def test_extend_line_segment(self):
"""
Tests :func:`colour.algebra.geometry.extend_line_segment` definition.
"""
np.testing.assert_almost_equal(
extend_line_segment(
np.array([0.95694934, 0.13720932]),
np.array([0.28382835, 0.60608318])),
np.array([-0.5367248, 1.17765341]),
decimal=7)
np.testing.assert_almost_equal(
extend_line_segment(
np.array([0.95694934, 0.13720932]),
np.array([0.28382835, 0.60608318]), 5),
np.array([-3.81893739, 3.46393435]),
decimal=7)
np.testing.assert_almost_equal(
extend_line_segment(
np.array([0.95694934, 0.13720932]),
np.array([0.28382835, 0.60608318]), -1),
np.array([1.1043815, 0.03451295]),
decimal=7)
def test_extend_line_segment(self):
"""
Tests :func:`colour.algebra.geometry.extend_line_segment` definition.
"""
np.testing.assert_almost_equal(
extend_line_segment(
np.array([0.95694934, 0.13720932]),
np.array([0.28382835, 0.60608318])),
np.array([-0.5367248, 1.17765341]),
decimal=7)
np.testing.assert_almost_equal(
extend_line_segment(
np.array([0.95694934, 0.13720932]),
np.array([0.28382835, 0.60608318]),
5),
np.array([-3.81893739, 3.46393435]),
decimal=7)
np.testing.assert_almost_equal(
extend_line_segment(
np.array([0.95694934, 0.13720932]),
np.array([0.28382835, 0.60608318]),
-1),
np.array([1.1043815, 0.03451295]),
decimal=7)
def closest_spectral_locus_wavelength(xy, xy_n, xy_s, inverse=False):
"""
Returns the coordinates and closest spectral locus wavelength index to the
point where the line defined by the given achromatic stimulus :math:`xy_n`
to colour stimulus :math:`xy_n` *CIE xy* chromaticity coordinates
intersects the spectral locus.
Parameters
----------
xy : array_like
Colour stimulus *CIE xy* chromaticity coordinates.
xy_n : array_like
Achromatic stimulus *CIE xy* chromaticity coordinates.
xy_s : array_like
Spectral locus *CIE xy* chromaticity coordinates.
inverse : bool, optional
The intersection will be computed using the colour stimulus :math:`xy`
to achromatic stimulus :math:`xy_n` inverse direction.
Returns
-------
tuple
Closest wavelength index, intersection point *CIE xy* chromaticity
coordinates.
Raises
------
ValueError
If no closest spectral locus wavelength index and coordinates found.
Examples
--------
>>> xy = np.array([0.54369557, 0.32107944])
>>> xy_n = np.array([0.31270000, 0.32900000])
>>> xy_s = XYZ_to_xy(CMFS['CIE 1931 2 Degree Standard Observer'].values)
>>> ix, intersect = closest_spectral_locus_wavelength(xy, xy_n, xy_s)
>>> print(ix) #
256
>>> print(intersect) # doctest: +ELLIPSIS
[ 0.6835474... 0.3162840...]
"""
xy = as_float_array(xy)
xy_n = np.resize(xy_n, xy.shape)
xy_s = as_float_array(xy_s)
xy_e = (extend_line_segment(xy, xy_n) if inverse else extend_line_segment(
xy_n, xy))
# Closing horse-shoe shape to handle line of purples intersections.
xy_s = np.vstack([xy_s, xy_s[0, :]])
xy_wl = intersect_line_segments(
np.concatenate((xy_n, xy_e), -1),
np.hstack([xy_s, np.roll(xy_s, 1, axis=0)])).xy
xy_wl = xy_wl[~np.isnan(xy_wl).any(axis=-1)]
if not len(xy_wl):
raise ValueError(
'No closest spectral locus wavelength index and coordinates found '
'for "{0}" colour stimulus and "{1}" achromatic stimulus "xy" '
'chromaticity coordinates!'.format(xy, xy_n))
i_wl = np.argmin(scipy.spatial.distance.cdist(xy_wl, xy_s), axis=-1)
i_wl = np.reshape(i_wl, xy.shape[0:-1])
xy_wl = np.reshape(xy_wl, xy.shape)
return i_wl, xy_wl
def dominant_wavelength(xy,
xy_n,
cmfs=CMFS['CIE 1931 2 Degree Standard Observer'],
inverse=False):
"""
Returns the *dominant wavelength* :math:`\\lambda_d` for given colour
stimulus :math:`xy` and the related :math:`xy_wl` first and :math:`xy_{cw}`
second intersection coordinates with the spectral locus.
In the eventuality where the :math:`xy_wl` first intersection coordinates
are on the line of purples, the *complementary wavelength* will be
computed in lieu.
The *complementary wavelength* is indicated by a negative sign
and the :math:`xy_{cw}` second intersection coordinates which are set by
default to the same value than :math:`xy_wl` first intersection coordinates
will be set to the *complementary dominant wavelength* intersection
coordinates with the spectral locus.
Parameters
----------
xy : array_like
Colour stimulus *CIE xy* chromaticity coordinates.
xy_n : array_like
Achromatic stimulus *CIE xy* chromaticity coordinates.
cmfs : XYZ_ColourMatchingFunctions, optional
Standard observer colour matching functions.
inverse : bool, optional
Inverse the computation direction to retrieve the
*complementary wavelength*.
Returns
-------
tuple
*Dominant wavelength*, first intersection point *CIE xy* chromaticity
coordinates, second intersection point *CIE xy* chromaticity
coordinates.
References
----------
:cite:`CIETC1-482004o`, :cite:`Erdogana`
Examples
--------
*Dominant wavelength* computation:
>>> from pprint import pprint
>>> xy = np.array([0.54369557, 0.32107944])
>>> xy_n = np.array([0.31270000, 0.32900000])
>>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
>>> pprint(dominant_wavelength(xy, xy_n, cmfs)) # doctest: +ELLIPSIS
(array(616...),
array([ 0.6835474..., 0.3162840...]),
array([ 0.6835474..., 0.3162840...]))
*Complementary dominant wavelength* is returned if the first intersection
is located on the line of purples:
>>> xy = np.array([0.37605506, 0.24452225])
>>> pprint(dominant_wavelength(xy, xy_n, cmfs)) # doctest: +ELLIPSIS
(array(-509.0),
array([ 0.4572314..., 0.1362814...]),
array([ 0.0104096..., 0.7320745...]))
"""
xy = as_float_array(xy)
xy_n = np.resize(xy_n, xy.shape)
xy_s = XYZ_to_xy(cmfs.values)
i_wl, xy_wl = closest_spectral_locus_wavelength(xy, xy_n, xy_s, inverse)
xy_cwl = xy_wl
wl = cmfs.wavelengths[i_wl]
xy_e = (extend_line_segment(xy, xy_n) if inverse else extend_line_segment(
xy_n, xy))
intersect = intersect_line_segments(np.concatenate((xy_n, xy_e), -1),
np.hstack([xy_s[0],
xy_s[-1]])).intersect
intersect = np.reshape(intersect, wl.shape)
i_wl_r, xy_cwl_r = closest_spectral_locus_wavelength(
xy, xy_n, xy_s, not inverse)
wl_r = -cmfs.wavelengths[i_wl_r]
wl = np.where(intersect, wl_r, wl)
xy_cwl = np.where(intersect[..., np.newaxis], xy_cwl_r, xy_cwl)
return wl, np.squeeze(xy_wl), np.squeeze(xy_cwl)
def closest_spectral_locus_wavelength(xy, xy_n, xy_s, reverse=False):
"""
Returns the coordinates and closest spectral locus wavelength index to the
point where the line defined by the given achromatic stimulus :math:`xy_n`
to colour stimulus :math:`xy_n` *xy* chromaticity coordinates intersects
the spectral locus.
Parameters
----------
xy : array_like
Colour stimulus *xy* chromaticity coordinates.
xy_n : array_like
Achromatic stimulus *xy* chromaticity coordinates.
xy_s : array_like
Spectral locus *xy* chromaticity coordinates.
reverse : bool, optional
The intersection will be computed using the colour stimulus :math:`xy`
to achromatic stimulus :math:`xy_n` reverse direction.
Returns
-------
tuple
Closest wavelength index, intersection point *xy* chromaticity
coordinates.
Raises
------
ValueError
If no closest spectral locus wavelength index and coordinates found.
Examples
--------
>>> xy = np.array([0.54369557, 0.32107944])
>>> xy_n = np.array([0.31270000, 0.32900000])
>>> xy_s = XYZ_to_xy(CMFS['CIE 1931 2 Degree Standard Observer'].values)
>>> ix, intersect = closest_spectral_locus_wavelength(xy, xy_n, xy_s)
>>> print(ix) #
256
>>> print(intersect) # doctest: +ELLIPSIS
[ 0.6835474... 0.3162840...]
"""
xy = as_float_array(xy)
xy_n = np.resize(xy_n, xy.shape)
xy_s = as_float_array(xy_s)
xy_e = (extend_line_segment(xy, xy_n)
if reverse else extend_line_segment(xy_n, xy))
# Closing horse-shoe shape to handle line of purples intersections.
xy_s = np.vstack([xy_s, xy_s[0, :]])
xy_wl = intersect_line_segments(
np.concatenate((xy_n, xy_e), -1),
np.hstack([xy_s, np.roll(xy_s, 1, axis=0)])).xy
xy_wl = xy_wl[~np.isnan(xy_wl).any(axis=-1)]
if not len(xy_wl):
raise ValueError(
'No closest spectral locus wavelength index and coordinates found '
'for "{0}" colour stimulus and "{1}" achromatic stimulus "xy" '
'chromaticity coordinates!'.format(xy, xy_n))
i_wl = np.argmin(scipy.spatial.distance.cdist(xy_wl, xy_s), axis=-1)
i_wl = np.reshape(i_wl, xy.shape[0:-1])
xy_wl = np.reshape(xy_wl, xy.shape)
return i_wl, xy_wl
def dominant_wavelength(xy,
xy_n,
cmfs=CMFS['CIE 1931 2 Degree Standard Observer'],
reverse=False):
"""
Returns the *dominant wavelength* :math:`\\lambda_d` for given colour
stimulus :math:`xy` and the related :math:`xy_wl` first and :math:`xy_{cw}`
second intersection coordinates with the spectral locus.
In the eventuality where the :math:`xy_wl` first intersection coordinates
are on the line of purples, the *complementary wavelength* will be
computed in lieu.
The *complementary wavelength* is indicated by a negative sign
and the :math:`xy_{cw}` second intersection coordinates which are set by
default to the same value than :math:`xy_wl` first intersection coordinates
will be set to the *complementary dominant wavelength* intersection
coordinates with the spectral locus.
Parameters
----------
xy : array_like
Colour stimulus *xy* chromaticity coordinates.
xy_n : array_like
Achromatic stimulus *xy* chromaticity coordinates.
cmfs : XYZ_ColourMatchingFunctions, optional
Standard observer colour matching functions.
reverse : bool, optional
Reverse the computation direction to retrieve the
*complementary wavelength*.
Returns
-------
tuple
*Dominant wavelength*, first intersection point *xy* chromaticity
coordinates, second intersection point *xy* chromaticity coordinates.
References
----------
:cite:`CIETC1-482004o`, :cite:`Erdogana`
Examples
--------
*Dominant wavelength* computation:
>>> from pprint import pprint
>>> xy = np.array([0.54369557, 0.32107944])
>>> xy_n = np.array([0.31270000, 0.32900000])
>>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
>>> pprint(dominant_wavelength(xy, xy_n, cmfs)) # doctest: +ELLIPSIS
(array(616...),
array([ 0.6835474..., 0.3162840...]),
array([ 0.6835474..., 0.3162840...]))
*Complementary dominant wavelength* is returned if the first intersection
is located on the line of purples:
>>> xy = np.array([0.37605506, 0.24452225])
>>> pprint(dominant_wavelength(xy, xy_n, cmfs)) # doctest: +ELLIPSIS
(array(-509.0),
array([ 0.4572314..., 0.1362814...]),
array([ 0.0104096..., 0.7320745...]))
"""
xy = as_float_array(xy)
xy_n = np.resize(xy_n, xy.shape)
xy_s = XYZ_to_xy(cmfs.values)
i_wl, xy_wl = closest_spectral_locus_wavelength(xy, xy_n, xy_s, reverse)
xy_cwl = xy_wl
wl = cmfs.wavelengths[i_wl]
xy_e = (extend_line_segment(xy, xy_n)
if reverse else extend_line_segment(xy_n, xy))
intersect = intersect_line_segments(
np.concatenate((xy_n, xy_e), -1), np.hstack([xy_s[0],
xy_s[-1]])).intersect
intersect = np.reshape(intersect, wl.shape)
i_wl_r, xy_cwl_r = closest_spectral_locus_wavelength(
xy, xy_n, xy_s, not reverse)
wl_r = -cmfs.wavelengths[i_wl_r]
wl = np.where(intersect, wl_r, wl)
xy_cwl = np.where(intersect[..., np.newaxis], xy_cwl_r, xy_cwl)
return wl, np.squeeze(xy_wl), np.squeeze(xy_cwl)
def dominant_wavelength(xy,
xy_n,
cmfs=CMFS['CIE 1931 2 Degree Standard Observer'],
reverse=False):
"""
Returns the *dominant wavelength* :math:`\lambda_d` for given colour
stimulus :math:`xy` and the related :math:`xy_wl` first and :math:`xy_{cw}`
second intersection coordinates with the spectral locus.
In the eventuality where the :math:`xy_wl` first intersection coordinates
are on the line of purples, the *complementary wavelength* will be
computed in lieu.
The *complementary wavelength* is indicated by a negative sign
and the :math:`xy_{cw}` second intersection coordinates which are set by
default to the same value than :math:`xy_wl` first intersection coordinates
will be set to the *complementary dominant wavelength* intersection
coordinates with the spectral locus.
Parameters
----------
xy : array_like
Colour stimulus *xy* chromaticity coordinates.
xy_n : array_like
Achromatic stimulus *xy* chromaticity coordinates.
cmfs : XYZ_ColourMatchingFunctions, optional
Standard observer colour matching functions.
reverse : bool, optional
Reverse the computation direction to retrieve the
*complementary wavelength*.
Returns
-------
tuple
*Dominant wavelength*, first intersection point *xy* chromaticity
coordinates, second intersection point *xy* chromaticity coordinates.
See Also
--------
complementary_wavelength
Examples
--------
*Dominant wavelength* computation:
>>> from pprint import pprint
>>> xy = np.array([0.26415, 0.37770])
>>> xy_n = np.array([0.31270, 0.32900])
>>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
>>> pprint(dominant_wavelength(xy, xy_n, cmfs)) # doctest: +ELLIPSIS
(array(504...),
array([ 0.0036969..., 0.6389577...]),
array([ 0.0036969..., 0.6389577...]))
*Complementary dominant wavelength* is returned if the first intersection
is located on the line of purples:
>>> xy = np.array([0.35000, 0.25000])
>>> pprint(dominant_wavelength(xy, xy_n, cmfs)) # doctest: +ELLIPSIS
(array(-520...),
array([ 0.4133314..., 0.1158663...]),
array([ 0.0743553..., 0.8338050...]))
"""
xy = np.asarray(xy)
xy_n = np.resize(xy_n, xy.shape)
xy_s = XYZ_to_xy(cmfs.values)
i_wl, xy_wl = closest_spectral_locus_wavelength(xy, xy_n, xy_s, reverse)
xy_cwl = xy_wl
wl = cmfs.wavelengths[i_wl]
xy_e = (extend_line_segment(xy, xy_n)
if reverse else
extend_line_segment(xy_n, xy))
intersect = intersect_line_segments(
np.concatenate((xy_n, xy_e), -1),
np.hstack((xy_s[0], xy_s[-1]))).intersect
intersect = np.reshape(intersect, wl.shape)
i_wl_r, xy_cwl_r = closest_spectral_locus_wavelength(
xy, xy_n, xy_s, not reverse)
wl_r = -cmfs.wavelengths[i_wl_r]
wl = np.where(intersect, wl_r, wl)
xy_cwl = np.where(intersect[..., np.newaxis], xy_cwl_r, xy_cwl)
return wl, np.squeeze(xy_wl), np.squeeze(xy_cwl)
def closest_spectral_locus_wavelength(
xy: ArrayLike,
xy_n: ArrayLike,
xy_s: ArrayLike,
inverse: Boolean = False) -> Tuple[NDArray, NDArray]:
"""
Return the coordinates and closest spectral locus wavelength index to the
point where the line defined by the given achromatic stimulus :math:`xy_n`
to colour stimulus :math:`xy_n` *CIE xy* chromaticity coordinates
intersects the spectral locus.
Parameters
----------
xy
Colour stimulus *CIE xy* chromaticity coordinates.
xy_n
Achromatic stimulus *CIE xy* chromaticity coordinates.
xy_s
Spectral locus *CIE xy* chromaticity coordinates.
inverse
The intersection will be computed using the colour stimulus :math:`xy`
to achromatic stimulus :math:`xy_n` inverse direction.
Returns
-------
:class:`tuple`
Closest wavelength index, intersection point *CIE xy* chromaticity
coordinates.
Raises
------
ValueError
If no closest spectral locus wavelength index and coordinates found.
Examples
--------
>>> from colour.colorimetry import MSDS_CMFS
>>> cmfs = MSDS_CMFS['CIE 1931 2 Degree Standard Observer']
>>> xy = np.array([0.54369557, 0.32107944])
>>> xy_n = np.array([0.31270000, 0.32900000])
>>> xy_s = XYZ_to_xy(cmfs.values)
>>> ix, intersect = closest_spectral_locus_wavelength(xy, xy_n, xy_s)
>>> print(ix) #
256
>>> print(intersect) # doctest: +ELLIPSIS
[ 0.6835474... 0.3162840...]
"""
xy = as_float_array(xy)
xy_n = np.resize(xy_n, xy.shape)
xy_s = as_float_array(xy_s)
xy_e = (extend_line_segment(xy, xy_n) if inverse else extend_line_segment(
xy_n, xy))
# Closing horse-shoe shape to handle line of purples intersections.
xy_s = np.vstack([xy_s, xy_s[0, :]])
xy_wl = intersect_line_segments(
np.concatenate((xy_n, xy_e), -1),
np.hstack([xy_s, np.roll(xy_s, 1, axis=0)]),
).xy
# Extracting the first intersection per-wavelength.
xy_wl = np.sort(xy_wl, 1)[:, 0, :]
if not len(xy_wl):
raise ValueError(
f"No closest spectral locus wavelength index and coordinates "
f'found for "{xy}" colour stimulus and "{xy_n}" achromatic '
f'stimulus "xy" chromaticity coordinates!')
i_wl = np.argmin(scipy.spatial.distance.cdist(xy_wl, xy_s), axis=-1)
i_wl = np.reshape(i_wl, xy.shape[0:-1])
xy_wl = np.reshape(xy_wl, xy.shape)
return i_wl, xy_wl
