def test_nan_polar_to_cartesian(self):
"""
Tests :func:`colour.algebra.coordinates.transformations.\
polar_to_cartesian` definition nan support.
"""
cases = [-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan]
cases = set(permutations(cases * 3, r=2))
for case in cases:
a_i = np.array(case)
polar_to_cartesian(a_i)
def final_opponent_signals(C_L, h_L):
"""
Returns the final opponent signals :math:`A_L` and :math:`B_L`.
Parameters
----------
C_L : numeric or array_like
Correlate of *colourfulness* :math:`C_L`.
h_L : numeric or array_like
Correlate of *hue* :math:`h_L` in degrees.
Returns
-------
ndarray
Final opponent signals :math:`A_L` and :math:`B_L`.
Examples
--------
>>> C_L = 0.0183832899143
>>> h_L = 229.46357270858391
>>> final_opponent_signals(C_L, h_L) # doctest: +ELLIPSIS
array([-0.0119478..., -0.0139711...])
"""
AB_L = polar_to_cartesian(tstack([C_L, np.radians(h_L)]))
return AB_L
def LCHuv_to_Luv(LCHuv):
"""
Converts from *CIE LCHuv* colourspace to *CIE Luv* colourspace.
Parameters
----------
LCHuv : array_like
*CIE LCHuv* colourspace array.
Returns
-------
ndarray
*CIE Luv* colourspace array.
Notes
-----
- Input / output :math:`L^*` is in domain / range [0, 100].
Examples
--------
>>> LCHuv = np.array([37.98562910, 28.83419279, 182.69946404])
>>> LCHuv_to_Luv(LCHuv) # doctest: +ELLIPSIS
array([ 37.9856291..., -28.8021959..., -1.3580070...])
"""
L, C, H = tsplit(LCHuv)
u, v = tsplit(polar_to_cartesian(tstack((C, np.radians(H)))))
Luv = tstack((L, u, v))
return Luv
def test_n_dimensional_polar_to_cartesian(self):
"""
Test :func:`colour.algebra.coordinates.transformations.\
polar_to_cartesian` definition n-dimensional arrays support.
"""
a_i = np.array([3.16227766, 0.32175055])
a_o = polar_to_cartesian(a_i)
a_i = np.tile(a_i, (6, 1))
a_o = np.tile(a_o, (6, 1))
np.testing.assert_almost_equal(polar_to_cartesian(a_i), a_o, decimal=7)
a_i = np.reshape(a_i, (2, 3, 2))
a_o = np.reshape(a_o, (2, 3, 2))
np.testing.assert_almost_equal(polar_to_cartesian(a_i), a_o, decimal=7)
def final_opponent_signals(C_L: FloatingOrArrayLike,
h_L: FloatingOrArrayLike) -> NDArray:
"""
Return the final opponent signals :math:`A_L` and :math:`B_L`.
Parameters
----------
C_L
Correlate of *colourfulness* :math:`C_L`.
h_L
Correlate of *hue* :math:`h_L` in degrees.
Returns
-------
:class:`numpy.ndarray`
Final opponent signals :math:`A_L` and :math:`B_L`.
Examples
--------
>>> C_L = 0.0183832899143
>>> h_L = 229.46357270858391
>>> final_opponent_signals(C_L, h_L) # doctest: +ELLIPSIS
array([-0.0119478..., -0.0139711...])
"""
AB_L = polar_to_cartesian(tstack([as_float_array(C_L), np.radians(h_L)]))
return AB_L
def LCHab_to_Lab(LCHab):
"""
Converts from *CIE LCHab* colourspace to *CIE Lab* colourspace.
Parameters
----------
LCHab : array_like
*CIE LCHab* colourspace array.
Returns
-------
ndarray
*CIE Lab* colourspace array.
Notes
-----
- *Lightness* :math:`L^*` is in domain [0, 100].
Examples
--------
>>> LCHab = np.array([37.98562910, 24.03845422, 190.58923377])
>>> LCHab_to_Lab(LCHab) # doctest: +ELLIPSIS
array([ 37.9856291..., -23.6290768..., -4.4174661...])
"""
L, C, H = tsplit(LCHab)
a, b = tsplit(polar_to_cartesian(tstack((C, np.radians(H)))))
Lab = tstack((L, a, b))
return Lab
def final_opponent_signals(C_L, h_L):
"""
Returns the final opponent signals :math:`A_L` and :math:`B_L`.
Parameters
----------
C_L : numeric or array_like
Correlate of *colourfulness* :math:`C_L`.
h_L : numeric or array_like
Correlate of *hue* :math:`h_L` in degrees.
Returns
-------
ndarray
Final opponent signals :math:`A_L` and :math:`B_L`.
Examples
--------
>>> C_L = 0.0183832899143
>>> h_L = 229.46357270858391
>>> final_opponent_signals(C_L, h_L) # doctest: +ELLIPSIS
array([-0.0119478..., -0.0139711...])
"""
AB_L = polar_to_cartesian(tstack([C_L, np.radians(h_L)]))
return AB_L
def LCHuv_to_Luv(LCHuv):
"""
Converts from *CIE L\\*C\\*Huv* colourspace to *CIE L\\*u\\*v\\**
colourspace.
Parameters
----------
LCHuv : array_like
*CIE L\\*C\\*Huv* colourspace array.
Returns
-------
ndarray
*CIE L\\*u\\*v\\** colourspace array.
Notes
-----
+------------+-----------------------+-----------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+=================+
| ``LCHuv`` | ``L`` : [0, 100] | ``L`` : [0, 1] |
| | | |
| | ``C`` : [0, 100] | ``C`` : [0, 1] |
| | | |
| | ``uv`` : [0, 360] | ``uv`` : [0, 1] |
+------------+-----------------------+-----------------+
+------------+-----------------------+-----------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+=================+
| ``Luv`` | ``L`` : [0, 100] | ``L`` : [0, 1] |
| | | |
| | ``u`` : [-100, 100] | ``u`` : [-1, 1] |
| | | |
| | ``v`` : [-100, 100] | ``v`` : [-1, 1] |
+------------+-----------------------+-----------------+
References
----------
:cite:`CIETC1-482004m`
Examples
--------
>>> LCHuv = np.array([41.52787529, 98.44997950, 10.38816348])
>>> LCHuv_to_Luv(LCHuv) # doctest: +ELLIPSIS
array([ 41.5278752..., 96.8362605..., 17.7521014...])
"""
L, C, H = tsplit(LCHuv)
u, v = tsplit(
polar_to_cartesian(tstack([C, np.radians(to_domain_degrees(H))])))
Luv = tstack([L, u, v])
return Luv
def LCHuv_to_Luv(LCHuv):
"""
Converts from *CIE L\\*C\\*Huv* colourspace to *CIE L\\*u\\*v\\**
colourspace.
Parameters
----------
LCHuv : array_like
*CIE L\\*C\\*Huv* colourspace array.
Returns
-------
ndarray
*CIE L\\*u\\*v\\** colourspace array.
Notes
-----
+------------+-----------------------+-----------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+=================+
| ``LCHuv`` | ``L`` : [0, 100] | ``L`` : [0, 1] |
| | | |
| | ``C`` : [0, 100] | ``C`` : [0, 1] |
| | | |
| | ``uv`` : [0, 360] | ``uv`` : [0, 1] |
+------------+-----------------------+-----------------+
+------------+-----------------------+-----------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+=================+
| ``Luv`` | ``L`` : [0, 100] | ``L`` : [0, 1] |
| | | |
| | ``u`` : [-100, 100] | ``u`` : [-1, 1] |
| | | |
| | ``v`` : [-100, 100] | ``v`` : [-1, 1] |
+------------+-----------------------+-----------------+
References
----------
:cite:`CIETC1-482004m`
Examples
--------
>>> LCHuv = np.array([41.52787529, 98.44997950, 10.38816348])
>>> LCHuv_to_Luv(LCHuv) # doctest: +ELLIPSIS
array([ 41.5278752..., 96.8362605..., 17.7521014...])
"""
L, C, H = tsplit(LCHuv)
u, v = tsplit(
polar_to_cartesian(tstack([C, np.radians(to_domain_degrees(H))])))
Luv = tstack([L, u, v])
return Luv
def LCHab_to_Lab(LCHab):
"""
Converts from *CIE L\\*C\\*Hab* colourspace to *CIE L\\*a\\*b\\**
colourspace.
Parameters
----------
LCHab : array_like
*CIE L\\*C\\*Hab* colourspace array.
Returns
-------
ndarray
*CIE L\\*a\\*b\\** colourspace array.
Notes
-----
+-------------+-----------------------+-----------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+=============+=======================+=================+
| ``LCHab`` | ``L`` : [0, 100] | ``L`` : [0, 1] |
| | | |
| | ``C`` : [0, 100] | ``C`` : [0, 1] |
| | | |
| | ``ab`` : [0, 360] | ``ab`` : [0, 1] |
+-------------+-----------------------+-----------------+
+-------------+-----------------------+-----------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+=============+=======================+=================+
| ``Lab`` | ``L`` : [0, 100] | ``L`` : [0, 1] |
| | | |
| | ``a`` : [-100, 100] | ``a`` : [-1, 1] |
| | | |
| | ``b`` : [-100, 100] | ``b`` : [-1, 1] |
+-------------+-----------------------+-----------------+
References
----------
:cite:`CIETC1-482004m`
Examples
--------
>>> LCHab = np.array([41.52787529, 59.12425901, 27.08848784])
>>> LCHab_to_Lab(LCHab) # doctest: +ELLIPSIS
array([ 41.5278752..., 52.6385830..., 26.9231792...])
"""
L, C, H = tsplit(LCHab)
a, b = tsplit(
polar_to_cartesian(tstack([C, np.radians(to_domain_degrees(H))])))
Lab = tstack([L, a, b])
return Lab
def test_polar_to_cartesian(self):
"""
Tests :func:`colour.algebra.coordinates.transformations.\
polar_to_cartesian` definition.
"""
np.testing.assert_almost_equal(
polar_to_cartesian(np.array([0.32175055, 1.08574654])),
np.array([0.15001697, 0.28463718]),
decimal=7)
np.testing.assert_almost_equal(
polar_to_cartesian(np.array([1.68145355, 1.05578119])),
np.array([0.82819662, 1.46334425]),
decimal=7)
np.testing.assert_almost_equal(
polar_to_cartesian(np.array([-0.14626640, 1.23829030])),
np.array([-0.04774323, -0.13825500]),
decimal=7)
def JMh_CIECAM02_to_UCS_Luo2006(JMh, coefficients):
"""
Converts from *CIECAM02* :math:`JMh` correlates array to one of the
*Luo et al. (2006)* *CAM02-LCD*, *CAM02-SCD*, or *CAM02-UCS* colourspaces
:math:`J'a'b'` array.
The :math:`JMh` correlates array is constructed using the CIECAM02
correlate of *Lightness* :math:`J`, the *CIECAM02* correlate of
*colourfulness* :math:`M` and the *CIECAM02* *Hue* angle :math:`h` in
degrees.
Parameters
----------
JMh : array_like
*CIECAM02* correlates array :math:`JMh`.
coefficients : array_like
Coefficients of one of the *Luo et al. (2006)* *CAM02-LCD*,
*CAM02-SCD*, or *CAM02-UCS* colourspaces.
Returns
-------
ndarray
*Luo et al. (2006)* *CAM02-LCD*, *CAM02-SCD*, or *CAM02-UCS*
colourspaces :math:`J'a'b'` array.
Examples
--------
>>> from colour.appearance import (
... CIECAM02_VIEWING_CONDITIONS,
... XYZ_to_CIECAM02)
>>> 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']
>>> specification = XYZ_to_CIECAM02(
... XYZ, XYZ_w, L_A, Y_b, surround)
>>> JMh = (specification.J, specification.M, specification.h)
>>> JMh_CIECAM02_to_UCS_Luo2006( # doctest: +ELLIPSIS
... JMh, COEFFICIENTS_UCS_LUO2006['CAM02-LCD'])
array([ 54.9043313..., -0.0845039..., -0.0685483...])
"""
J, M, h = tsplit(JMh)
_K_L, c_1, c_2 = tsplit(coefficients)
J_p = ((1 + 100 * c_1) * J) / (1 + c_1 * J)
M_p = (1 / c_2) * np.log(1 + c_2 * M)
a_p, b_p = tsplit(polar_to_cartesian(tstack((M_p, np.radians(h)))))
return tstack((J_p, a_p, b_p))
def JCh_to_Jab(JCh: ArrayLike) -> NDArray:
"""
Convert from *JCh* colour representation to *Jab* colour representation.
This definition is used to perform conversion from *CIE L\\*C\\*Hab*
colourspace to *CIE L\\*a\\*b\\** colourspace and for other similar
conversions. It implements a generic transformation from the correlates of
*Lightness* :math:`J`, chroma :math:`C` and hue angle :math:`h` to
*Lightness* :math:`J`, :math:`a` and :math:`b` opponent colour dimensions.
Parameters
----------
JCh
*JCh* colour representation array.
Returns
-------
:class:`numpy.ndarray`
*Jab* colour representation array.
Notes
-----
+-------------+-----------------------+-----------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+=============+=======================+=================+
| ``JCh`` | ``J`` : [0, 100] | ``J`` : [0, 1] |
| | | |
| | ``C`` : [0, 100] | ``C`` : [0, 1] |
| | | |
| | ``h`` : [0, 360] | ``h`` : [0, 1] |
+-------------+-----------------------+-----------------+
+-------------+-----------------------+-----------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+=============+=======================+=================+
| ``Jab`` | ``J`` : [0, 100] | ``J`` : [0, 1] |
| | | |
| | ``a`` : [-100, 100] | ``a`` : [-1, 1] |
| | | |
| | ``b`` : [-100, 100] | ``b`` : [-1, 1] |
+-------------+-----------------------+-----------------+
References
----------
:cite:`CIETC1-482004m`
Examples
--------
>>> JCh = np.array([41.52787529, 59.12425901, 27.08848784])
>>> JCh_to_Jab(JCh) # doctest: +ELLIPSIS
array([ 41.5278752..., 52.6385830..., 26.9231792...])
"""
L, C, H = tsplit(JCh)
a, b = tsplit(
polar_to_cartesian(tstack([C, np.radians(to_domain_degrees(H))])))
Jab = tstack([L, a, b])
return Jab
def JMh_CIECAM02_to_UCS_Luo2006(JMh: ArrayLike,
coefficients: ArrayLike) -> NDArray:
"""
Convert from *CIECAM02* :math:`JMh` correlates array to one of the
*Luo et al. (2006)* *CAM02-LCD*, *CAM02-SCD*, or *CAM02-UCS* colourspaces
:math:`J'a'b'` array.
The :math:`JMh` correlates array is constructed using the CIECAM02
correlate of *Lightness* :math:`J`, the *CIECAM02* correlate of
*colourfulness* :math:`M` and the *CIECAM02* *Hue* angle :math:`h` in
degrees.
Parameters
----------
JMh
*CIECAM02* correlates array :math:`JMh`.
coefficients
Coefficients of one of the *Luo et al. (2006)* *CAM02-LCD*,
*CAM02-SCD*, or *CAM02-UCS* colourspaces.
Returns
-------
:class:`numpy.ndarray`
*Luo et al. (2006)* *CAM02-LCD*, *CAM02-SCD*, or *CAM02-UCS*
colourspaces :math:`J'a'b'` array.
Notes
-----
+------------+------------------------+------------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+========================+==================+
| ``JMh`` | ``J`` : [0, 100] | ``J`` : [0, 1] |
| | | |
| | ``M`` : [0, 100] | ``M`` : [0, 1] |
| | | |
| | ``h`` : [0, 360] | ``h`` : [0, 1] |
+------------+------------------------+------------------+
+------------+------------------------+------------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+========================+==================+
| ``Jpapbp`` | ``Jp`` : [0, 100] | ``Jp`` : [0, 1] |
| | | |
| | ``ap`` : [-100, 100] | ``ap`` : [-1, 1] |
| | | |
| | ``bp`` : [-100, 100] | ``bp`` : [-1, 1] |
+------------+------------------------+------------------+
Examples
--------
>>> from colour.appearance import (
... VIEWING_CONDITIONS_CIECAM02,
... XYZ_to_CIECAM02)
>>> 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 = VIEWING_CONDITIONS_CIECAM02['Average']
>>> specification = XYZ_to_CIECAM02(
... XYZ, XYZ_w, L_A, Y_b, surround)
>>> JMh = (specification.J, specification.M, specification.h)
>>> JMh_CIECAM02_to_UCS_Luo2006(JMh, COEFFICIENTS_UCS_LUO2006['CAM02-LCD'])
... # doctest: +ELLIPSIS
array([ 54.9043313..., -0.0845039..., -0.0685483...])
"""
J, M, h = tsplit(JMh)
J = to_domain_100(J)
M = to_domain_100(M)
h = to_domain_degrees(h)
_K_L, c_1, c_2 = tsplit(coefficients)
J_p = ((1 + 100 * c_1) * J) / (1 + c_1 * J)
M_p = (1 / c_2) * np.log1p(c_2 * M)
a_p, b_p = tsplit(polar_to_cartesian(tstack([M_p, np.radians(h)])))
Jpapbp = tstack([J_p, a_p, b_p])
return from_range_100(Jpapbp)
def JMh_CIECAM02_to_UCS_Luo2006(JMh, coefficients):
"""
Converts from *CIECAM02* :math:`JMh` correlates array to one of the
*Luo et al. (2006)* *CAM02-LCD*, *CAM02-SCD*, or *CAM02-UCS* colourspaces
:math:`J'a'b'` array.
The :math:`JMh` correlates array is constructed using the CIECAM02
correlate of *Lightness* :math:`J`, the *CIECAM02* correlate of
*colourfulness* :math:`M` and the *CIECAM02* *Hue* angle :math:`h` in
degrees.
Parameters
----------
JMh : array_like
*CIECAM02* correlates array :math:`JMh`.
coefficients : array_like
Coefficients of one of the *Luo et al. (2006)* *CAM02-LCD*,
*CAM02-SCD*, or *CAM02-UCS* colourspaces.
Returns
-------
ndarray
*Luo et al. (2006)* *CAM02-LCD*, *CAM02-SCD*, or *CAM02-UCS*
colourspaces :math:`J'a'b'` array.
Notes
-----
+------------+------------------------+--------------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+========================+====================+
| ``JMh`` | ``J`` : [0, 100] | ``J`` : [0, 1] |
| | | |
| | ``M`` : [0, 100] | ``M`` : [0, 1] |
| | | |
| | ``h`` : [0, 360] | ``h`` : [0, 1] |
+------------+------------------------+--------------------+
+------------+------------------------+--------------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+========================+====================+
| ``Jpapbp`` | ``Jp_1`` : [0, 100] | ``Jp_1`` : [0, 1] |
| | | |
| | ``ap_1`` : [-100, 100] | ``ap_1`` : [-1, 1] |
| | | |
| | ``bp_1`` : [-100, 100] | ``bp_1`` : [-1, 1] |
+------------+------------------------+--------------------+
Examples
--------
>>> from colour.appearance import (
... CIECAM02_VIEWING_CONDITIONS,
... XYZ_to_CIECAM02)
>>> 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']
>>> specification = XYZ_to_CIECAM02(
... XYZ, XYZ_w, L_A, Y_b, surround)
>>> JMh = (specification.J, specification.M, specification.h)
>>> JMh_CIECAM02_to_UCS_Luo2006(JMh, COEFFICIENTS_UCS_LUO2006['CAM02-LCD'])
... # doctest: +ELLIPSIS
array([ 54.9043313..., -0.0845039..., -0.0685483...])
"""
J, M, h = tsplit(JMh)
J = to_domain_100(J)
M = to_domain_100(M)
h = to_domain_degrees(h)
_K_L, c_1, c_2 = tsplit(coefficients)
J_p = ((1 + 100 * c_1) * J) / (1 + c_1 * J)
M_p = (1 / c_2) * np.log(1 + c_2 * M)
a_p, b_p = tsplit(polar_to_cartesian(tstack([M_p, np.radians(h)])))
Jpapbp = tstack([J_p, a_p, b_p])
return from_range_100(Jpapbp)
