def test_metadata_to_file_name_fourth(self):
"""Ensures correct output from metadata_to_file_name.
In this case, using fourth set of metadata.
"""
this_file_name = plot_input_examples.metadata_to_file_name(
output_dir_name=FIGURE_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=FOURTH_RADAR_FIELD_NAME,
radar_height_m_agl=FOURTH_RADAR_HEIGHT_M_AGL,
layer_operation_dict=FOURTH_LAYER_OPERATION_DICT)
self.assertTrue(this_file_name == FOURTH_FIGURE_FILE_NAME)
def _plot_2d_regions(figure_objects,
axes_object_matrices,
model_metadata_dict,
list_of_polygon_objects,
output_dir_name,
full_storm_id_string=None,
storm_time_unix_sec=None):
"""Plots regions of interest for 2-D radar data.
:param figure_objects: See doc for `_plot_3d_radar_cam`.
:param axes_object_matrices: Same.
:param model_metadata_dict: Same.
:param list_of_polygon_objects: List of polygons (instances of
`shapely.geometry.Polygon`), demarcating regions of interest.
:param output_dir_name: See doc for `_plot_3d_radar_cam`.
:param full_storm_id_string: Same.
:param storm_time_unix_sec: Same.
"""
conv_2d3d = model_metadata_dict[cnn.CONV_2D3D_KEY]
figure_index = 1 if conv_2d3d else 0
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
num_grid_rows = training_option_dict[trainval_io.NUM_ROWS_KEY]
num_grid_rows *= 1 + int(conv_2d3d)
list_of_layer_operation_dicts = model_metadata_dict[
cnn.LAYER_OPERATIONS_KEY]
if list_of_layer_operation_dicts is None:
num_channels = len(training_option_dict[trainval_io.RADAR_FIELDS_KEY])
else:
num_channels = len(list_of_layer_operation_dicts)
for this_polygon_object in list_of_polygon_objects:
for k in range(num_channels):
i, j = numpy.unravel_index(
k, axes_object_matrices[figure_index].shape, order='F')
these_grid_columns = numpy.array(
this_polygon_object.exterior.xy[0])
these_grid_rows = num_grid_rows - numpy.array(
this_polygon_object.exterior.xy[1])
axes_object_matrices[figure_index][i, j].plot(
these_grid_columns,
these_grid_rows,
color=plotting_utils.colour_from_numpy_to_tuple(REGION_COLOUR),
linestyle='solid',
linewidth=REGION_LINE_WIDTH)
pmm_flag = full_storm_id_string is None and storm_time_unix_sec is None
output_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='shear' if conv_2d3d else None)
print('Saving figure to: "{0:s}"...'.format(output_file_name))
figure_objects[figure_index].savefig(output_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(figure_objects[figure_index])
def _plot_2d_radar_cam(colour_map_object,
max_colour_percentile,
figure_objects,
axes_object_matrices,
model_metadata_dict,
output_dir_name,
cam_matrix=None,
guided_cam_matrix=None,
significance_matrix=None,
full_storm_id_string=None,
storm_time_unix_sec=None):
"""Plots guided or unguided class-activation map for 2-D radar data.
M = number of rows in spatial grid
N = number of columns in spatial grid
C = number of radar channels
:param colour_map_object: See doc for `_plot_3d_radar_cam`.
:param max_colour_percentile: Same.
:param figure_objects: Same.
:param axes_object_matrices: Same.
:param model_metadata_dict: Same.
:param output_dir_name: Same.
:param cam_matrix: M-by-N numpy array of class activations.
:param guided_cam_matrix: M-by-N-by-C numpy array of guided-CAM output.
:param significance_matrix: See doc for `_plot_3d_radar_cam`.
:param full_storm_id_string: Same.
:param storm_time_unix_sec: Same.
"""
if cam_matrix is None:
quantity_string = 'max abs value'
else:
quantity_string = 'max activation'
pmm_flag = full_storm_id_string is None and storm_time_unix_sec is None
conv_2d3d = model_metadata_dict[cnn.CONV_2D3D_KEY]
figure_index = 1 if conv_2d3d else 0
list_of_layer_operation_dicts = model_metadata_dict[
cnn.LAYER_OPERATIONS_KEY]
if list_of_layer_operation_dicts is None:
training_option_dict = model_metadata_dict[
cnn.TRAINING_OPTION_DICT_KEY]
radar_field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY]
num_channels = len(radar_field_names)
else:
num_channels = len(list_of_layer_operation_dicts)
if cam_matrix is None:
this_matrix = guided_cam_matrix
else:
this_matrix = numpy.expand_dims(cam_matrix, axis=-1)
this_matrix = numpy.repeat(this_matrix, repeats=num_channels, axis=-1)
max_contour_level = numpy.percentile(numpy.absolute(this_matrix),
max_colour_percentile)
if cam_matrix is None:
saliency_plotting.plot_many_2d_grids_with_contours(
saliency_matrix_3d=numpy.flip(this_matrix, axis=0),
axes_object_matrix=axes_object_matrices[figure_index],
colour_map_object=colour_map_object,
max_absolute_contour_level=max_contour_level,
contour_interval=max_contour_level / HALF_NUM_CONTOURS,
row_major=False)
else:
cam_plotting.plot_many_2d_grids(
class_activation_matrix_3d=numpy.flip(this_matrix, axis=0),
axes_object_matrix=axes_object_matrices[figure_index],
colour_map_object=colour_map_object,
max_contour_level=max_contour_level,
contour_interval=max_contour_level / NUM_CONTOURS,
row_major=False)
if significance_matrix is not None:
if cam_matrix is None:
this_matrix = significance_matrix
else:
this_matrix = numpy.expand_dims(significance_matrix, axis=-1)
this_matrix = numpy.repeat(this_matrix,
repeats=num_channels,
axis=-1)
significance_plotting.plot_many_2d_grids_without_coords(
significance_matrix=numpy.flip(this_matrix, axis=0),
axes_object_matrix=axes_object_matrices[figure_index],
row_major=False)
this_title_string = figure_objects[figure_index]._suptitle
if this_title_string is not None:
this_title_string += ' ({0:s} = {1:.2e})'.format(
quantity_string, max_contour_level)
figure_objects[figure_index].suptitle(
this_title_string, fontsize=plot_input_examples.TITLE_FONT_SIZE)
output_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='shear' if conv_2d3d else None)
print('Saving figure to: "{0:s}"...'.format(output_file_name))
figure_objects[figure_index].savefig(output_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(figure_objects[figure_index])
def _plot_3d_radar_cam(colour_map_object,
max_colour_percentile,
figure_objects,
axes_object_matrices,
model_metadata_dict,
output_dir_name,
cam_matrix=None,
guided_cam_matrix=None,
significance_matrix=None,
full_storm_id_string=None,
storm_time_unix_sec=None):
"""Plots guided or unguided class-activation map for 3-D radar data.
This method will plot either `cam_matrix` or `guided_cam_matrix`, not both.
M = number of rows in spatial grid
N = number of columns in spatial grid
H = number of heights in spatial grid
F = number of radar fields
If this method is plotting a composite rather than single example (storm
object), `full_storm_id_string` and `storm_time_unix_sec` can be None.
:param colour_map_object: See documentation at top of file.
:param max_colour_percentile: Same.
:param figure_objects: See doc for `plot_input_examples._plot_3d_example`.
:param axes_object_matrices: Same.
:param model_metadata_dict: Dictionary returned by
`cnn.read_model_metadata`.
:param output_dir_name: Path to output directory. Figure(s) will be saved
here.
:param cam_matrix: M-by-N-by-H numpy array of class activations.
:param guided_cam_matrix: M-by-N-by-H-by-F numpy array of guided-CAM output.
:param significance_matrix: Boolean numpy array with the same dimensions as
the array being plotted (`cam_matrix` or `guided_cam_matrix`),
indicating where differences with some other CAM are significant.
:param full_storm_id_string: Full storm ID.
:param storm_time_unix_sec: Storm time.
"""
if cam_matrix is None:
quantity_string = 'max abs value'
else:
quantity_string = 'max activation'
pmm_flag = full_storm_id_string is None and storm_time_unix_sec is None
conv_2d3d = model_metadata_dict[cnn.CONV_2D3D_KEY]
if conv_2d3d:
loop_max = 1
radar_field_names = ['reflectivity']
else:
loop_max = len(figure_objects)
training_option_dict = model_metadata_dict[
cnn.TRAINING_OPTION_DICT_KEY]
radar_field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY]
for j in range(loop_max):
if cam_matrix is None:
this_matrix = guided_cam_matrix[..., j]
else:
this_matrix = cam_matrix
this_max_contour_level = numpy.percentile(numpy.absolute(this_matrix),
max_colour_percentile)
if cam_matrix is None:
saliency_plotting.plot_many_2d_grids_with_contours(
saliency_matrix_3d=numpy.flip(this_matrix, axis=0),
axes_object_matrix=axes_object_matrices[j],
colour_map_object=colour_map_object,
max_absolute_contour_level=this_max_contour_level,
contour_interval=this_max_contour_level / HALF_NUM_CONTOURS)
else:
cam_plotting.plot_many_2d_grids(
class_activation_matrix_3d=numpy.flip(this_matrix, axis=0),
axes_object_matrix=axes_object_matrices[j],
colour_map_object=colour_map_object,
max_contour_level=this_max_contour_level,
contour_interval=this_max_contour_level / NUM_CONTOURS)
if significance_matrix is not None:
if cam_matrix is None:
this_matrix = significance_matrix[..., j]
else:
this_matrix = significance_matrix
significance_plotting.plot_many_2d_grids_without_coords(
significance_matrix=numpy.flip(this_matrix, axis=0),
axes_object_matrix=axes_object_matrices[j])
this_title_string = figure_objects[j]._suptitle
if this_title_string is not None:
this_title_string += ' ({0:s} = {1:.2e})'.format(
quantity_string, this_max_contour_level)
figure_objects[j].suptitle(
this_title_string,
fontsize=plot_input_examples.TITLE_FONT_SIZE)
this_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=radar_field_names[j])
print('Saving figure to: "{0:s}"...'.format(this_file_name))
figure_objects[j].savefig(this_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(figure_objects[j])
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_2d_radar_saliency(
saliency_matrix, colour_map_object, max_colour_value, figure_objects,
axes_object_matrices, model_metadata_dict, output_dir_name,
significance_matrix=None, full_storm_id_string=None,
storm_time_unix_sec=None):
"""Plots saliency map for 2-D radar data.
M = number of rows in spatial grid
N = number of columns in spatial grid
C = number of radar channels
If this method is plotting a composite rather than single example (storm
object), `full_storm_id_string` and `storm_time_unix_sec` can be None.
:param saliency_matrix: M-by-N-by-C 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 figure_objects: See doc for
`plot_input_examples._plot_2d_radar_scan`.
:param axes_object_matrices: Same.
:param model_metadata_dict: Dictionary returned by
`cnn.read_model_metadata`.
:param output_dir_name: Path to output directory. Figure(s) will be saved
here.
:param significance_matrix: M-by-N-by-H numpy array of Boolean flags,
indicating where differences with some other saliency map are
significant.
:param full_storm_id_string: Full storm ID.
:param storm_time_unix_sec: Storm time.
"""
pmm_flag = full_storm_id_string is None and storm_time_unix_sec is None
conv_2d3d = model_metadata_dict[cnn.CONV_2D3D_KEY]
if conv_2d3d:
figure_index = 1
radar_field_name = 'shear'
else:
figure_index = 0
radar_field_name = None
list_of_layer_operation_dicts = model_metadata_dict[
cnn.LAYER_OPERATIONS_KEY]
if list_of_layer_operation_dicts is not None:
saliency_matrix = saliency_matrix[
..., plot_input_examples.LAYER_OP_INDICES_TO_KEEP
]
if significance_matrix is not None:
significance_matrix = significance_matrix[
..., plot_input_examples.LAYER_OP_INDICES_TO_KEEP
]
saliency_plotting.plot_many_2d_grids_with_contours(
saliency_matrix_3d=numpy.flip(saliency_matrix, axis=0),
axes_object_matrix=axes_object_matrices[figure_index],
colour_map_object=colour_map_object,
max_absolute_contour_level=max_colour_value,
contour_interval=max_colour_value / HALF_NUM_CONTOURS,
row_major=False)
if significance_matrix is not None:
significance_plotting.plot_many_2d_grids_without_coords(
significance_matrix=numpy.flip(significance_matrix, axis=0),
axes_object_matrix=axes_object_matrices[figure_index],
row_major=False)
this_title_object = figure_objects[figure_index]._suptitle
if this_title_object is not None:
this_title_string = '{0:s} (max abs saliency = {1:.2e})'.format(
this_title_object.get_text(), max_colour_value
)
figure_objects[figure_index].suptitle(
this_title_string, fontsize=plot_input_examples.TITLE_FONT_SIZE)
output_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=radar_field_name)
print('Saving figure to: "{0:s}"...'.format(output_file_name))
figure_objects[figure_index].savefig(
output_file_name, dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,
bbox_inches='tight'
)
pyplot.close(figure_objects[figure_index])
def _plot_3d_radar_saliency(
saliency_matrix, colour_map_object, max_colour_value, figure_objects,
axes_object_matrices, model_metadata_dict, output_dir_name,
significance_matrix=None, full_storm_id_string=None,
storm_time_unix_sec=None):
"""Plots saliency map for 3-D radar data.
M = number of rows in spatial grid
N = number of columns in spatial grid
H = number of heights in spatial grid
F = number of radar fields
If this method is plotting a composite rather than single example (storm
object), `full_storm_id_string` and `storm_time_unix_sec` can be None.
:param saliency_matrix: M-by-N-by-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 figure_objects: See doc for
`plot_input_examples._plot_3d_radar_scan`.
:param axes_object_matrices: Same.
:param model_metadata_dict: Dictionary returned by
`cnn.read_model_metadata`.
:param output_dir_name: Path to output directory. Figure(s) will be saved
here.
:param significance_matrix: M-by-N-by-H-by-F numpy array of Boolean flags,
indicating where differences with some other saliency map are
significant.
:param full_storm_id_string: Full storm ID.
:param storm_time_unix_sec: Storm time.
"""
pmm_flag = full_storm_id_string is None and storm_time_unix_sec is None
conv_2d3d = model_metadata_dict[cnn.CONV_2D3D_KEY]
if conv_2d3d:
loop_max = 1
radar_field_names = ['reflectivity']
else:
loop_max = len(figure_objects)
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
radar_field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY]
for j in range(loop_max):
saliency_plotting.plot_many_2d_grids_with_contours(
saliency_matrix_3d=numpy.flip(saliency_matrix[..., j], axis=0),
axes_object_matrix=axes_object_matrices[j],
colour_map_object=colour_map_object,
max_absolute_contour_level=max_colour_value,
contour_interval=max_colour_value / HALF_NUM_CONTOURS)
if significance_matrix is not None:
this_matrix = numpy.flip(significance_matrix[..., j], axis=0)
significance_plotting.plot_many_2d_grids_without_coords(
significance_matrix=this_matrix,
axes_object_matrix=axes_object_matrices[j]
)
this_title_object = figure_objects[j]._suptitle
if this_title_object is not None:
this_title_string = '{0:s} (max abs saliency = {1:.2e})'.format(
this_title_object.get_text(), max_colour_value
)
figure_objects[j].suptitle(
this_title_string, fontsize=plot_input_examples.TITLE_FONT_SIZE)
this_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=radar_field_names[j]
)
print('Saving figure to: "{0:s}"...'.format(this_file_name))
figure_objects[j].savefig(
this_file_name, dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,
bbox_inches='tight'
)
pyplot.close(figure_objects[j])
def _plot_sounding_saliency(
saliency_matrix, colour_map_object, max_colour_value,
sounding_figure_object, sounding_axes_object,
sounding_pressures_pascals, saliency_dict, model_metadata_dict,
add_title, output_dir_name, pmm_flag, example_index=None):
"""Plots saliency for sounding.
H = number of sounding heights
F = number of sounding fields
: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: Same.
:param sounding_figure_object: Figure handle (instance of
`matplotlib.figure.Figure`) for sounding itself.
:param sounding_axes_object: Axes handle (instance of
`matplotlib.axes._subplots.AxesSubplot`) for sounding itself.
:param sounding_pressures_pascals: length-H numpy array of sounding
pressures.
:param saliency_dict: Dictionary returned by `saliency_maps.read_file`.
:param model_metadata_dict: Dictionary returned by
`cnn.read_model_metadata`.
:param add_title: Boolean flag.
:param output_dir_name: Name of output directory. Figures will be saved
here.
:param pmm_flag: Boolean flag. If True, plotting PMM composite rather than
one example.
:param example_index: [used only if `pmm_flag == False`]
Plotting the [i]th example, where i = `example_index`.
"""
if max_colour_value is None:
max_colour_value = numpy.percentile(
numpy.absolute(saliency_matrix), MAX_COLOUR_PERCENTILE
)
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
sounding_field_names = training_option_dict[trainval_io.SOUNDING_FIELDS_KEY]
if pmm_flag:
full_storm_id_string = None
storm_time_unix_sec = None
else:
full_storm_id_string = saliency_dict[saliency_maps.FULL_STORM_IDS_KEY][
example_index]
storm_time_unix_sec = saliency_dict[saliency_maps.STORM_TIMES_KEY][
example_index]
if add_title:
title_string = 'Max absolute saliency = {0:.2e}'.format(
max_colour_value)
sounding_axes_object.set_title(title_string)
left_panel_file_name = plot_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))
sounding_figure_object.savefig(
left_panel_file_name, dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,
bbox_inches='tight')
pyplot.close(sounding_figure_object)
saliency_plotting.plot_saliency_for_sounding(
saliency_matrix=saliency_matrix,
sounding_field_names=sounding_field_names,
pressure_levels_mb=PASCALS_TO_MB * sounding_pressures_pascals,
colour_map_object=colour_map_object,
max_absolute_colour_value=max_colour_value)
right_panel_file_name = plot_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,
pad_inches=0, bbox_inches='tight')
pyplot.close()
concat_file_name = plot_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_2d_radar_saliency(
saliency_matrix, colour_map_object, max_colour_value, half_num_contours,
label_colour_bars, colour_bar_length, figure_objects,
axes_object_matrices, model_metadata_dict, output_dir_name,
significance_matrix=None, full_storm_id_string=None,
storm_time_unix_sec=None):
"""Plots saliency map for 2-D radar data.
M = number of rows in spatial grid
N = number of columns in spatial grid
C = number of radar channels
If this method is plotting a composite rather than single example (storm
object), `full_storm_id_string` and `storm_time_unix_sec` can be None.
:param saliency_matrix: M-by-N-by-C numpy array of saliency values.
:param colour_map_object: See documentation at top of file.
:param max_colour_value: Same.
:param half_num_contours: Same.
:param label_colour_bars: Same.
:param colour_bar_length: Same.
:param figure_objects: See doc for
`plot_input_examples._plot_2d_radar_scan`.
:param axes_object_matrices: Same.
:param model_metadata_dict: Dictionary returned by
`cnn.read_model_metadata`.
:param output_dir_name: Path to output directory. Figure(s) will be saved
here.
:param significance_matrix: M-by-N-by-H numpy array of Boolean flags,
indicating where differences with some other saliency map are
significant.
:param full_storm_id_string: Full storm ID.
:param storm_time_unix_sec: Storm time.
"""
if max_colour_value is None:
max_colour_value = numpy.percentile(
numpy.absolute(saliency_matrix), MAX_COLOUR_PERCENTILE
)
pmm_flag = full_storm_id_string is None and storm_time_unix_sec is None
conv_2d3d = model_metadata_dict[cnn.CONV_2D3D_KEY]
if conv_2d3d:
figure_index = 1
radar_field_name = 'shear'
else:
figure_index = 0
radar_field_name = None
saliency_plotting.plot_many_2d_grids_with_contours(
saliency_matrix_3d=numpy.flip(saliency_matrix, axis=0),
axes_object_matrix=axes_object_matrices[figure_index],
colour_map_object=colour_map_object,
max_absolute_contour_level=max_colour_value,
contour_interval=max_colour_value / half_num_contours,
row_major=False)
if significance_matrix is not None:
significance_plotting.plot_many_2d_grids_without_coords(
significance_matrix=numpy.flip(significance_matrix, axis=0),
axes_object_matrix=axes_object_matrices[figure_index],
row_major=False)
colour_bar_object = plotting_utils.plot_linear_colour_bar(
axes_object_or_matrix=axes_object_matrices[figure_index],
data_matrix=saliency_matrix,
colour_map_object=colour_map_object, min_value=0.,
max_value=max_colour_value, orientation_string='horizontal',
fraction_of_axis_length=colour_bar_length / (1 + int(conv_2d3d)),
extend_min=False, extend_max=True, font_size=COLOUR_BAR_FONT_SIZE)
if label_colour_bars:
colour_bar_object.set_label(
'Absolute saliency', fontsize=COLOUR_BAR_FONT_SIZE)
output_file_name = plot_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=radar_field_name)
print('Saving figure to: "{0:s}"...'.format(output_file_name))
figure_objects[figure_index].savefig(
output_file_name, dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,
bbox_inches='tight'
)
pyplot.close(figure_objects[figure_index])
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 _plot_2d_radar_cam(
colour_map_object, min_unguided_value, max_unguided_value,
num_unguided_contours, max_guided_value, half_num_guided_contours,
label_colour_bars, colour_bar_length, figure_objects,
axes_object_matrices, model_metadata_dict, output_dir_name,
cam_matrix=None, guided_cam_matrix=None, full_storm_id_string=None,
storm_time_unix_sec=None):
"""Plots class-activation map for 2-D radar data.
M = number of rows in spatial grid
N = number of columns in spatial grid
F = number of radar fields
If this method is plotting a composite rather than single example (storm
object), `full_storm_id_string` and `storm_time_unix_sec` can be None.
:param colour_map_object: See doc for `_plot_3d_radar_cam`.
:param min_unguided_value: Same.
:param max_unguided_value: Same.
:param num_unguided_contours: Same.
:param max_guided_value: Same.
:param half_num_guided_contours: Same.
:param label_colour_bars: Same.
:param colour_bar_length: Same.
:param figure_objects: See doc for
`plot_input_examples._plot_2d_radar_scan`.
:param axes_object_matrices: Same.
:param model_metadata_dict: See doc for `_plot_3d_radar_cam`.
:param output_dir_name: Same.
:param cam_matrix: M-by-N numpy array of unguided class activations.
:param guided_cam_matrix: [used only if `cam_matrix is None`]
M-by-N-by-F numpy array of guided class activations.
:param full_storm_id_string: Full storm ID.
:param storm_time_unix_sec: Storm time.
"""
pmm_flag = full_storm_id_string is None and storm_time_unix_sec is None
conv_2d3d = model_metadata_dict[cnn.CONV_2D3D_KEY]
if conv_2d3d:
figure_index = 1
radar_field_name = 'shear'
else:
figure_index = 0
radar_field_name = None
list_of_layer_operation_dicts = model_metadata_dict[
cnn.LAYER_OPERATIONS_KEY]
if list_of_layer_operation_dicts is None:
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
radar_field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY]
num_channels = len(radar_field_names)
else:
num_channels = len(list_of_layer_operation_dicts)
min_unguided_value_log10 = numpy.log10(min_unguided_value)
max_unguided_value_log10 = numpy.log10(max_unguided_value)
contour_interval_log10 = (
(max_unguided_value_log10 - min_unguided_value_log10) /
(num_unguided_contours - 1)
)
if cam_matrix is None:
saliency_plotting.plot_many_2d_grids_with_contours(
saliency_matrix_3d=numpy.flip(guided_cam_matrix, axis=0),
axes_object_matrix=axes_object_matrices[figure_index],
colour_map_object=colour_map_object,
max_absolute_contour_level=max_guided_value,
contour_interval=max_guided_value / half_num_guided_contours,
row_major=False
)
this_colour_bar_object = plotting_utils.plot_linear_colour_bar(
axes_object_or_matrix=axes_object_matrices[figure_index],
data_matrix=guided_cam_matrix,
colour_map_object=colour_map_object, min_value=0.,
max_value=max_guided_value, orientation_string='horizontal',
fraction_of_axis_length=colour_bar_length / (1 + int(conv_2d3d)),
extend_min=False, extend_max=True,
font_size=COLOUR_BAR_FONT_SIZE
)
if label_colour_bars:
this_colour_bar_object.set_label(
'Absolute guided class activation',
fontsize=COLOUR_BAR_FONT_SIZE
)
else:
this_cam_matrix_log10 = numpy.log10(
numpy.expand_dims(cam_matrix, axis=-1)
)
this_cam_matrix_log10 = numpy.repeat(
this_cam_matrix_log10, repeats=num_channels, axis=-1
)
cam_plotting.plot_many_2d_grids(
class_activation_matrix_3d=numpy.flip(
this_cam_matrix_log10, axis=0
),
axes_object_matrix=axes_object_matrices[figure_index],
colour_map_object=colour_map_object,
min_contour_level=min_unguided_value_log10,
max_contour_level=max_unguided_value_log10,
contour_interval=contour_interval_log10, row_major=False
)
this_colour_bar_object = plotting_utils.plot_linear_colour_bar(
axes_object_or_matrix=axes_object_matrices[figure_index],
data_matrix=this_cam_matrix_log10,
colour_map_object=colour_map_object,
min_value=min_unguided_value_log10,
max_value=max_unguided_value_log10,
orientation_string='horizontal',
fraction_of_axis_length=colour_bar_length / (1 + int(conv_2d3d)),
extend_min=True, extend_max=True,
font_size=COLOUR_BAR_FONT_SIZE
)
these_tick_values = this_colour_bar_object.get_ticks()
these_tick_strings = [
'{0:.2f}'.format(10 ** v)[:4] for v in these_tick_values
]
this_colour_bar_object.set_ticks(these_tick_values)
this_colour_bar_object.set_ticklabels(these_tick_strings)
if label_colour_bars:
this_colour_bar_object.set_label(
'Class activation', fontsize=COLOUR_BAR_FONT_SIZE
)
output_file_name = plot_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=radar_field_name
)
print('Saving figure to: "{0:s}"...'.format(output_file_name))
figure_objects[figure_index].savefig(
output_file_name, dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,
bbox_inches='tight'
)
pyplot.close(figure_objects[figure_index])
def _plot_3d_radar_cam(
colour_map_object, min_unguided_value, max_unguided_value,
num_unguided_contours, max_guided_value, half_num_guided_contours,
label_colour_bars, colour_bar_length, figure_objects,
axes_object_matrices, model_metadata_dict, output_dir_name,
cam_matrix=None, guided_cam_matrix=None, full_storm_id_string=None,
storm_time_unix_sec=None):
"""Plots class-activation map for 3-D radar data.
M = number of rows in spatial grid
N = number of columns in spatial grid
H = number of heights in spatial grid
F = number of radar fields
If this method is plotting a composite rather than single example (storm
object), `full_storm_id_string` and `storm_time_unix_sec` can be None.
:param colour_map_object: See documentation at top of file.
:param min_unguided_value: Same.
:param max_unguided_value: Same.
:param num_unguided_contours: Same.
:param max_guided_value: Same.
:param half_num_guided_contours: Same.
:param label_colour_bars: Same.
:param colour_bar_length: Same.
:param figure_objects: See doc for
`plot_input_examples._plot_3d_radar_scan`.
:param axes_object_matrices: Same.
:param model_metadata_dict: Dictionary returned by
`cnn.read_model_metadata`.
:param output_dir_name: Path to output directory. Figure(s) will be saved
here.
:param cam_matrix: M-by-N-by-H numpy array of unguided class activations.
:param guided_cam_matrix: [used only if `cam_matrix is None`]
M-by-N-by-H-by-F numpy array of guided class activations.
:param full_storm_id_string: Full storm ID.
:param storm_time_unix_sec: Storm time.
"""
pmm_flag = full_storm_id_string is None and storm_time_unix_sec is None
conv_2d3d = model_metadata_dict[cnn.CONV_2D3D_KEY]
if conv_2d3d:
loop_max = 1
radar_field_names = ['reflectivity']
else:
loop_max = len(figure_objects)
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
radar_field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY]
min_unguided_value_log10 = numpy.log10(min_unguided_value)
max_unguided_value_log10 = numpy.log10(max_unguided_value)
contour_interval_log10 = (
(max_unguided_value_log10 - min_unguided_value_log10) /
(num_unguided_contours - 1)
)
for j in range(loop_max):
if cam_matrix is None:
saliency_plotting.plot_many_2d_grids_with_contours(
saliency_matrix_3d=numpy.flip(
guided_cam_matrix[..., j], axis=0
),
axes_object_matrix=axes_object_matrices[j],
colour_map_object=colour_map_object,
max_absolute_contour_level=max_guided_value,
contour_interval=max_guided_value / half_num_guided_contours
)
this_colour_bar_object = plotting_utils.plot_linear_colour_bar(
axes_object_or_matrix=axes_object_matrices[j],
data_matrix=guided_cam_matrix[..., j],
colour_map_object=colour_map_object, min_value=0.,
max_value=max_guided_value, orientation_string='horizontal',
fraction_of_axis_length=colour_bar_length,
extend_min=False, extend_max=True,
font_size=COLOUR_BAR_FONT_SIZE
)
if label_colour_bars:
this_colour_bar_object.set_label(
'Absolute guided class activation',
fontsize=COLOUR_BAR_FONT_SIZE
)
else:
cam_matrix_log10 = numpy.log10(cam_matrix)
cam_plotting.plot_many_2d_grids(
class_activation_matrix_3d=numpy.flip(cam_matrix_log10, axis=0),
axes_object_matrix=axes_object_matrices[j],
colour_map_object=colour_map_object,
min_contour_level=min_unguided_value_log10,
max_contour_level=max_unguided_value_log10,
contour_interval=contour_interval_log10
)
this_colour_bar_object = plotting_utils.plot_linear_colour_bar(
axes_object_or_matrix=axes_object_matrices[j],
data_matrix=cam_matrix_log10,
colour_map_object=colour_map_object,
min_value=min_unguided_value_log10,
max_value=max_unguided_value_log10,
orientation_string='horizontal',
fraction_of_axis_length=colour_bar_length,
extend_min=True, extend_max=True,
font_size=COLOUR_BAR_FONT_SIZE
)
these_tick_values = this_colour_bar_object.get_ticks()
these_tick_strings = [
'{0:.2f}'.format(10 ** v)[:4] for v in these_tick_values
]
this_colour_bar_object.set_ticks(these_tick_values)
this_colour_bar_object.set_ticklabels(these_tick_strings)
if label_colour_bars:
this_colour_bar_object.set_label(
'Class activation', fontsize=COLOUR_BAR_FONT_SIZE
)
this_file_name = plot_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=radar_field_names[j]
)
print('Saving figure to: "{0:s}"...'.format(this_file_name))
figure_objects[j].savefig(
this_file_name, dpi=FIGURE_RESOLUTION_DPI, pad_inches=0,
bbox_inches='tight'
)
pyplot.close(figure_objects[j])