def sorted_spot_based_on_distance(self):
"""
Call Google API service first to obtain lat and lng data
Then call spitcast with lat and lng data from google api service
return a dict with the following formate
{
status: <either "thumbsup" or "thumbsdown">
error: [list of errors]
data: [spit cast response data]
}
"""
self.__google_location_api_call()
self.__nearby_surf_spots()
resulting_response = {"status": "thumbsup", "error": self.error, "data": self.response_data}
if len(self.error) > 0:
resulting_response["status"] = "thumbsdown"
else:
# Loop through the response data and estimating the distance between surf spot and current location
# Add distance data to response_data object
for item in self.response_data:
lng2, lat2 = item["coordinates"]
distance_from_location = haversine(float(self.lng), float(self.lat), float(lng2), float(lat2))
item["distance_from_location"] = distance_from_location
# sort the distance data in response_data object from closest to the farest
resulting_response["data"] = sort_list_with_dictionary_data(
self.response_data, "distance_from_location", False
)
return resulting_response
def sorted_top_spot_swell_data(self):
'''
Call spit cast api to get a list of top swell spots.
sort the response data then return a dict with the following formate
{
status: <either "thumbsup" or "thumbsdown">
error: [list of errors]
data: [spit cast response data]
}
'''
# TODO: Query the data first before making an API Call. Need to add db utility methods
self.__top_spot_swell_spots()
self.response_data = sort_list_with_dictionary_data(self.response_data, "avg_max_size")
resulting_response = {"status":"thumbsup", "error":self.error, "data":self.response_data}
if len(self.error) > 0:
resulting_response['status'] = "thumbsdown"
return resulting_response
