def XYZ_to_Nayatani95(XYZ, XYZ_n, Y_o, E_o, E_or, n=1):
"""
Computes the Nayatani (1995) colour appearance model correlates.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values of test sample / stimulus in domain
[0, 100].
XYZ_n : array_like
*CIE XYZ* tristimulus values of reference white in domain [0, 100].
Y_o : numeric or array_like
Luminance factor :math:`Y_o` of achromatic background as percentage in
domain [0.18, 1.0]
E_o : numeric or array_like
Illuminance :math:`E_o` of the viewing field in lux.
E_or : numeric or array_like
Normalising illuminance :math:`E_{or}` in lux usually in domain
[1000, 3000]
n : numeric or array_like, optional
Noise term used in the non linear chromatic adaptation model.
Returns
-------
Nayatani95_Specification
Nayatani (1995) colour appearance model specification.
Warning
-------
The input domain of that definition is non standard!
Notes
-----
- Input *CIE XYZ* tristimulus values are in domain [0, 100].
- Input *CIE XYZ_n* tristimulus values are in domain [0, 100].
Examples
--------
>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_n = np.array([95.05, 100.00, 108.88])
>>> Y_o = 20.0
>>> E_o = 5000.0
>>> E_or = 1000.0
>>> XYZ_to_Nayatani95(XYZ, XYZ_n, Y_o, E_o, E_or) # doctest: +ELLIPSIS
Nayatani95_Specification(Lstar_P=49.9998829..., C=0.0133550..., \
h=257.5232268..., s=0.0133550..., Q=62.6266734..., M=0.0167262..., H=None, \
HC=None, Lstar_N=50.0039154...)
"""
Y_o = np.asarray(Y_o)
E_o = np.asarray(E_o)
E_or = np.asarray(E_or)
# Computing adapting luminance :math:`L_o` and normalising luminance
# :math:`L_{or}` in in :math:`cd/m^2`.
# L_o = illuminance_to_luminance(E_o, Y_o)
L_or = illuminance_to_luminance(E_or, Y_o)
# Computing :math:`\xi`, :math:`\eta`, :math:`\zeta` values.
xez = intermediate_values(XYZ_to_xy(XYZ_n))
xi, eta, _zeta = tsplit(xez)
# Computing adapting field cone responses.
RGB_o = ((Y_o[..., np.newaxis] * E_o[..., np.newaxis]) / (100 * np.pi)) * xez
# Computing stimulus cone responses.
RGB = XYZ_to_RGB_Nayatani95(XYZ)
R, G, _B = tsplit(RGB)
# Computing exponential factors of the chromatic adaptation.
bRGB_o = exponential_factors(RGB_o)
bL_or = beta_1(L_or)
# Computing scaling coefficients :math:`e(R)` and :math:`e(G)`
eR = scaling_coefficient(R, xi)
eG = scaling_coefficient(G, eta)
# Computing opponent colour dimensions.
# Computing achromatic response :math:`Q`:
Q_response = achromatic_response(RGB, bRGB_o, xez, bL_or, eR, eG, n)
# Computing tritanopic response :math:`t`:
t_response = tritanopic_response(RGB, bRGB_o, xez, n)
# Computing protanopic response :math:`p`:
p_response = protanopic_response(RGB, bRGB_o, xez, n)
# -------------------------------------------------------------------------
# Computing the correlate of *brightness* :math:`B_r`.
# -------------------------------------------------------------------------
B_r = brightness_correlate(bRGB_o, bL_or, Q_response)
# Computing *brightness* :math:`B_{rw}` of ideal white.
brightness_ideal_white = ideal_white_brightness_correlate(bRGB_o, xez, bL_or, n)
# -------------------------------------------------------------------------
# Computing the correlate of achromatic *Lightness* :math:`L_p^\star`.
# -------------------------------------------------------------------------
Lstar_P = achromatic_lightness_correlate(Q_response)
# -------------------------------------------------------------------------
# Computing the correlate of normalised achromatic *Lightness*
# :math:`L_n^\star`.
# -------------------------------------------------------------------------
Lstar_N = normalised_achromatic_lightness_correlate(B_r, brightness_ideal_white)
# -------------------------------------------------------------------------
# Computing the *hue* angle :math:`\\theta`.
# -------------------------------------------------------------------------
theta = hue_angle(p_response, t_response)
# TODO: Implement hue quadrature & composition computation.
# -------------------------------------------------------------------------
# Computing the correlate of *saturation* :math:`S`.
# -------------------------------------------------------------------------
S_RG, S_YB = tsplit(saturation_components(theta, bL_or, t_response, p_response))
S = saturation_correlate(S_RG, S_YB)
# -------------------------------------------------------------------------
# Computing the correlate of *chroma* :math:`C`.
# -------------------------------------------------------------------------
# C_RG, C_YB = tsplit(chroma_components(Lstar_P, S_RG, S_YB))
C = chroma_correlate(Lstar_P, S)
# -------------------------------------------------------------------------
# Computing the correlate of *colourfulness* :math:`M`.
# -------------------------------------------------------------------------
# TODO: Investigate components usage.
# M_RG, M_YB = tsplit(colourfulness_components(C_RG, C_YB,
# brightness_ideal_white))
M = colourfulness_correlate(C, brightness_ideal_white)
return Nayatani95_Specification(Lstar_P, C, theta, S, B_r, M, None, None, Lstar_N)
def XYZ_to_Nayatani95(XYZ, XYZ_n, Y_o, E_o, E_or, n=1):
"""
Computes the *Nayatani (1995)* colour appearance model correlates.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values of test sample / stimulus.
XYZ_n : array_like
*CIE XYZ* tristimulus values of reference white.
Y_o : numeric or array_like
Luminance factor :math:`Y_o` of achromatic background as percentage
normalised to domain [0.18, 1.0] in **'Reference'** domain-range scale.
E_o : numeric or array_like
Illuminance :math:`E_o` of the viewing field in lux.
E_or : numeric or array_like
Normalising illuminance :math:`E_{or}` in lux usually normalised to
domain [1000, 3000].
n : numeric or array_like, optional
Noise term used in the non linear chromatic adaptation model.
Returns
-------
Nayatani95_Specification
*Nayatani (1995)* colour appearance model specification.
Notes
-----
+--------------------------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+================================+=======================+===============+
| ``XYZ`` | [0, 100] | [0, 1] |
+--------------------------------+-----------------------+---------------+
| ``XYZ_n`` | [0, 100] | [0, 1] |
+--------------------------------+-----------------------+---------------+
+--------------------------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+================================+=======================+===============+
| ``Nayatani95_Specification.h`` | [0, 360] | [0, 1] |
+--------------------------------+-----------------------+---------------+
References
----------
:cite:`Fairchild2013ba`, :cite:`Nayatani1995a`
Examples
--------
>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_n = np.array([95.05, 100.00, 108.88])
>>> Y_o = 20.0
>>> E_o = 5000.0
>>> E_or = 1000.0
>>> XYZ_to_Nayatani95(XYZ, XYZ_n, Y_o, E_o, E_or) # doctest: +ELLIPSIS
Nayatani95_Specification(L_star_P=49.9998829..., C=0.0133550..., \
h=257.5232268..., s=0.0133550..., Q=62.6266734..., M=0.0167262..., H=None, \
HC=None, L_star_N=50.0039154...)
"""
XYZ = to_domain_100(XYZ)
XYZ_n = to_domain_100(XYZ_n)
Y_o = as_float_array(Y_o)
E_o = as_float_array(E_o)
E_or = as_float_array(E_or)
# Computing adapting luminance :math:`L_o` and normalising luminance
# :math:`L_{or}` in in :math:`cd/m^2`.
# L_o = illuminance_to_luminance(E_o, Y_o)
L_or = illuminance_to_luminance(E_or, Y_o)
# Computing :math:`\\xi` :math:`\\eta`, :math:`\\zeta` values.
xez = intermediate_values(XYZ_to_xy(XYZ_n / 100))
xi, eta, _zeta = tsplit(xez)
# Computing adapting field cone responses.
RGB_o = (((Y_o[..., np.newaxis] * E_o[..., np.newaxis]) /
(100 * np.pi)) * xez)
# Computing stimulus cone responses.
RGB = XYZ_to_RGB_Nayatani95(XYZ)
R, G, _B = tsplit(RGB)
# Computing exponential factors of the chromatic adaptation.
bRGB_o = exponential_factors(RGB_o)
bL_or = beta_1(L_or)
# Computing scaling coefficients :math:`e(R)` and :math:`e(G)`
eR = scaling_coefficient(R, xi)
eG = scaling_coefficient(G, eta)
# Computing opponent colour dimensions.
# Computing achromatic response :math:`Q`:
Q_response = achromatic_response(RGB, bRGB_o, xez, bL_or, eR, eG, n)
# Computing tritanopic response :math:`t`:
t_response = tritanopic_response(RGB, bRGB_o, xez, n)
# Computing protanopic response :math:`p`:
p_response = protanopic_response(RGB, bRGB_o, xez, n)
# Computing the correlate of *brightness* :math:`B_r`.
B_r = brightness_correlate(bRGB_o, bL_or, Q_response)
# Computing *brightness* :math:`B_{rw}` of ideal white.
brightness_ideal_white = ideal_white_brightness_correlate(
bRGB_o, xez, bL_or, n)
# Computing the correlate of achromatic *Lightness* :math:`L_p^\\star`.
L_star_P = (achromatic_lightness_correlate(Q_response))
# Computing the correlate of normalised achromatic *Lightness*
# :math:`L_n^\\star`.
L_star_N = (normalised_achromatic_lightness_correlate(
B_r, brightness_ideal_white))
# Computing the *hue* angle :math:`\\theta`.
theta = hue_angle(p_response, t_response)
# TODO: Implement hue quadrature & composition computation.
# Computing the correlate of *saturation* :math:`S`.
S_RG, S_YB = tsplit(
saturation_components(theta, bL_or, t_response, p_response))
S = saturation_correlate(S_RG, S_YB)
# Computing the correlate of *chroma* :math:`C`.
# C_RG, C_YB = tsplit(chroma_components(L_star_P, S_RG, S_YB))
C = chroma_correlate(L_star_P, S)
# Computing the correlate of *colourfulness* :math:`M`.
# TODO: Investigate components usage.
# M_RG, M_YB = tsplit(colourfulness_components(C_RG, C_YB,
# brightness_ideal_white))
M = colourfulness_correlate(C, brightness_ideal_white)
return Nayatani95_Specification(L_star_P, C, from_range_degrees(theta), S,
B_r, M, None, None, L_star_N)
def XYZ_to_Nayatani95(XYZ, XYZ_n, Y_o, E_o, E_or, n=1):
"""
Computes the *Nayatani (1995)* colour appearance model correlates.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values of test sample / stimulus.
XYZ_n : array_like
*CIE XYZ* tristimulus values of reference white.
Y_o : numeric or array_like
Luminance factor :math:`Y_o` of achromatic background as percentage
normalised to domain [0.18, 1.0] in **'Reference'** domain-range scale.
E_o : numeric or array_like
Illuminance :math:`E_o` of the viewing field in lux.
E_or : numeric or array_like
Normalising illuminance :math:`E_{or}` in lux usually normalised to
domain [1000, 3000].
n : numeric or array_like, optional
Noise term used in the non linear chromatic adaptation model.
Returns
-------
Nayatani95_Specification
*Nayatani (1995)* colour appearance model specification.
Notes
-----
+--------------------------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+================================+=======================+===============+
| ``XYZ`` | [0, 100] | [0, 1] |
+--------------------------------+-----------------------+---------------+
| ``XYZ_n`` | [0, 100] | [0, 1] |
+--------------------------------+-----------------------+---------------+
+--------------------------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+================================+=======================+===============+
| ``Nayatani95_Specification.h`` | [0, 360] | [0, 1] |
+--------------------------------+-----------------------+---------------+
References
----------
:cite:`Fairchild2013ba`, :cite:`Nayatani1995a`
Examples
--------
>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_n = np.array([95.05, 100.00, 108.88])
>>> Y_o = 20.0
>>> E_o = 5000.0
>>> E_or = 1000.0
>>> XYZ_to_Nayatani95(XYZ, XYZ_n, Y_o, E_o, E_or) # doctest: +ELLIPSIS
Nayatani95_Specification(L_star_P=49.9998829..., C=0.0133550..., \
h=257.5232268..., s=0.0133550..., Q=62.6266734..., M=0.0167262..., H=None, \
HC=None, L_star_N=50.0039154...)
"""
XYZ = to_domain_100(XYZ)
XYZ_n = to_domain_100(XYZ_n)
Y_o = as_float_array(Y_o)
E_o = as_float_array(E_o)
E_or = as_float_array(E_or)
# Computing adapting luminance :math:`L_o` and normalising luminance
# :math:`L_{or}` in in :math:`cd/m^2`.
# L_o = illuminance_to_luminance(E_o, Y_o)
L_or = illuminance_to_luminance(E_or, Y_o)
# Computing :math:`\\xi` :math:`\\eta`, :math:`\\zeta` values.
xez = intermediate_values(XYZ_to_xy(XYZ_n / 100))
xi, eta, _zeta = tsplit(xez)
# Computing adapting field cone responses.
RGB_o = ((
(Y_o[..., np.newaxis] * E_o[..., np.newaxis]) / (100 * np.pi)) * xez)
# Computing stimulus cone responses.
RGB = XYZ_to_RGB_Nayatani95(XYZ)
R, G, _B = tsplit(RGB)
# Computing exponential factors of the chromatic adaptation.
bRGB_o = exponential_factors(RGB_o)
bL_or = beta_1(L_or)
# Computing scaling coefficients :math:`e(R)` and :math:`e(G)`
eR = scaling_coefficient(R, xi)
eG = scaling_coefficient(G, eta)
# Computing opponent colour dimensions.
# Computing achromatic response :math:`Q`:
Q_response = achromatic_response(RGB, bRGB_o, xez, bL_or, eR, eG, n)
# Computing tritanopic response :math:`t`:
t_response = tritanopic_response(RGB, bRGB_o, xez, n)
# Computing protanopic response :math:`p`:
p_response = protanopic_response(RGB, bRGB_o, xez, n)
# Computing the correlate of *brightness* :math:`B_r`.
B_r = brightness_correlate(bRGB_o, bL_or, Q_response)
# Computing *brightness* :math:`B_{rw}` of ideal white.
brightness_ideal_white = ideal_white_brightness_correlate(
bRGB_o, xez, bL_or, n)
# Computing the correlate of achromatic *Lightness* :math:`L_p^\\star`.
L_star_P = (achromatic_lightness_correlate(Q_response))
# Computing the correlate of normalised achromatic *Lightness*
# :math:`L_n^\\star`.
L_star_N = (normalised_achromatic_lightness_correlate(
B_r, brightness_ideal_white))
# Computing the *hue* angle :math:`\\theta`.
theta = hue_angle(p_response, t_response)
# TODO: Implement hue quadrature & composition computation.
# Computing the correlate of *saturation* :math:`S`.
S_RG, S_YB = tsplit(
saturation_components(theta, bL_or, t_response, p_response))
S = saturation_correlate(S_RG, S_YB)
# Computing the correlate of *chroma* :math:`C`.
# C_RG, C_YB = tsplit(chroma_components(L_star_P, S_RG, S_YB))
C = chroma_correlate(L_star_P, S)
# Computing the correlate of *colourfulness* :math:`M`.
# TODO: Investigate components usage.
# M_RG, M_YB = tsplit(colourfulness_components(C_RG, C_YB,
# brightness_ideal_white))
M = colourfulness_correlate(C, brightness_ideal_white)
return Nayatani95_Specification(L_star_P, C, from_range_degrees(theta), S,
B_r, M, None, None, L_star_N)