#!/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
import shutil
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()
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 = '.',
restart_dir: str = '.',
requested_var: str = 'HGT', line_type: str = 'SL1L2',
dpi: int = 100, 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,
include_all_requested_thresholds: 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"FATAL ERROR: 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 df.empty:
logger.warning(f"Empty Dataframe. Continuing onto next plot...")
plt.close(num)
logger.info("========================================")
return None
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"FATAL ERROR: 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"FATAL ERROR: 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])
)
requested_thresh_value = [float(str(item)[2:]) for item in requested_thresh_letter]
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 = ("FATAL ERROR: 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 = "FATAL ERROR: 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):
if not any(
group_name in str(models_removed_string)
for group_name in ["group", "set"]
):
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]
var_long_name_key = df['FCST_VAR'].tolist()[0]
if str(var_long_name_key).upper() == 'PROB_MXUPHL25_A24_GEHWT':
units = 'decimal'
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
elif any(field in str(var_long_name_key).upper() for field in ['WEASD', 'SNOD', 'ASNOW']):
if units in ['m']:
units = 'm_snow'
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"This often happens when processed data are all NaNs, "
+ f" which are removed. Check for seasonal cases where critical "
+ f" threshold is not reached. 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
)
requested_thresh_value = reference.unit_conversions[units]['formula'](
requested_thresh_value,
rounding=True
)
if units == '-':
units = ''
x_vals_argsort = np.argsort(x_vals)
x_vals = np.sort(x_vals)
if include_all_requested_thresholds:
x_axis_vals = np.array(requested_thresh_value)
else:
x_axis_vals = np.array(x_vals)
x_vals_incr = np.diff(x_axis_vals)
if len(x_axis_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=mod_setting_dicts[m]['markersize'],
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_axis_vals)
xticks_max = np.max(x_axis_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]
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_axis_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_axis_vals*precision_scale
]
xticks_argsort = np.argsort(np.concatenate((xticks, x_axis_vals.tolist())))
xticks = np.concatenate((
xticks, x_axis_vals.tolist()
))[xticks_argsort]
xtick_labels_with_blanks = np.concatenate((
res_xlabels, 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)
)
if round_to_nearest < 1.:
y_precision_scale = 100/round_to_nearest
else:
y_precision_scale = 1.
yticks = [
y_val for y_val
in np.arange(
ylim_min*y_precision_scale,
ylim_max*y_precision_scale+round_to_nearest*y_precision_scale,
round_to_nearest*y_precision_scale
)
]
yticks=np.divide(yticks,y_precision_scale)
ytick_labels = [f'{ytick}' for ytick in yticks]
show_ytick_every = len(yticks)//10+1
ytick_labels_with_blanks = ['' for item in ytick_labels]
for i, item in enumerate(ytick_labels[::int(show_ytick_every)]):
ytick_labels_with_blanks[int(show_ytick_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 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_yticklabels(ytick_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
)
majxticks = [i for i, item in enumerate(xtick_labels_with_blanks) if item]
for mt in majxticks:
ax.xaxis.get_major_ticks()[mt].tick1line.set_markersize(8)
majyticks = [i for i, item in enumerate(ytick_labels_with_blanks) if item]
for mt in majyticks:
ax.yaxis.get_major_ticks()[mt].tick1line.set_markersize(8)
ax.legend(
framealpha=1,
bbox_to_anchor=(0.5, -0.15), ncol=4, frameon=True, numpoints=2,
borderpad=.8, labelspacing=1.)
fig.subplots_adjust(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=(0,12),
textcoords='offset points', va='top', fontsize=11,
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'
)
# Title
domain = df['VX_MASK'].tolist()[0]
var_savename = df['FCST_VAR'].tolist()[0]
if any(field in var_savename.upper() for field in ['APCP']):
var_savename = re.sub('[^a-zA-Z \n\.]', '', var_savename)
elif any(field in var_savename.upper() for field in ['ASNOW','SNOD']):
var_savename = 'ASNOW'
elif 'PROB_MXUPHL25_A24_GEHWT' in var_savename.upper():
var_savename = 'MXUPHL25'
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_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', 'nohrsc']:
if 'A' in str(level):
level_num = level.replace('A', '')
level_string = f'{level_num}-hour '
level_savename = f'A{level_num.zfill(2)}'
elif 'Z' in str(level):
level_num = df['OBS_LEV'].tolist()[0].replace('A', '').replace('0', '')
level_string = f''
level_savename= f'A{level_num.zfill(2)}'
else:
level_string = f''
level_savename = f'{level}'
elif str(verif_type).lower() in ['lsr']:
if 'A' in str(level):
level_string = f'24-h '
level_savename = f'A24'
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=23
else:
title_pad=None
ax.set_title(title_center, 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)
if restart_dir:
shutil.copy2(
save_path,
os.path.join(
restart_dir,
f'{str(plot_group).lower()}',
f'{str(time_period_savename).lower()}',
save_name+'.png'
)
)
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"FATAL ERROR: 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"RESTART_DIR: {RESTART_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"Include all requested thresholds?"
+ f" {'yes' if include_all_requested_thresholds 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"FATAL ERROR: 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"FATAL ERROR: 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"FATAL ERROR: 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"FATAL ERROR: 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 FCST_LEVELS in presets.level_presets:
fcst_levels = re.split(r',(?![0*])', presets.level_presets[FCST_LEVELS].replace(' ',''))
else:
fcst_levels = re.split(r',(?![0*])', FCST_LEVELS.replace(' ',''))
if OBS_LEVELS in presets.level_presets:
obs_levels = re.split(r',(?![0*])', presets.level_presets[OBS_LEVELS].replace(' ',''))
else:
obs_levels = re.split(r',(?![0*])', OBS_LEVELS.replace(' ',''))
for l, fcst_level in enumerate(fcst_levels):
if len(fcst_levels) != len(obs_levels):
e = ("FATAL ERROR: 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, INTERP_PNTS, MET_VERSION, clear_prune_dir
)
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,
restart_dir=RESTART_DIR, eval_period=EVAL_PERIOD,
display_averages=display_averages,
include_all_requested_thresholds=include_all_requested_thresholds,
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'])
if 'RESTART_DIR' in os.environ:
RESTART_DIR = check_RESTART_DIR(os.environ['RESTART_DIR'])
else:
RESTART_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 = check_FCST_LEVEL(os.environ['FCST_LEVEL'])
OBS_LEVELS = check_OBS_LEVEL(os.environ['OBS_LEVEL'])
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 to include all requested thresholds in the x-axis labels or
# only those that remain after filtering
include_all_requested_thresholds = toggle.plot_settings['include_all_requested_thresholds']
# 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()