def load_observations_for_station(station_name, element_name):
"""Load observations for a specific stations."""
element_id = data_io.get_element_id(element_name, "fc")
# TODO Add support
if element_id != 999:
raise NotImplementedError()
observations = data_io.read_observations(element_id, station_name)
return observations
def load_and_interpolate_forecast(model, element_name,
station_name, issue):
"""Load a given dataset of forecast data with interpolated values.
parameters
----------
model: str, name of model
element_name: str, string representation of the provided parameter
station_name: str, canonical name of the station
issue: str, string representation of the model issue hour
"""
# Forecast columns: 1 to 4
forecast_cols = ['value' + str(x) for x in range(1, 5)]
# Station location
station_lat, station_lon = data_io.get_station_location(station_name)
# Read in forecast
element_id = data_io.get_element_id(element_name, model)
forecast_data = data_io.read_forecast_data(
model, element_id, station_name, issue)
# Check whether to do any interpolation
empty_columns = \
forecast_data.ix[:, forecast_cols].isnull().values.any(axis=0)
if empty_columns.sum() == 3:
# Just a single column provided. Don't do interpolation.
logging.debug(
"Not interpolating for model '%s', element '%s', station '%s'" %
(model, str(element_id), station_name))
# Select non-empty column
non_empty_col = forecast_cols[(~empty_columns).nonzero()[0][0]]
interpolated_forecast = forecast_data[non_empty_col]
else:
# Do interpolation using meta-data
meta_data = \
data_io.read_meta_data(model, element_id, station_name, issue)
interpolated_forecast = interpolate(
station_lat, station_lon,
forecast_data.ix[:, forecast_cols],
meta_data['latitude'], meta_data['longitude'],
meta_data['distance']
)
forecast_data['interpolated_forecast'] = interpolated_forecast
# Drop grid point data.
forecast_data.drop(forecast_cols, axis=1, inplace=True)
# Transform EPS data from row format to column format
forecast_data = \
transform_forecast_group_data(forecast_data, model, element_name)
return forecast_data