def _plot_one_composite(saliency_file_name, composite_name_abbrev,
composite_name_verbose, colour_map_object,
max_colour_value, half_num_contours,
smoothing_radius_grid_cells, output_dir_name):
"""Plots saliency map for one composite.
:param saliency_file_name: Path to input file (will be read by
`saliency.read_file`).
:param composite_name_abbrev: Abbrev composite name (will be used in file
names).
:param composite_name_verbose: Verbose composite name (will be used in
figure title).
:param colour_map_object: See documentation at top of file.
:param max_colour_value: Same.
:param half_num_contours: Same.
:param smoothing_radius_grid_cells: Same.
:param output_dir_name: Name of output directory (figures will be saved
here).
:return: main_figure_file_name: Path to main image file created by this
method.
:return: max_colour_value: See input doc.
"""
mean_radar_matrix, mean_saliency_matrix, model_metadata_dict = (
_read_one_composite(
saliency_file_name=saliency_file_name,
smoothing_radius_grid_cells=smoothing_radius_grid_cells))
if numpy.isnan(max_colour_value):
max_colour_value = numpy.percentile(mean_saliency_matrix,
MAX_COLOUR_PERCENTILE)
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY]
num_fields = mean_radar_matrix.shape[-1]
num_heights = mean_radar_matrix.shape[-2]
handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=[mean_radar_matrix],
model_metadata_dict=model_metadata_dict,
pmm_flag=True,
allow_whitespace=True,
plot_panel_names=True,
panel_name_font_size=PANEL_NAME_FONT_SIZE,
add_titles=False,
label_colour_bars=True,
colour_bar_length=COLOUR_BAR_LENGTH,
colour_bar_font_size=COLOUR_BAR_FONT_SIZE,
num_panel_rows=num_heights)
figure_objects = handle_dict[plot_examples.RADAR_FIGURES_KEY]
axes_object_matrices = handle_dict[plot_examples.RADAR_AXES_KEY]
for k in range(num_fields):
this_saliency_matrix = mean_saliency_matrix[0, ..., k]
saliency_plotting.plot_many_2d_grids_with_contours(
saliency_matrix_3d=numpy.flip(this_saliency_matrix, axis=0),
axes_object_matrix=axes_object_matrices[k],
colour_map_object=colour_map_object,
max_absolute_contour_level=max_colour_value,
contour_interval=max_colour_value / half_num_contours)
panel_file_names = [None] * num_fields
for k in range(num_fields):
panel_file_names[k] = '{0:s}/{1:s}_{2:s}.jpg'.format(
output_dir_name, composite_name_abbrev,
field_names[k].replace('_', '-'))
print('Saving figure to: "{0:s}"...'.format(panel_file_names[k]))
figure_objects[k].savefig(panel_file_names[k],
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(figure_objects[k])
main_figure_file_name = '{0:s}/{1:s}_saliency.jpg'.format(
output_dir_name, composite_name_abbrev)
print('Concatenating panels to: "{0:s}"...'.format(main_figure_file_name))
imagemagick_utils.concatenate_images(
input_file_names=panel_file_names,
output_file_name=main_figure_file_name,
num_panel_rows=1,
num_panel_columns=num_fields,
border_width_pixels=50)
imagemagick_utils.resize_image(input_file_name=main_figure_file_name,
output_file_name=main_figure_file_name,
output_size_pixels=CONCAT_FIGURE_SIZE_PX)
imagemagick_utils.trim_whitespace(input_file_name=main_figure_file_name,
output_file_name=main_figure_file_name,
border_width_pixels=TITLE_FONT_SIZE + 25)
_overlay_text(image_file_name=main_figure_file_name,
x_offset_from_center_px=0,
y_offset_from_top_px=0,
text_string=composite_name_verbose)
imagemagick_utils.trim_whitespace(input_file_name=main_figure_file_name,
output_file_name=main_figure_file_name,
border_width_pixels=10)
return main_figure_file_name, max_colour_value
def _run(input_file_name, allow_whitespace, plot_significance,
plot_regions_of_interest, colour_map_name, max_colour_percentile,
top_output_dir_name):
"""Plots Grad-CAM output (class-activation maps).
This is effectively the main method.
:param input_file_name: See documentation at top of file.
:param allow_whitespace: Same.
:param plot_significance: Same.
:param plot_regions_of_interest: Same.
:param colour_map_name: Same.
:param max_colour_percentile: Same.
:param top_output_dir_name: Same.
"""
if plot_significance:
plot_regions_of_interest = False
unguided_cam_dir_name = '{0:s}/main_gradcam'.format(top_output_dir_name)
guided_cam_dir_name = '{0:s}/guided_gradcam'.format(top_output_dir_name)
file_system_utils.mkdir_recursive_if_necessary(
directory_name=unguided_cam_dir_name)
file_system_utils.mkdir_recursive_if_necessary(
directory_name=guided_cam_dir_name)
# Check input args.
error_checking.assert_is_geq(max_colour_percentile, 0.)
error_checking.assert_is_leq(max_colour_percentile, 100.)
colour_map_object = pyplot.cm.get_cmap(colour_map_name)
print('Reading data from: "{0:s}"...'.format(input_file_name))
try:
gradcam_dict = gradcam.read_standard_file(input_file_name)
list_of_input_matrices = gradcam_dict[gradcam.INPUT_MATRICES_KEY]
list_of_cam_matrices = gradcam_dict[gradcam.CAM_MATRICES_KEY]
list_of_guided_cam_matrices = gradcam_dict[
gradcam.GUIDED_CAM_MATRICES_KEY]
full_storm_id_strings = gradcam_dict[gradcam.FULL_IDS_KEY]
storm_times_unix_sec = gradcam_dict[gradcam.STORM_TIMES_KEY]
except ValueError:
gradcam_dict = gradcam.read_pmm_file(input_file_name)
list_of_input_matrices = gradcam_dict[gradcam.MEAN_INPUT_MATRICES_KEY]
list_of_cam_matrices = gradcam_dict[gradcam.MEAN_CAM_MATRICES_KEY]
list_of_guided_cam_matrices = gradcam_dict[
gradcam.MEAN_GUIDED_CAM_MATRICES_KEY]
for i in range(len(list_of_input_matrices)):
list_of_input_matrices[i] = numpy.expand_dims(
list_of_input_matrices[i], axis=0)
if list_of_cam_matrices[i] is None:
continue
list_of_cam_matrices[i] = numpy.expand_dims(
list_of_cam_matrices[i], axis=0)
list_of_guided_cam_matrices[i] = numpy.expand_dims(
list_of_guided_cam_matrices[i], axis=0)
full_storm_id_strings = [None]
storm_times_unix_sec = [None]
pmm_flag = (full_storm_id_strings[0] is None
and storm_times_unix_sec[0] is None)
# Read metadata for CNN.
model_file_name = gradcam_dict[gradcam.MODEL_FILE_KEY]
model_metafile_name = '{0:s}/model_metadata.p'.format(
os.path.split(model_file_name)[0])
print(
'Reading model metadata from: "{0:s}"...'.format(model_metafile_name))
model_metadata_dict = cnn.read_model_metadata(model_metafile_name)
print(SEPARATOR_STRING)
cam_monte_carlo_dict = (gradcam_dict[gradcam.CAM_MONTE_CARLO_KEY]
if plot_significance else None)
guided_cam_monte_carlo_dict = (
gradcam_dict[gradcam.GUIDED_CAM_MONTE_CARLO_KEY]
if plot_significance else None)
region_dict = (gradcam_dict[gradcam.REGION_DICT_KEY]
if plot_regions_of_interest else None)
num_examples = list_of_input_matrices[0].shape[0]
num_input_matrices = len(list_of_input_matrices)
for i in range(num_examples):
these_figure_objects, these_axes_object_matrices = (
plot_input_examples.plot_one_example(
list_of_predictor_matrices=list_of_input_matrices,
model_metadata_dict=model_metadata_dict,
plot_sounding=False,
allow_whitespace=allow_whitespace,
pmm_flag=pmm_flag,
example_index=i,
full_storm_id_string=full_storm_id_strings[i],
storm_time_unix_sec=storm_times_unix_sec[i]))
for j in range(num_input_matrices):
if list_of_cam_matrices[j] is None:
continue
if cam_monte_carlo_dict is None:
this_significance_matrix = None
else:
this_significance_matrix = numpy.logical_or(
cam_monte_carlo_dict[
monte_carlo.TRIAL_PMM_MATRICES_KEY][j][i, ...] <
cam_monte_carlo_dict[monte_carlo.MIN_MATRICES_KEY][j][i,
...],
cam_monte_carlo_dict[
monte_carlo.TRIAL_PMM_MATRICES_KEY][j][i, ...] >
cam_monte_carlo_dict[monte_carlo.MAX_MATRICES_KEY][j][i,
...])
this_num_spatial_dim = len(list_of_input_matrices[j].shape) - 2
if this_num_spatial_dim == 3:
_plot_3d_radar_cam(
colour_map_object=colour_map_object,
max_colour_percentile=max_colour_percentile,
figure_objects=these_figure_objects,
axes_object_matrices=these_axes_object_matrices,
model_metadata_dict=model_metadata_dict,
output_dir_name=unguided_cam_dir_name,
cam_matrix=list_of_cam_matrices[j][i, ...],
significance_matrix=this_significance_matrix,
full_storm_id_string=full_storm_id_strings[i],
storm_time_unix_sec=storm_times_unix_sec[i])
else:
if region_dict is None:
_plot_2d_radar_cam(
colour_map_object=colour_map_object,
max_colour_percentile=max_colour_percentile,
figure_objects=these_figure_objects,
axes_object_matrices=these_axes_object_matrices,
model_metadata_dict=model_metadata_dict,
output_dir_name=unguided_cam_dir_name,
cam_matrix=list_of_cam_matrices[j][i, ...],
significance_matrix=this_significance_matrix,
full_storm_id_string=full_storm_id_strings[i],
storm_time_unix_sec=storm_times_unix_sec[i])
else:
_plot_2d_regions(
figure_objects=these_figure_objects,
axes_object_matrices=these_axes_object_matrices,
model_metadata_dict=model_metadata_dict,
list_of_polygon_objects=region_dict[
gradcam.POLYGON_OBJECTS_KEY][j][i],
output_dir_name=unguided_cam_dir_name,
full_storm_id_string=full_storm_id_strings[i],
storm_time_unix_sec=storm_times_unix_sec[i])
these_figure_objects, these_axes_object_matrices = (
plot_input_examples.plot_one_example(
list_of_predictor_matrices=list_of_input_matrices,
model_metadata_dict=model_metadata_dict,
plot_sounding=False,
allow_whitespace=allow_whitespace,
pmm_flag=pmm_flag,
example_index=i,
full_storm_id_string=full_storm_id_strings[i],
storm_time_unix_sec=storm_times_unix_sec[i]))
for j in range(num_input_matrices):
if list_of_guided_cam_matrices[j] is None:
continue
if guided_cam_monte_carlo_dict is None:
this_significance_matrix = None
else:
this_significance_matrix = numpy.logical_or(
guided_cam_monte_carlo_dict[
monte_carlo.TRIAL_PMM_MATRICES_KEY][j][i, ...] <
guided_cam_monte_carlo_dict[monte_carlo.MIN_MATRICES_KEY]
[j][i, ...], guided_cam_monte_carlo_dict[
monte_carlo.TRIAL_PMM_MATRICES_KEY][j][i, ...] >
guided_cam_monte_carlo_dict[
monte_carlo.MAX_MATRICES_KEY][j][i, ...])
this_num_spatial_dim = len(list_of_input_matrices[j].shape) - 2
if this_num_spatial_dim == 3:
_plot_3d_radar_cam(
colour_map_object=colour_map_object,
max_colour_percentile=max_colour_percentile,
figure_objects=these_figure_objects,
axes_object_matrices=these_axes_object_matrices,
model_metadata_dict=model_metadata_dict,
output_dir_name=guided_cam_dir_name,
guided_cam_matrix=list_of_guided_cam_matrices[j][i, ...],
significance_matrix=this_significance_matrix,
full_storm_id_string=full_storm_id_strings[i],
storm_time_unix_sec=storm_times_unix_sec[i])
else:
_plot_2d_radar_cam(
colour_map_object=colour_map_object,
max_colour_percentile=max_colour_percentile,
figure_objects=these_figure_objects,
axes_object_matrices=these_axes_object_matrices,
model_metadata_dict=model_metadata_dict,
output_dir_name=guided_cam_dir_name,
guided_cam_matrix=list_of_guided_cam_matrices[j][i, ...],
significance_matrix=this_significance_matrix,
full_storm_id_string=full_storm_id_strings[i],
storm_time_unix_sec=storm_times_unix_sec[i])
def _plot_one_composite(saliency_file_name, monte_carlo_file_name,
composite_name_abbrev, composite_name_verbose,
colour_map_object, max_colour_value, half_num_contours,
smoothing_radius_grid_cells, output_dir_name):
"""Plots saliency map for one composite.
:param saliency_file_name: Path to saliency file (will be read by
`saliency.read_file`).
:param monte_carlo_file_name: Path to Monte Carlo file (will be read by
`_read_monte_carlo_file`).
:param composite_name_abbrev: Abbrev composite name (will be used in file
names).
:param composite_name_verbose: Verbose composite name (will be used in
figure title).
:param colour_map_object: See documentation at top of file.
:param max_colour_value: Same.
:param half_num_contours: Same.
:param smoothing_radius_grid_cells: Same.
:param output_dir_name: Name of output directory (figures will be saved
here).
:return: main_figure_file_name: Path to main image file created by this
method.
"""
(mean_radar_matrices, mean_saliency_matrices, significance_matrices,
model_metadata_dict) = _read_one_composite(
saliency_file_name=saliency_file_name,
smoothing_radius_grid_cells=smoothing_radius_grid_cells,
monte_carlo_file_name=monte_carlo_file_name)
refl_heights_m_agl = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY][
trainval_io.RADAR_HEIGHTS_KEY]
num_refl_heights = len(refl_heights_m_agl)
handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=mean_radar_matrices,
model_metadata_dict=model_metadata_dict,
pmm_flag=True,
plot_sounding=False,
allow_whitespace=True,
plot_panel_names=True,
panel_name_font_size=PANEL_NAME_FONT_SIZE,
add_titles=False,
label_colour_bars=True,
colour_bar_length=COLOUR_BAR_LENGTH,
colour_bar_font_size=COLOUR_BAR_FONT_SIZE,
num_panel_rows=num_refl_heights)
axes_object_matrices = handle_dict[plot_examples.RADAR_AXES_KEY]
this_saliency_matrix = numpy.flip(mean_saliency_matrices[0][0, ..., 0],
axis=0)
saliency_plotting.plot_many_2d_grids_with_contours(
saliency_matrix_3d=this_saliency_matrix,
axes_object_matrix=axes_object_matrices[0],
colour_map_object=colour_map_object,
max_absolute_contour_level=max_colour_value,
contour_interval=max_colour_value / half_num_contours,
row_major=True)
this_sig_matrix = numpy.flip(significance_matrices[0][0, ..., 0], axis=0)
significance_plotting.plot_many_2d_grids_without_coords(
significance_matrix=this_sig_matrix,
axes_object_matrix=axes_object_matrices[0],
marker_size=2,
row_major=True)
this_saliency_matrix = numpy.flip(mean_saliency_matrices[1][0, ...],
axis=0)
saliency_plotting.plot_many_2d_grids_with_contours(
saliency_matrix_3d=this_saliency_matrix,
axes_object_matrix=axes_object_matrices[1],
colour_map_object=colour_map_object,
max_absolute_contour_level=max_colour_value,
contour_interval=max_colour_value / half_num_contours,
row_major=False)
this_sig_matrix = numpy.flip(significance_matrices[1][0, ...], axis=0)
significance_plotting.plot_many_2d_grids_without_coords(
significance_matrix=this_sig_matrix,
axes_object_matrix=axes_object_matrices[1],
marker_size=2,
row_major=False)
refl_figure_object = handle_dict[plot_examples.RADAR_FIGURES_KEY][0]
refl_figure_file_name = '{0:s}/{1:s}_reflectivity.jpg'.format(
output_dir_name, composite_name_abbrev)
print('Saving figure to: "{0:s}"...'.format(refl_figure_file_name))
refl_figure_object.savefig(refl_figure_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(refl_figure_object)
shear_figure_object = handle_dict[plot_examples.RADAR_FIGURES_KEY][1]
shear_figure_file_name = '{0:s}/{1:s}_shear.jpg'.format(
output_dir_name, composite_name_abbrev)
print('Saving figure to: "{0:s}"...'.format(shear_figure_file_name))
shear_figure_object.savefig(shear_figure_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(shear_figure_object)
main_figure_file_name = '{0:s}/{1:s}_saliency.jpg'.format(
output_dir_name, composite_name_abbrev)
print('Concatenating panels to: "{0:s}"...'.format(main_figure_file_name))
imagemagick_utils.concatenate_images(
input_file_names=[refl_figure_file_name, shear_figure_file_name],
output_file_name=main_figure_file_name,
num_panel_rows=1,
num_panel_columns=2,
border_width_pixels=50,
extra_args_string='-gravity south')
imagemagick_utils.resize_image(input_file_name=main_figure_file_name,
output_file_name=main_figure_file_name,
output_size_pixels=CONCAT_FIGURE_SIZE_PX)
imagemagick_utils.trim_whitespace(input_file_name=main_figure_file_name,
output_file_name=main_figure_file_name,
border_width_pixels=TITLE_FONT_SIZE + 25)
_overlay_text(image_file_name=main_figure_file_name,
x_offset_from_center_px=0,
y_offset_from_top_px=0,
text_string=composite_name_verbose)
imagemagick_utils.trim_whitespace(input_file_name=main_figure_file_name,
output_file_name=main_figure_file_name,
border_width_pixels=10)
return main_figure_file_name
def _run(input_file_name, plot_soundings, allow_whitespace, plot_significance,
colour_map_name, max_colour_percentile, output_dir_name):
"""Plots saliency maps for a CNN (convolutional neural network).
This is effectively the main method.
:param input_file_name: See documentation at top of file.
:param plot_soundings: Same.
:param allow_whitespace: Same.
:param plot_significance: Same.
:param colour_map_name: Same.
:param max_colour_percentile: Same.
:param output_dir_name: Same.
"""
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
error_checking.assert_is_geq(max_colour_percentile, 0.)
error_checking.assert_is_leq(max_colour_percentile, 100.)
colour_map_object = pyplot.cm.get_cmap(colour_map_name)
print('Reading data from: "{0:s}"...'.format(input_file_name))
try:
saliency_dict = saliency_maps.read_standard_file(input_file_name)
list_of_input_matrices = saliency_dict.pop(
saliency_maps.INPUT_MATRICES_KEY)
list_of_saliency_matrices = saliency_dict.pop(
saliency_maps.SALIENCY_MATRICES_KEY)
full_storm_id_strings = saliency_dict[saliency_maps.FULL_IDS_KEY]
storm_times_unix_sec = saliency_dict[saliency_maps.STORM_TIMES_KEY]
except ValueError:
saliency_dict = saliency_maps.read_pmm_file(input_file_name)
list_of_input_matrices = saliency_dict.pop(
saliency_maps.MEAN_INPUT_MATRICES_KEY)
list_of_saliency_matrices = saliency_dict.pop(
saliency_maps.MEAN_SALIENCY_MATRICES_KEY)
for i in range(len(list_of_input_matrices)):
list_of_input_matrices[i] = numpy.expand_dims(
list_of_input_matrices[i], axis=0
)
list_of_saliency_matrices[i] = numpy.expand_dims(
list_of_saliency_matrices[i], axis=0
)
full_storm_id_strings = [None]
storm_times_unix_sec = [None]
pmm_flag = (
full_storm_id_strings[0] is None and storm_times_unix_sec[0] is None
)
num_examples = list_of_input_matrices[0].shape[0]
max_colour_value_by_example = numpy.full(num_examples, numpy.nan)
for i in range(num_examples):
these_saliency_values = numpy.concatenate(
[numpy.ravel(s[i, ...]) for s in list_of_saliency_matrices]
)
max_colour_value_by_example[i] = numpy.percentile(
numpy.absolute(these_saliency_values), max_colour_percentile
)
model_file_name = saliency_dict[saliency_maps.MODEL_FILE_KEY]
model_metafile_name = '{0:s}/model_metadata.p'.format(
os.path.split(model_file_name)[0]
)
print('Reading metadata from: "{0:s}"...'.format(model_metafile_name))
model_metadata_dict = cnn.read_model_metadata(model_metafile_name)
print(SEPARATOR_STRING)
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
has_soundings = (
training_option_dict[trainval_io.SOUNDING_FIELDS_KEY] is not None
)
num_radar_matrices = len(list_of_input_matrices) - int(has_soundings)
monte_carlo_dict = (
saliency_dict[saliency_maps.MONTE_CARLO_DICT_KEY]
if plot_significance and
saliency_maps.MONTE_CARLO_DICT_KEY in saliency_dict
else None
)
for i in range(num_examples):
if has_soundings and plot_soundings:
_plot_sounding_saliency(
saliency_matrix=list_of_saliency_matrices[-1][i, ...],
colour_map_object=colour_map_object,
max_colour_value=max_colour_value_by_example[i],
sounding_matrix=list_of_input_matrices[-1][i, ...],
saliency_dict=saliency_dict,
model_metadata_dict=model_metadata_dict,
output_dir_name=output_dir_name,
pmm_flag=pmm_flag, example_index=i)
this_handle_dict = plot_input_examples.plot_one_example(
list_of_predictor_matrices=list_of_input_matrices,
model_metadata_dict=model_metadata_dict,
plot_sounding=False, allow_whitespace=allow_whitespace,
pmm_flag=pmm_flag, example_index=i,
full_storm_id_string=full_storm_id_strings[i],
storm_time_unix_sec=storm_times_unix_sec[i]
)
these_figure_objects = this_handle_dict[
plot_input_examples.RADAR_FIGURES_KEY]
these_axes_object_matrices = this_handle_dict[
plot_input_examples.RADAR_AXES_KEY]
for j in range(num_radar_matrices):
if monte_carlo_dict is None:
this_significance_matrix = None
else:
this_significance_matrix = numpy.logical_or(
monte_carlo_dict[
monte_carlo.TRIAL_PMM_MATRICES_KEY][j][i, ...] <
monte_carlo_dict[monte_carlo.MIN_MATRICES_KEY][j][i, ...],
monte_carlo_dict[
monte_carlo.TRIAL_PMM_MATRICES_KEY][j][i, ...] >
monte_carlo_dict[monte_carlo.MAX_MATRICES_KEY][j][i, ...]
)
this_num_spatial_dim = len(list_of_input_matrices[j].shape) - 2
if this_num_spatial_dim == 3:
_plot_3d_radar_saliency(
saliency_matrix=list_of_saliency_matrices[j][i, ...],
colour_map_object=colour_map_object,
max_colour_value=max_colour_value_by_example[i],
figure_objects=these_figure_objects,
axes_object_matrices=these_axes_object_matrices,
model_metadata_dict=model_metadata_dict,
output_dir_name=output_dir_name,
significance_matrix=this_significance_matrix,
full_storm_id_string=full_storm_id_strings[i],
storm_time_unix_sec=storm_times_unix_sec[i]
)
else:
_plot_2d_radar_saliency(
saliency_matrix=list_of_saliency_matrices[j][i, ...],
colour_map_object=colour_map_object,
max_colour_value=max_colour_value_by_example[i],
figure_objects=these_figure_objects,
axes_object_matrices=these_axes_object_matrices,
model_metadata_dict=model_metadata_dict,
output_dir_name=output_dir_name,
significance_matrix=this_significance_matrix,
full_storm_id_string=full_storm_id_strings[i],
storm_time_unix_sec=storm_times_unix_sec[i]
)
def _plot_one_example(
radar_matrix, sounding_matrix, sounding_pressures_pascals,
full_storm_id_string, storm_time_unix_sec, model_metadata_dict,
output_dir_name):
"""Plots predictors for one example.
M = number of rows in radar grid
N = number of columns in radar grid
H_r = number of heights in radar grid
F_r = number of radar fields
H_s = number of sounding heights
F_s = number of sounding fields
:param radar_matrix: numpy array (1 x M x N x H_r x F_r) of radar values.
:param sounding_matrix: numpy array (1 x H_s x F_s) of sounding values.
:param sounding_pressures_pascals: numpy array (length H_s) of sounding
pressures.
:param full_storm_id_string: Full storm ID.
:param storm_time_unix_sec: Valid time.
:param model_metadata_dict: Dictionary returned by
`cnn.read_model_metadata`.
:param output_dir_name: Name of output directory (figures will be saved
here).
:return: radar_figure_file_name: Path to radar figure created by this
method.
"""
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
radar_field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY]
num_radar_fields = radar_matrix.shape[-1]
num_radar_heights = radar_matrix.shape[-2]
handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=[radar_matrix, sounding_matrix],
model_metadata_dict=model_metadata_dict,
pmm_flag=False, example_index=0, plot_sounding=True,
sounding_pressures_pascals=sounding_pressures_pascals,
allow_whitespace=True, plot_panel_names=True,
panel_name_font_size=PANEL_NAME_FONT_SIZE,
add_titles=False, label_colour_bars=True,
colour_bar_length=COLOUR_BAR_LENGTH,
colour_bar_font_size=COLOUR_BAR_FONT_SIZE,
num_panel_rows=num_radar_heights
)
sounding_file_name = plot_examples.metadata_to_file_name(
output_dir_name=output_dir_name, is_sounding=True, pmm_flag=False,
full_storm_id_string=full_storm_id_string,
storm_time_unix_sec=storm_time_unix_sec
)
print('Saving figure to: "{0:s}"...'.format(sounding_file_name))
sounding_figure_object = handle_dict[plot_examples.SOUNDING_FIGURE_KEY]
sounding_figure_object.savefig(
sounding_file_name, dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0, bbox_inches='tight'
)
pyplot.close(sounding_figure_object)
figure_objects = handle_dict[plot_examples.RADAR_FIGURES_KEY]
panel_file_names = [None] * num_radar_fields
for k in range(num_radar_fields):
panel_file_names[k] = plot_examples.metadata_to_file_name(
output_dir_name=output_dir_name, is_sounding=False, pmm_flag=False,
full_storm_id_string=full_storm_id_string,
storm_time_unix_sec=storm_time_unix_sec,
radar_field_name=radar_field_names[k]
)
print('Saving figure to: "{0:s}"...'.format(panel_file_names[k]))
figure_objects[k].savefig(
panel_file_names[k], dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0, bbox_inches='tight'
)
pyplot.close(figure_objects[k])
radar_figure_file_name = plot_examples.metadata_to_file_name(
output_dir_name=output_dir_name, is_sounding=False, pmm_flag=False,
full_storm_id_string=full_storm_id_string,
storm_time_unix_sec=storm_time_unix_sec
)
print('Concatenating panels to: "{0:s}"...'.format(radar_figure_file_name))
imagemagick_utils.concatenate_images(
input_file_names=panel_file_names,
output_file_name=radar_figure_file_name,
num_panel_rows=1, num_panel_columns=num_radar_fields,
border_width_pixels=50
)
imagemagick_utils.resize_image(
input_file_name=radar_figure_file_name,
output_file_name=radar_figure_file_name,
output_size_pixels=CONCAT_FIGURE_SIZE_PX
)
imagemagick_utils.trim_whitespace(
input_file_name=radar_figure_file_name,
output_file_name=radar_figure_file_name,
border_width_pixels=10
)
for this_file_name in panel_file_names:
os.remove(this_file_name)
return radar_figure_file_name
def _run(input_file_name, colour_map_name, max_colour_value, half_num_contours,
smoothing_radius_grid_cells, plot_soundings, allow_whitespace,
plot_panel_names, add_titles, label_colour_bars, colour_bar_length,
output_dir_name):
"""Plots saliency maps.
This is effectively the main method.
:param input_file_name: See documentation at top of file.
:param colour_map_name: Same.
:param max_colour_value: Same.
:param half_num_contours: Same.
:param smoothing_radius_grid_cells: Same.
:param plot_soundings: Same.
:param allow_whitespace: Same.
:param plot_panel_names: Same.
:param add_titles: Same.
:param label_colour_bars: Same.
:param colour_bar_length: Same.
:param output_dir_name: Same.
"""
if max_colour_value <= 0:
max_colour_value = None
if smoothing_radius_grid_cells <= 0:
smoothing_radius_grid_cells = None
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
colour_map_object = pyplot.cm.get_cmap(colour_map_name)
error_checking.assert_is_geq(half_num_contours, 5)
print('Reading data from: "{0:s}"...'.format(input_file_name))
saliency_dict, pmm_flag = saliency_maps.read_file(input_file_name)
if pmm_flag:
predictor_matrices = saliency_dict.pop(
saliency_maps.MEAN_PREDICTOR_MATRICES_KEY)
saliency_matrices = saliency_dict.pop(
saliency_maps.MEAN_SALIENCY_MATRICES_KEY)
full_storm_id_strings = [None]
storm_times_unix_sec = [None]
mean_sounding_pressures_pa = saliency_dict[
saliency_maps.MEAN_SOUNDING_PRESSURES_KEY]
sounding_pressure_matrix_pa = numpy.reshape(
mean_sounding_pressures_pa, (1, len(mean_sounding_pressures_pa))
)
for i in range(len(predictor_matrices)):
predictor_matrices[i] = numpy.expand_dims(
predictor_matrices[i], axis=0
)
saliency_matrices[i] = numpy.expand_dims(
saliency_matrices[i], axis=0
)
else:
predictor_matrices = saliency_dict.pop(
saliency_maps.PREDICTOR_MATRICES_KEY)
saliency_matrices = saliency_dict.pop(
saliency_maps.SALIENCY_MATRICES_KEY)
full_storm_id_strings = saliency_dict[saliency_maps.FULL_STORM_IDS_KEY]
storm_times_unix_sec = saliency_dict[saliency_maps.STORM_TIMES_KEY]
sounding_pressure_matrix_pa = saliency_dict[
saliency_maps.SOUNDING_PRESSURES_KEY]
if smoothing_radius_grid_cells is not None:
saliency_matrices = _smooth_maps(
saliency_matrices=saliency_matrices,
smoothing_radius_grid_cells=smoothing_radius_grid_cells)
model_file_name = saliency_dict[saliency_maps.MODEL_FILE_KEY]
model_metafile_name = '{0:s}/model_metadata.p'.format(
os.path.split(model_file_name)[0]
)
print('Reading metadata from: "{0:s}"...'.format(model_metafile_name))
model_metadata_dict = cnn.read_model_metadata(model_metafile_name)
print(SEPARATOR_STRING)
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
num_radar_matrices = len(predictor_matrices)
if training_option_dict[trainval_io.SOUNDING_FIELDS_KEY] is None:
plot_soundings = False
else:
num_radar_matrices -= 1
num_examples = predictor_matrices[0].shape[0]
for i in range(num_examples):
this_handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=predictor_matrices,
model_metadata_dict=model_metadata_dict, pmm_flag=pmm_flag,
example_index=i, plot_sounding=plot_soundings,
sounding_pressures_pascals=sounding_pressure_matrix_pa[i, ...],
allow_whitespace=allow_whitespace,
plot_panel_names=plot_panel_names, add_titles=add_titles,
label_colour_bars=label_colour_bars,
colour_bar_length=colour_bar_length)
if plot_soundings:
_plot_sounding_saliency(
saliency_matrix=saliency_matrices[-1][i, ...],
colour_map_object=colour_map_object,
max_colour_value=max_colour_value,
sounding_figure_object=this_handle_dict[
plot_examples.SOUNDING_FIGURE_KEY],
sounding_axes_object=this_handle_dict[
plot_examples.SOUNDING_AXES_KEY],
sounding_pressures_pascals=sounding_pressure_matrix_pa[i, ...],
saliency_dict=saliency_dict,
model_metadata_dict=model_metadata_dict, add_title=add_titles,
output_dir_name=output_dir_name, pmm_flag=pmm_flag,
example_index=i)
these_figure_objects = this_handle_dict[plot_examples.RADAR_FIGURES_KEY]
these_axes_object_matrices = this_handle_dict[
plot_examples.RADAR_AXES_KEY]
for j in range(num_radar_matrices):
this_num_spatial_dim = len(predictor_matrices[j].shape) - 2
if this_num_spatial_dim == 3:
_plot_3d_radar_saliency(
saliency_matrix=saliency_matrices[j][i, ...],
colour_map_object=colour_map_object,
max_colour_value=max_colour_value,
half_num_contours=half_num_contours,
label_colour_bars=label_colour_bars,
colour_bar_length=colour_bar_length,
figure_objects=these_figure_objects,
axes_object_matrices=these_axes_object_matrices,
model_metadata_dict=model_metadata_dict,
output_dir_name=output_dir_name,
significance_matrix=None,
full_storm_id_string=full_storm_id_strings[i],
storm_time_unix_sec=storm_times_unix_sec[i]
)
else:
_plot_2d_radar_saliency(
saliency_matrix=saliency_matrices[j][i, ...],
colour_map_object=colour_map_object,
max_colour_value=max_colour_value,
half_num_contours=half_num_contours,
label_colour_bars=label_colour_bars,
colour_bar_length=colour_bar_length,
figure_objects=these_figure_objects,
axes_object_matrices=these_axes_object_matrices,
model_metadata_dict=model_metadata_dict,
output_dir_name=output_dir_name,
significance_matrix=None,
full_storm_id_string=full_storm_id_strings[i],
storm_time_unix_sec=storm_times_unix_sec[i]
)
def _plot_composite(composite_file_name, composite_name_abbrev,
composite_name_verbose, output_dir_name):
"""Plots one composite.
:param composite_file_name: Path to input file. Will be read by
`_read_composite`.
:param composite_name_abbrev: Abbreviated name for composite. Will be used
in names of output files.
:param composite_name_verbose: Verbose name for composite. Will be used as
figure title.
:param output_dir_name: Path to output directory. Figures will be saved
here.
:return: radar_figure_file_name: Path to file with radar figure for this
composite.
:return: sounding_figure_file_name: Path to file with sounding figure for
this composite.
"""
mean_predictor_matrices, model_metadata_dict, mean_sounding_pressures_pa = (
_read_composite(composite_file_name))
radar_field_names = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY][
trainval_io.RADAR_FIELDS_KEY]
radar_heights_m_agl = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY][
trainval_io.RADAR_HEIGHTS_KEY]
num_radar_fields = len(radar_field_names)
num_radar_heights = len(radar_heights_m_agl)
handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=mean_predictor_matrices,
model_metadata_dict=model_metadata_dict,
pmm_flag=True,
plot_sounding=True,
sounding_pressures_pascals=mean_sounding_pressures_pa,
allow_whitespace=True,
plot_panel_names=True,
panel_name_font_size=PANEL_NAME_FONT_SIZE,
add_titles=False,
label_colour_bars=True,
colour_bar_length=COLOUR_BAR_LENGTH,
colour_bar_font_size=COLOUR_BAR_FONT_SIZE,
sounding_font_size=SOUNDING_FONT_SIZE,
num_panel_rows=num_radar_heights)
sounding_figure_file_name = '{0:s}/{1:s}_sounding.jpg'.format(
output_dir_name, composite_name_abbrev)
print('Saving figure to: "{0:s}"...'.format(sounding_figure_file_name))
sounding_figure_object = handle_dict[plot_examples.SOUNDING_FIGURE_KEY]
sounding_figure_object.savefig(sounding_figure_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(sounding_figure_object)
imagemagick_utils.resize_image(input_file_name=sounding_figure_file_name,
output_file_name=sounding_figure_file_name,
output_size_pixels=CONCAT_FIGURE_SIZE_PX)
imagemagick_utils.trim_whitespace(
input_file_name=sounding_figure_file_name,
output_file_name=sounding_figure_file_name,
border_width_pixels=TITLE_FONT_SIZE + 25)
_overlay_text(image_file_name=sounding_figure_file_name,
x_offset_from_center_px=0,
y_offset_from_top_px=0,
text_string=composite_name_verbose)
imagemagick_utils.trim_whitespace(
input_file_name=sounding_figure_file_name,
output_file_name=sounding_figure_file_name,
border_width_pixels=10)
radar_figure_objects = handle_dict[plot_examples.RADAR_FIGURES_KEY]
panel_file_names = [None] * num_radar_fields
for j in range(num_radar_fields):
panel_file_names[j] = '{0:s}/{1:s}_{2:s}.jpg'.format(
output_dir_name, composite_name_abbrev,
radar_field_names[j].replace('_', '-'))
print('Saving figure to: "{0:s}"...'.format(panel_file_names[j]))
radar_figure_objects[j].savefig(panel_file_names[j],
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(radar_figure_objects[j])
radar_figure_file_name = '{0:s}/{1:s}_radar.jpg'.format(
output_dir_name, composite_name_abbrev)
print('Concatenating panels to: "{0:s}"...'.format(radar_figure_file_name))
imagemagick_utils.concatenate_images(
input_file_names=panel_file_names,
output_file_name=radar_figure_file_name,
num_panel_rows=1,
num_panel_columns=num_radar_fields,
border_width_pixels=50)
imagemagick_utils.resize_image(input_file_name=radar_figure_file_name,
output_file_name=radar_figure_file_name,
output_size_pixels=CONCAT_FIGURE_SIZE_PX)
imagemagick_utils.trim_whitespace(input_file_name=radar_figure_file_name,
output_file_name=radar_figure_file_name,
border_width_pixels=TITLE_FONT_SIZE + 25)
_overlay_text(image_file_name=radar_figure_file_name,
x_offset_from_center_px=0,
y_offset_from_top_px=0,
text_string=composite_name_verbose)
imagemagick_utils.trim_whitespace(input_file_name=radar_figure_file_name,
output_file_name=radar_figure_file_name,
border_width_pixels=10)
return radar_figure_file_name, sounding_figure_file_name
def _plot_one_composite(gradcam_file_name, monte_carlo_file_name,
composite_name_abbrev, composite_name_verbose,
colour_map_object, min_colour_value, max_colour_value,
num_contours, smoothing_radius_grid_cells,
monte_carlo_max_fdr, output_dir_name):
"""Plots class-activation map for one composite.
:param gradcam_file_name: Path to input file (will be read by
`gradcam.read_file`).
:param monte_carlo_file_name: Path to Monte Carlo file (will be read by
`_read_monte_carlo_file`).
:param composite_name_abbrev: Abbrev composite name (will be used in file
names).
:param composite_name_verbose: Verbose composite name (will be used in
figure title).
:param colour_map_object: See documentation at top of file.
:param min_colour_value: Minimum value in colour bar (may be NaN).
:param max_colour_value: Max value in colour bar (may be NaN).
:param num_contours: See documentation at top of file.
:param smoothing_radius_grid_cells: Same.
:param monte_carlo_max_fdr: Same.
:param output_dir_name: Name of output directory (figures will be saved
here).
:return: main_figure_file_name: Path to main image file created by this
method.
:return: min_colour_value: Same as input but cannot be None.
:return: max_colour_value: Same as input but cannot be None.
"""
(mean_radar_matrix, mean_class_activn_matrix, significance_matrix,
model_metadata_dict) = _read_one_composite(
gradcam_file_name=gradcam_file_name,
smoothing_radius_grid_cells=smoothing_radius_grid_cells,
monte_carlo_file_name=monte_carlo_file_name,
monte_carlo_max_fdr=monte_carlo_max_fdr)
if numpy.isnan(min_colour_value) or numpy.isnan(max_colour_value):
min_colour_value_log10 = numpy.log10(
numpy.percentile(mean_class_activn_matrix, 1.))
max_colour_value_log10 = numpy.log10(
numpy.percentile(mean_class_activn_matrix, 99.))
min_colour_value_log10 = max([min_colour_value_log10, -2.])
max_colour_value_log10 = max([max_colour_value_log10, -1.])
min_colour_value_log10 = min([min_colour_value_log10, 1.])
max_colour_value_log10 = min([max_colour_value_log10, 2.])
min_colour_value = 10**min_colour_value_log10
max_colour_value = 10**max_colour_value_log10
else:
min_colour_value_log10 = numpy.log10(min_colour_value)
max_colour_value_log10 = numpy.log10(max_colour_value)
contour_interval_log10 = (
(max_colour_value_log10 - min_colour_value_log10) / (num_contours - 1))
mean_activn_matrix_log10 = numpy.log10(mean_class_activn_matrix)
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY]
num_fields = mean_radar_matrix.shape[-1]
num_heights = mean_radar_matrix.shape[-2]
handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=[mean_radar_matrix],
model_metadata_dict=model_metadata_dict,
pmm_flag=True,
allow_whitespace=True,
plot_panel_names=True,
panel_name_font_size=PANEL_NAME_FONT_SIZE,
add_titles=False,
label_colour_bars=True,
colour_bar_length=COLOUR_BAR_LENGTH,
colour_bar_font_size=COLOUR_BAR_FONT_SIZE,
num_panel_rows=num_heights)
figure_objects = handle_dict[plot_examples.RADAR_FIGURES_KEY]
axes_object_matrices = handle_dict[plot_examples.RADAR_AXES_KEY]
for k in range(num_fields):
cam_plotting.plot_many_2d_grids(
class_activation_matrix_3d=numpy.flip(
mean_activn_matrix_log10[0, ...], axis=0),
axes_object_matrix=axes_object_matrices[k],
colour_map_object=colour_map_object,
min_contour_level=min_colour_value_log10,
max_contour_level=max_colour_value_log10,
contour_interval=contour_interval_log10)
significance_plotting.plot_many_2d_grids_without_coords(
significance_matrix=numpy.flip(significance_matrix[0, ...],
axis=0),
axes_object_matrix=axes_object_matrices[k])
panel_file_names = [None] * num_fields
for k in range(num_fields):
panel_file_names[k] = '{0:s}/{1:s}_{2:s}.jpg'.format(
output_dir_name, composite_name_abbrev,
field_names[k].replace('_', '-'))
print('Saving figure to: "{0:s}"...'.format(panel_file_names[k]))
figure_objects[k].savefig(panel_file_names[k],
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(figure_objects[k])
main_figure_file_name = '{0:s}/{1:s}_gradcam.jpg'.format(
output_dir_name, composite_name_abbrev)
print('Concatenating panels to: "{0:s}"...'.format(main_figure_file_name))
imagemagick_utils.concatenate_images(
input_file_names=panel_file_names,
output_file_name=main_figure_file_name,
num_panel_rows=1,
num_panel_columns=num_fields,
border_width_pixels=50)
imagemagick_utils.resize_image(input_file_name=main_figure_file_name,
output_file_name=main_figure_file_name,
output_size_pixels=CONCAT_FIGURE_SIZE_PX)
imagemagick_utils.trim_whitespace(input_file_name=main_figure_file_name,
output_file_name=main_figure_file_name,
border_width_pixels=TITLE_FONT_SIZE + 25)
_overlay_text(image_file_name=main_figure_file_name,
x_offset_from_center_px=0,
y_offset_from_top_px=0,
text_string=composite_name_verbose)
imagemagick_utils.trim_whitespace(input_file_name=main_figure_file_name,
output_file_name=main_figure_file_name,
border_width_pixels=10)
return main_figure_file_name, min_colour_value, max_colour_value
def _run(gridrad_example_dir_name, gridrad_full_id_string, gridrad_time_string,
myrorss_example_dir_name, myrorss_full_id_string, myrorss_time_string,
output_dir_name):
"""Makes figure with GridRad and MYRORSS predictors.
This is effectively the main method.
:param gridrad_example_dir_name: See documentation at top of file.
:param gridrad_full_id_string: Same.
:param gridrad_time_string: Same.
:param myrorss_example_dir_name: Same.
:param myrorss_full_id_string: Same.
:param myrorss_time_string: Same.
:param output_dir_name: Same.
"""
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
gridrad_time_unix_sec = time_conversion.string_to_unix_sec(
gridrad_time_string, TIME_FORMAT)
myrorss_time_unix_sec = time_conversion.string_to_unix_sec(
myrorss_time_string, TIME_FORMAT)
letter_label = None
num_gridrad_fields = len(GRIDRAD_FIELD_NAMES)
panel_file_names = [None] * num_gridrad_fields * 2
for j in range(num_gridrad_fields):
these_predictor_matrices, this_metadata_dict = _read_one_example(
top_example_dir_name=gridrad_example_dir_name,
full_storm_id_string=gridrad_full_id_string,
storm_time_unix_sec=gridrad_time_unix_sec,
source_name=radar_utils.GRIDRAD_SOURCE_ID,
radar_field_name=GRIDRAD_FIELD_NAMES[j],
include_sounding=False)[:2]
print(MINOR_SEPARATOR_STRING)
this_handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=these_predictor_matrices,
model_metadata_dict=this_metadata_dict,
pmm_flag=False,
example_index=0,
plot_sounding=False,
allow_whitespace=True,
plot_panel_names=False,
add_titles=False,
label_colour_bars=False,
colour_bar_font_size=COLOUR_BAR_FONT_SIZE,
colour_bar_length=COLOUR_BAR_LENGTH)
this_title_string = radar_plotting.fields_and_heights_to_names(
field_names=[GRIDRAD_FIELD_NAMES[j]],
heights_m_agl=RADAR_HEIGHTS_M_AGL[[0]],
include_units=True)[0]
this_title_string = this_title_string.replace('\n', ' ').replace(
' km AGL', ' km')
this_title_string = 'GridRad {0:s}{1:s}'.format(
this_title_string[0].lower(), this_title_string[1:])
this_figure_object = this_handle_dict[
plot_examples.RADAR_FIGURES_KEY][0]
this_axes_object = this_handle_dict[plot_examples.RADAR_AXES_KEY][0][0,
0]
this_figure_object.suptitle('')
this_axes_object.set_title(this_title_string, fontsize=TITLE_FONT_SIZE)
# this_axes_object.set_yticklabels(
# this_axes_object.get_yticks(), color=ALMOST_WHITE_COLOUR
# )
if letter_label is None:
letter_label = 'a'
else:
letter_label = chr(ord(letter_label) + 1)
plotting_utils.label_axes(axes_object=this_axes_object,
label_string='({0:s})'.format(letter_label),
font_size=PANEL_LETTER_FONT_SIZE,
x_coord_normalized=X_LABEL_COORD_NORMALIZED,
y_coord_normalized=Y_LABEL_COORD_NORMALIZED)
panel_file_names[j * 2] = '{0:s}/gridrad_{1:s}.jpg'.format(
output_dir_name, GRIDRAD_FIELD_NAMES[j].replace('_', '-'))
print('Saving figure to: "{0:s}"...'.format(panel_file_names[j * 2]))
this_figure_object.savefig(panel_file_names[j * 2],
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(this_figure_object)
print(SEPARATOR_STRING)
num_myrorss_shear_fields = len(MYRORSS_SHEAR_FIELD_NAMES)
for j in range(num_myrorss_shear_fields):
(these_predictor_matrices, this_metadata_dict,
these_pressures_pascals) = _read_one_example(
top_example_dir_name=myrorss_example_dir_name,
full_storm_id_string=myrorss_full_id_string,
storm_time_unix_sec=myrorss_time_unix_sec,
source_name=radar_utils.MYRORSS_SOURCE_ID,
radar_field_name=MYRORSS_SHEAR_FIELD_NAMES[j],
include_sounding=j == 0)
print(MINOR_SEPARATOR_STRING)
this_handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=these_predictor_matrices,
model_metadata_dict=this_metadata_dict,
pmm_flag=False,
example_index=0,
plot_sounding=j == 0,
sounding_pressures_pascals=these_pressures_pascals,
allow_whitespace=True,
plot_panel_names=False,
add_titles=False,
label_colour_bars=False,
colour_bar_font_size=COLOUR_BAR_FONT_SIZE,
colour_bar_length=COLOUR_BAR_LENGTH,
sounding_font_size=SOUNDING_FONT_SIZE)
if j == 0:
this_axes_object = this_handle_dict[
plot_examples.SOUNDING_AXES_KEY]
this_axes_object.set_title('Proximity sounding')
letter_label = chr(ord(letter_label) + 1)
plotting_utils.label_axes(
axes_object=this_axes_object,
label_string='({0:s})'.format(letter_label),
font_size=PANEL_LETTER_FONT_SIZE,
x_coord_normalized=X_LABEL_COORD_NORMALIZED,
y_coord_normalized=Y_LABEL_COORD_NORMALIZED)
this_figure_object = this_handle_dict[
plot_examples.SOUNDING_FIGURE_KEY]
panel_file_names[1] = '{0:s}/sounding.jpg'.format(output_dir_name)
print('Saving figure to: "{0:s}"...'.format(panel_file_names[1]))
this_figure_object.savefig(panel_file_names[1],
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(this_figure_object)
this_title_string = radar_plotting.fields_and_heights_to_names(
field_names=[radar_utils.REFL_NAME],
heights_m_agl=RADAR_HEIGHTS_M_AGL[[0]],
include_units=True)[0]
this_title_string = this_title_string.replace('\n', ' ').replace(
' km AGL', ' km')
this_title_string = 'MYRORSS {0:s}{1:s}'.format(
this_title_string[0].lower(), this_title_string[1:])
this_figure_object = this_handle_dict[
plot_examples.RADAR_FIGURES_KEY][0]
this_axes_object = this_handle_dict[
plot_examples.RADAR_AXES_KEY][0][0, 0]
this_figure_object.suptitle('')
this_axes_object.set_title(this_title_string,
fontsize=TITLE_FONT_SIZE)
letter_label = chr(ord(letter_label) + 1)
plotting_utils.label_axes(
axes_object=this_axes_object,
label_string='({0:s})'.format(letter_label),
font_size=PANEL_LETTER_FONT_SIZE,
x_coord_normalized=X_LABEL_COORD_NORMALIZED,
y_coord_normalized=Y_LABEL_COORD_NORMALIZED)
panel_file_names[3] = '{0:s}/myrorss_{1:s}.jpg'.format(
output_dir_name, radar_utils.REFL_NAME.replace('_', '-'))
print('Saving figure to: "{0:s}"...'.format(panel_file_names[3]))
this_figure_object.savefig(panel_file_names[3],
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(this_figure_object)
this_title_string = radar_plotting.fields_and_heights_to_names(
field_names=[MYRORSS_SHEAR_FIELD_NAMES[j]],
heights_m_agl=RADAR_HEIGHTS_M_AGL[[0]],
include_units=True)[0]
this_title_string = this_title_string.split('\n')[0]
this_title_string = 'MYRORSS {0:s}{1:s}'.format(
this_title_string[0].lower(), this_title_string[1:])
this_figure_object = this_handle_dict[
plot_examples.RADAR_FIGURES_KEY][1]
this_axes_object = this_handle_dict[plot_examples.RADAR_AXES_KEY][1][0,
0]
this_figure_object.suptitle('')
this_axes_object.set_title(this_title_string, fontsize=TITLE_FONT_SIZE)
letter_label = chr(ord(letter_label) + 1)
plotting_utils.label_axes(axes_object=this_axes_object,
label_string='({0:s})'.format(letter_label),
font_size=PANEL_LETTER_FONT_SIZE,
x_coord_normalized=X_LABEL_COORD_NORMALIZED,
y_coord_normalized=Y_LABEL_COORD_NORMALIZED)
panel_file_names[5 + j * 2] = '{0:s}/myrorss_{1:s}.jpg'.format(
output_dir_name, MYRORSS_SHEAR_FIELD_NAMES[j].replace('_', '-'))
print('Saving figure to: "{0:s}"...'.format(panel_file_names[5 +
j * 2]))
this_figure_object.savefig(panel_file_names[5 + j * 2],
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(this_figure_object)
if j != num_myrorss_shear_fields:
print(SEPARATOR_STRING)
concat_file_name = '{0:s}/predictors.jpg'.format(output_dir_name)
print('Concatenating panels to: "{0:s}"...'.format(concat_file_name))
imagemagick_utils.concatenate_images(input_file_names=panel_file_names,
output_file_name=concat_file_name,
num_panel_rows=4,
num_panel_columns=2)
imagemagick_utils.resize_image(input_file_name=concat_file_name,
output_file_name=concat_file_name,
output_size_pixels=CONCAT_FIGURE_SIZE_PX)
def _run(main_bwo_file_name, unconstrained_bwo_file_name, output_dir_name):
"""Makes figure with backwards-optimization results.
This is effectively the main method.
:param main_bwo_file_name: See documentation at top of file.
:param unconstrained_bwo_file_name: Same.
:param output_dir_name: Same.
"""
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
main_bwo_dict, model_metadata_dict = _read_bwo_file(main_bwo_file_name)
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
radar_field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY]
mean_before_matrices = main_bwo_dict[backwards_opt.MEAN_INPUT_MATRICES_KEY]
mean_after_matrices = main_bwo_dict[backwards_opt.MEAN_OUTPUT_MATRICES_KEY]
mean_sounding_pressures_pa = (
main_bwo_dict[backwards_opt.MEAN_SOUNDING_PRESSURES_KEY])
num_radar_heights = mean_before_matrices[0].shape[-2]
# Plot radar and sounding after optimization.
handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=mean_before_matrices,
model_metadata_dict=model_metadata_dict,
pmm_flag=True,
plot_sounding=True,
sounding_pressures_pascals=mean_sounding_pressures_pa,
allow_whitespace=True,
plot_panel_names=True,
panel_name_font_size=PANEL_NAME_FONT_SIZE,
add_titles=False,
label_colour_bars=True,
colour_bar_length=COLOUR_BAR_LENGTH,
colour_bar_font_size=COLOUR_BAR_FONT_SIZE,
sounding_font_size=SOUNDING_FONT_SIZE,
num_panel_rows=num_radar_heights,
plot_radar_diffs=False)
panel_file_names = [None] * 6
panel_file_names[3] = '{0:s}/sounding_before.jpg'.format(output_dir_name)
_write_sounding_figure(
figure_object=handle_dict[plot_examples.SOUNDING_FIGURE_KEY],
title_string='(d) Original sounding',
output_file_name=panel_file_names[3])
panel_file_names[0] = _write_radar_figures(
figure_objects=handle_dict[plot_examples.RADAR_FIGURES_KEY],
field_names=radar_field_names,
composite_name='before',
concat_title_string='(a) Original radar image',
output_dir_name=output_dir_name)
print(SEPARATOR_STRING)
# Plot radar and sounding after optimization.
handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=mean_after_matrices,
model_metadata_dict=model_metadata_dict,
pmm_flag=True,
plot_sounding=True,
sounding_pressures_pascals=mean_sounding_pressures_pa,
allow_whitespace=True,
plot_panel_names=True,
panel_name_font_size=PANEL_NAME_FONT_SIZE,
add_titles=False,
label_colour_bars=True,
colour_bar_length=COLOUR_BAR_LENGTH,
colour_bar_font_size=COLOUR_BAR_FONT_SIZE,
sounding_font_size=SOUNDING_FONT_SIZE,
num_panel_rows=num_radar_heights,
plot_radar_diffs=False)
panel_file_names[4] = '{0:s}/sounding_after.jpg'.format(output_dir_name)
_write_sounding_figure(
figure_object=handle_dict[plot_examples.SOUNDING_FIGURE_KEY],
title_string='(e) Sounding after constrained BWO',
output_file_name=panel_file_names[4])
panel_file_names[1] = _write_radar_figures(
figure_objects=handle_dict[plot_examples.RADAR_FIGURES_KEY],
field_names=radar_field_names,
composite_name='after',
concat_title_string='(b) Radar after constrained BWO',
output_dir_name=output_dir_name)
print(SEPARATOR_STRING)
# Plot radar and sounding after unconstrained optimization.
unconstrained_bwo_dict, model_metadata_dict = _read_bwo_file(
unconstrained_bwo_file_name)
mean_after_matrices = (
unconstrained_bwo_dict[backwards_opt.MEAN_OUTPUT_MATRICES_KEY])
mean_sounding_pressures_pa = (
unconstrained_bwo_dict[backwards_opt.MEAN_SOUNDING_PRESSURES_KEY])
handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=mean_after_matrices,
model_metadata_dict=model_metadata_dict,
pmm_flag=True,
plot_sounding=True,
sounding_pressures_pascals=mean_sounding_pressures_pa,
allow_whitespace=True,
plot_panel_names=True,
panel_name_font_size=PANEL_NAME_FONT_SIZE,
add_titles=False,
label_colour_bars=True,
colour_bar_length=COLOUR_BAR_LENGTH,
colour_bar_font_size=COLOUR_BAR_FONT_SIZE,
sounding_font_size=SOUNDING_FONT_SIZE,
num_panel_rows=num_radar_heights,
plot_radar_diffs=False)
panel_file_names[5] = (
'{0:s}/sounding_after_unconstrained.jpg'.format(output_dir_name))
_write_sounding_figure(
figure_object=handle_dict[plot_examples.SOUNDING_FIGURE_KEY],
title_string='(f) Sounding after unconstrained BWO',
output_file_name=panel_file_names[5])
panel_file_names[2] = _write_radar_figures(
figure_objects=handle_dict[plot_examples.RADAR_FIGURES_KEY],
field_names=radar_field_names,
composite_name='after_unconstrained',
concat_title_string='(c) Radar after unconstrained BWO',
output_dir_name=output_dir_name)
print(SEPARATOR_STRING)
figure_file_name = '{0:s}/bwo_concat.jpg'.format(output_dir_name)
print('Concatenating panels to: "{0:s}"...'.format(figure_file_name))
imagemagick_utils.concatenate_images(input_file_names=panel_file_names,
output_file_name=figure_file_name,
border_width_pixels=100,
num_panel_rows=2,
num_panel_columns=3,
extra_args_string='-gravity Center')
imagemagick_utils.trim_whitespace(input_file_name=figure_file_name,
output_file_name=figure_file_name,
border_width_pixels=10)
def _plot_composite(composite_file_name, composite_name_abbrev,
composite_name_verbose, plot_saliency, output_dir_name):
"""Plots one composite.
:param composite_file_name: Path to input file. Will be read by
`_read_composite`.
:param composite_name_abbrev: Abbreviated name for composite. Will be used
in names of output files.
:param composite_name_verbose: Verbose name for composite. Will be used as
figure title.
:param plot_saliency: See documentation at top of file.
:param output_dir_name: Path to output directory. Figures will be saved
here.
:return: radar_figure_file_name: Path to file with radar figure for this
composite.
:return: sounding_figure_file_name: Path to file with sounding figure for
this composite.
"""
(mean_predictor_matrices, model_metadata_dict, mean_sounding_pressures_pa,
mean_saliency_matrices) = _read_composite(
pickle_file_name=composite_file_name, read_saliency=plot_saliency)
refl_heights_m_agl = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY][
trainval_io.RADAR_HEIGHTS_KEY]
num_refl_heights = len(refl_heights_m_agl)
handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=mean_predictor_matrices,
model_metadata_dict=model_metadata_dict,
pmm_flag=True,
plot_sounding=True,
sounding_pressures_pascals=mean_sounding_pressures_pa,
allow_whitespace=True,
plot_panel_names=True,
panel_name_font_size=PANEL_NAME_FONT_SIZE,
add_titles=False,
label_colour_bars=True,
colour_bar_length=COLOUR_BAR_LENGTH,
colour_bar_font_size=COLOUR_BAR_FONT_SIZE,
sounding_font_size=SOUNDING_FONT_SIZE,
num_panel_rows=num_refl_heights)
if plot_saliency:
axes_object_matrices = handle_dict[plot_examples.RADAR_AXES_KEY]
all_saliency_values = numpy.concatenate(
(numpy.ravel(mean_saliency_matrices[0]),
numpy.ravel(mean_saliency_matrices[1])))
max_contour_value = numpy.percentile(
numpy.absolute(all_saliency_values), 99)
this_matrix = numpy.flip(mean_saliency_matrices[0][0, ..., 0], axis=0)
saliency_plotting.plot_many_2d_grids_with_contours(
saliency_matrix_3d=this_matrix,
axes_object_matrix=axes_object_matrices[0],
colour_map_object=SALIENCY_COLOUR_MAP_OBJECT,
max_absolute_contour_level=max_contour_value,
contour_interval=max_contour_value / 10,
row_major=True)
this_matrix = numpy.flip(mean_saliency_matrices[1][0, ...], axis=0)
saliency_plotting.plot_many_2d_grids_with_contours(
saliency_matrix_3d=this_matrix,
axes_object_matrix=axes_object_matrices[1],
colour_map_object=SALIENCY_COLOUR_MAP_OBJECT,
max_absolute_contour_level=max_contour_value,
contour_interval=max_contour_value / 10,
row_major=False)
sounding_figure_object = handle_dict[plot_examples.SOUNDING_FIGURE_KEY]
sounding_figure_file_name = '{0:s}/{1:s}_sounding.jpg'.format(
output_dir_name, composite_name_abbrev)
print('Saving figure to: "{0:s}"...'.format(sounding_figure_file_name))
sounding_figure_object.savefig(sounding_figure_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(sounding_figure_object)
imagemagick_utils.resize_image(input_file_name=sounding_figure_file_name,
output_file_name=sounding_figure_file_name,
output_size_pixels=CONCAT_FIGURE_SIZE_PX)
imagemagick_utils.trim_whitespace(
input_file_name=sounding_figure_file_name,
output_file_name=sounding_figure_file_name,
border_width_pixels=TITLE_FONT_SIZE + 25)
_overlay_text(image_file_name=sounding_figure_file_name,
x_offset_from_center_px=0,
y_offset_from_top_px=0,
text_string=composite_name_verbose)
imagemagick_utils.trim_whitespace(
input_file_name=sounding_figure_file_name,
output_file_name=sounding_figure_file_name,
border_width_pixels=10)
refl_figure_object = handle_dict[plot_examples.RADAR_FIGURES_KEY][0]
refl_figure_file_name = '{0:s}/{1:s}_reflectivity.jpg'.format(
output_dir_name, composite_name_abbrev)
print('Saving figure to: "{0:s}"...'.format(refl_figure_file_name))
refl_figure_object.savefig(refl_figure_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(refl_figure_object)
shear_figure_object = handle_dict[plot_examples.RADAR_FIGURES_KEY][1]
shear_figure_file_name = '{0:s}/{1:s}_shear.jpg'.format(
output_dir_name, composite_name_abbrev)
print('Saving figure to: "{0:s}"...'.format(shear_figure_file_name))
shear_figure_object.savefig(shear_figure_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(shear_figure_object)
radar_figure_file_name = '{0:s}/{1:s}_radar.jpg'.format(
output_dir_name, composite_name_abbrev)
print('Concatenating panels to: "{0:s}"...'.format(radar_figure_file_name))
imagemagick_utils.concatenate_images(
input_file_names=[refl_figure_file_name, shear_figure_file_name],
output_file_name=radar_figure_file_name,
num_panel_rows=1,
num_panel_columns=2,
border_width_pixels=50,
extra_args_string='-gravity south')
imagemagick_utils.resize_image(input_file_name=radar_figure_file_name,
output_file_name=radar_figure_file_name,
output_size_pixels=CONCAT_FIGURE_SIZE_PX)
imagemagick_utils.trim_whitespace(input_file_name=radar_figure_file_name,
output_file_name=radar_figure_file_name,
border_width_pixels=TITLE_FONT_SIZE + 25)
_overlay_text(image_file_name=radar_figure_file_name,
x_offset_from_center_px=0,
y_offset_from_top_px=0,
text_string=composite_name_verbose)
imagemagick_utils.trim_whitespace(input_file_name=radar_figure_file_name,
output_file_name=radar_figure_file_name,
border_width_pixels=10)
return radar_figure_file_name, sounding_figure_file_name
def _plot_one_composite(gradcam_file_name, composite_name_abbrev,
composite_name_verbose, colour_map_object,
max_colour_value, num_contours,
smoothing_radius_grid_cells, output_dir_name):
"""Plots class-activation map for one composite.
:param gradcam_file_name: Path to input file (will be read by
`gradcam.read_file`).
:param composite_name_abbrev: Abbrev composite name (will be used in file
names).
:param composite_name_verbose: Verbose composite name (will be used in
figure title).
:param colour_map_object: See documentation at top of file.
:param max_colour_value: Same.
:param num_contours: Same.
:param smoothing_radius_grid_cells: Same.
:param output_dir_name: Name of output directory (figures will be saved
here).
:return: main_figure_file_name: Path to main image file created by this
method.
"""
mean_predictor_matrices, mean_cam_matrices, model_metadata_dict = (
_read_one_composite(
gradcam_file_name=gradcam_file_name,
smoothing_radius_grid_cells=smoothing_radius_grid_cells))
refl_heights_m_agl = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY][
trainval_io.RADAR_HEIGHTS_KEY]
num_refl_heights = len(refl_heights_m_agl)
handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=mean_predictor_matrices,
model_metadata_dict=model_metadata_dict,
pmm_flag=True,
plot_sounding=False,
allow_whitespace=True,
plot_panel_names=True,
panel_name_font_size=PANEL_NAME_FONT_SIZE,
add_titles=False,
label_colour_bars=True,
colour_bar_length=COLOUR_BAR_LENGTH,
colour_bar_font_size=COLOUR_BAR_FONT_SIZE,
num_panel_rows=num_refl_heights)
axes_object_matrices = handle_dict[plot_examples.RADAR_AXES_KEY]
max_colour_value_log10 = numpy.log10(max_colour_value)
contour_interval_log10 = (
(max_colour_value_log10 - MIN_COLOUR_VALUE_LOG10) / (num_contours - 1))
this_matrix = numpy.flip(mean_cam_matrices[0][0, ...], axis=0)
this_matrix = numpy.log10(this_matrix)
cam_plotting.plot_many_2d_grids(class_activation_matrix_3d=this_matrix,
axes_object_matrix=axes_object_matrices[0],
colour_map_object=colour_map_object,
min_contour_level=MIN_COLOUR_VALUE_LOG10,
max_contour_level=max_colour_value_log10,
contour_interval=contour_interval_log10,
row_major=True)
this_matrix = numpy.flip(mean_cam_matrices[1][0, ...], axis=0)
this_num_channels = mean_predictor_matrices[1].shape[-1]
this_matrix = numpy.repeat(a=numpy.expand_dims(this_matrix, axis=-1),
axis=-1,
repeats=this_num_channels)
this_matrix = numpy.log10(this_matrix)
cam_plotting.plot_many_2d_grids(class_activation_matrix_3d=this_matrix,
axes_object_matrix=axes_object_matrices[1],
colour_map_object=colour_map_object,
min_contour_level=MIN_COLOUR_VALUE_LOG10,
max_contour_level=max_colour_value_log10,
contour_interval=contour_interval_log10,
row_major=False)
refl_figure_object = handle_dict[plot_examples.RADAR_FIGURES_KEY][0]
refl_figure_file_name = '{0:s}/{1:s}_reflectivity.jpg'.format(
output_dir_name, composite_name_abbrev)
print('Saving figure to: "{0:s}"...'.format(refl_figure_file_name))
refl_figure_object.savefig(refl_figure_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(refl_figure_object)
shear_figure_object = handle_dict[plot_examples.RADAR_FIGURES_KEY][1]
shear_figure_file_name = '{0:s}/{1:s}_shear.jpg'.format(
output_dir_name, composite_name_abbrev)
print('Saving figure to: "{0:s}"...'.format(shear_figure_file_name))
shear_figure_object.savefig(shear_figure_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(shear_figure_object)
main_figure_file_name = '{0:s}/{1:s}_radar.jpg'.format(
output_dir_name, composite_name_abbrev)
print('Concatenating panels to: "{0:s}"...'.format(main_figure_file_name))
imagemagick_utils.concatenate_images(
input_file_names=[refl_figure_file_name, shear_figure_file_name],
output_file_name=main_figure_file_name,
num_panel_rows=1,
num_panel_columns=2,
border_width_pixels=50,
extra_args_string='-gravity south')
imagemagick_utils.resize_image(input_file_name=main_figure_file_name,
output_file_name=main_figure_file_name,
output_size_pixels=CONCAT_FIGURE_SIZE_PX)
imagemagick_utils.trim_whitespace(input_file_name=main_figure_file_name,
output_file_name=main_figure_file_name,
border_width_pixels=TITLE_FONT_SIZE + 25)
_overlay_text(image_file_name=main_figure_file_name,
x_offset_from_center_px=0,
y_offset_from_top_px=0,
text_string=composite_name_verbose)
imagemagick_utils.trim_whitespace(input_file_name=main_figure_file_name,
output_file_name=main_figure_file_name,
border_width_pixels=10)
return main_figure_file_name
def _run(bwo_file_name, output_dir_name):
"""Makes figure with backwards-optimization results for MYRORSS model.
This is effectively the main method.
:param bwo_file_name: See documentation at top of file.
:param output_dir_name: Same.
"""
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name
)
bwo_dictionary, model_metadata_dict = _read_bwo_file(bwo_file_name)
mean_before_matrices = bwo_dictionary[backwards_opt.MEAN_INPUT_MATRICES_KEY]
mean_after_matrices = bwo_dictionary[backwards_opt.MEAN_OUTPUT_MATRICES_KEY]
mean_sounding_pressures_pa = (
bwo_dictionary[backwards_opt.MEAN_SOUNDING_PRESSURES_KEY]
)
num_radar_heights = mean_before_matrices[0].shape[-2]
# Plot sounding before optimization.
handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=mean_before_matrices,
model_metadata_dict=model_metadata_dict,
pmm_flag=True, plot_sounding=True,
sounding_pressures_pascals=mean_sounding_pressures_pa,
allow_whitespace=True, plot_panel_names=True,
panel_name_font_size=PANEL_NAME_FONT_SIZE, add_titles=False,
label_colour_bars=True, colour_bar_length=COLOUR_BAR_LENGTH,
colour_bar_font_size=COLOUR_BAR_FONT_SIZE,
sounding_font_size=SOUNDING_FONT_SIZE,
num_panel_rows=num_radar_heights, plot_radar_diffs=False
)
panel_file_names = [None] * 4
panel_file_names[2] = '{0:s}/sounding_before.jpg'.format(output_dir_name)
_write_sounding_figure(
figure_object=handle_dict[plot_examples.SOUNDING_FIGURE_KEY],
title_string='(c) Original sounding',
output_file_name=panel_file_names[2]
)
print(SEPARATOR_STRING)
# Plot radar and sounding after optimization.
handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=mean_after_matrices,
model_metadata_dict=model_metadata_dict,
pmm_flag=True, plot_sounding=True,
sounding_pressures_pascals=mean_sounding_pressures_pa,
allow_whitespace=True, plot_panel_names=True,
panel_name_font_size=PANEL_NAME_FONT_SIZE, add_titles=False,
label_colour_bars=True, colour_bar_length=COLOUR_BAR_LENGTH,
colour_bar_font_size=COLOUR_BAR_FONT_SIZE,
sounding_font_size=SOUNDING_FONT_SIZE,
num_panel_rows=num_radar_heights, plot_radar_diffs=False
)
panel_file_names[3] = '{0:s}/sounding_after.jpg'.format(output_dir_name)
_write_sounding_figure(
figure_object=handle_dict[plot_examples.SOUNDING_FIGURE_KEY],
title_string='(d) Synthetic sounding',
output_file_name=panel_file_names[3]
)
panel_file_names[0] = _write_radar_figures(
refl_figure_object=handle_dict[plot_examples.RADAR_FIGURES_KEY][0],
shear_figure_object=handle_dict[plot_examples.RADAR_FIGURES_KEY][1],
composite_name='after',
concat_title_string='(a) Synthetic radar image',
output_dir_name=output_dir_name
)
print(SEPARATOR_STRING)
mean_difference_matrices = [
a - b for a, b in zip(mean_after_matrices, mean_before_matrices)
]
handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=mean_difference_matrices,
model_metadata_dict=model_metadata_dict,
pmm_flag=True, plot_sounding=False,
allow_whitespace=True, plot_panel_names=True,
panel_name_font_size=PANEL_NAME_FONT_SIZE, add_titles=False,
label_colour_bars=True, colour_bar_length=COLOUR_BAR_LENGTH,
colour_bar_font_size=COLOUR_BAR_FONT_SIZE,
num_panel_rows=num_radar_heights, plot_radar_diffs=True,
diff_colour_map_object=DIFF_COLOUR_MAP_OBJECT,
max_diff_percentile=MAX_DIFF_PERCENTILE
)
panel_file_names[1] = _write_radar_figures(
refl_figure_object=handle_dict[plot_examples.RADAR_FIGURES_KEY][0],
shear_figure_object=handle_dict[plot_examples.RADAR_FIGURES_KEY][1],
composite_name='difference',
concat_title_string='(b) Radar difference',
output_dir_name=output_dir_name
)
print(SEPARATOR_STRING)
figure_file_name = '{0:s}/bwo_concat.jpg'.format(output_dir_name)
print('Concatenating panels to: "{0:s}"...'.format(figure_file_name))
imagemagick_utils.concatenate_images(
input_file_names=panel_file_names,
output_file_name=figure_file_name, border_width_pixels=100,
num_panel_rows=2, num_panel_columns=2,
extra_args_string='-gravity Center'
)
imagemagick_utils.trim_whitespace(
input_file_name=figure_file_name, output_file_name=figure_file_name,
border_width_pixels=10
)
def _run(input_file_name, colour_map_name, min_unguided_value,
max_unguided_value, max_guided_value, num_unguided_contours,
half_num_guided_contours, smoothing_radius_grid_cells,
allow_whitespace, plot_panel_names, add_titles, label_colour_bars,
colour_bar_length, top_output_dir_name):
"""Plots Grad-CAM output (guided and unguided class-activation maps).
This is effectively the main method.
:param input_file_name: See documentation at top of file.
:param colour_map_name: Same.
:param min_unguided_value: Same.
:param max_unguided_value: Same.
:param max_guided_value: Same.
:param num_unguided_contours: Same.
:param half_num_guided_contours: Same.
:param smoothing_radius_grid_cells: Same.
:param allow_whitespace: Same.
:param plot_panel_names: Same.
:param add_titles: Same.
:param label_colour_bars: Same.
:param colour_bar_length: Same.
:param top_output_dir_name: Same.
"""
if smoothing_radius_grid_cells <= 0:
smoothing_radius_grid_cells = None
unguided_cam_dir_name = '{0:s}/main_gradcam'.format(top_output_dir_name)
guided_cam_dir_name = '{0:s}/guided_gradcam'.format(top_output_dir_name)
file_system_utils.mkdir_recursive_if_necessary(
directory_name=unguided_cam_dir_name
)
file_system_utils.mkdir_recursive_if_necessary(
directory_name=guided_cam_dir_name
)
# Check input args.
colour_map_object = pyplot.get_cmap(colour_map_name)
error_checking.assert_is_greater(min_unguided_value, 0.)
error_checking.assert_is_greater(max_unguided_value, min_unguided_value)
error_checking.assert_is_greater(max_guided_value, 0.)
error_checking.assert_is_geq(num_unguided_contours, 10)
error_checking.assert_is_geq(half_num_guided_contours, 5)
print('Reading data from: "{0:s}"...'.format(input_file_name))
gradcam_dict, pmm_flag = gradcam.read_file(input_file_name)
if pmm_flag:
predictor_matrices = gradcam_dict.pop(
gradcam.MEAN_PREDICTOR_MATRICES_KEY
)
cam_matrices = gradcam_dict.pop(gradcam.MEAN_CAM_MATRICES_KEY)
guided_cam_matrices = gradcam_dict.pop(
gradcam.MEAN_GUIDED_CAM_MATRICES_KEY
)
full_storm_id_strings = [None]
storm_times_unix_sec = [None]
for j in range(len(predictor_matrices)):
predictor_matrices[j] = numpy.expand_dims(
predictor_matrices[j], axis=0
)
if cam_matrices[j] is None:
continue
cam_matrices[j] = numpy.expand_dims(
cam_matrices[j], axis=0
)
guided_cam_matrices[j] = numpy.expand_dims(
guided_cam_matrices[j], axis=0
)
else:
predictor_matrices = gradcam_dict.pop(gradcam.PREDICTOR_MATRICES_KEY)
cam_matrices = gradcam_dict.pop(gradcam.CAM_MATRICES_KEY)
guided_cam_matrices = gradcam_dict.pop(gradcam.GUIDED_CAM_MATRICES_KEY)
full_storm_id_strings = gradcam_dict[gradcam.FULL_STORM_IDS_KEY]
storm_times_unix_sec = gradcam_dict[gradcam.STORM_TIMES_KEY]
if smoothing_radius_grid_cells is not None:
cam_matrices, guided_cam_matrices = _smooth_maps(
cam_matrices=cam_matrices, guided_cam_matrices=guided_cam_matrices,
smoothing_radius_grid_cells=smoothing_radius_grid_cells
)
# Read metadata for CNN.
model_file_name = gradcam_dict[gradcam.MODEL_FILE_KEY]
model_metafile_name = '{0:s}/model_metadata.p'.format(
os.path.split(model_file_name)[0]
)
print('Reading model metadata from: "{0:s}"...'.format(model_metafile_name))
model_metadata_dict = cnn.read_model_metadata(model_metafile_name)
print(SEPARATOR_STRING)
num_examples = predictor_matrices[0].shape[0]
num_matrices = len(predictor_matrices)
for i in range(num_examples):
this_handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=predictor_matrices,
model_metadata_dict=model_metadata_dict, pmm_flag=pmm_flag,
example_index=i, plot_sounding=False,
allow_whitespace=allow_whitespace,
plot_panel_names=plot_panel_names, add_titles=add_titles,
label_colour_bars=label_colour_bars,
colour_bar_length=colour_bar_length
)
these_figure_objects = this_handle_dict[plot_examples.RADAR_FIGURES_KEY]
these_axes_object_matrices = (
this_handle_dict[plot_examples.RADAR_AXES_KEY]
)
for j in range(num_matrices):
if cam_matrices[j] is None:
continue
# print(numpy.percentile(cam_matrices[j][i, ...], 0.))
# print(numpy.percentile(cam_matrices[j][i, ...], 1.))
# print(numpy.percentile(cam_matrices[j][i, ...], 99.))
# print(numpy.percentile(cam_matrices[j][i, ...], 100.))
#
# print('\n\n')
#
# print(numpy.percentile(guided_cam_matrices[j][i, ...], 0.))
# print(numpy.percentile(guided_cam_matrices[j][i, ...], 1.))
# print(numpy.percentile(guided_cam_matrices[j][i, ...], 99.))
# print(numpy.percentile(guided_cam_matrices[j][i, ...], 100.))
#
# print('\n\n------------------------------\n\n')
this_num_spatial_dim = len(predictor_matrices[j].shape) - 2
if this_num_spatial_dim == 3:
_plot_3d_radar_cam(
colour_map_object=colour_map_object,
min_unguided_value=min_unguided_value,
max_unguided_value=max_unguided_value,
num_unguided_contours=num_unguided_contours,
max_guided_value=max_guided_value,
half_num_guided_contours=half_num_guided_contours,
label_colour_bars=label_colour_bars,
colour_bar_length=colour_bar_length,
figure_objects=these_figure_objects,
axes_object_matrices=these_axes_object_matrices,
model_metadata_dict=model_metadata_dict,
output_dir_name=unguided_cam_dir_name,
cam_matrix=cam_matrices[j][i, ...],
full_storm_id_string=full_storm_id_strings[i],
storm_time_unix_sec=storm_times_unix_sec[i]
)
else:
_plot_2d_radar_cam(
colour_map_object=colour_map_object,
min_unguided_value=min_unguided_value,
max_unguided_value=max_unguided_value,
num_unguided_contours=num_unguided_contours,
max_guided_value=max_guided_value,
half_num_guided_contours=half_num_guided_contours,
label_colour_bars=label_colour_bars,
colour_bar_length=colour_bar_length,
figure_objects=these_figure_objects,
axes_object_matrices=these_axes_object_matrices,
model_metadata_dict=model_metadata_dict,
output_dir_name=unguided_cam_dir_name,
cam_matrix=cam_matrices[j][i, ...],
full_storm_id_string=full_storm_id_strings[i],
storm_time_unix_sec=storm_times_unix_sec[i]
)
this_handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=predictor_matrices,
model_metadata_dict=model_metadata_dict, pmm_flag=pmm_flag,
example_index=i, plot_sounding=False,
allow_whitespace=allow_whitespace,
plot_panel_names=plot_panel_names, add_titles=add_titles,
label_colour_bars=label_colour_bars,
colour_bar_length=colour_bar_length
)
these_figure_objects = this_handle_dict[plot_examples.RADAR_FIGURES_KEY]
these_axes_object_matrices = (
this_handle_dict[plot_examples.RADAR_AXES_KEY]
)
for j in range(num_matrices):
if guided_cam_matrices[j] is None:
continue
this_num_spatial_dim = len(predictor_matrices[j].shape) - 2
if this_num_spatial_dim == 3:
_plot_3d_radar_cam(
colour_map_object=colour_map_object,
min_unguided_value=min_unguided_value,
max_unguided_value=max_unguided_value,
num_unguided_contours=num_unguided_contours,
max_guided_value=max_guided_value,
half_num_guided_contours=half_num_guided_contours,
label_colour_bars=label_colour_bars,
colour_bar_length=colour_bar_length,
figure_objects=these_figure_objects,
axes_object_matrices=these_axes_object_matrices,
model_metadata_dict=model_metadata_dict,
output_dir_name=guided_cam_dir_name,
guided_cam_matrix=guided_cam_matrices[j][i, ...],
full_storm_id_string=full_storm_id_strings[i],
storm_time_unix_sec=storm_times_unix_sec[i]
)
else:
_plot_2d_radar_cam(
colour_map_object=colour_map_object,
min_unguided_value=min_unguided_value,
max_unguided_value=max_unguided_value,
num_unguided_contours=num_unguided_contours,
max_guided_value=max_guided_value,
half_num_guided_contours=half_num_guided_contours,
label_colour_bars=label_colour_bars,
colour_bar_length=colour_bar_length,
figure_objects=these_figure_objects,
axes_object_matrices=these_axes_object_matrices,
model_metadata_dict=model_metadata_dict,
output_dir_name=guided_cam_dir_name,
guided_cam_matrix=guided_cam_matrices[j][i, ...],
full_storm_id_string=full_storm_id_strings[i],
storm_time_unix_sec=storm_times_unix_sec[i]
)
def _plot_one_example(orig_radar_matrix, translated_radar_matrix,
rotated_radar_matrix, noised_radar_matrix,
output_dir_name, full_storm_id_string,
storm_time_unix_sec):
"""Plots original and augmented radar images for one example.
M = number of rows in grid
N = number of columns in grid
:param orig_radar_matrix: M-by-N-by-1-by-1 numpy array with original values.
:param translated_radar_matrix: Same but with translated values.
:param rotated_radar_matrix: Same but with rotated values.
:param noised_radar_matrix: Same but with noised values.
:param output_dir_name: Name of output directory (figure will be saved
here).
:param full_storm_id_string: Storm ID.
:param storm_time_unix_sec: Storm time.
"""
dummy_heights_m_agl = numpy.array([1000, 2000, 3000, 4000], dtype=int)
concat_radar_matrix = numpy.concatenate(
(orig_radar_matrix, translated_radar_matrix, rotated_radar_matrix,
noised_radar_matrix),
axis=-2)
training_option_dict = {
trainval_io.SOUNDING_FIELDS_KEY: None,
trainval_io.RADAR_FIELDS_KEY: [RADAR_FIELD_NAME],
trainval_io.RADAR_HEIGHTS_KEY: dummy_heights_m_agl
}
model_metadata_dict = {cnn.TRAINING_OPTION_DICT_KEY: training_option_dict}
handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=[concat_radar_matrix],
model_metadata_dict=model_metadata_dict,
pmm_flag=True,
plot_sounding=False,
allow_whitespace=True,
plot_panel_names=False,
add_titles=False,
label_colour_bars=True,
num_panel_rows=2)
figure_object = handle_dict[plot_examples.RADAR_FIGURES_KEY][0]
axes_object_matrix = handle_dict[plot_examples.RADAR_AXES_KEY][0]
axes_object_matrix[0, 0].set_title('(a) Original',
fontsize=TITLE_FONT_SIZE)
axes_object_matrix[0, 1].set_title('(b) Translated',
fontsize=TITLE_FONT_SIZE)
axes_object_matrix[1, 0].set_title(r'(c) Rotated 30$^{\circ}$ clockwise',
fontsize=TITLE_FONT_SIZE)
axes_object_matrix[1, 1].set_title('(d) Noised', fontsize=TITLE_FONT_SIZE)
output_file_name = '{0:s}/storm={1:s}_time={2:s}.jpg'.format(
output_dir_name, full_storm_id_string.replace('_', '-'),
time_conversion.unix_sec_to_string(storm_time_unix_sec,
FILE_NAME_TIME_FORMAT))
print('Saving figure to: "{0:s}"...'.format(output_file_name))
figure_object.savefig(output_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(figure_object)
