colour.MultiSpectralDistributions¶

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

Bases: colour.continuous.multi_signals.MultiSignals

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

The multi-spectral distributions 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 MultiSignals or MultiSpectralDistributions or array_like or dict_like, optional) – Data to be stored in the multi-spectral distributions.
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 distributions 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 distributions labels for figures, default to colour.MultiSpectralDistributions.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 the multi-spectral distributions 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):
...     MultiSpectralDistributions(data, labels=labels)
... 
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):
...     MultiSpectralDistributions(data, labels=labels)
... 
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={...})

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

Aligns the multi-spectral distributions 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 multi-spectral distributions.

Return type

MultiSpectralDistributions

Examples

>>> 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)
... }
>>> msds = MultiSpectralDistributions(data)
>>> with numpy_print_options(suppress=True):
...     print(msds.align(SpectralShape(505, 565, 1)))
... 
[[ 505.            0.0031582...    0.4091067...    0.2126801...]
 [ 506.            0.0035019...    0.4268629...    0.2012748...]
 [ 507.            0.0042365...    0.4450668...    0.1900968...]
 [ 508.            0.0054192...    0.4638181...    0.1791709...]
 [ 509.            0.0070965...    0.4831505...    0.1685260...]
 [ 510.            0.0093   ...    0.503    ...    0.1582   ...]
 [ 511.            0.0120562...    0.5232543...    0.1482365...]
 [ 512.            0.0154137...    0.5439717...    0.1386625...]
 [ 513.            0.0193991...    0.565139 ...    0.1294993...]
 [ 514.            0.0240112...    0.5866255...    0.1207676...]
 [ 515.            0.0292289...    0.6082226...    0.1124864...]
 [ 516.            0.0350192...    0.6296821...    0.1046717...]
 [ 517.            0.0413448...    0.6507558...    0.0973361...]
 [ 518.            0.0481727...    0.6712346...    0.0904871...]
 [ 519.            0.0554816...    0.6909873...    0.0841267...]
 [ 520.            0.06327  ...    0.71     ...    0.07825  ...]
 [ 521.            0.0715642...    0.7283456...    0.0728614...]
 [ 522.            0.0803970...    0.7459679...    0.0680051...]
 [ 523.            0.0897629...    0.7628184...    0.0636823...]
 [ 524.            0.0996227...    0.7789004...    0.0598449...]
 [ 525.            0.1099142...    0.7942533...    0.0564111...]
 [ 526.            0.1205637...    0.8089368...    0.0532822...]
 [ 527.            0.1314973...    0.8230153...    0.0503588...]
 [ 528.            0.1426523...    0.8365417...    0.0475571...]
 [ 529.            0.1539887...    0.8495422...    0.0448253...]
 [ 530.            0.1655   ...    0.862    ...    0.04216  ...]
 [ 531.            0.1772055...    0.8738585...    0.0395936...]
 [ 532.            0.1890877...    0.8850940...    0.0371046...]
 [ 533.            0.2011304...    0.8957073...    0.0346733...]
 [ 534.            0.2133310...    0.9057092...    0.0323006...]
 [ 535.            0.2256968...    0.9151181...    0.0300011...]
 [ 536.            0.2382403...    0.9239560...    0.0277974...]
 [ 537.            0.2509754...    0.9322459...    0.0257131...]
 [ 538.            0.2639130...    0.9400080...    0.0237668...]
 [ 539.            0.2770569...    0.9472574...    0.0219659...]
 [ 540.            0.2904   ...    0.954    ...    0.0203   ...]
 [ 541.            0.3039194...    0.9602409...    0.0187414...]
 [ 542.            0.3175893...    0.9660106...    0.0172748...]
 [ 543.            0.3314022...    0.9713260...    0.0158947...]
 [ 544.            0.3453666...    0.9761850...    0.0146001...]
 [ 545.            0.3595019...    0.9805731...    0.0133933...]
 [ 546.            0.3738324...    0.9844703...    0.0122777...]
 [ 547.            0.3883818...    0.9878583...    0.0112562...]
 [ 548.            0.4031674...    0.9907270...    0.0103302...]
 [ 549.            0.4181943...    0.9930817...    0.0094972...]
 [ 550.            0.43345  ...    0.99495  ...    0.00875  ...]
 [ 551.            0.4489082...    0.9963738...    0.0080748...]
 [ 552.            0.4645599...    0.9973682...    0.0074580...]
 [ 553.            0.4803950...    0.9979568...    0.0068902...]
 [ 554.            0.4963962...    0.9981802...    0.0063660...]
 [ 555.            0.5125410...    0.9980910...    0.0058818...]
 [ 556.            0.5288034...    0.9977488...    0.0054349...]
 [ 557.            0.5451560...    0.9972150...    0.0050216...]
 [ 558.            0.5615719...    0.9965479...    0.0046357...]
 [ 559.            0.5780267...    0.9957974...    0.0042671...]
 [ 560.            0.5945   ...    0.995    ...    0.0039   ...]
 [ 561.            0.5945   ...    0.995    ...    0.0039   ...]
 [ 562.            0.5945   ...    0.995    ...    0.0039   ...]
 [ 563.            0.5945   ...    0.995    ...    0.0039   ...]
 [ 564.            0.5945   ...    0.995    ...    0.0039   ...]
 [ 565.            0.5945   ...    0.995    ...    0.0039   ...]]

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

Extrapolates the multi-spectral distributions 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 multi-spectral distributions.

Return type

MultiSpectralDistributions

References

[CIET13805c], [CIET14804h]

Examples

>>> 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)
... }
>>> msds = MultiSpectralDistributions(data)
>>> msds.extrapolate(SpectralShape(400, 700)).shape
SpectralShape(400.0, 700.0, 10.0)
>>> with numpy_print_options(suppress=True):
...     print(msds)
[[ 400.         0.0049     0.323      0.272  ]
 [ 410.         0.0049     0.323      0.272  ]
 [ 420.         0.0049     0.323      0.272  ]
 [ 430.         0.0049     0.323      0.272  ]
 [ 440.         0.0049     0.323      0.272  ]
 [ 450.         0.0049     0.323      0.272  ]
 [ 460.         0.0049     0.323      0.272  ]
 [ 470.         0.0049     0.323      0.272  ]
 [ 480.         0.0049     0.323      0.272  ]
 [ 490.         0.0049     0.323      0.272  ]
 [ 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 ]
 [ 570.         0.5945     0.995      0.0039 ]
 [ 580.         0.5945     0.995      0.0039 ]
 [ 590.         0.5945     0.995      0.0039 ]
 [ 600.         0.5945     0.995      0.0039 ]
 [ 610.         0.5945     0.995      0.0039 ]
 [ 620.         0.5945     0.995      0.0039 ]
 [ 630.         0.5945     0.995      0.0039 ]
 [ 640.         0.5945     0.995      0.0039 ]
 [ 650.         0.5945     0.995      0.0039 ]
 [ 660.         0.5945     0.995      0.0039 ]
 [ 670.         0.5945     0.995      0.0039 ]
 [ 680.         0.5945     0.995      0.0039 ]
 [ 690.         0.5945     0.995      0.0039 ]
 [ 700.         0.5945     0.995      0.0039 ]]

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

Interpolates the multi-spectral distributions 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 multi-spectral distributions.

Return type

MultiSpectralDistributions

Notes

See colour.SpectralDistribution.interpolate() method notes section.

Warning

See colour.SpectralDistribution.interpolate() method warning section.

References

[CIET13805a]

Examples

Multi-spectral distributions with a uniformly spaced independent variable uses Sprague (1880) interpolation:

>>> 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)
... }
>>> msds = MultiSpectralDistributions(data)
>>> with numpy_print_options(suppress=True):
...     print(msds.interpolate(SpectralShape(interval=1)))
... 
[[ 500.            0.0049   ...    0.323    ...    0.272    ...]
 [ 501.            0.0043252...    0.3400642...    0.2599848...]
 [ 502.            0.0037950...    0.3572165...    0.2479849...]
 [ 503.            0.0033761...    0.3744030...    0.2360688...]
 [ 504.            0.0031397...    0.3916650...    0.2242878...]
 [ 505.            0.0031582...    0.4091067...    0.2126801...]
 [ 506.            0.0035019...    0.4268629...    0.2012748...]
 [ 507.            0.0042365...    0.4450668...    0.1900968...]
 [ 508.            0.0054192...    0.4638181...    0.1791709...]
 [ 509.            0.0070965...    0.4831505...    0.1685260...]
 [ 510.            0.0093   ...    0.503    ...    0.1582   ...]
 [ 511.            0.0120562...    0.5232543...    0.1482365...]
 [ 512.            0.0154137...    0.5439717...    0.1386625...]
 [ 513.            0.0193991...    0.565139 ...    0.1294993...]
 [ 514.            0.0240112...    0.5866255...    0.1207676...]
 [ 515.            0.0292289...    0.6082226...    0.1124864...]
 [ 516.            0.0350192...    0.6296821...    0.1046717...]
 [ 517.            0.0413448...    0.6507558...    0.0973361...]
 [ 518.            0.0481727...    0.6712346...    0.0904871...]
 [ 519.            0.0554816...    0.6909873...    0.0841267...]
 [ 520.            0.06327  ...    0.71     ...    0.07825  ...]
 [ 521.            0.0715642...    0.7283456...    0.0728614...]
 [ 522.            0.0803970...    0.7459679...    0.0680051...]
 [ 523.            0.0897629...    0.7628184...    0.0636823...]
 [ 524.            0.0996227...    0.7789004...    0.0598449...]
 [ 525.            0.1099142...    0.7942533...    0.0564111...]
 [ 526.            0.1205637...    0.8089368...    0.0532822...]
 [ 527.            0.1314973...    0.8230153...    0.0503588...]
 [ 528.            0.1426523...    0.8365417...    0.0475571...]
 [ 529.            0.1539887...    0.8495422...    0.0448253...]
 [ 530.            0.1655   ...    0.862    ...    0.04216  ...]
 [ 531.            0.1772055...    0.8738585...    0.0395936...]
 [ 532.            0.1890877...    0.8850940...    0.0371046...]
 [ 533.            0.2011304...    0.8957073...    0.0346733...]
 [ 534.            0.2133310...    0.9057092...    0.0323006...]
 [ 535.            0.2256968...    0.9151181...    0.0300011...]
 [ 536.            0.2382403...    0.9239560...    0.0277974...]
 [ 537.            0.2509754...    0.9322459...    0.0257131...]
 [ 538.            0.2639130...    0.9400080...    0.0237668...]
 [ 539.            0.2770569...    0.9472574...    0.0219659...]
 [ 540.            0.2904   ...    0.954    ...    0.0203   ...]
 [ 541.            0.3039194...    0.9602409...    0.0187414...]
 [ 542.            0.3175893...    0.9660106...    0.0172748...]
 [ 543.            0.3314022...    0.9713260...    0.0158947...]
 [ 544.            0.3453666...    0.9761850...    0.0146001...]
 [ 545.            0.3595019...    0.9805731...    0.0133933...]
 [ 546.            0.3738324...    0.9844703...    0.0122777...]
 [ 547.            0.3883818...    0.9878583...    0.0112562...]
 [ 548.            0.4031674...    0.9907270...    0.0103302...]
 [ 549.            0.4181943...    0.9930817...    0.0094972...]
 [ 550.            0.43345  ...    0.99495  ...    0.00875  ...]
 [ 551.            0.4489082...    0.9963738...    0.0080748...]
 [ 552.            0.4645599...    0.9973682...    0.0074580...]
 [ 553.            0.4803950...    0.9979568...    0.0068902...]
 [ 554.            0.4963962...    0.9981802...    0.0063660...]
 [ 555.            0.5125410...    0.9980910...    0.0058818...]
 [ 556.            0.5288034...    0.9977488...    0.0054349...]
 [ 557.            0.5451560...    0.9972150...    0.0050216...]
 [ 558.            0.5615719...    0.9965479...    0.0046357...]
 [ 559.            0.5780267...    0.9957974...    0.0042671...]
 [ 560.            0.5945   ...    0.995    ...    0.0039   ...]]

Multi-spectral distributions with a non-uniformly spaced independent variable uses Cubic Spline interpolation:

>>> data[511] = (0.00314, 0.31416, 0.03142)
>>> msds = MultiSpectralDistributions(data)
>>> with numpy_print_options(suppress=True):
...     print(msds.interpolate(SpectralShape(interval=1)))
... 
[[ 500.            0.0049   ...    0.323    ...    0.272    ...]
 [ 501.            0.0300110...    0.9455153...    0.5985102...]
 [ 502.            0.0462136...    1.3563103...    0.8066498...]
 [ 503.            0.0547925...    1.5844039...    0.9126502...]
 [ 504.            0.0570325...    1.6588148...    0.9327429...]
 [ 505.            0.0542183...    1.6085619...    0.8831594...]
 [ 506.            0.0476346...    1.4626640...    0.7801312...]
 [ 507.            0.0385662...    1.2501401...    0.6398896...]
 [ 508.            0.0282978...    1.0000089...    0.4786663...]
 [ 509.            0.0181142...    0.7412892...    0.3126925...]
 [ 510.            0.0093   ...    0.503    ...    0.1582   ...]
 [ 511.            0.00314  ...    0.31416  ...    0.03142  ...]
 [ 512.            0.0006228...    0.1970419...   -0.0551709...]
 [ 513.            0.0015528...    0.1469341...   -0.1041165...]
 [ 514.            0.0054381...    0.1523785...   -0.1217152...]
 [ 515.            0.0117869...    0.2019173...   -0.1142659...]
 [ 516.            0.0201073...    0.2840925...   -0.0880670...]
 [ 517.            0.0299077...    0.3874463...   -0.0494174...]
 [ 518.            0.0406961...    0.5005208...   -0.0046156...]
 [ 519.            0.0519808...    0.6118579...    0.0400397...]
 [ 520.            0.06327  ...    0.71     ...    0.07825  ...]
 [ 521.            0.0741690...    0.7859059...    0.1050384...]
 [ 522.            0.0846726...    0.8402033...    0.1207164...]
 [ 523.            0.0948728...    0.8759363...    0.1269173...]
 [ 524.            0.1048614...    0.8961496...    0.1252743...]
 [ 525.            0.1147305...    0.9038874...    0.1174207...]
 [ 526.            0.1245719...    0.9021942...    0.1049899...]
 [ 527.            0.1344776...    0.8941145...    0.0896151...]
 [ 528.            0.1445395...    0.8826926...    0.0729296...]
 [ 529.            0.1548497...    0.8709729...    0.0565668...]
 [ 530.            0.1655   ...    0.862    ...    0.04216  ...]
 [ 531.            0.1765618...    0.858179 ...    0.0309976...]
 [ 532.            0.1880244...    0.8593588...    0.0229897...]
 [ 533.            0.1998566...    0.8647493...    0.0177013...]
 [ 534.            0.2120269...    0.8735601...    0.0146975...]
 [ 535.            0.2245042...    0.8850011...    0.0135435...]
 [ 536.            0.2372572...    0.8982820...    0.0138044...]
 [ 537.            0.2502546...    0.9126126...    0.0150454...]
 [ 538.            0.2634650...    0.9272026...    0.0168315...]
 [ 539.            0.2768572...    0.9412618...    0.0187280...]
 [ 540.            0.2904   ...    0.954    ...    0.0203   ...]
 [ 541.            0.3040682...    0.9647869...    0.0211987...]
 [ 542.            0.3178617...    0.9736329...    0.0214207...]
 [ 543.            0.3317865...    0.9807080...    0.0210486...]
 [ 544.            0.3458489...    0.9861825...    0.0201650...]
 [ 545.            0.3600548...    0.9902267...    0.0188525...]
 [ 546.            0.3744103...    0.9930107...    0.0171939...]
 [ 547.            0.3889215...    0.9947048...    0.0152716...]
 [ 548.            0.4035944...    0.9954792...    0.0131685...]
 [ 549.            0.4184352...    0.9955042...    0.0109670...]
 [ 550.            0.43345  ...    0.99495  ...    0.00875  ...]
 [ 551.            0.4486447...    0.9939867...    0.0065999...]
 [ 552.            0.4640255...    0.9927847...    0.0045994...]
 [ 553.            0.4795984...    0.9915141...    0.0028313...]
 [ 554.            0.4953696...    0.9903452...    0.0013781...]
 [ 555.            0.5113451...    0.9894483...    0.0003224...]
 [ 556.            0.5275310...    0.9889934...   -0.0002530...]
 [ 557.            0.5439334...    0.9891509...   -0.0002656...]
 [ 558.            0.5605583...    0.9900910...    0.0003672...]
 [ 559.            0.5774118...    0.9919840...    0.0017282...]
 [ 560.            0.5945   ...    0.995    ...    0.0039   ...]]

normalise(factor=1)[source]

Normalises the multi-spectral distributions with given normalization factor.

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

Notes

The implementation uses the maximum value for each colour.SpectralDistribution class instances.

Examples

>>> 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)
... }
>>> msds = MultiSpectralDistributions(data)
>>> with numpy_print_options(suppress=True):
...     print(msds.normalise())  
[[ 500.            0.0082422...    0.3246231...    1.       ...]
 [ 510.            0.0156434...    0.5055276...    0.5816176...]
 [ 520.            0.1064255...    0.7135678...    0.2876838...]
 [ 530.            0.2783852...    0.8663316...    0.155    ...]
 [ 540.            0.4884777...    0.9587939...    0.0746323...]
 [ 550.            0.7291000...    0.9999497...    0.0321691...]
 [ 560.            1.       ...    1.       ...    0.0143382...]]

property shape

Getter and setter property for the multi-spectral distributions shape.

Returns: Multi-spectral distributions shape.
Return type: SpectralShape

Notes

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

Warning

colour.MultiSpectralDistributions.shape attribute is read only.

Examples

Shape of the multi-spectral distributions 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)
... }
>>> MultiSpectralDistributions(data).shape
SpectralShape(500.0, 560.0, 10.0)

Shape of the multi-spectral distributions with a non-uniformly spaced independent variable:

>>> data[511] = (0.00314, 0.31416, 0.03142)
>>> MultiSpectralDistributions(data).shape
SpectralShape(500.0, 560.0, 1.0)

property strict_labels

Getter and setter property for the multi-spectral distributions strict labels.

Parameters: value (array_like) – Value to set the multi-spectral distributions strict labels with.
Returns: Multi-spectral distributions strict labels.
Return type: array_like

property strict_name

Getter and setter property for the multi-spectral distributions strict name.

Parameters: value (unicode) – Value to set the multi-spectral distributions strict name with.
Returns: Multi-spectral distributions strict name.
Return type: unicode

to_sds()[source]

Converts the multi-spectral distributions to a list of spectral distributions and update their name and strict name using the labels and strict labels.

Returns: List of spectral distributions.
Return type: list

Examples

>>> 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)
... }
>>> msds = MultiSpectralDistributions(data)
>>> with numpy_print_options(suppress=True):
...     for sd in msds.to_sds():
...         print(sd)  
[[ 500.         0.0049 ...]
 [ 510.         0.0093 ...]
 [ 520.         0.06327...]
 [ 530.         0.1655 ...]
 [ 540.         0.2904 ...]
 [ 550.         0.43345...]
 [ 560.         0.5945 ...]]
[[ 500.         0.323  ...]
 [ 510.         0.503  ...]
 [ 520.         0.71   ...]
 [ 530.         0.862  ...]
 [ 540.         0.954  ...]
 [ 550.         0.99495...]
 [ 560.         0.995  ...]]
[[ 500.         0.272  ...]
 [ 510.         0.1582 ...]
 [ 520.         0.07825...]
 [ 530.         0.04216...]
 [ 540.         0.0203 ...]
 [ 550.         0.00875...]
 [ 560.         0.0039 ...]]

trim(shape)[source]

Trims the multi-spectral distributions wavelengths to given shape.

Parameters: shape (SpectralShape) – Spectral shape used for trimming.
Returns: Trimmed multi-spectral distributions.
Return type: MultiSpectralDistributions

Examples

>>> 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)
... }
>>> msds = MultiSpectralDistributions(data)
>>> msds = msds.interpolate(SpectralShape(interval=1))
>>> with numpy_print_options(suppress=True):
...     print(msds.trim(SpectralShape(520, 580, 5)))
... 
[[ 520.            0.06327  ...    0.71     ...    0.07825  ...]
 [ 521.            0.0715642...    0.7283456...    0.0728614...]
 [ 522.            0.0803970...    0.7459679...    0.0680051...]
 [ 523.            0.0897629...    0.7628184...    0.0636823...]
 [ 524.            0.0996227...    0.7789004...    0.0598449...]
 [ 525.            0.1099142...    0.7942533...    0.0564111...]
 [ 526.            0.1205637...    0.8089368...    0.0532822...]
 [ 527.            0.1314973...    0.8230153...    0.0503588...]
 [ 528.            0.1426523...    0.8365417...    0.0475571...]
 [ 529.            0.1539887...    0.8495422...    0.0448253...]
 [ 530.            0.1655   ...    0.862    ...    0.04216  ...]
 [ 531.            0.1772055...    0.8738585...    0.0395936...]
 [ 532.            0.1890877...    0.8850940...    0.0371046...]
 [ 533.            0.2011304...    0.8957073...    0.0346733...]
 [ 534.            0.2133310...    0.9057092...    0.0323006...]
 [ 535.            0.2256968...    0.9151181...    0.0300011...]
 [ 536.            0.2382403...    0.9239560...    0.0277974...]
 [ 537.            0.2509754...    0.9322459...    0.0257131...]
 [ 538.            0.2639130...    0.9400080...    0.0237668...]
 [ 539.            0.2770569...    0.9472574...    0.0219659...]
 [ 540.            0.2904   ...    0.954    ...    0.0203   ...]
 [ 541.            0.3039194...    0.9602409...    0.0187414...]
 [ 542.            0.3175893...    0.9660106...    0.0172748...]
 [ 543.            0.3314022...    0.9713260...    0.0158947...]
 [ 544.            0.3453666...    0.9761850...    0.0146001...]
 [ 545.            0.3595019...    0.9805731...    0.0133933...]
 [ 546.            0.3738324...    0.9844703...    0.0122777...]
 [ 547.            0.3883818...    0.9878583...    0.0112562...]
 [ 548.            0.4031674...    0.9907270...    0.0103302...]
 [ 549.            0.4181943...    0.9930817...    0.0094972...]
 [ 550.            0.43345  ...    0.99495  ...    0.00875  ...]
 [ 551.            0.4489082...    0.9963738...    0.0080748...]
 [ 552.            0.4645599...    0.9973682...    0.0074580...]
 [ 553.            0.4803950...    0.9979568...    0.0068902...]
 [ 554.            0.4963962...    0.9981802...    0.0063660...]
 [ 555.            0.5125410...    0.9980910...    0.0058818...]
 [ 556.            0.5288034...    0.9977488...    0.0054349...]
 [ 557.            0.5451560...    0.9972150...    0.0050216...]
 [ 558.            0.5615719...    0.9965479...    0.0046357...]
 [ 559.            0.5780267...    0.9957974...    0.0042671...]
 [ 560.            0.5945   ...    0.995    ...    0.0039   ...]]

property values

Getter and setter property for the multi-spectral distributions values.

Parameters: value (array_like) – Value to set the multi-spectral distributions wavelengths values with.
Returns: Multi-spectral distributions values.
Return type: ndarray

property wavelengths

Getter and setter property for the multi-spectral distributions wavelengths \(\lambda_n\).

Parameters: value (array_like) – Value to set the multi-spectral distributions wavelengths \(\lambda_n\) with.
Returns: Multi-spectral distributions wavelengths \(\lambda_n\).
Return type: ndarray