magneticalc.Metric

Class	`Metric`	Provides different metrics, used for mapping some field vector properties to some color and alpha range.
Function	`color_map_cyclic`	Maps normalized value to color, cyclic.
Function	`color_map_divergent`	Maps normalized value to color, divergent.
Function	`metric_divergence`	Calculates the divergence of a sampling volume neighborhood.
Function	`metric_norm`	Calculates the selected norm of some vector.

@ConditionalDecorator(get_jit_enabled(), jit, nopython=True, parallel=False)
def color_map_cyclic(color_normalized):

Maps normalized value to color, cyclic.

Note: For JIT to work, this must be declared at the top level.

@ConditionalDecorator(get_jit_enabled(), jit, nopython=True, parallel=False)
def color_map_divergent(color_normalized):

Maps normalized value to color, divergent.

Note: For JIT to work, this must be declared at the top level.

@ConditionalDecorator(get_jit_enabled(), jit, nopython=True, parallel=False)
def metric_divergence(neighborhood_vectors, dL, polarity):

Calculates the divergence of a sampling volume neighborhood.

Note: For JIT to work, this must be declared at the top level.

Parameters
neighborhood_vectors:`np.ndarray`	Sampling volume neighborhood vectors (six 3D vectors)
dL:`float`	Length element
polarity:`int`	Polarity filter (-1: Keep values <= 0; 0: Keep all values; +1: Keep values >= 0)
Returns
`float`	Undocumented

@ConditionalDecorator(get_jit_enabled(), jit, nopython=True, parallel=False)
def metric_norm(norm_type, vector):

Calculates the selected norm of some vector.

Note: For JIT to work, this must be declared at the top level.

Parameters
norm_type:`int`	Norm type
vector:`np.ndarray`	3D vector
Returns
`float`	Scalar if successful, np.inf on error