# -*- coding: utf-8 -*-
"""
Colour Matching Functions Transformations
=========================================
Defines various educational objects for colour matching functions
transformations:
- :func:`colour.colorimetry.RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs`
- :func:`colour.colorimetry.RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs`
- :func:`colour.colorimetry.RGB_10_degree_cmfs_to_LMS_10_degree_cmfs`
- :func:`colour.colorimetry.LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs`
- :func:`colour.colorimetry.LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs`
See Also
--------
`Colour Matching Functions Jupyter Notebook
<http://nbviewer.jupyter.org/github/colour-science/colour-notebooks/\
blob/master/notebooks/colorimetry/cmfs.ipynb>`_
References
----------
- :cite:`CIETC1-362006a` : CIE TC 1-36. (2006). CIE 170-1:2006 Fundamental
Chromaticity Diagram with Physiological Axes - Part 1.
ISBN:978-3-901-90646-6
- :cite:`CVRLp` : 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
- :cite:`CVRLv` : 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
- :cite:`Wyszecki2000be` : 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
- :cite:`Wyszecki2000bg` : 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
"""
from __future__ import division, unicode_literals
import numpy as np
from colour.colorimetry import LMS_CMFS, RGB_CMFS, PHOTOPIC_LEFS
from colour.utilities import dot_vector, tstack
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2018 - Colour Developers'
__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-science@googlegroups.com'
__status__ = 'Production'
__all__ = [
'RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs',
'RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs',
'RGB_10_degree_cmfs_to_LMS_10_degree_cmfs',
'LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs',
'LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs'
]
[docs]def RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs(wavelength):
"""
Converts *Wright & Guild 1931 2 Degree RGB CMFs* colour matching functions
into the *CIE 1931 2 Degree Standard Observer* colour matching functions.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda` in nm.
Returns
-------
ndarray
*CIE 1931 2 Degree Standard Observer* spectral tristimulus values.
Notes
-----
- Data for the *CIE 1931 2 Degree Standard Observer* already exists,
this definition is intended for educational purpose.
References
----------
- :cite:`Wyszecki2000bg`
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 0.0113577..., 0.004102 , 0. ])
"""
cmfs = RGB_CMFS['Wright & Guild 1931 2 Degree RGB CMFs']
rgb_bar = cmfs[wavelength]
rgb = rgb_bar / np.sum(rgb_bar)
M1 = np.array([
[0.49000, 0.31000, 0.20000],
[0.17697, 0.81240, 0.01063],
[0.00000, 0.01000, 0.99000],
])
M2 = np.array([
[0.66697, 1.13240, 1.20063],
[0.66697, 1.13240, 1.20063],
[0.66697, 1.13240, 1.20063],
])
xyz = dot_vector(M1, rgb)
xyz /= dot_vector(M2, rgb)
x, y, z = xyz[..., 0], xyz[..., 1], xyz[..., 2]
V = PHOTOPIC_LEFS['CIE 1924 Photopic Standard Observer'].copy()
V.align(cmfs.shape)
L = V[wavelength]
x_bar = x / y * L
y_bar = L
z_bar = z / y * L
xyz_bar = tstack((x_bar, y_bar, z_bar))
return xyz_bar
[docs]def RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs(wavelength):
"""
Converts *Stiles & Burch 1959 10 Degree RGB CMFs* colour matching
functions into the *CIE 1964 10 Degree Standard Observer* colour matching
functions.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda` in nm.
Returns
-------
ndarray
*CIE 1964 10 Degree Standard Observer* spectral tristimulus values.
Notes
-----
- Data for the *CIE 1964 10 Degree Standard Observer* already exists,
this definition is intended for educational purpose.
References
----------
- :cite:`Wyszecki2000be`
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 0.0096432..., 0.0037526..., -0.0000041...])
"""
cmfs = RGB_CMFS['Stiles & Burch 1959 10 Degree RGB CMFs']
rgb_bar = cmfs[wavelength]
M = np.array([
[0.341080, 0.189145, 0.387529],
[0.139058, 0.837460, 0.073316],
[0.000000, 0.039553, 2.026200],
])
xyz_bar = dot_vector(M, rgb_bar)
return xyz_bar
[docs]def RGB_10_degree_cmfs_to_LMS_10_degree_cmfs(wavelength):
"""
Converts *Stiles & Burch 1959 10 Degree RGB CMFs* colour matching
functions into the *Stockman & Sharpe 10 Degree Cone Fundamentals*
spectral sensitivity functions.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda` in nm.
Returns
-------
ndarray
*Stockman & Sharpe 10 Degree Cone Fundamentals* spectral tristimulus
values.
Notes
-----
- Data for the *Stockman & Sharpe 10 Degree Cone Fundamentals* already
exists, this definition is intended for educational purpose.
References
----------
- :cite:`CIETC1-362006a`
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... RGB_10_degree_cmfs_to_LMS_10_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 0.0052860..., 0.0003252..., 0. ])
"""
cmfs = RGB_CMFS['Stiles & Burch 1959 10 Degree RGB CMFs']
rgb_bar = cmfs[wavelength]
M = np.array([
[0.1923252690, 0.749548882, 0.0675726702],
[0.0192290085, 0.940908496, 0.113830196],
[0.0000000000, 0.0105107859, 0.991427669],
])
lms_bar = dot_vector(M, rgb_bar)
lms_bar[..., -1][np.asarray(np.asarray(wavelength) > 505)] = 0
return lms_bar
[docs]def LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs(wavelength):
"""
Converts *Stockman & Sharpe 2 Degree Cone Fundamentals* colour matching
functions into the *CIE 2012 2 Degree Standard Observer* colour matching
functions.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda` in nm.
Returns
-------
ndarray
*CIE 2012 2 Degree Standard Observer* spectral tristimulus values.
Notes
-----
- Data for the *CIE 2012 2 Degree Standard Observer* already exists,
this definition is intended for educational purpose.
References
----------
- :cite:`CVRLv`
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 0.0109677..., 0.0041959..., 0. ])
"""
cmfs = LMS_CMFS['Stockman & Sharpe 2 Degree Cone Fundamentals']
lms_bar = cmfs[wavelength]
M = np.array([
[1.94735469, -1.41445123, 0.36476327],
[0.68990272, 0.34832189, 0.00000000],
[0.00000000, 0.00000000, 1.93485343],
])
xyz_bar = dot_vector(M, lms_bar)
return xyz_bar
[docs]def LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs(wavelength):
"""
Converts *Stockman & Sharpe 10 Degree Cone Fundamentals* colour matching
functions into the *CIE 2012 10 Degree Standard Observer* colour matching
functions.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda` in nm.
Returns
-------
ndarray
*CIE 2012 10 Degree Standard Observer* spectral tristimulus values.
Notes
-----
- Data for the *CIE 2012 10 Degree Standard Observer* already exists,
this definition is intended for educational purpose.
References
----------
- :cite:`CVRLp`
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 0.0098162..., 0.0037761..., 0. ])
"""
cmfs = LMS_CMFS['Stockman & Sharpe 10 Degree Cone Fundamentals']
lms_bar = cmfs[wavelength]
M = np.array([
[1.93986443, -1.34664359, 0.43044935],
[0.69283932, 0.34967567, 0.00000000],
[0.00000000, 0.00000000, 2.14687945],
])
xyz_bar = dot_vector(M, lms_bar)
return xyz_bar