def _plot_feature_map_after_conv(feature_matrix):
"""Plots new feature map (after convolution).
M = number of rows in grid
N = number of columns in grid
:param feature_matrix: Feature map as M-by-N numpy array.
"""
dummy_matrix = numpy.full(feature_matrix.shape, numpy.nan)
dummy_matrix[:2, :2] = HIGHLIGHTED_VALUE
_, axes_object = pyplot.subplots(1,
1,
figsize=(FIGURE_WIDTH_INCHES,
FIGURE_HEIGHT_INCHES))
pyplot.imshow(dummy_matrix,
cmap=COLOUR_MAP_OBJECT,
vmin=HIGHLIGHTED_VALUE - 1,
vmax=HIGHLIGHTED_VALUE,
axes=axes_object,
origin='upper')
pyplot.xticks([], [])
pyplot.yticks([], [])
for i in range(feature_matrix.shape[1]):
for j in range(feature_matrix.shape[0]):
if i == j == 1:
this_colour = SPECIAL_COLOUR + 0.
else:
this_colour = MAIN_COLOUR + 0.
axes_object.text(i,
j,
'{0:.1f}'.format(feature_matrix[j, i]),
fontsize=OVERLAY_FONT_SIZE,
color=this_colour,
horizontalalignment='center',
verticalalignment='center')
# polygon_x_coords = numpy.array([0, 2, 2, 0, 0], dtype=float) - 0.5
# polygon_y_coords = numpy.array([2, 2, 0, 0, 2], dtype=float) - 0.5
# axes_object.plot(
# polygon_x_coords, polygon_y_coords, color=LINE_COLOUR,
# linewidth=LINE_WIDTH)
plotting_utils.annotate_axes(axes_object=axes_object,
annotation_string='(c)',
font_colour=ANNOTATION_COLOUR)
print 'Saving figure to: "{0:s}"...'.format(AFTER_CONV_FILE_NAME)
file_system_utils.mkdir_recursive_if_necessary(
file_name=AFTER_CONV_FILE_NAME)
pyplot.savefig(AFTER_CONV_FILE_NAME, dpi=OUTPUT_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(input_file_name=AFTER_CONV_FILE_NAME,
output_file_name=AFTER_CONV_FILE_NAME)
return feature_matrix
def _write_sounding_figure(figure_object, title_string, output_file_name):
"""Writes sounding figure to file.
:param figure_object: Figure handle (instance of
`matplotlib.figure.Figure`).
:param title_string: Figure title.
:param output_file_name: Path to output file (figure will be saved as image
here).
"""
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)
imagemagick_utils.resize_image(input_file_name=output_file_name,
output_file_name=output_file_name,
output_size_pixels=SOUNDING_FIGURE_SIZE_PX)
imagemagick_utils.trim_whitespace(input_file_name=output_file_name,
output_file_name=output_file_name,
border_width_pixels=TITLE_FONT_SIZE + 25)
_overlay_text(image_file_name=output_file_name,
text_string=title_string,
x_offset_from_center_px=0,
y_offset_from_top_px=0)
imagemagick_utils.trim_whitespace(input_file_name=output_file_name,
output_file_name=output_file_name,
border_width_pixels=10)
def _plot_scores_as_grid(
score_matrix, colour_map_object, min_colour_value, max_colour_value,
x_tick_labels, x_axis_label, x_axis_text_colour, y_tick_labels,
y_axis_label, y_axis_text_colour, title_string, output_file_name):
"""Plots model scores as 2-D grid.
M = number of rows in grid
N = number of columns in grid
:param score_matrix: M-by-N numpy array of model scores.
:param colour_map_object: Instance of `matplotlib.colors.ListedColormap`.
:param min_colour_value: Minimum value in colour map.
:param max_colour_value: Max value in colour map.
:param x_tick_labels: length-N list of string labels.
:param x_axis_label: String label for the entire x-axis.
:param x_axis_text_colour: Colour for all text labels along x-axis.
:param y_tick_labels: length-M list of string labels.
:param y_axis_label: String label for the entire y-axis.
:param y_axis_text_colour: Colour for all text labels along y-axis.
:param title_string: Figure title.
:param output_file_name: Path to output file (the figure will be saved
here).
"""
_, axes_object = pyplot.subplots(
1, 1, figsize=(FIGURE_WIDTH_INCHES, FIGURE_HEIGHT_INCHES))
score_matrix_to_plot = score_matrix + 0.
score_matrix_to_plot[numpy.isnan(score_matrix_to_plot)] = 0.
pyplot.imshow(
score_matrix_to_plot, cmap=colour_map_object, origin='lower',
vmin=min_colour_value, vmax=max_colour_value)
x_tick_values = numpy.linspace(
0, score_matrix_to_plot.shape[1] - 1,
num=score_matrix_to_plot.shape[1], dtype=float)
pyplot.xticks(x_tick_values, x_tick_labels, color=x_axis_text_colour)
pyplot.xlabel(x_axis_label, color=x_axis_text_colour)
y_tick_values = numpy.linspace(
0, score_matrix_to_plot.shape[0] - 1,
num=score_matrix_to_plot.shape[0], dtype=float)
pyplot.yticks(y_tick_values, y_tick_labels, color=y_axis_text_colour)
pyplot.ylabel(y_axis_label, color=y_axis_text_colour)
pyplot.title(title_string)
plotting_utils.add_linear_colour_bar(
axes_object_or_list=axes_object,
values_to_colour=score_matrix_to_plot,
colour_map=colour_map_object, colour_min=min_colour_value,
colour_max=max_colour_value, orientation='vertical',
extend_min=True, extend_max=True, font_size=FONT_SIZE)
print 'Saving figure to: "{0:s}"...'.format(output_file_name)
file_system_utils.mkdir_recursive_if_necessary(file_name=output_file_name)
pyplot.savefig(output_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(
input_file_name=output_file_name, output_file_name=output_file_name)
def _run(input_dir_name, output_dir_name):
"""Creates figure showing overall model evaluation.
This is effectively the main method.
:param input_dir_name: See documentation at top of file.
:param output_dir_name: Same.
"""
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
panel_file_names = [
'{0:s}/{1:s}'.format(input_dir_name, p)
for p in PATHLESS_INPUT_FILE_NAMES
]
resized_panel_file_names = [
'{0:s}/{1:s}'.format(output_dir_name, p)
for p in PATHLESS_INPUT_FILE_NAMES
]
letter_label = None
for i in range(len(panel_file_names)):
print('Resizing panel and saving to: "{0:s}"...'.format(
resized_panel_file_names[i]))
imagemagick_utils.trim_whitespace(
input_file_name=panel_file_names[i],
output_file_name=resized_panel_file_names[i])
if letter_label is None:
letter_label = 'a'
else:
letter_label = chr(ord(letter_label) + 1)
_overlay_text(image_file_name=resized_panel_file_names[i],
x_offset_from_left_px=0,
y_offset_from_top_px=TITLE_FONT_SIZE,
text_string='({0:s})'.format(letter_label))
imagemagick_utils.resize_image(
input_file_name=resized_panel_file_names[i],
output_file_name=resized_panel_file_names[i],
output_size_pixels=PANEL_SIZE_PX)
concat_figure_file_name = '{0:s}/overall_evaluation.jpg'.format(
output_dir_name)
print(
'Concatenating panels to: "{0:s}"...'.format(concat_figure_file_name))
imagemagick_utils.concatenate_images(
input_file_names=resized_panel_file_names,
output_file_name=concat_figure_file_name,
num_panel_rows=NUM_PANEL_ROWS,
num_panel_columns=NUM_PANEL_COLUMNS)
imagemagick_utils.resize_image(input_file_name=concat_figure_file_name,
output_file_name=concat_figure_file_name,
output_size_pixels=CONCAT_FIGURE_SIZE_PX)
def _plot_locating_variable(
locating_var_matrix_m01_s01, title_string, annotation_string,
output_file_name):
"""Plots locating variable.
M = number of rows in grid
N = number of columns in grid
:param locating_var_matrix_m01_s01: M-by-N numpy array with values of
locating variable.
:param title_string: Title (will be placed above figure).
:param annotation_string: Text annotation (will be placed in top left of
figure).
:param output_file_name: Path to output file (figure will be saved here).
"""
(narr_row_limits, narr_column_limits, axes_object, basemap_object
) = _init_basemap(BORDER_COLOUR)
matrix_to_plot = locating_var_matrix_m01_s01[
narr_row_limits[0]:(narr_row_limits[1] + 1),
narr_column_limits[0]:(narr_column_limits[1] + 1)
] * LOCATING_VAR_MULTIPLIER
max_colour_value = numpy.nanpercentile(
numpy.absolute(matrix_to_plot), MAX_COLOUR_PERCENTILE)
min_colour_value = -1 * max_colour_value
nwp_plotting.plot_subgrid(
field_matrix=matrix_to_plot,
model_name=nwp_model_utils.NARR_MODEL_NAME, axes_object=axes_object,
basemap_object=basemap_object,
colour_map=LOCATING_VAR_COLOUR_MAP_OBJECT,
min_value_in_colour_map=min_colour_value,
max_value_in_colour_map=max_colour_value,
first_row_in_full_grid=narr_row_limits[0],
first_column_in_full_grid=narr_column_limits[0])
plotting_utils.add_linear_colour_bar(
axes_object_or_list=axes_object,
values_to_colour=matrix_to_plot,
colour_map=LOCATING_VAR_COLOUR_MAP_OBJECT, colour_min=min_colour_value,
colour_max=max_colour_value, orientation='vertical', extend_min=True,
extend_max=True, fraction_of_axis_length=COLOUR_BAR_LENGTH_FRACTION)
pyplot.title(title_string)
plotting_utils.annotate_axes(
axes_object=axes_object, annotation_string=annotation_string)
print 'Saving figure to: "{0:s}"...'.format(output_file_name)
pyplot.savefig(output_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(input_file_name=output_file_name,
output_file_name=output_file_name)
def _plot_kernel():
"""Plots convolutional kernel.
J = number of rows in kernel
K = number of columns in kernel
:return: kernel_matrix: Kernel as J-by-K numpy array.
"""
kernel_matrix = numpy.random.choice(a=POSSIBLE_KERNEL_VALUES,
size=(NUM_ROWS_IN_KERNEL,
NUM_COLUMNS_IN_KERNEL),
replace=True)
dummy_matrix = numpy.full((NUM_ROWS_IN_KERNEL, NUM_COLUMNS_IN_KERNEL),
HIGHLIGHTED_VALUE)
_, axes_object = pyplot.subplots(1,
1,
figsize=(FIGURE_WIDTH_INCHES,
FIGURE_HEIGHT_INCHES))
pyplot.imshow(dummy_matrix,
cmap=COLOUR_MAP_OBJECT,
vmin=HIGHLIGHTED_VALUE - 1,
vmax=HIGHLIGHTED_VALUE,
axes=axes_object,
origin='upper')
pyplot.xticks([], [])
pyplot.yticks([], [])
for i in range(kernel_matrix.shape[1]):
for j in range(kernel_matrix.shape[0]):
axes_object.text(i,
j,
'{0:.1f}'.format(kernel_matrix[j, i]),
fontsize=OVERLAY_FONT_SIZE,
color=MAIN_COLOUR,
horizontalalignment='center',
verticalalignment='center')
plotting_utils.annotate_axes(axes_object=axes_object,
annotation_string='(b)',
font_colour=ANNOTATION_COLOUR)
print 'Saving figure to: "{0:s}"...'.format(KERNEL_FILE_NAME)
file_system_utils.mkdir_recursive_if_necessary(file_name=KERNEL_FILE_NAME)
pyplot.savefig(KERNEL_FILE_NAME, dpi=OUTPUT_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(input_file_name=KERNEL_FILE_NAME,
output_file_name=KERNEL_FILE_NAME)
return kernel_matrix
def _run(input_dir_name, output_dir_name):
"""Creates figure showing model performance as a function of hyperparams.
This is effectively the main method.
:param input_dir_name: See documentation at top of file.
:param output_dir_name: Same.
"""
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
num_scores = len(SCORE_NAMES)
num_dense_layer_counts = len(DENSE_LAYER_COUNTS)
for j in range(num_scores):
panel_file_names = [
'{0:s}/num-dense-layers={1:d}_{2:s}_grid.jpg'.format(
input_dir_name, d, SCORE_NAMES[j]) for d in DENSE_LAYER_COUNTS
]
resized_panel_file_names = [
'{0:s}/num-dense-layers={1:d}_{2:s}_grid.jpg'.format(
output_dir_name, d, SCORE_NAMES[j]) for d in DENSE_LAYER_COUNTS
]
for i in range(num_dense_layer_counts):
imagemagick_utils.trim_whitespace(
input_file_name=panel_file_names[i],
output_file_name=resized_panel_file_names[i])
imagemagick_utils.resize_image(
input_file_name=resized_panel_file_names[i],
output_file_name=resized_panel_file_names[i],
output_size_pixels=PANEL_SIZE_PX)
concat_figure_file_name = '{0:s}/{1:s}_grid.jpg'.format(
output_dir_name, SCORE_NAMES[j])
print('Concatenating panels to: "{0:s}"...'.format(
concat_figure_file_name))
imagemagick_utils.concatenate_images(
input_file_names=resized_panel_file_names,
output_file_name=concat_figure_file_name,
num_panel_rows=NUM_PANEL_ROWS,
num_panel_columns=NUM_PANEL_COLUMNS)
imagemagick_utils.resize_image(
input_file_name=concat_figure_file_name,
output_file_name=concat_figure_file_name,
output_size_pixels=CONCAT_FIGURE_SIZE_PX)
imagemagick_utils.trim_whitespace(
input_file_name=concat_figure_file_name,
output_file_name=concat_figure_file_name)
def _run():
"""Concatenates figures showing dilation procedure and double penalty.
This is effectively the main method.
"""
dilation_image_object = Image.open(DILATION_FILE_NAME)
double_penalty_image_object = Image.open(DOUBLE_PENALTY_FILE_NAME)
new_dilation_width_px = double_penalty_image_object.size[0]
width_ratio = (float(new_dilation_width_px) /
dilation_image_object.size[0])
new_dilation_height_px = int(
numpy.round(dilation_image_object.size[1] * width_ratio))
small_dilation_file_name = DILATION_FILE_NAME.replace(
'.jpg', '_resized.jpg')
print 'Resizing dilation figure to: "{0:s}"...'.format(
small_dilation_file_name)
command_string = (
'/usr/bin/convert "{0:s}" -resize {1:d}x{2:d} "{3:s}"').format(
DILATION_FILE_NAME, new_dilation_width_px, new_dilation_height_px,
small_dilation_file_name)
os.system(command_string)
print('Concatenating dilation and double-penalty figures to: "{0:s}"...'
).format(CONCAT_FILE_NAME)
imagemagick_utils.concatenate_images(
input_file_names=[small_dilation_file_name, DOUBLE_PENALTY_FILE_NAME],
output_file_name=CONCAT_FILE_NAME,
num_panel_rows=2,
num_panel_columns=1)
imagemagick_utils.trim_whitespace(input_file_name=CONCAT_FILE_NAME,
output_file_name=CONCAT_FILE_NAME)
imagemagick_utils.resize_image(input_file_name=CONCAT_FILE_NAME,
output_file_name=CONCAT_FILE_NAME,
output_size_pixels=CONCAT_SIZE_PIXELS)
os.remove(small_dilation_file_name)
def _plot_ternary_table():
"""Plots ternary contingency table."""
_, axes_object = pyplot.subplots(1,
1,
figsize=(FIGURE_WIDTH_INCHES,
FIGURE_HEIGHT_INCHES))
pyplot.imshow(numpy.zeros((3, 3)),
cmap=COLOUR_MAP_OBJECT,
vmin=LARGE_NUMBER,
vmax=LARGE_NUMBER + 1,
axes=axes_object,
origin='upper')
tick_locations = numpy.array([0, 1, 2], dtype=int)
tick_labels = ['NF', 'WF', 'CF']
pyplot.xticks(tick_locations, tick_labels)
pyplot.xlabel('Actual')
pyplot.yticks(tick_locations, tick_labels)
pyplot.ylabel('Predicted')
for i in range(3):
for j in range(3):
axes_object.text(i,
j,
str(TERNARY_VARIABLE_NAME_MATRIX[i, j]),
fontsize=OVERLAY_FONT_SIZE,
color=MAIN_COLOUR,
horizontalalignment='center',
verticalalignment='center')
# plotting_utils.annotate_axes(
# axes_object=axes_object, annotation_string='(b)',
# font_colour=ANNOTATION_COLOUR)
print 'Saving figure to: "{0:s}"...'.format(TERNARY_CT_FILE_NAME)
file_system_utils.mkdir_recursive_if_necessary(
file_name=TERNARY_CT_FILE_NAME)
pyplot.savefig(TERNARY_CT_FILE_NAME, dpi=OUTPUT_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(input_file_name=TERNARY_CT_FILE_NAME,
output_file_name=TERNARY_CT_FILE_NAME)
def _plot_front_types(predicted_label_matrix, title_string, annotation_string,
output_file_name):
"""Plots front type at each grid cell.
M = number of rows in grid
N = number of columns in grid
:param predicted_label_matrix: M-by-N numpy array with predicted front type
at each grid cell. Each front type is from the list
`front_utils.VALID_INTEGER_IDS`.
:param title_string: Title (will be placed above figure).
:param annotation_string: Text annotation (will be placed in top left of
figure).
:param output_file_name: Path to output file (figure will be saved here).
"""
(narr_row_limits, narr_column_limits, axes_object, basemap_object
) = _init_basemap(BORDER_COLOUR)
matrix_to_plot = predicted_label_matrix[
narr_row_limits[0]:(narr_row_limits[1] + 1),
narr_column_limits[0]:(narr_column_limits[1] + 1)
]
front_plotting.plot_narr_grid(
frontal_grid_matrix=matrix_to_plot, axes_object=axes_object,
basemap_object=basemap_object,
first_row_in_narr_grid=narr_row_limits[0],
first_column_in_narr_grid=narr_column_limits[0])
pyplot.title(title_string)
plotting_utils.annotate_axes(
axes_object=axes_object, annotation_string=annotation_string)
print 'Saving figure to: "{0:s}"...'.format(output_file_name)
pyplot.savefig(output_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(input_file_name=output_file_name,
output_file_name=output_file_name)
def _run(tropical_example_dir_name, non_tropical_example_dir_name,
num_histogram_bins, output_dir_name):
"""Plots distribution of each target variable.
This is effectively the main method.
:param tropical_example_dir_name: See documentation at top of file.
:param non_tropical_example_dir_name: Same.
:param num_histogram_bins: Same.
:param output_dir_name: Same.
"""
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
first_time_unix_sec = (
time_conversion.first_and_last_times_in_year(FIRST_YEAR)[0])
last_time_unix_sec = (
time_conversion.first_and_last_times_in_year(LAST_YEAR)[-1])
example_file_names = example_io.find_many_files(
directory_name=tropical_example_dir_name,
first_time_unix_sec=first_time_unix_sec,
last_time_unix_sec=last_time_unix_sec,
raise_error_if_all_missing=True,
raise_error_if_any_missing=True)
example_file_names += example_io.find_many_files(
directory_name=non_tropical_example_dir_name,
first_time_unix_sec=first_time_unix_sec,
last_time_unix_sec=last_time_unix_sec,
raise_error_if_all_missing=True,
raise_error_if_any_missing=True)
example_dicts = []
for this_file_name in example_file_names:
print('Reading data from: "{0:s}"...'.format(this_file_name))
this_example_dict = example_io.read_file(this_file_name)
this_example_dict = example_utils.subset_by_field(
example_dict=this_example_dict, field_names=TARGET_NAMES_IN_FILE)
example_dicts.append(this_example_dict)
example_dict = example_utils.concat_examples(example_dicts)
del example_dicts
letter_label = None
panel_file_names = []
for this_target_name in TARGET_NAMES:
if this_target_name in TARGET_NAMES_IN_FILE:
these_target_values = example_utils.get_field_from_dict(
example_dict=example_dict, field_name=this_target_name)
else:
down_fluxes_w_m02 = example_utils.get_field_from_dict(
example_dict=example_dict,
field_name=example_utils.SHORTWAVE_SURFACE_DOWN_FLUX_NAME)
up_fluxes_w_m02 = example_utils.get_field_from_dict(
example_dict=example_dict,
field_name=example_utils.SHORTWAVE_TOA_UP_FLUX_NAME)
these_target_values = down_fluxes_w_m02 - up_fluxes_w_m02
these_target_values = numpy.ravel(these_target_values)
if letter_label is None:
letter_label = 'a'
else:
letter_label = chr(ord(letter_label) + 1)
this_file_name = _plot_histogram_one_target(
target_values=these_target_values,
target_name=this_target_name,
num_bins=num_histogram_bins,
letter_label=letter_label,
output_dir_name=output_dir_name)
panel_file_names.append(this_file_name)
concat_file_name = '{0:s}/target_distributions.jpg'.format(output_dir_name)
print('Concatenating panels to: "{0:s}"...'.format(concat_file_name))
imagemagick_utils.concatenate_images(input_file_names=panel_file_names,
output_file_name=concat_file_name,
num_panel_rows=2,
num_panel_columns=2,
border_width_pixels=25)
imagemagick_utils.trim_whitespace(input_file_name=concat_file_name,
output_file_name=concat_file_name)
def _plot_fronts(actual_binary_matrix, predicted_binary_matrix, title_string,
annotation_string, output_file_name):
"""Plots actual and predicted fronts.
M = number of rows in grid
N = number of columns in grid
:param actual_binary_matrix: M-by-N numpy array. If
actual_binary_matrix[i, j] = 1, there is an actual front passing through
grid cell [i, j].
:param predicted_binary_matrix: Same but for predicted fronts.
:param title_string: Title (will be placed above figure).
:param annotation_string: Text annotation (will be placed in top left of
figure).
:param output_file_name: Path to output file (figure will be saved here).
"""
(narr_row_limits,
narr_column_limits) = nwp_plotting.latlng_limits_to_rowcol_limits(
min_latitude_deg=MIN_LATITUDE_DEG,
max_latitude_deg=MAX_LATITUDE_DEG,
min_longitude_deg=MIN_LONGITUDE_DEG,
max_longitude_deg=MAX_LONGITUDE_DEG,
model_name=nwp_model_utils.NARR_MODEL_NAME)
_, axes_object, basemap_object = nwp_plotting.init_basemap(
model_name=nwp_model_utils.NARR_MODEL_NAME,
first_row_in_full_grid=narr_row_limits[0],
last_row_in_full_grid=narr_row_limits[1],
first_column_in_full_grid=narr_column_limits[0],
last_column_in_full_grid=narr_column_limits[1],
resolution_string='i')
plotting_utils.plot_coastlines(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_countries(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_states_and_provinces(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_parallels(basemap_object=basemap_object,
axes_object=axes_object,
bottom_left_lat_deg=-90.,
upper_right_lat_deg=90.,
parallel_spacing_deg=PARALLEL_SPACING_DEG)
plotting_utils.plot_meridians(basemap_object=basemap_object,
axes_object=axes_object,
bottom_left_lng_deg=0.,
upper_right_lng_deg=360.,
meridian_spacing_deg=MERIDIAN_SPACING_DEG)
this_colour_map_object, this_colour_norm_object = _get_colour_map(True)
this_matrix = actual_binary_matrix[0, narr_row_limits[0]:(
narr_row_limits[1] + 1), narr_column_limits[0]:(narr_column_limits[1] +
1)]
nwp_plotting.plot_subgrid(
field_matrix=this_matrix,
model_name=nwp_model_utils.NARR_MODEL_NAME,
axes_object=axes_object,
basemap_object=basemap_object,
colour_map=this_colour_map_object,
min_value_in_colour_map=this_colour_norm_object.boundaries[0],
max_value_in_colour_map=this_colour_norm_object.boundaries[-1],
first_row_in_full_grid=narr_row_limits[0],
first_column_in_full_grid=narr_column_limits[0],
opacity=ACTUAL_FRONT_OPACITY)
this_colour_map_object, this_colour_norm_object = _get_colour_map(False)
this_matrix = predicted_binary_matrix[0, narr_row_limits[0]:(
narr_row_limits[1] + 1), narr_column_limits[0]:(narr_column_limits[1] +
1)]
nwp_plotting.plot_subgrid(
field_matrix=this_matrix,
model_name=nwp_model_utils.NARR_MODEL_NAME,
axes_object=axes_object,
basemap_object=basemap_object,
colour_map=this_colour_map_object,
min_value_in_colour_map=this_colour_norm_object.boundaries[0],
max_value_in_colour_map=this_colour_norm_object.boundaries[-1],
first_row_in_full_grid=narr_row_limits[0],
first_column_in_full_grid=narr_column_limits[0],
opacity=PREDICTED_FRONT_OPACITY)
pyplot.title(title_string)
plotting_utils.annotate_axes(axes_object=axes_object,
annotation_string=annotation_string)
print 'Saving figure to: "{0:s}"...'.format(output_file_name)
file_system_utils.mkdir_recursive_if_necessary(file_name=output_file_name)
pyplot.savefig(output_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(input_file_name=output_file_name,
output_file_name=output_file_name)
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_one_time(
predictor_matrix, predictor_names, front_polyline_table, high_low_table,
thermal_colour_map_object, max_thermal_prctile_for_colours,
narr_row_limits, narr_column_limits, title_string, letter_label,
output_file_name):
"""Plots predictors at one time.
M = number of rows in grid
N = number of columns in grid
C = number of channels (predictors)
:param predictor_matrix: M-by-N-by-C numpy array of predictor values.
:param predictor_names: length-C list of predictor names.
:param front_polyline_table: pandas DataFrame returned by
`fronts_io.read_polylines_from_file`.
:param high_low_table: pandas DataFrame returned by
`wpc_bulletin_io.read_highs_and_lows`.
:param thermal_colour_map_object: See documentation at top of file.
:param max_thermal_prctile_for_colours: Same.
:param narr_row_limits: length-2 numpy array, indicating the first and last
NARR rows in `predictor_matrix`. If narr_row_limits = [i, k],
`predictor_matrix` spans rows i...k of the full NARR grid.
:param narr_column_limits: Same but for columns.
:param title_string: Title (will be placed above figure).
:param letter_label: Letter label. If this is "a", the label "(a)" will be
printed at the top left of the figure.
:param output_file_name: Path to output file (figure will be saved here).
"""
_, axes_object, basemap_object = nwp_plotting.init_basemap(
model_name=nwp_model_utils.NARR_MODEL_NAME,
first_row_in_full_grid=narr_row_limits[0],
last_row_in_full_grid=narr_row_limits[1],
first_column_in_full_grid=narr_column_limits[0],
last_column_in_full_grid=narr_column_limits[1]
)
plotting_utils.plot_coastlines(
basemap_object=basemap_object, axes_object=axes_object,
line_colour=BORDER_COLOUR
)
plotting_utils.plot_countries(
basemap_object=basemap_object, axes_object=axes_object,
line_colour=BORDER_COLOUR
)
plotting_utils.plot_states_and_provinces(
basemap_object=basemap_object, axes_object=axes_object,
line_colour=BORDER_COLOUR
)
plotting_utils.plot_parallels(
basemap_object=basemap_object, axes_object=axes_object,
bottom_left_lat_deg=-90., upper_right_lat_deg=90.,
parallel_spacing_deg=PARALLEL_SPACING_DEG
)
plotting_utils.plot_meridians(
basemap_object=basemap_object, axes_object=axes_object,
bottom_left_lng_deg=0., upper_right_lng_deg=360.,
meridian_spacing_deg=MERIDIAN_SPACING_DEG
)
num_predictors = len(predictor_names)
for j in range(num_predictors):
if predictor_names[j] in WIND_FIELD_NAMES:
continue
min_colour_value = numpy.percentile(
predictor_matrix[..., j], 100. - max_thermal_prctile_for_colours)
max_colour_value = numpy.percentile(
predictor_matrix[..., j], max_thermal_prctile_for_colours)
nwp_plotting.plot_subgrid(
field_matrix=predictor_matrix[..., j],
model_name=nwp_model_utils.NARR_MODEL_NAME, axes_object=axes_object,
basemap_object=basemap_object, colour_map=thermal_colour_map_object,
min_value_in_colour_map=min_colour_value,
max_value_in_colour_map=max_colour_value,
first_row_in_full_grid=narr_row_limits[0],
first_column_in_full_grid=narr_column_limits[0]
)
plotting_utils.add_linear_colour_bar(
axes_object_or_list=axes_object,
values_to_colour=predictor_matrix[..., j],
colour_map=thermal_colour_map_object, colour_min=min_colour_value,
colour_max=max_colour_value, orientation='horizontal',
extend_min=True, extend_max=True, fraction_of_axis_length=0.9)
u_wind_index = predictor_names.index(
processed_narr_io.U_WIND_GRID_RELATIVE_NAME)
v_wind_index = predictor_names.index(
processed_narr_io.V_WIND_GRID_RELATIVE_NAME)
nwp_plotting.plot_wind_barbs_on_subgrid(
u_wind_matrix_m_s01=predictor_matrix[..., u_wind_index],
v_wind_matrix_m_s01=predictor_matrix[..., v_wind_index],
model_name=nwp_model_utils.NARR_MODEL_NAME,
axes_object=axes_object, basemap_object=basemap_object,
first_row_in_full_grid=narr_row_limits[0],
first_column_in_full_grid=narr_column_limits[0],
plot_every_k_rows=PLOT_EVERY_KTH_WIND_BARB,
plot_every_k_columns=PLOT_EVERY_KTH_WIND_BARB,
barb_length=WIND_BARB_LENGTH, empty_barb_radius=EMPTY_WIND_BARB_RADIUS,
fill_empty_barb=False, colour_map=WIND_COLOUR_MAP_OBJECT,
colour_minimum_kt=MIN_COLOUR_WIND_SPEED_KT,
colour_maximum_kt=MAX_COLOUR_WIND_SPEED_KT)
if high_low_table is None:
num_pressure_systems = 0
else:
num_pressure_systems = len(high_low_table.index)
for i in range(num_pressure_systems):
this_system_type_string = high_low_table[
wpc_bulletin_io.SYSTEM_TYPE_COLUMN].values[i]
if this_system_type_string == wpc_bulletin_io.HIGH_PRESSURE_STRING:
this_string = 'H'
else:
this_string = 'L'
this_x_coord_metres, this_y_coord_metres = basemap_object(
high_low_table[wpc_bulletin_io.LONGITUDE_COLUMN].values[i],
high_low_table[wpc_bulletin_io.LATITUDE_COLUMN].values[i]
)
axes_object.text(
this_x_coord_metres, this_y_coord_metres, this_string,
fontsize=PRESSURE_SYSTEM_FONT_SIZE, color=PRESSURE_SYSTEM_COLOUR,
fontweight='bold', horizontalalignment='center',
verticalalignment='center')
num_fronts = len(front_polyline_table.index)
for i in range(num_fronts):
this_front_type_string = front_polyline_table[
front_utils.FRONT_TYPE_COLUMN].values[i]
if this_front_type_string == front_utils.WARM_FRONT_STRING_ID:
this_colour = WARM_FRONT_COLOUR
else:
this_colour = COLD_FRONT_COLOUR
front_plotting.plot_front_with_markers(
line_latitudes_deg=front_polyline_table[
front_utils.LATITUDES_COLUMN].values[i],
line_longitudes_deg=front_polyline_table[
front_utils.LONGITUDES_COLUMN].values[i],
axes_object=axes_object, basemap_object=basemap_object,
front_type_string=front_polyline_table[
front_utils.FRONT_TYPE_COLUMN].values[i],
marker_colour=this_colour)
pyplot.title(title_string)
if letter_label is not None:
plotting_utils.annotate_axes(
axes_object=axes_object,
annotation_string='({0:s})'.format(letter_label)
)
print 'Saving figure to: "{0:s}"...'.format(output_file_name)
pyplot.savefig(output_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(input_file_name=output_file_name,
output_file_name=output_file_name)
def _plot_one_time(valid_time_string, pressure_level_mb, title_string,
annotation_string, narr_rotation_cos_matrix,
narr_rotation_sin_matrix):
"""Plots WPC fronts and NARR fields at one time.
M = number of grid rows in the full NARR
N = number of grid columns in the full NARR
:param valid_time_string: Valid time (format "yyyy-mm-dd-HH").
:param pressure_level_mb: Pressure level (millibars).
:param title_string: Title (will be placed above figure).
:param annotation_string: Annotation (will be placed above and left of
figure).
:param narr_rotation_cos_matrix: M-by-N numpy array of cosines for wind-
rotation angles.
:param narr_rotation_sin_matrix: M-by-N numpy array of sines for wind-
rotation angles.
"""
narr_row_limits, narr_column_limits = (
nwp_plotting.latlng_limits_to_rowcol_limits(
min_latitude_deg=MIN_LATITUDE_DEG,
max_latitude_deg=MAX_LATITUDE_DEG,
min_longitude_deg=MIN_LONGITUDE_DEG,
max_longitude_deg=MAX_LONGITUDE_DEG,
model_name=nwp_model_utils.NARR_MODEL_NAME))
valid_time_unix_sec = time_conversion.string_to_unix_sec(
valid_time_string, DEFAULT_TIME_FORMAT)
front_file_name = fronts_io.find_file_for_one_time(
top_directory_name=TOP_FRONT_DIR_NAME,
file_type=fronts_io.POLYLINE_FILE_TYPE,
valid_time_unix_sec=valid_time_unix_sec)
print 'Reading data from: "{0:s}"...'.format(front_file_name)
front_line_table = fronts_io.read_polylines_from_file(front_file_name)
num_narr_fields = len(NARR_FIELD_NAMES)
narr_matrix_by_field = [numpy.array([])] * num_narr_fields
for j in range(num_narr_fields):
this_file_name = processed_narr_io.find_file_for_one_time(
top_directory_name=TOP_NARR_DIRECTORY_NAME,
field_name=NARR_FIELD_NAMES[j],
pressure_level_mb=pressure_level_mb,
valid_time_unix_sec=valid_time_unix_sec)
print 'Reading data from: "{0:s}"...'.format(this_file_name)
narr_matrix_by_field[j] = processed_narr_io.read_fields_from_file(
this_file_name)[0][0, ...]
narr_matrix_by_field[j] = utils.fill_nans(narr_matrix_by_field[j])
narr_matrix_by_field[j] = narr_matrix_by_field[j][narr_row_limits[0]:(
narr_row_limits[1] +
1), narr_column_limits[0]:(narr_column_limits[1] + 1)]
if NARR_FIELD_NAMES[j] == processed_narr_io.WET_BULB_THETA_NAME:
narr_matrix_by_field[j] = (narr_matrix_by_field[j] -
ZERO_CELSIUS_IN_KELVINS)
_, axes_object, basemap_object = nwp_plotting.init_basemap(
model_name=nwp_model_utils.NARR_MODEL_NAME,
first_row_in_full_grid=narr_row_limits[0],
last_row_in_full_grid=narr_row_limits[1],
first_column_in_full_grid=narr_column_limits[0],
last_column_in_full_grid=narr_column_limits[1])
plotting_utils.plot_coastlines(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_countries(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_states_and_provinces(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_parallels(basemap_object=basemap_object,
axes_object=axes_object,
bottom_left_lat_deg=-90.,
upper_right_lat_deg=90.,
parallel_spacing_deg=PARALLEL_SPACING_DEG)
plotting_utils.plot_meridians(basemap_object=basemap_object,
axes_object=axes_object,
bottom_left_lng_deg=0.,
upper_right_lng_deg=360.,
meridian_spacing_deg=MERIDIAN_SPACING_DEG)
for j in range(num_narr_fields):
if NARR_FIELD_NAMES[j] in WIND_FIELD_NAMES:
continue
min_colour_value = numpy.percentile(narr_matrix_by_field[j],
MIN_COLOUR_PERCENTILE)
max_colour_value = numpy.percentile(narr_matrix_by_field[j],
MAX_COLOUR_PERCENTILE)
nwp_plotting.plot_subgrid(
field_matrix=narr_matrix_by_field[j],
model_name=nwp_model_utils.NARR_MODEL_NAME,
axes_object=axes_object,
basemap_object=basemap_object,
colour_map=THERMAL_COLOUR_MAP_OBJECT,
min_value_in_colour_map=min_colour_value,
max_value_in_colour_map=max_colour_value,
first_row_in_full_grid=narr_row_limits[0],
first_column_in_full_grid=narr_column_limits[0])
plotting_utils.add_linear_colour_bar(
axes_object_or_list=axes_object,
values_to_colour=narr_matrix_by_field[j],
colour_map=THERMAL_COLOUR_MAP_OBJECT,
colour_min=min_colour_value,
colour_max=max_colour_value,
orientation='horizontal',
extend_min=True,
extend_max=True,
fraction_of_axis_length=0.9)
this_cos_matrix = narr_rotation_cos_matrix[narr_row_limits[0]:(
narr_row_limits[1] + 1), narr_column_limits[0]:(narr_column_limits[1] +
1)]
this_sin_matrix = narr_rotation_sin_matrix[narr_row_limits[0]:(
narr_row_limits[1] + 1), narr_column_limits[0]:(narr_column_limits[1] +
1)]
u_wind_index = NARR_FIELD_NAMES.index(
processed_narr_io.U_WIND_GRID_RELATIVE_NAME)
v_wind_index = NARR_FIELD_NAMES.index(
processed_narr_io.V_WIND_GRID_RELATIVE_NAME)
narr_matrix_by_field[u_wind_index], narr_matrix_by_field[v_wind_index] = (
nwp_model_utils.rotate_winds_to_earth_relative(
u_winds_grid_relative_m_s01=narr_matrix_by_field[u_wind_index],
v_winds_grid_relative_m_s01=narr_matrix_by_field[v_wind_index],
rotation_angle_cosines=this_cos_matrix,
rotation_angle_sines=this_sin_matrix))
nwp_plotting.plot_wind_barbs_on_subgrid(
u_wind_matrix_m_s01=narr_matrix_by_field[u_wind_index],
v_wind_matrix_m_s01=narr_matrix_by_field[v_wind_index],
model_name=nwp_model_utils.NARR_MODEL_NAME,
axes_object=axes_object,
basemap_object=basemap_object,
first_row_in_full_grid=narr_row_limits[0],
first_column_in_full_grid=narr_column_limits[0],
plot_every_k_rows=PLOT_EVERY_KTH_WIND_BARB,
plot_every_k_columns=PLOT_EVERY_KTH_WIND_BARB,
barb_length=WIND_BARB_LENGTH,
empty_barb_radius=EMPTY_WIND_BARB_RADIUS,
fill_empty_barb=False,
colour_map=WIND_COLOUR_MAP_OBJECT,
colour_minimum_kt=MIN_COLOUR_WIND_SPEED_KT,
colour_maximum_kt=MAX_COLOUR_WIND_SPEED_KT)
num_fronts = len(front_line_table.index)
for i in range(num_fronts):
this_front_type_string = front_line_table[
front_utils.FRONT_TYPE_COLUMN].values[i]
if this_front_type_string == front_utils.WARM_FRONT_STRING_ID:
this_colour = WARM_FRONT_COLOUR
else:
this_colour = COLD_FRONT_COLOUR
front_plotting.plot_front_with_markers(
line_latitudes_deg=front_line_table[
front_utils.LATITUDES_COLUMN].values[i],
line_longitudes_deg=front_line_table[
front_utils.LONGITUDES_COLUMN].values[i],
axes_object=axes_object,
basemap_object=basemap_object,
front_type_string=front_line_table[
front_utils.FRONT_TYPE_COLUMN].values[i],
marker_colour=this_colour)
pyplot.title(title_string)
plotting_utils.annotate_axes(axes_object=axes_object,
annotation_string=annotation_string)
file_system_utils.mkdir_recursive_if_necessary(
directory_name=OUTPUT_DIR_NAME)
figure_file_name = '{0:s}/fronts_{1:04d}mb_{2:s}.jpg'.format(
OUTPUT_DIR_NAME, pressure_level_mb, valid_time_string)
print 'Saving figure to: "{0:s}"...'.format(figure_file_name)
pyplot.savefig(figure_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(input_file_name=figure_file_name,
output_file_name=figure_file_name)
return figure_file_name
def _run():
"""Plots performance diagram on testing data.
This is effectively the main method.
"""
_, axes_object = pyplot.subplots(
1, 1, figsize=(FIGURE_WIDTH_INCHES, FIGURE_HEIGHT_INCHES)
)
model_eval_plotting.plot_performance_diagram(
axes_object=axes_object, pod_by_threshold=numpy.full(2, numpy.nan),
success_ratio_by_threshold=numpy.full(2, numpy.nan)
)
num_datasets = len(METHOD_NAME_BY_DATASET)
for i in range(num_datasets):
this_file_name = '{0:s}/obe_{1:d}km_min.p'.format(
METHOD_NAME_TO_DIRECTORY[METHOD_NAME_BY_DATASET[i]],
MATCHING_DIST_BY_DATASET_KM[i]
)
print 'Reading data from: "{0:s}"...'.format(this_file_name)
this_evaluation_dict = object_eval.read_evaluation_results(
this_file_name)
this_min_pod = this_evaluation_dict[object_eval.BINARY_POD_KEY]
this_min_success_ratio = this_evaluation_dict[
object_eval.BINARY_SUCCESS_RATIO_KEY]
this_file_name = '{0:s}/obe_{1:d}km_max.p'.format(
METHOD_NAME_TO_DIRECTORY[METHOD_NAME_BY_DATASET[i]],
MATCHING_DIST_BY_DATASET_KM[i]
)
print 'Reading data from: "{0:s}"...'.format(this_file_name)
this_evaluation_dict = object_eval.read_evaluation_results(
this_file_name)
this_max_pod = this_evaluation_dict[object_eval.BINARY_POD_KEY]
this_max_success_ratio = this_evaluation_dict[
object_eval.BINARY_SUCCESS_RATIO_KEY]
this_file_name = '{0:s}/obe_{1:d}km_mean.p'.format(
METHOD_NAME_TO_DIRECTORY[METHOD_NAME_BY_DATASET[i]],
MATCHING_DIST_BY_DATASET_KM[i]
)
print 'Reading data from: "{0:s}"...'.format(this_file_name)
this_evaluation_dict = object_eval.read_evaluation_results(
this_file_name)
this_mean_pod = this_evaluation_dict[object_eval.BINARY_POD_KEY]
this_mean_success_ratio = this_evaluation_dict[
object_eval.BINARY_SUCCESS_RATIO_KEY]
these_x_errors = numpy.array([
this_mean_success_ratio - this_min_success_ratio,
this_max_success_ratio - this_mean_success_ratio
])
these_y_errors = numpy.array([
this_mean_pod - this_min_pod, this_max_pod - this_mean_pod
])
these_x_errors = numpy.reshape(these_x_errors, (these_x_errors.size, 1))
these_y_errors = numpy.reshape(these_y_errors, (these_y_errors.size, 1))
axes_object.errorbar(
this_mean_success_ratio, this_mean_pod, xerr=these_x_errors,
yerr=these_y_errors,
ecolor=METHOD_NAME_TO_COLOUR[METHOD_NAME_BY_DATASET[i]],
elinewidth=LINE_WIDTH, capsize=ERROR_BAR_CAP_SIZE,
capthick=LINE_WIDTH)
this_label_string = '{0:s} ({1:d} km)'.format(
METHOD_NAME_BY_DATASET[i], MATCHING_DIST_BY_DATASET_KM[i])
this_x_coord = this_mean_success_ratio + 0.01
this_horiz_alignment_string = 'left'
if METHOD_NAME_BY_DATASET[i] == CNN_METHOD_NAME:
this_y_coord = this_mean_pod + 0.01
this_vert_alignment_string = 'bottom'
else:
this_y_coord = this_mean_pod - 0.01
this_vert_alignment_string = 'top'
if MATCHING_DIST_BY_DATASET_KM[i] == 250:
this_x_coord = this_mean_success_ratio - 0.01
this_horiz_alignment_string = 'right'
axes_object.text(
this_x_coord, this_y_coord, this_label_string,
fontsize=FONT_SIZE, fontweight='bold',
color=METHOD_NAME_TO_COLOUR[METHOD_NAME_BY_DATASET[i]],
horizontalalignment=this_horiz_alignment_string,
verticalalignment=this_vert_alignment_string, zorder=1e6)
file_system_utils.mkdir_recursive_if_necessary(file_name=OUTPUT_FILE_NAME)
print 'Saving figure to: "{0:s}"...'.format(OUTPUT_FILE_NAME)
pyplot.savefig(OUTPUT_FILE_NAME, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(input_file_name=OUTPUT_FILE_NAME,
output_file_name=OUTPUT_FILE_NAME)
def _add_colour_bar(figure_file_name, colour_map_object, max_colour_value,
temporary_dir_name):
"""Adds colour bar to saved image file.
:param figure_file_name: Path to saved image file. Colour bar will be added
to this image.
:param colour_map_object: Colour scheme (instance of `matplotlib.pyplot.cm`
or similar).
:param max_colour_value: Max value in colour scheme.
:param temporary_dir_name: Name of temporary output directory.
"""
this_image_matrix = Image.open(figure_file_name)
figure_width_px, figure_height_px = this_image_matrix.size
figure_width_inches = float(figure_width_px) / FIGURE_RESOLUTION_DPI
figure_height_inches = float(figure_height_px) / FIGURE_RESOLUTION_DPI
extra_figure_object, extra_axes_object = pyplot.subplots(
1, 1, figsize=(figure_width_inches, figure_height_inches))
extra_axes_object.axis('off')
dummy_values = numpy.array([0., max_colour_value])
colour_bar_object = plotting_utils.plot_linear_colour_bar(
axes_object_or_matrix=extra_axes_object,
data_matrix=dummy_values,
colour_map_object=colour_map_object,
min_value=0.,
max_value=max_colour_value,
orientation_string='vertical',
fraction_of_axis_length=1.25,
extend_min=False,
extend_max=True,
font_size=COLOUR_BAR_FONT_SIZE,
aspect_ratio=50.)
tick_values = colour_bar_object.get_ticks()
if max_colour_value <= 0.005:
tick_strings = ['{0:.4f}'.format(v) for v in tick_values]
elif max_colour_value <= 0.05:
tick_strings = ['{0:.3f}'.format(v) for v in tick_values]
else:
tick_strings = ['{0:.2f}'.format(v) for v in tick_values]
colour_bar_object.set_ticks(tick_values)
colour_bar_object.set_ticklabels(tick_strings)
extra_file_name = '{0:s}/saliency_colour-bar.jpg'.format(
temporary_dir_name)
print('Saving colour bar to: "{0:s}"...'.format(extra_file_name))
extra_figure_object.savefig(extra_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(extra_figure_object)
print('Concatenating colour bar to: "{0:s}"...'.format(figure_file_name))
imagemagick_utils.concatenate_images(
input_file_names=[figure_file_name, extra_file_name],
output_file_name=figure_file_name,
num_panel_rows=1,
num_panel_columns=2,
extra_args_string='-gravity Center')
os.remove(extra_file_name)
imagemagick_utils.trim_whitespace(input_file_name=figure_file_name,
output_file_name=figure_file_name)
def _plot_predictions_one_time(
output_file_name, title_string, annotation_string,
predicted_label_matrix=None, class_probability_matrix=None,
plot_warm_colour_bar=True, plot_cold_colour_bar=True):
"""Plots predictions (objects or probability grid) for one valid time.
:param output_file_name: Path to output file (figure will be saved here).
:param title_string: Title (will be placed above figure).
:param annotation_string: Text annotation (will be placed in top left of
figure).
:param predicted_label_matrix: See doc for `target_matrix` in
`machine_learning_utils.write_gridded_predictions`.
:param class_probability_matrix:
[used iff `predicted_label_matrix is None`]
See doc for `machine_learning_utils.write_gridded_predictions`.
:param plot_warm_colour_bar: [used iff `predicted_label_matrix is None`]
Boolean flag, indicating whether or not to plot colour bar for warm-
front probability.
:param plot_cold_colour_bar: Same but for cold-front probability.
"""
(narr_row_limits, narr_column_limits
) = nwp_plotting.latlng_limits_to_rowcol_limits(
min_latitude_deg=MIN_LATITUDE_DEG, max_latitude_deg=MAX_LATITUDE_DEG,
min_longitude_deg=MIN_LONGITUDE_DEG,
max_longitude_deg=MAX_LONGITUDE_DEG,
model_name=nwp_model_utils.NARR_MODEL_NAME)
_, axes_object, basemap_object = nwp_plotting.init_basemap(
model_name=nwp_model_utils.NARR_MODEL_NAME,
first_row_in_full_grid=narr_row_limits[0],
last_row_in_full_grid=narr_row_limits[1],
first_column_in_full_grid=narr_column_limits[0],
last_column_in_full_grid=narr_column_limits[1])
plotting_utils.plot_coastlines(
basemap_object=basemap_object, axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_countries(
basemap_object=basemap_object, axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_states_and_provinces(
basemap_object=basemap_object, axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_parallels(
basemap_object=basemap_object, axes_object=axes_object,
bottom_left_lat_deg=-90., upper_right_lat_deg=90.,
parallel_spacing_deg=PARALLEL_SPACING_DEG)
plotting_utils.plot_meridians(
basemap_object=basemap_object, axes_object=axes_object,
bottom_left_lng_deg=0., upper_right_lng_deg=360.,
meridian_spacing_deg=MERIDIAN_SPACING_DEG)
if class_probability_matrix is None:
this_matrix = predicted_label_matrix[
0, narr_row_limits[0]:(narr_row_limits[1] + 1),
narr_column_limits[0]:(narr_column_limits[1] + 1)
]
front_plotting.plot_narr_grid(
frontal_grid_matrix=this_matrix, axes_object=axes_object,
basemap_object=basemap_object,
first_row_in_narr_grid=narr_row_limits[0],
first_column_in_narr_grid=narr_column_limits[0],
opacity=DETERMINISTIC_OPACITY)
else:
this_matrix = class_probability_matrix[
0, narr_row_limits[0]:(narr_row_limits[1] + 1),
narr_column_limits[0]:(narr_column_limits[1] + 1),
front_utils.WARM_FRONT_INTEGER_ID
]
prediction_plotting.plot_narr_grid(
probability_matrix=this_matrix,
front_string_id=front_utils.WARM_FRONT_STRING_ID,
axes_object=axes_object, basemap_object=basemap_object,
first_row_in_narr_grid=narr_row_limits[0],
first_column_in_narr_grid=narr_column_limits[0],
opacity=PROBABILISTIC_OPACITY)
this_matrix = class_probability_matrix[
0, narr_row_limits[0]:(narr_row_limits[1] + 1),
narr_column_limits[0]:(narr_column_limits[1] + 1),
front_utils.COLD_FRONT_INTEGER_ID
]
prediction_plotting.plot_narr_grid(
probability_matrix=this_matrix,
front_string_id=front_utils.COLD_FRONT_STRING_ID,
axes_object=axes_object, basemap_object=basemap_object,
first_row_in_narr_grid=narr_row_limits[0],
first_column_in_narr_grid=narr_column_limits[0],
opacity=PROBABILISTIC_OPACITY)
if plot_warm_colour_bar:
(this_colour_map_object, this_colour_norm_object
) = prediction_plotting.get_warm_front_colour_map()[:2]
plotting_utils.add_colour_bar(
axes_object_or_list=axes_object,
colour_map=this_colour_map_object,
colour_norm_object=this_colour_norm_object,
values_to_colour=class_probability_matrix[
..., front_utils.WARM_FRONT_INTEGER_ID],
orientation='vertical', extend_min=True, extend_max=False,
fraction_of_axis_length=LENGTH_FRACTION_FOR_PROB_COLOUR_BAR)
if plot_cold_colour_bar:
(this_colour_map_object, this_colour_norm_object
) = prediction_plotting.get_cold_front_colour_map()[:2]
plotting_utils.add_colour_bar(
axes_object_or_list=axes_object,
colour_map=this_colour_map_object,
colour_norm_object=this_colour_norm_object,
values_to_colour=class_probability_matrix[
..., front_utils.COLD_FRONT_INTEGER_ID],
orientation='vertical', extend_min=True, extend_max=False,
fraction_of_axis_length=LENGTH_FRACTION_FOR_PROB_COLOUR_BAR)
pyplot.title(title_string)
plotting_utils.annotate_axes(
axes_object=axes_object, annotation_string=annotation_string)
print 'Saving figure to: "{0:s}"...'.format(output_file_name)
file_system_utils.mkdir_recursive_if_necessary(file_name=output_file_name)
pyplot.savefig(output_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(
input_file_name=output_file_name, output_file_name=output_file_name)
def plot_many_soundings(list_of_metpy_dictionaries,
title_strings,
num_panel_rows,
output_file_name,
temp_directory_name=None,
option_dict=None):
"""Creates paneled figure with many soundings.
N = number of soundings to plot
:param list_of_metpy_dictionaries: length-N list of dictionaries. Each
dictionary must satisfy the input format for `sounding_dict_for_metpy`
in `plot_sounding`.
:param title_strings: length-N list of titles.
:param num_panel_rows: Number of rows in paneled figure.
:param output_file_name: Path to output (image) file.
:param temp_directory_name: Name of temporary directory. Each panel will be
stored here, then deleted after the panels have been concatenated into
the final image. If `temp_directory_name is None`, will use the default
temp directory on the local machine.
:param option_dict: See doc for `plot_sounding`.
"""
error_checking.assert_is_numpy_array(numpy.array(title_strings),
num_dimensions=1)
num_soundings = len(title_strings)
error_checking.assert_is_list(list_of_metpy_dictionaries)
error_checking.assert_is_geq(len(list_of_metpy_dictionaries),
num_soundings)
error_checking.assert_is_leq(len(list_of_metpy_dictionaries),
num_soundings)
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_soundings)
file_system_utils.mkdir_recursive_if_necessary(file_name=output_file_name)
if temp_directory_name is not None:
file_system_utils.mkdir_recursive_if_necessary(
directory_name=temp_directory_name)
temp_file_names = [None] * num_soundings
num_panel_columns = int(numpy.ceil(float(num_soundings) / num_panel_rows))
for i in range(num_panel_rows):
for j in range(num_panel_columns):
this_sounding_index = i * num_panel_columns + j
if this_sounding_index >= num_soundings:
break
plot_sounding(sounding_dict_for_metpy=list_of_metpy_dictionaries[
this_sounding_index],
title_string=title_strings[this_sounding_index],
option_dict=option_dict)
temp_file_names[this_sounding_index] = '{0:s}.jpg'.format(
tempfile.NamedTemporaryFile(dir=temp_directory_name,
delete=False).name)
print('Saving sounding to: "{0:s}"...'.format(
temp_file_names[this_sounding_index]))
pyplot.savefig(temp_file_names[this_sounding_index],
dpi=DOTS_PER_INCH)
pyplot.close()
imagemagick_utils.trim_whitespace(
input_file_name=temp_file_names[this_sounding_index],
output_file_name=temp_file_names[this_sounding_index],
border_width_pixels=SINGLE_IMAGE_BORDER_WIDTH_PX)
imagemagick_utils.resize_image(
input_file_name=temp_file_names[this_sounding_index],
output_file_name=temp_file_names[this_sounding_index],
output_size_pixels=SINGLE_IMAGE_SIZE_PX)
print('Concatenating panels into one figure: "{0:s}"...'.format(
output_file_name))
imagemagick_utils.concatenate_images(
input_file_names=temp_file_names,
output_file_name=output_file_name,
num_panel_rows=num_panel_rows,
num_panel_columns=num_panel_columns,
border_width_pixels=PANELED_IMAGE_BORDER_WIDTH_PX)
for i in range(num_soundings):
os.remove(temp_file_names[i])
def _plot_predictions(predicted_label_matrix, title_string, annotation_string,
output_file_name):
"""Plots predicted front locations.
:param predicted_label_matrix: See doc for `target_matrix` in
`machine_learning_utils.write_gridded_predictions`.
:param title_string: Title (will be placed above figure).
:param annotation_string: Text annotation (will be placed in top left of
figure).
:param output_file_name: Path to output file (figure will be saved here).
"""
(narr_row_limits,
narr_column_limits) = nwp_plotting.latlng_limits_to_rowcol_limits(
min_latitude_deg=MIN_LATITUDE_DEG,
max_latitude_deg=MAX_LATITUDE_DEG,
min_longitude_deg=MIN_LONGITUDE_DEG,
max_longitude_deg=MAX_LONGITUDE_DEG,
model_name=nwp_model_utils.NARR_MODEL_NAME)
_, axes_object, basemap_object = nwp_plotting.init_basemap(
model_name=nwp_model_utils.NARR_MODEL_NAME,
first_row_in_full_grid=narr_row_limits[0],
last_row_in_full_grid=narr_row_limits[1],
first_column_in_full_grid=narr_column_limits[0],
last_column_in_full_grid=narr_column_limits[1])
plotting_utils.plot_coastlines(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_countries(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_states_and_provinces(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_parallels(basemap_object=basemap_object,
axes_object=axes_object,
bottom_left_lat_deg=-90.,
upper_right_lat_deg=90.,
parallel_spacing_deg=PARALLEL_SPACING_DEG)
plotting_utils.plot_meridians(basemap_object=basemap_object,
axes_object=axes_object,
bottom_left_lng_deg=0.,
upper_right_lng_deg=360.,
meridian_spacing_deg=MERIDIAN_SPACING_DEG)
this_matrix = predicted_label_matrix[0, narr_row_limits[0]:(
narr_row_limits[1] + 1), narr_column_limits[0]:(narr_column_limits[1] +
1)]
front_plotting.plot_narr_grid(
frontal_grid_matrix=this_matrix,
axes_object=axes_object,
basemap_object=basemap_object,
first_row_in_narr_grid=narr_row_limits[0],
first_column_in_narr_grid=narr_column_limits[0],
opacity=1.)
pyplot.title(title_string)
plotting_utils.annotate_axes(axes_object=axes_object,
annotation_string=annotation_string)
print 'Saving figure to: "{0:s}"...'.format(output_file_name)
file_system_utils.mkdir_recursive_if_necessary(file_name=output_file_name)
pyplot.savefig(output_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(input_file_name=output_file_name,
output_file_name=output_file_name)
def _plot_narr_fields(
wet_bulb_theta_matrix_kelvins, u_wind_matrix_m_s01, v_wind_matrix_m_s01,
title_string, annotation_string, output_file_name):
"""Plots NARR fields.
M = number of rows in grid
N = number of columns in grid
:param wet_bulb_theta_matrix_kelvins: M-by-N numpy array of wet-bulb
potential temperatures.
:param u_wind_matrix_m_s01: M-by-N numpy array of u-wind components (metres
per second).
:param v_wind_matrix_m_s01: Same but for v-wind.
:param title_string: Title (will be placed above figure).
:param annotation_string: Text annotation (will be placed in top left of
figure).
:param output_file_name: Path to output file (figure will be saved here).
"""
(narr_row_limits, narr_column_limits, axes_object, basemap_object
) = _init_basemap(BORDER_COLOUR)
wet_bulb_theta_matrix_to_plot = wet_bulb_theta_matrix_kelvins[
narr_row_limits[0]:(narr_row_limits[1] + 1),
narr_column_limits[0]:(narr_column_limits[1] + 1)
] - ZERO_CELSIUS_IN_KELVINS
u_wind_matrix_to_plot = u_wind_matrix_m_s01[
narr_row_limits[0]:(narr_row_limits[1] + 1),
narr_column_limits[0]:(narr_column_limits[1] + 1)
]
v_wind_matrix_to_plot = v_wind_matrix_m_s01[
narr_row_limits[0]:(narr_row_limits[1] + 1),
narr_column_limits[0]:(narr_column_limits[1] + 1)
]
nwp_plotting.plot_subgrid(
field_matrix=wet_bulb_theta_matrix_to_plot,
model_name=nwp_model_utils.NARR_MODEL_NAME, axes_object=axes_object,
basemap_object=basemap_object, colour_map=THERMAL_COLOUR_MAP_OBJECT,
min_value_in_colour_map=numpy.nanpercentile(
wet_bulb_theta_matrix_to_plot, MIN_COLOUR_PERCENTILE),
max_value_in_colour_map=numpy.nanpercentile(
wet_bulb_theta_matrix_to_plot, MAX_COLOUR_PERCENTILE),
first_row_in_full_grid=narr_row_limits[0],
first_column_in_full_grid=narr_column_limits[0])
plotting_utils.add_linear_colour_bar(
axes_object_or_list=axes_object,
values_to_colour=wet_bulb_theta_matrix_to_plot,
colour_map=THERMAL_COLOUR_MAP_OBJECT,
colour_min=numpy.nanpercentile(
wet_bulb_theta_matrix_to_plot, MIN_COLOUR_PERCENTILE),
colour_max=numpy.nanpercentile(
wet_bulb_theta_matrix_to_plot, MAX_COLOUR_PERCENTILE),
orientation='vertical', extend_min=True, extend_max=True,
fraction_of_axis_length=COLOUR_BAR_LENGTH_FRACTION)
nwp_plotting.plot_wind_barbs_on_subgrid(
u_wind_matrix_m_s01=u_wind_matrix_to_plot,
v_wind_matrix_m_s01=v_wind_matrix_to_plot,
model_name=nwp_model_utils.NARR_MODEL_NAME, axes_object=axes_object,
basemap_object=basemap_object,
first_row_in_full_grid=narr_row_limits[0],
first_column_in_full_grid=narr_column_limits[0],
plot_every_k_rows=PLOT_EVERY_KTH_WIND_BARB,
plot_every_k_columns=PLOT_EVERY_KTH_WIND_BARB,
barb_length=WIND_BARB_LENGTH, empty_barb_radius=EMPTY_WIND_BARB_RADIUS,
fill_empty_barb=False, colour_map=WIND_COLOUR_MAP_OBJECT,
colour_minimum_kt=MIN_COLOUR_WIND_SPEED_KT,
colour_maximum_kt=MAX_COLOUR_WIND_SPEED_KT)
pyplot.title(title_string)
plotting_utils.annotate_axes(
axes_object=axes_object, annotation_string=annotation_string)
print 'Saving figure to: "{0:s}"...'.format(output_file_name)
pyplot.savefig(output_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(input_file_name=output_file_name,
output_file_name=output_file_name)
def _plot_histogram_one_target(target_values, target_name, num_bins,
letter_label, output_dir_name):
"""Plots histogram for one target variable.
:param target_values: 1-D numpy array of values.
:param target_name: Name of target variable.
:param num_bins: Number of bins in histogram.
:param letter_label: Letter label (will be used to label panel).
:param output_dir_name: Name of output directory. Figure will be saved
here.
:return: output_file_name: Path to output file.
"""
min_value = (numpy.min(target_values)
if target_name == SHORTWAVE_NET_FLUX_NAME else 0.)
max_value = numpy.max(target_values)
num_examples_by_bin = histograms.create_histogram(
input_values=target_values,
num_bins=num_bins,
min_value=min_value,
max_value=max_value)[1]
frequency_by_bin = (num_examples_by_bin.astype(float) /
numpy.sum(num_examples_by_bin))
bin_edges = numpy.linspace(min_value, max_value, num=num_bins + 1)
bin_centers = numpy.array(
[numpy.mean(bin_edges[[k, k + 1]]) for k in range(num_bins)])
x_tick_coords = 0.5 + numpy.linspace(
0, num_bins - 1, num=num_bins, dtype=float)
if target_name == example_utils.SHORTWAVE_HEATING_RATE_NAME:
x_tick_labels = ['{0:.1f}'.format(c) for c in bin_centers]
else:
x_tick_labels = [
'{0:d}'.format(int(numpy.round(c))) for c in bin_centers
]
x_tick_labels = [
x_tick_labels[k] if numpy.mod(k, 3) == 0 else ' '
for k in range(num_bins)
]
figure_object, axes_object = pyplot.subplots(
1, 1, figsize=(FIGURE_WIDTH_INCHES, FIGURE_HEIGHT_INCHES))
axes_object.bar(x=x_tick_coords,
height=frequency_by_bin,
width=1.,
color=FACE_COLOUR,
edgecolor=EDGE_COLOUR,
linewidth=EDGE_WIDTH)
axes_object.set_xlim([x_tick_coords[0] - 0.5, x_tick_coords[-1] + 0.5])
axes_object.set_xticks(x_tick_coords)
axes_object.set_xticklabels(x_tick_labels, rotation=90.)
axes_object.set_ylabel('Frequency')
axes_object.set_xlabel(TARGET_NAME_TO_VERBOSE[target_name])
plotting_utils.label_axes(axes_object=axes_object,
label_string='({0:s})'.format(letter_label))
output_file_name = '{0:s}/histogram_{1:s}.jpg'.format(
output_dir_name, target_name.replace('_', '-'))
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)
imagemagick_utils.trim_whitespace(input_file_name=output_file_name,
output_file_name=output_file_name)
imagemagick_utils.resize_image(input_file_name=output_file_name,
output_file_name=output_file_name,
output_size_pixels=int(2.5e6))
return output_file_name
def _plot_one_composite(saliency_file_name, composite_name_abbrev,
composite_name_verbose, colour_map_object,
max_colour_value, half_num_contours,
smoothing_radius_grid_cells, output_dir_name):
"""Plots saliency map for one composite.
:param saliency_file_name: Path to input file (will be read by
`saliency.read_file`).
:param composite_name_abbrev: Abbrev composite name (will be used in file
names).
:param composite_name_verbose: Verbose composite name (will be used in
figure title).
:param colour_map_object: See documentation at top of file.
:param max_colour_value: Same.
:param half_num_contours: Same.
:param smoothing_radius_grid_cells: Same.
:param output_dir_name: Name of output directory (figures will be saved
here).
:return: main_figure_file_name: Path to main image file created by this
method.
:return: max_colour_value: See input doc.
"""
mean_radar_matrix, mean_saliency_matrix, model_metadata_dict = (
_read_one_composite(
saliency_file_name=saliency_file_name,
smoothing_radius_grid_cells=smoothing_radius_grid_cells))
if numpy.isnan(max_colour_value):
max_colour_value = numpy.percentile(mean_saliency_matrix,
MAX_COLOUR_PERCENTILE)
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY]
num_fields = mean_radar_matrix.shape[-1]
num_heights = mean_radar_matrix.shape[-2]
handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=[mean_radar_matrix],
model_metadata_dict=model_metadata_dict,
pmm_flag=True,
allow_whitespace=True,
plot_panel_names=True,
panel_name_font_size=PANEL_NAME_FONT_SIZE,
add_titles=False,
label_colour_bars=True,
colour_bar_length=COLOUR_BAR_LENGTH,
colour_bar_font_size=COLOUR_BAR_FONT_SIZE,
num_panel_rows=num_heights)
figure_objects = handle_dict[plot_examples.RADAR_FIGURES_KEY]
axes_object_matrices = handle_dict[plot_examples.RADAR_AXES_KEY]
for k in range(num_fields):
this_saliency_matrix = mean_saliency_matrix[0, ..., k]
saliency_plotting.plot_many_2d_grids_with_contours(
saliency_matrix_3d=numpy.flip(this_saliency_matrix, axis=0),
axes_object_matrix=axes_object_matrices[k],
colour_map_object=colour_map_object,
max_absolute_contour_level=max_colour_value,
contour_interval=max_colour_value / half_num_contours)
panel_file_names = [None] * num_fields
for k in range(num_fields):
panel_file_names[k] = '{0:s}/{1:s}_{2:s}.jpg'.format(
output_dir_name, composite_name_abbrev,
field_names[k].replace('_', '-'))
print('Saving figure to: "{0:s}"...'.format(panel_file_names[k]))
figure_objects[k].savefig(panel_file_names[k],
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(figure_objects[k])
main_figure_file_name = '{0:s}/{1:s}_saliency.jpg'.format(
output_dir_name, composite_name_abbrev)
print('Concatenating panels to: "{0:s}"...'.format(main_figure_file_name))
imagemagick_utils.concatenate_images(
input_file_names=panel_file_names,
output_file_name=main_figure_file_name,
num_panel_rows=1,
num_panel_columns=num_fields,
border_width_pixels=50)
imagemagick_utils.resize_image(input_file_name=main_figure_file_name,
output_file_name=main_figure_file_name,
output_size_pixels=CONCAT_FIGURE_SIZE_PX)
imagemagick_utils.trim_whitespace(input_file_name=main_figure_file_name,
output_file_name=main_figure_file_name,
border_width_pixels=TITLE_FONT_SIZE + 25)
_overlay_text(image_file_name=main_figure_file_name,
x_offset_from_center_px=0,
y_offset_from_top_px=0,
text_string=composite_name_verbose)
imagemagick_utils.trim_whitespace(input_file_name=main_figure_file_name,
output_file_name=main_figure_file_name,
border_width_pixels=10)
return main_figure_file_name, max_colour_value
def _run(input_dir_name, site_names_for_reliability, site_name_for_histogram,
output_dir_name):
"""Creates figure showing overall model evaluation.
This is effectively the main method.
:param input_dir_name: See documentation at top of file.
:param site_names_for_reliability: Same.
:param site_name_for_histogram: Same.
:param output_dir_name: Same.
"""
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
pathless_input_file_names = [
'shortwave-surface-down-flux-w-m02_attributes_{0:s}.jpg'.format(
site_name_for_histogram),
'shortwave-toa-up-flux-w-m02_attributes_{0:s}.jpg'.format(
site_name_for_histogram),
'net-shortwave-flux-w-m02_attributes_{0:s}.jpg'.format(
site_name_for_histogram),
'shortwave-heating-rate-k-day01_bias_profile.jpg',
'shortwave-heating-rate-k-day01_mean-absolute-error_profile.jpg',
'shortwave-heating-rate-k-day01_mae-skill-score_profile.jpg'
]
num_sites_for_relia = len(site_names_for_reliability)
for j in range(num_sites_for_relia):
this_file_name = (
'shortwave-heating-rate-k-day01_reliability_{0:s}.jpg').format(
site_names_for_reliability[j])
pathless_input_file_names.append(this_file_name)
panel_file_names = [
'{0:s}/{1:s}'.format(input_dir_name, p)
for p in pathless_input_file_names
]
resized_panel_file_names = [
'{0:s}/{1:s}'.format(output_dir_name, p)
for p in pathless_input_file_names
]
letter_label = None
for i in range(len(panel_file_names)):
print('Resizing panel and saving to: "{0:s}"...'.format(
resized_panel_file_names[i]))
imagemagick_utils.trim_whitespace(
input_file_name=panel_file_names[i],
output_file_name=resized_panel_file_names[i])
if letter_label is None:
letter_label = 'a'
else:
letter_label = chr(ord(letter_label) + 1)
_overlay_text(image_file_name=resized_panel_file_names[i],
x_offset_from_left_px=0,
y_offset_from_top_px=TITLE_FONT_SIZE,
text_string='({0:s})'.format(letter_label))
imagemagick_utils.resize_image(
input_file_name=resized_panel_file_names[i],
output_file_name=resized_panel_file_names[i],
output_size_pixels=PANEL_SIZE_PX)
concat_figure_file_name = '{0:s}/evaluation_by_site.jpg'.format(
output_dir_name)
print(
'Concatenating panels to: "{0:s}"...'.format(concat_figure_file_name))
imagemagick_utils.concatenate_images(
input_file_names=resized_panel_file_names,
output_file_name=concat_figure_file_name,
num_panel_rows=NUM_PANEL_ROWS,
num_panel_columns=NUM_PANEL_COLUMNS)
imagemagick_utils.resize_image(input_file_name=concat_figure_file_name,
output_file_name=concat_figure_file_name,
output_size_pixels=CONCAT_FIGURE_SIZE_PX)
def _run(saliency_file_names, composite_names, colour_map_name,
max_colour_values, half_num_contours, smoothing_radius_grid_cells,
output_dir_name):
"""Makes figure with saliency maps for MYRORSS model.
This is effectively the main method.
:param saliency_file_names: See documentation at top of file.
:param composite_names: Same.
:param colour_map_name: Same.
:param max_colour_values: Same.
:param half_num_contours: Same.
:param smoothing_radius_grid_cells: Same.
:param output_dir_name: Same.
"""
# Process input args.
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
if smoothing_radius_grid_cells <= 0:
smoothing_radius_grid_cells = None
colour_map_object = pyplot.cm.get_cmap(colour_map_name)
error_checking.assert_is_geq(half_num_contours, 5)
num_composites = len(saliency_file_names)
expected_dim = numpy.array([num_composites], dtype=int)
error_checking.assert_is_numpy_array(numpy.array(composite_names),
exact_dimensions=expected_dim)
max_colour_values[max_colour_values <= 0] = numpy.nan
error_checking.assert_is_numpy_array(max_colour_values,
exact_dimensions=expected_dim)
composite_names_abbrev = [
n.replace('_', '-').lower() for n in composite_names
]
composite_names_verbose = [
'({0:s}) {1:s}'.format(chr(ord('a') + i),
composite_names[i].replace('_', ' '))
for i in range(num_composites)
]
panel_file_names = [None] * num_composites
for i in range(num_composites):
panel_file_names[i], max_colour_values[i] = _plot_one_composite(
saliency_file_name=saliency_file_names[i],
composite_name_abbrev=composite_names_abbrev[i],
composite_name_verbose=composite_names_verbose[i],
colour_map_object=colour_map_object,
max_colour_value=max_colour_values[i],
half_num_contours=half_num_contours,
smoothing_radius_grid_cells=smoothing_radius_grid_cells,
output_dir_name=output_dir_name)
_add_colour_bar(figure_file_name=panel_file_names[i],
colour_map_object=colour_map_object,
max_colour_value=max_colour_values[i],
temporary_dir_name=output_dir_name)
print('\n')
figure_file_name = '{0:s}/saliency_concat.jpg'.format(output_dir_name)
print('Concatenating panels to: "{0:s}"...'.format(figure_file_name))
num_panel_rows = int(numpy.floor(numpy.sqrt(num_composites)))
num_panel_columns = int(numpy.ceil(float(num_composites) / num_panel_rows))
imagemagick_utils.concatenate_images(input_file_names=panel_file_names,
output_file_name=figure_file_name,
border_width_pixels=25,
num_panel_rows=num_panel_rows,
num_panel_columns=num_panel_columns)
imagemagick_utils.trim_whitespace(input_file_name=figure_file_name,
output_file_name=figure_file_name,
border_width_pixels=10)
def _run():
"""Plots input example.
This is effectively the main method.
:return: figure_file_name: Path to output file (where the figure was saved).
"""
valid_time_unix_sec = time_conversion.string_to_unix_sec(
VALID_TIME_STRING, TIME_FORMAT)
front_file_name = fronts_io.find_file_for_one_time(
top_directory_name=TOP_FRONT_DIR_NAME,
file_type=fronts_io.POLYLINE_FILE_TYPE,
valid_time_unix_sec=valid_time_unix_sec)
print 'Reading data from: "{0:s}"...'.format(front_file_name)
front_line_table = fronts_io.read_polylines_from_file(front_file_name)
num_narr_fields = len(NARR_FIELD_NAMES)
narr_matrix_by_field = [numpy.array([])] * num_narr_fields
for j in range(num_narr_fields):
if NARR_FIELD_NAMES[j] in WIND_FIELD_NAMES:
this_directory_name = '{0:s}/earth_relative_wind'.format(
TOP_NARR_DIRECTORY_NAME)
else:
this_directory_name = TOP_NARR_DIRECTORY_NAME + ''
this_file_name = processed_narr_io.find_file_for_one_time(
top_directory_name=this_directory_name,
field_name=NARR_FIELD_NAMES[j],
pressure_level_mb=PRESSURE_LEVEL_MB,
valid_time_unix_sec=valid_time_unix_sec)
print 'Reading data from: "{0:s}"...'.format(this_file_name)
narr_matrix_by_field[j] = processed_narr_io.read_fields_from_file(
this_file_name)[0][0, ...]
narr_matrix_by_field[j] = utils.fill_nans(narr_matrix_by_field[j])
if NARR_FIELD_NAMES[j] == processed_narr_io.WET_BULB_THETA_NAME:
narr_matrix_by_field[j] = (narr_matrix_by_field[j] -
ZERO_CELSIUS_IN_KELVINS)
# (_, front_centroid_latitude_deg, front_centroid_longitude_deg
# ) = _find_nearest_front(
# front_line_table=front_line_table,
# query_latitude_deg=APPROX_FRONT_LATITUDE_DEG,
# query_longitude_deg=APPROX_FRONT_LONGITUDE_DEG)
front_centroid_latitude_deg = APPROX_FRONT_LATITUDE_DEG + 0.
front_centroid_longitude_deg = APPROX_FRONT_LONGITUDE_DEG + 0.
projection_object = nwp_model_utils.init_model_projection(
nwp_model_utils.NARR_MODEL_NAME)
these_x_metres, these_y_metres = nwp_model_utils.project_latlng_to_xy(
latitudes_deg=numpy.array([front_centroid_latitude_deg]),
longitudes_deg=numpy.array([front_centroid_longitude_deg]),
projection_object=projection_object,
model_name=nwp_model_utils.NARR_MODEL_NAME)
front_centroid_x_metres = these_x_metres[0]
front_centroid_y_metres = these_y_metres[0]
grid_spacing_metres, _ = nwp_model_utils.get_xy_grid_spacing(
model_name=nwp_model_utils.NARR_MODEL_NAME)
center_narr_row_index = int(
numpy.round(front_centroid_y_metres / grid_spacing_metres))
center_narr_column_index = int(
numpy.round(front_centroid_x_metres / grid_spacing_metres))
first_narr_row_index = center_narr_row_index - NUM_ROWS_IN_HALF_GRID
last_narr_row_index = center_narr_row_index + NUM_ROWS_IN_HALF_GRID
first_narr_column_index = (center_narr_column_index -
NUM_COLUMNS_IN_HALF_GRID)
last_narr_column_index = center_narr_column_index + NUM_COLUMNS_IN_HALF_GRID
for j in range(num_narr_fields):
narr_matrix_by_field[j] = narr_matrix_by_field[j][
first_narr_row_index:(last_narr_row_index + 1),
first_narr_column_index:(last_narr_column_index + 1)]
_, axes_object, basemap_object = nwp_plotting.init_basemap(
model_name=nwp_model_utils.NARR_MODEL_NAME,
first_row_in_full_grid=first_narr_row_index,
last_row_in_full_grid=last_narr_row_index,
first_column_in_full_grid=first_narr_column_index,
last_column_in_full_grid=last_narr_column_index,
resolution_string='i')
plotting_utils.plot_coastlines(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_countries(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_states_and_provinces(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_parallels(basemap_object=basemap_object,
axes_object=axes_object,
bottom_left_lat_deg=-90.,
upper_right_lat_deg=90.,
parallel_spacing_deg=PARALLEL_SPACING_DEG)
plotting_utils.plot_meridians(basemap_object=basemap_object,
axes_object=axes_object,
bottom_left_lng_deg=0.,
upper_right_lng_deg=360.,
meridian_spacing_deg=MERIDIAN_SPACING_DEG)
for j in range(num_narr_fields):
if NARR_FIELD_NAMES[j] in WIND_FIELD_NAMES:
continue
min_colour_value = numpy.percentile(narr_matrix_by_field[j],
MIN_COLOUR_PERCENTILE)
max_colour_value = numpy.percentile(narr_matrix_by_field[j],
MAX_COLOUR_PERCENTILE)
nwp_plotting.plot_subgrid(
field_matrix=narr_matrix_by_field[j],
model_name=nwp_model_utils.NARR_MODEL_NAME,
axes_object=axes_object,
basemap_object=basemap_object,
colour_map=THERMAL_COLOUR_MAP_OBJECT,
min_value_in_colour_map=min_colour_value,
max_value_in_colour_map=max_colour_value,
first_row_in_full_grid=first_narr_row_index,
first_column_in_full_grid=first_narr_column_index)
plotting_utils.add_linear_colour_bar(
axes_object_or_list=axes_object,
values_to_colour=narr_matrix_by_field[j],
colour_map=THERMAL_COLOUR_MAP_OBJECT,
colour_min=min_colour_value,
colour_max=max_colour_value,
orientation='horizontal',
extend_min=True,
extend_max=True)
u_wind_index = NARR_FIELD_NAMES.index(
processed_narr_io.U_WIND_EARTH_RELATIVE_NAME)
v_wind_index = NARR_FIELD_NAMES.index(
processed_narr_io.V_WIND_EARTH_RELATIVE_NAME)
nwp_plotting.plot_wind_barbs_on_subgrid(
u_wind_matrix_m_s01=narr_matrix_by_field[u_wind_index],
v_wind_matrix_m_s01=narr_matrix_by_field[v_wind_index],
model_name=nwp_model_utils.NARR_MODEL_NAME,
axes_object=axes_object,
basemap_object=basemap_object,
first_row_in_full_grid=first_narr_row_index,
first_column_in_full_grid=first_narr_column_index,
plot_every_k_rows=PLOT_EVERY_KTH_WIND_BARB,
plot_every_k_columns=PLOT_EVERY_KTH_WIND_BARB,
barb_length=WIND_BARB_LENGTH,
empty_barb_radius=EMPTY_WIND_BARB_RADIUS,
colour_map=WIND_COLOUR_MAP_OBJECT,
colour_minimum_kt=MIN_COLOUR_WIND_SPEED_KT,
colour_maximum_kt=MAX_COLOUR_WIND_SPEED_KT)
num_fronts = len(front_line_table.index)
for i in range(num_fronts):
this_front_type_string = front_line_table[
front_utils.FRONT_TYPE_COLUMN].values[i]
if this_front_type_string == front_utils.WARM_FRONT_STRING_ID:
this_colour = WARM_FRONT_COLOUR
else:
this_colour = COLD_FRONT_COLOUR
# front_plotting.plot_polyline(
# latitudes_deg=front_line_table[
# front_utils.LATITUDES_COLUMN].values[i],
# longitudes_deg=front_line_table[
# front_utils.LONGITUDES_COLUMN].values[i],
# basemap_object=basemap_object, axes_object=axes_object,
# front_type=front_line_table[
# front_utils.FRONT_TYPE_COLUMN].values[i],
# line_width=FRONT_LINE_WIDTH, line_colour=this_colour)
print 'Saving figure to: "{0:s}"...'.format(OUTPUT_FILE_NAME)
file_system_utils.mkdir_recursive_if_necessary(file_name=OUTPUT_FILE_NAME)
pyplot.savefig(OUTPUT_FILE_NAME, dpi=OUTPUT_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(input_file_name=OUTPUT_FILE_NAME,
output_file_name=OUTPUT_FILE_NAME)
def _plot_one_composite(saliency_file_name, monte_carlo_file_name,
composite_name_abbrev, composite_name_verbose,
colour_map_object, max_colour_value, half_num_contours,
smoothing_radius_grid_cells, output_dir_name):
"""Plots saliency map for one composite.
:param saliency_file_name: Path to saliency file (will be read by
`saliency.read_file`).
:param monte_carlo_file_name: Path to Monte Carlo file (will be read by
`_read_monte_carlo_file`).
:param composite_name_abbrev: Abbrev composite name (will be used in file
names).
:param composite_name_verbose: Verbose composite name (will be used in
figure title).
:param colour_map_object: See documentation at top of file.
:param max_colour_value: Same.
:param half_num_contours: Same.
:param smoothing_radius_grid_cells: Same.
:param output_dir_name: Name of output directory (figures will be saved
here).
:return: main_figure_file_name: Path to main image file created by this
method.
"""
(mean_radar_matrices, mean_saliency_matrices, significance_matrices,
model_metadata_dict) = _read_one_composite(
saliency_file_name=saliency_file_name,
smoothing_radius_grid_cells=smoothing_radius_grid_cells,
monte_carlo_file_name=monte_carlo_file_name)
refl_heights_m_agl = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY][
trainval_io.RADAR_HEIGHTS_KEY]
num_refl_heights = len(refl_heights_m_agl)
handle_dict = plot_examples.plot_one_example(
list_of_predictor_matrices=mean_radar_matrices,
model_metadata_dict=model_metadata_dict,
pmm_flag=True,
plot_sounding=False,
allow_whitespace=True,
plot_panel_names=True,
panel_name_font_size=PANEL_NAME_FONT_SIZE,
add_titles=False,
label_colour_bars=True,
colour_bar_length=COLOUR_BAR_LENGTH,
colour_bar_font_size=COLOUR_BAR_FONT_SIZE,
num_panel_rows=num_refl_heights)
axes_object_matrices = handle_dict[plot_examples.RADAR_AXES_KEY]
this_saliency_matrix = numpy.flip(mean_saliency_matrices[0][0, ..., 0],
axis=0)
saliency_plotting.plot_many_2d_grids_with_contours(
saliency_matrix_3d=this_saliency_matrix,
axes_object_matrix=axes_object_matrices[0],
colour_map_object=colour_map_object,
max_absolute_contour_level=max_colour_value,
contour_interval=max_colour_value / half_num_contours,
row_major=True)
this_sig_matrix = numpy.flip(significance_matrices[0][0, ..., 0], axis=0)
significance_plotting.plot_many_2d_grids_without_coords(
significance_matrix=this_sig_matrix,
axes_object_matrix=axes_object_matrices[0],
marker_size=2,
row_major=True)
this_saliency_matrix = numpy.flip(mean_saliency_matrices[1][0, ...],
axis=0)
saliency_plotting.plot_many_2d_grids_with_contours(
saliency_matrix_3d=this_saliency_matrix,
axes_object_matrix=axes_object_matrices[1],
colour_map_object=colour_map_object,
max_absolute_contour_level=max_colour_value,
contour_interval=max_colour_value / half_num_contours,
row_major=False)
this_sig_matrix = numpy.flip(significance_matrices[1][0, ...], axis=0)
significance_plotting.plot_many_2d_grids_without_coords(
significance_matrix=this_sig_matrix,
axes_object_matrix=axes_object_matrices[1],
marker_size=2,
row_major=False)
refl_figure_object = handle_dict[plot_examples.RADAR_FIGURES_KEY][0]
refl_figure_file_name = '{0:s}/{1:s}_reflectivity.jpg'.format(
output_dir_name, composite_name_abbrev)
print('Saving figure to: "{0:s}"...'.format(refl_figure_file_name))
refl_figure_object.savefig(refl_figure_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(refl_figure_object)
shear_figure_object = handle_dict[plot_examples.RADAR_FIGURES_KEY][1]
shear_figure_file_name = '{0:s}/{1:s}_shear.jpg'.format(
output_dir_name, composite_name_abbrev)
print('Saving figure to: "{0:s}"...'.format(shear_figure_file_name))
shear_figure_object.savefig(shear_figure_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(shear_figure_object)
main_figure_file_name = '{0:s}/{1:s}_saliency.jpg'.format(
output_dir_name, composite_name_abbrev)
print('Concatenating panels to: "{0:s}"...'.format(main_figure_file_name))
imagemagick_utils.concatenate_images(
input_file_names=[refl_figure_file_name, shear_figure_file_name],
output_file_name=main_figure_file_name,
num_panel_rows=1,
num_panel_columns=2,
border_width_pixels=50,
extra_args_string='-gravity south')
imagemagick_utils.resize_image(input_file_name=main_figure_file_name,
output_file_name=main_figure_file_name,
output_size_pixels=CONCAT_FIGURE_SIZE_PX)
imagemagick_utils.trim_whitespace(input_file_name=main_figure_file_name,
output_file_name=main_figure_file_name,
border_width_pixels=TITLE_FONT_SIZE + 25)
_overlay_text(image_file_name=main_figure_file_name,
x_offset_from_center_px=0,
y_offset_from_top_px=0,
text_string=composite_name_verbose)
imagemagick_utils.trim_whitespace(input_file_name=main_figure_file_name,
output_file_name=main_figure_file_name,
border_width_pixels=10)
return main_figure_file_name
def _run():
"""Plots weird WPC fronts, along with theta_w and wind barbs from NARR.
This is effectively the main method.
"""
narr_latitude_matrix_deg, narr_longitude_matrix_deg = (
nwp_model_utils.get_latlng_grid_point_matrices(
model_name=nwp_model_utils.NARR_MODEL_NAME))
narr_rotation_cos_matrix, narr_rotation_sin_matrix = (
nwp_model_utils.get_wind_rotation_angles(
latitudes_deg=narr_latitude_matrix_deg,
longitudes_deg=narr_longitude_matrix_deg,
model_name=nwp_model_utils.NARR_MODEL_NAME))
num_panels = len(VALID_TIME_STRINGS)
panel_file_names = [''] * num_panels
this_annotation_string = None
for i in range(num_panels):
this_time_unix_sec = time_conversion.string_to_unix_sec(
VALID_TIME_STRINGS[i], DEFAULT_TIME_FORMAT)
this_title_string = time_conversion.unix_sec_to_string(
this_time_unix_sec, NICE_TIME_FORMAT)
if PRESSURE_LEVELS_MB[i] == 1000:
this_title_string += ' at 1000 mb'
else:
this_title_string += ' at surface'
if this_annotation_string is None:
this_annotation_string = '(a)'
else:
this_orig_character = this_annotation_string[1]
this_new_character = chr(ord(this_orig_character) + 1)
this_annotation_string = this_annotation_string.replace(
this_orig_character, this_new_character)
this_file_name = _plot_one_time(
valid_time_string=VALID_TIME_STRINGS[i],
pressure_level_mb=PRESSURE_LEVELS_MB[i],
title_string=this_title_string,
annotation_string=this_annotation_string,
narr_rotation_cos_matrix=narr_rotation_cos_matrix,
narr_rotation_sin_matrix=narr_rotation_sin_matrix)
panel_file_names[i] = this_file_name
print '\n'
concat_file_name = '{0:s}/weird_fronts.jpg'.format(OUTPUT_DIR_NAME)
print 'Concatenating figures to: "{0:s}"...'.format(concat_file_name)
num_panels = len(panel_file_names)
num_panel_columns = int(numpy.ceil(float(num_panels) / 2))
imagemagick_utils.concatenate_images(input_file_names=panel_file_names,
output_file_name=concat_file_name,
num_panel_rows=NUM_PANEL_ROWS,
num_panel_columns=num_panel_columns)
imagemagick_utils.trim_whitespace(input_file_name=concat_file_name,
output_file_name=concat_file_name)
imagemagick_utils.resize_image(input_file_name=concat_file_name,
output_file_name=concat_file_name,
output_size_pixels=CONCAT_SIZE_PIXELS)
def _plot_feature_map_before_conv():
"""Plots original feature map (before convolution).
M = number of rows in grid
N = number of columns in grid
:return: feature_matrix: Feature map as M-by-N numpy array.
"""
feature_matrix = numpy.random.random_integers(
low=MIN_FEATURE_VALUE,
high=MAX_FEATURE_VALUE,
size=(NUM_ROWS_BEFORE_POOLING, NUM_COLUMNS_BEFORE_POOLING))
dummy_matrix = numpy.full(
(NUM_ROWS_BEFORE_POOLING, NUM_COLUMNS_BEFORE_POOLING), numpy.nan)
dummy_matrix[:3, :3] = HIGHLIGHTED_VALUE
dummy_matrix = numpy.ma.masked_where(numpy.isnan(dummy_matrix),
dummy_matrix)
_, axes_object = pyplot.subplots(1,
1,
figsize=(FIGURE_WIDTH_INCHES,
FIGURE_HEIGHT_INCHES))
pyplot.imshow(dummy_matrix,
cmap=COLOUR_MAP_OBJECT,
vmin=HIGHLIGHTED_VALUE - 1,
vmax=HIGHLIGHTED_VALUE,
axes=axes_object,
origin='upper')
pyplot.xticks([], [])
pyplot.yticks([], [])
for i in range(feature_matrix.shape[1]):
for j in range(feature_matrix.shape[0]):
if i == j == 1:
this_colour = SPECIAL_COLOUR + 0.
else:
this_colour = MAIN_COLOUR + 0.
axes_object.text(i,
j,
'{0:d}'.format(feature_matrix[j, i]),
fontsize=OVERLAY_FONT_SIZE,
color=this_colour,
horizontalalignment='center',
verticalalignment='center')
# polygon_x_coords = numpy.array([0, 3, 3, 0, 0], dtype=float) - 0.5
# polygon_y_coords = numpy.array([3, 3, 0, 0, 3], dtype=float) - 0.5
# axes_object.plot(
# polygon_x_coords, polygon_y_coords, color=LINE_COLOUR,
# linewidth=LINE_WIDTH)
plotting_utils.annotate_axes(axes_object=axes_object,
annotation_string='(a)',
font_colour=ANNOTATION_COLOUR)
print 'Saving figure to: "{0:s}"...'.format(BEFORE_CONV_FILE_NAME)
file_system_utils.mkdir_recursive_if_necessary(
file_name=BEFORE_CONV_FILE_NAME)
pyplot.savefig(BEFORE_CONV_FILE_NAME, dpi=OUTPUT_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(input_file_name=BEFORE_CONV_FILE_NAME,
output_file_name=BEFORE_CONV_FILE_NAME)
return feature_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]
num_input_channels = INPUT_FEATURE_MATRIX.shape[2]
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))
this_axes_object_matrix[0, 2].axis('off')
this_axes_object_matrix[2, 2].axis('off')
letter_label = None
for k in range(num_input_channels):
_plot_feature_map(feature_matrix_2d=INPUT_FEATURE_MATRIX[...,
k],
kernel_row=i,
kernel_column=j,
is_output_map=False,
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=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[1, 2])
for k in range(num_input_channels):
this_bbox_object = this_axes_object_matrix[k, 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 += 0.005
this_bbox_object.x1 = this_bbox_object.x0 + this_width
this_bbox_object.y1 = this_bbox_object.y0 + this_height
this_axes_object_matrix[k, 1].set_position(this_bbox_object)
_plot_kernel(kernel_matrix_2d=KERNEL_MATRIX[..., k, 0],
feature_matrix_2d=INPUT_FEATURE_MATRIX[..., k],
feature_row_at_center=i,
feature_column_at_center=j,
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=1.04,
x_coord_normalized=0.2)
_plot_feature_to_kernel_lines(
kernel_matrix_2d=KERNEL_MATRIX[..., k, 0],
feature_matrix_2d=INPUT_FEATURE_MATRIX[..., k],
feature_row_at_center=i,
feature_column_at_center=j,
kernel_axes_object=this_axes_object_matrix[k, 1],
feature_axes_object=this_axes_object_matrix[k, 0])
letter_label = chr(ord(letter_label) + 1)
plotting_utils.label_axes(
axes_object=this_axes_object_matrix[1, 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.,
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)
imagemagick_utils.trim_whitespace(
input_file_name=image_file_names[-1],
output_file_name=image_file_names[-1])
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)
