colour.SpectralDistribution#

class colour.SpectralDistribution(data: Optional[Union[ArrayLike, dict, Series, Signal, SpectralDistribution]] = None, domain: Optional[Union[ArrayLike, SpectralShape]] = None, **kwargs: Any)[source]#

Bases: colour.continuous.signal.Signal

Define the spectral distribution: the base object for spectral computations.

The 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.

Important

Specific documentation about getting, setting, indexing and slicing the spectral power distribution values is available in the Spectral Representation and Continuous Signal section.

Parameters
  • data (Optional[Union[ArrayLike, dict, Series, Signal, SpectralDistribution]]) – Data to be stored in the spectral distribution.

  • domain (Optional[Union[ArrayLike, SpectralShape]]) – Values to initialise the colour.SpectralDistribution.wavelength property with. If both data and domain arguments are defined, the latter will be used to initialise the colour.SpectralDistribution.wavelength property.

  • extrapolator – Extrapolator class type to use as extrapolating function.

  • extrapolator_kwargs – Arguments to use when instantiating the extrapolating function.

  • interpolator – Interpolator class type to use as interpolating function.

  • interpolator_kwargs – Arguments to use when instantiating the interpolating function.

  • name – Spectral distribution name.

  • strict_name – Spectral distribution name for figures, default to colour.SpectralDistribution.name property value.

  • kwargs (Any) –

Attributes

Methods

References

[CIET13805a], [CIET13805d], [CIET14804c]

Examples

Instantiating a spectral 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):
...     SpectralDistribution(data)  
SpectralDistribution([[ 500.    ,    0.0651],
                      [ 520.    ,    0.0705],
                      [ 540.    ,    0.0772],
                      [ 560.    ,    0.087 ],
                      [ 580.    ,    0.1128],
                      [ 600.    ,    0.136 ]],
                     SpragueInterpolator,
                     {},
                     Extrapolator,
                     {'method': 'Constant', 'left': None, 'right': None})

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

>>> data[510] = 0.31416
>>> with numpy_print_options(suppress=True):
...     SpectralDistribution(data)  
SpectralDistribution([[ 500.     ,    0.0651 ],
                      [ 510.     ,    0.31416],
                      [ 520.     ,    0.0705 ],
                      [ 540.     ,    0.0772 ],
                      [ 560.     ,    0.087  ],
                      [ 580.     ,    0.1128 ],
                      [ 600.     ,    0.136  ]],
                     CubicSplineInterpolator,
                     {},
                     Extrapolator,
                     {'method': 'Constant', 'left': None, 'right': None})

Instantiation with a Pandas pandas.Series:

>>> from colour.utilities import is_pandas_installed
>>> if is_pandas_installed():
...     from pandas import Series
...     print(SpectralDistribution(Series(data)))  
[[  5.0000000...e+02   6.5100000...e-02]
 [  5.2000000...e+02   7.0500000...e-02]
 [  5.4000000...e+02   7.7200000...e-02]
 [  5.6000000...e+02   8.7000000...e-02]
 [  5.8000000...e+02   1.1280000...e-01]
 [  6.0000000...e+02   1.3600000...e-01]
 [  5.1000000...e+02   3.1416000...e-01]]
__init__(data: Optional[Union[ArrayLike, dict, Series, Signal, SpectralDistribution]] = None, domain: Optional[Union[ArrayLike, SpectralShape]] = None, **kwargs: Any)[source]#
Parameters
property strict_name: str#

Getter and setter property for the spectral distribution strict name.

Parameters

value – Value to set the spectral distribution strict name with.

Returns

Spectral distribution strict name.

Return type

str

property wavelengths: numpy.ndarray#

Getter and setter property for the spectral distribution wavelengths \(\lambda_n\).

Parameters

value – Value to set the spectral distribution wavelengths \(\lambda_n\) with.

Returns

Spectral distribution wavelengths \(\lambda_n\).

Return type

numpy.ndarray

property values: numpy.ndarray#

Getter and setter property for the spectral distribution values.

Parameters

value – Value to set the spectral distribution wavelengths values with.

Returns

Spectral distribution values.

Return type

numpy.ndarray

property shape: colour.colorimetry.spectrum.SpectralShape#

Getter property for the spectral distribution shape.

Returns

Spectral distribution shape.

Return type

colour.SpectralShape

Notes

  • A spectral distribution with a non-uniformly spaced independent variable have multiple intervals, in that case colour.SpectralDistribution.shape property returns the minimum interval size.

Examples

Shape of a spectral distribution with a uniformly spaced independent variable:

>>> data = {
...     500: 0.0651,
...     520: 0.0705,
...     540: 0.0772,
...     560: 0.0870,
...     580: 0.1128,
...     600: 0.1360
... }
>>> SpectralDistribution(data).shape
SpectralShape(500.0, 600.0, 20.0)

Shape of a spectral distribution with a non-uniformly spaced independent variable:

>>> data[510] = 0.31416
>>> SpectralDistribution(data).shape
SpectralShape(500.0, 600.0, 10.0)
interpolate(shape: colour.colorimetry.spectrum.SpectralShape, interpolator: Optional[Type[colour.hints.TypeInterpolator]] = None, interpolator_kwargs: Optional[Dict] = None) colour.colorimetry.spectrum.SpectralDistribution[source]#

Interpolate the spectral distribution in-place according to CIE 167:2005 recommendation (if the interpolator has not been changed at instantiation time) or given interpolation arguments.

The logic for choosing the interpolator class when interpolator is not given is as follows:

if self.interpolator not in (SpragueInterpolator,
                             CubicSplineInterpolator):
    interpolator = self.interpolator
elif self.is_uniform():
    interpolator = SpragueInterpolator
else:
    interpolator = CubicSplineInterpolator

The logic for choosing the interpolator keyword arguments when interpolator_kwargs is not given is as follows:

if self.interpolator not in (SpragueInterpolator,
                             CubicSplineInterpolator):
    interpolator_kwargs = self.interpolator_kwargs
else:
    interpolator_kwargs = {}
Parameters
Returns

Interpolated spectral distribution.

Return type

colour.SpectralDistribution

Notes

Warning

  • Cubic Spline interpolator requires at least 3 wavelengths \(\lambda_n\) for interpolation.

  • Sprague (1880) interpolator requires at least 6 wavelengths \(\lambda_n\) for interpolation.

References

[CIET13805a]

Examples

Spectral distribution with a uniformly spaced independent variable uses Sprague (1880) interpolation:

>>> 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
... }
>>> sd = SpectralDistribution(data)
>>> with numpy_print_options(suppress=True):
...     print(sd.interpolate(SpectralShape(500, 600, 1)))
... 
[[ 500.            0.0651   ...]
 [ 501.            0.0653522...]
 [ 502.            0.0656105...]
 [ 503.            0.0658715...]
 [ 504.            0.0661328...]
 [ 505.            0.0663929...]
 [ 506.            0.0666509...]
 [ 507.            0.0669069...]
 [ 508.            0.0671613...]
 [ 509.            0.0674150...]
 [ 510.            0.0676692...]
 [ 511.            0.0679253...]
 [ 512.            0.0681848...]
 [ 513.            0.0684491...]
 [ 514.            0.0687197...]
 [ 515.            0.0689975...]
 [ 516.            0.0692832...]
 [ 517.            0.0695771...]
 [ 518.            0.0698787...]
 [ 519.            0.0701870...]
 [ 520.            0.0705   ...]
 [ 521.            0.0708155...]
 [ 522.            0.0711336...]
 [ 523.            0.0714547...]
 [ 524.            0.0717789...]
 [ 525.            0.0721063...]
 [ 526.            0.0724367...]
 [ 527.            0.0727698...]
 [ 528.            0.0731051...]
 [ 529.            0.0734423...]
 [ 530.            0.0737808...]
 [ 531.            0.0741203...]
 [ 532.            0.0744603...]
 [ 533.            0.0748006...]
 [ 534.            0.0751409...]
 [ 535.            0.0754813...]
 [ 536.            0.0758220...]
 [ 537.            0.0761633...]
 [ 538.            0.0765060...]
 [ 539.            0.0768511...]
 [ 540.            0.0772   ...]
 [ 541.            0.0775527...]
 [ 542.            0.0779042...]
 [ 543.            0.0782507...]
 [ 544.            0.0785908...]
 [ 545.            0.0789255...]
 [ 546.            0.0792576...]
 [ 547.            0.0795917...]
 [ 548.            0.0799334...]
 [ 549.            0.0802895...]
 [ 550.            0.0806671...]
 [ 551.            0.0810740...]
 [ 552.            0.0815176...]
 [ 553.            0.0820049...]
 [ 554.            0.0825423...]
 [ 555.            0.0831351...]
 [ 556.            0.0837873...]
 [ 557.            0.0845010...]
 [ 558.            0.0852763...]
 [ 559.            0.0861110...]
 [ 560.            0.087    ...]
 [ 561.            0.0879383...]
 [ 562.            0.0889300...]
 [ 563.            0.0899793...]
 [ 564.            0.0910876...]
 [ 565.            0.0922541...]
 [ 566.            0.0934760...]
 [ 567.            0.0947487...]
 [ 568.            0.0960663...]
 [ 569.            0.0974220...]
 [ 570.            0.0988081...]
 [ 571.            0.1002166...]
 [ 572.            0.1016394...]
 [ 573.            0.1030687...]
 [ 574.            0.1044972...]
 [ 575.            0.1059186...]
 [ 576.            0.1073277...]
 [ 577.            0.1087210...]
 [ 578.            0.1100968...]
 [ 579.            0.1114554...]
 [ 580.            0.1128   ...]
 [ 581.            0.1141333...]
 [ 582.            0.1154495...]
 [ 583.            0.1167424...]
 [ 584.            0.1180082...]
 [ 585.            0.1192452...]
 [ 586.            0.1204536...]
 [ 587.            0.1216348...]
 [ 588.            0.1227915...]
 [ 589.            0.1239274...]
 [ 590.            0.1250465...]
 [ 591.            0.1261531...]
 [ 592.            0.1272517...]
 [ 593.            0.1283460...]
 [ 594.            0.1294393...]
 [ 595.            0.1305340...]
 [ 596.            0.1316310...]
 [ 597.            0.1327297...]
 [ 598.            0.1338277...]
 [ 599.            0.1349201...]
 [ 600.            0.136    ...]]

Spectral distribution with a non-uniformly spaced independent variable uses Cubic Spline interpolation:

>>> sd = SpectralDistribution(data)
>>> sd[510] = np.pi / 10
>>> with numpy_print_options(suppress=True):
...     print(sd.interpolate(SpectralShape(500, 600, 1)))
... 
[[ 500.            0.0651   ...]
 [ 501.            0.1365202...]
 [ 502.            0.1953263...]
 [ 503.            0.2423724...]
 [ 504.            0.2785126...]
 [ 505.            0.3046010...]
 [ 506.            0.3214916...]
 [ 507.            0.3300387...]
 [ 508.            0.3310962...]
 [ 509.            0.3255184...]
 [ 510.            0.3141592...]
 [ 511.            0.2978729...]
 [ 512.            0.2775135...]
 [ 513.            0.2539351...]
 [ 514.            0.2279918...]
 [ 515.            0.2005378...]
 [ 516.            0.1724271...]
 [ 517.            0.1445139...]
 [ 518.            0.1176522...]
 [ 519.            0.0926962...]
 [ 520.            0.0705   ...]
 [ 521.            0.0517370...]
 [ 522.            0.0363589...]
 [ 523.            0.0241365...]
 [ 524.            0.0148407...]
 [ 525.            0.0082424...]
 [ 526.            0.0041126...]
 [ 527.            0.0022222...]
 [ 528.            0.0023421...]
 [ 529.            0.0042433...]
 [ 530.            0.0076966...]
 [ 531.            0.0124729...]
 [ 532.            0.0183432...]
 [ 533.            0.0250785...]
 [ 534.            0.0324496...]
 [ 535.            0.0402274...]
 [ 536.            0.0481829...]
 [ 537.            0.0560870...]
 [ 538.            0.0637106...]
 [ 539.            0.0708246...]
 [ 540.            0.0772   ...]
 [ 541.            0.0826564...]
 [ 542.            0.0872086...]
 [ 543.            0.0909203...]
 [ 544.            0.0938549...]
 [ 545.            0.0960760...]
 [ 546.            0.0976472...]
 [ 547.            0.0986321...]
 [ 548.            0.0990942...]
 [ 549.            0.0990971...]
 [ 550.            0.0987043...]
 [ 551.            0.0979794...]
 [ 552.            0.0969861...]
 [ 553.            0.0957877...]
 [ 554.            0.0944480...]
 [ 555.            0.0930304...]
 [ 556.            0.0915986...]
 [ 557.            0.0902161...]
 [ 558.            0.0889464...]
 [ 559.            0.0878532...]
 [ 560.            0.087    ...]
 [ 561.            0.0864371...]
 [ 562.            0.0861623...]
 [ 563.            0.0861600...]
 [ 564.            0.0864148...]
 [ 565.            0.0869112...]
 [ 566.            0.0876336...]
 [ 567.            0.0885665...]
 [ 568.            0.0896945...]
 [ 569.            0.0910020...]
 [ 570.            0.0924735...]
 [ 571.            0.0940936...]
 [ 572.            0.0958467...]
 [ 573.            0.0977173...]
 [ 574.            0.0996899...]
 [ 575.            0.1017491...]
 [ 576.            0.1038792...]
 [ 577.            0.1060649...]
 [ 578.            0.1082906...]
 [ 579.            0.1105408...]
 [ 580.            0.1128   ...]
 [ 581.            0.1150526...]
 [ 582.            0.1172833...]
 [ 583.            0.1194765...]
 [ 584.            0.1216167...]
 [ 585.            0.1236884...]
 [ 586.            0.1256760...]
 [ 587.            0.1275641...]
 [ 588.            0.1293373...]
 [ 589.            0.1309798...]
 [ 590.            0.1324764...]
 [ 591.            0.1338114...]
 [ 592.            0.1349694...]
 [ 593.            0.1359349...]
 [ 594.            0.1366923...]
 [ 595.            0.1372262...]
 [ 596.            0.1375211...]
 [ 597.            0.1375614...]
 [ 598.            0.1373316...]
 [ 599.            0.1368163...]
 [ 600.            0.136    ...]]
extrapolate(shape: colour.colorimetry.spectrum.SpectralShape, extrapolator: Optional[Type[colour.hints.TypeExtrapolator]] = None, extrapolator_kwargs: Optional[Dict] = None) colour.colorimetry.spectrum.SpectralDistribution[source]#

Extrapolate the spectral distribution in-place according to CIE 15:2004 and CIE 167:2005 recommendations or given extrapolation arguments.

Parameters
Returns

Extrapolated spectral distribution.

Return type

colour.SpectralDistribution

References

[CIET13805d], [CIET14804c]

Examples

>>> 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
... }
>>> sd = SpectralDistribution(data)
>>> sd.extrapolate(SpectralShape(400, 700, 20)).shape
SpectralShape(400.0, 700.0, 20.0)
>>> with numpy_print_options(suppress=True):
...     print(sd)
[[ 400.        0.0651]
 [ 420.        0.0651]
 [ 440.        0.0651]
 [ 460.        0.0651]
 [ 480.        0.0651]
 [ 500.        0.0651]
 [ 520.        0.0705]
 [ 540.        0.0772]
 [ 560.        0.087 ]
 [ 580.        0.1128]
 [ 600.        0.136 ]
 [ 620.        0.136 ]
 [ 640.        0.136 ]
 [ 660.        0.136 ]
 [ 680.        0.136 ]
 [ 700.        0.136 ]]
align(shape: colour.colorimetry.spectrum.SpectralShape, interpolator: Optional[Type[colour.hints.TypeInterpolator]] = None, interpolator_kwargs: Optional[Dict] = None, extrapolator: Optional[Type[colour.hints.TypeExtrapolator]] = None, extrapolator_kwargs: Optional[Dict] = None) colour.colorimetry.spectrum.SpectralDistribution[source]#

Align the spectral distribution in-place to given spectral shape: Interpolates first then extrapolates to fit the given range.

Interpolation is performed according to CIE 167:2005 recommendation (if the interpolator has not been changed at instantiation time) or given interpolation arguments.

The logic for choosing the interpolator class when interpolator is not given is as follows:

if self.interpolator not in (SpragueInterpolator,
                             CubicSplineInterpolator):
    interpolator = self.interpolator
elif self.is_uniform():
    interpolator = SpragueInterpolator
else:
    interpolator = CubicSplineInterpolator

The logic for choosing the interpolator keyword arguments when interpolator_kwargs is not given is as follows:

if self.interpolator not in (SpragueInterpolator,
                             CubicSplineInterpolator):
    interpolator_kwargs = self.interpolator_kwargs
else:
    interpolator_kwargs = {}
Parameters
Returns

Aligned spectral distribution.

Return type

colour.SpectralDistribution

Examples

>>> 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
... }
>>> sd = SpectralDistribution(data)
>>> with numpy_print_options(suppress=True):
...     print(sd.align(SpectralShape(505, 565, 1)))
... 
[[ 505.            0.0663929...]
 [ 506.            0.0666509...]
 [ 507.            0.0669069...]
 [ 508.            0.0671613...]
 [ 509.            0.0674150...]
 [ 510.            0.0676692...]
 [ 511.            0.0679253...]
 [ 512.            0.0681848...]
 [ 513.            0.0684491...]
 [ 514.            0.0687197...]
 [ 515.            0.0689975...]
 [ 516.            0.0692832...]
 [ 517.            0.0695771...]
 [ 518.            0.0698787...]
 [ 519.            0.0701870...]
 [ 520.            0.0705   ...]
 [ 521.            0.0708155...]
 [ 522.            0.0711336...]
 [ 523.            0.0714547...]
 [ 524.            0.0717789...]
 [ 525.            0.0721063...]
 [ 526.            0.0724367...]
 [ 527.            0.0727698...]
 [ 528.            0.0731051...]
 [ 529.            0.0734423...]
 [ 530.            0.0737808...]
 [ 531.            0.0741203...]
 [ 532.            0.0744603...]
 [ 533.            0.0748006...]
 [ 534.            0.0751409...]
 [ 535.            0.0754813...]
 [ 536.            0.0758220...]
 [ 537.            0.0761633...]
 [ 538.            0.0765060...]
 [ 539.            0.0768511...]
 [ 540.            0.0772   ...]
 [ 541.            0.0775527...]
 [ 542.            0.0779042...]
 [ 543.            0.0782507...]
 [ 544.            0.0785908...]
 [ 545.            0.0789255...]
 [ 546.            0.0792576...]
 [ 547.            0.0795917...]
 [ 548.            0.0799334...]
 [ 549.            0.0802895...]
 [ 550.            0.0806671...]
 [ 551.            0.0810740...]
 [ 552.            0.0815176...]
 [ 553.            0.0820049...]
 [ 554.            0.0825423...]
 [ 555.            0.0831351...]
 [ 556.            0.0837873...]
 [ 557.            0.0845010...]
 [ 558.            0.0852763...]
 [ 559.            0.0861110...]
 [ 560.            0.087    ...]
 [ 561.            0.0879383...]
 [ 562.            0.0889300...]
 [ 563.            0.0899793...]
 [ 564.            0.0910876...]
 [ 565.            0.0922541...]]
trim(shape: colour.colorimetry.spectrum.SpectralShape) colour.colorimetry.spectrum.SpectralDistribution[source]#

Trim the spectral distribution wavelengths to given spectral shape.

Parameters

shape (colour.colorimetry.spectrum.SpectralShape) – Spectral shape used for trimming.

Returns

Trimmed spectral distribution.

Return type

colour.SpectralDistribution

Examples

>>> 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
... }
>>> sd = SpectralDistribution(data)
>>> sd = sd.interpolate(SpectralShape(500, 600, 1))
>>> with numpy_print_options(suppress=True):
...     print(sd.trim(SpectralShape(520, 580, 5)))
... 
[[ 520.            0.0705   ...]
 [ 521.            0.0708155...]
 [ 522.            0.0711336...]
 [ 523.            0.0714547...]
 [ 524.            0.0717789...]
 [ 525.            0.0721063...]
 [ 526.            0.0724367...]
 [ 527.            0.0727698...]
 [ 528.            0.0731051...]
 [ 529.            0.0734423...]
 [ 530.            0.0737808...]
 [ 531.            0.0741203...]
 [ 532.            0.0744603...]
 [ 533.            0.0748006...]
 [ 534.            0.0751409...]
 [ 535.            0.0754813...]
 [ 536.            0.0758220...]
 [ 537.            0.0761633...]
 [ 538.            0.0765060...]
 [ 539.            0.0768511...]
 [ 540.            0.0772   ...]
 [ 541.            0.0775527...]
 [ 542.            0.0779042...]
 [ 543.            0.0782507...]
 [ 544.            0.0785908...]
 [ 545.            0.0789255...]
 [ 546.            0.0792576...]
 [ 547.            0.0795917...]
 [ 548.            0.0799334...]
 [ 549.            0.0802895...]
 [ 550.            0.0806671...]
 [ 551.            0.0810740...]
 [ 552.            0.0815176...]
 [ 553.            0.0820049...]
 [ 554.            0.0825423...]
 [ 555.            0.0831351...]
 [ 556.            0.0837873...]
 [ 557.            0.0845010...]
 [ 558.            0.0852763...]
 [ 559.            0.0861110...]
 [ 560.            0.087    ...]
 [ 561.            0.0879383...]
 [ 562.            0.0889300...]
 [ 563.            0.0899793...]
 [ 564.            0.0910876...]
 [ 565.            0.0922541...]
 [ 566.            0.0934760...]
 [ 567.            0.0947487...]
 [ 568.            0.0960663...]
 [ 569.            0.0974220...]
 [ 570.            0.0988081...]
 [ 571.            0.1002166...]
 [ 572.            0.1016394...]
 [ 573.            0.1030687...]
 [ 574.            0.1044972...]
 [ 575.            0.1059186...]
 [ 576.            0.1073277...]
 [ 577.            0.1087210...]
 [ 578.            0.1100968...]
 [ 579.            0.1114554...]
 [ 580.            0.1128   ...]]
normalise(factor: Number = 1) colour.colorimetry.spectrum.SpectralDistribution[source]#

Normalise the spectral distribution using given normalization factor.

Parameters

factor (Number) – Normalization factor.

Returns

Normalised spectral distribution.

Return type

colour.SpectralDistribution

Examples

>>> 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
... }
>>> sd = SpectralDistribution(data)
>>> with numpy_print_options(suppress=True):
...     print(sd.normalise())  
[[ 500.            0.4786764...]
 [ 520.            0.5183823...]
 [ 540.            0.5676470...]
 [ 560.            0.6397058...]
 [ 580.            0.8294117...]
 [ 600.            1.       ...]]