def _run():
"""Plots dilation of WPC fronts.
This is effectively the main method.
"""
print 'Reading data from: "{0:s}"...'.format(FRONT_LINE_FILE_NAME)
front_line_table = fronts_io.read_polylines_from_file(FRONT_LINE_FILE_NAME)
print 'Reading data from: "{0:s}"...'.format(FRONTAL_GRID_FILE_NAME)
frontal_grid_table = fronts_io.read_narr_grids_from_file(
FRONTAL_GRID_FILE_NAME)
num_grid_rows, num_grid_columns = nwp_model_utils.get_grid_dimensions(
model_name=nwp_model_utils.NARR_MODEL_NAME)
ternary_front_matrix = ml_utils.front_table_to_images(
frontal_grid_table=frontal_grid_table,
num_rows_per_image=num_grid_rows,
num_columns_per_image=num_grid_columns)
_plot_fronts(front_line_table=front_line_table,
ternary_front_matrix=ternary_front_matrix,
title_string='Observed fronts before dilation',
annotation_string='(a)',
output_file_name=BEFORE_FILE_NAME)
ternary_front_matrix = ml_utils.dilate_ternary_target_images(
target_matrix=ternary_front_matrix,
dilation_distance_metres=DILATION_DISTANCE_METRES,
verbose=False)
_plot_fronts(front_line_table=front_line_table,
ternary_front_matrix=ternary_front_matrix,
title_string='Observed fronts after dilation',
annotation_string='(b)',
output_file_name=AFTER_FILE_NAME)
print 'Concatenating figures to: "{0:s}"...'.format(CONCAT_FILE_NAME)
imagemagick_utils.concatenate_images(
input_file_names=[BEFORE_FILE_NAME, AFTER_FILE_NAME],
output_file_name=CONCAT_FILE_NAME,
num_panel_rows=1,
num_panel_columns=2)
imagemagick_utils.resize_image(input_file_name=CONCAT_FILE_NAME,
output_file_name=CONCAT_FILE_NAME,
output_size_pixels=CONCAT_SIZE_PIXELS)
def _run():
"""Plots example of double penalty.
This is effectively the main method.
"""
print 'Reading data from: "{0:s}"...'.format(INPUT_FILE_NAME)
actual_grid_point_table = fronts_io.read_narr_grids_from_file(
INPUT_FILE_NAME)
predicted_grid_point_table = copy.deepcopy(actual_grid_point_table)
predicted_grid_point_table[
front_utils.WARM_FRONT_COLUMN_INDICES_COLUMN] += 1
predicted_grid_point_table[
front_utils.COLD_FRONT_COLUMN_INDICES_COLUMN] += 1
predicted_grid_point_table[front_utils.WARM_FRONT_ROW_INDICES_COLUMN] += 1
predicted_grid_point_table[front_utils.COLD_FRONT_ROW_INDICES_COLUMN] += 1
num_grid_rows, num_grid_columns = nwp_model_utils.get_grid_dimensions(
model_name=nwp_model_utils.NARR_MODEL_NAME)
actual_binary_matrix = ml_utils.front_table_to_images(
frontal_grid_table=actual_grid_point_table,
num_rows_per_image=num_grid_rows,
num_columns_per_image=num_grid_columns)
actual_binary_matrix = ml_utils.binarize_front_images(actual_binary_matrix)
predicted_binary_matrix = ml_utils.front_table_to_images(
frontal_grid_table=predicted_grid_point_table,
num_rows_per_image=num_grid_rows,
num_columns_per_image=num_grid_columns)
predicted_binary_matrix = ml_utils.binarize_front_images(
predicted_binary_matrix)
_plot_fronts(actual_binary_matrix=actual_binary_matrix,
predicted_binary_matrix=predicted_binary_matrix,
title_string='Observed and predicted front\nwithout dilation',
annotation_string='(c)',
output_file_name=NO_DILATION_FILE_NAME)
actual_binary_matrix = ml_utils.dilate_binary_target_images(
target_matrix=actual_binary_matrix,
dilation_distance_metres=DILATION_DISTANCE_METRES,
verbose=False)
predicted_binary_matrix = ml_utils.dilate_binary_target_images(
target_matrix=predicted_binary_matrix,
dilation_distance_metres=DILATION_DISTANCE_METRES,
verbose=False)
_plot_fronts(actual_binary_matrix=actual_binary_matrix,
predicted_binary_matrix=predicted_binary_matrix,
title_string='Observed and predicted front\nwith dilation',
annotation_string='(d)',
output_file_name=WITH_DILATION_FILE_NAME)
print 'Concatenating figures to: "{0:s}"...'.format(CONCAT_FILE_NAME)
imagemagick_utils.concatenate_images(
input_file_names=[NO_DILATION_FILE_NAME, WITH_DILATION_FILE_NAME],
output_file_name=CONCAT_FILE_NAME,
num_panel_rows=1,
num_panel_columns=2)
imagemagick_utils.resize_image(input_file_name=CONCAT_FILE_NAME,
output_file_name=CONCAT_FILE_NAME,
output_size_pixels=CONCAT_SIZE_PIXELS)
def create_full_size_4d_example(target_time_unix_sec, num_lead_time_steps,
predictor_time_step_offsets,
top_narr_directory_name,
top_frontal_grid_dir_name,
narr_predictor_names, pressure_level_mb,
dilation_distance_metres, num_classes):
"""Creates full-size 4-D examples from raw files.
:param target_time_unix_sec: See doc for `create_downsized_3d_examples`.
:param num_lead_time_steps: See doc for `create_downsized_4d_examples`.
:param predictor_time_step_offsets: Same.
:param top_narr_directory_name: See doc for `create_downsized_3d_examples`.
:param top_frontal_grid_dir_name: Same.
:param narr_predictor_names: Same.
:param pressure_level_mb: Same.
:param dilation_distance_metres: Same.
:param num_classes: Same.
:return: predictor_matrix: 1-by-M-by-N-by-T-by-C numpy array of predictor
values.
:return: target_matrix: 1-by-M-by-N numpy array of target values. Each
value is an integer from the list `front_utils.VALID_INTEGER_IDS`.
"""
error_checking.assert_is_integer(num_classes)
error_checking.assert_is_geq(num_classes, 2)
error_checking.assert_is_leq(num_classes, 3)
(narr_file_name_matrix,
frontal_grid_file_names) = trainval_io.find_input_files_for_4d_examples(
first_target_time_unix_sec=target_time_unix_sec,
last_target_time_unix_sec=target_time_unix_sec,
predictor_time_step_offsets=predictor_time_step_offsets,
num_lead_time_steps=num_lead_time_steps,
top_narr_directory_name=top_narr_directory_name,
top_frontal_grid_dir_name=top_frontal_grid_dir_name,
narr_predictor_names=narr_predictor_names,
pressure_level_mb=pressure_level_mb)
narr_file_name_matrix = narr_file_name_matrix[0, ...]
frontal_grid_file_name = frontal_grid_file_names[0]
num_predictor_times_per_example = len(predictor_time_step_offsets)
num_predictors = len(narr_predictor_names)
tuple_of_4d_predictor_matrices = ()
for i in range(num_predictor_times_per_example):
tuple_of_3d_predictor_matrices = ()
for j in range(num_predictors):
print 'Reading data from: "{0:s}"...'.format(
narr_file_name_matrix[i, j])
this_field_predictor_matrix = (
processed_narr_io.read_fields_from_file(
narr_file_name_matrix[i, j]))[0]
this_field_predictor_matrix = (
ml_utils.fill_nans_in_predictor_images(
this_field_predictor_matrix))
tuple_of_3d_predictor_matrices += (this_field_predictor_matrix, )
tuple_of_4d_predictor_matrices += (ml_utils.stack_predictor_variables(
tuple_of_3d_predictor_matrices), )
predictor_matrix = ml_utils.stack_time_steps(
tuple_of_4d_predictor_matrices)
print 'Reading data from: "{0:s}"...'.format(frontal_grid_file_name)
frontal_grid_table = fronts_io.read_narr_grids_from_file(
frontal_grid_file_name)
print 'Processing full-size 4-D machine-learning example...'
predictor_matrix, _ = ml_utils.normalize_predictors(
predictor_matrix=predictor_matrix)
target_matrix = ml_utils.front_table_to_images(
frontal_grid_table=frontal_grid_table,
num_rows_per_image=predictor_matrix.shape[1],
num_columns_per_image=predictor_matrix.shape[2])
if num_classes == 2:
target_matrix = ml_utils.binarize_front_images(target_matrix)
predictor_matrix = ml_utils.subset_narr_grid_for_fcn_input(
predictor_matrix)
target_matrix = ml_utils.subset_narr_grid_for_fcn_input(target_matrix)
if num_classes == 2:
target_matrix = ml_utils.dilate_binary_target_images(
target_matrix=target_matrix,
dilation_distance_metres=dilation_distance_metres,
verbose=False)
else:
target_matrix = ml_utils.dilate_ternary_target_images(
target_matrix=target_matrix,
dilation_distance_metres=dilation_distance_metres,
verbose=False)
predictor_matrix = predictor_matrix.astype('float32')
print 'Fraction of pixels with a front = {0:.4f}'.format(
numpy.mean(target_matrix > 0))
target_matrix = numpy.expand_dims(target_matrix, axis=-1)
return predictor_matrix, target_matrix
def create_downsized_3d_examples(center_row_indices,
center_column_indices,
num_rows_in_half_grid,
num_columns_in_half_grid,
full_predictor_matrix=None,
full_target_matrix=None,
target_time_unix_sec=None,
top_narr_directory_name=None,
top_frontal_grid_dir_name=None,
narr_predictor_names=None,
pressure_level_mb=None,
dilation_distance_metres=None,
num_classes=None):
"""Creates downsized 3-D examples from raw files.
This method creates one example for each center pixel, where center pixels
are specified by `center_row_indices` and `center_column_indices`.
If `full_predictor_matrix` and `full_target_matrix` are specified, all input
args thereafter will be ignored.
E = number of examples (center pixels)
P = number of rows in full NARR grid
Q = number of columns in full NARR grid
:param center_row_indices: length-E numpy array with row for each center
pixel.
:param center_column_indices: length-E numpy array with column for each
center pixel.
:param num_rows_in_half_grid: Number of rows in half-grid for each example.
Actual number of rows will be 2 * `num_rows_in_half_grid` + 1.
:param num_columns_in_half_grid: Same but for columns.
:param full_predictor_matrix: 1-by-P-by-Q-by-C numpy array of predictor
values.
:param full_target_matrix: 1-by-P-by-Q-by-C numpy array of target values.
Each value is an integer from the list `front_utils.VALID_INTEGER_IDS`.
:param target_time_unix_sec: Target time.
:param top_narr_directory_name: Name of top-level directory with NARR data.
Files therein will be found by
`processed_narr_io.find_file_for_one_time` and read by
`processed_narr_io.read_fields_from_file`.
:param top_frontal_grid_dir_name: Name of top-level directory with target
values (grids of front labels). Files therein will be found by
`fronts_io.find_file_for_one_time` and read by
`fronts_io.read_narr_grids_from_file`.
:param narr_predictor_names: length-C list with names of predictor
variables. Each must be accepted by
`processed_narr_io.check_field_name`.
:param pressure_level_mb: Pressure level (millibars) for predictors.
:param dilation_distance_metres: Dilation distance. Will be used to dilate
WF and CF labels, which effectively creates a distance buffer around
each front, thus accounting for spatial uncertainty in front placement.
:param num_classes: Number of target classes (either 2 or 3).
:return: downsized_predictor_matrix: E-by-M-by-N-by-C numpy array of
predictor values.
:return: target_values: length-E numpy array of target values (integers from
the list `front_utils.VALID_INTEGER_IDS`).
:return: full_predictor_matrix: 1-by-P-by-Q-by-C numpy array of predictor
values.
:return: full_target_matrix: 1-by-P-by-Q-by-C numpy array of target values.
Each value is an integer from the list `front_utils.VALID_INTEGER_IDS`.
"""
if full_predictor_matrix is None or full_target_matrix is None:
error_checking.assert_is_integer(num_classes)
error_checking.assert_is_geq(num_classes, 2)
error_checking.assert_is_leq(num_classes, 3)
(narr_file_name_matrix, frontal_grid_file_names
) = trainval_io.find_input_files_for_3d_examples(
first_target_time_unix_sec=target_time_unix_sec,
last_target_time_unix_sec=target_time_unix_sec,
top_narr_directory_name=top_narr_directory_name,
top_frontal_grid_dir_name=top_frontal_grid_dir_name,
narr_predictor_names=narr_predictor_names,
pressure_level_mb=pressure_level_mb)
narr_file_names = narr_file_name_matrix[0, :]
frontal_grid_file_name = frontal_grid_file_names[0]
num_predictors = len(narr_predictor_names)
tuple_of_full_predictor_matrices = ()
for j in range(num_predictors):
print 'Reading data from: "{0:s}"...'.format(narr_file_names[j])
this_field_predictor_matrix = (
processed_narr_io.read_fields_from_file(narr_file_names[j]))[0]
this_field_predictor_matrix = (
ml_utils.fill_nans_in_predictor_images(
this_field_predictor_matrix))
tuple_of_full_predictor_matrices += (this_field_predictor_matrix, )
print 'Reading data from: "{0:s}"...'.format(frontal_grid_file_name)
frontal_grid_table = fronts_io.read_narr_grids_from_file(
frontal_grid_file_name)
full_predictor_matrix = ml_utils.stack_predictor_variables(
tuple_of_full_predictor_matrices)
full_predictor_matrix, _ = ml_utils.normalize_predictors(
predictor_matrix=full_predictor_matrix)
full_target_matrix = ml_utils.front_table_to_images(
frontal_grid_table=frontal_grid_table,
num_rows_per_image=full_predictor_matrix.shape[1],
num_columns_per_image=full_predictor_matrix.shape[2])
if num_classes == 2:
full_target_matrix = ml_utils.binarize_front_images(
full_target_matrix)
if num_classes == 2:
full_target_matrix = ml_utils.dilate_binary_target_images(
target_matrix=full_target_matrix,
dilation_distance_metres=dilation_distance_metres,
verbose=False)
else:
full_target_matrix = ml_utils.dilate_ternary_target_images(
target_matrix=full_target_matrix,
dilation_distance_metres=dilation_distance_metres,
verbose=False)
print 'Creating {0:d} downsized 3-D examples...'.format(
len(center_row_indices))
this_target_point_dict = {
ml_utils.ROW_INDICES_BY_TIME_KEY: [center_row_indices],
ml_utils.COLUMN_INDICES_BY_TIME_KEY: [center_column_indices]
}
(downsized_predictor_matrix,
target_values) = ml_utils.downsize_grids_around_selected_points(
predictor_matrix=full_predictor_matrix,
target_matrix=full_target_matrix,
num_rows_in_half_window=num_rows_in_half_grid,
num_columns_in_half_window=num_columns_in_half_grid,
target_point_dict=this_target_point_dict,
verbose=False)[:2]
downsized_predictor_matrix = downsized_predictor_matrix.astype('float32')
return (downsized_predictor_matrix, target_values, full_predictor_matrix,
full_target_matrix)
def create_downsized_4d_examples(center_row_indices,
center_column_indices,
num_rows_in_half_grid,
num_columns_in_half_grid,
full_predictor_matrix=None,
full_target_matrix=None,
target_time_unix_sec=None,
num_lead_time_steps=None,
predictor_time_step_offsets=None,
top_narr_directory_name=None,
top_frontal_grid_dir_name=None,
narr_predictor_names=None,
pressure_level_mb=None,
dilation_distance_metres=None,
num_classes=None):
"""Creates downsized 4-D examples from raw files.
This method creates one example for each center pixel, where center pixels
are specified by `center_row_indices` and `center_column_indices`.
If `full_predictor_matrix` and `full_target_matrix` are specified, all input
args thereafter will be ignored.
E = number of examples (center pixels)
P = number of rows in full NARR grid
Q = number of columns in full NARR grid
:param center_row_indices: See doc for `create_downsized_3d_examples`.
:param center_column_indices: Same.
:param num_rows_in_half_grid: Same.
:param num_columns_in_half_grid: Same.
:param full_predictor_matrix: 1-by-P-by-Q-by-T-by-C numpy array of
predictor values.
:param full_target_matrix: 1-by-P-by-Q-by-C numpy array of target values.
Each value is an integer from the list `front_utils.VALID_INTEGER_IDS`.
:param target_time_unix_sec: See doc for `create_downsized_3d_examples`.
:param num_lead_time_steps: Number of time steps between target time and
latest possible predictor time.
:param predictor_time_step_offsets: length-T numpy array of offsets between
predictor time and latest possible predictor time (target time minus
lead time).
:param top_narr_directory_name: See doc for `create_downsized_3d_examples`.
:param top_frontal_grid_dir_name: Same.
:param narr_predictor_names: Same.
:param pressure_level_mb: Same.
:param dilation_distance_metres: Same.
:param num_classes: Same.
:return: downsized_predictor_matrix: E-by-M-by-N-by-T-by-C numpy array of
predictor values.
:return: target_values: length-E numpy array of target values (integers from
the list `front_utils.VALID_INTEGER_IDS`).
:return: full_predictor_matrix: 1-by-P-by-Q-by-T-by-C numpy array of
predictor values.
:return: full_target_matrix: 1-by-P-by-Q-by-C numpy array of target values.
Each value is an integer from the list `front_utils.VALID_INTEGER_IDS`.
"""
if full_predictor_matrix is None or full_target_matrix is None:
error_checking.assert_is_integer(num_classes)
error_checking.assert_is_geq(num_classes, 2)
error_checking.assert_is_leq(num_classes, 3)
(narr_file_name_matrix, frontal_grid_file_names
) = trainval_io.find_input_files_for_4d_examples(
first_target_time_unix_sec=target_time_unix_sec,
last_target_time_unix_sec=target_time_unix_sec,
predictor_time_step_offsets=predictor_time_step_offsets,
num_lead_time_steps=num_lead_time_steps,
top_narr_directory_name=top_narr_directory_name,
top_frontal_grid_dir_name=top_frontal_grid_dir_name,
narr_predictor_names=narr_predictor_names,
pressure_level_mb=pressure_level_mb)
narr_file_name_matrix = narr_file_name_matrix[0, ...]
frontal_grid_file_name = frontal_grid_file_names[0]
num_predictor_times_per_example = len(predictor_time_step_offsets)
num_predictors = len(narr_predictor_names)
tuple_of_4d_predictor_matrices = ()
for i in range(num_predictor_times_per_example):
tuple_of_3d_predictor_matrices = ()
for j in range(num_predictors):
print 'Reading data from: "{0:s}"...'.format(
narr_file_name_matrix[i, j])
this_field_predictor_matrix = (
processed_narr_io.read_fields_from_file(
narr_file_name_matrix[i, j]))[0]
this_field_predictor_matrix = (
ml_utils.fill_nans_in_predictor_images(
this_field_predictor_matrix))
tuple_of_3d_predictor_matrices += (
this_field_predictor_matrix, )
tuple_of_4d_predictor_matrices += (
ml_utils.stack_predictor_variables(
tuple_of_3d_predictor_matrices), )
full_predictor_matrix = ml_utils.stack_time_steps(
tuple_of_4d_predictor_matrices)
print 'Reading data from: "{0:s}"...'.format(frontal_grid_file_name)
frontal_grid_table = fronts_io.read_narr_grids_from_file(
frontal_grid_file_name)
full_predictor_matrix, _ = ml_utils.normalize_predictors(
predictor_matrix=full_predictor_matrix)
full_target_matrix = ml_utils.front_table_to_images(
frontal_grid_table=frontal_grid_table,
num_rows_per_image=full_predictor_matrix.shape[1],
num_columns_per_image=full_predictor_matrix.shape[2])
if num_classes == 2:
full_target_matrix = ml_utils.binarize_front_images(
full_target_matrix)
if num_classes == 2:
full_target_matrix = ml_utils.dilate_binary_target_images(
target_matrix=full_target_matrix,
dilation_distance_metres=dilation_distance_metres,
verbose=False)
else:
full_target_matrix = ml_utils.dilate_ternary_target_images(
target_matrix=full_target_matrix,
dilation_distance_metres=dilation_distance_metres,
verbose=False)
print 'Creating {0:d} downsized 4-D examples...'.format(
len(center_row_indices))
this_target_point_dict = {
ml_utils.ROW_INDICES_BY_TIME_KEY: [center_row_indices],
ml_utils.COLUMN_INDICES_BY_TIME_KEY: [center_column_indices]
}
(downsized_predictor_matrix,
target_values) = ml_utils.downsize_grids_around_selected_points(
predictor_matrix=full_predictor_matrix,
target_matrix=full_target_matrix,
num_rows_in_half_window=num_rows_in_half_grid,
num_columns_in_half_window=num_columns_in_half_grid,
target_point_dict=this_target_point_dict,
verbose=False)[:2]
downsized_predictor_matrix = downsized_predictor_matrix.astype('float32')
return (downsized_predictor_matrix, target_values, full_predictor_matrix,
full_target_matrix)
def _run(top_frontal_grid_dir_name, first_time_string, last_time_string,
dilation_distance_metres, min_num_fronts, output_dir_name):
"""Creates mask, indicating where human forecasters usually draw fronts.
This is effectively the main method.
:param top_frontal_grid_dir_name: See documentation at top of file.
:param first_time_string: Same.
:param last_time_string: Same.
:param dilation_distance_metres: Same.
:param min_num_fronts: Same.
:param output_dir_name: Same.
"""
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
error_checking.assert_is_greater(min_num_fronts, 0)
first_time_unix_sec = time_conversion.string_to_unix_sec(
first_time_string, INPUT_TIME_FORMAT)
last_time_unix_sec = time_conversion.string_to_unix_sec(
last_time_string, INPUT_TIME_FORMAT)
valid_times_unix_sec = time_periods.range_and_interval_to_list(
start_time_unix_sec=first_time_unix_sec,
end_time_unix_sec=last_time_unix_sec,
time_interval_sec=TIME_INTERVAL_SECONDS)
num_times = len(valid_times_unix_sec)
num_grid_rows, num_grid_columns = nwp_model_utils.get_grid_dimensions(
model_name=nwp_model_utils.NARR_MODEL_NAME)
num_cold_fronts_matrix = None
num_warm_fronts_matrix = None
for i in range(num_times):
this_file_name = fronts_io.find_file_for_one_time(
top_directory_name=top_frontal_grid_dir_name,
file_type=fronts_io.GRIDDED_FILE_TYPE,
valid_time_unix_sec=valid_times_unix_sec[i],
raise_error_if_missing=False)
if not os.path.isfile(this_file_name):
warning_string = ('POTENTIAL PROBLEM. Cannot find file: "{0:s}"'
).format(this_file_name)
warnings.warn(warning_string)
continue
print 'Reading data from: "{0:s}"...'.format(this_file_name)
this_frontal_grid_table = fronts_io.read_narr_grids_from_file(
this_file_name)
this_frontal_grid_matrix = ml_utils.front_table_to_images(
frontal_grid_table=this_frontal_grid_table,
num_rows_per_image=num_grid_rows,
num_columns_per_image=num_grid_columns)
this_frontal_grid_matrix = ml_utils.dilate_ternary_target_images(
target_matrix=this_frontal_grid_matrix,
dilation_distance_metres=dilation_distance_metres,
verbose=False)
this_frontal_grid_matrix = this_frontal_grid_matrix[0, ...]
this_num_cold_fronts_matrix = (this_frontal_grid_matrix == front_utils.
COLD_FRONT_INTEGER_ID).astype(int)
this_num_warm_fronts_matrix = (this_frontal_grid_matrix == front_utils.
WARM_FRONT_INTEGER_ID).astype(int)
if num_cold_fronts_matrix is None:
num_cold_fronts_matrix = this_num_cold_fronts_matrix + 0
num_warm_fronts_matrix = this_num_warm_fronts_matrix + 0
else:
num_cold_fronts_matrix = (num_cold_fronts_matrix +
this_num_cold_fronts_matrix)
num_warm_fronts_matrix = (num_warm_fronts_matrix +
this_num_warm_fronts_matrix)
print SEPARATOR_STRING
print 'Masking out grid cells with < {0:d} fronts...'.format(
min_num_fronts)
num_both_fronts_matrix = num_warm_fronts_matrix + num_cold_fronts_matrix
mask_matrix = (num_both_fronts_matrix >= min_num_fronts).astype(int)
pickle_file_name = '{0:s}/narr_mask.p'.format(output_dir_name)
print 'Writing mask to: "{0:s}"...'.format(pickle_file_name)
ml_utils.write_narr_mask(mask_matrix=mask_matrix,
pickle_file_name=pickle_file_name)
warm_front_map_file_name = '{0:s}/num_warm_fronts.jpg'.format(
output_dir_name)
_plot_front_densities(num_fronts_matrix=num_warm_fronts_matrix,
colour_map_object=WARM_FRONT_COLOUR_MAP_OBJECT,
title_string='Number of warm fronts',
annotation_string='(a)',
output_file_name=warm_front_map_file_name,
mask_matrix=None,
add_colour_bar=True)
cold_front_map_file_name = '{0:s}/num_cold_fronts.jpg'.format(
output_dir_name)
_plot_front_densities(num_fronts_matrix=num_cold_fronts_matrix,
colour_map_object=COLD_FRONT_COLOUR_MAP_OBJECT,
title_string='Number of cold fronts',
annotation_string='(b)',
output_file_name=cold_front_map_file_name,
mask_matrix=None,
add_colour_bar=True)
both_fronts_title_string = 'Grid cells with at least {0:d} fronts'.format(
min_num_fronts)
both_fronts_map_file_name = '{0:s}/num_both_fronts.jpg'.format(
output_dir_name)
num_both_fronts_matrix[num_both_fronts_matrix > 1] = 1
_plot_front_densities(num_fronts_matrix=num_both_fronts_matrix,
colour_map_object=BOTH_FRONTS_COLOUR_MAP_OBJECT,
title_string=both_fronts_title_string,
annotation_string='(c)',
output_file_name=both_fronts_map_file_name,
mask_matrix=mask_matrix,
add_colour_bar=False)