def test_read_write_grid_hdf5(self):
"""Test I/O functions for HDF5 format."""
filepath = pathlib.Path('grid.h5')
for dim in [2, 3]:
coords = [self.x, self.y, self.z][:dim]
petibmpy.write_grid_hdf5(filepath, 'name', *coords)
coords2 = petibmpy.read_grid_hdf5(filepath, 'name')
self.assertEqual(len(coords2), len(coords))
for i in range(dim):
self.assertTrue(numpy.allclose(coords2[i], coords[i]))
filepath.unlink()
name = 'wx_cc'
simudir = pathlib.Path(__file__).absolute().parents[1]
datadir = simudir / 'output'
gridpath = datadir / 'grid.h5'
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)
import petibmpy
name = 'wz' # name of the field variable
# Get directories.
simudir = pathlib.Path(__file__).absolute().parents[1]
datadir = simudir / 'output' / 'solution'
outdir = simudir / 'output' / 'postprocessing' / (name + '_avg')
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 + '-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)