# CrossCompute
my_address = '687 Central Ave N, St. Petersburg, FL 33701'
resources_select_path = 'resources.txt'
target_folder = '/tmp'

resources_text = open(resources_select_path, 'rt').read()
try:
    resources = resources_text.splitlines()[0].split()
except IndexError:
    resources = []
resources

GOOGLE_KEY = 'AIzaSyDqR8ePmVqzh-wF3R89tsZyhCZjaTI_mFI'

from geopy.geocoders import GoogleV3
geocode = GoogleV3(api_key=GOOGLE_KEY).geocode

my_location = geocode(my_address)
my_longitude = my_location.longitude
my_latitude = my_location.latitude

import pandas as pd
normalized_table_path = 'normalized-locations-20210706-1630.csv'
normalized_table = pd.read_csv(normalized_table_path)

from geopy.distance import distance

def get_distance(row):
    my_distance = distance((my_latitude, my_longitude), (row['Latitude'], row['Longitude']))
    row['Flying Distance in Miles'] = my_distance.miles
    row['Flying Distance in Meters'] = my_distance.m
    return row

distance_table = normalized_table.apply(get_distance, axis=1)

t = distance_table.sort_values('Flying Distance in Meters')

# TODO: Try reducing multiple conditions instead of hardcoding

if 'Sandbags' in resources:
    t = t.dropna(subset=['Resources'])
    t = t[t['Resources'].str.contains('Sandbags')]
    
t[:3]

destination_addresses = list(t[:3]['Normalized Address'])

base_url = 'https://maps.googleapis.com/maps/api/distancematrix/json'
d = {
    'origins': '|'.join([my_address]),
    'destinations': '|'.join(destination_addresses),
    'units': 'imperial',
    'key': GOOGLE_KEY,
}
url = base_url + '?' + '&'.join(f'{k}={v}' for k, v in d.items())
url

import requests
response = requests.get(url)
response_json = response.json()

distance_texts = []
duration_texts = []
for response_index, response_element in enumerate(response_json['rows'][0]['elements']):
    distance_text = response_element['distance']['text']
    duration_text = response_element['duration']['text']
    distance_texts.append(distance_text)
    duration_texts.append(duration_text)    

selected_t = t[:3].copy()

selected_t['Driving Distance'] = distance_texts
selected_t['Driving Time'] = duration_texts

from os.path import join

selected_locations_table_path = join(target_folder, 'selected-locations.csv')
selected_t[[
    'Driving Time',
    'Driving Distance',
    'Location Name',
    'Normalized Address',
    'Open',
    'Close',
    'Resources',
    'Type',
    'Location County',
    'Organization Name',
    'Source',
]].to_csv(selected_locations_table_path, index=False)
print('selected_locations_table_path = ' + selected_locations_table_path)

map_geotable_path = join(target_folder, 'map.csv')
map_table = t[[
    'Location Name',
    'Normalized Address',
    'Open',
    'Close',
    'Latitude',
    'Longitude',
]][:3].copy()
map_table['FillColor'] = 'blue'
map_table = map_table.append({
    'Location Name': 'Your Address',
    'Latitude': my_latitude,
    'Longitude': my_longitude,
    'FillColor': 'red',
}, ignore_index=True)
map_table;

map_table.to_csv(map_geotable_path, index=False)
print('map_geotable_path = ' + map_geotable_path)

destination_addresses

url_template = 'https://www.google.com/maps/dir/?api=1&destination=AAA&travelmode=driving'
directions_url = url_template.replace('AAA', destination_addresses[0].replace(' ', '%20'))

print('location_count = ' + str(len(t)))
print('directions_url = ' + directions_url)