def construct_interpolated_noon_prediction(prediction_a: ModelRunGridSubsetPrediction,
prediction_b: ModelRunGridSubsetPrediction):
""" Construct a noon prediction by interpolating.
"""
# create a noon prediction. (using utc hour 20, as that is solar noon in B.C.)
noon_prediction = ModelRunGridSubsetPrediction()
noon_prediction.prediction_timestamp = prediction_a.prediction_timestamp.replace(
hour=20)
# throw timestamps into their own variables.
timestamp_a = prediction_a.prediction_timestamp.timestamp()
timestamp_b = prediction_b.prediction_timestamp.timestamp()
noon_timestamp = noon_prediction.prediction_timestamp.timestamp()
# calculate interpolated values.
for key in SCALAR_MODEL_VALUE_KEYS:
value_a = getattr(prediction_a, key)
value_b = getattr(prediction_b, key)
if value_a is None or value_b is None:
logger.warning('can\'t interpolate between None values for %s', key)
continue
value = interpolate_between_two_points(timestamp_a, timestamp_b, value_a, value_b, noon_timestamp)
setattr(noon_prediction, key, value)
if noon_prediction.apcp_sfc_0 is None or prediction_a.apcp_sfc_0 is None:
noon_prediction.delta_precip = None
else:
noon_prediction.delta_precip = noon_prediction.apcp_sfc_0 - prediction_a.apcp_sfc_0
noon_prediction.wdir_tgl_10 = interpolate_wind_direction(
prediction_a, prediction_b, noon_prediction.prediction_timestamp)
return noon_prediction
def store_bounding_values(self, points, values, preduction_model_run: PredictionModelRunTimestamp,
grib_info: ModelRunInfo):
""" Store the values around the area of interest.
"""
# Convert points to geographic coordinates:
geographic_points = []
for point in points:
geographic_points.append(
calculate_geographic_coordinate(point, self.origin, self.pixel))
# Get the grid subset, i.e. the relevant bounding area for this particular model.
grid_subset = get_or_create_grid_subset(
self.session, self.prediction_model, geographic_points)
# Load the record if it exists.
# pylint: disable=no-member
prediction = self.session.query(ModelRunGridSubsetPrediction).\
filter(
ModelRunGridSubsetPrediction.prediction_model_run_timestamp_id == preduction_model_run.id).\
filter(ModelRunGridSubsetPrediction.prediction_timestamp == grib_info.prediction_timestamp).\
filter(ModelRunGridSubsetPrediction.prediction_model_grid_subset_id ==
grid_subset.id).first()
if not prediction:
# Record doesn't exist, so we create it.
prediction = ModelRunGridSubsetPrediction()
prediction.prediction_model_run_timestamp_id = preduction_model_run.id
prediction.prediction_timestamp = grib_info.prediction_timestamp
prediction.prediction_model_grid_subset_id = grid_subset.id
setattr(prediction, grib_info.variable_name.lower(), array(values))
self.session.add(prediction)
self.session.commit()