def test_n_dimensional_Hunter_Rdab_to_XYZ(self):
"""
Test :func:`colour.models.hunter_rdab.Hunter_Rdab_to_XYZ` definition
n-dimensional support.
"""
h_i = TVS_ILLUMINANTS_HUNTERLAB["CIE 1931 2 Degree Standard Observer"]
D65 = h_i["D65"]
R_d_ab = np.array([12.19722500, 57.12537874, 17.46241341])
XYZ_n = D65.XYZ_n
K_ab = D65.K_ab
XYZ = Hunter_Rdab_to_XYZ(R_d_ab, XYZ_n, K_ab)
R_d_ab = np.tile(R_d_ab, (6, 1))
XYZ = np.tile(XYZ, (6, 1))
np.testing.assert_almost_equal(
Hunter_Rdab_to_XYZ(R_d_ab, XYZ_n, K_ab), XYZ, decimal=7
)
K_ab = np.tile(K_ab, (6, 1))
XYZ_n = np.tile(XYZ_n, (6, 1))
np.testing.assert_almost_equal(
Hunter_Rdab_to_XYZ(R_d_ab, XYZ_n, K_ab), XYZ, decimal=7
)
R_d_ab = np.reshape(R_d_ab, (2, 3, 3))
XYZ_n = np.reshape(XYZ_n, (2, 3, 3))
K_ab = np.reshape(K_ab, (2, 3, 2))
XYZ = np.reshape(XYZ, (2, 3, 3))
np.testing.assert_almost_equal(
Hunter_Rdab_to_XYZ(R_d_ab, XYZ_n, K_ab), XYZ, decimal=7
)
def test_n_dimensional_Hunter_Rdab_to_XYZ(self):
"""
Tests :func:`colour.models.hunter_rdab.Hunter_Rdab_to_XYZ` definition
n-dimensions support.
"""
h_i = HUNTERLAB_ILLUMINANTS['CIE 1931 2 Degree Standard Observer']
D65 = h_i['D65']
R_d_ab = np.array([12.19722500, 57.12537874, 17.46241341])
XYZ_n = D65.XYZ_n
K_ab = D65.K_ab
XYZ = np.array([0.20654008, 0.12197225, 0.05136952]) * 100
np.testing.assert_almost_equal(
Hunter_Rdab_to_XYZ(R_d_ab, XYZ_n, K_ab), XYZ, decimal=7)
R_d_ab = np.tile(R_d_ab, (6, 1))
XYZ = np.tile(XYZ, (6, 1))
np.testing.assert_almost_equal(
Hunter_Rdab_to_XYZ(R_d_ab, XYZ_n, K_ab), XYZ, decimal=7)
K_ab = np.tile(K_ab, (6, 1))
XYZ_n = np.tile(XYZ_n, (6, 1))
np.testing.assert_almost_equal(
Hunter_Rdab_to_XYZ(R_d_ab, XYZ_n, K_ab), XYZ, decimal=7)
R_d_ab = np.reshape(R_d_ab, (2, 3, 3))
XYZ_n = np.reshape(XYZ_n, (2, 3, 3))
K_ab = np.reshape(K_ab, (2, 3, 2))
XYZ = np.reshape(XYZ, (2, 3, 3))
np.testing.assert_almost_equal(
Hunter_Rdab_to_XYZ(R_d_ab, XYZ_n, K_ab), XYZ, decimal=7)
def test_domain_range_scale_Hunter_Rdab_to_XYZ(self):
"""
Tests :func:`colour.models.hunter_lab.Hunter_Rdab_to_XYZ` definition
domain and range scale support.
"""
h_i = HUNTERLAB_ILLUMINANTS['CIE 1931 2 Degree Standard Observer']
D65 = h_i['D65']
R_d_ab = np.array([12.19722500, 57.12537874, 17.46241341])
XYZ_n = D65.XYZ_n
K_ab = D65.K_ab
XYZ = Hunter_Rdab_to_XYZ(R_d_ab, XYZ_n, K_ab)
d_r = (('reference', 1), (1, 0.01), (100, 1))
for scale, factor in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(
Hunter_Rdab_to_XYZ(R_d_ab * factor, XYZ_n * factor, K_ab),
XYZ * factor,
decimal=7)
def test_nan_Hunter_Rdab_to_XYZ(self):
"""
Tests :func:`colour.models.hunter_rdab.Hunter_Rdab_to_XYZ` definition
nan support.
"""
cases = [-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan]
cases = set(permutations(cases * 3, r=3))
for case in cases:
R_d_ab = np.array(case)
XYZ_n = np.array(case[0:3])
K_ab = np.array(case[0:2])
Hunter_Rdab_to_XYZ(R_d_ab, XYZ_n, K_ab)
def test_Hunter_Rdab_to_XYZ(self):
"""Test :func:`colour.models.hunter_rdab.Hunter_Rdab_to_XYZ` definition."""
np.testing.assert_almost_equal(
Hunter_Rdab_to_XYZ(
np.array([12.19722500, 57.12537874, 17.46241341])
),
np.array([0.20654008, 0.12197225, 0.05136952]) * 100,
decimal=7,
)
np.testing.assert_almost_equal(
Hunter_Rdab_to_XYZ(
np.array([23.04276800, -32.40057474, 20.96542183])
),
np.array([0.14222010, 0.23042768, 0.10495772]) * 100,
decimal=7,
)
np.testing.assert_almost_equal(
Hunter_Rdab_to_XYZ(
np.array([6.15720100, 18.13400284, -67.14408607])
),
np.array([0.07818780, 0.06157201, 0.28099326]) * 100,
decimal=7,
)
h_i = TVS_ILLUMINANTS_HUNTERLAB["CIE 1931 2 Degree Standard Observer"]
A = h_i["A"]
np.testing.assert_almost_equal(
Hunter_Rdab_to_XYZ(
np.array([12.19722500, 42.53572838, -3.00653110]),
A.XYZ_n,
A.K_ab,
),
np.array([0.20654008, 0.12197225, 0.05136952]) * 100,
decimal=7,
)
D65 = h_i["D65"]
np.testing.assert_almost_equal(
Hunter_Rdab_to_XYZ(
np.array([12.19722500, 57.12537874, 17.46241341]),
D65.XYZ_n,
D65.K_ab,
),
np.array([0.20654008, 0.12197225, 0.05136952]) * 100,
decimal=7,
)
np.testing.assert_almost_equal(
Hunter_Rdab_to_XYZ(
np.array([12.19722500, 57.11906384, 17.45962317]),
D65.XYZ_n,
K_ab=None,
),
np.array([0.20654008, 0.12197225, 0.05136952]) * 100,
decimal=7,
)