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.

Important

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

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.
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_kwargs (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_kwargs (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.

Attributes

strict_name
strict_labels
wavelengths
values
shape

Methods

__init__()
interpolate()
extrapolate()
align()
trim()
normalise()
to_sds()

References

[], [], []

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_kwargs={},
             ... extrapolator=Extrapolator,
             ... extrapolator_kwargs={...})

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_kwargs={},
             ... extrapolator=Extrapolator,
             ... extrapolator_kwargs={...})

Instantiation with a Pandas DataFrame:

>>> from colour.utilities import is_pandas_installed
>>> if is_pandas_installed():
...     from pandas import DataFrame
...     x_bar = [data[key][0] for key in sorted(data.keys())]
...     y_bar = [data[key][1] for key in sorted(data.keys())]
...     z_bar = [data[key][2] for key in sorted(data.keys())]
...     print(MultiSignals(  
...         DataFrame(
...             dict(zip(labels, [x_bar, y_bar, z_bar])), data.keys())))
[[  5.0000000...e+02   4.9000000...e-03   3.2300000...e-01   2.7200000...e-01]
 [  5.1000000...e+02   9.3000000...e-03   5.0300000...e-01   1.5820000...e-01]
 [  5.2000000...e+02   3.1400000...e-03   3.1416000...e-01   3.1420000...e-02]
 [  5.3000000...e+02   6.3270000...e-02   7.1000000...e-01   7.8250000...e-02]
 [  5.4000000...e+02   1.6550000...e-01   8.6200000...e-01   4.2160000...e-02]
 [  5.5000000...e+02   2.9040000...e-01   9.5400000...e-01   2.0300000...e-02]
 [  5.6000000...e+02   4.3345000...e-01   9.9495000...e-01   8.7500000...e-03]
 [  5.1100000...e+02   5.9450000...e-01   9.9500000...e-01   3.9000000...e-03]]

__init__(data=None, domain=None, labels=None, **kwargs)[source]¶

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

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 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

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 shape¶

Getter 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.

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)

interpolate(shape, interpolator=None, interpolator_kwargs=None, **kwargs)[source]¶

Interpolates the multi-spectral distributions 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

shape (SpectralShape, optional) – Spectral shape used for interpolation.
interpolator (object, optional) – Interpolator class type to use as interpolating function.
interpolator_kwargs (dict_like, optional) – Arguments to use when instantiating the interpolating function.
**kwargs (dict, optional) – Keywords arguments for deprecation management.

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

[]

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   ...]]

__repr__()¶

Returns an ellipsis string representation of the continuous signal for documentation purposes.

Returns: Ellipsis string representation.
Return type: unicode

extrapolate(shape, extrapolator=None, extrapolator_kwargs=None, **kwargs)[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_kwargs (dict_like, optional) – Arguments to use when instantiating the extrapolating function.
**kwargs (dict, optional) – Keywords arguments for deprecation management.

Returns

Extrapolated multi-spectral distributions.

Return type

MultiSpectralDistributions

References

[], []

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 ]]

align(shape, interpolator=None, interpolator_kwargs=None, extrapolator=None, extrapolator_kwargs=None, **kwargs)[source]¶

Aligns the multi-spectral distributions 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

shape (SpectralShape) – Spectral shape used for alignment.
interpolator (object, optional) – Interpolator class type to use as interpolating function.
interpolator_kwargs (dict_like, optional) – Arguments to use when instantiating the interpolating function.
extrapolator (object, optional) – Extrapolator class type to use as extrapolating function.
extrapolator_kwargs (dict_like, optional) – Arguments to use when instantiating the extrapolating function.
**kwargs (dict, optional) – Keywords arguments for deprecation management.

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   ...]]

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   ...]]

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...]]

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 ...]]