def _read_one_composite(saliency_file_name, smoothing_radius_grid_cells):
"""Reads saliency map for one composite.
T = number of input tensors for model
:param saliency_file_name: Path to input file (will be read by
`saliency.read_file`).
:param smoothing_radius_grid_cells: Radius for Gaussian smoother, used only
for saliency map.
:return: mean_predictor_matrices: length-T list of numpy arrays, where the
[i]th item has dimensions of the [i]th input tensor for the model.
:return: mean_saliency_matrices: Same as `mean_predictor_matrices` but with
saliency values.
:return: model_metadata_dict: Dictionary returned by
`cnn.read_model_metadata`.
"""
print('Reading data from: "{0:s}"...'.format(saliency_file_name))
saliency_dict = saliency_maps.read_file(saliency_file_name)[0]
mean_predictor_matrices = saliency_dict[MEAN_PREDICTOR_MATRICES_KEY]
mean_saliency_matrices = saliency_dict[MEAN_SALIENCY_MATRICES_KEY]
num_matrices = len(mean_predictor_matrices)
for i in range(num_matrices):
mean_predictor_matrices[i] = numpy.expand_dims(
mean_predictor_matrices[i], axis=0)
mean_saliency_matrices[i] = numpy.expand_dims(
mean_saliency_matrices[i], axis=0)
model_file_name = saliency_dict[MODEL_FILE_KEY]
model_metafile_name = cnn.find_metafile(model_file_name)
print('Reading CNN metadata from: "{0:s}"...'.format(model_metafile_name))
model_metadata_dict = cnn.read_model_metadata(model_metafile_name)
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
training_option_dict[trainval_io.UPSAMPLE_REFLECTIVITY_KEY] = False
all_refl_heights_m_agl = training_option_dict[
trainval_io.RADAR_HEIGHTS_KEY]
good_flags = numpy.array(
[h in REFL_HEIGHTS_M_AGL for h in all_refl_heights_m_agl], dtype=bool)
good_indices = numpy.where(good_flags)[0]
mean_predictor_matrices[0] = (mean_predictor_matrices[0][...,
good_indices, :])
mean_saliency_matrices[0] = (mean_saliency_matrices[0][...,
good_indices, :])
training_option_dict[trainval_io.RADAR_HEIGHTS_KEY] = REFL_HEIGHTS_M_AGL
model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY] = training_option_dict
if smoothing_radius_grid_cells is not None:
mean_saliency_matrices = _smooth_maps(
saliency_matrices=mean_saliency_matrices,
smoothing_radius_grid_cells=smoothing_radius_grid_cells)
return mean_predictor_matrices, mean_saliency_matrices, model_metadata_dict
def _composite_saliency_maps(
input_file_name, max_percentile_level, output_file_name):
"""Composites predictors and resulting saliency maps.
:param input_file_name: Path to input file. Will be read by
`saliency_maps.read_file`.
:param max_percentile_level: See documentation at top of file.
:param output_file_name: Path to output file. Will be written by
`saliency_maps.write_pmm_file`.
"""
print('Reading data from: "{0:s}"...'.format(input_file_name))
saliency_dict = saliency_maps.read_file(input_file_name)[0]
predictor_matrices = saliency_dict[saliency_maps.PREDICTOR_MATRICES_KEY]
saliency_matrices = saliency_dict[saliency_maps.SALIENCY_MATRICES_KEY]
sounding_pressure_matrix_pa = saliency_dict[
saliency_maps.SOUNDING_PRESSURES_KEY]
print('Compositing predictor matrices...')
mean_predictor_matrices, mean_sounding_pressures_pa = _composite_predictors(
predictor_matrices=predictor_matrices,
max_percentile_level=max_percentile_level,
sounding_pressure_matrix_pa=sounding_pressure_matrix_pa)
print('Compositing saliency maps...')
num_matrices = len(predictor_matrices)
mean_saliency_matrices = [None] * num_matrices
for i in range(num_matrices):
mean_saliency_matrices[i] = pmm.run_pmm_many_variables(
input_matrix=saliency_matrices[i],
max_percentile_level=max_percentile_level)
print('Writing output to: "{0:s}"...'.format(output_file_name))
saliency_maps.write_pmm_file(
pickle_file_name=output_file_name,
mean_denorm_predictor_matrices=mean_predictor_matrices,
mean_saliency_matrices=mean_saliency_matrices,
model_file_name=saliency_dict[saliency_maps.MODEL_FILE_KEY],
non_pmm_file_name=input_file_name,
pmm_max_percentile_level=max_percentile_level,
mean_sounding_pressures_pa=mean_sounding_pressures_pa)
def _read_one_composite(saliency_file_name, smoothing_radius_grid_cells):
"""Reads saliency map for one composite.
E = number of examples
M = number of rows in grid
N = number of columns in grid
H = number of heights in grid
F = number of radar fields
:param saliency_file_name: Path to input file (will be read by
`saliency.read_file`).
:param smoothing_radius_grid_cells: Radius for Gaussian smoother, used only
for saliency map.
:return: mean_radar_matrix: E-by-M-by-N-by-H-by-F numpy array with mean
radar fields.
:return: mean_saliency_matrix: E-by-M-by-N-by-H-by-F numpy array with mean
saliency fields.
:return: model_metadata_dict: Dictionary returned by
`cnn.read_model_metadata`.
"""
print('Reading data from: "{0:s}"...'.format(saliency_file_name))
saliency_dict = saliency_maps.read_file(saliency_file_name)[0]
mean_radar_matrix = numpy.expand_dims(
saliency_dict[saliency_maps.MEAN_PREDICTOR_MATRICES_KEY][0], axis=0)
mean_saliency_matrix = numpy.expand_dims(
saliency_dict[saliency_maps.MEAN_SALIENCY_MATRICES_KEY][0], axis=0)
if smoothing_radius_grid_cells is not None:
print((
'Smoothing saliency maps with Gaussian filter (e-folding radius of '
'{0:.1f} grid cells)...').format(smoothing_radius_grid_cells))
num_fields = mean_radar_matrix.shape[-1]
for k in range(num_fields):
mean_saliency_matrix[0, ..., k] = (
general_utils.apply_gaussian_filter(
input_matrix=mean_saliency_matrix[0, ..., k],
e_folding_radius_grid_cells=smoothing_radius_grid_cells))
model_file_name = saliency_dict[saliency_maps.MODEL_FILE_KEY]
model_metafile_name = cnn.find_metafile(model_file_name)
print('Reading CNN metadata from: "{0:s}"...'.format(model_metafile_name))
model_metadata_dict = cnn.read_model_metadata(model_metafile_name)
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
good_indices = numpy.array([
numpy.where(
training_option_dict[trainval_io.RADAR_HEIGHTS_KEY] == h)[0][0]
for h in RADAR_HEIGHTS_M_AGL
],
dtype=int)
mean_radar_matrix = mean_radar_matrix[..., good_indices, :]
mean_saliency_matrix = mean_saliency_matrix[..., good_indices, :]
good_indices = numpy.array([
training_option_dict[trainval_io.RADAR_FIELDS_KEY].index(f)
for f in RADAR_FIELD_NAMES
],
dtype=int)
mean_radar_matrix = mean_radar_matrix[..., good_indices]
mean_saliency_matrix = mean_saliency_matrix[..., good_indices]
training_option_dict[trainval_io.RADAR_HEIGHTS_KEY] = RADAR_HEIGHTS_M_AGL
training_option_dict[trainval_io.RADAR_FIELDS_KEY] = RADAR_FIELD_NAMES
training_option_dict[trainval_io.SOUNDING_FIELDS_KEY] = None
model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY] = training_option_dict
return mean_radar_matrix, mean_saliency_matrix, model_metadata_dict
def _read_one_composite(saliency_file_name, smoothing_radius_grid_cells,
monte_carlo_file_name):
"""Reads saliency map for one composite.
T = number of model-input tensors with radar data
:param saliency_file_name: Path to input file (will be read by
`saliency.read_file`).
:param smoothing_radius_grid_cells: Radius for Gaussian smoother, used only
for saliency map.
:param monte_carlo_file_name: Path to Monte Carlo file (will be read by
`_read_monte_carlo_file`).
:return: mean_radar_matrices: length-T list of numpy arrays with mean
predictor values. The [i]th array has the same dimensions as the [i]th
input tensor to the model.
:return: mean_saliency_matrices: Same but with saliency values.
:return: significance_matrices: Same but with Boolean significance flags.
:return: model_metadata_dict: Dictionary returned by
`cnn.read_model_metadata`.
"""
print('Reading data from: "{0:s}"...'.format(saliency_file_name))
saliency_dict = saliency_maps.read_file(saliency_file_name)[0]
mean_radar_matrices = saliency_dict[MEAN_PREDICTOR_MATRICES_KEY]
mean_saliency_matrices = saliency_dict[MEAN_SALIENCY_MATRICES_KEY]
num_matrices = len(mean_radar_matrices)
for i in range(num_matrices):
mean_radar_matrices[i] = numpy.expand_dims(mean_radar_matrices[i],
axis=0)
mean_saliency_matrices[i] = numpy.expand_dims(
mean_saliency_matrices[i], axis=0)
model_file_name = saliency_dict[MODEL_FILE_KEY]
model_metafile_name = cnn.find_metafile(model_file_name)
print('Reading CNN metadata from: "{0:s}"...'.format(model_metafile_name))
model_metadata_dict = cnn.read_model_metadata(model_metafile_name)
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
training_option_dict[trainval_io.UPSAMPLE_REFLECTIVITY_KEY] = False
has_soundings = (training_option_dict[trainval_io.SOUNDING_FIELDS_KEY]
is not None)
if has_soundings:
mean_radar_matrices = mean_radar_matrices[:-1]
mean_saliency_matrices = mean_saliency_matrices[:-1]
if smoothing_radius_grid_cells is not None:
mean_saliency_matrices = _smooth_maps(
saliency_matrices=mean_saliency_matrices,
smoothing_radius_grid_cells=smoothing_radius_grid_cells)
num_matrices = len(mean_radar_matrices)
significance_matrices = [None] * num_matrices
if monte_carlo_file_name is None:
for i in range(num_matrices):
significance_matrices[i] = numpy.full(mean_radar_matrices[i].shape,
False,
dtype=bool)
else:
print('Reading Monte Carlo test from: "{0:s}"...'.format(
monte_carlo_file_name))
this_file_handle = open(monte_carlo_file_name, 'rb')
monte_carlo_dict = pickle.load(this_file_handle)
this_file_handle.close()
for i in range(num_matrices):
significance_matrices[i] = (
monte_carlo_dict[monte_carlo.P_VALUE_MATRICES_KEY][i] <= 0.05)
significance_matrices[i] = numpy.expand_dims(
significance_matrices[i], axis=0)
all_sig_flags = numpy.concatenate(
[numpy.ravel(a) for a in significance_matrices])
print('Fraction of significant differences: {0:.4f}'.format(
numpy.mean(all_sig_flags.astype(float))))
all_refl_heights_m_agl = training_option_dict[
trainval_io.RADAR_HEIGHTS_KEY]
good_flags = numpy.array(
[h in REFL_HEIGHTS_M_AGL for h in all_refl_heights_m_agl], dtype=bool)
good_indices = numpy.where(good_flags)[0]
mean_radar_matrices[0] = (mean_radar_matrices[0][..., good_indices, :])
mean_saliency_matrices[0] = (mean_saliency_matrices[0][...,
good_indices, :])
significance_matrices[0] = (significance_matrices[0][..., good_indices, :])
training_option_dict[trainval_io.RADAR_HEIGHTS_KEY] = REFL_HEIGHTS_M_AGL
training_option_dict[trainval_io.SOUNDING_FIELDS_KEY] = None
model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY] = training_option_dict
return (mean_radar_matrices, mean_saliency_matrices, significance_matrices,
model_metadata_dict)
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 _read_one_composite(saliency_file_name, smoothing_radius_grid_cells,
monte_carlo_file_name):
"""Reads saliency map for one composite.
E = number of examples
M = number of rows in grid
N = number of columns in grid
H = number of heights in grid
F = number of radar fields
:param saliency_file_name: Path to input file (will be read by
`saliency.read_file`).
:param smoothing_radius_grid_cells: Radius for Gaussian smoother, used only
for saliency map.
:param monte_carlo_file_name: Path to Monte Carlo file (will be read by
`_read_monte_carlo_file`).
:return: mean_radar_matrix: E-by-M-by-N-by-H-by-F numpy array with mean
radar fields.
:return: mean_saliency_matrix: E-by-M-by-N-by-H-by-F numpy array with mean
saliency fields.
:return: significance_matrix: E-by-M-by-N-by-H-by-F numpy array of Boolean
flags.
:return: model_metadata_dict: Dictionary returned by
`cnn.read_model_metadata`.
"""
print('Reading saliency maps from: "{0:s}"...'.format(saliency_file_name))
saliency_dict = saliency_maps.read_file(saliency_file_name)[0]
mean_radar_matrix = numpy.expand_dims(
saliency_dict[saliency_maps.MEAN_PREDICTOR_MATRICES_KEY][0], axis=0)
mean_saliency_matrix = numpy.expand_dims(
saliency_dict[saliency_maps.MEAN_SALIENCY_MATRICES_KEY][0], axis=0)
if smoothing_radius_grid_cells is not None:
print((
'Smoothing saliency maps with Gaussian filter (e-folding radius of '
'{0:.1f} grid cells)...').format(smoothing_radius_grid_cells))
num_fields = mean_radar_matrix.shape[-1]
for k in range(num_fields):
mean_saliency_matrix[0, ..., k] = (
general_utils.apply_gaussian_filter(
input_matrix=mean_saliency_matrix[0, ..., k],
e_folding_radius_grid_cells=smoothing_radius_grid_cells))
model_file_name = saliency_dict[saliency_maps.MODEL_FILE_KEY]
model_metafile_name = cnn.find_metafile(model_file_name)
if monte_carlo_file_name is None:
significance_matrix = numpy.full(mean_radar_matrix.shape,
False,
dtype=bool)
else:
print('Reading Monte Carlo test from: "{0:s}"...'.format(
monte_carlo_file_name))
this_file_handle = open(monte_carlo_file_name, 'rb')
monte_carlo_dict = pickle.load(this_file_handle)
this_file_handle.close()
significance_matrix = numpy.logical_or(
monte_carlo_dict[monte_carlo.TRIAL_PMM_MATRICES_KEY][0] <
monte_carlo_dict[monte_carlo.MIN_MATRICES_KEY][0],
monte_carlo_dict[monte_carlo.TRIAL_PMM_MATRICES_KEY][0] >
monte_carlo_dict[monte_carlo.MAX_MATRICES_KEY][0])
significance_matrix = numpy.expand_dims(significance_matrix, axis=0)
print('Fraction of significant differences: {0:.4f}'.format(
numpy.mean(significance_matrix.astype(float))))
print('Reading CNN metadata from: "{0:s}"...'.format(model_metafile_name))
model_metadata_dict = cnn.read_model_metadata(model_metafile_name)
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
good_indices = numpy.array([
numpy.where(
training_option_dict[trainval_io.RADAR_HEIGHTS_KEY] == h)[0][0]
for h in RADAR_HEIGHTS_M_AGL
],
dtype=int)
mean_radar_matrix = mean_radar_matrix[..., good_indices, :]
mean_saliency_matrix = mean_saliency_matrix[..., good_indices, :]
significance_matrix = significance_matrix[..., good_indices, :]
good_indices = numpy.array([
training_option_dict[trainval_io.RADAR_FIELDS_KEY].index(f)
for f in RADAR_FIELD_NAMES
],
dtype=int)
mean_radar_matrix = mean_radar_matrix[..., good_indices]
mean_saliency_matrix = mean_saliency_matrix[..., good_indices]
significance_matrix = significance_matrix[..., good_indices]
training_option_dict[trainval_io.RADAR_HEIGHTS_KEY] = RADAR_HEIGHTS_M_AGL
training_option_dict[trainval_io.RADAR_FIELDS_KEY] = RADAR_FIELD_NAMES
training_option_dict[trainval_io.SOUNDING_FIELDS_KEY] = None
model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY] = training_option_dict
return (mean_radar_matrix, mean_saliency_matrix, significance_matrix,
model_metadata_dict)
def _run(actual_file_name, dummy_file_name, smoothing_radius_grid_cells,
max_pmm_percentile_level, num_iterations, confidence_level,
output_file_name):
"""Runs Monte Carlo test for saliency maps.
This is effectively the main method.
:param actual_file_name: See documentation at top of file.
:param dummy_file_name: Same.
:param smoothing_radius_grid_cells: Same.
:param max_pmm_percentile_level: Same.
:param num_iterations: Same.
:param confidence_level: Same.
:param output_file_name: Same.
"""
if smoothing_radius_grid_cells <= 0:
smoothing_radius_grid_cells = None
# Read saliency maps.
print('Reading actual saliency maps from: "{0:s}"...'.format(
actual_file_name))
actual_saliency_dict = saliency_maps.read_file(actual_file_name)[0]
actual_saliency_matrices = (
actual_saliency_dict[saliency_maps.SALIENCY_MATRICES_KEY])
print(
'Reading dummy saliency maps from: "{0:s}"...'.format(dummy_file_name))
dummy_saliency_dict = saliency_maps.read_file(dummy_file_name)[0]
dummy_saliency_matrices = (
dummy_saliency_dict[saliency_maps.SALIENCY_MATRICES_KEY])
# Ensure that the two files contain the same examples.
assert (actual_saliency_dict[saliency_maps.FULL_STORM_IDS_KEY] ==
dummy_saliency_dict[saliency_maps.FULL_STORM_IDS_KEY])
assert numpy.array_equal(
actual_saliency_dict[saliency_maps.STORM_TIMES_KEY],
dummy_saliency_dict[saliency_maps.STORM_TIMES_KEY])
if smoothing_radius_grid_cells is not None:
actual_saliency_matrices = _smooth_maps(
saliency_matrices=actual_saliency_matrices,
smoothing_radius_grid_cells=smoothing_radius_grid_cells)
dummy_saliency_matrices = _smooth_maps(
saliency_matrices=dummy_saliency_matrices,
smoothing_radius_grid_cells=smoothing_radius_grid_cells)
# Convert saliency from absolute values to percentiles.
num_matrices = len(actual_saliency_matrices)
num_examples = actual_saliency_matrices[0].shape[0]
for j in range(num_matrices):
for i in range(num_examples):
this_num_channels = actual_saliency_matrices[j].shape[-1]
for k in range(this_num_channels):
this_flat_array = numpy.ravel(actual_saliency_matrices[j][i,
...,
k])
these_flat_ranks = (
scipy.stats.rankdata(this_flat_array, method='average') /
len(this_flat_array))
actual_saliency_matrices[j][i, ..., k] = numpy.reshape(
these_flat_ranks, actual_saliency_matrices[j][i, ...,
k].shape)
this_flat_array = numpy.ravel(dummy_saliency_matrices[j][i,
...,
k])
these_flat_ranks = (
scipy.stats.rankdata(this_flat_array, method='average') /
len(this_flat_array))
dummy_saliency_matrices[j][i, ..., k] = numpy.reshape(
these_flat_ranks, dummy_saliency_matrices[j][i, ...,
k].shape)
# Do Monte Carlo test.
monte_carlo_dict = monte_carlo.run_monte_carlo_test(
list_of_baseline_matrices=dummy_saliency_matrices,
list_of_trial_matrices=actual_saliency_matrices,
max_pmm_percentile_level=max_pmm_percentile_level,
num_iterations=num_iterations,
confidence_level=confidence_level)
print('Writing results of Monte Carlo test to file: "{0:s}"...'.format(
output_file_name))
_write_results(monte_carlo_dict=monte_carlo_dict,
pickle_file_name=output_file_name)
