colour.colorimetry Package¶
Sub-Packages¶
- colour.colorimetry.dataset Package
- Sub-Packages
- colour.colorimetry.dataset.illuminants Package
- colour.colorimetry.dataset.light_sources Package
- Sub-Modules
- Module Contents
- Sub-Packages
Sub-Modules¶
- colour.colorimetry.blackbody Module
- colour.colorimetry.cmfs Module
- colour.colorimetry.correction Module
- colour.colorimetry.dominant Module
- colour.colorimetry.illuminants Module
- colour.colorimetry.lefs Module
- colour.colorimetry.lightness Module
- colour.colorimetry.luminance Module
- colour.colorimetry.photometry Module
- colour.colorimetry.spectrum Module
- colour.colorimetry.transformations Module
- colour.colorimetry.tristimulus Module
- colour.colorimetry.whiteness Module
- colour.colorimetry.yellowness Module
Module Contents¶
-
class
colour.colorimetry.
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 specific decimals count.
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.
Other Parameters: **kwargs (dict, optional) – Key / Value pairs to store into the mapping at initialisation.
-
wavelength_decimals
¶
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()) (380.1999999..., 380.2) >>> mapping = SpectralMapping(data1, wavelength_decimals=7) >>> # Doctests skip for Python 2.x compatibility. >>> tuple(mapping.keys()) (380.2,)
-
wavelength_decimals
Property for self.key_decimals attribute.
Returns: self.key_decimals. Return type: unicode
-
class
colour.colorimetry.
SpectralShape
(start=None, end=None, interval=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.
- interval (numeric, optional) – Wavelength \(\lambda_{i}\) range interval.
-
start
¶
-
end
¶
-
interval
¶
-
boundaries
¶
Examples
>>> # Doctests skip for Python 2.x compatibility. >>> SpectralShape(360, 830, 1) SpectralShape(360, 830, 1)
-
boundaries
Property for self._start and self._end private attributes.
Returns: self._start, self._end. Return type: tuple
-
end
Property for self._end private attribute.
Returns: self._end. Return type: numeric
-
interval
Property for self._interval private attribute.
Returns: self._interval. 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 interval 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
-
class
colour.colorimetry.
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
¶
-
data
¶
-
title
¶
-
wavelengths
¶
-
values
¶
-
items
¶
-
shape
¶
Examples
>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19} >>> spd = SpectralPowerDistribution('Sample', 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)
-
align
(shape, interpolation_method=None, extrapolation_method=u'Constant', extrapolation_left=None, extrapolation_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.
- interpolation_method (unicode, optional) – {None, ‘Cubic Spline’, ‘Linear’, ‘Pchip’, ‘Sprague’}, Enforce given interpolation method.
- extrapolation_method (unicode, optional) – {‘Constant’, ‘Linear’}, Extrapolation method.
- extrapolation_left (numeric, optional) – Value to return for low extrapolation range.
- extrapolation_right (numeric, optional) – Value to return for high extrapolation range.
Returns: Aligned spectral power distribution.
Return type: Examples
>>> data = { ... 510: 49.67, ... 520: 69.59, ... 530: 81.73, ... 540: 88.19, ... 550: 86.26, ... 560: 77.18} >>> spd = SpectralPowerDistribution('Sample', data) >>> print(spd.align(SpectralShape(505, 565, 1))) SpectralPowerDistribution('Sample', (505.0, 565.0, 1.0)) >>> # 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. TheSpectralPowerDistribution.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('Sample', data) >>> print(spd) SpectralPowerDistribution('Sample', (510..., 540..., 10...)) >>> spd_clone = spd.clone() >>> print(spd_clone) SpectralPowerDistribution('Sample (...)', (510..., 540..., 10...))
-
data
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: See also
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('Sample', 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('Sample', 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: 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
Notes
- Interpolation will be conducted over boundaries range, if you need
to extend the range of the spectral power distribution use the
SpectralPowerDistribution.extrapolate()
orSpectralPowerDistribution.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('Sample', data) >>> print(spd.interpolate(SpectralShape(interval=1))) SpectralPowerDistribution('Sample', (510.0, 560.0, 1.0)) >>> spd[515] array(60.3121800...)
Non uniform data is using Cubic Spline interpolation by default:
>>> spd = SpectralPowerDistribution('Sample', data) >>> spd[511] = 31.41 >>> print(spd.interpolate(SpectralShape(interval=1))) SpectralPowerDistribution('Sample', (510.0, 560.0, 1.0)) >>> spd[515] array(21.4835799...)
Enforcing Linear interpolation:
>>> spd = SpectralPowerDistribution('Sample', data) >>> print(spd.interpolate( ... SpectralShape(interval=1), method='Linear')) SpectralPowerDistribution('Sample', (510.0, 560.0, 1.0)) >>> spd[515] array(59.63...)
Enforcing Pchip interpolation:
>>> spd = SpectralPowerDistribution('Sample', data) >>> print(spd.interpolate( ... SpectralShape(interval=1), method='Pchip')) SpectralPowerDistribution('Sample', (510.0, 560.0, 1.0)) >>> spd[515] array(60.7204982...)
-
is_uniform
()[source] Returns if the spectral power distribution has uniformly spaced data.
Returns: Is uniform. Return type: bool See also
Examples
>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19} >>> spd = SpectralPowerDistribution('Sample', data) >>> spd.is_uniform() True
Breaking the interval 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. Return type: ndarray
-
name
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('Sample', data) >>> print(spd.normalise()) SpectralPowerDistribution('Sample', (510..., 540..., 10...)) >>> 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
Notes
- A non uniform spectral power distribution may will have multiple
different interval, in that case
SpectralPowerDistribution.shape
returns the minimum interval 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( ... 'Sample', 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( ... 'Sample', data).shape SpectralShape(512.3, 545.7, 7...)
- A non uniform spectral power distribution may will have multiple
different interval, in that case
-
title
Property for self._title private attribute.
Returns: self._title. Return type: unicode
-
trim_wavelengths
(shape)[source] Trims the spectral power distribution wavelengths to given spectral shape.
Parameters: shape (SpectralShape) – Spectral shape used for trimming. Returns: Trimed 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('Sample', data) >>> print(spd.trim_wavelengths( ... SpectralShape(520, 550, 10))) SpectralPowerDistribution('Sample', (520.0, 550.0, 10.0)) >>> # Doctests skip for Python 2.x compatibility. >>> spd.wavelengths array([ 520., 530., 540., 550.])
-
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))[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('Sample', data) >>> print(spd.zeros(SpectralShape(505, 565, 1))) SpectralPowerDistribution('Sample', (505.0, 565.0, 1.0)) >>> 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, 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
¶
-
mapping
¶
-
data
¶
-
title
¶
-
labels
¶
-
x
¶
-
y
¶
-
z
¶
-
wavelengths
¶
-
values
¶
-
items
¶
-
shape
¶
See also
colour.colorimetry.cmfs.LMS_ConeFundamentals
,colour.colorimetry.cmfs.RGB_ColourMatchingFunctions
,colour.colorimetry.cmfs.XYZ_ColourMatchingFunctions
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('Observer', 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)
-
align
(shape, interpolation_method=None, extrapolation_method=u'Constant', extrapolation_left=None, extrapolation_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.
- interpolation_method (unicode, optional) – {None, ‘Cubic Spline’, ‘Linear’, ‘Pchip’, ‘Sprague’}, Enforce given interpolation method.
- extrapolation_method (unicode, optional) – {‘Constant’, ‘Linear’}, Extrapolation method.
- extrapolation_left (numeric, optional) – Value to return for low extrapolation range.
- extrapolation_right (numeric, optional) – Value to return for high extrapolation range.
Returns: Aligned tri-spectral power distribution.
Return type: 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('Observer', data, mapping) >>> print(tri_spd.align(SpectralShape(505, 565, 1))) TriSpectralPowerDistribution('Observer', (505.0, 565.0, 1.0)) >>> # 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. TheTriSpectralPowerDistribution.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('Observer', data, mapping) >>> print(tri_spd) TriSpectralPowerDistribution('Observer', (510..., 560..., 10...)) >>> tri_spd_clone = tri_spd.clone() >>> print(tri_spd_clone) TriSpectralPowerDistribution('Observer (...)', (510..., 560..., 10...))
-
data
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: See also
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('Observer', 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('Observer', 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: See also
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('Observer', data, mapping) >>> print(tri_spd.interpolate(SpectralShape(interval=1))) TriSpectralPowerDistribution('Observer', (510.0, 560.0, 1.0)) >>> 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('Observer', data, mapping) >>> tri_spd[511] = np.array([31.41, 95.27, 15.06]) >>> print(tri_spd.interpolate(SpectralShape(interval=1))) TriSpectralPowerDistribution('Observer', (510.0, 560.0, 1.0)) >>> tri_spd[515] array([ 21.4752929..., 100.6436744..., 18.8153985...])
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('Observer', data, mapping) >>> print(tri_spd.interpolate( ... SpectralShape(interval=1), method='Linear')) TriSpectralPowerDistribution('Observer', (510.0, 560.0, 1.0)) >>> 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('Observer', data, mapping) >>> print(tri_spd.interpolate( ... SpectralShape(interval=1), method='Pchip')) TriSpectralPowerDistribution('Observer', (510.0, 560.0, 1.0)) >>> tri_spd[515] array([ 60.7204982..., 89.6971406..., 15.6271845...])
-
is_uniform
()[source] Returns if the tri-spectral power distribution has uniformly spaced data.
Returns: Is uniform. Return type: bool See also
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('Observer', data, mapping) >>> tri_spd.is_uniform() True
Breaking the interval by introducing new wavelength \(\lambda\) values:
>>> tri_spd[511] = np.array([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.
Notes
- In contrast to
SpectralPowerDistribution.items()
method,TriSpectralPowerDistribution.items()
returns a list as it is not possible to convert its output to a ndarray. It is possible to separate wavelengths from data by unpacking the return value into the zip definition as follows: zip(*tri_spd.items).
Returns: Tri-spectral power distribution data. Return type: list - In contrast to
-
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: 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('Observer', data, mapping) >>> print(tri_spd.normalise()) TriSpectralPowerDistribution('Observer', (510..., 560..., 10...)) >>> 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} >>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'} >>> tri_spd = TriSpectralPowerDistribution('Observer', data, mapping) >>> tri_spd.shape SpectralShape(510..., 540..., 10...)
-
title
Property for self._title private attribute.
Returns: self._title. Return type: unicode
-
trim_wavelengths
(shape)[source] Trims the tri-spectral power distribution wavelengths to given shape.
Parameters: shape (SpectralShape) – Spectral shape used for trimming. Returns: Trimmed 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('Observer', data, mapping) >>> # Doctests skip for Python 2.x compatibility. >>> print(tri_spd.trim_wavelengths( ... SpectralShape(520, 550, 10))) TriSpectralPowerDistribution('Observer', (520.0, 550.0, 10.0)) >>> tri_spd.wavelengths array([ 520., 530., 540., 550.])
-
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))[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('Observer', data, mapping) >>> print(tri_spd.zeros(SpectralShape(505, 565, 1))) TriSpectralPowerDistribution('Observer', (505.0, 565.0, 1.0)) >>> 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(360.0, 780.0, 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: 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, 780.0, 1.0) >>> spd[400] array(100.0)
-
colour.colorimetry.
zeros_spd
(shape=SpectralShape(360.0, 780.0, 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
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, 780.0, 1.0) >>> spd[400] array(0.0)
- By default, the spectral power distribution will use the shape given
by
-
colour.colorimetry.
ones_spd
(shape=SpectralShape(360.0, 780.0, 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
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, 780.0, 1.0) >>> spd[400] array(1.0)
- By default, the spectral power distribution will use the shape given
by
-
colour.colorimetry.
blackbody_spd
(temperature, shape=SpectralShape(360.0, 780.0, 1.0), c1=3.741771e-16, c2=0.014388, n=1)[source]¶ 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: Examples
>>> from colour import STANDARD_OBSERVERS_CMFS >>> cmfs = STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer'] >>> print(blackbody_spd(5000, cmfs.shape)) SpectralPowerDistribution('5000K Blackbody', (360.0, 830.0, 1.0))
-
colour.colorimetry.
blackbody_spectral_radiance
(wavelength, temperature, c1=3.741771e-16, c2=0.014388, n=1)¶ Returns the spectral radiance of a blackbody at thermodynamic temperature \(T[K]\) in a medium having index of refraction \(n\).
Parameters: - wavelength (numeric or array_like) – Wavelength in meters.
- temperature (numeric or array_like) – Temperature \(T[K]\) in kelvin degrees.
- c1 (numeric or array_like, 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 or array_like, 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 or array_like, 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 or ndarray
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 TC 1-48. (2004). APPENDIX E. INFORMATION ON THE USE OF PLANCK’S EQUATION FOR STANDARD AIR. In CIE 015:2004 Colorimetry, 3rd Edition (pp. 77–82). ISBN:978-3-901-90633-6 Examples
>>> # Doctests ellipsis for Python 2.x compatibility. >>> planck_law(500 * 1e-9, 5500) 20472701909806.5...
-
colour.colorimetry.
planck_law
(wavelength, temperature, c1=3.741771e-16, c2=0.014388, n=1)[source]¶ Returns the spectral radiance of a blackbody at thermodynamic temperature \(T[K]\) in a medium having index of refraction \(n\).
Parameters: - wavelength (numeric or array_like) – Wavelength in meters.
- temperature (numeric or array_like) – Temperature \(T[K]\) in kelvin degrees.
- c1 (numeric or array_like, 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 or array_like, 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 or array_like, 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 or ndarray
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 TC 1-48. (2004). APPENDIX E. INFORMATION ON THE USE OF PLANCK’S EQUATION FOR STANDARD AIR. In CIE 015:2004 Colorimetry, 3rd Edition (pp. 77–82). ISBN:978-3-901-90633-6 Examples
>>> # Doctests ellipsis for Python 2.x compatibility. >>> planck_law(500 * 1e-9, 5500) 20472701909806.5...
-
class
colour.colorimetry.
LMS_ConeFundamentals
(name, data, title=None)[source]¶ Bases:
colour.colorimetry.spectrum.TriSpectralPowerDistribution
Implements support for the Stockman and Sharpe LMS cone fundamentals colour matching functions.
Parameters: - name (unicode) – LMS colour matching functions name.
- data (dict) – LMS colour matching functions.
- title (unicode, optional) – LMS colour matching functions title for figures.
-
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, title=None)[source]¶ 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.
- title (unicode, optional) – CIE RGB colour matching functions title for figures.
-
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, title=None)[source]¶ 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.
- title (unicode, optional) – CIE Standard Observer XYZ colour matching functions title for figures.
-
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')[source]¶ Implements spectral bandpass dependence correction on given spectral power distribution using given method.
Parameters: - spd (SpectralPowerDistribution) – Spectral power distribution.
- method (unicode, optional) – (‘Stearns 1988’, ) Correction method.
Returns: Spectral bandpass dependence corrected spectral power distribution.
Return type:
-
colour.colorimetry.
bandpass_correction_Stearns1988
(spd)[source]¶ Implements spectral bandpass dependence correction on given spectral power distribution using Stearns and Stearns (1988) method.
Parameters: spd (SpectralPowerDistribution) – Spectral power distribution. Returns: Spectral bandpass dependence corrected spectral power distribution. Return type: SpectralPowerDistribution References
[1] Westland, S., Ripamonti, C., & Cheung, V. (2012). Correction for Spectral Bandpass. In Computational Colour Science Using MATLAB (2nd ed., p. 38). ISBN:978-0-470-66569-5 [2] Stearns, E. I., & Stearns, R. E. (1988). An example of a method for correcting radiance data for Bandpass error. Color Research & Application, 13(4), 257–259. doi:10.1002/col.5080130410 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)[source]¶ Returns the relative spectral power distribution of given CIE Standard Illuminant D Series using given xy chromaticity coordinates.
References
[1] Wyszecki, G., & Stiles, W. S. (2000). CIE Method of Calculating D-Illuminants. In Color Science: Concepts and Methods, Quantitative Data and Formulae (pp. 145–146). Wiley. ISBN:978-0471399186 [2] Lindbloom, B. (2007). Spectral Power Distribution of a CIE D-Illuminant. Retrieved April 05, 2014, from http://www.brucelindbloom.com/Eqn_DIlluminant.html Parameters: xy (array_like) – xy chromaticity coordinates. Returns: CIE Standard Illuminant D Series relative spectral power distribution. Return type: SpectralPowerDistribution Examples
>>> xy = np.array([0.34570, 0.35850]) >>> print(D_illuminant_relative_spd(xy)) SpectralPowerDistribution('CIE Standard Illuminant D Series', (300.0, 830.0, 10.0))
-
colour.colorimetry.
CIE_standard_illuminant_A_function
(wl)[source]¶ CIE Standard Illuminant A is intended to represent typical, domestic, tungsten-filament lighting. Its relative spectral power distribution is that of a Planckian radiator at a temperature of approximately 2856 K. CIE Standard Illuminant A should be used in all applications of colorimetry involving the use of incandescent lighting, unless there are specific reasons for using a different illuminant.
Parameters: wl (array_like) – Wavelength to evaluate the function at. Returns: CIE Standard Illuminant A value at given wavelength. Return type: ndarray References
[1] CIE TC 1-48. (2004). 3.1 Recommendations concerning standard physical data of illuminants. In CIE 015:2004 Colorimetry, 3rd Edition (pp. 12–13). ISBN:978-3-901-90633-6 Examples
>>> wl = np.array([560, 580, 581.5]) >>> CIE_standard_illuminant_A_function(wl) array([ 100. , 114.4363383..., 115.5285063...])
-
colour.colorimetry.
mesopic_luminous_efficiency_function
(Lp, source=u'Blue Heavy', method=u'MOVE', photopic_lef=SpectralPowerDistribution( 'CIE 1924 Photopic Standard Observer', {360.0: 3.917e-06, 361.0: 4.393581e-06, 362.0: 4.929604e-06, 363.0: 5.532136e-06, 364.0: 6.208245e-06, 365.0: 6.965e-06, 366.0: 7.813219e-06, 367.0: 8.767336e-06, 368.0: 9.839844e-06, 369.0: 1.104323e-05, 370.0: 1.239e-05, 371.0: 1.388641e-05, 372.0: 1.555728e-05, 373.0: 1.744296e-05, 374.0: 1.958375e-05, 375.0: 2.202e-05, 376.0: 2.483965e-05, 377.0: 2.804126e-05, 378.0: 3.153104e-05, 379.0: 3.521521e-05, 380.0: 3.9e-05, 381.0: 4.28264e-05, 382.0: 4.69146e-05, 383.0: 5.15896e-05, 384.0: 5.71764e-05, 385.0: 6.4e-05, 386.0: 7.234421e-05, 387.0: 8.221224e-05, 388.0: 9.350816e-05, 389.0: 0.0001061361, 390.0: 0.00012, 391.0: 0.000134984, 392.0: 0.000151492, 393.0: 0.000170208, 394.0: 0.000191816, 395.0: 0.000217, 396.0: 0.0002469067, 397.0: 0.00028124, 398.0: 0.00031852, 399.0: 0.0003572667, 400.0: 0.000396, 401.0: 0.0004337147, 402.0: 0.000473024, 403.0: 0.000517876, 404.0: 0.0005722187, 405.0: 0.00064, 406.0: 0.00072456, 407.0: 0.0008255, 408.0: 0.00094116, 409.0: 0.00106988, 410.0: 0.00121, 411.0: 0.001362091, 412.0: 0.001530752, 413.0: 0.001720368, 414.0: 0.001935323, 415.0: 0.00218, 416.0: 0.0024548, 417.0: 0.002764, 418.0: 0.0031178, 419.0: 0.0035264, 420.0: 0.004, 421.0: 0.00454624, 422.0: 0.00515932, 423.0: 0.00582928, 424.0: 0.00654616, 425.0: 0.0073, 426.0: 0.008086507, 427.0: 0.00890872, 428.0: 0.00976768, 429.0: 0.01066443, 430.0: 0.0116, 431.0: 0.01257317, 432.0: 0.01358272, 433.0: 0.01462968, 434.0: 0.01571509, 435.0: 0.01684, 436.0: 0.01800736, 437.0: 0.01921448, 438.0: 0.02045392, 439.0: 0.02171824, 440.0: 0.023, 441.0: 0.02429461, 442.0: 0.02561024, 443.0: 0.02695857, 444.0: 0.02835125, 445.0: 0.0298, 446.0: 0.03131083, 447.0: 0.03288368, 448.0: 0.03452112, 449.0: 0.03622571, 450.0: 0.038, 451.0: 0.03984667, 452.0: 0.041768, 453.0: 0.043766, 454.0: 0.04584267, 455.0: 0.048, 456.0: 0.05024368, 457.0: 0.05257304, 458.0: 0.05498056, 459.0: 0.05745872, 460.0: 0.06, 461.0: 0.06260197, 462.0: 0.06527752, 463.0: 0.06804208, 464.0: 0.07091109, 465.0: 0.0739, 466.0: 0.077016, 467.0: 0.0802664, 468.0: 0.0836668, 469.0: 0.0872328, 470.0: 0.09098, 471.0: 0.09491755, 472.0: 0.09904584, 473.0: 0.1033674, 474.0: 0.1078846, 475.0: 0.1126, 476.0: 0.117532, 477.0: 0.1226744, 478.0: 0.1279928, 479.0: 0.1334528, 480.0: 0.13902, 481.0: 0.1446764, 482.0: 0.1504693, 483.0: 0.1564619, 484.0: 0.1627177, 485.0: 0.1693, 486.0: 0.1762431, 487.0: 0.1835581, 488.0: 0.1912735, 489.0: 0.199418, 490.0: 0.20802, 491.0: 0.2171199, 492.0: 0.2267345, 493.0: 0.2368571, 494.0: 0.2474812, 495.0: 0.2586, 496.0: 0.2701849, 497.0: 0.2822939, 498.0: 0.2950505, 499.0: 0.308578, 500.0: 0.323, 501.0: 0.3384021, 502.0: 0.3546858, 503.0: 0.3716986, 504.0: 0.3892875, 505.0: 0.4073, 506.0: 0.4256299, 507.0: 0.4443096, 508.0: 0.4633944, 509.0: 0.4829395, 510.0: 0.503, 511.0: 0.5235693, 512.0: 0.544512, 513.0: 0.56569, 514.0: 0.5869653, 515.0: 0.6082, 516.0: 0.6293456, 517.0: 0.6503068, 518.0: 0.6708752, 519.0: 0.6908424, 520.0: 0.71, 521.0: 0.7281852, 522.0: 0.7454636, 523.0: 0.7619694, 524.0: 0.7778368, 525.0: 0.7932, 526.0: 0.8081104, 527.0: 0.8224962, 528.0: 0.8363068, 529.0: 0.8494916, 530.0: 0.862, 531.0: 0.8738108, 532.0: 0.8849624, 533.0: 0.8954936, 534.0: 0.9054432, 535.0: 0.9148501, 536.0: 0.9237348, 537.0: 0.9320924, 538.0: 0.9399226, 539.0: 0.9472252, 540.0: 0.954, 541.0: 0.9602561, 542.0: 0.9660074, 543.0: 0.9712606, 544.0: 0.9760225, 545.0: 0.9803, 546.0: 0.9840924, 547.0: 0.9874182, 548.0: 0.9903128, 549.0: 0.9928116, 550.0: 0.9949501, 551.0: 0.9967108, 552.0: 0.9980983, 553.0: 0.999112, 554.0: 0.9997482, 555.0: 1.0, 556.0: 0.9998567, 557.0: 0.9993046, 558.0: 0.9983255, 559.0: 0.9968987, 560.0: 0.995, 561.0: 0.9926005, 562.0: 0.9897426, 563.0: 0.9864444, 564.0: 0.9827241, 565.0: 0.9786, 566.0: 0.9740837, 567.0: 0.9691712, 568.0: 0.9638568, 569.0: 0.9581349, 570.0: 0.952, 571.0: 0.9454504, 572.0: 0.9384992, 573.0: 0.9311628, 574.0: 0.9234576, 575.0: 0.9154, 576.0: 0.9070064, 577.0: 0.8982772, 578.0: 0.8892048, 579.0: 0.8797816, 580.0: 0.87, 581.0: 0.8598613, 582.0: 0.849392, 583.0: 0.838622, 584.0: 0.8275813, 585.0: 0.8163, 586.0: 0.8047947, 587.0: 0.793082, 588.0: 0.781192, 589.0: 0.7691547, 590.0: 0.757, 591.0: 0.7447541, 592.0: 0.7324224, 593.0: 0.7200036, 594.0: 0.7074965, 595.0: 0.6949, 596.0: 0.6822192, 597.0: 0.6694716, 598.0: 0.6566744, 599.0: 0.6438448, 600.0: 0.631, 601.0: 0.6181555, 602.0: 0.6053144, 603.0: 0.5924756, 604.0: 0.5796379, 605.0: 0.5668, 606.0: 0.5539611, 607.0: 0.5411372, 608.0: 0.5283528, 609.0: 0.5156323, 610.0: 0.503, 611.0: 0.4904688, 612.0: 0.4780304, 613.0: 0.4656776, 614.0: 0.4534032, 615.0: 0.4412, 616.0: 0.42908, 617.0: 0.417036, 618.0: 0.405032, 619.0: 0.393032, 620.0: 0.381, 621.0: 0.3689184, 622.0: 0.3568272, 623.0: 0.3447768, 624.0: 0.3328176, 625.0: 0.321, 626.0: 0.3093381, 627.0: 0.2978504, 628.0: 0.2865936, 629.0: 0.2756245, 630.0: 0.265, 631.0: 0.2547632, 632.0: 0.2448896, 633.0: 0.2353344, 634.0: 0.2260528, 635.0: 0.217, 636.0: 0.2081616, 637.0: 0.1995488, 638.0: 0.1911552, 639.0: 0.1829744, 640.0: 0.175, 641.0: 0.1672235, 642.0: 0.1596464, 643.0: 0.1522776, 644.0: 0.1451259, 645.0: 0.1382, 646.0: 0.1315003, 647.0: 0.1250248, 648.0: 0.1187792, 649.0: 0.1127691, 650.0: 0.107, 651.0: 0.1014762, 652.0: 0.09618864, 653.0: 0.09112296, 654.0: 0.08626485, 655.0: 0.0816, 656.0: 0.07712064, 657.0: 0.07282552, 658.0: 0.06871008, 659.0: 0.06476976, 660.0: 0.061, 661.0: 0.05739621, 662.0: 0.05395504, 663.0: 0.05067376, 664.0: 0.04754965, 665.0: 0.04458, 666.0: 0.04175872, 667.0: 0.03908496, 668.0: 0.03656384, 669.0: 0.03420048, 670.0: 0.032, 671.0: 0.02996261, 672.0: 0.02807664, 673.0: 0.02632936, 674.0: 0.02470805, 675.0: 0.0232, 676.0: 0.02180077, 677.0: 0.02050112, 678.0: 0.01928108, 679.0: 0.01812069, 680.0: 0.017, 681.0: 0.01590379, 682.0: 0.01483718, 683.0: 0.01381068, 684.0: 0.01283478, 685.0: 0.01192, 686.0: 0.01106831, 687.0: 0.01027339, 688.0: 0.009533311, 689.0: 0.008846157, 690.0: 0.00821, 691.0: 0.007623781, 692.0: 0.007085424, 693.0: 0.006591476, 694.0: 0.006138485, 695.0: 0.005723, 696.0: 0.005343059, 697.0: 0.004995796, 698.0: 0.004676404, 699.0: 0.004380075, 700.0: 0.004102, 701.0: 0.003838453, 702.0: 0.003589099, 703.0: 0.003354219, 704.0: 0.003134093, 705.0: 0.002929, 706.0: 0.002738139, 707.0: 0.002559876, 708.0: 0.002393244, 709.0: 0.002237275, 710.0: 0.002091, 711.0: 0.001953587, 712.0: 0.00182458, 713.0: 0.00170358, 714.0: 0.001590187, 715.0: 0.001484, 716.0: 0.001384496, 717.0: 0.001291268, 718.0: 0.001204092, 719.0: 0.001122744, 720.0: 0.001047, 721.0: 0.0009765896, 722.0: 0.0009111088, 723.0: 0.0008501332, 724.0: 0.0007932384, 725.0: 0.00074, 726.0: 0.0006900827, 727.0: 0.00064331, 728.0: 0.000599496, 729.0: 0.0005584547, 730.0: 0.00052, 731.0: 0.0004839136, 732.0: 0.0004500528, 733.0: 0.0004183452, 734.0: 0.0003887184, 735.0: 0.0003611, 736.0: 0.0003353835, 737.0: 0.0003114404, 738.0: 0.0002891656, 739.0: 0.0002684539, 740.0: 0.0002492, 741.0: 0.0002313019, 742.0: 0.0002146856, 743.0: 0.0001992884, 744.0: 0.0001850475, 745.0: 0.0001719, 746.0: 0.0001597781, 747.0: 0.0001486044, 748.0: 0.0001383016, 749.0: 0.0001287925, 750.0: 0.00012, 751.0: 0.0001118595, 752.0: 0.0001043224, 753.0: 9.73356e-05, 754.0: 9.084587e-05, 755.0: 8.48e-05, 756.0: 7.914667e-05, 757.0: 7.3858e-05, 758.0: 6.8916e-05, 759.0: 6.430267e-05, 760.0: 6e-05, 761.0: 5.598187e-05, 762.0: 5.22256e-05, 763.0: 4.87184e-05, 764.0: 4.544747e-05, 765.0: 4.24e-05, 766.0: 3.956104e-05, 767.0: 3.691512e-05, 768.0: 3.444868e-05, 769.0: 3.214816e-05, 770.0: 3e-05, 771.0: 2.799125e-05, 772.0: 2.611356e-05, 773.0: 2.436024e-05, 774.0: 2.272461e-05, 775.0: 2.12e-05, 776.0: 1.977855e-05, 777.0: 1.845285e-05, 778.0: 1.721687e-05, 779.0: 1.606459e-05, 780.0: 1.499e-05, 781.0: 1.398728e-05, 782.0: 1.305155e-05, 783.0: 1.217818e-05, 784.0: 1.136254e-05, 785.0: 1.06e-05, 786.0: 9.885877e-06, 787.0: 9.217304e-06, 788.0: 8.592362e-06, 789.0: 8.009133e-06, 790.0: 7.4657e-06, 791.0: 6.959567e-06, 792.0: 6.487995e-06, 793.0: 6.048699e-06, 794.0: 5.639396e-06, 795.0: 5.2578e-06, 796.0: 4.901771e-06, 797.0: 4.56972e-06, 798.0: 4.260194e-06, 799.0: 3.971739e-06, 800.0: 3.7029e-06, 801.0: 3.452163e-06, 802.0: 3.218302e-06, 803.0: 3.0003e-06, 804.0: 2.797139e-06, 805.0: 2.6078e-06, 806.0: 2.43122e-06, 807.0: 2.266531e-06, 808.0: 2.113013e-06, 809.0: 1.969943e-06, 810.0: 1.8366e-06, 811.0: 1.71223e-06, 812.0: 1.596228e-06, 813.0: 1.48809e-06, 814.0: 1.387314e-06, 815.0: 1.2934e-06, 816.0: 1.20582e-06, 817.0: 1.124143e-06, 818.0: 1.048009e-06, 819.0: 9.770578e-07, 820.0: 9.1093e-07, 821.0: 8.492513e-07, 822.0: 7.917212e-07, 823.0: 7.380904e-07, 824.0: 6.881098e-07, 825.0: 6.4153e-07, 826.0: 5.980895e-07, 827.0: 5.575746e-07, 828.0: 5.19808e-07, 829.0: 4.846123e-07, 830.0: 4.5181e-07}), scotopic_lef=SpectralPowerDistribution( 'CIE 1951 Scotopic Standard Observer', {380.0: 0.000589, 381.0: 0.000665, 382.0: 0.000752, 383.0: 0.000854, 384.0: 0.000972, 385.0: 0.001108, 386.0: 0.001268, 387.0: 0.001453, 388.0: 0.001668, 389.0: 0.001918, 390.0: 0.002209, 391.0: 0.002547, 392.0: 0.002939, 393.0: 0.003394, 394.0: 0.003921, 395.0: 0.00453, 396.0: 0.00524, 397.0: 0.00605, 398.0: 0.00698, 399.0: 0.00806, 400.0: 0.00929, 401.0: 0.0107, 402.0: 0.01231, 403.0: 0.01413, 404.0: 0.01619, 405.0: 0.01852, 406.0: 0.02113, 407.0: 0.02405, 408.0: 0.0273, 409.0: 0.03089, 410.0: 0.03484, 411.0: 0.03916, 412.0: 0.0439, 413.0: 0.049, 414.0: 0.0545, 415.0: 0.0604, 416.0: 0.0668, 417.0: 0.0736, 418.0: 0.0808, 419.0: 0.0885, 420.0: 0.0966, 421.0: 0.1052, 422.0: 0.1141, 423.0: 0.1235, 424.0: 0.1334, 425.0: 0.1436, 426.0: 0.1541, 427.0: 0.1651, 428.0: 0.1764, 429.0: 0.1879, 430.0: 0.1998, 431.0: 0.2119, 432.0: 0.2243, 433.0: 0.2369, 434.0: 0.2496, 435.0: 0.2625, 436.0: 0.2755, 437.0: 0.2886, 438.0: 0.3017, 439.0: 0.3149, 440.0: 0.3281, 441.0: 0.3412, 442.0: 0.3543, 443.0: 0.3673, 444.0: 0.3803, 445.0: 0.3931, 446.0: 0.406, 447.0: 0.418, 448.0: 0.431, 449.0: 0.443, 450.0: 0.455, 451.0: 0.467, 452.0: 0.479, 453.0: 0.49, 454.0: 0.502, 455.0: 0.513, 456.0: 0.524, 457.0: 0.535, 458.0: 0.546, 459.0: 0.557, 460.0: 0.567, 461.0: 0.578, 462.0: 0.588, 463.0: 0.599, 464.0: 0.61, 465.0: 0.62, 466.0: 0.631, 467.0: 0.642, 468.0: 0.653, 469.0: 0.664, 470.0: 0.676, 471.0: 0.687, 472.0: 0.699, 473.0: 0.71, 474.0: 0.722, 475.0: 0.734, 476.0: 0.745, 477.0: 0.757, 478.0: 0.769, 479.0: 0.781, 480.0: 0.793, 481.0: 0.805, 482.0: 0.817, 483.0: 0.828, 484.0: 0.84, 485.0: 0.851, 486.0: 0.862, 487.0: 0.873, 488.0: 0.884, 489.0: 0.894, 490.0: 0.904, 491.0: 0.914, 492.0: 0.923, 493.0: 0.932, 494.0: 0.941, 495.0: 0.949, 496.0: 0.957, 497.0: 0.964, 498.0: 0.97, 499.0: 0.976, 500.0: 0.982, 501.0: 0.986, 502.0: 0.99, 503.0: 0.994, 504.0: 0.997, 505.0: 0.998, 506.0: 1.0, 507.0: 1.0, 508.0: 1.0, 509.0: 0.998, 510.0: 0.997, 511.0: 0.994, 512.0: 0.99, 513.0: 0.986, 514.0: 0.981, 515.0: 0.975, 516.0: 0.968, 517.0: 0.961, 518.0: 0.953, 519.0: 0.944, 520.0: 0.935, 521.0: 0.925, 522.0: 0.915, 523.0: 0.904, 524.0: 0.892, 525.0: 0.88, 526.0: 0.867, 527.0: 0.854, 528.0: 0.84, 529.0: 0.826, 530.0: 0.811, 531.0: 0.796, 532.0: 0.781, 533.0: 0.765, 534.0: 0.749, 535.0: 0.733, 536.0: 0.717, 537.0: 0.7, 538.0: 0.683, 539.0: 0.667, 540.0: 0.65, 541.0: 0.633, 542.0: 0.616, 543.0: 0.599, 544.0: 0.581, 545.0: 0.564, 546.0: 0.548, 547.0: 0.531, 548.0: 0.514, 549.0: 0.497, 550.0: 0.481, 551.0: 0.465, 552.0: 0.448, 553.0: 0.433, 554.0: 0.417, 555.0: 0.402, 556.0: 0.3864, 557.0: 0.3715, 558.0: 0.3569, 559.0: 0.3427, 560.0: 0.3288, 561.0: 0.3151, 562.0: 0.3018, 563.0: 0.2888, 564.0: 0.2762, 565.0: 0.2639, 566.0: 0.2519, 567.0: 0.2403, 568.0: 0.2291, 569.0: 0.2182, 570.0: 0.2076, 571.0: 0.1974, 572.0: 0.1876, 573.0: 0.1782, 574.0: 0.169, 575.0: 0.1602, 576.0: 0.1517, 577.0: 0.1436, 578.0: 0.1358, 579.0: 0.1284, 580.0: 0.1212, 581.0: 0.1143, 582.0: 0.1078, 583.0: 0.1015, 584.0: 0.0956, 585.0: 0.0899, 586.0: 0.0845, 587.0: 0.0793, 588.0: 0.0745, 589.0: 0.0699, 590.0: 0.0655, 591.0: 0.0613, 592.0: 0.0574, 593.0: 0.0537, 594.0: 0.0502, 595.0: 0.0469, 596.0: 0.0438, 597.0: 0.0409, 598.0: 0.03816, 599.0: 0.03558, 600.0: 0.03315, 601.0: 0.03087, 602.0: 0.02874, 603.0: 0.02674, 604.0: 0.02487, 605.0: 0.02312, 606.0: 0.02147, 607.0: 0.01994, 608.0: 0.01851, 609.0: 0.01718, 610.0: 0.01593, 611.0: 0.01477, 612.0: 0.01369, 613.0: 0.01269, 614.0: 0.01175, 615.0: 0.01088, 616.0: 0.01007, 617.0: 0.00932, 618.0: 0.00862, 619.0: 0.00797, 620.0: 0.00737, 621.0: 0.00682, 622.0: 0.0063, 623.0: 0.00582, 624.0: 0.00538, 625.0: 0.00497, 626.0: 0.00459, 627.0: 0.00424, 628.0: 0.003913, 629.0: 0.003613, 630.0: 0.003335, 631.0: 0.003079, 632.0: 0.002842, 633.0: 0.002623, 634.0: 0.002421, 635.0: 0.002235, 636.0: 0.002062, 637.0: 0.001903, 638.0: 0.001757, 639.0: 0.001621, 640.0: 0.001497, 641.0: 0.001382, 642.0: 0.001276, 643.0: 0.001178, 644.0: 0.001088, 645.0: 0.001005, 646.0: 0.000928, 647.0: 0.000857, 648.0: 0.000792, 649.0: 0.000732, 650.0: 0.000677, 651.0: 0.000626, 652.0: 0.000579, 653.0: 0.000536, 654.0: 0.000496, 655.0: 0.000459, 656.0: 0.000425, 657.0: 0.0003935, 658.0: 0.0003645, 659.0: 0.0003377, 660.0: 0.0003129, 661.0: 0.0002901, 662.0: 0.0002689, 663.0: 0.0002493, 664.0: 0.0002313, 665.0: 0.0002146, 666.0: 0.0001991, 667.0: 0.0001848, 668.0: 0.0001716, 669.0: 0.0001593, 670.0: 0.000148, 671.0: 0.0001375, 672.0: 0.0001277, 673.0: 0.0001187, 674.0: 0.0001104, 675.0: 0.0001026, 676.0: 9.54e-05, 677.0: 8.88e-05, 678.0: 8.26e-05, 679.0: 7.69e-05, 680.0: 7.15e-05, 681.0: 6.66e-05, 682.0: 6.2e-05, 683.0: 5.78e-05, 684.0: 5.38e-05, 685.0: 5.01e-05, 686.0: 4.67e-05, 687.0: 4.36e-05, 688.0: 4.06e-05, 689.0: 3.789e-05, 690.0: 3.533e-05, 691.0: 3.295e-05, 692.0: 3.075e-05, 693.0: 2.87e-05, 694.0: 2.679e-05, 695.0: 2.501e-05, 696.0: 2.336e-05, 697.0: 2.182e-05, 698.0: 2.038e-05, 699.0: 1.905e-05, 700.0: 1.78e-05, 701.0: 1.664e-05, 702.0: 1.556e-05, 703.0: 1.454e-05, 704.0: 1.36e-05, 705.0: 1.273e-05, 706.0: 1.191e-05, 707.0: 1.114e-05, 708.0: 1.043e-05, 709.0: 9.76e-06, 710.0: 9.14e-06, 711.0: 8.56e-06, 712.0: 8.02e-06, 713.0: 7.51e-06, 714.0: 7.04e-06, 715.0: 6.6e-06, 716.0: 6.18e-06, 717.0: 5.8e-06, 718.0: 5.44e-06, 719.0: 5.1e-06, 720.0: 4.78e-06, 721.0: 4.49e-06, 722.0: 4.21e-06, 723.0: 3.951e-06, 724.0: 3.709e-06, 725.0: 3.482e-06, 726.0: 3.27e-06, 727.0: 3.07e-06, 728.0: 2.884e-06, 729.0: 2.71e-06, 730.0: 2.546e-06, 731.0: 2.393e-06, 732.0: 2.25e-06, 733.0: 2.115e-06, 734.0: 1.989e-06, 735.0: 1.87e-06, 736.0: 1.759e-06, 737.0: 1.655e-06, 738.0: 1.557e-06, 739.0: 1.466e-06, 740.0: 1.379e-06, 741.0: 1.299e-06, 742.0: 1.223e-06, 743.0: 1.151e-06, 744.0: 1.084e-06, 745.0: 1.022e-06, 746.0: 9.62e-07, 747.0: 9.07e-07, 748.0: 8.55e-07, 749.0: 8.06e-07, 750.0: 7.6e-07, 751.0: 7.16e-07, 752.0: 6.75e-07, 753.0: 6.37e-07, 754.0: 6.01e-07, 755.0: 5.67e-07, 756.0: 5.35e-07, 757.0: 5.05e-07, 758.0: 4.77e-07, 759.0: 4.5e-07, 760.0: 4.25e-07, 761.0: 4.01e-07, 762.0: 3.79e-07, 763.0: 3.58e-07, 764.0: 3.382e-07, 765.0: 3.196e-07, 766.0: 3.021e-07, 767.0: 2.855e-07, 768.0: 2.699e-07, 769.0: 2.552e-07, 770.0: 2.413e-07, 771.0: 2.282e-07, 772.0: 2.159e-07, 773.0: 2.042e-07, 774.0: 1.932e-07, 775.0: 1.829e-07, 776.0: 1.731e-07, 777.0: 1.638e-07, 778.0: 1.551e-07, 779.0: 1.468e-07, 780.0: 1.39e-07}))[source]¶ Returns the mesopic luminous efficiency function \(V_m(\lambda)\) for given photopic luminance \(L_p\).
Parameters: - Lp (numeric) – Photopic luminance \(L_p\).
- source (unicode, optional) – {‘Blue Heavy’, ‘Red Heavy’}, Light source colour temperature.
- method (unicode, optional) – {‘MOVE’, ‘LRC’}, Method to calculate the weighting factor.
- photopic_lef (SpectralPowerDistribution, optional) – \(V(\lambda)\) photopic luminous efficiency function.
- scotopic_lef (SpectralPowerDistribution, optional) – \(V^\prime(\lambda)\) scotopic luminous efficiency function.
Returns: Mesopic luminous efficiency function \(V_m(\lambda)\).
Return type: Examples
>>> print(mesopic_luminous_efficiency_function(0.2)) SpectralPowerDistribution('0.2 Lp Mesopic Luminous Efficiency Function', (380.0, 780.0, 1.0))
-
colour.colorimetry.
mesopic_weighting_function
(wavelength, Lp, source=u'Blue Heavy', method=u'MOVE', photopic_lef=SpectralPowerDistribution( 'CIE 1924 Photopic Standard Observer', {360.0: 3.917e-06, 361.0: 4.393581e-06, 362.0: 4.929604e-06, 363.0: 5.532136e-06, 364.0: 6.208245e-06, 365.0: 6.965e-06, 366.0: 7.813219e-06, 367.0: 8.767336e-06, 368.0: 9.839844e-06, 369.0: 1.104323e-05, 370.0: 1.239e-05, 371.0: 1.388641e-05, 372.0: 1.555728e-05, 373.0: 1.744296e-05, 374.0: 1.958375e-05, 375.0: 2.202e-05, 376.0: 2.483965e-05, 377.0: 2.804126e-05, 378.0: 3.153104e-05, 379.0: 3.521521e-05, 380.0: 3.9e-05, 381.0: 4.28264e-05, 382.0: 4.69146e-05, 383.0: 5.15896e-05, 384.0: 5.71764e-05, 385.0: 6.4e-05, 386.0: 7.234421e-05, 387.0: 8.221224e-05, 388.0: 9.350816e-05, 389.0: 0.0001061361, 390.0: 0.00012, 391.0: 0.000134984, 392.0: 0.000151492, 393.0: 0.000170208, 394.0: 0.000191816, 395.0: 0.000217, 396.0: 0.0002469067, 397.0: 0.00028124, 398.0: 0.00031852, 399.0: 0.0003572667, 400.0: 0.000396, 401.0: 0.0004337147, 402.0: 0.000473024, 403.0: 0.000517876, 404.0: 0.0005722187, 405.0: 0.00064, 406.0: 0.00072456, 407.0: 0.0008255, 408.0: 0.00094116, 409.0: 0.00106988, 410.0: 0.00121, 411.0: 0.001362091, 412.0: 0.001530752, 413.0: 0.001720368, 414.0: 0.001935323, 415.0: 0.00218, 416.0: 0.0024548, 417.0: 0.002764, 418.0: 0.0031178, 419.0: 0.0035264, 420.0: 0.004, 421.0: 0.00454624, 422.0: 0.00515932, 423.0: 0.00582928, 424.0: 0.00654616, 425.0: 0.0073, 426.0: 0.008086507, 427.0: 0.00890872, 428.0: 0.00976768, 429.0: 0.01066443, 430.0: 0.0116, 431.0: 0.01257317, 432.0: 0.01358272, 433.0: 0.01462968, 434.0: 0.01571509, 435.0: 0.01684, 436.0: 0.01800736, 437.0: 0.01921448, 438.0: 0.02045392, 439.0: 0.02171824, 440.0: 0.023, 441.0: 0.02429461, 442.0: 0.02561024, 443.0: 0.02695857, 444.0: 0.02835125, 445.0: 0.0298, 446.0: 0.03131083, 447.0: 0.03288368, 448.0: 0.03452112, 449.0: 0.03622571, 450.0: 0.038, 451.0: 0.03984667, 452.0: 0.041768, 453.0: 0.043766, 454.0: 0.04584267, 455.0: 0.048, 456.0: 0.05024368, 457.0: 0.05257304, 458.0: 0.05498056, 459.0: 0.05745872, 460.0: 0.06, 461.0: 0.06260197, 462.0: 0.06527752, 463.0: 0.06804208, 464.0: 0.07091109, 465.0: 0.0739, 466.0: 0.077016, 467.0: 0.0802664, 468.0: 0.0836668, 469.0: 0.0872328, 470.0: 0.09098, 471.0: 0.09491755, 472.0: 0.09904584, 473.0: 0.1033674, 474.0: 0.1078846, 475.0: 0.1126, 476.0: 0.117532, 477.0: 0.1226744, 478.0: 0.1279928, 479.0: 0.1334528, 480.0: 0.13902, 481.0: 0.1446764, 482.0: 0.1504693, 483.0: 0.1564619, 484.0: 0.1627177, 485.0: 0.1693, 486.0: 0.1762431, 487.0: 0.1835581, 488.0: 0.1912735, 489.0: 0.199418, 490.0: 0.20802, 491.0: 0.2171199, 492.0: 0.2267345, 493.0: 0.2368571, 494.0: 0.2474812, 495.0: 0.2586, 496.0: 0.2701849, 497.0: 0.2822939, 498.0: 0.2950505, 499.0: 0.308578, 500.0: 0.323, 501.0: 0.3384021, 502.0: 0.3546858, 503.0: 0.3716986, 504.0: 0.3892875, 505.0: 0.4073, 506.0: 0.4256299, 507.0: 0.4443096, 508.0: 0.4633944, 509.0: 0.4829395, 510.0: 0.503, 511.0: 0.5235693, 512.0: 0.544512, 513.0: 0.56569, 514.0: 0.5869653, 515.0: 0.6082, 516.0: 0.6293456, 517.0: 0.6503068, 518.0: 0.6708752, 519.0: 0.6908424, 520.0: 0.71, 521.0: 0.7281852, 522.0: 0.7454636, 523.0: 0.7619694, 524.0: 0.7778368, 525.0: 0.7932, 526.0: 0.8081104, 527.0: 0.8224962, 528.0: 0.8363068, 529.0: 0.8494916, 530.0: 0.862, 531.0: 0.8738108, 532.0: 0.8849624, 533.0: 0.8954936, 534.0: 0.9054432, 535.0: 0.9148501, 536.0: 0.9237348, 537.0: 0.9320924, 538.0: 0.9399226, 539.0: 0.9472252, 540.0: 0.954, 541.0: 0.9602561, 542.0: 0.9660074, 543.0: 0.9712606, 544.0: 0.9760225, 545.0: 0.9803, 546.0: 0.9840924, 547.0: 0.9874182, 548.0: 0.9903128, 549.0: 0.9928116, 550.0: 0.9949501, 551.0: 0.9967108, 552.0: 0.9980983, 553.0: 0.999112, 554.0: 0.9997482, 555.0: 1.0, 556.0: 0.9998567, 557.0: 0.9993046, 558.0: 0.9983255, 559.0: 0.9968987, 560.0: 0.995, 561.0: 0.9926005, 562.0: 0.9897426, 563.0: 0.9864444, 564.0: 0.9827241, 565.0: 0.9786, 566.0: 0.9740837, 567.0: 0.9691712, 568.0: 0.9638568, 569.0: 0.9581349, 570.0: 0.952, 571.0: 0.9454504, 572.0: 0.9384992, 573.0: 0.9311628, 574.0: 0.9234576, 575.0: 0.9154, 576.0: 0.9070064, 577.0: 0.8982772, 578.0: 0.8892048, 579.0: 0.8797816, 580.0: 0.87, 581.0: 0.8598613, 582.0: 0.849392, 583.0: 0.838622, 584.0: 0.8275813, 585.0: 0.8163, 586.0: 0.8047947, 587.0: 0.793082, 588.0: 0.781192, 589.0: 0.7691547, 590.0: 0.757, 591.0: 0.7447541, 592.0: 0.7324224, 593.0: 0.7200036, 594.0: 0.7074965, 595.0: 0.6949, 596.0: 0.6822192, 597.0: 0.6694716, 598.0: 0.6566744, 599.0: 0.6438448, 600.0: 0.631, 601.0: 0.6181555, 602.0: 0.6053144, 603.0: 0.5924756, 604.0: 0.5796379, 605.0: 0.5668, 606.0: 0.5539611, 607.0: 0.5411372, 608.0: 0.5283528, 609.0: 0.5156323, 610.0: 0.503, 611.0: 0.4904688, 612.0: 0.4780304, 613.0: 0.4656776, 614.0: 0.4534032, 615.0: 0.4412, 616.0: 0.42908, 617.0: 0.417036, 618.0: 0.405032, 619.0: 0.393032, 620.0: 0.381, 621.0: 0.3689184, 622.0: 0.3568272, 623.0: 0.3447768, 624.0: 0.3328176, 625.0: 0.321, 626.0: 0.3093381, 627.0: 0.2978504, 628.0: 0.2865936, 629.0: 0.2756245, 630.0: 0.265, 631.0: 0.2547632, 632.0: 0.2448896, 633.0: 0.2353344, 634.0: 0.2260528, 635.0: 0.217, 636.0: 0.2081616, 637.0: 0.1995488, 638.0: 0.1911552, 639.0: 0.1829744, 640.0: 0.175, 641.0: 0.1672235, 642.0: 0.1596464, 643.0: 0.1522776, 644.0: 0.1451259, 645.0: 0.1382, 646.0: 0.1315003, 647.0: 0.1250248, 648.0: 0.1187792, 649.0: 0.1127691, 650.0: 0.107, 651.0: 0.1014762, 652.0: 0.09618864, 653.0: 0.09112296, 654.0: 0.08626485, 655.0: 0.0816, 656.0: 0.07712064, 657.0: 0.07282552, 658.0: 0.06871008, 659.0: 0.06476976, 660.0: 0.061, 661.0: 0.05739621, 662.0: 0.05395504, 663.0: 0.05067376, 664.0: 0.04754965, 665.0: 0.04458, 666.0: 0.04175872, 667.0: 0.03908496, 668.0: 0.03656384, 669.0: 0.03420048, 670.0: 0.032, 671.0: 0.02996261, 672.0: 0.02807664, 673.0: 0.02632936, 674.0: 0.02470805, 675.0: 0.0232, 676.0: 0.02180077, 677.0: 0.02050112, 678.0: 0.01928108, 679.0: 0.01812069, 680.0: 0.017, 681.0: 0.01590379, 682.0: 0.01483718, 683.0: 0.01381068, 684.0: 0.01283478, 685.0: 0.01192, 686.0: 0.01106831, 687.0: 0.01027339, 688.0: 0.009533311, 689.0: 0.008846157, 690.0: 0.00821, 691.0: 0.007623781, 692.0: 0.007085424, 693.0: 0.006591476, 694.0: 0.006138485, 695.0: 0.005723, 696.0: 0.005343059, 697.0: 0.004995796, 698.0: 0.004676404, 699.0: 0.004380075, 700.0: 0.004102, 701.0: 0.003838453, 702.0: 0.003589099, 703.0: 0.003354219, 704.0: 0.003134093, 705.0: 0.002929, 706.0: 0.002738139, 707.0: 0.002559876, 708.0: 0.002393244, 709.0: 0.002237275, 710.0: 0.002091, 711.0: 0.001953587, 712.0: 0.00182458, 713.0: 0.00170358, 714.0: 0.001590187, 715.0: 0.001484, 716.0: 0.001384496, 717.0: 0.001291268, 718.0: 0.001204092, 719.0: 0.001122744, 720.0: 0.001047, 721.0: 0.0009765896, 722.0: 0.0009111088, 723.0: 0.0008501332, 724.0: 0.0007932384, 725.0: 0.00074, 726.0: 0.0006900827, 727.0: 0.00064331, 728.0: 0.000599496, 729.0: 0.0005584547, 730.0: 0.00052, 731.0: 0.0004839136, 732.0: 0.0004500528, 733.0: 0.0004183452, 734.0: 0.0003887184, 735.0: 0.0003611, 736.0: 0.0003353835, 737.0: 0.0003114404, 738.0: 0.0002891656, 739.0: 0.0002684539, 740.0: 0.0002492, 741.0: 0.0002313019, 742.0: 0.0002146856, 743.0: 0.0001992884, 744.0: 0.0001850475, 745.0: 0.0001719, 746.0: 0.0001597781, 747.0: 0.0001486044, 748.0: 0.0001383016, 749.0: 0.0001287925, 750.0: 0.00012, 751.0: 0.0001118595, 752.0: 0.0001043224, 753.0: 9.73356e-05, 754.0: 9.084587e-05, 755.0: 8.48e-05, 756.0: 7.914667e-05, 757.0: 7.3858e-05, 758.0: 6.8916e-05, 759.0: 6.430267e-05, 760.0: 6e-05, 761.0: 5.598187e-05, 762.0: 5.22256e-05, 763.0: 4.87184e-05, 764.0: 4.544747e-05, 765.0: 4.24e-05, 766.0: 3.956104e-05, 767.0: 3.691512e-05, 768.0: 3.444868e-05, 769.0: 3.214816e-05, 770.0: 3e-05, 771.0: 2.799125e-05, 772.0: 2.611356e-05, 773.0: 2.436024e-05, 774.0: 2.272461e-05, 775.0: 2.12e-05, 776.0: 1.977855e-05, 777.0: 1.845285e-05, 778.0: 1.721687e-05, 779.0: 1.606459e-05, 780.0: 1.499e-05, 781.0: 1.398728e-05, 782.0: 1.305155e-05, 783.0: 1.217818e-05, 784.0: 1.136254e-05, 785.0: 1.06e-05, 786.0: 9.885877e-06, 787.0: 9.217304e-06, 788.0: 8.592362e-06, 789.0: 8.009133e-06, 790.0: 7.4657e-06, 791.0: 6.959567e-06, 792.0: 6.487995e-06, 793.0: 6.048699e-06, 794.0: 5.639396e-06, 795.0: 5.2578e-06, 796.0: 4.901771e-06, 797.0: 4.56972e-06, 798.0: 4.260194e-06, 799.0: 3.971739e-06, 800.0: 3.7029e-06, 801.0: 3.452163e-06, 802.0: 3.218302e-06, 803.0: 3.0003e-06, 804.0: 2.797139e-06, 805.0: 2.6078e-06, 806.0: 2.43122e-06, 807.0: 2.266531e-06, 808.0: 2.113013e-06, 809.0: 1.969943e-06, 810.0: 1.8366e-06, 811.0: 1.71223e-06, 812.0: 1.596228e-06, 813.0: 1.48809e-06, 814.0: 1.387314e-06, 815.0: 1.2934e-06, 816.0: 1.20582e-06, 817.0: 1.124143e-06, 818.0: 1.048009e-06, 819.0: 9.770578e-07, 820.0: 9.1093e-07, 821.0: 8.492513e-07, 822.0: 7.917212e-07, 823.0: 7.380904e-07, 824.0: 6.881098e-07, 825.0: 6.4153e-07, 826.0: 5.980895e-07, 827.0: 5.575746e-07, 828.0: 5.19808e-07, 829.0: 4.846123e-07, 830.0: 4.5181e-07}), scotopic_lef=SpectralPowerDistribution( 'CIE 1951 Scotopic Standard Observer', {380.0: 0.000589, 381.0: 0.000665, 382.0: 0.000752, 383.0: 0.000854, 384.0: 0.000972, 385.0: 0.001108, 386.0: 0.001268, 387.0: 0.001453, 388.0: 0.001668, 389.0: 0.001918, 390.0: 0.002209, 391.0: 0.002547, 392.0: 0.002939, 393.0: 0.003394, 394.0: 0.003921, 395.0: 0.00453, 396.0: 0.00524, 397.0: 0.00605, 398.0: 0.00698, 399.0: 0.00806, 400.0: 0.00929, 401.0: 0.0107, 402.0: 0.01231, 403.0: 0.01413, 404.0: 0.01619, 405.0: 0.01852, 406.0: 0.02113, 407.0: 0.02405, 408.0: 0.0273, 409.0: 0.03089, 410.0: 0.03484, 411.0: 0.03916, 412.0: 0.0439, 413.0: 0.049, 414.0: 0.0545, 415.0: 0.0604, 416.0: 0.0668, 417.0: 0.0736, 418.0: 0.0808, 419.0: 0.0885, 420.0: 0.0966, 421.0: 0.1052, 422.0: 0.1141, 423.0: 0.1235, 424.0: 0.1334, 425.0: 0.1436, 426.0: 0.1541, 427.0: 0.1651, 428.0: 0.1764, 429.0: 0.1879, 430.0: 0.1998, 431.0: 0.2119, 432.0: 0.2243, 433.0: 0.2369, 434.0: 0.2496, 435.0: 0.2625, 436.0: 0.2755, 437.0: 0.2886, 438.0: 0.3017, 439.0: 0.3149, 440.0: 0.3281, 441.0: 0.3412, 442.0: 0.3543, 443.0: 0.3673, 444.0: 0.3803, 445.0: 0.3931, 446.0: 0.406, 447.0: 0.418, 448.0: 0.431, 449.0: 0.443, 450.0: 0.455, 451.0: 0.467, 452.0: 0.479, 453.0: 0.49, 454.0: 0.502, 455.0: 0.513, 456.0: 0.524, 457.0: 0.535, 458.0: 0.546, 459.0: 0.557, 460.0: 0.567, 461.0: 0.578, 462.0: 0.588, 463.0: 0.599, 464.0: 0.61, 465.0: 0.62, 466.0: 0.631, 467.0: 0.642, 468.0: 0.653, 469.0: 0.664, 470.0: 0.676, 471.0: 0.687, 472.0: 0.699, 473.0: 0.71, 474.0: 0.722, 475.0: 0.734, 476.0: 0.745, 477.0: 0.757, 478.0: 0.769, 479.0: 0.781, 480.0: 0.793, 481.0: 0.805, 482.0: 0.817, 483.0: 0.828, 484.0: 0.84, 485.0: 0.851, 486.0: 0.862, 487.0: 0.873, 488.0: 0.884, 489.0: 0.894, 490.0: 0.904, 491.0: 0.914, 492.0: 0.923, 493.0: 0.932, 494.0: 0.941, 495.0: 0.949, 496.0: 0.957, 497.0: 0.964, 498.0: 0.97, 499.0: 0.976, 500.0: 0.982, 501.0: 0.986, 502.0: 0.99, 503.0: 0.994, 504.0: 0.997, 505.0: 0.998, 506.0: 1.0, 507.0: 1.0, 508.0: 1.0, 509.0: 0.998, 510.0: 0.997, 511.0: 0.994, 512.0: 0.99, 513.0: 0.986, 514.0: 0.981, 515.0: 0.975, 516.0: 0.968, 517.0: 0.961, 518.0: 0.953, 519.0: 0.944, 520.0: 0.935, 521.0: 0.925, 522.0: 0.915, 523.0: 0.904, 524.0: 0.892, 525.0: 0.88, 526.0: 0.867, 527.0: 0.854, 528.0: 0.84, 529.0: 0.826, 530.0: 0.811, 531.0: 0.796, 532.0: 0.781, 533.0: 0.765, 534.0: 0.749, 535.0: 0.733, 536.0: 0.717, 537.0: 0.7, 538.0: 0.683, 539.0: 0.667, 540.0: 0.65, 541.0: 0.633, 542.0: 0.616, 543.0: 0.599, 544.0: 0.581, 545.0: 0.564, 546.0: 0.548, 547.0: 0.531, 548.0: 0.514, 549.0: 0.497, 550.0: 0.481, 551.0: 0.465, 552.0: 0.448, 553.0: 0.433, 554.0: 0.417, 555.0: 0.402, 556.0: 0.3864, 557.0: 0.3715, 558.0: 0.3569, 559.0: 0.3427, 560.0: 0.3288, 561.0: 0.3151, 562.0: 0.3018, 563.0: 0.2888, 564.0: 0.2762, 565.0: 0.2639, 566.0: 0.2519, 567.0: 0.2403, 568.0: 0.2291, 569.0: 0.2182, 570.0: 0.2076, 571.0: 0.1974, 572.0: 0.1876, 573.0: 0.1782, 574.0: 0.169, 575.0: 0.1602, 576.0: 0.1517, 577.0: 0.1436, 578.0: 0.1358, 579.0: 0.1284, 580.0: 0.1212, 581.0: 0.1143, 582.0: 0.1078, 583.0: 0.1015, 584.0: 0.0956, 585.0: 0.0899, 586.0: 0.0845, 587.0: 0.0793, 588.0: 0.0745, 589.0: 0.0699, 590.0: 0.0655, 591.0: 0.0613, 592.0: 0.0574, 593.0: 0.0537, 594.0: 0.0502, 595.0: 0.0469, 596.0: 0.0438, 597.0: 0.0409, 598.0: 0.03816, 599.0: 0.03558, 600.0: 0.03315, 601.0: 0.03087, 602.0: 0.02874, 603.0: 0.02674, 604.0: 0.02487, 605.0: 0.02312, 606.0: 0.02147, 607.0: 0.01994, 608.0: 0.01851, 609.0: 0.01718, 610.0: 0.01593, 611.0: 0.01477, 612.0: 0.01369, 613.0: 0.01269, 614.0: 0.01175, 615.0: 0.01088, 616.0: 0.01007, 617.0: 0.00932, 618.0: 0.00862, 619.0: 0.00797, 620.0: 0.00737, 621.0: 0.00682, 622.0: 0.0063, 623.0: 0.00582, 624.0: 0.00538, 625.0: 0.00497, 626.0: 0.00459, 627.0: 0.00424, 628.0: 0.003913, 629.0: 0.003613, 630.0: 0.003335, 631.0: 0.003079, 632.0: 0.002842, 633.0: 0.002623, 634.0: 0.002421, 635.0: 0.002235, 636.0: 0.002062, 637.0: 0.001903, 638.0: 0.001757, 639.0: 0.001621, 640.0: 0.001497, 641.0: 0.001382, 642.0: 0.001276, 643.0: 0.001178, 644.0: 0.001088, 645.0: 0.001005, 646.0: 0.000928, 647.0: 0.000857, 648.0: 0.000792, 649.0: 0.000732, 650.0: 0.000677, 651.0: 0.000626, 652.0: 0.000579, 653.0: 0.000536, 654.0: 0.000496, 655.0: 0.000459, 656.0: 0.000425, 657.0: 0.0003935, 658.0: 0.0003645, 659.0: 0.0003377, 660.0: 0.0003129, 661.0: 0.0002901, 662.0: 0.0002689, 663.0: 0.0002493, 664.0: 0.0002313, 665.0: 0.0002146, 666.0: 0.0001991, 667.0: 0.0001848, 668.0: 0.0001716, 669.0: 0.0001593, 670.0: 0.000148, 671.0: 0.0001375, 672.0: 0.0001277, 673.0: 0.0001187, 674.0: 0.0001104, 675.0: 0.0001026, 676.0: 9.54e-05, 677.0: 8.88e-05, 678.0: 8.26e-05, 679.0: 7.69e-05, 680.0: 7.15e-05, 681.0: 6.66e-05, 682.0: 6.2e-05, 683.0: 5.78e-05, 684.0: 5.38e-05, 685.0: 5.01e-05, 686.0: 4.67e-05, 687.0: 4.36e-05, 688.0: 4.06e-05, 689.0: 3.789e-05, 690.0: 3.533e-05, 691.0: 3.295e-05, 692.0: 3.075e-05, 693.0: 2.87e-05, 694.0: 2.679e-05, 695.0: 2.501e-05, 696.0: 2.336e-05, 697.0: 2.182e-05, 698.0: 2.038e-05, 699.0: 1.905e-05, 700.0: 1.78e-05, 701.0: 1.664e-05, 702.0: 1.556e-05, 703.0: 1.454e-05, 704.0: 1.36e-05, 705.0: 1.273e-05, 706.0: 1.191e-05, 707.0: 1.114e-05, 708.0: 1.043e-05, 709.0: 9.76e-06, 710.0: 9.14e-06, 711.0: 8.56e-06, 712.0: 8.02e-06, 713.0: 7.51e-06, 714.0: 7.04e-06, 715.0: 6.6e-06, 716.0: 6.18e-06, 717.0: 5.8e-06, 718.0: 5.44e-06, 719.0: 5.1e-06, 720.0: 4.78e-06, 721.0: 4.49e-06, 722.0: 4.21e-06, 723.0: 3.951e-06, 724.0: 3.709e-06, 725.0: 3.482e-06, 726.0: 3.27e-06, 727.0: 3.07e-06, 728.0: 2.884e-06, 729.0: 2.71e-06, 730.0: 2.546e-06, 731.0: 2.393e-06, 732.0: 2.25e-06, 733.0: 2.115e-06, 734.0: 1.989e-06, 735.0: 1.87e-06, 736.0: 1.759e-06, 737.0: 1.655e-06, 738.0: 1.557e-06, 739.0: 1.466e-06, 740.0: 1.379e-06, 741.0: 1.299e-06, 742.0: 1.223e-06, 743.0: 1.151e-06, 744.0: 1.084e-06, 745.0: 1.022e-06, 746.0: 9.62e-07, 747.0: 9.07e-07, 748.0: 8.55e-07, 749.0: 8.06e-07, 750.0: 7.6e-07, 751.0: 7.16e-07, 752.0: 6.75e-07, 753.0: 6.37e-07, 754.0: 6.01e-07, 755.0: 5.67e-07, 756.0: 5.35e-07, 757.0: 5.05e-07, 758.0: 4.77e-07, 759.0: 4.5e-07, 760.0: 4.25e-07, 761.0: 4.01e-07, 762.0: 3.79e-07, 763.0: 3.58e-07, 764.0: 3.382e-07, 765.0: 3.196e-07, 766.0: 3.021e-07, 767.0: 2.855e-07, 768.0: 2.699e-07, 769.0: 2.552e-07, 770.0: 2.413e-07, 771.0: 2.282e-07, 772.0: 2.159e-07, 773.0: 2.042e-07, 774.0: 1.932e-07, 775.0: 1.829e-07, 776.0: 1.731e-07, 777.0: 1.638e-07, 778.0: 1.551e-07, 779.0: 1.468e-07, 780.0: 1.39e-07}))[source]¶ Calculates the mesopic weighting function factor at given wavelength \(\lambda\) using the photopic luminance \(L_p\).
Parameters: - wavelength (numeric or array_like) – Wavelength \(\lambda\) to calculate the mesopic weighting function factor.
- Lp (numeric) – Photopic luminance \(L_p\).
- source (unicode, optional) – {‘Blue Heavy’, ‘Red Heavy’}, Light source colour temperature.
- method (unicode, optional) – {‘MOVE’, ‘LRC’}, Method to calculate the weighting factor.
- photopic_lef (SpectralPowerDistribution, optional) – \(V(\lambda)\) photopic luminous efficiency function.
- scotopic_lef (SpectralPowerDistribution, optional) – \(V^\prime(\lambda)\) scotopic luminous efficiency function.
Returns: Mesopic weighting function factor.
Return type: numeric or ndarray
Examples
>>> mesopic_weighting_function(500, 0.2) 0.7052200...
-
colour.colorimetry.
lightness
(Y, method=u'CIE 1976', **kwargs)[source]¶ Returns the Lightness \(L\) using given method.
Parameters: - Y (numeric or array_like) – luminance \(Y\).
- method (unicode, optional) – {‘CIE 1976’, ‘Glasser 1958’, ‘Wyszecki 1963’, ‘Fairchild 2010’}, Computation method.
Other Parameters: - Y_n (numeric or array_like, optional) – {
lightness_CIE1976()
}, White reference luminance \(Y_n\). - epsilon (numeric or array_like, optional) – {
lightness_Fairchild2010()
}, \(\epsilon\) exponent.
Returns: Lightness \(L\).
Return type: numeric or array_like
Notes
- Input luminance \(Y\) and optional \(Y_n\) are in domain [0, 100] or [0, \(\infty\)].
- Output Lightness \(L\) is in range [0, 100].
Examples
>>> lightness(10.08) array(37.9856290...) >>> lightness(10.08, Y_n=100) array(37.9856290...) >>> lightness(10.08, Y_n=95) array(38.9165987...) >>> lightness(10.08, method='Glasser 1958') 36.2505626... >>> lightness(10.08, method='Wyszecki 1963') 37.0041149... >>> lightness( ... 10.08 / 100, ... epsilon=1.836, ... method='Fairchild 2010') 24.9022902...
-
colour.colorimetry.
lightness_Glasser1958
(Y)[source]¶ Returns the Lightness \(L\) of given luminance \(Y\) using Glasser et al. (1958) method.
Parameters: Y (numeric or array_like) – luminance \(Y\). Returns: Lightness \(L\). Return type: numeric or array_like Notes
- Input luminance \(Y\) is in domain [0, 100].
- Output Lightness \(L\) is in range [0, 100].
References
[2] Glasser, L. G., McKinney, A. H., Reilly, C. D., & Schnelle, P. D. (1958). Cube-Root Color Coordinate System. J. Opt. Soc. Am., 48(10), 736–740. doi:10.1364/JOSA.48.000736 Examples
>>> lightness_Glasser1958(10.08) 36.2505626...
-
colour.colorimetry.
lightness_Wyszecki1963
(Y)[source]¶ Returns the Lightness \(W\) of given luminance \(Y\) using Wyszecki (1963) method.
Parameters: Y (numeric or array_like) – luminance \(Y\). Returns: Lightness \(W\). Return type: numeric or array_like Notes
- Input luminance \(Y\) is in domain [0, 100].
- Output Lightness \(W\) is in range [0, 100].
References
[3] Wyszecki, G. (1963). Proposal for a New Color-Difference Formula. J. Opt. Soc. Am., 53(11), 1318–1319. doi:10.1364/JOSA.53.001318 Examples
>>> lightness_Wyszecki1963(10.08) 37.0041149...
-
colour.colorimetry.
lightness_CIE1976
(Y, Y_n=100)[source]¶ Returns the Lightness \(L^*\) of given luminance \(Y\) using given reference white luminance \(Y_n\) as per CIE 1976 recommendation.
Parameters: - Y (numeric or array_like) – luminance \(Y\).
- Y_n (numeric or array_like, optional) – White reference luminance \(Y_n\).
Returns: Lightness \(L^*\).
Return type: numeric or array_like
Notes
- Input luminance \(Y\) and \(Y_n\) are in domain [0, 100].
- Output Lightness \(L^*\) is in range [0, 100].
References
[4] Wyszecki, G., & Stiles, W. S. (2000). CIE 1976 (L*u*v*)-Space and Color-Difference Formula. In Color Science: Concepts and Methods, Quantitative Data and Formulae (p. 167). Wiley. ISBN:978-0471399186 [5] Lindbloom, B. (2003). A Continuity Study of the CIE L* Function. Retrieved February 24, 2014, from http://brucelindbloom.com/LContinuity.html Examples
>>> lightness_CIE1976(10.08) array(37.9856290...)
-
colour.colorimetry.
lightness_Fairchild2010
(Y, epsilon=2)[source]¶ Computes Lightness \(L_{hdr}\) of given luminance \(Y\) using Fairchild and Wyble (2010) method accordingly to Michealis-Menten kinetics.
Parameters: - Y (array_like) – luminance \(Y\).
- epsilon (numeric or array_like, optional) – \(\epsilon\) exponent.
Returns: Lightness \(L_{hdr}\).
Return type: array_like
Warning
The input domain of that definition is non standard!
Notes
- Input luminance \(Y\) is in domain [0, \(\infty\)].
References
[6] Fairchild, M. D., & Wyble, D. R. (2010). hdr-CIELAB and hdr-IPT: Simple Models for Describing the Color of High-Dynamic-Range and Wide-Color-Gamut Images. In Proc. of Color and Imaging Conference (pp. 322–326). ISBN:9781629932156 Examples
>>> lightness_Fairchild2010(10.08 / 100, 1.836) 24.9022902...
-
colour.colorimetry.
luminance
(LV, method=u'CIE 1976', **kwargs)[source]¶ Returns the luminance \(Y\) of given Lightness \(L^*\) or given Munsell value \(V\).
Parameters: - LV (numeric or array_like) – Lightness \(L^*\) or Munsell value \(V\).
- method (unicode, optional) – {‘CIE 1976’, ‘Newhall 1943’, ‘ASTM D1535-08’, ‘Fairchild 2010’}, Computation method.
Other Parameters: - Y_n (numeric or array_like, optional) – {
luminance_CIE1976()
}, White reference luminance \(Y_n\). - epsilon (numeric or array_like, optional) – {
lightness_Fairchild2010()
}, \(\epsilon\) exponent.
Returns: luminance \(Y\).
Return type: numeric or array_like
Notes
- Input LV is in domain [0, 100], [0, 10] or [0, 1] and optional luminance \(Y_n\) is in domain [0, 100].
- Output luminance \(Y\) is in range [0, 100] or [0, math:infty].
Examples
>>> luminance(37.98562910) array(10.0800000...) >>> luminance(37.98562910, Y_n=100) array(10.0800000...) >>> luminance(37.98562910, Y_n=95) array(9.5760000...) >>> luminance(3.74629715, method='Newhall 1943') 10.4089874... >>> luminance(3.74629715, method='ASTM D1535-08') 10.1488096... >>> luminance( ... 24.902290269546651, ... epsilon=1.836, ... method='Fairchild 2010') 0.1007999...
-
colour.colorimetry.
luminance_Newhall1943
(V)[source]¶ Returns the luminance \(R_Y\) of given Munsell value \(V\) using Newhall et al. (1943) method.
Parameters: V (numeric or array_like) – Munsell value \(V\). Returns: luminance \(R_Y\). Return type: numeric or array_like Notes
- Input Munsell value \(V\) is in domain [0, 10].
- Output luminance \(R_Y\) is in range [0, 100].
References
[1] Newhall, S. M., Nickerson, D., & Judd, D. B. (1943). Final report of the OSA subcommittee on the spacing of the munsell colors. JOSA, 33(7), 385. doi:10.1364/JOSA.33.000385 Examples
>>> luminance_Newhall1943(3.74629715382) 10.4089874...
-
colour.colorimetry.
luminance_ASTMD153508
(V)[source]¶ Returns the luminance \(Y\) of given Munsell value \(V\) using ASTM D1535-08e1 method.
Parameters: V (numeric or array_like) – Munsell value \(V\). Returns: luminance \(Y\). Return type: numeric or array_like Notes
- Input Munsell value \(V\) is in domain [0, 10].
- Output luminance \(Y\) is in range [0, 100].
References
[4] ASTM International. (n.d.). ASTM D1535-08e1 Standard Practice for Specifying Color by the Munsell System. doi:10.1520/D1535-08E01 Examples
>>> luminance_ASTMD153508(3.74629715382) 10.1488096...
-
colour.colorimetry.
luminance_CIE1976
(Lstar, Y_n=100)[source]¶ Returns the luminance \(Y\) of given Lightness \(L^*\) with given reference white luminance \(Y_n\).
Parameters: - Lstar (numeric or array_like) – Lightness \(L^*\)
- Y_n (numeric or array_like) – White reference luminance \(Y_n\).
Returns: luminance \(Y\).
Return type: numeric or array_like
Notes
- Input Lightness \(L^*\) and reference white luminance \(Y_n\) are in domain [0, 100].
- Output luminance \(Y\) is in range [0, 100].
References
[2] Wyszecki, G., & Stiles, W. S. (2000). CIE 1976 (L*u*v*)-Space and Color-Difference Formula. In Color Science: Concepts and Methods, Quantitative Data and Formulae (p. 167). Wiley. ISBN:978-0471399186 [3] Lindbloom, B. (2003). A Continuity Study of the CIE L* Function. Retrieved February 24, 2014, from http://brucelindbloom.com/LContinuity.html Examples
>>> luminance_CIE1976(37.98562910) array(10.0800000...) >>> luminance_CIE1976(37.98562910, 95) array(9.5760000...)
-
colour.colorimetry.
luminance_Fairchild2010
(L_hdr, epsilon=2)[source]¶ Computes luminance \(Y\) of given Lightness \(L_{hdr}\) using Fairchild and Wyble (2010) method accordingly to Michealis-Menten kinetics.
Parameters: - L_hdr (array_like) – Lightness \(L_{hdr}\).
- epsilon (numeric or array_like, optional) – \(\epsilon\) exponent.
Returns: luminance \(Y\).
Return type: array_like
Warning
The output range of that definition is non standard!
Notes
- Output luminance \(Y\) is in range [0, math:infty].
References
[4] Fairchild, M. D., & Wyble, D. R. (2010). hdr-CIELAB and hdr-IPT: Simple Models for Describing the Color of High-Dynamic-Range and Wide-Color-Gamut Images. In Proc. of Color and Imaging Conference (pp. 322–326). ISBN:9781629932156 Examples
>>> luminance_Fairchild2010( ... 24.902290269546651, 1.836) 0.1007999...
-
colour.colorimetry.
dominant_wavelength
(xy, xy_n, cmfs=XYZ_ColourMatchingFunctions( 'CIE 1931 2 Degree Standard Observer', {u'x_bar': {360.0: 0.0001299, 361.0: 0.000145847, 362.0: 0.0001638021, 363.0: 0.0001840037, 364.0: 0.0002066902, 365.0: 0.0002321, 366.0: 0.000260728, 367.0: 0.000293075, 368.0: 0.000329388, 369.0: 0.000369914, 370.0: 0.0004149, 371.0: 0.0004641587, 372.0: 0.000518986, 373.0: 0.000581854, 374.0: 0.0006552347, 375.0: 0.0007416, 376.0: 0.0008450296, 377.0: 0.0009645268, 378.0: 0.001094949, 379.0: 0.001231154, 380.0: 0.001368, 381.0: 0.00150205, 382.0: 0.001642328, 383.0: 0.001802382, 384.0: 0.001995757, 385.0: 0.002236, 386.0: 0.002535385, 387.0: 0.002892603, 388.0: 0.003300829, 389.0: 0.003753236, 390.0: 0.004243, 391.0: 0.004762389, 392.0: 0.005330048, 393.0: 0.005978712, 394.0: 0.006741117, 395.0: 0.00765, 396.0: 0.008751373, 397.0: 0.01002888, 398.0: 0.0114217, 399.0: 0.01286901, 400.0: 0.01431, 401.0: 0.01570443, 402.0: 0.01714744, 403.0: 0.01878122, 404.0: 0.02074801, 405.0: 0.02319, 406.0: 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738.0: 0.0, 739.0: 0.0, 740.0: 0.0, 741.0: 0.0, 742.0: 0.0, 743.0: 0.0, 744.0: 0.0, 745.0: 0.0, 746.0: 0.0, 747.0: 0.0, 748.0: 0.0, 749.0: 0.0, 750.0: 0.0, 751.0: 0.0, 752.0: 0.0, 753.0: 0.0, 754.0: 0.0, 755.0: 0.0, 756.0: 0.0, 757.0: 0.0, 758.0: 0.0, 759.0: 0.0, 760.0: 0.0, 761.0: 0.0, 762.0: 0.0, 763.0: 0.0, 764.0: 0.0, 765.0: 0.0, 766.0: 0.0, 767.0: 0.0, 768.0: 0.0, 769.0: 0.0, 770.0: 0.0, 771.0: 0.0, 772.0: 0.0, 773.0: 0.0, 774.0: 0.0, 775.0: 0.0, 776.0: 0.0, 777.0: 0.0, 778.0: 0.0, 779.0: 0.0, 780.0: 0.0, 781.0: 0.0, 782.0: 0.0, 783.0: 0.0, 784.0: 0.0, 785.0: 0.0, 786.0: 0.0, 787.0: 0.0, 788.0: 0.0, 789.0: 0.0, 790.0: 0.0, 791.0: 0.0, 792.0: 0.0, 793.0: 0.0, 794.0: 0.0, 795.0: 0.0, 796.0: 0.0, 797.0: 0.0, 798.0: 0.0, 799.0: 0.0, 800.0: 0.0, 801.0: 0.0, 802.0: 0.0, 803.0: 0.0, 804.0: 0.0, 805.0: 0.0, 806.0: 0.0, 807.0: 0.0, 808.0: 0.0, 809.0: 0.0, 810.0: 0.0, 811.0: 0.0, 812.0: 0.0, 813.0: 0.0, 814.0: 0.0, 815.0: 0.0, 816.0: 0.0, 817.0: 0.0, 818.0: 0.0, 819.0: 0.0, 820.0: 0.0, 821.0: 0.0, 822.0: 0.0, 823.0: 0.0, 824.0: 0.0, 825.0: 0.0, 826.0: 0.0, 827.0: 0.0, 828.0: 0.0, 829.0: 0.0, 830.0: 0.0}}, 'CIE 1931 2$^\circ$ Standard Observer'), reverse=False)[source]¶ Returns the dominant wavelength \(\lambda_d\) for given colour stimulus \(xy\) and the related \(xy_wl\) first and \(xy_{cw}\) second intersection coordinates with the spectral locus.
In the eventuality where the \(xy_wl\) first intersection coordinates are on the line of purples, the complementary wavelength will be computed in lieu.
The complementary wavelength is indicated by a negative sign and the \(xy_{cw}\) second intersection coordinates which are set by default to the same value than \(xy_wl\) first intersection coordinates will be set to the complementary dominant wavelength intersection coordinates with the spectral locus.
Parameters: - xy (array_like) – Colour stimulus xy chromaticity coordinates.
- xy_n (array_like) – Achromatic stimulus xy chromaticity coordinates.
- cmfs (XYZ_ColourMatchingFunctions, optional) – Standard observer colour matching functions.
- reverse (bool, optional) – Reverse the computation direction to retrieve the complementary wavelength.
Returns: Dominant wavelength, first intersection point xy chromaticity coordinates, second intersection point xy chromaticity coordinates.
Return type: See also
Examples
Dominant wavelength computation:
>>> from pprint import pprint >>> xy = np.array([0.26415, 0.37770]) >>> xy_n = np.array([0.31270, 0.32900]) >>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer'] >>> pprint(dominant_wavelength(xy, xy_n, cmfs)) (array(504...), array([ 0.0036969..., 0.6389577...]), array([ 0.0036969..., 0.6389577...]))
Complementary dominant wavelength is returned if the first intersection is located on the line of purples:
>>> xy = np.array([0.35000, 0.25000]) >>> pprint(dominant_wavelength(xy, xy_n, cmfs)) (array(-520...), array([ 0.4133314..., 0.1158663...]), array([ 0.0743553..., 0.8338050...]))
-
colour.colorimetry.
complementary_wavelength
(xy, xy_n, cmfs=XYZ_ColourMatchingFunctions( 'CIE 1931 2 Degree Standard Observer', {u'x_bar': {360.0: 0.0001299, 361.0: 0.000145847, 362.0: 0.0001638021, 363.0: 0.0001840037, 364.0: 0.0002066902, 365.0: 0.0002321, 366.0: 0.000260728, 367.0: 0.000293075, 368.0: 0.000329388, 369.0: 0.000369914, 370.0: 0.0004149, 371.0: 0.0004641587, 372.0: 0.000518986, 373.0: 0.000581854, 374.0: 0.0006552347, 375.0: 0.0007416, 376.0: 0.0008450296, 377.0: 0.0009645268, 378.0: 0.001094949, 379.0: 0.001231154, 380.0: 0.001368, 381.0: 0.00150205, 382.0: 0.001642328, 383.0: 0.001802382, 384.0: 0.001995757, 385.0: 0.002236, 386.0: 0.002535385, 387.0: 0.002892603, 388.0: 0.003300829, 389.0: 0.003753236, 390.0: 0.004243, 391.0: 0.004762389, 392.0: 0.005330048, 393.0: 0.005978712, 394.0: 0.006741117, 395.0: 0.00765, 396.0: 0.008751373, 397.0: 0.01002888, 398.0: 0.0114217, 399.0: 0.01286901, 400.0: 0.01431, 401.0: 0.01570443, 402.0: 0.01714744, 403.0: 0.01878122, 404.0: 0.02074801, 405.0: 0.02319, 406.0: 0.02620736, 407.0: 0.02978248, 408.0: 0.03388092, 409.0: 0.03846824, 410.0: 0.04351, 411.0: 0.0489956, 412.0: 0.0550226, 413.0: 0.0617188, 414.0: 0.069212, 415.0: 0.07763, 416.0: 0.08695811, 417.0: 0.09717672, 418.0: 0.1084063, 419.0: 0.1207672, 420.0: 0.13438, 421.0: 0.1493582, 422.0: 0.1653957, 423.0: 0.1819831, 424.0: 0.198611, 425.0: 0.21477, 426.0: 0.2301868, 427.0: 0.2448797, 428.0: 0.2587773, 429.0: 0.2718079, 430.0: 0.2839, 431.0: 0.2949438, 432.0: 0.3048965, 433.0: 0.3137873, 434.0: 0.3216454, 435.0: 0.3285, 436.0: 0.3343513, 437.0: 0.3392101, 438.0: 0.3431213, 439.0: 0.3461296, 440.0: 0.34828, 441.0: 0.3495999, 442.0: 0.3501474, 443.0: 0.350013, 444.0: 0.349287, 445.0: 0.34806, 446.0: 0.3463733, 447.0: 0.3442624, 448.0: 0.3418088, 449.0: 0.3390941, 450.0: 0.3362, 451.0: 0.3331977, 452.0: 0.3300411, 453.0: 0.3266357, 454.0: 0.3228868, 455.0: 0.3187, 456.0: 0.3140251, 457.0: 0.308884, 458.0: 0.3032904, 459.0: 0.2972579, 460.0: 0.2908, 461.0: 0.2839701, 462.0: 0.2767214, 463.0: 0.2689178, 464.0: 0.2604227, 465.0: 0.2511, 466.0: 0.2408475, 467.0: 0.2298512, 468.0: 0.2184072, 469.0: 0.2068115, 470.0: 0.19536, 471.0: 0.1842136, 472.0: 0.1733273, 473.0: 0.1626881, 474.0: 0.1522833, 475.0: 0.1421, 476.0: 0.1321786, 477.0: 0.1225696, 478.0: 0.1132752, 479.0: 0.1042979, 480.0: 0.09564, 481.0: 0.08729955, 482.0: 0.07930804, 483.0: 0.07171776, 484.0: 0.06458099, 485.0: 0.05795001, 486.0: 0.05186211, 487.0: 0.04628152, 488.0: 0.04115088, 489.0: 0.03641283, 490.0: 0.03201, 491.0: 0.0279172, 492.0: 0.0241444, 493.0: 0.020687, 494.0: 0.0175404, 495.0: 0.0147, 496.0: 0.01216179, 497.0: 0.00991996, 498.0: 0.00796724, 499.0: 0.006296346, 500.0: 0.0049, 501.0: 0.003777173, 502.0: 0.00294532, 503.0: 0.00242488, 504.0: 0.002236293, 505.0: 0.0024, 506.0: 0.00292552, 507.0: 0.00383656, 508.0: 0.00517484, 509.0: 0.00698208, 510.0: 0.0093, 511.0: 0.01214949, 512.0: 0.01553588, 513.0: 0.01947752, 514.0: 0.02399277, 515.0: 0.0291, 516.0: 0.03481485, 517.0: 0.04112016, 518.0: 0.04798504, 519.0: 0.05537861, 520.0: 0.06327, 521.0: 0.07163501, 522.0: 0.08046224, 523.0: 0.08973996, 524.0: 0.09945645, 525.0: 0.1096, 526.0: 0.1201674, 527.0: 0.1311145, 528.0: 0.1423679, 529.0: 0.1538542, 530.0: 0.1655, 531.0: 0.1772571, 532.0: 0.18914, 533.0: 0.2011694, 534.0: 0.2133658, 535.0: 0.2257499, 536.0: 0.2383209, 537.0: 0.2510668, 538.0: 0.2639922, 539.0: 0.2771017, 540.0: 0.2904, 541.0: 0.3038912, 542.0: 0.3175726, 543.0: 0.3314384, 544.0: 0.3454828, 545.0: 0.3597, 546.0: 0.3740839, 547.0: 0.3886396, 548.0: 0.4033784, 549.0: 0.4183115, 550.0: 0.4334499, 551.0: 0.4487953, 552.0: 0.464336, 553.0: 0.480064, 554.0: 0.4959713, 555.0: 0.5120501, 556.0: 0.5282959, 557.0: 0.5446916, 558.0: 0.5612094, 559.0: 0.5778215, 560.0: 0.5945, 561.0: 0.6112209, 562.0: 0.6279758, 563.0: 0.6447602, 564.0: 0.6615697, 565.0: 0.6784, 566.0: 0.6952392, 567.0: 0.7120586, 568.0: 0.7288284, 569.0: 0.7455188, 570.0: 0.7621, 571.0: 0.7785432, 572.0: 0.7948256, 573.0: 0.8109264, 574.0: 0.8268248, 575.0: 0.8425, 576.0: 0.8579325, 577.0: 0.8730816, 578.0: 0.8878944, 579.0: 0.9023181, 580.0: 0.9163, 581.0: 0.9297995, 582.0: 0.9427984, 583.0: 0.9552776, 584.0: 0.9672179, 585.0: 0.9786, 586.0: 0.9893856, 587.0: 0.9995488, 588.0: 1.0090892, 589.0: 1.0180064, 590.0: 1.0263, 591.0: 1.0339827, 592.0: 1.040986, 593.0: 1.047188, 594.0: 1.0524667, 595.0: 1.0567, 596.0: 1.0597944, 597.0: 1.0617992, 598.0: 1.0628068, 599.0: 1.0629096, 600.0: 1.0622, 601.0: 1.0607352, 602.0: 1.0584436, 603.0: 1.0552244, 604.0: 1.0509768, 605.0: 1.0456, 606.0: 1.0390369, 607.0: 1.0313608, 608.0: 1.0226662, 609.0: 1.0130477, 610.0: 1.0026, 611.0: 0.9913675, 612.0: 0.9793314, 613.0: 0.9664916, 614.0: 0.9528479, 615.0: 0.9384, 616.0: 0.923194, 617.0: 0.907244, 618.0: 0.890502, 619.0: 0.87292, 620.0: 0.8544499, 621.0: 0.835084, 622.0: 0.814946, 623.0: 0.794186, 624.0: 0.772954, 625.0: 0.7514, 626.0: 0.7295836, 627.0: 0.7075888, 628.0: 0.6856022, 629.0: 0.6638104, 630.0: 0.6424, 631.0: 0.6215149, 632.0: 0.6011138, 633.0: 0.5811052, 634.0: 0.5613977, 635.0: 0.5419, 636.0: 0.5225995, 637.0: 0.5035464, 638.0: 0.4847436, 639.0: 0.4661939, 640.0: 0.4479, 641.0: 0.4298613, 642.0: 0.412098, 643.0: 0.394644, 644.0: 0.3775333, 645.0: 0.3608, 646.0: 0.3444563, 647.0: 0.3285168, 648.0: 0.3130192, 649.0: 0.2980011, 650.0: 0.2835, 651.0: 0.2695448, 652.0: 0.2561184, 653.0: 0.2431896, 654.0: 0.2307272, 655.0: 0.2187, 656.0: 0.2070971, 657.0: 0.1959232, 658.0: 0.1851708, 659.0: 0.1748323, 660.0: 0.1649, 661.0: 0.1553667, 662.0: 0.14623, 663.0: 0.13749, 664.0: 0.1291467, 665.0: 0.1212, 666.0: 0.1136397, 667.0: 0.106465, 668.0: 0.09969044, 669.0: 0.09333061, 670.0: 0.0874, 671.0: 0.08190096, 672.0: 0.07680428, 673.0: 0.07207712, 674.0: 0.06768664, 675.0: 0.0636, 676.0: 0.05980685, 677.0: 0.05628216, 678.0: 0.05297104, 679.0: 0.04981861, 680.0: 0.04677, 681.0: 0.04378405, 682.0: 0.04087536, 683.0: 0.03807264, 684.0: 0.03540461, 685.0: 0.0329, 686.0: 0.03056419, 687.0: 0.02838056, 688.0: 0.02634484, 689.0: 0.02445275, 690.0: 0.0227, 691.0: 0.02108429, 692.0: 0.01959988, 693.0: 0.01823732, 694.0: 0.01698717, 695.0: 0.01584, 696.0: 0.01479064, 697.0: 0.01383132, 698.0: 0.01294868, 699.0: 0.0121292, 700.0: 0.01135916, 701.0: 0.01062935, 702.0: 0.009938846, 703.0: 0.009288422, 704.0: 0.008678854, 705.0: 0.008110916, 706.0: 0.007582388, 707.0: 0.007088746, 708.0: 0.006627313, 709.0: 0.006195408, 710.0: 0.005790346, 711.0: 0.005409826, 712.0: 0.005052583, 713.0: 0.004717512, 714.0: 0.004403507, 715.0: 0.004109457, 716.0: 0.003833913, 717.0: 0.003575748, 718.0: 0.003334342, 719.0: 0.003109075, 720.0: 0.002899327, 721.0: 0.002704348, 722.0: 0.00252302, 723.0: 0.002354168, 724.0: 0.002196616, 725.0: 0.00204919, 726.0: 0.00191096, 727.0: 0.001781438, 728.0: 0.00166011, 729.0: 0.001546459, 730.0: 0.001439971, 731.0: 0.001340042, 732.0: 0.001246275, 733.0: 0.001158471, 734.0: 0.00107643, 735.0: 0.0009999493, 736.0: 0.0009287358, 737.0: 0.0008624332, 738.0: 0.0008007503, 739.0: 0.000743396, 740.0: 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544.0: 0.01458461, 545.0: 0.0134, 546.0: 0.01230723, 547.0: 0.01130188, 548.0: 0.01037792, 549.0: 0.009529306, 550.0: 0.008749999, 551.0: 0.0080352, 552.0: 0.0073816, 553.0: 0.0067854, 554.0: 0.0062428, 555.0: 0.005749999, 556.0: 0.0053036, 557.0: 0.0048998, 558.0: 0.0045342, 559.0: 0.0042024, 560.0: 0.0039, 561.0: 0.0036232, 562.0: 0.0033706, 563.0: 0.0031414, 564.0: 0.0029348, 565.0: 0.002749999, 566.0: 0.0025852, 567.0: 0.0024386, 568.0: 0.0023094, 569.0: 0.0021968, 570.0: 0.0021, 571.0: 0.002017733, 572.0: 0.0019482, 573.0: 0.0018898, 574.0: 0.001840933, 575.0: 0.0018, 576.0: 0.001766267, 577.0: 0.0017378, 578.0: 0.0017112, 579.0: 0.001683067, 580.0: 0.001650001, 581.0: 0.001610133, 582.0: 0.0015644, 583.0: 0.0015136, 584.0: 0.001458533, 585.0: 0.0014, 586.0: 0.001336667, 587.0: 0.00127, 588.0: 0.001205, 589.0: 0.001146667, 590.0: 0.0011, 591.0: 0.0010688, 592.0: 0.0010494, 593.0: 0.0010356, 594.0: 0.0010212, 595.0: 0.001, 596.0: 0.00096864, 597.0: 0.00092992, 598.0: 0.00088688, 599.0: 0.00084256, 600.0: 0.0008, 601.0: 0.00076096, 602.0: 0.00072368, 603.0: 0.00068592, 604.0: 0.00064544, 605.0: 0.0006, 606.0: 0.0005478667, 607.0: 0.0004916, 608.0: 0.0004354, 609.0: 0.0003834667, 610.0: 0.00034, 611.0: 0.0003072533, 612.0: 0.00028316, 613.0: 0.00026544, 614.0: 0.0002518133, 615.0: 0.00024, 616.0: 0.0002295467, 617.0: 0.00022064, 618.0: 0.00021196, 619.0: 0.0002021867, 620.0: 0.00019, 621.0: 0.0001742133, 622.0: 0.00015564, 623.0: 0.00013596, 624.0: 0.0001168533, 625.0: 0.0001, 626.0: 8.613333e-05, 627.0: 7.46e-05, 628.0: 6.5e-05, 629.0: 5.693333e-05, 630.0: 4.999999e-05, 631.0: 4.416e-05, 632.0: 3.948e-05, 633.0: 3.572e-05, 634.0: 3.264e-05, 635.0: 3e-05, 636.0: 2.765333e-05, 637.0: 2.556e-05, 638.0: 2.364e-05, 639.0: 2.181333e-05, 640.0: 2e-05, 641.0: 1.813333e-05, 642.0: 1.62e-05, 643.0: 1.42e-05, 644.0: 1.213333e-05, 645.0: 1e-05, 646.0: 7.733333e-06, 647.0: 5.4e-06, 648.0: 3.2e-06, 649.0: 1.333333e-06, 650.0: 0.0, 651.0: 0.0, 652.0: 0.0, 653.0: 0.0, 654.0: 0.0, 655.0: 0.0, 656.0: 0.0, 657.0: 0.0, 658.0: 0.0, 659.0: 0.0, 660.0: 0.0, 661.0: 0.0, 662.0: 0.0, 663.0: 0.0, 664.0: 0.0, 665.0: 0.0, 666.0: 0.0, 667.0: 0.0, 668.0: 0.0, 669.0: 0.0, 670.0: 0.0, 671.0: 0.0, 672.0: 0.0, 673.0: 0.0, 674.0: 0.0, 675.0: 0.0, 676.0: 0.0, 677.0: 0.0, 678.0: 0.0, 679.0: 0.0, 680.0: 0.0, 681.0: 0.0, 682.0: 0.0, 683.0: 0.0, 684.0: 0.0, 685.0: 0.0, 686.0: 0.0, 687.0: 0.0, 688.0: 0.0, 689.0: 0.0, 690.0: 0.0, 691.0: 0.0, 692.0: 0.0, 693.0: 0.0, 694.0: 0.0, 695.0: 0.0, 696.0: 0.0, 697.0: 0.0, 698.0: 0.0, 699.0: 0.0, 700.0: 0.0, 701.0: 0.0, 702.0: 0.0, 703.0: 0.0, 704.0: 0.0, 705.0: 0.0, 706.0: 0.0, 707.0: 0.0, 708.0: 0.0, 709.0: 0.0, 710.0: 0.0, 711.0: 0.0, 712.0: 0.0, 713.0: 0.0, 714.0: 0.0, 715.0: 0.0, 716.0: 0.0, 717.0: 0.0, 718.0: 0.0, 719.0: 0.0, 720.0: 0.0, 721.0: 0.0, 722.0: 0.0, 723.0: 0.0, 724.0: 0.0, 725.0: 0.0, 726.0: 0.0, 727.0: 0.0, 728.0: 0.0, 729.0: 0.0, 730.0: 0.0, 731.0: 0.0, 732.0: 0.0, 733.0: 0.0, 734.0: 0.0, 735.0: 0.0, 736.0: 0.0, 737.0: 0.0, 738.0: 0.0, 739.0: 0.0, 740.0: 0.0, 741.0: 0.0, 742.0: 0.0, 743.0: 0.0, 744.0: 0.0, 745.0: 0.0, 746.0: 0.0, 747.0: 0.0, 748.0: 0.0, 749.0: 0.0, 750.0: 0.0, 751.0: 0.0, 752.0: 0.0, 753.0: 0.0, 754.0: 0.0, 755.0: 0.0, 756.0: 0.0, 757.0: 0.0, 758.0: 0.0, 759.0: 0.0, 760.0: 0.0, 761.0: 0.0, 762.0: 0.0, 763.0: 0.0, 764.0: 0.0, 765.0: 0.0, 766.0: 0.0, 767.0: 0.0, 768.0: 0.0, 769.0: 0.0, 770.0: 0.0, 771.0: 0.0, 772.0: 0.0, 773.0: 0.0, 774.0: 0.0, 775.0: 0.0, 776.0: 0.0, 777.0: 0.0, 778.0: 0.0, 779.0: 0.0, 780.0: 0.0, 781.0: 0.0, 782.0: 0.0, 783.0: 0.0, 784.0: 0.0, 785.0: 0.0, 786.0: 0.0, 787.0: 0.0, 788.0: 0.0, 789.0: 0.0, 790.0: 0.0, 791.0: 0.0, 792.0: 0.0, 793.0: 0.0, 794.0: 0.0, 795.0: 0.0, 796.0: 0.0, 797.0: 0.0, 798.0: 0.0, 799.0: 0.0, 800.0: 0.0, 801.0: 0.0, 802.0: 0.0, 803.0: 0.0, 804.0: 0.0, 805.0: 0.0, 806.0: 0.0, 807.0: 0.0, 808.0: 0.0, 809.0: 0.0, 810.0: 0.0, 811.0: 0.0, 812.0: 0.0, 813.0: 0.0, 814.0: 0.0, 815.0: 0.0, 816.0: 0.0, 817.0: 0.0, 818.0: 0.0, 819.0: 0.0, 820.0: 0.0, 821.0: 0.0, 822.0: 0.0, 823.0: 0.0, 824.0: 0.0, 825.0: 0.0, 826.0: 0.0, 827.0: 0.0, 828.0: 0.0, 829.0: 0.0, 830.0: 0.0}}, 'CIE 1931 2$^\circ$ Standard Observer'))[source]¶ Returns the complementary wavelength \(\lambda_c\) for given colour stimulus \(xy\) and the related \(xy_wl\) first and \(xy_{cw}\) second intersection coordinates with the spectral locus.
In the eventuality where the \(xy_wl\) first intersection coordinates are on the line of purples, the dominant wavelength will be computed in lieu.
The dominant wavelength is indicated by a negative sign and the \(xy_{cw}\) second intersection coordinates which are set by default to the same value than \(xy_wl\) first intersection coordinates will be set to the dominant wavelength intersection coordinates with the spectral locus.
Parameters: - xy (array_like) – Colour stimulus xy chromaticity coordinates.
- xy_n (array_like) – Achromatic stimulus xy chromaticity coordinates.
- cmfs (XYZ_ColourMatchingFunctions, optional) – Standard observer colour matching functions.
Returns: Complementary wavelength, first intersection point xy chromaticity coordinates, second intersection point xy chromaticity coordinates.
Return type: See also
Examples
Complementary wavelength computation:
>>> from pprint import pprint >>> xy = np.array([0.35000, 0.25000]) >>> xy_n = np.array([0.31270, 0.32900]) >>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer'] >>> pprint(complementary_wavelength(xy, xy_n, cmfs)) (array(520...), array([ 0.0743553..., 0.8338050...]), array([ 0.0743553..., 0.8338050...]))
Dominant wavelength is returned if the first intersection is located on the line of purples:
>>> xy = np.array([0.26415, 0.37770]) >>> pprint(complementary_wavelength(xy, xy_n, cmfs)) (array(-504...), array([ 0.4897494..., 0.1514035...]), array([ 0.0036969..., 0.6389577...]))
-
colour.colorimetry.
excitation_purity
(xy, xy_n, cmfs=XYZ_ColourMatchingFunctions( 'CIE 1931 2 Degree Standard Observer', {u'x_bar': {360.0: 0.0001299, 361.0: 0.000145847, 362.0: 0.0001638021, 363.0: 0.0001840037, 364.0: 0.0002066902, 365.0: 0.0002321, 366.0: 0.000260728, 367.0: 0.000293075, 368.0: 0.000329388, 369.0: 0.000369914, 370.0: 0.0004149, 371.0: 0.0004641587, 372.0: 0.000518986, 373.0: 0.000581854, 374.0: 0.0006552347, 375.0: 0.0007416, 376.0: 0.0008450296, 377.0: 0.0009645268, 378.0: 0.001094949, 379.0: 0.001231154, 380.0: 0.001368, 381.0: 0.00150205, 382.0: 0.001642328, 383.0: 0.001802382, 384.0: 0.001995757, 385.0: 0.002236, 386.0: 0.002535385, 387.0: 0.002892603, 388.0: 0.003300829, 389.0: 0.003753236, 390.0: 0.004243, 391.0: 0.004762389, 392.0: 0.005330048, 393.0: 0.005978712, 394.0: 0.006741117, 395.0: 0.00765, 396.0: 0.008751373, 397.0: 0.01002888, 398.0: 0.0114217, 399.0: 0.01286901, 400.0: 0.01431, 401.0: 0.01570443, 402.0: 0.01714744, 403.0: 0.01878122, 404.0: 0.02074801, 405.0: 0.02319, 406.0: 0.02620736, 407.0: 0.02978248, 408.0: 0.03388092, 409.0: 0.03846824, 410.0: 0.04351, 411.0: 0.0489956, 412.0: 0.0550226, 413.0: 0.0617188, 414.0: 0.069212, 415.0: 0.07763, 416.0: 0.08695811, 417.0: 0.09717672, 418.0: 0.1084063, 419.0: 0.1207672, 420.0: 0.13438, 421.0: 0.1493582, 422.0: 0.1653957, 423.0: 0.1819831, 424.0: 0.198611, 425.0: 0.21477, 426.0: 0.2301868, 427.0: 0.2448797, 428.0: 0.2587773, 429.0: 0.2718079, 430.0: 0.2839, 431.0: 0.2949438, 432.0: 0.3048965, 433.0: 0.3137873, 434.0: 0.3216454, 435.0: 0.3285, 436.0: 0.3343513, 437.0: 0.3392101, 438.0: 0.3431213, 439.0: 0.3461296, 440.0: 0.34828, 441.0: 0.3495999, 442.0: 0.3501474, 443.0: 0.350013, 444.0: 0.349287, 445.0: 0.34806, 446.0: 0.3463733, 447.0: 0.3442624, 448.0: 0.3418088, 449.0: 0.3390941, 450.0: 0.3362, 451.0: 0.3331977, 452.0: 0.3300411, 453.0: 0.3266357, 454.0: 0.3228868, 455.0: 0.3187, 456.0: 0.3140251, 457.0: 0.308884, 458.0: 0.3032904, 459.0: 0.2972579, 460.0: 0.2908, 461.0: 0.2839701, 462.0: 0.2767214, 463.0: 0.2689178, 464.0: 0.2604227, 465.0: 0.2511, 466.0: 0.2408475, 467.0: 0.2298512, 468.0: 0.2184072, 469.0: 0.2068115, 470.0: 0.19536, 471.0: 0.1842136, 472.0: 0.1733273, 473.0: 0.1626881, 474.0: 0.1522833, 475.0: 0.1421, 476.0: 0.1321786, 477.0: 0.1225696, 478.0: 0.1132752, 479.0: 0.1042979, 480.0: 0.09564, 481.0: 0.08729955, 482.0: 0.07930804, 483.0: 0.07171776, 484.0: 0.06458099, 485.0: 0.05795001, 486.0: 0.05186211, 487.0: 0.04628152, 488.0: 0.04115088, 489.0: 0.03641283, 490.0: 0.03201, 491.0: 0.0279172, 492.0: 0.0241444, 493.0: 0.020687, 494.0: 0.0175404, 495.0: 0.0147, 496.0: 0.01216179, 497.0: 0.00991996, 498.0: 0.00796724, 499.0: 0.006296346, 500.0: 0.0049, 501.0: 0.003777173, 502.0: 0.00294532, 503.0: 0.00242488, 504.0: 0.002236293, 505.0: 0.0024, 506.0: 0.00292552, 507.0: 0.00383656, 508.0: 0.00517484, 509.0: 0.00698208, 510.0: 0.0093, 511.0: 0.01214949, 512.0: 0.01553588, 513.0: 0.01947752, 514.0: 0.02399277, 515.0: 0.0291, 516.0: 0.03481485, 517.0: 0.04112016, 518.0: 0.04798504, 519.0: 0.05537861, 520.0: 0.06327, 521.0: 0.07163501, 522.0: 0.08046224, 523.0: 0.08973996, 524.0: 0.09945645, 525.0: 0.1096, 526.0: 0.1201674, 527.0: 0.1311145, 528.0: 0.1423679, 529.0: 0.1538542, 530.0: 0.1655, 531.0: 0.1772571, 532.0: 0.18914, 533.0: 0.2011694, 534.0: 0.2133658, 535.0: 0.2257499, 536.0: 0.2383209, 537.0: 0.2510668, 538.0: 0.2639922, 539.0: 0.2771017, 540.0: 0.2904, 541.0: 0.3038912, 542.0: 0.3175726, 543.0: 0.3314384, 544.0: 0.3454828, 545.0: 0.3597, 546.0: 0.3740839, 547.0: 0.3886396, 548.0: 0.4033784, 549.0: 0.4183115, 550.0: 0.4334499, 551.0: 0.4487953, 552.0: 0.464336, 553.0: 0.480064, 554.0: 0.4959713, 555.0: 0.5120501, 556.0: 0.5282959, 557.0: 0.5446916, 558.0: 0.5612094, 559.0: 0.5778215, 560.0: 0.5945, 561.0: 0.6112209, 562.0: 0.6279758, 563.0: 0.6447602, 564.0: 0.6615697, 565.0: 0.6784, 566.0: 0.6952392, 567.0: 0.7120586, 568.0: 0.7288284, 569.0: 0.7455188, 570.0: 0.7621, 571.0: 0.7785432, 572.0: 0.7948256, 573.0: 0.8109264, 574.0: 0.8268248, 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4.260194e-06, 799.0: 3.971739e-06, 800.0: 3.7029e-06, 801.0: 3.452163e-06, 802.0: 3.218302e-06, 803.0: 3.0003e-06, 804.0: 2.797139e-06, 805.0: 2.6078e-06, 806.0: 2.43122e-06, 807.0: 2.266531e-06, 808.0: 2.113013e-06, 809.0: 1.969943e-06, 810.0: 1.8366e-06, 811.0: 1.71223e-06, 812.0: 1.596228e-06, 813.0: 1.48809e-06, 814.0: 1.387314e-06, 815.0: 1.2934e-06, 816.0: 1.20582e-06, 817.0: 1.124143e-06, 818.0: 1.048009e-06, 819.0: 9.77058e-07, 820.0: 9.1093e-07, 821.0: 8.49251e-07, 822.0: 7.91721e-07, 823.0: 7.3809e-07, 824.0: 6.8811e-07, 825.0: 6.4153e-07, 826.0: 5.9809e-07, 827.0: 5.57575e-07, 828.0: 5.19808e-07, 829.0: 4.84612e-07, 830.0: 4.5181e-07}, u'z_bar': {360.0: 0.0006061, 361.0: 0.0006808792, 362.0: 0.0007651456, 363.0: 0.0008600124, 364.0: 0.0009665928, 365.0: 0.001086, 366.0: 0.001220586, 367.0: 0.001372729, 368.0: 0.001543579, 369.0: 0.001734286, 370.0: 0.001946, 371.0: 0.002177777, 372.0: 0.002435809, 373.0: 0.002731953, 374.0: 0.003078064, 375.0: 0.003486, 376.0: 0.003975227, 377.0: 0.00454088, 378.0: 0.00515832, 379.0: 0.005802907, 380.0: 0.006450001, 381.0: 0.007083216, 382.0: 0.007745488, 383.0: 0.008501152, 384.0: 0.009414544, 385.0: 0.01054999, 386.0: 0.0119658, 387.0: 0.01365587, 388.0: 0.01558805, 389.0: 0.01773015, 390.0: 0.02005001, 391.0: 0.02251136, 392.0: 0.02520288, 393.0: 0.02827972, 394.0: 0.03189704, 395.0: 0.03621, 396.0: 0.04143771, 397.0: 0.04750372, 398.0: 0.05411988, 399.0: 0.06099803, 400.0: 0.06785001, 401.0: 0.07448632, 402.0: 0.08136156, 403.0: 0.08915364, 404.0: 0.09854048, 405.0: 0.1102, 406.0: 0.1246133, 407.0: 0.1417017, 408.0: 0.1613035, 409.0: 0.1832568, 410.0: 0.2074, 411.0: 0.2336921, 412.0: 0.2626114, 413.0: 0.2947746, 414.0: 0.3307985, 415.0: 0.3713, 416.0: 0.4162091, 417.0: 0.4654642, 418.0: 0.5196948, 419.0: 0.5795303, 420.0: 0.6456, 421.0: 0.7184838, 422.0: 0.7967133, 423.0: 0.8778459, 424.0: 0.959439, 425.0: 1.0390501, 426.0: 1.1153673, 427.0: 1.1884971, 428.0: 1.2581233, 429.0: 1.3239296, 430.0: 1.3856, 431.0: 1.4426352, 432.0: 1.4948035, 433.0: 1.5421903, 434.0: 1.5848807, 435.0: 1.62296, 436.0: 1.6564048, 437.0: 1.6852959, 438.0: 1.7098745, 439.0: 1.7303821, 440.0: 1.74706, 441.0: 1.7600446, 442.0: 1.7696233, 443.0: 1.7762637, 444.0: 1.7804334, 445.0: 1.7826, 446.0: 1.7829682, 447.0: 1.7816998, 448.0: 1.7791982, 449.0: 1.7758671, 450.0: 1.77211, 451.0: 1.7682589, 452.0: 1.764039, 453.0: 1.7589438, 454.0: 1.7524663, 455.0: 1.7441, 456.0: 1.7335595, 457.0: 1.7208581, 458.0: 1.7059369, 459.0: 1.6887372, 460.0: 1.6692, 461.0: 1.6475287, 462.0: 1.6234127, 463.0: 1.5960223, 464.0: 1.564528, 465.0: 1.5281, 466.0: 1.4861114, 467.0: 1.4395215, 468.0: 1.3898799, 469.0: 1.3387362, 470.0: 1.28764, 471.0: 1.2374223, 472.0: 1.1878243, 473.0: 1.1387611, 474.0: 1.090148, 475.0: 1.0419, 476.0: 0.9941976, 477.0: 0.9473473, 478.0: 0.9014531, 479.0: 0.8566193, 480.0: 0.8129501, 481.0: 0.7705173, 482.0: 0.7294448, 483.0: 0.6899136, 484.0: 0.6521049, 485.0: 0.6162, 486.0: 0.5823286, 487.0: 0.5504162, 488.0: 0.5203376, 489.0: 0.4919673, 490.0: 0.46518, 491.0: 0.4399246, 492.0: 0.4161836, 493.0: 0.3938822, 494.0: 0.3729459, 495.0: 0.3533, 496.0: 0.3348578, 497.0: 0.3175521, 498.0: 0.3013375, 499.0: 0.2861686, 500.0: 0.272, 501.0: 0.2588171, 502.0: 0.2464838, 503.0: 0.2347718, 504.0: 0.2234533, 505.0: 0.2123, 506.0: 0.2011692, 507.0: 0.1901196, 508.0: 0.1792254, 509.0: 0.1685608, 510.0: 0.1582, 511.0: 0.1481383, 512.0: 0.1383758, 513.0: 0.1289942, 514.0: 0.1200751, 515.0: 0.1117, 516.0: 0.1039048, 517.0: 0.09666748, 518.0: 0.08998272, 519.0: 0.08384531, 520.0: 0.07824999, 521.0: 0.07320899, 522.0: 0.06867816, 523.0: 0.06456784, 524.0: 0.06078835, 525.0: 0.05725001, 526.0: 0.05390435, 527.0: 0.05074664, 528.0: 0.04775276, 529.0: 0.04489859, 530.0: 0.04216, 531.0: 0.03950728, 532.0: 0.03693564, 533.0: 0.03445836, 534.0: 0.03208872, 535.0: 0.02984, 536.0: 0.02771181, 537.0: 0.02569444, 538.0: 0.02378716, 539.0: 0.02198925, 540.0: 0.0203, 541.0: 0.01871805, 542.0: 0.01724036, 543.0: 0.01586364, 544.0: 0.01458461, 545.0: 0.0134, 546.0: 0.01230723, 547.0: 0.01130188, 548.0: 0.01037792, 549.0: 0.009529306, 550.0: 0.008749999, 551.0: 0.0080352, 552.0: 0.0073816, 553.0: 0.0067854, 554.0: 0.0062428, 555.0: 0.005749999, 556.0: 0.0053036, 557.0: 0.0048998, 558.0: 0.0045342, 559.0: 0.0042024, 560.0: 0.0039, 561.0: 0.0036232, 562.0: 0.0033706, 563.0: 0.0031414, 564.0: 0.0029348, 565.0: 0.002749999, 566.0: 0.0025852, 567.0: 0.0024386, 568.0: 0.0023094, 569.0: 0.0021968, 570.0: 0.0021, 571.0: 0.002017733, 572.0: 0.0019482, 573.0: 0.0018898, 574.0: 0.001840933, 575.0: 0.0018, 576.0: 0.001766267, 577.0: 0.0017378, 578.0: 0.0017112, 579.0: 0.001683067, 580.0: 0.001650001, 581.0: 0.001610133, 582.0: 0.0015644, 583.0: 0.0015136, 584.0: 0.001458533, 585.0: 0.0014, 586.0: 0.001336667, 587.0: 0.00127, 588.0: 0.001205, 589.0: 0.001146667, 590.0: 0.0011, 591.0: 0.0010688, 592.0: 0.0010494, 593.0: 0.0010356, 594.0: 0.0010212, 595.0: 0.001, 596.0: 0.00096864, 597.0: 0.00092992, 598.0: 0.00088688, 599.0: 0.00084256, 600.0: 0.0008, 601.0: 0.00076096, 602.0: 0.00072368, 603.0: 0.00068592, 604.0: 0.00064544, 605.0: 0.0006, 606.0: 0.0005478667, 607.0: 0.0004916, 608.0: 0.0004354, 609.0: 0.0003834667, 610.0: 0.00034, 611.0: 0.0003072533, 612.0: 0.00028316, 613.0: 0.00026544, 614.0: 0.0002518133, 615.0: 0.00024, 616.0: 0.0002295467, 617.0: 0.00022064, 618.0: 0.00021196, 619.0: 0.0002021867, 620.0: 0.00019, 621.0: 0.0001742133, 622.0: 0.00015564, 623.0: 0.00013596, 624.0: 0.0001168533, 625.0: 0.0001, 626.0: 8.613333e-05, 627.0: 7.46e-05, 628.0: 6.5e-05, 629.0: 5.693333e-05, 630.0: 4.999999e-05, 631.0: 4.416e-05, 632.0: 3.948e-05, 633.0: 3.572e-05, 634.0: 3.264e-05, 635.0: 3e-05, 636.0: 2.765333e-05, 637.0: 2.556e-05, 638.0: 2.364e-05, 639.0: 2.181333e-05, 640.0: 2e-05, 641.0: 1.813333e-05, 642.0: 1.62e-05, 643.0: 1.42e-05, 644.0: 1.213333e-05, 645.0: 1e-05, 646.0: 7.733333e-06, 647.0: 5.4e-06, 648.0: 3.2e-06, 649.0: 1.333333e-06, 650.0: 0.0, 651.0: 0.0, 652.0: 0.0, 653.0: 0.0, 654.0: 0.0, 655.0: 0.0, 656.0: 0.0, 657.0: 0.0, 658.0: 0.0, 659.0: 0.0, 660.0: 0.0, 661.0: 0.0, 662.0: 0.0, 663.0: 0.0, 664.0: 0.0, 665.0: 0.0, 666.0: 0.0, 667.0: 0.0, 668.0: 0.0, 669.0: 0.0, 670.0: 0.0, 671.0: 0.0, 672.0: 0.0, 673.0: 0.0, 674.0: 0.0, 675.0: 0.0, 676.0: 0.0, 677.0: 0.0, 678.0: 0.0, 679.0: 0.0, 680.0: 0.0, 681.0: 0.0, 682.0: 0.0, 683.0: 0.0, 684.0: 0.0, 685.0: 0.0, 686.0: 0.0, 687.0: 0.0, 688.0: 0.0, 689.0: 0.0, 690.0: 0.0, 691.0: 0.0, 692.0: 0.0, 693.0: 0.0, 694.0: 0.0, 695.0: 0.0, 696.0: 0.0, 697.0: 0.0, 698.0: 0.0, 699.0: 0.0, 700.0: 0.0, 701.0: 0.0, 702.0: 0.0, 703.0: 0.0, 704.0: 0.0, 705.0: 0.0, 706.0: 0.0, 707.0: 0.0, 708.0: 0.0, 709.0: 0.0, 710.0: 0.0, 711.0: 0.0, 712.0: 0.0, 713.0: 0.0, 714.0: 0.0, 715.0: 0.0, 716.0: 0.0, 717.0: 0.0, 718.0: 0.0, 719.0: 0.0, 720.0: 0.0, 721.0: 0.0, 722.0: 0.0, 723.0: 0.0, 724.0: 0.0, 725.0: 0.0, 726.0: 0.0, 727.0: 0.0, 728.0: 0.0, 729.0: 0.0, 730.0: 0.0, 731.0: 0.0, 732.0: 0.0, 733.0: 0.0, 734.0: 0.0, 735.0: 0.0, 736.0: 0.0, 737.0: 0.0, 738.0: 0.0, 739.0: 0.0, 740.0: 0.0, 741.0: 0.0, 742.0: 0.0, 743.0: 0.0, 744.0: 0.0, 745.0: 0.0, 746.0: 0.0, 747.0: 0.0, 748.0: 0.0, 749.0: 0.0, 750.0: 0.0, 751.0: 0.0, 752.0: 0.0, 753.0: 0.0, 754.0: 0.0, 755.0: 0.0, 756.0: 0.0, 757.0: 0.0, 758.0: 0.0, 759.0: 0.0, 760.0: 0.0, 761.0: 0.0, 762.0: 0.0, 763.0: 0.0, 764.0: 0.0, 765.0: 0.0, 766.0: 0.0, 767.0: 0.0, 768.0: 0.0, 769.0: 0.0, 770.0: 0.0, 771.0: 0.0, 772.0: 0.0, 773.0: 0.0, 774.0: 0.0, 775.0: 0.0, 776.0: 0.0, 777.0: 0.0, 778.0: 0.0, 779.0: 0.0, 780.0: 0.0, 781.0: 0.0, 782.0: 0.0, 783.0: 0.0, 784.0: 0.0, 785.0: 0.0, 786.0: 0.0, 787.0: 0.0, 788.0: 0.0, 789.0: 0.0, 790.0: 0.0, 791.0: 0.0, 792.0: 0.0, 793.0: 0.0, 794.0: 0.0, 795.0: 0.0, 796.0: 0.0, 797.0: 0.0, 798.0: 0.0, 799.0: 0.0, 800.0: 0.0, 801.0: 0.0, 802.0: 0.0, 803.0: 0.0, 804.0: 0.0, 805.0: 0.0, 806.0: 0.0, 807.0: 0.0, 808.0: 0.0, 809.0: 0.0, 810.0: 0.0, 811.0: 0.0, 812.0: 0.0, 813.0: 0.0, 814.0: 0.0, 815.0: 0.0, 816.0: 0.0, 817.0: 0.0, 818.0: 0.0, 819.0: 0.0, 820.0: 0.0, 821.0: 0.0, 822.0: 0.0, 823.0: 0.0, 824.0: 0.0, 825.0: 0.0, 826.0: 0.0, 827.0: 0.0, 828.0: 0.0, 829.0: 0.0, 830.0: 0.0}}, 'CIE 1931 2$^\circ$ Standard Observer'))[source]¶ Returns the excitation purity \(P_e\) for given colour stimulus \(xy\).
Parameters: - xy (array_like) – Colour stimulus xy chromaticity coordinates.
- xy_n (array_like) – Achromatic stimulus xy chromaticity coordinates.
- cmfs (XYZ_ColourMatchingFunctions, optional) – Standard observer colour matching functions.
Returns: Excitation purity \(P_e\).
Return type: numeric or array_like
Examples
>>> xy = np.array([0.28350, 0.68700]) >>> xy_n = np.array([0.31270, 0.32900]) >>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer'] >>> excitation_purity(xy, xy_n, cmfs) 0.9386035...
-
colour.colorimetry.
colorimetric_purity
(xy, xy_n, cmfs=XYZ_ColourMatchingFunctions( 'CIE 1931 2 Degree Standard Observer', {u'x_bar': {360.0: 0.0001299, 361.0: 0.000145847, 362.0: 0.0001638021, 363.0: 0.0001840037, 364.0: 0.0002066902, 365.0: 0.0002321, 366.0: 0.000260728, 367.0: 0.000293075, 368.0: 0.000329388, 369.0: 0.000369914, 370.0: 0.0004149, 371.0: 0.0004641587, 372.0: 0.000518986, 373.0: 0.000581854, 374.0: 0.0006552347, 375.0: 0.0007416, 376.0: 0.0008450296, 377.0: 0.0009645268, 378.0: 0.001094949, 379.0: 0.001231154, 380.0: 0.001368, 381.0: 0.00150205, 382.0: 0.001642328, 383.0: 0.001802382, 384.0: 0.001995757, 385.0: 0.002236, 386.0: 0.002535385, 387.0: 0.002892603, 388.0: 0.003300829, 389.0: 0.003753236, 390.0: 0.004243, 391.0: 0.004762389, 392.0: 0.005330048, 393.0: 0.005978712, 394.0: 0.006741117, 395.0: 0.00765, 396.0: 0.008751373, 397.0: 0.01002888, 398.0: 0.0114217, 399.0: 0.01286901, 400.0: 0.01431, 401.0: 0.01570443, 402.0: 0.01714744, 403.0: 0.01878122, 404.0: 0.02074801, 405.0: 0.02319, 406.0: 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544.0: 0.01458461, 545.0: 0.0134, 546.0: 0.01230723, 547.0: 0.01130188, 548.0: 0.01037792, 549.0: 0.009529306, 550.0: 0.008749999, 551.0: 0.0080352, 552.0: 0.0073816, 553.0: 0.0067854, 554.0: 0.0062428, 555.0: 0.005749999, 556.0: 0.0053036, 557.0: 0.0048998, 558.0: 0.0045342, 559.0: 0.0042024, 560.0: 0.0039, 561.0: 0.0036232, 562.0: 0.0033706, 563.0: 0.0031414, 564.0: 0.0029348, 565.0: 0.002749999, 566.0: 0.0025852, 567.0: 0.0024386, 568.0: 0.0023094, 569.0: 0.0021968, 570.0: 0.0021, 571.0: 0.002017733, 572.0: 0.0019482, 573.0: 0.0018898, 574.0: 0.001840933, 575.0: 0.0018, 576.0: 0.001766267, 577.0: 0.0017378, 578.0: 0.0017112, 579.0: 0.001683067, 580.0: 0.001650001, 581.0: 0.001610133, 582.0: 0.0015644, 583.0: 0.0015136, 584.0: 0.001458533, 585.0: 0.0014, 586.0: 0.001336667, 587.0: 0.00127, 588.0: 0.001205, 589.0: 0.001146667, 590.0: 0.0011, 591.0: 0.0010688, 592.0: 0.0010494, 593.0: 0.0010356, 594.0: 0.0010212, 595.0: 0.001, 596.0: 0.00096864, 597.0: 0.00092992, 598.0: 0.00088688, 599.0: 0.00084256, 600.0: 0.0008, 601.0: 0.00076096, 602.0: 0.00072368, 603.0: 0.00068592, 604.0: 0.00064544, 605.0: 0.0006, 606.0: 0.0005478667, 607.0: 0.0004916, 608.0: 0.0004354, 609.0: 0.0003834667, 610.0: 0.00034, 611.0: 0.0003072533, 612.0: 0.00028316, 613.0: 0.00026544, 614.0: 0.0002518133, 615.0: 0.00024, 616.0: 0.0002295467, 617.0: 0.00022064, 618.0: 0.00021196, 619.0: 0.0002021867, 620.0: 0.00019, 621.0: 0.0001742133, 622.0: 0.00015564, 623.0: 0.00013596, 624.0: 0.0001168533, 625.0: 0.0001, 626.0: 8.613333e-05, 627.0: 7.46e-05, 628.0: 6.5e-05, 629.0: 5.693333e-05, 630.0: 4.999999e-05, 631.0: 4.416e-05, 632.0: 3.948e-05, 633.0: 3.572e-05, 634.0: 3.264e-05, 635.0: 3e-05, 636.0: 2.765333e-05, 637.0: 2.556e-05, 638.0: 2.364e-05, 639.0: 2.181333e-05, 640.0: 2e-05, 641.0: 1.813333e-05, 642.0: 1.62e-05, 643.0: 1.42e-05, 644.0: 1.213333e-05, 645.0: 1e-05, 646.0: 7.733333e-06, 647.0: 5.4e-06, 648.0: 3.2e-06, 649.0: 1.333333e-06, 650.0: 0.0, 651.0: 0.0, 652.0: 0.0, 653.0: 0.0, 654.0: 0.0, 655.0: 0.0, 656.0: 0.0, 657.0: 0.0, 658.0: 0.0, 659.0: 0.0, 660.0: 0.0, 661.0: 0.0, 662.0: 0.0, 663.0: 0.0, 664.0: 0.0, 665.0: 0.0, 666.0: 0.0, 667.0: 0.0, 668.0: 0.0, 669.0: 0.0, 670.0: 0.0, 671.0: 0.0, 672.0: 0.0, 673.0: 0.0, 674.0: 0.0, 675.0: 0.0, 676.0: 0.0, 677.0: 0.0, 678.0: 0.0, 679.0: 0.0, 680.0: 0.0, 681.0: 0.0, 682.0: 0.0, 683.0: 0.0, 684.0: 0.0, 685.0: 0.0, 686.0: 0.0, 687.0: 0.0, 688.0: 0.0, 689.0: 0.0, 690.0: 0.0, 691.0: 0.0, 692.0: 0.0, 693.0: 0.0, 694.0: 0.0, 695.0: 0.0, 696.0: 0.0, 697.0: 0.0, 698.0: 0.0, 699.0: 0.0, 700.0: 0.0, 701.0: 0.0, 702.0: 0.0, 703.0: 0.0, 704.0: 0.0, 705.0: 0.0, 706.0: 0.0, 707.0: 0.0, 708.0: 0.0, 709.0: 0.0, 710.0: 0.0, 711.0: 0.0, 712.0: 0.0, 713.0: 0.0, 714.0: 0.0, 715.0: 0.0, 716.0: 0.0, 717.0: 0.0, 718.0: 0.0, 719.0: 0.0, 720.0: 0.0, 721.0: 0.0, 722.0: 0.0, 723.0: 0.0, 724.0: 0.0, 725.0: 0.0, 726.0: 0.0, 727.0: 0.0, 728.0: 0.0, 729.0: 0.0, 730.0: 0.0, 731.0: 0.0, 732.0: 0.0, 733.0: 0.0, 734.0: 0.0, 735.0: 0.0, 736.0: 0.0, 737.0: 0.0, 738.0: 0.0, 739.0: 0.0, 740.0: 0.0, 741.0: 0.0, 742.0: 0.0, 743.0: 0.0, 744.0: 0.0, 745.0: 0.0, 746.0: 0.0, 747.0: 0.0, 748.0: 0.0, 749.0: 0.0, 750.0: 0.0, 751.0: 0.0, 752.0: 0.0, 753.0: 0.0, 754.0: 0.0, 755.0: 0.0, 756.0: 0.0, 757.0: 0.0, 758.0: 0.0, 759.0: 0.0, 760.0: 0.0, 761.0: 0.0, 762.0: 0.0, 763.0: 0.0, 764.0: 0.0, 765.0: 0.0, 766.0: 0.0, 767.0: 0.0, 768.0: 0.0, 769.0: 0.0, 770.0: 0.0, 771.0: 0.0, 772.0: 0.0, 773.0: 0.0, 774.0: 0.0, 775.0: 0.0, 776.0: 0.0, 777.0: 0.0, 778.0: 0.0, 779.0: 0.0, 780.0: 0.0, 781.0: 0.0, 782.0: 0.0, 783.0: 0.0, 784.0: 0.0, 785.0: 0.0, 786.0: 0.0, 787.0: 0.0, 788.0: 0.0, 789.0: 0.0, 790.0: 0.0, 791.0: 0.0, 792.0: 0.0, 793.0: 0.0, 794.0: 0.0, 795.0: 0.0, 796.0: 0.0, 797.0: 0.0, 798.0: 0.0, 799.0: 0.0, 800.0: 0.0, 801.0: 0.0, 802.0: 0.0, 803.0: 0.0, 804.0: 0.0, 805.0: 0.0, 806.0: 0.0, 807.0: 0.0, 808.0: 0.0, 809.0: 0.0, 810.0: 0.0, 811.0: 0.0, 812.0: 0.0, 813.0: 0.0, 814.0: 0.0, 815.0: 0.0, 816.0: 0.0, 817.0: 0.0, 818.0: 0.0, 819.0: 0.0, 820.0: 0.0, 821.0: 0.0, 822.0: 0.0, 823.0: 0.0, 824.0: 0.0, 825.0: 0.0, 826.0: 0.0, 827.0: 0.0, 828.0: 0.0, 829.0: 0.0, 830.0: 0.0}}, 'CIE 1931 2$^\circ$ Standard Observer'))[source]¶ Returns the colorimetric purity \(P_c\) for given colour stimulus \(xy\).
Parameters: - xy (array_like) – Colour stimulus xy chromaticity coordinates.
- xy_n (array_like) – Achromatic stimulus xy chromaticity coordinates.
- cmfs (XYZ_ColourMatchingFunctions, optional) – Standard observer colour matching functions.
Returns: Colorimetric purity \(P_c\).
Return type: numeric or array_like
Examples
>>> xy = np.array([0.28350, 0.68700]) >>> xy_n = np.array([0.31270, 0.32900]) >>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer'] >>> colorimetric_purity(xy, xy_n, cmfs) 0.9705976...
-
colour.colorimetry.
luminous_flux
(spd, lef=SpectralPowerDistribution( 'CIE 1924 Photopic Standard Observer', {360.0: 3.917e-06, 361.0: 4.393581e-06, 362.0: 4.929604e-06, 363.0: 5.532136e-06, 364.0: 6.208245e-06, 365.0: 6.965e-06, 366.0: 7.813219e-06, 367.0: 8.767336e-06, 368.0: 9.839844e-06, 369.0: 1.104323e-05, 370.0: 1.239e-05, 371.0: 1.388641e-05, 372.0: 1.555728e-05, 373.0: 1.744296e-05, 374.0: 1.958375e-05, 375.0: 2.202e-05, 376.0: 2.483965e-05, 377.0: 2.804126e-05, 378.0: 3.153104e-05, 379.0: 3.521521e-05, 380.0: 3.9e-05, 381.0: 4.28264e-05, 382.0: 4.69146e-05, 383.0: 5.15896e-05, 384.0: 5.71764e-05, 385.0: 6.4e-05, 386.0: 7.234421e-05, 387.0: 8.221224e-05, 388.0: 9.350816e-05, 389.0: 0.0001061361, 390.0: 0.00012, 391.0: 0.000134984, 392.0: 0.000151492, 393.0: 0.000170208, 394.0: 0.000191816, 395.0: 0.000217, 396.0: 0.0002469067, 397.0: 0.00028124, 398.0: 0.00031852, 399.0: 0.0003572667, 400.0: 0.000396, 401.0: 0.0004337147, 402.0: 0.000473024, 403.0: 0.000517876, 404.0: 0.0005722187, 405.0: 0.00064, 406.0: 0.00072456, 407.0: 0.0008255, 408.0: 0.00094116, 409.0: 0.00106988, 410.0: 0.00121, 411.0: 0.001362091, 412.0: 0.001530752, 413.0: 0.001720368, 414.0: 0.001935323, 415.0: 0.00218, 416.0: 0.0024548, 417.0: 0.002764, 418.0: 0.0031178, 419.0: 0.0035264, 420.0: 0.004, 421.0: 0.00454624, 422.0: 0.00515932, 423.0: 0.00582928, 424.0: 0.00654616, 425.0: 0.0073, 426.0: 0.008086507, 427.0: 0.00890872, 428.0: 0.00976768, 429.0: 0.01066443, 430.0: 0.0116, 431.0: 0.01257317, 432.0: 0.01358272, 433.0: 0.01462968, 434.0: 0.01571509, 435.0: 0.01684, 436.0: 0.01800736, 437.0: 0.01921448, 438.0: 0.02045392, 439.0: 0.02171824, 440.0: 0.023, 441.0: 0.02429461, 442.0: 0.02561024, 443.0: 0.02695857, 444.0: 0.02835125, 445.0: 0.0298, 446.0: 0.03131083, 447.0: 0.03288368, 448.0: 0.03452112, 449.0: 0.03622571, 450.0: 0.038, 451.0: 0.03984667, 452.0: 0.041768, 453.0: 0.043766, 454.0: 0.04584267, 455.0: 0.048, 456.0: 0.05024368, 457.0: 0.05257304, 458.0: 0.05498056, 459.0: 0.05745872, 460.0: 0.06, 461.0: 0.06260197, 462.0: 0.06527752, 463.0: 0.06804208, 464.0: 0.07091109, 465.0: 0.0739, 466.0: 0.077016, 467.0: 0.0802664, 468.0: 0.0836668, 469.0: 0.0872328, 470.0: 0.09098, 471.0: 0.09491755, 472.0: 0.09904584, 473.0: 0.1033674, 474.0: 0.1078846, 475.0: 0.1126, 476.0: 0.117532, 477.0: 0.1226744, 478.0: 0.1279928, 479.0: 0.1334528, 480.0: 0.13902, 481.0: 0.1446764, 482.0: 0.1504693, 483.0: 0.1564619, 484.0: 0.1627177, 485.0: 0.1693, 486.0: 0.1762431, 487.0: 0.1835581, 488.0: 0.1912735, 489.0: 0.199418, 490.0: 0.20802, 491.0: 0.2171199, 492.0: 0.2267345, 493.0: 0.2368571, 494.0: 0.2474812, 495.0: 0.2586, 496.0: 0.2701849, 497.0: 0.2822939, 498.0: 0.2950505, 499.0: 0.308578, 500.0: 0.323, 501.0: 0.3384021, 502.0: 0.3546858, 503.0: 0.3716986, 504.0: 0.3892875, 505.0: 0.4073, 506.0: 0.4256299, 507.0: 0.4443096, 508.0: 0.4633944, 509.0: 0.4829395, 510.0: 0.503, 511.0: 0.5235693, 512.0: 0.544512, 513.0: 0.56569, 514.0: 0.5869653, 515.0: 0.6082, 516.0: 0.6293456, 517.0: 0.6503068, 518.0: 0.6708752, 519.0: 0.6908424, 520.0: 0.71, 521.0: 0.7281852, 522.0: 0.7454636, 523.0: 0.7619694, 524.0: 0.7778368, 525.0: 0.7932, 526.0: 0.8081104, 527.0: 0.8224962, 528.0: 0.8363068, 529.0: 0.8494916, 530.0: 0.862, 531.0: 0.8738108, 532.0: 0.8849624, 533.0: 0.8954936, 534.0: 0.9054432, 535.0: 0.9148501, 536.0: 0.9237348, 537.0: 0.9320924, 538.0: 0.9399226, 539.0: 0.9472252, 540.0: 0.954, 541.0: 0.9602561, 542.0: 0.9660074, 543.0: 0.9712606, 544.0: 0.9760225, 545.0: 0.9803, 546.0: 0.9840924, 547.0: 0.9874182, 548.0: 0.9903128, 549.0: 0.9928116, 550.0: 0.9949501, 551.0: 0.9967108, 552.0: 0.9980983, 553.0: 0.999112, 554.0: 0.9997482, 555.0: 1.0, 556.0: 0.9998567, 557.0: 0.9993046, 558.0: 0.9983255, 559.0: 0.9968987, 560.0: 0.995, 561.0: 0.9926005, 562.0: 0.9897426, 563.0: 0.9864444, 564.0: 0.9827241, 565.0: 0.9786, 566.0: 0.9740837, 567.0: 0.9691712, 568.0: 0.9638568, 569.0: 0.9581349, 570.0: 0.952, 571.0: 0.9454504, 572.0: 0.9384992, 573.0: 0.9311628, 574.0: 0.9234576, 575.0: 0.9154, 576.0: 0.9070064, 577.0: 0.8982772, 578.0: 0.8892048, 579.0: 0.8797816, 580.0: 0.87, 581.0: 0.8598613, 582.0: 0.849392, 583.0: 0.838622, 584.0: 0.8275813, 585.0: 0.8163, 586.0: 0.8047947, 587.0: 0.793082, 588.0: 0.781192, 589.0: 0.7691547, 590.0: 0.757, 591.0: 0.7447541, 592.0: 0.7324224, 593.0: 0.7200036, 594.0: 0.7074965, 595.0: 0.6949, 596.0: 0.6822192, 597.0: 0.6694716, 598.0: 0.6566744, 599.0: 0.6438448, 600.0: 0.631, 601.0: 0.6181555, 602.0: 0.6053144, 603.0: 0.5924756, 604.0: 0.5796379, 605.0: 0.5668, 606.0: 0.5539611, 607.0: 0.5411372, 608.0: 0.5283528, 609.0: 0.5156323, 610.0: 0.503, 611.0: 0.4904688, 612.0: 0.4780304, 613.0: 0.4656776, 614.0: 0.4534032, 615.0: 0.4412, 616.0: 0.42908, 617.0: 0.417036, 618.0: 0.405032, 619.0: 0.393032, 620.0: 0.381, 621.0: 0.3689184, 622.0: 0.3568272, 623.0: 0.3447768, 624.0: 0.3328176, 625.0: 0.321, 626.0: 0.3093381, 627.0: 0.2978504, 628.0: 0.2865936, 629.0: 0.2756245, 630.0: 0.265, 631.0: 0.2547632, 632.0: 0.2448896, 633.0: 0.2353344, 634.0: 0.2260528, 635.0: 0.217, 636.0: 0.2081616, 637.0: 0.1995488, 638.0: 0.1911552, 639.0: 0.1829744, 640.0: 0.175, 641.0: 0.1672235, 642.0: 0.1596464, 643.0: 0.1522776, 644.0: 0.1451259, 645.0: 0.1382, 646.0: 0.1315003, 647.0: 0.1250248, 648.0: 0.1187792, 649.0: 0.1127691, 650.0: 0.107, 651.0: 0.1014762, 652.0: 0.09618864, 653.0: 0.09112296, 654.0: 0.08626485, 655.0: 0.0816, 656.0: 0.07712064, 657.0: 0.07282552, 658.0: 0.06871008, 659.0: 0.06476976, 660.0: 0.061, 661.0: 0.05739621, 662.0: 0.05395504, 663.0: 0.05067376, 664.0: 0.04754965, 665.0: 0.04458, 666.0: 0.04175872, 667.0: 0.03908496, 668.0: 0.03656384, 669.0: 0.03420048, 670.0: 0.032, 671.0: 0.02996261, 672.0: 0.02807664, 673.0: 0.02632936, 674.0: 0.02470805, 675.0: 0.0232, 676.0: 0.02180077, 677.0: 0.02050112, 678.0: 0.01928108, 679.0: 0.01812069, 680.0: 0.017, 681.0: 0.01590379, 682.0: 0.01483718, 683.0: 0.01381068, 684.0: 0.01283478, 685.0: 0.01192, 686.0: 0.01106831, 687.0: 0.01027339, 688.0: 0.009533311, 689.0: 0.008846157, 690.0: 0.00821, 691.0: 0.007623781, 692.0: 0.007085424, 693.0: 0.006591476, 694.0: 0.006138485, 695.0: 0.005723, 696.0: 0.005343059, 697.0: 0.004995796, 698.0: 0.004676404, 699.0: 0.004380075, 700.0: 0.004102, 701.0: 0.003838453, 702.0: 0.003589099, 703.0: 0.003354219, 704.0: 0.003134093, 705.0: 0.002929, 706.0: 0.002738139, 707.0: 0.002559876, 708.0: 0.002393244, 709.0: 0.002237275, 710.0: 0.002091, 711.0: 0.001953587, 712.0: 0.00182458, 713.0: 0.00170358, 714.0: 0.001590187, 715.0: 0.001484, 716.0: 0.001384496, 717.0: 0.001291268, 718.0: 0.001204092, 719.0: 0.001122744, 720.0: 0.001047, 721.0: 0.0009765896, 722.0: 0.0009111088, 723.0: 0.0008501332, 724.0: 0.0007932384, 725.0: 0.00074, 726.0: 0.0006900827, 727.0: 0.00064331, 728.0: 0.000599496, 729.0: 0.0005584547, 730.0: 0.00052, 731.0: 0.0004839136, 732.0: 0.0004500528, 733.0: 0.0004183452, 734.0: 0.0003887184, 735.0: 0.0003611, 736.0: 0.0003353835, 737.0: 0.0003114404, 738.0: 0.0002891656, 739.0: 0.0002684539, 740.0: 0.0002492, 741.0: 0.0002313019, 742.0: 0.0002146856, 743.0: 0.0001992884, 744.0: 0.0001850475, 745.0: 0.0001719, 746.0: 0.0001597781, 747.0: 0.0001486044, 748.0: 0.0001383016, 749.0: 0.0001287925, 750.0: 0.00012, 751.0: 0.0001118595, 752.0: 0.0001043224, 753.0: 9.73356e-05, 754.0: 9.084587e-05, 755.0: 8.48e-05, 756.0: 7.914667e-05, 757.0: 7.3858e-05, 758.0: 6.8916e-05, 759.0: 6.430267e-05, 760.0: 6e-05, 761.0: 5.598187e-05, 762.0: 5.22256e-05, 763.0: 4.87184e-05, 764.0: 4.544747e-05, 765.0: 4.24e-05, 766.0: 3.956104e-05, 767.0: 3.691512e-05, 768.0: 3.444868e-05, 769.0: 3.214816e-05, 770.0: 3e-05, 771.0: 2.799125e-05, 772.0: 2.611356e-05, 773.0: 2.436024e-05, 774.0: 2.272461e-05, 775.0: 2.12e-05, 776.0: 1.977855e-05, 777.0: 1.845285e-05, 778.0: 1.721687e-05, 779.0: 1.606459e-05, 780.0: 1.499e-05, 781.0: 1.398728e-05, 782.0: 1.305155e-05, 783.0: 1.217818e-05, 784.0: 1.136254e-05, 785.0: 1.06e-05, 786.0: 9.885877e-06, 787.0: 9.217304e-06, 788.0: 8.592362e-06, 789.0: 8.009133e-06, 790.0: 7.4657e-06, 791.0: 6.959567e-06, 792.0: 6.487995e-06, 793.0: 6.048699e-06, 794.0: 5.639396e-06, 795.0: 5.2578e-06, 796.0: 4.901771e-06, 797.0: 4.56972e-06, 798.0: 4.260194e-06, 799.0: 3.971739e-06, 800.0: 3.7029e-06, 801.0: 3.452163e-06, 802.0: 3.218302e-06, 803.0: 3.0003e-06, 804.0: 2.797139e-06, 805.0: 2.6078e-06, 806.0: 2.43122e-06, 807.0: 2.266531e-06, 808.0: 2.113013e-06, 809.0: 1.969943e-06, 810.0: 1.8366e-06, 811.0: 1.71223e-06, 812.0: 1.596228e-06, 813.0: 1.48809e-06, 814.0: 1.387314e-06, 815.0: 1.2934e-06, 816.0: 1.20582e-06, 817.0: 1.124143e-06, 818.0: 1.048009e-06, 819.0: 9.770578e-07, 820.0: 9.1093e-07, 821.0: 8.492513e-07, 822.0: 7.917212e-07, 823.0: 7.380904e-07, 824.0: 6.881098e-07, 825.0: 6.4153e-07, 826.0: 5.980895e-07, 827.0: 5.575746e-07, 828.0: 5.19808e-07, 829.0: 4.846123e-07, 830.0: 4.5181e-07}), K_m=683)[source]¶ Returns the luminous flux for given spectral power distribution using given luminous efficiency function.
Parameters: - spd (SpectralPowerDistribution) – test spectral power distribution
- lef (SpectralPowerDistribution, optional) – \(V(\lambda)\) luminous efficiency function.
- K_m (numeric, optional) – \(lm\cdot W^{-1}\) maximum photopic luminous efficiency
Returns: Luminous flux.
Return type: numeric
Examples
>>> from colour import LIGHT_SOURCES_RELATIVE_SPDS >>> spd = LIGHT_SOURCES_RELATIVE_SPDS['Neodimium Incandescent'] >>> luminous_flux(spd) 23807.6555273...
-
colour.colorimetry.
luminous_efficiency
(spd, lef=SpectralPowerDistribution( 'CIE 1924 Photopic Standard Observer', {360.0: 3.917e-06, 361.0: 4.393581e-06, 362.0: 4.929604e-06, 363.0: 5.532136e-06, 364.0: 6.208245e-06, 365.0: 6.965e-06, 366.0: 7.813219e-06, 367.0: 8.767336e-06, 368.0: 9.839844e-06, 369.0: 1.104323e-05, 370.0: 1.239e-05, 371.0: 1.388641e-05, 372.0: 1.555728e-05, 373.0: 1.744296e-05, 374.0: 1.958375e-05, 375.0: 2.202e-05, 376.0: 2.483965e-05, 377.0: 2.804126e-05, 378.0: 3.153104e-05, 379.0: 3.521521e-05, 380.0: 3.9e-05, 381.0: 4.28264e-05, 382.0: 4.69146e-05, 383.0: 5.15896e-05, 384.0: 5.71764e-05, 385.0: 6.4e-05, 386.0: 7.234421e-05, 387.0: 8.221224e-05, 388.0: 9.350816e-05, 389.0: 0.0001061361, 390.0: 0.00012, 391.0: 0.000134984, 392.0: 0.000151492, 393.0: 0.000170208, 394.0: 0.000191816, 395.0: 0.000217, 396.0: 0.0002469067, 397.0: 0.00028124, 398.0: 0.00031852, 399.0: 0.0003572667, 400.0: 0.000396, 401.0: 0.0004337147, 402.0: 0.000473024, 403.0: 0.000517876, 404.0: 0.0005722187, 405.0: 0.00064, 406.0: 0.00072456, 407.0: 0.0008255, 408.0: 0.00094116, 409.0: 0.00106988, 410.0: 0.00121, 411.0: 0.001362091, 412.0: 0.001530752, 413.0: 0.001720368, 414.0: 0.001935323, 415.0: 0.00218, 416.0: 0.0024548, 417.0: 0.002764, 418.0: 0.0031178, 419.0: 0.0035264, 420.0: 0.004, 421.0: 0.00454624, 422.0: 0.00515932, 423.0: 0.00582928, 424.0: 0.00654616, 425.0: 0.0073, 426.0: 0.008086507, 427.0: 0.00890872, 428.0: 0.00976768, 429.0: 0.01066443, 430.0: 0.0116, 431.0: 0.01257317, 432.0: 0.01358272, 433.0: 0.01462968, 434.0: 0.01571509, 435.0: 0.01684, 436.0: 0.01800736, 437.0: 0.01921448, 438.0: 0.02045392, 439.0: 0.02171824, 440.0: 0.023, 441.0: 0.02429461, 442.0: 0.02561024, 443.0: 0.02695857, 444.0: 0.02835125, 445.0: 0.0298, 446.0: 0.03131083, 447.0: 0.03288368, 448.0: 0.03452112, 449.0: 0.03622571, 450.0: 0.038, 451.0: 0.03984667, 452.0: 0.041768, 453.0: 0.043766, 454.0: 0.04584267, 455.0: 0.048, 456.0: 0.05024368, 457.0: 0.05257304, 458.0: 0.05498056, 459.0: 0.05745872, 460.0: 0.06, 461.0: 0.06260197, 462.0: 0.06527752, 463.0: 0.06804208, 464.0: 0.07091109, 465.0: 0.0739, 466.0: 0.077016, 467.0: 0.0802664, 468.0: 0.0836668, 469.0: 0.0872328, 470.0: 0.09098, 471.0: 0.09491755, 472.0: 0.09904584, 473.0: 0.1033674, 474.0: 0.1078846, 475.0: 0.1126, 476.0: 0.117532, 477.0: 0.1226744, 478.0: 0.1279928, 479.0: 0.1334528, 480.0: 0.13902, 481.0: 0.1446764, 482.0: 0.1504693, 483.0: 0.1564619, 484.0: 0.1627177, 485.0: 0.1693, 486.0: 0.1762431, 487.0: 0.1835581, 488.0: 0.1912735, 489.0: 0.199418, 490.0: 0.20802, 491.0: 0.2171199, 492.0: 0.2267345, 493.0: 0.2368571, 494.0: 0.2474812, 495.0: 0.2586, 496.0: 0.2701849, 497.0: 0.2822939, 498.0: 0.2950505, 499.0: 0.308578, 500.0: 0.323, 501.0: 0.3384021, 502.0: 0.3546858, 503.0: 0.3716986, 504.0: 0.3892875, 505.0: 0.4073, 506.0: 0.4256299, 507.0: 0.4443096, 508.0: 0.4633944, 509.0: 0.4829395, 510.0: 0.503, 511.0: 0.5235693, 512.0: 0.544512, 513.0: 0.56569, 514.0: 0.5869653, 515.0: 0.6082, 516.0: 0.6293456, 517.0: 0.6503068, 518.0: 0.6708752, 519.0: 0.6908424, 520.0: 0.71, 521.0: 0.7281852, 522.0: 0.7454636, 523.0: 0.7619694, 524.0: 0.7778368, 525.0: 0.7932, 526.0: 0.8081104, 527.0: 0.8224962, 528.0: 0.8363068, 529.0: 0.8494916, 530.0: 0.862, 531.0: 0.8738108, 532.0: 0.8849624, 533.0: 0.8954936, 534.0: 0.9054432, 535.0: 0.9148501, 536.0: 0.9237348, 537.0: 0.9320924, 538.0: 0.9399226, 539.0: 0.9472252, 540.0: 0.954, 541.0: 0.9602561, 542.0: 0.9660074, 543.0: 0.9712606, 544.0: 0.9760225, 545.0: 0.9803, 546.0: 0.9840924, 547.0: 0.9874182, 548.0: 0.9903128, 549.0: 0.9928116, 550.0: 0.9949501, 551.0: 0.9967108, 552.0: 0.9980983, 553.0: 0.999112, 554.0: 0.9997482, 555.0: 1.0, 556.0: 0.9998567, 557.0: 0.9993046, 558.0: 0.9983255, 559.0: 0.9968987, 560.0: 0.995, 561.0: 0.9926005, 562.0: 0.9897426, 563.0: 0.9864444, 564.0: 0.9827241, 565.0: 0.9786, 566.0: 0.9740837, 567.0: 0.9691712, 568.0: 0.9638568, 569.0: 0.9581349, 570.0: 0.952, 571.0: 0.9454504, 572.0: 0.9384992, 573.0: 0.9311628, 574.0: 0.9234576, 575.0: 0.9154, 576.0: 0.9070064, 577.0: 0.8982772, 578.0: 0.8892048, 579.0: 0.8797816, 580.0: 0.87, 581.0: 0.8598613, 582.0: 0.849392, 583.0: 0.838622, 584.0: 0.8275813, 585.0: 0.8163, 586.0: 0.8047947, 587.0: 0.793082, 588.0: 0.781192, 589.0: 0.7691547, 590.0: 0.757, 591.0: 0.7447541, 592.0: 0.7324224, 593.0: 0.7200036, 594.0: 0.7074965, 595.0: 0.6949, 596.0: 0.6822192, 597.0: 0.6694716, 598.0: 0.6566744, 599.0: 0.6438448, 600.0: 0.631, 601.0: 0.6181555, 602.0: 0.6053144, 603.0: 0.5924756, 604.0: 0.5796379, 605.0: 0.5668, 606.0: 0.5539611, 607.0: 0.5411372, 608.0: 0.5283528, 609.0: 0.5156323, 610.0: 0.503, 611.0: 0.4904688, 612.0: 0.4780304, 613.0: 0.4656776, 614.0: 0.4534032, 615.0: 0.4412, 616.0: 0.42908, 617.0: 0.417036, 618.0: 0.405032, 619.0: 0.393032, 620.0: 0.381, 621.0: 0.3689184, 622.0: 0.3568272, 623.0: 0.3447768, 624.0: 0.3328176, 625.0: 0.321, 626.0: 0.3093381, 627.0: 0.2978504, 628.0: 0.2865936, 629.0: 0.2756245, 630.0: 0.265, 631.0: 0.2547632, 632.0: 0.2448896, 633.0: 0.2353344, 634.0: 0.2260528, 635.0: 0.217, 636.0: 0.2081616, 637.0: 0.1995488, 638.0: 0.1911552, 639.0: 0.1829744, 640.0: 0.175, 641.0: 0.1672235, 642.0: 0.1596464, 643.0: 0.1522776, 644.0: 0.1451259, 645.0: 0.1382, 646.0: 0.1315003, 647.0: 0.1250248, 648.0: 0.1187792, 649.0: 0.1127691, 650.0: 0.107, 651.0: 0.1014762, 652.0: 0.09618864, 653.0: 0.09112296, 654.0: 0.08626485, 655.0: 0.0816, 656.0: 0.07712064, 657.0: 0.07282552, 658.0: 0.06871008, 659.0: 0.06476976, 660.0: 0.061, 661.0: 0.05739621, 662.0: 0.05395504, 663.0: 0.05067376, 664.0: 0.04754965, 665.0: 0.04458, 666.0: 0.04175872, 667.0: 0.03908496, 668.0: 0.03656384, 669.0: 0.03420048, 670.0: 0.032, 671.0: 0.02996261, 672.0: 0.02807664, 673.0: 0.02632936, 674.0: 0.02470805, 675.0: 0.0232, 676.0: 0.02180077, 677.0: 0.02050112, 678.0: 0.01928108, 679.0: 0.01812069, 680.0: 0.017, 681.0: 0.01590379, 682.0: 0.01483718, 683.0: 0.01381068, 684.0: 0.01283478, 685.0: 0.01192, 686.0: 0.01106831, 687.0: 0.01027339, 688.0: 0.009533311, 689.0: 0.008846157, 690.0: 0.00821, 691.0: 0.007623781, 692.0: 0.007085424, 693.0: 0.006591476, 694.0: 0.006138485, 695.0: 0.005723, 696.0: 0.005343059, 697.0: 0.004995796, 698.0: 0.004676404, 699.0: 0.004380075, 700.0: 0.004102, 701.0: 0.003838453, 702.0: 0.003589099, 703.0: 0.003354219, 704.0: 0.003134093, 705.0: 0.002929, 706.0: 0.002738139, 707.0: 0.002559876, 708.0: 0.002393244, 709.0: 0.002237275, 710.0: 0.002091, 711.0: 0.001953587, 712.0: 0.00182458, 713.0: 0.00170358, 714.0: 0.001590187, 715.0: 0.001484, 716.0: 0.001384496, 717.0: 0.001291268, 718.0: 0.001204092, 719.0: 0.001122744, 720.0: 0.001047, 721.0: 0.0009765896, 722.0: 0.0009111088, 723.0: 0.0008501332, 724.0: 0.0007932384, 725.0: 0.00074, 726.0: 0.0006900827, 727.0: 0.00064331, 728.0: 0.000599496, 729.0: 0.0005584547, 730.0: 0.00052, 731.0: 0.0004839136, 732.0: 0.0004500528, 733.0: 0.0004183452, 734.0: 0.0003887184, 735.0: 0.0003611, 736.0: 0.0003353835, 737.0: 0.0003114404, 738.0: 0.0002891656, 739.0: 0.0002684539, 740.0: 0.0002492, 741.0: 0.0002313019, 742.0: 0.0002146856, 743.0: 0.0001992884, 744.0: 0.0001850475, 745.0: 0.0001719, 746.0: 0.0001597781, 747.0: 0.0001486044, 748.0: 0.0001383016, 749.0: 0.0001287925, 750.0: 0.00012, 751.0: 0.0001118595, 752.0: 0.0001043224, 753.0: 9.73356e-05, 754.0: 9.084587e-05, 755.0: 8.48e-05, 756.0: 7.914667e-05, 757.0: 7.3858e-05, 758.0: 6.8916e-05, 759.0: 6.430267e-05, 760.0: 6e-05, 761.0: 5.598187e-05, 762.0: 5.22256e-05, 763.0: 4.87184e-05, 764.0: 4.544747e-05, 765.0: 4.24e-05, 766.0: 3.956104e-05, 767.0: 3.691512e-05, 768.0: 3.444868e-05, 769.0: 3.214816e-05, 770.0: 3e-05, 771.0: 2.799125e-05, 772.0: 2.611356e-05, 773.0: 2.436024e-05, 774.0: 2.272461e-05, 775.0: 2.12e-05, 776.0: 1.977855e-05, 777.0: 1.845285e-05, 778.0: 1.721687e-05, 779.0: 1.606459e-05, 780.0: 1.499e-05, 781.0: 1.398728e-05, 782.0: 1.305155e-05, 783.0: 1.217818e-05, 784.0: 1.136254e-05, 785.0: 1.06e-05, 786.0: 9.885877e-06, 787.0: 9.217304e-06, 788.0: 8.592362e-06, 789.0: 8.009133e-06, 790.0: 7.4657e-06, 791.0: 6.959567e-06, 792.0: 6.487995e-06, 793.0: 6.048699e-06, 794.0: 5.639396e-06, 795.0: 5.2578e-06, 796.0: 4.901771e-06, 797.0: 4.56972e-06, 798.0: 4.260194e-06, 799.0: 3.971739e-06, 800.0: 3.7029e-06, 801.0: 3.452163e-06, 802.0: 3.218302e-06, 803.0: 3.0003e-06, 804.0: 2.797139e-06, 805.0: 2.6078e-06, 806.0: 2.43122e-06, 807.0: 2.266531e-06, 808.0: 2.113013e-06, 809.0: 1.969943e-06, 810.0: 1.8366e-06, 811.0: 1.71223e-06, 812.0: 1.596228e-06, 813.0: 1.48809e-06, 814.0: 1.387314e-06, 815.0: 1.2934e-06, 816.0: 1.20582e-06, 817.0: 1.124143e-06, 818.0: 1.048009e-06, 819.0: 9.770578e-07, 820.0: 9.1093e-07, 821.0: 8.492513e-07, 822.0: 7.917212e-07, 823.0: 7.380904e-07, 824.0: 6.881098e-07, 825.0: 6.4153e-07, 826.0: 5.980895e-07, 827.0: 5.575746e-07, 828.0: 5.19808e-07, 829.0: 4.846123e-07, 830.0: 4.5181e-07}))[source]¶ Returns the luminous efficiency of given spectral power distribution using given luminous efficiency function.
Parameters: - spd (SpectralPowerDistribution) – test spectral power distribution
- lef (SpectralPowerDistribution, optional) – \(V(\lambda)\) luminous efficiency function.
Returns: Luminous efficiency.
Return type: numeric
Examples
>>> from colour import LIGHT_SOURCES_RELATIVE_SPDS >>> spd = LIGHT_SOURCES_RELATIVE_SPDS['Neodimium Incandescent'] >>> luminous_efficiency(spd) 0.1994393...
-
colour.colorimetry.
luminous_efficacy
(spd, lef=SpectralPowerDistribution( 'CIE 1924 Photopic Standard Observer', {360.0: 3.917e-06, 361.0: 4.393581e-06, 362.0: 4.929604e-06, 363.0: 5.532136e-06, 364.0: 6.208245e-06, 365.0: 6.965e-06, 366.0: 7.813219e-06, 367.0: 8.767336e-06, 368.0: 9.839844e-06, 369.0: 1.104323e-05, 370.0: 1.239e-05, 371.0: 1.388641e-05, 372.0: 1.555728e-05, 373.0: 1.744296e-05, 374.0: 1.958375e-05, 375.0: 2.202e-05, 376.0: 2.483965e-05, 377.0: 2.804126e-05, 378.0: 3.153104e-05, 379.0: 3.521521e-05, 380.0: 3.9e-05, 381.0: 4.28264e-05, 382.0: 4.69146e-05, 383.0: 5.15896e-05, 384.0: 5.71764e-05, 385.0: 6.4e-05, 386.0: 7.234421e-05, 387.0: 8.221224e-05, 388.0: 9.350816e-05, 389.0: 0.0001061361, 390.0: 0.00012, 391.0: 0.000134984, 392.0: 0.000151492, 393.0: 0.000170208, 394.0: 0.000191816, 395.0: 0.000217, 396.0: 0.0002469067, 397.0: 0.00028124, 398.0: 0.00031852, 399.0: 0.0003572667, 400.0: 0.000396, 401.0: 0.0004337147, 402.0: 0.000473024, 403.0: 0.000517876, 404.0: 0.0005722187, 405.0: 0.00064, 406.0: 0.00072456, 407.0: 0.0008255, 408.0: 0.00094116, 409.0: 0.00106988, 410.0: 0.00121, 411.0: 0.001362091, 412.0: 0.001530752, 413.0: 0.001720368, 414.0: 0.001935323, 415.0: 0.00218, 416.0: 0.0024548, 417.0: 0.002764, 418.0: 0.0031178, 419.0: 0.0035264, 420.0: 0.004, 421.0: 0.00454624, 422.0: 0.00515932, 423.0: 0.00582928, 424.0: 0.00654616, 425.0: 0.0073, 426.0: 0.008086507, 427.0: 0.00890872, 428.0: 0.00976768, 429.0: 0.01066443, 430.0: 0.0116, 431.0: 0.01257317, 432.0: 0.01358272, 433.0: 0.01462968, 434.0: 0.01571509, 435.0: 0.01684, 436.0: 0.01800736, 437.0: 0.01921448, 438.0: 0.02045392, 439.0: 0.02171824, 440.0: 0.023, 441.0: 0.02429461, 442.0: 0.02561024, 443.0: 0.02695857, 444.0: 0.02835125, 445.0: 0.0298, 446.0: 0.03131083, 447.0: 0.03288368, 448.0: 0.03452112, 449.0: 0.03622571, 450.0: 0.038, 451.0: 0.03984667, 452.0: 0.041768, 453.0: 0.043766, 454.0: 0.04584267, 455.0: 0.048, 456.0: 0.05024368, 457.0: 0.05257304, 458.0: 0.05498056, 459.0: 0.05745872, 460.0: 0.06, 461.0: 0.06260197, 462.0: 0.06527752, 463.0: 0.06804208, 464.0: 0.07091109, 465.0: 0.0739, 466.0: 0.077016, 467.0: 0.0802664, 468.0: 0.0836668, 469.0: 0.0872328, 470.0: 0.09098, 471.0: 0.09491755, 472.0: 0.09904584, 473.0: 0.1033674, 474.0: 0.1078846, 475.0: 0.1126, 476.0: 0.117532, 477.0: 0.1226744, 478.0: 0.1279928, 479.0: 0.1334528, 480.0: 0.13902, 481.0: 0.1446764, 482.0: 0.1504693, 483.0: 0.1564619, 484.0: 0.1627177, 485.0: 0.1693, 486.0: 0.1762431, 487.0: 0.1835581, 488.0: 0.1912735, 489.0: 0.199418, 490.0: 0.20802, 491.0: 0.2171199, 492.0: 0.2267345, 493.0: 0.2368571, 494.0: 0.2474812, 495.0: 0.2586, 496.0: 0.2701849, 497.0: 0.2822939, 498.0: 0.2950505, 499.0: 0.308578, 500.0: 0.323, 501.0: 0.3384021, 502.0: 0.3546858, 503.0: 0.3716986, 504.0: 0.3892875, 505.0: 0.4073, 506.0: 0.4256299, 507.0: 0.4443096, 508.0: 0.4633944, 509.0: 0.4829395, 510.0: 0.503, 511.0: 0.5235693, 512.0: 0.544512, 513.0: 0.56569, 514.0: 0.5869653, 515.0: 0.6082, 516.0: 0.6293456, 517.0: 0.6503068, 518.0: 0.6708752, 519.0: 0.6908424, 520.0: 0.71, 521.0: 0.7281852, 522.0: 0.7454636, 523.0: 0.7619694, 524.0: 0.7778368, 525.0: 0.7932, 526.0: 0.8081104, 527.0: 0.8224962, 528.0: 0.8363068, 529.0: 0.8494916, 530.0: 0.862, 531.0: 0.8738108, 532.0: 0.8849624, 533.0: 0.8954936, 534.0: 0.9054432, 535.0: 0.9148501, 536.0: 0.9237348, 537.0: 0.9320924, 538.0: 0.9399226, 539.0: 0.9472252, 540.0: 0.954, 541.0: 0.9602561, 542.0: 0.9660074, 543.0: 0.9712606, 544.0: 0.9760225, 545.0: 0.9803, 546.0: 0.9840924, 547.0: 0.9874182, 548.0: 0.9903128, 549.0: 0.9928116, 550.0: 0.9949501, 551.0: 0.9967108, 552.0: 0.9980983, 553.0: 0.999112, 554.0: 0.9997482, 555.0: 1.0, 556.0: 0.9998567, 557.0: 0.9993046, 558.0: 0.9983255, 559.0: 0.9968987, 560.0: 0.995, 561.0: 0.9926005, 562.0: 0.9897426, 563.0: 0.9864444, 564.0: 0.9827241, 565.0: 0.9786, 566.0: 0.9740837, 567.0: 0.9691712, 568.0: 0.9638568, 569.0: 0.9581349, 570.0: 0.952, 571.0: 0.9454504, 572.0: 0.9384992, 573.0: 0.9311628, 574.0: 0.9234576, 575.0: 0.9154, 576.0: 0.9070064, 577.0: 0.8982772, 578.0: 0.8892048, 579.0: 0.8797816, 580.0: 0.87, 581.0: 0.8598613, 582.0: 0.849392, 583.0: 0.838622, 584.0: 0.8275813, 585.0: 0.8163, 586.0: 0.8047947, 587.0: 0.793082, 588.0: 0.781192, 589.0: 0.7691547, 590.0: 0.757, 591.0: 0.7447541, 592.0: 0.7324224, 593.0: 0.7200036, 594.0: 0.7074965, 595.0: 0.6949, 596.0: 0.6822192, 597.0: 0.6694716, 598.0: 0.6566744, 599.0: 0.6438448, 600.0: 0.631, 601.0: 0.6181555, 602.0: 0.6053144, 603.0: 0.5924756, 604.0: 0.5796379, 605.0: 0.5668, 606.0: 0.5539611, 607.0: 0.5411372, 608.0: 0.5283528, 609.0: 0.5156323, 610.0: 0.503, 611.0: 0.4904688, 612.0: 0.4780304, 613.0: 0.4656776, 614.0: 0.4534032, 615.0: 0.4412, 616.0: 0.42908, 617.0: 0.417036, 618.0: 0.405032, 619.0: 0.393032, 620.0: 0.381, 621.0: 0.3689184, 622.0: 0.3568272, 623.0: 0.3447768, 624.0: 0.3328176, 625.0: 0.321, 626.0: 0.3093381, 627.0: 0.2978504, 628.0: 0.2865936, 629.0: 0.2756245, 630.0: 0.265, 631.0: 0.2547632, 632.0: 0.2448896, 633.0: 0.2353344, 634.0: 0.2260528, 635.0: 0.217, 636.0: 0.2081616, 637.0: 0.1995488, 638.0: 0.1911552, 639.0: 0.1829744, 640.0: 0.175, 641.0: 0.1672235, 642.0: 0.1596464, 643.0: 0.1522776, 644.0: 0.1451259, 645.0: 0.1382, 646.0: 0.1315003, 647.0: 0.1250248, 648.0: 0.1187792, 649.0: 0.1127691, 650.0: 0.107, 651.0: 0.1014762, 652.0: 0.09618864, 653.0: 0.09112296, 654.0: 0.08626485, 655.0: 0.0816, 656.0: 0.07712064, 657.0: 0.07282552, 658.0: 0.06871008, 659.0: 0.06476976, 660.0: 0.061, 661.0: 0.05739621, 662.0: 0.05395504, 663.0: 0.05067376, 664.0: 0.04754965, 665.0: 0.04458, 666.0: 0.04175872, 667.0: 0.03908496, 668.0: 0.03656384, 669.0: 0.03420048, 670.0: 0.032, 671.0: 0.02996261, 672.0: 0.02807664, 673.0: 0.02632936, 674.0: 0.02470805, 675.0: 0.0232, 676.0: 0.02180077, 677.0: 0.02050112, 678.0: 0.01928108, 679.0: 0.01812069, 680.0: 0.017, 681.0: 0.01590379, 682.0: 0.01483718, 683.0: 0.01381068, 684.0: 0.01283478, 685.0: 0.01192, 686.0: 0.01106831, 687.0: 0.01027339, 688.0: 0.009533311, 689.0: 0.008846157, 690.0: 0.00821, 691.0: 0.007623781, 692.0: 0.007085424, 693.0: 0.006591476, 694.0: 0.006138485, 695.0: 0.005723, 696.0: 0.005343059, 697.0: 0.004995796, 698.0: 0.004676404, 699.0: 0.004380075, 700.0: 0.004102, 701.0: 0.003838453, 702.0: 0.003589099, 703.0: 0.003354219, 704.0: 0.003134093, 705.0: 0.002929, 706.0: 0.002738139, 707.0: 0.002559876, 708.0: 0.002393244, 709.0: 0.002237275, 710.0: 0.002091, 711.0: 0.001953587, 712.0: 0.00182458, 713.0: 0.00170358, 714.0: 0.001590187, 715.0: 0.001484, 716.0: 0.001384496, 717.0: 0.001291268, 718.0: 0.001204092, 719.0: 0.001122744, 720.0: 0.001047, 721.0: 0.0009765896, 722.0: 0.0009111088, 723.0: 0.0008501332, 724.0: 0.0007932384, 725.0: 0.00074, 726.0: 0.0006900827, 727.0: 0.00064331, 728.0: 0.000599496, 729.0: 0.0005584547, 730.0: 0.00052, 731.0: 0.0004839136, 732.0: 0.0004500528, 733.0: 0.0004183452, 734.0: 0.0003887184, 735.0: 0.0003611, 736.0: 0.0003353835, 737.0: 0.0003114404, 738.0: 0.0002891656, 739.0: 0.0002684539, 740.0: 0.0002492, 741.0: 0.0002313019, 742.0: 0.0002146856, 743.0: 0.0001992884, 744.0: 0.0001850475, 745.0: 0.0001719, 746.0: 0.0001597781, 747.0: 0.0001486044, 748.0: 0.0001383016, 749.0: 0.0001287925, 750.0: 0.00012, 751.0: 0.0001118595, 752.0: 0.0001043224, 753.0: 9.73356e-05, 754.0: 9.084587e-05, 755.0: 8.48e-05, 756.0: 7.914667e-05, 757.0: 7.3858e-05, 758.0: 6.8916e-05, 759.0: 6.430267e-05, 760.0: 6e-05, 761.0: 5.598187e-05, 762.0: 5.22256e-05, 763.0: 4.87184e-05, 764.0: 4.544747e-05, 765.0: 4.24e-05, 766.0: 3.956104e-05, 767.0: 3.691512e-05, 768.0: 3.444868e-05, 769.0: 3.214816e-05, 770.0: 3e-05, 771.0: 2.799125e-05, 772.0: 2.611356e-05, 773.0: 2.436024e-05, 774.0: 2.272461e-05, 775.0: 2.12e-05, 776.0: 1.977855e-05, 777.0: 1.845285e-05, 778.0: 1.721687e-05, 779.0: 1.606459e-05, 780.0: 1.499e-05, 781.0: 1.398728e-05, 782.0: 1.305155e-05, 783.0: 1.217818e-05, 784.0: 1.136254e-05, 785.0: 1.06e-05, 786.0: 9.885877e-06, 787.0: 9.217304e-06, 788.0: 8.592362e-06, 789.0: 8.009133e-06, 790.0: 7.4657e-06, 791.0: 6.959567e-06, 792.0: 6.487995e-06, 793.0: 6.048699e-06, 794.0: 5.639396e-06, 795.0: 5.2578e-06, 796.0: 4.901771e-06, 797.0: 4.56972e-06, 798.0: 4.260194e-06, 799.0: 3.971739e-06, 800.0: 3.7029e-06, 801.0: 3.452163e-06, 802.0: 3.218302e-06, 803.0: 3.0003e-06, 804.0: 2.797139e-06, 805.0: 2.6078e-06, 806.0: 2.43122e-06, 807.0: 2.266531e-06, 808.0: 2.113013e-06, 809.0: 1.969943e-06, 810.0: 1.8366e-06, 811.0: 1.71223e-06, 812.0: 1.596228e-06, 813.0: 1.48809e-06, 814.0: 1.387314e-06, 815.0: 1.2934e-06, 816.0: 1.20582e-06, 817.0: 1.124143e-06, 818.0: 1.048009e-06, 819.0: 9.770578e-07, 820.0: 9.1093e-07, 821.0: 8.492513e-07, 822.0: 7.917212e-07, 823.0: 7.380904e-07, 824.0: 6.881098e-07, 825.0: 6.4153e-07, 826.0: 5.980895e-07, 827.0: 5.575746e-07, 828.0: 5.19808e-07, 829.0: 4.846123e-07, 830.0: 4.5181e-07}))[source]¶ Returns the luminous efficacy in \(lm\cdot W^{-1}\) of given spectral power distribution using given luminous efficiency function.
Parameters: - spd (SpectralPowerDistribution) – test spectral power distribution
- lef (SpectralPowerDistribution, optional) – \(V(\lambda)\) luminous efficiency function.
Returns: Luminous efficacy in \(lm\cdot W^{-1}\).
Return type: numeric
Examples
>>> from colour import LIGHT_SOURCES_RELATIVE_SPDS >>> spd = LIGHT_SOURCES_RELATIVE_SPDS['Neodimium Incandescent'] >>> luminous_efficacy(spd) 136.2170803...
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colour.colorimetry.
RGB_10_degree_cmfs_to_LMS_10_degree_cmfs
(wavelength)[source]¶ 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 or array_like) – Wavelength \(\lambda\) in nm. Returns: Stockman & Sharpe 10 Degree Cone Fundamentals spectral tristimulus values. Return type: ndarray Notes
- Data for the Stockman & Sharpe 10 Degree Cone Fundamentals already exists, this definition is intended for educational purpose.
References
[3] CIE TC 1-36. (2006). CIE 170-1:2006 Fundamental Chromaticity Diagram with Physiological Axes - Part 1 (pp. 1–56). ISBN:978-3-901-90646-6 Examples
>>> RGB_10_degree_cmfs_to_LMS_10_degree_cmfs(700) array([ 0.0052860..., 0.0003252..., 0. ])
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colour.colorimetry.
RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs
(wavelength)[source]¶ 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 or array_like) – Wavelength \(\lambda\) in nm. Returns: CIE 1931 2 Degree Standard Observer spectral tristimulus values. Return type: ndarray Notes
- Data for the CIE 1931 2 Degree Standard Observer already exists, this definition is intended for educational purpose.
References
[1] Wyszecki, G., & Stiles, W. S. (2000). Table 1(3.3.3). In Color Science: Concepts and Methods, Quantitative Data and Formulae (pp. 138–139). Wiley. ISBN:978-0471399186 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)[source]¶ 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 or array_like) – Wavelength \(\lambda\) in nm. Returns: CIE 1964 10 Degree Standard Observer spectral tristimulus values. Return type: ndarray Notes
- Data for the CIE 1964 10 Degree Standard Observer already exists, this definition is intended for educational purpose.
References
[2] Wyszecki, G., & Stiles, W. S. (2000). The CIE 1964 Standard Observer. In Color Science: Concepts and Methods, Quantitative Data and Formulae (p. 141). Wiley. ISBN:978-0471399186 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)[source]¶ Converts Stockman & Sharpe 2 Degree Cone Fundamentals colour matching functions into the CIE 2012 2 Degree Standard Observer colour matching functions.
Parameters: wavelength (numeric or array_like) – Wavelength \(\lambda\) in nm. Returns: CIE 2012 2 Degree Standard Observer spectral tristimulus values. Return type: ndarray Notes
- Data for the CIE 2012 2 Degree Standard Observer already exists, this definition is intended for educational purpose.
References
[4] CVRL. (n.d.). CIE (2012) 2-deg XYZ “physiologically-relevant” colour matching functions. Retrieved June 25, 2014, from http://www.cvrl.org/database/text/cienewxyz/cie2012xyz2.htm Examples
>>> LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs(700) array([ 0.0109677..., 0.0041959..., 0. ])
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colour.colorimetry.
LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs
(wavelength)[source]¶ Converts Stockman & Sharpe 10 Degree Cone Fundamentals colour matching functions into the CIE 2012 10 Degree Standard Observer colour matching functions.
Parameters: wavelength (numeric or array_like) – Wavelength \(\lambda\) in nm. Returns: CIE 2012 10 Degree Standard Observer spectral tristimulus values. Return type: ndarray Notes
- Data for the CIE 2012 10 Degree Standard Observer already exists, this definition is intended for educational purpose.
References
[5] CVRL. (n.d.). CIE (2012) 10-deg XYZ “physiologically-relevant” colour matching functions. Retrieved June 25, 2014, from http://www.cvrl.org/database/text/cienewxyz/cie2012xyz10.htm Examples
>>> LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs(700) array([ 0.0098162..., 0.0037761..., 0. ])
-
colour.colorimetry.
spectral_to_XYZ
(spd, cmfs=XYZ_ColourMatchingFunctions( 'CIE 1931 2 Degree Standard Observer', {u'x_bar': {360.0: 0.0001299, 361.0: 0.000145847, 362.0: 0.0001638021, 363.0: 0.0001840037, 364.0: 0.0002066902, 365.0: 0.0002321, 366.0: 0.000260728, 367.0: 0.000293075, 368.0: 0.000329388, 369.0: 0.000369914, 370.0: 0.0004149, 371.0: 0.0004641587, 372.0: 0.000518986, 373.0: 0.000581854, 374.0: 0.0006552347, 375.0: 0.0007416, 376.0: 0.0008450296, 377.0: 0.0009645268, 378.0: 0.001094949, 379.0: 0.001231154, 380.0: 0.001368, 381.0: 0.00150205, 382.0: 0.001642328, 383.0: 0.001802382, 384.0: 0.001995757, 385.0: 0.002236, 386.0: 0.002535385, 387.0: 0.002892603, 388.0: 0.003300829, 389.0: 0.003753236, 390.0: 0.004243, 391.0: 0.004762389, 392.0: 0.005330048, 393.0: 0.005978712, 394.0: 0.006741117, 395.0: 0.00765, 396.0: 0.008751373, 397.0: 0.01002888, 398.0: 0.0114217, 399.0: 0.01286901, 400.0: 0.01431, 401.0: 0.01570443, 402.0: 0.01714744, 403.0: 0.01878122, 404.0: 0.02074801, 405.0: 0.02319, 406.0: 0.02620736, 407.0: 0.02978248, 408.0: 0.03388092, 409.0: 0.03846824, 410.0: 0.04351, 411.0: 0.0489956, 412.0: 0.0550226, 413.0: 0.0617188, 414.0: 0.069212, 415.0: 0.07763, 416.0: 0.08695811, 417.0: 0.09717672, 418.0: 0.1084063, 419.0: 0.1207672, 420.0: 0.13438, 421.0: 0.1493582, 422.0: 0.1653957, 423.0: 0.1819831, 424.0: 0.198611, 425.0: 0.21477, 426.0: 0.2301868, 427.0: 0.2448797, 428.0: 0.2587773, 429.0: 0.2718079, 430.0: 0.2839, 431.0: 0.2949438, 432.0: 0.3048965, 433.0: 0.3137873, 434.0: 0.3216454, 435.0: 0.3285, 436.0: 0.3343513, 437.0: 0.3392101, 438.0: 0.3431213, 439.0: 0.3461296, 440.0: 0.34828, 441.0: 0.3495999, 442.0: 0.3501474, 443.0: 0.350013, 444.0: 0.349287, 445.0: 0.34806, 446.0: 0.3463733, 447.0: 0.3442624, 448.0: 0.3418088, 449.0: 0.3390941, 450.0: 0.3362, 451.0: 0.3331977, 452.0: 0.3300411, 453.0: 0.3266357, 454.0: 0.3228868, 455.0: 0.3187, 456.0: 0.3140251, 457.0: 0.308884, 458.0: 0.3032904, 459.0: 0.2972579, 460.0: 0.2908, 461.0: 0.2839701, 462.0: 0.2767214, 463.0: 0.2689178, 464.0: 0.2604227, 465.0: 0.2511, 466.0: 0.2408475, 467.0: 0.2298512, 468.0: 0.2184072, 469.0: 0.2068115, 470.0: 0.19536, 471.0: 0.1842136, 472.0: 0.1733273, 473.0: 0.1626881, 474.0: 0.1522833, 475.0: 0.1421, 476.0: 0.1321786, 477.0: 0.1225696, 478.0: 0.1132752, 479.0: 0.1042979, 480.0: 0.09564, 481.0: 0.08729955, 482.0: 0.07930804, 483.0: 0.07171776, 484.0: 0.06458099, 485.0: 0.05795001, 486.0: 0.05186211, 487.0: 0.04628152, 488.0: 0.04115088, 489.0: 0.03641283, 490.0: 0.03201, 491.0: 0.0279172, 492.0: 0.0241444, 493.0: 0.020687, 494.0: 0.0175404, 495.0: 0.0147, 496.0: 0.01216179, 497.0: 0.00991996, 498.0: 0.00796724, 499.0: 0.006296346, 500.0: 0.0049, 501.0: 0.003777173, 502.0: 0.00294532, 503.0: 0.00242488, 504.0: 0.002236293, 505.0: 0.0024, 506.0: 0.00292552, 507.0: 0.00383656, 508.0: 0.00517484, 509.0: 0.00698208, 510.0: 0.0093, 511.0: 0.01214949, 512.0: 0.01553588, 513.0: 0.01947752, 514.0: 0.02399277, 515.0: 0.0291, 516.0: 0.03481485, 517.0: 0.04112016, 518.0: 0.04798504, 519.0: 0.05537861, 520.0: 0.06327, 521.0: 0.07163501, 522.0: 0.08046224, 523.0: 0.08973996, 524.0: 0.09945645, 525.0: 0.1096, 526.0: 0.1201674, 527.0: 0.1311145, 528.0: 0.1423679, 529.0: 0.1538542, 530.0: 0.1655, 531.0: 0.1772571, 532.0: 0.18914, 533.0: 0.2011694, 534.0: 0.2133658, 535.0: 0.2257499, 536.0: 0.2383209, 537.0: 0.2510668, 538.0: 0.2639922, 539.0: 0.2771017, 540.0: 0.2904, 541.0: 0.3038912, 542.0: 0.3175726, 543.0: 0.3314384, 544.0: 0.3454828, 545.0: 0.3597, 546.0: 0.3740839, 547.0: 0.3886396, 548.0: 0.4033784, 549.0: 0.4183115, 550.0: 0.4334499, 551.0: 0.4487953, 552.0: 0.464336, 553.0: 0.480064, 554.0: 0.4959713, 555.0: 0.5120501, 556.0: 0.5282959, 557.0: 0.5446916, 558.0: 0.5612094, 559.0: 0.5778215, 560.0: 0.5945, 561.0: 0.6112209, 562.0: 0.6279758, 563.0: 0.6447602, 564.0: 0.6615697, 565.0: 0.6784, 566.0: 0.6952392, 567.0: 0.7120586, 568.0: 0.7288284, 569.0: 0.7455188, 570.0: 0.7621, 571.0: 0.7785432, 572.0: 0.7948256, 573.0: 0.8109264, 574.0: 0.8268248, 575.0: 0.8425, 576.0: 0.8579325, 577.0: 0.8730816, 578.0: 0.8878944, 579.0: 0.9023181, 580.0: 0.9163, 581.0: 0.9297995, 582.0: 0.9427984, 583.0: 0.9552776, 584.0: 0.9672179, 585.0: 0.9786, 586.0: 0.9893856, 587.0: 0.9995488, 588.0: 1.0090892, 589.0: 1.0180064, 590.0: 1.0263, 591.0: 1.0339827, 592.0: 1.040986, 593.0: 1.047188, 594.0: 1.0524667, 595.0: 1.0567, 596.0: 1.0597944, 597.0: 1.0617992, 598.0: 1.0628068, 599.0: 1.0629096, 600.0: 1.0622, 601.0: 1.0607352, 602.0: 1.0584436, 603.0: 1.0552244, 604.0: 1.0509768, 605.0: 1.0456, 606.0: 1.0390369, 607.0: 1.0313608, 608.0: 1.0226662, 609.0: 1.0130477, 610.0: 1.0026, 611.0: 0.9913675, 612.0: 0.9793314, 613.0: 0.9664916, 614.0: 0.9528479, 615.0: 0.9384, 616.0: 0.923194, 617.0: 0.907244, 618.0: 0.890502, 619.0: 0.87292, 620.0: 0.8544499, 621.0: 0.835084, 622.0: 0.814946, 623.0: 0.794186, 624.0: 0.772954, 625.0: 0.7514, 626.0: 0.7295836, 627.0: 0.7075888, 628.0: 0.6856022, 629.0: 0.6638104, 630.0: 0.6424, 631.0: 0.6215149, 632.0: 0.6011138, 633.0: 0.5811052, 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592.0: 1.0, 593.0: 1.0, 594.0: 1.0, 595.0: 1.0, 596.0: 1.0, 597.0: 1.0, 598.0: 1.0, 599.0: 1.0, 600.0: 1.0, 601.0: 1.0, 602.0: 1.0, 603.0: 1.0, 604.0: 1.0, 605.0: 1.0, 606.0: 1.0, 607.0: 1.0, 608.0: 1.0, 609.0: 1.0, 610.0: 1.0, 611.0: 1.0, 612.0: 1.0, 613.0: 1.0, 614.0: 1.0, 615.0: 1.0, 616.0: 1.0, 617.0: 1.0, 618.0: 1.0, 619.0: 1.0, 620.0: 1.0, 621.0: 1.0, 622.0: 1.0, 623.0: 1.0, 624.0: 1.0, 625.0: 1.0, 626.0: 1.0, 627.0: 1.0, 628.0: 1.0, 629.0: 1.0, 630.0: 1.0, 631.0: 1.0, 632.0: 1.0, 633.0: 1.0, 634.0: 1.0, 635.0: 1.0, 636.0: 1.0, 637.0: 1.0, 638.0: 1.0, 639.0: 1.0, 640.0: 1.0, 641.0: 1.0, 642.0: 1.0, 643.0: 1.0, 644.0: 1.0, 645.0: 1.0, 646.0: 1.0, 647.0: 1.0, 648.0: 1.0, 649.0: 1.0, 650.0: 1.0, 651.0: 1.0, 652.0: 1.0, 653.0: 1.0, 654.0: 1.0, 655.0: 1.0, 656.0: 1.0, 657.0: 1.0, 658.0: 1.0, 659.0: 1.0, 660.0: 1.0, 661.0: 1.0, 662.0: 1.0, 663.0: 1.0, 664.0: 1.0, 665.0: 1.0, 666.0: 1.0, 667.0: 1.0, 668.0: 1.0, 669.0: 1.0, 670.0: 1.0, 671.0: 1.0, 672.0: 1.0, 673.0: 1.0, 674.0: 1.0, 675.0: 1.0, 676.0: 1.0, 677.0: 1.0, 678.0: 1.0, 679.0: 1.0, 680.0: 1.0, 681.0: 1.0, 682.0: 1.0, 683.0: 1.0, 684.0: 1.0, 685.0: 1.0, 686.0: 1.0, 687.0: 1.0, 688.0: 1.0, 689.0: 1.0, 690.0: 1.0, 691.0: 1.0, 692.0: 1.0, 693.0: 1.0, 694.0: 1.0, 695.0: 1.0, 696.0: 1.0, 697.0: 1.0, 698.0: 1.0, 699.0: 1.0, 700.0: 1.0, 701.0: 1.0, 702.0: 1.0, 703.0: 1.0, 704.0: 1.0, 705.0: 1.0, 706.0: 1.0, 707.0: 1.0, 708.0: 1.0, 709.0: 1.0, 710.0: 1.0, 711.0: 1.0, 712.0: 1.0, 713.0: 1.0, 714.0: 1.0, 715.0: 1.0, 716.0: 1.0, 717.0: 1.0, 718.0: 1.0, 719.0: 1.0, 720.0: 1.0, 721.0: 1.0, 722.0: 1.0, 723.0: 1.0, 724.0: 1.0, 725.0: 1.0, 726.0: 1.0, 727.0: 1.0, 728.0: 1.0, 729.0: 1.0, 730.0: 1.0, 731.0: 1.0, 732.0: 1.0, 733.0: 1.0, 734.0: 1.0, 735.0: 1.0, 736.0: 1.0, 737.0: 1.0, 738.0: 1.0, 739.0: 1.0, 740.0: 1.0, 741.0: 1.0, 742.0: 1.0, 743.0: 1.0, 744.0: 1.0, 745.0: 1.0, 746.0: 1.0, 747.0: 1.0, 748.0: 1.0, 749.0: 1.0, 750.0: 1.0, 751.0: 1.0, 752.0: 1.0, 753.0: 1.0, 754.0: 1.0, 755.0: 1.0, 756.0: 1.0, 757.0: 1.0, 758.0: 1.0, 759.0: 1.0, 760.0: 1.0, 761.0: 1.0, 762.0: 1.0, 763.0: 1.0, 764.0: 1.0, 765.0: 1.0, 766.0: 1.0, 767.0: 1.0, 768.0: 1.0, 769.0: 1.0, 770.0: 1.0, 771.0: 1.0, 772.0: 1.0, 773.0: 1.0, 774.0: 1.0, 775.0: 1.0, 776.0: 1.0, 777.0: 1.0, 778.0: 1.0, 779.0: 1.0, 780.0: 1.0}), method=u'ASTM E308-15', **kwargs)[source]¶ Converts given spectral power distribution to CIE XYZ tristimulus values using given colour matching functions, illuminant and method.
Parameters: - spd (SpectralPowerDistribution) – Spectral power distribution.
- cmfs (XYZ_ColourMatchingFunctions) – Standard observer colour matching functions.
- illuminant (SpectralPowerDistribution, optional) – Illuminant spectral power distribution.
- method (unicode, optional) – {‘ASTM E308-15’, ‘Integration’}, Computation method.
Other Parameters: - use_practice_range (bool, optional) – {
spectral_to_XYZ_ASTME30815()
}, Practise ASTM E308-15 working wavelengths range is [360, 780], if True this argument will trim the colour matching functions appropriately. - mi_5nm_omission_method (bool, optional) – {
spectral_to_XYZ_ASTME30815()
}, 5 nm measurement intervals spectral power distribution conversion to tristimulus values will use a 5 nm version of the colour matching functions instead of a table of tristimulus weighting factors. - mi_20nm_interpolation_method (bool, optional) – {
spectral_to_XYZ_ASTME30815()
}, 20 nm measurement intervals spectral power distribution conversion to tristimulus values will use a dedicated interpolation method instead of a table of tristimulus weighting factors.
Returns: CIE XYZ tristimulus values.
Return type: ndarray, (3,)
Warning
The output range of that definition is non standard!
Notes
- Output CIE XYZ tristimulus values are in range [0, 100].
Examples
>>> from colour import ( ... CMFS, ILLUMINANTS_RELATIVE_SPDS, SpectralPowerDistribution) >>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer'] >>> data = { ... 400: 0.0641, ... 420: 0.0645, ... 440: 0.0562, ... 460: 0.0537, ... 480: 0.0559, ... 500: 0.0651, ... 520: 0.0705, ... 540: 0.0772, ... 560: 0.0870, ... 580: 0.1128, ... 600: 0.1360, ... 620: 0.1511, ... 640: 0.1688, ... 660: 0.1996, ... 680: 0.2397, ... 700: 0.2852} >>> spd = SpectralPowerDistribution('Sample', data) >>> illuminant = ILLUMINANTS_RELATIVE_SPDS['D50'] >>> spectral_to_XYZ( ... spd, cmfs, illuminant) array([ 11.5290265..., 9.9502091..., 4.7098882...]) >>> spectral_to_XYZ( ... spd, cmfs, illuminant, use_practice_range=False) array([ 11.5291275..., 9.9502369..., 4.7098811...]) >>> spectral_to_XYZ( ... spd, cmfs, illuminant, method='Integration') array([ 11.5296285..., 9.9499467..., 4.7066079...])
-
colour.colorimetry.
lagrange_coefficients_ASTME202211
(interval=10, interval_type=u'inner')[source]¶ Computes the Lagrange Coefficients for given interval size using practise ASTM E2022-11 method [1]_.
Parameters: - interval (int) – Interval size in nm.
- interval_type (unicode, optional) – {‘inner’, ‘boundary’}, If the interval is an inner interval Lagrange Coefficients are computed for degree 4. Degree 3 is used for a boundary interval.
Returns: Lagrange Coefficients.
Return type: ndarray
See also
Examples
>>> lagrange_coefficients_ASTME202211( ... 10, 'inner') array([[-0.028..., 0.940..., 0.104..., -0.016...], [-0.048..., 0.864..., 0.216..., -0.032...], [-0.059..., 0.773..., 0.331..., -0.045...], [-0.064..., 0.672..., 0.448..., -0.056...], [-0.062..., 0.562..., 0.562..., -0.062...], [-0.056..., 0.448..., 0.672..., -0.064...], [-0.045..., 0.331..., 0.773..., -0.059...], [-0.032..., 0.216..., 0.864..., -0.048...], [-0.016..., 0.104..., 0.940..., -0.028...]]) >>> lagrange_coefficients_ASTME202211( ... 10, 'boundary') array([[ 0.85..., 0.19..., -0.04...], [ 0.72..., 0.36..., -0.08...], [ 0.59..., 0.51..., -0.10...], [ 0.48..., 0.64..., -0.12...], [ 0.37..., 0.75..., -0.12...], [ 0.28..., 0.84..., -0.12...], [ 0.19..., 0.91..., -0.10...], [ 0.12..., 0.96..., -0.08...], [ 0.05..., 0.99..., -0.04...]])
-
colour.colorimetry.
tristimulus_weighting_factors_ASTME202211
(cmfs, illuminant, shape)[source]¶ Returns a table of tristimulus weighting factors for given colour matching functions and illuminant using practise ASTM E2022-11 method [1]_.
The computed table of tristimulus weighting factors should be used with spectral data that has been corrected for spectral bandpass dependence.
Parameters: - cmfs (XYZ_ColourMatchingFunctions) – Standard observer colour matching functions.
- illuminant (SpectralPowerDistribution) – Illuminant spectral power distribution.
- shape (SpectralShape) – Shape used to build the table, only the interval is needed.
Returns: Tristimulus weighting factors table.
Return type: ndarray
Raises: ValueError
– If the colour matching functions or illuminant intervals are not equal to 1 nm.Warning
- The tables of tristimulus weighting factors are cached in
_TRISTIMULUS_WEIGHTING_FACTORS_CACHE
attribute. Their identifier key is defined by the colour matching functions and illuminant names along the current shape such as: CIE 1964 10 Degree Standard Observer, A, (360.0, 830.0, 10.0) Considering the above, one should be mindful that using similar colour matching functions and illuminant names but with different spectral data will lead to unexpected behaviour.
Notes
- Input colour matching functions and illuminant intervals are expected to be equal to 1 nm. If the illuminant data is not available at 1 nm interval, it needs to be interpolated 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.
Examples
>>> from colour import ( ... CMFS, ... CIE_standard_illuminant_A_function, ... SpectralPowerDistribution, ... SpectralShape) >>> cmfs = CMFS['CIE 1964 10 Degree Standard Observer'] >>> wl = cmfs.shape.range() >>> A = SpectralPowerDistribution( ... 'A (360, 830, 1)', ... dict(zip(wl, CIE_standard_illuminant_A_function(wl)))) >>> tristimulus_weighting_factors_ASTME202211( ... cmfs, A, SpectralShape(360, 830, 20)) array([[ -2.9816934...e-04, -3.1709762...e-05, -1.3301218...e-03], [ -8.7154955...e-03, -8.9154168...e-04, -4.0743684...e-02], [ 5.9967988...e-02, 5.0203497...e-03, 2.5650183...e-01], [ 7.7342255...e-01, 7.7983983...e-02, 3.6965732...e+00], [ 1.9000905...e+00, 3.0370051...e-01, 9.7554195...e+00], [ 1.9707727...e+00, 8.5528092...e-01, 1.1486732...e+01], [ 7.1836236...e-01, 2.1457000...e+00, 6.7845806...e+00], [ 4.2666758...e-02, 4.8985328...e+00, 2.3208000...e+00], [ 1.5223302...e+00, 9.6471138...e+00, 7.4306714...e-01], [ 5.6770329...e+00, 1.4460970...e+01, 1.9581949...e-01], [ 1.2445174...e+01, 1.7474254...e+01, 5.1826979...e-03], [ 2.0553577...e+01, 1.7583821...e+01, -2.6512696...e-03], [ 2.5331538...e+01, 1.4895703...e+01, 0.0000000...e+00], [ 2.1571157...e+01, 1.0079661...e+01, 0.0000000...e+00], [ 1.2178581...e+01, 5.0680655...e+00, 0.0000000...e+00], [ 4.6675746...e+00, 1.8303239...e+00, 0.0000000...e+00], [ 1.3236117...e+00, 5.1296946...e-01, 0.0000000...e+00], [ 3.1753258...e-01, 1.2300847...e-01, 0.0000000...e+00], [ 7.4634128...e-02, 2.9024389...e-02, 0.0000000...e+00], [ 1.8299016...e-02, 7.1606335...e-03, 0.0000000...e+00], [ 4.7942065...e-03, 1.8888730...e-03, 0.0000000...e+00], [ 1.3293045...e-03, 5.2774591...e-04, 0.0000000...e+00], [ 4.2546928...e-04, 1.7041978...e-04, 0.0000000...e+00], [ 9.6251115...e-05, 3.8955295...e-05, 0.0000000...e+00]])
-
colour.colorimetry.
adjust_tristimulus_weighting_factors_ASTME30815
(W, shape_r, shape_t)[source]¶ Adjusts given table of tristimulus weighting factors to account for a shorter wavelengths range of the test spectral shape compared to the reference spectral shape using practise ASTM E308-15 method [2]_: Weights at the wavelengths for which data are not available are added to the weights at the shortest and longest wavelength for which spectral data are available.
Parameters: - W (array_like) – Tristimulus weighting factors table.
- shape_r (SpectralShape) – Reference spectral shape.
- shape_t (SpectralShape) – Test spectral shape.
Returns: Adjusted tristimulus weighting factors.
Return type: ndarray
Examples
>>> from colour import ( ... CMFS, ... CIE_standard_illuminant_A_function, ... SpectralPowerDistribution, ... SpectralShape) >>> cmfs = CMFS['CIE 1964 10 Degree Standard Observer'] >>> wl = cmfs.shape.range() >>> A = SpectralPowerDistribution( ... 'A (360, 830, 1)', ... dict(zip(wl, CIE_standard_illuminant_A_function(wl)))) >>> W = tristimulus_weighting_factors_ASTME202211( ... cmfs, A, SpectralShape(360, 830, 20)) >>> adjust_tristimulus_weighting_factors_ASTME30815( ... W, SpectralShape(360, 830, 20), SpectralShape(400, 700, 20)) array([[ 5.0954324...e-02, 4.0970982...e-03, 2.1442802...e-01], [ 7.7342255...e-01, 7.7983983...e-02, 3.6965732...e+00], [ 1.9000905...e+00, 3.0370051...e-01, 9.7554195...e+00], [ 1.9707727...e+00, 8.5528092...e-01, 1.1486732...e+01], [ 7.1836236...e-01, 2.1457000...e+00, 6.7845806...e+00], [ 4.2666758...e-02, 4.8985328...e+00, 2.3208000...e+00], [ 1.5223302...e+00, 9.6471138...e+00, 7.4306714...e-01], [ 5.6770329...e+00, 1.4460970...e+01, 1.9581949...e-01], [ 1.2445174...e+01, 1.7474254...e+01, 5.1826979...e-03], [ 2.0553577...e+01, 1.7583821...e+01, -2.6512696...e-03], [ 2.5331538...e+01, 1.4895703...e+01, 0.0000000...e+00], [ 2.1571157...e+01, 1.0079661...e+01, 0.0000000...e+00], [ 1.2178581...e+01, 5.0680655...e+00, 0.0000000...e+00], [ 4.6675746...e+00, 1.8303239...e+00, 0.0000000...e+00], [ 1.3236117...e+00, 5.1296946...e-01, 0.0000000...e+00], [ 4.1711096...e-01, 1.6181949...e-01, 0.0000000...e+00]])
-
colour.colorimetry.
spectral_to_XYZ_integration
(spd, cmfs=XYZ_ColourMatchingFunctions( 'CIE 1931 2 Degree Standard Observer', {u'x_bar': {360.0: 0.0001299, 361.0: 0.000145847, 362.0: 0.0001638021, 363.0: 0.0001840037, 364.0: 0.0002066902, 365.0: 0.0002321, 366.0: 0.000260728, 367.0: 0.000293075, 368.0: 0.000329388, 369.0: 0.000369914, 370.0: 0.0004149, 371.0: 0.0004641587, 372.0: 0.000518986, 373.0: 0.000581854, 374.0: 0.0006552347, 375.0: 0.0007416, 376.0: 0.0008450296, 377.0: 0.0009645268, 378.0: 0.001094949, 379.0: 0.001231154, 380.0: 0.001368, 381.0: 0.00150205, 382.0: 0.001642328, 383.0: 0.001802382, 384.0: 0.001995757, 385.0: 0.002236, 386.0: 0.002535385, 387.0: 0.002892603, 388.0: 0.003300829, 389.0: 0.003753236, 390.0: 0.004243, 391.0: 0.004762389, 392.0: 0.005330048, 393.0: 0.005978712, 394.0: 0.006741117, 395.0: 0.00765, 396.0: 0.008751373, 397.0: 0.01002888, 398.0: 0.0114217, 399.0: 0.01286901, 400.0: 0.01431, 401.0: 0.01570443, 402.0: 0.01714744, 403.0: 0.01878122, 404.0: 0.02074801, 405.0: 0.02319, 406.0: 0.02620736, 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426.0: 1.0, 427.0: 1.0, 428.0: 1.0, 429.0: 1.0, 430.0: 1.0, 431.0: 1.0, 432.0: 1.0, 433.0: 1.0, 434.0: 1.0, 435.0: 1.0, 436.0: 1.0, 437.0: 1.0, 438.0: 1.0, 439.0: 1.0, 440.0: 1.0, 441.0: 1.0, 442.0: 1.0, 443.0: 1.0, 444.0: 1.0, 445.0: 1.0, 446.0: 1.0, 447.0: 1.0, 448.0: 1.0, 449.0: 1.0, 450.0: 1.0, 451.0: 1.0, 452.0: 1.0, 453.0: 1.0, 454.0: 1.0, 455.0: 1.0, 456.0: 1.0, 457.0: 1.0, 458.0: 1.0, 459.0: 1.0, 460.0: 1.0, 461.0: 1.0, 462.0: 1.0, 463.0: 1.0, 464.0: 1.0, 465.0: 1.0, 466.0: 1.0, 467.0: 1.0, 468.0: 1.0, 469.0: 1.0, 470.0: 1.0, 471.0: 1.0, 472.0: 1.0, 473.0: 1.0, 474.0: 1.0, 475.0: 1.0, 476.0: 1.0, 477.0: 1.0, 478.0: 1.0, 479.0: 1.0, 480.0: 1.0, 481.0: 1.0, 482.0: 1.0, 483.0: 1.0, 484.0: 1.0, 485.0: 1.0, 486.0: 1.0, 487.0: 1.0, 488.0: 1.0, 489.0: 1.0, 490.0: 1.0, 491.0: 1.0, 492.0: 1.0, 493.0: 1.0, 494.0: 1.0, 495.0: 1.0, 496.0: 1.0, 497.0: 1.0, 498.0: 1.0, 499.0: 1.0, 500.0: 1.0, 501.0: 1.0, 502.0: 1.0, 503.0: 1.0, 504.0: 1.0, 505.0: 1.0, 506.0: 1.0, 507.0: 1.0, 508.0: 1.0, 509.0: 1.0, 510.0: 1.0, 511.0: 1.0, 512.0: 1.0, 513.0: 1.0, 514.0: 1.0, 515.0: 1.0, 516.0: 1.0, 517.0: 1.0, 518.0: 1.0, 519.0: 1.0, 520.0: 1.0, 521.0: 1.0, 522.0: 1.0, 523.0: 1.0, 524.0: 1.0, 525.0: 1.0, 526.0: 1.0, 527.0: 1.0, 528.0: 1.0, 529.0: 1.0, 530.0: 1.0, 531.0: 1.0, 532.0: 1.0, 533.0: 1.0, 534.0: 1.0, 535.0: 1.0, 536.0: 1.0, 537.0: 1.0, 538.0: 1.0, 539.0: 1.0, 540.0: 1.0, 541.0: 1.0, 542.0: 1.0, 543.0: 1.0, 544.0: 1.0, 545.0: 1.0, 546.0: 1.0, 547.0: 1.0, 548.0: 1.0, 549.0: 1.0, 550.0: 1.0, 551.0: 1.0, 552.0: 1.0, 553.0: 1.0, 554.0: 1.0, 555.0: 1.0, 556.0: 1.0, 557.0: 1.0, 558.0: 1.0, 559.0: 1.0, 560.0: 1.0, 561.0: 1.0, 562.0: 1.0, 563.0: 1.0, 564.0: 1.0, 565.0: 1.0, 566.0: 1.0, 567.0: 1.0, 568.0: 1.0, 569.0: 1.0, 570.0: 1.0, 571.0: 1.0, 572.0: 1.0, 573.0: 1.0, 574.0: 1.0, 575.0: 1.0, 576.0: 1.0, 577.0: 1.0, 578.0: 1.0, 579.0: 1.0, 580.0: 1.0, 581.0: 1.0, 582.0: 1.0, 583.0: 1.0, 584.0: 1.0, 585.0: 1.0, 586.0: 1.0, 587.0: 1.0, 588.0: 1.0, 589.0: 1.0, 590.0: 1.0, 591.0: 1.0, 592.0: 1.0, 593.0: 1.0, 594.0: 1.0, 595.0: 1.0, 596.0: 1.0, 597.0: 1.0, 598.0: 1.0, 599.0: 1.0, 600.0: 1.0, 601.0: 1.0, 602.0: 1.0, 603.0: 1.0, 604.0: 1.0, 605.0: 1.0, 606.0: 1.0, 607.0: 1.0, 608.0: 1.0, 609.0: 1.0, 610.0: 1.0, 611.0: 1.0, 612.0: 1.0, 613.0: 1.0, 614.0: 1.0, 615.0: 1.0, 616.0: 1.0, 617.0: 1.0, 618.0: 1.0, 619.0: 1.0, 620.0: 1.0, 621.0: 1.0, 622.0: 1.0, 623.0: 1.0, 624.0: 1.0, 625.0: 1.0, 626.0: 1.0, 627.0: 1.0, 628.0: 1.0, 629.0: 1.0, 630.0: 1.0, 631.0: 1.0, 632.0: 1.0, 633.0: 1.0, 634.0: 1.0, 635.0: 1.0, 636.0: 1.0, 637.0: 1.0, 638.0: 1.0, 639.0: 1.0, 640.0: 1.0, 641.0: 1.0, 642.0: 1.0, 643.0: 1.0, 644.0: 1.0, 645.0: 1.0, 646.0: 1.0, 647.0: 1.0, 648.0: 1.0, 649.0: 1.0, 650.0: 1.0, 651.0: 1.0, 652.0: 1.0, 653.0: 1.0, 654.0: 1.0, 655.0: 1.0, 656.0: 1.0, 657.0: 1.0, 658.0: 1.0, 659.0: 1.0, 660.0: 1.0, 661.0: 1.0, 662.0: 1.0, 663.0: 1.0, 664.0: 1.0, 665.0: 1.0, 666.0: 1.0, 667.0: 1.0, 668.0: 1.0, 669.0: 1.0, 670.0: 1.0, 671.0: 1.0, 672.0: 1.0, 673.0: 1.0, 674.0: 1.0, 675.0: 1.0, 676.0: 1.0, 677.0: 1.0, 678.0: 1.0, 679.0: 1.0, 680.0: 1.0, 681.0: 1.0, 682.0: 1.0, 683.0: 1.0, 684.0: 1.0, 685.0: 1.0, 686.0: 1.0, 687.0: 1.0, 688.0: 1.0, 689.0: 1.0, 690.0: 1.0, 691.0: 1.0, 692.0: 1.0, 693.0: 1.0, 694.0: 1.0, 695.0: 1.0, 696.0: 1.0, 697.0: 1.0, 698.0: 1.0, 699.0: 1.0, 700.0: 1.0, 701.0: 1.0, 702.0: 1.0, 703.0: 1.0, 704.0: 1.0, 705.0: 1.0, 706.0: 1.0, 707.0: 1.0, 708.0: 1.0, 709.0: 1.0, 710.0: 1.0, 711.0: 1.0, 712.0: 1.0, 713.0: 1.0, 714.0: 1.0, 715.0: 1.0, 716.0: 1.0, 717.0: 1.0, 718.0: 1.0, 719.0: 1.0, 720.0: 1.0, 721.0: 1.0, 722.0: 1.0, 723.0: 1.0, 724.0: 1.0, 725.0: 1.0, 726.0: 1.0, 727.0: 1.0, 728.0: 1.0, 729.0: 1.0, 730.0: 1.0, 731.0: 1.0, 732.0: 1.0, 733.0: 1.0, 734.0: 1.0, 735.0: 1.0, 736.0: 1.0, 737.0: 1.0, 738.0: 1.0, 739.0: 1.0, 740.0: 1.0, 741.0: 1.0, 742.0: 1.0, 743.0: 1.0, 744.0: 1.0, 745.0: 1.0, 746.0: 1.0, 747.0: 1.0, 748.0: 1.0, 749.0: 1.0, 750.0: 1.0, 751.0: 1.0, 752.0: 1.0, 753.0: 1.0, 754.0: 1.0, 755.0: 1.0, 756.0: 1.0, 757.0: 1.0, 758.0: 1.0, 759.0: 1.0, 760.0: 1.0, 761.0: 1.0, 762.0: 1.0, 763.0: 1.0, 764.0: 1.0, 765.0: 1.0, 766.0: 1.0, 767.0: 1.0, 768.0: 1.0, 769.0: 1.0, 770.0: 1.0, 771.0: 1.0, 772.0: 1.0, 773.0: 1.0, 774.0: 1.0, 775.0: 1.0, 776.0: 1.0, 777.0: 1.0, 778.0: 1.0, 779.0: 1.0, 780.0: 1.0, 781.0: 1.0, 782.0: 1.0, 783.0: 1.0, 784.0: 1.0, 785.0: 1.0, 786.0: 1.0, 787.0: 1.0, 788.0: 1.0, 789.0: 1.0, 790.0: 1.0, 791.0: 1.0, 792.0: 1.0, 793.0: 1.0, 794.0: 1.0, 795.0: 1.0, 796.0: 1.0, 797.0: 1.0, 798.0: 1.0, 799.0: 1.0, 800.0: 1.0, 801.0: 1.0, 802.0: 1.0, 803.0: 1.0, 804.0: 1.0, 805.0: 1.0, 806.0: 1.0, 807.0: 1.0, 808.0: 1.0, 809.0: 1.0, 810.0: 1.0, 811.0: 1.0, 812.0: 1.0, 813.0: 1.0, 814.0: 1.0, 815.0: 1.0, 816.0: 1.0, 817.0: 1.0, 818.0: 1.0, 819.0: 1.0, 820.0: 1.0, 821.0: 1.0, 822.0: 1.0, 823.0: 1.0, 824.0: 1.0, 825.0: 1.0, 826.0: 1.0, 827.0: 1.0, 828.0: 1.0, 829.0: 1.0, 830.0: 1.0}))[source]¶ Converts given spectral power distribution to CIE XYZ tristimulus values using given colour matching functions and illuminant accordingly to classical integration method.
Parameters: - spd (SpectralPowerDistribution) – Spectral power distribution.
- cmfs (XYZ_ColourMatchingFunctions) – Standard observer colour matching functions.
- illuminant (SpectralPowerDistribution, optional) – Illuminant spectral power distribution.
Returns: CIE XYZ tristimulus values.
Return type: ndarray, (3,)
Warning
The output range of that definition is non standard!
Notes
- Output CIE XYZ tristimulus values are in range [0, 100].
References
[3] Wyszecki, G., & Stiles, W. S. (2000). Integration Replace by Summation. In Color Science: Concepts and Methods, Quantitative Data and Formulae (pp. 158–163). Wiley. ISBN:978-0471399186 Examples
>>> from colour import ( ... CMFS, ILLUMINANTS_RELATIVE_SPDS, SpectralPowerDistribution) >>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer'] >>> data = { ... 400: 0.0641, ... 420: 0.0645, ... 440: 0.0562, ... 460: 0.0537, ... 480: 0.0559, ... 500: 0.0651, ... 520: 0.0705, ... 540: 0.0772, ... 560: 0.0870, ... 580: 0.1128, ... 600: 0.1360, ... 620: 0.1511, ... 640: 0.1688, ... 660: 0.1996, ... 680: 0.2397, ... 700: 0.2852} >>> spd = SpectralPowerDistribution('Sample', data) >>> illuminant = ILLUMINANTS_RELATIVE_SPDS['D50'] >>> spectral_to_XYZ_integration( ... spd, cmfs, illuminant) array([ 11.5296285..., 9.9499467..., 4.7066079...])
-
colour.colorimetry.
spectral_to_XYZ_tristimulus_weighting_factors_ASTME30815
(spd, cmfs=XYZ_ColourMatchingFunctions( 'CIE 1931 2 Degree Standard Observer', {u'x_bar': {360.0: 0.0001299, 361.0: 0.000145847, 362.0: 0.0001638021, 363.0: 0.0001840037, 364.0: 0.0002066902, 365.0: 0.0002321, 366.0: 0.000260728, 367.0: 0.000293075, 368.0: 0.000329388, 369.0: 0.000369914, 370.0: 0.0004149, 371.0: 0.0004641587, 372.0: 0.000518986, 373.0: 0.000581854, 374.0: 0.0006552347, 375.0: 0.0007416, 376.0: 0.0008450296, 377.0: 0.0009645268, 378.0: 0.001094949, 379.0: 0.001231154, 380.0: 0.001368, 381.0: 0.00150205, 382.0: 0.001642328, 383.0: 0.001802382, 384.0: 0.001995757, 385.0: 0.002236, 386.0: 0.002535385, 387.0: 0.002892603, 388.0: 0.003300829, 389.0: 0.003753236, 390.0: 0.004243, 391.0: 0.004762389, 392.0: 0.005330048, 393.0: 0.005978712, 394.0: 0.006741117, 395.0: 0.00765, 396.0: 0.008751373, 397.0: 0.01002888, 398.0: 0.0114217, 399.0: 0.01286901, 400.0: 0.01431, 401.0: 0.01570443, 402.0: 0.01714744, 403.0: 0.01878122, 404.0: 0.02074801, 405.0: 0.02319, 406.0: 0.02620736, 407.0: 0.02978248, 408.0: 0.03388092, 409.0: 0.03846824, 410.0: 0.04351, 411.0: 0.0489956, 412.0: 0.0550226, 413.0: 0.0617188, 414.0: 0.069212, 415.0: 0.07763, 416.0: 0.08695811, 417.0: 0.09717672, 418.0: 0.1084063, 419.0: 0.1207672, 420.0: 0.13438, 421.0: 0.1493582, 422.0: 0.1653957, 423.0: 0.1819831, 424.0: 0.198611, 425.0: 0.21477, 426.0: 0.2301868, 427.0: 0.2448797, 428.0: 0.2587773, 429.0: 0.2718079, 430.0: 0.2839, 431.0: 0.2949438, 432.0: 0.3048965, 433.0: 0.3137873, 434.0: 0.3216454, 435.0: 0.3285, 436.0: 0.3343513, 437.0: 0.3392101, 438.0: 0.3431213, 439.0: 0.3461296, 440.0: 0.34828, 441.0: 0.3495999, 442.0: 0.3501474, 443.0: 0.350013, 444.0: 0.349287, 445.0: 0.34806, 446.0: 0.3463733, 447.0: 0.3442624, 448.0: 0.3418088, 449.0: 0.3390941, 450.0: 0.3362, 451.0: 0.3331977, 452.0: 0.3300411, 453.0: 0.3266357, 454.0: 0.3228868, 455.0: 0.3187, 456.0: 0.3140251, 457.0: 0.308884, 458.0: 0.3032904, 459.0: 0.2972579, 460.0: 0.2908, 461.0: 0.2839701, 462.0: 0.2767214, 463.0: 0.2689178, 464.0: 0.2604227, 465.0: 0.2511, 466.0: 0.2408475, 467.0: 0.2298512, 468.0: 0.2184072, 469.0: 0.2068115, 470.0: 0.19536, 471.0: 0.1842136, 472.0: 0.1733273, 473.0: 0.1626881, 474.0: 0.1522833, 475.0: 0.1421, 476.0: 0.1321786, 477.0: 0.1225696, 478.0: 0.1132752, 479.0: 0.1042979, 480.0: 0.09564, 481.0: 0.08729955, 482.0: 0.07930804, 483.0: 0.07171776, 484.0: 0.06458099, 485.0: 0.05795001, 486.0: 0.05186211, 487.0: 0.04628152, 488.0: 0.04115088, 489.0: 0.03641283, 490.0: 0.03201, 491.0: 0.0279172, 492.0: 0.0241444, 493.0: 0.020687, 494.0: 0.0175404, 495.0: 0.0147, 496.0: 0.01216179, 497.0: 0.00991996, 498.0: 0.00796724, 499.0: 0.006296346, 500.0: 0.0049, 501.0: 0.003777173, 502.0: 0.00294532, 503.0: 0.00242488, 504.0: 0.002236293, 505.0: 0.0024, 506.0: 0.00292552, 507.0: 0.00383656, 508.0: 0.00517484, 509.0: 0.00698208, 510.0: 0.0093, 511.0: 0.01214949, 512.0: 0.01553588, 513.0: 0.01947752, 514.0: 0.02399277, 515.0: 0.0291, 516.0: 0.03481485, 517.0: 0.04112016, 518.0: 0.04798504, 519.0: 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509.0: 1.0, 510.0: 1.0, 511.0: 1.0, 512.0: 1.0, 513.0: 1.0, 514.0: 1.0, 515.0: 1.0, 516.0: 1.0, 517.0: 1.0, 518.0: 1.0, 519.0: 1.0, 520.0: 1.0, 521.0: 1.0, 522.0: 1.0, 523.0: 1.0, 524.0: 1.0, 525.0: 1.0, 526.0: 1.0, 527.0: 1.0, 528.0: 1.0, 529.0: 1.0, 530.0: 1.0, 531.0: 1.0, 532.0: 1.0, 533.0: 1.0, 534.0: 1.0, 535.0: 1.0, 536.0: 1.0, 537.0: 1.0, 538.0: 1.0, 539.0: 1.0, 540.0: 1.0, 541.0: 1.0, 542.0: 1.0, 543.0: 1.0, 544.0: 1.0, 545.0: 1.0, 546.0: 1.0, 547.0: 1.0, 548.0: 1.0, 549.0: 1.0, 550.0: 1.0, 551.0: 1.0, 552.0: 1.0, 553.0: 1.0, 554.0: 1.0, 555.0: 1.0, 556.0: 1.0, 557.0: 1.0, 558.0: 1.0, 559.0: 1.0, 560.0: 1.0, 561.0: 1.0, 562.0: 1.0, 563.0: 1.0, 564.0: 1.0, 565.0: 1.0, 566.0: 1.0, 567.0: 1.0, 568.0: 1.0, 569.0: 1.0, 570.0: 1.0, 571.0: 1.0, 572.0: 1.0, 573.0: 1.0, 574.0: 1.0, 575.0: 1.0, 576.0: 1.0, 577.0: 1.0, 578.0: 1.0, 579.0: 1.0, 580.0: 1.0, 581.0: 1.0, 582.0: 1.0, 583.0: 1.0, 584.0: 1.0, 585.0: 1.0, 586.0: 1.0, 587.0: 1.0, 588.0: 1.0, 589.0: 1.0, 590.0: 1.0, 591.0: 1.0, 592.0: 1.0, 593.0: 1.0, 594.0: 1.0, 595.0: 1.0, 596.0: 1.0, 597.0: 1.0, 598.0: 1.0, 599.0: 1.0, 600.0: 1.0, 601.0: 1.0, 602.0: 1.0, 603.0: 1.0, 604.0: 1.0, 605.0: 1.0, 606.0: 1.0, 607.0: 1.0, 608.0: 1.0, 609.0: 1.0, 610.0: 1.0, 611.0: 1.0, 612.0: 1.0, 613.0: 1.0, 614.0: 1.0, 615.0: 1.0, 616.0: 1.0, 617.0: 1.0, 618.0: 1.0, 619.0: 1.0, 620.0: 1.0, 621.0: 1.0, 622.0: 1.0, 623.0: 1.0, 624.0: 1.0, 625.0: 1.0, 626.0: 1.0, 627.0: 1.0, 628.0: 1.0, 629.0: 1.0, 630.0: 1.0, 631.0: 1.0, 632.0: 1.0, 633.0: 1.0, 634.0: 1.0, 635.0: 1.0, 636.0: 1.0, 637.0: 1.0, 638.0: 1.0, 639.0: 1.0, 640.0: 1.0, 641.0: 1.0, 642.0: 1.0, 643.0: 1.0, 644.0: 1.0, 645.0: 1.0, 646.0: 1.0, 647.0: 1.0, 648.0: 1.0, 649.0: 1.0, 650.0: 1.0, 651.0: 1.0, 652.0: 1.0, 653.0: 1.0, 654.0: 1.0, 655.0: 1.0, 656.0: 1.0, 657.0: 1.0, 658.0: 1.0, 659.0: 1.0, 660.0: 1.0, 661.0: 1.0, 662.0: 1.0, 663.0: 1.0, 664.0: 1.0, 665.0: 1.0, 666.0: 1.0, 667.0: 1.0, 668.0: 1.0, 669.0: 1.0, 670.0: 1.0, 671.0: 1.0, 672.0: 1.0, 673.0: 1.0, 674.0: 1.0, 675.0: 1.0, 676.0: 1.0, 677.0: 1.0, 678.0: 1.0, 679.0: 1.0, 680.0: 1.0, 681.0: 1.0, 682.0: 1.0, 683.0: 1.0, 684.0: 1.0, 685.0: 1.0, 686.0: 1.0, 687.0: 1.0, 688.0: 1.0, 689.0: 1.0, 690.0: 1.0, 691.0: 1.0, 692.0: 1.0, 693.0: 1.0, 694.0: 1.0, 695.0: 1.0, 696.0: 1.0, 697.0: 1.0, 698.0: 1.0, 699.0: 1.0, 700.0: 1.0, 701.0: 1.0, 702.0: 1.0, 703.0: 1.0, 704.0: 1.0, 705.0: 1.0, 706.0: 1.0, 707.0: 1.0, 708.0: 1.0, 709.0: 1.0, 710.0: 1.0, 711.0: 1.0, 712.0: 1.0, 713.0: 1.0, 714.0: 1.0, 715.0: 1.0, 716.0: 1.0, 717.0: 1.0, 718.0: 1.0, 719.0: 1.0, 720.0: 1.0, 721.0: 1.0, 722.0: 1.0, 723.0: 1.0, 724.0: 1.0, 725.0: 1.0, 726.0: 1.0, 727.0: 1.0, 728.0: 1.0, 729.0: 1.0, 730.0: 1.0, 731.0: 1.0, 732.0: 1.0, 733.0: 1.0, 734.0: 1.0, 735.0: 1.0, 736.0: 1.0, 737.0: 1.0, 738.0: 1.0, 739.0: 1.0, 740.0: 1.0, 741.0: 1.0, 742.0: 1.0, 743.0: 1.0, 744.0: 1.0, 745.0: 1.0, 746.0: 1.0, 747.0: 1.0, 748.0: 1.0, 749.0: 1.0, 750.0: 1.0, 751.0: 1.0, 752.0: 1.0, 753.0: 1.0, 754.0: 1.0, 755.0: 1.0, 756.0: 1.0, 757.0: 1.0, 758.0: 1.0, 759.0: 1.0, 760.0: 1.0, 761.0: 1.0, 762.0: 1.0, 763.0: 1.0, 764.0: 1.0, 765.0: 1.0, 766.0: 1.0, 767.0: 1.0, 768.0: 1.0, 769.0: 1.0, 770.0: 1.0, 771.0: 1.0, 772.0: 1.0, 773.0: 1.0, 774.0: 1.0, 775.0: 1.0, 776.0: 1.0, 777.0: 1.0, 778.0: 1.0, 779.0: 1.0, 780.0: 1.0}))[source]¶ Converts given spectral power distribution to CIE XYZ tristimulus values using given colour matching functions and illuminant using a table of tristimulus weighting factors accordingly to practise ASTM E308-15 method [2]_.
Parameters: - spd (SpectralPowerDistribution) – Spectral power distribution.
- cmfs (XYZ_ColourMatchingFunctions) – Standard observer colour matching functions.
- illuminant (SpectralPowerDistribution, optional) – Illuminant spectral power distribution.
Returns: CIE XYZ tristimulus values.
Return type: ndarray, (3,)
Warning
The output range of that definition is non standard!
Notes
- Output CIE XYZ tristimulus values are in range [0, 100].
Examples
>>> from colour import ( ... CMFS, ILLUMINANTS_RELATIVE_SPDS, SpectralPowerDistribution) >>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer'] >>> data = { ... 400: 0.0641, ... 420: 0.0645, ... 440: 0.0562, ... 460: 0.0537, ... 480: 0.0559, ... 500: 0.0651, ... 520: 0.0705, ... 540: 0.0772, ... 560: 0.0870, ... 580: 0.1128, ... 600: 0.1360, ... 620: 0.1511, ... 640: 0.1688, ... 660: 0.1996, ... 680: 0.2397, ... 700: 0.2852} >>> spd = SpectralPowerDistribution('Sample', data) >>> illuminant = ILLUMINANTS_RELATIVE_SPDS['D50'] >>> spectral_to_XYZ_tristimulus_weighting_factors_ASTME30815( ... spd, cmfs, illuminant) array([ 11.5296311..., 9.9505845..., 4.7098037...])
-
colour.colorimetry.
spectral_to_XYZ_ASTME30815
(spd, cmfs=XYZ_ColourMatchingFunctions( 'CIE 1931 2 Degree Standard Observer', {u'x_bar': {360.0: 0.0001299, 361.0: 0.000145847, 362.0: 0.0001638021, 363.0: 0.0001840037, 364.0: 0.0002066902, 365.0: 0.0002321, 366.0: 0.000260728, 367.0: 0.000293075, 368.0: 0.000329388, 369.0: 0.000369914, 370.0: 0.0004149, 371.0: 0.0004641587, 372.0: 0.000518986, 373.0: 0.000581854, 374.0: 0.0006552347, 375.0: 0.0007416, 376.0: 0.0008450296, 377.0: 0.0009645268, 378.0: 0.001094949, 379.0: 0.001231154, 380.0: 0.001368, 381.0: 0.00150205, 382.0: 0.001642328, 383.0: 0.001802382, 384.0: 0.001995757, 385.0: 0.002236, 386.0: 0.002535385, 387.0: 0.002892603, 388.0: 0.003300829, 389.0: 0.003753236, 390.0: 0.004243, 391.0: 0.004762389, 392.0: 0.005330048, 393.0: 0.005978712, 394.0: 0.006741117, 395.0: 0.00765, 396.0: 0.008751373, 397.0: 0.01002888, 398.0: 0.0114217, 399.0: 0.01286901, 400.0: 0.01431, 401.0: 0.01570443, 402.0: 0.01714744, 403.0: 0.01878122, 404.0: 0.02074801, 405.0: 0.02319, 406.0: 0.02620736, 407.0: 0.02978248, 408.0: 0.03388092, 409.0: 0.03846824, 410.0: 0.04351, 411.0: 0.0489956, 412.0: 0.0550226, 413.0: 0.0617188, 414.0: 0.069212, 415.0: 0.07763, 416.0: 0.08695811, 417.0: 0.09717672, 418.0: 0.1084063, 419.0: 0.1207672, 420.0: 0.13438, 421.0: 0.1493582, 422.0: 0.1653957, 423.0: 0.1819831, 424.0: 0.198611, 425.0: 0.21477, 426.0: 0.2301868, 427.0: 0.2448797, 428.0: 0.2587773, 429.0: 0.2718079, 430.0: 0.2839, 431.0: 0.2949438, 432.0: 0.3048965, 433.0: 0.3137873, 434.0: 0.3216454, 435.0: 0.3285, 436.0: 0.3343513, 437.0: 0.3392101, 438.0: 0.3431213, 439.0: 0.3461296, 440.0: 0.34828, 441.0: 0.3495999, 442.0: 0.3501474, 443.0: 0.350013, 444.0: 0.349287, 445.0: 0.34806, 446.0: 0.3463733, 447.0: 0.3442624, 448.0: 0.3418088, 449.0: 0.3390941, 450.0: 0.3362, 451.0: 0.3331977, 452.0: 0.3300411, 453.0: 0.3266357, 454.0: 0.3228868, 455.0: 0.3187, 456.0: 0.3140251, 457.0: 0.308884, 458.0: 0.3032904, 459.0: 0.2972579, 460.0: 0.2908, 461.0: 0.2839701, 462.0: 0.2767214, 463.0: 0.2689178, 464.0: 0.2604227, 465.0: 0.2511, 466.0: 0.2408475, 467.0: 0.2298512, 468.0: 0.2184072, 469.0: 0.2068115, 470.0: 0.19536, 471.0: 0.1842136, 472.0: 0.1733273, 473.0: 0.1626881, 474.0: 0.1522833, 475.0: 0.1421, 476.0: 0.1321786, 477.0: 0.1225696, 478.0: 0.1132752, 479.0: 0.1042979, 480.0: 0.09564, 481.0: 0.08729955, 482.0: 0.07930804, 483.0: 0.07171776, 484.0: 0.06458099, 485.0: 0.05795001, 486.0: 0.05186211, 487.0: 0.04628152, 488.0: 0.04115088, 489.0: 0.03641283, 490.0: 0.03201, 491.0: 0.0279172, 492.0: 0.0241444, 493.0: 0.020687, 494.0: 0.0175404, 495.0: 0.0147, 496.0: 0.01216179, 497.0: 0.00991996, 498.0: 0.00796724, 499.0: 0.006296346, 500.0: 0.0049, 501.0: 0.003777173, 502.0: 0.00294532, 503.0: 0.00242488, 504.0: 0.002236293, 505.0: 0.0024, 506.0: 0.00292552, 507.0: 0.00383656, 508.0: 0.00517484, 509.0: 0.00698208, 510.0: 0.0093, 511.0: 0.01214949, 512.0: 0.01553588, 513.0: 0.01947752, 514.0: 0.02399277, 515.0: 0.0291, 516.0: 0.03481485, 517.0: 0.04112016, 518.0: 0.04798504, 519.0: 0.05537861, 520.0: 0.06327, 521.0: 0.07163501, 522.0: 0.08046224, 523.0: 0.08973996, 524.0: 0.09945645, 525.0: 0.1096, 526.0: 0.1201674, 527.0: 0.1311145, 528.0: 0.1423679, 529.0: 0.1538542, 530.0: 0.1655, 531.0: 0.1772571, 532.0: 0.18914, 533.0: 0.2011694, 534.0: 0.2133658, 535.0: 0.2257499, 536.0: 0.2383209, 537.0: 0.2510668, 538.0: 0.2639922, 539.0: 0.2771017, 540.0: 0.2904, 541.0: 0.3038912, 542.0: 0.3175726, 543.0: 0.3314384, 544.0: 0.3454828, 545.0: 0.3597, 546.0: 0.3740839, 547.0: 0.3886396, 548.0: 0.4033784, 549.0: 0.4183115, 550.0: 0.4334499, 551.0: 0.4487953, 552.0: 0.464336, 553.0: 0.480064, 554.0: 0.4959713, 555.0: 0.5120501, 556.0: 0.5282959, 557.0: 0.5446916, 558.0: 0.5612094, 559.0: 0.5778215, 560.0: 0.5945, 561.0: 0.6112209, 562.0: 0.6279758, 563.0: 0.6447602, 564.0: 0.6615697, 565.0: 0.6784, 566.0: 0.6952392, 567.0: 0.7120586, 568.0: 0.7288284, 569.0: 0.7455188, 570.0: 0.7621, 571.0: 0.7785432, 572.0: 0.7948256, 573.0: 0.8109264, 574.0: 0.8268248, 575.0: 0.8425, 576.0: 0.8579325, 577.0: 0.8730816, 578.0: 0.8878944, 579.0: 0.9023181, 580.0: 0.9163, 581.0: 0.9297995, 582.0: 0.9427984, 583.0: 0.9552776, 584.0: 0.9672179, 585.0: 0.9786, 586.0: 0.9893856, 587.0: 0.9995488, 588.0: 1.0090892, 589.0: 1.0180064, 590.0: 1.0263, 591.0: 1.0339827, 592.0: 1.040986, 593.0: 1.047188, 594.0: 1.0524667, 595.0: 1.0567, 596.0: 1.0597944, 597.0: 1.0617992, 598.0: 1.0628068, 599.0: 1.0629096, 600.0: 1.0622, 601.0: 1.0607352, 602.0: 1.0584436, 603.0: 1.0552244, 604.0: 1.0509768, 605.0: 1.0456, 606.0: 1.0390369, 607.0: 1.0313608, 608.0: 1.0226662, 609.0: 1.0130477, 610.0: 1.0026, 611.0: 0.9913675, 612.0: 0.9793314, 613.0: 0.9664916, 614.0: 0.9528479, 615.0: 0.9384, 616.0: 0.923194, 617.0: 0.907244, 618.0: 0.890502, 619.0: 0.87292, 620.0: 0.8544499, 621.0: 0.835084, 622.0: 0.814946, 623.0: 0.794186, 624.0: 0.772954, 625.0: 0.7514, 626.0: 0.7295836, 627.0: 0.7075888, 628.0: 0.6856022, 629.0: 0.6638104, 630.0: 0.6424, 631.0: 0.6215149, 632.0: 0.6011138, 633.0: 0.5811052, 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592.0: 1.0, 593.0: 1.0, 594.0: 1.0, 595.0: 1.0, 596.0: 1.0, 597.0: 1.0, 598.0: 1.0, 599.0: 1.0, 600.0: 1.0, 601.0: 1.0, 602.0: 1.0, 603.0: 1.0, 604.0: 1.0, 605.0: 1.0, 606.0: 1.0, 607.0: 1.0, 608.0: 1.0, 609.0: 1.0, 610.0: 1.0, 611.0: 1.0, 612.0: 1.0, 613.0: 1.0, 614.0: 1.0, 615.0: 1.0, 616.0: 1.0, 617.0: 1.0, 618.0: 1.0, 619.0: 1.0, 620.0: 1.0, 621.0: 1.0, 622.0: 1.0, 623.0: 1.0, 624.0: 1.0, 625.0: 1.0, 626.0: 1.0, 627.0: 1.0, 628.0: 1.0, 629.0: 1.0, 630.0: 1.0, 631.0: 1.0, 632.0: 1.0, 633.0: 1.0, 634.0: 1.0, 635.0: 1.0, 636.0: 1.0, 637.0: 1.0, 638.0: 1.0, 639.0: 1.0, 640.0: 1.0, 641.0: 1.0, 642.0: 1.0, 643.0: 1.0, 644.0: 1.0, 645.0: 1.0, 646.0: 1.0, 647.0: 1.0, 648.0: 1.0, 649.0: 1.0, 650.0: 1.0, 651.0: 1.0, 652.0: 1.0, 653.0: 1.0, 654.0: 1.0, 655.0: 1.0, 656.0: 1.0, 657.0: 1.0, 658.0: 1.0, 659.0: 1.0, 660.0: 1.0, 661.0: 1.0, 662.0: 1.0, 663.0: 1.0, 664.0: 1.0, 665.0: 1.0, 666.0: 1.0, 667.0: 1.0, 668.0: 1.0, 669.0: 1.0, 670.0: 1.0, 671.0: 1.0, 672.0: 1.0, 673.0: 1.0, 674.0: 1.0, 675.0: 1.0, 676.0: 1.0, 677.0: 1.0, 678.0: 1.0, 679.0: 1.0, 680.0: 1.0, 681.0: 1.0, 682.0: 1.0, 683.0: 1.0, 684.0: 1.0, 685.0: 1.0, 686.0: 1.0, 687.0: 1.0, 688.0: 1.0, 689.0: 1.0, 690.0: 1.0, 691.0: 1.0, 692.0: 1.0, 693.0: 1.0, 694.0: 1.0, 695.0: 1.0, 696.0: 1.0, 697.0: 1.0, 698.0: 1.0, 699.0: 1.0, 700.0: 1.0, 701.0: 1.0, 702.0: 1.0, 703.0: 1.0, 704.0: 1.0, 705.0: 1.0, 706.0: 1.0, 707.0: 1.0, 708.0: 1.0, 709.0: 1.0, 710.0: 1.0, 711.0: 1.0, 712.0: 1.0, 713.0: 1.0, 714.0: 1.0, 715.0: 1.0, 716.0: 1.0, 717.0: 1.0, 718.0: 1.0, 719.0: 1.0, 720.0: 1.0, 721.0: 1.0, 722.0: 1.0, 723.0: 1.0, 724.0: 1.0, 725.0: 1.0, 726.0: 1.0, 727.0: 1.0, 728.0: 1.0, 729.0: 1.0, 730.0: 1.0, 731.0: 1.0, 732.0: 1.0, 733.0: 1.0, 734.0: 1.0, 735.0: 1.0, 736.0: 1.0, 737.0: 1.0, 738.0: 1.0, 739.0: 1.0, 740.0: 1.0, 741.0: 1.0, 742.0: 1.0, 743.0: 1.0, 744.0: 1.0, 745.0: 1.0, 746.0: 1.0, 747.0: 1.0, 748.0: 1.0, 749.0: 1.0, 750.0: 1.0, 751.0: 1.0, 752.0: 1.0, 753.0: 1.0, 754.0: 1.0, 755.0: 1.0, 756.0: 1.0, 757.0: 1.0, 758.0: 1.0, 759.0: 1.0, 760.0: 1.0, 761.0: 1.0, 762.0: 1.0, 763.0: 1.0, 764.0: 1.0, 765.0: 1.0, 766.0: 1.0, 767.0: 1.0, 768.0: 1.0, 769.0: 1.0, 770.0: 1.0, 771.0: 1.0, 772.0: 1.0, 773.0: 1.0, 774.0: 1.0, 775.0: 1.0, 776.0: 1.0, 777.0: 1.0, 778.0: 1.0, 779.0: 1.0, 780.0: 1.0}), use_practice_range=True, mi_5nm_omission_method=True, mi_20nm_interpolation_method=True)[source]¶ Converts given spectral power distribution to CIE XYZ tristimulus values using given colour matching functions and illuminant accordingly to practise ASTM E308-15 method [2]_.
Parameters: - spd (SpectralPowerDistribution) – Spectral power distribution.
- cmfs (XYZ_ColourMatchingFunctions) – Standard observer colour matching functions.
- illuminant (SpectralPowerDistribution, optional) – Illuminant spectral power distribution.
- use_practice_range (bool, optional) – Practise ASTM E308-15 working wavelengths range is [360, 780], if True this argument will trim the colour matching functions appropriately.
- mi_5nm_omission_method (bool, optional) – 5 nm measurement intervals spectral power distribution conversion to tristimulus values will use a 5 nm version of the colour matching functions instead of a table of tristimulus weighting factors.
- mi_20nm_interpolation_method (bool, optional) – 20 nm measurement intervals spectral power distribution conversion to tristimulus values will use a dedicated interpolation method instead of a table of tristimulus weighting factors.
Returns: CIE XYZ tristimulus values.
Return type: ndarray, (3,)
Warning
- The tables of tristimulus weighting factors are cached in
_TRISTIMULUS_WEIGHTING_FACTORS_CACHE
attribute. Their identifier key is defined by the colour matching functions and illuminant names along the current shape such as: CIE 1964 10 Degree Standard Observer, A, (360.0, 830.0, 10.0) Considering the above, one should be mindful that using similar colour matching functions and illuminant names but with different spectral data will lead to unexpected behaviour. - The output range of that definition is non standard!
Notes
- Output CIE XYZ tristimulus values are in range [0, 100].
Examples
>>> from colour import ( ... CMFS, ILLUMINANTS_RELATIVE_SPDS, SpectralPowerDistribution) >>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer'] >>> data = { ... 400: 0.0641, ... 420: 0.0645, ... 440: 0.0562, ... 460: 0.0537, ... 480: 0.0559, ... 500: 0.0651, ... 520: 0.0705, ... 540: 0.0772, ... 560: 0.0870, ... 580: 0.1128, ... 600: 0.1360, ... 620: 0.1511, ... 640: 0.1688, ... 660: 0.1996, ... 680: 0.2397, ... 700: 0.2852} >>> spd = SpectralPowerDistribution('Sample', data) >>> illuminant = ILLUMINANTS_RELATIVE_SPDS['D50'] >>> spectral_to_XYZ_ASTME30815( ... spd, cmfs, illuminant) array([ 11.5290265..., 9.9502091..., 4.7098882...])
-
colour.colorimetry.
wavelength_to_XYZ
(wavelength, cmfs=XYZ_ColourMatchingFunctions( 'CIE 1931 2 Degree Standard Observer', {u'x_bar': {360.0: 0.0001299, 361.0: 0.000145847, 362.0: 0.0001638021, 363.0: 0.0001840037, 364.0: 0.0002066902, 365.0: 0.0002321, 366.0: 0.000260728, 367.0: 0.000293075, 368.0: 0.000329388, 369.0: 0.000369914, 370.0: 0.0004149, 371.0: 0.0004641587, 372.0: 0.000518986, 373.0: 0.000581854, 374.0: 0.0006552347, 375.0: 0.0007416, 376.0: 0.0008450296, 377.0: 0.0009645268, 378.0: 0.001094949, 379.0: 0.001231154, 380.0: 0.001368, 381.0: 0.00150205, 382.0: 0.001642328, 383.0: 0.001802382, 384.0: 0.001995757, 385.0: 0.002236, 386.0: 0.002535385, 387.0: 0.002892603, 388.0: 0.003300829, 389.0: 0.003753236, 390.0: 0.004243, 391.0: 0.004762389, 392.0: 0.005330048, 393.0: 0.005978712, 394.0: 0.006741117, 395.0: 0.00765, 396.0: 0.008751373, 397.0: 0.01002888, 398.0: 0.0114217, 399.0: 0.01286901, 400.0: 0.01431, 401.0: 0.01570443, 402.0: 0.01714744, 403.0: 0.01878122, 404.0: 0.02074801, 405.0: 0.02319, 406.0: 0.02620736, 407.0: 0.02978248, 408.0: 0.03388092, 409.0: 0.03846824, 410.0: 0.04351, 411.0: 0.0489956, 412.0: 0.0550226, 413.0: 0.0617188, 414.0: 0.069212, 415.0: 0.07763, 416.0: 0.08695811, 417.0: 0.09717672, 418.0: 0.1084063, 419.0: 0.1207672, 420.0: 0.13438, 421.0: 0.1493582, 422.0: 0.1653957, 423.0: 0.1819831, 424.0: 0.198611, 425.0: 0.21477, 426.0: 0.2301868, 427.0: 0.2448797, 428.0: 0.2587773, 429.0: 0.2718079, 430.0: 0.2839, 431.0: 0.2949438, 432.0: 0.3048965, 433.0: 0.3137873, 434.0: 0.3216454, 435.0: 0.3285, 436.0: 0.3343513, 437.0: 0.3392101, 438.0: 0.3431213, 439.0: 0.3461296, 440.0: 0.34828, 441.0: 0.3495999, 442.0: 0.3501474, 443.0: 0.350013, 444.0: 0.349287, 445.0: 0.34806, 446.0: 0.3463733, 447.0: 0.3442624, 448.0: 0.3418088, 449.0: 0.3390941, 450.0: 0.3362, 451.0: 0.3331977, 452.0: 0.3300411, 453.0: 0.3266357, 454.0: 0.3228868, 455.0: 0.3187, 456.0: 0.3140251, 457.0: 0.308884, 458.0: 0.3032904, 459.0: 0.2972579, 460.0: 0.2908, 461.0: 0.2839701, 462.0: 0.2767214, 463.0: 0.2689178, 464.0: 0.2604227, 465.0: 0.2511, 466.0: 0.2408475, 467.0: 0.2298512, 468.0: 0.2184072, 469.0: 0.2068115, 470.0: 0.19536, 471.0: 0.1842136, 472.0: 0.1733273, 473.0: 0.1626881, 474.0: 0.1522833, 475.0: 0.1421, 476.0: 0.1321786, 477.0: 0.1225696, 478.0: 0.1132752, 479.0: 0.1042979, 480.0: 0.09564, 481.0: 0.08729955, 482.0: 0.07930804, 483.0: 0.07171776, 484.0: 0.06458099, 485.0: 0.05795001, 486.0: 0.05186211, 487.0: 0.04628152, 488.0: 0.04115088, 489.0: 0.03641283, 490.0: 0.03201, 491.0: 0.0279172, 492.0: 0.0241444, 493.0: 0.020687, 494.0: 0.0175404, 495.0: 0.0147, 496.0: 0.01216179, 497.0: 0.00991996, 498.0: 0.00796724, 499.0: 0.006296346, 500.0: 0.0049, 501.0: 0.003777173, 502.0: 0.00294532, 503.0: 0.00242488, 504.0: 0.002236293, 505.0: 0.0024, 506.0: 0.00292552, 507.0: 0.00383656, 508.0: 0.00517484, 509.0: 0.00698208, 510.0: 0.0093, 511.0: 0.01214949, 512.0: 0.01553588, 513.0: 0.01947752, 514.0: 0.02399277, 515.0: 0.0291, 516.0: 0.03481485, 517.0: 0.04112016, 518.0: 0.04798504, 519.0: 0.05537861, 520.0: 0.06327, 521.0: 0.07163501, 522.0: 0.08046224, 523.0: 0.08973996, 524.0: 0.09945645, 525.0: 0.1096, 526.0: 0.1201674, 527.0: 0.1311145, 528.0: 0.1423679, 529.0: 0.1538542, 530.0: 0.1655, 531.0: 0.1772571, 532.0: 0.18914, 533.0: 0.2011694, 534.0: 0.2133658, 535.0: 0.2257499, 536.0: 0.2383209, 537.0: 0.2510668, 538.0: 0.2639922, 539.0: 0.2771017, 540.0: 0.2904, 541.0: 0.3038912, 542.0: 0.3175726, 543.0: 0.3314384, 544.0: 0.3454828, 545.0: 0.3597, 546.0: 0.3740839, 547.0: 0.3886396, 548.0: 0.4033784, 549.0: 0.4183115, 550.0: 0.4334499, 551.0: 0.4487953, 552.0: 0.464336, 553.0: 0.480064, 554.0: 0.4959713, 555.0: 0.5120501, 556.0: 0.5282959, 557.0: 0.5446916, 558.0: 0.5612094, 559.0: 0.5778215, 560.0: 0.5945, 561.0: 0.6112209, 562.0: 0.6279758, 563.0: 0.6447602, 564.0: 0.6615697, 565.0: 0.6784, 566.0: 0.6952392, 567.0: 0.7120586, 568.0: 0.7288284, 569.0: 0.7455188, 570.0: 0.7621, 571.0: 0.7785432, 572.0: 0.7948256, 573.0: 0.8109264, 574.0: 0.8268248, 575.0: 0.8425, 576.0: 0.8579325, 577.0: 0.8730816, 578.0: 0.8878944, 579.0: 0.9023181, 580.0: 0.9163, 581.0: 0.9297995, 582.0: 0.9427984, 583.0: 0.9552776, 584.0: 0.9672179, 585.0: 0.9786, 586.0: 0.9893856, 587.0: 0.9995488, 588.0: 1.0090892, 589.0: 1.0180064, 590.0: 1.0263, 591.0: 1.0339827, 592.0: 1.040986, 593.0: 1.047188, 594.0: 1.0524667, 595.0: 1.0567, 596.0: 1.0597944, 597.0: 1.0617992, 598.0: 1.0628068, 599.0: 1.0629096, 600.0: 1.0622, 601.0: 1.0607352, 602.0: 1.0584436, 603.0: 1.0552244, 604.0: 1.0509768, 605.0: 1.0456, 606.0: 1.0390369, 607.0: 1.0313608, 608.0: 1.0226662, 609.0: 1.0130477, 610.0: 1.0026, 611.0: 0.9913675, 612.0: 0.9793314, 613.0: 0.9664916, 614.0: 0.9528479, 615.0: 0.9384, 616.0: 0.923194, 617.0: 0.907244, 618.0: 0.890502, 619.0: 0.87292, 620.0: 0.8544499, 621.0: 0.835084, 622.0: 0.814946, 623.0: 0.794186, 624.0: 0.772954, 625.0: 0.7514, 626.0: 0.7295836, 627.0: 0.7075888, 628.0: 0.6856022, 629.0: 0.6638104, 630.0: 0.6424, 631.0: 0.6215149, 632.0: 0.6011138, 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377.0: 0.00454088, 378.0: 0.00515832, 379.0: 0.005802907, 380.0: 0.006450001, 381.0: 0.007083216, 382.0: 0.007745488, 383.0: 0.008501152, 384.0: 0.009414544, 385.0: 0.01054999, 386.0: 0.0119658, 387.0: 0.01365587, 388.0: 0.01558805, 389.0: 0.01773015, 390.0: 0.02005001, 391.0: 0.02251136, 392.0: 0.02520288, 393.0: 0.02827972, 394.0: 0.03189704, 395.0: 0.03621, 396.0: 0.04143771, 397.0: 0.04750372, 398.0: 0.05411988, 399.0: 0.06099803, 400.0: 0.06785001, 401.0: 0.07448632, 402.0: 0.08136156, 403.0: 0.08915364, 404.0: 0.09854048, 405.0: 0.1102, 406.0: 0.1246133, 407.0: 0.1417017, 408.0: 0.1613035, 409.0: 0.1832568, 410.0: 0.2074, 411.0: 0.2336921, 412.0: 0.2626114, 413.0: 0.2947746, 414.0: 0.3307985, 415.0: 0.3713, 416.0: 0.4162091, 417.0: 0.4654642, 418.0: 0.5196948, 419.0: 0.5795303, 420.0: 0.6456, 421.0: 0.7184838, 422.0: 0.7967133, 423.0: 0.8778459, 424.0: 0.959439, 425.0: 1.0390501, 426.0: 1.1153673, 427.0: 1.1884971, 428.0: 1.2581233, 429.0: 1.3239296, 430.0: 1.3856, 431.0: 1.4426352, 432.0: 1.4948035, 433.0: 1.5421903, 434.0: 1.5848807, 435.0: 1.62296, 436.0: 1.6564048, 437.0: 1.6852959, 438.0: 1.7098745, 439.0: 1.7303821, 440.0: 1.74706, 441.0: 1.7600446, 442.0: 1.7696233, 443.0: 1.7762637, 444.0: 1.7804334, 445.0: 1.7826, 446.0: 1.7829682, 447.0: 1.7816998, 448.0: 1.7791982, 449.0: 1.7758671, 450.0: 1.77211, 451.0: 1.7682589, 452.0: 1.764039, 453.0: 1.7589438, 454.0: 1.7524663, 455.0: 1.7441, 456.0: 1.7335595, 457.0: 1.7208581, 458.0: 1.7059369, 459.0: 1.6887372, 460.0: 1.6692, 461.0: 1.6475287, 462.0: 1.6234127, 463.0: 1.5960223, 464.0: 1.564528, 465.0: 1.5281, 466.0: 1.4861114, 467.0: 1.4395215, 468.0: 1.3898799, 469.0: 1.3387362, 470.0: 1.28764, 471.0: 1.2374223, 472.0: 1.1878243, 473.0: 1.1387611, 474.0: 1.090148, 475.0: 1.0419, 476.0: 0.9941976, 477.0: 0.9473473, 478.0: 0.9014531, 479.0: 0.8566193, 480.0: 0.8129501, 481.0: 0.7705173, 482.0: 0.7294448, 483.0: 0.6899136, 484.0: 0.6521049, 485.0: 0.6162, 486.0: 0.5823286, 487.0: 0.5504162, 488.0: 0.5203376, 489.0: 0.4919673, 490.0: 0.46518, 491.0: 0.4399246, 492.0: 0.4161836, 493.0: 0.3938822, 494.0: 0.3729459, 495.0: 0.3533, 496.0: 0.3348578, 497.0: 0.3175521, 498.0: 0.3013375, 499.0: 0.2861686, 500.0: 0.272, 501.0: 0.2588171, 502.0: 0.2464838, 503.0: 0.2347718, 504.0: 0.2234533, 505.0: 0.2123, 506.0: 0.2011692, 507.0: 0.1901196, 508.0: 0.1792254, 509.0: 0.1685608, 510.0: 0.1582, 511.0: 0.1481383, 512.0: 0.1383758, 513.0: 0.1289942, 514.0: 0.1200751, 515.0: 0.1117, 516.0: 0.1039048, 517.0: 0.09666748, 518.0: 0.08998272, 519.0: 0.08384531, 520.0: 0.07824999, 521.0: 0.07320899, 522.0: 0.06867816, 523.0: 0.06456784, 524.0: 0.06078835, 525.0: 0.05725001, 526.0: 0.05390435, 527.0: 0.05074664, 528.0: 0.04775276, 529.0: 0.04489859, 530.0: 0.04216, 531.0: 0.03950728, 532.0: 0.03693564, 533.0: 0.03445836, 534.0: 0.03208872, 535.0: 0.02984, 536.0: 0.02771181, 537.0: 0.02569444, 538.0: 0.02378716, 539.0: 0.02198925, 540.0: 0.0203, 541.0: 0.01871805, 542.0: 0.01724036, 543.0: 0.01586364, 544.0: 0.01458461, 545.0: 0.0134, 546.0: 0.01230723, 547.0: 0.01130188, 548.0: 0.01037792, 549.0: 0.009529306, 550.0: 0.008749999, 551.0: 0.0080352, 552.0: 0.0073816, 553.0: 0.0067854, 554.0: 0.0062428, 555.0: 0.005749999, 556.0: 0.0053036, 557.0: 0.0048998, 558.0: 0.0045342, 559.0: 0.0042024, 560.0: 0.0039, 561.0: 0.0036232, 562.0: 0.0033706, 563.0: 0.0031414, 564.0: 0.0029348, 565.0: 0.002749999, 566.0: 0.0025852, 567.0: 0.0024386, 568.0: 0.0023094, 569.0: 0.0021968, 570.0: 0.0021, 571.0: 0.002017733, 572.0: 0.0019482, 573.0: 0.0018898, 574.0: 0.001840933, 575.0: 0.0018, 576.0: 0.001766267, 577.0: 0.0017378, 578.0: 0.0017112, 579.0: 0.001683067, 580.0: 0.001650001, 581.0: 0.001610133, 582.0: 0.0015644, 583.0: 0.0015136, 584.0: 0.001458533, 585.0: 0.0014, 586.0: 0.001336667, 587.0: 0.00127, 588.0: 0.001205, 589.0: 0.001146667, 590.0: 0.0011, 591.0: 0.0010688, 592.0: 0.0010494, 593.0: 0.0010356, 594.0: 0.0010212, 595.0: 0.001, 596.0: 0.00096864, 597.0: 0.00092992, 598.0: 0.00088688, 599.0: 0.00084256, 600.0: 0.0008, 601.0: 0.00076096, 602.0: 0.00072368, 603.0: 0.00068592, 604.0: 0.00064544, 605.0: 0.0006, 606.0: 0.0005478667, 607.0: 0.0004916, 608.0: 0.0004354, 609.0: 0.0003834667, 610.0: 0.00034, 611.0: 0.0003072533, 612.0: 0.00028316, 613.0: 0.00026544, 614.0: 0.0002518133, 615.0: 0.00024, 616.0: 0.0002295467, 617.0: 0.00022064, 618.0: 0.00021196, 619.0: 0.0002021867, 620.0: 0.00019, 621.0: 0.0001742133, 622.0: 0.00015564, 623.0: 0.00013596, 624.0: 0.0001168533, 625.0: 0.0001, 626.0: 8.613333e-05, 627.0: 7.46e-05, 628.0: 6.5e-05, 629.0: 5.693333e-05, 630.0: 4.999999e-05, 631.0: 4.416e-05, 632.0: 3.948e-05, 633.0: 3.572e-05, 634.0: 3.264e-05, 635.0: 3e-05, 636.0: 2.765333e-05, 637.0: 2.556e-05, 638.0: 2.364e-05, 639.0: 2.181333e-05, 640.0: 2e-05, 641.0: 1.813333e-05, 642.0: 1.62e-05, 643.0: 1.42e-05, 644.0: 1.213333e-05, 645.0: 1e-05, 646.0: 7.733333e-06, 647.0: 5.4e-06, 648.0: 3.2e-06, 649.0: 1.333333e-06, 650.0: 0.0, 651.0: 0.0, 652.0: 0.0, 653.0: 0.0, 654.0: 0.0, 655.0: 0.0, 656.0: 0.0, 657.0: 0.0, 658.0: 0.0, 659.0: 0.0, 660.0: 0.0, 661.0: 0.0, 662.0: 0.0, 663.0: 0.0, 664.0: 0.0, 665.0: 0.0, 666.0: 0.0, 667.0: 0.0, 668.0: 0.0, 669.0: 0.0, 670.0: 0.0, 671.0: 0.0, 672.0: 0.0, 673.0: 0.0, 674.0: 0.0, 675.0: 0.0, 676.0: 0.0, 677.0: 0.0, 678.0: 0.0, 679.0: 0.0, 680.0: 0.0, 681.0: 0.0, 682.0: 0.0, 683.0: 0.0, 684.0: 0.0, 685.0: 0.0, 686.0: 0.0, 687.0: 0.0, 688.0: 0.0, 689.0: 0.0, 690.0: 0.0, 691.0: 0.0, 692.0: 0.0, 693.0: 0.0, 694.0: 0.0, 695.0: 0.0, 696.0: 0.0, 697.0: 0.0, 698.0: 0.0, 699.0: 0.0, 700.0: 0.0, 701.0: 0.0, 702.0: 0.0, 703.0: 0.0, 704.0: 0.0, 705.0: 0.0, 706.0: 0.0, 707.0: 0.0, 708.0: 0.0, 709.0: 0.0, 710.0: 0.0, 711.0: 0.0, 712.0: 0.0, 713.0: 0.0, 714.0: 0.0, 715.0: 0.0, 716.0: 0.0, 717.0: 0.0, 718.0: 0.0, 719.0: 0.0, 720.0: 0.0, 721.0: 0.0, 722.0: 0.0, 723.0: 0.0, 724.0: 0.0, 725.0: 0.0, 726.0: 0.0, 727.0: 0.0, 728.0: 0.0, 729.0: 0.0, 730.0: 0.0, 731.0: 0.0, 732.0: 0.0, 733.0: 0.0, 734.0: 0.0, 735.0: 0.0, 736.0: 0.0, 737.0: 0.0, 738.0: 0.0, 739.0: 0.0, 740.0: 0.0, 741.0: 0.0, 742.0: 0.0, 743.0: 0.0, 744.0: 0.0, 745.0: 0.0, 746.0: 0.0, 747.0: 0.0, 748.0: 0.0, 749.0: 0.0, 750.0: 0.0, 751.0: 0.0, 752.0: 0.0, 753.0: 0.0, 754.0: 0.0, 755.0: 0.0, 756.0: 0.0, 757.0: 0.0, 758.0: 0.0, 759.0: 0.0, 760.0: 0.0, 761.0: 0.0, 762.0: 0.0, 763.0: 0.0, 764.0: 0.0, 765.0: 0.0, 766.0: 0.0, 767.0: 0.0, 768.0: 0.0, 769.0: 0.0, 770.0: 0.0, 771.0: 0.0, 772.0: 0.0, 773.0: 0.0, 774.0: 0.0, 775.0: 0.0, 776.0: 0.0, 777.0: 0.0, 778.0: 0.0, 779.0: 0.0, 780.0: 0.0, 781.0: 0.0, 782.0: 0.0, 783.0: 0.0, 784.0: 0.0, 785.0: 0.0, 786.0: 0.0, 787.0: 0.0, 788.0: 0.0, 789.0: 0.0, 790.0: 0.0, 791.0: 0.0, 792.0: 0.0, 793.0: 0.0, 794.0: 0.0, 795.0: 0.0, 796.0: 0.0, 797.0: 0.0, 798.0: 0.0, 799.0: 0.0, 800.0: 0.0, 801.0: 0.0, 802.0: 0.0, 803.0: 0.0, 804.0: 0.0, 805.0: 0.0, 806.0: 0.0, 807.0: 0.0, 808.0: 0.0, 809.0: 0.0, 810.0: 0.0, 811.0: 0.0, 812.0: 0.0, 813.0: 0.0, 814.0: 0.0, 815.0: 0.0, 816.0: 0.0, 817.0: 0.0, 818.0: 0.0, 819.0: 0.0, 820.0: 0.0, 821.0: 0.0, 822.0: 0.0, 823.0: 0.0, 824.0: 0.0, 825.0: 0.0, 826.0: 0.0, 827.0: 0.0, 828.0: 0.0, 829.0: 0.0, 830.0: 0.0}}, 'CIE 1931 2$^\circ$ Standard Observer'), method=None)[source]¶ Converts given wavelength \(\lambda\) to CIE XYZ tristimulus values 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 or array_like) – Wavelength \(\lambda\) in nm.
- cmfs (XYZ_ColourMatchingFunctions, optional) – Standard observer colour matching functions.
- method (unicode, optional) – {None, ‘Cubic Spline’, ‘Linear’, ‘Pchip’, ‘Sprague’}, Enforce given interpolation method.
Returns: CIE XYZ tristimulus values.
Return type: ndarray
Raises: RuntimeError
– If Sprague (1880) interpolation method is forced with a non-uniformly spaced independent variable.ValueError
– If the interpolation method is not defined or if wavelength \(\lambda\) is not contained in the colour matching functions domain.
Notes
- Output CIE XYZ tristimulus values are in range [0, 1].
- If scipy is not unavailable the Cubic Spline method will fallback to legacy Linear interpolation.
- 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.
Examples
Uniform data is using Sprague (1880) interpolation by default:
>>> from colour import CMFS >>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer'] >>> wavelength_to_XYZ(480, cmfs) array([ 0.09564 , 0.13902 , 0.812950...]) >>> wavelength_to_XYZ(480.5, cmfs) array([ 0.0914287..., 0.1418350..., 0.7915726...])
Enforcing Cubic Spline interpolation:
>>> wavelength_to_XYZ(480.5, cmfs, 'Cubic Spline') array([ 0.0914288..., 0.1418351..., 0.7915729...])
Enforcing Linear interpolation:
>>> wavelength_to_XYZ(480.5, cmfs, 'Linear') array([ 0.0914697..., 0.1418482..., 0.7917337...])
Enforcing Pchip interpolation:
>>> wavelength_to_XYZ(480.5, cmfs, 'Pchip') array([ 0.0914280..., 0.1418341..., 0.7915711...])
-
colour.colorimetry.
whiteness
(method=u'CIE 2004', **kwargs)[source]¶ Returns the whiteness \(W\) using given method.
Parameters: method (unicode, optional) – {‘CIE 2004’, ‘Berger 1959’, ‘Taube 1960’, ‘Stensby 1968’, ‘ASTM E313’, ‘Ganz 1979’, ‘CIE 2004’}, Computation method.
Other Parameters: - XYZ (array_like) – {
whiteness_Berger1959()
,whiteness_Taube1960()
,whiteness_ASTME313()
}, CIE XYZ tristimulus values of sample. - XYZ_0 (array_like) – {
whiteness_Berger1959()
,whiteness_Taube1960()
}, CIE XYZ tristimulus values of reference white. - Lab (array_like) – {
whiteness_Stensby1968()
}, CIE Lab colourspace array of sample. - xy (array_like) – {
whiteness_Ganz1979()
,whiteness_CIE2004()
}, Chromaticity coordinates xy of sample. - Y (numeric or array_like) – {
whiteness_Ganz1979()
,whiteness_CIE2004()
}, Tristimulus \(Y\) value of sample. - xy_n (array_like) – {
whiteness_CIE2004()
}, Chromaticity coordinates xy_n of perfect diffuser. - observer (unicode, optional) – {
whiteness_CIE2004()
}, {‘CIE 1931 2 Degree Standard Observer’, ‘CIE 1964 10 Degree Standard Observer’}, CIE Standard Observer used for computations, tint \(T\) or \(T_{10}\) value is dependent on viewing field angular subtense.
Returns: whiteness \(W\).
Return type: numeric or ndarray
Examples
>>> xy = np.array([0.3167, 0.3334]) >>> Y = 100 >>> xy_n = np.array([0.3139, 0.3311]) >>> whiteness(xy=xy, Y=Y, xy_n=xy_n) array([ 93.85..., -1.305...]) >>> XYZ = np.array([95.00000000, 100.00000000, 105.00000000]) >>> XYZ_0 = np.array([94.80966767, 100.00000000, 107.30513595]) >>> method = 'Taube 1960' >>> whiteness(XYZ=XYZ, XYZ_0=XYZ_0, method=method) 91.4071738...
- XYZ (array_like) – {
-
colour.colorimetry.
whiteness_Berger1959
(XYZ, XYZ_0)[source]¶ Returns the whiteness index \(WI\) of given sample CIE XYZ tristimulus values using Berger (1959) method. [2]_
Parameters: - XYZ (array_like) – CIE XYZ tristimulus values of sample.
- XYZ_0 (array_like) – CIE XYZ tristimulus values of reference white.
Returns: Whiteness \(WI\).
Return type: numeric or ndarray
Notes
- Input CIE XYZ and CIE XYZ_0 tristimulus values are in domain [0, 100].
- Whiteness \(WI\) values larger than 33.33 indicate a bluish white and values smaller than 33.33 indicate a yellowish white.
Warning
The input domain of that definition is non standard!
Examples
>>> XYZ = np.array([95.00000000, 100.00000000, 105.00000000]) >>> XYZ_0 = np.array([94.80966767, 100.00000000, 107.30513595]) >>> whiteness_Berger1959(XYZ, XYZ_0) 30.3638017...
-
colour.colorimetry.
whiteness_Taube1960
(XYZ, XYZ_0)[source]¶ Returns the whiteness index \(WI\) of given sample CIE XYZ tristimulus values using Taube (1960) method. [2]_
Parameters: - XYZ (array_like) – CIE XYZ tristimulus values of sample.
- XYZ_0 (array_like) – CIE XYZ tristimulus values of reference white.
Returns: Whiteness \(WI\).
Return type: numeric or ndarray
Notes
- Input CIE XYZ and CIE XYZ_0 tristimulus values are in domain [0, 100].
- Whiteness \(WI\) values larger than 100 indicate a bluish white and values smaller than 100 indicate a yellowish white.
Examples
>>> XYZ = np.array([95.00000000, 100.00000000, 105.00000000]) >>> XYZ_0 = np.array([94.80966767, 100.00000000, 107.30513595]) >>> whiteness_Taube1960(XYZ, XYZ_0) 91.4071738...
-
colour.colorimetry.
whiteness_Stensby1968
(Lab)[source]¶ Returns the whiteness index \(WI\) of given sample CIE Lab colourspace array using Stensby (1968) method. [2]_
Parameters: Lab (array_like) – CIE Lab colourspace array of sample. Returns: Whiteness \(WI\). Return type: numeric or ndarray Notes
- Input CIE Lab colourspace array is in domain [0, 100].
- Whiteness \(WI\) values larger than 100 indicate a bluish white and values smaller than 100 indicate a yellowish white.
Examples
>>> Lab = np.array([100.00000000, -2.46875131, -16.72486654]) >>> whiteness_Stensby1968(Lab) 142.7683456...
-
colour.colorimetry.
whiteness_ASTME313
(XYZ)[source]¶ Returns the whiteness index \(WI\) of given sample CIE XYZ tristimulus values using ASTM E313 method. [2]_
Parameters: XYZ (array_like) – CIE XYZ tristimulus values of sample. Returns: Whiteness \(WI\). Return type: numeric or ndarray Notes
- Input CIE XYZ tristimulus values are in domain [0, 100].
Warning
The input domain of that definition is non standard!
Examples
>>> XYZ = np.array([95.00000000, 100.00000000, 105.00000000]) >>> whiteness_ASTME313(XYZ) 55.7400000...
-
colour.colorimetry.
whiteness_Ganz1979
(xy, Y)[source]¶ Returns the whiteness index \(W\) and tint \(T\) of given sample xy chromaticity coordinates using Ganz and Griesser (1979) method. [2]_
Parameters: - xy (array_like) – Chromaticity coordinates xy of sample.
- Y (numeric or array_like) – Tristimulus \(Y\) value of sample.
Returns: Whiteness \(W\) and tint \(T\).
Return type: ndarray
Notes
- Input tristimulus \(Y\) value is in domain [0, 100].
- The formula coefficients are valid for CIE Standard Illuminant D Series D65 and CIE 1964 10 Degree Standard Observer.
- Positive output tint \(T\) values indicate a greener tint while negative values indicate a redder tint.
- Whiteness differences of less than 5 Ganz units appear to be indistinguishable to the human eye.
- Tint differences of less than 0.5 Ganz units appear to be indistinguishable to the human eye.
Warning
The input domain of that definition is non standard!
Examples
>>> xy = np.array([0.3167, 0.3334]) >>> whiteness_Ganz1979(xy, 100) array([ 85.6003766..., 0.6789003...])
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colour.colorimetry.
whiteness_CIE2004
(xy, Y, xy_n, observer=u'CIE 1931 2 Degree Standard Observer')[source]¶ Returns the whiteness \(W\) or \(W_{10}\) and tint \(T\) or \(T_{10}\) of given sample xy chromaticity coordinates using CIE 2004 method.
Parameters: - xy (array_like) – Chromaticity coordinates xy of sample.
- Y (numeric or array_like) – Tristimulus \(Y\) value of sample.
- xy_n (array_like) – Chromaticity coordinates xy_n of perfect diffuser.
- observer (unicode, optional) – {‘CIE 1931 2 Degree Standard Observer’, ‘CIE 1964 10 Degree Standard Observer’}, CIE Standard Observer used for computations, tint \(T\) or \(T_{10}\) value is dependent on viewing field angular subtense.
Returns: Whiteness \(W\) or \(W_{10}\) and tint \(T\) or \(T_{10}\) of given sample.
Return type: ndarray
Notes
- Input tristimulus \(Y\) value is in domain [0, 100].
- This method may be used only for samples whose values of \(W\) or \(W_{10}\) lie within the following limits: greater than 40 and less than 5Y - 280, or 5Y10 - 280.
- This method may be used only for samples whose values of \(T\) or \(T_{10}\) lie within the following limits: greater than -4 and less than +2.
- Output whiteness \(W\) or \(W_{10}\) values larger than 100 indicate a bluish white while values smaller than 100 indicate a yellowish white. [2]_
- Positive output tint \(T\) or \(T_{10}\) values indicate a greener tint while negative values indicate a redder tint.
Warning
The input domain of that definition is non standard!
References
[4] CIE TC 1-48. (2004). The evaluation of whiteness. In CIE 015:2004 Colorimetry, 3rd Edition (p. 24). ISBN:978-3-901-90633-6 Examples
>>> xy = np.array([0.3167, 0.3334]) >>> xy_n = np.array([0.3139, 0.3311]) >>> whiteness_CIE2004(xy, 100, xy_n) array([ 93.85..., -1.305...])
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colour.colorimetry.
yellowness
(XYZ, method=u'ASTM E313')[source]¶ Returns the yellowness \(W\) using given method.
Parameters: - XYZ (array_like) – CIE XYZ tristimulus values of sample.
- method (unicode, optional) – {‘ASTM E313’, ‘ASTM D1925’}, Computation method.
Returns: yellowness \(Y\).
Return type: numeric or ndarray
Examples
>>> import numpy as np >>> XYZ = np.array([95.00000000, 100.00000000, 105.00000000]) >>> yellowness(XYZ) 11.0650000... >>> method = 'ASTM D1925' >>> yellowness(XYZ, method=method) 10.2999999...
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colour.colorimetry.
yellowness_ASTMD1925
(XYZ)[source]¶ Returns the yellowness index \(YI\) of given sample CIE XYZ tristimulus values using ASTM D1925 method. [1]_
ASTM D1925 has been specifically developed for the definition of the Yellowness of homogeneous, non-fluorescent, almost neutral-transparent, white-scattering or opaque plastics as they will be reviewed under daylight condition. It can be other materials as well, as long as they fit into this description.
Parameters: XYZ (array_like) – CIE XYZ tristimulus values of sample. Returns: Whiteness \(YI\). Return type: numeric or ndarray Notes
- Input CIE XYZ tristimulus values are in domain [0, 100].
Warning
The input domain of that definition is non standard!
Examples
>>> import numpy as np >>> XYZ = np.array([95.00000000, 100.00000000, 105.00000000]) >>> yellowness_ASTMD1925(XYZ) 10.2999999...
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colour.colorimetry.
yellowness_ASTME313
(XYZ)[source]¶ Returns the yellowness index \(YI\) of given sample CIE XYZ tristimulus values using ASTM E313 method. [1]_
ASTM E313 has successfully been used for a variety of white or near white materials. This includes coatings, Plastics, Textiles.
Parameters: XYZ (array_like) – CIE XYZ tristimulus values of sample. Returns: Whiteness \(YI\). Return type: numeric or ndarray Notes
- Input CIE XYZ tristimulus values are in domain [0, 100].
Warning
The input domain of that definition is non standard!
Examples
>>> import numpy as np >>> XYZ = np.array([95.00000000, 100.00000000, 105.00000000]) >>> yellowness_ASTME313(XYZ) 11.0650000...