magneticalc.Metric.Metric

class documentation

class Metric(Validatable):

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`
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, direction, colors):

"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, field, progress_callback):

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, alpha_preset):

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, vectors, dL, polarity):

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, vectors):

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, color_is_log, color_norm_values, color_norm_min, color_norm_max, alpha_is_log, alpha_norm_values, alpha_norm_min, alpha_norm_max, colors):

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 =

Undocumented

_limits =

Undocumented