def process_tavg(args):
infs = {}
# Interrogate files passed by the shell
for fname in args.infs:
# Load solution files and obtain solution times
inf = read_pyfr_data(fname)
tinf = Inifile(inf['stats']).getfloat('solver-time-integrator',
'tcurr')
# Retain if solution time is within limits
if args.limits is None or args.limits[0] <= tinf <= args.limits[1]:
infs[tinf] = inf
# Verify that solutions were computed on the same mesh
if inf['mesh_uuid'] != infs[infs.keys()[0]]['mesh_uuid']:
raise RuntimeError('Solution files in scope were not computed '
'on the same mesh')
# Sort the solution times, check for sufficient files in scope
stimes = sorted(infs.keys())
if len(infs) <= 1:
raise RuntimeError('More than one solution file is required to '
'compute an average')
# Initialise progress bar, and the average with first solution
pb = ProgressBar(0, 0, len(stimes), 0)
avgs = {name: infs[stimes[0]][name].copy() for name in infs[stimes[0]]}
solnfs = [name for name in avgs.keys() if name.startswith('soln')]
# Weight the initialised trapezoidal mean
dtnext = stimes[1] - stimes[0]
for name in solnfs:
avgs[name] *= 0.5 * dtnext
pb.advance_to(1)
# Compute the trapezoidal mean up to the last solution file
for i in xrange(len(stimes[2:])):
dtlast = dtnext
dtnext = stimes[i + 2] - stimes[i + 1]
# Weight the current solution, then add to the mean
for name in solnfs:
avgs[name] += 0.5 * (dtlast + dtnext) * infs[stimes[i + 1]][name]
pb.advance_to(i + 2)
# Weight final solution, update mean and normalise for elapsed time
for name in solnfs:
avgs[name] += 0.5 * dtnext * infs[stimes[-1]][name]
avgs[name] *= 1.0 / (stimes[-1] - stimes[0])
pb.advance_to(i + 3)
# Compute and assign stats for a time-averaged solution
stats = Inifile()
stats.set('time-average', 'tmin', stimes[0])
stats.set('time-average', 'tmax', stimes[-1])
stats.set('time-average', 'ntlevels', len(stimes))
avgs['stats'] = stats.tostr()
outf = open(args.outf, 'wb')
np.savez(outf, **avgs)
def _process_common(args, mesh, soln, cfg):
# Prefork to allow us to exec processes after MPI is initialised
if hasattr(os, 'fork'):
from pytools.prefork import enable_prefork
enable_prefork()
# Import but do not initialise MPI
from mpi4py import MPI
# Manually initialise MPI
MPI.Init()
# Ensure MPI is suitably cleaned up
register_finalize_handler()
# Create a backend
backend = get_backend(args.backend, cfg)
# Get the mapping from physical ranks to MPI ranks
rallocs = get_rank_allocation(mesh, cfg)
# Construct the solver
solver = get_solver(backend, rallocs, mesh, soln, cfg)
# If we are running interactively then create a progress bar
if args.progress and MPI.COMM_WORLD.rank == 0:
pb = ProgressBar(solver.tstart, solver.tcurr, solver.tend)
# Register a callback to update the bar after each step
callb = lambda intg: pb.advance_to(intg.tcurr)
solver.completed_step_handlers.append(callb)
# Execute!
solver.run()
def main():
from mpi4py import MPI
ap = ArgumentParser(prog='pyfr-sim', description='Runs a PyFR simulation')
ap.add_argument('--verbose', '-v', action='count')
ap.add_argument('--backend', '-b', default='cuda', help='Backend to use')
ap.add_argument('--progress',
'-p',
action='store_true',
help='show a progress bar')
ap.add_argument('--nansweep',
'-n',
metavar='N',
type=int,
help='check for NaNs every N steps')
sp = ap.add_subparsers(help='sub-command help')
ap_run = sp.add_parser('run', help='run --help')
ap_run.add_argument('mesh', help='mesh file')
ap_run.add_argument('cfg', type=FileType('r'), help='config file')
ap_run.set_defaults(process=process_run)
ap_restart = sp.add_parser('restart', help='restart --help')
ap_restart.add_argument('mesh', help='mesh file')
ap_restart.add_argument('soln', help='solution file')
ap_restart.add_argument('cfg',
nargs='?',
type=FileType('r'),
help='new config file')
ap_restart.set_defaults(process=process_restart)
# Parse the arguments
args = ap.parse_args()
mesh, soln, cfg = args.process(args)
# Ensure MPI is suitably cleaned up
register_finalize_handler()
# Create a backend
backend = get_backend(args.backend, cfg)
# Get the mapping from physical ranks to MPI ranks
rallocs = get_rank_allocation(mesh, cfg)
# Construct the solver
solver = get_solver(backend, rallocs, mesh, soln, cfg)
# If we are running interactively then create a progress bar
if args.progress and MPI.COMM_WORLD.rank == 0:
pb = ProgressBar(solver.tstart, solver.tcurr, solver.tend)
# Register a callback to update the bar after each step
callb = lambda intg: pb.advance_to(intg.tcurr)
solver.completed_step_handlers.append(callb)
# NaN sweeping
if args.nansweep:
def nansweep(intg):
if intg.nsteps % args.nansweep == 0:
if any(np.isnan(np.sum(s)) for s in intg.soln):
raise RuntimeError('NaNs detected at t = {}'.format(
intg.tcurr))
solver.completed_step_handlers.append(nansweep)
# Execute!
solver.run()
