remove old scripts

dartwrf/create_obs_upfront.py

-from multiprocessing.sharedctypes import Value
-import os, sys, shutil, warnings
-import time as time_module
-import datetime as dt
-import numpy as np
-
-from dartwrf.exp_config import exp
-from dartwrf.server_config import cluster
-from dartwrf.utils import copy, print
-import dartwrf.obs.create_obsseq_in as osq
-from dartwrf.obs import obsseq
-from dartwrf import assim_synth_obs as aso
-
-tformat = '%Y-%m-%d_%H:%M'
-
-
-if __name__ == "__main__":
-    """Create observations for multiple times upfront
-    """
-
-    args = sys.argv[1:]
-    times = []
-    for tstr in args:
-        times.append(dt.datetime.strptime(tstr, tformat))
-
-    # strange path?
-    # dir_for_obsseqout = exp.nature_wrfout + '/../../../obs_seq_out/'+exp.use_existing_obsseq  
-    raise NotImplementedError('where to save obsseq to?')  
-    dir_for_obsseqout = ''  # TODO: solve this when necessary
-    print('will save obsseq to', dir_for_obsseqout)
-    os.makedirs(dir_for_obsseqout, exist_ok=True) 
-
-    os.chdir(cluster.dart_rundir)
-
-    # link DART binaries to run_DART
-    os.system(cluster.python + " " + cluster.scripts_rundir + "/link_dart_rttov.py")  
-
-    # remove any existing observation files
-    os.system("rm -f input.nml obs_seq.in obs_seq.out obs_seq.out-orig obs_seq.final")  
-    aso.set_DART_nml()
-
-    for time in times:
-        print('obsseq for time', time)
-
-        aso.prepare_nature_dart(time)  # link WRF files to DART directory
-
-        osq.create_obsseqin_alltypes(time, exp.observations)  # obs_seq.in
-
-        aso.run_perfect_model_obs(nproc=6)  # create observations (obs_seq.out)
-
-        oso = obsseq.ObsSeq(cluster.dart_rundir + "/obs_seq.out")
-
-        if True:  # set reflectance < surface albedo to surface albedo
-            print(" 2.2) removing obs below surface albedo ")
-            if_vis_obs = oso.df['kind'].values == 262
-            if_obs_below_surface_albedo = oso.df['observations'].values < 0.2928
-
-            oso.df.loc[if_vis_obs & if_obs_below_surface_albedo, ('observations')] = 0.2928
-            oso.to_dart(f=cluster.dart_rundir + "/obs_seq.out")
-
-        if getattr(exp, "superob_km", False):
-            print(" 2.3) superobbing to", exp.superob_km, "km")
-            oso.df = oso.df.superob(window_km=exp.superob_km)
-            copy(cluster.dart_rundir + "/obs_seq.out", cluster.dart_rundir + "/obs_seq.out-orig")
-            oso.to_dart(f=cluster.dart_rundir + "/obs_seq.out")
-
-        aso.archive_osq_out(time, dir_obsseq=dir_for_obsseqout)
-

dartwrf/evaluate_posterior.py

-import os, sys, shutil, warnings
-import time as time_module
-import datetime as dt
-import numpy as np
-
-from dartwrf.exp_config import exp
-from dartwrf.server_config import cluster
-from dartwrf import assimilate as aso
-
-
-if __name__ == "__main__":
-
-     assim_time = dt.datetime.strptime(sys.argv[1], "%Y-%m-%d_%H:%M")
-
-     print(" computing posterior observations ")
-     aso.write_list_of_inputfiles_posterior(assim_time)
-
-    # prepare an obsseq without rejected observations
-     if exp.use_existing_obsseq:  # from another exp
-          oso_input = assim_time.strftime(exp.use_existing_obsseq)
-     else:  # from same exp
-          oso_input = cluster.archivedir+'/obs_seq_out' + assim_time.strftime("/%Y-%m-%d_%H:%M_obs_seq.out-beforeQC")
-          if not os.path.isfile(oso_input):
-               oso_input = cluster.archivedir+'/obs_seq_out' + assim_time.strftime("/%Y-%m-%d_%H:%M_obs_seq.out")
-          shutil.copy(oso_input, cluster.dart_rundir+'/obs_seq.out')
-
-     aso.evaluate(assim_time, output_format="%Y-%m-%d_%H:%M_obs_seq.final-evaluate")
\ No newline at end of file

evaluate_plus1.py

-#!/usr/bin/python3
-"""
-Evaluate the forecast in observation space one minute after the analysis time.
-"""
-import datetime as dt
-from dartwrf.workflows import WorkFlows
-
-w = WorkFlows(exp_config='exp_nonlin.py', server_config='jet.py')
-id = None
-
-assim_times = [dt.datetime(2008,7,30,12), ]
-                # dt.datetime(2008,7,30,12,30),  
-                # dt.datetime(2008,7,30,13),
-                # dt.datetime(2008,7,30,13,30),  
-                # dt.datetime(2008,7,30,14),]
-
-# generate observations at +1 minute after the assimilation time
-
-tuples = []
-for init in assim_times:
-    for s in range(30,3*60+1,30):
-        tuples.append((init, init+dt.timedelta(seconds=s)))
-
-w.evaluate_obs_posterior_after_analysis(tuples, depends_on=id)