mindscience.data.Disk

class mindscience.data.Disk(name, center, radius, dtype=numpy.float32, sampling_config=None)[source]

Definition of Disk object.

Parameters

name (str) – Name of the disk.
center (Union[tuple, list, numpy.ndarray]) – Center coordinates of the disk. If the parameter type is tuple or list, each element should be of type int or float, and its length must be 2.
radius (Union[int, float]) – Radius of the disk.
dtype (numpy.dtype) – Data type of sampled point data type. Default: numpy.float32.
sampling_config (SamplingConfig) – Sampling configuration. Default: None.

Raises

ValueError – If center is neither list nor tuple of length 2.
ValueError – If radius is negative.

Examples

>>> from mindscience.data import generate_sampling_config, Disk
>>> disk_mesh = dict({'domain': dict({'random_sampling': False, 'size' : [100, 180]}),
...                   'BC': dict({'random_sampling': False, 'size': 200, 'with_normal' : True,})})
>>> disk = Disk("disk", (-1.0, 0), 2.0, sampling_config=generate_sampling_config(disk_mesh))
>>> domain = disk.sampling(geom_type="domain")
>>> bc, bc_normal = disk.sampling(geom_type="BC")
>>> print(bc.shape)
(200, 2)

sampling(geom_type='domain')[source]

Sampling domain and boundary points.

Parameters

geom_type (str) –

geometry type: can be 'domain' or 'BC'. Default: 'domain'.

'domain', feasible domain of the problem.
'BC', boundary of the problem.

Returns

Numpy.array. If the with_normal property of boundary configuration is true, returns 2D numpy array with boundary normal vectors. Otherwise, returns 2D numpy array without boundary normal vectors.

Raises

KeyError – If geom_type is 'domain' but self.sampling_config.domain is None.
KeyError – If geom_type is 'BC' but self.sampling_config.bc is None.
ValueError – If geom_type is neither 'BC' nor 'domain'.