def RGB_colourspace_volume_coverage_MonteCarlo(
colourspace, coverage_sampler, samples=10e6, random_generator=random_triplet_generator, random_state=None
):
"""
Returns given *RGB* colourspace percentage coverage of an arbitrary volume.
Parameters
----------
colourspace : RGB_Colourspace
*RGB* colourspace to compute the volume coverage percentage.
coverage_sampler : object
Python object responsible for checking the volume coverage.
samples : numeric, optional
Samples count.
random_generator : generator, optional
Random triplet generator providing the random samples.
random_state : RandomState, optional
Mersenne Twister pseudo-random number generator to use in the random
number generator.
Returns
-------
float
Percentage coverage of volume.
Notes
-----
- This definition requires *scipy* to be installed.
Examples
--------
>>> from colour import sRGB_COLOURSPACE as sRGB
>>> prng = np.random.RandomState(2)
>>> RGB_colourspace_volume_coverage_MonteCarlo( # doctest: +ELLIPSIS
... sRGB,
... is_within_pointer_gamut,
... 10e3,
... random_state=prng)
83...
"""
if is_scipy_installed(raise_exception=True):
random_state = random_state if random_state is not None else np.random.RandomState()
# TODO: Investigate for generator yielding directly a ndarray.
XYZ = np.asarray(list(random_generator(samples, random_state=random_state)))
XYZ_vs = XYZ[coverage_sampler(XYZ)]
RGB = XYZ_to_RGB(XYZ_vs, colourspace.whitepoint, colourspace.whitepoint, colourspace.XYZ_to_RGB_matrix)
RGB_c = RGB[np.logical_and(np.min(RGB, axis=-1) >= 0, np.max(RGB, axis=-1) <= 1)]
return 100 * RGB_c.size / XYZ_vs.size
def is_within_macadam_limits(xyY, illuminant, tolerance=None):
"""
Returns if given *CIE xyY* colourspace array is within MacAdam limits of
given illuminant.
Parameters
----------
xyY : array_like
*CIE xyY* colourspace array.
illuminant : unicode
Illuminant.
tolerance : numeric, optional
Tolerance allowed in the inside-triangle check.
Returns
-------
bool
Is within MacAdam limits.
Notes
-----
- Input *CIE xyY* colourspace array is in domain [0, 1].
- This definition requires *scipy* to be installed.
Examples
--------
>>> is_within_macadam_limits(np.array([0.3205, 0.4131, 0.51]), 'A')
array(True, dtype=bool)
>>> a = np.array([[0.3205, 0.4131, 0.51],
... [0.0005, 0.0031, 0.001]])
>>> is_within_macadam_limits(a, 'A')
array([ True, False], dtype=bool)
"""
if is_scipy_installed(raise_exception=True):
from scipy.spatial import Delaunay
optimal_colour_stimuli = _XYZ_optimal_colour_stimuli(illuminant)
triangulation = _XYZ_OPTIMAL_COLOUR_STIMULI_TRIANGULATIONS_CACHE.get(
illuminant)
if triangulation is None:
_XYZ_OPTIMAL_COLOUR_STIMULI_TRIANGULATIONS_CACHE[illuminant] = \
triangulation = Delaunay(optimal_colour_stimuli)
simplex = triangulation.find_simplex(xyY_to_XYZ(xyY), tol=tolerance)
simplex = np.where(simplex >= 0, True, False)
return simplex
def is_within_macadam_limits(xyY, illuminant, tolerance=None):
"""
Returns if given *CIE xyY* colourspace array is within MacAdam limits of
given illuminant.
Parameters
----------
xyY : array_like
*CIE xyY* colourspace array.
illuminant : unicode
Illuminant.
tolerance : numeric, optional
Tolerance allowed in the inside-triangle check.
Returns
-------
bool
Is within MacAdam limits.
Notes
-----
- Input *CIE xyY* colourspace array is in domain [0, 1].
- This definition requires *scipy* to be installed.
Examples
--------
>>> is_within_macadam_limits(np.array([0.3205, 0.4131, 0.51]), 'A')
array(True, dtype=bool)
>>> a = np.array([[0.3205, 0.4131, 0.51],
... [0.0005, 0.0031, 0.001]])
>>> is_within_macadam_limits(a, 'A')
array([ True, False], dtype=bool)
"""
if is_scipy_installed(raise_exception=True):
from scipy.spatial import Delaunay
optimal_colour_stimuli = _XYZ_optimal_colour_stimuli(illuminant)
triangulation = _XYZ_OPTIMAL_COLOUR_STIMULI_TRIANGULATIONS_CACHE.get(
illuminant)
if triangulation is None:
_XYZ_OPTIMAL_COLOUR_STIMULI_TRIANGULATIONS_CACHE[illuminant] = \
triangulation = Delaunay(optimal_colour_stimuli)
simplex = triangulation.find_simplex(xyY_to_XYZ(xyY), tol=tolerance)
simplex = np.where(simplex >= 0, True, False)
return simplex
def is_within_mesh_volume(points, mesh, tolerance=None):
"""
Returns if given points are within given mesh volume using Delaunay
triangulation.
Parameters
----------
points : array_like
Points to check if they are within `mesh` volume.
mesh : array_like
Points of the volume used to generate the Delaunay triangulation.
tolerance : numeric, optional
Tolerance allowed in the inside-triangle check.
Returns
-------
bool
Is within mesh volume.
Notes
-----
- This definition requires *scipy* to be installed.
Examples
--------
>>> mesh = np.array([[-1.0, -1.0, 1.0],
... [1.0, -1.0, 1.0],
... [1.0, -1.0, -1.0],
... [-1.0, -1.0, -1.0],
... [0.0, 1.0, 0.0]])
>>> is_within_mesh_volume(np.array([0.0005, 0.0031, 0.0010]), mesh)
array(True, dtype=bool)
>>> a = np.array([[0.0005, 0.0031, 0.0010],
... [0.3205, 0.4131, 0.5100]])
>>> is_within_mesh_volume(a, mesh)
array([ True, False], dtype=bool)
"""
if is_scipy_installed(raise_exception=True):
from scipy.spatial import Delaunay
triangulation = Delaunay(mesh)
simplex = triangulation.find_simplex(points, tol=tolerance)
simplex = np.where(simplex >= 0, True, False)
return simplex
def is_within_macadam_limits(xyY, illuminant):
"""
Returns if given *CIE xyY* colourspace matrix is within *MacAdam* limits of
given illuminant.
Parameters
----------
xyY : array_like, (3,)
*CIE xyY* colourspace matrix.
illuminant : unicode
Illuminant.
Returns
-------
bool
Is within *MacAdam* limits.
Notes
-----
- Input *CIE xyY* colourspace matrix is in domain [0, 1].
- This definition requires *scipy* to be installed.
Examples
--------
>>> is_within_macadam_limits((0.3205, 0.4131, 0.51), 'A')
True
>>> is_within_macadam_limits((0.0005, 0.0031, 0.001), 'A')
False
"""
if is_scipy_installed(raise_exception=True):
from scipy.spatial import Delaunay
optimal_colour_stimuli = _XYZ_optimal_colour_stimuli(illuminant)
triangulation = _XYZ_OPTIMAL_COLOUR_STIMULI_TRIANGULATIONS_CACHE.get(
illuminant)
if triangulation is None:
_XYZ_OPTIMAL_COLOUR_STIMULI_TRIANGULATIONS_CACHE[illuminant] = \
triangulation = Delaunay(optimal_colour_stimuli)
simplex = triangulation.find_simplex(np.ravel(xyY_to_XYZ(xyY)))
return True if simplex != -1 else False
def is_within_macadam_limits(xyY, illuminant):
"""
Returns if given *CIE xyY* colourspace matrix is within *MacAdam* limits of
given illuminant.
Parameters
----------
xyY : array_like, (3,)
*CIE xyY* colourspace matrix.
illuminant : unicode
Illuminant.
Returns
-------
bool
Is within *MacAdam* limits.
Notes
-----
- Input *CIE xyY* colourspace matrix is in domain [0, 1].
- This definition requires *scipy* to be installed.
Examples
--------
>>> is_within_macadam_limits((0.3205, 0.4131, 0.51), 'A')
True
>>> is_within_macadam_limits((0.0005, 0.0031, 0.001), 'A')
False
"""
if is_scipy_installed(raise_exception=True):
from scipy.spatial import Delaunay
optimal_colour_stimuli = _XYZ_optimal_colour_stimuli(illuminant)
triangulation = _XYZ_OPTIMAL_COLOUR_STIMULI_TRIANGULATIONS_CACHE.get(
illuminant)
if triangulation is None:
_XYZ_OPTIMAL_COLOUR_STIMULI_TRIANGULATIONS_CACHE[illuminant] = \
triangulation = Delaunay(optimal_colour_stimuli)
simplex = triangulation.find_simplex(np.ravel(xyY_to_XYZ(xyY)))
return True if simplex != -1 else False
def __validate_interpolation_range(self, x):
"""
Validates given point to be in interpolation range.
"""
below_interpolation_range = x < self.__x[0]
above_interpolation_range = x > self.__x[-1]
if below_interpolation_range.any():
raise ValueError('"{0}" is below interpolation range.'.format(x))
if above_interpolation_range.any():
raise ValueError('"{0}" is above interpolation range.'.format(x))
if is_scipy_installed():
import scipy.interpolate
class CubicSplineInterpolator(scipy.interpolate.interp1d):
"""
Interpolates a 1-D function using cubic spline interpolation.
Notes
-----
This class is a wrapper around *scipy.interpolate.interp1d* class.
"""
def __init__(self, *args, **kwargs):
super(CubicSplineInterpolator, self).__init__(kind='cubic',
*args,
**kwargs)
def __validate_interpolation_range(self, x):
"""
Validates given point to be in interpolation range.
"""
below_interpolation_range = x < self.__x[0]
above_interpolation_range = x > self.__x[-1]
if below_interpolation_range.any():
raise ValueError('"{0}" is below interpolation range.'.format(x))
if above_interpolation_range.any():
raise ValueError('"{0}" is above interpolation range.'.format(x))
if is_scipy_installed():
import scipy.interpolate
class CubicSplineInterpolator(scipy.interpolate.interp1d):
"""
Interpolates a 1-D function using cubic spline interpolation.
Notes
-----
This class is a wrapper around *scipy.interpolate.interp1d* class.
"""
def __init__(self, *args, **kwargs):
super(CubicSplineInterpolator, self).__init__(
kind='cubic', *args, **kwargs)
def RGB_colourspace_volume_coverage_MonteCarlo(
colourspace,
coverage_sampler,
samples=10e6,
random_generator=random_triplet_generator,
random_state=None):
"""
Returns given *RGB* colourspace percentage coverage of an arbitrary volume.
Parameters
----------
colourspace : RGB_Colourspace
*RGB* colourspace to compute the volume coverage percentage.
coverage_sampler : object
Python object responsible for checking the volume coverage.
samples : numeric, optional
Samples count.
random_generator : generator, optional
Random triplet generator providing the random samples.
random_state : RandomState, optional
Mersenne Twister pseudo-random number generator to use in the random
number generator.
Returns
-------
float
Percentage coverage of volume.
Notes
-----
- This definition requires *scipy* to be installed.
Examples
--------
>>> from colour import sRGB_COLOURSPACE as sRGB
>>> prng = np.random.RandomState(2)
>>> RGB_colourspace_volume_coverage_MonteCarlo( # doctest: +ELLIPSIS
... sRGB,
... is_within_pointer_gamut,
... 10e3,
... random_state=prng)
83...
"""
if is_scipy_installed(raise_exception=True):
random_state = (random_state if random_state is not None else
np.random.RandomState())
# TODO: Investigate for generator yielding directly a ndarray.
XYZ = np.asarray(
list(random_generator(samples, random_state=random_state)))
XYZ_vs = XYZ[coverage_sampler(XYZ)]
RGB = XYZ_to_RGB(XYZ_vs, colourspace.whitepoint,
colourspace.whitepoint, colourspace.XYZ_to_RGB_matrix)
RGB_c = RGB[np.logical_and(
np.min(RGB, axis=-1) >= 0,
np.max(RGB, axis=-1) <= 1)]
return 100 * RGB_c.size / XYZ_vs.size
def CIE_1931_chromaticity_diagram_colours_plot(
surface=1,
samples=4096,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots the *CIE 1931 Chromaticity Diagram* colours.
Parameters
----------
surface : numeric, optional
Generated markers surface.
samples : numeric, optional
Samples count on one axis.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> CIE_1931_chromaticity_diagram_colours_plot() # doctest: +SKIP
True
"""
if is_scipy_installed(raise_exception=True):
from scipy.spatial import Delaunay
settings = {'figure_size': (64, 64)}
settings.update(kwargs)
canvas(**settings)
cmfs = get_cmfs(cmfs)
illuminant = CHROMATICITY_DIAGRAM_DEFAULT_ILLUMINANT
triangulation = Delaunay(XYZ_to_xy(cmfs.values, illuminant),
qhull_options='QJ')
xx, yy = np.meshgrid(np.linspace(0, 1, samples),
np.linspace(0, 1, samples))
xy = tstack((xx, yy))
xy = xy[triangulation.find_simplex(xy) > 0]
XYZ = xy_to_XYZ(xy)
RGB = normalise(XYZ_to_sRGB(XYZ, illuminant), axis=-1)
x_dot, y_dot = tsplit(xy)
pylab.scatter(x_dot, y_dot, color=RGB, s=surface)
settings.update({
'no_ticks': True,
'bounding_box': (0, 1, 0, 1),
'bbox_inches': 'tight',
'pad_inches': 0})
settings.update(kwargs)
ax = matplotlib.pyplot.gca()
matplotlib.pyplot.setp(ax, frame_on=False)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def CIE_1931_chromaticity_diagram_colours_plot(
surface=1,
samples=4096,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots the *CIE 1931 Chromaticity Diagram* colours.
Parameters
----------
surface : numeric, optional
Generated markers surface.
samples : numeric, optional
Samples count on one axis.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> CIE_1931_chromaticity_diagram_colours_plot() # doctest: +SKIP
True
"""
if is_scipy_installed(raise_exception=True):
from scipy.spatial import Delaunay
settings = {'figure_size': (64, 64)}
settings.update(kwargs)
canvas(**settings)
cmfs = get_cmfs(cmfs)
illuminant = DEFAULT_PLOTTING_ILLUMINANT
triangulation = Delaunay(XYZ_to_xy(cmfs.values, illuminant),
qhull_options='QJ')
xx, yy = np.meshgrid(np.linspace(0, 1, samples),
np.linspace(0, 1, samples))
xy = tstack((xx, yy))
xy = xy[triangulation.find_simplex(xy) > 0]
XYZ = xy_to_XYZ(xy)
RGB = normalise(XYZ_to_sRGB(XYZ, illuminant), axis=-1)
x_dot, y_dot = tsplit(xy)
pylab.scatter(x_dot, y_dot, color=RGB, s=surface)
settings.update({
'x_ticker': False,
'y_ticker': False,
'bounding_box': (0, 1, 0, 1),
'bbox_inches': 'tight',
'pad_inches': 0
})
settings.update(kwargs)
ax = matplotlib.pyplot.gca()
matplotlib.pyplot.setp(ax, frame_on=False)
boundaries(**settings)
decorate(**settings)
return display(**settings)