magneticalc.Metric.Metric

class documentation

class Metric(Validatable):

Constructor: Metric()

Provides different metrics, used for mapping some field vector properties to some color and alpha range.

Static Method	`boost_colors`	"Boosts" an array of color values.
Method	`__init__`	Initializes an empty metric.
Method	`recalculate`	Recalculates color and alpha values for field.
Method	`set`	Sets the parameters.
Class Variable	`LengthScale`	Undocumented
Class Variable	`LogNormMin`	Undocumented
Instance Variable	`alpha_preset`	Undocumented
Instance Variable	`color_preset`	Undocumented
Property	`colors`	Returns calculated colors.
Property	`limits`	Returns calculated limits.
Static Method	`_divergence_worker`	Calculates the divergence values of a list of vectors.
Static Method	`_norm_worker`	Calculates the norm values of a list of vectors.
Static Method	`_normalize_worker`	Normalizes color and alpha norm values, populates final color values.
Instance Variable	`_colors`	Undocumented
Instance Variable	`_limits`	Undocumented

Inherited from Validatable:

Method	`valid.setter`	No summary
Property	`valid`	True if valid, False if invalid
Instance Variable	`_valid`	Undocumented

@staticmethod
@ConditionalDecorator(get_jit_enabled(), jit, nopython=True, parallel=True)

def boost_colors(boost: float, direction: float, colors: np.ndarray) -> np.ndarray: ¶

"Boosts" an array of color values.

Parameters
boost:`float`	Boost value
direction:`float`	Boost direction
colors:`np.ndarray`	Colors (ordered list of 4-tuples)

Returns
`np.ndarray`	Colors (ordered list of 4-tuples)

def __init__(self): ¶

overrides magneticalc.Validatable.Validatable.__init__

Initializes an empty metric.

This class holds a pair of metric presets. Using these metric presets, colors (including alpha channel) and field limits are calculated.

@validator

def recalculate(self, sampling_volume: SamplingVolume, field: Field, progress_callback: Callable) -> bool: ¶

Recalculates color and alpha values for field.

Parameters
sampling_volume:`SamplingVolume`	SamplingVolume
field:`Field`	Field
progress_callback:`Callable`	Progress callback

Returns
`bool`	True (currently non-interruptable)

def set(self, color_preset: dict, alpha_preset: dict): ¶

Sets the parameters.

Parameters
color_preset:`dict`	Color metric preset (dictionary)
alpha_preset:`dict`	Alpha metric preset (dictionary)

LengthScale: float = ¶

Undocumented

LogNormMin: float = ¶

Undocumented

alpha_preset = ¶

Undocumented

color_preset = ¶

Undocumented

@property
@require_valid

colors: np.ndarray = ¶

Returns calculated colors.

@property
@require_valid

limits: dict = ¶

Returns calculated limits.

@staticmethod
@ConditionalDecorator(get_jit_enabled(), jit, nopython=True, parallel=True)

def _divergence_worker(sampling_volume_neighborhood_indices: list[np.ndarray], vectors: np.ndarray, dL: int, polarity: int) -> np.ndarray: ¶

Calculates the divergence values of a list of vectors.

Parameters
sampling_volume_neighborhood_indices:`list[np.ndarray]`	Ordered list of sampling volume neighborhood indices
vectors:`np.ndarray`	Ordered list of 3D vectors
dL:`int`	Length element
polarity:`int`	Polarity filter (-1: Keep values <= 0; 0: Keep all values; +1: Keep values >= 0)

Returns
`np.ndarray`	Norm values

@staticmethod
@ConditionalDecorator(get_jit_enabled(), jit, nopython=True, parallel=True)

def _norm_worker(norm_type: int, vectors: np.ndarray) -> np.ndarray: ¶

Calculates the norm values of a list of vectors.

Parameters
norm_type:`int`	Norm type
vectors:`np.ndarray`	Ordered list of 3D vectors

Returns
`np.ndarray`	Norm values

@staticmethod
@ConditionalDecorator(get_jit_enabled(), jit, nopython=True, parallel=True)

def _normalize_worker(color_map_id: str, color_is_log: bool, color_norm_values, color_norm_min: float, color_norm_max: float, alpha_is_log: bool, alpha_norm_values: np.ndarray, alpha_norm_min: float, alpha_norm_max: float, colors: np.ndarray) -> np.ndarray: ¶

Normalizes color and alpha norm values, populates final color values.

Parameters
color_map_id:`str`	Color map ID
color_is_log:`bool`	Selects logarithmic normalizer for color
color_norm_values	Color norm values
color_norm_min:`float`	Minimum color norm value
color_norm_max:`float`	Maximum color norm value
alpha_is_log:`bool`	Selects logarithmic normalizer for alpha
alpha_norm_values:`np.ndarray`	Alpha norm values
alpha_norm_min:`float`	Minimum alpha norm value
alpha_norm_max:`float`	Maximum alpha norm value
colors:`np.ndarray`	Color value list to populate (list of 4D vectors)

Returns
`np.ndarray`	Color values (list of 4D vectors)

_colors: np.ndarray = ¶

Undocumented

_limits: dict = ¶

Undocumented