def main(args):
"""The main loop"""
# this globbling logic is copied from `filedriver.py`. It may be worth
# cleaning this up to ensure it handles typical use-cases we encounter.
files = glob.glob(args.glob)
# In case the filenames aren't padded we sort first by shorter length and then
# alphabetically. This is a slight modification based on the question by Adrian and answer by
# Jochen Ritzel at:
# https://stackoverflow.com/questions/4659524/how-to-sort-by-length-of-string-followed-by-alphabetical-order
files.sort(key=lambda item: (len(item), item))
# initialize Catalyst
from paraview.catalyst import bridge
from paraview import print_info, print_warning
bridge.initialize()
# add analysis script
for script in args.script:
bridge.add_pipeline(script, args.script_version)
# Some MPI related stuff to figure out if we're running with MPI
# and if so, on how many ranks.
try:
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
num_ranks = comm.Get_size()
except ImportError:
print_warning("missing mpi4py, running in serial (non-distributed) mode")
rank = 0
num_ranks = 1
reader = create_reader(files)
timesteps = reader.TimestepValues[:]
step = 0
numsteps = len(timesteps)
for time in timesteps:
if args.delay > 0:
import time
time.sleep(args.delay)
if rank == 0:
print_info("timestep: {0} of {1}".format((step+1), numsteps))
dataset, wholeExtent = read_dataset(reader, time, rank, num_ranks)
# "perform" coprocessing. results are outputted only if
# the passed in script says we should at time/step
bridge.coprocess(time, step, dataset, name=args.channel, wholeExtent=wholeExtent)
del dataset
del wholeExtent
step += 1
# finalize Catalyst
bridge.finalize()
def main(args):
"""The main loop"""
# initialize Catalyst
from paraview.catalyst import bridge
from paraview import print_info, print_warning
bridge.initialize()
# add analysis script
for script in args.script:
bridge.add_pipeline(script, args.script_version)
# Some MPI related stuff to figure out if we're running with MPI
# and if so, on how many ranks.
try:
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
num_ranks = comm.Get_size()
except ImportError:
print_warning(
"missing mpi4py, running in serial (non-distributed) mode")
rank = 0
num_ranks = 1
numsteps = args.timesteps
for step in range(numsteps):
if args.delay > 0:
import time
time.sleep(args.delay)
if rank == 0:
print_info("timestep: {0}/{1}".format(step + 1, numsteps))
# assume simulation time starts at 0
time = step / float(numsteps)
dataset, wholeExtent = create_dataset(step, args, rank, num_ranks)
# "perform" coprocessing. results are outputted only if
# the passed in script says we should at time/step
bridge.coprocess(time,
step,
dataset,
name=args.channel,
wholeExtent=wholeExtent)
del dataset
del wholeExtent
# finalize Catalyst
bridge.finalize()
def finalize():
# finalize ParaView Catalyst.
bridge.finalize()
print("timestep: {0}/{1}".format(step + 1, numsteps))
# assume simulation time starts at 0
time = step / float(numsteps)
# put in some variation in the point data that changes with time
wavelet.SetMaximum(255 + 200 * math.sin(step))
# using 'UpdatePiece' lets us generate a subextent based on the
# 'rank' and 'num_ranks'; thus works seamlessly in distributed and
# non-distributed modes
wavelet.UpdatePiece(rank, num_ranks, 0)
# typically, here you'll have some adaptor code that converts your
# simulation data into a vtkDataObject subclass. In this example,
# there's nothing to do since we're directly generating a
# vtkDataObject.
dataset = wavelet.GetOutputDataObject(0)
# "perform" coprocessing. results are outputted only if
# the passed in script says we should at time/step
bridge.coprocess(time,
step,
dataset,
name=args.channel,
wholeExtent=wholeExtent)
del dataset
bridge.finalize()