def viewing_condition_dependent_parameters(Y_b, Y_w, L_A):
"""
Returns the viewing condition dependent parameters.
Parameters
----------
Y_b : numeric or array_like
Adapting field *Y* tristimulus value :math:`Y_b`.
Y_w : numeric or array_like
Whitepoint *Y* tristimulus value :math:`Y_w`.
L_A : numeric or array_like
Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`.
Returns
-------
ndarray
Viewing condition dependent parameters.
Examples
--------
>>> viewing_condition_dependent_parameters( # doctest: +ELLIPSIS
... 20.0, 100.0, 318.31)
array([ 0.2..., 1.1675444..., 1.000304..., 1.000304..., 1.9272136...])
"""
Y_b = np.asarray(Y_b)
Y_w = np.asarray(Y_w)
n = Y_b / Y_w
F_L = luminance_level_adaptation_factor(L_A)
N_bb, N_cb = tsplit(chromatic_induction_factors(n))
z = base_exponential_non_linearity(n)
return tstack((n, F_L, N_bb, N_cb, z))
def viewing_condition_dependent_parameters(Y_b, Y_w, L_A):
"""
Returns the viewing condition dependent parameters.
Parameters
----------
Y_b : numeric or array_like
Adapting field *Y* tristimulus value :math:`Y_b`.
Y_w : numeric or array_like
Whitepoint *Y* tristimulus value :math:`Y_w`.
L_A : numeric or array_like
Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`.
Returns
-------
ndarray
Viewing condition dependent parameters.
Examples
--------
>>> viewing_condition_dependent_parameters( # doctest: +ELLIPSIS
... 20.0, 100.0, 318.31)
array([ 0.2..., 1.1675444..., 1.000304..., 1.000304..., 1.9272136...])
"""
Y_b = np.asarray(Y_b)
Y_w = np.asarray(Y_w)
n = Y_b / Y_w
F_L = luminance_level_adaptation_factor(L_A)
N_bb, N_cb = tsplit(chromatic_induction_factors(n))
z = base_exponential_non_linearity(n)
return tstack((n, F_L, N_bb, N_cb, z))
def viewing_condition_dependent_parameters(Y_b, Y_w, L_A):
"""
Returns the viewing condition dependent parameters.
Parameters
----------
Y_b : numeric
Adapting field *Y* tristimulus value :math:`Y_b`.
Y_w : numeric
Whitepoint *Y* tristimulus value :math:`Y_w`.
L_A : numeric
Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`.
Returns
-------
tuple
Viewing condition dependent parameters.
Examples
--------
>>> viewing_condition_dependent_parameters(20.0, 100.0, 318.31) # noqa # doctest: +ELLIPSIS
(0.2000000..., 1.1675444..., 1.0003040..., 1.0003040..., 1.9272135...)
"""
n = Y_b / Y_w
F_L = luminance_level_adaptation_factor(L_A)
N_bb, N_cb = chromatic_induction_factors(n)
z = base_exponential_non_linearity(n)
return n, F_L, N_bb, N_cb, z
def viewing_condition_dependent_parameters(Y_b, Y_w, L_A):
"""
Returns the viewing condition dependent parameters.
Parameters
----------
Y_b : numeric
Adapting field *Y* tristimulus value :math:`Y_b`.
Y_w : numeric
Whitepoint *Y* tristimulus value :math:`Y_w`.
L_A : numeric
Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`.
Returns
-------
tuple
Viewing condition dependent parameters.
Examples
--------
>>> viewing_condition_dependent_parameters(20.0, 100.0, 318.31) # noqa # doctest: +ELLIPSIS
(0.2000000..., 1.1675444..., 1.0003040..., 1.0003040..., 1.9272135...)
"""
n = Y_b / Y_w
F_L = luminance_level_adaptation_factor(L_A)
N_bb, N_cb = chromatic_induction_factors(n)
z = base_exponential_non_linearity(n)
return n, F_L, N_bb, N_cb, z
def viewing_condition_dependent_parameters(
Y_b: FloatingOrArrayLike,
Y_w: FloatingOrArrayLike,
L_A: FloatingOrArrayLike,
) -> Tuple[FloatingOrNDArray, FloatingOrNDArray, FloatingOrNDArray,
FloatingOrNDArray, FloatingOrNDArray, ]:
"""
Return the viewing condition dependent parameters.
Parameters
----------
Y_b
Adapting field *Y* tristimulus value :math:`Y_b`.
Y_w
Whitepoint *Y* tristimulus value :math:`Y_w`.
L_A
Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`.
Returns
-------
:class:`tuple`
Viewing condition dependent parameters.
Examples
--------
>>> viewing_condition_dependent_parameters(20.0, 100.0, 318.31)
... # doctest: +ELLIPSIS
(0.2000000..., 1.1675444..., 1.0003040..., 1.0003040..., 1.9272135...)
"""
Y_b = as_float_array(Y_b)
Y_w = as_float_array(Y_w)
n = Y_b / Y_w
F_L = luminance_level_adaptation_factor(L_A)
N_bb, N_cb = tsplit(chromatic_induction_factors(n))
z = base_exponential_non_linearity(n)
return n, F_L, N_bb, N_cb, z
