def output_specification_from_data(self, data):
"""
Returns the LLAB(l:c) colour appearance model output specification
from given data.
Parameters
----------
data : list
Fixture data.
Returns
-------
LLAB_Specification
LLAB(l:c) colour appearance model specification.
"""
XYZ = tstack((data['X'], data['Y'], data['Z']))
XYZ_0 = tstack((data['X_0'], data['Y_0'], data['Z_0']))
specification = XYZ_to_LLAB(XYZ,
XYZ_0,
data['Y_b'],
data['L'],
LLAB_InductionFactors(1,
data['F_S'],
data['F_L'],
data['F_C']))
return specification
def XYZ_to_LMS_ATD95(XYZ):
"""
Converts from *CIE XYZ* tristimulus values to *LMS* cone responses.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
Returns
-------
ndarray
*LMS* cone responses.
Examples
--------
>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_to_LMS_ATD95(XYZ) # doctest: +ELLIPSIS
array([ 6.2283272..., 7.4780666..., 3.8859772...])
"""
X, Y, Z = tsplit(XYZ)
L = ((0.66 * (0.2435 * X + 0.8524 * Y - 0.0516 * Z))**0.7) + 0.024
M = ((-0.3954 * X + 1.1642 * Y + 0.0837 * Z)**0.7) + 0.036
S = ((0.43 * (0.04 * Y + 0.6225 * Z))**0.7) + 0.31
LMS = tstack((L, M, S))
return LMS
def XYZ_to_LMS_ATD95(XYZ):
"""
Converts from *CIE XYZ* tristimulus values to *LMS* cone responses.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
Returns
-------
ndarray
*LMS* cone responses.
Examples
--------
>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_to_LMS_ATD95(XYZ) # doctest: +ELLIPSIS
array([ 6.2283272..., 7.4780666..., 3.8859772...])
"""
X, Y, Z = tsplit(XYZ)
L = ((0.66 * (0.2435 * X + 0.8524 * Y - 0.0516 * Z)) ** 0.7) + 0.024
M = ((-0.3954 * X + 1.1642 * Y + 0.0837 * Z) ** 0.7) + 0.036
S = ((0.43 * (0.04 * Y + 0.6225 * Z)) ** 0.7) + 0.31
LMS = tstack((L, M, S))
return LMS
def output_specification_from_data(self, data):
"""
Returns the *LLAB(l:c)* colour appearance model output specification
from given data.
Parameters
----------
data : list
Fixture data.
Returns
-------
LLAB_Specification
*LLAB(l:c)* colour appearance model specification.
"""
XYZ = tstack([data['X'], data['Y'], data['Z']])
XYZ_0 = tstack([data['X_0'], data['Y_0'], data['Z_0']])
specification = XYZ_to_LLAB(
XYZ, XYZ_0, data['Y_b'], data['L'],
LLAB_InductionFactors(1, data['F_S'], data['F_L'], data['F_C']))
return specification
def opponent_colour_dimensions(LMS_g):
"""
Returns opponent colour dimensions from given post adaptation cone signals.
Parameters
----------
LMS_g : array_like
Post adaptation cone signals.
Returns
-------
ndarray
Opponent colour dimensions.
Examples
--------
>>> LMS_g = np.array([6.95457922, 7.08945043, 6.44069316])
>>> opponent_colour_dimensions(LMS_g) # doctest: +ELLIPSIS
array([ 0.1787931..., 0.0286942..., 0.0107584..., 0.0192182..., 0.0205377...,
0.0107584...])
"""
L_g, M_g, S_g = tsplit(LMS_g)
A_1i = 3.57 * L_g + 2.64 * M_g
T_1i = 7.18 * L_g - 6.21 * M_g
D_1i = -0.7 * L_g + 0.085 * M_g + S_g
A_2i = 0.09 * A_1i
T_2i = 0.43 * T_1i + 0.76 * D_1i
D_2i = D_1i
A_1 = final_response(A_1i)
T_1 = final_response(T_1i)
D_1 = final_response(D_1i)
A_2 = final_response(A_2i)
T_2 = final_response(T_2i)
D_2 = final_response(D_2i)
return tstack((A_1, T_1, D_1, A_2, T_2, D_2))
def opponent_colour_dimensions(LMS_g):
"""
Returns opponent colour dimensions from given post adaptation cone signals.
Parameters
----------
LMS_g : array_like
Post adaptation cone signals.
Returns
-------
ndarray
Opponent colour dimensions.
Examples
--------
>>> LMS_g = np.array([6.95457922, 7.08945043, 6.44069316])
>>> opponent_colour_dimensions(LMS_g) # doctest: +ELLIPSIS
array([ 0.1787931..., 0.0286942..., 0.0107584..., 0.0192182..., ...])
"""
L_g, M_g, S_g = tsplit(LMS_g)
A_1i = 3.57 * L_g + 2.64 * M_g
T_1i = 7.18 * L_g - 6.21 * M_g
D_1i = -0.7 * L_g + 0.085 * M_g + S_g
A_2i = 0.09 * A_1i
T_2i = 0.43 * T_1i + 0.76 * D_1i
D_2i = D_1i
A_1 = final_response(A_1i)
T_1 = final_response(T_1i)
D_1 = final_response(D_1i)
A_2 = final_response(A_2i)
T_2 = final_response(T_2i)
D_2 = final_response(D_2i)
return tstack((A_1, T_1, D_1, A_2, T_2, D_2))
