colour.MultiSpectralDistribution¶

class colour.MultiSpectralDistribution(data=None, domain=None, labels=None, **kwargs)[source]¶

Defines multi-spectral distribution: the base object for multi spectral computations. It is used to model colour matching functions, display primaries, camera sensitivities, etc…

The multi-spectral distribution will be initialised according to CIE 15:2004 recommendation: the method developed by Sprague (1880) will be used for interpolating functions having a uniformly spaced independent variable and the Cubic Spline method for non-uniformly spaced independent variable. Extrapolation is performed according to CIE 167:2005 recommendation.

Parameters:

data (Series or Dataframe or Signal or MultiSignal or MultiSpectralDistribution or array_like or dict_like, optional) – Data to be stored in the multi-spectral distribution.
domain (array_like, optional) – Values to initialise the multiple colour.SpectralDistribution class instances colour.continuous.Signal.wavelengths attribute with. If both data and domain arguments are defined, the latter will be used to initialise the colour.continuous.Signal.wavelengths attribute.
labels (array_like, optional) – Names to use for the colour.SpectralDistribution class instances.

Other Parameters:

name (unicode, optional) – Multi-spectral distribution name.
interpolator (object, optional) – Interpolator class type to use as interpolating function for the colour.SpectralDistribution class instances.
interpolator_args (dict_like, optional) – Arguments to use when instantiating the interpolating function of the colour.SpectralDistribution class instances.
extrapolator (object, optional) – Extrapolator class type to use as extrapolating function for the colour.SpectralDistribution class instances.
extrapolator_args (dict_like, optional) – Arguments to use when instantiating the extrapolating function of the colour.SpectralDistribution class instances.
strict_labels (array_like, optional) – Multi-spectral distribution labels for figures, default to colour.MultiSpectralDistribution.labels attribute value.

strict_name¶

strict_labels¶

wavelengths¶

values¶

shape¶

extrapolate()[source]¶

interpolate()[source]¶

align()[source]¶

trim()[source]¶

normalise()[source]¶

to_sds()[source]¶

References

[CIET13805a], [CIET13805c], [CIET14804h]

Examples

Instantiating a multi-spectral distribution with a uniformly spaced independent variable:

>>> from colour.utilities import numpy_print_options
>>> data = {
...     500: (0.004900, 0.323000, 0.272000),
...     510: (0.009300, 0.503000, 0.158200),
...     520: (0.063270, 0.710000, 0.078250),
...     530: (0.165500, 0.862000, 0.042160),
...     540: (0.290400, 0.954000, 0.020300),
...     550: (0.433450, 0.994950, 0.008750),
...     560: (0.594500, 0.995000, 0.003900)
... }
>>> labels = ('x_bar', 'y_bar', 'z_bar')
>>> with numpy_print_options(suppress=True):
...     MultiSpectralDistribution(data, labels=labels)
... # doctest: +ELLIPSIS
MultiSpectral...([[ 500.     ,    0.0049 ,    0.323  ,    0.272  ],
             ...  [ 510.     ,    0.0093 ,    0.503  ,    0.1582 ],
             ...  [ 520.     ,    0.06327,    0.71   ,    0.07825],
             ...  [ 530.     ,    0.1655 ,    0.862  ,    0.04216],
             ...  [ 540.     ,    0.2904 ,    0.954  ,    0.0203 ],
             ...  [ 550.     ,    0.43345,    0.99495,    0.00875],
             ...  [ 560.     ,    0.5945 ,    0.995  ,    0.0039 ]],
             ... labels=[...'x_bar', ...'y_bar', ...'z_bar'],
             ... interpolator=SpragueInterpolator,
             ... interpolator_args={},
             ... extrapolator=Extrapolator,
             ... extrapolator_args={...})

Instantiating a spectral distribution with a non-uniformly spaced independent variable:

>>> data[511] = (0.00314, 0.31416, 0.03142)
>>> with numpy_print_options(suppress=True):
...     MultiSpectralDistribution(data, labels=labels)
... # doctest: +ELLIPSIS
MultiSpectral...([[ 500.     ,    0.0049 ,    0.323  ,    0.272  ],
             ...  [ 510.     ,    0.0093 ,    0.503  ,    0.1582 ],
             ...  [ 511.     ,    0.00314,    0.31416,    0.03142],
             ...  [ 520.     ,    0.06327,    0.71   ,    0.07825],
             ...  [ 530.     ,    0.1655 ,    0.862  ,    0.04216],
             ...  [ 540.     ,    0.2904 ,    0.954  ,    0.0203 ],
             ...  [ 550.     ,    0.43345,    0.99495,    0.00875],
             ...  [ 560.     ,    0.5945 ,    0.995  ,    0.0039 ]],
             ... labels=[...'x_bar', ...'y_bar', ...'z_bar'],
             ... interpolator=CubicSplineInterpolator,
             ... interpolator_args={},
             ... extrapolator=Extrapolator,
             ... extrapolator_args={...})

__init__(data=None, domain=None, labels=None, **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`([data, domain, labels])	Initialize self.
`align`(shape[, interpolator, …])	Aligns the multi-spectral distribution in-place to given spectral shape: Interpolates first then extrapolates to fit the given range.
`arithmetical_operation`(a, operation[, in_place])	Performs given arithmetical operation with \(a\) operand, the operation can be either performed on a copy or in-place.
`clone`()
`copy`()	Returns a copy of the sub-class instance.
`domain_distance`(a)	Returns the euclidean distance between given array and independent domain \(x\) closest element.
`extrapolate`(shape[, extrapolator, …])	Extrapolates the multi-spectral distribution in-place according to CIE 15:2004 and CIE 167:2005 recommendations or given extrapolation arguments.
`fill_nan`([method, default])	Fill NaNs in independent domain \(x\) variable and corresponding range \(y\) variable using given method.
`get`()
`interpolate`(shape[, interpolator, …])	Interpolates the multi-spectral distribution in-place according to CIE 167:2005 recommendation or given interpolation arguments.
`is_uniform`()	Returns if independent domain \(x\) variable is uniform.
`multi_signal_unpack_data`([data, domain, …])	Unpack given data for multi-continuous signal instantiation.
`normalise`([factor])	Normalises the multi-spectral distribution with given normalization factor.
`to_dataframe`()	Converts the continuous signal to a Pandas `DataFrame` class instance.
`to_sds`()	Converts the multi-spectral distributions to a list of spectral distributions and update their name and strict name using the labels and strict labels.
`trim`(shape)	Trims the multi-spectral distribution wavelengths to given shape.
`trim_wavelengths`(shape)
`zeros`()

Attributes

`data`
`domain`	Getter and setter property for the `colour.continuous.Signal` sub-class instances independent domain \(x\) variable.
`dtype`	Getter and setter property for the continuous signal dtype.
`extrapolator`	Getter and setter property for the `colour.continuous.Signal` sub-class instances extrapolator type.
`extrapolator_args`	Getter and setter property for the `colour.continuous.Signal` sub-class instances extrapolator instantiation time arguments.
`function`	Getter and setter property for the `colour.continuous.Signal` sub-class instances callable.
`interpolator`	Getter and setter property for the `colour.continuous.Signal` sub-class instances interpolator type.
`interpolator_args`	Getter and setter property for the `colour.continuous.Signal` sub-class instances interpolator instantiation time arguments.
`items`
`labels`	Getter and setter property for the `colour.continuous.Signal` sub-class instances name.
`mapping`
`name`	Getter and setter property for the abstract continuous function name.
`range`	Getter and setter property for the `colour.continuous.Signal` sub-class instances corresponding range \(y\) variable.
`shape`	Getter and setter property for the multi-spectral distribution shape.
`signal_type`	Getter and setter property for the `colour.continuous.Signal` sub-class instances type.
`signals`	Getter and setter property for the `colour.continuous.Signal` sub-class instances.
`strict_labels`	Getter and setter property for the multi-spectral distribution strict labels.
`strict_name`	Getter and setter property for the multi-spectral distribution strict name.
`title`
`values`	Getter and setter property for the multi-spectral distribution values.
`wavelengths`	Getter and setter property for the multi-spectral distribution wavelengths \(\lambda_n\).
`x`
`y`
`z`