#!/usr/bin/env python3 ############################################################################### # # Name: threshold_average.py # Contact(s): Marcel Caron # Developed: Nov. 22, 2021 by Marcel Caron # Last Modified: Dec. 1, 2021 by Marcel Caron # Title: Line plot of verification metric as a function of # forecast threshold # Abstract: Plots METplus output (e.g., BCRMSE) as a line plot, # varying by forecast threshold, which represents the x-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 from decimal import Decimal 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=(28.,14.)) plotter.set_up_plots() toggle = Toggle() templates = Templates() paths = Paths() presets = Presets() model_colors = ModelSpecs() reference = Reference() # =================== FUNCTIONS ========================= def plot_threshold_average(df: pd.DataFrame, logger: logging.Logger, date_range: tuple, model_list: list, num: int = 0, level: str = '500', flead='all', thresh: list = ['<20'], metric_name: str = 'BCRMSE', y_min_limit: float = -10., y_max_limit: float = 10., y_lim_lock: bool = False, ylabel: str = '', date_type: str = 'VALID', date_hours: list = [0,6,12,18], verif_type: str = 'pres', save_dir: str = '.', requested_var: str = 'HGT', line_type: str = 'SL1L2', dpi: int = 300, confidence_intervals: bool = False, interp_pts: list = [], bs_nrep: int = 5000, bs_method: str = 'MATCHED_PAIRS', ci_lev: float = .95, bs_min_samp: int = 30, 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 level df = df[df['FCST_LEV'].astype(str).eq(str(level))] if df.empty: logger.warning(f"Empty Dataframe. Continuing onto next plot...") plt.close(num) logger.info("========================================") return None if str(line_type).upper() == 'CTC' and np.array(thresh).size == 0: logger.warning(f"Empty list of thresholds. Continuing onto next" + f" plot...") logger.info("========================================") return None # filter by forecast lead times if 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) if df.empty: logger.warning(f"Empty Dataframe. Continuing onto next plot...") plt.close(num) logger.info("========================================") return None # 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) requested_thresh_symbol, requested_thresh_letter = list( zip(*[plot_util.format_thresh(t) for t in thresh]) ) symbol_found = False for opt in ['>=', '>', '==','!=','<=', '<']: if any(opt in t for t in requested_thresh_symbol): if all(opt in t for t in requested_thresh_symbol): symbol_found = True break else: e = ("Threshold operands do not match among all requested" + f" thresholds.") logger.error(e) logger.error("Quitting ...") raise ValueError(e+"\nQuitting ...") if not symbol_found: e = "None of the requested thresholds contain a valid symbol." logger.error(e) logger.error("Quitting ...") raise ValueError(e+"\nQuitting ...") df_thresh_symbol, df_thresh_letter = list( zip(*[plot_util.format_thresh(t) for t in df['FCST_THRESH']]) ) df['FCST_THRESH_SYMBOL'] = df_thresh_symbol df['FCST_THRESH_VALUE'] = [str(item)[2:] for item in df_thresh_letter] df = df[df['FCST_THRESH_SYMBOL'].isin(requested_thresh_symbol)] thresholds_removed = ( np.array(requested_thresh_symbol)[ ~np.isin(requested_thresh_symbol, df['FCST_THRESH_SYMBOL']) ] ) requested_thresh_symbol = ( np.array(requested_thresh_symbol)[ np.isin(requested_thresh_symbol, df['FCST_THRESH_SYMBOL']) ] ) if thresholds_removed.size > 0: thresholds_removed_string = ', '.join(thresholds_removed) if len(thresholds_removed) > 1: warning_string = (f"{thresholds_removed_string} thresholds were" + f" not found and will not be plotted.") else: warning_string = (f"{thresholds_removed_string} threshold was" + f" not found and will not be plotted.") logger.warning(warning_string) logger.warning("Continuing ...") # 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','FCST_THRESH_VALUE'] 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='FCST_THRESH_VALUE', how='inner' ), df_split ) df_aggregated = df_aggregated[ df_aggregated.index.get_level_values('FCST_THRESH_VALUE') .isin(df_reduced.index) ] if df_aggregated.empty: logger.warning(f"Empty Dataframe. Continuing onto next plot...") plt.close(num) logger.info("========================================") return 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' ] coef, const = (None, None) 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 str(metric_name).upper() in metrics_using_var_units: coef, const = ( reference.unit_conversions[units]['formula']( None, return_terms=True ) ) # Calculate desired metric stat_output = plot_util.calculate_stat( logger, df_aggregated, str(metric_name).lower(), [coef, const] ) df_aggregated[str(metric_name).upper()] = stat_output[0] metric_long_name = stat_output[2] if confidence_intervals: ci_output = df_groups.apply( lambda x: plot_util.calculate_bootstrap_ci( logger, bs_method, x, str(metric_name).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 else: 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(metric_name).upper()+'_BLERR'] = ci_output[ 'CI_LOWER' ].values df_aggregated[str(metric_name).upper()+'_BUERR'] = ci_output[ 'CI_UPPER' ].values df_aggregated[str(metric_name).upper()] = ( df_aggregated[str(metric_name).upper()] ).astype(float).tolist() df_aggregated = df_aggregated[ df_aggregated.index.isin(model_list, level='MODEL') ] pivot_metric = pd.pivot_table( df_aggregated, values=str(metric_name).upper(), columns='MODEL', index='FCST_THRESH_VALUE' ) if sample_equalization: pivot_counts = pd.pivot_table( df_aggregated, values='COUNTS', columns='MODEL', index='FCST_THRESH_VALUE' ) pivot_metric = pivot_metric.dropna() if confidence_intervals: pivot_ci_lower = pd.pivot_table( df_aggregated, values=str(metric_name).upper()+'_BLERR', columns='MODEL', index='FCST_THRESH_VALUE' ) pivot_ci_upper = pd.pivot_table( df_aggregated, values=str(metric_name).upper()+'_BUERR', columns='MODEL', index='FCST_THRESH_VALUE' ) if pivot_metric.empty: print_varname = df['FCST_VAR'].tolist()[0] logger.warning( f"Could not find (and cannot plot) {metric_name}" + f" stats for {print_varname} at any level. " + f"Continuing ..." ) plt.close(num) logger.info("========================================") 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_common = list(set.intersection(*map( set, [ pivot_metric.index, pivot_ci_lower.index, pivot_ci_upper.index ] ))) if pivot_metric[pivot_metric.index.isin(indices_in_common)].empty: e = ("Some confidence intervals are missing. Turning " + f"confidence intervals off to avoid empty pivot tables.") logger.warning(e) confidence_intervals = False else: pivot_metric = pivot_metric[pivot_metric.index.isin(indices_in_common)] pivot_ci_lower = pivot_ci_lower[pivot_ci_lower.index.isin(indices_in_common)] pivot_ci_upper = pivot_ci_upper[pivot_ci_upper.index.isin(indices_in_common)] if sample_equalization: pivot_counts = pivot_counts[pivot_counts.index.isin(indices_in_common)] x_vals = pivot_metric.index.astype(float).tolist() if unit_convert: x_vals = reference.unit_conversions[units]['formula']( x_vals, rounding=True ) if units == '-': units = '' x_vals_argsort = np.argsort(x_vals) x_vals = np.sort(x_vals) x_vals_incr = np.diff(x_vals) if len(x_vals) > 1: min_incr = np.min(x_vals_incr) else: min_incr = 0 incrs = [.05,.1,.5,1.,5.,10.,50.,100.,500.,1E3,5E3,1E4,5E4,1E5,5E5] incr_idx = np.digitize(min_incr, incrs) if incr_idx < 1: incr_idx = 1 incr = incrs[incr_idx-1] y_min = y_min_limit y_max = y_max_limit 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_metric: continue y_vals_metric = pivot_metric[str(model_list[m])].values y_vals_metric = np.array([y_vals_metric[i] for i in x_vals_argsort]) y_vals_metric_mean = np.nanmean(y_vals_metric) if confidence_intervals: if (str(model_list[m]) not in pivot_ci_lower or str(model_list[m]) not in pivot_ci_upper): e = ("Some confidence intervals are missing. Turning " + f"confidence intervals off to avoid indexing errors.") logger.warning(e) confidence_intervals = False else: y_vals_ci_lower = pivot_ci_lower[ str(model_list[m]) ].values y_vals_ci_upper = pivot_ci_upper[ str(model_list[m]) ].values if not y_lim_lock: if np.any(y_vals_metric != np.inf): y_vals_metric_min = np.nanmin(y_vals_metric[y_vals_metric != np.inf]) y_vals_metric_max = np.nanmax(y_vals_metric[y_vals_metric != np.inf]) else: y_vals_metric_min = np.nanmin(y_vals_metric) y_vals_metric_max = np.nanmax(y_vals_metric) if n_mods == 0: y_mod_min = y_vals_metric_min y_mod_max = y_vals_metric_max n_mods+=1 else: if math.isinf(y_mod_min): y_mod_min = y_vals_metric_min else: y_mod_min = np.nanmin([y_mod_min, y_vals_metric_min]) if math.isinf(y_mod_max): y_mod_max = y_vals_metric_max else: y_mod_max = np.nanmax([y_mod_max, y_vals_metric_max]) if (y_vals_metric_min > y_min_limit and y_vals_metric_min <= y_mod_min): y_min = y_vals_metric_min if (y_vals_metric_max < y_max_limit and y_vals_metric_max >= y_mod_max): y_max = y_vals_metric_max if display_averages: if np.abs(y_vals_metric_mean) < 1E4: metric_mean_fmt_string = (f'{model_plot_name}' + f' ({y_vals_metric_mean:.2f})') else: metric_mean_fmt_string = (f'{model_plot_name}' + f' ({y_vals_metric_mean:.2E})') else: metric_mean_fmt_string = f'{model_plot_name}' plt.plot( x_vals, y_vals_metric, marker='o', c=mod_setting_dicts[m]['color'], mew=2., mec='white', figure=fig, ms=12, ls=mod_setting_dicts[m]['linestyle'], lw=mod_setting_dicts[m]['linewidth'], label=f'{metric_mean_fmt_string}' ) if confidence_intervals: plt.errorbar( x_vals.tolist(), y_vals_metric, yerr=[np.abs(y_vals_ci_lower), y_vals_ci_upper], fmt='none', ecolor=mod_setting_dicts[m]['color'], elinewidth=mod_setting_dicts[m]['linewidth'], capsize=10., capthick=mod_setting_dicts[m]['linewidth'], alpha=.70, zorder=0 ) # Zero line plt.axhline(y=0, color='black', linestyle='--', linewidth=1, zorder=0) metrics_with_axline_at_1 = [ 'FBIAS','RSD' ] if str(metric_name).upper() in metrics_with_axline_at_1: plt.axhline(y=1, color='black', linestyle='--', linewidth=1, zorder=0) # Configure axis ticks if unit_convert: x_vals_incr = reference.unit_conversions[units]['formula'](x_vals) units = reference.unit_conversions[units]['convert_to'] xticks_min = np.min(x_vals) xticks_max = np.max(x_vals) xlim_min = np.floor(xticks_min/incr)*incr xlim_max = np.ceil(xticks_max/incr)*incr if incr < 1.: precision_scale = 100/incr else: precision_scale = 1. xticks = [ x_val for x_val in np.arange( xlim_min*precision_scale, xlim_max*precision_scale+incr*precision_scale, incr*precision_scale ) ] xticks=np.divide(xticks,precision_scale) xtick_labels = [f'{opt}{xtick}' for xtick in xticks] number_of_ticks_dig = [25,50,75,100,125,150,175,200] show_xtick_every = np.ceil(( np.digitize(len(xtick_labels), number_of_ticks_dig) + 2 )/2.)*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 replace_xticks = [ xtick for xtick in xticks if np.any([ np.absolute(xtick-x_val) < incr/2.*show_xtick_every for x_val in x_vals.tolist() ]) ] res_xticks = [val for val in xticks if val not in replace_xticks] res_xlabels = [ xtick_labels_with_blanks[v] if val not in replace_xticks else '' for v, val in enumerate(xticks) ] add_labels = [ f'{opt}{np.round(x_val)/precision_scale}' for x_val in x_vals*precision_scale ] xticks_argsort = np.argsort(np.concatenate((xticks, x_vals.tolist()))) xticks = np.concatenate(( xticks, x_vals.tolist() ))[xticks_argsort] xtick_labels_with_blanks = np.concatenate(( res_xlabels, add_labels ))[xticks_argsort] #xticks_argsort = np.argsort(x_vals.tolist()) #xticks = np.array(x_vals.tolist())[xticks_argsort] #xtick_labels_with_blanks = np.array(add_labels)[xticks_argsort] res_diff = np.diff( [xtick for x, xtick in enumerate(xticks) if xtick_labels_with_blanks[x]] ) arg_xtick_labels = [ i for i, lab in enumerate(xtick_labels_with_blanks) if lab ] for i, d in enumerate(res_diff): if d < (incr/2.*show_xtick_every): xtick_labels_with_blanks[arg_xtick_labels[i+1]] = '' x_buffer_size = .015 ax.set_xlim( xlim_min-incr*x_buffer_size, xlim_max+incr*x_buffer_size ) y_range_categories = np.array([ [np.power(10.,y), 2.*np.power(10.,y)] for y in [-5,-4,-3,-2,-1,0,1,2,3,4,5] ]).flatten() round_to_nearest_categories = y_range_categories/20. if math.isinf(y_min): y_min = y_min_limit if math.isinf(y_max): y_max = y_max_limit y_range = y_max-y_min round_to_nearest = round_to_nearest_categories[ np.digitize(y_range, y_range_categories[:-1]) ] ylim_min = np.floor(y_min/round_to_nearest)*round_to_nearest ylim_max = np.ceil(y_max/round_to_nearest)*round_to_nearest if len(str(ylim_min)) > 5 and np.abs(ylim_min) < 1.: ylim_min = float( np.format_float_scientific(ylim_min, unique=False, precision=3) ) yticks = np.arange(ylim_min, ylim_max+round_to_nearest, round_to_nearest) 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 str(metric_name).upper() in metrics_using_var_units: if units: ylabel = f'{var_long_name} ({units})' else: ylabel = f'{var_long_name} (unitless)' else: ylabel = f'{metric_long_name}' ax.set_ylim(ylim_min, ylim_max) ax.set_ylabel(ylabel) if units: ax.set_xlabel(f'Forecast Threshold ({units})') else: ax.set_xlabel(f'Forecast Threshold (unitless)') ax.set_xticklabels(xtick_labels_with_blanks) ax.set_yticks(yticks) ax.set_xticks(xticks) 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', pad=15 ) majticks = [i for i, item in enumerate(xtick_labels_with_blanks) if item] for mt in majticks: ax.xaxis.get_major_ticks()[mt].tick1line.set_markersize(8) ax.legend( 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, xval in zip(counts, x_vals.tolist()): if not isinstance(count, str): count = str(int(count)) ax.annotate( f'{count}', xy=(xval,1.), xycoords=('data','axes fraction'), xytext=(0,18), textcoords='offset points', va='top', fontsize=16, color='dimgrey', ha='center' ) ax.annotate( '#SAMPLES', xy=(0.,1.), xycoords='axes fraction', xytext=(-50, 21), textcoords='offset points', va='top', fontsize=11, color='dimgrey', ha='center' ) fig.subplots_adjust(top=.9) # 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') metric_string = metric_long_name if str(level).upper() in ['CEILING', 'TOTAL', 'PBL']: if str(level).upper() == 'CEILING': level_string = '' level_savename = 'L0' elif str(level).upper() == 'TOTAL': level_string = 'Total ' level_savename = 'L0' elif str(level).upper() == 'PBL': level_string = '' level_savename = 'L0' elif str(verif_type).lower() in ['pres', 'upper_air', 'raob'] or 'P' in str(level): if 'P' in str(level): if str(level).upper() == 'P90-0': level_string = f'Mixed-Layer ' level_savename = f'L90' else: level_num = level.replace('P', '') level_string = f'{level_num} hPa ' level_savename = f'{level}' elif str(level).upper() == 'L0': level_string = f'Surface-Based ' level_savename = f'{level}' else: level_string = '' level_savename = f'{level}' elif str(verif_type).lower() in ['sfc', 'conus_sfc', 'polar_sfc', 'mrms', 'metar']: if 'Z' in str(level): if str(level).upper() == 'Z0': if str(var_long_name_key).upper() in ['MLSP', 'MSLET', 'MSLMA', 'PRMSL']: level_string = '' level_savename = f'{level}' else: level_string = 'Surface ' level_savename = f'{level}' else: level_num = level.replace('Z', '') if var_savename in ['TSOIL', 'SOILW']: level_string = f'{level_num}-cm ' level_savename = f'{level_num}CM' else: level_string = f'{level_num}-m ' level_savename = f'{level}' elif 'L' in str(level): level_string = '' level_savename = f'{level}' elif 'A' in str(level): level_num = level.replace('A', '') level_string = f'{level_num}-hour ' level_savename = f'A{level_num.zfill(2)}' else: level_string = f'{level} ' level_savename = f'{level}' elif str(verif_type).lower() in ['ccpa', 'mrms']: if 'A' in str(level): level_num = level.replace('A', '') level_string = f'{level_num}-hour ' level_savename = f'A{level_num.zfill(2)}' else: level_string = f'' level_savename = f'{level}' else: level_string = f'{level} ' level_savename = f'{level}' title1 = f'{metric_string}' if interp_pts and '' not in interp_pts: title1+=f' {interp_pts_string}' if units: title2 = f'{level_string}{var_long_name} ({units}), {domain_string}' else: title2 = f'{level_string}{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]) if sample_equalization: title_pad=40 else: title_pad=None ax.set_title(title_center, loc=plotter.title_loc, pad=title_pad) 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) ab_left = AnnotationBbox( left_image_box, xy=(0.,1.), xycoords='axes fraction', xybox=(0, 20), 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) right_image_box = OffsetImage(right_logo_arr, zoom=zoom_logo_right) ab_right = AnnotationBbox( right_image_box, xy=(1.,1.), xycoords='axes fraction', xybox=(0, 20), 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'threshmean', f'{str(date_type).lower()}{str(date_hours_savename).lower()}', f'{str(frange_save_string).lower()}', ] if item] ) save_name = (f'{str(metric_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()}' if level_savename: save_name+=f'_{str(level_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: Ignored for series by threshold") logger.debug(f"X_MAX_LIMIT: Ignored for series by threshold") logger.debug(f"X_LIM_LOCK: Ignored for series by threshold") 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('========================================') metrics = METRICS date_range = ( datetime.strptime(date_beg, '%Y%m%d'), datetime.strptime(date_end, '%Y%m%d')+td(days=1)-td(milliseconds=1) ) 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 (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'] for l, fcst_level in enumerate(FCST_LEVELS): 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 ...") if (FCST_LEVELS[l] not in var_specs['fcst_var_levels'] or OBS_LEVELS[l] not in var_specs['obs_var_levels']): e = (f"The requested variable/level combination is not valid:" + f" {requested_var}/{fcst_level}") logger.warning(e) logger.warning("Continuing ...") continue 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 ) logger.info("test") if df is None: continue for metric in metrics: 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.warning(e) logger.warning("Continuing ...") continue df_metric = df plot_threshold_average( df_metric, logger, date_range, MODELS, num=num, flead=FLEADS, level=fcst_level, thresh=fcst_thresh, metric_name=metric, date_type=DATE_TYPE, y_min_limit=Y_MIN_LIMIT, y_max_limit=Y_MAX_LIMIT, y_lim_lock=Y_LIM_LOCK, ylabel='Metric (unitless)', line_type=LINE_TYPE, verif_type=VERIF_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 = check_STATS(os.environ['STATS']).replace(' ','').split(',') # set the lowest possible lower (and highest possible upper) axis limits. # E.g.: If Y_LIM_LOCK == True, use Y_MIN_LIMIT as the definitive lower # limit (ditto with Y_MAX_LIMIT) # If Y_LIM_LOCK == False, then allow lower and upper limits to adjust to # data as long as limits don't overcome Y_MIN_LIMIT or Y_MAX_LIMIT 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'] # Still need to configure CIs (doesn't work yet) 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() FCST_LEVELS = [str(level) for level in FCST_LEVELS] OBS_LEVELS = [str(level) for level in OBS_LEVELS] CONFIDENCE_INTERVALS = str(CONFIDENCE_INTERVALS).lower() in [ 'true', '1', 't', 'y', 'yes' ] main()