def test_multi_sds_to_XYZ_integration(self):
"""
Tests :func:`colour.colorimetry.tristimulus.\
multi_sds_to_XYZ_integration`
definition.
"""
cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
np.testing.assert_almost_equal(
multi_sds_to_XYZ_integration(MSDS, cmfs, ILLUMINANTS_SDS['D65']),
XYZ_D65_INTEGRATION_MSDS,
decimal=7)
np.testing.assert_almost_equal(
multi_sds_to_XYZ_integration(
MSDS_ARRAY,
cmfs,
ILLUMINANTS_SDS['D65'],
shape=SpectralShape(400, 700, 60)),
XYZ_D65_ARRAY_INTEGRATION,
decimal=7)
np.testing.assert_almost_equal(
multi_sds_to_XYZ_integration(
MSDS_ARRAY,
cmfs,
ILLUMINANTS_SDS['D65'],
1,
shape=SpectralShape(400, 700, 60)),
XYZ_D65_ARRAY_K1_INTEGRATION,
decimal=7)
def test_msds_to_XYZ_integration(self):
"""
Test :func:`colour.colorimetry.tristimulus_values.\
msds_to_XYZ_integration` definition.
"""
cmfs = MSDS_CMFS["CIE 1931 2 Degree Standard Observer"]
np.testing.assert_almost_equal(
msds_to_XYZ_integration(MSDS_TWO, cmfs, SDS_ILLUMINANTS["D65"]),
TVS_D65_INTEGRATION_MSDS,
decimal=7,
)
np.testing.assert_almost_equal(
msds_to_XYZ_integration(
DATA_TWO,
cmfs,
SDS_ILLUMINANTS["D65"],
shape=SpectralShape(400, 700, 60),
),
TVS_D65_ARRAY_INTEGRATION,
decimal=7,
)
np.testing.assert_almost_equal(
msds_to_XYZ_integration(
DATA_TWO,
cmfs,
SDS_ILLUMINANTS["D65"],
1,
shape=SpectralShape(400, 700, 60),
),
TVS_D65_ARRAY_K1_INTEGRATION,
decimal=7,
)
def test_sd_to_XYZ_ASTME30815_mi_1nm(self):
"""
Tests :func:`colour.colorimetry.tristimulus.sd_to_XYZ_ASTME30815`
definition for 1 nm measurement intervals.
"""
np.testing.assert_almost_equal(
sd_to_XYZ_ASTME30815(self._sd.copy().align(self._cmfs.shape),
self._cmfs, self._A),
np.array([14.46372680, 10.85832950, 2.04663200]),
decimal=7)
np.testing.assert_almost_equal(
sd_to_XYZ_ASTME30815(self._sd.copy().align(self._cmfs.shape),
self._cmfs,
self._A,
use_practice_range=False),
np.array([14.46366018, 10.85827949, 2.04662258]),
decimal=7)
np.testing.assert_almost_equal(
sd_to_XYZ_ASTME30815(
self._sd.copy().align(SpectralShape(400, 700, 1)), self._cmfs,
self._A),
np.array([14.54173397, 10.88628632, 2.04965822]),
decimal=7)
np.testing.assert_almost_equal(
sd_to_XYZ_ASTME30815(self._sd.copy().align(
SpectralShape(400, 700, 1)),
self._cmfs,
self._A,
use_practice_range=False),
np.array([14.54203076, 10.88636754, 2.04964877]),
decimal=7)
def test_spectral_to_XYZ_tristimulus_weighting_factors_ASTME30815(self):
"""
Tests :func:`colour.colorimetry.tristimulus.\
spectral_to_XYZ_tristimulus_weighting_factors_ASTME30815`
definition.
"""
cmfs = CMFS.get('CIE 1931 2 Degree Standard Observer')
np.testing.assert_almost_equal(
spectral_to_XYZ_tristimulus_weighting_factors_ASTME30815(
SAMPLE_SPD,
cmfs,
ILLUMINANTS_RELATIVE_SPDS.get('A')),
np.array([14.46366344, 10.85828513, 2.04663792]),
decimal=7)
cmfs = CMFS.get('CIE 1964 10 Degree Standard Observer')
np.testing.assert_almost_equal(
spectral_to_XYZ_tristimulus_weighting_factors_ASTME30815(
SAMPLE_SPD,
cmfs,
ILLUMINANTS_RELATIVE_SPDS.get('C')),
np.array([10.77033881, 9.44864632, 6.62758924]),
decimal=7)
np.testing.assert_almost_equal(
spectral_to_XYZ_tristimulus_weighting_factors_ASTME30815(
SAMPLE_SPD,
cmfs,
ILLUMINANTS_RELATIVE_SPDS.get('F2')),
np.array([11.57837130, 9.98734511, 3.95499522]),
decimal=7)
np.testing.assert_almost_equal(
spectral_to_XYZ_tristimulus_weighting_factors_ASTME30815(
SAMPLE_SPD.clone().trim_wavelengths(
SpectralShape(400, 700, 5)),
cmfs,
ILLUMINANTS_RELATIVE_SPDS.get('A')),
np.array([14.38180830, 10.74512906, 2.01579131]),
decimal=7)
np.testing.assert_almost_equal(
spectral_to_XYZ_tristimulus_weighting_factors_ASTME30815(
SAMPLE_SPD.clone().interpolate(
SpectralShape(400, 700, 10)),
cmfs,
ILLUMINANTS_RELATIVE_SPDS.get('A')),
np.array([14.38284399, 10.74577954, 2.01553721]),
decimal=7)
np.testing.assert_almost_equal(
spectral_to_XYZ_tristimulus_weighting_factors_ASTME30815(
SAMPLE_SPD.clone().interpolate(
SpectralShape(400, 700, 20)),
cmfs,
ILLUMINANTS_RELATIVE_SPDS.get('A')),
np.array([14.38356848, 10.74613294, 2.01526418]),
decimal=7)
def test_sd_to_XYZ_ASTME308_mi_10nm(self):
"""
Test :func:`colour.colorimetry.tristimulus_values.sd_to_XYZ_ASTME308`
definition for 10 nm measurement intervals.
"""
np.testing.assert_almost_equal(
sd_to_XYZ_ASTME308(
reshape_sd(self._sd, SpectralShape(360, 830, 10)),
self._cmfs,
self._A,
),
np.array([14.47779980, 10.86358645, 2.04751388]),
decimal=7,
)
np.testing.assert_almost_equal(
sd_to_XYZ_ASTME308(
reshape_sd(self._sd, SpectralShape(360, 830, 10)),
self._cmfs,
self._A,
use_practice_range=False,
),
np.array([14.47773312, 10.86353641, 2.04750445]),
decimal=7,
)
np.testing.assert_almost_equal(
sd_to_XYZ_ASTME308(
reshape_sd(self._sd, SpectralShape(400, 700, 10)),
self._cmfs,
self._A,
),
np.array([14.54137532, 10.88641727, 2.04931318]),
decimal=7,
)
np.testing.assert_almost_equal(
sd_to_XYZ_ASTME308(
reshape_sd(self._sd, SpectralShape(400, 700, 10)),
self._cmfs,
self._A,
use_practice_range=False,
),
np.array([14.54167211, 10.88649849, 2.04930374]),
decimal=7,
)
np.testing.assert_almost_equal(
sd_to_XYZ_ASTME308(
reshape_sd(self._sd, SpectralShape(400, 700, 10)),
self._cmfs,
self._A,
k=1,
),
np.array([1568.94283333, 1174.59084705, 221.11080639]),
decimal=7,
)
def test_sd_to_XYZ_tristimulus_weighting_factors_ASTME308(self):
"""
Tests :func:`colour.colorimetry.tristimulus.\
sd_to_XYZ_tristimulus_weighting_factors_ASTME308`
definition.
"""
cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
np.testing.assert_almost_equal(
sd_to_XYZ_tristimulus_weighting_factors_ASTME308(
SAMPLE_SD, cmfs, ILLUMINANTS_SDS['A']),
np.array([14.46366344, 10.85828513, 2.04663792]),
decimal=7)
cmfs = CMFS['CIE 1964 10 Degree Standard Observer']
np.testing.assert_almost_equal(
sd_to_XYZ_tristimulus_weighting_factors_ASTME308(
SAMPLE_SD, cmfs, ILLUMINANTS_SDS['C']),
np.array([10.77033881, 9.44864632, 6.62758924]),
decimal=7)
np.testing.assert_almost_equal(
sd_to_XYZ_tristimulus_weighting_factors_ASTME308(
SAMPLE_SD, cmfs, ILLUMINANTS_SDS['FL2']),
np.array([11.57837130, 9.98734511, 3.95499522]),
decimal=7)
np.testing.assert_almost_equal(
sd_to_XYZ_tristimulus_weighting_factors_ASTME308(
SAMPLE_SD.copy().trim(SpectralShape(400, 700, 5)), cmfs,
ILLUMINANTS_SDS['A']),
np.array([14.38180830, 10.74512906, 2.01579131]),
decimal=7)
np.testing.assert_almost_equal(
sd_to_XYZ_tristimulus_weighting_factors_ASTME308(
SAMPLE_SD.copy().interpolate(SpectralShape(400, 700, 10)),
cmfs, ILLUMINANTS_SDS['A']),
np.array([14.38284399, 10.74577954, 2.01553721]),
decimal=7)
np.testing.assert_almost_equal(
sd_to_XYZ_tristimulus_weighting_factors_ASTME308(
SAMPLE_SD.copy().interpolate(SpectralShape(400, 700, 20)),
cmfs, ILLUMINANTS_SDS['A']),
np.array([14.38356848, 10.74613294, 2.01526418]),
decimal=7)
np.testing.assert_almost_equal(
sd_to_XYZ_tristimulus_weighting_factors_ASTME308(
SAMPLE_SD.copy().interpolate(SpectralShape(400, 700, 20)),
cmfs,
ILLUMINANTS_SDS['A'],
k=1),
np.array([1636.74286798, 1222.82981953, 229.32204062]),
decimal=7)
def test_sd_to_XYZ_tristimulus_weighting_factors_ASTME308(self):
"""
Tests :func:`colour.colorimetry.tristimulus.\
sd_to_XYZ_tristimulus_weighting_factors_ASTME308`
definition.
"""
cmfs = MSDS_CMFS['CIE 1931 2 Degree Standard Observer']
np.testing.assert_almost_equal(
sd_to_XYZ_tristimulus_weighting_factors_ASTME308(
SD_SAMPLE, cmfs, SDS_ILLUMINANTS['A']),
np.array([14.46341867, 10.85820227, 2.04697034]),
decimal=7)
cmfs = MSDS_CMFS['CIE 1964 10 Degree Standard Observer']
np.testing.assert_almost_equal(
sd_to_XYZ_tristimulus_weighting_factors_ASTME308(
SD_SAMPLE, cmfs, SDS_ILLUMINANTS['C']),
np.array([10.77005571, 9.44877491, 6.62428210]),
decimal=7)
np.testing.assert_almost_equal(
sd_to_XYZ_tristimulus_weighting_factors_ASTME308(
SD_SAMPLE, cmfs, SDS_ILLUMINANTS['FL2']),
np.array([11.57542759, 9.98605604, 3.95273304]),
decimal=7)
np.testing.assert_almost_equal(
sd_to_XYZ_tristimulus_weighting_factors_ASTME308(
SD_SAMPLE.copy().trim(SpectralShape(400, 700, 5)), cmfs,
SDS_ILLUMINANTS['A']),
np.array([14.38153638, 10.74503131, 2.01613844]),
decimal=7)
np.testing.assert_almost_equal(
sd_to_XYZ_tristimulus_weighting_factors_ASTME308(
SD_SAMPLE.copy().interpolate(SpectralShape(400, 700, 10)),
cmfs, SDS_ILLUMINANTS['A']),
np.array([14.38257202, 10.74568178, 2.01588427]),
decimal=7)
np.testing.assert_almost_equal(
sd_to_XYZ_tristimulus_weighting_factors_ASTME308(
SD_SAMPLE.copy().interpolate(SpectralShape(400, 700, 20)),
cmfs, SDS_ILLUMINANTS['A']),
np.array([14.38329645, 10.74603515, 2.01561113]),
decimal=7)
np.testing.assert_almost_equal(
sd_to_XYZ_tristimulus_weighting_factors_ASTME308(
SD_SAMPLE.copy().interpolate(SpectralShape(400, 700, 20)),
cmfs,
SDS_ILLUMINANTS['A'],
k=1),
np.array([1636.74881983, 1222.84626486, 229.36669308]),
decimal=7)
def mesopic_luminous_efficiency_function(
Lp,
source='Blue Heavy',
method='MOVE',
photopic_lef=PHOTOPIC_LEFS['CIE 1924 Photopic Standard Observer'],
scotopic_lef=SCOTOPIC_LEFS['CIE 1951 Scotopic Standard Observer']):
"""
Returns the mesopic luminous efficiency function :math:`V_m(\lambda)` for
given photopic luminance :math:`L_p`.
Parameters
----------
Lp : numeric
Photopic luminance :math:`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
:math:`V(\lambda)` photopic luminous efficiency function.
scotopic_lef : SpectralPowerDistribution, optional
:math:`V^\prime(\lambda)` scotopic luminous efficiency function.
Returns
-------
SpectralPowerDistribution
Mesopic luminous efficiency function :math:`V_m(\lambda)`.
Examples
--------
>>> print(mesopic_luminous_efficiency_function(0.2))
SpectralPowerDistribution(\
'0.2 Lp Mesopic Luminous Efficiency Function', (380.0, 780.0, 1.0))
"""
photopic_lef_shape = photopic_lef.shape
scotopic_lef_shape = scotopic_lef.shape
shape = SpectralShape(
max(photopic_lef_shape.start, scotopic_lef_shape.start),
min(photopic_lef_shape.end, scotopic_lef_shape.end),
max(photopic_lef_shape.interval, scotopic_lef_shape.interval))
wavelengths = shape.range()
spd_data = dict(
zip(
wavelengths,
mesopic_weighting_function(wavelengths, Lp, source, method,
photopic_lef, scotopic_lef)))
spd = SpectralPowerDistribution(
'{0} Lp Mesopic Luminous Efficiency Function'.format(Lp), spd_data)
return spd.normalise()
def test_sd_to_XYZ_ASTME308_mi_1nm(self):
"""
Test :func:`colour.colorimetry.tristimulus_values.sd_to_XYZ_ASTME308`
definition for 1 nm measurement intervals.
"""
np.testing.assert_almost_equal(
sd_to_XYZ_ASTME308(reshape_sd(self._sd, self._cmfs.shape),
self._cmfs, self._A),
np.array([14.46372680, 10.85832950, 2.04663200]),
decimal=7,
)
np.testing.assert_almost_equal(
sd_to_XYZ_ASTME308(
reshape_sd(self._sd, self._cmfs.shape),
self._cmfs,
self._A,
use_practice_range=False,
),
np.array([14.46366018, 10.85827949, 2.04662258]),
decimal=7,
)
np.testing.assert_almost_equal(
sd_to_XYZ_ASTME308(
reshape_sd(self._sd, SpectralShape(400, 700, 1)),
self._cmfs,
self._A,
),
np.array([14.54173397, 10.88628632, 2.04965822]),
decimal=7,
)
np.testing.assert_almost_equal(
sd_to_XYZ_ASTME308(
reshape_sd(self._sd, SpectralShape(400, 700, 1)),
self._cmfs,
self._A,
use_practice_range=False,
),
np.array([14.54203076, 10.88636754, 2.04964877]),
decimal=7,
)
np.testing.assert_almost_equal(
sd_to_XYZ_ASTME308(
reshape_sd(self._sd, SpectralShape(400, 700, 1)),
self._cmfs,
self._A,
k=1,
),
np.array([1568.98152997, 1174.57671769, 221.14803420]),
decimal=7,
)
def test_raise_exception_colour_fidelity_index_CFI2017(self):
"""
Test :func:`colour.quality.CIE2017.colour_fidelity_index_CFI2017`
definition raised exception.
"""
sd = reshape_sd(SDS_ILLUMINANTS["FL2"], SpectralShape(400, 700, 5))
self.assertWarns(ColourUsageWarning, colour_fidelity_index_CIE2017, sd)
sd = reshape_sd(SDS_ILLUMINANTS["FL2"], SpectralShape(380, 780, 10))
self.assertRaises(ValueError, colour_fidelity_index_CIE2017, sd)
def mesopic_luminous_efficiency_function(
Lp,
source="Blue Heavy",
method="MOVE",
photopic_lef=PHOTOPIC_LEFS.get("CIE 1924 Photopic Standard Observer"),
scotopic_lef=SCOTOPIC_LEFS.get("CIE 1951 Scotopic Standard Observer"),
):
"""
Returns the mesopic luminous efficiency function :math:`V_m(\lambda)` for
given photopic luminance :math:`L_p`.
Parameters
----------
Lp : numeric
Photopic luminance :math:`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
:math:`V(\lambda)` photopic luminous efficiency function.
scotopic_lef : SpectralPowerDistribution, optional
:math:`V^\prime(\lambda)` scotopic luminous efficiency function.
Returns
-------
SpectralPowerDistribution
Mesopic luminous efficiency function :math:`V_m(\lambda)`.
Examples
--------
>>> mesopic_luminous_efficiency_function(0.2) # doctest: +ELLIPSIS
<colour.colorimetry.spectrum.SpectralPowerDistribution object at 0x...>
"""
photopic_lef_shape = photopic_lef.shape
scotopic_lef_shape = scotopic_lef.shape
shape = SpectralShape(
max(photopic_lef_shape.start, scotopic_lef_shape.start),
min(photopic_lef_shape.end, scotopic_lef_shape.end),
max(photopic_lef_shape.steps, scotopic_lef_shape.steps),
)
wavelengths = shape.range()
spd_data = dict(
zip(wavelengths, mesopic_weighting_function(wavelengths, Lp, source, method, photopic_lef, scotopic_lef))
)
spd = SpectralPowerDistribution("{0} Lp Mesopic Luminous Efficiency Function".format(Lp), spd_data)
return spd.normalise()
def test_adjust_tristimulus_weighting_factors_ASTME30815(self):
"""
Tests :func:`colour.colorimetry.tristimulus.\
adjust_tristimulus_weighting_factors_ASTME30815` definition.
"""
np.testing.assert_almost_equal(
adjust_tristimulus_weighting_factors_ASTME30815(
D65_CIE_1931_2_20_TWF, SpectralShape(360, 830, 20),
SpectralShape(400, 700, 20)),
D65_CIE_1931_2_20_ATWF,
decimal=3)
def test_XYZ_to_sd_Meng2015(self):
"""
Tests :func:`colour.recovery.meng2015.XYZ_to_sd_Meng2015`
definition.
"""
cmfs = (STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer'].
copy().trim(DEFAULT_SPECTRAL_SHAPE))
shape = SpectralShape(cmfs.shape.start, cmfs.shape.end, 5)
cmfs_c = cmfs.copy().align(shape)
XYZ = np.array([0.21781186, 0.12541048, 0.04697113])
np.testing.assert_almost_equal(
sd_to_XYZ_integration(XYZ_to_sd_Meng2015(XYZ, cmfs_c), cmfs_c) /
100,
XYZ,
decimal=7)
shape = SpectralShape(cmfs.shape.start, cmfs.shape.end, 10)
cmfs_c = cmfs.copy().align(shape)
np.testing.assert_almost_equal(
sd_to_XYZ_integration(XYZ_to_sd_Meng2015(XYZ, cmfs_c), cmfs_c) /
100,
XYZ,
decimal=7)
np.testing.assert_almost_equal(sd_to_XYZ_integration(
XYZ_to_sd_Meng2015(XYZ, cmfs_c, ILLUMINANTS_SDS['D65']), cmfs_c,
ILLUMINANTS_SDS['D65']) / 100,
XYZ,
decimal=7)
np.testing.assert_almost_equal(sd_to_XYZ_integration(
XYZ_to_sd_Meng2015(
XYZ,
cmfs_c,
optimisation_parameters={'options': {
'ftol': 1e-10,
}}), cmfs_c) / 100,
XYZ,
decimal=7)
shape = SpectralShape(400, 700, 5)
cmfs_c = cmfs.copy().align(shape)
np.testing.assert_almost_equal(
sd_to_XYZ_integration(XYZ_to_sd_Meng2015(XYZ, cmfs_c), cmfs_c) /
100,
XYZ,
decimal=7)
def test_raise_exception_colour_fidelity_index_CFI2017(self):
"""
Tests :func:`colour.quality.CIE2017.colour_fidelity_index_CFI2017`
definition raised exception.
"""
if six.PY3: # pragma: no cover
sd = SDS_ILLUMINANTS['FL2'].copy().align(SpectralShape(
400, 700, 5))
self.assertWarns(ColourUsageWarning, colour_fidelity_index_CIE2017,
sd)
sd = SDS_ILLUMINANTS['FL2'].copy().align(SpectralShape(380, 780, 10))
self.assertRaises(ValueError, colour_fidelity_index_CIE2017, sd)
def test_adjust_tristimulus_weighting_factors_ASTME308(self):
"""
Test :func:`colour.colorimetry.tristimulus_values.\
adjust_tristimulus_weighting_factors_ASTME308` definition.
"""
np.testing.assert_almost_equal(
adjust_tristimulus_weighting_factors_ASTME308(
TWF_D65_CIE_1931_2_20,
SpectralShape(360, 830, 20),
SpectralShape(400, 700, 20),
),
TWF_D65_CIE_1931_2_20_A,
decimal=3,
)
def test_tristimulus_weighting_factors_ASTME2022(self):
"""
Tests :func:`colour.colorimetry.tristimulus.\
tristimulus_weighting_factors_ASTME2022` definition.
Notes
-----
- :attr:`TWF_A_CIE_1964_10_10`, :attr:`TWF_A_CIE_1964_10_20` and
:attr:`TWF_D65_CIE_1931_2_20` attributes data is matching
:cite:`ASTMInternational2015b`.
References
----------
:cite:`ASTMInternational2015b`
"""
cmfs = MSDS_CMFS['CIE 1964 10 Degree Standard Observer']
A = sd_CIE_standard_illuminant_A(cmfs.shape)
twf = tristimulus_weighting_factors_ASTME2022(
cmfs, A, SpectralShape(360, 830, 10))
np.testing.assert_almost_equal(np.round(twf, 3),
TWF_A_CIE_1964_10_10,
decimal=3)
twf = tristimulus_weighting_factors_ASTME2022(
cmfs, A, SpectralShape(360, 830, 20))
np.testing.assert_almost_equal(np.round(twf, 3),
TWF_A_CIE_1964_10_20,
decimal=3)
cmfs = MSDS_CMFS['CIE 1931 2 Degree Standard Observer']
D65 = SDS_ILLUMINANTS['D65'].copy().align(
cmfs.shape, interpolator=LinearInterpolator)
twf = tristimulus_weighting_factors_ASTME2022(
cmfs, D65, SpectralShape(360, 830, 20))
np.testing.assert_almost_equal(np.round(twf, 3),
TWF_D65_CIE_1931_2_20,
decimal=3)
twf = tristimulus_weighting_factors_ASTME2022(cmfs,
D65,
SpectralShape(
360, 830, 20),
k=1)
np.testing.assert_almost_equal(twf,
TWF_D65_CIE_1931_2_20_K1,
decimal=7)
def test_raise_exception_tristimulus_weighting_factors_ASTME2022(self):
"""
Test :func:`colour.colorimetry.tristimulus_values.\
tristimulus_weighting_factors_ASTME2022` definition raised exception.
"""
shape = SpectralShape(360, 830, 10)
cmfs_1 = MSDS_CMFS["CIE 1964 10 Degree Standard Observer"]
# pylint: disable=E1102
cmfs_2 = reshape_msds(cmfs_1, shape)
A_1 = sd_CIE_standard_illuminant_A(cmfs_1.shape)
A_2 = sd_CIE_standard_illuminant_A(cmfs_2.shape)
self.assertRaises(
ValueError,
tristimulus_weighting_factors_ASTME2022,
cmfs_1,
A_2,
shape,
)
self.assertRaises(
ValueError,
tristimulus_weighting_factors_ASTME2022,
cmfs_2,
A_1,
shape,
)
def test_handle_spectral_arguments(self):
"""
Test :func:`colour.colorimetry.tristimulus_values.\
handle_spectral_arguments` definition.
"""
cmfs, illuminant = handle_spectral_arguments()
# pylint: disable=E1102
self.assertEqual(
cmfs,
reshape_msds(MSDS_CMFS["CIE 1931 2 Degree Standard Observer"]),
)
self.assertEqual(illuminant, reshape_sd(SDS_ILLUMINANTS["D65"]))
shape = SpectralShape(400, 700, 20)
cmfs, illuminant = handle_spectral_arguments(shape_default=shape)
self.assertEqual(cmfs.shape, shape)
self.assertEqual(illuminant.shape, shape)
cmfs, illuminant = handle_spectral_arguments(
cmfs_default="CIE 2012 2 Degree Standard Observer",
illuminant_default="E",
shape_default=shape,
)
self.assertEqual(
cmfs,
reshape_msds(MSDS_CMFS["CIE 2012 2 Degree Standard Observer"],
shape=shape),
)
self.assertEqual(illuminant,
sd_ones(shape, interpolator=LinearInterpolator) * 100)
def sd_Jakob2019(
coefficients: ArrayLike, shape: SpectralShape = SPECTRAL_SHAPE_JAKOB2019
) -> SpectralDistribution:
"""
Return a spectral distribution following the spectral model given by
*Jakob and Hanika (2019)*.
Parameters
----------
coefficients
Dimensionless coefficients for *Jakob and Hanika (2019)* reflectance
spectral model.
shape
Shape used by the spectral distribution.
Returns
-------
:class:`colour.SpectralDistribution`
*Jakob and Hanika (2019)* spectral distribution.
References
----------
:cite:`Jakob2019`
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... sd_Jakob2019([-9e-05, 8.5e-02, -20], SpectralShape(400, 700, 20))
... # doctest: +ELLIPSIS
SpectralDistribution([[ 400. , 0.3143046...],
[ 420. , 0.4133320...],
[ 440. , 0.4880034...],
[ 460. , 0.5279562...],
[ 480. , 0.5319346...],
[ 500. , 0.5 ...],
[ 520. , 0.4326202...],
[ 540. , 0.3373544...],
[ 560. , 0.2353056...],
[ 580. , 0.1507665...],
[ 600. , 0.0931332...],
[ 620. , 0.0577434...],
[ 640. , 0.0367011...],
[ 660. , 0.0240879...],
[ 680. , 0.0163316...],
[ 700. , 0.0114118...]],
interpolator=SpragueInterpolator,
interpolator_kwargs={},
extrapolator=Extrapolator,
extrapolator_kwargs={...})
"""
c_0, c_1, c_2 = as_float_array(coefficients)
wl = shape.range()
U = c_0 * wl**2 + c_1 * wl + c_2
R = 1 / 2 + U / (2 * np.sqrt(1 + U**2))
name = f"{coefficients!r} (COEFF) - Jakob (2019)"
return SpectralDistribution(R, wl, name=name)
def test_XYZ_to_sd_Meng2015(self):
"""
Tests :func:`colour.recovery.meng2015.XYZ_to_sd_Meng2015`
definition.
"""
cmfs = STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer']
shape = SpectralShape(cmfs.shape.start, cmfs.shape.end, 5)
cmfs_c = cmfs.copy().align(shape)
XYZ = np.array([0.21781186, 0.12541048, 0.04697113])
np.testing.assert_almost_equal(
sd_to_XYZ_integration(XYZ_to_sd_Meng2015(XYZ), cmfs=cmfs_c) / 100,
XYZ,
decimal=7)
shape = SpectralShape(cmfs.shape.start, cmfs.shape.end, 10)
cmfs_c = cmfs.copy().align(shape)
np.testing.assert_almost_equal(
sd_to_XYZ_integration(
XYZ_to_sd_Meng2015(XYZ, interval=10), cmfs=cmfs_c) / 100,
XYZ,
decimal=7)
np.testing.assert_almost_equal(
sd_to_XYZ_integration(
XYZ_to_sd_Meng2015(
XYZ,
interval=10,
optimisation_parameters={
'options': {
'ftol': 1e-10,
'maxiter': 2000
}
}),
cmfs=cmfs_c) / 100,
XYZ,
decimal=7)
shape = SpectralShape(400, 700, 5)
cmfs_c = cmfs.copy().align(shape)
np.testing.assert_almost_equal(
sd_to_XYZ_integration(
XYZ_to_sd_Meng2015(XYZ, cmfs=cmfs_c), cmfs=cmfs_c) / 100,
XYZ,
decimal=7)
def test_tristimulus_weighting_factors_ASTME202211(self):
"""
Tests :func:`colour.colorimetry.tristimulus.\
tristimulus_weighting_factors_ASTME202211` definition.
Notes
-----
:attr:`A_CIE_1964_10_10_TWF`, :attr:`A_CIE_1964_10_20_TWF` and
:attr:`D65_CIE_1931_2_20_TWF` attributes data is matching [1]_.
References
----------
.. [1] ASTM International. (2015). ASTM E308–15 - Standard Practice
for Computing the Colors of Objects by Using the CIE System,
1–47. doi:10.1520/E0308-15
"""
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))))
twf = tristimulus_weighting_factors_ASTME202211(
cmfs, A, SpectralShape(360, 830, 10))
np.testing.assert_almost_equal(
np.round(twf, 3),
A_CIE_1964_10_10_TWF,
decimal=3)
twf = tristimulus_weighting_factors_ASTME202211(
cmfs, A, SpectralShape(360, 830, 20))
np.testing.assert_almost_equal(
np.round(twf, 3),
A_CIE_1964_10_20_TWF,
decimal=3)
cmfs = CMFS.get('CIE 1931 2 Degree Standard Observer')
D65 = ILLUMINANTS_RELATIVE_SPDS['D65'].clone().align(
cmfs.shape, interpolation_method='Linear')
twf = tristimulus_weighting_factors_ASTME202211(
cmfs, D65, SpectralShape(360, 830, 20))
np.testing.assert_almost_equal(
np.round(twf, 3),
D65_CIE_1931_2_20_TWF,
decimal=3)
def test_raise_exception_sd_to_XYZ_ASTME308(self):
"""
Tests :func:`colour.colorimetry.tristimulus.sd_to_XYZ_ASTME308`
definition raised exception.
"""
self.assertRaises(ValueError, sd_to_XYZ_ASTME308,
self._sd.copy().align(SpectralShape(360, 820, 2)))
def test_XYZ_to_sd_Meng2015(self):
"""Test :func:`colour.recovery.meng2015.XYZ_to_sd_Meng2015` definition."""
XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
np.testing.assert_almost_equal(
sd_to_XYZ_integration(
XYZ_to_sd_Meng2015(XYZ, self._cmfs, self._sd_D65),
self._cmfs,
self._sd_D65,
)
/ 100,
XYZ,
decimal=7,
)
np.testing.assert_almost_equal(
sd_to_XYZ_integration(
XYZ_to_sd_Meng2015(XYZ, self._cmfs, self._sd_E),
self._cmfs,
self._sd_E,
)
/ 100,
XYZ,
decimal=7,
)
np.testing.assert_almost_equal(
sd_to_XYZ_integration(
XYZ_to_sd_Meng2015(
XYZ,
self._cmfs,
self._sd_D65,
optimisation_kwargs={
"options": {
"ftol": 1e-10,
}
},
),
self._cmfs,
self._sd_D65,
)
/ 100,
XYZ,
decimal=7,
)
shape = SpectralShape(400, 700, 5)
# pylint: disable=E1102
cmfs = reshape_msds(self._cmfs, shape)
np.testing.assert_almost_equal(
sd_to_XYZ_integration(
XYZ_to_sd_Meng2015(XYZ, cmfs, self._sd_D65), cmfs, self._sd_D65
)
/ 100,
XYZ,
decimal=7,
)
def msds_constant(
k: Floating,
labels: Sequence,
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
**kwargs: Any,
) -> MultiSpectralDistributions:
"""
Return the multi-spectral distributions with given labels and given
spectral shape filled with constant :math:`k` values.
Parameters
----------
k
Constant :math:`k` to fill the multi-spectral distributions with.
labels
Names to use for the :class:`colour.SpectralDistribution` class
instances.
shape
Spectral shape used to create the multi-spectral distributions.
Other Parameters
----------------
kwargs
{:class:`colour.MultiSpectralDistributions`},
See the documentation of the previously listed class.
Returns
-------
:class:`colour.MultiSpectralDistributions`
Constant :math:`k` filled multi-spectral distributions.
Notes
-----
- By default, the multi-spectral distributions will use the shape given
by :attr:`colour.SPECTRAL_SHAPE_DEFAULT` attribute.
Examples
--------
>>> msds = msds_constant(100, labels=['a', 'b', 'c'])
>>> msds.shape
SpectralShape(360.0, 780.0, 1.0)
>>> msds[400]
array([ 100., 100., 100.])
>>> msds.labels # doctest: +SKIP
['a', 'b', 'c']
"""
settings = {"name": f"{k} Constant"}
settings.update(kwargs)
wavelengths = shape.range()
values = full((len(wavelengths), len(labels)), k)
return MultiSpectralDistributions(values,
wavelengths,
labels=labels,
**settings)
def sd_gaussian_fwhm(
peak_wavelength: Floating,
fwhm: Floating,
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
**kwargs: Any,
) -> SpectralDistribution:
"""
Return a gaussian spectral distribution of given spectral shape at given
peak wavelength and full width at half maximum.
Parameters
----------
peak_wavelength
Wavelength the gaussian spectral distribution will peak at.
fwhm
Full width at half maximum, i.e. width of the gaussian spectral
distribution measured between those points on the *y* axis which are
half the maximum amplitude.
shape
Spectral shape used to create the spectral distribution.
Other Parameters
----------------
kwargs
{:class:`colour.SpectralDistribution`},
See the documentation of the previously listed class.
Returns
-------
:class:`colour.SpectralDistribution`
Gaussian spectral distribution.
Notes
-----
- By default, the spectral distribution will use the shape given by
:attr:`colour.SPECTRAL_SHAPE_DEFAULT` attribute.
Examples
--------
>>> sd = sd_gaussian_fwhm(555, 25)
>>> sd.shape
SpectralShape(360.0, 780.0, 1.0)
>>> sd[555]
1.0
>>> sd[530] # doctest: +ELLIPSIS
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)
def setUp(self):
"""
Initialises common tests attributes.
"""
self._cmfs = MSDS_CMFS_STANDARD_OBSERVER[
'CIE 1931 2 Degree Standard Observer'].copy().align(
SpectralShape(360, 780, 10))
self._sd_D65 = SDS_ILLUMINANTS['D65'].copy().align(self._cmfs.shape)
self._sd_E = SDS_ILLUMINANTS['E'].copy().align(self._cmfs.shape)
def test_sd_CIE_standard_illuminant_A(self):
"""
Tests :func:`colour.colorimetry.illuminants.\
sd_CIE_standard_illuminant_A` definition.
"""
np.testing.assert_almost_equal(sd_CIE_standard_illuminant_A(
SpectralShape(360, 830, 5)).values,
A_DATA,
decimal=7)
def setUp(self):
"""Initialise the common tests attributes."""
# pylint: disable=E1102
self._cmfs = reshape_msds(
MSDS_CMFS["CIE 1931 2 Degree Standard Observer"],
SpectralShape(360, 780, 10),
)
self._sd_D65 = reshape_sd(SDS_ILLUMINANTS["D65"], self._cmfs.shape)
self._sd_E = reshape_sd(SDS_ILLUMINANTS["E"], self._cmfs.shape)
def matrix_anomalous_trichromacy_Machado2009(cmfs, primaries, d_LMS):
"""
Computes the *Machado et al. (2009)* *CVD* matrix for given *LMS* cone
fundamentals colour matching functions and display primaries tri-spectral
distributions with given :math:`\\Delta_{LMS}` shift amount in nanometers
to simulate anomalous trichromacy.
Parameters
----------
cmfs : LMS_ConeFundamentals
*LMS* cone fundamentals colour matching functions.
primaries : RGB_DisplayPrimaries
*RGB* display primaries tri-spectral distributions.
d_LMS : array_like
:math:`\\Delta_{LMS}` shift amount in nanometers.
Notes
-----
- Input *LMS* cone fundamentals colour matching functions interval is
expected to be 1 nanometer, incompatible input will be interpolated
at 1 nanometer interval.
- Input :math:`\\Delta_{LMS}` shift amount is in domain [0, 20].
Returns
-------
ndarray
Anomalous trichromacy matrix.
References
----------
:cite:`Colblindorb`, :cite:`Colblindora`, :cite:`Colblindorc`,
:cite:`Machado2009`
Examples
--------
>>> from colour.characterisation import MSDS_DISPLAY_PRIMARIES
>>> from colour.colorimetry import MSDS_CMFS_LMS
>>> cmfs = MSDS_CMFS_LMS['Stockman & Sharpe 2 Degree Cone Fundamentals']
>>> d_LMS = np.array([15, 0, 0])
>>> primaries = MSDS_DISPLAY_PRIMARIES['Apple Studio Display']
>>> matrix_anomalous_trichromacy_Machado2009(cmfs, primaries, d_LMS)
... # doctest: +ELLIPSIS
array([[-0.2777465..., 2.6515008..., -1.3737543...],
[ 0.2718936..., 0.2004786..., 0.5276276...],
[ 0.0064404..., 0.2592157..., 0.7343437...]])
"""
if cmfs.shape.interval != 1:
cmfs = cmfs.copy().interpolate(SpectralShape(interval=1))
M_n = matrix_RGB_to_WSYBRG(cmfs, primaries)
cmfs_a = msds_cmfs_anomalous_trichromacy_Machado2009(cmfs, d_LMS)
M_a = matrix_RGB_to_WSYBRG(cmfs_a, primaries)
return matrix_dot(np.linalg.inv(M_n), M_a)
def sd_gaussian_normal(
mu: Floating,
sigma: Floating,
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
**kwargs: Any,
) -> SpectralDistribution:
"""
Return a gaussian spectral distribution of given spectral shape at
given mean wavelength :math:`\\mu` and standard deviation :math:`sigma`.
Parameters
----------
mu
Mean wavelength :math:`\\mu` the gaussian spectral distribution will
peak at.
sigma
Standard deviation :math:`sigma` of the gaussian spectral distribution.
shape
Spectral shape used to create the spectral distribution.
Other Parameters
----------------
kwargs
{:class:`colour.SpectralDistribution`},
See the documentation of the previously listed class.
Returns
-------
:class:`colour.SpectralDistribution`
Gaussian spectral distribution.
Notes
-----
- By default, the spectral distribution will use the shape given by
:attr:`colour.SPECTRAL_SHAPE_DEFAULT` attribute.
Examples
--------
>>> sd = sd_gaussian_normal(555, 25)
>>> sd.shape
SpectralShape(360.0, 780.0, 1.0)
>>> sd[555] # doctest: +ELLIPSIS
1.0000000...
>>> sd[530] # doctest: +ELLIPSIS
0.6065306...
"""
settings = {"name": f"{mu}nm - {sigma} Sigma - Gaussian"}
settings.update(kwargs)
wavelengths = shape.range()
values = np.exp(-((wavelengths - mu)**2) / (2 * sigma**2))
return SpectralDistribution(values, wavelengths, **settings)
def test_tristimulus_weighting_factors_ASTME202211(self):
"""
Tests :func:`colour.colorimetry.tristimulus.\
tristimulus_weighting_factors_ASTME202211` definition.
Notes
-----
:attr:`A_CIE_1964_10_10_TWF`, :attr:`A_CIE_1964_10_20_TWF` and
:attr:`D65_CIE_1931_2_20_TWF` attributes data is matching
:cite:`ASTMInternational2015b`.
References
----------
- :cite:`ASTMInternational2015b`
"""
cmfs = CMFS['CIE 1964 10 Degree Standard Observer']
wl = cmfs.shape.range()
A = SpectralPowerDistribution(dict(
zip(wl, CIE_standard_illuminant_A_function(wl))),
name='A (360, 830, 1)')
twf = tristimulus_weighting_factors_ASTME202211(
cmfs, A, SpectralShape(360, 830, 10))
np.testing.assert_almost_equal(np.round(twf, 3),
A_CIE_1964_10_10_TWF,
decimal=3)
twf = tristimulus_weighting_factors_ASTME202211(
cmfs, A, SpectralShape(360, 830, 20))
np.testing.assert_almost_equal(np.round(twf, 3),
A_CIE_1964_10_20_TWF,
decimal=3)
cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
D65 = ILLUMINANTS_RELATIVE_SPDS['D65'].copy().align(
cmfs.shape, interpolator=LinearInterpolator)
twf = tristimulus_weighting_factors_ASTME202211(
cmfs, D65, SpectralShape(360, 830, 20))
np.testing.assert_almost_equal(np.round(twf, 3),
D65_CIE_1931_2_20_TWF,
decimal=3)
def sd_mesopic_luminous_efficiency_function(
Lp,
source='Blue Heavy',
method='MOVE',
photopic_lef=PHOTOPIC_LEFS['CIE 1924 Photopic Standard Observer'],
scotopic_lef=SCOTOPIC_LEFS['CIE 1951 Scotopic Standard Observer']):
"""
Returns the mesopic luminous efficiency function :math:`V_m(\\lambda)` for
given photopic luminance :math:`L_p`.
Parameters
----------
Lp : numeric
Photopic luminance :math:`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 : SpectralDistribution, optional
:math:`V(\\lambda)` photopic luminous efficiency function.
scotopic_lef : SpectralDistribution, optional
:math:`V^\\prime(\\lambda)` scotopic luminous efficiency function.
Returns
-------
SpectralDistribution
Mesopic luminous efficiency function :math:`V_m(\\lambda)`.
References
----------
:cite:`Wikipedia2005d`
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... sd_mesopic_luminous_efficiency_function(0.2) # doctest: +ELLIPSIS
SpectralDistribution([[ 380. , 0.000424 ...],
[ 381. , 0.0004781...],
[ 382. , 0.0005399...],
[ 383. , 0.0006122...],
[ 384. , 0.0006961...],
[ 385. , 0.0007929...],
[ 386. , 0.000907 ...],
[ 387. , 0.0010389...],
[ 388. , 0.0011923...],
[ 389. , 0.0013703...],
[ 390. , 0.0015771...],
[ 391. , 0.0018167...],
[ 392. , 0.0020942...],
[ 393. , 0.0024160...],
[ 394. , 0.0027888...],
[ 395. , 0.0032196...],
[ 396. , 0.0037222...],
[ 397. , 0.0042957...],
[ 398. , 0.0049531...],
[ 399. , 0.0057143...],
[ 400. , 0.0065784...],
[ 401. , 0.0075658...],
[ 402. , 0.0086912...],
[ 403. , 0.0099638...],
[ 404. , 0.0114058...],
[ 405. , 0.0130401...],
[ 406. , 0.0148750...],
[ 407. , 0.0169310...],
[ 408. , 0.0192211...],
[ 409. , 0.0217511...],
[ 410. , 0.0245342...],
[ 411. , 0.0275773...],
[ 412. , 0.0309172...],
[ 413. , 0.0345149...],
[ 414. , 0.0383998...],
[ 415. , 0.0425744...],
[ 416. , 0.0471074...],
[ 417. , 0.0519322...],
[ 418. , 0.0570541...],
[ 419. , 0.0625466...],
[ 420. , 0.0683463...],
[ 421. , 0.0745255...],
[ 422. , 0.0809440...],
[ 423. , 0.0877344...],
[ 424. , 0.0948915...],
[ 425. , 0.1022731...],
[ 426. , 0.109877 ...],
[ 427. , 0.1178421...],
[ 428. , 0.1260316...],
[ 429. , 0.1343772...],
[ 430. , 0.143017 ...],
[ 431. , 0.1518128...],
[ 432. , 0.1608328...],
[ 433. , 0.1700088...],
[ 434. , 0.1792726...],
[ 435. , 0.1886934...],
[ 436. , 0.1982041...],
[ 437. , 0.2078032...],
[ 438. , 0.2174184...],
[ 439. , 0.2271147...],
[ 440. , 0.2368196...],
[ 441. , 0.2464623...],
[ 442. , 0.2561153...],
[ 443. , 0.2657160...],
[ 444. , 0.2753387...],
[ 445. , 0.2848520...],
[ 446. , 0.2944648...],
[ 447. , 0.3034902...],
[ 448. , 0.3132347...],
[ 449. , 0.3223257...],
[ 450. , 0.3314513...],
[ 451. , 0.3406129...],
[ 452. , 0.3498117...],
[ 453. , 0.3583617...],
[ 454. , 0.3676377...],
[ 455. , 0.3762670...],
[ 456. , 0.3849392...],
[ 457. , 0.3936540...],
[ 458. , 0.4024077...],
[ 459. , 0.4111965...],
[ 460. , 0.4193298...],
[ 461. , 0.4281803...],
[ 462. , 0.4363804...],
[ 463. , 0.4453117...],
[ 464. , 0.4542949...],
[ 465. , 0.4626509...],
[ 466. , 0.4717570...],
[ 467. , 0.4809300...],
[ 468. , 0.4901776...],
[ 469. , 0.4995075...],
[ 470. , 0.5096145...],
[ 471. , 0.5191293...],
[ 472. , 0.5294259...],
[ 473. , 0.5391316...],
[ 474. , 0.5496217...],
[ 475. , 0.5602103...],
[ 476. , 0.5702197...],
[ 477. , 0.5810207...],
[ 478. , 0.5919093...],
[ 479. , 0.6028683...],
[ 480. , 0.6138806...],
[ 481. , 0.6249373...],
[ 482. , 0.6360619...],
[ 483. , 0.6465989...],
[ 484. , 0.6579538...],
[ 485. , 0.6687841...],
[ 486. , 0.6797939...],
[ 487. , 0.6909887...],
[ 488. , 0.7023827...],
[ 489. , 0.7133032...],
[ 490. , 0.7244513...],
[ 491. , 0.7358470...],
[ 492. , 0.7468118...],
[ 493. , 0.7580294...],
[ 494. , 0.7694964...],
[ 495. , 0.7805225...],
[ 496. , 0.7917805...],
[ 497. , 0.8026123...],
[ 498. , 0.8130793...],
[ 499. , 0.8239297...],
[ 500. , 0.8352251...],
[ 501. , 0.8456342...],
[ 502. , 0.8564818...],
[ 503. , 0.8676921...],
[ 504. , 0.8785021...],
[ 505. , 0.8881489...],
[ 506. , 0.8986405...],
[ 507. , 0.9079322...],
[ 508. , 0.9174255...],
[ 509. , 0.9257739...],
[ 510. , 0.9350656...],
[ 511. , 0.9432365...],
[ 512. , 0.9509063...],
[ 513. , 0.9586931...],
[ 514. , 0.9658413...],
[ 515. , 0.9722825...],
[ 516. , 0.9779924...],
[ 517. , 0.9836106...],
[ 518. , 0.9883465...],
[ 519. , 0.9920964...],
[ 520. , 0.9954436...],
[ 521. , 0.9976202...],
[ 522. , 0.9993457...],
[ 523. , 1. ...],
[ 524. , 0.9996498...],
[ 525. , 0.9990487...],
[ 526. , 0.9975356...],
[ 527. , 0.9957615...],
[ 528. , 0.9930143...],
[ 529. , 0.9899559...],
[ 530. , 0.9858741...],
[ 531. , 0.9814453...],
[ 532. , 0.9766885...],
[ 533. , 0.9709363...],
[ 534. , 0.9648947...],
[ 535. , 0.9585832...],
[ 536. , 0.952012 ...],
[ 537. , 0.9444916...],
[ 538. , 0.9367089...],
[ 539. , 0.9293506...],
[ 540. , 0.9210429...],
[ 541. , 0.9124772...],
[ 542. , 0.9036604...],
[ 543. , 0.8945958...],
[ 544. , 0.8845999...],
[ 545. , 0.8750500...],
[ 546. , 0.8659457...],
[ 547. , 0.8559224...],
[ 548. , 0.8456846...],
[ 549. , 0.8352499...],
[ 550. , 0.8253229...],
[ 551. , 0.8152079...],
[ 552. , 0.8042205...],
[ 553. , 0.7944209...],
[ 554. , 0.7837466...],
[ 555. , 0.7735680...],
[ 556. , 0.7627808...],
[ 557. , 0.7522710...],
[ 558. , 0.7417549...],
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[ 768. , 0.0000173...],
[ 769. , 0.0000161...],
[ 770. , 0.0000150...],
[ 771. , 0.0000140...],
[ 772. , 0.0000131...],
[ 773. , 0.0000122...],
[ 774. , 0.0000114...],
[ 775. , 0.0000106...],
[ 776. , 0.0000099...],
[ 777. , 0.0000092...],
[ 778. , 0.0000086...],
[ 779. , 0.0000080...],
[ 780. , 0.0000075...]],
interpolator=SpragueInterpolator,
interpolator_args={},
extrapolator=Extrapolator,
extrapolator_args={...})
"""
photopic_lef_shape = photopic_lef.shape
scotopic_lef_shape = scotopic_lef.shape
shape = SpectralShape(
max(photopic_lef_shape.start, scotopic_lef_shape.start),
min(photopic_lef_shape.end, scotopic_lef_shape.end),
max(photopic_lef_shape.interval, scotopic_lef_shape.interval))
wavelengths = shape.range()
sd_data = dict(
zip(
wavelengths,
mesopic_weighting_function(wavelengths, Lp, source, method,
photopic_lef, scotopic_lef)))
sd = SpectralDistribution(
sd_data, name='{0} Lp Mesopic Luminous Efficiency Function'.format(Lp))
return sd.normalise()
def mesopic_luminous_efficiency_function(
Lp,
source='Blue Heavy',
method='MOVE',
photopic_lef=PHOTOPIC_LEFS.get(
'CIE 1924 Photopic Standard Observer'),
scotopic_lef=SCOTOPIC_LEFS.get(
'CIE 1951 Scotopic Standard Observer')):
"""
Returns the mesopic luminous efficiency function :math:`V_m(\lambda)` for
given photopic luminance :math:`L_p`.
Parameters
----------
Lp : numeric
Photopic luminance :math:`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
:math:`V(\lambda)` photopic luminous efficiency function.
scotopic_lef : SpectralPowerDistribution, optional
:math:`V^\prime(\lambda)` scotopic luminous efficiency function.
Returns
-------
SpectralPowerDistribution
Mesopic luminous efficiency function :math:`V_m(\lambda)`.
Examples
--------
>>> print(mesopic_luminous_efficiency_function(0.2))
SpectralPowerDistribution(\
'0.2 Lp Mesopic Luminous Efficiency Function', (380.0, 780.0, 1.0))
"""
photopic_lef_shape = photopic_lef.shape
scotopic_lef_shape = scotopic_lef.shape
shape = SpectralShape(
max(photopic_lef_shape.start, scotopic_lef_shape.start),
min(photopic_lef_shape.end, scotopic_lef_shape.end),
max(photopic_lef_shape.interval, scotopic_lef_shape.interval))
wavelengths = shape.range()
spd_data = dict(zip(wavelengths,
mesopic_weighting_function(
wavelengths,
Lp,
source,
method,
photopic_lef,
scotopic_lef)))
spd = SpectralPowerDistribution(
'{0} Lp Mesopic Luminous Efficiency Function'.format(Lp),
spd_data)
return spd.normalise()
