def _init_basemap(border_colour):
"""Initializes basemap.
:param border_colour: Colour (in any format accepted by matplotlib) of
political borders.
:return: narr_row_limits: length-2 numpy array of (min, max) NARR rows to
plot.
:return: narr_column_limits: length-2 numpy array of (min, max) NARR columns
to plot.
:return: axes_object: Instance of `matplotlib.axes._subplots.AxesSubplot`.
:return: basemap_object: Instance of `mpl_toolkits.basemap.Basemap`.
"""
(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)
return narr_row_limits, narr_column_limits, axes_object, basemap_object
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 _run(top_narr_dir_name, top_front_line_dir_name, top_wpc_bulletin_dir_name,
first_time_string, last_time_string, pressure_level_mb,
thermal_field_name, thermal_colour_map_name,
max_thermal_prctile_for_colours, first_letter_label, letter_interval,
output_dir_name):
"""Plots predictors on full NARR grid.
This is effectively the main method.
:param top_narr_dir_name: See documentation at top of file.
:param top_front_line_dir_name: Same.
:param top_wpc_bulletin_dir_name: Same.
:param first_time_string: Same.
:param last_time_string: Same.
:param pressure_level_mb: Same.
:param thermal_field_name: Same.
:param thermal_colour_map_name: Same.
:param max_thermal_prctile_for_colours: Same.
:param first_letter_label: Same.
:param letter_interval: Same.
:param output_dir_name: Same.
:raises: ValueError: if
`thermal_field_name not in VALID_THERMAL_FIELD_NAMES`.
"""
# Check input args.
if top_wpc_bulletin_dir_name in ['', 'None']:
top_wpc_bulletin_dir_name = None
if first_letter_label in ['', 'None']:
first_letter_label = None
if thermal_field_name not in VALID_THERMAL_FIELD_NAMES:
error_string = (
'\n{0:s}\nValid thermal fields (listed above) do not include '
'"{1:s}".'
).format(str(VALID_THERMAL_FIELD_NAMES), thermal_field_name)
raise ValueError(error_string)
thermal_colour_map_object = pyplot.cm.get_cmap(thermal_colour_map_name)
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
first_time_unix_sec = time_conversion.string_to_unix_sec(
first_time_string, DEFAULT_TIME_FORMAT)
last_time_unix_sec = time_conversion.string_to_unix_sec(
last_time_string, DEFAULT_TIME_FORMAT)
valid_times_unix_sec = time_periods.range_and_interval_to_list(
start_time_unix_sec=first_time_unix_sec,
end_time_unix_sec=last_time_unix_sec,
time_interval_sec=NARR_TIME_INTERVAL_SEC, include_endpoint=True)
# Read metadata for NARR grid.
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)
)
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)
)
narr_rotation_cos_matrix = narr_rotation_cos_matrix[
narr_row_limits[0]:(narr_row_limits[1] + 1),
narr_column_limits[0]:(narr_column_limits[1] + 1)
]
narr_rotation_sin_matrix = narr_rotation_sin_matrix[
narr_row_limits[0]:(narr_row_limits[1] + 1),
narr_column_limits[0]:(narr_column_limits[1] + 1)
]
# Do plotting.
narr_field_names = [
processed_narr_io.U_WIND_GRID_RELATIVE_NAME,
processed_narr_io.V_WIND_GRID_RELATIVE_NAME,
thermal_field_name
]
this_letter_label = None
for this_time_unix_sec in valid_times_unix_sec:
this_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=this_time_unix_sec)
print 'Reading data from: "{0:s}"...'.format(this_file_name)
this_polyline_table = fronts_io.read_polylines_from_file(this_file_name)
if top_wpc_bulletin_dir_name is None:
this_high_low_table = None
else:
this_file_name = wpc_bulletin_io.find_file(
top_directory_name=top_wpc_bulletin_dir_name,
valid_time_unix_sec=this_time_unix_sec)
print 'Reading data from: "{0:s}"...'.format(this_file_name)
this_high_low_table = wpc_bulletin_io.read_highs_and_lows(
this_file_name)
this_predictor_matrix = None
for this_field_name in narr_field_names:
this_file_name = processed_narr_io.find_file_for_one_time(
top_directory_name=top_narr_dir_name,
field_name=this_field_name,
pressure_level_mb=pressure_level_mb,
valid_time_unix_sec=this_time_unix_sec)
print 'Reading data from: "{0:s}"...'.format(this_file_name)
this_field_matrix = processed_narr_io.read_fields_from_file(
this_file_name
)[0][0, ...]
this_field_matrix = utils.fill_nans(this_field_matrix)
this_field_matrix = this_field_matrix[
narr_row_limits[0]:(narr_row_limits[1] + 1),
narr_column_limits[0]:(narr_column_limits[1] + 1)
]
if this_field_name in [processed_narr_io.TEMPERATURE_NAME,
processed_narr_io.WET_BULB_THETA_NAME]:
this_field_matrix -= ZERO_CELSIUS_IN_KELVINS
if this_field_name == processed_narr_io.SPECIFIC_HUMIDITY_NAME:
this_field_matrix = this_field_matrix * KG_TO_GRAMS
this_field_matrix = numpy.expand_dims(this_field_matrix, axis=-1)
if this_predictor_matrix is None:
this_predictor_matrix = this_field_matrix + 0.
else:
this_predictor_matrix = numpy.concatenate(
(this_predictor_matrix, this_field_matrix), axis=-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)
(this_predictor_matrix[..., u_wind_index],
this_predictor_matrix[..., v_wind_index]
) = nwp_model_utils.rotate_winds_to_earth_relative(
u_winds_grid_relative_m_s01=this_predictor_matrix[
..., u_wind_index],
v_winds_grid_relative_m_s01=this_predictor_matrix[
..., v_wind_index],
rotation_angle_cosines=narr_rotation_cos_matrix,
rotation_angle_sines=narr_rotation_sin_matrix)
this_title_string = time_conversion.unix_sec_to_string(
this_time_unix_sec, NICE_TIME_FORMAT)
if pressure_level_mb == 1013:
this_title_string += ' at surface'
else:
this_title_string += ' at {0:d} mb'.format(pressure_level_mb)
this_default_time_string = time_conversion.unix_sec_to_string(
this_time_unix_sec, DEFAULT_TIME_FORMAT)
this_output_file_name = '{0:s}/predictors_{1:s}.jpg'.format(
output_dir_name, this_default_time_string)
if first_letter_label is not None:
if this_letter_label is None:
this_letter_label = first_letter_label
else:
this_letter_label = chr(
ord(this_letter_label) + letter_interval
)
_plot_one_time(
predictor_matrix=this_predictor_matrix,
predictor_names=narr_field_names,
front_polyline_table=this_polyline_table,
high_low_table=this_high_low_table,
thermal_colour_map_object=thermal_colour_map_object,
max_thermal_prctile_for_colours=max_thermal_prctile_for_colours,
narr_row_limits=narr_row_limits,
narr_column_limits=narr_column_limits,
title_string=this_title_string, letter_label=this_letter_label,
output_file_name=this_output_file_name)
print '\n'
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_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_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_rapruc_one_example(
full_storm_id_string, storm_time_unix_sec, top_tracking_dir_name,
latitude_buffer_deg, longitude_buffer_deg, lead_time_seconds,
field_name_grib1, output_dir_name, rap_file_name=None,
ruc_file_name=None):
"""Plots RAP or RUC field for one example.
:param full_storm_id_string: Full storm ID.
:param storm_time_unix_sec: Valid time.
:param top_tracking_dir_name: See documentation at top of file.
:param latitude_buffer_deg: Same.
:param longitude_buffer_deg: Same.
:param lead_time_seconds: Same.
:param field_name_grib1: Same.
:param output_dir_name: Same.
:param rap_file_name: Path to file with RAP analysis.
:param ruc_file_name: [used only if `rap_file_name is None`]
Path to file with RUC analysis.
"""
tracking_file_name = tracking_io.find_file(
top_tracking_dir_name=top_tracking_dir_name,
tracking_scale_metres2=DUMMY_TRACKING_SCALE_METRES2,
source_name=tracking_utils.SEGMOTION_NAME,
valid_time_unix_sec=storm_time_unix_sec,
spc_date_string=
time_conversion.time_to_spc_date_string(storm_time_unix_sec),
raise_error_if_missing=True
)
print('Reading data from: "{0:s}"...'.format(tracking_file_name))
storm_object_table = tracking_io.read_file(tracking_file_name)
storm_object_table = storm_object_table.loc[
storm_object_table[tracking_utils.FULL_ID_COLUMN] ==
full_storm_id_string
]
extrap_times_sec = numpy.array([0, lead_time_seconds], dtype=int)
storm_object_table = soundings._create_target_points_for_interp(
storm_object_table=storm_object_table,
lead_times_seconds=extrap_times_sec
)
orig_latitude_deg = (
storm_object_table[tracking_utils.CENTROID_LATITUDE_COLUMN].values[0]
)
orig_longitude_deg = (
storm_object_table[tracking_utils.CENTROID_LONGITUDE_COLUMN].values[0]
)
extrap_latitude_deg = (
storm_object_table[tracking_utils.CENTROID_LATITUDE_COLUMN].values[1]
)
extrap_longitude_deg = (
storm_object_table[tracking_utils.CENTROID_LONGITUDE_COLUMN].values[1]
)
if rap_file_name is None:
grib_file_name = ruc_file_name
model_name = nwp_model_utils.RUC_MODEL_NAME
else:
grib_file_name = rap_file_name
model_name = nwp_model_utils.RAP_MODEL_NAME
pathless_grib_file_name = os.path.split(grib_file_name)[-1]
grid_name = pathless_grib_file_name.split('_')[1]
host_name = socket.gethostname()
if 'casper' in host_name:
wgrib_exe_name = '/glade/work/ryanlage/wgrib/wgrib'
wgrib2_exe_name = '/glade/work/ryanlage/wgrib2/wgrib2/wgrib2'
else:
wgrib_exe_name = '/condo/swatwork/ralager/wgrib/wgrib'
wgrib2_exe_name = '/condo/swatwork/ralager/grib2/wgrib2/wgrib2'
print('Reading field "{0:s}" from: "{1:s}"...'.format(
field_name_grib1, grib_file_name
))
main_field_matrix = nwp_model_io.read_field_from_grib_file(
grib_file_name=grib_file_name, field_name_grib1=field_name_grib1,
model_name=model_name, grid_id=grid_name,
wgrib_exe_name=wgrib_exe_name, wgrib2_exe_name=wgrib2_exe_name
)
u_wind_name_grib1 = 'UGRD:{0:s}'.format(
field_name_grib1.split(':')[-1]
)
u_wind_name_grib1 = u_wind_name_grib1.replace('2 m', '10 m')
print('Reading field "{0:s}" from: "{1:s}"...'.format(
u_wind_name_grib1, grib_file_name
))
u_wind_matrix_m_s01 = nwp_model_io.read_field_from_grib_file(
grib_file_name=grib_file_name, field_name_grib1=u_wind_name_grib1,
model_name=model_name, grid_id=grid_name,
wgrib_exe_name=wgrib_exe_name, wgrib2_exe_name=wgrib2_exe_name
)
v_wind_name_grib1 = 'VGRD:{0:s}'.format(
u_wind_name_grib1.split(':')[-1]
)
print('Reading field "{0:s}" from: "{1:s}"...'.format(
v_wind_name_grib1, grib_file_name
))
v_wind_matrix_m_s01 = nwp_model_io.read_field_from_grib_file(
grib_file_name=grib_file_name, field_name_grib1=v_wind_name_grib1,
model_name=model_name, grid_id=grid_name,
wgrib_exe_name=wgrib_exe_name, wgrib2_exe_name=wgrib2_exe_name
)
latitude_matrix_deg, longitude_matrix_deg = (
nwp_model_utils.get_latlng_grid_point_matrices(
model_name=model_name, grid_name=grid_name)
)
cosine_matrix, sine_matrix = nwp_model_utils.get_wind_rotation_angles(
latitudes_deg=latitude_matrix_deg, longitudes_deg=longitude_matrix_deg,
model_name=model_name
)
u_wind_matrix_m_s01, v_wind_matrix_m_s01 = (
nwp_model_utils.rotate_winds_to_earth_relative(
u_winds_grid_relative_m_s01=u_wind_matrix_m_s01,
v_winds_grid_relative_m_s01=v_wind_matrix_m_s01,
rotation_angle_cosines=cosine_matrix,
rotation_angle_sines=sine_matrix)
)
min_plot_latitude_deg = (
min([orig_latitude_deg, extrap_latitude_deg]) - latitude_buffer_deg
)
max_plot_latitude_deg = (
max([orig_latitude_deg, extrap_latitude_deg]) + latitude_buffer_deg
)
min_plot_longitude_deg = (
min([orig_longitude_deg, extrap_longitude_deg]) - longitude_buffer_deg
)
max_plot_longitude_deg = (
max([orig_longitude_deg, extrap_longitude_deg]) + longitude_buffer_deg
)
row_limits, column_limits = nwp_plotting.latlng_limits_to_rowcol_limits(
min_latitude_deg=min_plot_latitude_deg,
max_latitude_deg=max_plot_latitude_deg,
min_longitude_deg=min_plot_longitude_deg,
max_longitude_deg=max_plot_longitude_deg,
model_name=model_name, grid_id=grid_name
)
main_field_matrix = main_field_matrix[
row_limits[0]:(row_limits[1] + 1),
column_limits[0]:(column_limits[1] + 1)
]
u_wind_matrix_m_s01 = u_wind_matrix_m_s01[
row_limits[0]:(row_limits[1] + 1),
column_limits[0]:(column_limits[1] + 1)
]
v_wind_matrix_m_s01 = v_wind_matrix_m_s01[
row_limits[0]:(row_limits[1] + 1),
column_limits[0]:(column_limits[1] + 1)
]
_, axes_object, basemap_object = nwp_plotting.init_basemap(
model_name=model_name, grid_id=grid_name,
first_row_in_full_grid=row_limits[0],
last_row_in_full_grid=row_limits[1],
first_column_in_full_grid=column_limits[0],
last_column_in_full_grid=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,
num_parallels=NUM_PARALLELS
)
plotting_utils.plot_meridians(
basemap_object=basemap_object, axes_object=axes_object,
num_meridians=NUM_MERIDIANS
)
min_colour_value = numpy.nanpercentile(
main_field_matrix, 100. - MAX_COLOUR_PERCENTILE
)
max_colour_value = numpy.nanpercentile(
main_field_matrix, MAX_COLOUR_PERCENTILE
)
nwp_plotting.plot_subgrid(
field_matrix=main_field_matrix,
model_name=model_name, grid_id=grid_name,
axes_object=axes_object, basemap_object=basemap_object,
colour_map_object=COLOUR_MAP_OBJECT, min_colour_value=min_colour_value,
max_colour_value=max_colour_value,
first_row_in_full_grid=row_limits[0],
first_column_in_full_grid=column_limits[0]
)
nwp_plotting.plot_wind_barbs_on_subgrid(
u_wind_matrix_m_s01=u_wind_matrix_m_s01,
v_wind_matrix_m_s01=v_wind_matrix_m_s01,
model_name=model_name, grid_id=grid_name,
axes_object=axes_object, basemap_object=basemap_object,
first_row_in_full_grid=row_limits[0],
first_column_in_full_grid=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=True, colour_map=WIND_COLOUR_MAP_OBJECT,
colour_minimum_kt=MIN_WIND_SPEED_KT, colour_maximum_kt=MAX_WIND_SPEED_KT
)
orig_x_metres, orig_y_metres = basemap_object(
orig_longitude_deg, orig_latitude_deg
)
axes_object.plot(
orig_x_metres, orig_y_metres, linestyle='None',
marker=ORIGIN_MARKER_TYPE, markersize=ORIGIN_MARKER_SIZE,
markeredgewidth=ORIGIN_MARKER_EDGE_WIDTH,
markerfacecolor=MARKER_COLOUR, markeredgecolor=MARKER_COLOUR
)
extrap_x_metres, extrap_y_metres = basemap_object(
extrap_longitude_deg, extrap_latitude_deg
)
axes_object.plot(
extrap_x_metres, extrap_y_metres, linestyle='None',
marker=EXTRAP_MARKER_TYPE, markersize=EXTRAP_MARKER_SIZE,
markeredgewidth=EXTRAP_MARKER_EDGE_WIDTH,
markerfacecolor=MARKER_COLOUR, markeredgecolor=MARKER_COLOUR
)
plotting_utils.plot_linear_colour_bar(
axes_object_or_matrix=axes_object, data_matrix=main_field_matrix,
colour_map_object=COLOUR_MAP_OBJECT,
min_value=min_colour_value, max_value=max_colour_value,
orientation_string='vertical'
)
output_file_name = '{0:s}/{1:s}_{2:s}.jpg'.format(
output_dir_name, full_storm_id_string.replace('_', '-'),
time_conversion.unix_sec_to_string(
storm_time_unix_sec, FILE_NAME_TIME_FORMAT
)
)
print('Saving figure to: "{0:s}"...'.format(output_file_name))
pyplot.savefig(
output_file_name, dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0, bbox_inches='tight'
)
pyplot.close()
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_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)
