#!/usr/bin/env python3 ############################################################################### # # Name: stat_by_level.py # Contact(s): Marcel Caron # Developed: Oct. 14, 2021 by Marcel Caron # Last Modified: Dec. 02, 2022 by Marcel Caron # Title: Line plot of pressure level as a function of # verification metric # Abstract: Plots METplus output (e.g., BCRMSE) as a line plot, # stratified by pressure level, which represents the y-axis. # Line colors and styles are unique for each model, and several # models can be plotted at once. # ############################################################################### import os import sys import numpy as np import math import pandas as pd import logging from functools import reduce import matplotlib matplotlib.use('agg') import matplotlib.pyplot as plt import matplotlib.colors as colors import matplotlib.image as mpimg from matplotlib.offsetbox import OffsetImage, AnnotationBbox from datetime import datetime, timedelta as td SETTINGS_DIR = os.environ['USH_DIR'] sys.path.insert(0, os.path.abspath(SETTINGS_DIR)) from settings import Toggle, Templates, Paths, Presets, ModelSpecs, Reference from plotter import Plotter from prune_stat_files import prune_data import plot_util import df_preprocessing from check_variables import * # ================ GLOBALS AND CONSTANTS ================ plotter = Plotter(fig_size=(18., 14.)) plotter.set_up_plots() toggle = Toggle() templates = Templates() paths = Paths() presets = Presets() model_colors = ModelSpecs() reference = Reference() # =================== FUNCTIONS ========================= def plot_stat_by_level(df: pd.DataFrame, logger: logging.Logger, date_range: tuple, model_list: list, num: int = 0, levels: list = ['P500'], flead='all', metric1_name: str = 'BCRMSE', metric2_name: str = 'ME', x_min_limit: float = -10., x_max_limit: float = 10., x_lim_lock: bool = False, y_min_limit: float = 50., y_max_limit: float = 1000., y_lim_lock: bool = False, ylabel: str = 'Pressure Level (hPa)', date_type: str = 'VALID', line_type: str = 'SL1L2', date_hours: list = [0,6,12,18], save_dir: str = '.', dpi: int = 300, confidence_intervals: bool = False, interp_pts: list = [], bs_nrep: int = 5000, bs_method: str = 'MATCHED_PAIRS', bs_min_samp: int = 300, ci_lev: float = .95, eval_period: str = 'TEST', save_header: str = '', display_averages: bool = True, plot_group: str = 'sfc_upper', sample_equalization: bool = True, plot_logo_left: bool = False, plot_logo_right: bool = False, path_logo_left: str = '.', path_logo_right: str = '.', zoom_logo_left: float = 1., zoom_logo_right: float = 1.): logger.info("========================================") logger.info(f"Creating Plot {num} ...") if df.empty: logger.warning(f"Empty Dataframe. Continuing onto next plot...") logger.info("========================================") return None fig, ax = plotter.get_plots(num) variable_translator = reference.variable_translator domain_translator = reference.domain_translator model_settings = model_colors.model_settings # filter by levels df = df[df['FCST_LEV'].astype(str).isin(levels)] # filter by forecast lead times if str(flead).upper() == 'ALL': frange_string = 'All Available Forecasts' frange_save_string = 'ALL_LEADS' pass elif isinstance(flead, list): if len(flead) <= 8: if len(flead) > 1: frange_phrase = 's '+', '.join([str(f) for f in flead]) else: frange_phrase = ' '+', '.join([str(f) for f in flead]) frange_save_phrase = '-'.join([str(f).zfill(3) for f in flead]) else: frange_phrase = f's {flead[0]}'+u'\u2013'+f'{flead[-1]}' frange_save_phrase = f'{flead[0]:03d}-F{flead[-1]:03d}' frange_string = f'Forecast Hour{frange_phrase}' frange_save_string = f'F{frange_save_phrase}' df = df[df['LEAD_HOURS'].isin(flead)] elif isinstance(flead, tuple): frange_string = (f'Forecast Hours {flead[0]:02d}' +u'\u2013'+f'{flead[1]:02d}') frange_save_string = f'F{flead[0]:03d}-F{flead[1]:03d}' df = df[ (df['LEAD_HOURS'] >= flead[0]) & (df['LEAD_HOURS'] <= flead[1]) ] elif isinstance(flead, np.int): frange_string = f'Forecast Hour {flead:02d}' frange_save_string = f'F{flead:03d}' df = df[df['LEAD_HOURS'] == flead] else: e1 = f"Invalid forecast lead: \'{flead}\'" e2 = f"Please check settings for forecast leads" logger.error(e1) logger.error(e2) raise ValueError(e1+"\n"+e2) # Remove from date_hours the valid/init hours that don't exist in the # dataframe date_hours = np.array(date_hours)[[ str(x) in df[str(date_type).upper()].dt.hour.astype(str).tolist() for x in date_hours ]] if interp_pts and '' not in interp_pts: interp_shape = list(df['INTERP_MTHD'])[0] if 'SQUARE' in interp_shape: widths = [int(np.sqrt(float(p))) for p in interp_pts] elif 'CIRCLE' in interp_shape: widths = [int(np.sqrt(float(p)+4)) for p in interp_pts] elif np.all([int(p) == 1 for p in interp_pts]): widths = [1 for p in interp_pts] else: error_string = ( f"Unknown INTERP_MTHD used to compute INTERP_PNTS: {interp_shape}." + f" Check the INTERP_MTHD column in your METplus stats files." + f" INTERP_MTHD must have either \"SQUARE\" or \"CIRCLE\"" + f" in the name." ) logger.error(error_string) raise ValueError(error_string) if isinstance(interp_pts, list): if len(interp_pts) <= 8: if len(interp_pts) > 1: interp_pts_phrase = 's '+', '.join([str(p) for p in widths]) else: interp_pts_phrase = ' '+', '.join([str(p) for p in widths]) interp_pts_save_phrase = '-'.join([str(p) for p in widths]) else: interp_pts_phrase = f's {widths[0]}'+u'\u2013'+f'{widths[-1]}' interp_pts_save_phrase = f'{widths[0]}-{widths[-1]}' interp_pts_string = f'(Width{interp_pts_phrase})' interp_pts_save_string = f'width{interp_pts_save_phrase}' df = df[df['INTERP_PNTS'].isin(interp_pts)] elif isinstance(interp_pts, np.int): interp_pts_string = f'(Width {widths:d})' interp_pts_save_string = f'width{widths:d}' df = df[df['INTERP_PNTS'] == widths] else: error_string = ( f"Invalid interpolation points entry: \'{interp_pts}\'\n" + f"Please check settings for interpolation points." ) logger.error(error_string) raise ValueError(error_string) plevs = df['OBS_LEV'].str[1:].astype(int) df['PLEV'] = plevs.tolist() # Remove from model_list the models that don't exist in the dataframe cols_to_keep = [ str(model) in df['MODEL'].tolist() for model in model_list ] models_removed = [ str(m) for (m, keep) in zip(model_list, cols_to_keep) if not keep ] models_removed_string = ', '.join(models_removed) model_list = [ str(m) for (m, keep) in zip(model_list, cols_to_keep) if keep ] if not all(cols_to_keep): logger.warning( f"{models_removed_string} data were not found and will not be" + f" plotted." ) if df.empty: logger.warning(f"Empty Dataframe. Continuing onto next plot...") plt.close(num) logger.info("========================================") return None group_by = ['MODEL','PLEV'] if sample_equalization: df, bool_success = plot_util.equalize_samples(logger, df, group_by) if not bool_success: sample_equalization = False if df.empty: logger.warning(f"Empty Dataframe. Continuing onto next plot...") plt.close(num) logger.info("========================================") return None df_groups = df.groupby(group_by) # Aggregate unit statistics before calculating metrics if str(line_type).upper() == 'CTC': df_aggregated = df_groups.sum() else: df_aggregated = df_groups.mean() if sample_equalization: df_aggregated['COUNTS']=df_groups.size() # Remove data if they exist for some but not all models at some value of # the indep. variable. Otherwise plot_util.calculate_stat will throw an # error df_split = [df_aggregated.xs(str(model)) for model in model_list] df_reduced = reduce( lambda x,y: pd.merge( x, y, on='PLEV', how='inner' ), df_split ) df_aggregated = df_aggregated[ df_aggregated.index.get_level_values('PLEV').isin(df_reduced.index) ] if df_aggregated.empty: logger.warning(f"Empty Dataframe. Continuing onto next plot...") plt.close(num) logger.info("========================================") return None coef, const = (None, None) units = df['FCST_UNITS'].tolist()[0] metrics_using_var_units = [ 'BCRMSE','RMSE','BIAS','ME','FBAR','OBAR','MAE','FBAR_OBAR', 'SPEED_ERR','DIR_ERR','RMSVE','VDIFF_SPEED','VDIF_DIR', 'FBAR_OBAR_SPEED','FBAR_OBAR_DIR','FBAR_SPEED','FBAR_DIR' ] unit_convert = False if units in reference.unit_conversions: unit_convert = True var_long_name_key = df['FCST_VAR'].tolist()[0] if str(var_long_name_key).upper() == 'HGT': if str(df['OBS_VAR'].tolist()[0]).upper() in ['CEILING']: if units in ['m', 'gpm']: units = 'gpm' elif str(df['OBS_VAR'].tolist()[0]).upper() in ['HPBL']: unit_convert = False elif str(df['OBS_VAR'].tolist()[0]).upper() in ['HGT']: unit_convert = False if unit_convert: if metric2_name is not None: if (str(metric1_name).upper() in metrics_using_var_units and str(metric2_name).upper() in metrics_using_var_units): coef, const = ( reference.unit_conversions[units]['formula']( None, return_terms=True ) ) elif str(metric1_name).upper() in metrics_using_var_units: coef, const = ( reference.unit_conversions[units]['formula']( None, return_terms=True ) ) # Calculate desired metrics metric_long_names = [] for stat in [metric1_name, metric2_name]: if stat: stat_output = plot_util.calculate_stat( logger, df_aggregated, str(stat).lower(), [coef, const] ) df_aggregated[str(stat).upper()] = stat_output[0] metric_long_names.append(stat_output[2]) if confidence_intervals: ci_output = df_groups.apply( lambda x: plot_util.calculate_bootstrap_ci( logger, bs_method, x, str(stat).lower(), bs_nrep, ci_lev, bs_min_samp, [coef, const] ) ) if any(ci_output['STATUS'] == 1): logger.warning(f"Failed attempt to compute bootstrap" + f" confidence intervals. Sample size" + f" for one or more groups is too small." + f" Minimum sample size can be changed" + f" in settings.py.") logger.warning(f"Confidence intervals will not be" + f" plotted.") confidence_intervals = False continue ci_output = ci_output.reset_index(level=2, drop=True) ci_output = ( ci_output .reindex(df_aggregated.index) .reindex(ci_output.index) ) df_aggregated[str(stat).upper()+'_BLERR'] = ci_output[ 'CI_LOWER' ].values df_aggregated[str(stat).upper()+'_BUERR'] = ci_output[ 'CI_UPPER' ].values df_aggregated[str(metric1_name).upper()] = ( df_aggregated[str(metric1_name).upper()] ).astype(float).tolist() if metric2_name is not None: df_aggregated[str(metric2_name).upper()] = ( df_aggregated[str(metric2_name).upper()] ).astype(float).tolist() df_aggregated = df_aggregated[ df_aggregated.index.isin(model_list, level='MODEL') ] pivot_metric1 = pd.pivot_table( df_aggregated, values=str(metric1_name).upper(), columns='MODEL', index='PLEV' ) if sample_equalization: pivot_counts = pd.pivot_table( df_aggregated, values='COUNTS', columns='MODEL', index='PLEV' ) if metric2_name: pivot_metric2 = pd.pivot_table( df_aggregated, values=str(metric2_name).upper(), columns='MODEL', index='PLEV' ) if confidence_intervals: pivot_ci_lower1 = pd.pivot_table( df_aggregated, values=str(metric1_name).upper()+'_BLERR', columns='MODEL', index='PLEV' ) pivot_ci_upper1 = pd.pivot_table( df_aggregated, values=str(metric1_name).upper()+'_BUERR', columns='MODEL', index='PLEV' ) if metric2_name: pivot_ci_lower2 = pd.pivot_table( df_aggregated, values=str(metric2_name).upper()+'_BLERR', columns='MODEL', index='PLEV' ) pivot_ci_upper2 = pd.pivot_table( df_aggregated, values=str(metric2_name).upper()+'_BUERR', columns='MODEL', index='PLEV' ) if (metric2_name and (pivot_metric1.empty or pivot_metric2.empty)): print_varname = df['FCST_VAR'].tolist()[0] logger.warning( f"Could not find (and cannot plot) {metric1_name} and/or" + f" {metric2_name} stats for {print_varname} at any pressure" + f" level. Continuing ..." ) plt.close(num) logger.info("========================================") print("Quitting due to missing data. Check the log file for details.") return None elif not metric2_name and pivot_metric1.empty: print_varname = df['FCST_VAR'].tolist()[0] logger.warning( f"Could not find (and cannot plot) {metric1_name}" + f" stats for {print_varname} at any pressure level. " + f"Continuing ..." ) plt.close(num) logger.info("========================================") print("Quitting due to missing data. Check the log file for details.") return None models_renamed = [] count_renamed = 1 for requested_model in model_list: if requested_model in model_colors.model_alias: requested_model = ( model_colors.model_alias[requested_model]['settings_key'] ) if requested_model in model_settings: models_renamed.append(requested_model) else: models_renamed.append('model'+str(count_renamed)) count_renamed+=1 models_renamed = np.array(models_renamed) # Check that there are no repeated colors temp_colors = [ model_colors.get_color_dict(name)['color'] for name in models_renamed ] colors_corrected=False loop_count=0 while not colors_corrected and loop_count < 10: unique, counts = np.unique(temp_colors, return_counts=True) repeated_colors = [u for i, u in enumerate(unique) if counts[i] > 1] if repeated_colors: for c in repeated_colors: models_sharing_colors = models_renamed[ np.array(temp_colors)==c ] if np.flatnonzero(np.core.defchararray.find( models_sharing_colors, 'model')!=-1): need_to_rename = models_sharing_colors[ np.flatnonzero(np.core.defchararray.find( models_sharing_colors, 'model' )!=-1)[0] ] else: continue models_renamed[models_renamed==need_to_rename] = ( 'model'+str(count_renamed) ) count_renamed+=1 temp_colors = [ model_colors.get_color_dict(name)['color'] for name in models_renamed ] loop_count+=1 else: colors_corrected = True mod_setting_dicts = [ model_colors.get_color_dict(name) for name in models_renamed ] # Plot data logger.info("Begin plotting ...") if confidence_intervals: indices_in_common1 = list(set.intersection(*map( set, [ pivot_metric1.index, pivot_ci_lower1.index, pivot_ci_upper1.index ] ))) pivot_metric1 = pivot_metric1[pivot_metric1.index.isin(indices_in_common1)] pivot_ci_lower1 = pivot_ci_lower1[pivot_ci_lower1.index.isin(indices_in_common1)] pivot_ci_upper1 = pivot_ci_upper1[pivot_ci_upper1.index.isin(indices_in_common1)] if sample_equalization: pivot_counts = pivot_counts[pivot_counts.index.isin(indices_in_common1)] if metric2_name is not None: indices_in_common2 = list(set.intersection(*map( set, [ pivot_metric2.index, pivot_ci_lower2.index, pivot_ci_upper2.index ] ))) pivot_metric2 = pivot_metric2[pivot_metric2.index.isin(indices_in_common2)] pivot_ci_lower2 = pivot_ci_lower2[pivot_ci_lower2.index.isin(indices_in_common2)] pivot_ci_upper2 = pivot_ci_upper2[pivot_ci_upper2.index.isin(indices_in_common2)] y_vals1 = pivot_metric1.index if metric2_name is not None: y_vals2 = pivot_metric2.index plev_incr = np.abs(np.diff(y_vals1)) if plev_incr.size == 0: min_incr = 100 else: min_incr = np.min(plev_incr) x_min = x_min_limit x_max = x_max_limit plot_reference = [False, False] ref_metrics = ['OBAR'] if str(metric1_name).upper() in ref_metrics: plot_reference[0] = True pivot_reference1 = pivot_metric1 reference1 = pivot_reference1.mean(axis=1) if confidence_intervals: reference_ci_lower1 = pivot_ci_lower1.mean(axis=1) reference_ci_upper1 = pivot_ci_upper1.mean(axis=1) if not np.any((pivot_reference1.T/reference1).T == 1.): logger.warning( f"{str(metric1_name).upper()} is requested, but the value " + f"varies from model to model. " + f"Will plot an individual line for each model. If a " + f"single reference line is preferred, set the " + f"sample_equalization toggle in ush/settings.py to 'True', " + f"and check in the log file if sample equalization " + f"completed successfully." ) plot_reference[0] = False if metric2_name is not None and str(metric2_name).upper() in ref_metrics: plot_reference[1] = True pivot_reference2 = pivot_metric2 reference2 = pivot_reference2.mean(axis=1) if confidence_intervals: reference_ci_lower2 = pivot_ci_lower2.mean(axis=1) reference_ci_upper2 = pivot_ci_upper2.mean(axis=1) if not np.any((pivot_reference2.T/reference2).T == 1.): logger.warning( f"{str(metric2_name).upper()} is requested, but the value " + f"varies from model to model. " + f"Will plot an individual line for each model. If a " + f"single reference line is preferred, set the " + f"sample_equalization toggle in ush/settings.py to 'True', " + f"and check in the log file if sample equalization " + f"completed successfully." ) plot_reference[1] = False if np.any(plot_reference): plotted_reference = [False, False] if confidence_intervals: plotted_reference_CIs = [False, False] f = lambda m,c,ls,lw,ms,mec: plt.plot( [], [], marker=m, mec=mec, mew=2., c=c, ls=ls, lw=lw, ms=ms )[0] if metric2_name is not None: if np.any(plot_reference): ref_color_dict = model_colors.get_color_dict('obs') handles = [] labels = [] line_settings = ['solid','dashed'] metric_names = [metric1_name, metric2_name] for p, rbool in enumerate(plot_reference): if rbool: handles += [ f('', ref_color_dict['color'], line_settings[p], 5., 0, 'white') ] else: handles += [ f('', 'black', line_settings[p], 5., 0, 'white') ] labels += [ str(metric_names[p]).upper() ] else: handles = [ f('', 'black', line_setting, 5., 0, 'white') for line_setting in ['solid','dashed'] ] labels = [ str(metric_name).upper() for metric_name in [metric1_name, metric2_name] ] else: handles = [] labels = [] n_mods = 0 for m in range(len(mod_setting_dicts)): if model_list[m] in model_colors.model_alias: model_plot_name = ( model_colors.model_alias[model_list[m]]['plot_name'] ) else: model_plot_name = model_list[m] if str(model_list[m]) not in pivot_metric1: continue x_vals_metric1 = pivot_metric1[str(model_list[m])].values x_vals_metric1_mean = np.nanmean(x_vals_metric1) if metric2_name is not None: x_vals_metric2 = pivot_metric2[str(model_list[m])].values x_vals_metric2_mean = np.nanmean(x_vals_metric2) if confidence_intervals: x_vals_ci_lower1 = pivot_ci_lower1[ str(model_list[m]) ].values x_vals_ci_upper1 = pivot_ci_upper1[ str(model_list[m]) ].values if metric2_name is not None: x_vals_ci_lower2 = pivot_ci_lower2[ str(model_list[m]) ].values x_vals_ci_upper2 = pivot_ci_upper2[ str(model_list[m]) ].values if not x_lim_lock: if metric2_name is not None: x_vals_both_metrics = np.concatenate((x_vals_metric1, x_vals_metric2)) if np.any(x_vals_both_metrics != np.inf): x_vals_metric_min = np.nanmin( x_vals_both_metrics[x_vals_both_metrics != np.inf] ) x_vals_metric_max = np.nanmax( x_vals_both_metrics[x_vals_both_metrics != np.inf] ) else: x_vals_metric_min = np.nanmin([ x_vals_both_metrics ]) x_vals_metric_max = np.nanmax([ x_vals_both_metrics ]) else: if np.any(x_vals_metric1 != np.inf): x_vals_metric_min = np.nanmin( x_vals_metric1[x_vals_metric1 != np.inf] ) x_vals_metric_max = np.nanmax( x_vals_metric1[x_vals_metric1 != np.inf] ) else: x_vals_metric_min = np.nanmin(x_vals_metric1) x_vals_metric_max = np.nanmax(x_vals_metric1) if n_mods == 0: x_mod_min = x_vals_metric_min x_mod_max = x_vals_metric_max n_mods+=1 else: if math.isinf(x_mod_min): x_mod_min = x_vals_metric_min else: x_mod_min = np.nanmin([x_mod_min, x_vals_metric_min]) if math.isinf(x_mod_max): x_mod_max = x_vals_metric_max else: x_mod_max = np.nanmax([x_mod_max, x_vals_metric_max]) if (x_vals_metric_min > x_min_limit and x_vals_metric_min <= x_mod_min): x_min = x_vals_metric_min if (x_vals_metric_max < x_max_limit and x_vals_metric_max >= x_mod_max): x_max = x_vals_metric_max if np.abs(x_vals_metric1_mean) < 1E4: metric1_mean_fmt_string = f'{x_vals_metric1_mean:.2f}' else: metric1_mean_fmt_string = f'{x_vals_metric1_mean:.2E}' if plot_reference[0]: if not plotted_reference[0]: ref_color_dict = model_colors.get_color_dict('obs') plt.plot( reference1, y_vals1.tolist(), marker=ref_color_dict['marker'], c=ref_color_dict['color'], mew=2., mec='white', figure=fig, ms=ref_color_dict['markersize'], ls='solid', lw=ref_color_dict['linewidth'] ) plotted_reference[0] = True else: plt.plot( x_vals_metric1, y_vals1.tolist(), marker=mod_setting_dicts[m]['marker'], c=mod_setting_dicts[m]['color'], mew=2., mec='white', figure=fig, ms=mod_setting_dicts[m]['markersize'], ls='solid', lw=mod_setting_dicts[m]['linewidth'] ) if metric2_name is not None: if np.abs(x_vals_metric2_mean) < 1E4: metric2_mean_fmt_string = f'{x_vals_metric2_mean:.2f}' else: metric2_mean_fmt_string = f'{x_vals_metric2_mean:.2E}' if plot_reference[1]: if not plotted_reference[0]: ref_color_dict = model_colors.get_color_dict('obs') plt.plot( reference2, y_vals2.tolist(), marker=ref_color_dict['marker'], c=ref_color_dict['color'], mew=2., mec='white', figure=fig, ms=ref_color_dict['markersize'], ls='dashed', lw=ref_color_dict['linewidth'] ) plotted_reference[1] = True else: plt.plot( x_vals_metric2, y_vals2.tolist(), marker=mod_setting_dicts[m]['marker'], c=mod_setting_dicts[m]['color'], mew=2., mec='white', figure=fig, ms=mod_setting_dicts[m]['markersize'], ls='dashed', lw=mod_setting_dicts[m]['linewidth'] ) if confidence_intervals: if plot_reference[0]: if not plotted_reference_CIs[0]: ref_color_dict = model_colors.get_color_dict('obs') plt.errorbar( reference1, y_vals1.tolist(), xerr=[np.abs(reference_ci_lower1), reference_ci_upper1], fmt='none', ecolor=ref_color_dict['color'], elinewidth=ref_color_dict['linewidth']/1.5, capsize=9., capthick=ref_color_dict['linewidth']/1.5, alpha=.70, zorder=0 ) plotted_reference_CIs[0] = True else: plt.errorbar( x_vals_metric1, y_vals1.tolist(), xerr=[np.abs(x_vals_ci_lower1), x_vals_ci_upper1], fmt='none', ecolor=mod_setting_dicts[m]['color'], elinewidth=mod_setting_dicts[m]['linewidth']/1.5, capsize=9., capthick=mod_setting_dicts[m]['linewidth']/1.5, alpha=.70, zorder=0 ) if metric2_name is not None: if plot_reference[1]: if not plotted_reference_CIs[1]: ref_color_dict = model_colors.get_color_dict('obs') plt.errorbar( reference2, y_vals2.tolist(), xerr=[np.abs(reference_ci_lower2), reference_ci_upper2], fmt='none', ecolor=ref_color_dict['color'], elinewidth=ref_color_dict['linewidth']/1.5, capsize=9., capthick=ref_color_dict['linewidth']/1.5, alpha=.70, zorder=0 ) plotted_reference_CIs[1] = True else: plt.errorbar( x_vals_metric2, y_vals2.tolist(), xerr=[np.abs(x_vals_ci_lower2), x_vals_ci_upper2], fmt='none', ecolor=mod_setting_dicts[m]['color'], elinewidth=mod_setting_dicts[m]['linewidth']/1.5, capsize=9., capthick=mod_setting_dicts[m]['linewidth']/1.5, alpha=.70, zorder=0 ) handles+=[ f( mod_setting_dicts[m]['marker'], mod_setting_dicts[m]['color'], 'solid', mod_setting_dicts[m]['linewidth'], mod_setting_dicts[m]['markersize'], 'white' ) ] if display_averages: if metric2_name is not None: labels+=[ f'{model_plot_name} ({metric1_mean_fmt_string},' + f' {metric2_mean_fmt_string})' ] else: labels+=[ f'{model_plot_name} ({metric1_mean_fmt_string})' ] else: labels+=[f'{model_plot_name}'] # Zero line plt.axvline(x=0, color='black', linestyle='--', linewidth=1, zorder=0) # Configure axis ticks yticks = [1000, 925, 850, 700, 500, 300, 250, 200, 100, 50, 10, 1] if metric2_name is not None: yticks = np.array([ ytick for ytick in yticks if ( ytick>=min([min(y_vals1),min(y_vals2)]) and ytick<=max([max(y_vals1),max(y_vals2)]) ) ]) else: yticks = np.array([ ytick for ytick in yticks if ( ytick>=min(y_vals1) and ytick<=max(y_vals1) ) ]) ytick_labels = yticks.astype(str) # x ticks and axis limits adjust based on the size of the x_range x_range_categories = np.array([ [np.power(10.,x), 2.*np.power(10.,x)] for x in [-5,-4,-3,-2,-1,0,1,2,3,4,5] ]).flatten() round_to_nearest_categories = x_range_categories/20. if math.isinf(x_max): x_max = x_max_limit if math.isinf(x_min): x_min = x_min_limit x_range = x_max-x_min round_to_nearest = round_to_nearest_categories[ np.digitize(x_range, x_range_categories[:-1]) ] xlim_min = np.floor(x_min/round_to_nearest)*round_to_nearest xlim_max = np.ceil(x_max/round_to_nearest)*round_to_nearest if len(str(xlim_min)) > 5 and np.abs(xlim_min) < 1.: xlim_min = float( np.format_float_scientific(xlim_min, unique=False, precision=3) ) xticks = np.arange(xlim_min, xlim_max+round_to_nearest, round_to_nearest) if any([len(str(xtick)) > 5 and np.abs(xtick) < 1. for xtick in xticks]): xtick_labels = [] for xtick in xticks: xtick_labels.append(float(np.format_float_scientific( xtick, unique=False, precision=3 ))) else: xtick_labels = [str(xtick) for xtick in xticks] if len(xticks) < 20: if max([len(str(xtick)) for xtick in xticks]) < 5: show_xtick_every = 1 else: show_xtick_every = 2 else: show_xtick_every = 2 xtick_labels_with_blanks = ['' for item in xtick_labels] for i, item in enumerate(xtick_labels[::int(show_xtick_every)]): xtick_labels_with_blanks[int(show_xtick_every)*i] = item var_long_name_key = df['FCST_VAR'].tolist()[0] if str(var_long_name_key).upper() == 'HGT': if str(df['OBS_VAR'].tolist()[0]).upper() in ['CEILING']: var_long_name_key = 'HGTCLDCEIL' elif str(df['OBS_VAR'].tolist()[0]).upper() in ['HPBL']: var_long_name_key = 'HPBL' var_long_name = variable_translator[var_long_name_key] if unit_convert: units = reference.unit_conversions[units]['convert_to'] if units == '-': units = '' if metric2_name is not None: metric1_string, metric2_string = metric_long_names if (str(metric1_name).upper() in metrics_using_var_units and str(metric2_name).upper() in metrics_using_var_units): if units: xlabel = f'{var_long_name} ({units})' else: xlabel = f'{var_long_name} (unitless)' else: xlabel = f'{metric1_string} and {metric2_string}' else: metric1_string = metric_long_names[0] if str(metric1_name).upper() in metrics_using_var_units: if units: xlabel = f'{var_long_name} ({units})' else: xlabel = f'{var_long_name} (unitless)' else: xlabel = f'{metric1_string}' ax.set_xlim(xlim_min, xlim_max) if y_lim_lock: y_min, y_max = [y_min_limit, y_max_limit] else: if metric2_name is not None: y_min, y_max = [ min([min(y_vals1),min(y_vals2)]), max([max(y_vals1),max(y_vals2)]) ] else: y_min, y_max = [ min(y_vals1), max(y_vals1) ] if y_min < y_min_limit: y_min = y_min_limit if y_max > y_max_limit: y_max = y_max_limit y_buffer_size = .015 ylim_min = np.exp(np.log(y_min)-y_buffer_size) ylim_max = np.exp(np.log(y_max)+y_buffer_size) ax.set_ylim(ylim_min, ylim_max) # Y-values should decrease upward; set the following after setting ax # limits ax.invert_yaxis() # Pressure decreases logarithmically with height ax.set_yscale('log') ax.set_xlabel(xlabel) ax.set_ylabel(ylabel) ax.set_yticklabels(ytick_labels) ax.set_xticklabels(xtick_labels_with_blanks) ax.set_xticks(xticks) ax.set_yticks(yticks) ax.tick_params( labelleft=True, labelright=False, labelbottom=True, labeltop=False ) ax.tick_params( left=False, labelleft=False, labelright=False, labelbottom=False, labeltop=False, which='minor', axis='y' ) ax.legend( handles, labels, loc='upper center', fontsize=15, framealpha=1, bbox_to_anchor=(0.5, -0.08), ncol=4, frameon=True, numpoints=2, borderpad=.8, labelspacing=2., columnspacing=3., handlelength=3., handletextpad=.4, borderaxespad=.5) fig.subplots_adjust(bottom=.2, wspace=0., hspace=0) ax.grid( visible=True, which='major', axis='both', alpha=.5, linestyle='--', linewidth=.5, zorder=0 ) if sample_equalization: counts = pivot_counts.mean(axis=1, skipna=True).fillna('') for count, yval in zip(counts, y_vals1.tolist()): if not isinstance(count, str): count = str(int(count)) ax.annotate( f'{count}', xy=(1.,yval), xycoords=('axes fraction','data'), xytext=(9,0), textcoords='offset points', va='center', fontsize=16, color='dimgrey', ha='left' ) ax.annotate( '#SAMPLES', xy=(1.,1.), xycoords='axes fraction', xytext=(9,5), textcoords='offset points', va='bottom', fontsize=11, color='dimgrey', ha='right' ) fig.subplots_adjust(right=.95) # Title domain = df['VX_MASK'].tolist()[0] var_savename = df['FCST_VAR'].tolist()[0] if 'APCP' in var_savename.upper(): var_savename = 'APCP' elif str(df['OBS_VAR'].tolist()[0]).upper() in ['HPBL']: var_savename = 'HPBL' elif str(df['OBS_VAR'].tolist()[0]).upper() in ['MSLET','MSLMA','PRMSL']: var_savename = 'MSLET' if domain in list(domain_translator.keys()): domain_string = domain_translator[domain]['long_name'] domain_save_string = domain_translator[domain]['save_name'] else: domain_string = domain domain_save_string = domain date_hours_string = plot_util.get_name_for_listed_items( [f'{date_hour:02d}' for date_hour in date_hours], ', ', '', 'Z', 'and ', '' ) ''' date_hours_string = ' '.join([ f'{date_hour:02d}Z,' for date_hour in date_hours ]) ''' date_start_string = date_range[0].strftime('%d %b %Y') date_end_string = date_range[1].strftime('%d %b %Y') if metric2_name is not None: title1 = f'{metric1_string} and {metric2_string}' else: title1 = f'{metric1_string}' if interp_pts and '' not in interp_pts: title1+=f' {interp_pts_string}' if units: title2 = f'{var_long_name} ({units}), {domain_string}' else: title2 = f'{var_long_name} (unitless), {domain_string}' title3 = (f'{str(date_type).capitalize()} {date_hours_string}' + f' {date_start_string} to {date_end_string}, {frange_string}') title_center = '\n'.join([title1, title2, title3]) ax.set_title(title_center, loc=plotter.title_loc) logger.info("... Plotting complete.") # Logos if plot_logo_left: if os.path.exists(path_logo_left): left_logo_arr = mpimg.imread(path_logo_left) left_image_box = OffsetImage(left_logo_arr, zoom=zoom_logo_left*0.9) ab_left = AnnotationBbox( left_image_box, xy=(0.,1.), xycoords='axes fraction', xybox=(0, 3), boxcoords='offset points', frameon = False, box_alignment=(0,0) ) ax.add_artist(ab_left) else: logger.warning( f"Left logo path ({path_logo_left}) doesn't exist. " + f"Left logo will not be plotted." ) if plot_logo_right: if os.path.exists(path_logo_right): right_logo_arr = mpimg.imread(path_logo_right) if sample_equalization: right_image_box = OffsetImage(right_logo_arr, zoom=zoom_logo_right*0.65) ab_right = AnnotationBbox( right_image_box, xy=(1.,1.), xycoords='axes fraction', xybox=(0, 20), boxcoords='offset points', frameon = False, box_alignment=(1,0) ) else: right_image_box = OffsetImage(right_logo_arr, zoom=zoom_logo_right*0.9) ab_right = AnnotationBbox( right_image_box, xy=(1.,1.), xycoords='axes fraction', xybox=(0, 3), boxcoords='offset points', frameon = False, box_alignment=(1,0) ) ax.add_artist(ab_right) else: logger.warning( f"Right logo path ({path_logo_right}) doesn't exist. " + f"Right logo will not be plotted." ) # Saving models_savename = '_'.join([str(model) for model in model_list]) if len(date_hours) <= 8: date_hours_savename = '_'.join([ f'{date_hour:02d}Z' for date_hour in date_hours ]) else: date_hours_savename = '-'.join([ f'{date_hour:02d}Z' for date_hour in [date_hours[0], date_hours[-1]] ]) date_start_savename = date_range[0].strftime('%Y%m%d') date_end_savename = date_range[1].strftime('%Y%m%d') if str(eval_period).upper() == 'TEST': time_period_savename = f'{date_start_savename}-{date_end_savename}' else: time_period_savename = f'{eval_period}' plot_info = '_'.join( [item for item in [ f'vertprof', f'{str(date_type).lower()}{str(date_hours_savename).lower()}', f'{str(frange_save_string).lower()}', ] if item] ) save_name = (f'{str(metric1_name).lower()}') if metric2_name is not None: save_name+=f'_{str(metric2_name).lower()}' if interp_pts and '' not in interp_pts: save_name+=f'_{str(interp_pts_save_string).lower()}' save_name+=f'.{str(var_savename).lower()}' save_name+=f'.{str(time_period_savename).lower()}' save_name+=f'.{plot_info}' save_name+=f'.{str(domain_save_string).lower()}' if save_header: save_name = f'{save_header}.'+save_name save_subdir = os.path.join( save_dir, f'{str(plot_group).lower()}', f'{str(time_period_savename).lower()}' ) if not os.path.isdir(save_subdir): os.makedirs(save_subdir) save_path = os.path.join(save_subdir, save_name+'.png') fig.savefig(save_path, dpi=dpi) logger.info(u"\u2713"+f" plot saved successfully as {save_path}") plt.close(num) logger.info('========================================') def main(): # Logging log_metplus_dir = '/' for subdir in LOG_TEMPLATE.split('/')[:-1]: log_metplus_dir = os.path.join(log_metplus_dir, subdir) if not os.path.isdir(log_metplus_dir): os.makedirs(log_metplus_dir) logger = logging.getLogger(LOG_TEMPLATE) logger.setLevel(LOG_LEVEL) formatter = logging.Formatter( '%(asctime)s.%(msecs)03d (%(filename)s:%(lineno)d) %(levelname)s: ' + '%(message)s', '%m/%d %H:%M:%S' ) file_handler = logging.FileHandler(LOG_TEMPLATE, mode='a') file_handler.setFormatter(formatter) logger.addHandler(file_handler) logger_info = f"Log file: {LOG_TEMPLATE}" print(logger_info) logger.info(logger_info) if str(EVAL_PERIOD).upper() == 'TEST': valid_beg = VALID_BEG valid_end = VALID_END init_beg = INIT_BEG init_end = INIT_END else: valid_beg = presets.date_presets[EVAL_PERIOD]['valid_beg'] valid_end = presets.date_presets[EVAL_PERIOD]['valid_end'] init_beg = presets.date_presets[EVAL_PERIOD]['init_beg'] init_end = presets.date_presets[EVAL_PERIOD]['init_end'] if str(DATE_TYPE).upper() == 'VALID': date_beg = valid_beg date_end = valid_end date_hours = VALID_HOURS date_type_string = DATE_TYPE elif str(DATE_TYPE).upper() == 'INIT': date_beg = init_beg date_end = init_end date_hours = INIT_HOURS date_type_string = 'Initialization' else: e = ( f"Invalid DATE_TYPE: {str(date_type).upper()}. Valid values are" + f" VALID or INIT" ) logger.error(e) raise ValueError(e) logger.debug('========================================') logger.debug("Config file settings") logger.debug(f"LOG_LEVEL: {LOG_LEVEL}") logger.debug(f"MET_VERSION: {MET_VERSION}") logger.debug(f"IMG_HEADER: {IMG_HEADER if IMG_HEADER else 'No header'}") logger.debug(f"STAT_OUTPUT_BASE_DIR: {STAT_OUTPUT_BASE_DIR}") logger.debug(f"STATS_DIR: {STATS_DIR}") logger.debug(f"PRUNE_DIR: {PRUNE_DIR}") logger.debug(f"SAVE_DIR: {SAVE_DIR}") logger.debug(f"VERIF_CASETYPE: {VERIF_CASETYPE}") logger.debug(f"MODELS: {MODELS}") logger.debug(f"VARIABLES: {VARIABLES}") logger.debug(f"DOMAINS: {DOMAINS}") logger.debug(f"INTERP: {INTERP}") logger.debug(f"DATE_TYPE: {DATE_TYPE}") logger.debug( f"EVAL_PERIOD: {EVAL_PERIOD}" ) logger.debug( f"{DATE_TYPE}_BEG: {date_beg}" ) logger.debug( f"{DATE_TYPE}_END: {date_end}" ) logger.debug(f"VALID_HOURS: {VALID_HOURS}") logger.debug(f"INIT_HOURS: {INIT_HOURS}") logger.debug(f"FCST_LEADS: {FLEADS}") logger.debug(f"FCST_LEVELS: {FCST_LEVELS}") logger.debug(f"OBS_LEVELS: {OBS_LEVELS}") logger.debug( f"FCST_THRESH: {FCST_THRESH if FCST_THRESH else 'No thresholds'}" ) logger.debug( f"OBS_THRESH: {OBS_THRESH if OBS_THRESH else 'No thresholds'}" ) logger.debug(f"LINE_TYPE: {LINE_TYPE}") logger.debug(f"METRICS: {METRICS}") logger.debug(f"CONFIDENCE_INTERVALS: {CONFIDENCE_INTERVALS}") logger.debug(f"INTERP_PNTS: {INTERP_PNTS if INTERP_PNTS else 'No interpolation points'}") logger.debug('----------------------------------------') logger.debug(f"Advanced settings (configurable in {SETTINGS_DIR}/settings.py)") logger.debug(f"Y_MIN_LIMIT: {Y_MIN_LIMIT}") logger.debug(f"Y_MAX_LIMIT: {Y_MAX_LIMIT}") logger.debug(f"Y_LIM_LOCK: {Y_LIM_LOCK}") logger.debug(f"X_MIN_LIMIT: {X_MIN_LIMIT}") logger.debug(f"X_MAX_LIMIT: {X_MAX_LIMIT}") logger.debug(f"X_LIM_LOCK: {X_LIM_LOCK}") logger.debug(f"Display averages? {'yes' if display_averages else 'no'}") logger.debug( f"Clear prune directories? {'yes' if clear_prune_dir else 'no'}" ) logger.debug(f"Plot upper-left logo? {'yes' if plot_logo_left else 'no'}") logger.debug( f"Plot upper-right logo? {'yes' if plot_logo_right else 'no'}" ) logger.debug(f"Upper-left logo path: {path_logo_left}") logger.debug(f"Upper-right logo path: {path_logo_right}") logger.debug( f"Upper-left logo fraction of original size: {zoom_logo_left}" ) logger.debug( f"Upper-right logo fraction of original size: {zoom_logo_right}" ) if CONFIDENCE_INTERVALS: logger.debug(f"Confidence Level: {int(ci_lev*100)}%") logger.debug(f"Bootstrap method: {bs_method}") logger.debug(f"Bootstrap repetitions: {bs_nrep}") logger.debug( f"Minimum sample size for confidence intervals: {bs_min_samp}" ) logger.debug('========================================') date_range = ( datetime.strptime(date_beg, '%Y%m%d'), datetime.strptime(date_end, '%Y%m%d')+td(days=1)-td(milliseconds=1) ) if len(METRICS) == 1: metrics = (METRICS[0], None) elif len(METRICS) > 1: metrics = METRICS[:2] else: e = (f"Received no list of metrics. Check that, for the METRICS" + f" setting, a comma-separated string of at least one metric is" + f" provided") logger.error(e) raise ValueError(e) fcst_thresh_symbol, fcst_thresh_letter = list( zip(*[plot_util.format_thresh(thresh) for thresh in FCST_THRESH]) ) obs_thresh_symbol, obs_thresh_letter = list( zip(*[plot_util.format_thresh(thresh) for thresh in OBS_THRESH]) ) num=0 e = '' if str(VERIF_CASETYPE).lower() not in list(reference.case_type.keys()): e = (f"The requested verification case/type combination is not valid:" + f" {VERIF_CASETYPE}") elif str(LINE_TYPE).upper() not in list( reference.case_type[str(VERIF_CASETYPE).lower()].keys()): e = (f"The requested line_type is not valid for {VERIF_CASETYPE}:" + f" {LINE_TYPE}") else: case_specs = ( reference.case_type [str(VERIF_CASETYPE).lower()] [str(LINE_TYPE).upper()] ) if e: logger.error(e) logger.error("Quitting ...") raise ValueError(e+"\nQuitting ...") if (str(INTERP).upper() not in case_specs['interp'].replace(' ','').split(',')): e = (f"The requested interp method is not valid for the" + f" requested case type ({VERIF_CASETYPE}) and" + f" line_type ({LINE_TYPE}): {INTERP}") logger.error(e) logger.error("Quitting ...") raise ValueError(e+"\nQuitting ...") for metric in metrics: if metric is not None: if (str(metric).lower() not in case_specs['plot_stats_list'] .replace(' ','').split(',')): e = (f"The requested metric is not valid for the" + f" requested case type ({VERIF_CASETYPE}) and" + f" line type ({LINE_TYPE}): {metric}") logger.error(e) logger.error("Quitting ...") raise ValueError(e+"\nQuitting ...") for requested_var in VARIABLES: if requested_var in list(case_specs['var_dict'].keys()): var_specs = case_specs['var_dict'][requested_var] else: e = (f"The requested variable is not valid for the requested case" + f" type ({VERIF_CASETYPE}) and line_type ({LINE_TYPE}):" + f"{requested_var}") logger.warning(e) logger.warning("Continuing ...") continue fcst_var_names = var_specs['fcst_var_names'] obs_var_names = var_specs['obs_var_names'] symbol_keep = [] letter_keep = [] for fcst_thresh, obs_thresh in list( zip(*[fcst_thresh_symbol, obs_thresh_symbol])): if (fcst_thresh in var_specs['fcst_var_thresholds'] and obs_thresh in var_specs['obs_var_thresholds']): symbol_keep.append(True) else: symbol_keep.append(False) for fcst_thresh, obs_thresh in list( zip(*[fcst_thresh_letter, obs_thresh_letter])): if (fcst_thresh in var_specs['fcst_var_thresholds'] and obs_thresh in var_specs['obs_var_thresholds']): letter_keep.append(True) else: letter_keep.append(False) keep = np.add(letter_keep, symbol_keep) dropped_items = np.array(FCST_THRESH)[~keep].tolist() fcst_thresh = np.array(FCST_THRESH)[keep].tolist() obs_thresh = np.array(OBS_THRESH)[keep].tolist() if dropped_items: dropped_items_string = ', '.join(dropped_items) e = (f"The requested thresholds are not valid for the requested" + f" case type ({VERIF_CASETYPE}) and line_type" + f" ({LINE_TYPE}): {dropped_items_string}") logger.warning(e) logger.warning("Continuing ...") plot_group = var_specs['plot_group'] if len(FCST_LEVELS) != len(OBS_LEVELS): e = ("FCST_LEVELS and OBS_LEVELS must be lists of the same" + f" size") logger.error(e) logger.error("Quitting ...") raise ValueError(e+"\nQuitting ...") keep = [] for l, fcst_level in enumerate(FCST_LEVELS): if (FCST_LEVELS[l] not in var_specs['fcst_var_levels'] or OBS_LEVELS[l] not in var_specs['obs_var_levels']): keep.append(False) else: keep.append(True) keep = np.array(keep) dropped_items = np.array(FCST_LEVELS)[~keep].tolist() fcst_levels = np.array(FCST_LEVELS)[keep].tolist() if dropped_items: dropped_items_string = ', '.join(dropped_items) e = (f"The requested levels are not valid for the requested" + f" case type ({VERIF_CASETYPE}) and line_type" + f" ({LINE_TYPE}): {dropped_items_string}") logger.warning(e) logger.warning("Continuing ...") for domain in DOMAINS: if str(domain) not in case_specs['vx_mask_list']: e = (f"The requested domain is not valid for the requested" + f" case type ({VERIF_CASETYPE}) and line_type" + f" ({LINE_TYPE}): {domain}") logger.warning(e) logger.warning("Continuing ...") continue df = df_preprocessing.get_preprocessed_data( logger, STATS_DIR, PRUNE_DIR, OUTPUT_BASE_TEMPLATE, VERIF_CASE, VERIF_TYPE, LINE_TYPE, DATE_TYPE, date_range, EVAL_PERIOD, date_hours, FLEADS, requested_var, fcst_var_names, obs_var_names, MODELS, domain, INTERP, MET_VERSION, clear_prune_dir ) if df is None: continue plot_stat_by_level( df, logger, date_range, MODELS, num=num, flead=FLEADS, levels=fcst_levels, metric1_name=metrics[0], metric2_name=metrics[1], date_type=DATE_TYPE, x_min_limit=X_MIN_LIMIT, x_max_limit=X_MAX_LIMIT, x_lim_lock=X_LIM_LOCK, y_min_limit=Y_MIN_LIMIT, y_max_limit=Y_MAX_LIMIT, y_lim_lock=Y_LIM_LOCK, ylabel='Pressure Level (hPa)', line_type=LINE_TYPE, date_hours=date_hours, save_dir=SAVE_DIR, eval_period=EVAL_PERIOD, display_averages=display_averages, save_header=IMG_HEADER, plot_group=plot_group, confidence_intervals=CONFIDENCE_INTERVALS, interp_pts=INTERP_PNTS, bs_nrep=bs_nrep, bs_method=bs_method, ci_lev=ci_lev, bs_min_samp=bs_min_samp, sample_equalization=sample_equalization, plot_logo_left=plot_logo_left, plot_logo_right=plot_logo_right, path_logo_left=path_logo_left, path_logo_right=path_logo_right, zoom_logo_left=zoom_logo_left, zoom_logo_right=zoom_logo_right ) num+=1 # ============ START USER CONFIGURATIONS ================ if __name__ == "__main__": print("\n=================== CHECKING CONFIG VARIABLES =====================\n") LOG_TEMPLATE = check_LOG_TEMPLATE(os.environ['LOG_TEMPLATE']) LOG_LEVEL = check_LOG_LEVEL(os.environ['LOG_LEVEL']) MET_VERSION = check_MET_VERSION(os.environ['MET_VERSION']) IMG_HEADER = check_IMG_HEADER(os.environ['IMG_HEADER']) VERIF_CASE = check_VERIF_CASE(os.environ['VERIF_CASE']) VERIF_TYPE = check_VERIF_TYPE(os.environ['VERIF_TYPE']) STAT_OUTPUT_BASE_DIR = check_STAT_OUTPUT_BASE_DIR(os.environ['STAT_OUTPUT_BASE_DIR']) STATS_DIR = STAT_OUTPUT_BASE_DIR PRUNE_DIR = check_PRUNE_DIR(os.environ['PRUNE_DIR']) SAVE_DIR = check_SAVE_DIR(os.environ['SAVE_DIR']) DATE_TYPE = check_DATE_TYPE(os.environ['DATE_TYPE']) LINE_TYPE = check_LINE_TYPE(os.environ['LINE_TYPE']) INTERP = check_INTERP(os.environ['INTERP']) MODELS = check_MODELS(os.environ['MODELS']).replace(' ','').split(',') DOMAINS = check_VX_MASK_LIST(os.environ['VX_MASK_LIST']).replace(' ','').split(',') # valid hour (each plot will use all available valid_hours listed below) VALID_HOURS = check_FCST_VALID_HOUR(os.environ['FCST_VALID_HOUR'], DATE_TYPE).replace(' ','').split(',') INIT_HOURS = check_FCST_INIT_HOUR(os.environ['FCST_INIT_HOUR'], DATE_TYPE).replace(' ','').split(',') # time period to cover (inclusive) EVAL_PERIOD = check_EVAL_PERIOD(os.environ['EVAL_PERIOD']) VALID_BEG = check_VALID_BEG(os.environ['VALID_BEG'], DATE_TYPE, EVAL_PERIOD, plot_type='time_series') VALID_END = check_VALID_END(os.environ['VALID_END'], DATE_TYPE, EVAL_PERIOD, plot_type='time_series') INIT_BEG = check_INIT_BEG(os.environ['INIT_BEG'], DATE_TYPE, EVAL_PERIOD, plot_type='time_series') INIT_END = check_INIT_END(os.environ['INIT_END'], DATE_TYPE, EVAL_PERIOD, plot_type='time_series') # list of variables # Options: {'TMP','HGT','CAPE','RH','DPT','UGRD','VGRD','UGRD_VGRD','TCDC', # 'VIS'} VARIABLES = check_var_name(os.environ['var_name']).replace(' ','').split(',') # list of lead hours # Options: {list of lead hours; string, 'all'; tuple, start/stop flead; # string, single flead} FLEADS = check_FCST_LEAD(os.environ['FCST_LEAD']).replace(' ','').split(',') # list of levels FCST_LEVELS = re.split(r',(?![0*])', check_FCST_LEVEL(os.environ['FCST_LEVEL']).replace(' ','')) OBS_LEVELS = re.split(r',(?![0*])', check_OBS_LEVEL(os.environ['OBS_LEVEL']).replace(' ','')) FCST_THRESH = check_FCST_THRESH(os.environ['FCST_THRESH'], LINE_TYPE) OBS_THRESH = check_OBS_THRESH(os.environ['OBS_THRESH'], FCST_THRESH, LINE_TYPE).replace(' ','').split(',') FCST_THRESH = FCST_THRESH.replace(' ','').split(',') # requires two metrics to plot METRICS = list(filter(None, check_STATS(os.environ['STATS']).replace(' ','').split(','))) # set the lowest possible lower (and highest possible upper) axis limits. # E.g.: If X_LIM_LOCK == True, use X_MIN_LIMIT as the definitive lower # limit (ditto with X_MAX_LIMIT) # If X_LIM_LOCK == False, then allow lower and upper limits to adjust to # data as long as limits don't overcome X_MIN_LIMIT or X_MAX_LIMIT X_MIN_LIMIT = toggle.plot_settings['x_min_limit'] X_MAX_LIMIT = toggle.plot_settings['x_max_limit'] X_LIM_LOCK = toggle.plot_settings['x_lim_lock'] Y_MIN_LIMIT = toggle.plot_settings['y_min_limit'] Y_MAX_LIMIT = toggle.plot_settings['y_max_limit'] Y_LIM_LOCK = toggle.plot_settings['y_lim_lock'] # configure CIs CONFIDENCE_INTERVALS = check_CONFIDENCE_INTERVALS(os.environ['CONFIDENCE_INTERVALS']).replace(' ','') bs_nrep = toggle.plot_settings['bs_nrep'] bs_method = toggle.plot_settings['bs_method'] ci_lev = toggle.plot_settings['ci_lev'] bs_min_samp = toggle.plot_settings['bs_min_samp'] # list of points used in interpolation method INTERP_PNTS = check_INTERP_PTS(os.environ['INTERP_PNTS']).replace(' ','').split(',') # At each value of the independent variable, whether or not to remove # samples used to aggregate each statistic if the samples are not shared # by all models. Required to display sample sizes sample_equalization = toggle.plot_settings['sample_equalization'] # Whether or not to display average values beside legend labels display_averages = toggle.plot_settings['display_averages'] # Whether or not to clear the intermediate directory that stores pruned data clear_prune_dir = toggle.plot_settings['clear_prune_directory'] # Information about logos plot_logo_left = toggle.plot_settings['plot_logo_left'] plot_logo_right = toggle.plot_settings['plot_logo_right'] zoom_logo_left = toggle.plot_settings['zoom_logo_left'] zoom_logo_right = toggle.plot_settings['zoom_logo_right'] path_logo_left = paths.logo_left_path path_logo_right = paths.logo_right_path OUTPUT_BASE_TEMPLATE = os.environ['STAT_OUTPUT_BASE_TEMPLATE'] print("\n===================================================================\n") # ============= END USER CONFIGURATIONS ================= LOG_TEMPLATE = str(LOG_TEMPLATE) LOG_LEVEL = str(LOG_LEVEL) MET_VERSION = float(MET_VERSION) VALID_HOURS = [ int(valid_hour) if valid_hour else None for valid_hour in VALID_HOURS ] INIT_HOURS = [ int(init_hour) if init_hour else None for init_hour in INIT_HOURS ] FLEADS = [int(flead) for flead in FLEADS] INTERP_PNTS = [str(pts) for pts in INTERP_PNTS] VERIF_CASETYPE = str(VERIF_CASE).lower() + '_' + str(VERIF_TYPE).lower() CONFIDENCE_INTERVALS = str(CONFIDENCE_INTERVALS).lower() in [ 'true', '1', 't', 'y', 'yes' ] main()