def __init__(self, recreate=False):
datamodel = Datamodel()
self.table, self.table_config = datamodel.population()
self.postgres = Postgresql(user_name='postgres',
password='******',
host='localhost',
port='5432',
db='TestProject')
self.postgres.initialize_table(self.table,
recreate=False,
**self.table_config)
self.googlegeo = GoogleGeo()
# myan: only get major cities data once per request
self.major_cities_postgres = Postgresql(user_name='postgres',
password='******',
host='localhost',
port='5432',
db='TestProject')
self.major_cities = self.major_cities_postgres.get(
"select * from TestMajorCities")
self.all_states = self.major_cities['state'].values
self.all_cities = self.major_cities['city'].values
self.all_lats = self.major_cities['lat'].values
self.all_lngs = self.major_cities['lng'].values
self.all_population = self.major_cities['population'].values
def _get_city_info(major_cities):
place_ids = []
lats = []
lngs = []
states = []
# base_url = 'http://maps.googleapis.com/maps/api/geocode/json?address='
progress = 0.0
google_geo = GoogleGeo()
for i, city in enumerate(major_cities.city.values):
sleep(0.5)
output = dict(place_id=None, state=None, lat=None, lng=None)
try:
# response = requests.get(base_url + '{string}'.format(string=city))
# output = _parse_google_geo_response(response.json())
output = google_geo.get(city, fields_to_get=('place_id', 'lat', 'lng', 'state'))
if float(i) / len(major_cities) - progress >= 0.1:
print "Processed {pct}%".format(pct=str(float(i) / len(major_cities) * 100))
progress = float(i) / len(major_cities)
except:
pass
place_ids.append(output['place_id'])
states.append(output['state'])
lats.append(output['lat'])
lngs.append(output['lng'])
major_cities['place_id'] = place_ids
major_cities['state'] = states
major_cities['lat'] = lats
major_cities['lng'] = lngs
major_cities.drop_duplicates(subset='place_id', inplace=True)
major_cities = major_cities.loc[np.logical_not(major_cities.place_id.isnull())]
return major_cities
def _get_city_info(major_cities):
place_ids = []
lats = []
lngs = []
states = []
# base_url = 'http://maps.googleapis.com/maps/api/geocode/json?address='
progress = 0.0
google_geo = GoogleGeo()
for i, city in enumerate(major_cities.city.values):
sleep(0.5)
output = dict(place_id=None, state=None, lat=None, lng=None)
try:
# response = requests.get(base_url + '{string}'.format(string=city))
# output = _parse_google_geo_response(response.json())
output = google_geo.get(city,
fields_to_get=('place_id', 'lat', 'lng',
'state'))
if float(i) / len(major_cities) - progress >= 0.1:
print "Processed {pct}%".format(
pct=str(float(i) / len(major_cities) * 100))
progress = float(i) / len(major_cities)
except:
pass
place_ids.append(output['place_id'])
states.append(output['state'])
lats.append(output['lat'])
lngs.append(output['lng'])
major_cities['place_id'] = place_ids
major_cities['state'] = states
major_cities['lat'] = lats
major_cities['lng'] = lngs
major_cities.drop_duplicates(subset='place_id', inplace=True)
major_cities = major_cities.loc[np.logical_not(
major_cities.place_id.isnull())]
return major_cities
def __init__(self,recreate=False):
datamodel = Datamodel()
self.table, self.table_config = datamodel.population()
self.postgres = Postgresql(user_name='postgres',
password='******',
host='localhost',
port='5432',
db='TestProject')
self.postgres.initialize_table(self.table, recreate=False, **self.table_config)
self.googlegeo = GoogleGeo()
# myan: only get major cities data once per request
self.major_cities_postgres = Postgresql(user_name='postgres',
password='******',
host='localhost',
port='5432',
db='TestProject')
self.major_cities = self.major_cities_postgres.get("select * from TestMajorCities")
self.all_states = self.major_cities['state'].values
self.all_cities = self.major_cities['city'].values
self.all_lats = self.major_cities['lat'].values
self.all_lngs = self.major_cities['lng'].values
self.all_population = self.major_cities['population'].values
class Population:
"""
This object connects to the GreatSchools.org API and retrieves information about schools and GS ratings.
See more information on: http://www.greatschools.org/api/docs/main.page
"""
def __init__(self,recreate=False):
datamodel = Datamodel()
self.table, self.table_config = datamodel.population()
self.postgres = Postgresql(user_name='postgres',
password='******',
host='localhost',
port='5432',
db='TestProject')
self.postgres.initialize_table(self.table, recreate=False, **self.table_config)
self.googlegeo = GoogleGeo()
# myan: only get major cities data once per request
self.major_cities_postgres = Postgresql(user_name='postgres',
password='******',
host='localhost',
port='5432',
db='TestProject')
self.major_cities = self.major_cities_postgres.get("select * from TestMajorCities")
self.all_states = self.major_cities['state'].values
self.all_cities = self.major_cities['city'].values
self.all_lats = self.major_cities['lat'].values
self.all_lngs = self.major_cities['lng'].values
self.all_population = self.major_cities['population'].values
def run(self, **kwargs):
# myan: seems python has a strange way of handling memory pointers when deleting elements from lists in a loop
# therefore create a separate list tmp_results to hold all the results from API calls first and decide what to
# include.
results_df = pd.DataFrame(self._closest_city_population(self._geo_info(**kwargs)))
existing_keys = self.postgres.get("select place_id from {table};".format(table=self.table))
addition_results = results_df.loc[np.logical_not(results_df['place_id'].isin(existing_keys['place_id'].values))]
addition_results.drop_duplicates(subset='place_id', inplace=True)
if len(addition_results) > 0:
self.postgres.put_dataframe(addition_results, self.table_config['fields_types'], table=self.table)
return results_df
def _geo_info(self, addresses=None, fields_to_get=('place_id', 'state', 'city', 'county', 'lat', 'lng')):
"""
Get geo info from Google API
Args:
addresses: list of addresses
Returns:
list of dict, [{field1:value1, field2:value2, ...}, {...]]
Examples:
p = Population()
results = p._geo_info(address=['Houston,TX', 'Dallas, TX'])
"""
if fields_to_get is None:
raise ValueError('Argument fields_to_get must not be None.')
results = []
for entry in addresses:
output = self.googlegeo.get(entry, fields_to_get=fields_to_get)
output.update(address=str(entry))
if isinstance(entry, int):
output.update(zip_code=entry)
results.append(output)
return results
def _closest_city_population(self, tmp_results):
for entry in tmp_results:
is_curr_state = self.all_states == entry['state']
same_state_cities = self.all_cities[is_curr_state]
# myan: use a simple squared distance between two points to simply get the minimum
euc_distance = (self.all_lats[is_curr_state] - entry['lat']) ** 2 + (self.all_lngs[is_curr_state] - entry['lng']) ** 2
closest_idx = euc_distance.argmin()
# myan: once we locate the closest major city, we can then calculate the actual haversine distance
haversine_distance = haversine((self.all_lats[is_curr_state][closest_idx], self.all_lngs[is_curr_state][closest_idx]),
(entry['lat'], entry['lng']))
if haversine_distance <= MAX_DISTANCE_KM:
entry.update(closest_city=same_state_cities[closest_idx])
entry.update(closest_city_population=self.all_population[is_curr_state][closest_idx])
else:
entry.update(closest_city='NULL')
entry.update(closest_city_population='NULL')
return tmp_results
class Population:
"""
This object connects to the GreatSchools.org API and retrieves information about schools and GS ratings.
See more information on: http://www.greatschools.org/api/docs/main.page
"""
def __init__(self, recreate=False):
datamodel = Datamodel()
self.table, self.table_config = datamodel.population()
self.postgres = Postgresql(user_name='postgres',
password='******',
host='localhost',
port='5432',
db='TestProject')
self.postgres.initialize_table(self.table,
recreate=False,
**self.table_config)
self.googlegeo = GoogleGeo()
# myan: only get major cities data once per request
self.major_cities_postgres = Postgresql(user_name='postgres',
password='******',
host='localhost',
port='5432',
db='TestProject')
self.major_cities = self.major_cities_postgres.get(
"select * from TestMajorCities")
self.all_states = self.major_cities['state'].values
self.all_cities = self.major_cities['city'].values
self.all_lats = self.major_cities['lat'].values
self.all_lngs = self.major_cities['lng'].values
self.all_population = self.major_cities['population'].values
def run(self, **kwargs):
# myan: seems python has a strange way of handling memory pointers when deleting elements from lists in a loop
# therefore create a separate list tmp_results to hold all the results from API calls first and decide what to
# include.
results_df = pd.DataFrame(
self._closest_city_population(self._geo_info(**kwargs)))
existing_keys = self.postgres.get(
"select place_id from {table};".format(table=self.table))
addition_results = results_df.loc[np.logical_not(
results_df['place_id'].isin(existing_keys['place_id'].values))]
addition_results.drop_duplicates(subset='place_id', inplace=True)
if len(addition_results) > 0:
self.postgres.put_dataframe(addition_results,
self.table_config['fields_types'],
table=self.table)
return results_df
def _geo_info(self,
addresses=None,
fields_to_get=('place_id', 'state', 'city', 'county', 'lat',
'lng')):
"""
Get geo info from Google API
Args:
addresses: list of addresses
Returns:
list of dict, [{field1:value1, field2:value2, ...}, {...]]
Examples:
p = Population()
results = p._geo_info(address=['Houston,TX', 'Dallas, TX'])
"""
if fields_to_get is None:
raise ValueError('Argument fields_to_get must not be None.')
results = []
for entry in addresses:
output = self.googlegeo.get(entry, fields_to_get=fields_to_get)
output.update(address=str(entry))
if isinstance(entry, int):
output.update(zip_code=entry)
results.append(output)
return results
def _closest_city_population(self, tmp_results):
for entry in tmp_results:
is_curr_state = self.all_states == entry['state']
same_state_cities = self.all_cities[is_curr_state]
# myan: use a simple squared distance between two points to simply get the minimum
euc_distance = (self.all_lats[is_curr_state] - entry['lat'])**2 + (
self.all_lngs[is_curr_state] - entry['lng'])**2
closest_idx = euc_distance.argmin()
# myan: once we locate the closest major city, we can then calculate the actual haversine distance
haversine_distance = haversine(
(self.all_lats[is_curr_state][closest_idx],
self.all_lngs[is_curr_state][closest_idx]),
(entry['lat'], entry['lng']))
if haversine_distance <= MAX_DISTANCE_KM:
entry.update(closest_city=same_state_cities[closest_idx])
entry.update(closest_city_population=self.
all_population[is_curr_state][closest_idx])
else:
entry.update(closest_city='NULL')
entry.update(closest_city_population='NULL')
return tmp_results
