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 _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 _plot_binary_table():
"""Plots binary contingency table."""
_, axes_object = pyplot.subplots(1,
1,
figsize=(FIGURE_WIDTH_INCHES,
FIGURE_HEIGHT_INCHES))
pyplot.imshow(numpy.zeros((2, 2)),
cmap=COLOUR_MAP_OBJECT,
vmin=LARGE_NUMBER,
vmax=LARGE_NUMBER + 1,
axes=axes_object,
origin='upper')
tick_locations = numpy.array([0, 1], dtype=int)
tick_labels = ['Yes', 'No']
pyplot.xticks(tick_locations, tick_labels)
pyplot.xlabel('Actual')
pyplot.yticks(tick_locations, tick_labels)
pyplot.ylabel('Predicted')
for i in range(2):
for j in range(2):
axes_object.text(i,
j,
str(BINARY_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='(a)',
font_colour=ANNOTATION_COLOUR)
print 'Saving figure to: "{0:s}"...'.format(BINARY_CT_FILE_NAME)
file_system_utils.mkdir_recursive_if_necessary(
file_name=BINARY_CT_FILE_NAME)
pyplot.savefig(BINARY_CT_FILE_NAME, dpi=OUTPUT_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(input_file_name=BINARY_CT_FILE_NAME,
output_file_name=BINARY_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 _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_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_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_fronts(front_line_table, ternary_front_matrix, title_string,
annotation_string, output_file_name):
"""Plots one set of WPC fronts (either before or after dilation).
:param front_line_table: See doc for `fronts_io.write_polylines_to_file`.
:param ternary_front_matrix: numpy array created by
`machine_learning_utils.dilate_ternary_target_images`.
: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 = ternary_front_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,
first_row_in_narr_grid=narr_row_limits[0],
first_column_in_narr_grid=narr_column_limits[0],
basemap_object=basemap_object,
opacity=FRONT_LINE_OPACITY)
num_fronts = len(front_line_table.index)
for i in range(num_fronts):
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)
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_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_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_observations_one_time(
valid_time_string, title_string, annotation_string, output_file_name):
"""Plots observations (NARR predictors and WPC fronts) for one valid time.
:param valid_time_string: Valid time (format "yyyy-mm-dd-HH").
: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)
valid_time_unix_sec = time_conversion.string_to_unix_sec(
valid_time_string, INPUT_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 = TOP_NARR_WIND_DIR_NAME + ''
else:
this_directory_name = TOP_NARR_DIR_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])
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='vertical',
extend_min=True, extend_max=True,
fraction_of_axis_length=LENGTH_FRACTION_FOR_THETA_COLOUR_BAR)
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=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,
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)
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_histogram(num_fronts_by_bin, front_type_string, title_string,
annotation_string, output_file_name):
"""Plots histogram of either cold-front or warm-front lengths.
B = number of bins
:param num_fronts_by_bin: length-B numpy array with number of fronts in each
bin.
:param front_type_string: Front type (must be accepted by
`front_utils.check_front_type`).
:param title_string: Figure title (will be placed above the 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).
"""
bin_frequencies = (num_fronts_by_bin.astype(float) /
numpy.sum(num_fronts_by_bin))
num_bins = len(num_fronts_by_bin)
bin_edges_metres = numpy.linspace(MIN_HISTOGRAM_LENGTH_METRES,
MAX_HISTOGRAM_LENGTH_METRES,
num=num_bins + 1)
bin_width_metres = bin_edges_metres[1] - bin_edges_metres[0]
bin_centers_metres = bin_edges_metres[:-1] + bin_width_metres / 2
_, axes_object = pyplot.subplots(1,
1,
figsize=(FIGURE_WIDTH_INCHES,
FIGURE_HEIGHT_INCHES))
if front_type_string == front_utils.WARM_FRONT_STRING_ID:
this_colour = front_plotting.DEFAULT_WARM_FRONT_COLOUR
else:
this_colour = front_plotting.DEFAULT_COLD_FRONT_COLOUR
axes_object.bar(bin_centers_metres * METRES_TO_KM,
bin_frequencies,
bin_width_metres * METRES_TO_KM,
color=this_colour,
edgecolor=LINE_COLOUR,
linewidth=LINE_WIDTH)
max_y_tick_value = rounder.floor_to_nearest(
1.05 * numpy.max(bin_frequencies), Y_TICK_SPACING)
num_y_ticks = 1 + int(numpy.round(max_y_tick_value / Y_TICK_SPACING))
y_tick_values = numpy.linspace(0., max_y_tick_value, num=num_y_ticks)
pyplot.yticks(y_tick_values)
axes_object.set_xlim(MIN_HISTOGRAM_LENGTH_METRES * METRES_TO_KM,
MAX_HISTOGRAM_LENGTH_METRES * METRES_TO_KM)
axes_object.set_ylim(0., 1.05 * numpy.max(bin_frequencies))
x_tick_values, _ = pyplot.xticks()
x_tick_labels = []
for i in range(len(x_tick_values)):
this_label = '{0:d}'.format(int(numpy.round(x_tick_values[i])))
if i == len(x_tick_values) - 1:
this_label += '+'
x_tick_labels.append(this_label)
pyplot.xticks(x_tick_values, x_tick_labels)
pyplot.xlabel('Front length (km)')
pyplot.ylabel('Frequency')
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=OUTPUT_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(input_file_name=output_file_name,
output_file_name=output_file_name)
def _plot_front_densities(num_fronts_matrix,
colour_map_object,
title_string,
annotation_string,
output_file_name,
mask_matrix=None,
add_colour_bar=True):
"""Plots number of fronts at each NARR grid cell.
M = number of grid rows (unique y-coordinates at grid points)
N = number of grid columns (unique x-coordinates at grid points)
:param num_fronts_matrix: M-by-N numpy array with number of fronts at each
grid cell.
:param colour_map_object: Instance of `matplotlib.pyplot.cm`.
: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 (image) file. The figure will be
saved here.
:param mask_matrix: M-by-N numpy array of integers. If
mask_matrix[i, j] = 0, grid cell [i, j] will be masked out in the map.
If `mask_matrix is None`, there will be no masking.
:param add_colour_bar: Boolean flag. If True, will add colour bar.
"""
_, axes_object, basemap_object = narr_plotting.init_basemap()
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_fronts_matrix = num_fronts_matrix.astype(float)
max_colour_value = numpy.percentile(num_fronts_matrix,
MAX_COLOUR_PERCENTILE)
if mask_matrix is not None:
num_fronts_matrix[mask_matrix == 0] = numpy.nan
narr_plotting.plot_xy_grid(data_matrix=num_fronts_matrix,
axes_object=axes_object,
basemap_object=basemap_object,
colour_map=colour_map_object,
colour_minimum=0.,
colour_maximum=max_colour_value)
if add_colour_bar:
plotting_utils.add_linear_colour_bar(
axes_object_or_list=axes_object,
values_to_colour=num_fronts_matrix,
colour_map=colour_map_object,
colour_min=0.,
colour_max=max_colour_value,
orientation='horizontal',
extend_min=False,
extend_max=True)
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=OUTPUT_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 _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 _plot_one_time(predicted_region_table,
title_string,
letter_label,
output_file_name,
class_probability_matrix=None,
predicted_label_matrix=None,
plot_wf_colour_bar=True,
plot_cf_colour_bar=True):
"""Plots predictions at one time.
Either `class_probability_matrix` or `predicted_label_matrix` will be
plotted -- not both.
M = number of rows in NARR grid
N = number of columns in NARR grid
:param predicted_region_table: Subset of pandas DataFrame returned by
`object_eval.read_predictions_and_obs`, containing predicted fronts at
only one time.
: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.
:param class_probability_matrix: M-by-N-by-3 numpy array of class
probabilities.
:param predicted_label_matrix: M-by-N numpy array of predicted labels
(integers in `front_utils.VALID_INTEGER_IDS`).
:param plot_wf_colour_bar: Boolean flag. If True, will plot colour bar for
warm-front probability.
:param plot_cf_colour_bar: Boolean flag. If True, will plot colour bar 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[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=0.25)
else:
this_wf_probability_matrix = class_probability_matrix[
narr_row_limits[0]:(narr_row_limits[1] + 1),
narr_column_limits[0]:(narr_column_limits[1] + 1),
front_utils.WARM_FRONT_INTEGER_ID]
this_wf_probability_matrix[numpy.isnan(
this_wf_probability_matrix)] = 0.
prediction_plotting.plot_narr_grid(
probability_matrix=this_wf_probability_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=0.5)
this_cf_probability_matrix = class_probability_matrix[
narr_row_limits[0]:(narr_row_limits[1] + 1),
narr_column_limits[0]:(narr_column_limits[1] + 1),
front_utils.COLD_FRONT_INTEGER_ID]
this_cf_probability_matrix[numpy.isnan(
this_cf_probability_matrix)] = 0.
prediction_plotting.plot_narr_grid(
probability_matrix=this_cf_probability_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=0.5)
if plot_wf_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=this_wf_probability_matrix,
orientation='horizontal',
extend_min=True,
extend_max=False,
fraction_of_axis_length=0.9)
if plot_cf_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=this_cf_probability_matrix,
orientation='horizontal',
extend_min=True,
extend_max=False,
fraction_of_axis_length=0.9)
narr_latitude_matrix_deg, narr_longitude_matrix_deg = (
nwp_model_utils.get_latlng_grid_point_matrices(
model_name=nwp_model_utils.NARR_MODEL_NAME))
num_objects = len(predicted_region_table.index)
for i in range(num_objects):
these_rows = predicted_region_table[
object_eval.ROW_INDICES_COLUMN].values[i]
these_columns = predicted_region_table[
object_eval.COLUMN_INDICES_COLUMN].values[i]
front_plotting.plot_polyline(
latitudes_deg=narr_latitude_matrix_deg[these_rows, these_columns],
longitudes_deg=narr_longitude_matrix_deg[these_rows,
these_columns],
axes_object=axes_object,
basemap_object=basemap_object,
front_type=predicted_region_table[
front_utils.FRONT_TYPE_COLUMN].values[i],
line_width=4)
# predicted_object_matrix = object_eval.regions_to_images(
# predicted_region_table=predicted_region_table,
# num_grid_rows=num_grid_rows, num_grid_columns=num_grid_columns)
#
# this_matrix = predicted_object_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)
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)
