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 _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_feature_maps_one_layer(feature_matrix, layer_name, output_dir_name):
"""Plots all feature maps for one layer.
E = number of examples
M = number of spatial rows
N = number of spatial columns
C = number of channels
:param feature_matrix: E-by-M-by-N-by-C numpy array of feature values.
:param layer_name: Name of layer that output feature values.
:param output_dir_name: Name of output directory for this layer.
"""
num_examples = feature_matrix.shape[0]
num_channels = feature_matrix.shape[-1]
num_panel_rows = int(numpy.round(numpy.sqrt(num_channels)))
annotation_string_by_channel = [None] * num_channels
# annotation_string_by_channel = [
# 'Filter {0:d}'.format(c + 1) for c in range(num_channels)
# ]
max_colour_value = numpy.percentile(numpy.absolute(feature_matrix), 99)
min_colour_value = -1 * max_colour_value
for i in range(num_examples):
_, these_axes_objects = (
feature_map_plotting.plot_many_2d_feature_maps(
feature_matrix=feature_matrix[i, 2:-2, 2:-2, ...],
annotation_string_by_panel=annotation_string_by_channel,
num_panel_rows=num_panel_rows,
colour_map_object=pyplot.cm.seismic,
min_colour_value=min_colour_value,
max_colour_value=max_colour_value))
plotting_utils.add_linear_colour_bar(
axes_object_or_list=these_axes_objects,
values_to_colour=feature_matrix[i, ...],
colour_map=pyplot.cm.seismic,
colour_min=min_colour_value,
colour_max=max_colour_value,
orientation='horizontal',
extend_min=True,
extend_max=True)
# this_title_string = 'Layer "{0:s}", example {1:d}'.format(layer_name, i)
# pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE)
this_figure_file_name = '{0:s}/example{1:06d}.jpg'.format(
output_dir_name, i)
print 'Saving figure to: "{0:s}"...'.format(this_figure_file_name)
pyplot.savefig(this_figure_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
def _plot_temperature_grid(temperature_matrix_kelvins):
"""Plots temperature grid as colour map.
M = number of rows in grid
N = number of columns in grid
:param temperature_matrix_kelvins: M-by-N numpy array of temperatures.
:return: axes_object: Instance of `matplotlib.axes._subplots.AxesSubplot`.
"""
(edge_temperature_matrix_kelvins, edge_x_coords_metres,
edge_y_coords_metres) = grids.xy_field_grid_points_to_edges(
field_matrix=temperature_matrix_kelvins,
x_min_metres=0.,
y_min_metres=0.,
x_spacing_metres=1.,
y_spacing_metres=1.)
_, axes_object = pyplot.subplots(1,
1,
figsize=(FIGURE_WIDTH_INCHES,
FIGURE_HEIGHT_INCHES))
pyplot.pcolormesh(edge_x_coords_metres,
edge_y_coords_metres,
edge_temperature_matrix_kelvins,
cmap=_create_colour_scheme(temperature_matrix_kelvins),
vmin=numpy.min(temperature_matrix_kelvins),
vmax=numpy.max(temperature_matrix_kelvins),
shading='flat',
edgecolors='None',
axes=axes_object)
colour_bar_object = plotting_utils.add_linear_colour_bar(
axes_object_or_list=axes_object,
values_to_colour=temperature_matrix_kelvins,
colour_map=_create_colour_scheme(temperature_matrix_kelvins),
colour_min=numpy.min(temperature_matrix_kelvins),
colour_max=numpy.max(temperature_matrix_kelvins),
orientation='vertical',
extend_min=False,
extend_max=False)
colour_bar_object.set_ticks([])
colour_bar_object.set_label(r'Thermal variable ($\tau$)')
pyplot.xticks([], [])
pyplot.yticks([], [])
return axes_object
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 _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_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_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_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 _run(example_file_name, top_front_line_dir_name, num_examples,
example_indices, thetaw_colour_map_name, thetaw_max_colour_percentile,
output_dir_name):
"""Plots one or more input examples.
This is effectively the main method.
:param example_file_name: See documentation at top of file.
:param top_front_line_dir_name: Same.
:param num_examples: Same.
:param example_indices: Same.
:param thetaw_colour_map_name: Same.
:param thetaw_max_colour_percentile: Same.
:param output_dir_name: Same.
"""
if num_examples <= 0:
num_examples = None
if num_examples is None:
error_checking.assert_is_geq_numpy_array(example_indices, 0)
else:
error_checking.assert_is_greater(num_examples, 0)
error_checking.assert_is_geq(thetaw_max_colour_percentile, 0)
error_checking.assert_is_leq(thetaw_max_colour_percentile, 100)
thetaw_colour_map_object = pyplot.cm.get_cmap(thetaw_colour_map_name)
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
print 'Reading normalized examples from: "{0:s}"...'.format(
example_file_name)
example_dict = trainval_io.read_downsized_3d_examples(
netcdf_file_name=example_file_name,
num_half_rows_to_keep=NUM_HALF_ROWS,
num_half_columns_to_keep=NUM_HALF_COLUMNS,
predictor_names_to_keep=NARR_PREDICTOR_NAMES)
# TODO(thunderhoser): This is a HACK (assuming that normalization method is
# z-score and not min-max).
mean_value_matrix = example_dict[trainval_io.FIRST_NORM_PARAM_KEY]
standard_deviation_matrix = example_dict[trainval_io.SECOND_NORM_PARAM_KEY]
normalization_dict = {
ml_utils.MIN_VALUE_MATRIX_KEY: None,
ml_utils.MAX_VALUE_MATRIX_KEY: None,
ml_utils.MEAN_VALUE_MATRIX_KEY: mean_value_matrix,
ml_utils.STDEV_MATRIX_KEY: standard_deviation_matrix
}
example_dict[trainval_io.PREDICTOR_MATRIX_KEY] = (
ml_utils.denormalize_predictors(
predictor_matrix=example_dict[trainval_io.PREDICTOR_MATRIX_KEY],
normalization_dict=normalization_dict))
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_examples_total = len(example_dict[trainval_io.TARGET_TIMES_KEY])
example_indices = numpy.linspace(0,
num_examples_total - 1,
num=num_examples_total,
dtype=int)
if num_examples is not None:
num_examples = min([num_examples, num_examples_total])
example_indices = numpy.random.choice(example_indices,
size=num_examples,
replace=False)
thetaw_index = NARR_PREDICTOR_NAMES.index(
processed_narr_io.WET_BULB_THETA_NAME)
u_wind_index = NARR_PREDICTOR_NAMES.index(
processed_narr_io.U_WIND_GRID_RELATIVE_NAME)
v_wind_index = NARR_PREDICTOR_NAMES.index(
processed_narr_io.V_WIND_GRID_RELATIVE_NAME)
for i in example_indices:
this_center_row_index = example_dict[trainval_io.ROW_INDICES_KEY][i]
this_first_row_index = this_center_row_index - NUM_HALF_ROWS
this_last_row_index = this_center_row_index + NUM_HALF_ROWS
this_center_column_index = example_dict[
trainval_io.COLUMN_INDICES_KEY][i]
this_first_column_index = this_center_column_index - NUM_HALF_COLUMNS
this_last_column_index = this_center_column_index + NUM_HALF_COLUMNS
this_u_wind_matrix_m_s01 = example_dict[
trainval_io.PREDICTOR_MATRIX_KEY][i, ..., u_wind_index]
this_v_wind_matrix_m_s01 = example_dict[
trainval_io.PREDICTOR_MATRIX_KEY][i, ..., v_wind_index]
this_cos_matrix = narr_rotation_cos_matrix[this_first_row_index:(
this_last_row_index +
1), this_first_column_index:(this_last_column_index + 1)]
this_sin_matrix = narr_rotation_sin_matrix[this_first_row_index:(
this_last_row_index +
1), this_first_column_index:(this_last_column_index + 1)]
this_u_wind_matrix_m_s01, this_v_wind_matrix_m_s01 = (
nwp_model_utils.rotate_winds_to_earth_relative(
u_winds_grid_relative_m_s01=this_u_wind_matrix_m_s01,
v_winds_grid_relative_m_s01=this_v_wind_matrix_m_s01,
rotation_angle_cosines=this_cos_matrix,
rotation_angle_sines=this_sin_matrix))
_, axes_object, basemap_object = nwp_plotting.init_basemap(
model_name=nwp_model_utils.NARR_MODEL_NAME,
first_row_in_full_grid=this_first_row_index,
last_row_in_full_grid=this_last_row_index,
first_column_in_full_grid=this_first_column_index,
last_column_in_full_grid=this_last_column_index,
resolution_string='i')
plotting_utils.plot_coastlines(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR,
line_width=BORDER_WIDTH)
plotting_utils.plot_countries(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR,
line_width=BORDER_WIDTH)
plotting_utils.plot_states_and_provinces(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR,
line_width=BORDER_WIDTH)
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_thetaw_matrix_kelvins = example_dict[
trainval_io.PREDICTOR_MATRIX_KEY][i, ..., thetaw_index]
this_min_value = numpy.percentile(this_thetaw_matrix_kelvins,
100. - thetaw_max_colour_percentile)
this_max_value = numpy.percentile(this_thetaw_matrix_kelvins,
thetaw_max_colour_percentile)
nwp_plotting.plot_subgrid(
field_matrix=this_thetaw_matrix_kelvins,
model_name=nwp_model_utils.NARR_MODEL_NAME,
axes_object=axes_object,
basemap_object=basemap_object,
colour_map=thetaw_colour_map_object,
min_value_in_colour_map=this_min_value,
max_value_in_colour_map=this_max_value,
first_row_in_full_grid=this_first_row_index,
first_column_in_full_grid=this_first_column_index)
colour_bar_object = plotting_utils.add_linear_colour_bar(
axes_object_or_list=axes_object,
values_to_colour=this_thetaw_matrix_kelvins,
colour_map=thetaw_colour_map_object,
colour_min=this_min_value,
colour_max=this_max_value,
orientation='vertical',
extend_min=True,
extend_max=True,
fraction_of_axis_length=0.8)
colour_bar_object.set_label(
r'Wet-bulb potential temperature ($^{\circ}$C)')
nwp_plotting.plot_wind_barbs_on_subgrid(
u_wind_matrix_m_s01=this_u_wind_matrix_m_s01,
v_wind_matrix_m_s01=this_v_wind_matrix_m_s01,
model_name=nwp_model_utils.NARR_MODEL_NAME,
axes_object=axes_object,
basemap_object=basemap_object,
first_row_in_full_grid=this_first_row_index,
first_column_in_full_grid=this_first_column_index,
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)
this_front_file_name = fronts_io.find_file_for_one_time(
top_directory_name=top_front_line_dir_name,
file_type=fronts_io.POLYLINE_FILE_TYPE,
valid_time_unix_sec=example_dict[trainval_io.TARGET_TIMES_KEY][i])
print time_conversion.unix_sec_to_string(
example_dict[trainval_io.TARGET_TIMES_KEY][i], '%Y-%m-%d-%H')
this_polyline_table = fronts_io.read_polylines_from_file(
this_front_file_name)
this_num_fronts = len(this_polyline_table.index)
for j in range(this_num_fronts):
this_front_type_string = this_polyline_table[
front_utils.FRONT_TYPE_COLUMN].values[j]
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=this_polyline_table[
front_utils.LATITUDES_COLUMN].values[j],
line_longitudes_deg=this_polyline_table[
front_utils.LONGITUDES_COLUMN].values[j],
axes_object=axes_object,
basemap_object=basemap_object,
front_type_string=this_polyline_table[
front_utils.FRONT_TYPE_COLUMN].values[j],
marker_colour=this_colour,
marker_size=FRONT_MARKER_SIZE,
marker_spacing_metres=FRONT_SPACING_METRES)
this_output_file_name = '{0:s}/example{1:06d}.jpg'.format(
output_dir_name, i)
print 'Saving figure to: "{0:s}"...'.format(this_output_file_name)
pyplot.savefig(this_output_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
def plot_performance_diagram(axes_object=None,
pod_by_threshold=None,
success_ratio_by_threshold=None,
line_colour=DEFAULT_PERF_DIAG_LINE_COLOUR,
line_width=DEFAULT_PERF_DIAG_LINE_WIDTH,
bias_line_colour=DEFAULT_FREQ_BIAS_LINE_COLOUR,
bias_line_width=DEFAULT_FREQ_BIAS_LINE_WIDTH,
csi_colour_map=DEFAULT_CSI_COLOUR_MAP):
"""Plots performance diagram.
T = number of binarization thresholds
For the definition of a "binarization threshold" and the role they play in
performance diagrams, see
`model_evaluation.get_points_in_performance_diagram`.
:param axes_object: Instance of `matplotlib.axes._subplots.AxesSubplot`.
:param pod_by_threshold: length-T numpy array of POD (probability of
detection) values.
:param success_ratio_by_threshold: length-T numpy array of success ratios.
:param line_colour: Colour (in any format accepted by `matplotlib.colors`).
:param line_width: Line width (real positive number).
:param bias_line_colour: Colour of contour lines for frequency bias.
:param bias_line_width: Width of contour lines for frequency bias.
:param csi_colour_map: Colour map (instance of `matplotlib.pyplot.cm`) for
CSI (critical success index) contours.
"""
error_checking.assert_is_numpy_array(pod_by_threshold, num_dimensions=1)
error_checking.assert_is_geq_numpy_array(pod_by_threshold,
0.,
allow_nan=True)
error_checking.assert_is_leq_numpy_array(pod_by_threshold,
1.,
allow_nan=True)
num_thresholds = len(pod_by_threshold)
error_checking.assert_is_numpy_array(success_ratio_by_threshold,
exact_dimensions=numpy.array(
[num_thresholds]))
error_checking.assert_is_geq_numpy_array(success_ratio_by_threshold,
0.,
allow_nan=True)
error_checking.assert_is_leq_numpy_array(success_ratio_by_threshold,
1.,
allow_nan=True)
success_ratio_matrix, pod_matrix = model_eval.get_sr_pod_grid()
csi_matrix = model_eval.csi_from_sr_and_pod(success_ratio_matrix,
pod_matrix)
frequency_bias_matrix = model_eval.frequency_bias_from_sr_and_pod(
success_ratio_matrix, pod_matrix)
pyplot.contourf(success_ratio_matrix,
pod_matrix,
csi_matrix,
LEVELS_FOR_CSI_CONTOURS,
cmap=csi_colour_map,
vmin=0.,
vmax=1.,
axes=axes_object)
colour_bar_object = plotting_utils.add_linear_colour_bar(
axes_object,
values_to_colour=csi_matrix,
colour_map=csi_colour_map,
colour_min=0.,
colour_max=1.,
orientation='vertical',
extend_min=False,
extend_max=False)
colour_bar_object.set_label('CSI (critical success index)')
bias_colour_tuple = ()
for _ in range(len(LEVELS_FOR_FREQ_BIAS_CONTOURS)):
bias_colour_tuple += (bias_line_colour, )
bias_contour_object = pyplot.contour(success_ratio_matrix,
pod_matrix,
frequency_bias_matrix,
LEVELS_FOR_FREQ_BIAS_CONTOURS,
colors=bias_colour_tuple,
linewidths=bias_line_width,
linestyles='dashed',
axes=axes_object)
pyplot.clabel(bias_contour_object,
inline=True,
inline_spacing=PIXEL_PADDING_FOR_FREQ_BIAS_LABELS,
fmt=STRING_FORMAT_FOR_FREQ_BIAS_LABELS,
fontsize=FONT_SIZE)
nan_flags = numpy.logical_or(numpy.isnan(success_ratio_by_threshold),
numpy.isnan(pod_by_threshold))
if not numpy.all(nan_flags):
real_indices = numpy.where(numpy.invert(nan_flags))[0]
axes_object.plot(success_ratio_by_threshold[real_indices],
pod_by_threshold[real_indices],
color=line_colour,
linestyle='solid',
linewidth=line_width)
axes_object.set_xlabel('Success ratio (1 - FAR)')
axes_object.set_ylabel('POD (probability of detection)')
axes_object.set_xlim(0., 1.)
axes_object.set_ylim(0., 1.)
def plot_saliency_for_sounding(saliency_matrix,
sounding_field_names,
pressure_levels_mb,
colour_map_object,
max_absolute_colour_value,
min_font_size=DEFAULT_MIN_SOUNDING_FONT_SIZE,
max_font_size=DEFAULT_MAX_SOUNDING_FONT_SIZE):
"""Plots saliency for one sounding.
P = number of pressure levels
F = number of fields
:param saliency_matrix: P-by-F numpy array of saliency values.
:param sounding_field_names: length-F list of field names.
:param pressure_levels_mb: length-P list of pressure levels (millibars).
:param colour_map_object: See doc for `plot_2d_grid`.
:param max_absolute_colour_value: Same.
:param min_font_size: Same.
:param max_font_size: Same.
"""
error_checking.assert_is_greater_numpy_array(pressure_levels_mb, 0.)
error_checking.assert_is_numpy_array(pressure_levels_mb, num_dimensions=1)
error_checking.assert_is_list(sounding_field_names)
error_checking.assert_is_numpy_array(numpy.array(sounding_field_names),
num_dimensions=1)
num_pressure_levels = len(pressure_levels_mb)
num_sounding_fields = len(sounding_field_names)
error_checking.assert_is_numpy_array_without_nan(saliency_matrix)
error_checking.assert_is_numpy_array(saliency_matrix,
exact_dimensions=numpy.array([
num_pressure_levels,
num_sounding_fields
]))
try:
u_wind_index = sounding_field_names.index(soundings.U_WIND_NAME)
v_wind_index = sounding_field_names.index(soundings.V_WIND_NAME)
plot_wind_barbs = True
except ValueError:
plot_wind_barbs = False
if plot_wind_barbs:
u_wind_saliency_values = saliency_matrix[:, u_wind_index]
v_wind_saliency_values = saliency_matrix[:, v_wind_index]
wind_saliency_magnitudes = numpy.sqrt(u_wind_saliency_values**2 +
v_wind_saliency_values**2)
colour_norm_object = pyplot.Normalize(vmin=0.,
vmax=max_absolute_colour_value)
rgb_matrix_for_wind = colour_map_object(
colour_norm_object(wind_saliency_magnitudes))[..., :-1]
non_wind_flags = numpy.array(
[f not in WIND_COMPONENT_NAMES for f in sounding_field_names],
dtype=bool)
non_wind_indices = numpy.where(non_wind_flags)[0]
saliency_matrix = saliency_matrix[:, non_wind_indices]
sounding_field_names = [
sounding_field_names[k] for k in non_wind_indices
]
sounding_field_names.append(WIND_NAME)
num_sounding_fields = len(sounding_field_names)
rgb_matrix, font_size_matrix = _saliency_to_colour_and_size(
saliency_matrix=saliency_matrix,
colour_map_object=colour_map_object,
max_absolute_colour_value=max_absolute_colour_value,
min_font_size=min_font_size,
max_font_size=max_font_size)
_, axes_object = pyplot.subplots(1,
1,
figsize=(FIGURE_WIDTH_INCHES,
FIGURE_HEIGHT_INCHES))
axes_object.set_axis_bgcolor(SOUNDING_SALIENCY_BACKGROUND_COLOUR)
for k in range(num_sounding_fields):
if sounding_field_names[k] == WIND_NAME:
for j in range(num_pressure_levels):
this_vector = numpy.array(
[u_wind_saliency_values[j], v_wind_saliency_values[j]])
this_vector = (WIND_SALIENCY_MULTIPLIER * this_vector /
numpy.linalg.norm(this_vector, ord=2))
axes_object.barbs(k,
pressure_levels_mb[j],
this_vector[0],
this_vector[1],
length=WIND_BARB_LENGTH,
fill_empty=True,
rounding=False,
sizes={'emptybarb': EMPTY_WIND_BARB_RADIUS},
color=rgb_matrix_for_wind[j, ...])
continue
for j in range(num_pressure_levels):
if saliency_matrix[j, k] >= 0:
axes_object.text(k,
pressure_levels_mb[j],
'+',
fontsize=font_size_matrix[j, k],
color=rgb_matrix[j, k, ...],
horizontalalignment='center',
verticalalignment='center')
else:
axes_object.text(k,
pressure_levels_mb[j],
'_',
fontsize=font_size_matrix[j, k],
color=rgb_matrix[j, k, ...],
horizontalalignment='center',
verticalalignment='bottom')
axes_object.set_xlim(-0.5, num_sounding_fields - 0.5)
axes_object.set_ylim(100, 1000)
axes_object.invert_yaxis()
pyplot.yscale('log')
pyplot.minorticks_off()
y_tick_locations = numpy.linspace(100, 1000, num=10, dtype=int)
y_tick_labels = ['{0:d}'.format(p) for p in y_tick_locations]
pyplot.yticks(y_tick_locations, y_tick_labels)
x_tick_locations = numpy.linspace(0,
num_sounding_fields - 1,
num=num_sounding_fields,
dtype=float)
x_tick_labels = [FIELD_NAME_TO_LATEX_DICT[f] for f in sounding_field_names]
pyplot.xticks(x_tick_locations, x_tick_labels)
colour_bar_object = plotting_utils.add_linear_colour_bar(
axes_object_or_list=axes_object,
values_to_colour=saliency_matrix,
colour_map=colour_map_object,
colour_min=0.,
colour_max=max_absolute_colour_value,
orientation='vertical',
extend_min=True,
extend_max=True)
colour_bar_object.set_label('Saliency (absolute value)')
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_feature_maps_one_layer(feature_matrix, storm_ids,
storm_times_unix_sec, layer_name,
output_dir_name):
"""Plots all feature maps for one layer.
E = number of examples (storm objects)
M = number of spatial rows
N = number of spatial columns
H = number of spatial depths (heights)
C = number of channels
:param feature_matrix: numpy array (E x M x N x C or E x M x N x H x C) of
feature maps.
:param storm_ids: length-E list of storm IDs.
:param storm_times_unix_sec: length-E numpy array of storm times.
:param layer_name: Name of layer.
:param output_dir_name: Name of output directory for this layer.
"""
num_spatial_dimensions = len(feature_matrix.shape) - 2
num_storm_objects = feature_matrix.shape[0]
num_channels = feature_matrix.shape[-1]
if num_spatial_dimensions == 3:
num_heights = feature_matrix.shape[-2]
else:
num_heights = None
num_panel_rows = int(numpy.round(numpy.sqrt(num_channels)))
annotation_string_by_channel = [None] * num_channels
# annotation_string_by_channel = [
# 'Filter {0:d}'.format(c + 1) for c in range(num_channels)
# ]
if num_channels >= NUM_PANELS_FOR_NO_FONT:
annotation_string_by_channel = [''] * num_channels
font_size = TINY_FONT_SIZE + 0
elif num_channels >= NUM_PANELS_FOR_TINY_FONT:
font_size = TINY_FONT_SIZE + 0
elif num_channels >= NUM_PANELS_FOR_SMALL_FONT:
font_size = SMALL_FONT_SIZE + 0
else:
font_size = MAIN_FONT_SIZE + 0
max_colour_value = numpy.percentile(numpy.absolute(feature_matrix), 99)
min_colour_value = -1 * max_colour_value
for i in range(num_storm_objects):
this_time_string = time_conversion.unix_sec_to_string(
storm_times_unix_sec[i], TIME_FORMAT)
if num_spatial_dimensions == 2:
_, these_axes_objects = (
feature_map_plotting.plot_many_2d_feature_maps(
feature_matrix=numpy.flip(feature_matrix[i, ...], axis=0),
annotation_string_by_panel=annotation_string_by_channel,
num_panel_rows=num_panel_rows,
colour_map_object=pyplot.cm.seismic,
min_colour_value=min_colour_value,
max_colour_value=max_colour_value,
font_size=font_size))
plotting_utils.add_linear_colour_bar(
axes_object_or_list=these_axes_objects,
values_to_colour=feature_matrix[i, ...],
colour_map=pyplot.cm.seismic,
colour_min=min_colour_value,
colour_max=max_colour_value,
orientation='horizontal',
extend_min=True,
extend_max=True)
this_title_string = 'Layer "{0:s}", storm "{1:s}" at {2:s}'.format(
layer_name, storm_ids[i], this_time_string)
pyplot.suptitle(this_title_string, fontsize=MAIN_FONT_SIZE)
this_figure_file_name = (
'{0:s}/storm={1:s}_{2:s}_features.jpg').format(
output_dir_name, storm_ids[i].replace('_', '-'),
this_time_string)
print 'Saving figure to: "{0:s}"...'.format(this_figure_file_name)
pyplot.savefig(this_figure_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
else:
for k in range(num_heights):
_, these_axes_objects = (
feature_map_plotting.plot_many_2d_feature_maps(
feature_matrix=numpy.flip(feature_matrix[i, :, :,
k, :],
axis=0),
annotation_string_by_panel=annotation_string_by_channel,
num_panel_rows=num_panel_rows,
colour_map_object=pyplot.cm.seismic,
min_colour_value=min_colour_value,
max_colour_value=max_colour_value,
font_size=font_size))
plotting_utils.add_linear_colour_bar(
axes_object_or_list=these_axes_objects,
values_to_colour=feature_matrix[i, :, :, k, :],
colour_map=pyplot.cm.seismic,
colour_min=min_colour_value,
colour_max=max_colour_value,
orientation='horizontal',
extend_min=True,
extend_max=True)
this_title_string = (
'Layer "{0:s}", height {1:d} of {2:d}, storm "{3:s}" at '
'{4:s}').format(layer_name, k + 1, num_heights,
storm_ids[i], this_time_string)
pyplot.suptitle(this_title_string, fontsize=MAIN_FONT_SIZE)
this_figure_file_name = (
'{0:s}/storm={1:s}_{2:s}_features_height{3:02d}.jpg'
).format(output_dir_name, storm_ids[i].replace('_', '-'),
this_time_string, k + 1)
print 'Saving figure to: "{0:s}"...'.format(
this_figure_file_name)
pyplot.savefig(this_figure_file_name,
dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()