def test_plot_hull_section_colours(self):
"""
Test :func:`colour.plotting.section.plot_hull_section_colours`
definition.
"""
if not is_trimesh_installed: # pragma: no cover
return
import trimesh
vertices, faces, _outline = primitive_cube(1, 1, 1, 64, 64, 64)
XYZ_vertices = RGB_to_XYZ(
vertices["position"] + 0.5,
RGB_COLOURSPACE_sRGB.whitepoint,
RGB_COLOURSPACE_sRGB.whitepoint,
RGB_COLOURSPACE_sRGB.matrix_RGB_to_XYZ,
)
hull = trimesh.Trimesh(XYZ_vertices, faces, process=False)
figure, axes = plot_hull_section_colours(hull)
self.assertIsInstance(figure, Figure)
self.assertIsInstance(axes, Axes)
figure, axes = plot_hull_section_colours(hull, axis="+x")
self.assertIsInstance(figure, Figure)
self.assertIsInstance(axes, Axes)
figure, axes = plot_hull_section_colours(hull, axis="+y")
self.assertIsInstance(figure, Figure)
self.assertIsInstance(axes, Axes)
def RGB_colourspace_volume_visual(colourspace=PRIMARY_COLOURSPACE,
colourspace_model=COLOURSPACE_MODEL,
segments=16,
wireframe=False):
"""
Returns a RGB colourspace volume visual geometry formatted as *JSON*.
Parameters
----------
colourspace : unicode, optional
RGB colourspace used to generate the visual geometry.
colourspace_model : unicode, optional
Colourspace model used to generate the visual geometry.
segments : int, optional
Segments count per side of the *box* used to generate the visual
geometry.
wireframe : bool, optional
Whether the visual geometry must represent a wireframe visual.
Returns
-------
unicode
RGB colourspace volume visual geometry formatted as *JSON*.
"""
colourspace = first_item(
filter_RGB_colourspaces(re.escape(colourspace)).values())
cube = conform_primitive_dtype(
primitive_cube(width_segments=segments,
height_segments=segments,
depth_segments=segments))
vertices = cube[0]['position'] + 0.5
faces = colourspace_model_faces_reorder(np.reshape(cube[1], (-1, 1)),
colourspace_model)
RGB = cube[0]['colour']
XYZ = RGB_to_XYZ(
vertices,
colourspace.whitepoint,
colourspace.whitepoint,
colourspace.matrix_RGB_to_XYZ,
)
vertices = colourspace_model_axis_reorder(
XYZ_to_colourspace_model(
XYZ,
colourspace.whitepoint,
colourspace_model,
), colourspace_model)
return buffer_geometry(position=vertices, color=RGB, index=faces)
def plot_RGB_colourspace_section(
colourspace: Union[RGB_Colourspace, str, Sequence[Union[RGB_Colourspace,
str]]],
model: Union[Literal["CAM02LCD", "CAM02SCD", "CAM02UCS", "CAM16LCD",
"CAM16SCD", "CAM16UCS", "CIE XYZ", "CIE xyY",
"CIE Lab", "CIE Luv", "CIE UCS", "CIE UVW", "DIN99",
"Hunter Lab", "Hunter Rdab", "ICaCb", "ICtCp", "IPT",
"IgPgTg", "Jzazbz", "OSA UCS", "Oklab", "hdr-CIELAB",
"hdr-IPT", ], str, ] = "CIE xyY",
axis: Union[Literal["+z", "+x", "+y"], str] = "+z",
origin: Floating = 0.5,
normalise: Boolean = True,
show_section_colours: Boolean = True,
show_section_contour: Boolean = True,
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot given *RGB* colourspace section colours along given axis and origin.
Parameters
----------
colourspace
*RGB* colourspace of the *RGB* array. ``colourspace`` can be of any
type or form supported by the
:func:`colour.plotting.filter_RGB_colourspaces` definition.
model
Colourspace model, see :attr:`colour.COLOURSPACE_MODELS` attribute for
the list of supported colourspace models.
axis
Axis the hull section will be normal to.
origin
Coordinate along ``axis`` at which to plot the hull section.
normalise
Whether to normalise ``axis`` to the extent of the hull along it.
show_section_colours
Whether to show the hull section colours.
show_section_contour
Whether to show the hull section contour.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`,
:func:`colour.plotting.render`,
:func:`colour.plotting.section.plot_hull_section_colours`
:func:`colour.plotting.section.plot_hull_section_contour`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> from colour.utilities import is_trimesh_installed
>>> if is_trimesh_installed:
... plot_RGB_colourspace_section(
... 'sRGB', section_colours='RGB', section_opacity=0.15)
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_RGB_Colourspace_Section.png
:align: center
:alt: plot_RGB_colourspace_section
"""
import trimesh
settings: Dict[str, Any] = {"uniform": True}
settings.update(kwargs)
_figure, axes = artist(**settings)
colourspace = cast(
RGB_Colourspace,
first_item(filter_RGB_colourspaces(colourspace).values()),
)
vertices, faces, _outline = primitive_cube(1, 1, 1, 64, 64, 64)
XYZ_vertices = RGB_to_XYZ(
vertices["position"] + 0.5,
colourspace.whitepoint,
colourspace.whitepoint,
colourspace.matrix_RGB_to_XYZ,
)
hull = trimesh.Trimesh(XYZ_vertices, faces, process=False)
if show_section_colours:
settings = {"axes": axes}
settings.update(kwargs)
settings["standalone"] = False
plot_hull_section_colours(hull, model, axis, origin, normalise,
**settings)
if show_section_contour:
settings = {"axes": axes}
settings.update(kwargs)
settings["standalone"] = False
plot_hull_section_contour(hull, model, axis, origin, normalise,
**settings)
title = (f"{colourspace.name} Section - "
f"{f'{origin * 100}%' if normalise else origin} - "
f"{model}")
plane = MAPPING_AXIS_TO_PLANE[axis]
labels = np.array(COLOURSPACE_MODELS_AXIS_LABELS[model])[as_int_array(
colourspace_model_axis_reorder([0, 1, 2], model))]
x_label, y_label = labels[plane[0]], labels[plane[1]]
settings.update({
"axes": axes,
"standalone": True,
"title": title,
"x_label": x_label,
"y_label": y_label,
})
settings.update(kwargs)
return render(**settings)
def test_primitive_cube(self):
"""
Tests :func:`colour.geometry.primitives.primitive_cube`
definition.
"""
vertices, faces, outline = primitive_cube()
np.testing.assert_almost_equal(
vertices['position'],
np.array([
[-0.5, 0.5, -0.5],
[0.5, 0.5, -0.5],
[-0.5, -0.5, -0.5],
[0.5, -0.5, -0.5],
[-0.5, 0.5, 0.5],
[0.5, 0.5, 0.5],
[-0.5, -0.5, 0.5],
[0.5, -0.5, 0.5],
[0.5, -0.5, -0.5],
[0.5, -0.5, 0.5],
[-0.5, -0.5, -0.5],
[-0.5, -0.5, 0.5],
[0.5, 0.5, -0.5],
[0.5, 0.5, 0.5],
[-0.5, 0.5, -0.5],
[-0.5, 0.5, 0.5],
[-0.5, -0.5, 0.5],
[-0.5, 0.5, 0.5],
[-0.5, -0.5, -0.5],
[-0.5, 0.5, -0.5],
[0.5, -0.5, 0.5],
[0.5, 0.5, 0.5],
[0.5, -0.5, -0.5],
[0.5, 0.5, -0.5],
]),
decimal=7)
np.testing.assert_almost_equal(
vertices['uv'],
np.array([
[0, 1],
[1, 1],
[0, 0],
[1, 0],
[0, 1],
[1, 1],
[0, 0],
[1, 0],
[0, 1],
[1, 1],
[0, 0],
[1, 0],
[0, 1],
[1, 1],
[0, 0],
[1, 0],
[0, 1],
[1, 1],
[0, 0],
[1, 0],
[0, 1],
[1, 1],
[0, 0],
[1, 0],
]),
decimal=7)
np.testing.assert_almost_equal(
vertices['normal'],
np.array([
[0, 0, -1.],
[0, 0, -1],
[0, 0, -1],
[0, 0, -1],
[0, 0, 1],
[0, 0, 1],
[0, 0, 1],
[0, 0, 1],
[0, -1, 0],
[0, -1, 0],
[0, -1, 0],
[0, -1, 0],
[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
[-1, 0, 0],
[-1, 0, 0],
[-1, 0, 0],
[-1, 0, 0],
[1, 0, 0],
[1, 0, 0],
[1, 0, 0],
[1, 0, 0],
]),
decimal=7)
np.testing.assert_almost_equal(
vertices['colour'],
np.array([
[0, 1, 0, 1],
[1, 1, 0, 1],
[0, 0, 0, 1],
[1, 0, 0, 1],
[0, 1, 1, 1],
[1, 1, 1, 1],
[0, 0, 1, 1],
[1, 0, 1, 1],
[1, 0, 0, 1],
[1, 0, 1, 1],
[0, 0, 0, 1],
[0, 0, 1, 1],
[1, 1, 0, 1],
[1, 1, 1, 1],
[0, 1, 0, 1],
[0, 1, 1, 1],
[0, 0, 1, 1],
[0, 1, 1, 1],
[0, 0, 0, 1],
[0, 1, 0, 1],
[1, 0, 1, 1],
[1, 1, 1, 1],
[1, 0, 0, 1],
[1, 1, 0, 1],
]),
decimal=7)
np.testing.assert_equal(
faces,
np.array([
[1, 2, 0],
[1, 3, 2],
[4, 6, 5],
[6, 7, 5],
[9, 10, 8],
[9, 11, 10],
[12, 14, 13],
[14, 15, 13],
[17, 18, 16],
[17, 19, 18],
[20, 22, 21],
[22, 23, 21],
]))
np.testing.assert_equal(
outline,
np.array([
[0, 2],
[2, 3],
[3, 1],
[1, 0],
[4, 6],
[6, 7],
[7, 5],
[5, 4],
[8, 10],
[10, 11],
[11, 9],
[9, 8],
[12, 14],
[14, 15],
[15, 13],
[13, 12],
[16, 18],
[18, 19],
[19, 17],
[17, 16],
[20, 22],
[22, 23],
[23, 21],
[21, 20],
]))
vertices, faces, outline = primitive_cube(
width=0.2,
height=0.4,
depth=0.6,
width_segments=1,
height_segments=2,
depth_segments=3)
np.testing.assert_almost_equal(
vertices['position'],
np.array([
[-0.10000000, 0.30000001, -0.20000000],
[0.10000000, 0.30000001, -0.20000000],
[-0.10000000, 0.10000000, -0.20000000],
[0.10000000, 0.10000000, -0.20000000],
[-0.10000000, -0.10000000, -0.20000000],
[0.10000000, -0.10000000, -0.20000000],
[-0.10000000, -0.30000001, -0.20000000],
[0.10000000, -0.30000001, -0.20000000],
[-0.10000000, 0.30000001, 0.20000000],
[0.10000000, 0.30000001, 0.20000000],
[-0.10000000, 0.10000000, 0.20000000],
[0.10000000, 0.10000000, 0.20000000],
[-0.10000000, -0.10000000, 0.20000000],
[0.10000000, -0.10000000, 0.20000000],
[-0.10000000, -0.30000001, 0.20000000],
[0.10000000, -0.30000001, 0.20000000],
[0.10000000, -0.30000001, -0.20000000],
[0.10000000, -0.30000001, 0.00000000],
[0.10000000, -0.30000001, 0.20000000],
[-0.10000000, -0.30000001, -0.20000000],
[-0.10000000, -0.30000001, 0.00000000],
[-0.10000000, -0.30000001, 0.20000000],
[0.10000000, 0.30000001, -0.20000000],
[0.10000000, 0.30000001, 0.00000000],
[0.10000000, 0.30000001, 0.20000000],
[-0.10000000, 0.30000001, -0.20000000],
[-0.10000000, 0.30000001, 0.00000000],
[-0.10000000, 0.30000001, 0.20000000],
[-0.10000000, -0.30000001, 0.20000000],
[-0.10000000, -0.10000000, 0.20000000],
[-0.10000000, 0.10000000, 0.20000000],
[-0.10000000, 0.30000001, 0.20000000],
[-0.10000000, -0.30000001, -0.00000000],
[-0.10000000, -0.10000000, -0.00000000],
[-0.10000000, 0.10000000, -0.00000000],
[-0.10000000, 0.30000001, -0.00000000],
[-0.10000000, -0.30000001, -0.20000000],
[-0.10000000, -0.10000000, -0.20000000],
[-0.10000000, 0.10000000, -0.20000000],
[-0.10000000, 0.30000001, -0.20000000],
[0.10000000, -0.30000001, 0.20000000],
[0.10000000, -0.10000000, 0.20000000],
[0.10000000, 0.10000000, 0.20000000],
[0.10000000, 0.30000001, 0.20000000],
[0.10000000, -0.30000001, -0.00000000],
[0.10000000, -0.10000000, -0.00000000],
[0.10000000, 0.10000000, -0.00000000],
[0.10000000, 0.30000001, -0.00000000],
[0.10000000, -0.30000001, -0.20000000],
[0.10000000, -0.10000000, -0.20000000],
[0.10000000, 0.10000000, -0.20000000],
[0.10000000, 0.30000001, -0.20000000],
]),
decimal=7)
np.testing.assert_almost_equal(
vertices['uv'],
np.array([
[0.00000000, 1.00000000],
[1.00000000, 1.00000000],
[0.00000000, 0.66666669],
[1.00000000, 0.66666669],
[0.00000000, 0.33333334],
[1.00000000, 0.33333334],
[0.00000000, 0.00000000],
[1.00000000, 0.00000000],
[0.00000000, 1.00000000],
[1.00000000, 1.00000000],
[0.00000000, 0.66666669],
[1.00000000, 0.66666669],
[0.00000000, 0.33333334],
[1.00000000, 0.33333334],
[0.00000000, 0.00000000],
[1.00000000, 0.00000000],
[0.00000000, 1.00000000],
[0.50000000, 1.00000000],
[1.00000000, 1.00000000],
[0.00000000, 0.00000000],
[0.50000000, 0.00000000],
[1.00000000, 0.00000000],
[0.00000000, 1.00000000],
[0.50000000, 1.00000000],
[1.00000000, 1.00000000],
[0.00000000, 0.00000000],
[0.50000000, 0.00000000],
[1.00000000, 0.00000000],
[0.00000000, 1.00000000],
[0.33333334, 1.00000000],
[0.66666669, 1.00000000],
[1.00000000, 1.00000000],
[0.00000000, 0.50000000],
[0.33333334, 0.50000000],
[0.66666669, 0.50000000],
[1.00000000, 0.50000000],
[0.00000000, 0.00000000],
[0.33333334, 0.00000000],
[0.66666669, 0.00000000],
[1.00000000, 0.00000000],
[0.00000000, 1.00000000],
[0.33333334, 1.00000000],
[0.66666669, 1.00000000],
[1.00000000, 1.00000000],
[0.00000000, 0.50000000],
[0.33333334, 0.50000000],
[0.66666669, 0.50000000],
[1.00000000, 0.50000000],
[0.00000000, 0.00000000],
[0.33333334, 0.00000000],
[0.66666669, 0.00000000],
[1.00000000, 0.00000000],
]),
decimal=7)
np.testing.assert_almost_equal(
vertices['normal'],
np.array([
[-0., -0., -1.],
[0, 0, -1],
[0, 0, -1],
[0, 0, -1],
[0, 0, -1],
[0, 0, -1],
[0, 0, -1],
[0, 0, -1],
[0, 0, 1],
[0, 0, 1],
[0, 0, 1],
[0, 0, 1],
[0, 0, 1],
[0, 0, 1],
[0, 0, 1],
[0, 0, 1],
[0, -1, 0],
[0, -1, 0],
[0, -1, 0],
[0, -1, 0],
[0, -1, 0],
[0, -1, 0],
[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
[-1, 0, 0],
[-1, 0, 0],
[-1, 0, 0],
[-1, 0, 0],
[-1, 0, 0],
[-1, 0, 0],
[-1, 0, 0],
[-1, 0, 0],
[-1, 0, 0],
[-1, 0, 0],
[-1, 0, 0],
[-1, 0, 0],
[1, 0, 0],
[1, 0, 0],
[1, 0, 0],
[1, 0, 0],
[1, 0, 0],
[1, 0, 0],
[1, 0, 0],
[1, 0, 0],
[1, 0, 0],
[1, 0, 0],
[1, 0, 0],
[1, 0, 0],
]),
decimal=7)
np.testing.assert_almost_equal(
vertices['colour'],
np.array([
[0.33333334, 1.00000000, 0.16666667, 1.00000000],
[0.66666669, 1.00000000, 0.16666667, 1.00000000],
[0.33333334, 0.66666669, 0.16666667, 1.00000000],
[0.66666669, 0.66666669, 0.16666667, 1.00000000],
[0.33333334, 0.33333334, 0.16666667, 1.00000000],
[0.66666669, 0.33333334, 0.16666667, 1.00000000],
[0.33333334, 0.00000000, 0.16666667, 1.00000000],
[0.66666669, 0.00000000, 0.16666667, 1.00000000],
[0.33333334, 1.00000000, 0.83333331, 1.00000000],
[0.66666669, 1.00000000, 0.83333331, 1.00000000],
[0.33333334, 0.66666669, 0.83333331, 1.00000000],
[0.66666669, 0.66666669, 0.83333331, 1.00000000],
[0.33333334, 0.33333334, 0.83333331, 1.00000000],
[0.66666669, 0.33333334, 0.83333331, 1.00000000],
[0.33333334, 0.00000000, 0.83333331, 1.00000000],
[0.66666669, 0.00000000, 0.83333331, 1.00000000],
[0.66666669, 0.00000000, 0.16666667, 1.00000000],
[0.66666669, 0.00000000, 0.50000000, 1.00000000],
[0.66666669, 0.00000000, 0.83333331, 1.00000000],
[0.33333334, 0.00000000, 0.16666667, 1.00000000],
[0.33333334, 0.00000000, 0.50000000, 1.00000000],
[0.33333334, 0.00000000, 0.83333331, 1.00000000],
[0.66666669, 1.00000000, 0.16666667, 1.00000000],
[0.66666669, 1.00000000, 0.50000000, 1.00000000],
[0.66666669, 1.00000000, 0.83333331, 1.00000000],
[0.33333334, 1.00000000, 0.16666667, 1.00000000],
[0.33333334, 1.00000000, 0.50000000, 1.00000000],
[0.33333334, 1.00000000, 0.83333331, 1.00000000],
[0.33333334, 0.00000000, 0.83333331, 1.00000000],
[0.33333334, 0.33333334, 0.83333331, 1.00000000],
[0.33333334, 0.66666669, 0.83333331, 1.00000000],
[0.33333334, 1.00000000, 0.83333331, 1.00000000],
[0.33333334, 0.00000000, 0.50000000, 1.00000000],
[0.33333334, 0.33333334, 0.50000000, 1.00000000],
[0.33333334, 0.66666669, 0.50000000, 1.00000000],
[0.33333334, 1.00000000, 0.50000000, 1.00000000],
[0.33333334, 0.00000000, 0.16666667, 1.00000000],
[0.33333334, 0.33333334, 0.16666667, 1.00000000],
[0.33333334, 0.66666669, 0.16666667, 1.00000000],
[0.33333334, 1.00000000, 0.16666667, 1.00000000],
[0.66666669, 0.00000000, 0.83333331, 1.00000000],
[0.66666669, 0.33333334, 0.83333331, 1.00000000],
[0.66666669, 0.66666669, 0.83333331, 1.00000000],
[0.66666669, 1.00000000, 0.83333331, 1.00000000],
[0.66666669, 0.00000000, 0.50000000, 1.00000000],
[0.66666669, 0.33333334, 0.50000000, 1.00000000],
[0.66666669, 0.66666669, 0.50000000, 1.00000000],
[0.66666669, 1.00000000, 0.50000000, 1.00000000],
[0.66666669, 0.00000000, 0.16666667, 1.00000000],
[0.66666669, 0.33333334, 0.16666667, 1.00000000],
[0.66666669, 0.66666669, 0.16666667, 1.00000000],
[0.66666669, 1.00000000, 0.16666667, 1.00000000],
]),
decimal=7)
np.testing.assert_equal(
faces,
np.array([
[1, 2, 0],
[1, 3, 2],
[3, 4, 2],
[3, 5, 4],
[5, 6, 4],
[5, 7, 6],
[8, 10, 9],
[10, 11, 9],
[10, 12, 11],
[12, 13, 11],
[12, 14, 13],
[14, 15, 13],
[17, 19, 16],
[17, 20, 19],
[18, 20, 17],
[18, 21, 20],
[22, 25, 23],
[25, 26, 23],
[23, 26, 24],
[26, 27, 24],
[29, 32, 28],
[29, 33, 32],
[30, 33, 29],
[30, 34, 33],
[31, 34, 30],
[31, 35, 34],
[33, 36, 32],
[33, 37, 36],
[34, 37, 33],
[34, 38, 37],
[35, 38, 34],
[35, 39, 38],
[40, 44, 41],
[44, 45, 41],
[41, 45, 42],
[45, 46, 42],
[42, 46, 43],
[46, 47, 43],
[44, 48, 45],
[48, 49, 45],
[45, 49, 46],
[49, 50, 46],
[46, 50, 47],
[50, 51, 47],
]))
np.testing.assert_equal(
outline,
np.array([
[0, 2],
[2, 3],
[3, 1],
[1, 0],
[2, 4],
[4, 5],
[5, 3],
[3, 2],
[4, 6],
[6, 7],
[7, 5],
[5, 4],
[8, 10],
[10, 11],
[11, 9],
[9, 8],
[10, 12],
[12, 13],
[13, 11],
[11, 10],
[12, 14],
[14, 15],
[15, 13],
[13, 12],
[16, 19],
[19, 20],
[20, 17],
[17, 16],
[17, 20],
[20, 21],
[21, 18],
[18, 17],
[22, 25],
[25, 26],
[26, 23],
[23, 22],
[23, 26],
[26, 27],
[27, 24],
[24, 23],
[28, 32],
[32, 33],
[33, 29],
[29, 28],
[29, 33],
[33, 34],
[34, 30],
[30, 29],
[30, 34],
[34, 35],
[35, 31],
[31, 30],
[32, 36],
[36, 37],
[37, 33],
[33, 32],
[33, 37],
[37, 38],
[38, 34],
[34, 33],
[34, 38],
[38, 39],
[39, 35],
[35, 34],
[40, 44],
[44, 45],
[45, 41],
[41, 40],
[41, 45],
[45, 46],
[46, 42],
[42, 41],
[42, 46],
[46, 47],
[47, 43],
[43, 42],
[44, 48],
[48, 49],
[49, 45],
[45, 44],
[45, 49],
[49, 50],
[50, 46],
[46, 45],
[46, 50],
[50, 51],
[51, 47],
[47, 46],
]))
for plane in PLANE_TO_AXIS_MAPPING.keys():
np.testing.assert_almost_equal(
primitive_cube(planes=[plane])[0]['position'],
primitive_cube(
planes=[PLANE_TO_AXIS_MAPPING[plane]])[0]['position'],
decimal=7)
def test_hull_section(self):
"""Test :func:`colour.geometry.section.hull_section` definition."""
if not is_trimesh_installed: # pragma: no cover
return
import trimesh
vertices, faces, _outline = primitive_cube(1, 1, 1, 2, 2, 2)
hull = trimesh.Trimesh(vertices["position"], faces, process=False)
np.testing.assert_almost_equal(
hull_section(hull, origin=0),
np.array([
[0.0, -0.5, 0.0],
[0.5, -0.5, 0.0],
[0.5, 0.0, 0.0],
[0.5, 0.5, 0.0],
[0.0, 0.5, 0.0],
[-0.5, 0.5, 0.0],
[-0.5, 0.0, 0.0],
[-0.5, -0.5, 0.0],
[0.0, -0.5, 0.0],
]),
)
np.testing.assert_almost_equal(
hull_section(hull, axis="+x", origin=0),
np.array([
[0.0, 0.0, -0.5],
[0.0, 0.5, -0.5],
[0.0, 0.5, 0.0],
[0.0, 0.5, 0.5],
[0.0, 0.0, 0.5],
[0.0, -0.5, 0.5],
[0.0, -0.5, 0.0],
[0.0, -0.5, -0.5],
[0.0, 0.0, -0.5],
]),
)
np.testing.assert_almost_equal(
hull_section(hull, axis="+y", origin=0),
np.array([
[0.0, 0.0, -0.5],
[-0.5, 0.0, -0.5],
[-0.5, 0.0, 0.0],
[-0.5, 0.0, 0.5],
[0.0, 0.0, 0.5],
[0.5, 0.0, 0.5],
[0.5, 0.0, 0.0],
[0.5, 0.0, -0.5],
[0.0, 0.0, -0.5],
]),
)
hull.vertices = (hull.vertices + 0.5) * 2
np.testing.assert_almost_equal(
hull_section(hull, origin=0.5, normalise=True),
np.array([
[1.0, 0.0, 1.0],
[2.0, 0.0, 1.0],
[2.0, 1.0, 1.0],
[2.0, 2.0, 1.0],
[1.0, 2.0, 1.0],
[0.0, 2.0, 1.0],
[0.0, 1.0, 1.0],
[0.0, 0.0, 1.0],
[1.0, 0.0, 1.0],
]),
)
self.assertRaises(ValueError, hull_section, hull, origin=-1)
def generate_documentation_plots(output_directory: str):
"""
Generate documentation plots.
Parameters
----------
output_directory
Output directory.
"""
filter_warnings()
colour_style()
np.random.seed(0)
# *************************************************************************
# "README.rst"
# *************************************************************************
filename = os.path.join(
output_directory, "Examples_Colour_Automatic_Conversion_Graph.png"
)
plot_automatic_colour_conversion_graph(filename)
arguments = {
"tight_layout": True,
"transparent_background": True,
"filename": os.path.join(
output_directory, "Examples_Plotting_Visible_Spectrum.png"
),
}
plt.close(
plot_visible_spectrum(
"CIE 1931 2 Degree Standard Observer", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Illuminant_F1_SD.png"
)
plt.close(plot_single_illuminant_sd("FL1", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Blackbodies.png"
)
blackbody_sds = [
sd_blackbody(i, SpectralShape(0, 10000, 10))
for i in range(1000, 15000, 1000)
]
plt.close(
plot_multi_sds(
blackbody_sds,
y_label="W / (sr m$^2$) / m",
plot_kwargs={"use_sd_colours": True, "normalise_sd_colours": True},
legend_location="upper right",
bounding_box=(0, 1250, 0, 2.5e6),
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Cone_Fundamentals.png"
)
plt.close(
plot_single_cmfs(
"Stockman & Sharpe 2 Degree Cone Fundamentals",
y_label="Sensitivity",
bounding_box=(390, 870, 0, 1.1),
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Luminous_Efficiency.png"
)
plt.close(
plot_multi_sds(
(
sd_mesopic_luminous_efficiency_function(0.2),
SDS_LEFS_PHOTOPIC["CIE 1924 Photopic Standard Observer"],
SDS_LEFS_SCOTOPIC["CIE 1951 Scotopic Standard Observer"],
),
y_label="Luminous Efficiency",
legend_location="upper right",
y_tighten=True,
margins=(0, 0, 0, 0.1),
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_BabelColor_Average.png"
)
plt.close(
plot_multi_sds(
SDS_COLOURCHECKERS["BabelColor Average"].values(),
plot_kwargs={"use_sd_colours": True},
title=("BabelColor Average - " "Spectral Distributions"),
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_ColorChecker_2005.png"
)
plt.close(
plot_single_colour_checker(
"ColorChecker 2005", text_kwargs={"visible": False}, **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Chromaticities_Prediction.png"
)
plt.close(
plot_corresponding_chromaticities_prediction(
2, "Von Kries", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Examples_Plotting_Chromaticities_CIE_1931_Chromaticity_Diagram.png",
)
RGB = np.random.random((32, 32, 3))
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931(
RGB,
"ITU-R BT.709",
colourspaces=["ACEScg", "S-Gamut"],
show_pointer_gamut=True,
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_CRI.png"
)
plt.close(
plot_single_sd_colour_rendering_index_bars(
SDS_ILLUMINANTS["FL2"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Colour_Rendition_Report.png"
)
plt.close(
plot_single_sd_colour_rendition_report(
SDS_ILLUMINANTS["FL2"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Plot_Visible_Spectrum_Section.png"
)
plt.close(
plot_visible_spectrum_section(
section_colours="RGB", section_opacity=0.15, **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Plot_RGB_Colourspace_Section.png"
)
plt.close(
plot_RGB_colourspace_section(
"sRGB", section_colours="RGB", section_opacity=0.15, **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Examples_Plotting_CCT_CIE_1960_UCS_Chromaticity_Diagram.png",
)
plt.close(
plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(
["A", "B", "C"], **arguments
)[0]
)
# *************************************************************************
# Documentation
# *************************************************************************
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_CVD_Simulation_Machado2009.png"
)
plt.close(plot_cvd_simulation_Machado2009(RGB, **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Colour_Checker.png"
)
plt.close(plot_single_colour_checker("ColorChecker 2005", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_Colour_Checkers.png"
)
plt.close(
plot_multi_colour_checkers(
["ColorChecker 1976", "ColorChecker 2005"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_SD.png"
)
data = {
500: 0.0651,
520: 0.0705,
540: 0.0772,
560: 0.0870,
580: 0.1128,
600: 0.1360,
}
sd = SpectralDistribution(data, name="Custom")
plt.close(plot_single_sd(sd, **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_SDS.png"
)
data_1 = {
500: 0.004900,
510: 0.009300,
520: 0.063270,
530: 0.165500,
540: 0.290400,
550: 0.433450,
560: 0.594500,
}
data_2 = {
500: 0.323000,
510: 0.503000,
520: 0.710000,
530: 0.862000,
540: 0.954000,
550: 0.994950,
560: 0.995000,
}
spd1 = SpectralDistribution(data_1, name="Custom 1")
spd2 = SpectralDistribution(data_2, name="Custom 2")
plt.close(plot_multi_sds([spd1, spd2], **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_CMFS.png"
)
plt.close(
plot_single_cmfs("CIE 1931 2 Degree Standard Observer", **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_CMFS.png"
)
cmfs = (
"CIE 1931 2 Degree Standard Observer",
"CIE 1964 10 Degree Standard Observer",
)
plt.close(plot_multi_cmfs(cmfs, **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Illuminant_SD.png"
)
plt.close(plot_single_illuminant_sd("A", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_Illuminant_SDS.png"
)
plt.close(plot_multi_illuminant_sds(["A", "B", "C"], **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Visible_Spectrum.png"
)
plt.close(plot_visible_spectrum(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Lightness_Function.png"
)
plt.close(plot_single_lightness_function("CIE 1976", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_Lightness_Functions.png"
)
plt.close(
plot_multi_lightness_functions(
["CIE 1976", "Wyszecki 1963"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Luminance_Function.png"
)
plt.close(plot_single_luminance_function("CIE 1976", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_Luminance_Functions.png"
)
plt.close(
plot_multi_luminance_functions(
["CIE 1976", "Newhall 1943"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Blackbody_Spectral_Radiance.png"
)
plt.close(
plot_blackbody_spectral_radiance(
3500, blackbody="VY Canis Major", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Blackbody_Colours.png"
)
plt.close(
plot_blackbody_colours(SpectralShape(150, 12500, 50), **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Colour_Swatch.png"
)
RGB = ColourSwatch((0.45620519, 0.03081071, 0.04091952))
plt.close(plot_single_colour_swatch(RGB, **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_Colour_Swatches.png"
)
RGB_1 = ColourSwatch((0.45293517, 0.31732158, 0.26414773))
RGB_2 = ColourSwatch((0.77875824, 0.57726450, 0.50453169))
plt.close(plot_multi_colour_swatches([RGB_1, RGB_2], **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Function.png"
)
plt.close(plot_single_function(lambda x: x ** (1 / 2.2), **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_Functions.png"
)
functions = {
"Gamma 2.2": lambda x: x ** (1 / 2.2),
"Gamma 2.4": lambda x: x ** (1 / 2.4),
"Gamma 2.6": lambda x: x ** (1 / 2.6),
}
plt.close(plot_multi_functions(functions, **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Image.png"
)
path = os.path.join(
colour.__path__[0],
"examples",
"plotting",
"resources",
"Ishihara_Colour_Blindness_Test_Plate_3.png",
)
plt.close(plot_image(read_image(str(path)), **arguments)[0])
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Corresponding_Chromaticities_Prediction.png",
)
plt.close(
plot_corresponding_chromaticities_prediction(
1, "Von Kries", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Spectral_Locus.png"
)
plt.close(
plot_spectral_locus(spectral_locus_colours="RGB", **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Chromaticity_Diagram_Colours.png"
)
plt.close(
plot_chromaticity_diagram_colours(diagram_colours="RGB", **arguments)[
0
]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Chromaticity_Diagram.png"
)
plt.close(plot_chromaticity_diagram(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Chromaticity_Diagram_CIE1931.png"
)
plt.close(plot_chromaticity_diagram_CIE1931(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Chromaticity_Diagram_CIE1960UCS.png"
)
plt.close(plot_chromaticity_diagram_CIE1960UCS(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Chromaticity_Diagram_CIE1976UCS.png"
)
plt.close(plot_chromaticity_diagram_CIE1976UCS(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_SDS_In_Chromaticity_Diagram.png"
)
A = SDS_ILLUMINANTS["A"]
D65 = SDS_ILLUMINANTS["D65"]
plt.close(plot_sds_in_chromaticity_diagram([A, D65], **arguments)[0])
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1931.png",
)
plt.close(
plot_sds_in_chromaticity_diagram_CIE1931([A, D65], **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1960UCS.png",
)
plt.close(
plot_sds_in_chromaticity_diagram_CIE1960UCS([A, D65], **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1976UCS.png",
)
plt.close(
plot_sds_in_chromaticity_diagram_CIE1976UCS([A, D65], **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Pointer_Gamut.png"
)
plt.close(plot_pointer_gamut(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Colourspaces_In_Chromaticity_Diagram.png",
)
plt.close(
plot_RGB_colourspaces_in_chromaticity_diagram(
["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Colourspaces_In_Chromaticity_Diagram_CIE1931.png",
)
plt.close(
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1931(
["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Colourspaces_In_"
"Chromaticity_Diagram_CIE1960UCS.png",
)
plt.close(
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1960UCS(
["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Colourspaces_In_"
"Chromaticity_Diagram_CIE1976UCS.png",
)
plt.close(
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1976UCS(
["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Chromaticities_In_" "Chromaticity_Diagram.png",
)
RGB = np.random.random((128, 128, 3))
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram(
RGB, "ITU-R BT.709", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Chromaticities_In_"
"Chromaticity_Diagram_CIE1931.png",
)
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931(
RGB, "ITU-R BT.709", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Chromaticities_In_"
"Chromaticity_Diagram_CIE1960UCS.png",
)
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1960UCS(
RGB, "ITU-R BT.709", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Chromaticities_In_"
"Chromaticity_Diagram_CIE1976UCS.png",
)
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1976UCS(
RGB, "ITU-R BT.709", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Ellipses_MacAdam1942_In_Chromaticity_Diagram.png",
)
plt.close(
plot_ellipses_MacAdam1942_in_chromaticity_diagram(**arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Ellipses_MacAdam1942_In_"
"Chromaticity_Diagram_CIE1931.png",
)
plt.close(
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1931(**arguments)[
0
]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Ellipses_MacAdam1942_In_"
"Chromaticity_Diagram_CIE1960UCS.png",
)
plt.close(
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1960UCS(
**arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Ellipses_MacAdam1942_In_"
"Chromaticity_Diagram_CIE1976UCS.png",
)
plt.close(
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1976UCS(
**arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_CCTF.png"
)
plt.close(plot_single_cctf("ITU-R BT.709", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_CCTFs.png"
)
plt.close(plot_multi_cctfs(["ITU-R BT.709", "sRGB"], **arguments)[0])
data = np.array(
[
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.40920000, 0.28120000, 0.30600000]),
np.array(
[
[0.02495100, 0.01908600, 0.02032900],
[0.10944300, 0.06235900, 0.06788100],
[0.27186500, 0.18418700, 0.19565300],
[0.48898900, 0.40749400, 0.44854600],
]
),
None,
],
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.30760000, 0.48280000, 0.42770000]),
np.array(
[
[0.02108000, 0.02989100, 0.02790400],
[0.06194700, 0.11251000, 0.09334400],
[0.15255800, 0.28123300, 0.23234900],
[0.34157700, 0.56681300, 0.47035300],
]
),
None,
],
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.39530000, 0.28120000, 0.18450000]),
np.array(
[
[0.02436400, 0.01908600, 0.01468800],
[0.10331200, 0.06235900, 0.02854600],
[0.26311900, 0.18418700, 0.12109700],
[0.43158700, 0.40749400, 0.39008600],
]
),
None,
],
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.20510000, 0.18420000, 0.57130000]),
np.array(
[
[0.03039800, 0.02989100, 0.06123300],
[0.08870000, 0.08498400, 0.21843500],
[0.18405800, 0.18418700, 0.40111400],
[0.32550100, 0.34047200, 0.50296900],
[0.53826100, 0.56681300, 0.80010400],
]
),
None,
],
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.35770000, 0.28120000, 0.11250000]),
np.array(
[
[0.03678100, 0.02989100, 0.01481100],
[0.17127700, 0.11251000, 0.01229900],
[0.30080900, 0.28123300, 0.21229800],
[0.52976000, 0.40749400, 0.11720000],
]
),
None,
],
]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Constant_Hue_Loci.png"
)
plt.close(plot_constant_hue_loci(data, "IPT", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Munsell_Value_Function.png"
)
plt.close(plot_single_munsell_value_function("ASTM D1535", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_Munsell_Value_Functions.png"
)
plt.close(
plot_multi_munsell_value_functions(
["ASTM D1535", "McCamy 1987"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_SD_Rayleigh_Scattering.png"
)
plt.close(plot_single_sd_rayleigh_scattering(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_The_Blue_Sky.png"
)
plt.close(plot_the_blue_sky(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Colour_Quality_Bars.png"
)
illuminant = SDS_ILLUMINANTS["FL2"]
light_source = SDS_LIGHT_SOURCES["Kinoton 75P"]
light_source = light_source.copy().align(SpectralShape(360, 830, 1))
cqs_i = colour_quality_scale(illuminant, additional_data=True)
cqs_l = colour_quality_scale(light_source, additional_data=True)
plt.close(plot_colour_quality_bars([cqs_i, cqs_l], **arguments)[0])
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Single_SD_Colour_Rendering_Index_Bars.png",
)
illuminant = SDS_ILLUMINANTS["FL2"]
plt.close(
plot_single_sd_colour_rendering_index_bars(illuminant, **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Multi_SDS_Colour_Rendering_Indexes_Bars.png",
)
light_source = SDS_LIGHT_SOURCES["Kinoton 75P"]
plt.close(
plot_multi_sds_colour_rendering_indexes_bars(
[illuminant, light_source], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Single_SD_Colour_Quality_Scale_Bars.png",
)
illuminant = SDS_ILLUMINANTS["FL2"]
plt.close(
plot_single_sd_colour_quality_scale_bars(illuminant, **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Multi_SDS_Colour_Quality_Scales_Bars.png",
)
light_source = SDS_LIGHT_SOURCES["Kinoton 75P"]
plt.close(
plot_multi_sds_colour_quality_scales_bars(
[illuminant, light_source], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Hull_Section_Colours.png"
)
vertices, faces, _outline = primitive_cube(1, 1, 1, 64, 64, 64)
XYZ_vertices = RGB_to_XYZ(
vertices["position"] + 0.5,
RGB_COLOURSPACE_sRGB.whitepoint,
RGB_COLOURSPACE_sRGB.whitepoint,
RGB_COLOURSPACE_sRGB.matrix_RGB_to_XYZ,
)
hull = trimesh.Trimesh(XYZ_vertices, faces, process=False)
plt.close(
plot_hull_section_colours(hull, section_colours="RGB", **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Hull_Section_Contour.png"
)
plt.close(
plot_hull_section_contour(hull, section_colours="RGB", **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Visible_Spectrum_Section.png"
)
plt.close(
plot_visible_spectrum_section(
section_colours="RGB", section_opacity=0.15, **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_RGB_Colourspace_Section.png"
)
plt.close(
plot_RGB_colourspace_section(
"sRGB", section_colours="RGB", section_opacity=0.15, **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Planckian_Locus.png"
)
plt.close(
plot_planckian_locus(planckian_locus_colours="RGB", **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram.png",
)
plt.close(
plot_planckian_locus_in_chromaticity_diagram(
["A", "B", "C"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram_CIE1931.png",
)
plt.close(
plot_planckian_locus_in_chromaticity_diagram_CIE1931(
["A", "B", "C"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram_CIE1960UCS.png",
)
plt.close(
plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(
["A", "B", "C"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Single_SD_Colour_Rendition_Report_Full.png",
)
plt.close(
plot_single_sd_colour_rendition_report(
SDS_ILLUMINANTS["FL2"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Single_SD_Colour_Rendition_Report_Intermediate.png",
)
plt.close(
plot_single_sd_colour_rendition_report(
SDS_ILLUMINANTS["FL2"], "Intermediate", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Single_SD_Colour_Rendition_Report_Simple.png",
)
plt.close(
plot_single_sd_colour_rendition_report(
SDS_ILLUMINANTS["FL2"], "Simple", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_RGB_Colourspaces_Gamuts.png"
)
plt.close(
plot_RGB_colourspaces_gamuts(
["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_RGB_Colourspaces_Gamuts.png"
)
plt.close(
plot_RGB_colourspaces_gamuts(
["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_RGB_Scatter.png"
)
plt.close(plot_RGB_scatter(RGB, "ITU-R BT.709", **arguments)[0])
filename = os.path.join(
output_directory, "Plotting_Plot_Colour_Automatic_Conversion_Graph.png"
)
plot_automatic_colour_conversion_graph(filename)
# *************************************************************************
# "tutorial.rst"
# *************************************************************************
arguments["filename"] = os.path.join(
output_directory, "Tutorial_Visible_Spectrum.png"
)
plt.close(plot_visible_spectrum(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Tutorial_Sample_SD.png"
)
sample_sd_data = {
380: 0.048,
385: 0.051,
390: 0.055,
395: 0.060,
400: 0.065,
405: 0.068,
410: 0.068,
415: 0.067,
420: 0.064,
425: 0.062,
430: 0.059,
435: 0.057,
440: 0.055,
445: 0.054,
450: 0.053,
455: 0.053,
460: 0.052,
465: 0.052,
470: 0.052,
475: 0.053,
480: 0.054,
485: 0.055,
490: 0.057,
495: 0.059,
500: 0.061,
505: 0.062,
510: 0.065,
515: 0.067,
520: 0.070,
525: 0.072,
530: 0.074,
535: 0.075,
540: 0.076,
545: 0.078,
550: 0.079,
555: 0.082,
560: 0.087,
565: 0.092,
570: 0.100,
575: 0.107,
580: 0.115,
585: 0.122,
590: 0.129,
595: 0.134,
600: 0.138,
605: 0.142,
610: 0.146,
615: 0.150,
620: 0.154,
625: 0.158,
630: 0.163,
635: 0.167,
640: 0.173,
645: 0.180,
650: 0.188,
655: 0.196,
660: 0.204,
665: 0.213,
670: 0.222,
675: 0.231,
680: 0.242,
685: 0.251,
690: 0.261,
695: 0.271,
700: 0.282,
705: 0.294,
710: 0.305,
715: 0.318,
720: 0.334,
725: 0.354,
730: 0.372,
735: 0.392,
740: 0.409,
745: 0.420,
750: 0.436,
755: 0.450,
760: 0.462,
765: 0.465,
770: 0.448,
775: 0.432,
780: 0.421,
}
sd = SpectralDistribution(sample_sd_data, name="Sample")
plt.close(plot_single_sd(sd, **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Tutorial_SD_Interpolation.png"
)
sd_copy = sd.copy()
sd_copy.interpolate(SpectralShape(400, 770, 1))
plt.close(
plot_multi_sds(
[sd, sd_copy], bounding_box=[730, 780, 0.25, 0.5], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Tutorial_Sample_Swatch.png"
)
sd = SpectralDistribution(sample_sd_data)
cmfs = MSDS_CMFS_STANDARD_OBSERVER["CIE 1931 2 Degree Standard Observer"]
illuminant = SDS_ILLUMINANTS["D65"]
with domain_range_scale("1"):
XYZ = sd_to_XYZ(sd, cmfs, illuminant)
RGB = XYZ_to_sRGB(XYZ)
plt.close(
plot_single_colour_swatch(
ColourSwatch(RGB, "Sample"),
text_kwargs={"size": "x-large"},
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Tutorial_Neutral5.png"
)
patch_name = "neutral 5 (.70 D)"
patch_sd = SDS_COLOURCHECKERS["ColorChecker N Ohta"][patch_name]
with domain_range_scale("1"):
XYZ = sd_to_XYZ(patch_sd, cmfs, illuminant)
RGB = XYZ_to_sRGB(XYZ)
plt.close(
plot_single_colour_swatch(
ColourSwatch(RGB, patch_name.title()),
text_kwargs={"size": "x-large"},
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Tutorial_Colour_Checker.png"
)
plt.close(
plot_single_colour_checker(
colour_checker="ColorChecker 2005",
text_kwargs={"visible": False},
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Tutorial_CIE_1931_Chromaticity_Diagram.png"
)
xy = XYZ_to_xy(XYZ)
plot_chromaticity_diagram_CIE1931(standalone=False)
x, y = xy
plt.plot(x, y, "o-", color="white")
# Annotating the plot.
plt.annotate(
patch_sd.name.title(),
xy=xy,
xytext=(-50, 30),
textcoords="offset points",
arrowprops=dict(arrowstyle="->", connectionstyle="arc3, rad=-0.2"),
)
plt.close(
render(
standalone=True,
limits=(-0.1, 0.9, -0.1, 0.9),
x_tighten=True,
y_tighten=True,
**arguments,
)[0]
)
# *************************************************************************
# "basics.rst"
# *************************************************************************
arguments["filename"] = os.path.join(
output_directory, "Basics_Logo_Small_001_CIE_XYZ.png"
)
RGB = read_image(os.path.join(output_directory, "Logo_Small_001.png"))[
..., 0:3
]
XYZ = sRGB_to_XYZ(RGB)
plt.close(
plot_image(XYZ, text_kwargs={"text": "sRGB to XYZ"}, **arguments)[0]
)