def post(self):
data = self.get_body()
geo = geocode(data['address'])
pos1 = np.array([geo.lat, geo.lon])
pos2Dictionary = collections.OrderedDict()
for user in User.query().fetch():
user_geo = geocode(user.full_address())
if (user_geo != None):
pos2Dictionary[user.key.id()] = [user_geo.lat, user_geo.lon]
dists = distance.get_distance_sorted_responders(pos1, pos2Dictionary)
user = User.get_by_id(dists.keys()[0])
return JsonResponse(user)
def findLat(addr):
# Function to find latitude and longitude using google maps API
gmaps = Client(key='AIzaSyAr6MafwMH3Anrx_aJNb3_y7KrWKlCcNOA')
geocode_result = gmaps.geocode(addr)
lat = geocode_result[0]["geometry"]["location"]["lat"]
lon = geocode_result[0]["geometry"]["location"]["lng"]
return [lat, lon]
def Geocode(query):
# do geocoding
try:
geocode_result = gm.geocode(query)[0]
latitude = geocode_result['geometry']['location']['lat']
longitude = geocode_result['geometry']['location']['lng']
return latitude, longitude
except IndexError:
return 0
def geo(address):
gmaps = googlemaps.Client(key="AIzaSyB1Udy3X-6-BGZaJt-SIT0OrvUWo_i4uWs")
results = gmaps.geocode(address)
for result in results:
geo = result['geometry']
lat, lng = geo['location']['lat'], geo['location']['lng']
metrics = ("{lat: %s, lng: %s}" % (lat, lng))
return (metrics)
def save(self, force_insert=False, force_update=False):
self.city = self.city.capitalize()
# update geocoding infos
try:
loc = gmaps.geocode(self.__unicode__())
except Exception:
return False
self.lat = loc['lat']
self.long = loc['lng']
super(Address, self).save(force_insert, force_update)
def findPos(pollStations, data):
gmaps = googlemaps.Client(key='AIzaSyA-Mb5xxYe0XkHkkoEMl_YqbRjCcnwQjRY')
positions = pd.DataFrame(columns=['lat', 'lon'], index=pollStations)
for i in pollStations:
geocode_result = gmaps.geocode(i)
positions.loc[i]['lat'] = geocode_result[0]["geometry"]["location"][
"lat"]
positions.loc[i]['lon'] = geocode_result[0]["geometry"]["location"][
"lng"]
return positions
print(i, result)
if isSuccess == True: # if geocoding is successful,
# store the results
lat.append(result[0]) # latitude
lng.append(result[1]) # longitude
return lat, lng
# call the geocoding function
[lat, lng] = GeocodeStreetLocationCity(data)
# we put the list of latitude,longitude into pandas data frame
df = pd.DataFrame({'latitude': lat, 'longitude': lng})
# do geocode for the whole mega city
geocode_result = gm.geocode('Metro Manila')[
0] # change the name into your city of interest
# get the center of the city
center_lat = geocode_result['geometry']['location']['lat']
center_lng = geocode_result['geometry']['location']['lng']
print('center=', center_lat, center_lng)
# get the bounding box of the city i.e. Metro Manila
bounding_box = geocode_result['geometry']['bounds']
lat_boundary = [
bounding_box['southwest']['lat'], bounding_box['northeast']['lat']
]
lng_boundary = [
bounding_box['southwest']['lng'], bounding_box['northeast']['lng']
]
print('boundary:', lat_boundary, lng_boundary)
plt.show()
# Maps integration
api_key = payload['api_key']
gmaps = googlemaps.Client(key=api_key)
# Geocoding a city
# geocode_result = gmaps.geocode(all_cities[0])
latitudes = list()
longitudes = list()
# loop over all cities and get the geocoding --> also for duplicates for density on heatmap
for city in all_cities:
try:
geocode_result = gmaps.geocode(city)
lngDif = (
geocode_result[0]['geometry']['bounds']['southwest']['lng'] -
geocode_result[0]['geometry']['bounds']['northeast']['lng']) / 2
latDif = (
geocode_result[0]['geometry']['bounds']['southwest']['lat'] -
geocode_result[0]['geometry']['bounds']['northeast']['lat']) / 2
lat = geocode_result[0]['geometry']['bounds']['northeast'][
'lat'] + latDif
lng = geocode_result[0]['geometry']['bounds']['northeast'][
'lng'] + lngDif
latitudes.append(lat)
longitudes.append(lng)