFVU0ERODwBXN1qcWXXYnUasOfz+C88sz/0Pwn6T4KsgeZra0IYBv6169FDW2VpMUmSJEnqAmTQ2xGSzDjwBXz84cs/RG738/TjliNvMacSZgc2ZxD2pjTMHbRCmcVsInHeEoPVfZJPcyjJUBEYLS5X1I1riQgGCe/di1HXDlqvqkZxOBA1taCL6KoIke1eK8WPPEp4586Y+WwDB5AxdSquCROiAkPH4IEE1m2tu9XcqhUcgwfWLUyYNXUDVRCOLUbcuOJCzpVTKHriPzhSakntFcCd40eztVDaovuwhh3d9N4tvj/VPyyn9OU3CW7bidAFitWKraBANpGQJEmSpE4kg96OIqLzQpsfJrhvyX0U15il1iyqhb8c9xecFicAfXdDfpm5wxsT5ylQ5TCv922+90azfhhmJ/hhLfYWOgAHLea45tTv7jZOS9E8HlTNAEcYI6SZ+bL18aNS16YtGIoJeB3Dh5E+bRrOI46IaRUMoEZKIdTNEcO8X3VnQXUphGqj/hziVlwI1sCWRbiV5fQ/Yy+KaKZTh6JC/hHQb5K5oxuvLEMc1T8sp+ivT2LU1KJ50lEcTkQwSGD9eoruvpu8e++Vga8kSZIkdQIZ9LaXqP+/RkGZqLsdJ057d9O7fLz148jtm0bfxJCsIZHbabUCi26mNMQTskCK3xyXrCDBhOr0BokNBJvu7jZm79sbq72KoF/D4gojdAUjpGKEVRCxu8bOMUeSPnUqzuHDW1yLdWDdDm5k27hpBWDztjU/2wxmAUVVUByO6IoL/grY8KF5EG3rEswexrF/PELRoNc4lP4nmgfSnBktri+KqiEsTkr/+y5GbRBLbn4kkFccDhS7nXBxMaXPPofrqKNkqoMkSZIk7Wcy6G23eLuQ8YI02F65nQeWPhC5PTZ3LNOHTo8aU+FUCGtmDm/QCim1DQfZqupye8OaOS5ZR6/UsbQS9FoMc1xj8XZ3o/w4m4z+VRT/nEa42oIQSsNptgiBe2gP0q//HfYBAxJarzUzE9xuqK4mbhcJAJcTa1Y6WooL1eVEsdvNYLO6FFZ/AJsWws7vwWhaBq3+BTugzzHQ7wSUvseBPTWhtaGqYHGC1QlWF1hs+FeuIrh1u3lQrsnOtaIoaB4PwcJC/KvX4Bw+LLHnkSRJkiSpQ8igtz1Ecx+7Y94vRCTVIWyE+f0Xv6emrjJCqi2VB459AE2NrjC7Jdes0jBwJ9hD0X2is30QsMKGHua4ZB3ZQrWteONa2t1tTN+6An+ZDSOkIoymO5gCZ3aQrKGV2IYfBgkGvAD2Af1xDBqEf8VPEG4arQvQVJxDBpJ6TN3OaUURrP0UNn0KRctptrSGLQUKjof+JyJ6jMNfuAvdW4FWuAfHIHf8XVhFMQPk+iDX6ogZopeXI0IhFJst7tMqNhui/vCfJEmSJEn7lQx695PnVjzHTyU/RW7PGj+LXHds5CoUhd3pMHJL7Bwq4AzB7vT21etNhO71onu9LZaUC5eX43tnLhX/K0QEU5pcNbub2dJCpPevxpaigyMt8QXU1dR1ZdXijwS8TXZ6DQNP7yqUZbPNHd2Stc3P58w0Uxb6TYKeY0GzmgfO/vmgeeAsFEaxWrD17kH2pRfhPnIUWGx1Aa7LDHZbec+1jAwUqxURDKI4YoNiEQyiWK1oGUmkTUiSJEmS1CFk0LsfLC9ZzjPLn4ncntxvMqcXnB53rGoYHL+i5fmOXwEvndZKnkIcPwxQmLCu9VzgdT3MgLY54eJivG+9TeWHHyKCTfN/BYrFQLUYoECo2kLJijS6jajAOeKilp9YAKEas8xY2A/hIDXLV6BYVPNwXN3uuSMjTGrPWlJ7+rHbi+CbL+PPl5rbUHEhdyQ02lWPOnCWloqSZkWEQgQ2b6PokWfIu+ce3Ecf3ep71Zhj6BBsBQUE1q9vSLOof2lCoPt82AcNwjF0SAuzSJIkSZK0L8igtz0UpYUUh4bUhju/uBNdmDmlPVJ68Ptxv292yqNXGDiaST+t59DNcclaPioV470KWjpCZQAbB7rjXgvu2IHvjTepXLgwtlWwUlefTAERVtHDdc+iCfQglG9Ow9mtmTzWJjV1VbsN1ZNG4Is5hKsVrC4dR1YIV3aQlNwAVncLrz29T13FhZMgZ0izNXRLX34To6YWS7duKKoGiopic6K4Us0DZ8+/gGv8+KQOnCmqSva111B0992Ei4vRPB4zpSEYRPf5UN1usq+9Rh5ikyRJkqROIIPedmu5sgCYB9gAVEXlweMeJMXWNBWgwZifEnvWRMc1lqU4UKlocYwKpCvRJcsCmzfjnfM61V9+GdPoQcvIwH3sMVR88L+6dmxNJtQVBAoBr0Fg4wbshw027xeGGeQGq1H0EIrdhpqeZlZcsGigh1C2fUm3EZWk5AWwOJoPdHVS0MZdZu7oZvZr+U2w2PFv3EZw+260jCwUS3T+bXsPnLnHjyfv3nspffY5goWFCJ8PxWrFPmiQrNMrSZIkSZ1IBr3tpYDHq/P4s2DTIajBzdeCLz32rb16xNWMzhnd4nRjdyX2tImOa+xXC7XWBwH/b4kG54J/zRq8r71GzbffxYyx5OTgufgiUk85hdr3n6ci3PLupQiphFcvxN6vLwSrUHS/Geh63GYNXVU1Uxq2f2keRCv8HKdegbMg/nw1pVYqtzuoKbaRe4IT59ir4w9UNbC5Gw6gqRp67VZEOLzPDpy5x4/HddRR+FevQS8vlx3ZJEmSJKkLkEFvO736YJjGoaRDh2efgtKUMDfc1PD2Ds8aznWjrmt1vkTDoraET9lr9iY0LmtdGbvu+B3+n3+OuWbt2ZP0KReTMmkSisV8feGt6xOaN7x5BZqoQk1zoDjrynoFq2Djgroaul+ZDSbiEAbUlNio3OGgapedUK0GhoJqM7BnNWkMUn/wrK6UWFMxB85CNWCEQbWA1dUhB84UVZVlySRJkiSpC5FBbzs0DXjrGcAzZzeEpU6LkwePexCram11zkRbTiTfmgLCoVBC4xx+PSbgtfXrR/q0qbiPPtrsbNZIxbrShOat3OwlJysDar2wZoFZcWH7UjDir0ugUF1kpWKbk8pdDoxQ4wYgdc0/FAgYfXC6Ms1A1+JotcpC5MDZ6pUodj+KHqQ+JUVoNvSAA/vQ4fLAmSRJkiQdRGTQ20YeX/yAF+DDMQo/FzQEvbePvZ2+nr4JzfttT5iwI7FxIxKasYGrpuVQOV6oaB86hIyp03COHRO3VTBAqKiq1ee2OHTc9mKYex3sXAaimdN6Vhf0PRb6TaK6yMquuX81Wxzr0Q0vVE2g2XUMXUUf+xtwZba6hnqKqpJ99lEUrVpCuMJAc6goFg0RNtCrA6i2ENlny65pkiRJknQwkUFvGz3+f/Hv39YNXp3UECwdsd7gwssvTHzi2PKu7RtHXbksr7fZlIh4oazziCNInzYVx/DhzQa7DRPED/+tKWFSe/pJ7enHlV23m7ujLHag3RNpFkGvcWAxD9Jp+kYUixVFDWBNMdsb1/f7UDSB0BUU1YbWLa/l9TVlGLirPiDv6DClqxwEy3VEQKBoCvZuNrKH+XFXfQDGNWbnNUmSJEmSDngy6G2jeEegghr88xyNkMUMEtOrBL9830D5cxKNJDo4qVcEg4RLSjBi6uk2W2gNA8j7859aX4ImUFUdW0F3gmu2AAK7pyHQdWSEm3+wKxv6TzLr6PY4wsynjSzM7H7mGD0W24ijCCz/FovNj2pp2KkWAnTdjv3wccmnIexeDqUbcPdPx3WYA39xGL3WQHOqOHIsKGE/lG4wx+W3fPBQkiRJkqQDgwx62yheosBrE1W25TSEkte/Z5Aa/1xWs7Z1gwkJnAvb1q31MXpdBQIhBHpVFUJVwBAxwW5MlTFr8xG1arehWnRUJYyiGCCg94yj8P37Z7NZRFrzRYaFIxNl8Jl1zSJGgNLoeSz2RgfQzO5nCpD9y19SdPcuwlXVaEo1ihJCCCu6cKN2S2lb3duavWYescVsIOHs3iTX2mIHv9ccJ0mSJEnSQUEGvW20Mw16Nyp5+3NfhffGNQRfp39vcPhmwba05HJvqxNMW2hpnAgGCZeWYgQC6F4fvrlz8c2bh8WIDm+by/CtGdwr8ntFUVCcTlQrqEoIxfCDHoZdP5mlxTZ/hrVqD9lD488V8Fmo2O6gZq+NPk/8HbrXVTTQLNFVFtT4KRJRdW83rkOEQmbd2wGHtb3urSsLVCuEA+bzNxUOmNddWcnPLUmSJElSlySD3jZa3xd61xU4qHTC/zWq1tCzRPCLz4zIuPgNh5tT3+wikXGx6nd3Q8UleN96y2wVHAhEjWlt9pRbrkd1u1HtVlQ1jBKqgpAfdnwLmz6DwkVQ23wN29oys4Zu5Q4HwUrzS8yeY0PkH4HiTDODXK31Shb13DkBXBNL8XcvQ68NozktOAaXouQEWn9wPLmjIHsg7FkVW+1BCPO1dR9mjpMkSZIk6aAgg942Kk1r+P2HR6qUp5qBkyUsuOldHVs4dlwiauytj4k3rn53N1BYiPeNN6j8ZCGEo3NqFacDo7aW5gJmAIHgMI8Nqz0INXtg2xJzR3fLF2ZN3biPUagtsVKx3UHlDjvhGkvUVVAIlBnUbvfiGtkr7hzN2rwY5s9ECVTizNbqSpXpULwa5s+Es/8O/SYmN6eqwrG3mI+vLAJnhpnSEA6YAa891bwuD7FJkiRJ0kFDBr1tVJKuYmCgAhd+ZWDRBW8cpzJtsUHfYnOMUTcuGRW2xCrwNh6ne73U/PQT5a+9RvXnX8S0ClY9HtIvOB993SK8Xxe2OK9mNah98S5so3rBtq/MQDAeVYOeR0H/E/Gu9LN7zn9BESiKQNGapFEIQNfxL/8J18gkkj0MA758DKr2QLAW8x2NLMBsKvHlY9D3uOQD1H4TzYD5y8fMQ2t+r5nS0H2YGfAmG0hLkiRJktSlyaC3jb4eBld8Ail+UAWcv0QwZoNOj7o+DQIz7/brJJtyTVqd+DgjGKT6iy8p+89/qPnmm5gxWrdupF94AamnnYbqcLDx12+gRNInGnZ7NbtOag8/aT39uLsHULQ9sHlD7JNa7NB7AvQ/yQw0nR6wuhHLnzHnFCJ+6oSou7diZ2Ivrt7u5bBrGQSr6+5ovENtmDvPu5a1vcpCv4nm69i93Dy05soyUxo6YofXMPbNvJIkSZIktYkMetvIUFXemSD4xWKBaoAO5O019yIVwFDhnQkKRpKBTkpNYuPySmHb5dOp/emnmGvWHvl4Lp5C6omTUKxWFKsN1e0iqAeoz4qwuMKk9fCT2suPKzsYVUghis1tBob9ToS+x5hNICItfs3ZnP3zUBQQhtJswrCigrNvkgfDqkrAX39aUImOeUVd8O6vMMe1lap2fFmyzYsbdpCNkLmDnD1Q7iBLkiRJUieSQW87mNUaDC74UuAOgSLqdnit8Pax0dUcEpXnTWxcQRnUlv0UdZ+toID0qVNwH3ssmtOJ4nKZB9JsNggHSMnyk2arJa2nH2dW8y2Jw2EVy8jJ0P9kszuaM73FFr+O/BQsrjChqua/nCyuMI78lMReXL1dPxKJops+beS8nzDHDTo1ubn3lbocZAJV0bnCe1a1PQdZkiRJkqR2k0FvW+k6qCqTvxGkNIofFSAlBJO/Ebw3rm5cEsKtDI/bKnjwYNKnTSXluOPQ3G5UlwvFZjOfe8dSWP0/2LCAgf2Km503VKNSsd1J5Q47Qb+TQXf9X10psQQCd2cmaivFGFSrOS4pTasqNL0db1yyOjINoT4HOVAFqXkN66r/gaGyqO05yJIkSZIktYsMetvh6X/oZDTTfCKj1rx+3U3JBWRZzcwXt1Xw6MPJuPRS3Mccg+Z2o1itZuC17WtY+Tas/wgqdjT7XIFKjcrtDip2OPCXNfSYU+2APfFdWX9RJXpAxeIKYwQ1DL1htaomUG06ekDFX1SJc3DC00J6b8zWc3UH2ES83Am1blwbdHQaQl2nN5wZsYG4opj3y05vkiRJktQpZNDbRg6/aDbgrZdRa45LRtMWDS21Cu7zn/+gWCxms4jCxbCqLtCtbj7H1V9uoWKHWUM34LM2egYReT6LK6klo3urEAZYHALFGsaotIChgCrQ3GFUBcJ+c1xShl8EH/6uriawWrfG+kN4ivkuOD3muGTtizSERp3e4pKd3iRJkiSp08igt43ufyWJcb9KbKxeVRUJQVtrFayoAmXTAlj1Dqz/EPy+5ifOHQmHnUlg+f8ofM0bd+bGz9fz0tzEFlxHcxooqkLAa4meyVAI+ayAwOIyxyU3sQWOvRUW3gtGGNT6+UXD7WNvNcclY1+lIchOb5IkSZLUZcmgt40yWogxkx0XLi+n7N//pvzlV2gaYjUOdhWLQUpegLSeflLy/fDfafEnVFSzhu7gs2DY+ZBuNoSw/fAfzD1i81niJ14Y2Gt3t77oRhxDRxL2Nz8jKIT9AsfQkUnNC8AxN5v//fJRM7Cvz+11ZpgBb/31ZDRKQxCAf08IvdZAc6o4ciwobU1DkJ3eJEmSJKnLkkFvG+kaEG51mDmuGaE9xex94Xm8r7+B8PujrtUHu4rVIK2+hm6u39zsjEezmjuTQ84xf7ljdxP9xSEsLgjXNAS+0QwsLoG/OEScfcpmBasrzXSGlhgKwepKEmw4Fy1vFCJnBP5VK9FrdTSnhqP3cJS8NgaPdWkI1UUKpd95CZbrCB0UDWwZGtljnbgzQsmnIchOb5IkSZLUZcmgt41Sm2lUlsi4QGEhe599Dt/8+RCKLh0mAEUz8PSsxVNQizunhRq6VpfZKGLouTDoNHC03PNYD6cSrqkifsALoBKuMdDDyZUW23nbgwmP6zd/UlJzs3kx1U/fROn3AYIVToRhbmTblq0ne81NuK/7Z/K5t64sqossFH1ZiRECzaGiOEDoECgNU/RJJXnHWnC3JQ1BdnqTJEmSpC5JBr1tlGhNhsgxMV2nduVKyp5/nsqFn8a0CtY8aWSeNAT73vdJyQ00W4VLDyhU7nLg2+Ggz4dr4ueONkMolcQelWtKrRuXuHBZBU0PxEVTGo1LgmFQ/fKfKFoUxAhrZnCq1QWnXihaFCQv5U+4/5hc7q3IGUHpaidGoBpLqgWl7s1WLKC4VcJVYUpXu3HljEj4zznKvuz0JkmSJElSm8igdz+oXLyY8n/9m+qvv465Zkl3kjVKJb3bOlTLWsiLfXy4VqVip1lxoXqPHYRiFvFKIuAF8C4P0XrQa45LTWJeS15P9DIfTdsbNzDvt+T1TGJWELt+pHTxDoyQgiVFjQ1OqwWli3fguupHlJ5HJjyvf+06glV2NGcNiggDmrl9LAwUoaM5FIJVdvxr1+EcnmQf6Xr7otObJEmSJEltJoPefUwFdvzyupj7rWkKWYeV4+m7CzVOHBqs1qisKy1WU2qra7vbINSGTcNgZesBbzLj6qX96npKbriRhjZpUf2CI7fTfnV9UvP6Vy4n6BVozobd2HqKoqA5NILeMP6Vy3EmEfTq5eUIoaFk9ISaEjPnVujmOi0OlLRuiAo/enl5UuuVJEmSJKnrkkHvPhS3e5onRNbQKtJ61cbm6mYP4tuVu8he7sBfbm1mBtMXgwTJ7iMqVhvx0w/ijUtcxfMvNH503X8b19NtGJd90kkJz6sHVIShoKgG8XaoFdVAGAp6ILmfALSMDBSrFaHYUbIGQKimoQSa1YXw+1GsOlpGRlLzSpIkSZLUdcmgt42aq38A8UNVR2aQ7KFVpPTwR+fr5o2CwZNh6DnQ7TBqf5mLv7z1oHNUd2/Sa3YOTCFQ2nq+rnNgcgfZ9D174twb+y7EH9c8rd+RKFYLIhxCscUGvSJsoFitaP0S3+UFcAwdgq2ggMD69Sh2O4q1oRuHEALd58M+aBCOoUOSmleSJEmSpK5LBr1ttDYLhjeqaKU0k8/qygmQPbQSV/dgXbCrQO/xMPhsGDIZMvpEjc/4OrGAM9FxjTmPnYJ3yQsJjUuGpXt3wrt2JTQuGY5hw7D1H0hg7RpQQ2CoCKGgKAJUAz2gYB88EMew5PJuFVUl+9prKLr7bsLFxWgeD4rNhggG0X0+VLeb7GuvQZEHzyRJkiTpoCG/q7dRWqOA15kdoGnAm5Lvp+/JJfQ5cS+ufAOl/yQ461H4zTq46kM4+saYgBdASzBZN9FxUWs+ZgSqreWuaKrNIO2YEUnNm/2b33TouHqKqpL969vB5SHoVQj6BKEKnaBPEPQq4Eon+9e3tyk4dY8fT96992IfNAijpoZwSQlGTQ32QYPIu/ce3OPHJz2nJEmSJEldl9zpbaMejX4f9qs4MoP4y6yk9faTNaQSW2qY6t0Otn/jptfbP4IrM6F5VUA0WwWhnmjTTytqWneyR2sUfyvqUnvrn6fuvwpkj9ZQ05LbkU05YjTYbBAMNj/IZjPHtYFisZvNNwy9oSObqqFYkss9bso9fjyuo47Cv3oNenk5WkYGjqFD5A6vJEmSJB2EZNDbRo1D0lCVle5Hl2FNCROstFKyOpWqIjsirGIAvRIMeOPP3pbrzcgdRdbROVRv20p1kaPRPOZ/3bl+so7uk3SbXGGYubWihaBXsVrNccnU0zUMSp99DhHWsQ8ciPD7EbqOomkoDgfh4mJKn30O11FHtTlQVVS17WXJJEmSJEk6YMgtrQ5SvCKNLZ90Y+eSDCq3OxHhtr21FT1DrQ9KYlxTe7+vrgt4Y1UXOdj7fXXSc1a89z6ithY0LbYBg6qCpiFqa6l47/2k5vWvXkOwsBAtPR1FUVCdTrSUFFSn0yxZ5vEQLCzEv3pN0muWJEmSJOnQIoPeNmqaGRustCCM2B3YljNoYy0/Ndyh46LWsuMHir+opvmdYoXiL6oxdvyQ1LyhXbvMtAOLBaxWM9Wh8X8tFhDCHJcEvbwcEQqh2OKnMSg2GyIUkvV0JUmSJElq1UET9D755JP07dsXh8PBuHHj+Pbbb/fp8z1weseOq5cnEvsjSXRcY2X/m09DnV4lzi8AUTcucdb8fDPP1jBQFAVFVc0UBLWui5phgKKY45IQqafbTNqECAbNkmWynq4kSZIkSa04KILeOXPmcOutt3L33XezbNkyRo0axWmnnUZxcfE+e86UBDdDEx1Xb/gXif2RJDqusfK3PiORfGFzXOLSzjoTNTUVwmGEiG5+IYSAcBg1NZW0s85Mat76erq6zxd3Xt3nw1ZQIOvpSpIkSZLUqoMi6H300Ue55ppruPLKKxk6dChPP/00LpeLF198cZ89580lHTuunn+jg9a7pom6cckxgnrk8c3NGz0uMarFQvYvrzVzeoNBhK4jhEDoulnRQdPI/uW1qJbkzk3W19NV3S7CxcUYfj/CMDD8fsLFxbKeriRJkiRJCTvgo4VgMMgPP/zAySefHLlPVVVOPvlklixZEvcxgUCAioqKqF/JSvSNa8sbbO7FNh+YtrF2A7b+/Rs9Q9P5G8qkNYxLXNZVV5Fz6y2oaWlQH+zqOmpaGjm33kLWVVe1ac2ynq4kSZIkSR3hgC9ZVlpaiq7rdG/S7at79+6sXbs27mMefPBB7r333nY9r4GOSmxr3HjjkmK3Q8AfCXwbh6ZK03FJ6vHU/7FpzNgms9UHu0rUuLbIuuoqMi6/nIr33ie0axfW/Hwz9SHJHd6mZD1dSZIkSZLa65CMGu688058Pl/k1/bt25Oew3r5tA4dV6/He/NpHIA2PWZWS4E0/gAAFEpJREFUf685Ljm2lBSs/fo1uTd6Zmu/fthSkm9xXE+1WEg/9xy6XX8d6eee0+6At159Pd2U447FOXyYDHglSZIkSUrKAR85ZGdno2kae/bsibp/z5495Obmxn2M3W4nLS0t6leyhvz+vlbLkRl145KR1rOnWQkhTqgbuU9RzHFtMOD99+IEviZrv34MeP+9Ns0rSZIkSZLUlR3wQa/NZuPII49k4cKFkfsMw2DhwoVMmDBhnz738LVrmg18jbrrbTFkzeq6wBdi9noVxbzeDgPef4/+33+Hc9w4rL164Rw3jv7ffycDXkmSJEmSDlqKaFoL6gA0Z84cpk+fzjPPPMNRRx3F3//+d15//XXWrl0bk+sbT0VFBR6PB5/P16Zd3zUPzCL079dRUTAQWC+fkvQOb9x17djBzrMnQyAAdjs95s9r8w6vJEmSJEnSoeygCHoBnnjiCf7617+ye/duDj/8cB5//HHGjRuX0GPbG/RKkiRJkiRJXdtBE/S2hwx6JUmSJEmSDm4HfE6vJEmSJEmSJLVGBr2SJEmSJEnSQU8GvZIkSZIkSdJBTwa9kiRJkiRJ0kFPBr2SJEmSJEnSQU8GvZIkSZIkSdJBTwa9kiRJkiRJ0kFPBr2SJEmSJEnSQU8GvZIkSZIkSdJBTwa9kiRJkiRJ0kFPBr2SJEmSJEnSQc/S2QvoCoQQAFRUVHTySiRJkqSuIDU1FUVROnsZkiR1IBn0ApWVlQD06tWrk1ciSZIkdQU+n4+0tLTOXoYkSR1IEfXbnIcwwzDYtWtXl/zJvqKigl69erF9+/YD5h/gA23Ncr37llzvviXXu290xe8HkiS1j9zpBVRVpWfPnp29jBalpaV16W8Q8Rxoa5br3bfkevctuV5JkqSWyYNskiRJkiRJ0kFPBr2SJEmSJEnSQU8GvV2c3W7n7rvvxm63d/ZSEnagrVmud9+S69235HolSZISIw+ySZIkSZIkSQc9udMrSZIkSZIkHfRk0CtJkiRJkiQd9GTQK0mSJEmSJB30ZNArSZIkSZIkHfRk0NtFPfjgg4wdO5bU1FRycnI477zzWLduXWcvK2F/+ctfUBSFmTNndvZSmrVz504uvfRSsrKycDqdjBgxgu+//76zlxWXruvcddddFBQU4HQ66d+/P/fffz9d6Rzq559/zuTJk8nPz0dRFObOnRt1XQjBrFmzyMvLw+l0cvLJJ7Nhw4bOWSwtrzcUCnHHHXcwYsQI3G43+fn5XH755ezatatLrrep6667DkVR+Pvf/77f1tdUIutds2YN55xzDh6PB7fbzdixY9m2bdv+X6wkSYcEGfR2UYsXL+aGG27gm2++YcGCBYRCIU499VSqq6s7e2mt+u6773jmmWcYOXJkZy+lWeXl5RxzzDFYrVY++OADVq9ezSOPPEJGRkZnLy2uhx56iKeeeoonnniCNWvW8NBDD/Hwww/zz3/+s7OXFlFdXc2oUaN48skn415/+OGHefzxx3n66adZunQpbreb0047Db/fv59XamppvTU1NSxbtoy77rqLZcuW8fbbb7Nu3TrOOeecTlipqbX3t94777zDN998Q35+/n5aWXytrXfTpk0ce+yxDB48mEWLFvHzzz9z11134XA49vNKJUk6ZAjpgFBcXCwAsXjx4s5eSosqKyvFwIEDxYIFC8TEiRPFr3/9685eUlx33HGHOPbYYzt7GQk766yzxFVXXRV13wUXXCB+8YtfdNKKWgaId955J3LbMAyRm5sr/vrXv0bu83q9wm63i//+97+dsMJoTdcbz7fffisAsXXr1v2zqBY0t94dO3aIHj16iJUrV4o+ffqIxx57bL+vLZ546506daq49NJLO2dBkiQdkuRO7wHC5/MBkJmZ2ckradkNN9zAWWedxcknn9zZS2nRu+++y5gxY7j44ovJyclh9OjRPPfcc529rGYdffTRLFy4kPXr1wOwfPlyvvzyS84444xOXlliCgsL2b17d9TXhcfjYdy4cSxZsqQTV5Y4n8+Hoiikp6d39lLiMgyDyy67jNtuu41hw4Z19nJaZBgG7733HoMGDeK0004jJyeHcePGtZiyIUmS1F4y6D0AGIbBzJkzOeaYYxg+fHhnL6dZr732GsuWLePBBx/s7KW0avPmzTz11FMMHDiQjz76iOuvv56bb76Zf/3rX529tLh+97vfMW3aNAYPHozVamX06NHMnDmTX/ziF529tITs3r0bgO7du0fd371798i1rszv93PHHXdwySWXkJaW1tnLieuhhx7CYrFw8803d/ZSWlVcXExVVRV/+ctfOP300/n44485//zzueCCC1i8eHFnL0+SpIOUpbMXILXuhhtuYOXKlXz55ZedvZRmbd++nV//+tcsWLDggMjJMwyDMWPG8MADDwAwevRoVq5cydNPP8306dM7eXWxXn/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" ] }, "metadata": {}, @@ -1065,36 +478,13 @@ } ], "source": [ - "gtfs_utils_v2.get_stops?" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "45ab654a-d525-4872-8dec-db9ade78d21d", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'shared_utils' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m metro_st \u001b[38;5;241m=\u001b[39m \u001b[43mshared_utils\u001b[49m\u001b[38;5;241m.\u001b[39mgtfs_utils_v2\u001b[38;5;241m.\u001b[39mget_stops(selected_date\u001b[38;5;241m=\u001b[39manalysis_dt, operator_feeds\u001b[38;5;241m=\u001b[39mfeed_list,\n\u001b[1;32m 2\u001b[0m trip_df\u001b[38;5;241m=\u001b[39mmetro_trips, get_df\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'shared_utils' is not defined" - ] - } - ], - "source": [ - "metro_st = shared_utils.gtfs_utils_v2.get_stops(selected_date=analysis_dt, operator_feeds=feed_list,\n", - " trip_df=metro_trips, get_df=True)" + "sns.lmplot(x=\"n_routes_weekday\",y=\"n_trips_weekday\", hue=\"name\", data=valid_weekday_data).set(title=\"weekday routes vs trips\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "ceddb65f-e0f6-4ba8-8aaf-f70a41dd474e", + "id": "1eb53400-c483-4217-84ec-b6238c045a3b", "metadata": {}, "outputs": [], "source": [] @@ -1117,6 +507,13 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, diff --git a/ahsc_grant/create_stop_freq_refactor.py b/ahsc_grant/create_stop_freq_refactor.py index 3eccaf9e3..a89a3b589 100644 --- a/ahsc_grant/create_stop_freq_refactor.py +++ b/ahsc_grant/create_stop_freq_refactor.py @@ -160,7 +160,7 @@ def plot_histogram(data, column, title): stops_all.append(stop_merged) merge_cols = ["name","route_type", "stop_id","geometry", "stop_code", "stop_name", "location_type"] -final_cols = ["name","feed_key","location_type","route_type","stop_name","stop_id","stop_code","geometry","n_trips_weekday","n_trips_saturday","n_trips_sunday","n_routes_weekday","n_routes_saturday","n_routes_sunday", "stop_desc"] +final_cols = ["name","location_type","route_type","stop_name","stop_id","stop_code","geometry","n_trips_weekday","n_trips_saturday","n_trips_sunday","n_routes_weekday","n_routes_saturday","n_routes_sunday", "stop_desc"] stoptimes_all = merge_stoptimes(*stops_all, merge_cols=merge_cols, final_cols=final_cols) stoptimes_all_gdf = gpd.GeoDataFrame(stoptimes_all, geometry='geometry') diff --git a/ahsc_grant/join_analytical_file_refactor.ipynb b/ahsc_grant/join_analytical_file_refactor.ipynb new file mode 100644 index 000000000..54e1adcc2 --- /dev/null +++ b/ahsc_grant/join_analytical_file_refactor.ipynb @@ -0,0 +1,1011 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "15ba7a7b-53fd-448c-be1d-b0db9cca59e1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: shared_utils in /home/jovyan/data-analyses/_shared_utils (2.5)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install shared_utils" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e569b7c3-9cc8-4d69-8128-83efea0f7d63", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"CALITP_BQ_MAX_BYTES\"] = str(800_000_000_000)\n", + "\n", + "import shared_utils\n", + "\n", + "from siuba import *\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "pd.set_option('display.max_columns', None) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9e570a7b-80fd-4e35-97d5-b40bbfd5ce56", + "metadata": {}, + "outputs": [], + "source": [ + "GCS_FILE_PATH = 'gs://calitp-analytics-data/data-analyses/ahsc_grant/'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "75ccd0e2-7eaa-4bb6-93e7-e578e20afebd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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namelocation_typeroute_typestop_namestop_idstop_codegeometryn_trips_weekdayn_trips_saturdayn_trips_sundayn_routes_weekdayn_routes_saturdayn_routes_sundaystop_desc
95Monterey Salinas Schedule0.03Del Monte Center / Gate 100020002POINT (-121.89736 36.58461)38.030.026.07.05.05.0None
96Monterey Salinas Schedule0.03Del Monte Center / Gate 200030003POINT (-121.89728 36.58473)31.027.023.06.05.04.0None
97Monterey Salinas Schedule0.03Del Monte Center / Gate 300040004POINT (-121.89893 36.58445)25.012.011.05.03.02.0None
98Monterey Salinas Schedule0.036th / Mission Street00060006POINT (-121.92076 36.55564)91.086.077.06.06.06.0None
99Monterey Salinas Schedule0.03Northridge Mall00110011POINT (-121.65803 36.71563)142.0136.0132.06.07.07.0None
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" + ], + "text/plain": [ + " name location_type route_type \\\n", + "95 Monterey Salinas Schedule 0.0 3 \n", + "96 Monterey Salinas Schedule 0.0 3 \n", + "97 Monterey Salinas Schedule 0.0 3 \n", + "98 Monterey Salinas Schedule 0.0 3 \n", + "99 Monterey Salinas Schedule 0.0 3 \n", + "\n", + " stop_name stop_id stop_code geometry \\\n", + "95 Del Monte Center / Gate 1 0002 0002 POINT (-121.89736 36.58461) \n", + "96 Del Monte Center / Gate 2 0003 0003 POINT (-121.89728 36.58473) \n", + "97 Del Monte Center / Gate 3 0004 0004 POINT (-121.89893 36.58445) \n", + "98 6th / Mission Street 0006 0006 POINT (-121.92076 36.55564) \n", + "99 Northridge Mall 0011 0011 POINT (-121.65803 36.71563) \n", + "\n", + " n_trips_weekday n_trips_saturday n_trips_sunday n_routes_weekday \\\n", + "95 38.0 30.0 26.0 7.0 \n", + "96 31.0 27.0 23.0 6.0 \n", + "97 25.0 12.0 11.0 5.0 \n", + "98 91.0 86.0 77.0 6.0 \n", + "99 142.0 136.0 132.0 6.0 \n", + "\n", + " n_routes_saturday n_routes_sunday stop_desc \n", + "95 5.0 5.0 None \n", + "96 5.0 4.0 None \n", + "97 3.0 2.0 None \n", + "98 6.0 6.0 None \n", + "99 7.0 7.0 None " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# start with trips per stop and ridership\n", + "stoptrips = gpd.read_parquet(f\"{GCS_FILE_PATH}tbl1_trips_perstop_07_08_2024.parquet\")\n", + "stoptrips >> head (5)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f19b305a-46a5-4e4c-a4da-6d72d353e27e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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feed_keystop_idstop_namegeometrySTOP_NAMEsat_onssun_onsweekday_onsname
006d1f3ac2b0ae5e74424edbbfefa19ed12591LA ZooPOINT (158199.490 -428414.858)LA ZOO857.679453381.1908682775.546009LA Metro Bus Schedule
106d1f3ac2b0ae5e74424edbbfefa19ed53771st / HillPOINT (161833.578 -438634.619)1ST / HILL14914.09271711531.023762159742.798188LA Metro Bus Schedule
206d1f3ac2b0ae5e74424edbbfefa19ed156121st / HillPOINT (161849.863 -438611.462)1ST / HILL4800.6224963383.06895543551.056687LA Metro Bus Schedule
306d1f3ac2b0ae5e74424edbbfefa19ed12176th / WallPOINT (161822.154 -439849.592)6TH / WALL3502.1911013061.43916024682.108713LA Metro Bus Schedule
406d1f3ac2b0ae5e74424edbbfefa19ed73767th / AlmaPOINT (157649.168 -473829.124)7TH / ALMA0.00000035.736644559.874088LA Metro Bus Schedule
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" + ], + "text/plain": [ + " feed_key stop_id stop_name \\\n", + "0 06d1f3ac2b0ae5e74424edbbfefa19ed 12591 LA Zoo \n", + "1 06d1f3ac2b0ae5e74424edbbfefa19ed 5377 1st / Hill \n", + "2 06d1f3ac2b0ae5e74424edbbfefa19ed 15612 1st / Hill \n", + "3 06d1f3ac2b0ae5e74424edbbfefa19ed 1217 6th / Wall \n", + "4 06d1f3ac2b0ae5e74424edbbfefa19ed 7376 7th / Alma \n", + "\n", + " geometry STOP_NAME sat_ons sun_ons \\\n", + "0 POINT (158199.490 -428414.858) LA ZOO 857.679453 381.190868 \n", + "1 POINT (161833.578 -438634.619) 1ST / HILL 14914.092717 11531.023762 \n", + "2 POINT (161849.863 -438611.462) 1ST / HILL 4800.622496 3383.068955 \n", + "3 POINT (161822.154 -439849.592) 6TH / WALL 3502.191101 3061.439160 \n", + "4 POINT (157649.168 -473829.124) 7TH / ALMA 0.000000 35.736644 \n", + "\n", + " weekday_ons name \n", + "0 2775.546009 LA Metro Bus Schedule \n", + "1 159742.798188 LA Metro Bus Schedule \n", + "2 43551.056687 LA Metro Bus Schedule \n", + "3 24682.108713 LA Metro Bus Schedule \n", + "4 559.874088 LA Metro Bus Schedule " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ridership_metro = gpd.read_parquet(f\"{GCS_FILE_PATH}ridership_metro_08_26_2024.parquet\")\n", + "ridership_metro >> head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7c9a3e16-41ce-4109-a929-4e84d0c9f7cc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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feed_keystop_idstop_namegeometrysat_onssun_onsweekday_onsname
052201caab047b98ae19b7547c0d7c2ad1Modoc & PortesuelloPOINT (25170.737 -398993.625)1573.01287.013964.0SBMTD Schedule
152201caab047b98ae19b7547c0d7c2ad2Milpas & MontecitoPOINT (29196.552 -399052.308)3901.03139.030225.0SBMTD Schedule
252201caab047b98ae19b7547c0d7c2ad4Cathedral Oaks & Camino Del RioPOINT (20247.563 -395908.292)NaNNaN1.0SBMTD Schedule
352201caab047b98ae19b7547c0d7c2ad5Via Real & Sandpiper MHPPOINT (42143.060 -400995.911)217.0109.01485.0SBMTD Schedule
452201caab047b98ae19b7547c0d7c2ad6UCSB Elings Hall OutboundPOINT (14738.802 -400125.683)1374.01199.09777.0SBMTD Schedule
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" + ], + "text/plain": [ + " feed_key stop_id stop_name \\\n", + "0 52201caab047b98ae19b7547c0d7c2ad 1 Modoc & Portesuello \n", + "1 52201caab047b98ae19b7547c0d7c2ad 2 Milpas & Montecito \n", + "2 52201caab047b98ae19b7547c0d7c2ad 4 Cathedral Oaks & Camino Del Rio \n", + "3 52201caab047b98ae19b7547c0d7c2ad 5 Via Real & Sandpiper MHP \n", + "4 52201caab047b98ae19b7547c0d7c2ad 6 UCSB Elings Hall Outbound \n", + "\n", + " geometry sat_ons sun_ons weekday_ons \\\n", + "0 POINT (25170.737 -398993.625) 1573.0 1287.0 13964.0 \n", + "1 POINT (29196.552 -399052.308) 3901.0 3139.0 30225.0 \n", + "2 POINT (20247.563 -395908.292) NaN NaN 1.0 \n", + "3 POINT (42143.060 -400995.911) 217.0 109.0 1485.0 \n", + "4 POINT (14738.802 -400125.683) 1374.0 1199.0 9777.0 \n", + "\n", + " name \n", + "0 SBMTD Schedule \n", + "1 SBMTD Schedule \n", + "2 SBMTD Schedule \n", + "3 SBMTD Schedule \n", + "4 SBMTD Schedule " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ridership_sbmtd = gpd.read_parquet(f\"{GCS_FILE_PATH}ridership_sbmtd_08_26_2024.parquet\")\n", + "ridership_sbmtd >> head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "89a1f78d-006f-42a7-97d6-9e5dcf8ed65c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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feed_keystop_idstop_namegeometrysat_onssun_onsweekday_onsname
0efb3d4ea58f58d541e2c452e253bec5d1001Fremont / Hilby AvenuePOINT (-165123.570 -155506.782)112.058.0753.0Monterey Salinas Schedule
1efb3d4ea58f58d541e2c452e253bec5d1004Fremont / Trinity AvenuePOINT (-165043.149 -155309.483)672.0754.08534.0Monterey Salinas Schedule
2efb3d4ea58f58d541e2c452e253bec5d1007Fremont / Elm AvenuePOINT (-164902.027 -154954.295)112.058.0753.0Monterey Salinas Schedule
3efb3d4ea58f58d541e2c452e253bec5d1010Fremont / BroadwayPOINT (-164827.944 -154788.329)224.0232.01757.0Monterey Salinas Schedule
4efb3d4ea58f58d541e2c452e253bec5d1016Fremont / EchoPOINT (-164607.221 -154335.862)112.0116.01004.0Monterey Salinas Schedule
\n", + "
" + ], + "text/plain": [ + " feed_key stop_id stop_name \\\n", + "0 efb3d4ea58f58d541e2c452e253bec5d 1001 Fremont / Hilby Avenue \n", + "1 efb3d4ea58f58d541e2c452e253bec5d 1004 Fremont / Trinity Avenue \n", + "2 efb3d4ea58f58d541e2c452e253bec5d 1007 Fremont / Elm Avenue \n", + "3 efb3d4ea58f58d541e2c452e253bec5d 1010 Fremont / Broadway \n", + "4 efb3d4ea58f58d541e2c452e253bec5d 1016 Fremont / Echo \n", + "\n", + " geometry sat_ons sun_ons weekday_ons \\\n", + "0 POINT (-165123.570 -155506.782) 112.0 58.0 753.0 \n", + "1 POINT (-165043.149 -155309.483) 672.0 754.0 8534.0 \n", + "2 POINT (-164902.027 -154954.295) 112.0 58.0 753.0 \n", + "3 POINT (-164827.944 -154788.329) 224.0 232.0 1757.0 \n", + "4 POINT (-164607.221 -154335.862) 112.0 116.0 1004.0 \n", + "\n", + " name \n", + "0 Monterey Salinas Schedule \n", + "1 Monterey Salinas Schedule \n", + "2 Monterey Salinas Schedule \n", + "3 Monterey Salinas Schedule \n", + "4 Monterey Salinas Schedule " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ridership_mst = gpd.read_parquet(f\"{GCS_FILE_PATH}ridership_mst_08_26_2024.parquet\")\n", + "ridership_mst >> head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8472601d-7600-40f3-a98a-37b983b858ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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feed_keystop_idstop_namegeometrysat_onssun_onsweekday_onsname
497206d1f3ac2b0ae5e74424edbbfefa19ed7963Slauson / 6thPOINT (154927.260 -446021.786)714.732878512.2252297326.011997LA Metro Bus Schedule
156506d1f3ac2b0ae5e74424edbbfefa19ed17352Rosecrans / VermontPOINT (157953.951 -455609.191)833.855024690.9084498838.863255LA Metro Bus Schedule
1192706d1f3ac2b0ae5e74424edbbfefa19ed8666Agoura / Lakeview CanyonPOINT (108860.167 -429061.804)83.38550211.912215702.820663LA Metro Bus Schedule
1081906d1f3ac2b0ae5e74424edbbfefa19ed16626Whittier / KeenanPOINT (171283.010 -442487.956)1095.923746869.5916686015.668388LA Metro Bus Schedule
364406d1f3ac2b0ae5e74424edbbfefa19ed10577Foothill / Home DepotPOINT (145681.002 -412215.403)154.858790154.8587901774.919980LA Metro Bus Schedule
\n", + "
" + ], + "text/plain": [ + " feed_key stop_id stop_name \\\n", + "4972 06d1f3ac2b0ae5e74424edbbfefa19ed 7963 Slauson / 6th \n", + "1565 06d1f3ac2b0ae5e74424edbbfefa19ed 17352 Rosecrans / Vermont \n", + "11927 06d1f3ac2b0ae5e74424edbbfefa19ed 8666 Agoura / Lakeview Canyon \n", + "10819 06d1f3ac2b0ae5e74424edbbfefa19ed 16626 Whittier / Keenan \n", + "3644 06d1f3ac2b0ae5e74424edbbfefa19ed 10577 Foothill / Home Depot \n", + "\n", + " geometry sat_ons sun_ons weekday_ons \\\n", + "4972 POINT (154927.260 -446021.786) 714.732878 512.225229 7326.011997 \n", + "1565 POINT (157953.951 -455609.191) 833.855024 690.908449 8838.863255 \n", + "11927 POINT (108860.167 -429061.804) 83.385502 11.912215 702.820663 \n", + "10819 POINT (171283.010 -442487.956) 1095.923746 869.591668 6015.668388 \n", + "3644 POINT (145681.002 -412215.403) 154.858790 154.858790 1774.919980 \n", + "\n", + " name \n", + "4972 LA Metro Bus Schedule \n", + "1565 LA Metro Bus Schedule \n", + "11927 LA Metro Bus Schedule \n", + "10819 LA Metro Bus Schedule \n", + "3644 LA Metro Bus Schedule " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# contatenate gdfs, keeping common columns\n", + "ridership_all = pd.concat([ridership_metro,ridership_sbmtd,ridership_mst], join='inner', ignore_index=\"True\")\n", + "ridership_all.sample(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c3a86515-0daa-42a3-8f6c-e911bba05714", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13516" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(ridership_all)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e19cf2f7-e11a-4c95-9b7d-1fedc9425352", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13730" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(stoptrips)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b58266f9-66cc-4341-854a-27092a7aa91d", + "metadata": {}, + "outputs": [], + "source": [ + "ridership_all = ridership_all.drop(columns=['feed_key'])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "271d1dde-fdeb-4326-a9d4-11a63beece10", + "metadata": {}, + "outputs": [], + "source": [ + "# join together, keep buses, create total trips per weekday\n", + "trips_ridership_joined = (stoptrips\n", + " >> left_join(_,ridership_all, \n", + " on = ['name', 'stop_name', 'stop_id'])\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "86c67e0b-c512-4b14-b13d-2ffaddf5d599", + "metadata": {}, + "outputs": [], + "source": [ + "columns_to_check = ['weekday_ons', 'sat_ons', 'sun_ons']" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "cdef705c-7ce1-4770-a20f-87833a07dc44", + "metadata": {}, + "outputs": [], + "source": [ + "trips_ridership_joined_all_na = trips_ridership_joined[\n", + " trips_ridership_joined[columns_to_check].isna().all(axis=1)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5343c09f-576e-45f8-8423-737e00b37b36", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 364 entries, 0 to 13871\n", + "Data columns (total 18 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 name 364 non-null object \n", + " 1 location_type 208 non-null float64 \n", + " 2 route_type 364 non-null object \n", + " 3 stop_name 364 non-null object \n", + " 4 stop_id 364 non-null object \n", + " 5 stop_code 364 non-null object \n", + " 6 geometry_x 364 non-null geometry\n", + " 7 n_trips_weekday 344 non-null float64 \n", + " 8 n_trips_saturday 296 non-null float64 \n", + " 9 n_trips_sunday 292 non-null float64 \n", + " 10 n_routes_weekday 344 non-null float64 \n", + " 11 n_routes_saturday 296 non-null float64 \n", + " 12 n_routes_sunday 292 non-null float64 \n", + " 13 stop_desc 0 non-null object \n", + " 14 geometry_y 0 non-null geometry\n", + " 15 sat_ons 0 non-null float64 \n", + " 16 sun_ons 0 non-null float64 \n", + " 17 weekday_ons 0 non-null float64 \n", + "dtypes: float64(10), geometry(2), object(6)\n", + "memory usage: 54.0+ KB\n" + ] + } + ], + "source": [ + "trips_ridership_joined_all_na.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "0c89b02f-1918-4449-bd41-59d576bd6fdb", + "metadata": {}, + "outputs": [], + "source": [ + "trips_ridership_joined.to_excel('trips_ridership_joined_all.xlsx', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "78423f9d-4278-4a5b-a90f-6336147393a9", + "metadata": {}, + "outputs": [], + "source": [ + "trips_ridership_joined_all_na.to_excel('trips_ridership_joined_all_na.xlsx', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "94d14b9c-190b-41fc-830f-9da8f2955524", + "metadata": {}, + "outputs": [], + "source": [ + "trips_ridership_joined_filtered = trips_ridership_joined[\n", + " trips_ridership_joined[['weekday_ons', 'sat_ons', 'sun_ons']].isna().all(axis=1)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "963a56ca-19cd-4dee-8036-ff8353f2d403", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Monterey Salinas Schedule', 'SBMTD Schedule',\n", + " 'LA Metro Bus Schedule'], dtype=object)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trips_ridership_joined_filtered.name.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "8d7e034a-f5f0-42ed-9a59-219e1b59f1ea", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13730" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(trips_ridership_joined)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3bdee905-90b7-4e94-b7b7-89dec5f5af54", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Monterey Salinas Schedule', 'LA Metro Bus Schedule',\n", + " 'SBMTD Schedule'], dtype=object)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# add .25mi (10min walk) buffers to stops\n", + "# this replaces our point geometry with polygons\n", + "trips_ridership_joined.geometry = trips_ridership_joined.buffer(402.336)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cbeb98aa-81f3-45ef-b7ee-cba97fa9d9d6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ahsc_grant/process_metro_refactor.ipynb b/ahsc_grant/process_metro_refactor.ipynb index 898f7ce96..a07295095 100644 --- a/ahsc_grant/process_metro_refactor.ipynb +++ b/ahsc_grant/process_metro_refactor.ipynb @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "8f569093-29f5-47b5-9f24-d7acb7d4fe5e", "metadata": {}, "outputs": [], @@ -91,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "20bd9b8a-1fe1-402e-a5ef-4095e67685dd", "metadata": {}, "outputs": [], @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "id": "bbfd896c-4797-4022-9cd8-472d157ac5d0", "metadata": {}, "outputs": [], @@ -111,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "id": "b47dc5c1-0464-4ec3-9312-a0c5d1baf822", "metadata": {}, "outputs": [], @@ -128,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "id": "8160abe7-4459-4425-99e9-bfe920670829", "metadata": {}, "outputs": [], @@ -138,12 +138,13 @@ " >> spread(\"DAY_TYPE\", \"stop_total_ons\")\n", " >> rename(stop_id = _.STOP_ID)\n", " >> mutate(feed_key = '06d1f3ac2b0ae5e74424edbbfefa19ed')\n", + " >> mutate(name = 'LA Metro Bus Schedule')\n", " )" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "id": "9cad0548-911a-4539-bbb7-9c175117be1b", "metadata": {}, "outputs": [], @@ -153,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "id": "3889b66f-e516-4f6b-81a0-9755b49c26bc", "metadata": {}, "outputs": [], @@ -163,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "id": "f83432fa-3767-46bc-93e5-ef465534ae62", "metadata": {}, "outputs": [ @@ -196,6 +197,7 @@ " sat_ons\n", " sun_ons\n", " weekday_ons\n", + " name\n", " \n", " \n", " \n", @@ -209,6 +211,7 @@ " 857.679453\n", " 381.190868\n", " 2775.546009\n", + " LA Metro Bus Schedule\n", " \n", " \n", " 1\n", @@ -220,6 +223,7 @@ " 14914.092717\n", " 11531.023762\n", " 159742.798188\n", + " LA Metro Bus Schedule\n", " \n", " \n", " 2\n", @@ -231,6 +235,7 @@ " 4800.622496\n", " 3383.068955\n", " 43551.056687\n", + " LA Metro Bus Schedule\n", " \n", " \n", " 3\n", @@ -242,6 +247,7 @@ " 3502.191101\n", " 3061.439160\n", " 24682.108713\n", + " LA Metro Bus Schedule\n", " \n", " \n", " 4\n", @@ -253,148 +259,36 @@ " 0.000000\n", " 35.736644\n", " 559.874088\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 12146\n", - " 06d1f3ac2b0ae5e74424edbbfefa19ed\n", - " 1537\n", - " Manchester / Harbor Transitway Station\n", - " POINT (158894.193 -449166.908)\n", - " MANCHESTER / HARBOR TRANSITWAY STATION\n", - " 1215.045892\n", - " 1179.309248\n", - " 14282.745341\n", - " \n", - " \n", - " 12147\n", - " 06d1f3ac2b0ae5e74424edbbfefa19ed\n", - " 300\n", - " Ave 26 / Lacy - Lincoln / Cypress Station\n", - " POINT (164252.826 -435551.507)\n", - " AVE 26 / LACY - LINCOLN / CYPRESS STATIO\n", - " 428.839727\n", - " 405.015297\n", - " 3263.946809\n", - " \n", - " \n", - " 12148\n", - " 06d1f3ac2b0ae5e74424edbbfefa19ed\n", - " 8886\n", - " Ave 26 / Lacy - Lincoln / Cypress Station\n", - " POINT (164238.291 -435568.866)\n", - " AVE 26 / LACY - LINCOLN / CYPRESS STATIO\n", - " 3240.122379\n", - " 2513.477287\n", - " 30769.250389\n", - " \n", - " \n", - " 12149\n", - " 06d1f3ac2b0ae5e74424edbbfefa19ed\n", - " 4236\n", - " Pacific Coast Highway / Malibu Cove Colony\n", - " POINT (114012.013 -442574.810)\n", - " PACIFIC COAST / MALIBU COVE COLONY\n", - " 297.805366\n", - " 297.805366\n", - " 3442.630028\n", - " \n", - " \n", - " 12150\n", - " 06d1f3ac2b0ae5e74424edbbfefa19ed\n", - " 24247\n", - " Medical Center Dr / Community Medical Center\n", - " POINT (126277.095 -422858.067)\n", - " MEDICAL CENTER DR / COMMUNITY MEDICAL CE\n", - " 750.469522\n", - " 369.278654\n", - " 8612.531177\n", + " LA Metro Bus Schedule\n", " \n", " \n", "\n", - "

12151 rows × 8 columns

\n", "" ], "text/plain": [ - " feed_key stop_id \\\n", - "0 06d1f3ac2b0ae5e74424edbbfefa19ed 12591 \n", - "1 06d1f3ac2b0ae5e74424edbbfefa19ed 5377 \n", - "2 06d1f3ac2b0ae5e74424edbbfefa19ed 15612 \n", - "3 06d1f3ac2b0ae5e74424edbbfefa19ed 1217 \n", - "4 06d1f3ac2b0ae5e74424edbbfefa19ed 7376 \n", - "... ... ... \n", - "12146 06d1f3ac2b0ae5e74424edbbfefa19ed 1537 \n", - "12147 06d1f3ac2b0ae5e74424edbbfefa19ed 300 \n", - "12148 06d1f3ac2b0ae5e74424edbbfefa19ed 8886 \n", - "12149 06d1f3ac2b0ae5e74424edbbfefa19ed 4236 \n", - "12150 06d1f3ac2b0ae5e74424edbbfefa19ed 24247 \n", - "\n", - " stop_name \\\n", - "0 LA Zoo \n", - "1 1st / Hill \n", - "2 1st / Hill \n", - "3 6th / Wall \n", - "4 7th / Alma \n", - "... ... \n", - "12146 Manchester / Harbor Transitway Station \n", - "12147 Ave 26 / Lacy - Lincoln / Cypress Station \n", - "12148 Ave 26 / Lacy - Lincoln / Cypress Station \n", - "12149 Pacific Coast Highway / Malibu Cove Colony \n", - "12150 Medical Center Dr / Community Medical Center \n", - "\n", - " geometry \\\n", - "0 POINT (158199.490 -428414.858) \n", - "1 POINT (161833.578 -438634.619) \n", - "2 POINT (161849.863 -438611.462) \n", - "3 POINT (161822.154 -439849.592) \n", - "4 POINT (157649.168 -473829.124) \n", - "... ... \n", - "12146 POINT (158894.193 -449166.908) \n", - "12147 POINT (164252.826 -435551.507) \n", - "12148 POINT (164238.291 -435568.866) \n", - "12149 POINT (114012.013 -442574.810) \n", - "12150 POINT (126277.095 -422858.067) \n", - "\n", - " STOP_NAME sat_ons sun_ons \\\n", - "0 LA ZOO 857.679453 381.190868 \n", - "1 1ST / HILL 14914.092717 11531.023762 \n", - "2 1ST / HILL 4800.622496 3383.068955 \n", - "3 6TH / WALL 3502.191101 3061.439160 \n", - "4 7TH / ALMA 0.000000 35.736644 \n", - "... ... ... ... \n", - "12146 MANCHESTER / HARBOR TRANSITWAY STATION 1215.045892 1179.309248 \n", - "12147 AVE 26 / LACY - LINCOLN / CYPRESS STATIO 428.839727 405.015297 \n", - "12148 AVE 26 / LACY - LINCOLN / CYPRESS STATIO 3240.122379 2513.477287 \n", - "12149 PACIFIC COAST / MALIBU COVE COLONY 297.805366 297.805366 \n", - "12150 MEDICAL CENTER DR / COMMUNITY MEDICAL CE 750.469522 369.278654 \n", + " feed_key stop_id stop_name \\\n", + "0 06d1f3ac2b0ae5e74424edbbfefa19ed 12591 LA Zoo \n", + "1 06d1f3ac2b0ae5e74424edbbfefa19ed 5377 1st / Hill \n", + "2 06d1f3ac2b0ae5e74424edbbfefa19ed 15612 1st / Hill \n", + "3 06d1f3ac2b0ae5e74424edbbfefa19ed 1217 6th / Wall \n", + "4 06d1f3ac2b0ae5e74424edbbfefa19ed 7376 7th / Alma \n", "\n", - " weekday_ons \n", - "0 2775.546009 \n", - "1 159742.798188 \n", - "2 43551.056687 \n", - "3 24682.108713 \n", - "4 559.874088 \n", - "... ... \n", - "12146 14282.745341 \n", - "12147 3263.946809 \n", - "12148 30769.250389 \n", - "12149 3442.630028 \n", - "12150 8612.531177 \n", + " geometry STOP_NAME sat_ons sun_ons \\\n", + "0 POINT (158199.490 -428414.858) LA ZOO 857.679453 381.190868 \n", + "1 POINT (161833.578 -438634.619) 1ST / HILL 14914.092717 11531.023762 \n", + "2 POINT (161849.863 -438611.462) 1ST / HILL 4800.622496 3383.068955 \n", + "3 POINT (161822.154 -439849.592) 6TH / WALL 3502.191101 3061.439160 \n", + "4 POINT (157649.168 -473829.124) 7TH / ALMA 0.000000 35.736644 \n", "\n", - "[12151 rows x 8 columns]" + " weekday_ons name \n", + "0 2775.546009 LA Metro Bus Schedule \n", + "1 159742.798188 LA Metro Bus Schedule \n", + "2 43551.056687 LA Metro Bus Schedule \n", + "3 24682.108713 LA Metro Bus Schedule \n", + "4 559.874088 LA Metro Bus Schedule " ] }, - "execution_count": 15, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -405,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "id": "d2a4fe81-4fd3-4d28-be90-a8a59215bf12", "metadata": {}, "outputs": [ @@ -415,7 +309,7 @@ "5" ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -430,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 15, "id": "e2aec214-2f75-4291-965e-232bcc0558bb", "metadata": {}, "outputs": [ @@ -498,7 +392,7 @@ "4 Cesar E Chavez / Broadway 63500002" ] }, - "execution_count": 58, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -509,7 +403,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "id": "24b3f9bb-d806-4df8-a357-484a922d0a95", "metadata": {}, "outputs": [ @@ -519,7 +413,7 @@ "8" ] }, - "execution_count": 18, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -534,7 +428,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "id": "a3e52ea9-e6d4-4ea0-8489-4d2f92e1bd3a", "metadata": {}, "outputs": [ @@ -565,6 +459,7 @@ " sun_ons\n", " weekday_ons\n", " feed_key\n", + " name\n", " \n", " \n", " \n", @@ -576,6 +471,7 @@ " 774.293951\n", " NaN\n", " 06d1f3ac2b0ae5e74424edbbfefa19ed\n", + " LA Metro Bus Schedule\n", " \n", " \n", " 6819\n", @@ -585,6 +481,7 @@ " 0.000000\n", " 47.648859\n", " 06d1f3ac2b0ae5e74424edbbfefa19ed\n", + " LA Metro Bus Schedule\n", " \n", " \n", " 6820\n", @@ -594,6 +491,7 @@ " 11.912215\n", " 11.912215\n", " 06d1f3ac2b0ae5e74424edbbfefa19ed\n", + " LA Metro Bus Schedule\n", " \n", " \n", " 7022\n", @@ -603,6 +501,7 @@ " 35.736644\n", " 238.244293\n", " 06d1f3ac2b0ae5e74424edbbfefa19ed\n", + " LA Metro Bus Schedule\n", " \n", " \n", " 7275\n", @@ -612,6 +511,7 @@ " 0.000000\n", " 23.824429\n", " 06d1f3ac2b0ae5e74424edbbfefa19ed\n", + " LA Metro Bus Schedule\n", " \n", " \n", " 7486\n", @@ -621,6 +521,7 @@ " 0.000000\n", " 262.068722\n", " 06d1f3ac2b0ae5e74424edbbfefa19ed\n", + " LA Metro Bus Schedule\n", " \n", " \n", " 7674\n", @@ -630,6 +531,7 @@ " 238.244293\n", " NaN\n", " 06d1f3ac2b0ae5e74424edbbfefa19ed\n", + " LA Metro Bus Schedule\n", " \n", " \n", " 7706\n", @@ -639,6 +541,7 @@ " 0.000000\n", " 23.824429\n", " 06d1f3ac2b0ae5e74424edbbfefa19ed\n", + " LA Metro Bus Schedule\n", " \n", " \n", "\n", @@ -655,18 +558,18 @@ "7674 3513 ROSCOE / TOPANGA CANYON 571.786302 238.244293 \n", "7706 3559 DO NOT ANNOUNCE THIS STOP! 47.648859 0.000000 \n", "\n", - " weekday_ons feed_key \n", - "6324 NaN 06d1f3ac2b0ae5e74424edbbfefa19ed \n", - "6819 47.648859 06d1f3ac2b0ae5e74424edbbfefa19ed \n", - "6820 11.912215 06d1f3ac2b0ae5e74424edbbfefa19ed \n", - "7022 238.244293 06d1f3ac2b0ae5e74424edbbfefa19ed \n", - "7275 23.824429 06d1f3ac2b0ae5e74424edbbfefa19ed \n", - "7486 262.068722 06d1f3ac2b0ae5e74424edbbfefa19ed \n", - "7674 NaN 06d1f3ac2b0ae5e74424edbbfefa19ed \n", - "7706 23.824429 06d1f3ac2b0ae5e74424edbbfefa19ed " + " weekday_ons feed_key name \n", + "6324 NaN 06d1f3ac2b0ae5e74424edbbfefa19ed LA Metro Bus Schedule \n", + "6819 47.648859 06d1f3ac2b0ae5e74424edbbfefa19ed LA Metro Bus Schedule \n", + "6820 11.912215 06d1f3ac2b0ae5e74424edbbfefa19ed LA Metro Bus Schedule \n", + "7022 238.244293 06d1f3ac2b0ae5e74424edbbfefa19ed LA Metro Bus Schedule \n", + "7275 23.824429 06d1f3ac2b0ae5e74424edbbfefa19ed LA Metro Bus Schedule \n", + "7486 262.068722 06d1f3ac2b0ae5e74424edbbfefa19ed LA Metro Bus Schedule \n", + "7674 NaN 06d1f3ac2b0ae5e74424edbbfefa19ed LA Metro Bus Schedule \n", + "7706 23.824429 06d1f3ac2b0ae5e74424edbbfefa19ed LA Metro Bus Schedule " ] }, - "execution_count": 19, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -677,10 +580,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "a579f297-7e13-43d3-b4e9-d6eabb76983b", "metadata": {}, "outputs": [], + "source": [ + "GCS_FILE_PATH = 'gs://calitp-analytics-data/data-analyses/ahsc_grant'\n", + "yr_metro_joined.to_parquet(f\"{GCS_FILE_PATH}/ridership_metro_08_26_2024.parquet\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38dc248b-fa90-445e-9abe-898bfbb1650c", + "metadata": {}, + "outputs": [], "source": [] } ], diff --git a/ahsc_grant/process_mst_refactor.ipynb b/ahsc_grant/process_mst_refactor.ipynb index 355151c06..02bf3d18c 100644 --- a/ahsc_grant/process_mst_refactor.ipynb +++ b/ahsc_grant/process_mst_refactor.ipynb @@ -454,6 +454,7 @@ " >> spread(\"DAY_TYPE\", \"stop_total_ons\")\n", " >> rename(stop_id = _.Stop_ID)\n", " >> mutate(feed_key = feed_key)\n", + " >> mutate(name = 'Monterey Salinas Schedule')\n", " )" ] }, @@ -489,6 +490,7 @@ " sun_ons\n", " weekday_ons\n", " feed_key\n", + " name\n", " \n", " \n", " \n", @@ -499,6 +501,7 @@ " 58.0\n", " 753.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " \n", " \n", " 1\n", @@ -507,6 +510,7 @@ " 754.0\n", " 8534.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " \n", " \n", " 2\n", @@ -515,16 +519,22 @@ " 58.0\n", " 753.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " \n", " \n", "\n", "" ], "text/plain": [ - " stop_id sat_ons sun_ons weekday_ons feed_key\n", - "0 1001 112.0 58.0 753.0 efb3d4ea58f58d541e2c452e253bec5d\n", - "1 1004 672.0 754.0 8534.0 efb3d4ea58f58d541e2c452e253bec5d\n", - "2 1007 112.0 58.0 753.0 efb3d4ea58f58d541e2c452e253bec5d" + " stop_id sat_ons sun_ons weekday_ons feed_key \\\n", + "0 1001 112.0 58.0 753.0 efb3d4ea58f58d541e2c452e253bec5d \n", + "1 1004 672.0 754.0 8534.0 efb3d4ea58f58d541e2c452e253bec5d \n", + "2 1007 112.0 58.0 753.0 efb3d4ea58f58d541e2c452e253bec5d \n", + "\n", + " name \n", + "0 Monterey Salinas Schedule \n", + "1 Monterey Salinas Schedule \n", + "2 Monterey Salinas Schedule " ] }, "execution_count": 16, @@ -581,7 +591,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "id": "52e017f4-afa8-47e4-b639-005ef6d41149", "metadata": {}, "outputs": [ @@ -613,6 +623,7 @@ " sat_ons\n", " sun_ons\n", " weekday_ons\n", + " name\n", " \n", " \n", " \n", @@ -625,6 +636,7 @@ " 112.0\n", " 58.0\n", " 753.0\n", + " Monterey Salinas Schedule\n", " \n", " \n", " 1\n", @@ -635,6 +647,7 @@ " 672.0\n", " 754.0\n", " 8534.0\n", + " Monterey Salinas Schedule\n", " \n", " \n", " 2\n", @@ -645,6 +658,7 @@ " 112.0\n", " 58.0\n", " 753.0\n", + " Monterey Salinas Schedule\n", " \n", " \n", "\n", @@ -656,13 +670,18 @@ "1 efb3d4ea58f58d541e2c452e253bec5d 1004 Fremont / Trinity Avenue \n", "2 efb3d4ea58f58d541e2c452e253bec5d 1007 Fremont / Elm Avenue \n", "\n", - " geometry sat_ons sun_ons weekday_ons \n", - "0 POINT (-165123.570 -155506.782) 112.0 58.0 753.0 \n", - "1 POINT (-165043.149 -155309.483) 672.0 754.0 8534.0 \n", - "2 POINT (-164902.027 -154954.295) 112.0 58.0 753.0 " + " geometry sat_ons sun_ons weekday_ons \\\n", + "0 POINT (-165123.570 -155506.782) 112.0 58.0 753.0 \n", + "1 POINT (-165043.149 -155309.483) 672.0 754.0 8534.0 \n", + "2 POINT (-164902.027 -154954.295) 112.0 58.0 753.0 \n", + "\n", + " name \n", + "0 Monterey Salinas Schedule \n", + "1 Monterey Salinas Schedule \n", + "2 Monterey Salinas Schedule " ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -673,7 +692,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "id": "94a73dc9-fac3-4704-8a22-277562d48148", "metadata": {}, "outputs": [ @@ -683,7 +702,7 @@ "185" ] }, - "execution_count": 22, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -698,7 +717,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 22, "id": "0de0f292-8097-4561-ade9-1f9d42c82f42", "metadata": {}, "outputs": [ @@ -708,7 +727,7 @@ "209" ] }, - "execution_count": 26, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -724,7 +743,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 23, "id": "744ddd4c-bb16-462f-b308-86afdc5bf79c", "metadata": {}, "outputs": [], @@ -736,7 +755,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 24, "id": "a5811d41-224c-4e74-9de1-46645118f44b", "metadata": {}, "outputs": [ @@ -746,7 +765,7 @@ "text": [ "\n", "Int64Index: 163 entries, 0 to 162\n", - "Data columns (total 7 columns):\n", + "Data columns (total 8 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 feed_key 163 non-null object \n", @@ -756,8 +775,9 @@ " 4 sat_ons 136 non-null float64 \n", " 5 sun_ons 136 non-null float64 \n", " 6 weekday_ons 163 non-null float64 \n", - "dtypes: float64(3), geometry(1), object(3)\n", - "memory usage: 10.2+ KB\n" + " 7 name 163 non-null object \n", + "dtypes: float64(3), geometry(1), object(4)\n", + "memory usage: 11.5+ KB\n" ] } ], @@ -769,7 +789,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 25, "id": "853abc77-626f-4628-830a-73ca40581b75", "metadata": {}, "outputs": [ @@ -1033,7 +1053,7 @@ "17446 POINT (-170610.985 -154956.800) " ] }, - "execution_count": 29, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1046,7 +1066,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 26, "id": "f397a471-dd71-4c8a-8c5d-3bbe1b0c78fe", "metadata": {}, "outputs": [], @@ -1056,7 +1076,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 27, "id": "ba34fe18-45c8-4a93-9be7-2bee8931ea7e", "metadata": {}, "outputs": [ @@ -1086,6 +1106,7 @@ " sun_ons\n", " weekday_ons\n", " feed_key\n", + " name\n", " Stop_Name\n", " \n", " \n", @@ -1097,6 +1118,7 @@ " 0.0\n", " 251.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " INTERGARRISON/OTTERSPORTSCT\n", " \n", " \n", @@ -1106,6 +1128,7 @@ " 0.0\n", " 251.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " INTERGARRISON/OTTERSPORTSCTR\n", " \n", " \n", @@ -1115,6 +1138,7 @@ " NaN\n", " 0.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " SCHILLING/COUNTYGOVERNMENTC\n", " \n", " \n", @@ -1124,6 +1148,7 @@ " 8062.0\n", " 23343.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " DELMONTE/CONFERENCECENTER\n", " \n", " \n", @@ -1133,6 +1158,7 @@ " 22562.0\n", " 74296.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " CANNERYROW/PRESCOTT\n", " \n", " \n", @@ -1142,6 +1168,7 @@ " 17922.0\n", " 33634.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " OLIVIER/FISHERMAN'SWHARF\n", " \n", " \n", @@ -1151,6 +1178,7 @@ " 1856.0\n", " 5020.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " FRANKLIN/ALVARADO\n", " \n", " \n", @@ -1160,6 +1188,7 @@ " 58.0\n", " 502.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " ALVARADO/FRANKLIN\n", " \n", " \n", @@ -1169,6 +1198,7 @@ " 4640.0\n", " 20582.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " CANNERYROW/DRAKE\n", " \n", " \n", @@ -1178,6 +1208,7 @@ " 31204.0\n", " 74045.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " CANNERYROW/AQUARIUM\n", " \n", " \n", @@ -1187,6 +1218,7 @@ " 2726.0\n", " 6526.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " CANNERYROW/DRAKE\n", " \n", " \n", @@ -1196,6 +1228,7 @@ " 754.0\n", " 2259.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " REESIDE/FOAM\n", " \n", " \n", @@ -1205,6 +1238,7 @@ " 4408.0\n", " 7028.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " WAVE/CR1PARKINGGARAGE\n", " \n", " \n", @@ -1214,6 +1248,7 @@ " 58.0\n", " 0.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " LAUREL/AMERICANLEGION\n", " \n", " \n", @@ -1223,6 +1258,7 @@ " 0.0\n", " NaN\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " LAUREL/PARKSIDE\n", " \n", " \n", @@ -1232,6 +1268,7 @@ " 0.0\n", " NaN\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " LAUREL/MARYAL\n", " \n", " \n", @@ -1241,6 +1278,7 @@ " 0.0\n", " 1004.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " LAUREL/MARYAL\n", " \n", " \n", @@ -1250,6 +1288,7 @@ " 0.0\n", " 0.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " LAUREL/BALDWIN\n", " \n", " \n", @@ -1259,6 +1298,7 @@ " 0.0\n", " NaN\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " LAUREL/TYLER\n", " \n", " \n", @@ -1268,6 +1308,7 @@ " 0.0\n", " 0.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " LAUREL/DAVIS\n", " \n", " \n", @@ -1277,6 +1318,7 @@ " 0.0\n", " NaN\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " LAUREL/TYLER\n", " \n", " \n", @@ -1286,6 +1328,7 @@ " 0.0\n", " 251.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " LAUREL/DAVIS\n", " \n", " \n", @@ -1295,6 +1338,7 @@ " 0.0\n", " NaN\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " LAUREL/PARKSIDE\n", " \n", " \n", @@ -1304,6 +1348,7 @@ " 232.0\n", " 251.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " LAUREL/MAIN\n", " \n", " \n", @@ -1313,6 +1358,7 @@ " 0.0\n", " 0.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " LAUREL/SANTATERESA\n", " \n", " \n", @@ -1322,6 +1368,7 @@ " 116.0\n", " 502.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " LAUREL/GRANADA\n", " \n", " \n", @@ -1331,6 +1378,7 @@ " 0.0\n", " NaN\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " LAUREL/LINWOOD\n", " \n", " \n", @@ -1340,6 +1388,7 @@ " 0.0\n", " 502.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " LAUREL/TAPADERO\n", " \n", " \n", @@ -1349,6 +1398,7 @@ " 0.0\n", " NaN\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " LAUREL/RAMONA\n", " \n", " \n", @@ -1358,6 +1408,7 @@ " 116.0\n", " NaN\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " LAUREL/ADAMS\n", " \n", " \n", @@ -1367,6 +1418,7 @@ " 0.0\n", " NaN\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " LAUREL/MONROE\n", " \n", " \n", @@ -1376,6 +1428,7 @@ " 58.0\n", " 502.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " ELAUREL/ST.EDWARDS\n", " \n", " \n", @@ -1385,6 +1438,7 @@ " NaN\n", " 0.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " NaN\n", " \n", " \n", @@ -1394,6 +1448,7 @@ " NaN\n", " 0.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " AIRPORTROAD/NAVYANNEX(PENDI\n", " \n", " \n", @@ -1403,6 +1458,7 @@ " 0.0\n", " NaN\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " 2NDAVE/8THSTREET\n", " \n", " \n", @@ -1412,6 +1468,7 @@ " 0.0\n", " NaN\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " 2NDAVE/6THST\n", " \n", " \n", @@ -1421,6 +1478,7 @@ " 0.0\n", " NaN\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " 2NDAVE/5THST\n", " \n", " \n", @@ -1430,6 +1488,7 @@ " 0.0\n", " 251.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " 2NDAVE/8THSTREET(NORTH)\n", " \n", " \n", @@ -1439,6 +1498,7 @@ " 0.0\n", " 251.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " 2NDAVE/8THSTREET\n", " \n", " \n", @@ -1448,6 +1508,7 @@ " 0.0\n", " NaN\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " 2NDAVE/6THST\n", " \n", " \n", @@ -1457,6 +1518,7 @@ " 0.0\n", " NaN\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " 2NDAVE/5THST\n", " \n", " \n", @@ -1466,6 +1528,7 @@ " 0.0\n", " 251.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " 2NDAVE/INTER-GARRISONRD(NOR\n", " \n", " \n", @@ -1475,6 +1538,7 @@ " 0.0\n", " 251.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " 2NDAVE/INTER-GARRISONRD\n", " \n", " \n", @@ -1484,6 +1548,7 @@ " 0.0\n", " 502.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " INTER-GARRISON/BUILDING90\n", " \n", " \n", @@ -1493,6 +1558,7 @@ " NaN\n", " 0.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " 2NDAVE/DIVARTYST(NORTH)\n", " \n", " \n", @@ -1502,6 +1568,7 @@ " 0.0\n", " 0.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " INTER-GARRISON/BUILDING82(O\n", " \n", " \n", @@ -1511,6 +1578,7 @@ " 0.0\n", " 0.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " INTER-GARRISON/BUILDING82\n", " \n", " \n", @@ -1520,6 +1588,7 @@ " 5510.0\n", " 13052.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " MONTEREYTRANSITPLAZA/GATE5(\n", " \n", " \n", @@ -1529,6 +1598,7 @@ " NaN\n", " 0.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " SALINASTRANSITCENTER/GATE6\n", " \n", " \n", @@ -1538,6 +1608,7 @@ " 0.0\n", " 251.0\n", " efb3d4ea58f58d541e2c452e253bec5d\n", + " Monterey Salinas Schedule\n", " MARINATRANSITEXCHANGEGATE5\n", " \n", " \n", @@ -1597,60 +1668,60 @@ "48 9206 NaN NaN 0.0 efb3d4ea58f58d541e2c452e253bec5d \n", "49 9305 616.0 0.0 251.0 efb3d4ea58f58d541e2c452e253bec5d \n", "\n", - " Stop_Name \n", - "0 INTERGARRISON/OTTERSPORTSCT \n", - "1 INTERGARRISON/OTTERSPORTSCTR \n", - "2 SCHILLING/COUNTYGOVERNMENTC \n", - "3 DELMONTE/CONFERENCECENTER \n", - "4 CANNERYROW/PRESCOTT \n", - "5 OLIVIER/FISHERMAN'SWHARF \n", - "6 FRANKLIN/ALVARADO \n", - "7 ALVARADO/FRANKLIN \n", - "8 CANNERYROW/DRAKE \n", - "9 CANNERYROW/AQUARIUM \n", - "10 CANNERYROW/DRAKE \n", - "11 REESIDE/FOAM \n", - "12 WAVE/CR1PARKINGGARAGE \n", - "13 LAUREL/AMERICANLEGION \n", - "14 LAUREL/PARKSIDE \n", - "15 LAUREL/MARYAL \n", - "16 LAUREL/MARYAL \n", - "17 LAUREL/BALDWIN \n", - "18 LAUREL/TYLER \n", - "19 LAUREL/DAVIS \n", - "20 LAUREL/TYLER \n", - "21 LAUREL/DAVIS \n", - "22 LAUREL/PARKSIDE \n", - "23 LAUREL/MAIN \n", - "24 LAUREL/SANTATERESA \n", - "25 LAUREL/GRANADA \n", - "26 LAUREL/LINWOOD \n", - "27 LAUREL/TAPADERO \n", - "28 LAUREL/RAMONA \n", - "29 LAUREL/ADAMS \n", - "30 LAUREL/MONROE \n", - "31 ELAUREL/ST.EDWARDS \n", - "32 NaN \n", - "33 AIRPORTROAD/NAVYANNEX(PENDI \n", - "34 2NDAVE/8THSTREET \n", - "35 2NDAVE/6THST \n", - "36 2NDAVE/5THST \n", - "37 2NDAVE/8THSTREET(NORTH) \n", - "38 2NDAVE/8THSTREET \n", - "39 2NDAVE/6THST \n", - "40 2NDAVE/5THST \n", - "41 2NDAVE/INTER-GARRISONRD(NOR \n", - "42 2NDAVE/INTER-GARRISONRD \n", - "43 INTER-GARRISON/BUILDING90 \n", - "44 2NDAVE/DIVARTYST(NORTH) \n", - "45 INTER-GARRISON/BUILDING82(O \n", - "46 INTER-GARRISON/BUILDING82 \n", - "47 MONTEREYTRANSITPLAZA/GATE5( \n", - "48 SALINASTRANSITCENTER/GATE6 \n", - "49 MARINATRANSITEXCHANGEGATE5 " + " name Stop_Name \n", + "0 Monterey Salinas Schedule INTERGARRISON/OTTERSPORTSCT \n", + "1 Monterey Salinas Schedule INTERGARRISON/OTTERSPORTSCTR \n", + "2 Monterey Salinas Schedule SCHILLING/COUNTYGOVERNMENTC \n", + "3 Monterey Salinas Schedule DELMONTE/CONFERENCECENTER \n", + "4 Monterey Salinas Schedule CANNERYROW/PRESCOTT \n", + "5 Monterey Salinas Schedule OLIVIER/FISHERMAN'SWHARF \n", + "6 Monterey Salinas Schedule FRANKLIN/ALVARADO \n", + "7 Monterey Salinas Schedule ALVARADO/FRANKLIN \n", + "8 Monterey Salinas Schedule CANNERYROW/DRAKE \n", + "9 Monterey Salinas Schedule CANNERYROW/AQUARIUM \n", + "10 Monterey Salinas Schedule CANNERYROW/DRAKE \n", + "11 Monterey Salinas Schedule REESIDE/FOAM \n", + "12 Monterey Salinas Schedule WAVE/CR1PARKINGGARAGE \n", + "13 Monterey Salinas Schedule LAUREL/AMERICANLEGION \n", + "14 Monterey Salinas Schedule LAUREL/PARKSIDE \n", + "15 Monterey Salinas Schedule LAUREL/MARYAL \n", + "16 Monterey Salinas Schedule LAUREL/MARYAL \n", + "17 Monterey Salinas Schedule LAUREL/BALDWIN \n", + "18 Monterey Salinas Schedule LAUREL/TYLER \n", + "19 Monterey Salinas Schedule LAUREL/DAVIS \n", + "20 Monterey Salinas Schedule LAUREL/TYLER \n", + "21 Monterey Salinas Schedule LAUREL/DAVIS \n", + "22 Monterey Salinas Schedule LAUREL/PARKSIDE \n", + "23 Monterey Salinas Schedule LAUREL/MAIN \n", + "24 Monterey Salinas Schedule LAUREL/SANTATERESA \n", + "25 Monterey Salinas Schedule LAUREL/GRANADA \n", + "26 Monterey Salinas Schedule LAUREL/LINWOOD \n", + "27 Monterey Salinas Schedule LAUREL/TAPADERO \n", + "28 Monterey Salinas Schedule LAUREL/RAMONA \n", + "29 Monterey Salinas Schedule LAUREL/ADAMS \n", + "30 Monterey Salinas Schedule LAUREL/MONROE \n", + "31 Monterey Salinas Schedule ELAUREL/ST.EDWARDS \n", + "32 Monterey Salinas Schedule NaN \n", + "33 Monterey Salinas Schedule AIRPORTROAD/NAVYANNEX(PENDI \n", + "34 Monterey Salinas Schedule 2NDAVE/8THSTREET \n", + "35 Monterey Salinas Schedule 2NDAVE/6THST \n", + "36 Monterey Salinas Schedule 2NDAVE/5THST \n", + "37 Monterey Salinas Schedule 2NDAVE/8THSTREET(NORTH) \n", + "38 Monterey Salinas Schedule 2NDAVE/8THSTREET \n", + "39 Monterey Salinas Schedule 2NDAVE/6THST \n", + "40 Monterey Salinas Schedule 2NDAVE/5THST \n", + "41 Monterey Salinas Schedule 2NDAVE/INTER-GARRISONRD(NOR \n", + "42 Monterey Salinas Schedule 2NDAVE/INTER-GARRISONRD \n", + "43 Monterey Salinas Schedule INTER-GARRISON/BUILDING90 \n", + "44 Monterey Salinas Schedule 2NDAVE/DIVARTYST(NORTH) \n", + "45 Monterey Salinas Schedule INTER-GARRISON/BUILDING82(O \n", + "46 Monterey Salinas Schedule INTER-GARRISON/BUILDING82 \n", + "47 Monterey Salinas Schedule MONTEREYTRANSITPLAZA/GATE5( \n", + "48 Monterey Salinas Schedule SALINASTRANSITCENTER/GATE6 \n", + "49 Monterey Salinas Schedule MARINATRANSITEXCHANGEGATE5 " ] }, - "execution_count": 31, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1661,7 +1732,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 28, "id": "6c396026-6a9e-4f0f-b05e-27c23bbf6e87", "metadata": {}, "outputs": [ @@ -1693,6 +1764,7 @@ " sat_ons\n", " sun_ons\n", " weekday_ons\n", + " name\n", " \n", " \n", " \n", @@ -1705,6 +1777,7 @@ " 2240.0\n", " 1740.0\n", " 9287.0\n", + " Monterey Salinas Schedule\n", " \n", " \n", " 1\n", @@ -1715,6 +1788,7 @@ " 616.0\n", " 348.0\n", " 1757.0\n", + " Monterey Salinas Schedule\n", " \n", " \n", " 2\n", @@ -1725,6 +1799,7 @@ " 56.0\n", " 58.0\n", " 502.0\n", + " Monterey Salinas Schedule\n", " \n", " \n", " 3\n", @@ -1735,6 +1810,7 @@ " 4312.0\n", " 4234.0\n", " 21837.0\n", + " Monterey Salinas Schedule\n", " \n", " \n", " 4\n", @@ -1745,6 +1821,7 @@ " 9128.0\n", " 13224.0\n", " 40662.0\n", + " Monterey Salinas Schedule\n", " \n", " \n", " ...\n", @@ -1755,6 +1832,7 @@ " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", " 923\n", @@ -1765,6 +1843,7 @@ " 1960.0\n", " 1914.0\n", " 13554.0\n", + " Monterey Salinas Schedule\n", " \n", " \n", " 924\n", @@ -1775,6 +1854,7 @@ " 952.0\n", " 754.0\n", " 7781.0\n", + " Monterey Salinas Schedule\n", " \n", " \n", " 925\n", @@ -1785,6 +1865,7 @@ " 896.0\n", " 812.0\n", " 7279.0\n", + " Monterey Salinas Schedule\n", " \n", " \n", " 926\n", @@ -1795,6 +1876,7 @@ " 2184.0\n", " 1798.0\n", " 7530.0\n", + " Monterey Salinas Schedule\n", " \n", " \n", " 927\n", @@ -1805,10 +1887,11 @@ " 1288.0\n", " 1044.0\n", " 6526.0\n", + " Monterey Salinas Schedule\n", " \n", " \n", "\n", - "

928 rows × 7 columns

\n", + "

928 rows × 8 columns

\n", "" ], "text/plain": [ @@ -1838,23 +1921,23 @@ "926 Marina Transit Exchange Gate 3 POINT (-160117.309 -146605.866) \n", "927 Marina Transit Exchange Gate 4 POINT (-160122.042 -146615.448) \n", "\n", - " sat_ons sun_ons weekday_ons \n", - "0 2240.0 1740.0 9287.0 \n", - "1 616.0 348.0 1757.0 \n", - "2 56.0 58.0 502.0 \n", - "3 4312.0 4234.0 21837.0 \n", - "4 9128.0 13224.0 40662.0 \n", - ".. ... ... ... \n", - "923 1960.0 1914.0 13554.0 \n", - "924 952.0 754.0 7781.0 \n", - "925 896.0 812.0 7279.0 \n", - "926 2184.0 1798.0 7530.0 \n", - "927 1288.0 1044.0 6526.0 \n", + " sat_ons sun_ons weekday_ons name \n", + "0 2240.0 1740.0 9287.0 Monterey Salinas Schedule \n", + "1 616.0 348.0 1757.0 Monterey Salinas Schedule \n", + "2 56.0 58.0 502.0 Monterey Salinas Schedule \n", + "3 4312.0 4234.0 21837.0 Monterey Salinas Schedule \n", + "4 9128.0 13224.0 40662.0 Monterey Salinas Schedule \n", + ".. ... ... ... ... \n", + "923 1960.0 1914.0 13554.0 Monterey Salinas Schedule \n", + "924 952.0 754.0 7781.0 Monterey Salinas Schedule \n", + "925 896.0 812.0 7279.0 Monterey Salinas Schedule \n", + "926 2184.0 1798.0 7530.0 Monterey Salinas Schedule \n", + "927 1288.0 1044.0 6526.0 Monterey Salinas Schedule \n", "\n", - "[928 rows x 7 columns]" + "[928 rows x 8 columns]" ] }, - "execution_count": 32, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1870,7 +1953,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 29, "id": "746c7e99-fceb-4c26-ac05-4fe7a7321805", "metadata": {}, "outputs": [], @@ -1882,20 +1965,20 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 30, "id": "030e959d-06c8-4b84-b3da-add76ed639dd", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ - "" + "" ] }, - "execution_count": 34, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1910,11 +1993,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "b378153b-16c4-43d9-b333-e2a9aed818a2", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "GCS_FILE_PATH = 'gs://calitp-analytics-data/data-analyses/ahsc_grant'\n", + "yr_mst_joined.to_parquet(f\"{GCS_FILE_PATH}/ridership_mst_08_26_2024.parquet\")" + ] } ], "metadata": { diff --git a/ahsc_grant/process_sbmtd_refactor.ipynb b/ahsc_grant/process_sbmtd_refactor.ipynb index 4ad7ebd62..92a4c446c 100644 --- a/ahsc_grant/process_sbmtd_refactor.ipynb +++ b/ahsc_grant/process_sbmtd_refactor.ipynb @@ -22,6 +22,44 @@ { "cell_type": "code", "execution_count": 2, + "id": "34c771c1-79be-4094-a428-e111cbe4bce4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: fuzzywuzzy in /opt/conda/lib/python3.9/site-packages (0.18.0)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install fuzzywuzzy" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c634a3a4-ef9f-4c63-ba50-8f51692e09b2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.9/site-packages/fuzzywuzzy/fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning\n", + " warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')\n" + ] + } + ], + "source": [ + "from fuzzywuzzy import process, fuzz" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "id": "b3a3667c-b23f-4136-bae9-a58286b8b16a", "metadata": {}, "outputs": [], @@ -46,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "id": "c187a09f-1914-435a-ba2b-54c836aa1d34", "metadata": {}, "outputs": [], @@ -59,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "id": "e585de4e-ed3b-46a2-9298-b6c82374d444", "metadata": {}, "outputs": [], @@ -70,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "18eeb689-320a-49dd-930d-6ece7ed108a5", "metadata": {}, "outputs": [], @@ -84,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "693e9a3e-d48e-433d-a064-e8390ea00035", "metadata": {}, "outputs": [], @@ -99,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "e2a2f873-5f6b-47ae-9497-399b11688052", "metadata": {}, "outputs": [], @@ -109,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "id": "96660be6-8b4b-4c34-b9a4-f08dc4e6724f", "metadata": {}, "outputs": [], @@ -121,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "id": "922d765d-cdfc-42d1-8d49-048f13b54498", "metadata": {}, "outputs": [], @@ -132,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "id": "22e787d0-9b33-4eab-9e32-530e22d690ef", "metadata": { "tags": [] @@ -155,7 +193,7 @@ "Name: daytype, Length: 2215, dtype: object" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -169,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "id": "c1ba2a9f-cbb4-4874-a7f2-572c468489d5", "metadata": {}, "outputs": [], @@ -179,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "id": "91a83f4c-86f5-4daf-9c9a-8f4e44b06432", "metadata": {}, "outputs": [], @@ -195,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "id": "1cb016a0-1b98-4925-a411-9438bdf1de0c", "metadata": {}, "outputs": [], @@ -210,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "id": "be29dc6f-1b93-4856-85e3-104bed466cbd", "metadata": {}, "outputs": [ @@ -290,7 +328,7 @@ "4 -49 Pueblo/Castillo Out sun_ons 109" ] }, - "execution_count": 14, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -307,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "id": "e7d5f69d-f20d-461f-bd1d-fef4b9211f64", "metadata": {}, "outputs": [ @@ -333,11 +371,12 @@ " \n", " \n", " stop_id\n", - " STOP_NAME_clean\n", + " stop_name\n", " sat_ons\n", " sun_ons\n", " weekday_ons\n", " feed_key\n", + " name\n", " \n", " \n", " \n", @@ -349,6 +388,7 @@ " 381.0\n", " 3926.0\n", " 52201caab047b98ae19b7547c0d7c2ad\n", + " SBMTD Schedule\n", " \n", " \n", " 1\n", @@ -358,6 +398,7 @@ " 13.0\n", " 173.0\n", " 52201caab047b98ae19b7547c0d7c2ad\n", + " SBMTD Schedule\n", " \n", " \n", " 2\n", @@ -367,6 +408,7 @@ " 80.0\n", " 658.0\n", " 52201caab047b98ae19b7547c0d7c2ad\n", + " SBMTD Schedule\n", " \n", " \n", " 3\n", @@ -376,6 +418,7 @@ " 22.0\n", " 396.0\n", " 52201caab047b98ae19b7547c0d7c2ad\n", + " SBMTD Schedule\n", " \n", " \n", " 4\n", @@ -385,28 +428,29 @@ " 43.0\n", " 659.0\n", " 52201caab047b98ae19b7547c0d7c2ad\n", + " SBMTD Schedule\n", " \n", " \n", "\n", "" ], "text/plain": [ - " stop_id STOP_NAME_clean sat_ons sun_ons weekday_ons \\\n", + " stop_id stop_name sat_ons sun_ons weekday_ons \\\n", "0 -1 Hollister/Sumida 319.0 381.0 3926.0 \n", "1 -10 Hollister/Robin Hill 29.0 13.0 173.0 \n", "2 -11 Hollister/Willow Springs 78.0 80.0 658.0 \n", "3 -12 Hollister/Los Carneros Way 40.0 22.0 396.0 \n", "4 -13 Hollister/Cremona 49.0 43.0 659.0 \n", "\n", - " feed_key \n", - "0 52201caab047b98ae19b7547c0d7c2ad \n", - "1 52201caab047b98ae19b7547c0d7c2ad \n", - "2 52201caab047b98ae19b7547c0d7c2ad \n", - "3 52201caab047b98ae19b7547c0d7c2ad \n", - "4 52201caab047b98ae19b7547c0d7c2ad " + " feed_key name \n", + "0 52201caab047b98ae19b7547c0d7c2ad SBMTD Schedule \n", + "1 52201caab047b98ae19b7547c0d7c2ad SBMTD Schedule \n", + "2 52201caab047b98ae19b7547c0d7c2ad SBMTD Schedule \n", + "3 52201caab047b98ae19b7547c0d7c2ad SBMTD Schedule \n", + "4 52201caab047b98ae19b7547c0d7c2ad SBMTD Schedule " ] }, - "execution_count": 15, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -415,8 +459,10 @@ "yr_sbmtd_grouped = (yr_sbmtd_grouped\n", " >> mutate(STOP_ID_clean = _.STOP_ID_clean.astype(str))\n", " >> rename(stop_id = _.STOP_ID_clean)\n", + " >> rename(stop_name = _.STOP_NAME_clean)\n", " >> spread(\"DAY_TYPE\", \"stop_total_ons\")\n", " >> mutate(feed_key = feed_key)\n", + " >> mutate(name = 'SBMTD Schedule')\n", " )\n", "\n", "yr_sbmtd_grouped >> head (5)" @@ -424,30 +470,115 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, + "id": "99f062ea-35cd-437c-b32d-9a09553b05bc", + "metadata": {}, + "outputs": [], + "source": [ + "stops_data = (\n", + " stops_data\n", + " >> rename(STOP_NAME=_.stop_name) \n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "id": "de9e653d-8873-4c18-b113-8ab316041780", "metadata": {}, "outputs": [], "source": [ "stops_to_join = (stops_data\n", - " >> select(_.feed_key, _.stop_id, _.stop_name, _.geometry)\n", + " >> select(_.feed_key, _.stop_id, _.STOP_NAME, _.geometry)\n", " )" ] }, { "cell_type": "code", - "execution_count": 17, - "id": "4c30d984-c5ec-49e8-b498-f41f4ee1770f", + "execution_count": 20, + "id": "d2724f88-a1a0-4ddd-b504-b34b1c5f8eb6", "metadata": {}, "outputs": [], "source": [ - "yr_sbmtd_geo_code = stops_to_join >> inner_join(_, yr_sbmtd_grouped, on = ['feed_key', 'stop_id'])" + "stop_name_to_id = stops_to_join.set_index('STOP_NAME')['stop_id'].to_dict()" ] }, { "cell_type": "code", - "execution_count": 18, - "id": "35d862cf-560f-43a9-826a-d5cfd2b54944", + "execution_count": 21, + "id": "8e90085f-5e68-496a-a912-8923987d50a8", + "metadata": {}, + "outputs": [], + "source": [ + "def get_best_match(name, choices, scorer=fuzz.ratio, threshold=90):\n", + " best_match, score = process.extractOne(name, choices, scorer=scorer)\n", + " if score >= threshold:\n", + " return best_match\n", + " else:\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "db69fcb2-6a5b-4211-a69c-fee3d44e2345", + "metadata": {}, + "outputs": [], + "source": [ + "stops_to_join['matched_stop_name'] = stops_to_join['STOP_NAME'].apply(lambda x: get_best_match(x, yr_sbmtd_grouped['stop_name'].unique()))\n", + "stops_to_join['matched_stop_id'] = stops_to_join['matched_stop_name'].map(stop_name_to_id)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "6624a034-9a44-44f6-ab8e-3e745486e923", + "metadata": {}, + "outputs": [], + "source": [ + "fuzzy_matches = stops_to_join.dropna(subset=['matched_stop_name'])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "331aeb5b-dba3-4b98-9aa4-1ab0c22721ea", + "metadata": {}, + "outputs": [], + "source": [ + "name_to_stop_id_mapping = fuzzy_matches.set_index('matched_stop_name')['stop_id'].to_dict()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "c8c6152a-56d2-4f20-b4dc-3cd050dca883", + "metadata": {}, + "outputs": [], + "source": [ + "yr_sbmtd_grouped_updated = yr_sbmtd_grouped.copy()\n", + "yr_sbmtd_grouped_updated['stop_id'] = yr_sbmtd_grouped_updated['stop_name'].map(name_to_stop_id_mapping).fillna(yr_sbmtd_grouped_updated['stop_id'])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "7d88e45e-bbea-4518-a7c0-1697e48322f9", + "metadata": {}, + "outputs": [], + "source": [ + "final_join = pd.merge(\n", + " stops_to_join,\n", + " yr_sbmtd_grouped_updated,\n", + " on=['feed_key', 'stop_id'],\n", + " how='inner'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "337ef7a7-dbee-4322-8507-81908a21c9b2", "metadata": {}, "outputs": [ { @@ -455,71 +586,374 @@ "output_type": "stream", "text": [ "\n", - "Int64Index: 336 entries, 0 to 335\n", - "Data columns (total 8 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 feed_key 336 non-null object \n", - " 1 stop_id 336 non-null object \n", - " 2 stop_name 336 non-null object \n", - " 3 geometry 336 non-null geometry\n", - " 4 STOP_NAME_clean 336 non-null object \n", - " 5 sat_ons 282 non-null float64 \n", - " 6 sun_ons 275 non-null float64 \n", - " 7 weekday_ons 336 non-null float64 \n", - "dtypes: float64(3), geometry(1), object(4)\n", - "memory usage: 23.6+ KB\n" + "Int64Index: 602 entries, 0 to 601\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 feed_key 602 non-null object \n", + " 1 stop_id 602 non-null object \n", + " 2 STOP_NAME 602 non-null object \n", + " 3 geometry 602 non-null geometry\n", + " 4 matched_stop_name 602 non-null object \n", + " 5 matched_stop_id 26 non-null object \n", + " 6 stop_name 602 non-null object \n", + " 7 sat_ons 500 non-null float64 \n", + " 8 sun_ons 466 non-null float64 \n", + " 9 weekday_ons 602 non-null float64 \n", + " 10 name 602 non-null object \n", + "dtypes: float64(3), geometry(1), object(7)\n", + "memory usage: 56.4+ KB\n" ] } ], "source": [ - "yr_sbmtd_geo_code.info() " + "final_join.info()" ] }, { "cell_type": "code", - "execution_count": 19, - "id": "328d98c1-d89a-4780-96cd-60e99948a346", + "execution_count": 28, + "id": "c092093a-0247-4069-af51-4df9c6b32bd0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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feed_keystop_idSTOP_NAMEgeometrymatched_stop_namematched_stop_idstop_namesat_onssun_onsweekday_onsname
052201caab047b98ae19b7547c0d7c2ad1Modoc & PortesuelloPOINT (25170.737 -398993.625)Modoc/PortesuelloNaNModoc/Portesuello1573.01287.013964.0SBMTD Schedule
152201caab047b98ae19b7547c0d7c2ad2Milpas & MontecitoPOINT (29196.552 -399052.308)Milpas/MontecitoNaNMilpas/Montecito3901.03139.030225.0SBMTD Schedule
252201caab047b98ae19b7547c0d7c2ad4Cathedral Oaks & Camino Del RioPOINT (20247.563 -395908.292)Cathedral Oaks/Camino Del RioNaNCathedral Oaks/Camino Del RioNaNNaN1.0SBMTD Schedule
352201caab047b98ae19b7547c0d7c2ad5Via Real & Sandpiper MHPPOINT (42143.060 -400995.911)Via Real/Sandpiper MHPNaNVia Real/Sandpiper MHP217.0109.01485.0SBMTD Schedule
452201caab047b98ae19b7547c0d7c2ad6UCSB Elings Hall OutboundPOINT (14738.802 -400125.683)UCSB Elings Hall Outbound6UCSB Elings Hall Outbound1374.01199.09777.0SBMTD Schedule
552201caab047b98ae19b7547c0d7c2ad10Anapamu & Santa BarbaraPOINT (27354.741 -398937.880)Anapamu/Santa BarbaraNaNAnapamu/Santa Barbara238.0164.01705.0SBMTD Schedule
652201caab047b98ae19b7547c0d7c2ad16Seville & Embarcadero Del MarPOINT (13110.869 -400474.402)Seville/Embarcadero Del MarNaNSeville/Embarcadero Del Mar197.0242.01706.0SBMTD Schedule
752201caab047b98ae19b7547c0d7c2ad16Seville & Embarcadero Del MarPOINT (13110.869 -400474.402)Seville/Embarcadero Del MarNaNSeville/Embarcadero Del Mar53.073.01191.0SBMTD Schedule
852201caab047b98ae19b7547c0d7c2ad18Embarcadero & Sabado TardePOINT (13206.817 -400596.547)Embarcadero/Sabado TardeNaNEmbarcadero/Sabado Tarde254.0326.02763.0SBMTD Schedule
952201caab047b98ae19b7547c0d7c2ad18Embarcadero & Sabado TardePOINT (13206.817 -400596.547)Embarcadero/Sabado TardeNaNEmbarcadero/Sabado Tarde99.099.01279.0SBMTD Schedule
\n", + "
" + ], + "text/plain": [ + " feed_key stop_id STOP_NAME \\\n", + "0 52201caab047b98ae19b7547c0d7c2ad 1 Modoc & Portesuello \n", + "1 52201caab047b98ae19b7547c0d7c2ad 2 Milpas & Montecito \n", + "2 52201caab047b98ae19b7547c0d7c2ad 4 Cathedral Oaks & Camino Del Rio \n", + "3 52201caab047b98ae19b7547c0d7c2ad 5 Via Real & Sandpiper MHP \n", + "4 52201caab047b98ae19b7547c0d7c2ad 6 UCSB Elings Hall Outbound \n", + "5 52201caab047b98ae19b7547c0d7c2ad 10 Anapamu & Santa Barbara \n", + "6 52201caab047b98ae19b7547c0d7c2ad 16 Seville & Embarcadero Del Mar \n", + "7 52201caab047b98ae19b7547c0d7c2ad 16 Seville & Embarcadero Del Mar \n", + "8 52201caab047b98ae19b7547c0d7c2ad 18 Embarcadero & Sabado Tarde \n", + "9 52201caab047b98ae19b7547c0d7c2ad 18 Embarcadero & Sabado Tarde \n", + "\n", + " geometry matched_stop_name \\\n", + "0 POINT (25170.737 -398993.625) Modoc/Portesuello \n", + "1 POINT (29196.552 -399052.308) Milpas/Montecito \n", + "2 POINT (20247.563 -395908.292) Cathedral Oaks/Camino Del Rio \n", + "3 POINT (42143.060 -400995.911) Via Real/Sandpiper MHP \n", + "4 POINT (14738.802 -400125.683) UCSB Elings Hall Outbound \n", + "5 POINT (27354.741 -398937.880) Anapamu/Santa Barbara \n", + "6 POINT (13110.869 -400474.402) Seville/Embarcadero Del Mar \n", + "7 POINT (13110.869 -400474.402) Seville/Embarcadero Del Mar \n", + "8 POINT (13206.817 -400596.547) Embarcadero/Sabado Tarde \n", + "9 POINT (13206.817 -400596.547) Embarcadero/Sabado Tarde \n", + "\n", + " matched_stop_id stop_name sat_ons sun_ons \\\n", + "0 NaN Modoc/Portesuello 1573.0 1287.0 \n", + "1 NaN Milpas/Montecito 3901.0 3139.0 \n", + "2 NaN Cathedral Oaks/Camino Del Rio NaN NaN \n", + "3 NaN Via Real/Sandpiper MHP 217.0 109.0 \n", + "4 6 UCSB Elings Hall Outbound 1374.0 1199.0 \n", + "5 NaN Anapamu/Santa Barbara 238.0 164.0 \n", + "6 NaN Seville/Embarcadero Del Mar 197.0 242.0 \n", + "7 NaN Seville/Embarcadero Del Mar 53.0 73.0 \n", + "8 NaN Embarcadero/Sabado Tarde 254.0 326.0 \n", + "9 NaN Embarcadero/Sabado Tarde 99.0 99.0 \n", + "\n", + " weekday_ons name \n", + "0 13964.0 SBMTD Schedule \n", + "1 30225.0 SBMTD Schedule \n", + "2 1.0 SBMTD Schedule \n", + "3 1485.0 SBMTD Schedule \n", + "4 9777.0 SBMTD Schedule \n", + "5 1705.0 SBMTD Schedule \n", + "6 1706.0 SBMTD Schedule \n", + "7 1191.0 SBMTD Schedule \n", + "8 2763.0 SBMTD Schedule \n", + "9 1279.0 SBMTD Schedule " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_join.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "d7e5964f-79bf-42de-a1cb-942f2cda8f67", + "metadata": {}, + "outputs": [], + "source": [ + "strings_to_drop = ['Fairview Ave/Encina Road', 'Encina/Fairview 164']" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "4c26c17f-5c37-4ab3-91e9-f31cf2389000", + "metadata": {}, + "outputs": [], + "source": [ + "final_join = final_join[~final_join['stop_name'].str.contains('|'.join(strings_to_drop))]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "f88219b8-d47e-43e7-97b8-5d7227e904b1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 600 entries, 0 to 601\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 feed_key 600 non-null object \n", + " 1 stop_id 600 non-null object \n", + " 2 STOP_NAME 600 non-null object \n", + " 3 geometry 600 non-null geometry\n", + " 4 matched_stop_name 600 non-null object \n", + " 5 matched_stop_id 26 non-null object \n", + " 6 stop_name 600 non-null object \n", + " 7 sat_ons 498 non-null float64 \n", + " 8 sun_ons 464 non-null float64 \n", + " 9 weekday_ons 600 non-null float64 \n", + " 10 name 600 non-null object \n", + "dtypes: float64(3), geometry(1), object(7)\n", + "memory usage: 56.2+ KB\n" + ] + } + ], + "source": [ + "final_join.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "67ec195f-84a8-4c65-83c2-7a85bbda22d4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "271" + "149" ] }, - "execution_count": 19, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "stops_remainder = (stops_to_join \n", - " >> anti_join(_, yr_sbmtd_grouped, on = ['feed_key', 'stop_id'])\n", - " )\n", + "stops_remainder = (\n", + " stops_to_join\n", + " >> anti_join(_, yr_sbmtd_grouped_updated, on=['feed_key', 'stop_id'])\n", + ")\n", "\n", "len(stops_remainder)" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 33, "id": "1225423d-148b-496e-88d8-6fdf842ff037", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "419" + "153" ] }, - "execution_count": 21, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "yr_sbmtd_remainders = (yr_sbmtd_grouped \n", + "yr_sbmtd_remainders = (yr_sbmtd_grouped_updated \n", " >> anti_join(_, stops_to_join, on = ['feed_key', 'stop_id'])\n", " )\n", "\n", @@ -528,11 +962,150 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, + "id": "6f61e55f-8e69-4e5f-ba51-a967e56c68cd", + "metadata": {}, + "outputs": [], + "source": [ + "columns_to_keep = ['feed_key', 'stop_id', 'STOP_NAME', 'geometry', 'sat_ons', 'sun_ons', 'weekday_ons', 'name']\n", + "final_join = final_join[columns_to_keep]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "a679b398-b994-4a41-a5bd-f60f1f32d1ce", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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feed_keystop_idSTOP_NAMEgeometrysat_onssun_onsweekday_onsname
052201caab047b98ae19b7547c0d7c2ad1Modoc & PortesuelloPOINT (25170.737 -398993.625)1573.01287.013964.0SBMTD Schedule
152201caab047b98ae19b7547c0d7c2ad2Milpas & MontecitoPOINT (29196.552 -399052.308)3901.03139.030225.0SBMTD Schedule
252201caab047b98ae19b7547c0d7c2ad4Cathedral Oaks & Camino Del RioPOINT (20247.563 -395908.292)NaNNaN1.0SBMTD Schedule
352201caab047b98ae19b7547c0d7c2ad5Via Real & Sandpiper MHPPOINT (42143.060 -400995.911)217.0109.01485.0SBMTD Schedule
\n", + "
" + ], + "text/plain": [ + " feed_key stop_id STOP_NAME \\\n", + "0 52201caab047b98ae19b7547c0d7c2ad 1 Modoc & Portesuello \n", + "1 52201caab047b98ae19b7547c0d7c2ad 2 Milpas & Montecito \n", + "2 52201caab047b98ae19b7547c0d7c2ad 4 Cathedral Oaks & Camino Del Rio \n", + "3 52201caab047b98ae19b7547c0d7c2ad 5 Via Real & Sandpiper MHP \n", + "\n", + " geometry sat_ons sun_ons weekday_ons \\\n", + "0 POINT (25170.737 -398993.625) 1573.0 1287.0 13964.0 \n", + "1 POINT (29196.552 -399052.308) 3901.0 3139.0 30225.0 \n", + "2 POINT (20247.563 -395908.292) NaN NaN 1.0 \n", + "3 POINT (42143.060 -400995.911) 217.0 109.0 1485.0 \n", + "\n", + " name \n", + "0 SBMTD Schedule \n", + "1 SBMTD Schedule \n", + "2 SBMTD Schedule \n", + "3 SBMTD Schedule " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_join.head(4)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "9e8aba70-c79a-4c75-bdca-1ea7cc6c6427", + "metadata": {}, + "outputs": [], + "source": [ + "final_join = final_join.rename(columns={'STOP_NAME': 'stop_name'})" + ] + }, + { + "cell_type": "code", + "execution_count": 37, "id": "60b1712e-bc8f-40ca-ba50-ff0fb740af1a", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "GCS_FILE_PATH = 'gs://calitp-analytics-data/data-analyses/ahsc_grant'\n", + "final_join.to_parquet(f\"{GCS_FILE_PATH}/ridership_sbmtd_08_26_2024.parquet\")" + ] } ], "metadata": {