colour.SpectralPowerDistribution

class colour.SpectralPowerDistribution(data=None, domain=None, **kwargs)

Defines the spectral power distribution: the base object for spectral computations.

The spectral power 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 Signal, SpectralPowerDistribution or array_like or dict_like, optional) – Data to be stored in the spectral power distribution.
• domain (array_like, optional) – Values to initialise the colour.SpectralPowerDistribution.wavelength attribute with. If both data and domain arguments are defined, the latter will be used to initialise the colour.SpectralPowerDistribution.wavelength attribute.

Other Parameters:  

• name (unicode, optional) – Spectral power distribution name.
• interpolator (object, optional) – Interpolator class type to use as interpolating function.
• interpolator_args (dict_like, optional) – Arguments to use when instantiating the interpolating function.
• extrapolator (object, optional) – Extrapolator class type to use as extrapolating function.
• extrapolator_args (dict_like, optional) – Arguments to use when instantiating the extrapolating function.
• strict_name (unicode, optional) – Spectral power distribution name for figures, default to colour.SpectralPowerDistribution.name attribute value.

strict_name

wavelengths

values

shape

__init__()

extrapolate()

interpolate()

align()

trim()

normalise()

References

• [CIET13805a]
• [CIET13805c]
• [CIET14804g]

Examples

Instantiating a spectral power distribution with a uniformly spaced independent variable:

>>> from colour.utilities import numpy_print_options
>>> data = {
...     500: 0.0651,
...     520: 0.0705,
...     540: 0.0772,
...     560: 0.0870,
...     580: 0.1128,
...     600: 0.1360
... }
>>> with numpy_print_options(suppress=True):
...     SpectralPowerDistribution(data)  
SpectralPowerDistribution([[ 500.    ,    0.0651],
                           [ 520.    ,    0.0705],
                           [ 540.    ,    0.0772],
                           [ 560.    ,    0.087 ],
                           [ 580.    ,    0.1128],
                           [ 600.    ,    0.136 ]],
                          interpolator=SpragueInterpolator,
                          interpolator_args={},
                          extrapolator=Extrapolator,
                          extrapolator_args={...})

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

>>> data[510] = 0.31416
>>> with numpy_print_options(suppress=True):
...     SpectralPowerDistribution(data)  
SpectralPowerDistribution([[ 500.     ,    0.0651 ],
                           [ 510.     ,    0.31416],
                           [ 520.     ,    0.0705 ],
                           [ 540.     ,    0.0772 ],
                           [ 560.     ,    0.087  ],
                           [ 580.     ,    0.1128 ],
                           [ 600.     ,    0.136  ]],
                          interpolator=CubicSplineInterpolator,
                          interpolator_args={},
                          extrapolator=Extrapolator,
                          extrapolator_args={...})

__init__(data=None, domain=None, **kwargs)

Methods

__init__([data, domain])
align(shape[, interpolator, …])	Aligns the spectral power 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, must be reimplemented by sub-classes.
domain_distance(a)	Returns the euclidean distance between given array and independent domain \(x\) closest element.
extrapolate(shape[, extrapolator, …])	Extrapolates the spectral power 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 spectral power distribution in-place according to CIE 167:2005 recommendation or given interpolation arguments.
is_uniform()	Returns if independent domain \(x\) variable is uniform.
normalise([factor])	Normalises the spectral power distribution using given normalization factor.
signal_unpack_data([data, domain, dtype])	Unpack given data for continuous signal instantiation.
to_series()	Converts the continuous signal to a Pandas Series class instance.
trim(shape)	Trims the spectral power distribution wavelengths to given spectral shape.
trim_wavelengths(shape)
zeros()