colour.colorimetry Package¶

Sub-Packages¶

colour.colorimetry.dataset Package

Module Contents¶

class colour.colorimetry.SpectralShape(start=None, end=None, steps=None)¶

Bases: object

Defines the base object for spectral power distribution shape.

Parameters:	start (numeric, optional) – Wavelengths \(\lambda_{i}\): range start in nm. end (numeric, optional) – Wavelengths \(\lambda_{i}\): range end in nm. steps (numeric, optional) – Wavelengths \(\lambda_{i}\): range steps.

start¶

end¶

steps¶

__repr__()¶

__contains__()¶

__len__()¶

__eq__()¶

__ne__()¶

range()¶

Examples

>>> SpectralShape(360, 830, 1)
SpectralShape(360, 830, 1)

__contains__(wavelength)

Returns if the spectral shape contains the given wavelength \(\lambda\).

Parameters:	wavelength (numeric) – Wavelength \(\lambda\).
Returns:	Is wavelength \(\lambda\) in the spectral shape.
Return type:	bool

Notes

Reimplements the object.__contains__() method.

Examples

>>> 0.5 in SpectralShape(0, 10, 0.1)
True
>>> 0.51 in SpectralShape(0, 10, 0.1)
False

__eq__(shape)

Returns the spectral shape equality with given other spectral shape.

Parameters:	shape (SpectralShape) – Spectral shape to compare for equality.
Returns:	Spectral shape equality.
Return type:	bool

Notes

Reimplements the object.__eq__() method.

Examples

>>> SpectralShape(0, 10, 0.1) == SpectralShape(0, 10, 0.1)
True
>>> SpectralShape(0, 10, 0.1) == SpectralShape(0, 10, 1)
False

__iter__()¶

Returns a generator for the spectral power distribution data.

Returns:	Spectral power distribution data generator.
Return type:	generator

Notes

Reimplements the object.__iter__() method.

Examples

>>> shape = SpectralShape(0, 10, 1)
>>> for wavelength in shape: print(wavelength)
0
1
2
3
4
5
6
7
8
9
10

__len__()

Returns the spectral shape wavelengths \(\lambda_n\) count.

Returns:	Spectral shape wavelengths \(\lambda_n\) count.
Return type:	int

Notes

Reimplements the object.__len__() method.

Examples

>>> len(SpectralShape(0, 10, 0.1))
101

__ne__(shape)

Returns the spectral shape inequality with given other spectral shape.

Parameters:	shape (SpectralShape) – Spectral shape to compare for inequality.
Returns:	Spectral shape inequality.
Return type:	bool

Notes

Reimplements the object.__ne__() method.

Examples

>>> SpectralShape(0, 10, 0.1) != SpectralShape(0, 10, 0.1)
False
>>> SpectralShape(0, 10, 0.1) != SpectralShape(0, 10, 1)
True

__repr__()

Returns a formatted string representation.

Returns:	Formatted string representation.
Return type:	unicode

__str__()¶

Returns a nice formatted string representation.

Returns:	Nice formatted string representation.
Return type:	unicode

end

Property for self.__end private attribute.

Returns:	self.__end.
Return type:	numeric

range()

Returns an iterable range for the spectral power distribution shape.

Returns:	Iterable range for the spectral power distribution shape
Return type:	ndarray
Raises:	`RuntimeError` – If one of spectral shape start, end or steps attributes is not defined.

Examples

>>> SpectralShape(0, 10, 0.1).range()
array([  0. ,   0.1,   0.2,   0.3,   0.4,   0.5,   0.6,   0.7,   0.8,
         0.9,   1. ,   1.1,   1.2,   1.3,   1.4,   1.5,   1.6,   1.7,
         1.8,   1.9,   2. ,   2.1,   2.2,   2.3,   2.4,   2.5,   2.6,
         2.7,   2.8,   2.9,   3. ,   3.1,   3.2,   3.3,   3.4,   3.5,
         3.6,   3.7,   3.8,   3.9,   4. ,   4.1,   4.2,   4.3,   4.4,
         4.5,   4.6,   4.7,   4.8,   4.9,   5. ,   5.1,   5.2,   5.3,
         5.4,   5.5,   5.6,   5.7,   5.8,   5.9,   6. ,   6.1,   6.2,
         6.3,   6.4,   6.5,   6.6,   6.7,   6.8,   6.9,   7. ,   7.1,
         7.2,   7.3,   7.4,   7.5,   7.6,   7.7,   7.8,   7.9,   8. ,
         8.1,   8.2,   8.3,   8.4,   8.5,   8.6,   8.7,   8.8,   8.9,
         9. ,   9.1,   9.2,   9.3,   9.4,   9.5,   9.6,   9.7,   9.8,
         9.9,  10. ])

start

Property for self.__start private attribute.

Returns:	self.__start.
Return type:	numeric

steps

Property for self.__steps private attribute.

Returns:	self.__steps.
Return type:	numeric

class colour.colorimetry.SpectralPowerDistribution(name, data)¶

Bases: object

Defines the base object for spectral data computations.

Parameters:	name (str or unicode) – Spectral power distribution name. data (dict) – Spectral power distribution data in a dict as follows: {wavelength \(\lambda_{i}\): value, wavelength \(\lambda_{i+1}\), ..., wavelength \(\lambda_{i+n}\)}

name¶

data¶

wavelengths¶

values¶

items¶

shape¶

__getitem__()¶

__init__()¶

__setitem__()¶

__iter__()¶

__contains__()¶

__len__()¶

__eq__()¶

__ne__()¶

__add__()¶

__sub__()¶

__mul__()¶

__div__()¶

__truediv__()¶

get()¶

is_uniform()¶

extrapolate()¶

interpolate()¶

align()¶

zeros()¶

normalise()¶

clone()¶

Examples

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd.wavelengths
array([510, 520, 530, 540])
>>> spd.values
array([ 49.67,  69.59,  81.73,  88.19])
>>> spd.shape
SpectralShape(510, 540, 10)

__add__(x)

Implements support for spectral power distribution addition.

Parameters:	x (numeric or array_like or SpectralPowerDistribution) – Variable to add.
Returns:	Variable added spectral power distribution.
Return type:	SpectralPowerDistribution

Notes

Reimplements the object.__add__() method.

Warning

The addition operation happens in place.

Examples

Adding a single numeric variable:

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd + 10  
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([ 59.67,  79.59,  91.73,  98.19])

Adding an array_like variable:

>>> spd + [1, 2, 3, 4]  
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([  60.67,   81.59,   94.73,  102.19])

Adding a SpectralPowerDistribution class variable:

>>> spd_alternate = SpectralPowerDistribution('Spd', data)
>>> spd + spd_alternate  
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([ 110.34,  151.18,  176.46,  190.38])

__contains__(wavelength)

Returns if the spectral power distribution contains the given wavelength \(\lambda\).

Parameters:	wavelength (numeric) – Wavelength \(\lambda\).
Returns:	Is wavelength \(\lambda\) in the spectral power distribution.
Return type:	bool

Notes

Reimplements the object.__contains__() method.

Examples

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> 510 in spd
True

__div__(x)

Implements support for spectral power distribution division.

Parameters:	x (numeric or array_like or SpectralPowerDistribution) – Variable to divide.
Returns:	Variable divided spectral power distribution.
Return type:	SpectralPowerDistribution

Notes

Reimplements the object.__div__() method.

Warning

The division operation happens in place.

Examples

Dividing a single numeric variable:

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd / 10  
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([ 4.967,  6.959,  8.173,  8.819])

Dividing an array_like variable:

>>> spd / [1, 2, 3, 4]  
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([ 4.967     ,  3.4795    ,  2.72433333,  2.20475   ])

Dividing a SpectralPowerDistribution class variable:

>>> spd_alternate = SpectralPowerDistribution('Spd', data)
>>> spd / spd_alternate  
<...SpectralPowerDistribution object at 0x...>
>>> spd.values  
array([ 0.1       ,  0.05      ,  0.0333333...,  0.025     ])

__eq__(spd)

Returns the spectral power distribution equality with given other spectral power distribution.

Parameters:	spd (SpectralPowerDistribution) – Spectral power distribution to compare for equality.
Returns:	Spectral power distribution equality.
Return type:	bool

Notes

Reimplements the object.__eq__() method.

Examples

>>> data1 = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> data2 = {510: 48.6700, 520: 69.5900, 530: 81.7300, 540: 88.1900}
>>> spd1 = SpectralPowerDistribution('Spd', data1)
>>> spd2 = SpectralPowerDistribution('Spd', data2)
>>> spd3 = SpectralPowerDistribution('Spd', data2)
>>> spd1 == spd2
False
>>> spd2 == spd3
True

__getitem__(wavelength)

Returns the value for given wavelength \(\lambda\).

Parameters:	wavelength (numeric) – Wavelength \(\lambda\) to retrieve the value.
Returns:	Wavelength \(\lambda\) value.
Return type:	numeric

See also

SpectralPowerDistribution.get()

Notes

Reimplements the object.__getitem__() method.

Examples

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> # Doctests ellipsis for Python 2.x compatibility.
>>> spd[510]  
49.67...

__hash__()¶

Returns the spectral power distribution hash value.

Returns:	Object hash.
Return type:	int

Notes

Reimplements the object.__hash__() method.

Warning

SpectralPowerDistribution class is mutable and should not be hashable. However, so that it can be used as a key in some data caches, we provide a __hash__ implementation, assuming that the underlying data will not change for those specific cases.

References

[1]	http://stackoverflow.com/a/16162138/931625 (Last accessed 8 August 2014)

__iter__()

Returns a generator for the spectral power distribution data.

Returns:	Spectral power distribution data generator.
Return type:	generator

Notes

Reimplements the object.__iter__() method.

Examples

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> # Doctests ellipsis for Python 2.x compatibility.
>>> for wavelength, value in spd: print((wavelength, value))    
(510, 49.6...)
(520, 69.5...)
(530, 81.7...)
(540, 88.1...)

__len__()

Returns the spectral power distribution wavelengths \(\lambda_n\) count.

Returns:	Spectral power distribution wavelengths \(\lambda_n\) count.
Return type:	int

Notes

Reimplements the object.__len__() method.

Examples

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> len(spd)
4

__mul__(x)

Implements support for spectral power distribution multiplication.

Parameters:	x (numeric or array_like or SpectralPowerDistribution) – Variable to multiply.
Returns:	Variable multiplied spectral power distribution.
Return type:	SpectralPowerDistribution

Notes

Reimplements the object.__mul__() method.

Warning

The multiplication operation happens in place.

Examples

Multiplying a single numeric variable:

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd * 10  
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([ 496.7,  695.9,  817.3,  881.9])

Multiplying an array_like variable:

>>> spd * [1, 2, 3, 4]  
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([  496.7,  1391.8,  2451.9,  3527.6])

Multiplying a SpectralPowerDistribution class variable:

>>> spd_alternate = SpectralPowerDistribution('Spd', data)
>>> spd * spd_alternate  
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([  24671.089,   96855.362,  200393.787,  311099.044])

__ne__(spd)

Returns the spectral power distribution inequality with given other spectral power distribution.

Parameters:	spd (SpectralPowerDistribution) – Spectral power distribution to compare for inequality.
Returns:	Spectral power distribution inequality.
Return type:	bool

Notes

Reimplements the object.__ne__() method.

Examples

>>> data1 = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> data2 = {510: 48.6700, 520: 69.5900, 530: 81.7300, 540: 88.1900}
>>> spd1 = SpectralPowerDistribution('Spd', data1)
>>> spd2 = SpectralPowerDistribution('Spd', data2)
>>> spd3 = SpectralPowerDistribution('Spd', data2)
>>> spd1 != spd2
True
>>> spd2 != spd3
False

__setitem__(wavelength, value)

Sets the wavelength \(\lambda\) with given value.

Parameters:	wavelength (numeric) – Wavelength \(\lambda\) to set. value (numeric) – Value for wavelength \(\lambda\).

Notes

Reimplements the object.__setitem__() method.

Examples

>>> spd = SpectralPowerDistribution('Spd', {})
>>> spd[510] = 49.6700
>>> spd.values
array([ 49.67])

__sub__(x)

Implements support for spectral power distribution subtraction.

Parameters:	x (numeric or array_like or SpectralPowerDistribution) – Variable to subtract.
Returns:	Variable subtracted spectral power distribution.
Return type:	SpectralPowerDistribution

Notes

Reimplements the object.__sub__() method.

Warning

The subtraction operation happens in place.

Examples

Subtracting a single numeric variable:

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd - 10  
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([ 39.67,  59.59,  71.73,  78.19])

Subtracting an array_like variable:

>>> spd - [1, 2, 3, 4]  
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([ 38.67,  57.59,  68.73,  74.19])

Subtracting a SpectralPowerDistribution class variable:

>>> spd_alternate = SpectralPowerDistribution('Spd', data)
>>> spd - spd_alternate  
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([-11., -12., -13., -14.])

__truediv__(x)

Implements support for spectral power distribution division.

Parameters:	x (numeric or array_like or SpectralPowerDistribution) – Variable to divide.
Returns:	Variable divided spectral power distribution.
Return type:	SpectralPowerDistribution

Notes

Reimplements the object.__div__() method.

Warning

The division operation happens in place.

Examples

Dividing a single numeric variable:

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd / 10  
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([ 4.967,  6.959,  8.173,  8.819])

Dividing an array_like variable:

>>> spd / [1, 2, 3, 4]  
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([ 4.967     ,  3.4795    ,  2.72433333,  2.20475   ])

Dividing a SpectralPowerDistribution class variable:

>>> spd_alternate = SpectralPowerDistribution('Spd', data)
>>> spd / spd_alternate  
<...SpectralPowerDistribution object at 0x...>
>>> spd.values  
array([ 0.1       ,  0.05      ,  0.0333333...,  0.025     ])

align(shape, method=u'Constant', left=None, right=None)

Aligns the spectral power distribution to given spectral shape: Interpolates first then extrapolates to fit the given range.

Parameters:	shape (SpectralShape) – Spectral shape used for alignment. method (unicode, optional) – (‘Linear’, ‘Constant’), Extrapolation method. left (numeric, optional) – Value to return for low extrapolation range. right (numeric, optional) – Value to return for high extrapolation range.
Returns:	Aligned spectral power distribution.
Return type:	SpectralPowerDistribution

Examples

>>> data = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 86.26,
...     560: 77.18}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd.align(SpectralShape(505, 565, 1))  
<...SpectralPowerDistribution object at 0x...>
>>> # Doctests skip for Python 2.x compatibility.
>>> spd.wavelengths  
array([505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517,
       518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530,
       531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543,
       544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556,
       557, 558, 559, 560, 561, 562, 563, 564, 565])
>>> spd.values  
array([ 49.67     ...,  49.67     ...,  49.67     ...,  49.67     ...,
        49.67     ...,  49.67     ...,  51.8341162...,  53.9856467...,
        56.1229464...,  58.2366197...,  60.3121800...,  62.3327095...,
        64.2815187...,  66.1448055...,  67.9143153...,  69.59     ...,
        71.1759958...,  72.6627938...,  74.0465756...,  75.3329710...,
        76.5339542...,  77.6647421...,  78.7406907...,  79.7741932...,
        80.7715767...,  81.73     ...,  82.6407518...,  83.507872 ...,
        84.3326333...,  85.109696 ...,  85.8292968...,  86.47944  ...,
        87.0480863...,  87.525344 ...,  87.9056578...,  88.19     ...,
        88.3858347...,  88.4975634...,  88.5258906...,  88.4696570...,
        88.3266460...,  88.0943906...,  87.7709802...,  87.3558672...,
        86.8506741...,  86.26     ...,  85.5911699...,  84.8503430...,
        84.0434801...,  83.1771110...,  82.2583874...,  81.2951360...,
        80.2959122...,  79.2700525...,  78.2277286...,  77.18     ...,
        77.18     ...,  77.18     ...,  77.18     ...,  77.18     ...,  77.18      ])

clone()

Clones the spectral power distribution.

Most of the SpectralPowerDistribution class operations are conducted in-place. The SpectralPowerDistribution.clone() method provides a convenient way to copy the spectral power distribution to a new object.

Returns:	Cloned spectral power distribution.
Return type:	SpectralPowerDistribution

Examples

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> print(spd)  
<...SpectralPowerDistribution object at 0x...>
>>> spd_clone = spd.clone()
>>> print(spd_clone)  
<...SpectralPowerDistribution object at 0x...>

data

Property for self.__data private attribute.

Returns:	self.__data.
Return type:	dict

extrapolate(shape, method=u'Constant', left=None, right=None)

Extrapolates the spectral power distribution following CIE 15:2004 recommendation.

Parameters:	shape (SpectralShape) – Spectral shape used for extrapolation. method (unicode, optional) – (‘Linear’, ‘Constant’), Extrapolation method. left (numeric, optional) – Value to return for low extrapolation range. right (numeric, optional) – Value to return for high extrapolation range.
Returns:	Extrapolated spectral power distribution.
Return type:	SpectralPowerDistribution

See also

SpectralPowerDistribution.align()

References

[2]	CIE 015:2004 Colorimetry, 3rd edition: 7.2.2.1 Extrapolation, ISBN-13: 978-3-901-90633-6

[3]	CIE 167:2005 Recommended Practice for Tabulating Spectral Data for Use in Colour Computations: 10. EXTRAPOLATION, ISBN-13: 978-3-901-90641-1

Examples

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd.extrapolate(SpectralShape(400, 700)).shape
SpectralShape(400, 700, 10)
>>> # Doctests ellipsis for Python 2.x compatibility.
>>> spd[400]  
49.67...
>>> # Doctests ellipsis for Python 2.x compatibility.
>>> spd[700]  
88.1...

get(wavelength, default=None)

Returns the value for given wavelength \(\lambda\).

Parameters:	wavelength (numeric) – Wavelength \(\lambda\) to retrieve the value. default (None or numeric, optional) – Wavelength \(\lambda\) default value.
Returns:	Wavelength \(\lambda\) value.
Return type:	numeric

Examples

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> # Doctests ellipsis for Python 2.x compatibility.
>>> spd.get(510)  
49.67...
>>> spd.get(511)  
None

interpolate(shape=SpectralShape(None, None, None), method=None)

Interpolates the spectral power distribution following CIE 167:2005 recommendations: the method developed by Sprague (1880) should be used for interpolating functions having a uniformly spaced independent variable and a Cubic Spline method for non-uniformly spaced independent variable.

Parameters:	shape (SpectralShape, optional) – Spectral shape used for interpolation. method (unicode, optional) – (‘Sprague’, ‘Cubic Spline’, ‘Linear’), Enforce given interpolation method.
Returns:	Interpolated spectral power distribution.
Return type:	SpectralPowerDistribution
Raises:	`RuntimeError` – If the Sprague interpolation method is forced with a non-uniformly spaced independent variable. `ValueError` – If the interpolation method is not defined.

See also

SpectralPowerDistribution.align()

Notes

Interpolation will be conducted over boundaries range, if you need to extend the range of the spectral power distribution use the SpectralPowerDistribution.extrapolate() or SpectralPowerDistribution.align() methods.
Sprague interpolator cannot be used for interpolating functions having a non-uniformly spaced independent variable.

Warning

If scipy is not unavailable the Cubic Spline method will fallback to legacy Linear interpolation.
Linear interpolator requires at least 2 wavelengths \(\lambda_n\) for interpolation.
Cubic Spline interpolator requires at least 3 wavelengths \(\lambda_n\) for interpolation.
Sprague interpolator requires at least 6 wavelengths \(\lambda_n\) for interpolation.

References

[4]	CIE 167:2005 Recommended Practice for Tabulating Spectral Data for Use in Colour Computations: 9. INTERPOLATION, ISBN-13: 978-3-901-90641-1

Examples

Uniform data is using Sprague interpolation by default:

>>> data = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 86.26,
...     560: 77.18}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd.interpolate(SpectralShape(steps=1))  
<...SpectralPowerDistribution object at 0x...>
>>> spd[515]  
60.3121800...

Non uniform data is using Cubic Spline interpolation by default:

>>> data = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 86.26,
...     560: 77.18}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd[511] = 31.41
>>> spd.interpolate(SpectralShape(steps=1))  
<...SpectralPowerDistribution object at 0x...>
>>> spd[515]  
21.4792222...

Enforcing Linear interpolation:

>>> data = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 86.26,
...     560: 77.18}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd.interpolate(SpectralShape(steps=1), method='Linear')    
<...SpectralPowerDistribution object at 0x...>
>>> # Doctests ellipsis for Python 2.x compatibility.
>>> spd[515]  
59.63...

is_uniform()

Returns if the spectral power distribution has uniformly spaced data.

Returns:	Is uniform.
Return type:	bool

See also

SpectralPowerDistribution.shape()

Examples

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd.is_uniform()
True

Breaking the steps by introducing a new wavelength \(\lambda\) value:

>>> spd[511] = 3.1415
>>> spd.is_uniform()
False

items

Property for self.items attribute. This is a convenient attribute used to iterate over the spectral power distribution.

Returns:	Spectral power distribution data generator.
Return type:	generator

name

Property for self.__name private attribute.

Returns:	self.__name.
Return type:	str or unicode

normalise(factor=1)

Normalises the spectral power distribution with given normalization factor.

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

Examples

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd.normalise()  
<...SpectralPowerDistribution object at 0x...>
>>> spd.values  
array([ 0.5632157...,  0.7890917...,  0.9267490...,  1.        ])

shape

Property for self.shape attribute.

Returns the shape of the spectral power distribution in the form of a SpectralShape class instance.

Returns:	Spectral power distribution shape.
Return type:	SpectralShape

See also

SpectralPowerDistribution.is_uniform

Notes

A non uniform spectral power distribution may will have multiple different steps, in that case SpectralPowerDistribution.shape returns the minimum steps size.

Warning

SpectralPowerDistribution.shape is read only.

Examples

Uniform spectral power distribution:

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> SpectralPowerDistribution('Spd', data).shape
SpectralShape(510, 540, 10)

Non uniform spectral power distribution:

>>> data = {512.3: 49.67, 524.5: 69.59, 532.4: 81.73, 545.7: 88.19}
>>> # Doctests ellipsis for Python 2.x compatibility.
>>> SpectralPowerDistribution('Spd', data).shape  
SpectralShape(512.3, 545.7, 7...)

values

Property for self.values attribute.

Returns:	Spectral power distribution wavelengths \(\lambda_n\) values.
Return type:	ndarray

Warning

SpectralPowerDistribution.values is read only.

wavelengths

Property for self.wavelengths attribute.

Returns:	Spectral power distribution wavelengths \(\lambda_n\).
Return type:	ndarray

Warning

SpectralPowerDistribution.wavelengths is read only.

zeros(shape=SpectralShape(None, None, None))

Zeros fills the spectral power distribution: Missing values will be replaced with zeros to fit the defined range.

Parameters:	shape (SpectralShape, optional) – Spectral shape used for zeros fill.
Returns:	Zeros filled spectral power distribution.
Return type:	SpectralPowerDistribution

Examples

>>> data = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 86.26,
...     560: 77.18}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd.zeros(SpectralShape(505, 565, 1))  
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([  0.  ,   0.  ,   0.  ,   0.  ,   0.  ,  49.67,   0.  ,   0.  ,
         0.  ,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,  69.59,
         0.  ,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,
         0.  ,  81.73,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,
         0.  ,   0.  ,   0.  ,  88.19,   0.  ,   0.  ,   0.  ,   0.  ,
         0.  ,   0.  ,   0.  ,   0.  ,   0.  ,  86.26,   0.  ,   0.  ,
         0.  ,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,  77.18,
         0.  ,   0.  ,   0.  ,   0.  ,   0.  ])

class colour.colorimetry.TriSpectralPowerDistribution(name, data, mapping, labels)¶

Bases: object

Defines the base object for colour matching functions.

A compound of three SpectralPowerDistribution is used to store the underlying axis data.

Parameters:	name (str or unicode) – Tri-spectral power distribution name. data (dict) – Tri-spectral power distribution data. mapping (dict) – Tri-spectral power distribution attributes mapping. labels (dict) – Tri-spectral power distribution axis labels mapping.

name¶

mapping¶

data¶

labels¶

x¶

y¶

z¶

wavelengths¶

values¶

items¶

shape¶

__init__()¶

__getitem__()¶

__setitem__()¶

__iter__()¶

__contains__()¶

__len__()¶

__eq__()¶

__ne__()¶

__add__()¶

__sub__()¶

__mul__()¶

__div__()¶

__truediv__()¶

get()¶

is_uniform()¶

extrapolate()¶

interpolate()¶

align()¶

zeros()¶

normalise()¶

clone()¶

Examples

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> tri_spd.wavelengths
array([510, 520, 530, 540])
>>> tri_spd.values
array([[ 49.67,  90.56,  12.43],
       [ 69.59,  87.34,  23.15],
       [ 81.73,  45.76,  67.98],
       [ 88.19,  23.45,  90.28]])
>>> tri_spd.shape
SpectralShape(510, 540, 10)

__add__(x)

Implements support for tri-spectral power distribution addition.

Parameters:	x (numeric or array_like or TriSpectralPowerDistribution) – Variable to add.
Returns:	Variable added tri-spectral power distribution.
Return type:	TriSpectralPowerDistribution

Notes

Reimplements the object.__add__() method.

Warning

The addition operation happens in place.

Examples

Adding a single numeric variable:

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> tri_spd + 10  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[  59.67,  100.56,   22.43],
       [  79.59,   97.34,   33.15],
       [  91.73,   55.76,   77.98],
       [  98.19,   33.45,  100.28]])

Adding an array_like variable:

>>> tri_spd + [(1, 2, 3)] * 4  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[  60.67,  102.56,   25.43],
       [  80.59,   99.34,   36.15],
       [  92.73,   57.76,   80.98],
       [  99.19,   35.45,  103.28]])

Adding a TriSpectralPowerDistribution class variable:

>>> data1 = {'x_bar': z_bar, 'y_bar': x_bar, 'z_bar': y_bar}
>>> tri_spd1 = TriSpectralPowerDistribution('Tri Spd', data1, mpg, lbl)
>>> tri_spd + tri_spd1  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[  73.1 ,  152.23,  115.99],
       [ 103.74,  168.93,  123.49],
       [ 160.71,  139.49,  126.74],
       [ 189.47,  123.64,  126.73]])

__contains__(wavelength)

Returns if the tri-spectral power distribution contains the given wavelength \(\lambda\).

Parameters:	wavelength (numeric) – Wavelength \(\lambda\).
Returns:	Is wavelength \(\lambda\) in the tri-spectral power distribution.
Return type:	bool

Notes

Reimplements the object.__contains__() method.

Examples

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> 510 in tri_spd
True

__div__(x)

Implements support for tri-spectral power distribution division.

Parameters:	x (numeric or array_like or TriSpectralPowerDistribution) – Variable to divide.
Returns:	Variable divided tri-spectral power distribution.
Return type:	TriSpectralPowerDistribution

Notes

Reimplements the object.__mul__() method.

Warning

The division operation happens in place.

Examples

Dividing a single numeric variable:

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> tri_spd / 10  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[ 4.967,  9.056,  1.243],
       [ 6.959,  8.734,  2.315],
       [ 8.173,  4.576,  6.798],
       [ 8.819,  2.345,  9.028]])

Dividing an array_like variable:

>>> tri_spd / [(1, 2, 3)] * 4  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values  
array([[ 19.868     ,  18.112     ,   1.6573333...],
       [ 27.836     ,  17.468     ,   3.0866666...],
       [ 32.692     ,   9.152     ,   9.064    ...],
       [ 35.276     ,   4.69      ,  12.0373333...]])

Dividing a TriSpectralPowerDistribution class variable:

>>> data1 = {'x_bar': z_bar, 'y_bar': x_bar, 'z_bar': y_bar}
>>> tri_spd1 = TriSpectralPowerDistribution('Tri Spd', data1, mpg, lbl)
>>> tri_spd / tri_spd1  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values  
array([[ 1.5983909...,  0.3646466...,  0.0183009...],
       [ 1.2024190...,  0.2510130...,  0.0353408...],
       [ 0.4809061...,  0.1119784...,  0.1980769...],
       [ 0.3907399...,  0.0531806...,  0.5133191...]])

__eq__(tri_spd)

Returns the tri-spectral power distribution equality with given other tri-spectral power distribution.

Parameters:	spd (TriSpectralPowerDistribution) – Tri-spectral power distribution to compare for equality.
Returns:	Tri-spectral power distribution equality.
Return type:	bool

Notes

Reimplements the object.__eq__() method.

Examples

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data1 = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> data2 = {'x_bar': y_bar, 'y_bar': x_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd1 = TriSpectralPowerDistribution('Tri Spd', data1, mpg, lbl)
>>> tri_spd2 = TriSpectralPowerDistribution('Tri Spd', data2, mpg, lbl)
>>> tri_spd3 = TriSpectralPowerDistribution('Tri Spd', data1, mpg, lbl)
>>> tri_spd1 == tri_spd2
False
>>> tri_spd1 == tri_spd3
True

__getitem__(wavelength)

Returns the values for given wavelength \(\lambda\).

Parameters:	wavelength (numeric) – Wavelength \(\lambda\) to retrieve the values.
Returns:	Wavelength \(\lambda\) values.
Return type:	ndarray, (3,)

See also

TriSpectralPowerDistribution.get()

Notes

Reimplements the object.__getitem__() method.

Examples

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> tri_spd[510]
array([ 49.67,  90.56,  12.43])

__hash__()¶

Returns the spectral power distribution hash value.

Returns:	Object hash.
Return type:	int

Notes

Reimplements the object.__hash__() method.

Warning

See SpectralPowerDistribution.__hash__() method warning section.

References

[5]	http://stackoverflow.com/a/16162138/931625 (Last accessed 8 August 2014)

__iter__()

Returns a generator for the tri-spectral power distribution data.

Returns:	Tri-spectral power distribution data generator.
Return type:	generator

Notes

Reimplements the object.__iter__() method.

Examples

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> for wavelength, value in tri_spd: print((wavelength, value))
(510, array([ 49.67,  90.56,  12.43]))
(520, array([ 69.59,  87.34,  23.15]))
(530, array([ 81.73,  45.76,  67.98]))
(540, array([ 88.19,  23.45,  90.28]))

__len__()

Returns the tri-spectral power distribution wavelengths \(\lambda_n\) count.

Returns:	Tri-Spectral power distribution wavelengths \(\lambda_n\) count.
Return type:	int

Notes

Reimplements the object.__len__() method.

Examples

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> len(tri_spd)
4

__mul__(x)

Implements support for tri-spectral power distribution multiplication.

Parameters:	x (numeric or array_like or TriSpectralPowerDistribution) – Variable to multiply.
Returns:	Variable multiplied tri-spectral power distribution.
Return type:	TriSpectralPowerDistribution

Notes

Reimplements the object.__mul__() method.

Warning

The multiplication operation happens in place.

Examples

Multiplying a single numeric variable:

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> tri_spd * 10  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[ 496.7,  905.6,  124.3],
       [ 695.9,  873.4,  231.5],
       [ 817.3,  457.6,  679.8],
       [ 881.9,  234.5,  902.8]])

Multiplying an array_like variable:

>>> tri_spd * [(1, 2, 3)] * 4  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[  1986.8,   7244.8,   1491.6],
       [  2783.6,   6987.2,   2778. ],
       [  3269.2,   3660.8,   8157.6],
       [  3527.6,   1876. ,  10833.6]])

Multiplying a TriSpectralPowerDistribution class variable:

>>> data1 = {'x_bar': z_bar, 'y_bar': x_bar, 'z_bar': y_bar}
>>> tri_spd1 = TriSpectralPowerDistribution('Tri Spd', data1, mpg, lbl)
>>> tri_spd * tri_spd1  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[  24695.924,  359849.216,  135079.296],
       [  64440.34 ,  486239.248,  242630.52 ],
       [ 222240.216,  299197.184,  373291.776],
       [ 318471.728,  165444.44 ,  254047.92 ]])

__ne__(tri_spd)

Returns the tri-spectral power distribution inequality with given other tri-spectral power distribution.

Parameters:	spd (TriSpectralPowerDistribution) – Tri-spectral power distribution to compare for inequality.
Returns:	Tri-spectral power distribution inequality.
Return type:	bool

Notes

Reimplements the object.__eq__() method.

Examples

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data1 = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> data2 = {'x_bar': y_bar, 'y_bar': x_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd1 = TriSpectralPowerDistribution('Tri Spd', data1, mpg, lbl)
>>> tri_spd2 = TriSpectralPowerDistribution('Tri Spd', data2, mpg, lbl)
>>> tri_spd3 = TriSpectralPowerDistribution('Tri Spd', data1, mpg, lbl)
>>> tri_spd1 != tri_spd2
True
>>> tri_spd1 != tri_spd3
False

__setitem__(wavelength, value)

Sets the wavelength \(\lambda\) with given value.

Parameters:	wavelength (numeric) – Wavelength \(\lambda\) to set. value (array_like) – Value for wavelength \(\lambda\).

Notes

Reimplements the object.__setitem__() method.

Examples

>>> x_bar = {}
>>> y_bar = {}
>>> z_bar = {}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> tri_spd[510] = (49.6700, 49.6700, 49.6700)
>>> tri_spd.values
array([[ 49.67,  49.67,  49.67]])

__sub__(x)

Implements support for tri-spectral power distribution subtraction.

Parameters:	x (numeric or array_like or TriSpectralPowerDistribution) – Variable to subtract.
Returns:	Variable subtracted tri-spectral power distribution.
Return type:	TriSpectralPowerDistribution

Notes

Reimplements the object.__sub__() method.

Warning

The subtraction operation happens in place.

Examples

Subtracting a single numeric variable:

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> tri_spd - 10  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[ 39.67,  80.56,   2.43],
       [ 59.59,  77.34,  13.15],
       [ 71.73,  35.76,  57.98],
       [ 78.19,  13.45,  80.28]])

Subtracting an array_like variable:

>>> tri_spd - [(1, 2, 3)] * 4  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[ 38.67,  78.56,  -0.57],
       [ 58.59,  75.34,  10.15],
       [ 70.73,  33.76,  54.98],
       [ 77.19,  11.45,  77.28]])

Subtracting a TriSpectralPowerDistribution class variable:

>>> data1 = {'x_bar': z_bar, 'y_bar': x_bar, 'z_bar': y_bar}
>>> tri_spd1 = TriSpectralPowerDistribution('Tri Spd', data1, mpg, lbl)
>>> tri_spd - tri_spd1  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[ 26.24,  28.89, -91.13],
       [ 35.44,   5.75, -77.19],
       [  2.75, -47.97,   9.22],
       [-13.09, -76.74,  53.83]])

__truediv__(x)

Implements support for tri-spectral power distribution division.

Parameters:	x (numeric or array_like or TriSpectralPowerDistribution) – Variable to divide.
Returns:	Variable divided tri-spectral power distribution.
Return type:	TriSpectralPowerDistribution

Notes

Reimplements the object.__mul__() method.

Warning

The division operation happens in place.

Examples

Dividing a single numeric variable:

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> tri_spd / 10  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[ 4.967,  9.056,  1.243],
       [ 6.959,  8.734,  2.315],
       [ 8.173,  4.576,  6.798],
       [ 8.819,  2.345,  9.028]])

Dividing an array_like variable:

>>> tri_spd / [(1, 2, 3)] * 4  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values  
array([[ 19.868     ,  18.112     ,   1.6573333...],
       [ 27.836     ,  17.468     ,   3.0866666...],
       [ 32.692     ,   9.152     ,   9.064    ...],
       [ 35.276     ,   4.69      ,  12.0373333...]])

Dividing a TriSpectralPowerDistribution class variable:

>>> data1 = {'x_bar': z_bar, 'y_bar': x_bar, 'z_bar': y_bar}
>>> tri_spd1 = TriSpectralPowerDistribution('Tri Spd', data1, mpg, lbl)
>>> tri_spd / tri_spd1  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values  
array([[ 1.5983909...,  0.3646466...,  0.0183009...],
       [ 1.2024190...,  0.2510130...,  0.0353408...],
       [ 0.4809061...,  0.1119784...,  0.1980769...],
       [ 0.3907399...,  0.0531806...,  0.5133191...]])

align(shape, method=u'Constant', left=None, right=None)

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

Parameters:	shape (SpectralShape) – Spectral shape used for alignment. method (unicode, optional) – (‘Linear’, ‘Constant’), Extrapolation method. left (numeric, optional) – Value to return for low extrapolation range. right (numeric, optional) – Value to return for high extrapolation range.
Returns:	Aligned tri-spectral power distribution.
Return type:	TriSpectralPowerDistribution

Examples

>>> x_bar = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 89.76,
...     560: 90.28}
>>> y_bar = {
...     510: 90.56,
...     520: 87.34,
...     530: 45.76,
...     540: 23.45,
...     550: 15.34,
...     560: 10.11}
>>> z_bar = {
...     510: 12.43,
...     520: 23.15,
...     530: 67.98,
...     540: 90.28,
...     550: 91.61,
...     560: 98.24}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> tri_spd.align(SpectralShape(505, 565, 1))  
<...TriSpectralPowerDistribution object at 0x...>
>>> # Doctests skip for Python 2.x compatibility.
>>> tri_spd.wavelengths  
array([505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517,
       518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530,
       531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543,
       544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556,
       557, 558, 559, 560, 561, 562, 563, 564, 565])
>>> tri_spd.values  
array([[ 49.67     ...,  90.56     ...,  12.43     ...],
       [ 49.67     ...,  90.56     ...,  12.43     ...],
       [ 49.67     ...,  90.56     ...,  12.43     ...],
       [ 49.67     ...,  90.56     ...,  12.43     ...],
       [ 49.67     ...,  90.56     ...,  12.43     ...],
       [ 49.67     ...,  90.56     ...,  12.43     ...],
       [ 51.8325938...,  91.2994928...,  12.5377184...],
       [ 53.9841952...,  91.9502387...,  12.7233193...],
       [ 56.1205452...,  92.5395463...,  12.9651679...],
       [ 58.2315395...,  93.0150037...,  13.3123777...],
       [ 60.3033208...,  93.2716331...,  13.8605136...],
       [ 62.3203719...,  93.1790455...,  14.7272944...],
       [ 64.2676077...,  92.6085951...,  16.0282961...],
       [ 66.1324679...,  91.4605335...,  17.8526544...],
       [ 67.9070097...,  89.6911649...,  20.2387677...],
       [ 69.59     ...,  87.34     ...,  23.15     ...],
       [ 71.1837378...,  84.4868033...,  26.5150469...],
       [ 72.6800056...,  81.0666018...,  30.3964852...],
       [ 74.0753483...,  77.0766254...,  34.7958422...],
       [ 75.3740343...,  72.6153870...,  39.6178858...],
       [ 76.5856008...,  67.8490714...,  44.7026805...],
       [ 77.7223995...,  62.9779261...,  49.8576432...],
       [ 78.7971418...,  58.2026503...,  54.8895997...],
       [ 79.8204447...,  53.6907852...,  59.6368406...],
       [ 80.798376 ...,  49.5431036...,  64.0011777...],
       [ 81.73     ...,  45.76     ...,  67.98     ...],
       [ 82.6093606...,  42.2678534...,  71.6460893...],
       [ 83.439232 ...,  39.10608  ...,  74.976976 ...],
       [ 84.2220071...,  36.3063728...,  77.9450589...],
       [ 84.956896 ...,  33.85464  ...,  80.552    ...],
       [ 85.6410156...,  31.7051171...,  82.8203515...],
       [ 86.27048  ...,  29.79448  ...,  84.785184 ...],
       [ 86.8414901...,  28.0559565...,  86.4857131...],
       [ 87.351424 ...,  26.43344  ...,  87.956928 ...],
       [ 87.7999266...,  24.8956009...,  89.2212178...],
       [ 88.19     ...,  23.45     ...,  90.28     ...],
       [ 88.5265036...,  22.1424091...,  91.1039133...],
       [ 88.8090803...,  20.9945234...,  91.6538035...],
       [ 89.0393279...,  20.0021787...,  91.9333499...],
       [ 89.2222817...,  19.1473370...,  91.9858818...],
       [ 89.3652954...,  18.4028179...,  91.8811002...],
       [ 89.4769231...,  17.7370306...,  91.7018000...],
       [ 89.5657996...,  17.1187058...,  91.5305910...],
       [ 89.6395227...,  16.5216272...,  91.4366204...],
       [ 89.7035339...,  15.9293635...,  91.4622944...],
       [ 89.76     ...,  15.34     ...,  91.61     ...],
       [ 89.8094041...,  14.7659177...,  91.8528616...],
       [ 89.8578890...,  14.2129190...,  92.2091737...],
       [ 89.9096307...,  13.6795969...,  92.6929664...],
       [ 89.9652970...,  13.1613510...,  93.2988377...],
       [ 90.0232498...,  12.6519811...,  94.0078786...],
       [ 90.0807467...,  12.1452800...,  94.7935995...],
       [ 90.1351435...,  11.6366269...,  95.6278555...],
       [ 90.1850956...,  11.1245805...,  96.4867724...],
       [ 90.2317606...,  10.6124724...,  97.3566724...],
       [ 90.28     ...,  10.11     ...,  98.24     ...],
       [ 90.28     ...,  10.11     ...,  98.24     ...],
       [ 90.28     ...,  10.11     ...,  98.24     ...],
       [ 90.28     ...,  10.11     ...,  98.24     ...],
       [ 90.28     ...,  10.11     ...,  98.24     ...],
       [ 90.28     ...,  10.11     ...,  98.24     ...]])

clone()

Clones the tri-spectral power distribution.

Most of the TriSpectralPowerDistribution class operations are conducted in-place. The TriSpectralPowerDistribution.clone() method provides a convenient way to copy the tri-spectral power distribution to a new object.

Returns:	Cloned tri-spectral power distribution.
Return type:	TriSpectralPowerDistribution

Examples

>>> x_bar = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 89.76,
...     560: 90.28}
>>> y_bar = {
...     510: 90.56,
...     520: 87.34,
...     530: 45.76,
...     540: 23.45,
...     550: 15.34,
...     560: 10.11}
>>> z_bar = {
...     510: 12.43,
...     520: 23.15,
...     530: 67.98,
...     540: 90.28,
...     550: 91.61,
...     560: 98.24}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> print(tri_spd)  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd_clone = tri_spd.clone()
>>> print(tri_spd_clone)  
<...TriSpectralPowerDistribution object at 0x...>

data

Property for self.__data private attribute.

Returns:	self.__data.
Return type:	dict

extrapolate(shape, method=u'Constant', left=None, right=None)

Extrapolates the tri-spectral power distribution following CIE 15:2004 recommendation.

Parameters:	shape (SpectralShape) – Spectral shape used for extrapolation. method (unicode, optional) – (‘Linear’, ‘Constant’), Extrapolation method. left (numeric, optional) – Value to return for low extrapolation range. right (numeric, optional) – Value to return for high extrapolation range.
Returns:	Extrapolated tri-spectral power distribution.
Return type:	TriSpectralPowerDistribution

See also

TriSpectralPowerDistribution.align()

References

[6]	CIE 015:2004 Colorimetry, 3rd edition: 7.2.2.1 Extrapolation, ISBN-13: 978-3-901-90633-6

[7]	CIE 167:2005 Recommended Practice for Tabulating Spectral Data for Use in Colour Computations: 10. EXTRAPOLATION, ISBN-13: 978-3-901-90641-1

Examples

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> tri_spd.extrapolate(SpectralShape(400, 700)).shape
SpectralShape(400, 700, 10)
>>> tri_spd[400]
array([ 49.67,  90.56,  12.43])
>>> tri_spd[700]
array([ 88.19,  23.45,  90.28])

get(wavelength, default=None)

Returns the values for given wavelength \(\lambda\).

Parameters:	wavelength (numeric) – Wavelength \(\lambda\) to retrieve the values. default (None or numeric, optional) – Wavelength \(\lambda\) default values.
Returns:	Wavelength \(\lambda\) values.
Return type:	numeric

Examples

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> tri_spd[510]
array([ 49.67,  90.56,  12.43])
>>> tri_spd.get(511)  
None

interpolate(shape=SpectralShape(None, None, None), method=None)

Interpolates the tri-spectral power distribution following CIE 167:2005 recommendations: the method developed by Sprague (1880) should be used for interpolating functions having a uniformly spaced independent variable and a Cubic Spline method for non-uniformly spaced independent variable.

Parameters:	shape (SpectralShape, optional) – Spectral shape used for interpolation. method (unicode, optional) – (‘Sprague’, ‘Cubic Spline’, ‘Linear’), Enforce given interpolation method.
Returns:	Interpolated tri-spectral power distribution.
Return type:	TriSpectralPowerDistribution

See also

TriSpectralPowerDistribution.align()

Notes

See SpectralPowerDistribution.interpolate() method notes section.

Warning

See SpectralPowerDistribution.interpolate() method warning section.

References

[8]	CIE 167:2005 Recommended Practice for Tabulating Spectral Data for Use in Colour Computations: 9. INTERPOLATION, ISBN-13: 978-3-901-90641-1

Examples

Uniform data is using Sprague interpolation by default:

>>> x_bar = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 89.76,
...     560: 90.28}
>>> y_bar = {
...     510: 90.56,
...     520: 87.34,
...     530: 45.76,
...     540: 23.45,
...     550: 15.34,
...     560: 10.11}
>>> z_bar = {
...     510: 12.43,
...     520: 23.15,
...     530: 67.98,
...     540: 90.28,
...     550: 91.61,
...     560: 98.24}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> tri_spd.interpolate(SpectralShape(steps=1))  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd[515]
array([ 60.30332087,  93.27163315,  13.86051361])

Non uniform data is using Cubic Spline interpolation by default:

>>> x_bar = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 89.76,
...     560: 90.28}
>>> y_bar = {
...     510: 90.56,
...     520: 87.34,
...     530: 45.76,
...     540: 23.45,
...     550: 15.34,
...     560: 10.11}
>>> z_bar = {
...     510: 12.43,
...     520: 23.15,
...     530: 67.98,
...     540: 90.28,
...     550: 91.61,
...     560: 98.24}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> tri_spd[511] = (31.41, 95.27, 15.06)
>>> tri_spd.interpolate(SpectralShape(steps=1))  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd[515]
array([  21.47104053,  100.64300155,   18.8165196 ])

Enforcing Linear interpolation:

>>> x_bar = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 89.76,
...     560: 90.28}
>>> y_bar = {
...     510: 90.56,
...     520: 87.34,
...     530: 45.76,
...     540: 23.45,
...     550: 15.34,
...     560: 10.11}
>>> z_bar = {
...     510: 12.43,
...     520: 23.15,
...     530: 67.98,
...     540: 90.28,
...     550: 91.61,
...     560: 98.24}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> tri_spd.interpolate(SpectralShape(steps=1), method='Linear')    
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd[515]
array([ 59.63,  88.95,  17.79])

is_uniform()

Returns if the tri-spectral power distribution has uniformly spaced data.

Returns:	Is uniform.
Return type:	bool

See also

TriSpectralPowerDistribution.shape()

Examples

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> tri_spd.is_uniform()
True

Breaking the steps by introducing new wavelength \(\lambda\) values:

>>> tri_spd[511] = (49.6700, 49.6700, 49.6700)
>>> tri_spd.is_uniform()
False

items

Property for self.items attribute. This is a convenient attribute used to iterate over the tri-spectral power distribution.

Returns:	Tri-spectral power distribution data generator.
Return type:	generator

labels

Property for self.__labels private attribute.

Returns:	self.__labels.
Return type:	dict

mapping

Property for self.__mapping private attribute.

Returns:	self.__mapping.
Return type:	dict

name

Property for self.__name private attribute.

Returns:	self.__name.
Return type:	str or unicode

normalise(factor=1)

Normalises the tri-spectral power distribution with given normalization factor.

Parameters:	factor (numeric, optional) – Normalization factor
Returns:	Normalised tri- spectral power distribution.
Return type:	TriSpectralPowerDistribution

Notes

The implementation uses the maximum value for all axis.

Examples

>>> x_bar = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 89.76,
...     560: 90.28}
>>> y_bar = {
...     510: 90.56,
...     520: 87.34,
...     530: 45.76,
...     540: 23.45,
...     550: 15.34,
...     560: 10.11}
>>> z_bar = {
...     510: 12.43,
...     520: 23.15,
...     530: 67.98,
...     540: 90.28,
...     550: 91.61,
...     560: 98.24}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> tri_spd.normalise()  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values  
array([[ 0.5055985...,  0.9218241...,  0.1265268...],
       [ 0.7083672...,  0.8890472...,  0.2356473...],
       [ 0.8319421...,  0.4657980...,  0.6919788...],
       [ 0.8976995...,  0.2387011...,  0.9189739...],
       [ 0.9136807...,  0.1561482...,  0.9325122...],
       [ 0.9189739...,  0.1029112...,  1.       ...]])

shape

Property for self.shape attribute.

Returns the shape of the tri-spectral power distribution in the form of a SpectralShape class instance.

Returns:	Tri-spectral power distribution shape.
Return type:	SpectralShape

Warning

TriSpectralPowerDistribution.shape is read only.

Examples

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> tri_spd.shape
SpectralShape(510, 540, 10)

values

Property for self.values attribute.

Returns:	Tri-spectral power distribution wavelengths \(\lambda_n\) values.
Return type:	ndarray

Warning

TriSpectralPowerDistribution.values is read only.

wavelengths

Property for self.wavelengths attribute.

Returns:	Tri-spectral power distribution wavelengths \(\lambda_n\).
Return type:	ndarray

Warning

TriSpectralPowerDistribution.wavelengths is read only.

x

Property for self.x attribute.

Returns:	Spectral power distribution for x axis.
Return type:	SpectralPowerDistribution

Warning

TriSpectralPowerDistribution.x is read only.

y

Property for self.y attribute.

Returns:	Spectral power distribution for y axis.
Return type:	SpectralPowerDistribution

Warning

TriSpectralPowerDistribution.y is read only.

z

Property for self.z attribute.

Returns:	Spectral power distribution for z axis.
Return type:	SpectralPowerDistribution

Warning

TriSpectralPowerDistribution.z is read only.

zeros(shape=SpectralShape(None, None, None))

Zeros fills the tri-spectral power distribution: Missing values will be replaced with zeros to fit the defined range.

Parameters:	shape (SpectralShape, optional) – Spectral shape used for zeros fill.
Returns:	Zeros filled tri-spectral power distribution.
Return type:	TriSpectralPowerDistribution

Examples

>>> x_bar = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 89.76,
...     560: 90.28}
>>> y_bar = {
...     510: 90.56,
...     520: 87.34,
...     530: 45.76,
...     540: 23.45,
...     550: 15.34,
...     560: 10.11}
>>> z_bar = {
...     510: 12.43,
...     520: 23.15,
...     530: 67.98,
...     540: 90.28,
...     550: 91.61,
...     560: 98.24}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mpg = lbl = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mpg, lbl)
>>> tri_spd.zeros(SpectralShape(505, 565, 1))  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [ 49.67,  90.56,  12.43],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [ 69.59,  87.34,  23.15],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [ 81.73,  45.76,  67.98],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [ 88.19,  23.45,  90.28],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [ 89.76,  15.34,  91.61],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [ 90.28,  10.11,  98.24],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ]])

colour.colorimetry.constant_spd(k, shape=SpectralShape(360, 830, 1))¶

Returns a spectral power distribution of given spectral shape filled with constant \(k\) values.

Parameters:	k (numeric) – Constant \(k\) to fill the spectral power distribution with. shape (SpectralShape, optional) – Spectral shape used to create the spectral power distribution.
Returns:	Constant \(k\) to filled spectral power distribution.
Return type:	SpectralPowerDistribution

Notes

By default, the spectral power distribution will use the shape given by DEFAULT_SPECTRAL_SHAPE attribute.

Examples

>>> spd = constant_spd(100)
>>> spd.shape
SpectralShape(360, 830, 1)
>>> spd[400]
100.0

colour.colorimetry.zeros_spd(shape=SpectralShape(360, 830, 1))¶

Returns a spectral power distribution of given spectral shape filled with zeros.

Parameters:	shape (SpectralShape, optional) – Spectral shape used to create the spectral power distribution.
Returns:	Zeros filled spectral power distribution.
Return type:	SpectralPowerDistribution

See also

constant_spd()

Notes

By default, the spectral power distribution will use the shape given by DEFAULT_SPECTRAL_SHAPE attribute.

Examples

>>> spd = zeros_spd()
>>> spd.shape
SpectralShape(360, 830, 1)
>>> spd[400]
0.0

colour.colorimetry.ones_spd(shape=SpectralShape(360, 830, 1))¶

Returns a spectral power distribution of given spectral shape filled with ones.

Parameters:	shape (SpectralShape, optional) – Spectral shape used to create the spectral power distribution.
Returns:	Ones filled spectral power distribution.
Return type:	SpectralPowerDistribution

See also

constant_spd()

Notes

By default, the spectral power distribution will use the shape given by DEFAULT_SPECTRAL_SHAPE attribute.

Examples

>>> spd = ones_spd()
>>> spd.shape
SpectralShape(360, 830, 1)
>>> spd[400]
1.0

colour.colorimetry.blackbody_spd(temperature, shape=SpectralShape(360, 830, 1), c1=3.741771e-16, c2=0.014388, n=1)¶

Returns the spectral power distribution of the planckian radiator for given temperature \(T[K]\).

Parameters:	temperature (numeric) – Temperature \(T[K]\) in kelvin degrees. shape (SpectralShape, optional) – Spectral shape used to create the spectral power distribution of the planckian radiator. c1 (numeric, optional) – The official value of \(c1\) is provided by the Committee on Data for Science and Technology (CODATA), and is \(c1=3,741771x10.16\ W/m_2\) (Mohr and Taylor, 2000). c2 (numeric, optional) – Since \(T\) is measured on the International Temperature Scale, the value of \(c2\) used in colorimetry should follow that adopted in the current International Temperature Scale (ITS-90) (Preston-Thomas, 1990; Mielenz et aI., 1991), namely \(c2=1,4388x10.2\ m/K\). n (numeric, optional) – Medium index of refraction. For dry air at 15°C and 101 325 Pa, containing 0,03 percent by volume of carbon dioxide, it is approximately 1,00028 throughout the visible region although CIE 15:2004 recommends using \(n=1\).
Returns:	Blackbody spectral power distribution.
Return type:	SpectralPowerDistribution

Examples

>>> from colour import STANDARD_OBSERVERS_CMFS
>>> cmfs = STANDARD_OBSERVERS_CMFS.get('CIE 1931 2 Degree Standard Observer')  
>>> blackbody_spd(5000, cmfs.shape)  
<colour.colorimetry.spectrum.SpectralPowerDistribution object at 0x...>

colour.colorimetry.blackbody_spectral_radiance(*args, **kwds)¶

Returns the spectral radiance of a blackbody at thermodynamic temperature \(T[K]\) in a medium having index of refraction \(n\).

Notes

The following form implementation is expressed in term of wavelength. The SI unit of radiance is watts per steradian per square metre.

References

[1]	CIE 015:2004 Colorimetry, 3rd edition: Appendix E. Information on the Use of Planck”s Equation for Standard Air., ISBN-13: 978-3-901-90633-6

Parameters:	wavelength (numeric) – Wavelength in meters. temperature (numeric) – Temperature \(T[K]\) in kelvin degrees. c1 (numeric, optional) – The official value of \(c1\) is provided by the Committee on Data for Science and Technology (CODATA), and is \(c1=3,741771x10.16\ W/m_2\) (Mohr and Taylor, 2000). c2 (numeric, optional) – Since \(T\) is measured on the International Temperature Scale, the value of \(c2\) used in colorimetry should follow that adopted in the current International Temperature Scale (ITS-90) (Preston-Thomas, 1990; Mielenz et aI., 1991), namely \(c2=1,4388x10.2\ m/K\). n (numeric, optional) – Medium index of refraction. For dry air at 15°C and 101 325 Pa, containing 0,03 percent by volume of carbon dioxide, it is approximately 1,00028 throughout the visible region although CIE 15:2004 recommends using \(n=1\).
Returns:	Radiance in watts per steradian per square metre.
Return type:	numeric

Examples

>>> # Doctests ellipsis for Python 2.x compatibility.
>>> planck_law(500 * 1e-9, 5500)  
20472701909806.5...

colour.colorimetry.planck_law(*args, **kwds)¶

Returns the spectral radiance of a blackbody at thermodynamic temperature \(T[K]\) in a medium having index of refraction \(n\).

Notes

The following form implementation is expressed in term of wavelength. The SI unit of radiance is watts per steradian per square metre.

References

[1]	CIE 015:2004 Colorimetry, 3rd edition: Appendix E. Information on the Use of Planck”s Equation for Standard Air., ISBN-13: 978-3-901-90633-6

Parameters:	wavelength (numeric) – Wavelength in meters. temperature (numeric) – Temperature \(T[K]\) in kelvin degrees. c1 (numeric, optional) – The official value of \(c1\) is provided by the Committee on Data for Science and Technology (CODATA), and is \(c1=3,741771x10.16\ W/m_2\) (Mohr and Taylor, 2000). c2 (numeric, optional) – Since \(T\) is measured on the International Temperature Scale, the value of \(c2\) used in colorimetry should follow that adopted in the current International Temperature Scale (ITS-90) (Preston-Thomas, 1990; Mielenz et aI., 1991), namely \(c2=1,4388x10.2\ m/K\). n (numeric, optional) – Medium index of refraction. For dry air at 15°C and 101 325 Pa, containing 0,03 percent by volume of carbon dioxide, it is approximately 1,00028 throughout the visible region although CIE 15:2004 recommends using \(n=1\).
Returns:	Radiance in watts per steradian per square metre.
Return type:	numeric

Examples

>>> # Doctests ellipsis for Python 2.x compatibility.
>>> planck_law(500 * 1e-9, 5500)  
20472701909806.5...

class colour.colorimetry.LMS_ConeFundamentals(name, data)¶

Bases: colour.colorimetry.spectrum.TriSpectralPowerDistribution

Implements support for the Stockman & Sharpe LMS cone fundamentals colour matching functions.

Parameters:	name (unicode) – LMS colour matching functions name. data (dict) – LMS colour matching functions.

l_bar¶

m_bar¶

s_bar¶

l_bar

Property for self.x attribute.

Returns:	self.x
Return type:	SpectralPowerDistribution

Warning

LMS_ConeFundamentals.l_bar is read only.

m_bar

Property for self.y attribute.

Returns:	self.y
Return type:	SpectralPowerDistribution

Warning

LMS_ConeFundamentals.m_bar is read only.

s_bar

Property for self.z attribute.

Returns:	self.z
Return type:	SpectralPowerDistribution

Warning

LMS_ConeFundamentals.s_bar is read only.

class colour.colorimetry.RGB_ColourMatchingFunctions(name, data)¶

Bases: colour.colorimetry.spectrum.TriSpectralPowerDistribution

Implements support for the CIE RGB colour matching functions.

Parameters:	name (unicode) – CIE RGB colour matching functions name. data (dict) – CIE RGB colour matching functions.

r_bar¶

g_bar¶

b_bar¶

b_bar

Property for self.z attribute.

Returns:	self.z
Return type:	SpectralPowerDistribution

Warning

RGB_ColourMatchingFunctions.b_bar is read only.

g_bar

Property for self.y attribute.

Returns:	self.y
Return type:	SpectralPowerDistribution

Warning

RGB_ColourMatchingFunctions.g_bar is read only.

r_bar

Property for self.x attribute.

Returns:	self.x
Return type:	SpectralPowerDistribution

Warning

RGB_ColourMatchingFunctions.r_bar is read only.

class colour.colorimetry.XYZ_ColourMatchingFunctions(name, data)¶

Bases: colour.colorimetry.spectrum.TriSpectralPowerDistribution

Implements support for the CIE Standard Observers XYZ colour matching functions.

Parameters:	name (unicode) – CIE Standard Observer XYZ colour matching functions name. data (dict) – CIE Standard Observer XYZ colour matching functions.

x_bar¶

y_bar¶

z_bar¶

x_bar

Property for self.x attribute.

Returns:	self.x
Return type:	SpectralPowerDistribution

Warning

XYZ_ColourMatchingFunctions.x_bar is read only.

y_bar

Property for self.y attribute.

Returns:	self.y
Return type:	SpectralPowerDistribution

Warning

XYZ_ColourMatchingFunctions.y_bar is read only.

z_bar

Property for self.z attribute.

Returns:	self.z
Return type:	SpectralPowerDistribution

Warning

XYZ_ColourMatchingFunctions.z_bar is read only.

colour.colorimetry.bandpass_correction(spd, method=u'Stearns 1988')¶

Implements spectral bandpass dependence correction on given spectral power distribution using given method.

Parameters:	spd (SpectralPowerDistribution) – Spectral power distribution. method (unicode) – (‘Stearns 1988’,) Correction method.
Returns:	Spectral bandpass dependence corrected spectral power distribution.
Return type:	SpectralPowerDistribution

colour.colorimetry.bandpass_correction_stearns1988(spd)¶

Implements spectral bandpass dependence correction on given spectral power distribution using Stearns and Stearns (1988) method.

References

[1]	Stephen Westland, Caterina Ripamonti, Vien Cheung, Computational Colour Science Using MATLAB, 2nd Edition, The Wiley-IS&T Series in Imaging Science and Technology, published July 2012, ISBN-13: 978-0-470-66569-5, page 38.

Parameters:	spd (SpectralPowerDistribution) – Spectral power distribution.
Returns:	Spectral bandpass dependence corrected spectral power distribution.
Return type:	SpectralPowerDistribution

Examples

>>> from colour import SpectralPowerDistribution
>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> corrected_spd = bandpass_correction_stearns1988(spd)
>>> corrected_spd.values  
array([ 48.01664   ,  70.3729688...,  82.0919506...,  88.72618   ])

colour.colorimetry.D_illuminant_relative_spd(xy)¶

Returns the relative spectral power distribution of given CIE Standard Illuminant D Series using given xy chromaticity coordinates.

References

[1]	Wyszecki & Stiles, Color Science - Concepts and Methods Data and Formulae - Second Edition, Wiley Classics Library Edition, published 2000, ISBN-10: 0-471-39918-3, page 146.

[2]	http://www.brucelindbloom.com/Eqn_DIlluminant.html (Last accessed 5 April 2014)

Parameters:	xy (array_like) – xy chromaticity coordinates.
Returns:	CIE Standard Illuminant D Series relative spectral power distribution.
Return type:	SpectralPowerDistribution

Examples

>>> D_illuminant_relative_spd((0.34567, 0.35850))  
<colour.colorimetry.spectrum.SpectralPowerDistribution object at 0x...>

colour.colorimetry.mesopic_luminous_efficiency_function(Lp, source=u'Blue Heavy', method=u'MOVE', photopic_lef=<colour.colorimetry.spectrum.SpectralPowerDistribution object at 0x102cf8fd0>, scotopic_lef=<colour.colorimetry.spectrum.SpectralPowerDistribution object at 0x102e061d0>)¶

Returns the mesopic luminous efficiency function \(V_m(\lambda)\) for given photopic luminance \(L_p\).

Parameters:	Lp (numeric) – Photopic luminance \(L_p\). source (unicode) – (‘Blue Heavy’, ‘Red Heavy’), Light source colour temperature. method (unicode) – (‘MOVE’, ‘LRC’), Method to calculate the weighting factor. photopic_lef (SpectralPowerDistribution) – \(V(\lambda)\) photopic luminous efficiency function. scotopic_lef (SpectralPowerDistribution) – \(V^\prime(\lambda)\) scotopic luminous efficiency function.
Returns:	Mesopic luminous efficiency function \(V_m(\lambda)\).
Return type:	SpectralPowerDistribution

Examples

>>> mesopic_luminous_efficiency_function(0.2)  
<colour.colorimetry.spectrum.SpectralPowerDistribution object at 0x...>

colour.colorimetry.mesopic_weighting_function(wavelength, Lp, source=u'Blue Heavy', method=u'MOVE', photopic_lef=<colour.colorimetry.spectrum.SpectralPowerDistribution object at 0x102cf8fd0>, scotopic_lef=<colour.colorimetry.spectrum.SpectralPowerDistribution object at 0x102e061d0>)¶

Calculates the mesopic weighting function factor at given wavelength \(\lambda\) using the photopic luminance \(L_p\).

Parameters:	wavelength (numeric) – Wavelength \(\lambda\) to calculate the mesopic weighting function factor. Lp (numeric) – Photopic luminance \(L_p\). source (unicode) – (‘Blue Heavy’, ‘Red Heavy’), Light source colour temperature. method (unicode) – (‘MOVE’, ‘LRC’), Method to calculate the weighting factor. photopic_lef (SpectralPowerDistribution) – \(V(\lambda)\) photopic luminous efficiency function. scotopic_lef (SpectralPowerDistribution) – \(V^\prime(\lambda)\) scotopic luminous efficiency function.
Returns:	Mesopic weighting function factor.
Return type:	numeric
Raises:	`KeyError` – If wavelength \(\lambda\) is not available in either luminous efficiency function.

Examples

>>> mesopic_weighting_function(500, 0.2)  
0.7052200...

colour.colorimetry.lightness(Y, method=u'CIE 1976', **kwargs)¶

Returns the Lightness \(L^*\) using given method.

Parameters:	Y (numeric) – luminance \(Y\). method (unicode, optional) – (‘Glasser 1958’, ‘Wyszecki 1964’, ‘CIE 1976’), Computation method. kwargs () – Keywords arguments.
Returns:	Lightness \(L^*\).
Return type:	numeric

Notes

Input luminance \(Y\) and optional \(Y_n\) are in domain [0, 100].
Output Lightness \(L^*\) is in domain [0, 100].

Examples

>>> lightness(10.08)  
37.9856290...
>>> lightness(10.08, Yn=100)  
37.9856290...
>>> lightness(10.08, Yn=95)  
38.9165987...
>>> lightness(10.08, method='Glasser 1958')  
36.2505626...
>>> lightness(10.08, method='Wyszecki 1964')  
37.0041149...

colour.colorimetry.lightness_glasser1958(Y, **kwargs)¶

Returns the Lightness \(L^*\) of given luminance \(Y\) using Glasser et al. (1958) method.

Parameters:	Y (numeric) – luminance \(Y\). kwargs (, optional) – Unused parameter provided for signature compatibility with other Lightness computation objects.
Returns:	Lightness \(L^*\).
Return type:	numeric

Notes

Input luminance \(Y\) is in domain [0, 100].
Output Lightness \(L^*\) is in domain [0, 100].

References

[1]	http://en.wikipedia.org/wiki/Lightness (Last accessed 13 April 2014)

Examples

>>> lightness_glasser1958(10.08)  
36.2505626...

colour.colorimetry.lightness_wyszecki1964(Y, **kwargs)¶

Returns the Lightness \(W^*\) of given luminance \(Y\) using Wyszecki (1964) method.

Parameters:	Y (numeric) – luminance \(Y\). kwargs (, optional) – Unused parameter provided for signature compatibility with other Lightness computation objects.
Returns:	Lightness \(W^*\).
Return type:	numeric

Notes

Input luminance \(Y\) is in domain [0, 100].
Output Lightness \(W^*\) is in domain [0, 100].

References

[2]	http://en.wikipedia.org/wiki/Lightness (Last accessed 13 April 2014)

Examples

>>> lightness_wyszecki1964(10.08)  
37.0041149...

colour.colorimetry.lightness_1976(Y, Yn=100)¶

Returns the Lightness \(L^*\) of given luminance \(Y\) using given reference white luminance \(Y_n\) as per CIE Lab implementation.

Parameters:	Y (numeric) – luminance \(Y\). Yn (numeric, optional) – White reference luminance \(Y_n\).
Returns:	Lightness \(L^*\).
Return type:	numeric

Notes

Input luminance \(Y\) and \(Y_n\) are in domain [0, 100].
Output Lightness \(L^*\) is in domain [0, 100].

References

[3]	http://www.poynton.com/PDFs/GammaFAQ.pdf (Last accessed 12 April 2014)

Examples

>>> lightness_1976(10.08)  
37.9856290...

colour.colorimetry.luminance(LV, method=u'CIE 1976', **kwargs)¶

Returns the luminance \(Y\) of given Lightness \(L^*\) or given Munsell value \(V\).

Parameters:	LV (numeric) – Lightness \(L^\) or Munsell* value \(V\). method (unicode, optional) – (‘Newhall 1943’, ‘CIE 1976’, ‘ASTM D1535-08’) Computation method. kwargs () – Keywords arguments.
Returns:	luminance \(Y\).
Return type:	numeric

Notes

Input LV is in domain [0, 100] or [0, 10] and optional luminance \(Y_n\) is in domain [0, 100].
Output luminance \(Y\) is in domain [0, 100].

Examples

>>> luminance(37.9856290977)  
10.0800000...
>>> luminance(37.9856290977, Yn=100)  
10.0800000...
>>> luminance(37.9856290977, Yn=95)  
9.5760000...
>>> luminance(3.74629715382, method='Newhall 1943')  
10.4089874...
>>> luminance(3.74629715382, method='ASTM D1535-08')  
10.1488096...

colour.colorimetry.luminance_newhall1943(V, **kwargs)¶

Returns the luminance \(Y\) of given Munsell value \(V\) using Newhall, Nickerson, and Judd (1943) method.

Parameters:	V (numeric) – Munsell value \(V\). kwargs (, optional) – Unused parameter provided for signature compatibility with other luminance computation objects.
Returns:	luminance \(Y\).
Return type:	numeric

Notes

Input Munsell value \(V\) is in domain [0, 10].
Output luminance \(Y\) is in domain [0, 100].

References

[1]	http://en.wikipedia.org/wiki/Lightness (Last accessed 13 April 2014)

Examples

>>> luminance_newhall1943(3.74629715382)  
10.4089874...

colour.colorimetry.luminance_1976(L, Yn=100)¶

Returns the luminance \(Y\) of given Lightness \(L^*\) with given reference white luminance \(Y_n\).

Parameters:	L (numeric) – Lightness \(L^\) Yn (numeric) – White reference luminance* \(Y_n\).
Returns:	luminance \(Y\).
Return type:	numeric

Notes

Input Lightness \(L^*\) is in domain [0, 100].
Output luminance \(Y\) is in domain [0, 100].

References

[2]	http://www.poynton.com/PDFs/GammaFAQ.pdf (Last accessed 12 April 2014)

Examples

>>> luminance_1976(37.9856290977)  
10.0800000...

colour.colorimetry.luminance_ASTM_D1535_08(V, **kwargs)¶

Returns the luminance \(Y\) of given Munsell value \(V\) using ASTM D1535-08e1 (2008) method.

Parameters:	V (numeric) – Munsell value \(V\). kwargs (, optional) – Unused parameter provided for signature compatibility with other luminance computation objects.
Returns:	luminance \(Y\).
Return type:	numeric

Notes

Input Munsell value \(V\) is in domain [0, 10].
Output luminance \(Y\) is in domain [0, 100].

References

http://www.scribd.com/doc/89648322/ASTM-D1535-08e1-Standard-Practice-for-Specifying-Color-by-the-Munsell-System

Examples

>>> luminance_ASTM_D1535_08(3.74629715382)  
10.1488096...

colour.colorimetry.RGB_10_degree_cmfs_to_LMS_10_degree_cmfs(wavelength)¶

Converts Stiles & Burch 1959 10 Degree RGB CMFs colour matching functions into the Stockman & Sharpe 10 Degree Cone Fundamentals spectral sensitivity functions.

Parameters:	wavelength (numeric) – Wavelength \(\lambda\) in nm.
Returns:	Stockman & Sharpe 10 Degree Cone Fundamentals spectral tristimulus values.
Return type:	ndarray, (3,)
Raises:	`KeyError` – If wavelength \(\lambda\) is not available in the colour matching functions.

Notes

Data for the Stockman & Sharpe 10 Degree Cone Fundamentals already exists, this definition is intended for educational purpose.

References

[3]	CIE 170-1:2006 Fundamental Chromaticity Diagram with Physiological Axes - Part 1, ISBN-13: 978-3-901-90646-6

Examples

>>> RGB_10_degree_cmfs_to_LMS_10_degree_cmfs(700)  
array([ 0.0052860...,  0.0003252...,  0.        ])

colour.colorimetry.RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs(wavelength)¶

Converts Wright & Guild 1931 2 Degree RGB CMFs colour matching functions into the CIE 1931 2 Degree Standard Observer colour matching functions.

Parameters:	wavelength (numeric) – Wavelength \(\lambda\) in nm.
Returns:	CIE 1931 2 Degree Standard Observer spectral tristimulus values.
Return type:	ndarray, (3,)
Raises:	`KeyError` – If wavelength \(\lambda\) is not available in the colour matching functions.

Notes

Data for the CIE 1931 2 Degree Standard Observer already exists, this definition is intended for educational purpose.

References

[1]	Wyszecki & Stiles, Color Science - Concepts and Methods Data and Formulae - Second Edition, Wiley Classics Library Edition, published 2000, ISBN-10: 0-471-39918-3, pages 138, 139.

Examples

>>> RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs(700)  
array([ 0.0113577...,  0.004102  ,  0.        ])

colour.colorimetry.RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs(wavelength)¶

Converts Stiles & Burch 1959 10 Degree RGB CMFs colour matching functions into the CIE 1964 10 Degree Standard Observer colour matching functions.

Parameters:	wavelength (numeric) – Wavelength \(\lambda\) in nm.
Returns:	CIE 1964 10 Degree Standard Observer spectral tristimulus values.
Return type:	ndarray, (3,)
Raises:	`KeyError` – If wavelength \(\lambda\) is not available in the colour matching functions.

Notes

Data for the CIE 1964 10 Degree Standard Observer already exists, this definition is intended for educational purpose.

References

[2]	Wyszecki & Stiles, Color Science - Concepts and Methods Data and Formulae - Second Edition, Wiley Classics Library Edition, published 2000, ISBN-10: 0-471-39918-3, page 141.

Examples

>>> RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs(700)  
array([  9.6432150...e-03,   3.7526317...e-03,  -4.1078830...e-06])

colour.colorimetry.LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs(wavelength)¶

Converts Stockman & Sharpe 2 Degree Cone Fundamentals colour matching functions into the CIE 2012 2 Degree Standard Observer colour matching functions.

Parameters:	wavelength (numeric) – Wavelength \(\lambda\) in nm.
Returns:	CIE 2012 2 Degree Standard Observer spectral tristimulus values.
Return type:	ndarray, (3,)
Raises:	`KeyError` – If wavelength \(\lambda\) is not available in the colour matching functions.

Notes

Data for the CIE 2012 2 Degree Standard Observer already exists, this definition is intended for educational purpose.

References

[4]	http://www.cvrl.org/database/text/cienewxyz/cie2012xyz2.htm (Last accessed 25 June 2014)

Examples

>>> LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs(700)  
array([ 0.0109677...,  0.0041959...,  0.        ])

colour.colorimetry.LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs(wavelength)¶

Converts Stockman & Sharpe 10 Degree Cone Fundamentals colour matching functions into the CIE 2012 10 Degree Standard Observer colour matching functions.

Parameters:	wavelength (numeric) – Wavelength \(\lambda\) in nm.
Returns:	CIE 2012 10 Degree Standard Observer spectral tristimulus values.
Return type:	ndarray, (3,)
Raises:	`KeyError` – If wavelength \(\lambda\) is not available in the colour matching functions.

Notes

Data for the CIE 2012 10 Degree Standard Observer already exists, this definition is intended for educational purpose.

References

[5]	http://www.cvrl.org/database/text/cienewxyz/cie2012xyz10.htm (Last accessed 25 June 2014)

Examples

>>> LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs(700)  
array([ 0.0098162...,  0.0037761...,  0.        ])

colour.colorimetry.spectral_to_XYZ(spd, cmfs=<colour.colorimetry.cmfs.XYZ_ColourMatchingFunctions object at 0x102cf5c10>, illuminant=None)¶

Converts given spectral power distribution to CIE XYZ colourspace using given colour matching functions and illuminant.

Parameters:	spd (SpectralPowerDistribution) – Spectral power distribution. cmfs (XYZ_ColourMatchingFunctions) – Standard observer colour matching functions. illuminant (SpectralPowerDistribution, optional) – Illuminant spectral power distribution.
Returns:	CIE XYZ colourspace matrix.
Return type:	ndarray, (3,)

Warning

The output domain of that definition is non standard!

Notes

Output CIE XYZ colourspace matrix is in domain [0, 100].

References

[1]	Wyszecki & Stiles, Color Science - Concepts and Methods Data and Formulae - Second Edition, Wiley Classics Library Edition, published 2000, ISBN-10: 0-471-39918-3, page 158.

Examples

>>> from colour import CMFS, ILLUMINANTS_RELATIVE_SPDS, SpectralPowerDistribution  
>>> cmfs = CMFS.get('CIE 1931 2 Degree Standard Observer')
>>> data = {380: 0.0600, 390: 0.0600}
>>> spd = SpectralPowerDistribution('Custom', data)
>>> illuminant = ILLUMINANTS_RELATIVE_SPDS.get('D50')
>>> spectral_to_XYZ(spd, cmfs, illuminant)  
array([  4.5764852...e-04,   1.2964866...e-05,   2.1615807...e-03])

colour.colorimetry.wavelength_to_XYZ(*args, **kwds)¶

Converts given wavelength \(\lambda\) to CIE XYZ colourspace using given colour matching functions.

If the wavelength \(\lambda\) is not available in the colour matching function, its value will be calculated using CIE recommendations: The method developed by Sprague (1880) should be used for interpolating functions having a uniformly spaced independent variable and a Cubic Spline method for non-uniformly spaced independent variable.

Parameters:	wavelength (numeric) – Wavelength \(\lambda\) in nm. cmfs (XYZ_ColourMatchingFunctions, optional) – Standard observer colour matching functions.
Returns:	CIE XYZ colourspace matrix.
Return type:	ndarray, (3,)
Raises:	`ValueError` – If wavelength \(\lambda\) is not in the colour matching functions domain.

Notes

Output CIE XYZ colourspace matrix is in domain [0, 1].
If scipy is not unavailable the Cubic Spline method will fallback to legacy Linear interpolation.

Examples

>>> from colour import CMFS
>>> cmfs = CMFS.get('CIE 1931 2 Degree Standard Observer')
>>> wavelength_to_XYZ(480)  
array([ 0.09564  ,  0.13902  ,  0.812950...])

colour.colorimetry Package¶

Sub-Packages¶

Sub-Modules¶

Module Contents¶