mindscience.data.Triangle

class mindscience.data.Triangle(name, vertices, boundary_type='uniform', dtype=numpy.float32, sampling_config=None)[source]

Definition of triangle object.

Parameters

name (str) – Name of the triangle.
vertices (numpy.ndarray) – Vertices of the triangle.
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.

Examples

>>> from mindscience.data import generate_sampling_config, Triangle
>>> triangle_mesh = dict({'domain': dict({'random_sampling': True, 'size': 300}),
...                       'BC': dict({'random_sampling': True, 'size': 300, 'with_normal': False,}),})
>>> vertices = np.array([[0., .1], [.9, .2], [.5, .6]])
>>> triangle = Triangle("triangle", vertices,
...                     sampling_config=generate_sampling_config(triangle_mesh))
>>> domain = triangle.sampling(geom_type="domain")
>>> bc = triangle.sampling(geom_type="bc")
>>> print(domain.shape)
(300, 2)