colour.appearance.hunt Module¶

Hunt Colour Appearance Model¶

Defines Hunt colour appearance model objects:

Hunt_InductionFactors
HUNT_VIEWING_CONDITIONS
Hunt_Specification
XYZ_to_Hunt()

References

[1]	Mark D. Fairchild, Color Appearance Models, 3nd Edition, The Wiley-IS&T Series in Imaging Science and Technology, published June 2013, ASIN: B00DAYO8E2, locations 5094-5556.

[2]	Dr. R.W.G. Hunt, The reproduction of colour, 6th Edition, John Wiley & Sons, published 10 August 2005, ISBN-13: 978-0470024256

class colour.appearance.hunt.Hunt_InductionFactors[source]¶

Bases: colour.appearance.hunt.Hunt_InductionFactors

Hunt colour appearance model induction factors.

Parameters:

Parameters:	N_c (numeric) – Chromatic surround induction factor $N_c$ . N_b (numeric) – Brightness surround induction factor $N_b$ . N_cb (numeric, optional) – Chromatic background induction factor $N_{cb}$ , approximated using tristimulus values $Y_w$ and $Y_b$ of respectively the reference white and the background if not specified. N_bb (numeric, optional) – Brightness background induction factor $N_{bb}$ , approximated using tristimulus values $Y_w$ and $Y_b$ of respectively the reference white and the background if not specified.

N_c (numeric) – Chromatic surround induction factor $N_c$ .
N_b (numeric) – Brightness surround induction factor $N_b$ .
N_cb (numeric, optional) – Chromatic background induction factor $N_{cb}$ , approximated using tristimulus values $Y_w$ and $Y_b$ of respectively the reference white and the background if not specified.
N_bb (numeric, optional) – Brightness background induction factor $N_{bb}$ , approximated using tristimulus values $Y_w$ and $Y_b$ of respectively the reference white and the background if not specified.

colour.appearance.hunt.HUNT_VIEWING_CONDITIONS = CaseInsensitiveMapping({u'Large Transparencies On Light Boxes': Hunt_InductionFactors(N_c=0.7, N_b=25, N_cb=None, N_bb=None), u'Television & CRT, Dim Surrounds': Hunt_InductionFactors(N_c=1, N_b=25, N_cb=None, N_bb=None), u'normal': Hunt_InductionFactors(N_c=1, N_b=75, N_cb=None, N_bb=None), u'Normal Scenes': Hunt_InductionFactors(N_c=1, N_b=75, N_cb=None, N_bb=None), u'Small Areas, Uniform Background & Surrounds': Hunt_InductionFactors(N_c=1, N_b=300, N_cb=None, N_bb=None), u'small_uniform': Hunt_InductionFactors(N_c=1, N_b=300, N_cb=None, N_bb=None), u'Projected Transparencies, Dark Surrounds': Hunt_InductionFactors(N_c=0.7, N_b=10, N_cb=None, N_bb=None), u'projected_dark': Hunt_InductionFactors(N_c=0.7, N_b=10, N_cb=None, N_bb=None), u'light_boxes': Hunt_InductionFactors(N_c=0.7, N_b=25, N_cb=None, N_bb=None), u'tv_dim': Hunt_InductionFactors(N_c=1, N_b=25, N_cb=None, N_bb=None)})¶

Reference Hunt colour appearance model viewing conditions.

HUNT_VIEWING_CONDITIONS : dict: (‘Small Areas, Uniform Background & Surrounds’, ‘Normal Scenes’, ‘Television & CRT, Dim Surrounds’, ‘Large Transparencies On Light Boxes’, ‘Projected Transparencies, Dark Surrounds’)

Aliases:

‘small_uniform’: ‘Small Areas, Uniform Background & Surrounds’
‘normal’: ‘Normal Scenes’
‘tv_dim’: ‘Television & CRT, Dim Surrounds’
‘light_boxes’: ‘Large Transparencies On Light Boxes’
‘projected_dark’: ‘Projected Transparencies, Dark Surrounds’

colour.appearance.hunt.XYZ_TO_HPE_MATRIX = array([[ 0.38971, 0.68898, -0.07868], [-0.22981, 1.1834 , 0.04641], [ 0. , 0. , 1. ]])¶

Hunt colour appearance model CIE XYZ colourspace matrix to Hunt-Pointer-Estevez $\rho\gamma\beta$ colourspace matrix.

XYZ_TO_HPE_MATRIX : array_like, (3, 3)

colour.appearance.hunt.HPE_TO_XYZ_MATRIX = array([[ 1.91019683e+00, -1.11212389e+00, 2.01907957e-01], [ 3.70950088e-01, 6.29054257e-01, -8.05514218e-06], [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])¶

Hunt colour appearance model Hunt-Pointer-Estevez $\rho\gamma\beta$ colourspace to CIE XYZ colourspace matrix matrix.

HPE_TO_XYZ_MATRIX : array_like, (3, 3)

class colour.appearance.hunt.Hunt_ReferenceSpecification[source]¶

Bases: colour.appearance.hunt.Hunt_ReferenceSpecification

Defines the Hunt colour appearance model reference specification.

This specification has field names consistent with Mark D. Fairchild reference.

Parameters:

Parameters:	J (numeric) – Correlate of Lightness $J$ . C_94 (numeric) – Correlate of chroma $C_94$ . h_S (numeric) – Hue angle $h_S$ in degrees. s (numeric) – Correlate of saturation $s$ . Q (numeric) – Correlate of brightness $Q$ . M_94 (numeric) – Correlate of colourfulness $M_94$ . H (numeric) – Hue $h$ quadrature $H$ . H_C (numeric) – Hue $h$ composition $H_C$ .

J (numeric) – Correlate of Lightness $J$ .
C_94 (numeric) – Correlate of chroma $C_94$ .
h_S (numeric) – Hue angle $h_S$ in degrees.
s (numeric) – Correlate of saturation $s$ .
Q (numeric) – Correlate of brightness $Q$ .
M_94 (numeric) – Correlate of colourfulness $M_94$ .
H (numeric) – Hue $h$ quadrature $H$ .
H_C (numeric) – Hue $h$ composition $H_C$ .

class colour.appearance.hunt.Hunt_Specification[source]¶

Bases: colour.appearance.hunt.Hunt_Specification

Defines the Hunt colour appearance model specification.

This specification has field names consistent with the remaining colour appearance models in colour.appearance but diverge from Mark D. Fairchild reference.

Parameters:

Parameters:	J (numeric) – Correlate of Lightness $J$ . C (numeric) – Correlate of chroma $C_94$ . h (numeric) – Hue angle $h_S$ in degrees. s (numeric) – Correlate of saturation $s$ . Q (numeric) – Correlate of brightness $Q$ . M (numeric) – Correlate of colourfulness $M_94$ . H (numeric) – Hue $h$ quadrature $H$ . HC (numeric) – Hue $h$ composition $H_C$ .

J (numeric) – Correlate of Lightness $J$ .
C (numeric) – Correlate of chroma $C_94$ .
h (numeric) – Hue angle $h_S$ in degrees.
s (numeric) – Correlate of saturation $s$ .
Q (numeric) – Correlate of brightness $Q$ .
M (numeric) – Correlate of colourfulness $M_94$ .
H (numeric) – Hue $h$ quadrature $H$ .
HC (numeric) – Hue $h$ composition $H_C$ .

colour.appearance.hunt.XYZ_to_Hunt(XYZ, XYZ_w, XYZ_b, L_A, surround=Hunt_InductionFactors(N_c=1, N_b=75, N_cb=None, N_bb=None), L_AS=None, CCT_w=None, XYZ_p=None, p=None, S=None, S_W=None, helson_judd_effect=False, discount_illuminant=True)[source]¶

Computes the Hunt colour appearance model correlates.

Parameters:

Parameters:	XYZ (array_like, (3,)) – CIE XYZ colourspace matrix of test sample / stimulus in domain [0, 100]. XYZ_w (array_like, (3,)) – CIE XYZ colourspace matrix of reference white in domain [0, 100]. XYZ_b (array_like, (3,)) – CIE XYZ colourspace matrix of background in domain [0, 100]. L_A (numeric) – Adapting field luminance $L_A$ in $cd/m^2$ . surround (Hunt_InductionFactors, optional) – Surround viewing conditions induction factors. L_AS (numeric, optional) – Scotopic luminance $L_{AS}$ of the illuminant, approximated if not specified. CCT_w (numeric, optional) – Correlated color temperature $T_{cp}$ : of the illuminant, needed to approximate $L_{AS}$ . XYZ_p (array_like, (3,), optional) – CIE XYZ colourspace matrix of proximal field in domain [0, 100], assumed to be equal to background if not specified. p (numeric, optional) – Simultaneous contrast / assimilation factor $p$ with value in domain [-1, 0] when simultaneous contrast occurs and domain [0, 1] when assimilation occurs. S (numeric, optional) – Scotopic response $S$ to the stimulus, approximated using tristimulus values $Y$ of the stimulus if not specified. S_w (numeric, optional) – Scotopic response $S_w$ for the reference white, approximated using the tristimulus values $Y_w$ of the reference white if not specified. helson_judd_effect (bool, optional) – Truth value indicating whether the Helson-Judd effect should be accounted for. discount_illuminant (bool, optional) – Truth value indicating if the illuminant should be discounted.

XYZ (array_like, (3,)) – CIE XYZ colourspace matrix of test sample / stimulus in domain [0, 100].
XYZ_w (array_like, (3,)) – CIE XYZ colourspace matrix of reference white in domain [0, 100].
XYZ_b (array_like, (3,)) – CIE XYZ colourspace matrix of background in domain [0, 100].
L_A (numeric) – Adapting field luminance $L_A$ in $cd/m^2$ .
surround (Hunt_InductionFactors, optional) – Surround viewing conditions induction factors.
L_AS (numeric, optional) – Scotopic luminance $L_{AS}$ of the illuminant, approximated if not specified.
CCT_w (numeric, optional) – Correlated color temperature $T_{cp}$ : of the illuminant, needed to approximate $L_{AS}$ .
XYZ_p (array_like, (3,), optional) – CIE XYZ colourspace matrix of proximal field in domain [0, 100], assumed to be equal to background if not specified.
p (numeric, optional) – Simultaneous contrast / assimilation factor $p$ with value in domain [-1, 0] when simultaneous contrast occurs and domain [0, 1] when assimilation occurs.
S (numeric, optional) – Scotopic response $S$ to the stimulus, approximated using tristimulus values $Y$ of the stimulus if not specified.
S_w (numeric, optional) – Scotopic response $S_w$ for the reference white, approximated using the tristimulus values $Y_w$ of the reference white if not specified.
helson_judd_effect (bool, optional) – Truth value indicating whether the Helson-Judd effect should be accounted for.
discount_illuminant (bool, optional) – Truth value indicating if the illuminant should be discounted.

Warning

The input domain of that definition is non standard!

Notes

Input CIE XYZ colourspace matrix is in domain [0, 100].
Input CIE XYZ_b colourspace matrix is in domain [0, 100].
Input CIE XYZ_w colourspace matrix is in domain [0, 100].
Input CIE XYZ_p colourspace matrix is in domain [0, 100].

Returns:	Hunt colour appearance model specification.
Return type:	Hunt_Specification
Raises:	`ValueError` – If an illegal arguments combination is specified.

Examples

>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_w = np.array([95.05, 100.00, 108.88])
>>> XYZ_b = np.array([95.05, 100.00, 108.88])
>>> L_A = 318.31
>>> surround = HUNT_VIEWING_CONDITIONS['Normal Scenes']
>>> CCT_w = 6504.0
>>> XYZ_to_Hunt(XYZ, XYZ_w, XYZ_b, L_A, surround, CCT_w=CCT_w)    
Hunt_Specification(J=30.0462678..., C=0.1210508..., h=269.2737594..., s=0.0199093..., Q=22.2097654..., M=0.1238964..., H=None, HC=None)

colour.appearance.hunt.luminance_level_adaptation_factor(L_A)[source]¶

Returns the luminance level adaptation factor $F_L$ .

Parameters:	L_A (numeric) – Adapting field luminance $L_A$ in $cd/m^2$ .
Returns:	Luminance level adaptation factor $F_L$
Return type:	numeric

Examples

>>> luminance_level_adaptation_factor(318.31)  
1.1675444...

colour.appearance.hunt.illuminant_scotopic_luminance(L_A, CCT)[source]¶

Returns the approximate scotopic luminance $L_{AS}$ of the illuminant.

Parameters:	L_A (numeric) – Adapting field luminance $L_A$ in $cd/m^2$ . CCT (numeric) – Correlated color temperature $T_{cp}$ of the illuminant.
Returns:	Approximate scotopic luminance $L_{AS}$ .
Return type:	numeric

Examples

>>> illuminant_scotopic_luminance(318.31, 6504.0)  
769.9376286...

colour.appearance.hunt.XYZ_to_rgb(XYZ)[source]¶

Converts from CIE XYZ colourspace to Hunt-Pointer-Estevez $\rho\gamma\beta$ colourspace.

Parameters:	XYZ (array_like, (3,)) – CIE XYZ colourspace matrix.
Returns:	Hunt-Pointer-Estevez $\rho\gamma\beta$ colourspace matrix.
Return type:	ndarray, (3,)

Examples

>>> XYZ = np.array([19.01, 20, 21.78])
>>> XYZ_to_rgb(XYZ)  
array([ 19.4743367...,  20.3101217...,  21.78     ])

colour.appearance.hunt.f_n(x)[source]¶

Defines the nonlinear response function of the Hunt colour appearance model used to model the nonlinear behavior of various visual responses.

Parameters:	x (numeric or array_like) – Visual response variable $x$ .
Returns:	Modeled visual response variable $x$ .
Return type:	numeric or array_like

Examples

>>> x = np.array([0.23350512, 0.23351103, 0.23355179])
>>> f_n(x)  
array([ 5.8968592...,  5.8969521...,  5.8975927...])

colour.appearance.hunt.chromatic_adaptation(XYZ, XYZ_w, XYZ_b, L_A, F_L, XYZ_p=None, p=None, helson_judd_effect=False, discount_illuminant=True)[source]¶

Applies chromatic adaptation to given CIE XYZ colourspace matrix.

Parameters:	XYZ (array_like, (3,)) – CIE XYZ colourspace matrix of test sample in domain [0, 100]. XYZ_b (array_like, (3,)) – CIE XYZ colourspace matrix of background in domain [0, 100]. XYZ_w (array_like, (3,)) – CIE XYZ colourspace matrix of reference white in domain [0, 100]. L_A (numeric) – Adapting field luminance $L_A$ in $cd/m^2$ . F_L (numeric) – Luminance adaptation factor $F_L$ . XYZ_p (array_like, (3,), optional) – CIE XYZ colourspace matrix of proximal field in domain [0, 100], assumed to be equal to background if not specified. p (numeric, optional) – Simultaneous contrast / assimilation factor $p$ with value in domain [-1, 0] when simultaneous contrast occurs and domain [0, 1] when assimilation occurs. helson_judd_effect (bool, optional) – Truth value indicating whether the Helson-Judd effect should be accounted for. discount_illuminant (bool, optional) – Truth value indicating if the illuminant should be discounted.
Returns:	Adapted CIE XYZ colourspace matrix.
Return type:	ndarray, (3,)

Examples

>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_b = np.array([95.05, 100.00, 108.88])
>>> XYZ_w = np.array([95.05, 100.00, 108.88])
>>> L_A = 318.31
>>> F_L = 1.16754446415
>>> chromatic_adaptation(XYZ, XYZ_w, XYZ_b, L_A, F_L)  
array([ 6.8959454...,  6.8959991...,  6.8965708...])

colour.appearance.hunt.adjusted_reference_white_signals(rgb_p, rgb_b, rgb_w, p)[source]¶

Adjusts the white point for simultaneous chromatic contrast.

Parameters:	rgb_p (array_like, (3,)) – Cone signals Hunt-Pointer-Estevez $\rho\gamma\beta$ matrix of the proximal field. rgb_b (array_like, (3,)) – Cone signals Hunt-Pointer-Estevez $\rho\gamma\beta$ matrix of the background. rgb_w (array_like, (3,)) – Cone signals matrix Hunt-Pointer-Estevez $\rho\gamma\beta$ of the reference white. p (numeric) – Simultaneous contrast / assimilation factor $p$ with value in domain [-1, 0] when simultaneous contrast occurs and domain [0, 1] when assimilation occurs.
Returns:	Adjusted cone signals Hunt-Pointer-Estevez $\rho\gamma\beta$ matrix of the reference white.
Return type:	ndarray

Examples

>>> rgb_p = np.array([98.0719355, 101.1375595, 100])
>>> rgb_b = np.array([0.99984505, 0.9998384, 0.99982674])
>>> rgb_w = np.array([97.3732571, 101.5496803, 108.88])
>>> p = 0.1
>>> adjusted_reference_white_signals(rgb_p, rgb_b, rgb_w, p)    
array([ 88.0792742...,  91.8569553...,  98.4876543...])

colour.appearance.hunt.achromatic_post_adaptation_signal(rgb)[source]¶

Returns the achromatic post adaptation signal $A$ from given Hunt-Pointer-Estevez $\rho\gamma\beta$ colourspace matrix.

Parameters:	rgb (array_like, (3,)) – Hunt-Pointer-Estevez $\rho\gamma\beta$ colourspace matrix.
Returns:	Achromatic post adaptation signal $A$ .
Return type:	numeric

Examples

>>> rgb = np.array([6.89594549, 6.89599915, 6.89657085])
>>> achromatic_post_adaptation_signal(rgb)  
18.9827186...

colour.appearance.hunt.colour_difference_signals(rgb)[source]¶

Returns the colour difference signals $C_1$ , $C_2$ and $C_3$ from given Hunt-Pointer-Estevez $\rho\gamma\beta$ colourspace matrix.

Parameters:	rgb (array_like, (3,)) – Hunt-Pointer-Estevez $\rho\gamma\beta$ colourspace matrix.
Returns:	Colour difference signals $C_1$ , $C_2$ and $C_3$ .
Return type:	tuple

Examples

>>> rgb = np.array([6.89594549, 6.89599915, 6.89657085])
>>> colour_difference_signals(rgb)  
(-5.3659999...e-05, -0.0005717..., 0.0006253...)

colour.appearance.hunt.hue_angle(C)[source]¶

Returns the hue angle $h$ from given colour difference signals $C$ .

Parameters:	C (array_like) – Colour difference signals $C$ .
Returns:	Hue angle $h$ .
Return type:	numeric

Examples

>>> C = (-5.3658655819965873e-05,
...      -0.00057169938364687312,
...      0.00062535803946683899)
>>> hue_angle(C)  
269.2737594...

colour.appearance.hunt.eccentricity_factor(hue)[source]¶

Returns eccentricity factor $e_s$ from given hue angle $h$ .

Parameters:	numeric – Hue angle $h$ .
Returns:	Eccentricity factor $e_s$ .
Return type:	float

Examples

>>> eccentricity_factor(269.273759)  
1.1108365...

colour.appearance.hunt.low_luminance_tritanopia_factor(L_A)[source]¶

Returns the low luminance tritanopia factor $F_t$ from given adapting field luminance $L_A$ in $cd/m^2$ .

Parameters:	L_A (numeric) – Adapting field luminance $L_A$ in $cd/m^2$ .
Returns:	Low luminance tritanopia factor $F_t$ .
Return type:	numeric

Examples

>>> low_luminance_tritanopia_factor(318.31)  
0.9996859...

colour.appearance.hunt.yellowness_blueness_response(C, e_s, N_c, N_cb, F_t)[source]¶

Returns the yellowness / blueness response $M_{yb}$ .

Parameters:	C (array_like) – Colour difference signals $C$ . e_s (numeric) – Eccentricity factor $e_s$ . N_c (numeric) – Chromatic surround induction factor $N_c$ . N_b (numeric) – Brightness surround induction factor $N_b$ . F_t (numeric) – Low luminance tritanopia factor $F_t$ .
Returns:	Yellowness / blueness response $M_{yb}$ .
Return type:	numeric

Examples

>>> C = (-5.3658655819965873e-05,
...      -0.00057169938364687312,
...      0.00062535803946683899)
>>> e_s = 1.1108365048626296
>>> N_c = 1.0
>>> N_cb = 0.72499999999999998
>>> F_t =0.99968593951195
>>> yellowness_blueness_response(C, e_s, N_c, N_cb, F_t)    
-0.0082372...

colour.appearance.hunt.redness_greenness_response(C, e_s, N_c, N_cb)[source]¶

Returns the redness / greenness response $M_{yb}$ .

Parameters:	C (array_like) – Colour difference signals $C$ . e_s (numeric) – Eccentricity factor $e_s$ . N_c (numeric) – Chromatic surround induction factor $N_c$ . N_b (numeric) – Brightness surround induction factor $N_b$ .
Returns:	Redness / greenness response $M_{rg}$ .
Return type:	numeric

Examples

>>> C = (-5.3658655819965873e-05,
...      -0.00057169938364687312,
...      0.00062535803946683899)
>>> e_s = 1.1108365048626296
>>> N_c = 1.0
>>> N_cb = 0.72499999999999998
>>> redness_greenness_response(C, e_s, N_c, N_cb)  
-0.0001044...

colour.appearance.hunt.overall_chromatic_response(M_yb, M_rg)[source]¶

Returns the overall chromatic response $M$ .

Parameters:	M_yb (numeric) – Yellowness / blueness response $M_{yb}$ . M_rg (numeric) – Redness / greenness response $M_{rg}$ .
Returns:	Overall chromatic response $M$ .
Return type:	numeric

Examples

>>> M_yb = -0.008237223618824608
>>> M_rg = -0.00010444758327626432
>>> overall_chromatic_response(M_yb, M_rg)  
0.0082378...

colour.appearance.hunt.saturation_correlate(M, rgb_a)[source]¶

Returns the saturation correlate $s$ .

Parameters:	M (numeric) – Overall chromatic response $M$ . rgb_a (array_like, (3,)) – Adapted Hunt-Pointer-Estevez $\rho\gamma\beta$ colourspace matrix.
Returns:	Saturation correlate $s$ .
Return type:	numeric

Examples

>>> M = 0.008237885787274198
>>> rgb_a = np.array([6.89594549, 6.89599915, 6.89657085])
>>> saturation_correlate(M, rgb_a)  
0.0199093...

colour.appearance.hunt.achromatic_signal(L_AS, S, S_W, N_bb, A_a)[source]¶

Returns the achromatic signal $A$ .

Parameters:	L_AS (numeric) – Scotopic luminance $L_{AS}$ of the illuminant. S (numeric) – Scotopic response $S$ to the stimulus. S_w (numeric) – Scotopic response $S_w$ for the reference white. N_bb (numeric) – Brightness background induction factor $N_{bb}$ . A_a (numeric) – Achromatic post adaptation signal of the stimulus $A_a$ .
Returns:	Achromatic signal $A$ .
Return type:	numeric

Examples

>>> L_AS = 769.9376286541402
>>> S = 20.0
>>> S_W = 100.0
>>> N_bb = 0.72499999999999998
>>> A_a = 18.982718664838487
>>> achromatic_signal(L_AS, S, S_W, N_bb, A_a)  
15.5068546...

colour.appearance.hunt.brightness_correlate(A, A_w, M, N_b)[source]¶

Returns the brightness correlate $Q$ .

Parameters:	A (numeric) – Achromatic signal $A$ . A_a (numeric) – Achromatic post adaptation signal of the reference white $A_w$ . M (numeric) – Overall chromatic response $M$ . N_b (numeric) – Brightness surround induction factor $N_b$ .
Returns:	Brightness correlate $Q$ .
Return type:	numeric

Examples

>>> A = 15.506854623621885
>>> A_w = 35.718916676317086
>>> M = 0.0082378857872741976
>>> N_b = 75.0
>>> brightness_correlate(A, A_w, M, N_b)  
22.2097654...

colour.appearance.hunt.lightness_correlate(Y_b, Y_w, Q, Q_w)[source]¶

Returns the Lightness correlate $J$ .

Parameters:	Y_b (numeric) – Tristimulus values $Y_b$ the background. Y_w (numeric) – Tristimulus values $Y_b$ the reference white. Q (numeric) – Brightness correlate $Q$ of the stimulus. Q_w (numeric) – Brightness correlate $Q$ of the reference white.
Returns:	Lightness correlate $J$ .
Return type:	numeric

Examples

>>> Y_b = 100.0
>>> Y_w = 100.0
>>> Q = 22.209765491265024
>>> Q_w = 40.518065821226081
>>> lightness_correlate(Y_b, Y_w, Q, Q_w)  
30.0462678...

colour.appearance.hunt.chroma_correlate(s, Y_b, Y_w, Q, Q_w)[source]¶

Returns the chroma correlate $C_94$ .

Parameters:	s (numeric) – Saturation correlate $s$ . Y_b (numeric) – Tristimulus values $Y_b$ the background. Y_w (numeric) – Tristimulus values $Y_b$ the reference white. Q (numeric) – Brightness correlate $Q$ of the stimulus. Q_w (numeric) – Brightness correlate $Q$ of the reference white.
Returns:	Chroma correlate $C_94$ .
Return type:	numeric

Examples

>>> s = 0.0199093206929
>>> Y_b = 100.0
>>> Y_w = 100.0
>>> Q = 22.209765491265024
>>> Q_w = 40.518065821226081
>>> chroma_correlate(s, Y_b, Y_w, Q, Q_w)  
0.1210508...

colour.appearance.hunt.colourfulness_correlate(F_L, C_94)[source]¶

Returns the colourfulness correlate $M_94$ .

Parameters:	F_L (numeric) – Luminance adaptation factor $F_L$ . numeric – Chroma correlate $C_94$ .
Returns:	Colourfulness correlate $M_94$ .
Return type:	numeric

Examples

>>> F_L = 1.16754446414718
>>> C_94 = 0.12105083993617581
>>> colourfulness_correlate(F_L, C_94)  
0.1238964...