def test_buffer_to_column_name_large_buffer_grid_columns(self):
"""Ensures correct output from _buffer_to_column_name.
In this case, the buffer is "5-10 km outside storm" and the column name
is for grid columns inside the polygon.
"""
this_column_name = gridded_forecasts._buffer_to_column_name(
min_buffer_dist_metres=LARGE_BUFFER_MIN_DISTANCE_METRES,
max_buffer_dist_metres=LARGE_BUFFER_MAX_DISTANCE_METRES,
column_type=gridded_forecasts.GRID_COLUMNS_IN_POLYGON_COLUMN_TYPE)
self.assertTrue(this_column_name == LARGE_BUFFER_GRID_COLUMNS_COLUMN)
def test_buffer_to_column_name_large_buffer_latlng(self):
"""Ensures correct output from _buffer_to_column_name.
In this case, the buffer is "5-10 km outside storm" and the column name
is for lat-long polygons.
"""
this_column_name = gridded_forecasts._buffer_to_column_name(
min_buffer_dist_metres=LARGE_BUFFER_MIN_DISTANCE_METRES,
max_buffer_dist_metres=LARGE_BUFFER_MAX_DISTANCE_METRES,
column_type=gridded_forecasts.LATLNG_POLYGON_COLUMN_TYPE)
self.assertTrue(this_column_name == LARGE_BUFFER_LATLNG_COLUMN)
def test_buffer_to_column_name_large_buffer_forecast(self):
"""Ensures correct output from _buffer_to_column_name.
In this case, the buffer is "5-10 km outside storm" and the column name
is for forecast probabilities.
"""
this_column_name = gridded_forecasts._buffer_to_column_name(
min_buffer_dist_metres=LARGE_BUFFER_MIN_DISTANCE_METRES,
max_buffer_dist_metres=LARGE_BUFFER_MAX_DISTANCE_METRES,
column_type=gridded_forecasts.FORECAST_COLUMN_TYPE)
self.assertTrue(this_column_name == LARGE_BUFFER_FORECAST_COLUMN)
def test_buffer_to_column_name_small_buffer_xy(self):
"""Ensures correct output from _buffer_to_column_name.
In this case, the buffer is "inside storm" and the column name is for
x-y polygons.
"""
this_column_name = gridded_forecasts._buffer_to_column_name(
min_buffer_dist_metres=SMALL_BUFFER_MIN_DISTANCE_METRES,
max_buffer_dist_metres=SMALL_BUFFER_MAX_DISTANCE_METRES,
column_type=gridded_forecasts.XY_POLYGON_COLUMN_TYPE)
self.assertTrue(this_column_name == SMALL_BUFFER_XY_COLUMN)
def _run(input_prediction_file_name, top_tracking_dir_name,
tracking_scale_metres2, x_spacing_metres, y_spacing_metres,
effective_radius_metres, smoothing_method_name,
smoothing_cutoff_radius_metres, smoothing_efold_radius_metres,
top_output_dir_name):
"""Projects CNN forecasts onto the RAP grid.
This is effectively the same method.
:param input_prediction_file_name: See documentation at top of file.
:param top_tracking_dir_name: Same.
:param tracking_scale_metres2: Same.
:param x_spacing_metres: Same.
:param y_spacing_metres: Same.
:param effective_radius_metres: Same.
:param smoothing_method_name: Same.
:param smoothing_cutoff_radius_metres: Same.
:param smoothing_efold_radius_metres: Same.
:param top_output_dir_name: Same.
"""
print('Reading data from: "{0:s}"...'.format(input_prediction_file_name))
ungridded_forecast_dict = prediction_io.read_ungridded_predictions(
input_prediction_file_name)
target_param_dict = target_val_utils.target_name_to_params(
ungridded_forecast_dict[prediction_io.TARGET_NAME_KEY])
min_buffer_dist_metres = target_param_dict[
target_val_utils.MIN_LINKAGE_DISTANCE_KEY]
# TODO(thunderhoser): This is HACKY.
if min_buffer_dist_metres == 0:
min_buffer_dist_metres = numpy.nan
max_buffer_dist_metres = target_param_dict[
target_val_utils.MAX_LINKAGE_DISTANCE_KEY]
min_lead_time_seconds = target_param_dict[
target_val_utils.MIN_LEAD_TIME_KEY]
max_lead_time_seconds = target_param_dict[
target_val_utils.MAX_LEAD_TIME_KEY]
forecast_column_name = gridded_forecasts._buffer_to_column_name(
min_buffer_dist_metres=min_buffer_dist_metres,
max_buffer_dist_metres=max_buffer_dist_metres,
column_type=gridded_forecasts.FORECAST_COLUMN_TYPE)
init_times_unix_sec = numpy.unique(
ungridded_forecast_dict[prediction_io.STORM_TIMES_KEY])
tracking_file_names = []
for this_time_unix_sec in init_times_unix_sec:
this_tracking_file_name = tracking_io.find_file(
top_tracking_dir_name=top_tracking_dir_name,
tracking_scale_metres2=tracking_scale_metres2,
source_name=tracking_utils.SEGMOTION_NAME,
valid_time_unix_sec=this_time_unix_sec,
spc_date_string=time_conversion.time_to_spc_date_string(
this_time_unix_sec),
raise_error_if_missing=True)
tracking_file_names.append(this_tracking_file_name)
storm_object_table = tracking_io.read_many_files(tracking_file_names)
print(SEPARATOR_STRING)
tracking_utils.find_storm_objects(
all_id_strings=ungridded_forecast_dict[prediction_io.STORM_IDS_KEY],
all_times_unix_sec=ungridded_forecast_dict[
prediction_io.STORM_TIMES_KEY],
id_strings_to_keep=storm_object_table[
tracking_utils.FULL_ID_COLUMN].values.tolist(),
times_to_keep_unix_sec=storm_object_table[
tracking_utils.VALID_TIME_COLUMN].values,
allow_missing=False)
sort_indices = tracking_utils.find_storm_objects(
all_id_strings=storm_object_table[
tracking_utils.FULL_ID_COLUMN].values.tolist(),
all_times_unix_sec=storm_object_table[
tracking_utils.VALID_TIME_COLUMN].values,
id_strings_to_keep=ungridded_forecast_dict[
prediction_io.STORM_IDS_KEY],
times_to_keep_unix_sec=ungridded_forecast_dict[
prediction_io.STORM_TIMES_KEY],
allow_missing=False)
forecast_probabilities = ungridded_forecast_dict[
prediction_io.PROBABILITY_MATRIX_KEY][sort_indices, 1]
storm_object_table = storm_object_table.assign(
**{forecast_column_name: forecast_probabilities})
gridded_forecast_dict = gridded_forecasts.create_forecast_grids(
storm_object_table=storm_object_table,
min_lead_time_sec=min_lead_time_seconds,
max_lead_time_sec=max_lead_time_seconds,
lead_time_resolution_sec=gridded_forecasts.
DEFAULT_LEAD_TIME_RES_SECONDS,
grid_spacing_x_metres=x_spacing_metres,
grid_spacing_y_metres=y_spacing_metres,
interp_to_latlng_grid=False,
prob_radius_for_grid_metres=effective_radius_metres,
smoothing_method=smoothing_method_name,
smoothing_e_folding_radius_metres=smoothing_efold_radius_metres,
smoothing_cutoff_radius_metres=smoothing_cutoff_radius_metres)
print(SEPARATOR_STRING)
output_file_name = prediction_io.find_file(
top_prediction_dir_name=top_output_dir_name,
first_init_time_unix_sec=numpy.min(
storm_object_table[tracking_utils.VALID_TIME_COLUMN].values),
last_init_time_unix_sec=numpy.max(
storm_object_table[tracking_utils.VALID_TIME_COLUMN].values),
gridded=True,
raise_error_if_missing=False)
print(('Writing results (forecast grids for {0:d} initial times) to: '
'"{1:s}"...').format(
len(gridded_forecast_dict[prediction_io.INIT_TIMES_KEY]),
output_file_name))
prediction_io.write_gridded_predictions(
gridded_forecast_dict=gridded_forecast_dict,
pickle_file_name=output_file_name)
