colour.SpectralDistribution¶

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

Bases: colour.continuous.signal.Signal

Defines 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.

Parameters

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

Other Parameters

name (unicode, optional) – Spectral 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 distribution name for figures, default to colour.SpectralDistribution.name attribute value.

strict_name¶

wavelengths¶

values¶

shape¶

__init__()[source]¶

extrapolate()[source]¶

interpolate()[source]¶

align()[source]¶

trim()[source]¶

normalise()[source]¶

References

[CIET13805a], [CIET13805c], [CIET14804h]

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 ]],
                     interpolator=SpragueInterpolator,
                     interpolator_args={},
                     extrapolator=Extrapolator,
                     extrapolator_args={...})

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  ]],
                     interpolator=CubicSplineInterpolator,
                     interpolator_args={},
                     extrapolator=Extrapolator,
                     extrapolator_args={...})

align(shape, interpolator=None, interpolator_args=None, extrapolator=None, extrapolator_args=None)[source]

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

Parameters

shape (SpectralShape) – Spectral shape used for alignment.
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.

Returns

Aligned spectral distribution.

Return type

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...]]

extrapolate(shape, extrapolator=None, extrapolator_args=None)[source]

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

Parameters

shape (SpectralShape) – Spectral shape used for extrapolation.
extrapolator (object, optional) – Extrapolator class type to use as extrapolating function.
extrapolator_args (dict_like, optional) – Arguments to use when instantiating the extrapolating function.

Returns

Extrapolated spectral distribution.

Return type

SpectralDistribution

References

[CIET13805c], [CIET14804h]

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)).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 ]]

interpolate(shape, interpolator=None, interpolator_args=None)[source]

Interpolates the spectral distribution in-place according to CIE 167:2005 recommendation or given interpolation arguments.

Parameters

shape (SpectralShape, optional) – Spectral shape used for interpolation.
interpolator (object, optional) – Interpolator class type to use as interpolating function.
interpolator_args (dict_like, optional) – Arguments to use when instantiating the interpolating function.

Returns

Interpolated spectral distribution.

Return type

SpectralDistribution

Notes

Interpolation will be performed over boundaries range, if you need to extend the range of the spectral distribution use the colour.SpectralDistribution.extrapolate() or colour.SpectralDistribution.align() methods.

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(interval=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 no-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(interval=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    ...]]

normalise(factor=1)[source]

Normalises the spectral distribution using given normalization factor.

Parameters: factor (numeric, optional) – Normalization factor
Returns: Normalised spectral distribution.
Return type: 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.       ...]]

property shape

Getter and setter property for the spectral distribution shape.

Returns: Spectral distribution shape.
Return type: SpectralShape

Notes

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

Warning

colour.SpectralDistribution.shape attribute is read only.

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)

property strict_name

Getter and setter property for the spectral distribution strict name.

Parameters: value (unicode) – Value to set the spectral distribution strict name with.
Returns: Spectral distribution strict name.
Return type: unicode

trim(shape)[source]

Trims the spectral distribution wavelengths to given spectral shape.

Parameters: shape (SpectralShape) – Spectral shape used for trimming.
Returns: Trimmed spectral distribution.
Return type: 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(interval=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   ...]]

property values

Getter and setter property for the spectral distribution values.

Parameters: value (array_like) – Value to set the spectral distribution wavelengths values with.
Returns: Spectral distribution values.
Return type: ndarray

property wavelengths

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

Parameters: value (array_like) – Value to set the spectral distribution wavelengths \(\lambda_n\) with.
Returns: Spectral distribution wavelengths \(\lambda_n\).
Return type: ndarray