def test_least_square_mapping_MoorePenrose(self):
"""
Test :func:`colour.algebra.regression.\
least_square_mapping_MoorePenrose` definition.
"""
prng = np.random.RandomState(2)
y = prng.random_sample((24, 3))
x = y + (prng.random_sample((24, 3)) - 0.5) * 0.5
np.testing.assert_almost_equal(
least_square_mapping_MoorePenrose(y, x),
np.array([
[1.05263767, 0.13780789, -0.22763399],
[0.07395843, 1.02939945, -0.10601150],
[0.05725508, -0.20526336, 1.10151945],
]),
decimal=7,
)
y = prng.random_sample((4, 3, 2))
x = y + (prng.random_sample((4, 3, 2)) - 0.5) * 0.5
np.testing.assert_almost_equal(
least_square_mapping_MoorePenrose(y, x),
np.array([
[
[
[1.05968114, -0.0896093, -0.02923021],
[3.77254737, 0.06682885, -2.78161763],
],
[
[-0.77388532, 1.78761209, -0.44050114],
[-4.1282882, 0.55185528, 5.049136],
],
[
[0.36246422, -0.56421525, 1.4208154],
[2.07589501, 0.40261387, -1.47059455],
],
],
[
[
[0.237067, 0.4794514, 0.04004058],
[0.67778963, 0.15901967, 0.23854131],
],
[
[-0.4225357, 0.99316309, -0.14598921],
[-3.46789045, 1.09102153, 3.31051434],
],
[
[-0.91661817, 1.49060435, -0.45074387],
[-4.18896905, 0.25487186, 4.75951391],
],
],
]),
decimal=7,
)
def test_least_square_mapping_MoorePenrose(self):
"""
Tests :func:`colour.algebra.regression.\
least_square_mapping_MoorePenrose` definition.
"""
prng = np.random.RandomState(2)
y = prng.random_sample((24, 3))
x = y + (prng.random_sample((24, 3)) - 0.5) * 0.5
np.testing.assert_almost_equal(
least_square_mapping_MoorePenrose(y, x),
np.array([
[1.05263767, 0.13780789, -0.22763399],
[0.07395843, 1.02939945, -0.10601150],
[0.05725508, -0.20526336, 1.10151945],
]),
decimal=7)
y = prng.random_sample((4, 3, 2))
x = y + (prng.random_sample((4, 3, 2)) - 0.5) * 0.5
np.testing.assert_almost_equal(
least_square_mapping_MoorePenrose(y, x),
np.array([
[
[[1.05968114, -0.0896093, -0.02923021],
[3.77254737, 0.06682885, -2.78161763]],
[[-0.77388532, 1.78761209, -0.44050114],
[-4.1282882, 0.55185528, 5.049136]],
[[0.36246422, -0.56421525, 1.4208154],
[2.07589501, 0.40261387, -1.47059455]],
],
[
[[0.237067, 0.4794514, 0.04004058],
[0.67778963, 0.15901967, 0.23854131]],
[[-0.4225357, 0.99316309, -0.14598921],
[-3.46789045, 1.09102153, 3.31051434]],
[[-0.91661817, 1.49060435, -0.45074387],
[-4.18896905, 0.25487186, 4.75951391]],
],
]),
decimal=7)
def matrix_colour_correction_Finlayson2015(
M_T: ArrayLike,
M_R: ArrayLike,
degree: Literal[1, 2, 3, 4] = 1,
root_polynomial_expansion: Boolean = True,
) -> NDArray:
"""
Compute a colour correction matrix from given :math:`M_T` colour array to
:math:`M_R` colour array using *Finlayson et al. (2015)* method.
Parameters
----------
M_T
Test array :math:`M_T` to fit onto array :math:`M_R`.
M_R
Reference array the array :math:`M_T` will be colour fitted against.
degree
Expanded polynomial degree.
root_polynomial_expansion
Whether to use the root-polynomials set for the expansion.
Returns
-------
:class:`numpy.ndarray`
Colour correction matrix.
References
----------
:cite:`Finlayson2015`
Examples
--------
>>> prng = np.random.RandomState(2)
>>> M_T = prng.random_sample((24, 3))
>>> M_R = M_T + (prng.random_sample((24, 3)) - 0.5) * 0.5
>>> matrix_colour_correction_Finlayson2015(M_T, M_R) # doctest: +ELLIPSIS
array([[ 1.0526376..., 0.1378078..., -0.2276339...],
[ 0.0739584..., 1.0293994..., -0.1060115...],
[ 0.0572550..., -0.2052633..., 1.1015194...]])
"""
return least_square_mapping_MoorePenrose(
polynomial_expansion_Finlayson2015(M_T, degree,
root_polynomial_expansion),
M_R,
)
def colour_correction_matrix_Finlayson2015(M_T,
M_R,
degree=1,
root_polynomial_expansion=True):
"""
Computes a colour correction matrix from given :math:`M_T` colour array to
:math:`M_R` colour array using *Finlayson et al. (2015)* method.
Parameters
----------
M_T : array_like, (n, 3)
Test array :math:`M_T` to fit onto array :math:`M_R`.
M_R : array_like, (n, 3)
Reference array the array :math:`M_T` will be colour fitted against.
degree : int, optional
Expanded polynomial degree.
root_polynomial_expansion : bool
Whether to use the root-polynomials set for the expansion.
Returns
-------
ndarray, (n, 3)
Colour correction matrix.
References
----------
:cite:`Finlayson2015`
Examples
--------
>>> prng = np.random.RandomState(2)
>>> M_T = prng.random_sample((24, 3))
>>> M_R = M_T + (prng.random_sample((24, 3)) - 0.5) * 0.5
>>> colour_correction_matrix(M_T, M_R) # doctest: +ELLIPSIS
array([[ 1.0526376..., 0.1378078..., -0.2276339...],
[ 0.0739584..., 1.0293994..., -0.1060115...],
[ 0.0572550..., -0.2052633..., 1.1015194...]])
"""
return least_square_mapping_MoorePenrose(
polynomial_expansion_Finlayson2015(M_T, degree,
root_polynomial_expansion), M_R)
def colour_correction_matrix_Finlayson2015(M_T,
M_R,
degree=1,
root_polynomial_expansion=True):
"""
Computes a colour correction matrix from given :math:`M_T` colour array to
:math:`M_R` colour array using *Finlayson et al. (2015)* method.
Parameters
----------
M_T : array_like, (3, n)
Test array :math:`M_T` to fit onto array :math:`M_R`.
M_R : array_like, (3, n)
Reference array the array :math:`M_T` will be colour fitted against.
degree : int, optional
Expanded polynomial degree.
root_polynomial_expansion : bool
Whether to use the root-polynomials set for the expansion.
Returns
-------
ndarray, (3, n)
Colour correction matrix.
References
----------
:cite:`Finlayson2015`
Examples
--------
>>> prng = np.random.RandomState(2)
>>> M_T = prng.random_sample((24, 3))
>>> M_R = M_T + (prng.random_sample((24, 3)) - 0.5) * 0.5
>>> colour_correction_matrix(M_T, M_R) # doctest: +ELLIPSIS
array([[ 1.0526376..., 0.1378078..., -0.2276339...],
[ 0.0739584..., 1.0293994..., -0.1060115...],
[ 0.0572550..., -0.2052633..., 1.1015194...]])
"""
return least_square_mapping_MoorePenrose(
polynomial_expansion_Finlayson2015(M_T, degree,
root_polynomial_expansion), M_R)
def matrix_colour_correction_Cheung2004(
M_T: ArrayLike,
M_R: ArrayLike,
terms: Literal[3, 5, 7, 8, 10, 11, 14, 16, 17, 19, 20, 22] = 3,
) -> NDArray:
"""
Compute a colour correction matrix from given :math:`M_T` colour array to
:math:`M_R` colour array using *Cheung et al. (2004)* method.
Parameters
----------
M_T
Test array :math:`M_T` to fit onto array :math:`M_R`.
M_R
Reference array the array :math:`M_T` will be colour fitted against.
terms
Number of terms of the expanded polynomial.
Returns
-------
:class:`numpy.ndarray`
Colour correction matrix.
References
----------
:cite:`Cheung2004`, :cite:`Westland2004`
Examples
--------
>>> prng = np.random.RandomState(2)
>>> M_T = prng.random_sample((24, 3))
>>> M_R = M_T + (prng.random_sample((24, 3)) - 0.5) * 0.5
>>> matrix_colour_correction_Cheung2004(M_T, M_R) # doctest: +ELLIPSIS
array([[ 1.0526376..., 0.1378078..., -0.2276339...],
[ 0.0739584..., 1.0293994..., -0.1060115...],
[ 0.0572550..., -0.2052633..., 1.1015194...]])
"""
return least_square_mapping_MoorePenrose(
matrix_augmented_Cheung2004(M_T, terms), M_R)
def matrix_colour_correction_Vandermonde(M_T: ArrayLike,
M_R: ArrayLike,
degree: Integer = 1) -> NDArray:
"""
Compute a colour correction matrix from given :math:`M_T` colour array to
:math:`M_R` colour array using *Vandermonde* method.
Parameters
----------
M_T
Test array :math:`M_T` to fit onto array :math:`M_R`.
M_R
Reference array the array :math:`M_T` will be colour fitted against.
degree
Expanded polynomial degree.
Returns
-------
:class:`numpy.ndarray`
Colour correction matrix.
References
----------
:cite:`Wikipedia2003e`
Examples
--------
>>> prng = np.random.RandomState(2)
>>> M_T = prng.random_sample((24, 3))
>>> M_R = M_T + (prng.random_sample((24, 3)) - 0.5) * 0.5
>>> matrix_colour_correction_Vandermonde(M_T, M_R) # doctest: +ELLIPSIS
array([[ 1.0300256..., 0.1141770..., -0.2621816..., 0.0418022...],
[ 0.0670209..., 1.0221494..., -0.1166108..., 0.0128250...],
[ 0.0744612..., -0.1872819..., 1.1278078..., -0.0318085...]])
"""
return least_square_mapping_MoorePenrose(
polynomial_expansion_Vandermonde(M_T, degree), M_R)
def colour_correction_matrix_Cheung2004(M_T, M_R, terms=3):
"""
Computes a colour correction from given :math:`M_T` colour array to
:math:`M_R` colour array using *Cheung et al. (2004)* method.
Parameters
----------
M_T : array_like, (3, n)
Test array :math:`M_T` to fit onto array :math:`M_R`.
M_R : array_like, (3, n)
Reference array the array :math:`M_T` will be colour fitted against.
terms : int, optional
Number of terms of the expanded polynomial, must be one of
*[3, 5, 7, 8, 10, 11, 14, 16, 17, 19, 20, 22]*.
Returns
-------
ndarray, (3, n)
Colour correction matrix.
References
----------
:cite:`Cheung2004`, :cite:`Westland2004`
Examples
--------
>>> prng = np.random.RandomState(2)
>>> M_T = prng.random_sample((24, 3))
>>> M_R = M_T + (prng.random_sample((24, 3)) - 0.5) * 0.5
>>> colour_correction_matrix(M_T, M_R) # doctest: +ELLIPSIS
array([[ 1.0526376..., 0.1378078..., -0.2276339...],
[ 0.0739584..., 1.0293994..., -0.1060115...],
[ 0.0572550..., -0.2052633..., 1.1015194...]])
"""
return least_square_mapping_MoorePenrose(
augmented_matrix_Cheung2004(M_T, terms), M_R)
def colour_correction_matrix_Cheung2004(M_T, M_R, terms=3):
"""
Computes a colour correction from given :math:`M_T` colour array to
:math:`M_R` colour array using *Cheung et al. (2004)* method.
Parameters
----------
M_T : array_like, (3, n)
Test array :math:`M_T` to fit onto array :math:`M_R`.
M_R : array_like, (3, n)
Reference array the array :math:`M_T` will be colour fitted against.
terms : int, optional
Number of terms of the expanded polynomial, must be one of
*[3, 5, 7, 8, 10, 11, 14, 16, 17, 19, 20, 22]*.
Returns
-------
ndarray, (3, n)
Colour correction matrix.
References
----------
:cite:`Cheung2004`, :cite:`Westland2004`
Examples
--------
>>> prng = np.random.RandomState(2)
>>> M_T = prng.random_sample((24, 3))
>>> M_R = M_T + (prng.random_sample((24, 3)) - 0.5) * 0.5
>>> colour_correction_matrix(M_T, M_R) # doctest: +ELLIPSIS
array([[ 1.0526376..., 0.1378078..., -0.2276339...],
[ 0.0739584..., 1.0293994..., -0.1060115...],
[ 0.0572550..., -0.2052633..., 1.1015194...]])
"""
return least_square_mapping_MoorePenrose(
augmented_matrix_Cheung2004(M_T, terms), M_R)
def colour_correction_matrix_Vandermonde(M_T, M_R, degree=1):
"""
Computes a colour correction matrix from given :math:`M_T` colour array to
:math:`M_R` colour array using *Vandermonde* method.
Parameters
----------
M_T : array_like, (3, n)
Test array :math:`M_T` to fit onto array :math:`M_R`.
M_R : array_like, (3, n)
Reference array the array :math:`M_T` will be colour fitted against.
degree : int, optional
Expanded polynomial degree.
Returns
-------
ndarray, (3, n)
Colour correction matrix.
References
----------
:cite:`Wikipedia2003e`
Examples
--------
>>> prng = np.random.RandomState(2)
>>> M_T = prng.random_sample((24, 3))
>>> M_R = M_T + (prng.random_sample((24, 3)) - 0.5) * 0.5
>>> colour_correction_matrix(M_T, M_R) # doctest: +ELLIPSIS
array([[ 1.0526376..., 0.1378078..., -0.2276339...],
[ 0.0739584..., 1.0293994..., -0.1060115...],
[ 0.0572550..., -0.2052633..., 1.1015194...]])
"""
return least_square_mapping_MoorePenrose(
polynomial_expansion_Vandermonde(M_T, degree), M_R)
