def test_nan_chromatic_adaptation_Zhai2018(self): """ Test :func:`colour.adaptation.zhai2018.chromatic_adaptation_Zhai2018` 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: XYZ_b = np.array(case) XYZ_wb = np.array(case) XYZ_wd = np.array(case) D_b = case[0] D_d = case[0] XYZ_wo = np.array(case) chromatic_adaptation_Zhai2018(XYZ_b, XYZ_wb, XYZ_wd, D_b, D_d, XYZ_wo)
def test_n_dimensional_chromatic_adaptation_Zhai2018(self): """ Test :func:`colour.adaptation.zhai2018.chromatic_adaptation_Zhai2018` definition n-dimensional arrays support. """ XYZ_b = np.array([48.900, 43.620, 6.250]) XYZ_wb = np.array([109.850, 100, 35.585]) XYZ_wd = np.array([95.047, 100, 108.883]) D_b = 0.9407 D_d = 0.9800 XYZ_d = chromatic_adaptation_Zhai2018(XYZ_b, XYZ_wb, XYZ_wd, D_b, D_d) XYZ_b = np.tile(XYZ_b, (6, 1)) XYZ_d = np.tile(XYZ_d, (6, 1)) np.testing.assert_almost_equal( chromatic_adaptation_Zhai2018(XYZ_b, XYZ_wb, XYZ_wd, D_b, D_d), XYZ_d, decimal=7, ) XYZ_wb = np.tile(XYZ_wb, (6, 1)) XYZ_wd = np.tile(XYZ_wd, (6, 1)) D_b = np.tile(D_b, (6, 1)) D_d = np.tile(D_d, (6, 1)) np.testing.assert_almost_equal( chromatic_adaptation_Zhai2018(XYZ_b, XYZ_wb, XYZ_wd, D_b, D_d), XYZ_d, decimal=7, ) XYZ_b = np.reshape(XYZ_b, (2, 3, 3)) XYZ_wb = np.reshape(XYZ_wb, (2, 3, 3)) XYZ_wd = np.reshape(XYZ_wd, (2, 3, 3)) D_b = np.reshape(D_b, (2, 3, 1)) D_d = np.reshape(D_d, (2, 3, 1)) XYZ_d = np.reshape(XYZ_d, (2, 3, 3)) np.testing.assert_almost_equal( chromatic_adaptation_Zhai2018(XYZ_b, XYZ_wb, XYZ_wd, D_b, D_d), XYZ_d, decimal=7, )
def test_domain_range_scale_chromatic_adaptation_Zhai2018(self): """ Test :func:`colour.adaptation.zhai2018.chromatic_adaptation_Zhai2018` definition domain and range scale support. """ XYZ_b = np.array([48.900, 43.620, 6.250]) XYZ_wb = np.array([109.850, 100, 35.585]) XYZ_wd = np.array([95.047, 100, 108.883]) XYZ_d = chromatic_adaptation_Zhai2018(XYZ_b, XYZ_wb, XYZ_wd) 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( chromatic_adaptation_Zhai2018(XYZ_b * factor, XYZ_wb * factor, XYZ_wd * factor), XYZ_d * factor, decimal=7, )
def test_chromatic_adaptation_Zhai2018(self): """ Test :func:`colour.adaptation.zhai2018.chromatic_adaptation_Zhai2018` definition. """ np.testing.assert_almost_equal( chromatic_adaptation_Zhai2018( XYZ_b=np.array([48.900, 43.620, 6.250]), XYZ_wb=np.array([109.850, 100, 35.585]), XYZ_wd=np.array([95.047, 100, 108.883]), D_b=0.9407, D_d=0.9800, XYZ_wo=np.array([100, 100, 100]), ), np.array([39.18561644, 42.15461798, 19.23672036]), decimal=7, ) np.testing.assert_almost_equal( chromatic_adaptation_Zhai2018( XYZ_b=np.array([48.900, 43.620, 6.250]), XYZ_wb=np.array([109.850, 100, 35.585]), XYZ_wd=np.array([95.047, 100, 108.883]), D_b=0.9407, D_d=0.9800, XYZ_wo=np.array([100, 100, 100]), transform="CAT16", ), np.array([40.37398343, 43.69426311, 20.51733764]), decimal=7, ) np.testing.assert_almost_equal( chromatic_adaptation_Zhai2018( XYZ_b=np.array([52.034, 58.824, 23.703]), XYZ_wb=np.array([92.288, 100, 38.775]), XYZ_wd=np.array([105.432, 100, 137.392]), D_b=0.6709, D_d=0.5331, XYZ_wo=np.array([97.079, 100, 141.798]), ), np.array([57.03242915, 58.93434364, 64.76261333]), decimal=7, ) np.testing.assert_almost_equal( chromatic_adaptation_Zhai2018( XYZ_b=np.array([52.034, 58.824, 23.703]), XYZ_wb=np.array([92.288, 100, 38.775]), XYZ_wd=np.array([105.432, 100, 137.392]), D_b=0.6709, D_d=0.5331, XYZ_wo=np.array([97.079, 100, 141.798]), transform="CAT16", ), np.array([56.77130011, 58.81317888, 64.66922808]), decimal=7, ) np.testing.assert_almost_equal( chromatic_adaptation_Zhai2018( XYZ_b=np.array([48.900, 43.620, 6.250]), XYZ_wb=np.array([109.850, 100, 35.585]), XYZ_wd=np.array([95.047, 100, 108.883]), ), np.array([38.72444735, 42.09232891, 20.05297620]), decimal=7, )
def ZCAM_to_XYZ( specification: CAM_Specification_ZCAM, XYZ_w: ArrayLike, L_A: FloatingOrArrayLike, Y_b: FloatingOrArrayLike, surround: InductionFactors_ZCAM = VIEWING_CONDITIONS_ZCAM["Average"], discount_illuminant: Boolean = False, ) -> NDArray: """ Convert from *ZCAM* specification to *CIE XYZ* tristimulus values. Parameters ---------- specification *ZCAM* colour appearance model specification. Correlate of *Lightness* :math:`J`, correlate of *chroma* :math:`C` or correlate of *colourfulness* :math:`M` and *hue* angle :math:`h` in degrees must be specified, e.g. :math:`JCh` or :math:`JMh`. XYZ_w Absolute *CIE XYZ* tristimulus values of the white under reference illuminant. L_A Test adapting field *luminance* :math:`L_A` in :math:`cd/m^2` such as :math:`L_A = L_w * Y_b / 100` (where :math:`L_w` is luminance of the reference white and :math:`Y_b` is the background luminance factor). Y_b Luminous factor of background :math:`Y_b` such as :math:`Y_b = 100 x L_b / L_w` where :math:`L_w` is the luminance of the light source and :math:`L_b` is the luminance of the background. For viewing images, :math:`Y_b` can be the average :math:`Y` value for the pixels in the entire image, or frequently, a :math:`Y` value of 20, approximate an :math:`L^*` of 50 is used. surround Surround viewing conditions induction factors. discount_illuminant Truth value indicating if the illuminant should be discounted. Returns ------- :class:`numpy.ndarray` *CIE XYZ* tristimulus values. Raises ------ ValueError If neither *C* or *M* correlates have been defined in the ``CAM_Specification_ZCAM`` argument. Warnings -------- The underlying *SMPTE ST 2084:2014* transfer function is an absolute transfer function. Notes ----- - *Safdar, Hardeberg and Luo (2021)* does not specify how the chromatic adaptation to *CIE Standard Illuminant D65* in *Step 0* should be performed. A one-step *Von Kries* chromatic adaptation transform is not symetrical or transitive when a degree of adptation is involved. *Safdar, Hardeberg and Luo (2018)* uses *Zhai and Luo (2018)* two-steps chromatic adaptation transform, thus it seems sensible to adopt this transform for the *ZCAM* colour appearance model until more information is available. It is worth noting that a one-step *Von Kries* chromatic adaptation transform with support for degree of adaptation produces values closer to the supplemental document compared to the *Zhai and Luo (2018)* two-steps chromatic adaptation transform but then the *ZCAM* colour appearance model does not round-trip properly. - *Step 4* of the inverse model uses a rounded exponent of 1.3514 preventing the model to round-trip properly. Given that this implementation takes some liberties with respect to the chromatic adaptation transform to use, it was deemed appropriate to use an exponent value that enables the *ZCAM* colour appearance model to round-trip. - The underlying *SMPTE ST 2084:2014* transfer function is an absolute transfer function, thus the domain and range values for the *Reference* and *1* scales are only indicative that the data is not affected by scale transformations. +-------------------------------+-----------------------+---------------+ | **Domain** | **Scale - Reference** | **Scale - 1** | +===============================+=======================+===============+ | ``CAM_Specification_ZCAM.J`` | [UN] | [0, 1] | +-------------------------------+-----------------------+---------------+ | ``CAM_Specification_ZCAM.C`` | [UN] | [0, 1] | +-------------------------------+-----------------------+---------------+ | ``CAM_Specification_ZCAM.h`` | [0, 360] | [0, 1] | +-------------------------------+-----------------------+---------------+ | ``CAM_Specification_ZCAM.s`` | [UN] | [0, 1] | +-------------------------------+-----------------------+---------------+ | ``CAM_Specification_ZCAM.Q`` | [UN] | [0, 1] | +-------------------------------+-----------------------+---------------+ | ``CAM_Specification_ZCAM.M`` | [UN] | [0, 1] | +-------------------------------+-----------------------+---------------+ | ``CAM_Specification_ZCAM.H`` | [0, 400] | [0, 1] | +-------------------------------+-----------------------+---------------+ | ``CAM_Specification_ZCAM.HC`` | [UN] | [0, 1] | +-------------------------------+-----------------------+---------------+ | ``CAM_Specification_ZCAM.V`` | [UN] | [0, 1] | +-------------------------------+-----------------------+---------------+ | ``CAM_Specification_ZCAM.K`` | [UN] | [0, 1] | +-------------------------------+-----------------------+---------------+ | ``CAM_Specification_ZCAM.H`` | [UN] | [0, 1] | +-------------------------------+-----------------------+---------------+ +-----------+-----------------------+---------------+ | **Range** | **Scale - Reference** | **Scale - 1** | +===========+=======================+===============+ | ``XYZ`` | [UN] | [UN] | +-----------+-----------------------+---------------+ References ---------- :cite:`Safdar2018`, :cite:`Safdar2021`, :cite:`Zhai2018` Examples -------- >>> specification = CAM_Specification_ZCAM(J=92.250443780723629, ... C=3.0216926733329013, ... h=196.32457375575581) >>> XYZ_w = np.array([256, 264, 202]) >>> L_A = 264 >>> Y_b = 100 >>> surround = VIEWING_CONDITIONS_ZCAM['Average'] >>> ZCAM_to_XYZ(specification, XYZ_w, L_A, Y_b, surround) ... # doctest: +ELLIPSIS array([ 185., 206., 163.]) """ J_z, C_z, h_z, _S_z, _Q_z, M_z, _H, _H_Z, _V_z, _K_z, _W_z = astuple( specification) J_z = to_domain_1(J_z) C_z = to_domain_1(C_z) h_z = to_domain_degrees(h_z) M_z = to_domain_1(M_z) XYZ_w = to_domain_1(XYZ_w) _X_w, Y_w, _Z_w = tsplit(XYZ_w) L_A = as_float_array(L_A) Y_b = as_float_array(Y_b) F_s, F, c, N_c = surround # Step 0 (Forward) - Chromatic adaptation from reference illuminant to # "CIE Standard Illuminant D65" illuminant using "CAT02". # Computing degree of adaptation :math:`D`. D = (degree_of_adaptation(surround.F, L_A) if not discount_illuminant else ones(L_A.shape)) # Step 1 (Forward) - Computing factors related with viewing conditions and # independent of the test stimulus. # Background factor :math:`F_b` F_b = np.sqrt(Y_b / Y_w) # Luminance level adaptation factor :math:`F_L` F_L = 0.171 * spow(L_A, 1 / 3) * (1 - np.exp(-48 / 9 * L_A)) # Step 2 (Forward) - Computing achromatic response (:math:`I_{z,w}`), # redness-greenness (:math:`a_{z,w}`), and yellowness-blueness # (:math:`b_{z,w}`). with domain_range_scale("ignore"): I_z_w, _A_z_w, B_z_w = tsplit( XYZ_to_Izazbz(XYZ_w, method="Safdar 2021")) # Step 1 (Inverse) - Computing achromatic response (:math:`I_z`). Q_z_p = (1.6 * F_s) / F_b**0.12 Q_z_m = F_s**2.2 * F_b**0.5 * spow(F_L, 0.2) Q_z_w = 2700 * spow(I_z_w, Q_z_p) * Q_z_m I_z_p = (F_b**0.12) / (1.6 * F_s) I_z_d = 2700 * 100 * Q_z_m I_z = spow((J_z * Q_z_w) / I_z_d, I_z_p) # Step 2 (Inverse) - Computing chroma :math:`C_z`. if has_only_nan(M_z) and not has_only_nan(C_z): M_z = (C_z * Q_z_w) / 100 elif has_only_nan(M_z): raise ValueError('Either "C" or "M" correlate must be defined in ' 'the "CAM_Specification_ZCAM" argument!') # Step 3 (Inverse) - Computing hue angle :math:`h_z` # :math:`h_z` is currently required as an input. # Computing eccentricity factor :math:`e_z`. e_z = 1.015 + np.cos(np.radians(89.038 + h_z % 360)) h_z_r = np.radians(h_z) # Step 4 (Inverse) - Computing redness-greenness (:math:`a_z`), and # yellowness-blueness (:math:`b_z`). # C_z_p_e = 1.3514 C_z_p_e = 50 / 37 C_z_p = spow( (M_z * spow(I_z_w, 0.78) * F_b**0.1) / (100 * e_z**0.068 * spow(F_L, 0.2)), C_z_p_e, ) a_z = C_z_p * np.cos(h_z_r) b_z = C_z_p * np.sin(h_z_r) # Step 5 (Inverse) - Computing tristimulus values :math:`XYZ_{D65}`. with domain_range_scale("ignore"): XYZ_D65 = Izazbz_to_XYZ(tstack([I_z, a_z, b_z]), method="Safdar 2021") XYZ = chromatic_adaptation_Zhai2018(XYZ_D65, TVS_D65, XYZ_w, D, D, transform="CAT02") return from_range_1(XYZ)
def XYZ_to_ZCAM( XYZ: ArrayLike, XYZ_w: ArrayLike, L_A: FloatingOrArrayLike, Y_b: FloatingOrArrayLike, surround: InductionFactors_ZCAM = VIEWING_CONDITIONS_ZCAM["Average"], discount_illuminant: Boolean = False, ) -> CAM_Specification_ZCAM: """ Compute the *ZCAM* colour appearance model correlates from given *CIE XYZ* tristimulus values. Parameters ---------- XYZ Absolute *CIE XYZ* tristimulus values of test sample / stimulus. XYZ_w Absolute *CIE XYZ* tristimulus values of the white under reference illuminant. L_A Test adapting field *luminance* :math:`L_A` in :math:`cd/m^2` such as :math:`L_A = L_w * Y_b / 100` (where :math:`L_w` is luminance of the reference white and :math:`Y_b` is the background luminance factor). Y_b Luminous factor of background :math:`Y_b` such as :math:`Y_b = 100 * L_b / L_w` where :math:`L_w` is the luminance of the light source and :math:`L_b` is the luminance of the background. For viewing images, :math:`Y_b` can be the average :math:`Y` value for the pixels in the entire image, or frequently, a :math:`Y` value of 20, approximate an :math:`L^*` of 50 is used. surround Surround viewing conditions induction factors. discount_illuminant Truth value indicating if the illuminant should be discounted. Returns ------- :class:`colour.CAM_Specification_ZCAM` *ZCAM* colour appearance model specification. Warnings -------- The underlying *SMPTE ST 2084:2014* transfer function is an absolute transfer function. Notes ----- - *Safdar, Hardeberg and Luo (2021)* does not specify how the chromatic adaptation to *CIE Standard Illuminant D65* in *Step 0* should be performed. A one-step *Von Kries* chromatic adaptation transform is not symmetrical or transitive when a degree of adaptation is involved. *Safdar, Hardeberg and Luo (2018)* uses *Zhai and Luo (2018)* two-steps chromatic adaptation transform, thus it seems sensible to adopt this transform for the *ZCAM* colour appearance model until more information is available. It is worth noting that a one-step *Von Kries* chromatic adaptation transform with support for degree of adaptation produces values closer to the supplemental document compared to the *Zhai and Luo (2018)* two-steps chromatic adaptation transform but then the *ZCAM* colour appearance model does not round-trip properly. - The underlying *SMPTE ST 2084:2014* transfer function is an absolute transfer function, thus the domain and range values for the *Reference* and *1* scales are only indicative that the data is not affected by scale transformations. +------------+-----------------------+---------------+ | **Domain** | **Scale - Reference** | **Scale - 1** | +============+=======================+===============+ | ``XYZ`` | [UN] | [UN] | +------------+-----------------------+---------------+ | ``XYZ_tw`` | [UN] | [UN] | +------------+-----------------------+---------------+ | ``XYZ_rw`` | [UN] | [UN] | +------------+-----------------------+---------------+ +-------------------------------+-----------------------+---------------+ | **Range** | **Scale - Reference** | **Scale - 1** | +===============================+=======================+===============+ | ``CAM_Specification_ZCAM.J`` | [UN] | [0, 1] | +-------------------------------+-----------------------+---------------+ | ``CAM_Specification_ZCAM.C`` | [UN] | [0, 1] | +-------------------------------+-----------------------+---------------+ | ``CAM_Specification_ZCAM.h`` | [0, 360] | [0, 1] | +-------------------------------+-----------------------+---------------+ | ``CAM_Specification_ZCAM.s`` | [UN] | [0, 1] | +-------------------------------+-----------------------+---------------+ | ``CAM_Specification_ZCAM.Q`` | [UN] | [0, 1] | +-------------------------------+-----------------------+---------------+ | ``CAM_Specification_ZCAM.M`` | [UN] | [0, 1] | +-------------------------------+-----------------------+---------------+ | ``CAM_Specification_ZCAM.H`` | [0, 400] | [0, 1] | +-------------------------------+-----------------------+---------------+ | ``CAM_Specification_ZCAM.HC`` | [UN] | [0, 1] | +-------------------------------+-----------------------+---------------+ | ``CAM_Specification_ZCAM.V`` | [UN] | [0, 1] | +-------------------------------+-----------------------+---------------+ | ``CAM_Specification_ZCAM.K`` | [UN] | [0, 1] | +-------------------------------+-----------------------+---------------+ | ``CAM_Specification_ZCAM.H`` | [UN] | [0, 1] | +-------------------------------+-----------------------+---------------+ References ---------- :cite:`Safdar2018`, :cite:`Safdar2021`, :cite:`Zhai2018` Examples -------- >>> XYZ = np.array([185, 206, 163]) >>> XYZ_w = np.array([256, 264, 202]) >>> L_A = 264 >>> Y_b = 100 >>> surround = VIEWING_CONDITIONS_ZCAM['Average'] >>> XYZ_to_ZCAM(XYZ, XYZ_w, L_A, Y_b, surround) ... # doctest: +ELLIPSIS CAM_Specification_ZCAM(J=92.2504437..., C=3.0216926..., h=196.3245737..., \ s=19.1319556..., Q=321.3408463..., M=10.5256217..., H=237.6114442..., \ HC=None, V=34.7006776..., K=25.8835968..., W=91.6821728...) """ XYZ = to_domain_1(XYZ) XYZ_w = to_domain_1(XYZ_w) _X_w, Y_w, _Z_w = tsplit(XYZ_w) L_A = as_float_array(L_A) Y_b = as_float_array(Y_b) F_s, _F, _c, _N_c = surround # Step 0 (Forward) - Chromatic adaptation from reference illuminant to # "CIE Standard Illuminant D65" illuminant using "CAT02". # Computing degree of adaptation :math:`D`. D = (degree_of_adaptation(surround.F, L_A) if not discount_illuminant else ones(L_A.shape)) XYZ_D65 = chromatic_adaptation_Zhai2018(XYZ, XYZ_w, TVS_D65, D, D, transform="CAT02") # Step 1 (Forward) - Computing factors related with viewing conditions and # independent of the test stimulus. # Background factor :math:`F_b` F_b = np.sqrt(Y_b / Y_w) # Luminance level adaptation factor :math:`F_L` F_L = 0.171 * spow(L_A, 1 / 3) * (1 - np.exp(-48 / 9 * L_A)) # Step 2 (Forward) - Computing achromatic response (:math:`I_z` and # :math:`I_{z,w}`), redness-greenness (:math:`a_z` and :math:`a_{z,w}`), # and yellowness-blueness (:math:`b_z`, :math:`b_{z,w}`). with domain_range_scale("ignore"): I_z, a_z, b_z = tsplit(XYZ_to_Izazbz(XYZ_D65, method="Safdar 2021")) I_z_w, _a_z_w, b_z_w = tsplit( XYZ_to_Izazbz(XYZ_w, method="Safdar 2021")) # Step 3 (Forward) - Computing hue angle :math:`h_z` h_z = hue_angle(a_z, b_z) # Step 4 (Forward) - Computing hue quadrature :math:`H`. H = hue_quadrature(h_z) # Computing eccentricity factor :math:`e_z`. e_z = 1.015 + np.cos(np.radians(89.038 + h_z % 360)) # Step 5 (Forward) - Computing brightness :math:`Q_z`, # lightness :math:`J_z`, colourfulness :math`M_z`, and chroma :math:`C_z` Q_z_p = (1.6 * F_s) / F_b**0.12 Q_z_m = F_s**2.2 * F_b**0.5 * spow(F_L, 0.2) Q_z = 2700 * spow(I_z, Q_z_p) * Q_z_m Q_z_w = 2700 * spow(I_z_w, Q_z_p) * Q_z_m J_z = 100 * (Q_z / Q_z_w) M_z = (100 * (a_z**2 + b_z**2)**0.37 * ((spow(e_z, 0.068) * spow(F_L, 0.2)) / (F_b**0.1 * spow(I_z_w, 0.78)))) C_z = 100 * (M_z / Q_z_w) # Step 6 (Forward) - Computing saturation :math:`S_z`, # vividness :math:`V_z`, blackness :math:`K_z`, and whiteness :math:`W_z`. S_z = 100 * spow(F_L, 0.6) * np.sqrt(M_z / Q_z) V_z = np.sqrt((J_z - 58)**2 + 3.4 * C_z**2) K_z = 100 - 0.8 * np.sqrt(J_z**2 + 8 * C_z**2) W_z = 100 - np.sqrt((100 - J_z)**2 + C_z**2) return CAM_Specification_ZCAM( as_float(from_range_1(J_z)), as_float(from_range_1(C_z)), as_float(from_range_degrees(h_z)), as_float(from_range_1(S_z)), as_float(from_range_1(Q_z)), as_float(from_range_1(M_z)), as_float(from_range_degrees(H, 400)), None, as_float(from_range_1(V_z)), as_float(from_range_1(K_z)), as_float(from_range_1(W_z)), )
def corresponding_chromaticities_prediction_Zhai2018( experiment: Union[Literal[1, 2, 3, 4, 6, 8, 9, 11, 12], CorrespondingColourDataset] = 1, D_b: FloatingOrArrayLike = 1, D_d: FloatingOrArrayLike = 1, XYZ_wo: ArrayLike = np.array([1, 1, 1]), transform: Union[Literal["CAT02", "CAT16", ], str, ] = "CAT02", ) -> Tuple[CorrespondingChromaticitiesPrediction, ...]: """ Return the corresponding chromaticities prediction for *Zhai and Luo (2018)* chromatic adaptation model using given transform. Parameters ---------- experiment *Breneman (1987)* experiment number or :class:`colour.CorrespondingColourDataset` class instance. D_b Degree of adaptation :math:`D_{\\beta}` of input illuminant :math:`\\beta`. D_d Degree of adaptation :math:`D_{\\delta}` of output illuminant :math:`\\delta`. XYZ_wo Baseline illuminant (:math:`BI`) :math:`o`. transform Chromatic adaptation transform. Returns ------- :class:`tuple` Corresponding chromaticities prediction. References ---------- :cite:`Breneman1987b`, :cite:`Zhai2018` Examples -------- >>> from pprint import pprint >>> pr = corresponding_chromaticities_prediction_Zhai2018(2) >>> pr = [(p.uv_m, p.uv_p) for p in pr] >>> pprint(pr) # doctest: +ELLIPSIS [(array([ 0.207, 0.486]), array([ 0.2082238..., 0.4727943...])), (array([ 0.449, 0.511]), array([ 0.4474691..., 0.5076681...])), (array([ 0.263, 0.505]), array([ 0.2640379..., 0.4954003...])), (array([ 0.322, 0.545]), array([ 0.3336937..., 0.5435500...])), (array([ 0.316, 0.537]), array([ 0.3238490..., 0.5359889...])), (array([ 0.265, 0.553]), array([ 0.2724846..., 0.5506939...])), (array([ 0.221, 0.538]), array([ 0.2267596..., 0.5295259...])), (array([ 0.135, 0.532]), array([ 0.1443208..., 0.5190035...])), (array([ 0.145, 0.472]), array([ 0.1500723..., 0.4561352...])), (array([ 0.163, 0.331]), array([ 0.1570902..., 0.3245137...])), (array([ 0.176, 0.431]), array([ 0.1763887..., 0.4146000...])), (array([ 0.244, 0.349]), array([ 0.2267005..., 0.3551480...]))] """ experiment_results = (experiment if isinstance( experiment, CorrespondingColourDataset) else convert_experiment_results_Breneman1987(experiment)) with domain_range_scale("1"): XYZ_w, XYZ_wr = experiment_results.XYZ_t, experiment_results.XYZ_r xy_w, xy_wr = XYZ_to_xy([XYZ_w, XYZ_wr]) uv_t = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_ct, xy_w), xy_w) uv_m = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_cr, xy_wr), xy_wr) XYZ_1 = experiment_results.XYZ_ct XYZ_2 = chromatic_adaptation_Zhai2018(XYZ_1, XYZ_w, XYZ_wr, D_b, D_d, XYZ_wo, transform) uv_p = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr) return tuple( CorrespondingChromaticitiesPrediction(experiment_results.name, uv_t[i], uv_m[i], uv_p[i]) for i in range(len(uv_t)))