def _run(output_file_name):
"""Plots Laplacian kernel used for edge-detector test.
This is effectively the main method.
:param output_file_name: See documentation at top of file.
"""
num_heights = KERNEL_MATRIX_3D.shape[-1]
figure_object, axes_object_matrix = plotting_utils.create_paneled_figure(
num_rows=1,
num_columns=num_heights,
horizontal_spacing=0.1,
vertical_spacing=0.1,
shared_x_axis=False,
shared_y_axis=False,
keep_aspect_ratio=True)
for k in range(num_heights):
_plot_kernel_one_height(kernel_matrix_2d=KERNEL_MATRIX_3D[..., k],
axes_object=axes_object_matrix[0, k])
axes_object_matrix[0, 0].set_title('Bottom height')
axes_object_matrix[0, 1].set_title('Middle height')
axes_object_matrix[0, 2].set_title('Top height')
file_system_utils.mkdir_recursive_if_necessary(file_name=output_file_name)
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)
def plot_many_1d_feature_maps(
feature_matrix, colour_map_object, colour_norm_object=None,
min_colour_value=None, max_colour_value=None,
figure_width_inches=DEFAULT_FIG_WIDTH_INCHES,
figure_height_inches=DEFAULT_FIG_HEIGHT_INCHES):
"""Plots many 1-D feature maps in the same figure (one per column).
N = number of points in spatial grid
C = number of channels
:param feature_matrix: N-by-C numpy array of feature values.
:param colour_map_object: See doc for `plot_many_2d_feature_maps`.
:param colour_norm_object: Same.
:param min_colour_value: Same.
:param max_colour_value: Same.
:param figure_width_inches: Same.
:param figure_height_inches: Same.
:return: figure_object: See doc for `plotting_utils.create_paneled_figure`.
:return: axes_object_matrix: Same.
"""
pyplot.rc('axes', linewidth=1)
error_checking.assert_is_numpy_array(feature_matrix, num_dimensions=2)
num_channels = feature_matrix.shape[1]
num_spatial_points = feature_matrix.shape[0]
figure_object, axes_object_matrix = plotting_utils.create_paneled_figure(
num_rows=1, num_columns=num_channels,
figure_width_inches=figure_width_inches,
figure_height_inches=figure_height_inches,
horizontal_spacing=0., vertical_spacing=0.,
shared_x_axis=False, shared_y_axis=False, keep_aspect_ratio=False)
for k in range(num_channels):
this_matrix = numpy.reshape(
feature_matrix[..., k], (num_spatial_points, 1)
)
plot_2d_feature_map(
feature_matrix=this_matrix, axes_object=axes_object_matrix[0, k],
font_size=30, colour_map_object=colour_map_object,
colour_norm_object=colour_norm_object,
min_colour_value=min_colour_value,
max_colour_value=max_colour_value,
annotation_string=''
)
return figure_object, axes_object_matrix
def _run(include_caption, output_dir_name):
"""Makes animation to explain multivariate convolution.
This is effectively the main method.
:param include_caption: See documentation at top of file.
:param output_dir_name: Same.
"""
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
output_feature_matrix = standalone_utils.do_2d_convolution(
feature_matrix=INPUT_FEATURE_MATRIX, kernel_matrix=KERNEL_MATRIX,
pad_edges=True, stride_length_px=1)
output_feature_matrix = output_feature_matrix[0, ..., 0]
num_grid_rows = INPUT_FEATURE_MATRIX.shape[0]
num_grid_columns = INPUT_FEATURE_MATRIX.shape[1]
image_file_names = []
kernel_width_ratio = float(KERNEL_MATRIX.shape[1]) / num_grid_columns
kernel_height_ratio = float(KERNEL_MATRIX.shape[0]) / num_grid_rows
for i in range(num_grid_rows):
for j in range(num_grid_columns):
this_figure_object, this_axes_object_matrix = (
plotting_utils.create_paneled_figure(
num_rows=NUM_PANEL_ROWS, num_columns=NUM_PANEL_COLUMNS,
horizontal_spacing=0.2, vertical_spacing=0.,
shared_x_axis=False, shared_y_axis=False,
keep_aspect_ratio=True)
)
letter_label = None
_plot_feature_map(
feature_matrix_2d=INPUT_FEATURE_MATRIX[..., 0],
kernel_row=i, kernel_column=j, is_output_map=False,
axes_object=this_axes_object_matrix[0, 0]
)
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_matrix[0, 0],
label_string='({0:s})'.format(letter_label),
font_size=PANEL_LETTER_FONT_SIZE,
y_coord_normalized=1.04, x_coord_normalized=0.1
)
_plot_feature_map(
feature_matrix_2d=output_feature_matrix,
kernel_row=i, kernel_column=j, is_output_map=True,
axes_object=this_axes_object_matrix[0, 2]
)
this_bbox_object = this_axes_object_matrix[0, 1].get_position()
this_width = kernel_width_ratio * (
this_bbox_object.x1 - this_bbox_object.x0
)
this_height = kernel_height_ratio * (
this_bbox_object.y1 - this_bbox_object.y0
)
this_bbox_object.x0 += 0.5 * this_width
this_bbox_object.y0 = (
this_axes_object_matrix[0, 0].get_position().y0 + 0.1
)
this_bbox_object.x1 = this_bbox_object.x0 + this_width
this_bbox_object.y1 = this_bbox_object.y0 + this_height
this_axes_object_matrix[0, 1].set_position(this_bbox_object)
_plot_kernel(
kernel_matrix_2d=KERNEL_MATRIX[..., 0, 0],
feature_matrix_2d=INPUT_FEATURE_MATRIX[..., 0],
feature_row_at_center=i, feature_column_at_center=j,
axes_object=this_axes_object_matrix[0, 1]
)
letter_label = chr(ord(letter_label) + 1)
plotting_utils.label_axes(
axes_object=this_axes_object_matrix[0, 1],
label_string='({0:s})'.format(letter_label),
font_size=PANEL_LETTER_FONT_SIZE,
y_coord_normalized=1.04, x_coord_normalized=0.2
)
_plot_feature_to_kernel_lines(
kernel_matrix_2d=KERNEL_MATRIX[..., 0, 0],
feature_matrix_2d=INPUT_FEATURE_MATRIX[..., 0],
feature_row_at_center=i, feature_column_at_center=j,
kernel_axes_object=this_axes_object_matrix[0, 1],
feature_axes_object=this_axes_object_matrix[0, 0]
)
letter_label = chr(ord(letter_label) + 1)
plotting_utils.label_axes(
axes_object=this_axes_object_matrix[0, 2],
label_string='({0:s})'.format(letter_label),
font_size=PANEL_LETTER_FONT_SIZE,
y_coord_normalized=1.04, x_coord_normalized=0.1
)
if include_caption:
this_figure_object.text(
0.5, 0.35, FIGURE_CAPTION,
fontsize=DEFAULT_FONT_SIZE, color='k',
horizontalalignment='center', verticalalignment='top')
image_file_names.append(
'{0:s}/conv_animation_row{1:d}_column{2:d}.jpg'.format(
output_dir_name, i, j)
)
print('Saving figure to: "{0:s}"...'.format(image_file_names[-1]))
this_figure_object.savefig(
image_file_names[-1], dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0, bbox_inches='tight'
)
pyplot.close(this_figure_object)
animation_file_name = '{0:s}/conv_animation.gif'.format(output_dir_name)
print('Creating animation: "{0:s}"...'.format(animation_file_name))
imagemagick_utils.create_gif(
input_file_names=image_file_names, output_file_name=animation_file_name,
num_seconds_per_frame=0.5, resize_factor=0.5)
def plot_3d_grid_without_coords(field_matrix,
field_name,
grid_point_heights_metres,
ground_relative,
num_panel_rows=None,
figure_object=None,
axes_object_matrix=None,
font_size=DEFAULT_FONT_SIZE,
colour_map_object=None,
colour_norm_object=None):
"""Plots 3-D grid as many colour maps (one per height).
M = number of grid rows
N = number of grid columns
H = number of grid heights
To use the default colour scheme for the given radar field, leave
`colour_map_object` and `colour_norm_object` empty.
If `num_panel_rows is None`, this method needs arguments `figure_object` and
`axes_object_matrix` -- and vice-versa.
:param field_matrix: M-by-N-by-H numpy array with values of radar field.
:param field_name: Name of radar field (must be accepted by
`radar_utils.check_field_name`).
:param grid_point_heights_metres: length-H integer numpy array of heights.
:param ground_relative: Boolean flag. If True, heights in
`height_by_pair_metres` are ground-relative. If False,
sea-level-relative.
:param num_panel_rows: Number of rows in paneled figure (different than M,
the number of grid rows).
:param figure_object: See doc for `plotting_utils.create_paneled_figure`.
:param axes_object_matrix: See above.
:param font_size: Font size for colour-bar ticks and panel labels.
:param colour_map_object: See doc for `plot_latlng_grid`.
:param colour_norm_object: Same.
:return: figure_object: See doc for `plotting_utils.init_panels`.
:return: axes_object_matrix: Same.
"""
error_checking.assert_is_numpy_array(field_matrix, num_dimensions=3)
error_checking.assert_is_geq_numpy_array(grid_point_heights_metres, 0)
grid_point_heights_metres = numpy.round(grid_point_heights_metres).astype(
int)
num_heights = field_matrix.shape[2]
these_expected_dim = numpy.array([num_heights], dtype=int)
error_checking.assert_is_numpy_array(grid_point_heights_metres,
exact_dimensions=these_expected_dim)
error_checking.assert_is_boolean(ground_relative)
if figure_object is None:
error_checking.assert_is_integer(num_panel_rows)
error_checking.assert_is_geq(num_panel_rows, 1)
error_checking.assert_is_leq(num_panel_rows, num_heights)
num_panel_columns = int(numpy.ceil(
float(num_heights) / num_panel_rows))
figure_object, axes_object_matrix = (
plotting_utils.create_paneled_figure(num_rows=num_panel_rows,
num_columns=num_panel_columns,
shared_x_axis=False,
shared_y_axis=False,
keep_aspect_ratio=True))
else:
error_checking.assert_is_numpy_array(axes_object_matrix,
num_dimensions=2)
num_panel_rows = axes_object_matrix.shape[0]
num_panel_columns = axes_object_matrix.shape[1]
for i in range(num_panel_rows):
for j in range(num_panel_columns):
this_height_index = i * num_panel_columns + j
if this_height_index >= num_heights:
axes_object_matrix[i, j].axis('off')
continue
this_annotation_string = '{0:.1f} km'.format(
grid_point_heights_metres[this_height_index] * METRES_TO_KM)
if ground_relative:
this_annotation_string += ' AGL'
else:
this_annotation_string += ' ASL'
plot_2d_grid_without_coords(
field_matrix=field_matrix[..., this_height_index],
field_name=field_name,
axes_object=axes_object_matrix[i, j],
annotation_string=this_annotation_string,
colour_map_object=colour_map_object,
colour_norm_object=colour_norm_object,
font_size=font_size)
return figure_object, axes_object_matrix
def plot_many_2d_grids_without_coords(field_matrix,
field_name_by_panel,
num_panel_rows=None,
figure_object=None,
axes_object_matrix=None,
panel_names=None,
colour_map_object_by_panel=None,
colour_norm_object_by_panel=None,
plot_colour_bar_by_panel=None,
font_size=DEFAULT_FONT_SIZE,
row_major=True):
"""Plots 2-D colour map in each panel (one per field/height pair).
M = number of rows in spatial grid
N = number of columns in spatial grid
P = number of panels (field/height pairs)
This method uses the default colour scheme for each radar field.
If `num_panel_rows is None`, this method needs arguments `figure_object` and
`axes_object_matrix` -- and vice-versa.
:param field_matrix: M-by-N-by-P numpy array of radar values.
:param field_name_by_panel: length-P list of field names.
:param num_panel_rows: Number of rows in paneled figure (different than M,
which is number of rows in spatial grid).
:param figure_object: See doc for `plotting_utils.create_paneled_figure`.
:param axes_object_matrix: See above.
:param panel_names: length-P list of panel names (will be printed at bottoms
of panels). If you do not want panel names, make this None.
:param colour_map_object_by_panel: length-P list of `matplotlib.pyplot.cm`
objects. If this is None, the default will be used for each field.
:param colour_norm_object_by_panel: length-P list of
`matplotlib.colors.BoundaryNorm` objects. If this is None, the default
will be used for each field.
:param plot_colour_bar_by_panel: length-P numpy array of Boolean flags. If
plot_colour_bar_by_panel[k] = True, horizontal colour bar will be
plotted under [k]th panel. If you want to plot colour bar for every
panel, leave this as None.
:param font_size: Font size.
:param row_major: Boolean flag. If True, panels will be filled along rows
first, then down columns. If False, down columns first, then along
rows.
:return: figure_object: See doc for `plotting_utils.create_paneled_figure`.
:return: axes_object_matrix: Same.
:raises: ValueError: if `colour_map_object_by_panel` or
`colour_norm_object_by_panel` has different length than number of
panels.
"""
error_checking.assert_is_boolean(row_major)
error_checking.assert_is_numpy_array(field_matrix, num_dimensions=3)
num_panels = field_matrix.shape[2]
if panel_names is None:
panel_names = [None] * num_panels
if plot_colour_bar_by_panel is None:
plot_colour_bar_by_panel = numpy.full(num_panels, True, dtype=bool)
these_expected_dim = numpy.array([num_panels], dtype=int)
error_checking.assert_is_numpy_array(numpy.array(panel_names),
exact_dimensions=these_expected_dim)
error_checking.assert_is_numpy_array(numpy.array(field_name_by_panel),
exact_dimensions=these_expected_dim)
error_checking.assert_is_boolean_numpy_array(plot_colour_bar_by_panel)
error_checking.assert_is_numpy_array(plot_colour_bar_by_panel,
exact_dimensions=these_expected_dim)
if (colour_map_object_by_panel is None
or colour_norm_object_by_panel is None):
colour_map_object_by_panel = [None] * num_panels
colour_norm_object_by_panel = [None] * num_panels
error_checking.assert_is_list(colour_map_object_by_panel)
error_checking.assert_is_list(colour_norm_object_by_panel)
if len(colour_map_object_by_panel) != num_panels:
error_string = (
'Number of colour maps ({0:d}) should equal number of panels '
'({1:d}).').format(len(colour_map_object_by_panel), num_panels)
raise ValueError(error_string)
if len(colour_norm_object_by_panel) != num_panels:
error_string = (
'Number of colour-normalizers ({0:d}) should equal number of panels'
' ({1:d}).').format(len(colour_norm_object_by_panel), num_panels)
raise ValueError(error_string)
if figure_object is None:
error_checking.assert_is_integer(num_panel_rows)
error_checking.assert_is_geq(num_panel_rows, 1)
error_checking.assert_is_leq(num_panel_rows, num_panels)
num_panel_columns = int(numpy.ceil(float(num_panels) / num_panel_rows))
figure_object, axes_object_matrix = (
plotting_utils.create_paneled_figure(num_rows=num_panel_rows,
num_columns=num_panel_columns,
shared_x_axis=False,
shared_y_axis=False,
keep_aspect_ratio=True))
else:
error_checking.assert_is_numpy_array(axes_object_matrix,
num_dimensions=2)
num_panel_rows = axes_object_matrix.shape[0]
num_panel_columns = axes_object_matrix.shape[1]
if row_major:
order_string = 'C'
else:
order_string = 'F'
for k in range(num_panels):
this_panel_row, this_panel_column = numpy.unravel_index(
k, (num_panel_rows, num_panel_columns), order=order_string)
# this_colour_map_object, this_colour_norm_object = (
# plot_2d_grid_without_coords(
# field_matrix=field_matrix[..., k],
# field_name=field_name_by_panel[k],
# axes_object=axes_object_matrix[
# this_panel_row, this_panel_column],
# annotation_string=panel_names[k], font_size=font_size,
# colour_map_object=colour_map_object_by_panel[k],
# colour_norm_object=colour_norm_object_by_panel[k]
# )
# )
this_colour_map_object, this_colour_norm_object = (
plot_2d_grid_without_coords(
field_matrix=field_matrix[..., k],
field_name=field_name_by_panel[k],
axes_object=axes_object_matrix[this_panel_row,
this_panel_column],
annotation_string=None,
font_size=font_size,
colour_map_object=colour_map_object_by_panel[k],
colour_norm_object=colour_norm_object_by_panel[k]))
if not plot_colour_bar_by_panel[k]:
continue
this_extend_min_flag = field_name_by_panel[k] in SHEAR_VORT_DIV_NAMES
this_colour_bar_object = plotting_utils.plot_colour_bar(
axes_object_or_matrix=axes_object_matrix[this_panel_row,
this_panel_column],
data_matrix=field_matrix[..., k],
colour_map_object=this_colour_map_object,
colour_norm_object=this_colour_norm_object,
orientation_string='horizontal',
extend_min=this_extend_min_flag,
extend_max=True,
fraction_of_axis_length=0.75,
font_size=font_size)
this_colour_bar_object.set_label(panel_names[k].replace('\n', '; '),
fontsize=font_size,
fontweight='bold')
for k in range(num_panel_rows * num_panel_columns):
if k < num_panels:
continue
this_panel_row, this_panel_column = numpy.unravel_index(
k, (num_panel_rows, num_panel_columns), order=order_string)
axes_object_matrix[this_panel_row, this_panel_column].axis('off')
return figure_object, axes_object_matrix
def _run(input_file_name, num_predictors_to_plot, confidence_level,
output_dir_name):
"""Plots results of permutation-based importance test.
This is effectively the main method.
:param input_file_name: See documentation at top of file.
:param num_predictors_to_plot: Same.
:param confidence_level: Same.
:param output_dir_name: Same.
"""
if num_predictors_to_plot <= 0:
num_predictors_to_plot = None
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
print('Reading data from: "{0:s}"...'.format(input_file_name))
permutation_dict = ml4rt_permutation.read_file(input_file_name)
permutation_dict = _results_to_gg_format(permutation_dict)
figure_object, axes_object_matrix = plotting_utils.create_paneled_figure(
num_rows=1,
num_columns=2,
shared_x_axis=False,
shared_y_axis=True,
keep_aspect_ratio=False,
horizontal_spacing=0.1,
vertical_spacing=0.05)
permutation_plotting.plot_single_pass_test(
permutation_dict=permutation_dict,
axes_object=axes_object_matrix[0, 0],
num_predictors_to_plot=num_predictors_to_plot,
plot_percent_increase=False,
confidence_level=confidence_level,
bar_face_colour=BAR_FACE_COLOUR)
axes_object_matrix[0, 0].set_title('Single-pass test')
axes_object_matrix[0, 0].set_xlabel('Mean squared error')
permutation_plotting.plot_multipass_test(
permutation_dict=permutation_dict,
axes_object=axes_object_matrix[0, 1],
num_predictors_to_plot=num_predictors_to_plot,
plot_percent_increase=False,
confidence_level=confidence_level,
bar_face_colour=BAR_FACE_COLOUR)
axes_object_matrix[0, 1].set_title('Multi-pass test')
axes_object_matrix[0, 1].set_xlabel('Mean squared error')
axes_object_matrix[0, 1].set_ylabel('')
figure_file_name = '{0:s}/permutation_test_abs-values.jpg'.format(
output_dir_name)
print('Saving figure to: "{0:s}"...'.format(figure_file_name))
figure_object.savefig(figure_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(figure_object)
figure_object, axes_object_matrix = plotting_utils.create_paneled_figure(
num_rows=1,
num_columns=2,
shared_x_axis=False,
shared_y_axis=True,
keep_aspect_ratio=False,
horizontal_spacing=0.1,
vertical_spacing=0.05)
permutation_plotting.plot_single_pass_test(
permutation_dict=permutation_dict,
axes_object=axes_object_matrix[0, 0],
num_predictors_to_plot=num_predictors_to_plot,
plot_percent_increase=True,
confidence_level=confidence_level,
bar_face_colour=BAR_FACE_COLOUR)
axes_object_matrix[0, 0].set_title('Single-pass test')
axes_object_matrix[0, 0].set_xlabel('MSE (fraction of original)')
permutation_plotting.plot_multipass_test(
permutation_dict=permutation_dict,
axes_object=axes_object_matrix[0, 1],
num_predictors_to_plot=num_predictors_to_plot,
plot_percent_increase=True,
confidence_level=confidence_level,
bar_face_colour=BAR_FACE_COLOUR)
axes_object_matrix[0, 1].set_title('Multi-pass test')
axes_object_matrix[0, 1].set_xlabel('MSE (fraction of original)')
axes_object_matrix[0, 1].set_ylabel('')
figure_file_name = '{0:s}/permutation_test_percentage.jpg'.format(
output_dir_name)
print('Saving figure to: "{0:s}"...'.format(figure_file_name))
figure_object.savefig(figure_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(figure_object)
def _run(input_file_name, num_predictors_to_plot, output_dir_name):
"""Plots results of permutation test.
This is effectively the main method.
:param input_file_name: See documentation at top of file.
:param output_dir_name: Same.
"""
if num_predictors_to_plot <= 0:
num_predictors_to_plot = None
if output_dir_name in ['', 'None']:
output_dir_name = os.path.split(input_file_name)[0]
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
print(
'Reading permutation results from: "{0:s}"...'.format(input_file_name))
permutation_dict = permutation.read_results(input_file_name)
_, axes_object_matrix = plotting_utils.create_paneled_figure(
num_rows=1,
num_columns=2,
shared_x_axis=False,
shared_y_axis=True,
keep_aspect_ratio=False)
permutation_plotting.plot_breiman_results(
permutation_dict=permutation_dict,
axes_object=axes_object_matrix[0, 0],
plot_percent_increase=False,
num_predictors_to_plot=num_predictors_to_plot)
axes_object_matrix[0, 0].set_xlabel('AUC')
axes_object_matrix[0, 0].set_title('Single-pass')
permutation_plotting.plot_lakshmanan_results(
permutation_dict=permutation_dict,
axes_object=axes_object_matrix[0, 1],
plot_percent_increase=False,
num_steps_to_plot=num_predictors_to_plot)
axes_object_matrix[0, 1].set_xlabel('AUC')
axes_object_matrix[0, 1].set_ylabel('')
axes_object_matrix[0, 1].set_title('Multi-pass')
pyplot.tight_layout()
absolute_value_file_name = '{0:s}/permutation_absolute-values.jpg'.format(
output_dir_name)
print('Saving figure to file: "{0:s}"...'.format(absolute_value_file_name))
pyplot.savefig(absolute_value_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
_, axes_object_matrix = plotting_utils.create_paneled_figure(
num_rows=1,
num_columns=2,
shared_x_axis=False,
shared_y_axis=True,
keep_aspect_ratio=False)
permutation_plotting.plot_breiman_results(
permutation_dict=permutation_dict,
axes_object=axes_object_matrix[0, 0],
plot_percent_increase=True,
num_predictors_to_plot=num_predictors_to_plot)
axes_object_matrix[0, 0].set_title('Single-pass')
permutation_plotting.plot_lakshmanan_results(
permutation_dict=permutation_dict,
axes_object=axes_object_matrix[0, 1],
plot_percent_increase=True,
num_steps_to_plot=num_predictors_to_plot)
axes_object_matrix[0, 1].set_ylabel('')
axes_object_matrix[0, 1].set_title('Multi-pass')
pyplot.tight_layout()
percentage_file_name = '{0:s}/permutation_percentage.jpg'.format(
output_dir_name)
print('Saving figure to file: "{0:s}"...'.format(percentage_file_name))
pyplot.savefig(percentage_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
def plot_many_2d_feature_maps(
feature_matrix, annotation_string_by_panel, num_panel_rows,
colour_map_object, colour_norm_object=None, min_colour_value=None,
max_colour_value=None, figure_width_inches=DEFAULT_FIG_WIDTH_INCHES,
figure_height_inches=DEFAULT_FIG_HEIGHT_INCHES,
font_size=DEFAULT_FONT_SIZE):
"""Plots many 2-D feature maps in the same figure (one per panel).
M = number of rows in spatial grid
N = number of columns in spatial grid
P = number of panels
:param feature_matrix: M-by-N-by-P numpy array of feature values (either
before or after activation function -- this method doesn't care).
:param annotation_string_by_panel: length-P list of annotations.
annotation_string_by_panel[k] will be printed in the bottom-center of
the [k]th panel.
:param num_panel_rows: Number of panel rows.
:param colour_map_object: See doc for `plot_2d_feature_map`.
:param colour_norm_object: Same.
:param min_colour_value: Same.
:param max_colour_value: Same.
:param figure_width_inches: Figure width.
:param figure_height_inches: Figure height.
:param font_size: Font size for panel labels.
:return: figure_object: See doc for `plotting_utils.create_paneled_figure`.
:return: axes_object_matrix: Same.
"""
pyplot.rc('axes', linewidth=3)
error_checking.assert_is_numpy_array(feature_matrix, num_dimensions=3)
num_panels = feature_matrix.shape[-1]
error_checking.assert_is_numpy_array(
numpy.array(annotation_string_by_panel),
exact_dimensions=numpy.array([num_panels])
)
error_checking.assert_is_integer(num_panel_rows)
error_checking.assert_is_geq(num_panel_rows, 1)
error_checking.assert_is_leq(num_panel_rows, num_panels)
num_panel_columns = int(numpy.ceil(
float(num_panels) / num_panel_rows
))
figure_object, axes_object_matrix = plotting_utils.create_paneled_figure(
num_rows=num_panel_rows, num_columns=num_panel_columns,
figure_width_inches=figure_width_inches,
figure_height_inches=figure_height_inches,
horizontal_spacing=0., vertical_spacing=0.,
shared_x_axis=False, shared_y_axis=False, keep_aspect_ratio=False)
for i in range(num_panel_rows):
for j in range(num_panel_columns):
this_linear_index = i * num_panel_columns + j
if this_linear_index >= num_panels:
axes_object_matrix[i, j].axis('off')
continue
plot_2d_feature_map(
feature_matrix=feature_matrix[..., this_linear_index],
axes_object=axes_object_matrix[i, j], font_size=font_size,
colour_map_object=colour_map_object,
colour_norm_object=colour_norm_object,
min_colour_value=min_colour_value,
max_colour_value=max_colour_value,
annotation_string=annotation_string_by_panel[this_linear_index]
)
return figure_object, axes_object_matrix
def _run(forward_test_file_name, backwards_test_file_name, num_predictors,
confidence_level, output_file_name):
"""Makes figure with results of all 4 permutation tests.
This is effectively the main method.
:param forward_test_file_name: See documentation at top of file.
:param backwards_test_file_name: Same.
:param num_predictors: Same.
:param confidence_level: Same.
:param output_file_name: Same.
"""
if num_predictors <= 0:
num_predictors = None
file_system_utils.mkdir_recursive_if_necessary(file_name=output_file_name)
print('Reading data from: "{0:s}"...'.format(forward_test_file_name))
forward_test_dict = permutation_utils.read_results(forward_test_file_name)
print('Reading data from: "{0:s}"...'.format(backwards_test_file_name))
backwards_test_dict = permutation_utils.read_results(
backwards_test_file_name
)
figure_object, axes_object_matrix = plotting_utils.create_paneled_figure(
num_rows=2, num_columns=2, shared_x_axis=False, shared_y_axis=True,
keep_aspect_ratio=False, horizontal_spacing=0.1, vertical_spacing=0.05
)
permutation_plotting.plot_single_pass_test(
permutation_dict=forward_test_dict,
axes_object=axes_object_matrix[0, 0],
plot_percent_increase=False, confidence_level=confidence_level,
num_predictors_to_plot=num_predictors
)
axes_object_matrix[0, 0].set_title('Forward single-pass test')
axes_object_matrix[0, 0].set_xticks([])
axes_object_matrix[0, 0].set_xlabel('')
plotting_utils.label_axes(
axes_object=axes_object_matrix[0, 0], label_string='(a)',
x_coord_normalized=-0.01, y_coord_normalized=0.925
)
permutation_plotting.plot_multipass_test(
permutation_dict=forward_test_dict,
axes_object=axes_object_matrix[0, 1],
plot_percent_increase=False, confidence_level=confidence_level,
num_predictors_to_plot=num_predictors
)
axes_object_matrix[0, 1].set_title('Forward multi-pass test')
axes_object_matrix[0, 1].set_xticks([])
axes_object_matrix[0, 1].set_xlabel('')
axes_object_matrix[0, 1].set_ylabel('')
plotting_utils.label_axes(
axes_object=axes_object_matrix[0, 1], label_string='(b)',
x_coord_normalized=1.15, y_coord_normalized=0.925
)
permutation_plotting.plot_single_pass_test(
permutation_dict=backwards_test_dict,
axes_object=axes_object_matrix[1, 0],
plot_percent_increase=False, confidence_level=confidence_level,
num_predictors_to_plot=num_predictors
)
axes_object_matrix[1, 0].set_title('Backward single-pass test')
axes_object_matrix[1, 0].set_xlabel('Area under ROC curve (AUC)')
plotting_utils.label_axes(
axes_object=axes_object_matrix[1, 0], label_string='(c)',
x_coord_normalized=-0.01, y_coord_normalized=0.925
)
permutation_plotting.plot_multipass_test(
permutation_dict=backwards_test_dict,
axes_object=axes_object_matrix[1, 1],
plot_percent_increase=False, confidence_level=confidence_level,
num_predictors_to_plot=num_predictors
)
axes_object_matrix[1, 1].set_title('Backward multi-pass test')
axes_object_matrix[1, 1].set_xlabel('Area under ROC curve (AUC)')
axes_object_matrix[1, 1].set_ylabel('')
plotting_utils.label_axes(
axes_object=axes_object_matrix[1, 1], label_string='(d)',
x_coord_normalized=1.15, y_coord_normalized=0.925
)
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)
def _run(include_caption, output_dir_name):
"""Makes animation to explain pooling.
This is effectively the main method.
:param include_caption: See documentation at top of file.
:param output_dir_name: Same.
"""
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
num_input_rows = INPUT_FEATURE_MATRIX.shape[0]
num_input_columns = INPUT_FEATURE_MATRIX.shape[1]
num_channels = INPUT_FEATURE_MATRIX.shape[2]
image_file_names = []
for i in range(num_input_rows):
for j in range(num_input_columns):
this_figure_object, this_axes_object_matrix = (
plotting_utils.create_paneled_figure(
num_rows=num_channels,
num_columns=NUM_PANEL_COLUMNS,
horizontal_spacing=HORIZ_PANEL_SPACING,
vertical_spacing=VERTICAL_PANEL_SPACING,
shared_x_axis=False,
shared_y_axis=False,
keep_aspect_ratio=True))
letter_label = None
for k in range(num_channels):
_plot_feature_map(feature_matrix_2d=INPUT_FEATURE_MATRIX[...,
k],
pooled_row=i,
pooled_column=j,
pooled=True,
axes_object=this_axes_object_matrix[k, 0])
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_matrix[k, 0],
label_string='({0:s})'.format(letter_label),
font_size=PANEL_LETTER_FONT_SIZE,
y_coord_normalized=0.85,
x_coord_normalized=-0.02)
for k in range(num_channels):
_plot_feature_map(feature_matrix_2d=OUTPUT_FEATURE_MATRIX[...,
k],
pooled_row=i,
pooled_column=j,
pooled=False,
axes_object=this_axes_object_matrix[k, 1])
letter_label = chr(ord(letter_label) + 1)
plotting_utils.label_axes(
axes_object=this_axes_object_matrix[k, 1],
label_string='({0:s})'.format(letter_label),
font_size=PANEL_LETTER_FONT_SIZE,
y_coord_normalized=0.85,
x_coord_normalized=-0.02)
_plot_interpanel_lines(
pooled_row=i,
pooled_column=j,
input_fm_axes_object=this_axes_object_matrix[k, 0],
output_fm_axes_object=this_axes_object_matrix[k, 1])
if include_caption:
this_figure_object.text(0.5,
CAPTION_Y_COORD,
FIGURE_CAPTION,
fontsize=DEFAULT_FONT_SIZE,
color='k',
horizontalalignment='center',
verticalalignment='top')
image_file_names.append(
'{0:s}/upsampling_animation_row{1:d}_column{2:d}.jpg'.format(
output_dir_name, i, j))
print('Saving figure to: "{0:s}"...'.format(image_file_names[-1]))
this_figure_object.savefig(image_file_names[-1],
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(this_figure_object)
animation_file_name = '{0:s}/upsampling_animation.gif'.format(
output_dir_name)
print('Creating animation: "{0:s}"...'.format(animation_file_name))
imagemagick_utils.create_gif(input_file_names=image_file_names,
output_file_name=animation_file_name,
num_seconds_per_frame=0.5,
resize_factor=0.5)
def _plot_one_example(
input_feature_matrix, feature_matrix_after_conv,
feature_matrix_after_activn, feature_matrix_after_bn,
feature_matrix_after_pooling, output_file_name):
"""Plots entire figure for one example (storm object).
:param input_feature_matrix: 2-D numpy array with input features.
:param feature_matrix_after_conv: 2-D numpy array with features after
convolution.
:param feature_matrix_after_activn: 2-D numpy array with features after
activation.
:param feature_matrix_after_bn: 2-D numpy array with features after batch
normalization.
:param feature_matrix_after_pooling: 2-D numpy array with features after
pooling.
:param output_file_name: Path to output file. Figure will be saved here.
"""
num_output_channels = feature_matrix_after_conv.shape[-1]
figure_object, axes_object_matrix = plotting_utils.create_paneled_figure(
num_rows=num_output_channels, num_columns=NUM_PANEL_COLUMNS,
horizontal_spacing=0., vertical_spacing=0.,
shared_x_axis=False, shared_y_axis=False, keep_aspect_ratio=True)
max_colour_value = numpy.percentile(
numpy.absolute(input_feature_matrix), MAX_COLOUR_PERCENTILE
)
axes_object_matrix[0, 0].set_title('Input', fontsize=TITLE_FONT_SIZE)
for k in range(num_output_channels):
if k == 0:
_plot_one_feature_map(
feature_matrix_2d=input_feature_matrix[..., k],
max_colour_value=max_colour_value, plot_colour_bar=False,
axes_object=axes_object_matrix[k, 0]
)
continue
axes_object_matrix[k, 0].axis('off')
colour_bar_object = plotting_utils.plot_linear_colour_bar(
axes_object_or_matrix=axes_object_matrix[num_output_channels - 1, 0],
data_matrix=input_feature_matrix[..., 0],
colour_map_object=COLOUR_MAP_OBJECT, min_value=-1 * max_colour_value,
max_value=max_colour_value, orientation_string='horizontal',
padding=0.015, fraction_of_axis_length=0.9,
extend_min=True, extend_max=True, font_size=DEFAULT_FONT_SIZE)
tick_values = colour_bar_object.ax.get_xticks()
tick_label_strings = ['{0:.1f}'.format(x) for x in tick_values]
colour_bar_object.set_ticks(tick_values)
colour_bar_object.set_ticklabels(tick_label_strings)
letter_label = 'a'
plotting_utils.label_axes(
axes_object=axes_object_matrix[0, 0],
label_string='({0:s})'.format(letter_label),
font_size=TITLE_FONT_SIZE,
x_coord_normalized=0.125, y_coord_normalized=1.025
)
this_matrix = numpy.stack(
(feature_matrix_after_conv, feature_matrix_after_activn), axis=0
)
max_colour_value = numpy.percentile(
numpy.absolute(this_matrix), MAX_COLOUR_PERCENTILE
)
axes_object_matrix[0, 1].set_title(
' After convolution', fontsize=TITLE_FONT_SIZE)
for k in range(num_output_channels):
_plot_one_feature_map(
feature_matrix_2d=feature_matrix_after_conv[..., k],
max_colour_value=max_colour_value,
plot_colour_bar=k == num_output_channels - 1,
axes_object=axes_object_matrix[k, 1]
)
letter_label = chr(ord(letter_label) + 1)
plotting_utils.label_axes(
axes_object=axes_object_matrix[k, 1],
label_string='({0:s})'.format(letter_label),
font_size=TITLE_FONT_SIZE,
x_coord_normalized=0.125, y_coord_normalized=1.025
)
axes_object_matrix[0, 2].set_title(
' After activation', fontsize=TITLE_FONT_SIZE)
for k in range(num_output_channels):
_plot_one_feature_map(
feature_matrix_2d=feature_matrix_after_activn[..., k],
max_colour_value=max_colour_value,
plot_colour_bar=k == num_output_channels - 1,
axes_object=axes_object_matrix[k, 2]
)
letter_label = chr(ord(letter_label) + 1)
plotting_utils.label_axes(
axes_object=axes_object_matrix[k, 2],
label_string='({0:s})'.format(letter_label),
font_size=TITLE_FONT_SIZE,
x_coord_normalized=0.125, y_coord_normalized=1.025
)
max_colour_value = numpy.percentile(
numpy.absolute(feature_matrix_after_bn), MAX_COLOUR_PERCENTILE
)
axes_object_matrix[0, 3].set_title(
' After batch norm', fontsize=TITLE_FONT_SIZE)
for k in range(num_output_channels):
_plot_one_feature_map(
feature_matrix_2d=feature_matrix_after_bn[..., k],
max_colour_value=max_colour_value,
plot_colour_bar=k == num_output_channels - 1,
axes_object=axes_object_matrix[k, 3]
)
letter_label = chr(ord(letter_label) + 1)
plotting_utils.label_axes(
axes_object=axes_object_matrix[k, 3],
label_string='({0:s})'.format(letter_label),
font_size=TITLE_FONT_SIZE,
x_coord_normalized=0.125, y_coord_normalized=1.025
)
max_colour_value = numpy.percentile(
numpy.absolute(feature_matrix_after_pooling), MAX_COLOUR_PERCENTILE
)
axes_object_matrix[0, 4].set_title(
'After pooling', fontsize=TITLE_FONT_SIZE)
for k in range(num_output_channels):
_plot_one_feature_map(
feature_matrix_2d=feature_matrix_after_pooling[..., k],
max_colour_value=max_colour_value,
plot_colour_bar=k == num_output_channels - 1,
axes_object=axes_object_matrix[k, 4]
)
letter_label = chr(ord(letter_label) + 1)
plotting_utils.label_axes(
axes_object=axes_object_matrix[k, 4],
label_string='({0:s})'.format(letter_label),
font_size=TITLE_FONT_SIZE,
x_coord_normalized=0.125, y_coord_normalized=1.025
)
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)
def _plot_2d_radar_scan(list_of_predictor_matrices,
model_metadata_dict,
allow_whitespace,
title_string=None):
"""Plots 2-D radar scan for one example.
J = number of panel rows in image
K = number of panel columns in image
:param list_of_predictor_matrices: See doc for `_plot_3d_radar_scan`.
:param model_metadata_dict: Same.
:param allow_whitespace: Same.
:param title_string: Same.
:return: figure_objects: length-1 list of figure handles (instances of
`matplotlib.figure.Figure`).
:return: axes_object_matrices: length-1 list. Each element is a J-by-K
numpy array of axes handles (instances of
`matplotlib.axes._subplots.AxesSubplot`).
"""
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
list_of_layer_operation_dicts = model_metadata_dict[
cnn.LAYER_OPERATIONS_KEY]
if list_of_layer_operation_dicts is None:
field_name_by_panel = training_option_dict[
trainval_io.RADAR_FIELDS_KEY]
num_panels = len(field_name_by_panel)
panel_names = radar_plotting.radar_fields_and_heights_to_panel_names(
field_names=field_name_by_panel,
heights_m_agl=training_option_dict[trainval_io.RADAR_HEIGHTS_KEY])
plot_cbar_by_panel = numpy.full(num_panels, True, dtype=bool)
else:
list_of_layer_operation_dicts = [
list_of_layer_operation_dicts[k] for k in LAYER_OP_INDICES_TO_KEEP
]
list_of_predictor_matrices[0] = list_of_predictor_matrices[0][
..., LAYER_OP_INDICES_TO_KEEP]
field_name_by_panel, panel_names = (
radar_plotting.layer_ops_to_field_and_panel_names(
list_of_layer_operation_dicts=list_of_layer_operation_dicts))
num_panels = len(field_name_by_panel)
plot_cbar_by_panel = numpy.full(num_panels, True, dtype=bool)
# if allow_whitespace:
# if len(field_name_by_panel) == 12:
# plot_cbar_by_panel[2::3] = True
# else:
# plot_cbar_by_panel[:] = True
num_panel_rows = int(numpy.floor(numpy.sqrt(num_panels)))
num_panel_columns = int(numpy.ceil(float(num_panels) / num_panel_rows))
if allow_whitespace:
figure_object = None
axes_object_matrix = None
else:
figure_object, axes_object_matrix = (
plotting_utils.create_paneled_figure(num_rows=num_panel_rows,
num_columns=num_panel_columns,
horizontal_spacing=0.,
vertical_spacing=0.,
shared_x_axis=False,
shared_y_axis=False,
keep_aspect_ratio=True))
figure_object, axes_object_matrix = (
radar_plotting.plot_many_2d_grids_without_coords(
field_matrix=numpy.flip(list_of_predictor_matrices[0], axis=0),
field_name_by_panel=field_name_by_panel,
panel_names=panel_names,
num_panel_rows=num_panel_rows,
figure_object=figure_object,
axes_object_matrix=axes_object_matrix,
plot_colour_bar_by_panel=plot_cbar_by_panel,
font_size=FONT_SIZE_WITH_COLOUR_BARS,
row_major=False))
if allow_whitespace and title_string is not None:
pyplot.suptitle(title_string, fontsize=TITLE_FONT_SIZE)
return [figure_object], [axes_object_matrix]
def _plot_2d3d_radar_scan(list_of_predictor_matrices,
model_metadata_dict,
allow_whitespace,
title_string=None):
"""Plots 3-D reflectivity and 2-D azimuthal shear for one example.
:param list_of_predictor_matrices: See doc for `_plot_3d_radar_scan`.
:param model_metadata_dict: Same.
:param allow_whitespace: Same.
:param title_string: Same.
:return: figure_objects: length-2 list of figure handles (instances of
`matplotlib.figure.Figure`). The first is for reflectivity; the second
is for azimuthal shear.
:return: axes_object_matrices: length-2 list (the first is for reflectivity;
the second is for azimuthal shear). Each element is a 2-D numpy
array of axes handles (instances of
`matplotlib.axes._subplots.AxesSubplot`).
"""
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
az_shear_field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY]
refl_heights_m_agl = training_option_dict[trainval_io.RADAR_HEIGHTS_KEY]
num_az_shear_fields = len(az_shear_field_names)
num_refl_heights = len(refl_heights_m_agl)
this_num_panel_rows = int(numpy.floor(numpy.sqrt(num_refl_heights)))
this_num_panel_columns = int(
numpy.ceil(float(num_refl_heights) / this_num_panel_rows))
if allow_whitespace:
refl_figure_object = None
refl_axes_object_matrix = None
else:
refl_figure_object, refl_axes_object_matrix = (
plotting_utils.create_paneled_figure(
num_rows=this_num_panel_rows,
num_columns=this_num_panel_columns,
horizontal_spacing=0.,
vertical_spacing=0.,
shared_x_axis=False,
shared_y_axis=False,
keep_aspect_ratio=True))
refl_figure_object, refl_axes_object_matrix = (
radar_plotting.plot_3d_grid_without_coords(
field_matrix=numpy.flip(list_of_predictor_matrices[0][..., 0],
axis=0),
field_name=radar_utils.REFL_NAME,
grid_point_heights_metres=refl_heights_m_agl,
ground_relative=True,
num_panel_rows=this_num_panel_rows,
figure_object=refl_figure_object,
axes_object_matrix=refl_axes_object_matrix,
font_size=FONT_SIZE_SANS_COLOUR_BARS))
if allow_whitespace:
this_colour_map_object, this_colour_norm_object = (
radar_plotting.get_default_colour_scheme(radar_utils.REFL_NAME))
plotting_utils.plot_colour_bar(
axes_object_or_matrix=refl_axes_object_matrix,
data_matrix=list_of_predictor_matrices[0],
colour_map_object=this_colour_map_object,
colour_norm_object=this_colour_norm_object,
orientation_string='horizontal',
extend_min=True,
extend_max=True)
if title_string is not None:
this_title_string = '{0:s}; {1:s}'.format(title_string,
radar_utils.REFL_NAME)
pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE)
if allow_whitespace:
shear_figure_object = None
shear_axes_object_matrix = None
else:
shear_figure_object, shear_axes_object_matrix = (
plotting_utils.create_paneled_figure(
num_rows=1,
num_columns=num_az_shear_fields,
horizontal_spacing=0.,
vertical_spacing=0.,
shared_x_axis=False,
shared_y_axis=False,
keep_aspect_ratio=True))
shear_figure_object, shear_axes_object_matrix = (
radar_plotting.plot_many_2d_grids_without_coords(
field_matrix=numpy.flip(list_of_predictor_matrices[1], axis=0),
field_name_by_panel=az_shear_field_names,
panel_names=az_shear_field_names,
num_panel_rows=1,
figure_object=shear_figure_object,
axes_object_matrix=shear_axes_object_matrix,
plot_colour_bar_by_panel=numpy.full(num_az_shear_fields,
False,
dtype=bool),
font_size=FONT_SIZE_SANS_COLOUR_BARS))
if allow_whitespace:
this_colour_map_object, this_colour_norm_object = (
radar_plotting.get_default_colour_scheme(
radar_utils.LOW_LEVEL_SHEAR_NAME))
plotting_utils.plot_colour_bar(
axes_object_or_matrix=shear_axes_object_matrix,
data_matrix=list_of_predictor_matrices[1],
colour_map_object=this_colour_map_object,
colour_norm_object=this_colour_norm_object,
orientation_string='horizontal',
extend_min=True,
extend_max=True)
if title_string is not None:
pyplot.suptitle(title_string, fontsize=TITLE_FONT_SIZE)
figure_objects = [refl_figure_object, shear_figure_object]
axes_object_matrices = [refl_axes_object_matrix, shear_axes_object_matrix]
return figure_objects, axes_object_matrices
def _plot_3d_radar_scan(list_of_predictor_matrices,
model_metadata_dict,
allow_whitespace,
title_string=None):
"""Plots 3-D radar scan for one example.
J = number of panel rows in image
K = number of panel columns in image
F = number of radar fields
:param list_of_predictor_matrices: List created by
`testing_io.read_specific_examples`, except that the first axis (example
dimension) is removed.
:param model_metadata_dict: Dictionary returned by
`cnn.read_model_metadata`.
:param allow_whitespace: See documentation at top of file.
:param title_string: Title (may be None).
:return: figure_objects: length-F list of figure handles (instances of
`matplotlib.figure.Figure`).
:return: axes_object_matrices: length-F list. Each element is a J-by-K
numpy array of axes handles (instances of
`matplotlib.axes._subplots.AxesSubplot`).
"""
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
radar_field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY]
radar_heights_m_agl = training_option_dict[trainval_io.RADAR_HEIGHTS_KEY]
num_radar_fields = len(radar_field_names)
num_radar_heights = len(radar_heights_m_agl)
num_panel_rows = int(numpy.floor(numpy.sqrt(num_radar_heights)))
num_panel_columns = int(
numpy.ceil(float(num_radar_heights) / num_panel_rows))
figure_objects = [None] * num_radar_fields
axes_object_matrices = [None] * num_radar_fields
radar_matrix = list_of_predictor_matrices[0]
for j in range(num_radar_fields):
this_radar_matrix = numpy.flip(radar_matrix[..., j], axis=0)
if not allow_whitespace:
figure_objects[j], axes_object_matrices[j] = (
plotting_utils.create_paneled_figure(
num_rows=num_panel_rows,
num_columns=num_panel_columns,
horizontal_spacing=0.,
vertical_spacing=0.,
shared_x_axis=False,
shared_y_axis=False,
keep_aspect_ratio=True))
figure_objects[j], axes_object_matrices[j] = (
radar_plotting.plot_3d_grid_without_coords(
field_matrix=this_radar_matrix,
field_name=radar_field_names[j],
grid_point_heights_metres=radar_heights_m_agl,
ground_relative=True,
num_panel_rows=num_panel_rows,
figure_object=figure_objects[j],
axes_object_matrix=axes_object_matrices[j],
font_size=FONT_SIZE_SANS_COLOUR_BARS))
if allow_whitespace:
this_colour_map_object, this_colour_norm_object = (
radar_plotting.get_default_colour_scheme(radar_field_names[j]))
plotting_utils.plot_colour_bar(
axes_object_or_matrix=axes_object_matrices[j],
data_matrix=this_radar_matrix,
colour_map_object=this_colour_map_object,
colour_norm_object=this_colour_norm_object,
orientation_string='horizontal',
extend_min=True,
extend_max=True)
if title_string is not None:
this_title_string = '{0:s}; {1:s}'.format(
title_string, radar_field_names[j])
pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE)
return figure_objects, axes_object_matrices
def _plot_one_score(score_matrix, colour_map_object, min_colour_value,
max_colour_value, colour_bar_label, is_score_bias,
best_model_index, output_file_name):
"""Plots one score.
:param score_matrix: 4-D numpy array of scores, where the first axis
represents dropout rate; second represents L2 weight; third represents
num dense layers; and fourth is data augmentation (yes or no).
:param colour_map_object: See documentation at top of file.
:param min_colour_value: Minimum value in colour scheme.
:param max_colour_value: Max value in colour scheme.
:param colour_bar_label: Label string for colour bar.
:param is_score_bias: Boolean flag. If True, score to be plotted is
frequency bias, which changes settings for colour scheme.
:param best_model_index: Linear index of best model.
:param output_file_name: Path to output file (figure will be saved here).
"""
if is_score_bias:
colour_map_object, colour_norm_object = _get_bias_colour_scheme(
max_value=max_colour_value)
else:
colour_norm_object = None
num_dense_layer_counts = len(DENSE_LAYER_COUNTS)
num_data_aug_flags = len(DATA_AUGMENTATION_FLAGS)
figure_object, axes_object_matrix = plotting_utils.create_paneled_figure(
num_rows=num_dense_layer_counts * num_data_aug_flags,
num_columns=1,
horizontal_spacing=0.15,
vertical_spacing=0.15,
shared_x_axis=False,
shared_y_axis=False,
keep_aspect_ratio=True)
axes_object_matrix = numpy.reshape(
axes_object_matrix, (num_dense_layer_counts, num_data_aug_flags))
x_axis_label = r'L$_2$ weight (log$_{10}$)'
y_axis_label = 'Dropout rate'
x_tick_labels = ['{0:.1f}'.format(w) for w in numpy.log10(L2_WEIGHTS)]
y_tick_labels = ['{0:.3f}'.format(d) for d in DROPOUT_RATES]
best_model_index_tuple = numpy.unravel_index(best_model_index,
score_matrix.shape)
for k in range(num_dense_layer_counts):
for m in range(num_data_aug_flags):
model_eval.plot_hyperparam_grid(
score_matrix=score_matrix[..., k, m],
min_colour_value=min_colour_value,
max_colour_value=max_colour_value,
colour_map_object=colour_map_object,
colour_norm_object=colour_norm_object,
axes_object=axes_object_matrix[k, m])
axes_object_matrix[k, m].set_xticklabels(
x_tick_labels, fontsize=TICK_LABEL_FONT_SIZE, rotation=90.)
axes_object_matrix[k, m].set_yticklabels(
y_tick_labels, fontsize=TICK_LABEL_FONT_SIZE)
axes_object_matrix[k, m].set_ylabel(y_axis_label,
fontsize=TICK_LABEL_FONT_SIZE)
if k == num_dense_layer_counts - 1 and m == num_data_aug_flags - 1:
axes_object_matrix[k, m].set_xlabel(x_axis_label)
else:
axes_object_matrix[k, m].set_xticks([], [])
this_title_string = '{0:d} dense layer{1:s}, DA {2:s}'.format(
DENSE_LAYER_COUNTS[k],
's' if DENSE_LAYER_COUNTS[k] > 1 else '',
'on' if DATA_AUGMENTATION_FLAGS[m] else 'off')
axes_object_matrix[k, m].set_title(this_title_string)
i = best_model_index_tuple[0]
j = best_model_index_tuple[1]
k = best_model_index_tuple[2]
m = best_model_index_tuple[3]
axes_object_matrix[k, m].plot(j,
i,
linestyle='None',
marker=BEST_MODEL_MARKER_TYPE,
markersize=BEST_MODEL_MARKER_SIZE,
markerfacecolor=MARKER_COLOUR,
markeredgecolor=MARKER_COLOUR,
markeredgewidth=BEST_MODEL_MARKER_WIDTH)
corrupt_model_indices = numpy.where(numpy.isnan(
numpy.ravel(score_matrix)))[0]
for this_linear_index in corrupt_model_indices:
i, j, k, m = numpy.unravel_index(this_linear_index, score_matrix.shape)
axes_object_matrix[k,
m].plot(j,
i,
linestyle='None',
marker=CORRUPT_MODEL_MARKER_TYPE,
markersize=CORRUPT_MODEL_MARKER_SIZE,
markerfacecolor=MARKER_COLOUR,
markeredgecolor=MARKER_COLOUR,
markeredgewidth=CORRUPT_MODEL_MARKER_WIDTH)
if is_score_bias:
colour_bar_object = plotting_utils.plot_colour_bar(
axes_object_or_matrix=axes_object_matrix,
data_matrix=score_matrix,
colour_map_object=colour_map_object,
colour_norm_object=colour_norm_object,
orientation_string='vertical',
extend_min=False,
extend_max=True,
font_size=DEFAULT_FONT_SIZE)
tick_values = colour_bar_object.get_ticks()
tick_strings = ['{0:.1f}'.format(v) for v in tick_values]
colour_bar_object.set_ticks(tick_values)
colour_bar_object.set_ticklabels(tick_strings)
else:
colour_bar_object = plotting_utils.plot_linear_colour_bar(
axes_object_or_matrix=axes_object_matrix,
data_matrix=score_matrix,
colour_map_object=colour_map_object,
min_value=min_colour_value,
max_value=max_colour_value,
orientation_string='vertical',
extend_min=True,
extend_max=True,
font_size=DEFAULT_FONT_SIZE)
colour_bar_object.set_label(colour_bar_label)
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)
def _run(exp1_permutation_dir_name, exp2_permutation_dir_name,
use_forward_test, use_multipass_test, output_file_name):
"""Creates figure showing permutation-test results for both models.
This is effectively the main method.
:param exp1_permutation_dir_name: See documentation at top of file.
:param exp2_permutation_dir_name: Same.
:param use_forward_test: Same.
:param use_multipass_test: Same.
:param output_file_name: Same.
"""
file_system_utils.mkdir_recursive_if_necessary(file_name=output_file_name)
exp1_flux_file_name = '{0:s}/{1:s}_perm_test_fluxes-only.nc'.format(
exp1_permutation_dir_name,
'forward' if use_forward_test else 'backwards')
exp1_heating_rate_file_name = '{0:s}/{1:s}_perm_test_hr-only.nc'.format(
exp1_permutation_dir_name,
'forward' if use_forward_test else 'backwards')
exp2_flux_file_name = '{0:s}/{1:s}_perm_test_fluxes-only.nc'.format(
exp2_permutation_dir_name,
'forward' if use_forward_test else 'backwards')
exp2_heating_rate_file_name = '{0:s}/{1:s}_perm_test_hr-only.nc'.format(
exp2_permutation_dir_name,
'forward' if use_forward_test else 'backwards')
print('Reading data from: "{0:s}"...'.format(exp1_heating_rate_file_name))
exp1_heating_permutation_dict = ml4rt_permutation.read_file(
exp1_heating_rate_file_name)
exp1_heating_permutation_dict = _results_to_gg_format(
exp1_heating_permutation_dict)
figure_object, axes_object_matrix = plotting_utils.create_paneled_figure(
num_rows=2,
num_columns=2,
shared_x_axis=False,
shared_y_axis=True,
keep_aspect_ratio=False,
horizontal_spacing=0.25,
vertical_spacing=0.25)
if use_multipass_test:
permutation_plotting.plot_multipass_test(
permutation_dict=exp1_heating_permutation_dict,
axes_object=axes_object_matrix[0, 0],
plot_percent_increase=False,
confidence_level=CONFIDENCE_LEVEL)
else:
permutation_plotting.plot_single_pass_test(
permutation_dict=exp1_heating_permutation_dict,
axes_object=axes_object_matrix[0, 0],
plot_percent_increase=False,
confidence_level=CONFIDENCE_LEVEL)
plotting_utils.label_axes(axes_object=axes_object_matrix[0, 0],
label_string='(a)',
font_size=30,
x_coord_normalized=0.1,
y_coord_normalized=1.01)
axes_object_matrix[0, 0].set_title('Exp 1, heating rates only')
axes_object_matrix[0,
0].set_xlabel(r'Dual-weighted MSE (K$^3$ day$^{-3}$)')
axes_object_matrix[0, 0].set_ylabel('')
print('Reading data from: "{0:s}"...'.format(exp1_flux_file_name))
exp1_flux_permutation_dict = ml4rt_permutation.read_file(
exp1_flux_file_name)
exp1_flux_permutation_dict = _results_to_gg_format(
exp1_flux_permutation_dict)
if use_multipass_test:
permutation_plotting.plot_multipass_test(
permutation_dict=exp1_flux_permutation_dict,
axes_object=axes_object_matrix[0, 1],
plot_percent_increase=False,
confidence_level=CONFIDENCE_LEVEL)
else:
permutation_plotting.plot_single_pass_test(
permutation_dict=exp1_flux_permutation_dict,
axes_object=axes_object_matrix[0, 1],
plot_percent_increase=False,
confidence_level=CONFIDENCE_LEVEL)
plotting_utils.label_axes(axes_object=axes_object_matrix[0, 1],
label_string='(b)',
font_size=30,
x_coord_normalized=0.1,
y_coord_normalized=1.01)
axes_object_matrix[0, 1].set_title('Exp 1, fluxes only')
axes_object_matrix[0, 1].set_xlabel(r'MSE (K day$^{-1}$)')
axes_object_matrix[0, 1].set_ylabel('')
print('Reading data from: "{0:s}"...'.format(exp2_heating_rate_file_name))
exp2_heating_permutation_dict = ml4rt_permutation.read_file(
exp2_heating_rate_file_name)
exp2_heating_permutation_dict = _results_to_gg_format(
exp2_heating_permutation_dict)
if use_multipass_test:
permutation_plotting.plot_multipass_test(
permutation_dict=exp2_heating_permutation_dict,
axes_object=axes_object_matrix[1, 0],
plot_percent_increase=False,
confidence_level=CONFIDENCE_LEVEL)
else:
permutation_plotting.plot_single_pass_test(
permutation_dict=exp2_heating_permutation_dict,
axes_object=axes_object_matrix[1, 0],
plot_percent_increase=False,
confidence_level=CONFIDENCE_LEVEL)
plotting_utils.label_axes(axes_object=axes_object_matrix[1, 0],
label_string='(c)',
font_size=30,
x_coord_normalized=0.1,
y_coord_normalized=1.01)
axes_object_matrix[1, 0].set_title('Exp 2, heating rates only')
axes_object_matrix[1,
0].set_xlabel(r'Dual-weighted MSE (K$^3$ day$^{-3}$)')
axes_object_matrix[1, 0].set_ylabel('')
print('Reading data from: "{0:s}"...'.format(exp2_flux_file_name))
exp2_flux_permutation_dict = ml4rt_permutation.read_file(
exp2_flux_file_name)
exp2_flux_permutation_dict = _results_to_gg_format(
exp2_flux_permutation_dict)
if use_multipass_test:
permutation_plotting.plot_multipass_test(
permutation_dict=exp2_flux_permutation_dict,
axes_object=axes_object_matrix[1, 1],
plot_percent_increase=False,
confidence_level=CONFIDENCE_LEVEL)
else:
permutation_plotting.plot_single_pass_test(
permutation_dict=exp2_flux_permutation_dict,
axes_object=axes_object_matrix[1, 1],
plot_percent_increase=False,
confidence_level=CONFIDENCE_LEVEL)
plotting_utils.label_axes(axes_object=axes_object_matrix[1, 1],
label_string='(d)',
font_size=30,
x_coord_normalized=0.1,
y_coord_normalized=1.01)
axes_object_matrix[1, 1].set_title('Exp 2, fluxes only')
axes_object_matrix[1, 1].set_xlabel(r'MSE (K day$^{-1}$)')
axes_object_matrix[1, 1].set_ylabel('')
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)
