colour.colorimetry.spectrum Module¶

Spectrum¶

Defines the classes handling spectral data computation:

SpectralMapping
SpectralShape
SpectralPowerDistribution
TriSpectralPowerDistribution

See also

Spectrum IPython Notebook

colour.colorimetry.spectrum.DEFAULT_WAVELENGTH_DECIMALS = 10¶

Default wavelength precision decimals.

DEFAULT_WAVELENGTH_DECIMALS : int

class colour.colorimetry.spectrum.SpectralMapping(data=None, wavelength_decimals=10, **kwargs)[source]¶

Bases: colour.utilities.data_structures.ArbitraryPrecisionMapping

Defines the base mapping for spectral data.

It enables usage of floating point wavelengths as keys by rounding them at a specfic decimals count.

Parameters:

Parameters:	data (dict or SpectralMapping, optional) – Spectral data in a dict or SpectralMapping as follows: {wavelength \(\lambda_{i}\): value, wavelength \(\lambda_{i+1}\): value, ..., wavelength \(\lambda_{i+n}\): value} wavelength_decimals (int, optional) – Decimals count the keys will be rounded at. *kwargs (dict*) – Key / Value pairs to store into the mapping at initialisation.

data (dict or SpectralMapping, optional) – Spectral data in a dict or SpectralMapping as follows: {wavelength \(\lambda_{i}\): value, wavelength \(\lambda_{i+1}\): value, ..., wavelength \(\lambda_{i+n}\): value}
wavelength_decimals (int, optional) – Decimals count the keys will be rounded at.
**kwargs (dict) – Key / Value pairs to store into the mapping at initialisation.

wavelength_decimals[source]¶

Examples

>>> data1 = {380.1999999998: 0.000039, 380.2000000000: 0.000039}
>>> mapping = SpectralMapping(data1, wavelength_decimals=10)
>>> # Doctests skip for Python 2.x compatibility.
>>> tuple(mapping.keys())  
(380.1999999998, 380.2)
>>> mapping = SpectralMapping(data1, wavelength_decimals=7)
>>> # Doctests skip for Python 2.x compatibility.
>>> tuple(mapping.keys())  
(380.2,)

wavelength_decimals[source]

Property for self.key_decimals attribute.

Returns:	self.key_decimals.
Return type:	unicode

class colour.colorimetry.spectrum.SpectralShape(start=None, end=None, steps=None)[source]¶

Bases: object

Defines the base object for spectral power distribution shape.

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

start[source]¶

end[source]¶

steps[source]¶

__repr__()[source]¶

__contains__()[source]¶

__len__()[source]¶

__eq__()[source]¶

__ne__()[source]¶

range()[source]¶

Examples

>>> # Doctests skip for Python 2.x compatibility.
>>> SpectralShape(360, 830, 1)  
SpectralShape(360, 830, 1)

__contains__(wavelength)[source]

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

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

Warning

wavelength argument is tested to be contained in the spectral shape within the tolerance defined by colour.constants.common.EPSILON attribute value.

Notes

Reimplements the object.__contains__() method.

Examples

>>> 0.5 in SpectralShape(0, 10, 0.1)
True
>>> 0.6 in SpectralShape(0, 10, 0.1)
True
>>> 0.51 in SpectralShape(0, 10, 0.1)
False
>>> np.array([0.5, 0.6]) in SpectralShape(0, 10, 0.1)
True
>>> np.array([0.51, 0.6]) in SpectralShape(0, 10, 0.1)
False

__eq__(shape)[source]

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__()[source]¶

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
0
0
0
0
0
0
0
0
0
0

__len__()[source]

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

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

Notes

Reimplements the object.__len__() method.

Examples

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

__ne__(shape)[source]

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__()[source]

Returns a formatted string representation.

Returns:	Formatted string representation.
Return type:	unicode

__str__()[source]¶

Returns a nice formatted string representation.

Returns:	Nice formatted string representation.
Return type:	unicode

end[source]

Property for self.__end private attribute.

Returns:	self.__end.
Return type:	numeric

range()[source]

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[source]

Property for self.__start private attribute.

Returns:	self.__start.
Return type:	numeric

steps[source]

Property for self.__steps private attribute.

Returns:	self.__steps.
Return type:	numeric

class colour.colorimetry.spectrum.SpectralPowerDistribution(name, data, title=None)[source]¶

Bases: object

Defines the base object for spectral data computations.

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

Notes

Underlying spectral data is stored within a colour.SpectralMapping class mapping which implies that wavelengths keys will be rounded.

name[source]¶

data[source]¶

title[source]¶

wavelengths[source]¶

values[source]¶

items[source]¶

shape[source]¶

__init__()[source]¶

__getitem__()[source]¶

__setitem__()[source]¶

__iter__()[source]¶

__contains__()[source]¶

__len__()[source]¶

__eq__()[source]¶

__ne__()[source]¶

__add__()[source]¶

__sub__()[source]¶

__mul__()[source]¶

__div__()[source]¶

__truediv__()¶

__pow__()[source]¶

get()[source]¶

is_uniform()[source]¶

extrapolate()[source]¶

interpolate()[source]¶

align()[source]¶

zeros()[source]¶

normalise()[source]¶

clone()[source]¶

Examples

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

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)[source]

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

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

Warning

wavelength argument is tested to be contained in the spectral power distribution within the tolerance defined by colour.constants.common.EPSILON attribute value.

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
>>> np.array([510, 520]) in spd
True
>>> np.array([510, 520, 521]) in spd
False

__div__(x)[source]

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)[source]

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)[source]

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

Parameters:	wavelength (numeric, array_like or slice) – Wavelength \(\lambda\) to retrieve the value.
Returns:	Wavelength \(\lambda\) value.
Return type:	numeric or ndarray

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]  
array(49.67...)
>>> spd[np.array([510, 520])]
array([ 49.67,  69.59])
>>> spd[:]
array([ 49.67,  69.59,  81.73,  88.19])

__hash__()[source]¶

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]	Hettinger, R. (n.d.). Python hashable dicts. Retrieved August 08, 2014, from http://stackoverflow.com/a/16162138/931625

__iter__()[source]

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__()[source]

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)[source]

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)[source]

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

__pow__(x)[source]

Implements support for spectral power distribution exponentiation.

Parameters:	x (numeric or array_like or SpectralPowerDistribution) – Variable to exponentiate by.
Returns:	Spectral power distribution raised by power of x.
Return type:	SpectralPowerDistribution

Notes

Reimplements the object.__pow__() method.

Warning

The power operation happens in place.

Examples

Exponentiation by a single numeric variable:

>>> data = {510: 1.67, 520: 2.59, 530: 3.73, 540: 4.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd ** 2  
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([  2.7889,   6.7081,  13.9129,  17.5561])

Exponentiation by an array_like variable:

>>> spd ** [1, 2, 3, 4]  
<...SpectralPowerDistribution object at 0x...>
>>> spd.values  
array([  2.7889000...e+00,   4.4998605...e+01,   2.6931031...e+03,
         9.4997501...e+04])

Exponentiation by a SpectralPowerDistribution class variable:

>>> spd_alternate = SpectralPowerDistribution('Spd', data)
>>> spd ** spd_alternate  
<...SpectralPowerDistribution object at 0x...>
>>> spd.values  
array([  5.5446356...e+00,   1.9133109...e+04,   6.2351033...e+12,
         7.1880990...e+20])

__setitem__(wavelength, value)[source]

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

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

Warning

value parameter is resized to match wavelength parameter size.

Notes

Reimplements the object.__setitem__() method.

Examples

>>> spd = SpectralPowerDistribution('Spd', {})
>>> spd[510] = 49.67
>>> spd.values
array([ 49.67])
>>> spd[np.array([520, 530])] = np.array([69.59, 81.73])
>>> spd.values
array([ 49.67,  69.59,  81.73])
>>> spd[np.array([540, 550])] = 88.19
>>> spd.values
array([ 49.67,  69.59,  81.73,  88.19,  88.19])
>>> spd[:] = 49.67
>>> spd.values
array([ 49.67,  49.67,  49.67,  49.67,  49.67])

__sub__(x)[source]

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)[source]

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) – {‘Constant’, ‘Linear’}, 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     ...])

clone()[source]

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[source]

Property for self.__data private attribute.

Returns:	self.__data.
Return type:	SpectralMapping

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

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

Parameters:	shape (SpectralShape) – Spectral shape used for extrapolation. method (unicode, optional) – {‘Constant’, ‘Linear’},, 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 TC 1-48. (2004). Extrapolation. In CIE 015:2004 Colorimetry, 3rd Edition (p. 24). ISBN:978-3-901-90633-6

[3]	CIE TC 1-38. (2005). EXTRAPOLATION. In CIE 167:2005 Recommended Practice for Tabulating Spectral Data for Use in Colour Computations (pp. 19–20). ISBN: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...)
>>> spd[400]  
array(49.67...)
>>> spd[700]  
array(88.1...)

get(wavelength, default=nan)[source]

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

Parameters:	wavelength (numeric or ndarray) – Wavelength \(\lambda\) to retrieve the value. default (nan or numeric, optional) – Wavelength \(\lambda\) default value.
Returns:	Wavelength \(\lambda\) value.
Return type:	numeric or ndarray

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)  
array(49.67...)
>>> spd.get(511)
array(nan)
>>> spd.get(np.array([510, 520]))
array([ 49.67,  69.59])

interpolate(shape=SpectralShape(None, None, None), method=None)[source]

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) – {None, ‘Cubic Spline’, ‘Linear’, ‘Pchip’, ‘Sprague’}, Enforce given interpolation method.
Returns:	Interpolated spectral power distribution.
Return type:	SpectralPowerDistribution
Raises:	`RuntimeError` – If Sprague (1880) 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 (1880) 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.
Cubic Spline interpolator requires at least 3 wavelengths \(\lambda_n\) for interpolation.
Linear interpolator requires at least 2 wavelengths \(\lambda_n\) for interpolation.
Pchip interpolator requires at least 2 wavelengths \(\lambda_n\) for interpolation.
Sprague (1880) interpolator requires at least 6 wavelengths \(\lambda_n\) for interpolation.

References

[4]	CIE TC 1-38. (2005). 9. INTERPOLATION. In CIE 167:2005 Recommended Practice for Tabulating Spectral Data for Use in Colour Computations (pp. 14–19). ISBN:978-3-901-90641-1

Examples

Uniform data is using Sprague (1880) 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]  
array(60.3121800...)

Non uniform data is using Cubic Spline interpolation by default:

>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd[511] = 31.41
>>> spd.interpolate(SpectralShape(steps=1))  
<...SpectralPowerDistribution object at 0x...>
>>> spd[515]  
array(21.4792222...)

Enforcing Linear interpolation:

>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd.interpolate(  
...     SpectralShape(steps=1), method='Linear')
<...SpectralPowerDistribution object at 0x...>
>>> spd[515]  
array(59.63...)

Enforcing Pchip interpolation:

>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd.interpolate(  
...     SpectralShape(steps=1), method='Pchip')
<...SpectralPowerDistribution object at 0x...>
>>> spd[515]  
array(58.8173260...)

is_uniform()[source]

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[source]

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[source]

Property for self.__name private attribute.

Returns:	self.__name.
Return type:	unicode

normalise(factor=1)[source]

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[source]

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...)

title[source]

Property for self.__title private attribute.

Returns:	self.__title.
Return type:	unicode

values[source]

Property for self.values attribute.

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

Warning

SpectralPowerDistribution.values is read only.

wavelengths[source]

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))[source]

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
Raises:	`RuntimeError` – If the spectral power distribution cannot be zeros filled.

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.spectrum.TriSpectralPowerDistribution(name, data, mapping, title=None, labels=None)[source]¶

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 (unicode) – Tri-spectral power distribution name. data (dict) – Tri-spectral power distribution data. mapping (dict) – Tri-spectral power distribution attributes mapping. title (unicode, optional) – Tri-spectral power distribution title for figures. labels (dict, optional) – Tri-spectral power distribution axis labels mapping for figures.

name[source]¶

mapping[source]¶

data[source]¶

title[source]¶

labels[source]¶

x[source]¶

y[source]¶

z[source]¶

wavelengths[source]¶

values[source]¶

items[source]¶

shape[source]¶

__init__()[source]¶

__getitem__()[source]¶

__setitem__()[source]¶

__iter__()[source]¶

__contains__()[source]¶

__len__()[source]¶

__eq__()[source]¶

__ne__()[source]¶

__add__()[source]¶

__sub__()[source]¶

__mul__()[source]¶

__div__()[source]¶

__truediv__()¶

get()[source]¶

is_uniform()[source]¶

extrapolate()[source]¶

interpolate()[source]¶

align()[source]¶

zeros()[source]¶

normalise()[source]¶

clone()[source]¶

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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> # Doctests skip for Python 2.x compatibility.
>>> 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]])
>>> # Doctests skip for Python 2.x compatibility.
>>> tri_spd.shape  
SpectralShape(510, 540, 10)

__add__(x)[source]

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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> 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, mapping)
>>> 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)[source]

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

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

Warning

wavelength argument is tested to be contained in the tri-spectral power distribution within the tolerance defined by colour.constants.common.EPSILON attribute value.

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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> 510 in tri_spd
True
>>> np.array([510, 520]) in tri_spd
True
>>> np.array([510, 520, 521]) in tri_spd
False

__div__(x)[source]

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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> 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, mapping)
>>> 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)[source]

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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd1 = TriSpectralPowerDistribution('Tri Spd', data1, mapping)
>>> tri_spd2 = TriSpectralPowerDistribution('Tri Spd', data2, mapping)
>>> tri_spd3 = TriSpectralPowerDistribution('Tri Spd', data1, mapping)
>>> tri_spd1 == tri_spd2
False
>>> tri_spd1 == tri_spd3
True

__getitem__(wavelength)[source]

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

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

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}
>>> mapping  = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> tri_spd[510]
array([ 49.67,  90.56,  12.43])
>>> tri_spd[np.array([510, 520])]
array([[ 49.67,  90.56,  12.43],
       [ 69.59,  87.34,  23.15]])
>>> tri_spd[:]
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]])

__hash__()[source]¶

Returns the spectral power distribution hash value. [1]

Returns:	Object hash.
Return type:	int

Notes

Reimplements the object.__hash__() method.

Warning

See SpectralPowerDistribution.__hash__() method warning section.

__iter__()[source]

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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> # Doctests skip for Python 2.x compatibility.
>>> 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__()[source]

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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> len(tri_spd)
4

__mul__(x)[source]

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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> 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, mapping)
>>> 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)[source]

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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd1 = TriSpectralPowerDistribution('Tri Spd', data1, mapping)
>>> tri_spd2 = TriSpectralPowerDistribution('Tri Spd', data2, mapping)
>>> tri_spd3 = TriSpectralPowerDistribution('Tri Spd', data1, mapping)
>>> tri_spd1 != tri_spd2
True
>>> tri_spd1 != tri_spd3
False

__pow__(x)[source]¶

Implements support for tri-spectral power distribution exponentiation.

Parameters:	x (numeric or array_like or TriSpectralPowerDistribution) – Variable to exponentiate by.
Returns:	TriSpectral power distribution raised by power of x.
Return type:	TriSpectralPowerDistribution

Notes

Reimplements the object.__pow__() method.

Warning

The power operation happens in place.

Examples

Exponentiation by a single numeric variable:

>>> x_bar = {510: 1.67, 520: 1.59, 530: 1.73, 540: 1.19}
>>> y_bar = {510: 1.56, 520: 1.34, 530: 1.76, 540: 1.45}
>>> z_bar = {510: 1.43, 520: 1.15, 530: 1.98, 540: 1.28}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> tri_spd ** 1.1  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values  
array([[ 1.7578755...,  1.6309365...,  1.4820731...],
       [ 1.6654700...,  1.3797972...,  1.1661854...],
       [ 1.8274719...,  1.8623612...,  2.1199797...],
       [ 1.2108815...,  1.5048901...,  1.3119913...]])

Exponentiation by an array_like variable:

>>> tri_spd ** ([(1, 2, 3)] * 4)  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values  
array([[ 1.7578755...,  2.6599539...,  3.2554342...],
       [ 1.6654700...,  1.9038404...,  1.5859988...],
       [ 1.8274719...,  3.4683895...,  9.5278547...],
       [ 1.2108815...,  2.2646943...,  2.2583585...]])

Exponentiation by a TriSpectralPowerDistribution class variable:

>>> data1 = {'x_bar': z_bar, 'y_bar': x_bar, 'z_bar': y_bar}
>>> tri_spd1 = TriSpectralPowerDistribution('Tri Spd', data1, mapping)
>>> tri_spd ** tri_spd1  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values  
array([[  2.2404384...,   5.1231818...,   6.3047797...],
       [  1.7979075...,   2.7836369...,   1.8552645...],
       [  3.2996236...,   8.5984706...,  52.8483490...],
       [  1.2775271...,   2.6452177...,   3.2583647...]])

__setitem__(wavelength, value)[source]

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

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

Warning

value parameter is resized to match wavelength parameter size.

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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> tri_spd[510] = np.array([49.67, 49.67, 49.67])
>>> tri_spd.values
array([[ 49.67,  49.67,  49.67]])
>>> tri_spd[np.array([520, 530])] = np.array([[69.59, 69.59, 69.59],
...                                           [81.73, 81.73, 81.73]])
>>> tri_spd.values
array([[ 49.67,  49.67,  49.67],
       [ 69.59,  69.59,  69.59],
       [ 81.73,  81.73,  81.73]])
>>> tri_spd[np.array([540, 550])] = 88.19
>>> tri_spd.values
array([[ 49.67,  49.67,  49.67],
       [ 69.59,  69.59,  69.59],
       [ 81.73,  81.73,  81.73],
       [ 88.19,  88.19,  88.19],
       [ 88.19,  88.19,  88.19]])
>>> tri_spd[:] = 49.67
>>> tri_spd.values
array([[ 49.67,  49.67,  49.67],
       [ 49.67,  49.67,  49.67],
       [ 49.67,  49.67,  49.67],
       [ 49.67,  49.67,  49.67],
       [ 49.67,  49.67,  49.67]])

__sub__(x)[source]

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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> 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, mapping)
>>> 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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> 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, mapping)
>>> 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)[source]

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) – {‘Constant’, ‘Linear’}, 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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> 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()[source]

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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> print(tri_spd)  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd_clone = tri_spd.clone()
>>> print(tri_spd_clone)  
<...TriSpectralPowerDistribution object at 0x...>

data[source]

Property for self.__data private attribute.

Returns:	self.__data.
Return type:	dict

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

Extrapolates the tri-spectral power distribution following CIE 15:2004 recommendation. [2] [3]

Parameters:	shape (SpectralShape) – Spectral shape used for extrapolation. method (unicode, optional) – {‘Constant’, ‘Linear’}, 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()

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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> 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=nan)[source]

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

Parameters:	wavelength (numeric or array_like) – Wavelength \(\lambda\) to retrieve the values. default (nan, numeric or array_like, optional) – Wavelength \(\lambda\) default values.
Returns:	Wavelength \(\lambda\) values.
Return type:	numeric or array_like

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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> tri_spd.get(510)
array([ 49.67,  90.56,  12.43])
>>> tri_spd.get(np.array([510, 520]))
array([[ 49.67,  90.56,  12.43],
       [ 69.59,  87.34,  23.15]])
>>> tri_spd.get(511)
array([ nan,  nan,  nan])
>>> tri_spd.get(np.array([510, 520]))
array([[ 49.67,  90.56,  12.43],
       [ 69.59,  87.34,  23.15]])

interpolate(shape=SpectralShape(None, None, None), method=None)[source]

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. [4]

Parameters:	shape (SpectralShape, optional) – Spectral shape used for interpolation. method (unicode, optional) – {None, ‘Cubic Spline’, ‘Linear’, ‘Pchip’, ‘Sprague’}, 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.

Examples

Uniform data is using Sprague (1880) 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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> tri_spd.interpolate(SpectralShape(steps=1))  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd[515]  
array([ 60.3033208...,  93.2716331...,  13.8605136...])

Non uniform data is using Cubic Spline interpolation by default:

>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> tri_spd[511] = np.array([31.41, 95.27, 15.06])
>>> tri_spd.interpolate(SpectralShape(steps=1))  
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd[515]  
array([  21.4710405...,  100.6430015...,   18.8165196...])

Enforcing Linear interpolation:

>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> tri_spd.interpolate(  
...     SpectralShape(steps=1), method='Linear')
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd[515]  
array([ 59.63...,  88.95...,  17.79...])

Enforcing Pchip interpolation:

>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> tri_spd.interpolate(  
...     SpectralShape(steps=1), method='Pchip')
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd[515]  
array([ 58.8173260...,  89.4355596...,  16.4545683...])

is_uniform()[source]

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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> tri_spd.is_uniform()
True

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

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

items[source]

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[source]

Property for self.__labels private attribute.

Returns:	self.__labels.
Return type:	dict

mapping[source]

Property for self.__mapping private attribute.

Returns:	self.__mapping.
Return type:	dict

name[source]

Property for self.__name private attribute.

Returns:	self.__name.
Return type:	unicode

normalise(factor=1)[source]

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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> 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[source]

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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> tri_spd.shape  
SpectralShape(510..., 540..., 10...)

title[source]

Property for self.__title private attribute.

Returns:	self.__title.
Return type:	unicode

values[source]

Property for self.values attribute.

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

Warning

TriSpectralPowerDistribution.values is read only.

wavelengths[source]

Property for self.wavelengths attribute.

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

Warning

TriSpectralPowerDistribution.wavelengths is read only.

x[source]

Property for self.x attribute.

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

Warning

TriSpectralPowerDistribution.x is read only.

y[source]

Property for self.y attribute.

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

Warning

TriSpectralPowerDistribution.y is read only.

z[source]

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))[source]

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}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Tri Spd', data, mapping)
>>> 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.spectrum.DEFAULT_SPECTRAL_SHAPE = SpectralShape(360.0, 830.0, 1.0)¶

Default spectral shape using the shape of CIE 1931 2 Degree Standard Observer.

DEFAULT_SPECTRAL_SHAPE : SpectralShape

colour.colorimetry.spectrum.constant_spd(k, shape=SpectralShape(360.0, 830.0, 1.0))[source]¶

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(360.0, 830.0, 1.0)
>>> spd[400]
array(100.0)

colour.colorimetry.spectrum.zeros_spd(shape=SpectralShape(360.0, 830.0, 1.0))[source]¶

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(360.0, 830.0, 1.0)
>>> spd[400]
array(0.0)

colour.colorimetry.spectrum.ones_spd(shape=SpectralShape(360.0, 830.0, 1.0))[source]¶

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(360.0, 830.0, 1.0)
>>> spd[400]
array(1.0)