def geocode_location(location, api_key=None):
"""Use Google to geocode a location string.
For high-volume traffic, you will need to specify an API-key.
"""
GEOCODE_URL = "http://maps.google.com/maps/geo"
params = [('q', location),
('sensor', 'false'),
('output', 'json')]
if api_key:
params += [('key', api_key)]
resp = utils.open_url(GEOCODE_URL, params)
data = json.loads(resp.read())
if data['Status']['code'] != 200:
raise exceptions.GeocodeException('Unable to geocode this location')
best_match = data['Placemark'][0]
address = best_match['address']
lon, lat, _ = best_match['Point']['coordinates']
location = models.Location(lat, lon, address)
return location
def _daily_forecast_from_location_info(location_info,
start_date=None,
num_days=6,
metric=False):
if not start_date:
start_date = datetime.date.today()
# NOTE: the order of the query-string parameters seems to matter; so,
# we can't use a dictionary to hold the params
params = location_info + [("format", "24 hourly"),
("startDate", start_date.strftime("%Y-%m-%d")),
("numDays", str(num_days)),
("Unit", "m" if metric else "e")]
FORECAST_BY_DAY_URL = ("http://www.weather.gov/forecasts/xml"
"/sample_products/browser_interface"
"/ndfdBrowserClientByDay.php")
resp = utils.open_url(FORECAST_BY_DAY_URL, params)
tree = utils.parse_xml(resp)
if tree.getroot().tag == 'error':
raise exceptions.NOAAException("Unable to retrieve forecast")
time_layouts = _parse_time_layouts(tree)
min_temp_tlk, min_temps = _parse_temperatures_for_type(tree, 'minimum')
max_temp_tlk, max_temps = _parse_temperatures_for_type(tree, 'maximum')
conditions_tlk, conditions = _parse_conditions(tree)
# Time layout keys have to match for us to sequence and group by them
assert (min_temp_tlk == max_temp_tlk == conditions_tlk)
time_layout_key = min_temp_tlk
time_layout = time_layouts[time_layout_key]
dates = [dt.date() for dt, _ in time_layout]
forecast = []
for date, min_temp_value, max_temp_value, condition in zip(
dates, min_temps, max_temps, conditions):
# If we're missing any data, don't create the data point
if utils.any_none([min_temp_value, max_temp_value, condition]):
continue
temp_unit = 'C' if metric else 'F'
min_temp = models.Temperature(min_temp_value, unit=temp_unit)
max_temp = models.Temperature(max_temp_value, unit=temp_unit)
datapoint = models.ForecastedCondition(date, min_temp, max_temp,
condition)
forecast.append(datapoint)
return forecast
def _daily_forecast_from_location_info(location_info, start_date=None,
num_days=6, metric=False):
if not start_date:
start_date = datetime.date.today()
# NOTE: the order of the query-string parameters seems to matter; so,
# we can't use a dictionary to hold the params
params = location_info + [("format", "24 hourly"),
("startDate", start_date.strftime("%Y-%m-%d")),
("numDays", str(num_days)),
("Unit", "m" if metric else "e")]
FORECAST_BY_DAY_URL = ("http://www.weather.gov/forecasts/xml"
"/sample_products/browser_interface"
"/ndfdBrowserClientByDay.php")
resp = utils.open_url(FORECAST_BY_DAY_URL, params)
tree = utils.parse_xml(resp)
if tree.getroot().tag == 'error':
raise exceptions.NOAAException("Unable to retrieve forecast")
time_layouts = _parse_time_layouts(tree)
min_temp_tlk, min_temps = _parse_temperatures_for_type(tree, 'minimum')
max_temp_tlk, max_temps = _parse_temperatures_for_type(tree, 'maximum')
conditions_tlk, conditions = _parse_conditions(tree)
# Time layout keys have to match for us to sequence and group by them
assert (min_temp_tlk == max_temp_tlk == conditions_tlk)
time_layout_key = min_temp_tlk
time_layout = time_layouts[time_layout_key]
dates = [dt.date() for dt, _ in time_layout]
forecast = []
for date, min_temp_value, max_temp_value, condition in zip(
dates, min_temps, max_temps, conditions):
# If we're missing any data, don't create the data point
if utils.any_none([min_temp_value, max_temp_value, condition]):
continue
temp_unit = 'C' if metric else 'F'
min_temp = models.Temperature(min_temp_value, unit=temp_unit)
max_temp = models.Temperature(max_temp_value, unit=temp_unit)
datapoint = models.ForecastedCondition(
date, min_temp, max_temp, condition)
forecast.append(datapoint)
return forecast
def geocode_location(location, api_key=None):
"""Use Google to geocode a location string.
For high-volume traffic, you will need to specify an API-key.
"""
GEOCODE_URL = "http://maps.google.com/maps/geo"
params = [('q', location), ('sensor', 'false'), ('output', 'json')]
if api_key:
params += [('key', api_key)]
resp = utils.open_url(GEOCODE_URL, params)
data = json.loads(resp.read())
if data['Status']['code'] != 200:
raise exceptions.GeocodeException('Unable to geocode this location')
best_match = data['Placemark'][0]
address = best_match['address']
lon, lat, _ = best_match['Point']['coordinates']
location = models.Location(lat, lon, address)
return location
def geocode_location(location, api_key=None):
"""Use Google to geocode a location string.
For high-volume traffic, you will need to specify an API-key.
"""
GEOCODE_URL = "http://maps.googleapis.com/maps/api/geocode/json"
params = [('address', location)]
if api_key:
params += [('key', api_key)]
resp = utils.open_url(GEOCODE_URL, params)
data = json.loads(resp.read().decode())
if data['status'] != 'OK':
raise exceptions.GeocodeException('Unable to geocode this location')
address = data['results'][0]['formatted_address']
lon = data['results'][0]['geometry']['location']['lng']
lat = data['results'][0]['geometry']['location']['lat']
location = models.Location(lat, lon, address)
return location
