outdir = datadir / 'postprocessing' / name
outdir.mkdir(parents=True, exist_ok=True)
# Read the cell-centered grid.
x, y, z = petibmpy.read_grid_hdf5(gridpath, 'p')
# Read the grid of the x-component of the vorticity.
grid_wx = petibmpy.read_grid_hdf5(gridpath, 'wx')
# Save the grid on which is defined the Q-criterion.
gridpath = outdir / 'grid.h5'
petibmpy.write_grid_hdf5(gridpath, name, x, y, z)
# List of time-step indices to process.
timesteps = [7750, 7875, 8000, 8250, 8375, 8500, 8625, 8750, 8875]
interp_args = dict(bounds_error=False, method='linear', fill_value=None)
for timestep in timesteps:
print('[time step {}] Computing the cell-centered x-vorticity ...'
.format(timestep))
filepath = datadir / '{:0>7}.h5'.format(timestep)
# Load and interpolate the x-vorticity field on the cell-centered grid.
wx = petibmpy.read_field_hdf5(filepath, 'wx')
wx = petibmpy.interpolate3d(wx, grid_wx, (x, y, z), **interp_args)
# Save the cell-centered x-vorticity field into file.
filepath = outdir / '{:0>7}.h5'.format(timestep)
petibmpy.write_field_hdf5(filepath, name, wx)
# Write the XDMF file to visualize with VisIt.
filepath = outdir / (name + '.xmf')
petibmpy.write_xdmf(filepath, outdir, gridpath, name, states=timesteps)
def test_read_write_field_hdf5(self):
"""Test the I/O functions."""
petibmpy.write_field_hdf5(self.filepath, self.name, self.values)
values = petibmpy.read_field_hdf5(self.filepath, self.name)
self.assertTrue(numpy.allclose(values, self.values))
self.filepath.unlink()
# Read 3D grid and write 2D grid.
gridpath = simudir / 'output' / 'grid.h5'
x, y, _ = petibmpy.read_grid_hdf5(gridpath, name)
gridpath = outdir / 'grid.h5'
petibmpy.write_grid_hdf5(gridpath, name + '-avg', x, y)
# Get temporal parameters.
filepath = simudir / 'config.yaml'
with open(filepath, 'r') as infile:
config = yaml.load(infile, Loader=yaml.FullLoader)['parameters']
dt = config['dt']
timesteps = [80000, 100000]
# Average the scalar field along the z-direction and write field.
for timestep in timesteps:
print('[time step {}]'.format(timestep))
filepath = datadir / '{:0>7}.h5'.format(timestep)
data = petibmpy.read_field_hdf5(filepath, name)
data_avg = numpy.mean(data, axis=0)
filepath = outdir / '{:0>7}.h5'.format(timestep)
petibmpy.write_field_hdf5(filepath, name + '-avg', data_avg)
# Write XDMF file to visualize field with VisIt.
filepath = outdir / (name + '-avg.xmf')
petibmpy.write_xdmf(filepath,
outdir,
gridpath,
name + '-avg',
dt,
states=timesteps)
gridpath = outdir / 'grid.h5'
petibmpy.write_grid_hdf5(gridpath, name, x, y, z)
# Get temporal parameters.
filepath = simudir / 'config.yaml'
with open(filepath, 'r') as infile:
config = yaml.load(infile, Loader=yaml.FullLoader)['parameters']
nstart, nt, nsave = config['startStep'], config['nt'], config['nsave']
timesteps = list(range(nstart, nstart + nt + 1, nsave))
interp_args = dict(bounds_error=False, method='linear', fill_value=None)
for timestep in timesteps:
print('[time step {}] Computing the Q-criterion ...'.format(timestep))
filepath = datadir / '{:0>7}.h5'.format(timestep)
# Load and interpolate the velocity field on the cell-centered grid.
u = petibmpy.read_field_hdf5(filepath, 'u')
u = petibmpy.interpolate3d(u, grid_u, (x, y, z), **interp_args)
v = petibmpy.read_field_hdf5(filepath, 'v')
v = petibmpy.interpolate3d(v, grid_v, (x, y, z), **interp_args)
w = petibmpy.read_field_hdf5(filepath, 'w')
w = petibmpy.interpolate3d(w, grid_w, (x, y, z), **interp_args)
# Compute the Q-criterion.
qcrit = petibmpy.qcriterion((u, v, w), (x, y, z))
# Save the Q-criterion into file.
filepath = outdir / '{:0>7}.h5'.format(timestep)
petibmpy.write_field_hdf5(filepath, name, qcrit)
# Write the XDMF file to visualize with VisIt.
filepath = outdir / (name + '.xmf')
petibmpy.write_xdmf(filepath, outdir, gridpath, name, states=timesteps)
# Get directories.
simudir = pathlib.Path(__file__).absolute().parents[1]
datadir = simudir / 'output' / 'solution'
outdir = simudir / 'output' / 'postprocessing' / name
outdir.mkdir(parents=True, exist_ok=True)
# Read 3D grid and write 2D grid.
gridpath = simudir / 'output' / 'grid.h5'
x, y = petibmpy.read_grid_hdf5(gridpath, name)
gridpath = outdir / 'grid.h5'
petibmpy.write_grid_hdf5(gridpath, name, x, y)
# Get temporal parameters.
filepath = simudir / 'config.yaml'
with open(filepath, 'r') as infile:
config = yaml.load(infile, Loader=yaml.FullLoader)['parameters']
dt = config['dt']
states = sorted([int(p.stem) for p in datadir.iterdir()])
for state in states:
print('[time step {}]'.format(state))
filename = '{:0>7}.h5'.format(state)
filepath = datadir / filename
data = petibmpy.read_field_hdf5(filepath, name)
filepath = outdir / filename
petibmpy.write_field_hdf5(filepath, name, data)
# Write XDMF file to visualize field with VisIt.
filepath = outdir / (name + '.xmf')
petibmpy.write_xdmf(filepath, outdir, gridpath, name, dt, states=states)
