def test_count_events_on_equidistant_grid_no_ids(self):
"""Ensures correct output from count_events_on_equidistant_grid.
In this case event IDs are *not* included as input.
"""
this_num_events_matrix, _ = grids.count_events_on_equidistant_grid(
event_x_coords_metres=EVENT_X_COORDS_METRES,
event_y_coords_metres=EVENT_Y_COORDS_METRES,
integer_event_ids=None,
grid_point_x_coords_metres=EVENT_GRID_POINTS_X_METRES,
grid_point_y_coords_metres=EVENT_GRID_POINTS_Y_METRES)
self.assertTrue(
numpy.array_equal(this_num_events_matrix, NUM_EVENTS_MATRIX))
def test_count_events_on_equidistant_grid_with_ids(self):
"""Ensures correct output from count_events_on_equidistant_grid.
In this case event IDs are included as input.
"""
this_num_events_matrix, _ = grids.count_events_on_equidistant_grid(
event_x_coords_metres=EVENT_X_COORDS_METRES,
event_y_coords_metres=EVENT_Y_COORDS_METRES,
integer_event_ids=INTEGER_EVENT_IDS,
grid_point_x_coords_metres=GRID_POINTS_X_FOR_COUNTING_METRES,
grid_point_y_coords_metres=GRID_POINTS_Y_FOR_COUNTING_METRES)
self.assertTrue(
numpy.array_equal(this_num_events_matrix,
NUM_UNIQUE_EVENTS_MATRIX_EQUIDISTANT))
def _run(prediction_file_name, top_tracking_dir_name, prob_threshold,
grid_spacing_metres, output_dir_name):
"""Plots spatial distribution of false alarms.
This is effectively the main method.
:param prediction_file_name: See documentation at top of file.
:param top_tracking_dir_name: Same.
:param prob_threshold: Same.
:param grid_spacing_metres: Same.
:param output_dir_name: Same.
"""
# Process input args.
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
error_checking.assert_is_greater(prob_threshold, 0.)
error_checking.assert_is_less_than(prob_threshold, 1.)
grid_metadata_dict = grids.create_equidistant_grid(
min_latitude_deg=MIN_LATITUDE_DEG,
max_latitude_deg=MAX_LATITUDE_DEG,
min_longitude_deg=MIN_LONGITUDE_DEG,
max_longitude_deg=MAX_LONGITUDE_DEG,
x_spacing_metres=grid_spacing_metres,
y_spacing_metres=grid_spacing_metres,
azimuthal=False)
# Read predictions and find positive forecasts and false alarms.
print('Reading predictions from: "{0:s}"...'.format(prediction_file_name))
prediction_dict = prediction_io.read_ungridded_predictions(
prediction_file_name)
observed_labels = prediction_dict[prediction_io.OBSERVED_LABELS_KEY]
forecast_labels = (
prediction_dict[prediction_io.PROBABILITY_MATRIX_KEY][:, -1] >=
prob_threshold).astype(int)
pos_forecast_indices = numpy.where(forecast_labels == 1)[0]
false_alarm_indices = numpy.where(
numpy.logical_and(observed_labels == 0, forecast_labels == 1))[0]
num_examples = len(observed_labels)
num_positive_forecasts = len(pos_forecast_indices)
num_false_alarms = len(false_alarm_indices)
print(('Probability threshold = {0:.3f} ... number of examples, positive '
'forecasts, false alarms = {1:d}, {2:d}, {3:d}').format(
prob_threshold, num_examples, num_positive_forecasts,
num_false_alarms))
# Find and read tracking files.
pos_forecast_id_strings = [
prediction_dict[prediction_io.STORM_IDS_KEY][k]
for k in pos_forecast_indices
]
pos_forecast_times_unix_sec = (
prediction_dict[prediction_io.STORM_TIMES_KEY][pos_forecast_indices])
file_times_unix_sec = numpy.unique(pos_forecast_times_unix_sec)
num_files = len(file_times_unix_sec)
storm_object_tables = [None] * num_files
print(SEPARATOR_STRING)
for i in range(num_files):
this_tracking_file_name = tracking_io.find_file(
top_tracking_dir_name=top_tracking_dir_name,
tracking_scale_metres2=DUMMY_TRACKING_SCALE_METRES2,
source_name=tracking_utils.SEGMOTION_NAME,
valid_time_unix_sec=file_times_unix_sec[i],
spc_date_string=time_conversion.time_to_spc_date_string(
file_times_unix_sec[i]),
raise_error_if_missing=True)
print('Reading data from: "{0:s}"...'.format(this_tracking_file_name))
this_table = tracking_io.read_file(this_tracking_file_name)
storm_object_tables[i] = this_table.loc[this_table[
tracking_utils.FULL_ID_COLUMN].isin(pos_forecast_id_strings)]
if i == 0:
continue
storm_object_tables[i] = storm_object_tables[i].align(
storm_object_tables[0], axis=1)[0]
storm_object_table = pandas.concat(storm_object_tables,
axis=0,
ignore_index=True)
print(SEPARATOR_STRING)
# Find latitudes and longitudes of false alarms.
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)
good_indices = tracking_utils.find_storm_objects(
all_id_strings=all_id_strings,
all_times_unix_sec=all_times_unix_sec,
id_strings_to_keep=pos_forecast_id_strings,
times_to_keep_unix_sec=pos_forecast_times_unix_sec,
allow_missing=False)
pos_forecast_latitudes_deg = storm_object_table[
tracking_utils.CENTROID_LATITUDE_COLUMN].values[good_indices]
pos_forecast_longitudes_deg = storm_object_table[
tracking_utils.CENTROID_LONGITUDE_COLUMN].values[good_indices]
false_alarm_id_strings = [
prediction_dict[prediction_io.STORM_IDS_KEY][k]
for k in false_alarm_indices
]
false_alarm_times_unix_sec = (
prediction_dict[prediction_io.STORM_TIMES_KEY][false_alarm_indices])
good_indices = tracking_utils.find_storm_objects(
all_id_strings=all_id_strings,
all_times_unix_sec=all_times_unix_sec,
id_strings_to_keep=false_alarm_id_strings,
times_to_keep_unix_sec=false_alarm_times_unix_sec,
allow_missing=False)
false_alarm_latitudes_deg = storm_object_table[
tracking_utils.CENTROID_LATITUDE_COLUMN].values[good_indices]
false_alarm_longitudes_deg = storm_object_table[
tracking_utils.CENTROID_LONGITUDE_COLUMN].values[good_indices]
pos_forecast_x_coords_metres, pos_forecast_y_coords_metres = (
projections.project_latlng_to_xy(
latitudes_deg=pos_forecast_latitudes_deg,
longitudes_deg=pos_forecast_longitudes_deg,
projection_object=grid_metadata_dict[grids.PROJECTION_KEY]))
num_pos_forecasts_matrix = grids.count_events_on_equidistant_grid(
event_x_coords_metres=pos_forecast_x_coords_metres,
event_y_coords_metres=pos_forecast_y_coords_metres,
grid_point_x_coords_metres=grid_metadata_dict[grids.X_COORDS_KEY],
grid_point_y_coords_metres=grid_metadata_dict[grids.Y_COORDS_KEY])[0]
print(SEPARATOR_STRING)
false_alarm_x_coords_metres, false_alarm_y_coords_metres = (
projections.project_latlng_to_xy(
latitudes_deg=false_alarm_latitudes_deg,
longitudes_deg=false_alarm_longitudes_deg,
projection_object=grid_metadata_dict[grids.PROJECTION_KEY]))
num_false_alarms_matrix = grids.count_events_on_equidistant_grid(
event_x_coords_metres=false_alarm_x_coords_metres,
event_y_coords_metres=false_alarm_y_coords_metres,
grid_point_x_coords_metres=grid_metadata_dict[grids.X_COORDS_KEY],
grid_point_y_coords_metres=grid_metadata_dict[grids.Y_COORDS_KEY])[0]
print(SEPARATOR_STRING)
num_pos_forecasts_matrix = num_pos_forecasts_matrix.astype(float)
num_pos_forecasts_matrix[num_pos_forecasts_matrix == 0] = numpy.nan
num_false_alarms_matrix = num_false_alarms_matrix.astype(float)
num_false_alarms_matrix[num_false_alarms_matrix == 0] = numpy.nan
far_matrix = num_false_alarms_matrix / num_pos_forecasts_matrix
this_max_value = numpy.nanpercentile(num_false_alarms_matrix,
MAX_COUNT_PERCENTILE_TO_PLOT)
if this_max_value < 10:
this_max_value = numpy.nanmax(num_false_alarms_matrix)
figure_object = plotter._plot_one_value(
data_matrix=num_false_alarms_matrix,
grid_metadata_dict=grid_metadata_dict,
colour_map_object=CMAP_OBJECT_FOR_COUNTS,
min_colour_value=0,
max_colour_value=this_max_value,
plot_cbar_min_arrow=False,
plot_cbar_max_arrow=True)[0]
num_false_alarms_file_name = '{0:s}/num_false_alarms.jpg'.format(
output_dir_name)
print('Saving figure to: "{0:s}"...'.format(num_false_alarms_file_name))
figure_object.savefig(num_false_alarms_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(figure_object)
this_max_value = numpy.nanpercentile(num_pos_forecasts_matrix,
MAX_COUNT_PERCENTILE_TO_PLOT)
if this_max_value < 10:
this_max_value = numpy.nanmax(num_pos_forecasts_matrix)
figure_object = plotter._plot_one_value(
data_matrix=num_pos_forecasts_matrix,
grid_metadata_dict=grid_metadata_dict,
colour_map_object=CMAP_OBJECT_FOR_COUNTS,
min_colour_value=0,
max_colour_value=this_max_value,
plot_cbar_min_arrow=False,
plot_cbar_max_arrow=True)[0]
num_pos_forecasts_file_name = '{0:s}/num_positive_forecasts.jpg'.format(
output_dir_name)
print('Saving figure to: "{0:s}"...'.format(num_pos_forecasts_file_name))
figure_object.savefig(num_pos_forecasts_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(figure_object)
this_max_value = numpy.nanpercentile(far_matrix,
MAX_FAR_PERCENTILE_TO_PLOT)
this_min_value = numpy.nanpercentile(far_matrix,
100. - MAX_FAR_PERCENTILE_TO_PLOT)
figure_object = plotter._plot_one_value(
data_matrix=far_matrix,
grid_metadata_dict=grid_metadata_dict,
colour_map_object=CMAP_OBJECT_FOR_FAR,
min_colour_value=this_min_value,
max_colour_value=this_max_value,
plot_cbar_min_arrow=this_min_value > 0.,
plot_cbar_max_arrow=this_max_value < 1.)[0]
far_file_name = '{0:s}/false_alarm_ratio.jpg'.format(output_dir_name)
print('Saving figure to: "{0:s}"...'.format(far_file_name))
figure_object.savefig(far_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(figure_object)