def _plot_saliency_for_2d3d_radar( list_of_input_matrices, list_of_saliency_matrices, training_option_dict, saliency_colour_map_object, max_colour_value_by_example, output_dir_name, storm_ids=None, storm_times_unix_sec=None): """Plots saliency for 2-D azimuthal-shear and 3-D reflectivity fields. E = number of examples (storm objects) If `storm_ids is None` and `storm_times_unix_sec is None`, will assume that the input matrices contain probability-matched means. :param list_of_input_matrices: See doc for `saliency_maps.read_standard_file`. :param list_of_saliency_matrices: Same. :param training_option_dict: Dictionary returned by `cnn.read_model_metadata`. :param saliency_colour_map_object: See documentation at top of file. :param max_colour_value_by_example: length-E numpy array with max value in colour scheme for each example. Minimum value for [i]th example will be -1 * max_colour_value_by_example[i], since the colour scheme is zero-centered and divergent. :param output_dir_name: Name of output directory (figures will be saved here). :param storm_ids: length-E list of storm IDs (strings). :param storm_times_unix_sec: length-E numpy array of storm times. """ pmm_flag = storm_ids is None and storm_times_unix_sec is None reflectivity_matrix_dbz = list_of_input_matrices[0] reflectivity_saliency_matrix = list_of_saliency_matrices[0] az_shear_matrix_s01 = list_of_input_matrices[1] az_shear_saliency_matrix = list_of_saliency_matrices[1] num_examples = reflectivity_matrix_dbz.shape[0] num_reflectivity_heights = len( training_option_dict[trainval_io.RADAR_HEIGHTS_KEY] ) num_panel_rows_for_reflectivity = int(numpy.floor( numpy.sqrt(num_reflectivity_heights) )) az_shear_field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY] num_az_shear_fields = len(az_shear_field_names) plot_colour_bar_flags = numpy.full(num_az_shear_fields, False, dtype=bool) for i in range(num_examples): _, these_axes_objects = radar_plotting.plot_3d_grid_without_coords( field_matrix=numpy.flip(reflectivity_matrix_dbz[i, ..., 0], axis=0), field_name=radar_utils.REFL_NAME, grid_point_heights_metres=training_option_dict[ trainval_io.RADAR_HEIGHTS_KEY], ground_relative=True, num_panel_rows=num_panel_rows_for_reflectivity, font_size=FONT_SIZE_SANS_COLOUR_BARS) saliency_plotting.plot_many_2d_grids_with_pm_signs( saliency_matrix_3d=numpy.flip( reflectivity_saliency_matrix[i, ..., 0], axis=0), axes_objects_2d_list=these_axes_objects, colour_map_object=saliency_colour_map_object, max_absolute_colour_value=max_colour_value_by_example[i]) this_colour_map_object, this_colour_norm_object = ( radar_plotting.get_default_colour_scheme(radar_utils.REFL_NAME) ) plotting_utils.add_colour_bar( axes_object_or_list=these_axes_objects, values_to_colour=reflectivity_matrix_dbz[i, ..., 0], colour_map=this_colour_map_object, colour_norm_object=this_colour_norm_object, orientation='horizontal', extend_min=True, extend_max=True) if pmm_flag: this_title_string = 'Probability-matched mean' this_file_name = '{0:s}/saliency_pmm_reflectivity.jpg'.format( output_dir_name) else: this_storm_time_string = time_conversion.unix_sec_to_string( storm_times_unix_sec[i], TIME_FORMAT) this_title_string = 'Storm "{0:s}" at {1:s}'.format( storm_ids[i], this_storm_time_string) this_file_name = ( '{0:s}/saliency_{1:s}_{2:s}_reflectivity.jpg' ).format( output_dir_name, storm_ids[i].replace('_', '-'), this_storm_time_string ) this_title_string += ' (max absolute saliency = {0:.3f})'.format( max_colour_value_by_example[i]) pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE) print 'Saving figure to file: "{0:s}"...'.format(this_file_name) pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI) pyplot.close() _, these_axes_objects = ( radar_plotting.plot_many_2d_grids_without_coords( field_matrix=numpy.flip(az_shear_matrix_s01[i, ...], axis=0), field_name_by_panel=az_shear_field_names, panel_names=az_shear_field_names, num_panel_rows=1, plot_colour_bar_by_panel=plot_colour_bar_flags, font_size=FONT_SIZE_SANS_COLOUR_BARS) ) saliency_plotting.plot_many_2d_grids_with_pm_signs( saliency_matrix_3d=numpy.flip( az_shear_saliency_matrix[i, ...], axis=0), axes_objects_2d_list=these_axes_objects, colour_map_object=saliency_colour_map_object, max_absolute_colour_value=max_colour_value_by_example[i]) this_colour_map_object, this_colour_norm_object = ( radar_plotting.get_default_colour_scheme( radar_utils.LOW_LEVEL_SHEAR_NAME) ) plotting_utils.add_colour_bar( axes_object_or_list=these_axes_objects, values_to_colour=az_shear_saliency_matrix[i, ...], colour_map=this_colour_map_object, colour_norm_object=this_colour_norm_object, orientation='horizontal', extend_min=True, extend_max=True) pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE) this_file_name = this_file_name.replace( '_reflectivity.jpg', '_azimuthal-shear.jpg') print 'Saving figure to file: "{0:s}"...'.format(this_file_name) pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI) pyplot.close()
def _plot_3d_radar(training_option_dict, output_dir_name, pmm_flag, diff_colour_map_object=None, max_colour_percentile_for_diff=None, full_id_strings=None, storm_time_strings=None, novel_radar_matrix=None, novel_radar_matrix_upconv=None, novel_radar_matrix_upconv_svd=None): """Plots results of novelty detection for 3-D radar fields. E = number of examples (storm objects) M = number of rows in spatial grid N = number of columns in spatial grid H = number of heights in spatial grid F = number of fields If `novel_radar_matrix` is the only matrix given, this method will plot the original (not reconstructed) radar fields. If `novel_radar_matrix_upconv` is the only matrix given, will plot upconvnet-reconstructed fields. If `novel_radar_matrix_upconv_svd` is the only matrix given, will plot upconvnet-and-SVD-reconstructed fields. If both `novel_radar_matrix_upconv` and `novel_radar_matrix_upconv_svd` are given, will plot novelty fields (upconvnet/SVD reconstruction minus upconvnet reconstruction). :param training_option_dict: See doc for `cnn.read_model_metadata`. :param output_dir_name: Name of output directory (figures will be saved here). :param pmm_flag: Boolean flag. If True, the input matrices contain probability-matched means. :param diff_colour_map_object: [used only if both `novel_radar_matrix_upconv` and `novel_radar_matrix_upconv_svd` are given] See documentation at top of file. :param max_colour_percentile_for_diff: Same. :param full_id_strings: [optional and used only if `pmm_flag = False`] length-E list of full storm IDs. :param storm_time_strings: [optional and used only if `pmm_flag = False`] length-E list of storm times. :param novel_radar_matrix: E-by-M-by-N-by-H-by-F numpy array of original (not reconstructed) radar fields. :param novel_radar_matrix_upconv: E-by-M-by-N-by-H-by-F numpy array of upconvnet-reconstructed radar fields. :param novel_radar_matrix_upconv_svd: E-by-M-by-N-by-H-by-F numpy array of upconvnet-and-SVD-reconstructed radar fields. """ if pmm_flag: have_storm_ids = False else: have_storm_ids = not (full_id_strings is None or storm_time_strings is None) plot_difference = False if novel_radar_matrix is not None: plot_type_abbrev = 'actual' plot_type_verbose = 'actual' radar_matrix_to_plot = novel_radar_matrix else: if (novel_radar_matrix_upconv is not None and novel_radar_matrix_upconv_svd is not None): plot_difference = True plot_type_abbrev = 'novelty' plot_type_verbose = 'novelty' radar_matrix_to_plot = (novel_radar_matrix_upconv - novel_radar_matrix_upconv_svd) else: if novel_radar_matrix_upconv is not None: plot_type_abbrev = 'upconv' plot_type_verbose = 'upconvnet reconstruction' radar_matrix_to_plot = novel_radar_matrix_upconv else: plot_type_abbrev = 'upconv-svd' plot_type_verbose = 'upconvnet/SVD reconstruction' radar_matrix_to_plot = novel_radar_matrix_upconv_svd radar_field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY] radar_heights_m_agl = training_option_dict[trainval_io.RADAR_HEIGHTS_KEY] num_storms = novel_radar_matrix.shape[0] num_heights = novel_radar_matrix.shape[-2] num_panel_rows = int(numpy.floor(numpy.sqrt(num_heights))) for i in range(num_storms): if pmm_flag: this_title_string = 'Probability-matched mean' this_base_file_name = 'pmm' else: if have_storm_ids: this_title_string = 'Storm "{0:s}" at {1:s}'.format( full_id_strings[i], storm_time_strings[i]) this_base_file_name = '{0:s}_{1:s}'.format( full_id_strings[i].replace('_', '-'), storm_time_strings[i]) else: this_title_string = 'Example {0:d}'.format(i + 1) this_base_file_name = 'example{0:06d}'.format(i) this_title_string += ' ({0:s})'.format(plot_type_verbose) for j in range(len(radar_field_names)): this_file_name = '{0:s}/{1:s}_{2:s}_{3:s}.jpg'.format( output_dir_name, this_base_file_name, plot_type_abbrev, radar_field_names[j].replace('_', '-')) if plot_difference: this_colour_map_object = diff_colour_map_object this_max_value = numpy.percentile( numpy.absolute(radar_matrix_to_plot[i, ..., j]), max_colour_percentile_for_diff) this_colour_norm_object = matplotlib.colors.Normalize( vmin=-1 * this_max_value, vmax=this_max_value, clip=False) else: this_colour_map_object, this_colour_norm_object = ( radar_plotting.get_default_colour_scheme( radar_field_names[j])) _, this_axes_object_matrix = ( radar_plotting.plot_3d_grid_without_coords( field_matrix=numpy.flip(radar_matrix_to_plot[i, ..., j], axis=0), field_name=radar_field_names[j], grid_point_heights_metres=radar_heights_m_agl, ground_relative=True, num_panel_rows=num_panel_rows, font_size=FONT_SIZE_SANS_COLOUR_BARS, colour_map_object=this_colour_map_object, colour_norm_object=this_colour_norm_object)) plotting_utils.plot_colour_bar( axes_object_or_matrix=this_axes_object_matrix, data_matrix=radar_matrix_to_plot[i, ..., j], colour_map_object=this_colour_map_object, colour_norm_object=this_colour_norm_object, orientation_string='horizontal', extend_min=True, extend_max=True) pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE) print('Saving figure to: "{0:s}"...'.format(this_file_name)) pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI) pyplot.close()
def _plot_storm_outlines_one_time(storm_object_table, valid_time_unix_sec, axes_object, basemap_object, storm_colour, storm_opacity, include_secondary_ids, output_dir_name, radar_matrix=None, radar_field_name=None, radar_latitudes_deg=None, radar_longitudes_deg=None): """Plots storm outlines (and may underlay radar data) at one time step. M = number of rows in radar grid N = number of columns in radar grid K = number of storm objects :param storm_object_table: See doc for `storm_plotting.plot_storm_outlines`. :param valid_time_unix_sec: Will plot storm outlines only at this time. Will plot tracks up to and including this time. :param axes_object: Same. :param basemap_object: Same. :param storm_colour: Same. :param storm_opacity: Same. :param include_secondary_ids: Same. :param output_dir_name: See documentation at top of file. :param radar_matrix: M-by-N numpy array of radar values. If `radar_matrix is None`, radar data will simply not be plotted. :param radar_field_name: [used only if `radar_matrix is not None`] See documentation at top of file. :param radar_latitudes_deg: [used only if `radar_matrix is not None`] length-M numpy array of grid-point latitudes (deg N). :param radar_longitudes_deg: [used only if `radar_matrix is not None`] length-N numpy array of grid-point longitudes (deg E). """ min_plot_latitude_deg = basemap_object.llcrnrlat max_plot_latitude_deg = basemap_object.urcrnrlat min_plot_longitude_deg = basemap_object.llcrnrlon max_plot_longitude_deg = basemap_object.urcrnrlon 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) if radar_matrix is not None: good_indices = numpy.where( numpy.logical_and(radar_latitudes_deg >= min_plot_latitude_deg, radar_latitudes_deg <= max_plot_latitude_deg))[0] radar_latitudes_deg = radar_latitudes_deg[good_indices] radar_matrix = radar_matrix[good_indices, :] good_indices = numpy.where( numpy.logical_and( radar_longitudes_deg >= min_plot_longitude_deg, radar_longitudes_deg <= max_plot_longitude_deg))[0] radar_longitudes_deg = radar_longitudes_deg[good_indices] radar_matrix = radar_matrix[:, good_indices] latitude_spacing_deg = radar_latitudes_deg[1] - radar_latitudes_deg[0] longitude_spacing_deg = (radar_longitudes_deg[1] - radar_longitudes_deg[0]) radar_plotting.plot_latlng_grid( field_matrix=radar_matrix, field_name=radar_field_name, axes_object=axes_object, min_grid_point_latitude_deg=numpy.min(radar_latitudes_deg), min_grid_point_longitude_deg=numpy.min(radar_longitudes_deg), latitude_spacing_deg=latitude_spacing_deg, longitude_spacing_deg=longitude_spacing_deg) colour_map_object, colour_norm_object = ( radar_plotting.get_default_colour_scheme(radar_field_name)) latitude_range_deg = max_plot_latitude_deg - min_plot_latitude_deg longitude_range_deg = max_plot_longitude_deg - min_plot_longitude_deg if latitude_range_deg > longitude_range_deg: orientation_string = 'vertical' else: orientation_string = 'horizontal' colour_bar_object = plotting_utils.plot_colour_bar( axes_object_or_matrix=axes_object, data_matrix=radar_matrix, colour_map_object=colour_map_object, colour_norm_object=colour_norm_object, orientation_string=orientation_string, extend_min=radar_field_name in radar_plotting.SHEAR_VORT_DIV_NAMES, extend_max=True, fraction_of_axis_length=0.9) colour_bar_object.set_label( radar_plotting.FIELD_NAME_TO_VERBOSE_DICT[radar_field_name]) valid_time_rows = numpy.where(storm_object_table[ tracking_utils.VALID_TIME_COLUMN].values == valid_time_unix_sec)[0] line_colour = matplotlib.colors.to_rgba(storm_colour, storm_opacity) storm_plotting.plot_storm_outlines( storm_object_table=storm_object_table.iloc[valid_time_rows], axes_object=axes_object, basemap_object=basemap_object, line_colour=line_colour) storm_plotting.plot_storm_ids( storm_object_table=storm_object_table.iloc[valid_time_rows], axes_object=axes_object, basemap_object=basemap_object, plot_near_centroids=False, include_secondary_ids=include_secondary_ids, font_colour=storm_plotting.DEFAULT_FONT_COLOUR) storm_plotting.plot_storm_tracks(storm_object_table=storm_object_table, axes_object=axes_object, basemap_object=basemap_object, colour_map_object=None, line_colour=TRACK_COLOUR) nice_time_string = time_conversion.unix_sec_to_string( valid_time_unix_sec, NICE_TIME_FORMAT) abbrev_time_string = time_conversion.unix_sec_to_string( valid_time_unix_sec, FILE_NAME_TIME_FORMAT) pyplot.title('Storm objects at {0:s}'.format(nice_time_string)) output_file_name = '{0:s}/storm_outlines_{1:s}.jpg'.format( output_dir_name, abbrev_time_string) print('Saving figure to: "{0:s}"...'.format(output_file_name)) pyplot.savefig(output_file_name, dpi=FIGURE_RESOLUTION_DPI) pyplot.close() imagemagick_utils.trim_whitespace(input_file_name=output_file_name, output_file_name=output_file_name)
def _plot_saliency_for_3d_radar( radar_matrix, radar_saliency_matrix, model_metadata_dict, saliency_colour_map_object, max_colour_value_by_example, output_dir_name, storm_ids=None, storm_times_unix_sec=None): """Plots saliency for 3-D radar fields. E = number of examples M = number of rows in spatial grid N = number of columns in spatial grid H = number of heights in spatial grid F = number of fields If `storm_ids is None` and `storm_times_unix_sec is None`, will assume that the input matrices contain probability-matched means. :param radar_matrix: E-by-M-by-N-by-H-by-F numpy array of radar values (predictors). :param radar_saliency_matrix: E-by-M-by-N-by-H-by-F numpy array of saliency values. :param model_metadata_dict: See doc for `cnn.read_model_metadata`. :param saliency_colour_map_object: See doc for `_plot_saliency_for_2d3d_radar`. :param max_colour_value_by_example: Same. :param output_dir_name: Same. :param storm_ids: Same. :param storm_times_unix_sec: Same. """ pmm_flag = storm_ids is None and storm_times_unix_sec is None training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY] num_examples = radar_matrix.shape[0] num_fields = radar_matrix.shape[-1] num_heights = len(training_option_dict[trainval_io.RADAR_HEIGHTS_KEY]) num_panel_rows = int(numpy.floor(numpy.sqrt(num_heights))) for i in range(num_examples): for k in range(num_fields): this_field_name = training_option_dict[ trainval_io.RADAR_FIELDS_KEY][k] _, these_axes_objects = ( radar_plotting.plot_3d_grid_without_coords( field_matrix=numpy.flip(radar_matrix[i, ..., k], axis=0), field_name=this_field_name, grid_point_heights_metres=training_option_dict[ trainval_io.RADAR_HEIGHTS_KEY], ground_relative=True, num_panel_rows=num_panel_rows, font_size=FONT_SIZE_SANS_COLOUR_BARS) ) saliency_plotting.plot_many_2d_grids_with_pm_signs( saliency_matrix_3d=numpy.flip( radar_saliency_matrix[i, ..., k], axis=0), axes_objects_2d_list=these_axes_objects, colour_map_object=saliency_colour_map_object, max_absolute_colour_value=max_colour_value_by_example[i]) this_colour_map_object, this_colour_norm_object = ( radar_plotting.get_default_colour_scheme(this_field_name) ) plotting_utils.add_colour_bar( axes_object_or_list=these_axes_objects, values_to_colour=radar_matrix[i, ..., k], colour_map=this_colour_map_object, colour_norm_object=this_colour_norm_object, orientation='horizontal', extend_min=True, extend_max=True) if pmm_flag: this_title_string = 'Probability-matched mean' this_file_name = '{0:s}/saliency_pmm_{1:s}.jpg'.format( output_dir_name, this_field_name.replace('_', '-') ) else: this_storm_time_string = time_conversion.unix_sec_to_string( storm_times_unix_sec[i], TIME_FORMAT) this_title_string = 'Storm "{0:s}" at {1:s}'.format( storm_ids[i], this_storm_time_string) this_file_name = '{0:s}/saliency_{1:s}_{2:s}_{3:s}.jpg'.format( output_dir_name, storm_ids[i].replace('_', '-'), this_storm_time_string, this_field_name.replace('_', '-') ) this_title_string += ' (max absolute saliency = {0:.3f})'.format( max_colour_value_by_example[i]) pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE) print 'Saving figure to file: "{0:s}"...'.format(this_file_name) pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI) pyplot.close()
def _plot_3d_radar_cams( radar_matrix, model_metadata_dict, cam_colour_map_object, max_colour_prctile_for_cam, output_dir_name, class_activation_matrix=None, ggradcam_output_matrix=None, storm_ids=None, storm_times_unix_sec=None): """Plots class-activation maps for 3-D radar data. E = number of examples M = number of rows in spatial grid N = number of columns in spatial grid H = number of heights in spatial grid F = number of radar fields This method will plot either `class_activation_matrix` or `ggradcam_output_matrix`, not both. If `storm_ids is None` and `storm_times_unix_sec is None`, will assume that the input matrices contain probability-matched means. :param radar_matrix: E-by-M-by-N-by-H-by-F numpy array of radar values. :param model_metadata_dict: Dictionary with CNN metadata (see doc for `cnn.read_model_metadata`). :param cam_colour_map_object: See documentation at top of file. :param max_colour_prctile_for_cam: Same. :param output_dir_name: Same. :param class_activation_matrix: E-by-M-by-N-by-H numpy array of class activations. :param ggradcam_output_matrix: E-by-M-by-N-by-H-by-F numpy array of output values from guided Grad-CAM. :param storm_ids: length-E list of storm IDs (strings). :param storm_times_unix_sec: length-E numpy array of storm times. """ pmm_flag = storm_ids is None and storm_times_unix_sec is None num_examples = radar_matrix.shape[0] num_heights = radar_matrix.shape[-2] num_fields = radar_matrix.shape[-1] num_panel_rows = int(numpy.floor(numpy.sqrt(num_heights))) if class_activation_matrix is None: quantity_string = 'max absolute guided Grad-CAM output' pathless_file_name_prefix = 'guided-gradcam' else: quantity_string = 'max class activation' pathless_file_name_prefix = 'gradcam' training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY] for i in range(num_examples): for k in range(num_fields): this_field_name = training_option_dict[ trainval_io.RADAR_FIELDS_KEY][k] _, these_axes_objects = radar_plotting.plot_3d_grid_without_coords( field_matrix=numpy.flip(radar_matrix[i, ..., k], axis=0), field_name=this_field_name, grid_point_heights_metres=training_option_dict[ trainval_io.RADAR_HEIGHTS_KEY], ground_relative=True, num_panel_rows=num_panel_rows, font_size=FONT_SIZE_SANS_COLOUR_BARS) if class_activation_matrix is None: this_matrix = ggradcam_output_matrix[i, ..., k] this_max_contour_level = numpy.percentile( numpy.absolute(this_matrix), max_colour_prctile_for_cam) if this_max_contour_level == 0: this_max_contour_level = 10. saliency_plotting.plot_many_2d_grids_with_contours( saliency_matrix_3d=numpy.flip(this_matrix, axis=0), axes_objects_2d_list=these_axes_objects, colour_map_object=cam_colour_map_object, max_absolute_contour_level=this_max_contour_level, contour_interval=this_max_contour_level / 10) else: this_matrix = class_activation_matrix[i, ...] this_max_contour_level = numpy.percentile( this_matrix, max_colour_prctile_for_cam) if this_max_contour_level == 0: this_max_contour_level = 10. cam_plotting.plot_many_2d_grids( class_activation_matrix_3d=numpy.flip(this_matrix, axis=0), axes_objects_2d_list=these_axes_objects, colour_map_object=cam_colour_map_object, max_contour_level=this_max_contour_level, contour_interval=this_max_contour_level / NUM_CONTOURS) this_colour_map_object, this_colour_norm_object = ( radar_plotting.get_default_colour_scheme(this_field_name) ) plotting_utils.add_colour_bar( axes_object_or_list=these_axes_objects, values_to_colour=radar_matrix[i, ..., k], colour_map=this_colour_map_object, colour_norm_object=this_colour_norm_object, orientation='horizontal', extend_min=True, extend_max=True) if pmm_flag: this_title_string = 'Probability-matched mean' this_figure_file_name = '{0:s}/{1:s}_pmm_{2:s}.jpg'.format( output_dir_name, pathless_file_name_prefix, this_field_name.replace('_', '-') ) else: this_storm_time_string = time_conversion.unix_sec_to_string( storm_times_unix_sec[i], TIME_FORMAT) this_title_string = 'Storm "{0:s}" at {1:s}'.format( storm_ids[i], this_storm_time_string) this_figure_file_name = ( '{0:s}/{1:s}_{2:s}_{3:s}_{4:s}.jpg' ).format( output_dir_name, pathless_file_name_prefix, storm_ids[i].replace('_', '-'), this_storm_time_string, this_field_name.replace('_', '-') ) this_title_string += ' ({0:s} = {1:.3f})'.format( quantity_string, this_max_contour_level) pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE) print 'Saving figure to file: "{0:s}"...'.format( this_figure_file_name) pyplot.savefig(this_figure_file_name, dpi=FIGURE_RESOLUTION_DPI) pyplot.close()
def _plot_storm_outlines_one_time(storm_object_table, valid_time_unix_sec, warning_table, axes_object, basemap_object, storm_outline_colour, storm_outline_opacity, include_secondary_ids, output_dir_name, primary_id_to_track_colour=None, radar_matrix=None, radar_field_name=None, radar_latitudes_deg=None, radar_longitudes_deg=None, radar_colour_map_object=None): """Plots storm outlines (and may underlay radar data) at one time step. M = number of rows in radar grid N = number of columns in radar grid K = number of storm objects If `primary_id_to_track_colour is None`, all storm tracks will be the same colour. :param storm_object_table: See doc for `storm_plotting.plot_storm_outlines`. :param valid_time_unix_sec: Will plot storm outlines only at this time. Will plot tracks up to and including this time. :param warning_table: None or a pandas table with the following columns. warning_table.start_time_unix_sec: Start time. warning_table.end_time_unix_sec: End time. warning_table.polygon_object_latlng: Polygon (instance of `shapely.geometry.Polygon`) with lat-long coordinates of warning boundary. :param axes_object: See doc for `storm_plotting.plot_storm_outlines`. :param basemap_object: Same. :param storm_outline_colour: Same. :param storm_outline_opacity: Same. :param include_secondary_ids: Same. :param output_dir_name: See documentation at top of file. :param primary_id_to_track_colour: Dictionary created by `_assign_colours_to_storms`. If this is None, all storm tracks will be the same colour. :param radar_matrix: M-by-N numpy array of radar values. If `radar_matrix is None`, radar data will simply not be plotted. :param radar_field_name: [used only if `radar_matrix is not None`] See documentation at top of file. :param radar_latitudes_deg: [used only if `radar_matrix is not None`] length-M numpy array of grid-point latitudes (deg N). :param radar_longitudes_deg: [used only if `radar_matrix is not None`] length-N numpy array of grid-point longitudes (deg E). :param radar_colour_map_object: [used only if `radar_matrix is not None`] Colour map (instance of `matplotlib.pyplot.cm`). If None, will use default for the given field. """ # plot_storm_ids = radar_matrix is None or radar_colour_map_object is None plot_storm_ids = False min_plot_latitude_deg = basemap_object.llcrnrlat max_plot_latitude_deg = basemap_object.urcrnrlat min_plot_longitude_deg = basemap_object.llcrnrlon max_plot_longitude_deg = basemap_object.urcrnrlon 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) if radar_matrix is not None: custom_colour_map = radar_colour_map_object is not None good_indices = numpy.where( numpy.logical_and(radar_latitudes_deg >= min_plot_latitude_deg, radar_latitudes_deg <= max_plot_latitude_deg))[0] radar_latitudes_deg = radar_latitudes_deg[good_indices] radar_matrix = radar_matrix[good_indices, :] good_indices = numpy.where( numpy.logical_and( radar_longitudes_deg >= min_plot_longitude_deg, radar_longitudes_deg <= max_plot_longitude_deg))[0] radar_longitudes_deg = radar_longitudes_deg[good_indices] radar_matrix = radar_matrix[:, good_indices] latitude_spacing_deg = radar_latitudes_deg[1] - radar_latitudes_deg[0] longitude_spacing_deg = (radar_longitudes_deg[1] - radar_longitudes_deg[0]) if radar_colour_map_object is None: colour_map_object, colour_norm_object = ( radar_plotting.get_default_colour_scheme(radar_field_name)) else: colour_map_object = radar_colour_map_object colour_norm_object = radar_plotting.get_default_colour_scheme( radar_field_name)[-1] this_ratio = radar_plotting._field_to_plotting_units( field_matrix=1., field_name=radar_field_name) colour_norm_object = pyplot.Normalize( colour_norm_object.vmin / this_ratio, colour_norm_object.vmax / this_ratio) radar_plotting.plot_latlng_grid( field_matrix=radar_matrix, field_name=radar_field_name, axes_object=axes_object, min_grid_point_latitude_deg=numpy.min(radar_latitudes_deg), min_grid_point_longitude_deg=numpy.min(radar_longitudes_deg), latitude_spacing_deg=latitude_spacing_deg, longitude_spacing_deg=longitude_spacing_deg, colour_map_object=colour_map_object, colour_norm_object=colour_norm_object) latitude_range_deg = max_plot_latitude_deg - min_plot_latitude_deg longitude_range_deg = max_plot_longitude_deg - min_plot_longitude_deg if latitude_range_deg > longitude_range_deg: orientation_string = 'vertical' else: orientation_string = 'horizontal' colour_bar_object = plotting_utils.plot_colour_bar( axes_object_or_matrix=axes_object, data_matrix=radar_matrix, colour_map_object=colour_map_object, colour_norm_object=colour_norm_object, orientation_string=orientation_string, padding=0.05, extend_min=radar_field_name in radar_plotting.SHEAR_VORT_DIV_NAMES, extend_max=True, fraction_of_axis_length=1.) radar_field_name_verbose = radar_utils.field_name_to_verbose( field_name=radar_field_name, include_units=True) radar_field_name_verbose = radar_field_name_verbose.replace( 'm ASL', 'kft ASL') colour_bar_object.set_label(radar_field_name_verbose) if custom_colour_map: tick_values = colour_bar_object.get_ticks() tick_label_strings = ['{0:.1f}'.format(v) for v in tick_values] colour_bar_object.set_ticks(tick_values) colour_bar_object.set_ticklabels(tick_label_strings) valid_time_rows = numpy.where(storm_object_table[ tracking_utils.VALID_TIME_COLUMN].values == valid_time_unix_sec)[0] this_colour = matplotlib.colors.to_rgba(storm_outline_colour, storm_outline_opacity) storm_plotting.plot_storm_outlines( storm_object_table=storm_object_table.iloc[valid_time_rows], axes_object=axes_object, basemap_object=basemap_object, line_colour=this_colour) if plot_storm_ids: storm_plotting.plot_storm_ids( storm_object_table=storm_object_table.iloc[valid_time_rows], axes_object=axes_object, basemap_object=basemap_object, plot_near_centroids=False, include_secondary_ids=include_secondary_ids, font_colour=storm_plotting.DEFAULT_FONT_COLOUR) if warning_table is not None: warning_indices = numpy.where( numpy.logical_and( warning_table[WARNING_START_TIME_KEY].values <= valid_time_unix_sec, warning_table[WARNING_END_TIME_KEY].values >= valid_time_unix_sec))[0] for k in warning_indices: this_vertex_dict = polygons.polygon_object_to_vertex_arrays( warning_table[WARNING_LATLNG_POLYGON_KEY].values[k]) these_latitudes_deg = this_vertex_dict[polygons.EXTERIOR_Y_COLUMN] these_longitudes_deg = this_vertex_dict[polygons.EXTERIOR_X_COLUMN] these_latitude_flags = numpy.logical_and( these_latitudes_deg >= min_plot_latitude_deg, these_latitudes_deg <= max_plot_latitude_deg) these_longitude_flags = numpy.logical_and( these_longitudes_deg >= min_plot_longitude_deg, these_longitudes_deg <= max_plot_longitude_deg) these_coord_flags = numpy.logical_and(these_latitude_flags, these_longitude_flags) if not numpy.any(these_coord_flags): continue these_x_metres, these_y_metres = basemap_object( these_longitudes_deg, these_latitudes_deg) axes_object.plot(these_x_metres, these_y_metres, color=this_colour, linestyle='dashed', linewidth=storm_plotting.DEFAULT_POLYGON_WIDTH) axes_object.text(numpy.mean(these_x_metres), numpy.mean(these_y_metres), 'W{0:d}'.format(k), fontsize=storm_plotting.DEFAULT_FONT_SIZE, fontweight='bold', color=this_colour, horizontalalignment='center', verticalalignment='center') these_sec_id_strings = ( warning_table[LINKED_SECONDARY_IDS_KEY].values[k]) if len(these_sec_id_strings) == 0: continue these_object_indices = numpy.array([], dtype=int) for this_sec_id_string in these_sec_id_strings: these_subindices = numpy.where( storm_object_table[tracking_utils.SECONDARY_ID_COLUMN]. values[valid_time_rows] == this_sec_id_string)[0] these_object_indices = numpy.concatenate( (these_object_indices, valid_time_rows[these_subindices])) for i in these_object_indices: this_vertex_dict = polygons.polygon_object_to_vertex_arrays( storm_object_table[ tracking_utils.LATLNG_POLYGON_COLUMN].values[i]) these_x_metres, these_y_metres = basemap_object( this_vertex_dict[polygons.EXTERIOR_X_COLUMN], this_vertex_dict[polygons.EXTERIOR_Y_COLUMN]) axes_object.text(numpy.mean(these_x_metres), numpy.mean(these_y_metres), 'W{0:d}'.format(k), fontsize=storm_plotting.DEFAULT_FONT_SIZE, fontweight='bold', color=this_colour, horizontalalignment='center', verticalalignment='center') if primary_id_to_track_colour is None: storm_plotting.plot_storm_tracks(storm_object_table=storm_object_table, axes_object=axes_object, basemap_object=basemap_object, colour_map_object=None, constant_colour=DEFAULT_TRACK_COLOUR) else: for this_primary_id_string in primary_id_to_track_colour: this_storm_object_table = storm_object_table.loc[ storm_object_table[tracking_utils.PRIMARY_ID_COLUMN] == this_primary_id_string] if len(this_storm_object_table.index) == 0: continue storm_plotting.plot_storm_tracks( storm_object_table=this_storm_object_table, axes_object=axes_object, basemap_object=basemap_object, colour_map_object=None, constant_colour=primary_id_to_track_colour[ this_primary_id_string]) nice_time_string = time_conversion.unix_sec_to_string( valid_time_unix_sec, NICE_TIME_FORMAT) abbrev_time_string = time_conversion.unix_sec_to_string( valid_time_unix_sec, FILE_NAME_TIME_FORMAT) pyplot.title('Storm objects at {0:s}'.format(nice_time_string)) output_file_name = '{0:s}/storm_outlines_{1:s}.jpg'.format( output_dir_name, abbrev_time_string) 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_bwo_for_3d_radar( optimized_radar_matrix, training_option_dict, diff_colour_map_object, max_colour_percentile_for_diff, top_output_dir_name, pmm_flag, input_radar_matrix=None, storm_ids=None, storm_times_unix_sec=None): """Plots BWO results for 3-D radar fields. E = number of examples (storm objects) M = number of rows in spatial grid N = number of columns in spatial grid H = number of heights in spatial grid F = number of fields :param optimized_radar_matrix: E-by-M-by-N-by-H-by-F numpy array of radar values (predictors). :param training_option_dict: See doc for `_plot_bwo_for_2d3d_radar`. :param diff_colour_map_object: Same. :param max_colour_percentile_for_diff: Same. :param top_output_dir_name: Same. :param pmm_flag: Same. :param input_radar_matrix: Same as `optimized_radar_matrix` but with non-optimized input. :param storm_ids: See doc for `_plot_bwo_for_2d3d_radar`. :param storm_times_unix_sec: Same. """ before_optimization_dir_name = '{0:s}/before_optimization'.format( top_output_dir_name) after_optimization_dir_name = '{0:s}/after_optimization'.format( top_output_dir_name) difference_dir_name = '{0:s}/after_minus_before_optimization'.format( top_output_dir_name) file_system_utils.mkdir_recursive_if_necessary( directory_name=before_optimization_dir_name) file_system_utils.mkdir_recursive_if_necessary( directory_name=after_optimization_dir_name) file_system_utils.mkdir_recursive_if_necessary( directory_name=difference_dir_name) if pmm_flag: have_storm_ids = False else: have_storm_ids = not (storm_ids is None or storm_times_unix_sec is None) radar_field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY] radar_heights_m_agl = training_option_dict[trainval_io.RADAR_HEIGHTS_KEY] num_storms = optimized_radar_matrix.shape[0] num_heights = optimized_radar_matrix.shape[-2] num_panel_rows = int(numpy.floor( numpy.sqrt(num_heights) )) for i in range(num_storms): print '\n' if pmm_flag: this_base_title_string = 'Probability-matched mean' this_base_pathless_file_name = 'pmm' else: if have_storm_ids: this_storm_time_string = time_conversion.unix_sec_to_string( storm_times_unix_sec[i], TIME_FORMAT) this_base_title_string = 'Storm "{0:s}" at {1:s}'.format( storm_ids[i], this_storm_time_string) this_base_pathless_file_name = '{0:s}_{1:s}'.format( storm_ids[i].replace('_', '-'), this_storm_time_string) else: this_base_title_string = 'Example {0:d}'.format(i + 1) this_base_pathless_file_name = 'example{0:06d}'.format(i) for j in range(len(radar_field_names)): _, these_axes_objects = ( radar_plotting.plot_3d_grid_without_coords( field_matrix=numpy.flip( optimized_radar_matrix[i, ..., j], axis=0), field_name=radar_field_names[j], grid_point_heights_metres=radar_heights_m_agl, ground_relative=True, num_panel_rows=num_panel_rows, font_size=FONT_SIZE_SANS_COLOUR_BARS) ) this_colour_map_object, this_colour_norm_object = ( radar_plotting.get_default_colour_scheme( radar_field_names[j]) ) plotting_utils.add_colour_bar( axes_object_or_list=these_axes_objects, values_to_colour=optimized_radar_matrix[i, ..., j], colour_map=this_colour_map_object, colour_norm_object=this_colour_norm_object, orientation='horizontal', extend_min=True, extend_max=True) this_title_string = '{0:s} (after optimization)'.format( this_base_title_string) this_file_name = ( '{0:s}/{1:s}_after-optimization_{2:s}.jpg' ).format( after_optimization_dir_name, this_base_pathless_file_name, radar_field_names[j].replace('_', '-') ) pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE) print 'Saving figure to: "{0:s}"...'.format(this_file_name) pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI) pyplot.close() if input_radar_matrix is None: continue _, these_axes_objects = ( radar_plotting.plot_3d_grid_without_coords( field_matrix=numpy.flip( input_radar_matrix[i, ..., j], axis=0), field_name=radar_field_names[j], grid_point_heights_metres=radar_heights_m_agl, ground_relative=True, num_panel_rows=num_panel_rows, font_size=FONT_SIZE_SANS_COLOUR_BARS) ) this_colour_map_object, this_colour_norm_object = ( radar_plotting.get_default_colour_scheme( radar_field_names[j]) ) plotting_utils.add_colour_bar( axes_object_or_list=these_axes_objects, values_to_colour=input_radar_matrix[i, ..., j], colour_map=this_colour_map_object, colour_norm_object=this_colour_norm_object, orientation='horizontal', extend_min=True, extend_max=True) this_title_string = '{0:s} (before optimization)'.format( this_base_title_string) this_file_name = ( '{0:s}/{1:s}_before-optimization_{2:s}.jpg' ).format( before_optimization_dir_name, this_base_pathless_file_name, radar_field_names[j].replace('_', '-') ) pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE) print 'Saving figure to: "{0:s}"...'.format(this_file_name) pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI) pyplot.close() this_diff_matrix = ( optimized_radar_matrix[i, ..., j] - input_radar_matrix[i, ..., j] ) this_max_value = numpy.percentile( numpy.absolute(this_diff_matrix), max_colour_percentile_for_diff) this_colour_norm_object = matplotlib.colors.Normalize( vmin=-1 * this_max_value, vmax=this_max_value, clip=False) _, these_axes_objects = ( radar_plotting.plot_3d_grid_without_coords( field_matrix=numpy.flip(this_diff_matrix, axis=0), field_name=radar_field_names[j], grid_point_heights_metres=radar_heights_m_agl, ground_relative=True, num_panel_rows=num_panel_rows, font_size=FONT_SIZE_SANS_COLOUR_BARS, colour_map_object=diff_colour_map_object, colour_norm_object=this_colour_norm_object) ) plotting_utils.add_colour_bar( axes_object_or_list=these_axes_objects, values_to_colour=this_diff_matrix, colour_map=diff_colour_map_object, colour_norm_object=this_colour_norm_object, orientation='horizontal', extend_min=True, extend_max=True) this_title_string = ( '{0:s} (after minus before optimization)' ).format(this_base_title_string) this_file_name = ( '{0:s}/{1:s}_optimization-diff_{2:s}.jpg' ).format( difference_dir_name, this_base_pathless_file_name, radar_field_names[j].replace('_', '-') ) pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE) print 'Saving figure to: "{0:s}"...'.format(this_file_name) pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI) pyplot.close()
def _plot_bwo_for_2d3d_radar( list_of_optimized_matrices, training_option_dict, diff_colour_map_object, max_colour_percentile_for_diff, top_output_dir_name, pmm_flag, list_of_input_matrices=None, storm_ids=None, storm_times_unix_sec=None): """Plots BWO results for 2-D azimuthal-shear and 3-D reflectivity fields. E = number of examples (storm objects) T = number of input tensors to the model :param list_of_optimized_matrices: length-T list of numpy arrays, where the [i]th array is the optimized version of the [i]th input matrix to the model. :param training_option_dict: See doc for `cnn.read_model_metadata`. :param diff_colour_map_object: See documentation at top of file. :param max_colour_percentile_for_diff: Same. :param top_output_dir_name: Path to top-level output directory (figures will be saved here). :param pmm_flag: Boolean flag. If True, `list_of_predictor_matrices` contains probability-matched means. :param list_of_input_matrices: Same as `list_of_optimized_matrices` but with non-optimized input matrices. :param storm_ids: [optional and used only if `pmm_flag = False`] length-E list of storm IDs (strings). :param storm_times_unix_sec: [optional and used only if `pmm_flag = False`] length-E numpy array of storm times. """ before_optimization_dir_name = '{0:s}/before_optimization'.format( top_output_dir_name) after_optimization_dir_name = '{0:s}/after_optimization'.format( top_output_dir_name) difference_dir_name = '{0:s}/after_minus_before_optimization'.format( top_output_dir_name) file_system_utils.mkdir_recursive_if_necessary( directory_name=before_optimization_dir_name) file_system_utils.mkdir_recursive_if_necessary( directory_name=after_optimization_dir_name) file_system_utils.mkdir_recursive_if_necessary( directory_name=difference_dir_name) if pmm_flag: have_storm_ids = False else: have_storm_ids = not (storm_ids is None or storm_times_unix_sec is None) az_shear_field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY] num_az_shear_fields = len(az_shear_field_names) plot_colour_bar_flags = numpy.full(num_az_shear_fields, False, dtype=bool) num_storms = list_of_optimized_matrices[0].shape[0] for i in range(num_storms): print '\n' if pmm_flag: this_base_title_string = 'Probability-matched mean' this_base_pathless_file_name = 'pmm' else: if have_storm_ids: this_storm_time_string = time_conversion.unix_sec_to_string( storm_times_unix_sec[i], TIME_FORMAT) this_base_title_string = 'Storm "{0:s}" at {1:s}'.format( storm_ids[i], this_storm_time_string) this_base_pathless_file_name = '{0:s}_{1:s}'.format( storm_ids[i].replace('_', '-'), this_storm_time_string) else: this_base_title_string = 'Example {0:d}'.format(i + 1) this_base_pathless_file_name = 'example{0:06d}'.format(i) this_reflectivity_matrix_dbz = numpy.flip( list_of_optimized_matrices[0][i, ..., 0], axis=0) this_num_heights = this_reflectivity_matrix_dbz.shape[-1] this_num_panel_rows = int(numpy.floor( numpy.sqrt(this_num_heights) )) _, these_axes_objects = radar_plotting.plot_3d_grid_without_coords( field_matrix=this_reflectivity_matrix_dbz, field_name=radar_utils.REFL_NAME, grid_point_heights_metres=training_option_dict[ trainval_io.RADAR_HEIGHTS_KEY], ground_relative=True, num_panel_rows=this_num_panel_rows, font_size=FONT_SIZE_SANS_COLOUR_BARS) this_colour_map_object, this_colour_norm_object = ( radar_plotting.get_default_colour_scheme(radar_utils.REFL_NAME) ) plotting_utils.add_colour_bar( axes_object_or_list=these_axes_objects, values_to_colour=this_reflectivity_matrix_dbz, colour_map=this_colour_map_object, colour_norm_object=this_colour_norm_object, orientation='horizontal', extend_min=True, extend_max=True) this_title_string = '{0:s} (after optimization)'.format( this_base_title_string) this_file_name = ( '{0:s}/{1:s}_after-optimization_reflectivity.jpg' ).format(after_optimization_dir_name, this_base_pathless_file_name) pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE) print 'Saving figure to: "{0:s}"...'.format(this_file_name) pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI) pyplot.close() this_az_shear_matrix_s01 = numpy.flip( list_of_optimized_matrices[1][i, ..., 0], axis=0) _, these_axes_objects = ( radar_plotting.plot_many_2d_grids_without_coords( field_matrix=this_az_shear_matrix_s01, field_name_by_panel=az_shear_field_names, num_panel_rows=1, panel_names=az_shear_field_names, plot_colour_bar_by_panel=plot_colour_bar_flags, font_size=FONT_SIZE_SANS_COLOUR_BARS) ) this_colour_map_object, this_colour_norm_object = ( radar_plotting.get_default_colour_scheme( radar_utils.LOW_LEVEL_SHEAR_NAME) ) plotting_utils.add_colour_bar( axes_object_or_list=these_axes_objects, values_to_colour=this_az_shear_matrix_s01, colour_map=this_colour_map_object, colour_norm_object=this_colour_norm_object, orientation='horizontal', extend_min=True, extend_max=True) this_title_string = '{0:s} (after optimization)'.format( this_base_title_string) this_file_name = ( '{0:s}/{1:s}_after-optimization_azimuthal-shear.jpg' ).format(after_optimization_dir_name, this_base_pathless_file_name) pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE) print 'Saving figure to: "{0:s}"...'.format(this_file_name) pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI) pyplot.close() if list_of_input_matrices is None: continue this_reflectivity_matrix_dbz = numpy.flip( list_of_input_matrices[0][i, ..., 0], axis=0) _, these_axes_objects = radar_plotting.plot_3d_grid_without_coords( field_matrix=this_reflectivity_matrix_dbz, field_name=radar_utils.REFL_NAME, grid_point_heights_metres=training_option_dict[ trainval_io.RADAR_HEIGHTS_KEY], ground_relative=True, num_panel_rows=this_num_panel_rows, font_size=FONT_SIZE_SANS_COLOUR_BARS) this_colour_map_object, this_colour_norm_object = ( radar_plotting.get_default_colour_scheme(radar_utils.REFL_NAME) ) plotting_utils.add_colour_bar( axes_object_or_list=these_axes_objects, values_to_colour=this_reflectivity_matrix_dbz, colour_map=this_colour_map_object, colour_norm_object=this_colour_norm_object, orientation='horizontal', extend_min=True, extend_max=True) this_title_string = '{0:s} (before optimization)'.format( this_base_title_string) this_file_name = ( '{0:s}/{1:s}_before-optimization_reflectivity.jpg' ).format(before_optimization_dir_name, this_base_pathless_file_name) pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE) print 'Saving figure to: "{0:s}"...'.format(this_file_name) pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI) pyplot.close() this_az_shear_matrix_s01 = numpy.flip( list_of_input_matrices[1][i, ..., 0], axis=0) _, these_axes_objects = ( radar_plotting.plot_many_2d_grids_without_coords( field_matrix=this_az_shear_matrix_s01, field_name_by_panel=az_shear_field_names, num_panel_rows=1, panel_names=az_shear_field_names, plot_colour_bar_by_panel=plot_colour_bar_flags, font_size=FONT_SIZE_SANS_COLOUR_BARS) ) this_colour_map_object, this_colour_norm_object = ( radar_plotting.get_default_colour_scheme( radar_utils.LOW_LEVEL_SHEAR_NAME) ) plotting_utils.add_colour_bar( axes_object_or_list=these_axes_objects, values_to_colour=this_az_shear_matrix_s01, colour_map=this_colour_map_object, colour_norm_object=this_colour_norm_object, orientation='horizontal', extend_min=True, extend_max=True) this_title_string = '{0:s} (before optimization)'.format( this_base_title_string) this_file_name = ( '{0:s}/{1:s}_before-optimization_azimuthal-shear.jpg' ).format(before_optimization_dir_name, this_base_pathless_file_name) pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE) print 'Saving figure to: "{0:s}"...'.format(this_file_name) pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI) pyplot.close() this_refl_diff_matrix_dbz = ( list_of_optimized_matrices[0][i, ..., 0] - list_of_input_matrices[0][i, ..., 0] ) this_refl_diff_matrix_dbz = numpy.flip( this_refl_diff_matrix_dbz, axis=0) this_max_value_dbz = numpy.percentile( numpy.absolute(this_refl_diff_matrix_dbz), max_colour_percentile_for_diff) this_colour_norm_object = matplotlib.colors.Normalize( vmin=-1 * this_max_value_dbz, vmax=this_max_value_dbz, clip=False) _, these_axes_objects = radar_plotting.plot_3d_grid_without_coords( field_matrix=this_refl_diff_matrix_dbz, field_name=radar_utils.REFL_NAME, grid_point_heights_metres=training_option_dict[ trainval_io.RADAR_HEIGHTS_KEY], ground_relative=True, num_panel_rows=this_num_panel_rows, font_size=FONT_SIZE_SANS_COLOUR_BARS, colour_map_object=diff_colour_map_object, colour_norm_object=this_colour_norm_object) plotting_utils.add_colour_bar( axes_object_or_list=these_axes_objects, values_to_colour=this_refl_diff_matrix_dbz, colour_map=diff_colour_map_object, colour_norm_object=this_colour_norm_object, orientation='horizontal', extend_min=True, extend_max=True) this_title_string = '{0:s} (after minus before optimization)'.format( this_base_title_string) this_file_name = ( '{0:s}/{1:s}_optimization-diff_reflectivity.jpg' ).format(difference_dir_name, this_base_pathless_file_name) pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE) print 'Saving figure to: "{0:s}"...'.format(this_file_name) pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI) pyplot.close() this_shear_diff_matrix_s01 = ( list_of_optimized_matrices[1][i, ..., 0] - list_of_input_matrices[1][i, ..., 0] ) this_shear_diff_matrix_s01 = numpy.flip( this_shear_diff_matrix_s01, axis=0) this_max_value_s01 = numpy.percentile( numpy.absolute(this_shear_diff_matrix_s01), max_colour_percentile_for_diff) this_colour_norm_object = matplotlib.colors.Normalize( vmin=-1 * this_max_value_s01, vmax=this_max_value_s01, clip=False) _, these_axes_objects = ( radar_plotting.plot_many_2d_grids_without_coords( field_matrix=this_shear_diff_matrix_s01, field_name_by_panel=az_shear_field_names, num_panel_rows=1, panel_names=az_shear_field_names, colour_map_object_by_panel= [diff_colour_map_object] * num_az_shear_fields, colour_norm_object_by_panel= [copy.deepcopy(this_colour_norm_object)] * num_az_shear_fields, plot_colour_bar_by_panel=plot_colour_bar_flags, font_size=FONT_SIZE_SANS_COLOUR_BARS) ) plotting_utils.add_colour_bar( axes_object_or_list=these_axes_objects, values_to_colour=this_shear_diff_matrix_s01, colour_map=diff_colour_map_object, colour_norm_object=this_colour_norm_object, orientation='horizontal', extend_min=True, extend_max=True) this_title_string = '{0:s} (after minus before optimization)'.format( this_base_title_string) this_file_name = ( '{0:s}/{1:s}_optimization-diff_azimuthal-shear.jpg' ).format(difference_dir_name, this_base_pathless_file_name) pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE) print 'Saving figure to: "{0:s}"...'.format(this_file_name) pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI) pyplot.close()
def _plot_one_field(reflectivity_matrix_dbz, latitudes_deg, longitudes_deg, add_colour_bar, panel_letter, output_file_name): """Plots reflectivity field from one dataset. :param reflectivity_matrix_dbz: See doc for `_read_file`. :param latitudes_deg: Same. :param longitudes_deg: Same. :param add_colour_bar: Boolean flag. :param panel_letter: Panel letter (will be printed at top left of figure). :param output_file_name: Path to output file (figure will be saved here). """ (figure_object, axes_object, basemap_object) = plotting_utils.create_equidist_cylindrical_map( min_latitude_deg=numpy.min(latitudes_deg), max_latitude_deg=numpy.max(latitudes_deg), min_longitude_deg=numpy.min(longitudes_deg), max_longitude_deg=numpy.max(longitudes_deg), 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, num_parallels=NUM_PARALLELS) plotting_utils.plot_meridians(basemap_object=basemap_object, axes_object=axes_object, num_meridians=NUM_MERIDIANS) radar_plotting.plot_latlng_grid( field_matrix=reflectivity_matrix_dbz, field_name=RADAR_FIELD_NAME, axes_object=axes_object, min_grid_point_latitude_deg=numpy.min(latitudes_deg), min_grid_point_longitude_deg=numpy.min(longitudes_deg), latitude_spacing_deg=latitudes_deg[1] - latitudes_deg[0], longitude_spacing_deg=longitudes_deg[1] - longitudes_deg[0]) if add_colour_bar: colour_map_object, colour_norm_object = ( radar_plotting.get_default_colour_scheme(RADAR_FIELD_NAME)) plotting_utils.plot_colour_bar(axes_object_or_matrix=axes_object, data_matrix=reflectivity_matrix_dbz, colour_map_object=colour_map_object, colour_norm_object=colour_norm_object, orientation_string='horizontal', padding=0.05, extend_min=False, extend_max=True, fraction_of_axis_length=1.) plotting_utils.label_axes(axes_object=axes_object, label_string='({0:s})'.format(panel_letter), y_coord_normalized=1.03) print('Saving figure to: "{0:s}"...'.format(output_file_name)) figure_object.savefig(output_file_name, dpi=FIGURE_RESOLUTION_DPI, pad_inches=0, bbox_inches='tight') pyplot.close(figure_object)
def _plot_one_example_one_time(storm_object_table, full_id_string, valid_time_unix_sec, tornado_table, top_myrorss_dir_name, radar_field_name, radar_height_m_asl, latitude_limits_deg, longitude_limits_deg): """Plots one example with surrounding context at one time. :param storm_object_table: pandas DataFrame, containing only storm objects at one time with the relevant primary ID. Columns are documented in `storm_tracking_io.write_file`. :param full_id_string: Full ID of storm of interest. :param valid_time_unix_sec: Valid time. :param tornado_table: pandas DataFrame created by `linkage._read_input_tornado_reports`. :param top_myrorss_dir_name: See documentation at top of file. :param radar_field_name: Same. :param radar_height_m_asl: Same. :param latitude_limits_deg: See doc for `_get_plotting_limits`. :param longitude_limits_deg: Same. """ min_plot_latitude_deg = latitude_limits_deg[0] max_plot_latitude_deg = latitude_limits_deg[1] min_plot_longitude_deg = longitude_limits_deg[0] max_plot_longitude_deg = longitude_limits_deg[1] radar_file_name = myrorss_and_mrms_io.find_raw_file( top_directory_name=top_myrorss_dir_name, spc_date_string=time_conversion.time_to_spc_date_string( valid_time_unix_sec), unix_time_sec=valid_time_unix_sec, data_source=radar_utils.MYRORSS_SOURCE_ID, field_name=radar_field_name, height_m_asl=radar_height_m_asl, raise_error_if_missing=True) print('Reading data from: "{0:s}"...'.format(radar_file_name)) radar_metadata_dict = myrorss_and_mrms_io.read_metadata_from_raw_file( netcdf_file_name=radar_file_name, data_source=radar_utils.MYRORSS_SOURCE_ID) sparse_grid_table = (myrorss_and_mrms_io.read_data_from_sparse_grid_file( netcdf_file_name=radar_file_name, field_name_orig=radar_metadata_dict[ myrorss_and_mrms_io.FIELD_NAME_COLUMN_ORIG], data_source=radar_utils.MYRORSS_SOURCE_ID, sentinel_values=radar_metadata_dict[radar_utils.SENTINEL_VALUE_COLUMN]) ) radar_matrix, grid_point_latitudes_deg, grid_point_longitudes_deg = ( radar_s2f.sparse_to_full_grid(sparse_grid_table=sparse_grid_table, metadata_dict=radar_metadata_dict)) radar_matrix = numpy.flip(radar_matrix, axis=0) grid_point_latitudes_deg = grid_point_latitudes_deg[::-1] axes_object, basemap_object = ( plotting_utils.create_equidist_cylindrical_map( 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, resolution_string='i')[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) radar_plotting.plot_latlng_grid( field_matrix=radar_matrix, field_name=radar_field_name, axes_object=axes_object, min_grid_point_latitude_deg=numpy.min(grid_point_latitudes_deg), min_grid_point_longitude_deg=numpy.min(grid_point_longitudes_deg), latitude_spacing_deg=numpy.diff(grid_point_latitudes_deg[:2])[0], longitude_spacing_deg=numpy.diff(grid_point_longitudes_deg[:2])[0]) colour_map_object, colour_norm_object = ( radar_plotting.get_default_colour_scheme(radar_field_name)) plotting_utils.plot_colour_bar(axes_object_or_matrix=axes_object, data_matrix=radar_matrix, colour_map_object=colour_map_object, colour_norm_object=colour_norm_object, orientation_string='horizontal', extend_min=False, extend_max=True, fraction_of_axis_length=0.8) first_list, second_list = temporal_tracking.full_to_partial_ids( [full_id_string]) primary_id_string = first_list[0] secondary_id_string = second_list[0] # Plot outlines of unrelated storms (with different primary IDs). this_storm_object_table = storm_object_table.loc[storm_object_table[ tracking_utils.PRIMARY_ID_COLUMN] != primary_id_string] storm_plotting.plot_storm_outlines( storm_object_table=this_storm_object_table, axes_object=axes_object, basemap_object=basemap_object, line_width=2, line_colour='k', line_style='dashed') # Plot outlines of related storms (with the same primary ID). this_storm_object_table = storm_object_table.loc[ (storm_object_table[tracking_utils.PRIMARY_ID_COLUMN] == primary_id_string) & (storm_object_table[ tracking_utils.SECONDARY_ID_COLUMN] != secondary_id_string)] this_num_storm_objects = len(this_storm_object_table.index) if this_num_storm_objects > 0: storm_plotting.plot_storm_outlines( storm_object_table=this_storm_object_table, axes_object=axes_object, basemap_object=basemap_object, line_width=2, line_colour='k', line_style='solid') for j in range(len(this_storm_object_table)): axes_object.text( this_storm_object_table[ tracking_utils.CENTROID_LONGITUDE_COLUMN].values[j], this_storm_object_table[ tracking_utils.CENTROID_LATITUDE_COLUMN].values[j], 'P', fontsize=FONT_SIZE, color=FONT_COLOUR, fontweight='bold', horizontalalignment='center', verticalalignment='center') # Plot outline of storm of interest (same secondary ID). this_storm_object_table = storm_object_table.loc[storm_object_table[ tracking_utils.SECONDARY_ID_COLUMN] == secondary_id_string] storm_plotting.plot_storm_outlines( storm_object_table=this_storm_object_table, axes_object=axes_object, basemap_object=basemap_object, line_width=4, line_colour='k', line_style='solid') this_num_storm_objects = len(this_storm_object_table.index) plot_forecast = (this_num_storm_objects > 0 and FORECAST_PROBABILITY_COLUMN in list(this_storm_object_table)) if plot_forecast: this_polygon_object_latlng = this_storm_object_table[ tracking_utils.LATLNG_POLYGON_COLUMN].values[0] this_latitude_deg = numpy.min( numpy.array(this_polygon_object_latlng.exterior.xy[1])) this_longitude_deg = this_storm_object_table[ tracking_utils.CENTROID_LONGITUDE_COLUMN].values[0] label_string = 'Prob = {0:.3f}\nat {1:s}'.format( this_storm_object_table[FORECAST_PROBABILITY_COLUMN].values[0], time_conversion.unix_sec_to_string(valid_time_unix_sec, TORNADO_TIME_FORMAT)) bounding_box_dict = { 'facecolor': plotting_utils.colour_from_numpy_to_tuple( PROBABILITY_BACKGROUND_COLOUR), 'alpha': PROBABILITY_BACKGROUND_OPACITY, 'edgecolor': 'k', 'linewidth': 1 } axes_object.text(this_longitude_deg, this_latitude_deg, label_string, fontsize=FONT_SIZE, color=plotting_utils.colour_from_numpy_to_tuple( PROBABILITY_FONT_COLOUR), fontweight='bold', bbox=bounding_box_dict, horizontalalignment='center', verticalalignment='top', zorder=1e10) tornado_latitudes_deg = tornado_table[linkage.EVENT_LATITUDE_COLUMN].values tornado_longitudes_deg = tornado_table[ linkage.EVENT_LONGITUDE_COLUMN].values tornado_times_unix_sec = tornado_table[linkage.EVENT_TIME_COLUMN].values tornado_time_strings = [ time_conversion.unix_sec_to_string(t, TORNADO_TIME_FORMAT) for t in tornado_times_unix_sec ] axes_object.plot(tornado_longitudes_deg, tornado_latitudes_deg, linestyle='None', marker=TORNADO_MARKER_TYPE, markersize=TORNADO_MARKER_SIZE, markeredgewidth=TORNADO_MARKER_EDGE_WIDTH, markerfacecolor=plotting_utils.colour_from_numpy_to_tuple( TORNADO_MARKER_COLOUR), markeredgecolor=plotting_utils.colour_from_numpy_to_tuple( TORNADO_MARKER_COLOUR)) num_tornadoes = len(tornado_latitudes_deg) for j in range(num_tornadoes): axes_object.text(tornado_longitudes_deg[j] + 0.02, tornado_latitudes_deg[j] - 0.02, tornado_time_strings[j], fontsize=FONT_SIZE, color=FONT_COLOUR, fontweight='bold', horizontalalignment='left', verticalalignment='top')
def _plot_2d3d_radar_scan(list_of_predictor_matrices, model_metadata_dict, allow_whitespace, title_string=None): """Plots 3-D reflectivity and 2-D azimuthal shear for one example. :param list_of_predictor_matrices: See doc for `_plot_3d_radar_scan`. :param model_metadata_dict: Same. :param allow_whitespace: Same. :param title_string: Same. :return: figure_objects: length-2 list of figure handles (instances of `matplotlib.figure.Figure`). The first is for reflectivity; the second is for azimuthal shear. :return: axes_object_matrices: length-2 list (the first is for reflectivity; the second is for azimuthal shear). Each element is a 2-D numpy array of axes handles (instances of `matplotlib.axes._subplots.AxesSubplot`). """ training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY] az_shear_field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY] refl_heights_m_agl = training_option_dict[trainval_io.RADAR_HEIGHTS_KEY] num_az_shear_fields = len(az_shear_field_names) num_refl_heights = len(refl_heights_m_agl) this_num_panel_rows = int(numpy.floor(numpy.sqrt(num_refl_heights))) this_num_panel_columns = int( numpy.ceil(float(num_refl_heights) / this_num_panel_rows)) if allow_whitespace: refl_figure_object = None refl_axes_object_matrix = None else: refl_figure_object, refl_axes_object_matrix = ( plotting_utils.create_paneled_figure( num_rows=this_num_panel_rows, num_columns=this_num_panel_columns, horizontal_spacing=0., vertical_spacing=0., shared_x_axis=False, shared_y_axis=False, keep_aspect_ratio=True)) refl_figure_object, refl_axes_object_matrix = ( radar_plotting.plot_3d_grid_without_coords( field_matrix=numpy.flip(list_of_predictor_matrices[0][..., 0], axis=0), field_name=radar_utils.REFL_NAME, grid_point_heights_metres=refl_heights_m_agl, ground_relative=True, num_panel_rows=this_num_panel_rows, figure_object=refl_figure_object, axes_object_matrix=refl_axes_object_matrix, font_size=FONT_SIZE_SANS_COLOUR_BARS)) if allow_whitespace: this_colour_map_object, this_colour_norm_object = ( radar_plotting.get_default_colour_scheme(radar_utils.REFL_NAME)) plotting_utils.plot_colour_bar( axes_object_or_matrix=refl_axes_object_matrix, data_matrix=list_of_predictor_matrices[0], colour_map_object=this_colour_map_object, colour_norm_object=this_colour_norm_object, orientation_string='horizontal', extend_min=True, extend_max=True) if title_string is not None: this_title_string = '{0:s}; {1:s}'.format(title_string, radar_utils.REFL_NAME) pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE) if allow_whitespace: shear_figure_object = None shear_axes_object_matrix = None else: shear_figure_object, shear_axes_object_matrix = ( plotting_utils.create_paneled_figure( num_rows=1, num_columns=num_az_shear_fields, horizontal_spacing=0., vertical_spacing=0., shared_x_axis=False, shared_y_axis=False, keep_aspect_ratio=True)) shear_figure_object, shear_axes_object_matrix = ( radar_plotting.plot_many_2d_grids_without_coords( field_matrix=numpy.flip(list_of_predictor_matrices[1], axis=0), field_name_by_panel=az_shear_field_names, panel_names=az_shear_field_names, num_panel_rows=1, figure_object=shear_figure_object, axes_object_matrix=shear_axes_object_matrix, plot_colour_bar_by_panel=numpy.full(num_az_shear_fields, False, dtype=bool), font_size=FONT_SIZE_SANS_COLOUR_BARS)) if allow_whitespace: this_colour_map_object, this_colour_norm_object = ( radar_plotting.get_default_colour_scheme( radar_utils.LOW_LEVEL_SHEAR_NAME)) plotting_utils.plot_colour_bar( axes_object_or_matrix=shear_axes_object_matrix, data_matrix=list_of_predictor_matrices[1], colour_map_object=this_colour_map_object, colour_norm_object=this_colour_norm_object, orientation_string='horizontal', extend_min=True, extend_max=True) if title_string is not None: pyplot.suptitle(title_string, fontsize=TITLE_FONT_SIZE) figure_objects = [refl_figure_object, shear_figure_object] axes_object_matrices = [refl_axes_object_matrix, shear_axes_object_matrix] return figure_objects, axes_object_matrices
def _plot_3d_radar_scan(list_of_predictor_matrices, model_metadata_dict, allow_whitespace, title_string=None): """Plots 3-D radar scan for one example. J = number of panel rows in image K = number of panel columns in image F = number of radar fields :param list_of_predictor_matrices: List created by `testing_io.read_specific_examples`, except that the first axis (example dimension) is removed. :param model_metadata_dict: Dictionary returned by `cnn.read_model_metadata`. :param allow_whitespace: See documentation at top of file. :param title_string: Title (may be None). :return: figure_objects: length-F list of figure handles (instances of `matplotlib.figure.Figure`). :return: axes_object_matrices: length-F list. Each element is a J-by-K numpy array of axes handles (instances of `matplotlib.axes._subplots.AxesSubplot`). """ training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY] radar_field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY] radar_heights_m_agl = training_option_dict[trainval_io.RADAR_HEIGHTS_KEY] num_radar_fields = len(radar_field_names) num_radar_heights = len(radar_heights_m_agl) num_panel_rows = int(numpy.floor(numpy.sqrt(num_radar_heights))) num_panel_columns = int( numpy.ceil(float(num_radar_heights) / num_panel_rows)) figure_objects = [None] * num_radar_fields axes_object_matrices = [None] * num_radar_fields radar_matrix = list_of_predictor_matrices[0] for j in range(num_radar_fields): this_radar_matrix = numpy.flip(radar_matrix[..., j], axis=0) if not allow_whitespace: figure_objects[j], axes_object_matrices[j] = ( plotting_utils.create_paneled_figure( num_rows=num_panel_rows, num_columns=num_panel_columns, horizontal_spacing=0., vertical_spacing=0., shared_x_axis=False, shared_y_axis=False, keep_aspect_ratio=True)) figure_objects[j], axes_object_matrices[j] = ( radar_plotting.plot_3d_grid_without_coords( field_matrix=this_radar_matrix, field_name=radar_field_names[j], grid_point_heights_metres=radar_heights_m_agl, ground_relative=True, num_panel_rows=num_panel_rows, figure_object=figure_objects[j], axes_object_matrix=axes_object_matrices[j], font_size=FONT_SIZE_SANS_COLOUR_BARS)) if allow_whitespace: this_colour_map_object, this_colour_norm_object = ( radar_plotting.get_default_colour_scheme(radar_field_names[j])) plotting_utils.plot_colour_bar( axes_object_or_matrix=axes_object_matrices[j], data_matrix=this_radar_matrix, colour_map_object=this_colour_map_object, colour_norm_object=this_colour_norm_object, orientation_string='horizontal', extend_min=True, extend_max=True) if title_string is not None: this_title_string = '{0:s}; {1:s}'.format( title_string, radar_field_names[j]) pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE) return figure_objects, axes_object_matrices
def _plot_one_example_one_time( storm_object_table, full_id_string, valid_time_unix_sec, tornado_table, top_myrorss_dir_name, radar_field_name, radar_height_m_asl, latitude_limits_deg, longitude_limits_deg): """Plots one example with surrounding context at one time. :param storm_object_table: pandas DataFrame, containing only storm objects at one time with the relevant primary ID. Columns are documented in `storm_tracking_io.write_file`. :param full_id_string: Full ID of storm of interest. :param valid_time_unix_sec: Valid time. :param tornado_table: pandas DataFrame created by `linkage._read_input_tornado_reports`. :param top_myrorss_dir_name: See documentation at top of file. :param radar_field_name: Same. :param radar_height_m_asl: Same. :param latitude_limits_deg: See doc for `_get_plotting_limits`. :param longitude_limits_deg: Same. """ min_plot_latitude_deg = latitude_limits_deg[0] max_plot_latitude_deg = latitude_limits_deg[1] min_plot_longitude_deg = longitude_limits_deg[0] max_plot_longitude_deg = longitude_limits_deg[1] radar_file_name = myrorss_and_mrms_io.find_raw_file_inexact_time( top_directory_name=top_myrorss_dir_name, desired_time_unix_sec=valid_time_unix_sec, spc_date_string=time_conversion.time_to_spc_date_string( valid_time_unix_sec), data_source=radar_utils.MYRORSS_SOURCE_ID, field_name=radar_field_name, height_m_asl=radar_height_m_asl, max_time_offset_sec= myrorss_and_mrms_io.DEFAULT_MAX_TIME_OFFSET_FOR_NON_SHEAR_SEC, raise_error_if_missing=True) print('Reading data from: "{0:s}"...'.format(radar_file_name)) radar_metadata_dict = myrorss_and_mrms_io.read_metadata_from_raw_file( netcdf_file_name=radar_file_name, data_source=radar_utils.MYRORSS_SOURCE_ID) sparse_grid_table = ( myrorss_and_mrms_io.read_data_from_sparse_grid_file( netcdf_file_name=radar_file_name, field_name_orig=radar_metadata_dict[ myrorss_and_mrms_io.FIELD_NAME_COLUMN_ORIG], data_source=radar_utils.MYRORSS_SOURCE_ID, sentinel_values=radar_metadata_dict[ radar_utils.SENTINEL_VALUE_COLUMN] ) ) radar_matrix, grid_point_latitudes_deg, grid_point_longitudes_deg = ( radar_s2f.sparse_to_full_grid( sparse_grid_table=sparse_grid_table, metadata_dict=radar_metadata_dict) ) radar_matrix = numpy.flip(radar_matrix, axis=0) grid_point_latitudes_deg = grid_point_latitudes_deg[::-1] axes_object, basemap_object = ( plotting_utils.create_equidist_cylindrical_map( 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, resolution_string='h' )[1:] ) plotting_utils.plot_coastlines( basemap_object=basemap_object, axes_object=axes_object, line_colour=plotting_utils.DEFAULT_COUNTRY_COLOUR) plotting_utils.plot_countries( basemap_object=basemap_object, axes_object=axes_object) plotting_utils.plot_states_and_provinces( basemap_object=basemap_object, axes_object=axes_object) plotting_utils.plot_parallels( basemap_object=basemap_object, axes_object=axes_object, num_parallels=NUM_PARALLELS, line_width=0) plotting_utils.plot_meridians( basemap_object=basemap_object, axes_object=axes_object, num_meridians=NUM_MERIDIANS, line_width=0) radar_plotting.plot_latlng_grid( field_matrix=radar_matrix, field_name=radar_field_name, axes_object=axes_object, min_grid_point_latitude_deg=numpy.min(grid_point_latitudes_deg), min_grid_point_longitude_deg=numpy.min(grid_point_longitudes_deg), latitude_spacing_deg=numpy.diff(grid_point_latitudes_deg[:2])[0], longitude_spacing_deg=numpy.diff(grid_point_longitudes_deg[:2])[0] ) colour_map_object, colour_norm_object = ( radar_plotting.get_default_colour_scheme(radar_field_name) ) plotting_utils.plot_colour_bar( axes_object_or_matrix=axes_object, data_matrix=radar_matrix, colour_map_object=colour_map_object, colour_norm_object=colour_norm_object, orientation_string='horizontal', padding=0.05, extend_min=False, extend_max=True, fraction_of_axis_length=0.8) first_list, second_list = temporal_tracking.full_to_partial_ids( [full_id_string] ) primary_id_string = first_list[0] secondary_id_string = second_list[0] # Plot outlines of unrelated storms (with different primary IDs). this_storm_object_table = storm_object_table.loc[ storm_object_table[tracking_utils.PRIMARY_ID_COLUMN] != primary_id_string ] storm_plotting.plot_storm_outlines( storm_object_table=this_storm_object_table, axes_object=axes_object, basemap_object=basemap_object, line_width=AUXILIARY_STORM_WIDTH, line_colour='k', line_style='dashed') # Plot outlines of related storms (with the same primary ID). this_storm_object_table = storm_object_table.loc[ (storm_object_table[tracking_utils.PRIMARY_ID_COLUMN] == primary_id_string) & (storm_object_table[tracking_utils.SECONDARY_ID_COLUMN] != secondary_id_string) ] this_num_storm_objects = len(this_storm_object_table.index) if this_num_storm_objects > 0: storm_plotting.plot_storm_outlines( storm_object_table=this_storm_object_table, axes_object=axes_object, basemap_object=basemap_object, line_width=AUXILIARY_STORM_WIDTH, line_colour='k', line_style='solid' ) for j in range(len(this_storm_object_table)): axes_object.text( this_storm_object_table[ tracking_utils.CENTROID_LONGITUDE_COLUMN ].values[j], this_storm_object_table[ tracking_utils.CENTROID_LATITUDE_COLUMN ].values[j], 'P', fontsize=MAIN_FONT_SIZE, color=FONT_COLOUR, fontweight='bold', horizontalalignment='center', verticalalignment='center' ) # Plot outline of storm of interest (same secondary ID). this_storm_object_table = storm_object_table.loc[ storm_object_table[tracking_utils.SECONDARY_ID_COLUMN] == secondary_id_string ] storm_plotting.plot_storm_outlines( storm_object_table=this_storm_object_table, axes_object=axes_object, basemap_object=basemap_object, line_width=MAIN_STORM_WIDTH, line_colour='k', line_style='solid') this_num_storm_objects = len(this_storm_object_table.index) plot_forecast = ( this_num_storm_objects > 0 and FORECAST_PROBABILITY_COLUMN in list(this_storm_object_table) ) if plot_forecast: label_string = 'Prob = {0:.3f}\nat {1:s}'.format( this_storm_object_table[FORECAST_PROBABILITY_COLUMN].values[0], time_conversion.unix_sec_to_string( valid_time_unix_sec, TORNADO_TIME_FORMAT) ) axes_object.set_title( label_string.replace('\n', ' '), fontsize=TITLE_FONT_SIZE ) tornado_id_strings = tornado_table[tornado_io.TORNADO_ID_COLUMN].values for this_tornado_id_string in numpy.unique(tornado_id_strings): these_rows = numpy.where( tornado_id_strings == this_tornado_id_string )[0] this_tornado_table = tornado_table.iloc[these_rows].sort_values( linkage.EVENT_TIME_COLUMN, axis=0, ascending=True, inplace=False ) _plot_one_tornado( tornado_table=this_tornado_table, axes_object=axes_object )
def plot_examples(list_of_predictor_matrices, storm_ids, storm_times_unix_sec, model_metadata_dict, output_dir_name, storm_activations=None): """Plots one or more learning examples. E = number of examples (storm objects) :param list_of_predictor_matrices: List created by `testing_io.read_specific_examples`. Contains data to be plotted. :param storm_ids: length-E list of storm IDs. :param storm_times_unix_sec: length-E numpy array of storm times. :param model_metadata_dict: See doc for `cnn.read_model_metadata`. :param output_dir_name: Name of output directory (figures will be saved here). :param storm_activations: length-E numpy array of storm activations (may be None). Will be included in title of each figure. """ training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY] sounding_field_names = training_option_dict[ trainval_io.SOUNDING_FIELDS_KEY] plot_soundings = sounding_field_names is not None if plot_soundings: list_of_metpy_dictionaries = dl_utils.soundings_to_metpy_dictionaries( sounding_matrix=list_of_predictor_matrices[-1], field_names=sounding_field_names) else: list_of_metpy_dictionaries = None num_radar_dimensions = len(list_of_predictor_matrices[0].shape) - 2 list_of_layer_operation_dicts = model_metadata_dict[ cnn.LAYER_OPERATIONS_KEY] if num_radar_dimensions == 2: if list_of_layer_operation_dicts is None: field_name_by_panel = training_option_dict[ trainval_io.RADAR_FIELDS_KEY] panel_names = ( radar_plotting.radar_fields_and_heights_to_panel_names( field_names=field_name_by_panel, heights_m_agl=training_option_dict[ trainval_io.RADAR_HEIGHTS_KEY])) plot_colour_bar_by_panel = numpy.full(len(panel_names), True, dtype=bool) else: field_name_by_panel, panel_names = ( radar_plotting.layer_ops_to_field_and_panel_names( list_of_layer_operation_dicts)) plot_colour_bar_by_panel = numpy.full(len(panel_names), False, dtype=bool) plot_colour_bar_by_panel[2::3] = True else: field_name_by_panel = None panel_names = None plot_colour_bar_by_panel = None az_shear_field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY] num_az_shear_fields = len(az_shear_field_names) num_storms = len(storm_ids) myrorss_2d3d = len(list_of_predictor_matrices) == 3 for i in range(num_storms): this_time_string = time_conversion.unix_sec_to_string( storm_times_unix_sec[i], TIME_FORMAT) this_base_title_string = 'Storm "{0:s}" at {1:s}'.format( storm_ids[i], this_time_string) if storm_activations is not None: this_base_title_string += ' (activation = {0:.3f})'.format( storm_activations[i]) this_base_file_name = '{0:s}/storm={1:s}_{2:s}'.format( output_dir_name, storm_ids[i].replace('_', '-'), this_time_string) if plot_soundings: sounding_plotting.plot_sounding( sounding_dict_for_metpy=list_of_metpy_dictionaries[i], title_string=this_base_title_string) this_file_name = '{0:s}_sounding.jpg'.format(this_base_file_name) print 'Saving figure to: "{0:s}"...'.format(this_file_name) pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI) pyplot.close() if myrorss_2d3d: this_reflectivity_matrix_dbz = numpy.flip( list_of_predictor_matrices[0][i, ..., 0], axis=0) this_num_heights = this_reflectivity_matrix_dbz.shape[-1] this_num_panel_rows = int(numpy.floor( numpy.sqrt(this_num_heights))) _, these_axes_objects = radar_plotting.plot_3d_grid_without_coords( field_matrix=this_reflectivity_matrix_dbz, field_name=radar_utils.REFL_NAME, grid_point_heights_metres=training_option_dict[ trainval_io.RADAR_HEIGHTS_KEY], ground_relative=True, num_panel_rows=this_num_panel_rows, font_size=FONT_SIZE_SANS_COLOUR_BARS) this_colour_map_object, this_colour_norm_object = ( radar_plotting.get_default_colour_scheme( radar_utils.REFL_NAME)) plotting_utils.add_colour_bar( axes_object_or_list=these_axes_objects, values_to_colour=this_reflectivity_matrix_dbz, colour_map=this_colour_map_object, colour_norm_object=this_colour_norm_object, orientation='horizontal', extend_min=True, extend_max=True) this_title_string = '{0:s}; {1:s}'.format(this_base_title_string, radar_utils.REFL_NAME) this_file_name = '{0:s}_reflectivity.jpg'.format( this_base_file_name) pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE) print 'Saving figure to: "{0:s}"...'.format(this_file_name) pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI) pyplot.close() this_az_shear_matrix_s01 = numpy.flip( list_of_predictor_matrices[1][i, ..., 0], axis=0) _, these_axes_objects = ( radar_plotting.plot_many_2d_grids_without_coords( field_matrix=this_az_shear_matrix_s01, field_name_by_panel=az_shear_field_names, panel_names=az_shear_field_names, num_panel_rows=1, plot_colour_bar_by_panel=numpy.full(num_az_shear_fields, False, dtype=bool), font_size=FONT_SIZE_SANS_COLOUR_BARS)) this_colour_map_object, this_colour_norm_object = ( radar_plotting.get_default_colour_scheme( radar_utils.LOW_LEVEL_SHEAR_NAME)) plotting_utils.add_colour_bar( axes_object_or_list=these_axes_objects, values_to_colour=this_az_shear_matrix_s01, colour_map=this_colour_map_object, colour_norm_object=this_colour_norm_object, orientation='horizontal', extend_min=True, extend_max=True) this_file_name = '{0:s}_shear.jpg'.format(this_base_file_name) pyplot.suptitle(this_base_title_string, fontsize=TITLE_FONT_SIZE) print 'Saving figure to: "{0:s}"...'.format(this_file_name) pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI) pyplot.close() continue this_radar_matrix = list_of_predictor_matrices[0] if num_radar_dimensions == 2: this_num_channels = this_radar_matrix.shape[-1] this_num_panel_rows = int( numpy.floor(numpy.sqrt(this_num_channels))) radar_plotting.plot_many_2d_grids_without_coords( field_matrix=numpy.flip(this_radar_matrix[i, ...], axis=0), field_name_by_panel=field_name_by_panel, panel_names=panel_names, num_panel_rows=this_num_panel_rows, plot_colour_bar_by_panel=plot_colour_bar_by_panel, font_size=FONT_SIZE_WITH_COLOUR_BARS, row_major=False) this_title_string = this_base_title_string + '' pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE) this_file_name = '{0:s}.jpg'.format(this_base_file_name) print 'Saving figure to: "{0:s}"...'.format(this_file_name) pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI) pyplot.close() continue radar_field_names = training_option_dict[trainval_io.RADAR_FIELDS_KEY] radar_heights_m_agl = training_option_dict[ trainval_io.RADAR_HEIGHTS_KEY] for j in range(len(radar_field_names)): this_num_heights = this_radar_matrix.shape[-2] this_num_panel_rows = int(numpy.floor( numpy.sqrt(this_num_heights))) _, these_axes_objects = radar_plotting.plot_3d_grid_without_coords( field_matrix=numpy.flip(this_radar_matrix[i, ..., j], axis=0), field_name=radar_field_names[j], grid_point_heights_metres=radar_heights_m_agl, ground_relative=True, num_panel_rows=this_num_panel_rows, font_size=FONT_SIZE_SANS_COLOUR_BARS) this_colour_map_object, this_colour_norm_object = ( radar_plotting.get_default_colour_scheme(radar_field_names[j])) plotting_utils.add_colour_bar( axes_object_or_list=these_axes_objects, values_to_colour=this_radar_matrix[i, ..., j], colour_map=this_colour_map_object, colour_norm_object=this_colour_norm_object, orientation='horizontal', extend_min=True, extend_max=True) this_title_string = '{0:s}; {1:s}'.format(this_base_title_string, radar_field_names[j]) this_file_name = '{0:s}_{1:s}.jpg'.format( this_base_file_name, radar_field_names[j].replace('_', '-')) pyplot.suptitle(this_title_string, fontsize=TITLE_FONT_SIZE) print 'Saving figure to: "{0:s}"...'.format(this_file_name) pyplot.savefig(this_file_name, dpi=FIGURE_RESOLUTION_DPI) pyplot.close()
def _plot_echo_tops(echo_top_matrix_km_asl, latitudes_deg, longitudes_deg, plot_colour_bar, convective_flag_matrix=None): """Plots grid of 40-dBZ echo tops. M = number of rows in grid N = number of columns in grid :param echo_top_matrix_km_asl: M-by-N numpy array of echo tops (km above sea level). :param latitudes_deg: length-M numpy array of latitudes (deg N). :param longitudes_deg: length-N numpy array of longitudes (deg E). :param plot_colour_bar: Boolean flag. :param convective_flag_matrix: M-by-N numpy array of Boolean flags, indicating which grid cells are convective. If `convective_flag_matrix is None`, all grid cells will be plotted. If `convective_flag_matrix is not None`, only convective grid cells will be plotted. :return: figure_object: Figure handle (instance of `matplotlib.figure.Figure`). :return: axes_object: Axes handle (instance of `matplotlib.axes._subplots.AxesSubplot`). :return: basemap_object: Basemap handle (instance of `mpl_toolkits.basemap.Basemap`). """ figure_object, axes_object, basemap_object = ( plotting_utils.create_equidist_cylindrical_map( min_latitude_deg=numpy.min(latitudes_deg), max_latitude_deg=numpy.max(latitudes_deg), min_longitude_deg=numpy.min(longitudes_deg), max_longitude_deg=numpy.max(longitudes_deg), resolution_string='h' ) ) # plotting_utils.plot_coastlines( # basemap_object=basemap_object, axes_object=axes_object, # line_colour=plotting_utils.DEFAULT_COUNTRY_COLOUR # ) plotting_utils.plot_countries( basemap_object=basemap_object, axes_object=axes_object ) plotting_utils.plot_states_and_provinces( basemap_object=basemap_object, axes_object=axes_object ) plotting_utils.plot_parallels( basemap_object=basemap_object, axes_object=axes_object, num_parallels=NUM_PARALLELS, line_width=0 ) plotting_utils.plot_meridians( basemap_object=basemap_object, axes_object=axes_object, num_meridians=NUM_MERIDIANS, line_width=0 ) matrix_to_plot = echo_top_matrix_km_asl + 0. if convective_flag_matrix is not None: matrix_to_plot[convective_flag_matrix == False] = numpy.nan radar_plotting.plot_latlng_grid( field_matrix=matrix_to_plot, field_name=radar_utils.ECHO_TOP_40DBZ_NAME, axes_object=axes_object, min_grid_point_latitude_deg=numpy.min(latitudes_deg), min_grid_point_longitude_deg=numpy.min(longitudes_deg), latitude_spacing_deg=numpy.diff(latitudes_deg[:2])[0], longitude_spacing_deg=numpy.diff(longitudes_deg[:2])[0] ) if not plot_colour_bar: return figure_object, axes_object, basemap_object colour_map_object, colour_norm_object = ( radar_plotting.get_default_colour_scheme( radar_utils.ECHO_TOP_40DBZ_NAME) ) colour_bar_object = plotting_utils.plot_colour_bar( axes_object_or_matrix=axes_object, data_matrix=matrix_to_plot, colour_map_object=colour_map_object, colour_norm_object=colour_norm_object, orientation_string='horizontal', extend_min=False, extend_max=True, fraction_of_axis_length=1. ) colour_bar_object.set_label('40-dBZ echo top (kft ASL)') return figure_object, axes_object, basemap_object