def _read_actual_polylines(top_input_dir_name, unix_times_sec): """Reads actual fronts (polylines) for each time step. :param top_input_dir_name: See documentation at top of file. :param unix_times_sec: 1-D numpy array of valid times. :return: polyline_table: See doc for `fronts_io.write_polylines_to_file`. """ list_of_polyline_tables = [] for this_time_unix_sec in unix_times_sec: this_file_name = fronts_io.find_file_for_one_time( top_directory_name=top_input_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) list_of_polyline_tables.append( fronts_io.read_polylines_from_file(this_file_name) ) if len(list_of_polyline_tables) == 1: continue list_of_polyline_tables[-1] = list_of_polyline_tables[-1].align( list_of_polyline_tables[0], axis=1)[0] # print 'Removing fronts in masked area...' # return front_utils.remove_polylines_in_masked_area( # polyline_table=polyline_table, narr_mask_matrix=narr_mask_matrix) return pandas.concat( list_of_polyline_tables, axis=0, ignore_index=True)
def test_find_file_for_one_time_grids(self): """Ensures correct output from find_file_for_one_time. In this case, looking for file with NARR grids. """ this_file_name = fronts_io.find_file_for_one_time( top_directory_name=DIRECTORY_NAME, file_type=fronts_io.GRIDDED_FILE_TYPE, valid_time_unix_sec=VALID_TIME_UNIX_SEC, raise_error_if_missing=False) self.assertTrue(this_file_name == GRIDDED_FILE_NAME_ONE_TIME)
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 _run(): """Plots input example. This is effectively the main method. :return: figure_file_name: Path to output file (where the figure was saved). """ valid_time_unix_sec = time_conversion.string_to_unix_sec( VALID_TIME_STRING, TIME_FORMAT) front_file_name = fronts_io.find_file_for_one_time( top_directory_name=TOP_FRONT_DIR_NAME, file_type=fronts_io.POLYLINE_FILE_TYPE, valid_time_unix_sec=valid_time_unix_sec) print 'Reading data from: "{0:s}"...'.format(front_file_name) front_line_table = fronts_io.read_polylines_from_file(front_file_name) num_narr_fields = len(NARR_FIELD_NAMES) narr_matrix_by_field = [numpy.array([])] * num_narr_fields for j in range(num_narr_fields): if NARR_FIELD_NAMES[j] in WIND_FIELD_NAMES: this_directory_name = '{0:s}/earth_relative_wind'.format( TOP_NARR_DIRECTORY_NAME) else: this_directory_name = TOP_NARR_DIRECTORY_NAME + '' this_file_name = processed_narr_io.find_file_for_one_time( top_directory_name=this_directory_name, field_name=NARR_FIELD_NAMES[j], pressure_level_mb=PRESSURE_LEVEL_MB, valid_time_unix_sec=valid_time_unix_sec) print 'Reading data from: "{0:s}"...'.format(this_file_name) narr_matrix_by_field[j] = processed_narr_io.read_fields_from_file( this_file_name)[0][0, ...] narr_matrix_by_field[j] = utils.fill_nans(narr_matrix_by_field[j]) if NARR_FIELD_NAMES[j] == processed_narr_io.WET_BULB_THETA_NAME: narr_matrix_by_field[j] = (narr_matrix_by_field[j] - ZERO_CELSIUS_IN_KELVINS) # (_, front_centroid_latitude_deg, front_centroid_longitude_deg # ) = _find_nearest_front( # front_line_table=front_line_table, # query_latitude_deg=APPROX_FRONT_LATITUDE_DEG, # query_longitude_deg=APPROX_FRONT_LONGITUDE_DEG) front_centroid_latitude_deg = APPROX_FRONT_LATITUDE_DEG + 0. front_centroid_longitude_deg = APPROX_FRONT_LONGITUDE_DEG + 0. projection_object = nwp_model_utils.init_model_projection( nwp_model_utils.NARR_MODEL_NAME) these_x_metres, these_y_metres = nwp_model_utils.project_latlng_to_xy( latitudes_deg=numpy.array([front_centroid_latitude_deg]), longitudes_deg=numpy.array([front_centroid_longitude_deg]), projection_object=projection_object, model_name=nwp_model_utils.NARR_MODEL_NAME) front_centroid_x_metres = these_x_metres[0] front_centroid_y_metres = these_y_metres[0] grid_spacing_metres, _ = nwp_model_utils.get_xy_grid_spacing( model_name=nwp_model_utils.NARR_MODEL_NAME) center_narr_row_index = int( numpy.round(front_centroid_y_metres / grid_spacing_metres)) center_narr_column_index = int( numpy.round(front_centroid_x_metres / grid_spacing_metres)) first_narr_row_index = center_narr_row_index - NUM_ROWS_IN_HALF_GRID last_narr_row_index = center_narr_row_index + NUM_ROWS_IN_HALF_GRID first_narr_column_index = (center_narr_column_index - NUM_COLUMNS_IN_HALF_GRID) last_narr_column_index = center_narr_column_index + NUM_COLUMNS_IN_HALF_GRID for j in range(num_narr_fields): narr_matrix_by_field[j] = narr_matrix_by_field[j][ first_narr_row_index:(last_narr_row_index + 1), first_narr_column_index:(last_narr_column_index + 1)] _, axes_object, basemap_object = nwp_plotting.init_basemap( model_name=nwp_model_utils.NARR_MODEL_NAME, first_row_in_full_grid=first_narr_row_index, last_row_in_full_grid=last_narr_row_index, first_column_in_full_grid=first_narr_column_index, last_column_in_full_grid=last_narr_column_index, resolution_string='i') plotting_utils.plot_coastlines(basemap_object=basemap_object, axes_object=axes_object, line_colour=BORDER_COLOUR) plotting_utils.plot_countries(basemap_object=basemap_object, axes_object=axes_object, line_colour=BORDER_COLOUR) plotting_utils.plot_states_and_provinces(basemap_object=basemap_object, axes_object=axes_object, line_colour=BORDER_COLOUR) plotting_utils.plot_parallels(basemap_object=basemap_object, axes_object=axes_object, bottom_left_lat_deg=-90., upper_right_lat_deg=90., parallel_spacing_deg=PARALLEL_SPACING_DEG) plotting_utils.plot_meridians(basemap_object=basemap_object, axes_object=axes_object, bottom_left_lng_deg=0., upper_right_lng_deg=360., meridian_spacing_deg=MERIDIAN_SPACING_DEG) for j in range(num_narr_fields): if NARR_FIELD_NAMES[j] in WIND_FIELD_NAMES: continue min_colour_value = numpy.percentile(narr_matrix_by_field[j], MIN_COLOUR_PERCENTILE) max_colour_value = numpy.percentile(narr_matrix_by_field[j], MAX_COLOUR_PERCENTILE) nwp_plotting.plot_subgrid( field_matrix=narr_matrix_by_field[j], model_name=nwp_model_utils.NARR_MODEL_NAME, axes_object=axes_object, basemap_object=basemap_object, colour_map=THERMAL_COLOUR_MAP_OBJECT, min_value_in_colour_map=min_colour_value, max_value_in_colour_map=max_colour_value, first_row_in_full_grid=first_narr_row_index, first_column_in_full_grid=first_narr_column_index) plotting_utils.add_linear_colour_bar( axes_object_or_list=axes_object, values_to_colour=narr_matrix_by_field[j], colour_map=THERMAL_COLOUR_MAP_OBJECT, colour_min=min_colour_value, colour_max=max_colour_value, orientation='horizontal', extend_min=True, extend_max=True) u_wind_index = NARR_FIELD_NAMES.index( processed_narr_io.U_WIND_EARTH_RELATIVE_NAME) v_wind_index = NARR_FIELD_NAMES.index( processed_narr_io.V_WIND_EARTH_RELATIVE_NAME) nwp_plotting.plot_wind_barbs_on_subgrid( u_wind_matrix_m_s01=narr_matrix_by_field[u_wind_index], v_wind_matrix_m_s01=narr_matrix_by_field[v_wind_index], model_name=nwp_model_utils.NARR_MODEL_NAME, axes_object=axes_object, basemap_object=basemap_object, first_row_in_full_grid=first_narr_row_index, first_column_in_full_grid=first_narr_column_index, plot_every_k_rows=PLOT_EVERY_KTH_WIND_BARB, plot_every_k_columns=PLOT_EVERY_KTH_WIND_BARB, barb_length=WIND_BARB_LENGTH, empty_barb_radius=EMPTY_WIND_BARB_RADIUS, colour_map=WIND_COLOUR_MAP_OBJECT, colour_minimum_kt=MIN_COLOUR_WIND_SPEED_KT, colour_maximum_kt=MAX_COLOUR_WIND_SPEED_KT) num_fronts = len(front_line_table.index) for i in range(num_fronts): this_front_type_string = front_line_table[ front_utils.FRONT_TYPE_COLUMN].values[i] if this_front_type_string == front_utils.WARM_FRONT_STRING_ID: this_colour = WARM_FRONT_COLOUR else: this_colour = COLD_FRONT_COLOUR # front_plotting.plot_polyline( # latitudes_deg=front_line_table[ # front_utils.LATITUDES_COLUMN].values[i], # longitudes_deg=front_line_table[ # front_utils.LONGITUDES_COLUMN].values[i], # basemap_object=basemap_object, axes_object=axes_object, # front_type=front_line_table[ # front_utils.FRONT_TYPE_COLUMN].values[i], # line_width=FRONT_LINE_WIDTH, line_colour=this_colour) print 'Saving figure to: "{0:s}"...'.format(OUTPUT_FILE_NAME) file_system_utils.mkdir_recursive_if_necessary(file_name=OUTPUT_FILE_NAME) pyplot.savefig(OUTPUT_FILE_NAME, dpi=OUTPUT_RESOLUTION_DPI) pyplot.close() imagemagick_utils.trim_whitespace(input_file_name=OUTPUT_FILE_NAME, output_file_name=OUTPUT_FILE_NAME)
def _plot_one_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 _process_wpc_bulletins(first_time_string, last_time_string, top_bulletin_dir_name, top_polyline_dir_name, top_frontal_grid_dir_name): """Turns warm/cold fronts from WPC bulletins into polylines and NARR grids. :param first_time_string: Time (format "yyyymmddHH"). This script turns warm/cold fronts into polylines and NARR grids for all 3-hour time steps from `first_time_string`...`last_time_string`. :param last_time_string: See above. :param top_bulletin_dir_name: [input] Name of top-level directory with WPC bulletins. :param top_polyline_dir_name: [output] Name of top-level directory for Pickle files with frontal polylines. :param top_frontal_grid_dir_name: [output] Name of top-level directory for Pickle files with frontal grids. """ first_time_unix_sec = time_conversion.string_to_unix_sec( first_time_string, INPUT_TIME_FORMAT) last_time_unix_sec = time_conversion.string_to_unix_sec( last_time_string, INPUT_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=TIME_INTERVAL_SECONDS, include_endpoint=True) num_times = len(valid_times_unix_sec) for i in range(num_times): this_bulletin_file_name = wpc_bulletin_io.find_file( valid_time_unix_sec=valid_times_unix_sec[i], top_directory_name=top_bulletin_dir_name, raise_error_if_missing=False) if not os.path.isfile(this_bulletin_file_name): warning_string = 'Cannot find file. Expected at: "{0:s}"'.format( this_bulletin_file_name) warnings.warn(warning_string) continue print 'Reading data from: "{0:s}"...'.format(this_bulletin_file_name) this_polyline_table = wpc_bulletin_io.read_fronts_from_file( this_bulletin_file_name) this_polyline_file_name = fronts_io.find_file_for_one_time( top_directory_name=top_polyline_dir_name, file_type=fronts_io.POLYLINE_FILE_TYPE, valid_time_unix_sec=valid_times_unix_sec[i], raise_error_if_missing=False) print 'Writing polylines to file: "{0:s}"...'.format( this_polyline_file_name) fronts_io.write_polylines_to_file( front_table=this_polyline_table, pickle_file_name=this_polyline_file_name) print 'Converting polylines to NARR grids...' this_frontal_grid_table = front_utils.many_polylines_to_narr_grid( polyline_table=this_polyline_table, dilation_distance_metres=DILATION_DISTANCE_METRES) this_gridded_file_name = fronts_io.find_file_for_one_time( top_directory_name=top_frontal_grid_dir_name, file_type=fronts_io.GRIDDED_FILE_TYPE, valid_time_unix_sec=valid_times_unix_sec[i], raise_error_if_missing=False) print 'Writing NARR grids to file: "{0:s}"...\n'.format( this_gridded_file_name) fronts_io.write_narr_grids_to_file( frontal_grid_table=this_frontal_grid_table, pickle_file_name=this_gridded_file_name)
def _run(top_frontal_grid_dir_name, first_time_string, last_time_string, dilation_distance_metres, min_num_fronts, output_dir_name): """Creates mask, indicating where human forecasters usually draw fronts. This is effectively the main method. :param top_frontal_grid_dir_name: See documentation at top of file. :param first_time_string: Same. :param last_time_string: Same. :param dilation_distance_metres: Same. :param min_num_fronts: Same. :param output_dir_name: Same. """ file_system_utils.mkdir_recursive_if_necessary( directory_name=output_dir_name) error_checking.assert_is_greater(min_num_fronts, 0) first_time_unix_sec = time_conversion.string_to_unix_sec( first_time_string, INPUT_TIME_FORMAT) last_time_unix_sec = time_conversion.string_to_unix_sec( last_time_string, INPUT_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=TIME_INTERVAL_SECONDS) num_times = len(valid_times_unix_sec) num_grid_rows, num_grid_columns = nwp_model_utils.get_grid_dimensions( model_name=nwp_model_utils.NARR_MODEL_NAME) num_cold_fronts_matrix = None num_warm_fronts_matrix = None for i in range(num_times): this_file_name = fronts_io.find_file_for_one_time( top_directory_name=top_frontal_grid_dir_name, file_type=fronts_io.GRIDDED_FILE_TYPE, valid_time_unix_sec=valid_times_unix_sec[i], raise_error_if_missing=False) if not os.path.isfile(this_file_name): warning_string = ('POTENTIAL PROBLEM. Cannot find file: "{0:s}"' ).format(this_file_name) warnings.warn(warning_string) continue print 'Reading data from: "{0:s}"...'.format(this_file_name) this_frontal_grid_table = fronts_io.read_narr_grids_from_file( this_file_name) this_frontal_grid_matrix = ml_utils.front_table_to_images( frontal_grid_table=this_frontal_grid_table, num_rows_per_image=num_grid_rows, num_columns_per_image=num_grid_columns) this_frontal_grid_matrix = ml_utils.dilate_ternary_target_images( target_matrix=this_frontal_grid_matrix, dilation_distance_metres=dilation_distance_metres, verbose=False) this_frontal_grid_matrix = this_frontal_grid_matrix[0, ...] this_num_cold_fronts_matrix = (this_frontal_grid_matrix == front_utils. COLD_FRONT_INTEGER_ID).astype(int) this_num_warm_fronts_matrix = (this_frontal_grid_matrix == front_utils. WARM_FRONT_INTEGER_ID).astype(int) if num_cold_fronts_matrix is None: num_cold_fronts_matrix = this_num_cold_fronts_matrix + 0 num_warm_fronts_matrix = this_num_warm_fronts_matrix + 0 else: num_cold_fronts_matrix = (num_cold_fronts_matrix + this_num_cold_fronts_matrix) num_warm_fronts_matrix = (num_warm_fronts_matrix + this_num_warm_fronts_matrix) print SEPARATOR_STRING print 'Masking out grid cells with < {0:d} fronts...'.format( min_num_fronts) num_both_fronts_matrix = num_warm_fronts_matrix + num_cold_fronts_matrix mask_matrix = (num_both_fronts_matrix >= min_num_fronts).astype(int) pickle_file_name = '{0:s}/narr_mask.p'.format(output_dir_name) print 'Writing mask to: "{0:s}"...'.format(pickle_file_name) ml_utils.write_narr_mask(mask_matrix=mask_matrix, pickle_file_name=pickle_file_name) warm_front_map_file_name = '{0:s}/num_warm_fronts.jpg'.format( output_dir_name) _plot_front_densities(num_fronts_matrix=num_warm_fronts_matrix, colour_map_object=WARM_FRONT_COLOUR_MAP_OBJECT, title_string='Number of warm fronts', annotation_string='(a)', output_file_name=warm_front_map_file_name, mask_matrix=None, add_colour_bar=True) cold_front_map_file_name = '{0:s}/num_cold_fronts.jpg'.format( output_dir_name) _plot_front_densities(num_fronts_matrix=num_cold_fronts_matrix, colour_map_object=COLD_FRONT_COLOUR_MAP_OBJECT, title_string='Number of cold fronts', annotation_string='(b)', output_file_name=cold_front_map_file_name, mask_matrix=None, add_colour_bar=True) both_fronts_title_string = 'Grid cells with at least {0:d} fronts'.format( min_num_fronts) both_fronts_map_file_name = '{0:s}/num_both_fronts.jpg'.format( output_dir_name) num_both_fronts_matrix[num_both_fronts_matrix > 1] = 1 _plot_front_densities(num_fronts_matrix=num_both_fronts_matrix, colour_map_object=BOTH_FRONTS_COLOUR_MAP_OBJECT, title_string=both_fronts_title_string, annotation_string='(c)', output_file_name=both_fronts_map_file_name, mask_matrix=mask_matrix, add_colour_bar=False)