def _plot_sounding(list_of_predictor_matrices,
model_metadata_dict,
allow_whitespace,
title_string=None):
"""Plots sounding for one example.
:param list_of_predictor_matrices: See doc for `_plot_3d_radar_scan`.
:param model_metadata_dict: Same.
:param allow_whitespace: Same.
:param title_string: Same.
:return: figure_object: Figure handle (instance of
`matplotlib.figure.Figure`).
:return: axes_object: Axes handle (instance of
`matplotlib.axes._subplots.AxesSubplot`).
"""
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
sounding_field_names = training_option_dict[
trainval_io.SOUNDING_FIELDS_KEY]
sounding_heights_m_agl = training_option_dict[
trainval_io.SOUNDING_HEIGHTS_KEY]
sounding_matrix = numpy.expand_dims(list_of_predictor_matrices[-1], axis=0)
# TODO(thunderhoser): Allow actual storm elevation to be used.
metpy_dict = dl_utils.soundings_to_metpy_dictionaries(
sounding_matrix=sounding_matrix,
field_names=sounding_field_names,
height_levels_m_agl=sounding_heights_m_agl,
storm_elevations_m_asl=numpy.array([0.]))[0]
return sounding_plotting.plot_sounding(
sounding_dict_for_metpy=metpy_dict,
title_string=title_string if allow_whitespace else None)
def test_soundings_to_metpy_dictionaries_with_pressure(self):
"""Ensures correct output from soundings_to_metpy_dictionaries.
In this case the soundings contain pressure.
"""
these_metpy_dictionaries = dl_utils.soundings_to_metpy_dictionaries(
sounding_matrix=SOUNDING_MATRIX_UNNORMALIZED,
field_names=SOUNDING_FIELD_NAMES)
self.assertTrue(
_compare_lists_of_metpy_dicts(these_metpy_dictionaries,
LIST_OF_METPY_DICTIONARIES))
def test_soundings_to_metpy_dictionaries_no_pressure(self):
"""Ensures correct output from soundings_to_metpy_dictionaries.
In this case the soundings do not contain pressure.
"""
these_metpy_dictionaries = dl_utils.soundings_to_metpy_dictionaries(
sounding_matrix=SOUNDING_MATRIX_NO_PRESSURE,
field_names=SOUNDING_FIELD_NAMES_NO_PRESSURE,
height_levels_m_agl=HEIGHT_LEVELS_M_AGL,
storm_elevations_m_asl=STORM_ELEVATIONS_M_ASL)
self.assertTrue(
_compare_lists_of_metpy_dicts(these_metpy_dictionaries,
LIST_OF_METPY_DICTIONARIES))
def _plot_sounding_saliency(
saliency_matrix, colour_map_object, max_colour_value, sounding_matrix,
saliency_dict, model_metadata_dict, output_dir_name, pmm_flag,
example_index=None):
"""Plots saliency for sounding.
H = number of sounding heights
F = number of sounding fields
If plotting a composite rather than one example, `full_storm_id_string` and
`storm_time_unix_sec` can be None.
:param saliency_matrix: H-by-F numpy array of saliency values.
:param colour_map_object: See documentation at top of file.
:param max_colour_value: Max value in colour scheme for saliency.
:param sounding_matrix: H-by-F numpy array of actual sounding values.
:param saliency_dict: Dictionary returned from
`saliency_maps.read_standard_file` or `saliency_maps.read_pmm_file`.
:param model_metadata_dict: Dictionary returned by
`cnn.read_model_metadata`.
:param output_dir_name: Path to output directory. Figure will be saved
here.
:param pmm_flag: Boolean flag. If True, will plot composite rather than one
example.
:param example_index: [used only if `pmm_flag == False`]
Will plot the [i]th example, where i = `example_index`.
"""
if pmm_flag:
example_index = 0
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
sounding_field_names = training_option_dict[trainval_io.SOUNDING_FIELDS_KEY]
sounding_heights_m_agl = training_option_dict[
trainval_io.SOUNDING_HEIGHTS_KEY]
sounding_matrix = numpy.expand_dims(sounding_matrix, axis=0)
if saliency_maps.SOUNDING_PRESSURES_KEY in saliency_dict:
pressure_matrix_pascals = numpy.expand_dims(
saliency_dict[saliency_maps.SOUNDING_PRESSURES_KEY], axis=-1
)
pressure_matrix_pascals = pressure_matrix_pascals[[example_index], ...]
sounding_matrix = numpy.concatenate(
(sounding_matrix, pressure_matrix_pascals), axis=-1
)
sounding_dict_for_metpy = dl_utils.soundings_to_metpy_dictionaries(
sounding_matrix=sounding_matrix,
field_names=sounding_field_names + [soundings.PRESSURE_NAME]
)[0]
else:
sounding_dict_for_metpy = dl_utils.soundings_to_metpy_dictionaries(
sounding_matrix=sounding_matrix, field_names=sounding_field_names,
height_levels_m_agl=sounding_heights_m_agl,
storm_elevations_m_asl=numpy.array([0.])
)[0]
if pmm_flag:
full_storm_id_string = None
storm_time_unix_sec = None
title_string = 'PMM composite'
else:
full_storm_id_string = saliency_dict[saliency_maps.FULL_IDS_KEY][
example_index]
storm_time_unix_sec = saliency_dict[saliency_maps.STORM_TIMES_KEY][
example_index]
title_string = 'Storm "{0:s}" at {1:s}'.format(
full_storm_id_string,
time_conversion.unix_sec_to_string(
storm_time_unix_sec, plot_input_examples.TIME_FORMAT)
)
sounding_plotting.plot_sounding(
sounding_dict_for_metpy=sounding_dict_for_metpy,
title_string=title_string)
left_panel_file_name = plot_input_examples.metadata_to_file_name(
output_dir_name=output_dir_name, is_sounding=False, pmm_flag=pmm_flag,
full_storm_id_string=full_storm_id_string,
storm_time_unix_sec=storm_time_unix_sec,
radar_field_name='sounding-actual')
print('Saving figure to file: "{0:s}"...'.format(left_panel_file_name))
pyplot.savefig(left_panel_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(
input_file_name=left_panel_file_name,
output_file_name=left_panel_file_name)
saliency_plotting.plot_saliency_for_sounding(
saliency_matrix=saliency_matrix,
sounding_field_names=sounding_field_names,
pressure_levels_mb=sounding_dict_for_metpy[
soundings.PRESSURE_COLUMN_METPY],
colour_map_object=colour_map_object,
max_absolute_colour_value=max_colour_value)
right_panel_file_name = plot_input_examples.metadata_to_file_name(
output_dir_name=output_dir_name, is_sounding=False, pmm_flag=pmm_flag,
full_storm_id_string=full_storm_id_string,
storm_time_unix_sec=storm_time_unix_sec,
radar_field_name='sounding-saliency')
print('Saving figure to file: "{0:s}"...'.format(right_panel_file_name))
pyplot.savefig(right_panel_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(
input_file_name=right_panel_file_name,
output_file_name=right_panel_file_name)
concat_file_name = plot_input_examples.metadata_to_file_name(
output_dir_name=output_dir_name, is_sounding=True, pmm_flag=pmm_flag,
full_storm_id_string=full_storm_id_string,
storm_time_unix_sec=storm_time_unix_sec)
print('Concatenating figures to: "{0:s}"...\n'.format(concat_file_name))
imagemagick_utils.concatenate_images(
input_file_names=[left_panel_file_name, right_panel_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=SOUNDING_IMAGE_SIZE_PX)
os.remove(left_panel_file_name)
os.remove(right_panel_file_name)
def _plot_sounding_saliency(
sounding_matrix, sounding_saliency_matrix, sounding_field_names,
saliency_metadata_dict, colour_map_object, max_colour_value_by_example,
output_dir_name):
"""Plots soundings along with their saliency maps.
E = number of examples
H = number of sounding heights
F = number of sounding fields
:param sounding_matrix: E-by-H-by-F numpy array of sounding values.
:param sounding_saliency_matrix: E-by-H-by-F numpy array of corresponding
saliency values.
:param sounding_field_names: length-F list of field names.
:param saliency_metadata_dict: Dictionary returned by
`saliency_maps.read_standard_file`.
:param colour_map_object: See doc for `_plot_2d3d_radar_saliency`.
:param max_colour_value_by_example: Same.
:param output_dir_name: Name of output directory (figures will be saved
here).
"""
# TODO(thunderhoser): Generalize this method to deal with
# probability-matched means.
num_examples = sounding_matrix.shape[0]
try:
metpy_dict_by_example = dl_utils.soundings_to_metpy_dictionaries(
sounding_matrix=sounding_matrix, field_names=sounding_field_names)
except:
sounding_pressure_matrix_pa = numpy.expand_dims(
saliency_metadata_dict[saliency_maps.SOUNDING_PRESSURES_KEY],
axis=-1)
this_sounding_matrix = numpy.concatenate(
(sounding_matrix, sounding_pressure_matrix_pa), axis=-1)
metpy_dict_by_example = dl_utils.soundings_to_metpy_dictionaries(
sounding_matrix=this_sounding_matrix,
field_names=sounding_field_names + [soundings.PRESSURE_NAME])
for i in range(num_examples):
this_storm_id = saliency_metadata_dict[
saliency_maps.STORM_IDS_KEY][i]
this_storm_time_string = time_conversion.unix_sec_to_string(
saliency_metadata_dict[saliency_maps.STORM_TIMES_KEY][i],
TIME_FORMAT)
this_title_string = 'Storm "{0:s}" at {1:s}'.format(
this_storm_id, this_storm_time_string)
sounding_plotting.plot_sounding(
sounding_dict_for_metpy=metpy_dict_by_example[i],
title_string=this_title_string)
this_left_file_name = (
'{0:s}/{1:s}_{2:s}_sounding-actual.jpg'
).format(
output_dir_name, this_storm_id.replace('_', '-'),
this_storm_time_string
)
print 'Saving figure to file: "{0:s}"...'.format(
this_left_file_name)
pyplot.savefig(this_left_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(
input_file_name=this_left_file_name,
output_file_name=this_left_file_name)
saliency_plotting.plot_saliency_for_sounding(
saliency_matrix=sounding_saliency_matrix[i, ...],
sounding_field_names=sounding_field_names,
pressure_levels_mb=metpy_dict_by_example[i][
soundings.PRESSURE_COLUMN_METPY],
colour_map_object=colour_map_object,
max_absolute_colour_value=max_colour_value_by_example[i])
this_right_file_name = (
'{0:s}/{1:s}_{2:s}_sounding-saliency.jpg'
).format(
output_dir_name, this_storm_id.replace('_', '-'),
this_storm_time_string
)
print 'Saving figure to file: "{0:s}"...'.format(
this_right_file_name)
pyplot.savefig(this_right_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(
input_file_name=this_right_file_name,
output_file_name=this_right_file_name)
this_file_name = (
'{0:s}/saliency_{1:s}_{2:s}_sounding.jpg'
).format(
output_dir_name, this_storm_id.replace('_', '-'),
this_storm_time_string
)
print 'Concatenating panels into file: "{0:s}"...\n'.format(
this_file_name)
imagemagick_utils.concatenate_images(
input_file_names=[this_left_file_name, this_right_file_name],
output_file_name=this_file_name, num_panel_rows=1,
num_panel_columns=2)
imagemagick_utils.resize_image(
input_file_name=this_file_name,
output_file_name=this_file_name,
output_size_pixels=SOUNDING_IMAGE_SIZE_PX)
os.remove(this_left_file_name)
os.remove(this_right_file_name)
def _plot_bwo_for_soundings(
optimized_sounding_matrix, training_option_dict, top_output_dir_name,
pmm_flag, input_sounding_matrix=None, storm_ids=None,
storm_times_unix_sec=None):
"""Plots BWO results for soundings.
E = number of examples (storm objects)
H = number of sounding heights
F = number of sounding fields
:param optimized_sounding_matrix: E-by-H-by-F numpy array of sounding values
(predictors).
:param training_option_dict: See doc for `_plot_bwo_for_2d3d_radar`.
:param top_output_dir_name: Same.
:param pmm_flag: Same.
:param input_sounding_matrix: Same as `optimized_sounding_matrix` but with
non-optimized input.
:param storm_ids: See doc for `_plot_bwo_for_2d3d_radar`.
:param storm_times_unix_sec: Same.
"""
before_optimization_dir_name = '{0:s}/before_optimization'.format(
top_output_dir_name)
after_optimization_dir_name = '{0:s}/after_optimization'.format(
top_output_dir_name)
file_system_utils.mkdir_recursive_if_necessary(
directory_name=before_optimization_dir_name)
file_system_utils.mkdir_recursive_if_necessary(
directory_name=after_optimization_dir_name)
if pmm_flag:
have_storm_ids = False
else:
have_storm_ids = not (storm_ids is None or storm_times_unix_sec is None)
num_storms = optimized_sounding_matrix.shape[0]
list_of_optimized_metpy_dicts = dl_utils.soundings_to_metpy_dictionaries(
sounding_matrix=optimized_sounding_matrix,
field_names=training_option_dict[trainval_io.SOUNDING_FIELDS_KEY],
height_levels_m_agl=training_option_dict[
trainval_io.SOUNDING_HEIGHTS_KEY],
storm_elevations_m_asl=numpy.zeros(num_storms))
if input_sounding_matrix is None:
list_of_input_metpy_dicts = None
else:
list_of_input_metpy_dicts = dl_utils.soundings_to_metpy_dictionaries(
sounding_matrix=input_sounding_matrix,
field_names=training_option_dict[trainval_io.SOUNDING_FIELDS_KEY],
height_levels_m_agl=training_option_dict[
trainval_io.SOUNDING_HEIGHTS_KEY],
storm_elevations_m_asl=numpy.zeros(num_storms))
for i in range(num_storms):
if pmm_flag:
this_base_title_string = 'Probability-matched mean'
this_base_pathless_file_name = 'pmm'
else:
if have_storm_ids:
this_storm_time_string = time_conversion.unix_sec_to_string(
storm_times_unix_sec[i], TIME_FORMAT)
this_base_title_string = 'Storm "{0:s}" at {1:s}'.format(
storm_ids[i], this_storm_time_string)
this_base_pathless_file_name = '{0:s}_{1:s}'.format(
storm_ids[i].replace('_', '-'), this_storm_time_string)
else:
this_base_title_string = 'Example {0:d}'.format(i + 1)
this_base_pathless_file_name = 'example{0:06d}'.format(i)
this_title_string = '{0:s} (after optimization)'.format(
this_base_title_string)
this_file_name = '{0:s}/{1:s}_after-optimization_sounding.jpg'.format(
after_optimization_dir_name, this_base_pathless_file_name)
sounding_plotting.plot_sounding(
sounding_dict_for_metpy=list_of_optimized_metpy_dicts[i],
title_string=this_title_string)
print 'Saving figure to: "{0:s}"...'.format(this_file_name)
pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
if input_sounding_matrix is None:
continue
this_title_string = '{0:s} (before optimization)'.format(
this_base_title_string)
this_file_name = '{0:s}/{1:s}_before-optimization_sounding.jpg'.format(
before_optimization_dir_name, this_base_pathless_file_name)
sounding_plotting.plot_sounding(
sounding_dict_for_metpy=list_of_input_metpy_dicts[i],
title_string=this_title_string)
print 'Saving figure to: "{0:s}"...'.format(this_file_name)
pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
def plot_examples(list_of_predictor_matrices,
storm_ids,
storm_times_unix_sec,
model_metadata_dict,
output_dir_name,
storm_activations=None):
"""Plots one or more learning examples.
E = number of examples (storm objects)
:param list_of_predictor_matrices: List created by
`testing_io.read_specific_examples`. Contains data to be plotted.
:param storm_ids: length-E list of storm IDs.
:param storm_times_unix_sec: length-E numpy array of storm times.
:param model_metadata_dict: See doc for `cnn.read_model_metadata`.
:param output_dir_name: Name of output directory (figures will be saved
here).
:param storm_activations: length-E numpy array of storm activations (may be
None). Will be included in title of each figure.
"""
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
sounding_field_names = training_option_dict[
trainval_io.SOUNDING_FIELDS_KEY]
plot_soundings = sounding_field_names is not None
if plot_soundings:
list_of_metpy_dictionaries = dl_utils.soundings_to_metpy_dictionaries(
sounding_matrix=list_of_predictor_matrices[-1],
field_names=sounding_field_names)
else:
list_of_metpy_dictionaries = None
num_radar_dimensions = len(list_of_predictor_matrices[0].shape) - 2
list_of_layer_operation_dicts = model_metadata_dict[
cnn.LAYER_OPERATIONS_KEY]
if num_radar_dimensions == 2:
if list_of_layer_operation_dicts is None:
field_name_by_panel = training_option_dict[
trainval_io.RADAR_FIELDS_KEY]
panel_names = (
radar_plotting.radar_fields_and_heights_to_panel_names(
field_names=field_name_by_panel,
heights_m_agl=training_option_dict[
trainval_io.RADAR_HEIGHTS_KEY]))
plot_colour_bar_by_panel = numpy.full(len(panel_names),
True,
dtype=bool)
else:
field_name_by_panel, panel_names = (
radar_plotting.layer_ops_to_field_and_panel_names(
list_of_layer_operation_dicts))
plot_colour_bar_by_panel = numpy.full(len(panel_names),
False,
dtype=bool)
plot_colour_bar_by_panel[2::3] = True
else:
field_name_by_panel = None
panel_names = None
plot_colour_bar_by_panel = None
az_shear_field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY]
num_az_shear_fields = len(az_shear_field_names)
num_storms = len(storm_ids)
myrorss_2d3d = len(list_of_predictor_matrices) == 3
for i in range(num_storms):
this_time_string = time_conversion.unix_sec_to_string(
storm_times_unix_sec[i], TIME_FORMAT)
this_base_title_string = 'Storm "{0:s}" at {1:s}'.format(
storm_ids[i], this_time_string)
if storm_activations is not None:
this_base_title_string += ' (activation = {0:.3f})'.format(
storm_activations[i])
this_base_file_name = '{0:s}/storm={1:s}_{2:s}'.format(
output_dir_name, storm_ids[i].replace('_', '-'), this_time_string)
if plot_soundings:
sounding_plotting.plot_sounding(
sounding_dict_for_metpy=list_of_metpy_dictionaries[i],
title_string=this_base_title_string)
this_file_name = '{0:s}_sounding.jpg'.format(this_base_file_name)
print 'Saving figure to: "{0:s}"...'.format(this_file_name)
pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
if myrorss_2d3d:
this_reflectivity_matrix_dbz = numpy.flip(
list_of_predictor_matrices[0][i, ..., 0], axis=0)
this_num_heights = this_reflectivity_matrix_dbz.shape[-1]
this_num_panel_rows = int(numpy.floor(
numpy.sqrt(this_num_heights)))
_, these_axes_objects = radar_plotting.plot_3d_grid_without_coords(
field_matrix=this_reflectivity_matrix_dbz,
field_name=radar_utils.REFL_NAME,
grid_point_heights_metres=training_option_dict[
trainval_io.RADAR_HEIGHTS_KEY],
ground_relative=True,
num_panel_rows=this_num_panel_rows,
font_size=FONT_SIZE_SANS_COLOUR_BARS)
this_colour_map_object, this_colour_norm_object = (
radar_plotting.get_default_colour_scheme(
radar_utils.REFL_NAME))
plotting_utils.add_colour_bar(
axes_object_or_list=these_axes_objects,
values_to_colour=this_reflectivity_matrix_dbz,
colour_map=this_colour_map_object,
colour_norm_object=this_colour_norm_object,
orientation='horizontal',
extend_min=True,
extend_max=True)
this_title_string = '{0:s}; {1:s}'.format(this_base_title_string,
radar_utils.REFL_NAME)
this_file_name = '{0:s}_reflectivity.jpg'.format(
this_base_file_name)
pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE)
print 'Saving figure to: "{0:s}"...'.format(this_file_name)
pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
this_az_shear_matrix_s01 = numpy.flip(
list_of_predictor_matrices[1][i, ..., 0], axis=0)
_, these_axes_objects = (
radar_plotting.plot_many_2d_grids_without_coords(
field_matrix=this_az_shear_matrix_s01,
field_name_by_panel=az_shear_field_names,
panel_names=az_shear_field_names,
num_panel_rows=1,
plot_colour_bar_by_panel=numpy.full(num_az_shear_fields,
False,
dtype=bool),
font_size=FONT_SIZE_SANS_COLOUR_BARS))
this_colour_map_object, this_colour_norm_object = (
radar_plotting.get_default_colour_scheme(
radar_utils.LOW_LEVEL_SHEAR_NAME))
plotting_utils.add_colour_bar(
axes_object_or_list=these_axes_objects,
values_to_colour=this_az_shear_matrix_s01,
colour_map=this_colour_map_object,
colour_norm_object=this_colour_norm_object,
orientation='horizontal',
extend_min=True,
extend_max=True)
this_file_name = '{0:s}_shear.jpg'.format(this_base_file_name)
pyplot.suptitle(this_base_title_string, fontsize=TITLE_FONT_SIZE)
print 'Saving figure to: "{0:s}"...'.format(this_file_name)
pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
continue
this_radar_matrix = list_of_predictor_matrices[0]
if num_radar_dimensions == 2:
this_num_channels = this_radar_matrix.shape[-1]
this_num_panel_rows = int(
numpy.floor(numpy.sqrt(this_num_channels)))
radar_plotting.plot_many_2d_grids_without_coords(
field_matrix=numpy.flip(this_radar_matrix[i, ...], axis=0),
field_name_by_panel=field_name_by_panel,
panel_names=panel_names,
num_panel_rows=this_num_panel_rows,
plot_colour_bar_by_panel=plot_colour_bar_by_panel,
font_size=FONT_SIZE_WITH_COLOUR_BARS,
row_major=False)
this_title_string = this_base_title_string + ''
pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE)
this_file_name = '{0:s}.jpg'.format(this_base_file_name)
print 'Saving figure to: "{0:s}"...'.format(this_file_name)
pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
continue
radar_field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY]
radar_heights_m_agl = training_option_dict[
trainval_io.RADAR_HEIGHTS_KEY]
for j in range(len(radar_field_names)):
this_num_heights = this_radar_matrix.shape[-2]
this_num_panel_rows = int(numpy.floor(
numpy.sqrt(this_num_heights)))
_, these_axes_objects = radar_plotting.plot_3d_grid_without_coords(
field_matrix=numpy.flip(this_radar_matrix[i, ..., j], axis=0),
field_name=radar_field_names[j],
grid_point_heights_metres=radar_heights_m_agl,
ground_relative=True,
num_panel_rows=this_num_panel_rows,
font_size=FONT_SIZE_SANS_COLOUR_BARS)
this_colour_map_object, this_colour_norm_object = (
radar_plotting.get_default_colour_scheme(radar_field_names[j]))
plotting_utils.add_colour_bar(
axes_object_or_list=these_axes_objects,
values_to_colour=this_radar_matrix[i, ..., j],
colour_map=this_colour_map_object,
colour_norm_object=this_colour_norm_object,
orientation='horizontal',
extend_min=True,
extend_max=True)
this_title_string = '{0:s}; {1:s}'.format(this_base_title_string,
radar_field_names[j])
this_file_name = '{0:s}_{1:s}.jpg'.format(
this_base_file_name, radar_field_names[j].replace('_', '-'))
pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE)
print 'Saving figure to: "{0:s}"...'.format(this_file_name)
pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
