SO(3) Data

class symdet.data.so3_data.SO3(noise: bool = True, variance: float = 0.05, radius: float = 1.0)[source]

Class for the double well potential implementation.

noise

If true, noise is included in the data generation.

Type

bool

variance

Variance to use in the noise generation.

Type

float

radius

Radius if the circle.

Type

float

radial_values

Radial values to use in the data generation.

Type

Union[float, list]

See also

symdet.data.data_generator.DataGenerator

Examples

>>> from symdet import DoubleWellPotential
>>> generator = SO3()
>>> generator.load_data()
>>> generator.plot_data()

Methods

build_clusters(**kwargs)

Split the raw function data into classes.

load_data(points[, save])

Load / generate the data.

plot_clusters([save])

Plot the clusters generated.

plot_data([save, show])

Plot the data.
build_clusters(**kwargs)[source]

Split the raw function data into classes.

Return type

Updates the class state.

Notes

Not required for this data.

load_data(points: Union[int, ndarray], save: bool = False)[source]

Load / generate the data.

Parameters

  • points (Union[int, np.ndarray]) – Points to generate, either an np.ndarray or an integer. If an integer, N points will be generated, if an array, it will either be treated as input to a function to generate values or those indices will be drawn from a pool.

  • save (bool) – If true, save the data after generating it.

Return type

Updates the class state.

plot_clusters(save: bool = False)[source]

Plot the clusters generated.

Parameters

save –

Notes

Not required for this analysis.

plot_data(save: bool = False, show: bool = True)[source]

Plot the data.

Parameters

  • save (bool) – If true, save the plot.

  • show (bool (default=True)) – If true, show the result

Return type

Plots the data.