#!/usr/bin/env python3
###############################################################################
#
# Name: valid_hour_average.py
# Contact(s): Marcel Caron
# Developed: Nov. 22, 2021 by Marcel Caron
# Last Modified: Dec. 01, 2022 by Marcel Caron
# Title: Line plot of verification metric as a function of
# valid or init hour
# Abstract: Plots METplus output (e.g., BCRMSE) as a line plot,
# varying by valid or init hour, 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
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_valid_hour_average(df: pd.DataFrame, logger: logging.Logger,
date_range: tuple, model_list: list, num: int = 0,
level: str = '500', flead='all',
fcst_thresh: list = ['<20'], obs_thresh: list = [''],
metric1_name: str = 'BCRMSE', metric2_name: str = 'ME',
y_min_limit: float = -10., y_max_limit: float = 10.,
y_lim_lock: bool = False,
xlabel: str = 'Forecast Lead Hour',
date_type: str = 'VALID', date_hours: list = [0,6,12,18],
anti_date_hours: list = [0,3,6,9,12,15,18,21],
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
if str(line_type).upper() == 'CTC' and np.array(fcst_thresh).size == 0:
logger.warning(f"Empty list of thresholds. Continuing onto next"
+ f" 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
# 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 str(date_type).upper() == 'INIT':
anti_date_type = 'VALID'
elif str(date_type).upper() == 'VALID':
anti_date_type = 'INIT'
# Filter ANTI_DATE_HOURS column based on what is in anti_date_hours
# Remove from date_hours and anti_date_hours the init and valid hours that
# don't exist in the dataframe.
df['DATE_HOURS'] = df[str(date_type).upper()].dt.hour.astype(int)
df['ANTI_DATE_HOURS'] = df[str(anti_date_type).upper()].dt.hour.astype(int)
df = df[df['ANTI_DATE_HOURS'].isin([int(x) for x in anti_date_hours])]
date_hours = np.array(date_hours)[[
int(x) in df['DATE_HOURS'].tolist() for x in date_hours
]]
anti_date_hours = np.array(anti_date_hours)[[
int(x) in df['ANTI_DATE_HOURS'].tolist() for x in anti_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)
if obs_thresh and '' not in obs_thresh:
requested_obs_thresh_symbol, requested_obs_thresh_letter = list(
zip(*[plot_util.format_thresh(t) for t in obs_thresh])
)
symbol_found = False
for opt in ['>=', '>', '==', '!=', '<=', '<']:
if any(opt in t for t in requested_obs_thresh_symbol):
if all(opt in t for t in requested_obs_thresh_symbol):
symbol_found = True
opt_letter = requested_obs_thresh_letter[0][:2]
break
else:
e = ("Threshold operands do not match among all requested"
+ f" obs thresholds.")
logger.error(e)
logger.error("Quitting ...")
raise ValueError(e+"\nQuitting ...")
if not symbol_found:
e = "None of the requested obs thresholds contain a valid symbol."
logger.error(e)
logger.error("Quitting ...")
raise ValueError(e+"\nQuitting ...")
df_obs_thresh_symbol, df_obs_thresh_letter = list(
zip(*[
plot_util.format_thresh(t)
for t in df['OBS_THRESH'].replace(np.nan, '', regex=True)
])
)
df['OBS_THRESH_SYMBOL'] = df_obs_thresh_symbol
df['OBS_THRESH_VALUE'] = [str(item)[2:] for item in df_obs_thresh_letter]
requested_obs_thresh_value = [
str(item)[2:] for item in requested_obs_thresh_letter
]
df = df[df['OBS_THRESH_SYMBOL'].isin(requested_obs_thresh_symbol)]
thresholds_removed = (
np.array(requested_obs_thresh_symbol)[
~np.isin(requested_obs_thresh_symbol, df['OBS_THRESH_SYMBOL'])
]
)
requested_obs_thresh_symbol = (
np.array(requested_obs_thresh_symbol)[
np.isin(requested_obs_thresh_symbol, df['OBS_THRESH_SYMBOL'])
]
)
if thresholds_removed.size > 0:
thresholds_removed_string = ', '.join(thresholds_removed)
if len(thresholds_removed) > 1:
warning_string = (f"{thresholds_removed_string} obs thresholds"
+ f" were not found and will not be"
+ f" plotted.")
else:
warning_string = (f"{thresholds_removed_string} obs threshold was"
+ f" not found and will not be plotted.")
logger.warning(warning_string)
logger.warning("Continuing ...")
if fcst_thresh and '' not in fcst_thresh:
requested_fcst_thresh_symbol, requested_fcst_thresh_letter = list(
zip(*[plot_util.format_thresh(t) for t in fcst_thresh])
)
symbol_found = False
for opt in ['>=', '>', '==', '!=', '<=', '<']:
if any(opt in t for t in requested_fcst_thresh_symbol):
if all(opt in t for t in requested_fcst_thresh_symbol):
symbol_found = True
opt_letter = requested_fcst_thresh_letter[0][:2]
break
else:
e = ("Threshold operands do not match among all requested"
+ f" fcst thresholds.")
logger.error(e)
logger.error("Quitting ...")
raise ValueError(e+"\nQuitting ...")
if not symbol_found:
e = "None of the requested fcst thresholds contain a valid symbol."
logger.error(e)
logger.error("Quitting ...")
raise ValueError(e+"\nQuitting ...")
df_fcst_thresh_symbol, df_fcst_thresh_letter = list(
zip(*[plot_util.format_thresh(t) for t in df['FCST_THRESH']])
)
df['FCST_THRESH_SYMBOL'] = df_fcst_thresh_symbol
df['FCST_THRESH_VALUE'] = [str(item)[2:] for item in df_fcst_thresh_letter]
requested_fcst_thresh_value = [
str(item)[2:] for item in requested_fcst_thresh_letter
]
df = df[df['FCST_THRESH_SYMBOL'].isin(requested_fcst_thresh_symbol)]
thresholds_removed = (
np.array(requested_fcst_thresh_symbol)[
~np.isin(requested_fcst_thresh_symbol, df['FCST_THRESH_SYMBOL'])
]
)
requested_fcst_thresh_symbol = (
np.array(requested_fcst_thresh_symbol)[
np.isin(requested_fcst_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} fcst thresholds"
+ f" were not found and will not be"
+ f" plotted.")
else:
warning_string = (f"{thresholds_removed_string} fcst 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','ANTI_DATE_HOURS']
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='ANTI_DATE_HOURS', how='inner'
),
df_split
)
df_aggregated = df_aggregated[
df_aggregated.index.get_level_values('ANTI_DATE_HOURS')
.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 metric
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='ANTI_DATE_HOURS'
)
if sample_equalization:
pivot_counts=pd.pivot_table(
df_aggregated, values='COUNTS', columns='MODEL',
index='ANTI_DATE_HOURS'
)
pivot_metric1 = pivot_metric1.dropna()
if metric2_name is not None:
pivot_metric2 = pd.pivot_table(
df_aggregated, values=str(metric2_name).upper(), columns='MODEL',
index='ANTI_DATE_HOURS'
)
pivot_metric2 = pivot_metric2.dropna()
if confidence_intervals:
pivot_ci_lower1 = pd.pivot_table(
df_aggregated, values=str(metric1_name).upper()+'_BLERR',
columns='MODEL', index='ANTI_DATE_HOURS'
)
pivot_ci_upper1 = pd.pivot_table(
df_aggregated, values=str(metric1_name).upper()+'_BUERR',
columns='MODEL', index='ANTI_DATE_HOURS'
)
if metric2_name is not None:
pivot_ci_lower2 = pd.pivot_table(
df_aggregated, values=str(metric2_name).upper()+'_BLERR',
columns='MODEL', index='ANTI_DATE_HOURS'
)
pivot_ci_upper2 = pd.pivot_table(
df_aggregated, values=str(metric2_name).upper()+'_BUERR',
columns='MODEL', index='ANTI_DATE_HOURS'
)
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 level. "
+ f"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 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 ...")
incr = 1
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)]
x_vals1 = pivot_metric1.index
if metric2_name is not None:
x_vals2 = pivot_metric2.index
y_min = y_min_limit
y_max = y_max_limit
if obs_thresh and '' not in obs_thresh:
obs_thresh_labels = np.unique(df['OBS_THRESH_VALUE'])
obs_thresh_argsort = np.argsort(obs_thresh_labels.astype(float))
requested_obs_thresh_argsort = np.argsort([
float(item) for item in requested_obs_thresh_value
])
obs_thresh_labels = [obs_thresh_labels[i] for i in obs_thresh_argsort]
requested_obs_thresh_labels = [
requested_obs_thresh_value[i] for i in requested_obs_thresh_argsort
]
if fcst_thresh and '' not in fcst_thresh:
fcst_thresh_labels = np.unique(df['FCST_THRESH_VALUE'])
fcst_thresh_argsort = np.argsort(fcst_thresh_labels.astype(float))
requested_fcst_thresh_argsort = np.argsort([
float(item) for item in requested_fcst_thresh_value
])
fcst_thresh_labels = [fcst_thresh_labels[i] for i in fcst_thresh_argsort]
requested_fcst_thresh_labels = [
requested_fcst_thresh_value[i] for i in requested_fcst_thresh_argsort
]
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
y_vals_metric1 = pivot_metric1[str(model_list[m])].values
y_vals_metric1_mean = np.nanmean(y_vals_metric1)
if metric2_name is not None:
y_vals_metric2 = pivot_metric2[str(model_list[m])].values
y_vals_metric2_mean = np.nanmean(y_vals_metric2)
if confidence_intervals:
y_vals_ci_lower1 = pivot_ci_lower1[
str(model_list[m])
].values
y_vals_ci_upper1 = pivot_ci_upper1[
str(model_list[m])
].values
if metric2_name is not None:
y_vals_ci_lower2 = pivot_ci_lower2[
str(model_list[m])
].values
y_vals_ci_upper2 = pivot_ci_upper2[
str(model_list[m])
].values
if not y_lim_lock:
if metric2_name is not None:
y_vals_both_metrics = np.concatenate((y_vals_metric1, y_vals_metric2))
if np.any(y_vals_both_metrics != np.inf):
y_vals_metric_min = np.nanmin(y_vals_both_metrics[y_vals_both_metrics != np.inf])
y_vals_metric_max = np.nanmax(y_vals_both_metrics[y_vals_both_metrics != np.inf])
else:
y_vals_metric_min = np.nanmin(y_vals_both_metrics)
y_vals_metric_max = np.nanmax(y_vals_both_metrics)
else:
if np.any(y_vals_metric1 != np.inf):
y_vals_metric_min = np.nanmin(y_vals_metric1[y_vals_metric1 != np.inf])
y_vals_metric_max = np.nanmax(y_vals_metric1[y_vals_metric1 != np.inf])
else:
y_vals_metric_min = np.nanmin(y_vals_metric1)
y_vals_metric_max = np.nanmax(y_vals_metric1)
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 np.abs(y_vals_metric1_mean) < 1E4:
metric1_mean_fmt_string = f'{y_vals_metric1_mean:.2f}'
else:
metric1_mean_fmt_string = f'{y_vals_metric1_mean:.2E}'
if plot_reference[0]:
if not plotted_reference[0]:
ref_color_dict = model_colors.get_color_dict('obs')
plt.plot(
x_vals1.tolist(), reference1,
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_vals1.tolist(), y_vals_metric1,
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(y_vals_metric2_mean) < 1E4:
metric2_mean_fmt_string = f'{y_vals_metric2_mean:.2f}'
else:
metric2_mean_fmt_string = f'{y_vals_metric2_mean:.2E}'
if plot_reference[1]:
if not plotted_reference[1]:
ref_color_dict = model_colors.get_color_dict('obs')
plt.plot(
x_vals2.tolist(), reference2,
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_vals2.tolist(), y_vals_metric2,
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(
x_vals1.tolist(), reference1,
yerr=[np.abs(reference_ci_lower1), reference_ci_upper1],
fmt='none', ecolor=ref_color_dict['color'],
elinewidth=ref_color_dict['linewidth'],
capsize=10., capthick=ref_color_dict['linewidth'],
alpha=.70, zorder=0
)
plotted_reference_CIs[0] = True
else:
plt.errorbar(
x_vals1.tolist(), y_vals_metric1,
yerr=[np.abs(y_vals_ci_lower1), y_vals_ci_upper1],
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
)
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(
x_vals2.tolist(), reference2,
yerr=[np.abs(reference_ci_lower2), reference_ci_upper2],
fmt='none', ecolor=ref_color_dict['color'],
elinewidth=ref_color_dict['linewidth'],
capsize=10., capthick=ref_color_dict['linewidth'],
alpha=.70, zorder=0
)
plotted_reference_CIs[1] = True
else:
plt.errorbar(
x_vals2.tolist(), y_vals_metric2,
yerr=[np.abs(y_vals_ci_lower2), y_vals_ci_upper2],
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
)
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.axhline(y=0, color='black', linestyle='--', linewidth=1, zorder=0)
metrics_with_axline_at_1 = [
'FBIAS','RSD'
]
if (str(metric1_name).upper() in metrics_with_axline_at_1
or str(metric2_name).upper() in metrics_with_axline_at_1):
plt.axhline(y=1, color='black', linestyle='--', linewidth=1, zorder=0)
# Configure axis ticks
xticks_min = 0
xticks_max = 23
xticks = [
x_val for x_val in np.arange(xticks_min, xticks_max+incr, incr)
]
xtick_labels = [str(xtick) for xtick in xticks]
if len(xticks) < 48:
show_xtick_every = 1
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
x_buffer_size = .15
ax.set_xlim(
xticks_min-incr*x_buffer_size, xticks_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 unit_convert:
if fcst_thresh and '' not in fcst_thresh:
fcst_thresh_labels = [float(tlab) for tlab in fcst_thresh_labels]
fcst_thresh_labels = (
reference.unit_conversions[units]['formula'](
fcst_thresh_labels,
rounding=True
)
)
fcst_thresh_labels = [str(tlab) for tlab in fcst_thresh_labels]
if obs_thresh and '' not in obs_thresh:
obs_thresh_labels = [float(tlab) for tlab in obs_thresh_labels]
obs_thresh_labels = (
reference.unit_conversions[units]['formula'](
obs_thresh_labels,
rounding=True
)
)
obs_thresh_labels = [str(tlab) for tlab in obs_thresh_labels]
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:
ylabel = f'{var_long_name} ({units})'
else:
ylabel = f'{var_long_name} (unitless)'
else:
ylabel = 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:
ylabel = f'{var_long_name} ({units})'
else:
ylabel = f'{var_long_name} (unitless)'
else:
ylabel = f'{metric1_string}'
ax.set_ylim(ylim_min, ylim_max)
ax.set_ylabel(ylabel)
ax.set_xlabel(xlabel)
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
)
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, xval in zip(counts, x_vals1.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')
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}'
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}'
fcst_thresh_on = (fcst_thresh and '' not in fcst_thresh)
obs_thresh_on = (obs_thresh and '' not in obs_thresh)
if fcst_thresh_on:
fcst_thresholds_phrase = ', '.join([
f'{opt}{fcst_thresh_label}'
for fcst_thresh_label in fcst_thresh_labels
])
fcst_thresholds_save_phrase = ''.join([
f'{opt_letter}{fcst_thresh_label}'
for fcst_thresh_label in requested_fcst_thresh_labels
]).replace('.','p')
if obs_thresh_on:
obs_thresholds_phrase = ', '.join([
f'obs{opt}{obs_thresh_label}'
for obs_thresh_label in obs_thresh_labels
])
obs_thresholds_save_phrase = ''.join([
f'obs{opt_letter}{obs_thresh_label}'
for obs_thresh_label in requested_obs_thresh_labels
]).replace('.','p')
if fcst_thresh_on:
if units:
title2 = (f'{level_string}{var_long_name} ({fcst_thresholds_phrase}'
+ f' {units}), {domain_string}')
else:
title2 = (f'{level_string}{var_long_name} ({fcst_thresholds_phrase}'
+ f' unitless), {domain_string}')
elif obs_thresh_on:
if units:
title2 = (f'{level_string}{var_long_name} ({obs_thresholds_phrase}'
+ f' {units}), {domain_string}')
else:
title2 = (f'{level_string}{var_long_name} ({obs_thresholds_phrase}'
+ f' unitless), {domain_string}')
else:
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 = f'vhrmean'
save_name = (f'{str(metric1_name).lower()}')
if metric2_name is not None:
save_name+=f'_{str(metric2_name).lower()}'
if fcst_thresh_on:
save_name+=f'_{str(fcst_thresholds_save_phrase).lower()}'
elif obs_thresh_on:
save_name+=f'_{str(obs_thresholds_save_phrase).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':
e = (f"You requested a valid_hour_average plot with the DATE_TYPE "
+ f"set to 'VALID'. Valid time is already used as the independent"
+ f" variable in valid_hour_average plots. Set the DATE_TYPE to"
+ f" 'INIT' to equalize data by initialization"
+ f" time. Make sure neither FCST_INIT_HOUR nor"
+ f" FCST_VALID_HOUR is an empty list.")
logger.error(e)
raise ValueError(e)
elif str(DATE_TYPE).upper() == 'INIT':
date_beg = init_beg
date_end = init_end
date_hours = INIT_HOURS
anti_date_hours = VALID_HOURS
anti_date_type_string = 'Valid'
xlabel=f'{str(anti_date_type_string).capitalize()} Hour (UTC)'
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 valid hour")
logger.debug(f"X_MAX_LIMIT: Ignored for series by valid hour")
logger.debug(f"X_LIM_LOCK: Ignored for series by valid hour")
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']
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)
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
)
if df is None:
continue
df_metric = df
plot_valid_hour_average(
df_metric, logger, date_range, MODELS, num=num,
flead=FLEADS, level=fcst_level, fcst_thresh=fcst_thresh,
obs_thresh=obs_thresh,
metric1_name=metrics[0], metric2_name=metrics[1],
date_type=DATE_TYPE, y_min_limit=Y_MIN_LIMIT,
y_max_limit=Y_MAX_LIMIT, y_lim_lock=Y_LIM_LOCK,
xlabel=xlabel, line_type=LINE_TYPE, verif_type=VERIF_TYPE,
date_hours=date_hours, anti_date_hours=anti_date_hours,
eval_period=EVAL_PERIOD, save_dir=SAVE_DIR,
display_averages=display_averages, save_header=IMG_HEADER,
plot_group=plot_group,
confidence_intervals=CONFIDENCE_INTERVALS, bs_nrep=bs_nrep,
interp_pts=INTERP_PNTS, 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 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']
# 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()
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()