def test_lower_items(self):
"""
Tests :meth:`colour.utilities.data_structures.\
CaseInsensitiveMapping.lower_items` method.
"""
mapping = CaseInsensitiveMapping(John='Doe', Jane='Doe')
self.assertListEqual(sorted([item for item in mapping.lower_items()]),
[('jane', 'Doe'), ('john', 'Doe')])
def test_copy(self):
"""
Tests
:meth:`colour.utilities.data_structures.CaseInsensitiveMapping.copy`
method.
"""
mapping1 = CaseInsensitiveMapping(John='Doe', Jane='Doe')
mapping2 = mapping1.copy()
self.assertEqual(mapping1, mapping2)
self.assertNotEqual(id(mapping1), id(mapping2))
RGB_COLOURSPACES = CaseInsensitiveMapping({
ACES_2065_1_COLOURSPACE.name:
ACES_2065_1_COLOURSPACE,
ACES_CC_COLOURSPACE.name:
ACES_CC_COLOURSPACE,
ACES_CCT_COLOURSPACE.name:
ACES_CCT_COLOURSPACE,
ACES_PROXY_COLOURSPACE.name:
ACES_PROXY_COLOURSPACE,
ACES_CG_COLOURSPACE.name:
ACES_CG_COLOURSPACE,
ADOBE_RGB_1998_COLOURSPACE.name:
ADOBE_RGB_1998_COLOURSPACE,
ADOBE_WIDE_GAMUT_RGB_COLOURSPACE.name:
ADOBE_WIDE_GAMUT_RGB_COLOURSPACE,
APPLE_RGB_COLOURSPACE.name:
APPLE_RGB_COLOURSPACE,
ALEXA_WIDE_GAMUT_COLOURSPACE.name:
ALEXA_WIDE_GAMUT_COLOURSPACE,
BEST_RGB_COLOURSPACE.name:
BEST_RGB_COLOURSPACE,
BETA_RGB_COLOURSPACE.name:
BETA_RGB_COLOURSPACE,
BT470_525_COLOURSPACE.name:
BT470_525_COLOURSPACE,
BT470_625_COLOURSPACE.name:
BT470_625_COLOURSPACE,
BT709_COLOURSPACE.name:
BT709_COLOURSPACE,
BT2020_COLOURSPACE.name:
BT2020_COLOURSPACE,
CIE_RGB_COLOURSPACE.name:
CIE_RGB_COLOURSPACE,
CINEMA_GAMUT_COLOURSPACE.name:
CINEMA_GAMUT_COLOURSPACE,
COLOR_MATCH_RGB_COLOURSPACE.name:
COLOR_MATCH_RGB_COLOURSPACE,
DCDM_XYZ_COLOURSPACE.name:
DCDM_XYZ_COLOURSPACE,
DCI_P3_COLOURSPACE.name:
DCI_P3_COLOURSPACE,
DCI_P3_P_COLOURSPACE.name:
DCI_P3_P_COLOURSPACE,
DJI_D_GAMUT_COLOURSPACE.name:
DJI_D_GAMUT_COLOURSPACE,
DON_RGB_4_COLOURSPACE.name:
DON_RGB_4_COLOURSPACE,
ECI_RGB_V2_COLOURSPACE.name:
ECI_RGB_V2_COLOURSPACE,
EKTA_SPACE_PS_5_COLOURSPACE.name:
EKTA_SPACE_PS_5_COLOURSPACE,
FILMLIGHT_E_GAMUT_COLOURSPACE.name:
FILMLIGHT_E_GAMUT_COLOURSPACE,
PROTUNE_NATIVE_COLOURSPACE.name:
PROTUNE_NATIVE_COLOURSPACE,
MAX_RGB_COLOURSPACE.name:
MAX_RGB_COLOURSPACE,
P3_D65_COLOURSPACE.name:
P3_D65_COLOURSPACE,
PAL_SECAM_COLOURSPACE.name:
PAL_SECAM_COLOURSPACE,
RED_COLOR_COLOURSPACE.name:
RED_COLOR_COLOURSPACE,
RED_COLOR_2_COLOURSPACE.name:
RED_COLOR_2_COLOURSPACE,
RED_COLOR_3_COLOURSPACE.name:
RED_COLOR_3_COLOURSPACE,
RED_COLOR_4_COLOURSPACE.name:
RED_COLOR_4_COLOURSPACE,
RED_WIDE_GAMUT_RGB_COLOURSPACE.name:
RED_WIDE_GAMUT_RGB_COLOURSPACE,
DRAGON_COLOR_COLOURSPACE.name:
DRAGON_COLOR_COLOURSPACE,
DRAGON_COLOR_2_COLOURSPACE.name:
DRAGON_COLOR_2_COLOURSPACE,
ROMM_RGB_COLOURSPACE.name:
ROMM_RGB_COLOURSPACE,
RIMM_RGB_COLOURSPACE.name:
RIMM_RGB_COLOURSPACE,
ERIMM_RGB_COLOURSPACE.name:
ERIMM_RGB_COLOURSPACE,
F_GAMUT_COLOURSPACE.name:
F_GAMUT_COLOURSPACE,
PROPHOTO_RGB_COLOURSPACE.name:
PROPHOTO_RGB_COLOURSPACE,
RUSSELL_RGB_COLOURSPACE.name:
RUSSELL_RGB_COLOURSPACE,
SHARP_RGB_COLOURSPACE.name:
SHARP_RGB_COLOURSPACE,
SMPTE_240M_COLOURSPACE.name:
SMPTE_240M_COLOURSPACE,
SMPTE_C_COLOURSPACE.name:
SMPTE_C_COLOURSPACE,
NTSC_1953_COLOURSPACE.name:
NTSC_1953_COLOURSPACE,
NTSC_1987_COLOURSPACE.name:
NTSC_1987_COLOURSPACE,
S_GAMUT_COLOURSPACE.name:
S_GAMUT_COLOURSPACE,
S_GAMUT3_COLOURSPACE.name:
S_GAMUT3_COLOURSPACE,
S_GAMUT3_CINE_COLOURSPACE.name:
S_GAMUT3_CINE_COLOURSPACE,
sRGB_COLOURSPACE.name:
sRGB_COLOURSPACE,
V_GAMUT_COLOURSPACE.name:
V_GAMUT_COLOURSPACE,
XTREME_RGB_COLOURSPACE.name:
XTREME_RGB_COLOURSPACE
})
values = np.copy(sd.values)
values[0] = (1 + ALPHA_STEARNS) * values[0] - ALPHA_STEARNS * values[1]
values[-1] = (1 + ALPHA_STEARNS) * values[-1] - ALPHA_STEARNS * values[-2]
for i in range(1, len(values) - 1):
values[i] = (-ALPHA_STEARNS * values[i - 1] +
(1 + 2 * ALPHA_STEARNS) * values[i] -
ALPHA_STEARNS * values[i + 1])
sd.values = values
return sd
BANDPASS_CORRECTION_METHODS = CaseInsensitiveMapping({
'Stearns 1988': bandpass_correction_Stearns1988
})
BANDPASS_CORRECTION_METHODS.__doc__ = """
Supported spectral bandpass dependence correction methods.
BANDPASS_CORRECTION_METHODS : CaseInsensitiveMapping
**{'Stearns 1988', }**
"""
def bandpass_correction(sd, method='Stearns 1988'):
"""
Implements spectral bandpass dependence correction on given spectral
distribution using given method.
Parameters
*CMCCAT2000* chromatic adaptation model induction factors.
Parameters
----------
F : numeric or array_like
:math:`F` surround condition.
References
----------
:cite:`Li2002a`, :cite:`Westland2012k`
"""
CMCCAT2000_VIEWING_CONDITIONS = CaseInsensitiveMapping({
'Average': CMCCAT2000_InductionFactors(1),
'Dim': CMCCAT2000_InductionFactors(0.8),
'Dark': CMCCAT2000_InductionFactors(0.8)
})
CMCCAT2000_VIEWING_CONDITIONS.__doc__ = """
Reference *CMCCAT2000* chromatic adaptation model viewing conditions.
References
----------
:cite:`Li2002a`, :cite:`Westland2012k`
CMCCAT2000_VIEWING_CONDITIONS : CaseInsensitiveMapping
('Average', 'Dim', 'Dark')
"""
def chromatic_adaptation_forward_CMCCAT2000(
# -*- coding: utf-8 -*-
"""
References
----------
- :cite:`Darrodi2015a` : Darrodi, M. M., Finlayson, G., Goodman, T., &
Mackiewicz, M. (2015). Reference data set for camera spectral sensitivity
estimation. Journal of the Optical Society of America A, 32(3), 381.
doi:10.1364/JOSAA.32.000381
"""
from __future__ import absolute_import
from .dslr import DSLR_CAMERAS_RGB_SPECTRAL_SENSITIVITIES
from colour.utilities import CaseInsensitiveMapping
CAMERAS_RGB_SPECTRAL_SENSITIVITIES = CaseInsensitiveMapping(
DSLR_CAMERAS_RGB_SPECTRAL_SENSITIVITIES)
CAMERAS_RGB_SPECTRAL_SENSITIVITIES.__doc__ = """
Cameras *RGB* spectral sensitivities.
References
----------
:cite:`Darrodi2015a`
CAMERAS_RGB_SPECTRAL_SENSITIVITIES : CaseInsensitiveMapping
**{Nikon 5100 (NPL), Sigma SDMerill (NPL)}**
"""
__all__ = ['CAMERAS_RGB_SPECTRAL_SENSITIVITIES']
1.0
>>> sd[530] # doctest: +ELLIPSIS
0.3678794...
"""
wavelengths = shape.range()
values = np.exp(-((wavelengths - peak_wavelength) / fwhm)**2)
name = '{0}nm - {1} FWHM - Gaussian'.format(peak_wavelength, fwhm)
return SpectralDistribution(values, wavelengths, name=name)
SD_GAUSSIAN_METHODS = CaseInsensitiveMapping({
'Normal': sd_gaussian_normal,
'FWHM': sd_gaussian_fwhm
})
SD_GAUSSIAN_METHODS.__doc__ = """
Supported gaussian spectral distribution computation methods.
SD_GAUSSIAN_METHODS : CaseInsensitiveMapping
**{'Normal', 'FWHM'}**
"""
def sd_gaussian(mu_peak_wavelength,
sigma_fwhm,
shape=DEFAULT_SPECTRAL_SHAPE,
method='Normal'):
"""
Returns a gaussian spectral distribution of given spectral shape using
ALEXA_LOG_C_CURVE_BCL_DATA = CaseInsensitiveMapping({
'SUP 3.x': {
160: (0.0928, 0.8128),
200: (0.0928, 0.8341),
250: (0.0928, 0.8549),
320: (0.0928, 0.8773),
400: (0.0928, 0.8968),
500: (0.0928, 0.9158),
640: (0.0928, 0.9362),
800: (0.0928, 0.9539),
1000: (0.0928, 0.9711),
1280: (0.0928, 0.9895),
1600: (0.0928, 1.0000),
2000: (0.0928, 1.0000),
2560: (0.0928, 1.0000),
3200: (0.0928, 1.0000)
},
'SUP 2.x': {
160: (0.1083, 0.8110),
200: (0.1115, 0.8320),
250: (0.1146, 0.8524),
320: (0.1181, 0.8743),
400: (0.1213, 0.8935),
500: (0.1245, 0.9121),
640: (0.1280, 0.9320),
800: (0.1311, 0.9494),
1000: (0.1343, 0.9662),
1280: (0.1378, 0.9841),
1600: (0.1409, 0.9997)
}
})
'ILLUMINANTS_CIE_1931_2_DEGREE_STANDARD_OBSERVER',
'ILLUMINANTS_CIE_1964_10_DEGREE_STANDARD_OBSERVER', 'ILLUMINANTS'
]
ILLUMINANTS_CIE_1931_2_DEGREE_STANDARD_OBSERVER = CaseInsensitiveMapping({
'A': np.array([0.44758, 0.40745]),
'B': np.array([0.34842, 0.35161]),
'C': np.array([0.31006, 0.31616]),
'D50': np.array([0.34570, 0.35850]),
'D55': np.array([0.33243, 0.34744]),
'D60': np.array([0.321626242047397, 0.337736995955436]),
'D65': np.array([0.31270, 0.32900]),
'D75': np.array([0.29903, 0.31488]),
'E': np.array([1 / 3, 1 / 3]),
'FL1': np.array([0.31310, 0.33710]),
'FL2': np.array([0.37210, 0.37510]),
'FL3': np.array([0.40910, 0.39410]),
'FL4': np.array([0.44020, 0.40310]),
'FL5': np.array([0.31380, 0.34520]),
'FL6': np.array([0.37790, 0.38820]),
'FL7': np.array([0.31290, 0.32920]),
'FL8': np.array([0.34580, 0.35860]),
'FL9': np.array([0.37410, 0.37270]),
'FL10': np.array([0.34580, 0.35880]),
'FL11': np.array([0.38050, 0.37690]),
'FL12': np.array([0.43700, 0.40420])
})
"""
*CIE* illuminant chromaticity coordinates for
*CIE 1931 2 Degree Standard Observer*.
__all__ = [
'R_MATRIX', 'RLAB_VIEWING_CONDITIONS', 'RLAB_D_FACTOR',
'RLAB_ReferenceSpecification', 'RLAB_Specification', 'XYZ_to_RLAB'
]
R_MATRIX = np.array([[1.9569, -1.1882, 0.2313], [0.3612, 0.6388, 0.0000],
[0.0000, 0.0000, 1.0000]])
"""
RLAB colour appearance model precomputed helper matrix.
R_MATRIX : array_like, (3, 3)
"""
RLAB_VIEWING_CONDITIONS = CaseInsensitiveMapping({
'Average': 1 / 2.3,
'Dim': 1 / 2.9,
'Dark': 1 / 3.5
})
"""
Reference RLAB colour appearance model viewing conditions.
RLAB_VIEWING_CONDITIONS : CaseInsensitiveMapping
**{'Average', 'Dim', 'Dark'}**
"""
RLAB_D_FACTOR = CaseInsensitiveMapping({
'Hard Copy Images':
1,
'Soft Copy Images':
0,
'Projected Transparencies, Dark Room':
__all__ += dataset.__all__
__all__ += [
'chromatic_adaptation_matrix_VonKries', 'chromatic_adaptation_VonKries'
]
__all__ += ['chromatic_adaptation_Fairchild1990']
__all__ += [
'CMCCAT2000_InductionFactors', 'CMCCAT2000_VIEWING_CONDITIONS',
'chromatic_adaptation_forward_CMCCAT2000',
'chromatic_adaptation_reverse_CMCCAT2000',
'chromatic_adaptation_CMCCAT2000'
]
__all__ += ['chromatic_adaptation_CIE1994']
CHROMATIC_ADAPTATION_METHODS = CaseInsensitiveMapping({
'CIE 1994': chromatic_adaptation_CIE1994,
'CMCCAT2000': chromatic_adaptation_CMCCAT2000,
'Fairchild 1990': chromatic_adaptation_Fairchild1990,
'Von Kries': chromatic_adaptation_VonKries,
})
CHROMATIC_ADAPTATION_METHODS.__doc__ = """
Supported chromatic adaptation methods.
References
----------
:cite:`CIETC1-321994b`, :cite:`Fairchild1991a`, :cite:`Fairchild2013s`,
:cite:`Fairchild2013t`, :cite:`Li2002a`, :cite:`Westland2012k`
CHROMATIC_ADAPTATION_METHODS : CaseInsensitiveMapping
**{'CIE 1994', 'CMCCAT2000', 'Fairchild 1990', 'Von Kries'}**
"""
SMITS_1999_SDS = CaseInsensitiveMapping({
'white':
SpectralDistribution(
SMITS_1999_SDS_DATA['white'],
name='white'),
'cyan':
SpectralDistribution(
SMITS_1999_SDS_DATA['cyan'],
name='cyan'),
'magenta':
SpectralDistribution(
SMITS_1999_SDS_DATA['magenta'],
name='magenta'),
'yellow':
SpectralDistribution(
SMITS_1999_SDS_DATA['yellow'],
name='yellow'),
'red':
SpectralDistribution(
SMITS_1999_SDS_DATA['red'],
name='red'),
'green':
SpectralDistribution(
SMITS_1999_SDS_DATA['green'],
name='green'),
'blue':
SpectralDistribution(
SMITS_1999_SDS_DATA['blue'],
name='blue')
}) # yapf: disable
SMITS_1999_SDS.__doc__ = """
Maximum degree of adaptation :math:`F`.
c : numeric or array_like
Exponential non linearity :math:`c`.
N_c : numeric or array_like
Chromatic induction factor :math:`N_c`.
References
----------
:cite:`Fairchild2004c`, :cite:`Luo2013`, :cite:`Moroneya`,
:cite:`Wikipedia2007a`
"""
CIECAM02_VIEWING_CONDITIONS = CaseInsensitiveMapping({
'Average': CIECAM02_InductionFactors(1, 0.69, 1),
'Dim': CIECAM02_InductionFactors(0.9, 0.59, 0.9),
'Dark': CIECAM02_InductionFactors(0.8, 0.525, 0.8)
})
CIECAM02_VIEWING_CONDITIONS.__doc__ = """
Reference *CIECAM02* colour appearance model viewing conditions.
References
----------
:cite:`Fairchild2004c`, :cite:`Luo2013`, :cite:`Moroneya`,
:cite:`Wikipedia2007a`
CIECAM02_VIEWING_CONDITIONS : CaseInsensitiveMapping
**{'Average', 'Dim', 'Dark'}**
"""
HUE_DATA_FOR_HUE_QUADRATURE = {
from __future__ import absolute_import
from colour.utilities import CaseInsensitiveMapping, filter_kwargs
from .barten1999 import (optical_MTF_Barten1999, pupil_diameter_Barten1999,
sigma_Barten1999, retinal_illuminance_Barten1999,
maximum_angular_size_Barten1999,
contrast_sensitivity_function_Barten1999)
__all__ = [
'optical_MTF_Barten1999', 'pupil_diameter_Barten1999', 'sigma_Barten1999',
'retinal_illuminance_Barten1999', 'maximum_angular_size_Barten1999',
'contrast_sensitivity_function_Barten1999'
]
CONTRAST_SENSITIVITY_METHODS = CaseInsensitiveMapping({
'Barten 1999': contrast_sensitivity_function_Barten1999,
})
CONTRAST_SENSITIVITY_METHODS.__doc__ = """
Supported contrast sensitivity methods.
References
----------
:cite:`Barten1999`, :cite:`Barten2003`, :cite:`Cowan2004`,
:cite:`InternationalTelecommunicationUnion2015`,
CONTRAST_SENSITIVITY_METHODS : CaseInsensitiveMapping
**{'Barten 1999'}**
"""
def contrast_sensitivity_function(method='Barten 1999', **kwargs):
*Lightness* induction factor :math:`F_L`.
F_C : numeric or array_like
*Chroma* induction factor :math:`F_C`.
References
----------
:cite:`Fairchild2013x`, :cite:`Luo1996b`, :cite:`Luo1996c`
"""
LLAB_VIEWING_CONDITIONS = CaseInsensitiveMapping({
'Reference Samples & Images, Average Surround, Subtending > 4': (
LLAB_InductionFactors(1, 3, 0, 1)),
'Reference Samples & Images, Average Surround, Subtending < 4': (
LLAB_InductionFactors(1, 3, 1, 1)),
'Television & VDU Displays, Dim Surround': (LLAB_InductionFactors(
0.7, 3.5, 1, 1)),
'Cut Sheet Transparency, Dim Surround': (LLAB_InductionFactors(
1, 5, 1, 1.1)),
'35mm Projection Transparency, Dark Surround': (LLAB_InductionFactors(
0.7, 4, 1, 1))
})
LLAB_VIEWING_CONDITIONS.__doc__ = """
Reference :math:`LLAB(l:c)` colour appearance model viewing conditions.
References
----------
:cite:`Fairchild2013x`, :cite:`Luo1996b`, :cite:`Luo1996c`
LLAB_VIEWING_CONDITIONS : CaseInsensitiveMapping
**{'Reference Samples & Images, Average Surround, Subtending > 4',
'Reference Samples & Images, Average Surround, Subtending < 4',
class CMCCAT2000_InductionFactors(
namedtuple('CMCCAT2000_InductionFactors', ('F', ))):
"""
CMCCAT2000 chromatic adaptation model induction factors.
Parameters
----------
F : numeric or array_like
:math:`F` surround condition.
"""
CMCCAT2000_VIEWING_CONDITIONS = CaseInsensitiveMapping({
'Average':
CMCCAT2000_InductionFactors(1.),
'Dim':
CMCCAT2000_InductionFactors(0.8),
'Dark':
CMCCAT2000_InductionFactors(0.8)
})
"""
Reference CMCCAT2000 chromatic adaptation model viewing conditions.
CMCCAT2000_VIEWING_CONDITIONS : CaseInsensitiveMapping
('Average', 'Dim', 'Dark')
"""
def chromatic_adaptation_forward_CMCCAT2000(
XYZ,
XYZ_w,
XYZ_wr,
CVD_MATRICES_MACHADO2010 = CaseInsensitiveMapping({
'Protanomaly': {
0.0:
np.array([
[1.000000, 0.000000, -0.000000],
[0.000000, 1.000000, 0.000000],
[-0.000000, -0.000000, 1.000000],
]),
0.1:
np.array([
[0.856167, 0.182038, -0.038205],
[0.029342, 0.955115, 0.015544],
[-0.002880, -0.001563, 1.004443],
]),
0.2:
np.array([
[0.734766, 0.334872, -0.069637],
[0.051840, 0.919198, 0.028963],
[-0.004928, -0.004209, 1.009137],
]),
0.3:
np.array([
[0.630323, 0.465641, -0.095964],
[0.069181, 0.890046, 0.040773],
[-0.006308, -0.007724, 1.014032],
]),
0.4:
np.array([
[0.539009, 0.579343, -0.118352],
[0.082546, 0.866121, 0.051332],
[-0.007136, -0.011959, 1.019095],
]),
0.5:
np.array([
[0.458064, 0.679578, -0.137642],
[0.092785, 0.846313, 0.060902],
[-0.007494, -0.016807, 1.024301],
]),
0.6:
np.array([
[0.385450, 0.769005, -0.154455],
[0.100526, 0.829802, 0.069673],
[-0.007442, -0.022190, 1.029632],
]),
0.7:
np.array([
[0.319627, 0.849633, -0.169261],
[0.106241, 0.815969, 0.077790],
[-0.007025, -0.028051, 1.035076],
]),
0.8:
np.array([
[0.259411, 0.923008, -0.182420],
[0.110296, 0.804340, 0.085364],
[-0.006276, -0.034346, 1.040622],
]),
0.9:
np.array([
[0.203876, 0.990338, -0.194214],
[0.112975, 0.794542, 0.092483],
[-0.005222, -0.041043, 1.046265],
]),
1.0:
np.array([
[0.152286, 1.052583, -0.204868],
[0.114503, 0.786281, 0.099216],
[-0.003882, -0.048116, 1.051998],
])
},
'Deuteranomaly': {
0.0:
np.array([
[1.000000, 0.000000, -0.000000],
[0.000000, 1.000000, 0.000000],
[-0.000000, -0.000000, 1.000000],
]),
0.1:
np.array([
[0.866435, 0.177704, -0.044139],
[0.049567, 0.939063, 0.011370],
[-0.003453, 0.007233, 0.996220],
]),
0.2:
np.array([
[0.760729, 0.319078, -0.079807],
[0.090568, 0.889315, 0.020117],
[-0.006027, 0.013325, 0.992702],
]),
0.3:
np.array([
[0.675425, 0.433850, -0.109275],
[0.125303, 0.847755, 0.026942],
[-0.007950, 0.018572, 0.989378],
]),
0.4:
np.array([
[0.605511, 0.528560, -0.134071],
[0.155318, 0.812366, 0.032316],
[-0.009376, 0.023176, 0.986200],
]),
0.5:
np.array([
[0.547494, 0.607765, -0.155259],
[0.181692, 0.781742, 0.036566],
[-0.010410, 0.027275, 0.983136],
]),
0.6:
np.array([
[0.498864, 0.674741, -0.173604],
[0.205199, 0.754872, 0.039929],
[-0.011131, 0.030969, 0.980162],
]),
0.7:
np.array([
[0.457771, 0.731899, -0.189670],
[0.226409, 0.731012, 0.042579],
[-0.011595, 0.034333, 0.977261],
]),
0.8:
np.array([
[0.422823, 0.781057, -0.203881],
[0.245752, 0.709602, 0.044646],
[-0.011843, 0.037423, 0.974421],
]),
0.9:
np.array([
[0.392952, 0.823610, -0.216562],
[0.263559, 0.690210, 0.046232],
[-0.011910, 0.040281, 0.971630],
]),
1.0:
np.array([
[0.367322, 0.860646, -0.227968],
[0.280085, 0.672501, 0.047413],
[-0.011820, 0.042940, 0.968881],
])
},
'Tritanomaly': {
0.0:
np.array([
[1.000000, 0.000000, -0.000000],
[0.000000, 1.000000, 0.000000],
[-0.000000, -0.000000, 1.000000],
]),
0.1:
np.array([
[0.926670, 0.092514, -0.019184],
[0.021191, 0.964503, 0.014306],
[0.008437, 0.054813, 0.936750],
]),
0.2:
np.array([
[0.895720, 0.133330, -0.029050],
[0.029997, 0.945400, 0.024603],
[0.013027, 0.104707, 0.882266],
]),
0.3:
np.array([
[0.905871, 0.127791, -0.033662],
[0.026856, 0.941251, 0.031893],
[0.013410, 0.148296, 0.838294],
]),
0.4:
np.array([
[0.948035, 0.089490, -0.037526],
[0.014364, 0.946792, 0.038844],
[0.010853, 0.193991, 0.795156],
]),
0.5:
np.array([
[1.017277, 0.027029, -0.044306],
[-0.006113, 0.958479, 0.047634],
[0.006379, 0.248708, 0.744913],
]),
0.6:
np.array([
[1.104996, -0.046633, -0.058363],
[-0.032137, 0.971635, 0.060503],
[0.001336, 0.317922, 0.680742],
]),
0.7:
np.array([
[1.193214, -0.109812, -0.083402],
[-0.058496, 0.979410, 0.079086],
[-0.002346, 0.403492, 0.598854],
]),
0.8:
np.array([
[1.257728, -0.139648, -0.118081],
[-0.078003, 0.975409, 0.102594],
[-0.003316, 0.501214, 0.502102],
]),
0.9:
np.array([
[1.278864, -0.125333, -0.153531],
[-0.084748, 0.957674, 0.127074],
[-0.000989, 0.601151, 0.399838],
]),
1.0:
np.array([
[1.255528, -0.076749, -0.178779],
[-0.078411, 0.930809, 0.147602],
[0.004733, 0.691367, 0.303900],
])
}
})
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t))
XYZ_2 = chromatic_adaptation_VonKries(XYZ_1, XYZ_w, XYZ_wr, transform)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)
prediction.append(
CorrespondingChromaticitiesPrediction(result.name, result.uvp_t,
result.uvp_m, uvp))
return tuple(prediction)
CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS = CaseInsensitiveMapping({
'CIE 1994':
corresponding_chromaticities_prediction_CIE1994,
'CMCCAT2000':
corresponding_chromaticities_prediction_CMCCAT2000,
'Fairchild 1990':
corresponding_chromaticities_prediction_Fairchild1990,
'Von Kries':
corresponding_chromaticities_prediction_VonKries
})
CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS.__doc__ = """
Aggregated corresponding chromaticities prediction models.
References
----------
- :cite:`Breneman1987b`
- :cite:`CIETC1-321994b`
- :cite:`Fairchild1991a`
- :cite:`Fairchild2013s`
- :cite:`Fairchild2013t`
- :cite:`Li2002a`
'.cube': 'Iridas Cube',
'.spi1d': 'Sony SPI1D',
'.spi3d': 'Sony SPI3D',
'.csp': 'Cinespace'
})
"""
Extension to *LUT* format.
EXTENSION_TO_LUT_FORMAT_MAPPING : CaseInsensitiveMapping
**{'.cube', '.spi1d'}**
"""
LUT_READ_METHODS = CaseInsensitiveMapping({
'Cinespace': read_LUT_Cinespace,
'Iridas Cube': read_LUT_IridasCube,
'Resolve Cube': read_LUT_ResolveCube,
'Sony SPI1D': read_LUT_SonySPI1D,
'Sony SPI3D': read_LUT_SonySPI3D,
})
LUT_READ_METHODS.__doc__ = """
Supported *LUT* reading methods.
References
----------
:cite:`AdobeSystems2013b`, :cite:`Chamberlain2015`
LUT_READ_METHODS : CaseInsensitiveMapping
**{'Cinespace', 'Iridas Cube', 'Resolve Cube', 'Sony SPI1D',
'Sony SPI3D'}**
"""
delta_A = a_1 - a_2
delta_B = b_1 - b_2
delta_H2 = delta_A * delta_A + delta_B * delta_B - delta_C * delta_C
v_1 = delta_L / (l * s_l)
v_2 = delta_C / (c * s_c)
v_3 = s_h
d_E = np.sqrt(v_1**2 + v_2**2 + (delta_H2 / (v_3 * v_3)))
return d_E
DELTA_E_METHODS = CaseInsensitiveMapping({
'CIE 1976': delta_E_CIE1976,
'CIE 1994': delta_E_CIE1994,
'CIE 2000': delta_E_CIE2000,
'CMC': delta_E_CMC
})
"""
Supported *Delta E* computations methods.
DELTA_E_METHODS : CaseInsensitiveMapping
**{'CIE 1976', 'CIE 1994', 'CIE 2000', 'CMC'}**
Aliases:
- 'cie1976': 'CIE 1976'
- 'cie1994': 'CIE 1994'
- 'cie2000': 'CIE 2000'
"""
DELTA_E_METHODS['cie1976'] = DELTA_E_METHODS['CIE 1976']
>>> CCT_to_uv_Krystek1985(6504.38938305) # doctest: +ELLIPSIS
array([ 0.1837669..., 0.3093443...])
"""
T = as_float_array(CCT)
u = ((0.860117757 + 1.54118254 * 10e-4 * T + 1.28641212 * 10e-7 * T ** 2) /
(1 + 8.42420235 * 10e-4 * T + 7.08145163 * 10e-7 * T ** 2))
v = ((0.317398726 + 4.22806245 * 10e-5 * T + 4.20481691 * 10e-8 * T ** 2) /
(1 - 2.89741816 * 10e-5 * T + 1.61456053 * 10e-7 * T ** 2))
return tstack([u, v])
UV_TO_CCT_METHODS = CaseInsensitiveMapping({
'Ohno 2013': uv_to_CCT_Ohno2013,
'Robertson 1968': uv_to_CCT_Robertson1968
})
UV_TO_CCT_METHODS.__doc__ = """
Supported *CIE UCS* colourspace *uv* chromaticity coordinates to correlated
colour temperature :math:`T_{cp}` computation methods.
References
----------
:cite:`AdobeSystems2013`, :cite:`AdobeSystems2013a`, :cite:`Ohno2014a`,
:cite:`Wyszecki2000y`
UV_TO_CCT_METHODS : CaseInsensitiveMapping
**{'Ohno 2013', 'Robertson 1968'}**
Aliases:
}
"""
CQS test colour samples indexes to names mapping.
VS_INDEXES_TO_NAMES : dict
"""
VS_APPROXIMATE_MUNSELL_NOTATIONS = CaseInsensitiveMapping({
'VS1': '7.5P 4/10',
'VS2': '10PB 4/10',
'VS3': '5PB 4/2',
'VS4': '7.5B 5/10',
'VS5': '10B G6/8',
'VS6': '2.5BG 6/10',
'VS7': '2.5G 6/12',
'VS8': '7.5GY 7/10',
'VS9': '2.5GY 8/10',
'VS10': '5Y 8.5/12',
'VS11': '10YR 7/12',
'VS12': '5YR 7/12',
'VS13': '10R 6/12',
'VS14': '5R 4/14',
'VS15': '7.5RP 4/12'
})
"""
CQS test colour samples *Munsell* colour approximations.
VS_APPROXIMATE_MUNSELL_NOTATIONS : CaseInsensitiveMapping
"""
VS_SPDS_DATA = {
Parameters
----------
F : numeric or array_like
Maximum degree of adaptation :math:`F`.
c : numeric or array_like
Exponential non linearity :math:`c`.
N_c : numeric or array_like
Chromatic induction factor :math:`N_c`.
References
----------
:cite:`Li2017`
"""
CAM16_VIEWING_CONDITIONS = CaseInsensitiveMapping(CIECAM02_VIEWING_CONDITIONS)
CAM16_VIEWING_CONDITIONS.__doc__ = """
Reference *CAM16* colour appearance model viewing conditions.
References
----------
:cite:`Li2017`
CAM16_VIEWING_CONDITIONS : CaseInsensitiveMapping
**{'Average', 'Dim', 'Dark'}**
"""
class CAM16_Specification(
namedtuple('CAM16_Specification',
('J', 'C', 'h', 's', 'Q', 'M', 'H', 'HC'))):
"""
values = np.copy(sd.values)
values[0] = (1 + ALPHA_STEARNS) * values[0] - ALPHA_STEARNS * values[1]
values[-1] = (1 + ALPHA_STEARNS) * values[-1] - ALPHA_STEARNS * values[-2]
for i in range(1, len(values) - 1):
values[i] = (-ALPHA_STEARNS * values[i - 1] +
(1 + 2 * ALPHA_STEARNS) * values[i] -
ALPHA_STEARNS * values[i + 1])
sd.values = values
return sd
BANDPASS_CORRECTION_METHODS = CaseInsensitiveMapping(
{'Stearns 1988': bandpass_correction_Stearns1988})
BANDPASS_CORRECTION_METHODS.__doc__ = """
Supported spectral bandpass dependence correction methods.
BANDPASS_CORRECTION_METHODS : CaseInsensitiveMapping
**{'Stearns 1988', }**
"""
def bandpass_correction(sd, method='Stearns 1988'):
"""
Implements spectral bandpass dependence correction on given spectral
distribution using given method.
Parameters
----------
>>> RGB = np.array([0.17224810, 0.09170660, 0.06416938])
>>> polynomial_expansion_Vandermonde(RGB) # doctest: +ELLIPSIS
array([ 0.1722481 , 0.0917066 , 0.06416938, 1. ])
"""
a = as_float_array(a)
a_e = np.transpose(np.vander(np.ravel(a), degree + 1))
a_e = np.hstack(a_e.reshape(a_e.shape[0], -1, 3))
return np.squeeze(a_e[:, 0:a_e.shape[-1] - a.shape[-1] + 1])
POLYNOMIAL_EXPANSION_METHODS = CaseInsensitiveMapping({
'Cheung 2004': augmented_matrix_Cheung2004,
'Finlayson 2015': polynomial_expansion_Finlayson2015,
'Vandermonde': polynomial_expansion_Vandermonde,
})
POLYNOMIAL_EXPANSION_METHODS.__doc__ = """
Supported polynomial expansion methods.
References
----------
:cite:`Cheung2004`, :cite:`Finlayson2015`, :cite:`Westland2004`,
:cite:`Wikipedia2003e`
POLYNOMIAL_EXPANSION_METHODS : CaseInsensitiveMapping
**{'Cheung 2004', 'Finlayson 2015', 'Vandermonde'}**
"""
if method.lower() == 'hdr-cielab':
maximum_perception = 247
else:
maximum_perception = 246
L_hdr = reaction_rate_MichealisMenten(
spow(Y, epsilon), maximum_perception, 2 ** epsilon) + 0.02
return from_range_100(L_hdr)
LIGHTNESS_METHODS = CaseInsensitiveMapping({
'Glasser 1958': lightness_Glasser1958,
'Wyszecki 1963': lightness_Wyszecki1963,
'CIE 1976': lightness_CIE1976,
'Fairchild 2010': lightness_Fairchild2010,
'Fairchild 2011': lightness_Fairchild2011
})
LIGHTNESS_METHODS.__doc__ = """
Supported *Lightness* computation methods.
References
----------
:cite:`CIETC1-482004m`, :cite:`Fairchild2010`, :cite:`Fairchild2011`,
:cite:`Glasser1958a`, :cite:`Wyszecki1963b`, :cite:`Wyszecki2000bd`
LIGHTNESS_METHODS : CaseInsensitiveMapping
**{'Glasser 1958', 'Wyszecki 1963', 'CIE 1976', 'Fairchild 2010',
'Fairchild 2011'}**
730.0: (0.0108, 0.0009, 0.0011),
735.0: (0.0097, 0.0011, 0.0010),
740.0: (0.0091, 0.0009, 0.0010),
745.0: (0.0093, 0.0010, 0.0012),
750.0: (0.0083, 0.0011, 0.0013),
755.0: (0.0073, 0.0013, 0.0012),
760.0: (0.0081, 0.0015, 0.0016),
765.0: (0.0067, 0.0018, 0.0015),
770.0: (0.0070, 0.0021, 0.0028),
775.0: (0.0073, 0.0015, 0.0046),
780.0: (0.0066, 0.0018, 0.0058)
}
}
CRT_DISPLAYS_RGB_PRIMARIES = CaseInsensitiveMapping({
'Typical CRT Brainard 1997':
RGB_DisplayPrimaries(
CRT_DISPLAYS_RGB_PRIMARIES_DATA['Typical CRT Brainard 1997'],
name='Typical CRT Brainard 1997')
})
"""
*CRT* displays *RGB* primaries multi-spectral power distributions.
References
----------
- :cite:`Machado2010a`
CRT_DISPLAYS_RGB_PRIMARIES : CaseInsensitiveMapping
**{'Typical CRT Brainard 1997'}**
"""
from .din99 import delta_E_DIN99
__all__ = ['delta_E_CAM02LCD', 'delta_E_CAM02SCD', 'delta_E_CAM02UCS']
__all__ += ['delta_E_CAM16LCD', 'delta_E_CAM16SCD', 'delta_E_CAM16UCS']
__all__ += [
'delta_E_CIE1976', 'delta_E_CIE1994', 'delta_E_CIE2000', 'delta_E_CMC'
]
__all__ += ['delta_E_DIN99']
DELTA_E_METHODS = CaseInsensitiveMapping({
'CIE 1976': delta_E_CIE1976,
'CIE 1994': delta_E_CIE1994,
'CIE 2000': delta_E_CIE2000,
'CMC': delta_E_CMC,
'CAM02-LCD': delta_E_CAM02LCD,
'CAM02-SCD': delta_E_CAM02SCD,
'CAM02-UCS': delta_E_CAM02UCS,
'CAM16-LCD': delta_E_CAM16LCD,
'CAM16-SCD': delta_E_CAM16SCD,
'CAM16-UCS': delta_E_CAM16UCS,
'DIN99': delta_E_DIN99,
})
DELTA_E_METHODS.__doc__ = """
Supported :math:`\\Delta E_{ab}` computation methods.
References
----------
:cite:`ASTMInternational2007`, :cite:`Li2017`, :cite:`Lindbloom2003c`,
:cite:`Lindbloom2011a`, :cite:`Lindbloom2009e`, :cite:`Lindbloom2009f`,
:cite:`Luo2006b`, :cite:`Melgosa2013b`, :cite:`Wikipedia2008b`
Parameters
----------
F : numeric or array_like
Maximum degree of adaptation :math:`F`.
c : numeric or array_like
Exponential non linearity :math:`c`.
N_c : numeric or array_like
Chromatic induction factor :math:`N_c`.
"""
CIECAM02_VIEWING_CONDITIONS = CaseInsensitiveMapping({
'Average':
CIECAM02_InductionFactors(1, 0.69, 1),
'Dim':
CIECAM02_InductionFactors(0.9, 0.59, 0.95),
'Dark':
CIECAM02_InductionFactors(0.8, 0.525, 0.8)
})
"""
Reference CIECAM02 colour appearance model viewing conditions.
CIECAM02_VIEWING_CONDITIONS : CaseInsensitiveMapping
**{'Average', 'Dim', 'Dark'}**
"""
HUE_DATA_FOR_HUE_QUADRATURE = {
'h_i': np.array([20.14, 90.00, 164.25, 237.53, 380.14]),
'e_i': np.array([0.8, 0.7, 1.0, 1.2, 0.8]),
'H_i': np.array([0.0, 100.0, 200.0, 300.0, 400.0])
}
>>> polynomial_expansion_Vandermonde(RGB) # doctest: +ELLIPSIS
array([ 0.1722481 , 0.0917066 , 0.06416938, 1. ])
"""
a = as_float_array(a)
a_e = np.transpose(np.vander(np.ravel(a), degree + 1))
a_e = np.hstack(a_e.reshape(a_e.shape[0], -1, 3))
return np.squeeze(a_e[:, 0:a_e.shape[-1] - a.shape[-1] + 1])
POLYNOMIAL_EXPANSION_METHODS = CaseInsensitiveMapping({
'Cheung 2004':
matrix_augmented_Cheung2004,
'Finlayson 2015':
polynomial_expansion_Finlayson2015,
'Vandermonde':
polynomial_expansion_Vandermonde,
})
POLYNOMIAL_EXPANSION_METHODS.__doc__ = """
Supported polynomial expansion methods.
References
----------
:cite:`Cheung2004`, :cite:`Finlayson2015`, :cite:`Westland2004`,
:cite:`Wikipedia2003e`
POLYNOMIAL_EXPANSION_METHODS : CaseInsensitiveMapping
**{'Cheung 2004', 'Finlayson 2015', 'Vandermonde'}**
"""
x, y = tsplit(xy)
Y = to_domain_100(Y)
x_n, y_n = tsplit(xy_n)
W = Y + 800 * (x_n - x) + 1700 * (y_n - y)
T = (1000 if '1931' in observer else 900) * (x_n - x) - 650 * (y_n - y)
WT = tstack([W, T])
return from_range_100(WT)
WHITENESS_METHODS = CaseInsensitiveMapping({
'Berger 1959': whiteness_Berger1959,
'Taube 1960': whiteness_Taube1960,
'Stensby 1968': whiteness_Stensby1968,
'ASTM E313': whiteness_ASTME313,
'Ganz 1979': whiteness_Ganz1979,
'CIE 2004': whiteness_CIE2004
})
WHITENESS_METHODS.__doc__ = """
Supported *whiteness* computation methods.
References
----------
:cite:`CIETC1-482004k`, :cite:`X-Rite2012a`
WHITENESS_METHODS : CaseInsensitiveMapping
**{'CIE 2004', 'Berger 1959', 'Taube 1960', 'Stensby 1968', 'ASTM E313',
'Ganz 1979', 'CIE 2004'}**
Aliases:
'JMh_CIECAM02_to_CAM02UCS', 'CAM02UCS_to_JMh_CIECAM02'
]
class Coefficients_UCS_Luo2006(
namedtuple('Coefficients_UCS_Luo2006', ('K_L', 'c_1', 'c_2'))):
"""
Defines the the class storing *Luo et al. (2006)* fitting coefficients for
the *CAM02-LCD*, *CAM02-SCD*, and *CAM02-UCS* colourspaces.
"""
COEFFICIENTS_UCS_LUO2006 = CaseInsensitiveMapping({
'CAM02-LCD':
Coefficients_UCS_Luo2006(0.77, 0.007, 0.0053),
'CAM02-SCD':
Coefficients_UCS_Luo2006(1.24, 0.007, 0.0363),
'CAM02-UCS':
Coefficients_UCS_Luo2006(1.00, 0.007, 0.0228)
})
"""
*Luo et al. (2006)* fitting coefficients for the *CAM02-LCD*, *CAM02-SCD*, and
*CAM02-UCS* colourspaces.
COEFFICIENTS_UCS_LUO2006 : CaseInsensitiveMapping
**{'CAM02-LCD', 'CAM02-SCD', 'CAM02-UCS'}**
"""
def JMh_CIECAM02_to_UCS_Luo2006(JMh, coefficients):
"""
Converts from *CIECAM02* :math:`JMh` correlates array to one of the
]
R_MATRIX = np.array([
[1.9569, -1.1882, 0.2313],
[0.3612, 0.6388, 0.0000],
[0.0000, 0.0000, 1.0000],
])
"""
*RLAB* colour appearance model precomputed helper matrix.
R_MATRIX : array_like, (3, 3)
"""
RLAB_VIEWING_CONDITIONS = CaseInsensitiveMapping({
'Average': 1 / 2.3,
'Dim': 1 / 2.9,
'Dark': 1 / 3.5
})
RLAB_VIEWING_CONDITIONS.__doc__ = """
Reference *RLAB* colour appearance model viewing conditions.
References
----------
:cite:`Fairchild1996a`, :cite:`Fairchild2013w`
RLAB_VIEWING_CONDITIONS : CaseInsensitiveMapping
**{'Average', 'Dim', 'Dark'}**
"""
RLAB_D_FACTOR = CaseInsensitiveMapping({
'Hard Copy Images': 1,
Examples
--------
>>> luminance_ASTM_D1535_08(3.74629715382) # doctest: +ELLIPSIS
10.1488096...
"""
Y = 1.1914 * V - 0.22533 * (V * V) + 0.23352 * (V**3) - 0.020484 * (
V**4) + 0.00081939 * (V**5)
return Y
LUMINANCE_METHODS = CaseInsensitiveMapping({
'Newhall 1943':
luminance_newhall1943,
'CIE 1976':
luminance_1976,
'ASTM D1535-08':
luminance_ASTM_D1535_08
})
"""
Supported *luminance* computations methods.
LUMINANCE_METHODS : dict
('Newhall 1943', 'CIE 1976', 'ASTM D1535-08')
Aliases:
- 'cie1976': 'CIE 1976'
- 'astm2008': 'ASTM D1535-08'
"""
LUMINANCE_METHODS['cie1976'] = (LUMINANCE_METHODS['CIE 1976'])
maximum_perception = 247
else:
maximum_perception = 246
Y = np.exp(
np.log(
substrate_concentration_MichealisMenten(
L_hdr - 0.02, maximum_perception, 2 ** epsilon)) / epsilon)
return from_range_1(Y)
LUMINANCE_METHODS = CaseInsensitiveMapping({
'Newhall 1943': luminance_Newhall1943,
'ASTM D1535-08': luminance_ASTMD153508,
'CIE 1976': luminance_CIE1976,
'Fairchild 2010': luminance_Fairchild2010,
'Fairchild 2011': luminance_Fairchild2011
})
LUMINANCE_METHODS.__doc__ = """
Supported *luminance* computation methods.
References
----------
:cite:`ASTMInternational2008a`, :cite:`CIETC1-482004m`, :cite:`Fairchild2010`,
:cite:`Fairchild2011`, :cite:`Newhall1943a`, :cite:`Wyszecki2000bd`
LUMINANCE_METHODS : CaseInsensitiveMapping
**{'Newhall 1943', 'ASTM D1535-08', 'CIE 1976', 'Fairchild 2010'}**
Aliases:
def __new__(cls, N_c, N_b, N_cb=None, N_bb=None):
"""
Returns a new instance of the
:class:`colour.appearance.Hunt_InductionFactors` class.
"""
return super(Hunt_InductionFactors,
cls).__new__(cls, N_c, N_b, N_cb, N_bb)
HUNT_VIEWING_CONDITIONS = CaseInsensitiveMapping({
'Small Areas, Uniform Background & Surrounds':
Hunt_InductionFactors(1, 300),
'Normal Scenes':
Hunt_InductionFactors(1, 75),
'Television & CRT, Dim Surrounds':
Hunt_InductionFactors(1, 25),
'Large Transparencies On Light Boxes':
Hunt_InductionFactors(0.7, 25),
'Projected Transparencies, Dark Surrounds':
Hunt_InductionFactors(0.7, 10)
})
HUNT_VIEWING_CONDITIONS.__doc__ = """
Reference *Hunt* colour appearance model viewing conditions.
References
----------
:cite:`Fairchild2013u`, :cite:`Hunt2004b`
HUNT_VIEWING_CONDITIONS : CaseInsensitiveMapping
**{'Small Areas, Uniform Background & Surrounds',
'Normal Scenes',
__all__ += ['oetf_sRGB', 'oetf_reverse_sRGB']
__all__ += ['log_encoding_ViperLog', 'log_decoding_ViperLog']
LOG_ENCODING_CURVES = CaseInsensitiveMapping({
'ACEScc': log_encoding_ACEScc,
'ACEScct': log_encoding_ACEScct,
'ACESproxy': log_encoding_ACESproxy,
'ALEXA Log C': log_encoding_ALEXALogC,
'Canon Log 2': log_encoding_CanonLog2,
'Canon Log 3': log_encoding_CanonLog3,
'Canon Log': log_encoding_CanonLog,
'Cineon': log_encoding_Cineon,
'D-Log': log_encoding_DJIDLog,
'ERIMM RGB': log_encoding_ERIMMRGB,
'Filmic Pro 6': log_encoding_FilmicPro6,
'Log3G10': log_encoding_Log3G10,
'Log3G12': log_encoding_Log3G12,
'Panalog': log_encoding_Panalog,
'PLog': log_encoding_PivotedLog,
'Protune': log_encoding_Protune,
'REDLog': log_encoding_REDLog,
'REDLogFilm': log_encoding_REDLogFilm,
'S-Log': log_encoding_SLog,
'S-Log2': log_encoding_SLog2,
'S-Log3': log_encoding_SLog3,
'T-Log': log_encoding_FilmLightTLog,
'V-Log': log_encoding_VLog,
'ViperLog': log_encoding_ViperLog
})
LOG_ENCODING_CURVES.__doc__ = """
Supported *log* encoding curves.
'CCS_LIGHT_SOURCES_NIST_PHILIPS_STANDARD_OBSERVER_10_DEGREE_CIE1964',
'CCS_LIGHT_SOURCES_COMMON_STANDARD_OBSERVER_2_DEGREE_CIE1931',
'CCS_LIGHT_SOURCES_COMMON_STANDARD_OBSERVER_10_DEGREE_CIE1964',
'CCS_LIGHT_SOURCES'
]
CCS_LIGHT_SOURCES_RIT_STANDARD_OBSERVER_2_DEGREE_CIE1931 = (
CaseInsensitiveMapping({
'Natural':
np.array([0.381585730647787, 0.359224138274067]),
'Philips TL-84':
np.array([0.378413599970988, 0.379290254544090]),
'SA':
np.array([0.447573030734154, 0.407438137156467]),
'SC':
np.array([0.310056734303928, 0.316145704789204]),
'T8 Luxline Plus White':
np.array([0.410492204086250, 0.388932529676840]),
'T8 Polylux 3000':
np.array([0.431706082207185, 0.413877736072647]),
'T8 Polylux 4000':
np.array([0.379219473139794, 0.384469085577631]),
'Thorn Kolor-rite':
np.array([0.381919124282806, 0.374309261641251])
}))
"""
Chromaticity coordinates of the light sources from the *RIT* *PointerData.xls*
spreadsheet for the *CIE 1931 2 Degree Standard Observer*.
Warnings
--------
The chromaticity coordinates have been calculated from *PointerData.xls*
LIGHT_SOURCES_PROJECTORS_CIE_1964_10_DEGREE_STANDARD_OBSERVER = (
CaseInsensitiveMapping({
'Kinoton 75P': np.array([0.317086642148234, 0.336222428041514])
}))
"""
Projectors and Xenon Arc Lamps. [3_]
LIGHT_SOURCES_PROJECTORS_CIE_1964_10_DEGREE_STANDARD_OBSERVER :
CaseInsensitiveMapping
**{'Kinoton 75P', }**
"""
LIGHT_SOURCES = CaseInsensitiveMapping({
'CIE 1931 2 Degree Standard Observer':
CaseInsensitiveMapping(
LIGHT_SOURCES_RIT_CIE_1931_2_DEGREE_STANDARD_OBSERVER),
'CIE 1964 10 Degree Standard Observer':
CaseInsensitiveMapping(
LIGHT_SOURCES_RIT_CIE_1964_10_DEGREE_STANDARD_OBSERVER)
})
LIGHT_SOURCES.__doc__ = """
Aggregated light sources chromaticity coordinates.
LIGHT_SOURCES : CaseInsensitiveMapping
**{'CIE 1931 2 Degree Standard Observer',
'CIE 1964 10 Degree Standard Observer'}**
Aliases:
- 'cie_2_1931': 'CIE 1931 2 Degree Standard Observer'
- 'cie_10_1964': 'CIE 1964 10 Degree Standard Observer'
"""
DNG_ILLUMINANTS_CORRELATED_COLOUR_TEMPERATURE = (CaseInsensitiveMapping({
'Standard light A':
2850,
'Tungsten (incandescent light)':
2850,
'ISO studio tungsten':
3200,
'D50':
5000,
'D55':
5500,
'Daylight':
5500,
'Fine weather':
5500,
'Flash':
5500,
'Standard light B':
5500,
'D65':
6500,
'Standard light C':
6500,
'Cloudy weather':
6500,
'D75':
7500,
'Shade':
7500,
'Daylight fluorescent (D 5700 - 7100K)': (5700 + 7100) * 0.5,
'Day white fluorescent (N 4600 - 5500K)': (4600 + 5400) * 0.5,
'Cool white fluorescent (W 3800 - 4500K)': (3900 + 4500) * 0.5,
'Fluorescent': (3900 + 4500) * 0.5,
'White fluorescent (WW 3250 - 3800K)': (3200 + 3700) * 0.5,
'Warm white fluorescent (L 2600 - 3250K)': (2600 + 3250) * 0.5
}))
HUNTERLAB_ILLUMINANTS_CIE_1964_10_DEGREE_STANDARD_OBSERVER_DATA
}))
"""
*Hunter L,a,b* illuminant dataset for *CIE 1964 10 Degree Standard Observer*.
References
----------
:cite:`HunterLab2008b`, :cite:`HunterLab2008c`
HUNTERLAB_ILLUMINANTS_CIE_1964_10_DEGREE_STANDARD_OBSERVER :
CaseInsensitiveMapping
"""
HUNTERLAB_ILLUMINANTS = CaseInsensitiveMapping({
'CIE 1931 2 Degree Standard Observer':
HUNTERLAB_ILLUMINANTS_CIE_1931_2_DEGREE_STANDARD_OBSERVER,
'CIE 1964 10 Degree Standard Observer':
HUNTERLAB_ILLUMINANTS_CIE_1964_10_DEGREE_STANDARD_OBSERVER
})
HUNTERLAB_ILLUMINANTS.__doc__ = """
Aggregated *Hunter L,a,b* illuminant dataset.
References
----------
:cite:`HunterLab2008b`, :cite:`HunterLab2008c`
HUNTERLAB_ILLUMINANTS : CaseInsensitiveMapping
**{'CIE 1931 2 Degree Standard Observer',
'CIE 1964 10 Degree Standard Observer'}**
Aliases:
'chromatic_adaptation_matrix_VonKries', 'chromatic_adaptation_VonKries'
]
__all__ += ['chromatic_adaptation_Fairchild1990']
__all__ += [
'CMCCAT2000_InductionFactors', 'CMCCAT2000_VIEWING_CONDITIONS',
'chromatic_adaptation_forward_CMCCAT2000',
'chromatic_adaptation_reverse_CMCCAT2000',
'chromatic_adaptation_CMCCAT2000'
]
__all__ += ['chromatic_adaptation_CIE1994']
CHROMATIC_ADAPTATION_METHODS = CaseInsensitiveMapping({
'CIE 1994':
chromatic_adaptation_CIE1994,
'CMCCAT2000':
chromatic_adaptation_CMCCAT2000,
'Fairchild 1990':
chromatic_adaptation_Fairchild1990,
'Von Kries':
chromatic_adaptation_VonKries,
})
CHROMATIC_ADAPTATION_METHODS.__doc__ = """
Supported chromatic adaptation methods.
References
----------
:cite:`CIETC1-321994b`, :cite:`Fairchild1991a`, :cite:`Fairchild2013s`,
:cite:`Fairchild2013t`, :cite:`Li2002a`, :cite:`Westland2012k`
CHROMATIC_ADAPTATION_METHODS : CaseInsensitiveMapping
**{'CIE 1994', 'CMCCAT2000', 'Fairchild 1990', 'Von Kries'}**
"""
0.00630978795372749960,
0.00414367215817645990,
0.00059244446107236802),
680.0: (
0.00528874383171553000,
0.00183198958165669010,
0.00468563680483140980)
}
} # yapf: disable
DSLR_CAMERAS_RGB_SPECTRAL_SENSITIVITIES = CaseInsensitiveMapping({
'Nikon 5100 (NPL)':
RGB_SpectralSensitivities(
DSLR_CAMERAS_RGB_SPECTRAL_SENSITIVITIES_DATA['Nikon 5100 (NPL)'],
name='Nikon 5100 (NPL)'),
'Sigma SDMerill (NPL)':
RGB_SpectralSensitivities(
DSLR_CAMERAS_RGB_SPECTRAL_SENSITIVITIES_DATA[
'Sigma SDMerill (NPL)'],
name='Sigma SDMerill (NPL)')
})
"""
*DSLR* cameras *RGB* spectral sensitivities.
References
----------
:cite:`Darrodi2015a`
DSLR_CAMERAS_RGB_SPECTRAL_SENSITIVITIES : CaseInsensitiveMapping
**{Nikon 5100 (NPL), Sigma SDMerill (NPL)}**
"""
__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__ = '*****@*****.**'
__status__ = 'Development'
__all__ = [
'YCBCR_WEIGHTS', 'YCbCr_ranges', 'RGB_to_YCbCr', 'YCbCr_to_RGB',
'RGB_to_YcCbcCrc', 'YcCbcCrc_to_RGB'
]
YCBCR_WEIGHTS = CaseInsensitiveMapping({
'ITU-R BT.601': np.array([0.2990, 0.1140]),
'ITU-R BT.709': np.array([0.2126, 0.0722]),
'ITU-R BT.2020': np.array([0.2627, 0.0593]),
'SMPTE-240M': np.array([0.2122, 0.0865])
})
"""
Luma weightings presets.
References
----------
- :cite:`InternationalTelecommunicationUnion2011e`
- :cite:`InternationalTelecommunicationUnion2015i`
- :cite:`InternationalTelecommunicationUnion2015h`
- :cite:`SocietyofMotionPictureandTelevisionEngineers1999b`
- :cite:`Wikipediaca`
YCBCR_WEIGHTS : dict
**{'ITU-R BT.601', 'ITU-R BT.709', 'ITU-R BT.2020', 'SMPTE-240M}**
0.5625 * spd.values[i - 1] +
0.5625 * spd.values[i + 1] -
0.0625 * spd.values[i + 3])
# Discarding the additional 20nm padding intervals.
spd.trim_wavelengths(
SpectralShape(spd.shape.start + 20, spd.shape.end - 20, 10))
XYZ = method(spd, cmfs, illuminant)
return XYZ
SPECTRAL_TO_XYZ_METHODS = CaseInsensitiveMapping({
'ASTM E308–15':
spectral_to_XYZ_ASTME30815,
'Integration':
spectral_to_XYZ_integration
})
"""
Supported spectral power distribution to *CIE XYZ* tristimulus values
conversion methods
SPECTRAL_TO_XYZ_METHODS : CaseInsensitiveMapping
**{'ASTM E308–15', 'Integration'}**
Aliases:
- 'astm2015': 'ASTM E308–15'
"""
SPECTRAL_TO_XYZ_METHODS['astm2015'] = (SPECTRAL_TO_XYZ_METHODS['ASTM E308–15'])
0.3678794...
"""
settings = {"name": f"{peak_wavelength}nm - {fwhm} FWHM - Gaussian"}
settings.update(kwargs)
wavelengths = shape.range()
values = np.exp(-(((wavelengths - peak_wavelength) / fwhm)**2))
return SpectralDistribution(values, wavelengths, **settings)
SD_GAUSSIAN_METHODS: CaseInsensitiveMapping = CaseInsensitiveMapping({
"Normal":
sd_gaussian_normal,
"FWHM":
sd_gaussian_fwhm
})
SD_GAUSSIAN_METHODS.__doc__ = """
Supported gaussian spectral distribution computation methods.
"""
def sd_gaussian(
mu_peak_wavelength: Floating,
sigma_fwhm: Floating,
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
method: Union[Literal["Normal", "FWHM"], str] = "Normal",
**kwargs: Any,
) -> SpectralDistribution:
"""
def tristimulus_weighting_factors_ASTME202211(cmfs, illuminant, shape):
"""
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
-------
ndarray
Tristimulus weighting factors table.
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
:attr:`_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.get('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( # doctest: +ELLIPSIS
... 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]])
"""
if cmfs.shape.interval != 1:
raise ValueError('"{0}" shape "interval" must be 1!'.format(cmfs))
if illuminant.shape.interval != 1:
raise ValueError(
'"{0}" shape "interval" must be 1!'.format(illuminant))
global _TRISTIMULUS_WEIGHTING_FACTORS_CACHE
if _TRISTIMULUS_WEIGHTING_FACTORS_CACHE is None:
_TRISTIMULUS_WEIGHTING_FACTORS_CACHE = CaseInsensitiveMapping()
name_twf = ', '.join((cmfs.name, illuminant.name, str(shape)))
if name_twf in _TRISTIMULUS_WEIGHTING_FACTORS_CACHE:
return _TRISTIMULUS_WEIGHTING_FACTORS_CACHE[name_twf]
Y = cmfs.values
S = illuminant.values
W = S[::shape.interval, np.newaxis] * Y[::shape.interval, :]
# First and last measurement intervals *Lagrange Coefficients*.
c_c = lagrange_coefficients_ASTME202211(shape.interval, 'boundary')
# Intermediate measurement intervals *Lagrange Coefficients*.
c_b = lagrange_coefficients_ASTME202211(shape.interval, 'inner')
# Total wavelengths count.
w_c = len(Y)
# Measurement interval interpolated values count.
r_c = c_b.shape[0]
# Last interval first interpolated wavelength.
w_lif = w_c - (w_c - 1) % shape.interval - 1 - r_c
# Intervals count.
i_c = W.shape[0]
i_cm = i_c - 1
for i in range(3):
# First interval.
for j in range(r_c):
for k in range(3):
W[k, i] = W[k, i] + c_c[j, k] * S[j + 1] * Y[j + 1, i]
# Last interval.
for j in range(r_c):
for k in range(i_cm, i_cm - 3, -1):
W[k, i] = (W[k, i] + c_c[r_c - j - 1, i_cm - k] *
S[j + w_lif] * Y[j + w_lif, i])
# Intermediate intervals.
for j in range(i_c - 3):
for k in range(r_c):
w_i = (r_c + 1) * (j + 1) + 1 + k
W[j, i] = W[j, i] + c_b[k, 0] * S[w_i] * Y[w_i, i]
W[j + 1, i] = W[j + 1, i] + c_b[k, 1] * S[w_i] * Y[w_i, i]
W[j + 2, i] = W[j + 2, i] + c_b[k, 2] * S[w_i] * Y[w_i, i]
W[j + 3, i] = W[j + 3, i] + c_b[k, 3] * S[w_i] * Y[w_i, i]
# Extrapolation of potential incomplete interval.
for j in range(int(w_c - ((w_c - 1) % shape.interval)), w_c, 1):
W[i_cm, i] = W[i_cm, i] + S[j] * Y[j, i]
W *= 100 / np.sum(W, axis=0)[1]
_TRISTIMULUS_WEIGHTING_FACTORS_CACHE[name_twf] = W
return W
*Lightness* induction factor :math:`F_L`.
F_C : numeric or array_like
*Chroma* induction factor :math:`F_C`.
References
----------
:cite:`Fairchild2013x`, :cite:`Luo1996b`, :cite:`Luo1996c`
"""
VIEWING_CONDITIONS_LLAB = CaseInsensitiveMapping({
'Reference Samples & Images, Average Surround, Subtending > 4':
(InductionFactors_LLAB(1, 3, 0, 1)),
'Reference Samples & Images, Average Surround, Subtending < 4':
(InductionFactors_LLAB(1, 3, 1, 1)),
'Television & VDU Displays, Dim Surround':
(InductionFactors_LLAB(0.7, 3.5, 1, 1)),
'Cut Sheet Transparency, Dim Surround':
(InductionFactors_LLAB(1, 5, 1, 1.1)),
'35mm Projection Transparency, Dark Surround':
(InductionFactors_LLAB(0.7, 4, 1, 1))
})
VIEWING_CONDITIONS_LLAB.__doc__ = """
Reference :math:`LLAB(l:c)` colour appearance model viewing conditions.
References
----------
:cite:`Fairchild2013x`, :cite:`Luo1996b`, :cite:`Luo1996c`
VIEWING_CONDITIONS_LLAB : CaseInsensitiveMapping
**{'Reference Samples & Images, Average Surround, Subtending > 4',
'Reference Samples & Images, Average Surround, Subtending < 4',
for i in range(3, len(sd.domain) - 3, 2):
sd[sd.wavelengths[i]] = (-0.0625 * sd.values[i - 3] +
0.5625 * sd.values[i - 1] +
0.5625 * sd.values[i + 1] -
0.0625 * sd.values[i + 3])
# Discarding the additional 20nm padding intervals.
sd.trim(SpectralShape(sd.shape.start + 20, sd.shape.end - 20, 10))
XYZ = method(sd, cmfs, illuminant)
return XYZ
SD_TO_XYZ_METHODS = CaseInsensitiveMapping({
'ASTM E308-15': sd_to_XYZ_ASTME30815,
'Integration': sd_to_XYZ_integration
})
SD_TO_XYZ_METHODS.__doc__ = """
Supported spectral distribution to *CIE XYZ* tristimulus values conversion
methods.
References
----------
:cite:`ASTMInternational2011a`, :cite:`ASTMInternational2015b`,
:cite:`Wyszecki2000bf`
SD_TO_XYZ_METHODS : CaseInsensitiveMapping
**{'ASTM E308-15', 'Integration'}**
Aliases:
ILLUMINANTS_CIE_1931_2_DEGREE_STANDARD_OBSERVER = CaseInsensitiveMapping({
'A':
np.array([0.44758, 0.40745]),
'B':
np.array([0.34842, 0.35161]),
'C':
np.array([0.31006, 0.31616]),
'D50':
np.array([0.34570, 0.35850]),
'D55':
np.array([0.33243, 0.34744]),
'D60':
np.array([0.321626242047397, 0.337736995955436]),
'D65':
np.array([0.31270, 0.32900]),
'D75':
np.array([0.29903, 0.31488]),
'E':
np.array([1 / 3, 1 / 3]),
'FL1':
np.array([0.31310, 0.33710]),
'FL2':
np.array([0.37210, 0.37510]),
'FL3':
np.array([0.40910, 0.39410]),
'FL4':
np.array([0.44020, 0.40310]),
'FL5':
np.array([0.31380, 0.34520]),
'FL6':
np.array([0.37790, 0.38820]),
'FL7':
np.array([0.31290, 0.32920]),
'FL8':
np.array([0.34580, 0.35860]),
'FL9':
np.array([0.37410, 0.37270]),
'FL10':
np.array([0.34580, 0.35880]),
'FL11':
np.array([0.38050, 0.37690]),
'FL12':
np.array([0.43700, 0.40420])
})
def tristimulus_weighting_factors_ASTME202211(cmfs, illuminant, shape):
"""
Returns a table of tristimulus weighting factors for given colour matching
functions and illuminant using practise *ASTM E2022-11* method.
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 : SpectralDistribution
Illuminant spectral distribution.
shape : SpectralShape
Shape used to build the table, only the interval is needed.
Returns
-------
ndarray
Tristimulus weighting factors table.
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
:attr:`colour.colorimetry.tristimulus.\
_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.
References
----------
:cite:`ASTMInternational2011a`
Examples
--------
>>> from colour import (CMFS, sd_CIE_standard_illuminant_A,
... SpectralDistribution, SpectralShape)
>>> from colour.utilities import numpy_print_options
>>> cmfs = CMFS['CIE 1964 10 Degree Standard Observer']
>>> A = sd_CIE_standard_illuminant_A(cmfs.shape)
>>> with numpy_print_options(suppress=True):
... tristimulus_weighting_factors_ASTME202211(
... cmfs, A, SpectralShape(360, 830, 20))
... # doctest: +ELLIPSIS
array([[ -0.0002981..., -0.0000317..., -0.0013301...],
[ -0.0087155..., -0.0008915..., -0.0407436...],
[ 0.0599679..., 0.0050203..., 0.2565018...],
[ 0.7734225..., 0.0779839..., 3.6965732...],
[ 1.9000905..., 0.3037005..., 9.7554195...],
[ 1.9707727..., 0.8552809..., 11.4867325...],
[ 0.7183623..., 2.1457000..., 6.7845806...],
[ 0.0426667..., 4.8985328..., 2.3208000...],
[ 1.5223302..., 9.6471138..., 0.7430671...],
[ 5.6770329..., 14.4609708..., 0.1958194...],
[ 12.4451744..., 17.4742541..., 0.0051827...],
[ 20.5535772..., 17.5838219..., -0.0026512...],
[ 25.3315384..., 14.8957035..., 0. ...],
[ 21.5711570..., 10.0796619..., 0. ...],
[ 12.1785817..., 5.0680655..., 0. ...],
[ 4.6675746..., 1.8303239..., 0. ...],
[ 1.3236117..., 0.5129694..., 0. ...],
[ 0.3175325..., 0.1230084..., 0. ...],
[ 0.0746341..., 0.0290243..., 0. ...],
[ 0.0182990..., 0.0071606..., 0. ...],
[ 0.0047942..., 0.0018888..., 0. ...],
[ 0.0013293..., 0.0005277..., 0. ...],
[ 0.0004254..., 0.0001704..., 0. ...],
[ 0.0000962..., 0.0000389..., 0. ...]])
"""
if cmfs.shape.interval != 1:
raise ValueError('"{0}" shape "interval" must be 1!'.format(cmfs))
if illuminant.shape.interval != 1:
raise ValueError(
'"{0}" shape "interval" must be 1!'.format(illuminant))
global _TRISTIMULUS_WEIGHTING_FACTORS_CACHE
if _TRISTIMULUS_WEIGHTING_FACTORS_CACHE is None:
_TRISTIMULUS_WEIGHTING_FACTORS_CACHE = CaseInsensitiveMapping()
name_twf = ', '.join((cmfs.name, illuminant.name, str(shape)))
if name_twf in _TRISTIMULUS_WEIGHTING_FACTORS_CACHE:
return _TRISTIMULUS_WEIGHTING_FACTORS_CACHE[name_twf]
Y = cmfs.values
S = illuminant.values
interval_i = DEFAULT_INT_DTYPE(shape.interval)
W = S[::interval_i, np.newaxis] * Y[::interval_i, :]
# First and last measurement intervals *Lagrange Coefficients*.
c_c = lagrange_coefficients_ASTME202211(interval_i, 'boundary')
# Intermediate measurement intervals *Lagrange Coefficients*.
c_b = lagrange_coefficients_ASTME202211(interval_i, 'inner')
# Total wavelengths count.
w_c = len(Y)
# Measurement interval interpolated values count.
r_c = c_b.shape[0]
# Last interval first interpolated wavelength.
w_lif = w_c - (w_c - 1) % interval_i - 1 - r_c
# Intervals count.
i_c = W.shape[0]
i_cm = i_c - 1
for i in range(3):
# First interval.
for j in range(r_c):
for k in range(3):
W[k, i] = W[k, i] + c_c[j, k] * S[j + 1] * Y[j + 1, i]
# Last interval.
for j in range(r_c):
for k in range(i_cm, i_cm - 3, -1):
W[k, i] = (W[k, i] + c_c[r_c - j - 1, i_cm - k] *
S[j + w_lif] * Y[j + w_lif, i])
# Intermediate intervals.
for j in range(i_c - 3):
for k in range(r_c):
w_i = (r_c + 1) * (j + 1) + 1 + k
W[j, i] = W[j, i] + c_b[k, 0] * S[w_i] * Y[w_i, i]
W[j + 1, i] = W[j + 1, i] + c_b[k, 1] * S[w_i] * Y[w_i, i]
W[j + 2, i] = W[j + 2, i] + c_b[k, 2] * S[w_i] * Y[w_i, i]
W[j + 3, i] = W[j + 3, i] + c_b[k, 3] * S[w_i] * Y[w_i, i]
# Extrapolation of potential incomplete interval.
for j in range(DEFAULT_INT_DTYPE(w_c - ((w_c - 1) % interval_i)), w_c,
1):
W[i_cm, i] = W[i_cm, i] + S[j] * Y[j, i]
W *= 100 / np.sum(W, axis=0)[1]
_TRISTIMULUS_WEIGHTING_FACTORS_CACHE[name_twf] = W
return W
"""
COLORCHECKER24_AFTER_NOV2014 = ColourChecker(
'ColorChecker24 - After November 2014', COLORCHECKER24_AFTER_NOV2014_DATA,
COLORCHECKER24_AFTER_NOV2014_ILLUMINANT)
"""
Reference *ColourChecker* data from *X-Rite (2015)* and matching the
*ColourChecker* edition after November 2014.
COLORCHECKER24_AFTER_NOV2014 : ColourChecker
"""
COLOURCHECKERS = CaseInsensitiveMapping({
'ColorChecker 1976': COLORCHECKER_1976,
'ColorChecker 2005': COLORCHECKER_2005,
'BabelColor Average': BABELCOLOR_AVERAGE,
'ColorChecker24 - Before November 2014': COLORCHECKER24_BEFORE_NOV2014,
'ColorChecker24 - After November 2014': COLORCHECKER24_AFTER_NOV2014,
})
COLOURCHECKERS.__doc__ = """
Aggregated *ColourCheckers* chromaticity coordinates.
References
----------
:cite:`BabelColor2012b`, :cite:`BabelColor2012c`, :cite:`X-Rite2015`
COLOURCHECKERS : CaseInsensitiveMapping
**{'ColorChecker 1976', 'ColorChecker 2005', 'BabelColor Average',
'ColorChecker24 - Before November 2014',
'ColorChecker24 - After November 2014'}**
def lagrange_coefficients_ASTME202211(interval=10, interval_type='inner'):
"""
Computes the *Lagrange Coefficients* for given interval size using practise
*ASTM E2022-11* method.
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
-------
ndarray
*Lagrange Coefficients*.
References
----------
:cite:`ASTMInternational2011a`
Examples
--------
>>> lagrange_coefficients_ASTME202211(10, 'inner')
... # doctest: +ELLIPSIS
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')
... # doctest: +ELLIPSIS
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...]])
"""
global _LAGRANGE_INTERPOLATING_COEFFICIENTS_CACHE
if _LAGRANGE_INTERPOLATING_COEFFICIENTS_CACHE is None:
_LAGRANGE_INTERPOLATING_COEFFICIENTS_CACHE = CaseInsensitiveMapping()
name_lica = ', '.join((str(interval), interval_type))
if name_lica in _LAGRANGE_INTERPOLATING_COEFFICIENTS_CACHE:
return _LAGRANGE_INTERPOLATING_COEFFICIENTS_CACHE[name_lica]
r_n = np.linspace(1 / interval, 1 - (1 / interval), interval - 1)
d = 3
if interval_type.lower() == 'inner':
r_n += 1
d = 4
lica = _LAGRANGE_INTERPOLATING_COEFFICIENTS_CACHE[name_lica] = (
as_float_array([lagrange_coefficients(r, d) for r in r_n]))
return lica
--------
>>> import numpy as np
>>> XYZ = np.array([95.00000000, 100.00000000, 105.00000000])
>>> yellowness_ASTME313(XYZ) # doctest: +ELLIPSIS
11.0650000...
"""
_X, Y, Z = tsplit(to_domain_100(XYZ))
WI = 100 * (1 - (0.847 * Z) / Y)
return from_range_100(WI)
YELLOWNESS_METHODS = CaseInsensitiveMapping({
'ASTM D1925': yellowness_ASTMD1925,
'ASTM E313': yellowness_ASTME313
})
YELLOWNESS_METHODS.__doc__ = """
Supported *yellowness* computation methods.
References
----------
:cite:`X-Rite2012a`
YELLOWNESS_METHODS : CaseInsensitiveMapping
**{'ASTM E313', 'ASTM D1925'}**
"""
def yellowness(XYZ, method='ASTM E313'):
"""
x, y = tsplit(xy)
Y = to_domain_100(Y)
x_n, y_n = tsplit(xy_n)
W = Y + 800 * (x_n - x) + 1700 * (y_n - y)
T = (1000 if '1931' in observer else 900) * (x_n - x) - 650 * (y_n - y)
WT = tstack([W, T])
return from_range_100(WT)
WHITENESS_METHODS = CaseInsensitiveMapping({
'Berger 1959': whiteness_Berger1959,
'Taube 1960': whiteness_Taube1960,
'Stensby 1968': whiteness_Stensby1968,
'ASTM E313': whiteness_ASTME313,
'Ganz 1979': whiteness_Ganz1979,
'CIE 2004': whiteness_CIE2004
})
WHITENESS_METHODS.__doc__ = """
Supported *whiteness* computations methods.
References
----------
:cite:`CIETC1-482004k`, :cite:`X-Rite2012a`
WHITENESS_METHODS : CaseInsensitiveMapping
**{'CIE 2004', 'Berger 1959', 'Taube 1960', 'Stensby 1968', 'ASTM E313',
'Ganz 1979', 'CIE 2004'}**
Aliases:
[0.3295, 0.3331, 95],
[0.3167, 0.3189, 95],
[0.3148, 0.3195, 95],
[0.3103, 0.3214, 95],
[0.3006, 0.3259, 95],
[0.2900, 0.3308, 95],
[0.3127, 0.3290, 100]]) # yapf: disable
"""
*CIE Standard Illuminant D Series D65* *Optimal Colour Stimuli*.
D65_OPTIMAL_COLOUR_STIMULI : ndarray
References
----------
.. [4] Wyszecki, G., & Stiles, W. S. (2000). Table II(3.7). In Color Science:
Concepts and Methods, Quantitative Data and Formulae (pp. 778–779).
Wiley. ISBN:978-0471399186
"""
ILLUMINANTS_OPTIMAL_COLOUR_STIMULI = CaseInsensitiveMapping({
'A': A_OPTIMAL_COLOUR_STIMULI,
'C': C_OPTIMAL_COLOUR_STIMULI,
'D65': D65_OPTIMAL_COLOUR_STIMULI
})
"""
Illuminants *Optimal Colour Stimuli*.
ILLUMINANTS_OPTIMAL_COLOUR_STIMULI : CaseInsensitiveMapping
**{'A', 'C', 'D65'}**
"""
:cite:`Bianco2010a`
BS_PC_CAT : array_like, (3, 3)
Notes
-----
- This chromatic adaptation transform has no negative lobes.
"""
CHROMATIC_ADAPTATION_TRANSFORMS = CaseInsensitiveMapping({
'XYZ Scaling': XYZ_SCALING_CAT,
'Von Kries': VON_KRIES_CAT,
'Bradford': BRADFORD_CAT,
'Sharp': SHARP_CAT,
'Fairchild': FAIRCHILD_CAT,
'CMCCAT97': CMCCAT97_CAT,
'CMCCAT2000': CMCCAT2000_CAT,
'CAT02': CAT02_CAT,
'CAT02_BRILL_CAT': CAT02_BRILL_CAT,
'Bianco': BS_CAT,
'Bianco PC': BS_PC_CAT
})
CHROMATIC_ADAPTATION_TRANSFORMS.__doc__ = """
Supported chromatic adaptation transforms.
References
----------
:cite:`Bianco2010a`, :cite:`Brill2008a`, :cite:`Fairchildb`, :cite:`Li2007e`,
:cite:`Lindbloom2009g`, :cite:`Westland2012g`, :cite:`Westland2012k`,
:cite:`Wikipedia2007`
