mindflow.geometry.Tetrahedron
- class mindflow.geometry.Tetrahedron(name, vertices, boundary_type='uniform', dtype=numpy.float32, sampling_config=None)[source]
Definition of tetrahedron object.
- Parameters
name (str) – name of the tetrahedron.
vertices (numpy.ndarray) – vertices of the tetrahedron.
boundary_type (str) –
this can be
'uniform'
or'unweighted'
. Default:'uniform'
.'uniform'
, the expected number of samples in each boundary is proportional to the area (length) of the boundary.'unweighted'
, the expected number of samples in each boundary is the same.
dtype (numpy.dtype) – data type of sampled point data type. Default:
numpy.float32
.sampling_config (SamplingConfig) – sampling configuration. Default:
none
.
- Supported Platforms:
Ascend
GPU
Examples
>>> import numpy as np >>> from mindflow.geometry import generate_sampling_config, Tetrahedron >>> tetrahedron_mesh = dict({'domain': dict({'random_sampling': True, 'size': 300}), ... 'BC': dict({'random_sampling': True, 'size': 300, 'with_normal': False,}),}) >>> vertices = np.array([[0., .1, 0.], [.9, .2, .1], [.5, .6, 0.1], [.6, .5, .8]]) >>> tetrahedron = Tetrahedron("tetrahedron", vertices, ... sampling_config=generate_sampling_config(tetrahedron_mesh)) >>> domain = tetrahedron.sampling(geom_type="domain") >>> bc = tetrahedron.sampling(geom_type="bc") >>> print(domain.shape) (300, 2)