def merge_results(self, label, full_merge=False, force=False):
"""Merge the different process result files together.
Parameters:
label (str): The name of the subdirectory containing folders where
each process computes results. For example, if label="states",
then self.records_dir/states/ should exist and contain at least
one subfolder named states_s1/ (or similar), which in turn
contains .h5 files from each process (states_s1_p0.h5, etc.).
full_merge (bool): If true, merge the process files AND merge
the resulting files into one large file. For example,
states_s1_p0.h5 and states_s1_p1.h5 are merged into
states_s1.h5 like usual, and then states_s1.h5 and states_s2.h5
are merged into one large states.h5.
force (bool): If true, merge the files even if there is an existing
merged file.
"""
# Check that the relevant folder exists.
subdir = os.path.join(self.records_dir, label)
if not os.path.isdir(subdir):
raise NotADirectoryError(subdir)
# Check for existing folders and missing files before merging.
work_todo = False
if full_merge:
work_todo = not os.path.isfile(os.path.join(subdir, label + ".h5"))
else:
for d in os.listdir(subdir):
target = os.path.join(subdir, d)
if os.path.isdir(target) and not os.path.isfile(target +
".h5"):
work_todo = True
break
if work_todo or force:
self.logger.info("Merging {} files...".format(label))
post.merge_process_files(subdir,
cleanup=False,
comm=MPI.COMM_WORLD)
if full_merge:
# Wait for other processes to finish.
MPI.COMM_WORLD.Barrier()
# Do the last merge.
set_paths = glob(os.path.join(subdir, label + "_s*.h5"))
post.merge_sets(os.path.join(subdir, label + ".h5"),
set_paths,
cleanup=True,
comm=MPI.COMM_WORLD)
self.logger.info("\t{} files now {}merged".format(
label, "fully " if full_merge else ""))
def join_temporal(base_path,data_types=None, cleanup=False):
logger.info("joining data in time from Dedalus run {:s}".format(base_path))
if data_types is None:
data_types = ['scalar', 'profiles']
for data_type in data_types:
logger.info("merging {}".format(data_type))
try:
path = os.path.join(base_path,data_type)
files = glob.glob(os.path.join(path,"*.h5"))
joined_filename = os.path.join(path,"{}_joined.h5".format(data_type))
post.merge_sets(joined_filename, files, cleanup=cleanup)
except:
logger.info("missing {}".format(data_type))
raise
logger.info("done temporal join operation for {:s}".format(base_path))
def merge_move(rundir, outdir):
# Merge snapshots from different processes:
post.merge_process_files(rundir + "snapshots", cleanup=True)
set_paths = list(
pathlib.Path(rundir + "snapshots").glob("snapshots_s*.h5"))
post.merge_sets(rundir + "snapshots/snapshots.h5", set_paths, cleanup=True)
# ifields
post.merge_process_files(rundir + "ifields", cleanup=True)
set_paths = list(pathlib.Path(rundir + "ifields").glob("ifields_s*.h5"))
post.merge_sets(rundir + "ifields/ifields.h5", set_paths, cleanup=True)
# moments
post.merge_process_files(rundir + "moments", cleanup=True)
set_paths = list(pathlib.Path(rundir + "moments").glob("moments_s*.h5"))
post.merge_sets(rundir + "moments/moments.h5", set_paths, cleanup=True)
############################# These strings need editting ############################# #-----# #-----# #-----# #-----# #-----# #-----# #-----# #-----# #-----# run_name = "test1" direc_folder = "raw_data2/" #-----# #-----# #-----# #-----# #-----# #-----# #-----# #-----# #-----# ####################################################################################### #-----# #-----# #-----# #-----# #-----# #-----# #-----# #-----# #-----# subfolder = "snapshots" direc = direc_folder + subfolder print(direc_folder) # name of the resulting output file output_name = subfolder + "_" + run_name set_paths = list(pathlib.Path(direc).glob(subfolder + "_s*.h5")) post.merge_sets(direc + "/" + output_name + ".h5", set_paths, cleanup=True) # direc is the folder in which the various data folders are located subfolder = "analysis" direc = direc_folder + subfolder # name of the resulting output file output_name = subfolder + "_" + run_name set_paths = list(pathlib.Path(direc).glob(subfolder + "_s*.h5")) post.merge_sets(direc + "/" + output_name + ".h5", set_paths, cleanup=True) # direc is the folder in which the various data folders are located subfolder = "run_parameters" direc = direc_folder + subfolder # name of the resulting output file output_name = subfolder + "_" + run_name
--cleanup Delete distributed files after merging
"""
import h5py
import h5py
import subprocess
from dedalus.tools import post
import pathlib
import sys
import os
import argparse
import glob
from docopt import docopt
args = docopt(__doc__)
base = args['<base_path>']
iter_paths = os.listdir(args['<base_path>'])
for path in iter_paths:
print(base + "/" + path)
folder = base + "/" + path
post.merge_analysis(folder)
for path in iter_paths:
print(base + "/" + path)
set_paths = list(glob.glob(base + "/" + path + "/*h5"))
print(set_paths)
post.merge_sets(base + "/" + path + ".h5", set_paths, cleanup=False)
#~ print(file_name)
print(os.path.join(r, file))
# Useful for error hunting if the loop above doesn't appear to work as it should
#print(list_of_run_parameter_files)
# If there are 0 or 1 files nothing needs to be merged. But useful to know how many files there are.
if len(list_of_run_parameter_files) == 1:
final_file = list_of_run_parameter_files[0]
print(final_file)
# are we just going to pass?
elif len(list_of_run_parameter_files) == 0:
print("No Run Parameter files found")
else:
# Do the merge for run_parameters here.
final_file = post.merge_sets(results_direc + "/raw_data2/" + section +
".h5",
list_of_run_parameter_files,
cleanup=True)
section = "analysis"
specificdir = specificdir = os.path.join(direc, section)
# r=root, d=directories, f = files
list_of_analysis_files = []
for r, d, f in os.walk(specificdir): #you need thisdir not specificdir
for file in f:
if file.endswith(".h5"):
list_of_analysis_files.append(os.path.join(r, file))
print(os.path.join(r, file))
# Useful for error hunting if the loop above doesn't appear to work as it should
#print(list_of_analysis_files)
# Analysis handler
analysis = solver.evaluator.add_file_handler('analysis', iter=5, max_writes=100)
analysis.add_task("integ(u,'x')", layout='g', name='<u>')
analysis.add_task("integ(u**2,'x')", layout='g', name='<uu>')
analysis.add_system(solver.state, layout='g')
# Simulation
import time
# Main loop
dt = 1e-2
start_time = time.time()
while solver.ok:
solver.step(dt)
if solver.iteration % 100 == 0:
print('Completed iteration {}'.format(solver.iteration))
end_time = time.time()
print('Runtime:', end_time-start_time)
# Merge process files
from dedalus.tools import post
post.merge_process_files("analysis", cleanup=True)
# Merge files into one
import pathlib
set_paths = list(pathlib.Path("analysis").glob("analysis_s*.h5"))
post.merge_sets("analysis/1d_kdv_analysis.h5", set_paths, cleanup=True)
import pathlib
from dedalus.tools import post
set_paths = list(pathlib.Path("ugm_28_1hr/").glob("ugm_28_1hr_s*.h5"))
post.merge_sets("ugm_28_1hr/ugm_28_1hr.h5", set_paths, cleanup=True)
"""
Merge h5 data into one file.
Usage:
merge2.py <joint_path> <set_path> [--cleanup]
Options:
--cleanup Delete distributed files after merging
"""
if __name__ == "__main__":
import pathlib
from docopt import docopt
from dedalus.tools import logging
from dedalus.tools import post
print('0')
args = docopt(__doc__)
set_paths = list(pathlib.Path(args['<set_path>']).glob("*.h5"))
#print(set_paths)
post.merge_sets(args['<joint_path>'], set_paths, args['--cleanup'])
print('2')
# will take approximately forever***
from dedalus.tools import post
# post.merge_process_files("slices", cleanup=True)
# post.merge_process_files("dump",cleanup=True)
post.merge_process_files("checkpoint",cleanup=True)
import subprocess
# print(subprocess.check_output("find slices", shell=True).decode())
# print(subprocess.check_output("find dump", shell=True).decode())
print(subprocess.check_output("find checkpoint", shell=True).decode())
import pathlib
# set_paths=list(pathlib.Path("slices").glob("slices_s*.h5"))
# post.merge_sets("slices/slices.h5",set_paths,cleanup=True)
# set_paths=list(pathlib.Path("dump").glob("dump_s*.h5"))
# post.merge_sets("dump/dump.h5",set_paths,cleanup=True)
set_paths=list(pathlib.Path("checkpoint").glob("checkpoint_s*.h5"))
post.merge_sets("checkpoint/checkpoint.h5",set_paths,cleanup=True)
# Rename folders
import os
# os.rename("dump", "dump_old")
# os.rename("slices", "slices_old")
os.rename("checkpoint", "checkpoint_old")
from dedalus.tools import post
import pathlib
print('Merging data files...')
post.merge_process_files("analysis_tasks", cleanup=True)
set_paths = list(pathlib.Path("analysis_tasks").glob("analysis_tasks_s*.h5"))
post.merge_sets("analysis_tasks/analysis.h5", set_paths, cleanup=True)
if __name__ == "__main__":
import pathlib
import shutil
import tarfile
from docopt import docopt
from dedalus.tools import logging
from dedalus.tools import post
from dedalus.tests import test, bench, cov
args = docopt(__doc__)
if args['test']:
test()
elif args['bench']:
bench()
elif args['cov']:
cov()
elif args['get_config']:
config_path = pathlib.Path(__file__).parent.joinpath('dedalus.cfg')
shutil.copy(str(config_path), '.')
elif args['get_examples']:
example_path = pathlib.Path(__file__).parent.joinpath('examples.tar.gz')
with tarfile.open(str(example_path), mode='r:gz') as archive:
archive.extractall('dedalus_examples')
elif args['merge_procs']:
post.merge_process_files(args['<base_path>'], cleanup=args['--cleanup'])
elif args['merge_sets']:
post.merge_sets(args['<joint_path>'], args['<set_paths>'], cleanup=args['--cleanup'])
"""
Perform after execution of the merge.py script to combine all .h5 data files
into a singular .h5 file.
WARNING: This script may produce an extremely large output file.
"""
import pathlib
import sys
from dedalus.tools import post
direc = sys.argv[1]
run_name = sys.argv[2]
for subfolder in ['snapshots', 'analysis', 'run_parameters']:
# print(f"Creating file {direc}{subfolder}/{subfolder}_{run_name}.h5")
set_paths = list(pathlib.Path(f"{direc}{subfolder}").glob(f"{subfolder}_s*.h5"))
post.merge_sets(f"{direc}{subfolder}/{subfolder}_{run_name}.h5", set_paths, cleanup=True)
"""
Merge analysis sets from a FileHandler.
Usage:
merge_sets.py <joint_path> <set_paths>...
"""
if __name__ == "__main__":
from docopt import docopt
from mpi4py import MPI
from dedalus.tools import logging
from dedalus.tools import post
args = docopt(__doc__)
if MPI.COMM_WORLD.rank == 0:
post.merge_sets(args['<joint_path>'], args['<set_paths>'])
# Main loop ------------------------------------------------------------------|
try:
logger.info('Starting loop')
start_time = time.time()
while solver.ok:
# We start with a very small dt and increase it.
dt = dtm + (dtM - dtm) * (1 - np.exp(-sigma_dt * solver.sim_time))
solver.step(dt)
if (solver.iteration - 1) % 100 == 0:
logger.info('Ite #{0:d}, Time: {1:.2e} IPs, dt: {2:e}'.format(
solver.iteration, solver.sim_time / TF, dt))
except NameError:
logger.error('Exception raised, triggering end of main loop.')
raise
finally:
end_time = time.time()
logger.info('Iterations: {0:d}'.format(solver.iteration))
logger.info('Sim end time: {0:f}'.format(solver.sim_time))
logger.info('Run time: {0:.2f} sec'.format(end_time - start_time))
logger.info('Run time: {0:f} cpu-min'.format(
(end_time - start_time) / 30 * domain.dist.comm_cart.size))
# Post-processing ------------------------------------------------------------|
print(subprocess.check_output("find anim", shell=True).decode())
post.merge_process_files("anim", cleanup=True) # merges the sub-domains if any
set_paths = list(
pathlib.Path("anim").glob("anim_s*.h5")) # finds all of the time series
# merges the time series
post.merge_sets("anim/anim.h5", set_paths, cleanup=True)
print(subprocess.check_output("find anim", shell=True).decode())
