def test_generate_forecast_figure_components(ac_power_forecast_metadata,
forecast_values):
# not great, just testing that script and div are returned
out = timeseries.generate_forecast_figure(ac_power_forecast_metadata,
forecast_values,
return_components=True)
assert '<script' in out[0]
assert '<div' in out[1]
def timeseries_adapter(type_, metadata, json_value_response):
metadata = deepcopy(metadata)
# ignores any modeling parameters as they aren't need for this
site = datamodel.Site.from_dict(metadata['site'], raise_on_extra=False)
metadata['site'] = site
if type_ == 'forecast':
obj = datamodel.Forecast.from_dict(metadata)
data = io_utils.json_payload_to_forecast_series(json_value_response)
return timeseries.generate_forecast_figure(obj,
data,
return_components=True)
else:
obj = datamodel.Observation.from_dict(metadata)
data = io_utils.json_payload_to_observation_df(json_value_response)
return timeseries.generate_observation_figure(obj,
data,
return_components=True)
def timeseries_adapter(type_, metadata, json_value_response):
metadata = deepcopy(metadata)
# ignores any modeling parameters as they aren't need for this
if 'site' in metadata:
site = datamodel.Site.from_dict(metadata['site'], raise_on_extra=False)
metadata['site'] = site
elif 'aggregate' in metadata:
# Patch aggregates so data model doesn't throw an error. We don't
# need to load all of the observations when plotting forecasts.
metadata['aggregate']['observations'] = []
if type_ == 'forecast':
obj = datamodel.Forecast.from_dict(metadata)
data = io_utils.json_payload_to_forecast_series(json_value_response)
return timeseries.generate_forecast_figure(obj,
data,
return_components=True,
limit=None)
elif type_ == 'observation':
obj = datamodel.Observation.from_dict(metadata)
data = io_utils.json_payload_to_observation_df(json_value_response)
return timeseries.generate_observation_figure(obj,
data,
return_components=True,
limit=None)
elif type_ == 'probabilistic_forecast':
cvs = []
for constant_value in metadata['constant_values']:
cv_dict = metadata.copy()
cv_dict.update(constant_value)
cvs.append(
datamodel.ProbabilisticForecastConstantValue.from_dict(
cv_dict))
metadata['constant_values'] = cvs
obj = datamodel.ProbabilisticForecast.from_dict(metadata,
raise_on_extra=False)
return timeseries.generate_probabilistic_forecast_figure(
obj, json_value_response)
else:
# remove observations, we aren't using them for plotting aggregates
metadata['observations'] = []
obj = datamodel.Aggregate.from_dict(metadata, raise_on_extra=False)
data = io_utils.json_payload_to_observation_df(json_value_response)
return timeseries.generate_observation_figure(obj,
data,
return_components=True,
limit=None)
def test_generate_forecast_figure_empty(ac_power_forecast_metadata, rc):
assert timeseries.generate_forecast_figure(ac_power_forecast_metadata,
pd.Series(dtype=float),
return_components=rc) is None
def test_generate_forecast_figure(ac_power_forecast_metadata, forecast_values):
# not great, just testing that script and div are returned
out = timeseries.generate_forecast_figure(ac_power_forecast_metadata,
forecast_values)
assert isinstance(out, bokeh.models.Column)