def wave_force_from_capytaine(self, filename, mode):
"""
Reads hydrodynamic data from a netCFD file created with capytaine and copies the
data for the requested mode into the object.
Args:
filename: .nc file to read from
mode: Name of the mode to read ('Heave' 'Pitch' ... 'Sway' 'Yaw')
Returns:
None
Examples:
test = Rao()
test.wave_force_from_capytaine(r"capytaine.nc", "Heave")
"""
from capytaine.io.xarray import merge_complex_values
dataset = merge_complex_values(xr.open_dataset(filename))
dataset['wave_direction'] *= 180 / np.pi # convert rad/s to deg
if 'excitation_force' not in dataset:
dataset['excitation_force'] = dataset[
'Froude_Krylov_force'] + dataset['diffraction_force']
da = dataset['excitation_force'].sel(influenced_dof=mode)
self._data['amplitude'] = abs(da)
self._data['complex_unit'] = xr.DataArray(da / abs(da),
dims=('omega',
'wave_direction'))
self._data.coords['mode'] = mode
def load_data(self):
from capytaine.io.xarray import merge_complex_values
results_data = merge_complex_values(
xarray.open_dataset(self.hydrodynamics_file_name))
return results_data
def __read_bem__(self, fpath=None) -> xr.Dataset:
if fpath is None: #TODO - should be able to do this as "from_dir" and build entire object
fpath = os.path.join(self.params['wrk_dir'], self.params['name'] + '.nc')
assert isinstance(fpath, str), 'fpath must be type(str), received {:}'.format(type(fpath))
assert os.path.exists(fpath)
hydro = merge_complex_values(xr.open_dataset(fpath)) # TODO - this could result in a mismatch between mesh and hydro
self.hydro = hydro
self.__post_proc_bem__()
return hydro
def load_from_capytaine(self, filename):
from capytaine.io.xarray import merge_complex_values
dataset = merge_complex_values(xr.open_dataset(filename))
dataset['wave_direction'] *= 180 / np.pi # convert rad/s to deg
self._force.clear()
for mode in self._modes:
r = Rao()
r.wave_force_from_capytaine(filename, mode)
r.scale(self._N_to_kN)
self._force.append(r)
self._damping = dataset['radiation_damping'] * self._N_to_kN
self._mass = dataset['added_mass'] * self._kg_to_mt
def test_remove_complex_values():
original_dataset = xr.Dataset(
data_vars={
'variable1': (['x', 'y'], np.random.rand(4, 5)),
'variable2': (['x', 'y', 'z'], np.random.rand(4, 5, 3) + 1j * np.random.rand(4, 5, 3)),
'variable3': (['y', 'z'], np.random.rand(5, 3) + 1j * np.random.rand(5, 3)),
},
coords={
'x': (['x'], np.linspace(0, 10, 4))
})
real_dataset = separate_complex_values(original_dataset)
assert np.allclose(real_dataset['variable3'].sel(complex='re').data,
np.real(original_dataset['variable3'].data),
atol=0.1)
assert set(original_dataset.dims) == set(real_dataset.dims) - {'complex'}
complex_dataset = merge_complex_values(real_dataset)
assert set(original_dataset.dims) == set(complex_dataset.dims)