colour.appearance.ciecam02 Module

CIECAM02 Colour Appearance Model

Defines CIECAM02 colour appearance model objects:

See also

CIECAM02 Colour Appearance Model Jupyter Notebook

References

[1]Wikipedia. (n.d.). CIECAM02. Retrieved August 14, 2014, from http://en.wikipedia.org/wiki/CIECAM02
[2]Fairchild, M. D. (2004). CIECAM02. In Color Appearance Models (2nd ed., pp. 289–301). Wiley. ISBN:978-0470012161
[3]Westland, S., Ripamonti, C., & Cheung, V. (2012). Extrapolation Methods. Computational Colour Science Using MATLAB (2nd ed., p. 38). ISBN:978-0-470-66569-5
[4]Moroney, N., Fairchild, M. D., Hunt, R. W. G., Li, C., Luo, M. R., & Newman, T. (n.d.). The CIECAM02 Color Appearance Model. Color and Imaging Conference, 2002(1), 23–27. Retrieved from http://www.ingentaconnect.com/content/ist/cic/2002/00002002/00000001/art00006
[5](1, 2, 3) Luo, M. R., & Li, C. (2013). CIECAM02 and Its Recent Developments. In Advanced Color Image Processing and Analysis (pp. 19–58). doi:10.1007/978-1-4419-6190-7
colour.appearance.ciecam02.CAT02_INVERSE_CAT = array([[ 1.09612382, -0.278869 , 0.18274518], [ 0.45436904, 0.47353315, 0.0720978 ], [-0.00962761, -0.00569803, 1.01532564]])

Inverse CAT02 chromatic adaptation transform.

CAT02_INVERSE_CAT : array_like, (3, 3)

class colour.appearance.ciecam02.CIECAM02_InductionFactors[source]

Bases: colour.appearance.ciecam02.CIECAM02_InductionFactors

CIECAM02 colour appearance model induction factors.

Parameters:
  • F (numeric or array_like) – Maximum degree of adaptation \(F\).
  • c (numeric or array_like) – Exponential non linearity \(c\).
  • N_c (numeric or array_like) – Chromatic induction factor \(N_c\).

Create new instance of CIECAM02_InductionFactors(F, c, N_c)

colour.appearance.ciecam02.CIECAM02_VIEWING_CONDITIONS = CaseInsensitiveMapping({u'Dark': CIECAM02_InductionFactors(F=0.8, c=0.525, N_c=0.8), u'Dim': CIECAM02_InductionFactors(F=0.9, c=0.59, N_c=0.95), u'Average': CIECAM02_InductionFactors(F=1, c=0.69, N_c=1)})

Reference CIECAM02 colour appearance model viewing conditions.

CIECAM02_VIEWING_CONDITIONS : CaseInsensitiveMapping
{‘Average’, ‘Dim’, ‘Dark’}
class colour.appearance.ciecam02.CIECAM02_Specification[source]

Bases: colour.appearance.ciecam02.CIECAM02_Specification

Defines the CIECAM02 colour appearance model specification.

Parameters:
  • J (numeric or array_like) – Correlate of Lightness \(J\).
  • C (numeric or array_like) – Correlate of chroma \(C\).
  • h (numeric or array_like) – Hue angle \(h\) in degrees.
  • s (numeric or array_like) – Correlate of saturation \(s\).
  • Q (numeric or array_like) – Correlate of brightness \(Q\).
  • M (numeric or array_like) – Correlate of colourfulness \(M\).
  • H (numeric or array_like) – Hue \(h\) quadrature \(H\).
  • HC (numeric or array_like) – Hue \(h\) composition \(H^C\).

Returns a new instance of the CIECAM02_Specification class.

colour.appearance.ciecam02.XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b, surround=CIECAM02_InductionFactors(F=1, c=0.69, N_c=1), discount_illuminant=False)[source]

Computes the CIECAM02 colour appearance model correlates from given CIE XYZ tristimulus values.

This is the forward implementation.

Parameters:
  • XYZ (array_like) – CIE XYZ tristimulus values of test sample / stimulus in domain [0, 100].
  • XYZ_w (array_like) – CIE XYZ tristimulus values of reference white in domain [0, 100].
  • L_A (numeric or array_like) – Adapting field luminance \(L_A\) in \(cd/m^2\), (often taken to be 20% of the luminance of a white object in the scene).
  • Y_b (numeric or array_like) – Relative luminance of background \(Y_b\) in \(cd/m^2\).
  • surround (CIECAM02_InductionFactors, optional) – Surround viewing conditions induction factors.
  • discount_illuminant (bool, optional) – Truth value indicating if the illuminant should be discounted.
Returns:

CIECAM02 colour appearance model specification.
Return type:

CIECAM02_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_w tristimulus values are in domain [0, 100].

Examples

>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_w = np.array([95.05, 100.00, 108.88])
>>> L_A = 318.31
>>> Y_b = 20.0
>>> surround = CIECAM02_VIEWING_CONDITIONS['Average']
>>> XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b, surround)  
CIECAM02_Specification(J=41.7310911..., C=0.1047077..., h=219.0484326..., s=2.3603053..., Q=195.3713259..., M=0.1088421..., H=278.0607358..., HC=None)
colour.appearance.ciecam02.CIECAM02_to_XYZ(CIECAM02_specification, XYZ_w, L_A, Y_b, surround=CIECAM02_InductionFactors(F=1, c=0.69, N_c=1), discount_illuminant=False)[source]

Converts CIECAM02 specification to CIE XYZ tristimulus values.

This is the reverse implementation.

Parameters:
  • CIECAM02_specification (CIECAM02_Specification) – CIECAM02 colour appearance model specification. Correlate of Lightness \(J\), correlate of chroma \(C\) or correlate of colourfulness \(M\) and hue angle \(h\) in degrees must be specified, e.g. \(JCh\) or \(JMh\).
  • XYZ_w (array_like) – CIE XYZ tristimulus values of reference white.
  • L_A (numeric or array_like) – Adapting field luminance \(L_A\) in \(cd/m^2\), (often taken to be 20% of the luminance of a white object in the scene).
  • Y_b (numeric or array_like) – Relative luminance of background \(Y_b\) in \(cd/m^2\).
  • surround (CIECAM02_Surround, optional) – Surround viewing conditions.
  • discount_illuminant (bool, optional) – Discount the illuminant.
Returns:

XYZ – CIE XYZ tristimulus values.
Return type:

ndarray
Raises:

ValueError – If neither C or M correlates have been defined in the CIECAM02_specification argument.

Warning

The output range of that definition is non standard!

Notes

  • CIECAM02_specification can also be passed as a compatible argument colour.as_namedtuple() definition.
  • Input CIE XYZ_w tristimulus values are in domain [0, 100].
  • Output CIE XYZ tristimulus values are in range [0, 100].

Examples

>>> specification = CIECAM02_Specification(
...     J=41.731091132513917,
...     C=0.104707757171031,
...     h=219.048432658311780)
>>> XYZ_w = np.array([95.05, 100.00, 108.88])
>>> L_A = 318.31
>>> Y_b = 20.0
>>> CIECAM02_to_XYZ(specification, XYZ_w, L_A, Y_b)  
array([ 19.01...,  20...  ,  21.78...])
colour.appearance.ciecam02.chromatic_induction_factors(n)[source]

Returns the chromatic induction factors \(N_{bb}\) and \(N_{cb}\).

Parameters:n (numeric or array_like) – Function of the luminance factor of the background \(n\).
Returns:Chromatic induction factors \(N_{bb}\) and \(N_{cb}\).
Return type:ndarray

Examples

>>> chromatic_induction_factors(0.2)  
array([ 1.000304...,  1.000304...])
colour.appearance.ciecam02.base_exponential_non_linearity(n)[source]

Returns the base exponential non linearity \(n\).

Parameters:n (numeric or array_like) – Function of the luminance factor of the background \(n\).
Returns:Base exponential non linearity \(z\).
Return type:numeric or ndarray

Examples

>>> base_exponential_non_linearity(0.2)  
1.9272135...
colour.appearance.ciecam02.viewing_condition_dependent_parameters(Y_b, Y_w, L_A)[source]

Returns the viewing condition dependent parameters.

Parameters:
  • Y_b (numeric or array_like) – Adapting field Y tristimulus value \(Y_b\).
  • Y_w (numeric or array_like) – Whitepoint Y tristimulus value \(Y_w\).
  • L_A (numeric or array_like) – Adapting field luminance \(L_A\) in \(cd/m^2\).
Returns:

Viewing condition dependent parameters.
Return type:

ndarray

Examples

>>> viewing_condition_dependent_parameters(  
...     20.0, 100.0, 318.31)
array([ 0.2...,  1.1675444...,  1.000304...,  1.000304...,  1.9272136...])
colour.appearance.ciecam02.degree_of_adaptation(F, L_A)[source]

Returns the degree of adaptation \(D\) from given surround maximum degree of adaptation \(F\) and Adapting field luminance \(L_A\) in \(cd/m^2\).

Parameters:
  • F (numeric or array_like) – Surround maximum degree of adaptation \(F\).
  • L_A (numeric or array_like) – Adapting field luminance \(L_A\) in \(cd/m^2\).
Returns:

Degree of adaptation \(D\).
Return type:

numeric or ndarray

Examples

>>> degree_of_adaptation(1.0, 318.31)  
0.9944687...
colour.appearance.ciecam02.full_chromatic_adaptation_forward(RGB, RGB_w, Y_w, D)[source]

Applies full chromatic adaptation to given CMCCAT2000 transform sharpened RGB array using given CMCCAT2000 transform sharpened whitepoint RGB_w array.

Parameters:
  • RGB (array_like) – CMCCAT2000 transform sharpened RGB array.
  • RGB_w (array_like) – CMCCAT2000 transform sharpened whitepoint RGB_w array.
  • Y_w (numeric or array_like) – Whitepoint Y tristimulus value \(Y_w\).
  • D (numeric or array_like) – Degree of adaptation \(D\).
Returns:

Adapted RGB array.
Return type:

ndarray

Examples

>>> RGB = np.array([18.985456, 20.707422, 21.747482])
>>> RGB_w = np.array([94.930528, 103.536988, 108.717742])
>>> Y_w = 100.0
>>> D = 0.994468780088
>>> full_chromatic_adaptation_forward(  
...     RGB, RGB_w, Y_w, D)
array([ 19.9937078...,  20.0039363...,  20.0132638...])
colour.appearance.ciecam02.full_chromatic_adaptation_reverse(RGB, RGB_w, Y_w, D)[source]

Reverts full chromatic adaptation of given CMCCAT2000 transform sharpened RGB array using given CMCCAT2000 transform sharpened whitepoint RGB_w array.

Parameters:
  • RGB (array_like) – CMCCAT2000 transform sharpened RGB array.
  • RGB_w (array_like) – CMCCAT2000 transform sharpened whitepoint RGB_w array.
  • Y_w (numeric or array_like) – Whitepoint Y tristimulus value \(Y_w\).
  • D (numeric or array_like) – Degree of adaptation \(D\).
Returns:

Adapted RGB array.
Return type:

ndarray

Examples

>>> RGB = np.array([19.99370783, 20.00393634, 20.01326387])
>>> RGB_w = np.array([94.930528, 103.536988, 108.717742])
>>> Y_w = 100.0
>>> D = 0.994468780088
>>> full_chromatic_adaptation_reverse(RGB, RGB_w, Y_w, D)
array([ 18.985456,  20.707422,  21.747482])
colour.appearance.ciecam02.RGB_to_rgb(RGB)[source]

Converts given RGB array to Hunt-Pointer-Estevez \(\rho\gamma\beta\) colourspace.

Parameters:RGB (array_like) – RGB array.
Returns:Hunt-Pointer-Estevez \(\rho\gamma\beta\) colourspace array.
Return type:ndarray

Examples

>>> RGB = np.array([19.99370783, 20.00393634, 20.01326387])
>>> RGB_to_rgb(RGB)  
array([ 19.9969397...,  20.0018612...,  20.0135053...])
colour.appearance.ciecam02.rgb_to_RGB(rgb)[source]

Converts given Hunt-Pointer-Estevez \(\rho\gamma\beta\) colourspace array to RGB array.

Parameters:rgb (array_like) – Hunt-Pointer-Estevez \(\rho\gamma\beta\) colourspace array.
Returns:RGB array.
Return type:ndarray

Examples

>>> rgb = np.array([19.99693975, 20.00186123, 20.01350530])
>>> rgb_to_RGB(rgb)  
array([ 19.9937078...,  20.0039363...,  20.0132638...])
colour.appearance.ciecam02.post_adaptation_non_linear_response_compression_forward(RGB, F_L)[source]

Returns given CMCCAT2000 transform sharpened RGB array with post adaptation non linear response compression.

Parameters:
  • RGB (array_like) – CMCCAT2000 transform sharpened RGB array.
  • F_L (array_like) – Luminance level adaptation factor \(F_L\).
Returns:

Compressed CMCCAT2000 transform sharpened RGB array.
Return type:

ndarray

Notes

  • This definition implements negative values handling as per [5].

Examples

>>> RGB = np.array([19.99693975, 20.00186123, 20.01350530])
>>> F_L = 1.16754446415
>>> post_adaptation_non_linear_response_compression_forward(
...     RGB, F_L)  
array([ 7.9463202...,  7.9471152...,  7.9489959...])
colour.appearance.ciecam02.post_adaptation_non_linear_response_compression_reverse(RGB, F_L)[source]

Returns given CMCCAT2000 transform sharpened RGB array without post adaptation non linear response compression.

Parameters:
  • RGB (array_like) – CMCCAT2000 transform sharpened RGB array.
  • F_L (array_like) – Luminance level adaptation factor \(F_L\).
Returns:

Uncompressed CMCCAT2000 transform sharpened RGB array.
Return type:

ndarray

Examples

>>> RGB = np.array([7.94632020, 7.94711528, 7.94899595])
>>> F_L = 1.16754446415
>>> post_adaptation_non_linear_response_compression_reverse(
...     RGB, F_L)  
array([ 19.9969397...,  20.0018612...,  20.0135052...])
colour.appearance.ciecam02.opponent_colour_dimensions_forward(RGB)[source]

Returns opponent colour dimensions from given compressed CMCCAT2000 transform sharpened RGB array for forward CIECAM02 implementation.

Parameters:RGB (array_like) – Compressed CMCCAT2000 transform sharpened RGB array.
Returns:Opponent colour dimensions.
Return type:ndarray

Examples

>>> RGB = np.array([7.94632020, 7.94711528, 7.94899595])
>>> opponent_colour_dimensions_forward(RGB)  
array([-0.0006241..., -0.0005062...])
colour.appearance.ciecam02.opponent_colour_dimensions_reverse(P_n, h)[source]

Returns opponent colour dimensions from given points \(P_n\) and hue \(h\) in degrees for reverse CIECAM02 implementation.

Parameters:
  • P_n (array_like) – Points \(P_n\).
  • h (numeric or array_like) – Hue \(h\) in degrees.
Returns:

Opponent colour dimensions.
Return type:

ndarray

Notes

  • This definition implements negative values handling as per [5].

Examples

>>> P_n = np.array([30162.89081534, 24.23720547, 1.05000000])
>>> h = -140.95156734
>>> opponent_colour_dimensions_reverse(P_n, h)  
array([-0.0006241..., -0.0005062...])
colour.appearance.ciecam02.hue_angle(a, b)[source]

Returns the hue angle \(h\) in degrees.

Parameters:
  • a (numeric or array_like) – Opponent colour dimension \(a\).
  • b (numeric or array_like) – Opponent colour dimension \(b\).
Returns:

Hue angle \(h\) in degrees.
Return type:

numeric or ndarray

Examples

>>> a = -0.000624112068243
>>> b = -0.000506270106773
>>> hue_angle(a, b)  
219.0484326...
colour.appearance.ciecam02.hue_quadrature(h)[source]

Returns the hue quadrature from given hue \(h\) angle in degrees.

Parameters:h (numeric or array_like) – Hue \(h\) angle in degrees.
Returns:Hue quadrature.
Return type:numeric or ndarray

Examples

>>> hue_quadrature(219.0484326582719)  
278.0607358...
colour.appearance.ciecam02.eccentricity_factor(h)[source]

Returns the eccentricity factor \(e_t\) from given hue \(h\) angle in degrees for forward CIECAM02 implementation.

Parameters:h (numeric or array_like) – Hue \(h\) angle in degrees.
Returns:Eccentricity factor \(e_t\).
Return type:numeric or ndarray

Examples

>>> eccentricity_factor(-140.951567342)  
1.1740054...
colour.appearance.ciecam02.achromatic_response_forward(RGB, N_bb)[source]

Returns the achromatic response \(A\) from given compressed CMCCAT2000 transform sharpened RGB array and \(N_{bb}\) chromatic induction factor for forward CIECAM02 implementation.

Parameters:
  • RGB (array_like) – Compressed CMCCAT2000 transform sharpened RGB array.
  • N_bb (numeric or array_like) – Chromatic induction factor \(N_{bb}\).
Returns:

Achromatic response \(A\).
Return type:

numeric or ndarray

Examples

>>> RGB = np.array([7.94632020, 7.94711528, 7.94899595])
>>> N_bb = 1.000304004559381
>>> achromatic_response_forward(RGB, N_bb)  
23.9394809...
colour.appearance.ciecam02.achromatic_response_reverse(A_w, J, c, z)[source]

Returns the achromatic response \(A\) from given achromatic response \(A_w\) for the whitepoint, Lightness correlate \(J\), surround exponential non linearity \(c\) and base exponential non linearity \(z\) for reverse CIECAM02 implementation.

Parameters:
  • A_w (numeric or array_like) – Achromatic response \(A_w\) for the whitepoint.
  • J (numeric or array_like) – Lightness correlate \(J\).
  • c (numeric or array_like) – Surround exponential non linearity \(c\).
  • z (numeric or array_like) – Base exponential non linearity \(z\).
Returns:

Achromatic response \(A\).
Return type:

numeric or ndarray

Examples

>>> A_w = 46.1882087914
>>> J = 41.73109113251392
>>> c = 0.69
>>> z = 1.927213595499958
>>> achromatic_response_reverse(A_w, J, c, z)  
23.9394809...
colour.appearance.ciecam02.lightness_correlate(A, A_w, c, z)[source]

Returns the Lightness correlate \(J\).

Parameters:
  • A (numeric or array_like) – Achromatic response \(A\) for the stimulus.
  • A_w (numeric or array_like) – Achromatic response \(A_w\) for the whitepoint.
  • c (numeric or array_like) – Surround exponential non linearity \(c\).
  • z (numeric or array_like) – Base exponential non linearity \(z\).
Returns:

Lightness correlate \(J\).
Return type:

numeric or ndarray

Examples

>>> A = 23.9394809667
>>> A_w = 46.1882087914
>>> c = 0.69
>>> z = 1.9272135955
>>> lightness_correlate(A, A_w, c, z)  
41.7310911...
colour.appearance.ciecam02.brightness_correlate(c, J, A_w, F_L)[source]

Returns the brightness correlate \(Q\).

Parameters:
  • c (numeric or array_like) – Surround exponential non linearity \(c\).
  • J (numeric or array_like) – Lightness correlate \(J\).
  • A_w (numeric or array_like) – Achromatic response \(A_w\) for the whitepoint.
  • F_L (numeric or array_like) – Luminance level adaptation factor \(F_L\).
Returns:

Brightness correlate \(Q\).
Return type:

numeric or ndarray

Examples

>>> c = 0.69
>>> J = 41.7310911325
>>> A_w = 46.1882087914
>>> F_L = 1.16754446415
>>> brightness_correlate(c, J, A_w, F_L)  
195.3713259...
colour.appearance.ciecam02.temporary_magnitude_quantity_forward(N_c, N_cb, e_t, a, b, RGB_a)[source]

Returns the temporary magnitude quantity \(t\). for forward CIECAM02 implementation.

Parameters:
  • N_c (numeric or array_like) – Surround chromatic induction factor \(N_{c}\).
  • N_cb (numeric or array_like) – Chromatic induction factor \(N_{cb}\).
  • e_t (numeric or array_like) – Eccentricity factor \(e_t\).
  • a (numeric or array_like) – Opponent colour dimension \(a\).
  • b (numeric or array_like) – Opponent colour dimension \(b\).
  • RGB_a (array_like) – Compressed stimulus CMCCAT2000 transform sharpened RGB array.
Returns:

Temporary magnitude quantity \(t\).
Return type:

numeric or ndarray

Examples

>>> N_c = 1.0
>>> N_cb = 1.00030400456
>>> e_t = 1.174005472851914
>>> a = -0.000624112068243
>>> b = -0.000506270106773
>>> RGB_a = np.array([7.94632020, 7.94711528, 7.94899595])
>>> temporary_magnitude_quantity_forward(  
...     N_c, N_cb, e_t, a, b, RGB_a)
0.1497462...
colour.appearance.ciecam02.temporary_magnitude_quantity_reverse(C, J, n)[source]

Returns the temporary magnitude quantity \(t\). for reverse CIECAM02 implementation.

Parameters:
  • C (numeric or array_like) – Chroma correlate \(C\).
  • J (numeric or array_like) – Lightness correlate \(J\).
  • n (numeric or array_like) – Function of the luminance factor of the background \(n\).
Returns:

Temporary magnitude quantity \(t\).
Return type:

numeric or ndarray

Notes

  • This definition implements negative values handling as per [5].

Examples

>>> C = 68.8364136888275
>>> J = 41.749268505999
>>> n = 0.2
>>> temporary_magnitude_quantity_reverse(C, J, n)  
202.3873619...
colour.appearance.ciecam02.chroma_correlate(J, n, N_c, N_cb, e_t, a, b, RGB_a)[source]

Returns the chroma correlate \(C\).

Parameters:
  • J (numeric or array_like) – Lightness correlate \(J\).
  • n (numeric or array_like) – Function of the luminance factor of the background \(n\).
  • N_c (numeric or array_like) – Surround chromatic induction factor \(N_{c}\).
  • N_cb (numeric or array_like) – Chromatic induction factor \(N_{cb}\).
  • e_t (numeric or array_like) – Eccentricity factor \(e_t\).
  • a (numeric or array_like) – Opponent colour dimension \(a\).
  • b (numeric or array_like) – Opponent colour dimension \(b\).
  • RGB_a (array_like) – Compressed stimulus CMCCAT2000 transform sharpened RGB array.
Returns:

Chroma correlate \(C\).
Return type:

numeric or ndarray

Examples

>>> J = 41.7310911325
>>> n = 0.2
>>> N_c = 1.0
>>> N_cb = 1.00030400456
>>> e_t = 1.17400547285
>>> a = -0.000624112068243
>>> b = -0.000506270106773
>>> RGB_a = np.array([7.94632020, 7.94711528, 7.94899595])
>>> chroma_correlate(  
...     J, n, N_c, N_cb, e_t, a, b, RGB_a)
0.1047077...
colour.appearance.ciecam02.colourfulness_correlate(C, F_L)[source]

Returns the colourfulness correlate \(M\).

Parameters:
  • C (numeric or array_like) – Chroma correlate \(C\).
  • F_L (numeric or array_like) – Luminance level adaptation factor \(F_L\).
Returns:

Colourfulness correlate \(M\).
Return type:

numeric or ndarray

Examples

>>> C = 0.104707757171
>>> F_L = 1.16754446415
>>> colourfulness_correlate(C, F_L)  
0.1088421...
colour.appearance.ciecam02.saturation_correlate(M, Q)[source]

Returns the saturation correlate \(s\).

Parameters:
  • M (numeric or array_like) – Colourfulness correlate \(M\).
  • Q (numeric or array_like) – Brightness correlate \(C\).
Returns:

Saturation correlate \(s\).
Return type:

numeric or ndarray

Examples

>>> M = 0.108842175669
>>> Q = 195.371325966
>>> saturation_correlate(M, Q)  
2.3603053...
colour.appearance.ciecam02.P(N_c, N_cb, e_t, t, A, N_bb)[source]

Returns the points \(P_1\), \(P_2\) and \(P_3\).

Parameters:
  • N_c (numeric or array_like) – Surround chromatic induction factor \(N_{c}\).
  • N_cb (numeric or array_like) – Chromatic induction factor \(N_{cb}\).
  • e_t (numeric or array_like) – Eccentricity factor \(e_t\).
  • t (numeric or array_like) – Temporary magnitude quantity \(t\).
  • A (numeric or array_like) – Achromatic response \(A\) for the stimulus.
  • N_bb (numeric or array_like) – Chromatic induction factor \(N_{bb}\).
Returns:

Points \(P\).
Return type:

ndarray

Examples

>>> N_c = 1.0
>>> N_cb = 1.00030400456
>>> e_t = 1.174005472851914
>>> t = 0.149746202921
>>> A = 23.9394809667
>>> N_bb = 1.00030400456
>>> P(N_c, N_cb, e_t, t, A, N_bb)  
array([  3.0162890...e+04,   2.4237205...e+01,   1.0500000...e+00])
colour.appearance.ciecam02.post_adaptation_non_linear_response_compression_matrix(P_2, a, b)[source]

Returns the post adaptation non linear response compression matrix.

Parameters:
  • P_2 (numeric or array_like) – Point \(P_2\).
  • a (numeric or array_like) – Opponent colour dimension \(a\).
  • b (numeric or array_like) – Opponent colour dimension \(b\).
Returns:

Points \(P\).
Return type:

ndarray

Examples

>>> P_2 = 24.2372054671
>>> a = -0.000624112068243
>>> b = -0.000506270106773
>>> post_adaptation_non_linear_response_compression_matrix(
...     P_2, a, b)  
array([ 7.9463202...,  7.9471152...,  7.9489959...])