#!/usr/bin/env python3
'''
Program Name: subseasonal_plots_grid2grid_create_job_scripts.py
Contact(s): Shannon Shields
Abstract: This script is run by exevs_subseasonal_grid2grid_plots.sh
in scripts/plots/subseasonal.
This creates multiple independent job scripts. These
jobs contain all the necessary environment variables
and commands needed to run the specific
use case.
'''
import sys
import os
import glob
import datetime
import itertools
import numpy as np
import subprocess
import subseasonal_util as sub_util
print("BEGIN: "+os.path.basename(__file__))
# Read in environment variables
DATA = os.environ['DATA']
NET = os.environ['NET']
RUN = os.environ['RUN']
VERIF_CASE = os.environ['VERIF_CASE']
STEP = os.environ['STEP']
COMPONENT = os.environ['COMPONENT']
JOB_GROUP = os.environ['JOB_GROUP']
machine = os.environ['machine']
USE_CFP = os.environ['USE_CFP']
nproc = os.environ['nproc']
start_date = os.environ['start_date']
end_date = os.environ['end_date']
VERIF_CASE_STEP_abbrev = os.environ['VERIF_CASE_STEP_abbrev']
VERIF_CASE_STEP_type_list = (os.environ[VERIF_CASE_STEP_abbrev+'_type_list'] \
.split(' '))
PBS_NODEFILE = os.environ['PBS_NODEFILE']
VERIF_CASE_STEP = VERIF_CASE+'_'+STEP
model_list = os.environ['model_list'].split(' ')
njobs = 0
JOB_GROUP_jobs_dir = os.path.join(DATA, VERIF_CASE_STEP,
'plot_job_scripts', JOB_GROUP)
if not os.path.exists(JOB_GROUP_jobs_dir):
os.makedirs(JOB_GROUP_jobs_dir)
################################################
#### Plotting jobs
################################################
plot_jobs_dict = {
'anom': {
'WeeklyAvg_TempAnom2m': {'line_type_stat_list': ['SL1L2/RMSE',
'SL1L2/ME'],
'vx_mask_list': ['GLOBAL', 'CONUS',
'Alaska', 'Hawaii'],
'fcst_var_dict': {'name': 'TMP_ANOM_WEEKLYAVG',
'levels': 'Z2',
'threshs': 'NA'},
'obs_var_dict': {'name': 'TMP_ANOM_WEEKLYAVG',
'levels': 'Z2',
'threshs': 'NA'},
'interp_dict': {'method': 'NEAREST',
'points': '1'},
'fhr_start': '168',
'fhr_end': '840',
'fhr_inc': '168',
'grid': 'G003',
'obs_name': 'ecmwf',
'plots_list': 'time_series, lead_average'},
'Days6_10Avg_TempAnom2m': {'line_type_stat_list': ['SL1L2/RMSE',
'SL1L2/ME'],
'vx_mask_list': ['GLOBAL', 'CONUS',
'Alaska', 'Hawaii'],
'fcst_var_dict': {'name': 'TMP_ANOM_DAYS6_10AVG',
'levels': 'Z2',
'threshs': 'NA'},
'obs_var_dict': {'name': 'TMP_ANOM_DAYS6_10AVG',
'levels': 'Z2',
'threshs': 'NA'},
'interp_dict': {'method': 'NEAREST',
'points': '1'},
'fhr_start': '240',
'fhr_end': '240',
'fhr_inc': '24',
'grid': 'G003',
'obs_name': 'ecmwf',
'plots_list': 'time_series'},
'Weeks3_4Avg_TempAnom2m': {'line_type_stat_list': ['SL1L2/RMSE',
'SL1L2/ME'],
'vx_mask_list': ['GLOBAL', 'CONUS',
'Alaska', 'Hawaii'],
'fcst_var_dict': {'name': 'TMP_ANOM_WEEKS3_4AVG',
'levels': 'Z2',
'threshs': 'NA'},
'obs_var_dict': {'name': 'TMP_ANOM_WEEKS3_4AVG',
'levels': 'Z2',
'threshs': 'NA'},
'interp_dict': {'method': 'NEAREST',
'points': '1'},
'fhr_start': '672',
'fhr_end': '672',
'fhr_inc': '24',
'grid': 'G003',
'obs_name': 'ecmwf',
'plots_list': 'time_series'},
},
'pres_lvls': {
'WeeklyAvg_GeoHeightAnom': {'line_type_stat_list': ['SL1L2/RMSE',
'SL1L2/ME'],
'vx_mask_list': ['NHEM', 'SHEM',
'TROPICS', 'CONUS',
'Alaska', 'Hawaii'],
'fcst_var_dict': {'name': 'HGT_ANOM_WEEKLYAVG',
'levels': 'P500',
'threshs': 'NA'},
'obs_var_dict': {'name': 'HGT_ANOM_WEEKLYAVG',
'levels': 'P500',
'threshs': 'NA'},
'interp_dict': {'method': 'NEAREST',
'points': '1'},
'fhr_start': '168',
'fhr_end': '840',
'fhr_inc': '168',
'grid': 'G003',
'obs_name': 'gfs',
'plots_list': 'time_series, lead_average'},
'Days6_10Avg_GeoHeightAnom': {'line_type_stat_list': ['SL1L2/RMSE',
'SL1L2/ME'],
'vx_mask_list': ['NHEM', 'SHEM',
'TROPICS', 'CONUS',
'Alaska', 'Hawaii'],
'fcst_var_dict': {'name': 'HGT_ANOM_DAYS6_10AVG',
'levels': 'P500',
'threshs': 'NA'},
'obs_var_dict': {'name': 'HGT_ANOM_DAYS6_10AVG',
'levels': 'P500',
'threshs': 'NA'},
'interp_dict': {'method': 'NEAREST',
'points': '1'},
'fhr_start': '240',
'fhr_end': '240',
'fhr_inc': '24',
'grid': 'G003',
'obs_name': 'gfs',
'plots_list': 'time_series'},
'Weeks3_4Avg_GeoHeightAnom': {'line_type_stat_list': ['SL1L2/RMSE',
'SL1L2/ME'],
'vx_mask_list': ['NHEM', 'SHEM',
'TROPICS', 'CONUS',
'Alaska', 'Hawaii'],
'fcst_var_dict': {'name': 'HGT_ANOM_WEEKS3_4AVG',
'levels': 'P500',
'threshs': 'NA'},
'obs_var_dict': {'name': 'HGT_ANOM_WEEKS3_4AVG',
'levels': 'P500',
'threshs': 'NA'},
'interp_dict': {'method': 'NEAREST',
'points': '1'},
'fhr_start': '672',
'fhr_end': '672',
'fhr_inc': '24',
'grid': 'G003',
'obs_name': 'gfs',
'plots_list': 'time_series'},
},
'sea_ice': {
'WeeklyAvg_Concentration': {'line_type_stat_list': ['SL1L2/RMSE',
'SL1L2/ME'],
'vx_mask_list': ['ARCTIC', 'ANTARCTIC'],
'fcst_var_dict': {'name': 'ICEC_WEEKLYAVG',
'levels': 'Z0',
'threshs': 'NA'},
'obs_var_dict': {'name': 'ice_conc',
'levels': "'*,*'",
'threshs': 'NA'},
'interp_dict': {'method': 'NEAREST',
'points': '1'},
'fhr_start': '168',
'fhr_end': '840',
'fhr_inc': '168',
'grid': 'G003',
'obs_name': 'osi_saf',
'plots_list': 'time_series, lead_average'},
'WeeklyAvg_Concentration_Thresh': {'line_type_stat_list': ['CTC/CSI'],
'vx_mask_list': ['ARCTIC',
'ANTARCTIC'],
'fcst_var_dict': {'name': (
'ICEC_WEEKLYAVG'
),
'levels': 'Z0',
'threshs': ('ge15, '
+'ge40, '
+'ge80')},
'obs_var_dict': {'name': 'ice_conc',
'levels': "'*,*'",
'threshs': ('ge15, '
+'ge40, '
+'ge80')},
'interp_dict': {'method': 'NEAREST',
'points': '1'},
'fhr_start': '168',
'fhr_end': '840',
'fhr_inc': '168',
'grid': 'G003',
'obs_name': 'osi_saf',
'plots_list': ('time_series, '
+'lead_average')},
'WeeklyAvg_Concentration_PerfDia': {'line_type_stat_list': \
['CTC/PERF_DIA'],
'vx_mask_list': \
['ARCTIC', 'ANTARCTIC'],
'fcst_var_dict': \
{'name': 'ICEC_WEEKLYAVG',
'levels': 'Z0',
'threshs': ('ge15, ge40, '
+'ge80')},
'obs_var_dict': \
{'name': 'ice_conc',
'levels': "'*,*'",
'threshs': ('ge15, ge40, '
+'ge80')},
'interp_dict': {'method': 'NEAREST',
'points': '1'},
'fhr_start': '168',
'fhr_end': '840',
'fhr_inc': '168',
'grid': 'G003',
'obs_name': 'osi_saf',
'plots_list': 'performance_diagram'},
'MonthlyAvg_Concentration': {'line_type_stat_list': ['SL1L2/RMSE',
'SL1L2/ME'],
'vx_mask_list': ['ARCTIC', 'ANTARCTIC'],
'fcst_var_dict': {'name': 'ICEC_MONTHLYAVG',
'levels': 'Z0',
'threshs': 'NA'},
'obs_var_dict': {'name': 'ice_conc',
'levels': "'*,*'",
'threshs': 'NA'},
'interp_dict': {'method': 'NEAREST',
'points': '1'},
'fhr_start': '720',
'fhr_end': '720',
'fhr_inc': '24',
'grid': 'G003',
'obs_name': 'osi_saf',
'plots_list': 'time_series'},
'MonthlyAvg_Concentration_Thresh': {'line_type_stat_list': ['CTC/CSI'],
'vx_mask_list': ['ARCTIC',
'ANTARCTIC'],
'fcst_var_dict': {'name': (
'ICEC_MONTHLYAVG'
),
'levels': 'Z0',
'threshs': ('ge15, '
+'ge40, '
+'ge80')},
'obs_var_dict': {'name': 'ice_conc',
'levels': "'*,*'",
'threshs': ('ge15, '
+'ge40, '
+'ge80')},
'interp_dict': {'method': 'NEAREST',
'points': '1'},
'fhr_start': '720',
'fhr_end': '720',
'fhr_inc': '24',
'grid': 'G003',
'obs_name': 'osi_saf',
'plots_list': 'time_series'},
'MonthlyAvg_Concentration_PerfDia': {'line_type_stat_list': \
['CTC/PERF_DIA'],
'vx_mask_list': \
['ARCTIC', 'ANTARCTIC'],
'fcst_var_dict': \
{'name': 'ICEC_MONTHLYAVG',
'levels': 'Z0',
'threshs': ('ge15, ge40, '
+'ge80')},
'obs_var_dict': \
{'name': 'ice_conc',
'levels': "'*,*'",
'threshs': ('ge15, ge40, '
+'ge80')},
'interp_dict': {'method': 'NEAREST',
'points': '1'},
'fhr_start': '720',
'fhr_end': '720',
'fhr_inc': '24',
'grid': 'G003',
'obs_name': 'osi_saf',
'plots_list': 'performance_diagram'},
},
'sst': {
'DailyAvg_SST': {'line_type_stat_list': ['SL1L2/RMSE', 'SL1L2/ME'],
'vx_mask_list': ['NHEM', 'SHEM', 'TROPICS'],
'fcst_var_dict': {'name': 'SST_DAILYAVG',
'levels': 'Z0',
'threshs': 'NA'},
'obs_var_dict': {'name': 'analysed_sst',
'levels': "'0,*,*'",
'threshs': 'NA'},
'interp_dict': {'method': 'NEAREST',
'points': '1'},
'fhr_start': '24',
'fhr_end': '840',
'fhr_inc': '24',
'grid': 'G003',
'obs_name': 'ghrsst_ospo',
'plots_list': 'time_series, lead_average'},
'WeeklyAvg_SST': {'line_type_stat_list': ['SL1L2/RMSE', 'SL1L2/ME'],
'vx_mask_list': ['NHEM', 'SHEM', 'TROPICS'],
'fcst_var_dict': {'name': 'SST_WEEKLYAVG',
'levels': 'Z0',
'threshs': 'NA'},
'obs_var_dict': {'name': 'analysed_sst',
'levels': "'0,*,*'",
'threshs': 'NA'},
'interp_dict': {'method': 'NEAREST',
'points': '1'},
'fhr_start': '168',
'fhr_end': '840',
'fhr_inc': '168',
'grid': 'G003',
'obs_name': 'ghrsst_ospo',
'plots_list': 'time_series, lead_average'},
'MonthlyAvg_SST': {'line_type_stat_list': ['SL1L2/RMSE', 'SL1L2/ME'],
'vx_mask_list': ['NHEM', 'SHEM', 'TROPICS'],
'fcst_var_dict': {'name': 'SST_MONTHLYAVG',
'levels': 'Z0',
'threshs': 'NA'},
'obs_var_dict': {'name': 'analysed_sst',
'levels': "'0,*,*'",
'threshs': 'NA'},
'interp_dict': {'method': 'NEAREST',
'points': '1'},
'fhr_start': '720',
'fhr_end': '720',
'fhr_inc': '24',
'grid': 'G003',
'obs_name': 'ghrsst_ospo',
'plots_list': 'time_series'},
}
}
for verif_type in VERIF_CASE_STEP_type_list:
print("----> Making job scripts for "+VERIF_CASE_STEP+" "
+verif_type+" for job group "+JOB_GROUP)
VERIF_CASE_STEP_abbrev_type = (VERIF_CASE_STEP_abbrev+'_'
+verif_type)
model_plot_name_list = (
os.environ[VERIF_CASE_STEP_abbrev+'_model_plot_name_list'].split(' ')
)
verif_type_plot_jobs_dict = plot_jobs_dict[verif_type]
for verif_type_job in list(verif_type_plot_jobs_dict.keys()):
obs_list = [
verif_type_plot_jobs_dict[verif_type_job]['obs_name']
for m in model_list
]
fcst_var_levels = (verif_type_plot_jobs_dict[verif_type_job]\
['fcst_var_dict']['levels'].split(', '))
fcst_var_threshs = (verif_type_plot_jobs_dict[verif_type_job]\
['fcst_var_dict']['threshs'].split(', '))
obs_var_levels = (verif_type_plot_jobs_dict[verif_type_job]\
['obs_var_dict']['levels'].split(', '))
obs_var_threshs = (verif_type_plot_jobs_dict[verif_type_job]\
['obs_var_dict']['threshs'].split(', '))
# Initialize job environment dictionary
job_env_dict = sub_util.initialize_job_env_dict(
verif_type, JOB_GROUP,
VERIF_CASE_STEP_abbrev_type, verif_type_job
)
job_env_dict['event_equalization'] = (
os.environ[VERIF_CASE_STEP_abbrev+'_event_eq']
)
job_env_dict['fhr_start'] = (
verif_type_plot_jobs_dict[verif_type_job]['fhr_start']
)
job_env_dict['fhr_end'] = (
verif_type_plot_jobs_dict[verif_type_job]['fhr_end']
)
job_env_dict['fhr_inc'] = (
verif_type_plot_jobs_dict[verif_type_job]['fhr_inc']
)
job_env_dict['start_date'] = start_date
job_env_dict['end_date'] = end_date
job_env_dict['date_type'] = 'VALID'
job_env_dict['grid'] = (
verif_type_plot_jobs_dict[verif_type_job]['grid']
)
job_env_dict['interp_method'] = (
verif_type_plot_jobs_dict[verif_type_job]\
['interp_dict']['method']
)
job_env_dict['interp_points_list'] = ("'"+
verif_type_plot_jobs_dict[verif_type_job]\
['interp_dict']['points']
+"'")
valid_hr_start = int(job_env_dict['valid_hr_start'])
valid_hr_end = int(job_env_dict['valid_hr_end'])
valid_hr_inc = int(job_env_dict['valid_hr_inc'])
valid_hrs = list(range(valid_hr_start,
valid_hr_end+valid_hr_inc,
valid_hr_inc))
for data_name in ['fcst', 'obs']:
job_env_dict[data_name+'_var_name'] = (
verif_type_plot_jobs_dict[verif_type_job]\
[data_name+'_var_dict']['name']
)
verif_type_job_loop_list = []
for line_type_stat \
in verif_type_plot_jobs_dict[verif_type_job]\
['line_type_stat_list']:
if JOB_GROUP in ['condense_stats', 'filter_stats', 'tar_images']:
if line_type_stat.split('/')[0] not in verif_type_job_loop_list:
verif_type_job_loop_list.append(line_type_stat.split('/')[0])
else:
verif_type_job_loop_list.append(line_type_stat)
for verif_type_job_loop in list(
itertools.product(verif_type_job_loop_list,
verif_type_plot_jobs_dict[verif_type_job]\
['vx_mask_list'])
):
if '/' in verif_type_job_loop[0]:
job_env_dict['line_type'] = (
verif_type_job_loop[0].split('/')[0]
)
job_env_dict['stat'] = (
verif_type_job_loop[0].split('/')[1]
)
else:
job_env_dict['line_type'] = verif_type_job_loop[0]
job_env_dict['vx_mask'] = verif_type_job_loop[1]
job_env_dict['job_name'] = (
job_env_dict['line_type']+'/'
+verif_type_job+'/'
+job_env_dict['vx_mask']
)
job_output_dir = os.path.join(
DATA, VERIF_CASE+'_'+STEP, 'plot_output',
RUN+'.'+end_date, verif_type,
job_env_dict['job_name'].replace('/','_')
)
if not os.path.exists(job_output_dir):
os.makedirs(job_output_dir)
if JOB_GROUP == 'condense_stats':
for JOB_GROUP_loop in list(
itertools.product(model_list, [fcst_var_levels],
[fcst_var_threshs])
):
job_env_dict['model_list'] = "'"+f"{JOB_GROUP_loop[0]}"+"'"
job_env_dict['model_plot_name_list'] = (
"'"+f"{model_plot_name_list[model_list.index(JOB_GROUP_loop[0])]}"+"'"
)
job_env_dict['obs_list'] = (
"'"+f"{obs_list[model_list.index(JOB_GROUP_loop[0])]}"+"'"
)
job_env_dict['fcst_var_level_list'] = (
"'"+f"{', '.join(JOB_GROUP_loop[1])}"+"'"
)
job_env_dict['fcst_var_thresh_list'] = (
"'"+f"{', '.join(JOB_GROUP_loop[2])}"+"'"
)
job_env_dict['obs_var_level_list'] = (
"'"+f"{', '.join(obs_var_levels)}"+"'"
)
job_env_dict['obs_var_thresh_list'] = (
"'"+f"{', '.join(obs_var_threshs)}"+"'"
)
job_env_dict['valid_hr_inc'] = '24'
# Create job file
njobs+=1
job_file = os.path.join(JOB_GROUP_jobs_dir,
'job'+str(njobs))
print("Creating job script: "+job_file)
job = open(job_file, 'w')
job.write('#!/bin/bash\n')
job.write('set -x\n')
job.write('\n')
# Set any environment variables for special cases
# Write environment variables
job_env_dict['job_id'] = 'job'+str(njobs)
for name, value in job_env_dict.items():
job.write('export '+name+'='+value+'\n')
job.write('\n')
# Write job commands
job.write(
sub_util.python_command('subseasonal_plots.py',[])
+'\n'
)
job.write('export err=$?; err_chk'+'\n')
job.close()
elif JOB_GROUP == 'filter_stats':
for JOB_GROUP_loop in list(
itertools.product(model_list, fcst_var_levels,
fcst_var_threshs, valid_hrs)
):
job_env_dict['model_list'] = "'"+f"{JOB_GROUP_loop[0]}"+"'"
job_env_dict['model_plot_name_list'] = (
"'"+f"{model_plot_name_list[model_list.index(JOB_GROUP_loop[0])]}"+"'"
)
job_env_dict['obs_list'] = (
"'"+f"{obs_list[model_list.index(JOB_GROUP_loop[0])]}"+"'"
)
job_env_dict['fcst_var_level_list'] = (
"'"+f"{JOB_GROUP_loop[1]}"+"'"
)
job_env_dict['fcst_var_thresh_list'] = (
"'"+f"{JOB_GROUP_loop[2]}"+"'"
)
job_env_dict['obs_var_level_list'] = (
"'"+f"{obs_var_levels[fcst_var_levels.index(JOB_GROUP_loop[1])]}"+"'"
)
job_env_dict['obs_var_thresh_list'] = (
"'"+f"{obs_var_threshs[fcst_var_threshs.index(JOB_GROUP_loop[2])]}"+"'"
)
job_env_dict['valid_hr_start'] = (
str(JOB_GROUP_loop[3]).zfill(2)
)
job_env_dict['valid_hr_end'] = (
job_env_dict['valid_hr_start']
)
job_env_dict['valid_hr_inc'] = '24'
# Create job file
njobs+=1
job_file = os.path.join(JOB_GROUP_jobs_dir,
'job'+str(njobs))
print("Creating job script: "+job_file)
job = open(job_file, 'w')
job.write('#!/bin/bash\n')
job.write('set -x\n')
job.write('\n')
# Write environment variables
job_env_dict['job_id'] = 'job'+str(njobs)
for name, value in job_env_dict.items():
job.write('export '+name+'='+value+'\n')
job.write('\n')
job.write(
sub_util.python_command('subseasonal_plots.py',[])
+'\n'
)
job.write('export err=$?; err_chk'+'\n')
job.close()
elif JOB_GROUP == 'make_plots':
job_output_images_dir = os.path.join(
DATA, VERIF_CASE+'_'+STEP, 'plot_output',
RUN+'.'+end_date, verif_type,
job_env_dict['job_name'].replace('/','_'), 'images'
)
if not os.path.exists(job_output_images_dir):
os.makedirs(job_output_images_dir)
job_env_dict['model_list'] = "'"+f"{', '.join(model_list)}"+"'"
job_env_dict['model_plot_name_list'] = (
"'"+f"{', '.join(model_plot_name_list)}"+"'"
)
job_env_dict['obs_list'] = (
"'"+f"{', '.join(obs_list)}"+"'"
)
for plot in verif_type_plot_jobs_dict\
[verif_type_job]['plots_list'].split(', '):
job_env_dict['plot'] = plot
if plot == 'valid_hour_average':
plot_valid_hrs_loop = [valid_hrs]
else:
plot_valid_hrs_loop = valid_hrs
if plot in ['threshold_average', 'performance_diagram']:
plot_fcst_threshs_loop = [fcst_var_threshs]
else:
plot_fcst_threshs_loop = fcst_var_threshs
if plot in ['stat_by_level', 'lead_by_level']:
plot_fcst_levels_loop = ['all', 'trop', 'strat',
'ltrop', 'utrop']
else:
plot_fcst_levels_loop = fcst_var_levels
for JOB_GROUP_loop in list(
itertools.product(plot_valid_hrs_loop,
plot_fcst_threshs_loop,
plot_fcst_levels_loop)
):
if plot == 'valid_hour_average':
job_env_dict['valid_hr_start'] = str(
JOB_GROUP_loop[0][0]
).zfill(2)
job_env_dict['valid_hr_end'] = str(
JOB_GROUP_loop[0][-1]
).zfill(2)
job_env_dict['valid_hr_inc'] = str(valid_hr_inc)
else:
job_env_dict['valid_hr_start'] = str(
JOB_GROUP_loop[0]
).zfill(2)
job_env_dict['valid_hr_end'] = str(
JOB_GROUP_loop[0]
).zfill(2)
job_env_dict['valid_hr_inc'] = '24'
if plot in ['threshold_average',
'performance_diagram']:
job_env_dict['fcst_var_thresh_list'] = (
"'"+f"{', '.join(JOB_GROUP_loop[1])}"+"'"
)
job_env_dict['obs_var_thresh_list'] = (
"'"+f"{', '.join(obs_var_threshs)}"+"'"
)
else:
job_env_dict['fcst_var_thresh_list'] = (
"'"+f"{JOB_GROUP_loop[1]}"+"'"
)
job_env_dict['obs_var_thresh_list'] = (
"'"+f"{obs_var_threshs[fcst_var_threshs.index(JOB_GROUP_loop[1])]}"+"'"
)
if plot in ['stat_by_level', 'lead_by_level']:
job_env_dict['vert_profile'] = (
"'"+f"{JOB_GROUP_loop[2]}"+"'"
)
job_env_dict['fcst_var_level_list'] = (
"'"+f"{', '.join(fcst_var_levels)}"+"'"
)
job_env_dict['obs_var_level_list'] = (
"'"+f"{', '.join(obs_var_levels)}"+"'"
)
else:
job_env_dict['fcst_var_level_list'] = (
"'"+f"{JOB_GROUP_loop[2]}"+"'"
)
job_env_dict['obs_var_level_list'] = (
"'"+f"{obs_var_levels[fcst_var_levels.index(JOB_GROUP_loop[2])]}"+"'"
)
run_subseasonal_plots = ['subseasonal_plots.py']
for run_subseasonal_plot in run_subseasonal_plots:
# Create job file
njobs+=1
job_file = os.path.join(JOB_GROUP_jobs_dir,
'job'+str(njobs))
print("Creating job script: "+job_file)
job = open(job_file, 'w')
job.write('#!/bin/bash\n')
job.write('set -x\n')
job.write('\n')
# Write environment variables
job_env_dict['job_id'] = 'job'+str(njobs)
for name, value in job_env_dict.items():
job.write('export '+name+'='+value+'\n')
job.write('\n')
job.write(
sub_util.python_command(run_subseasonal_plot,
[])+'\n'
)
job.write('export err=$?; err_chk'+'\n')
job.close()
elif JOB_GROUP == 'tar_images':
job_env_dict['model_list'] = "'"+f"{', '.join(model_list)}"+"'"
job_env_dict['model_plot_name_list'] = (
"'"+f"{', '.join(model_plot_name_list)}"+"'"
)
job_env_dict['obs_list'] = (
"'"+f"{', '.join(obs_list)}"+"'"
)
job_env_dict['fcst_var_level_list'] = (
"'"+f"{', '.join(fcst_var_levels)}"+"'"
)
job_env_dict['fcst_var_thresh_list'] = (
"'"+f"{', '.join(fcst_var_threshs)}"+"'"
)
job_env_dict['obs_var_level_list'] = (
"'"+f"{', '.join(obs_var_levels)}"+"'"
)
job_env_dict['obs_var_thresh_list'] = (
"'"+f"{', '.join(obs_var_threshs)}"+"'"
)
job_env_dict['valid_hr_inc'] = '24'
# Create job file
njobs+=1
job_file = os.path.join(JOB_GROUP_jobs_dir,
'job'+str(njobs))
print("Creating job script: "+job_file)
job = open(job_file, 'w')
job.write('#!/bin/bash\n')
job.write('set -x\n')
job.write('\n')
# Write environment variables
job_env_dict['job_id'] = 'job'+str(njobs)
for name, value in job_env_dict.items():
job.write('export '+name+'='+value+'\n')
job.write('\n')
job.write(
sub_util.python_command('subseasonal_plots.py',[])
+'\n'
)
job.write('export err=$?; err_chk'+'\n')
job.close()
# If running USE_CFP, create POE scripts
if USE_CFP == 'YES':
job_files = glob.glob(os.path.join(JOB_GROUP_jobs_dir, 'job*'))
njob_files = len(job_files)
if njob_files == 0:
print("FATAL ERROR: No job files created in "+JOB_GROUP_jobs_dir)
sys.exit(1)
poe_files = glob.glob(os.path.join(JOB_GROUP_jobs_dir, 'poe*'))
npoe_files = len(poe_files)
if npoe_files > 0:
for poe_file in poe_files:
os.remove(poe_file)
njob, iproc, node = 1, 0, 1
while njob <= njob_files:
job = 'job'+str(njob)
if machine in ['HERA', 'ORION', 'S4', 'JET']:
if iproc >= int(nproc):
poe_file.close()
iproc = 0
node+=1
poe_filename = os.path.join(JOB_GROUP_jobs_dir,
'poe_jobs'+str(node))
poe_file = open(poe_filename, 'a')
iproc+=1
if machine in ['HERA', 'ORION', 'S4', 'JET']:
poe_file.write(
str(iproc-1)+' '
+os.path.join(JOB_GROUP_jobs_dir, job)+'\n'
)
else:
poe_file.write(
os.path.join(JOB_GROUP_jobs_dir, job)+'\n'
)
poe_file.close()
njob+=1
# If at final record and have not reached the
# final processor then write echo's to
# poe script for remaining processors
poe_filename = os.path.join(JOB_GROUP_jobs_dir,
'poe_jobs'+str(node))
poe_file = open(poe_filename, 'a')
if machine == 'WCOSS2':
nselect = subprocess.check_output(
'cat '+PBS_NODEFILE+'| wc -l', shell=True, encoding='UTF-8'
).replace('\n', '')
nnp = int(nselect) * int(nproc)
else:
nnp = nproc
iproc+=1
while iproc <= int(nnp):
if machine in ['HERA', 'ORION', 'S4', 'JET']:
poe_file.write(
str(iproc-1)+' /bin/echo '+str(iproc)+'\n'
)
else:
poe_file.write(
'/bin/echo '+str(iproc)+'\n'
)
iproc+=1
poe_file.close()
print("END: "+os.path.basename(__file__))