""" Common Plotting =============== Define the common plotting objects. - :func:`colour.plotting.colour_style` - :func:`colour.plotting.override_style` - :func:`colour.plotting.font_scaling` - :func:`colour.plotting.XYZ_to_plotting_colourspace` - :class:`colour.plotting.ColourSwatch` - :func:`colour.plotting.colour_cycle` - :func:`colour.plotting.artist` - :func:`colour.plotting.camera` - :func:`colour.plotting.decorate` - :func:`colour.plotting.boundaries` - :func:`colour.plotting.display` - :func:`colour.plotting.render` - :func:`colour.plotting.label_rectangles` - :func:`colour.plotting.uniform_axes3d` - :func:`colour.plotting.plot_single_colour_swatch` - :func:`colour.plotting.plot_multi_colour_swatches` - :func:`colour.plotting.plot_single_function` - :func:`colour.plotting.plot_multi_functions` - :func:`colour.plotting.plot_image` """ from __future__ import annotations import contextlib import functools import itertools import typing from contextlib import contextmanager from dataclasses import dataclass, field from functools import partial import matplotlib.cm import matplotlib.font_manager import matplotlib.pyplot as plt import matplotlib.ticker import numpy as np from cycler import cycler from matplotlib.colors import LinearSegmentedColormap from matplotlib.figure import Figure, SubFigure if typing.TYPE_CHECKING: from matplotlib.axes import Axes from matplotlib.patches import Patch from mpl_toolkits.mplot3d.axes3d import Axes3D from colour.characterisation import CCS_COLOURCHECKERS, ColourChecker from colour.colorimetry import ( MSDS_CMFS, SDS_ILLUMINANTS, SDS_LIGHT_SOURCES, MultiSpectralDistributions, SpectralDistribution, ) if typing.TYPE_CHECKING: from colour.hints import ( Any, Callable, Dict, Domain1, Generator, Literal, LiteralChromaticAdaptationTransform, LiteralFontScaling, LiteralRGBColourspace, Mapping, PathLike, Range1, Real, Sequence, Tuple, ) from colour.hints import ArrayLike, List, TypedDict, cast from colour.models import RGB_COLOURSPACES, RGB_Colourspace, XYZ_to_RGB from colour.utilities import ( CanonicalMapping, Structure, as_float_array, as_int_scalar, attest, filter_mapping, first_item, is_sibling, optional, runtime_warning, validate_method, ) from colour.utilities.deprecation import handle_arguments_deprecation __author__ = "Colour Developers" __copyright__ = "Copyright 2013 Colour Developers" __license__ = "BSD-3-Clause - https://opensource.org/licenses/BSD-3-Clause" __maintainer__ = "Colour Developers" __email__ = "colour-developers@colour-science.org" __status__ = "Production" __all__ = [ "CONSTANTS_COLOUR_STYLE", "CONSTANTS_ARROW_STYLE", "colour_style", "override_style", "font_scaling", "XYZ_to_plotting_colourspace", "ColourSwatch", "colour_cycle", "KwargsArtist", "artist", "KwargsCamera", "camera", "KwargsRender", "render", "label_rectangles", "uniform_axes3d", "filter_passthrough", "filter_RGB_colourspaces", "filter_cmfs", "filter_illuminants", "filter_colour_checkers", "update_settings_collection", "plot_single_colour_swatch", "plot_multi_colour_swatches", "plot_single_function", "plot_multi_functions", "plot_image", "plot_ray", ] CONSTANTS_COLOUR_STYLE: Structure = Structure( colour=Structure( darkest="#111111", darker="#222222", dark="#333333", dim="#505050", average="#808080", light="#D5D5D5", bright="#EEEEEE", brighter="#F0F0F0", brightest="#F5F5F5", cycle=( "#F44336", "#9C27B0", "#3F51B5", "#03A9F4", "#009688", "#8BC34A", "#FFEB3B", "#FF9800", "#795548", "#607D8B", ), map=LinearSegmentedColormap.from_list( "colour", ( "#F44336", "#9C27B0", "#3F51B5", "#03A9F4", "#009688", "#8BC34A", "#FFEB3B", "#FF9800", "#795548", "#607D8B", ), ), cmap="inferno", colourspace=RGB_COLOURSPACES["sRGB"], ), font=Structure( { "size": 10, "scaling": Structure( xx_small=0.579, x_small=0.694, small=0.833, medium=1, large=1 / 0.579, x_large=1 / 0.694, xx_large=1 / 0.833, ), } ), opacity=Structure(high=0.75, medium=0.5, low=0.25), geometry=Structure(x_long=10, long=5, medium=2.5, short=1, x_short=0.5), hatch=Structure( patterns=( "\\\\", "o", "x", ".", "*", "//", ) ), zorder=Structure( { "background_polygon": -140, "background_scatter": -130, "background_line": -120, "background_annotation": -110, "background_label": -100, "midground_polygon": -90, "midground_scatter": -80, "midground_line": -70, "midground_annotation": -60, "midground_label": -50, "foreground_polygon": -40, "foreground_scatter": -30, "foreground_line": -20, "foreground_annotation": -10, "foreground_label": 0, } ), ) """Various defaults settings used across the plotting sub-package.""" # NOTE: Adding our font scaling items so that they can be tweaked without # affecting *Matplotplib* ones. for _scaling, _value in CONSTANTS_COLOUR_STYLE.font.scaling.items(): matplotlib.font_manager.font_scalings[ f"{_scaling.replace('_', '-')}-colour-science" ] = _value del _scaling, _value CONSTANTS_ARROW_STYLE: Structure = Structure( color=CONSTANTS_COLOUR_STYLE.colour.dark, headwidth=CONSTANTS_COLOUR_STYLE.geometry.short * 4, headlength=CONSTANTS_COLOUR_STYLE.geometry.long, width=CONSTANTS_COLOUR_STYLE.geometry.short * 0.5, shrink=CONSTANTS_COLOUR_STYLE.geometry.short * 0.1, connectionstyle="arc3,rad=-0.2", ) """Annotation arrow settings used across the plotting sub-package.""" def colour_style(use_style: bool = True) -> dict: """ Return the *Colour* plotting style configuration. Parameters ---------- use_style Whether to apply the style configuration to *Matplotlib*. Returns ------- :class:`dict` *Colour* plotting style configuration dictionary. """ constants = CONSTANTS_COLOUR_STYLE style = { # Figure Size Settings "figure.figsize": (12.80, 7.20), "figure.dpi": 100, "savefig.dpi": 100, "savefig.bbox": "standard", # Font Settings # 'font.size': 12, "axes.titlesize": "x-large", "axes.labelsize": "larger", "legend.fontsize": "small", "xtick.labelsize": "medium", "ytick.labelsize": "medium", # Text Settings "text.color": constants.colour.darkest, # Tick Settings "xtick.top": False, "xtick.bottom": True, "ytick.right": False, "ytick.left": True, "xtick.minor.visible": True, "ytick.minor.visible": True, "xtick.direction": "out", "ytick.direction": "out", "xtick.major.size": constants.geometry.long * 1.25, "xtick.minor.size": constants.geometry.long * 0.75, "ytick.major.size": constants.geometry.long * 1.25, "ytick.minor.size": constants.geometry.long * 0.75, "xtick.major.width": constants.geometry.short, "xtick.minor.width": constants.geometry.short, "ytick.major.width": constants.geometry.short, "ytick.minor.width": constants.geometry.short, # Spine Settings "axes.linewidth": constants.geometry.short, "axes.edgecolor": constants.colour.dark, # Title Settings "axes.titlepad": plt.rcParams["font.size"] * 0.75, # Axes Settings "axes.facecolor": constants.colour.brightest, "axes.grid": True, "axes.grid.which": "major", "axes.grid.axis": "both", # Grid Settings "axes.axisbelow": True, "grid.linewidth": constants.geometry.short * 0.5, "grid.linestyle": "--", "grid.color": constants.colour.light, # Legend "legend.frameon": True, "legend.framealpha": constants.opacity.high, "legend.fancybox": False, "legend.facecolor": constants.colour.brighter, "legend.borderpad": constants.geometry.short * 0.5, # Lines "lines.linewidth": constants.geometry.short, "lines.markersize": constants.geometry.short * 3, "lines.markeredgewidth": constants.geometry.short * 0.75, # Cycle "axes.prop_cycle": cycler(color=constants.colour.cycle), } if use_style: plt.rcParams.update(style) return style def override_style(**kwargs: Any) -> Callable: """ Decorate a function to override *Matplotlib* style. Other Parameters ---------------- kwargs Keywords arguments for *Matplotlib* style configuration. Returns ------- Callable Decorated function with overridden *Matplotlib* style. Examples -------- >>> @override_style(**{"text.color": "red"}) ... def f(*args, **kwargs): ... plt.text(0.5, 0.5, "This is a text!") ... plt.show() >>> f() # doctest: +SKIP """ keywords = dict(kwargs) def wrapper(function: Callable) -> Callable: """Wrap specified function wrapper.""" @functools.wraps(function) def wrapped(*args: Any, **kwargs: Any) -> Any: """Wrap specified function.""" keywords.update(kwargs) style_overrides = { key: value for key, value in keywords.items() if key in plt.rcParams } with plt.style.context(style_overrides): return function(*args, **kwargs) return wrapped return wrapper @contextmanager def font_scaling(scaling: LiteralFontScaling, value: float) -> Generator: """ Set a temporary *Matplotlib* font scaling using a context manager. Parameters ---------- scaling Font scaling to temporarily set. value Value to temporarily set the font scaling with. Yields ------ Generator. Examples -------- >>> with font_scaling("medium-colour-science", 2): ... print(matplotlib.font_manager.font_scalings["medium-colour-science"]) 2 >>> print(matplotlib.font_manager.font_scalings["medium-colour-science"]) 1 """ current_value = matplotlib.font_manager.font_scalings[scaling] matplotlib.font_manager.font_scalings[scaling] = value yield matplotlib.font_manager.font_scalings[scaling] = current_value def XYZ_to_plotting_colourspace( XYZ: Domain1, illuminant: ArrayLike = RGB_COLOURSPACES["sRGB"].whitepoint, chromatic_adaptation_transform: ( LiteralChromaticAdaptationTransform | str | None ) = "CAT02", apply_cctf_encoding: bool = True, ) -> Range1: """ Convert from *CIE XYZ* tristimulus values to the default plotting colourspace. Parameters ---------- XYZ *CIE XYZ* tristimulus values. illuminant Source illuminant chromaticity coordinates. chromatic_adaptation_transform *Chromatic adaptation* transform. apply_cctf_encoding Apply the default plotting colourspace encoding colour component transfer function / opto-electronic transfer function. Returns ------- :class:`numpy.ndarray` Default plotting colourspace colour array. Notes ----- +------------+-----------------------+---------------+ | **Domain** | **Scale - Reference** | **Scale - 1** | +============+=======================+===============+ | ``XYZ`` | 1 | 1 | +------------+-----------------------+---------------+ +------------+-----------------------+---------------+ | **Range** | **Scale - Reference** | **Scale - 1** | +============+=======================+===============+ | ``RGB`` | 1 | 1 | +------------+-----------------------+---------------+ Examples -------- >>> import numpy as np >>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952]) >>> XYZ_to_plotting_colourspace(XYZ) # doctest: +ELLIPSIS array([ 0.7057393..., 0.1924826..., 0.2235416...]) """ return XYZ_to_RGB( XYZ, CONSTANTS_COLOUR_STYLE.colour.colourspace, illuminant, chromatic_adaptation_transform, apply_cctf_encoding, ) @dataclass class ColourSwatch: """ Define a data structure for a colour swatch. Parameters ---------- RGB RGB colour values representing the swatch. name Name identifier for the colour swatch. """ RGB: ArrayLike name: str | None = field(default_factory=lambda: None) def colour_cycle(**kwargs: Any) -> itertools.cycle: """ Create a colour cycle iterator using the specified colour map. Other Parameters ---------------- colour_cycle_map Matplotlib colourmap name. colour_cycle_count Colours count to pick in the colourmap. Returns ------- :class:`itertools.cycle` Colour cycle iterator. """ settings = Structure( colour_cycle_map=CONSTANTS_COLOUR_STYLE.colour.map, colour_cycle_count=len(CONSTANTS_COLOUR_STYLE.colour.cycle), ) settings.update(kwargs) samples = np.linspace(0, 1, settings.colour_cycle_count) if isinstance(settings.colour_cycle_map, LinearSegmentedColormap): cycle = settings.colour_cycle_map(samples) else: cycle = getattr(plt.cm, settings.colour_cycle_map)(samples) return itertools.cycle(cycle) class KwargsArtist(TypedDict): """ Define keyword argument types for the :func:`colour.plotting.artist` definition. Parameters ---------- axes Axes that will be passed through without creating a new figure. uniform Whether to create the figure with an equal aspect ratio. """ axes: Axes uniform: bool def artist(**kwargs: KwargsArtist | Any) -> Tuple[Figure, Axes]: """ Return the current figure and its axes or create a new one. Other Parameters ---------------- kwargs {:func:`colour.plotting.common.KwargsArtist`}, See the documentation of the previously listed class. Returns ------- :class:`tuple` Current figure and axes. """ width, height = plt.rcParams["figure.figsize"] figure_size = (width, width) if kwargs.get("uniform") else (width, height) axes = kwargs.get("axes") if axes is None: figure = plt.figure(figsize=figure_size) return figure, figure.gca() axes = cast("Axes", axes) figure = axes.figure if isinstance(figure, SubFigure): figure = figure.get_figure() return cast("Figure", figure), axes class KwargsCamera(TypedDict): """ Define the keyword argument types for the :func:`colour.plotting.camera` definition. Parameters ---------- figure Figure to apply the render elements onto. axes Axes to apply the render elements onto. azimuth Camera azimuth. elevation Camera elevation. camera_aspect Matplotlib axes aspect. Default is *equal*. """ figure: Figure axes: Axes azimuth: float | None elevation: float | None camera_aspect: Literal["equal"] | str def camera(**kwargs: KwargsCamera | Any) -> Tuple[Figure, Axes3D]: """ Configure camera settings for the current 3D visualization. Other Parameters ---------------- kwargs {:func:`colour.plotting.common.KwargsCamera`}, See the documentation of the previously listed class. Returns ------- :class:`tuple` Current figure and axes. """ figure = cast("Figure", kwargs.get("figure", plt.gcf())) axes = cast("Axes3D", kwargs.get("axes", plt.gca())) settings = Structure(camera_aspect="equal", elevation=None, azimuth=None) settings.update(kwargs) if settings.camera_aspect == "equal": uniform_axes3d(axes=axes) axes.view_init(elev=settings.elevation, azim=settings.azimuth) return figure, axes class KwargsRender(TypedDict): """ Define the keyword argument types for the :func:`colour.plotting.render` definition. Parameters ---------- figure Figure to apply the render elements onto. axes Axes to apply the render elements onto. filename Figure will be saved using the specified ``filename`` argument. show Whether to show the figure and call :func:`matplotlib.pyplot.show` definition. block Whether to wait for all figures to be closed before returning. If `True` block and run the GUI main loop until all figure windows are closed. If `False` ensure that all figure windows are displayed and return immediately. In this case, you are responsible for ensuring that the event loop is running to have responsive figures. Defaults to True in non-interactive mode and to False in interactive mode. aspect Matplotlib axes aspect. axes_visible Whether the axes are visible. Default is *True*. bounding_box Array defining current axes limits such as `bounding_box = (x min, x max, y min, y max)`. tight_layout Whether to invoke the :func:`matplotlib.pyplot.tight_layout` definition. legend Whether to display the legend. Default is *False*. legend_columns Number of columns in the legend. Default is *1*. transparent_background Whether to turn off the background patch. Default is *True*. title Figure title. wrap_title Whether to wrap the figure title. Default is *True*. x_label *X* axis label. y_label *Y* axis label. x_ticker Whether to display the *X* axis ticker. Default is *True*. y_ticker Whether to display the *Y* axis ticker. Default is *True*. """ figure: Figure axes: Axes filename: str | PathLike show: bool block: bool aspect: Literal["auto", "equal"] | float axes_visible: bool bounding_box: ArrayLike tight_layout: bool legend: bool legend_columns: int transparent_background: bool title: str wrap_title: bool x_label: str y_label: str x_ticker: bool y_ticker: bool def render( **kwargs: KwargsRender | Any, ) -> Tuple[Figure, Axes] | Tuple[Figure, Axes3D]: """ Render the current figure while adjusting various settings such as the bounding box, title, or background transparency. Other Parameters ---------------- kwargs {:func:`colour.plotting.common.KwargsRender`}, See the documentation of the previously listed class. Returns ------- :class:`tuple` Current figure and axes. """ figure = cast("Figure", kwargs.get("figure", plt.gcf())) axes = cast("Axes", kwargs.get("axes", plt.gca())) kwargs = handle_arguments_deprecation( { "ArgumentRenamed": [["standalone", "show"]], }, **kwargs, ) settings = Structure( filename=None, show=True, block=True, aspect=None, axes_visible=True, bounding_box=None, tight_layout=True, legend=False, legend_columns=1, transparent_background=True, title=None, wrap_title=True, x_label=None, y_label=None, x_ticker=True, y_ticker=True, ) settings.update(kwargs) if settings.aspect: axes.set_aspect(settings.aspect) if not settings.axes_visible: axes.set_axis_off() if settings.bounding_box: axes.set_xlim(settings.bounding_box[0], settings.bounding_box[1]) axes.set_ylim(settings.bounding_box[2], settings.bounding_box[3]) if settings.title: axes.set_title(settings.title, wrap=settings.wrap_title) if settings.x_label: axes.set_xlabel(settings.x_label) if settings.y_label: axes.set_ylabel(settings.y_label) if not settings.x_ticker: axes.set_xticks([]) if not settings.y_ticker: axes.set_yticks([]) if settings.legend: axes.legend(ncol=settings.legend_columns) if settings.tight_layout: figure.tight_layout() if settings.transparent_background: figure.patch.set_alpha(0) if settings.filename is not None: figure.savefig(str(settings.filename)) if settings.show: plt.show(block=settings.block) return figure, axes def label_rectangles( labels: Sequence[str | Real], rectangles: Sequence[Patch], rotation: Literal["horizontal", "vertical"] | str = "vertical", text_size: float = CONSTANTS_COLOUR_STYLE.font.scaling.medium, offset: ArrayLike | None = None, **kwargs: Any, ) -> Tuple[Figure, Axes]: """ Add labels above specified rectangles. Parameters ---------- labels Text labels to display above the rectangles. rectangles Rectangle patches used to determine label positions and values. rotation Orientation of the labels. text_size Font size for the labels. offset Label offset as percentages of the largest rectangle dimensions. Other Parameters ---------------- figure Figure to apply the render elements onto. axes Axes to apply the render elements onto. Returns ------- :class:`tuple` Current figure and axes. """ rotation = validate_method( rotation, ("horizontal", "vertical"), '"{0}" rotation is invalid, it must be one of {1}!', ) figure = kwargs.get("figure", plt.gcf()) axes = kwargs.get("axes", plt.gca()) offset = as_float_array(optional(offset, (0.0, 0.025))) x_m, y_m = 0, 0 for rectangle in rectangles: x_m = max(x_m, rectangle.get_width()) # pyright: ignore y_m = max(y_m, rectangle.get_height()) # pyright: ignore for i, rectangle in enumerate(rectangles): x = rectangle.get_x() # pyright: ignore height = rectangle.get_height() # pyright: ignore width = rectangle.get_width() # pyright: ignore axes.text( x + width / 2 + offset[0] * width, height + offset[1] * y_m, labels[i], ha="center", va="bottom", rotation=rotation, fontsize=text_size, clip_on=True, zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_label, ) return figure, axes def uniform_axes3d(**kwargs: Any) -> Tuple[Figure, Axes3D]: """ Set equal aspect ratio to the specified 3D axes. Other Parameters ---------------- figure Figure to apply the render elements onto. axes Axes to apply the render elements onto. Returns ------- :class:`tuple` Current figure and axes. """ figure = kwargs.get("figure", plt.gcf()) axes = kwargs.get("axes", plt.gca()) with contextlib.suppress(NotImplementedError): # pragma: no cover # TODO: Reassess according to # https://github.com/matplotlib/matplotlib/issues/1077 axes.set_aspect("equal") extents = np.array([getattr(axes, f"get_{axis}lim")() for axis in "xyz"]) centers = np.mean(extents, axis=1) extent = np.max(np.abs(extents[..., 1] - extents[..., 0])) for center, axis in zip(centers, "xyz", strict=True): getattr(axes, f"set_{axis}lim")(center - extent / 2, center + extent / 2) return figure, axes def filter_passthrough( mapping: Mapping, filterers: Any | str | Sequence[Any | str], allow_non_siblings: bool = True, ) -> dict: """ Filter mapping objects matching specified filterers while passing through class instances whose type is one of the mapping element types. Enable passing custom but compatible objects to plotting definitions that by default expect keys from dataset elements. For example, a typical call to the :func:`colour.plotting.plot_multi_illuminant_sds` definition is as follows: >>> import colour >>> colour.plotting.plot_multi_illuminant_sds(["A"]) ... # doctest: +SKIP With the previous example, it is also possible to pass a custom spectral distribution as follows: >>> data = { ... 500: 0.0651, ... 520: 0.0705, ... 540: 0.0772, ... 560: 0.0870, ... 580: 0.1128, ... 600: 0.1360, ... } >>> colour.plotting.plot_multi_illuminant_sds( ... ["A", colour.SpectralDistribution(data)] ... ) ... # doctest: +SKIP Similarly, a typical call to the :func:`colour.plotting.plot_planckian_locus_in_chromaticity_diagram_CIE1931` definition is as follows: >>> colour.plotting.plot_planckian_locus_in_chromaticity_diagram_CIE1931(["A"]) ... # doctest: +SKIP But it is also possible to pass a custom whitepoint as follows: >>> colour.plotting.plot_planckian_locus_in_chromaticity_diagram_CIE1931( ... ["A", {"Custom": np.array([1 / 3 + 0.05, 1 / 3 + 0.05])}] ... ) ... # doctest: +SKIP Parameters ---------- mapping Mapping to filter. filterers Filterer or object class instance (which is passed through directly if its type is one of the mapping element types) or list of filterers. allow_non_siblings Whether to allow non-siblings to be also passed through. Returns ------- :class:`dict` Filtered mapping. Notes ----- - If the mapping passed is a :class:`colour.utilities.CanonicalMapping` class instance, then the lower, slugified and canonical keys are also used for matching. """ if isinstance(filterers, str) or not isinstance(filterers, (list, tuple)): filterers = [filterers] string_filterers: List[str] = [ filterer for filterer in filterers if isinstance(filterer, str) ] object_filterers: List[Any] = [ filterer for filterer in filterers if is_sibling(filterer, mapping) ] if allow_non_siblings: non_siblings = [ filterer for filterer in filterers if filterer not in string_filterers and filterer not in object_filterers ] if non_siblings: runtime_warning( f'Non-sibling elements are passed-through: "{non_siblings}"' ) object_filterers.extend(non_siblings) filtered_mapping = filter_mapping(mapping, string_filterers) for filterer in object_filterers: # TODO: Consider using "MutableMapping" here. if isinstance(filterer, (dict, CanonicalMapping)): for key, value in filterer.items(): filtered_mapping[key] = value else: try: name = filterer.name except AttributeError: try: name = filterer.__name__ except AttributeError: name = str(id(filterer)) filtered_mapping[name] = filterer return filtered_mapping def filter_RGB_colourspaces( filterers: ( RGB_Colourspace | LiteralRGBColourspace | str | Sequence[RGB_Colourspace | LiteralRGBColourspace | str] ), allow_non_siblings: bool = True, ) -> Dict[str, RGB_Colourspace]: """ Filter the *RGB* colourspaces matching the specified filterers. Parameters ---------- filterers Filterer, :class:`colour.RGB_Colourspace` class instance (which is passed through directly if its type is one of the mapping element types), or list of filterers. The ``filterers`` elements can also be of any form supported by the :func:`colour.plotting.common.filter_passthrough` definition. allow_non_siblings Whether to allow non-siblings to be also passed through. Returns ------- :class:`dict` Filtered *RGB* colourspaces. """ return filter_passthrough(RGB_COLOURSPACES, filterers, allow_non_siblings) def filter_cmfs( filterers: ( MultiSpectralDistributions | str | Sequence[MultiSpectralDistributions | str] ), allow_non_siblings: bool = True, ) -> Dict[str, MultiSpectralDistributions]: """ Filter the colour matching functions matching the specified filterers. Parameters ---------- filterers Filterer or :class:`colour.LMS_ConeFundamentals`, :class:`colour.RGB_ColourMatchingFunctions` or :class:`colour.XYZ_ColourMatchingFunctions` class instance (which is passed through directly if its type is one of the mapping element types) or list of filterers. ``filterers`` elements can also be of any form supported by the :func:`colour.plotting.common.filter_passthrough` definition. allow_non_siblings Whether to allow non-siblings to be also passed through. Returns ------- :class:`dict` Filtered colour matching functions. """ return filter_passthrough(MSDS_CMFS, filterers, allow_non_siblings) def filter_illuminants( filterers: SpectralDistribution | str | Sequence[SpectralDistribution | str], allow_non_siblings: bool = True, ) -> Dict[str, SpectralDistribution]: """ Filter the illuminants matching the specified filterers. Parameters ---------- filterers Filterer or :class:`colour.SpectralDistribution` class instance (which is passed through directly if its type is one of the mapping element types) or list of filterers. ``filterers`` elements can also be of any form supported by the :func:`colour.plotting.common.filter_passthrough` definition. allow_non_siblings Whether to allow non-siblings to be also passed through. Returns ------- :class:`dict` Filtered illuminants. """ illuminants = {} illuminants.update( filter_passthrough(SDS_ILLUMINANTS, filterers, allow_non_siblings) ) illuminants.update( filter_passthrough(SDS_LIGHT_SOURCES, filterers, allow_non_siblings) ) return illuminants def filter_colour_checkers( filterers: ColourChecker | str | Sequence[ColourChecker | str], allow_non_siblings: bool = True, ) -> Dict[str, ColourChecker]: """ Filter the colour checkers matching the specified filterers. Parameters ---------- filterers Filterer or :class:`colour.characterisation.ColourChecker` class instance (which is passed through directly if its type is one of the mapping element types) or list of filterers. ``filterers`` elements can also be of any form supported by the :func:`colour.plotting.common.filter_passthrough` definition. allow_non_siblings Whether to allow non-siblings to be also passed through. Returns ------- :class:`dict` Filtered colour checkers. """ return filter_passthrough(CCS_COLOURCHECKERS, filterers, allow_non_siblings) def update_settings_collection( settings_collection: dict | List[dict], keyword_arguments: dict | List[dict], expected_count: int, ) -> None: """ Update the specified settings collection *in-place* with the specified keyword arguments and expected count of settings collection elements. Parameters ---------- settings_collection Settings collection to update. keyword_arguments Keyword arguments to update the settings collection. expected_count Expected count of settings collection elements. Examples -------- >>> settings_collection = [{1: 2}, {3: 4}] >>> keyword_arguments = {5: 6} >>> update_settings_collection(settings_collection, keyword_arguments, 2) >>> print(settings_collection) [{1: 2, 5: 6}, {3: 4, 5: 6}] >>> settings_collection = [{1: 2}, {3: 4}] >>> keyword_arguments = [{5: 6}, {7: 8}] >>> update_settings_collection(settings_collection, keyword_arguments, 2) >>> print(settings_collection) [{1: 2, 5: 6}, {3: 4, 7: 8}] """ if not isinstance(keyword_arguments, dict): attest( len(keyword_arguments) == expected_count, "Multiple keyword arguments defined, but they do not " "match the expected count!", ) for i, settings in enumerate(settings_collection): if isinstance(keyword_arguments, dict): settings.update(keyword_arguments) else: settings.update(keyword_arguments[i]) @override_style( **{ "axes.grid": False, "xtick.bottom": False, "ytick.left": False, "xtick.labelbottom": False, "ytick.labelleft": False, } ) def plot_single_colour_swatch( colour_swatch: ArrayLike | ColourSwatch, **kwargs: Any ) -> Tuple[Figure, Axes]: """ Plot a single colour swatch. Parameters ---------- colour_swatch Colour swatch to plot, either a regular `ArrayLike` or a :class:`colour.plotting.ColourSwatch` class instance. Other Parameters ---------------- kwargs {:func:`colour.plotting.artist`, :func:`colour.plotting.plot_multi_colour_swatches`, :func:`colour.plotting.render`}, See the documentation of the previously listed definitions. Returns ------- :class:`tuple` Current figure and axes. Examples -------- >>> RGB = ColourSwatch((0.45620519, 0.03081071, 0.04091952)) >>> plot_single_colour_swatch(RGB) # doctest: +ELLIPSIS (
, <...Axes...>) .. image:: ../_static/Plotting_Plot_Single_Colour_Swatch.png :align: center :alt: plot_single_colour_swatch """ return plot_multi_colour_swatches((colour_swatch,), **kwargs) @override_style( **{ "axes.grid": False, "xtick.bottom": False, "ytick.left": False, "xtick.labelbottom": False, "ytick.labelleft": False, } ) def plot_multi_colour_swatches( colour_swatches: ArrayLike | Sequence[ArrayLike | ColourSwatch], width: float = 1, height: float = 1, spacing: float = 0, columns: int | None = None, direction: Literal["+y", "-y"] | str = "+y", text_kwargs: dict | None = None, background_colour: ArrayLike = (1.0, 1.0, 1.0), compare_swatches: Literal["Diagonal", "Stacked"] | str | None = None, **kwargs: Any, ) -> Tuple[Figure, Axes]: """ Plot colour swatches with configurable layout and comparison options. Parameters ---------- colour_swatches Colour swatch sequence, either a regular `ArrayLike` or a sequence of :class:`colour.plotting.ColourSwatch` class instances. width Colour swatch width. height Colour swatch height. spacing Colour swatches spacing. columns Colour swatches columns count, defaults to the colour swatch count or half of it if comparing. direction Row stacking direction. text_kwargs Keyword arguments for the :func:`matplotlib.pyplot.text` definition. The following special keywords can also be used: - ``offset``: Sets the text offset. - ``visible``: Sets the text visibility. background_colour Background colour. compare_swatches Whether to compare the swatches, in which case the colour swatch count must be an even number with alternating reference colour swatches and test colour swatches. *Stacked* will draw the test colour swatch in the center of the reference colour swatch, *Diagonal* will draw the reference colour swatch in the upper left diagonal area and the test colour swatch in the bottom right diagonal area. Other Parameters ---------------- kwargs {:func:`colour.plotting.artist`, :func:`colour.plotting.render`}, See the documentation of the previously listed definitions. Returns ------- :class:`tuple` Current figure and axes. Examples -------- >>> RGB_1 = ColourSwatch((0.45293517, 0.31732158, 0.26414773)) >>> RGB_2 = ColourSwatch((0.77875824, 0.57726450, 0.50453169)) >>> plot_multi_colour_swatches([RGB_1, RGB_2]) # doctest: +ELLIPSIS (
, <...Axes...>) .. image:: ../_static/Plotting_Plot_Multi_Colour_Swatches.png :align: center :alt: plot_multi_colour_swatches """ direction = validate_method( direction, ("+y", "-y"), '"{0}" direction is invalid, it must be one of {1}!', ) if compare_swatches is not None: compare_swatches = validate_method( compare_swatches, ("Diagonal", "Stacked"), '"{0}" compare swatches method is invalid, it must be one of {1}!', ) _figure, axes = artist(**kwargs) # Handling case where `colour_swatches` is a regular *ArrayLike*. colour_swatches = list(colour_swatches) # pyright: ignore colour_swatches_converted = [] if not isinstance(first_item(colour_swatches), ColourSwatch): for _i, colour_swatch in enumerate( np.reshape( as_float_array(cast("ArrayLike", colour_swatches))[..., :3], (-1, 3) ) ): colour_swatches_converted.append(ColourSwatch(colour_swatch)) else: colour_swatches_converted = cast("List[ColourSwatch]", colour_swatches) colour_swatches = colour_swatches_converted if compare_swatches is not None: attest( len(colour_swatches) % 2 == 0, "Cannot compare an odd number of colour swatches!", ) colour_swatches_reference = colour_swatches[0::2] colour_swatches_test = colour_swatches[1::2] else: colour_swatches_reference = colour_swatches_test = colour_swatches columns = optional(columns, len(colour_swatches_reference)) text_settings = { "offset": 0.05, "visible": True, "zorder": CONSTANTS_COLOUR_STYLE.zorder.midground_label, } if text_kwargs is not None: text_settings.update(text_kwargs) text_offset = text_settings.pop("offset") offset_X: float = 0 offset_Y: float = 0 x_min, x_max, y_min, y_max = 0, width, 0, height y = 1 if direction == "+y" else -1 for i, colour_swatch in enumerate(colour_swatches_reference): if i % columns == 0 and i != 0: offset_X = 0 offset_Y += (height + spacing) * y x_0, x_1 = offset_X, offset_X + width y_0, y_1 = offset_Y, offset_Y + height * y axes.fill( (x_0, x_1, x_1, x_0), (y_0, y_0, y_1, y_1), color=np.clip(colour_swatch.RGB, 0, 1), zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_polygon, ) if compare_swatches == "stacked": margin_X = width * 0.25 margin_Y = height * 0.25 axes.fill( ( x_0 + margin_X, x_1 - margin_X, x_1 - margin_X, x_0 + margin_X, ), ( y_0 + margin_Y * y, y_0 + margin_Y * y, y_1 - margin_Y * y, y_1 - margin_Y * y, ), color=np.clip(colour_swatches_test[i].RGB, 0, 1), zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_polygon, ) else: axes.fill( (x_0, x_1, x_1), (y_0, y_0, y_1), color=np.clip(colour_swatches_test[i].RGB, 0, 1), zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_polygon, ) if colour_swatch.name is not None and text_settings["visible"]: axes.text( x_0 + text_offset, y_0 + text_offset * y, colour_swatch.name, verticalalignment="bottom" if y == 1 else "top", clip_on=True, **text_settings, ) offset_X += width + spacing x_max = min(len(colour_swatches), as_int_scalar(columns)) x_max = x_max * width + x_max * spacing - spacing y_max = offset_Y axes.patch.set_facecolor(background_colour) # pyright: ignore if y == 1: bounding_box = [ x_min - spacing, x_max + spacing, y_min - spacing, y_max + spacing + height, ] else: bounding_box = [ x_min - spacing, x_max + spacing, y_max - spacing - height, y_min + spacing, ] settings: Dict[str, Any] = { "axes": axes, "bounding_box": bounding_box, "aspect": "equal", } settings.update(kwargs) return render(**settings) @override_style() def plot_single_function( function: Callable, samples: ArrayLike | None = None, log_x: int | None = None, log_y: int | None = None, plot_kwargs: dict | List[dict] | None = None, **kwargs: Any, ) -> Tuple[Figure, Axes]: """ Plot the specified function. Parameters ---------- function Function to plot. samples Samples to evaluate the functions with. log_x Log base to use for the *x* axis scale, if *None*, the *x* axis scale will be linear. log_y Log base to use for the *y* axis scale, if *None*, the *y* axis scale will be linear. plot_kwargs Keyword arguments for the :func:`matplotlib.pyplot.plot` definition, used to control the style of the plotted function. Other Parameters ---------------- kwargs {:func:`colour.plotting.artist`, :func:`colour.plotting.plot_multi_functions`, :func:`colour.plotting.render`}, See the documentation of the previously listed definitions. Returns ------- :class:`tuple` Current figure and axes. Examples -------- >>> from colour.models import gamma_function >>> plot_single_function(partial(gamma_function, exponent=1 / 2.2)) ... # doctest: +ELLIPSIS (
, <...Axes...>) .. image:: ../_static/Plotting_Plot_Single_Function.png :align: center :alt: plot_single_function """ try: name = function.__name__ except AttributeError: name = "Unnamed" settings: Dict[str, Any] = { "title": f"{name} - Function", "legend": False, } settings.update(kwargs) return plot_multi_functions( {name: function}, samples, log_x, log_y, plot_kwargs, **settings ) @override_style() def plot_multi_functions( functions: Dict[str, Callable], samples: ArrayLike | None = None, log_x: int | None = None, log_y: int | None = None, plot_kwargs: dict | List[dict] | None = None, **kwargs: Any, ) -> Tuple[Figure, Axes]: """ Plot specified functions. Parameters ---------- functions Functions to plot. samples Samples to evaluate the functions with. log_x Log base to use for the *x* axis scale, if *None*, the *x* axis scale will be linear. log_y Log base to use for the *y* axis scale, if *None*, the *y* axis scale will be linear. plot_kwargs Keyword arguments for the :func:`matplotlib.pyplot.plot` definition, used to control the style of the plotted functions. ``plot_kwargs`` can be either a single dictionary applied to all the plotted functions with the same settings or a sequence of dictionaries with different settings for each plotted function. Other Parameters ---------------- kwargs {:func:`colour.plotting.artist`, :func:`colour.plotting.render`}, See the documentation of the previously listed definitions. Returns ------- :class:`tuple` Current figure and axes. Examples -------- >>> 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), ... } >>> plot_multi_functions(functions) ... # doctest: +ELLIPSIS (
, <...Axes...>) .. image:: ../_static/Plotting_Plot_Multi_Functions.png :align: center :alt: plot_multi_functions """ settings: Dict[str, Any] = dict(kwargs) _figure, axes = artist(**settings) plot_settings_collection = [ { "label": f"{name}", "zorder": CONSTANTS_COLOUR_STYLE.zorder.midground_label, } for name in functions ] if plot_kwargs is not None: update_settings_collection( plot_settings_collection, plot_kwargs, len(functions) ) if log_x is not None and log_y is not None: attest( log_x >= 2 and log_y >= 2, "Log base must be equal or greater than 2.", ) plotting_function = axes.loglog axes.set_xscale("log", base=log_x) axes.set_yscale("log", base=log_y) elif log_x is not None: attest(log_x >= 2, "Log base must be equal or greater than 2.") plotting_function = partial(axes.semilogx, base=log_x) elif log_y is not None: attest(log_y >= 2, "Log base must be equal or greater than 2.") plotting_function = partial(axes.semilogy, base=log_y) else: plotting_function = axes.plot samples = optional(samples, np.linspace(0, 1, 1000)) for i, (_name, function) in enumerate(functions.items()): plotting_function(samples, function(samples), **plot_settings_collection[i]) x_label = f"x - Log Base {log_x} Scale" if log_x is not None else "x - Linear Scale" y_label = f"y - Log Base {log_y} Scale" if log_y is not None else "y - Linear Scale" settings = { "axes": axes, "legend": True, "title": f"{', '.join(functions)} - Functions", "x_label": x_label, "y_label": y_label, } settings.update(kwargs) return render(**settings) @override_style() def plot_image( image: ArrayLike, imshow_kwargs: dict | None = None, text_kwargs: dict | None = None, **kwargs: Any, ) -> Tuple[Figure, Axes]: """ Plot the specified image using matplotlib. Parameters ---------- image Image array to plot, typically as RGB or grayscale data. imshow_kwargs Keyword arguments for the :func:`matplotlib.pyplot.imshow` definition, controlling image display properties. text_kwargs Keyword arguments for the :func:`matplotlib.pyplot.text` definition, controlling text overlay properties. The following special keyword arguments can also be used: - ``offset`` : Sets the text offset position. Other Parameters ---------------- kwargs {:func:`colour.plotting.artist`, :func:`colour.plotting.render`}, See the documentation of the previously listed definitions for additional plotting controls. Returns ------- :class:`tuple` Current figure and axes objects from matplotlib. Examples -------- >>> import os >>> import colour >>> from colour import read_image >>> path = os.path.join( ... colour.__path__[0], ... "examples", ... "plotting", ... "resources", ... "Ishihara_Colour_Blindness_Test_Plate_3.png", ... ) >>> plot_image(read_image(path)) # doctest: +ELLIPSIS (
, <...Axes...>) .. image:: ../_static/Plotting_Plot_Image.png :align: center :alt: plot_image """ _figure, axes = artist(**kwargs) imshow_settings = { "interpolation": "nearest", "cmap": matplotlib.colormaps["Greys_r"], "zorder": CONSTANTS_COLOUR_STYLE.zorder.background_polygon, } if imshow_kwargs is not None: imshow_settings.update(imshow_kwargs) text_settings = { "text": None, "offset": 0.005, "color": CONSTANTS_COLOUR_STYLE.colour.brightest, "alpha": CONSTANTS_COLOUR_STYLE.opacity.high, "zorder": CONSTANTS_COLOUR_STYLE.zorder.midground_label, } if text_kwargs is not None: text_settings.update(text_kwargs) text_offset = text_settings.pop("offset") image = as_float_array(image) axes.imshow(np.clip(image, 0, 1), **imshow_settings) if text_settings["text"] is not None: text = text_settings.pop("text") axes.text( text_offset, text_offset, text, transform=axes.transAxes, ha="left", va="bottom", **text_settings, ) settings: Dict[str, Any] = { "axes": axes, "axes_visible": False, } settings.update(kwargs) return render(**settings) def plot_ray( axes: Axes, x_coords: ArrayLike, y_coords: ArrayLike, style: Literal["solid", "dashed"] | str = "solid", label: str | None = None, show_arrow: bool = True, show_dots: bool = False, ) -> None: """ Draw a ray path with optional arrow and interface dots. Parameters ---------- axes Axes to draw the ray on. x_coords X coordinates of the ray path. y_coords Y coordinates of the ray path. style Line style: 'solid' for transmitted rays, 'dashed' for reflected rays. label Label for the legend (only on first segment). show_arrow Whether to show directional arrow at midpoint. show_dots Whether to show dots at intermediate points. Examples -------- >>> import matplotlib.pyplot as plt >>> import numpy as np >>> _fig, axes = plt.subplots() >>> x = np.array([0, 1, 2]) >>> y = np.array([0, 1, 0]) >>> plot_ray(axes, x, y, style="solid", label="Ray") >>> plt.close() """ x_coords = as_float_array(x_coords) y_coords = as_float_array(y_coords) # Validate style style = validate_method(style, ("solid", "dashed")) # Draw the ray line linestyle = "-" if style == "solid" else "--" axes.plot( x_coords, y_coords, linestyle=linestyle, color="black", linewidth=2, label=label, zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_line, ) # Draw arrows on each segment if show_arrow: for i in range(len(x_coords) - 1): x_start, x_end = x_coords[i], x_coords[i + 1] y_start, y_end = y_coords[i], y_coords[i + 1] # Calculate midpoint mid_x = (x_start + x_end) / 2 mid_y = (y_start + y_end) / 2 # Calculate direction dx = x_end - x_start dy = y_end - y_start # Draw arrow at midpoint axes.annotate( "", xy=(mid_x + dx * 0.1, mid_y + dy * 0.1), xytext=(mid_x, mid_y), arrowprops=dict(arrowstyle="->", color="black", lw=1.5), zorder=CONSTANTS_COLOUR_STYLE.zorder.foreground_annotation, ) # Draw dots at intermediate points (exclude first and last) if show_dots and len(x_coords) > 2: axes.plot( x_coords[1:-1], y_coords[1:-1], "ko", markersize=6, zorder=CONSTANTS_COLOUR_STYLE.zorder.foreground_scatter, )