Source code for colour.plotting.common

# -*- coding: utf-8 -*-
"""
Common Plotting
===============

Defines the common plotting objects:

-   :func:`colour.plotting.colour_style`
-   :func:`colour.plotting.override_style`
-   :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 division

import functools
import itertools
import matplotlib
import matplotlib.cm
import matplotlib.pyplot as plt
import matplotlib.ticker
import numpy as np
import re
import textwrap
from collections import OrderedDict, namedtuple
from functools import partial
from matplotlib.colors import LinearSegmentedColormap

from colour.characterisation import COLOURCHECKERS
from colour.colorimetry import CMFS, ILLUMINANTS_SDS, LIGHT_SOURCES_SDS
from colour.models import RGB_COLOURSPACES, XYZ_to_RGB
from colour.utilities import (CaseInsensitiveMapping, Structure,
                              as_float_array, is_sibling, is_string,
                              filter_mapping, runtime_warning)

__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2019 - Colour Developers'
__license__ = 'New BSD License - https://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = '[email protected]'
__status__ = 'Production'

__all__ = [
    'COLOUR_STYLE_CONSTANTS', 'COLOUR_ARROW_STYLE', 'colour_style',
    'override_style', 'XYZ_to_plotting_colourspace', 'ColourSwatch',
    'colour_cycle', 'artist', 'camera', 'render', 'wrap_title',
    'label_rectangles', 'uniform_axes3d', 'filter_passthrough',
    'filter_RGB_colourspaces', 'filter_cmfs', 'filter_illuminants',
    'filter_colour_checkers', 'plot_single_colour_swatch',
    'plot_multi_colour_swatches', 'plot_single_function',
    'plot_multi_functions', 'plot_image'
]

COLOUR_STYLE_CONSTANTS = 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',
                            )),
                    'colourspace':
                        RGB_COLOURSPACES['sRGB']
                }),
        'opacity':
            Structure(**{
                'high': 0.75,
                'low': 0.25
            }),
        'hatch':
            Structure(**{'patterns': (
                '\\\\',
                'o',
                'x',
                '.',
                '*',
                '//',
            )}),
        'geometry':
            Structure(**{
                'long': 5,
                'short': 1
            })
    })
"""
Various defaults settings used across the plotting sub-package.

COLOUR_STYLE_CONSTANTS : Structure
"""

COLOUR_ARROW_STYLE = Structure(
    **{
        'color': COLOUR_STYLE_CONSTANTS.colour.dark,
        'headwidth': COLOUR_STYLE_CONSTANTS.geometry.short * 4,
        'headlength': COLOUR_STYLE_CONSTANTS.geometry.long,
        'width': COLOUR_STYLE_CONSTANTS.geometry.short * 0.5,
        'shrink': COLOUR_STYLE_CONSTANTS.geometry.short * 0.1,
        'connectionstyle': 'arc3,rad=-0.2',
    })
"""
Annotation arrow settings used across the plotting sub-package.

COLOUR_ARROW_STYLE : Structure
"""


[docs]def colour_style(use_style=True): """ Returns *Colour* plotting style. Parameters ---------- use_style : bool, optional Whether to use the style and load it into *Matplotlib*. Returns ------- dict *Colour* style. """ constants = COLOUR_STYLE_CONSTANTS 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': matplotlib.cycler(color=constants.colour.cycle), } if use_style: plt.rcParams.update(style) return style
def override_style(**kwargs): """ Decorator for overriding *Matplotlib* style. Other Parameters ---------------- \\**kwargs : dict, optional Keywords arguments. Returns ------- object Examples -------- >>> @override_style(**{'text.color': 'red'}) ... def f(): ... plt.text(0.5, 0.5, 'This is a text!') ... plt.show() >>> f() # doctest: +SKIP """ keyword_overrides = dict(kwargs) def wrapper(function): """ Wrapper for given function. """ @functools.wraps(function) def wrapped(*args, **kwargs): """ Wrapped function. """ keywords = dict(kwargs) keywords.update(keyword_overrides) style_overrides = { key: value for key, value in keywords.items() if key in plt.rcParams.keys() } with plt.style.context(style_overrides): return function(*args, **kwargs) return wrapped return wrapper def XYZ_to_plotting_colourspace(XYZ, illuminant=RGB_COLOURSPACES['sRGB'].whitepoint, chromatic_adaptation_transform='CAT02', apply_cctf_encoding=True): """ Converts from *CIE XYZ* tristimulus values to :attr:`colour.plotting.DEFAULT_PLOTTING_COLOURSPACE` colourspace. Parameters ---------- XYZ : array_like *CIE XYZ* tristimulus values. illuminant : array_like, optional Source illuminant chromaticity coordinates. chromatic_adaptation_transform : unicode, optional **{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp', 'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco', 'Bianco PC'}**, *Chromatic adaptation* transform. apply_cctf_encoding : bool, optional Apply :attr:`colour.plotting.DEFAULT_PLOTTING_COLOURSPACE` colourspace encoding colour component transfer function / opto-electronic transfer function. Returns ------- ndarray :attr:`colour.plotting.DEFAULT_PLOTTING_COLOURSPACE` colourspace colour array. 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, illuminant, COLOUR_STYLE_CONSTANTS.colour.colourspace.whitepoint, COLOUR_STYLE_CONSTANTS.colour.colourspace.XYZ_to_RGB_matrix, chromatic_adaptation_transform, COLOUR_STYLE_CONSTANTS.colour.colourspace.cctf_encoding if apply_cctf_encoding else None) class ColourSwatch(namedtuple('ColourSwatch', ('name', 'RGB'))): """ Defines a data structure for a colour swatch. Parameters ---------- name : unicode, optional Colour name. RGB : array_like, optional RGB Colour. """ def __new__(cls, name=None, RGB=None): """ Returns a new instance of the :class:`colour.plotting.ColourSwatch` class. """ return super(ColourSwatch, cls).__new__(cls, name, RGB)
[docs]def colour_cycle(**kwargs): """ Returns a colour cycle iterator using given colour map. Other Parameters ---------------- colour_cycle_map : unicode or LinearSegmentedColormap, optional Matplotlib colourmap name. colour_cycle_count : int, optional Colours count to pick in the colourmap. Returns ------- cycle Colour cycle iterator. """ settings = Structure( **{ 'colour_cycle_map': COLOUR_STYLE_CONSTANTS.colour.map, 'colour_cycle_count': len(COLOUR_STYLE_CONSTANTS.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)
[docs]def artist(**kwargs): """ Returns the current figure and its axes or creates a new one. Other Parameters ---------------- axes : Axes, optional Axes that will be passed through without creating a new figure. uniform : unicode, optional Whether to create the figure with an equal aspect ratio. Returns ------- 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() else: return plt.gcf(), axes
[docs]def camera(**kwargs): """ Sets the camera settings. Other Parameters ---------------- figure : Figure, optional Figure to apply the render elements onto. axes : Axes, optional Axes to apply the render elements onto. azimuth : numeric, optional Camera azimuth. camera_aspect : unicode, optional Matplotlib axes aspect. Default is *equal*. elevation : numeric, optional Camera elevation. Returns ------- tuple Current figure and axes. """ figure = kwargs.get('figure', plt.gcf()) axes = 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
[docs]def render(**kwargs): """ Renders the current figure while adjusting various settings such as the bounding box, the title or background transparency. Other Parameters ---------------- figure : Figure, optional Figure to apply the render elements onto. axes : Axes, optional Axes to apply the render elements onto. filename : unicode, optional Figure will be saved using given ``filename`` argument. standalone : bool, optional Whether to show the figure and call :func:`plt.show` definition. aspect : unicode, optional Matplotlib axes aspect. axes_visible : bool, optional Whether the axes are visible. Default is *True*. bounding_box : array_like, optional Array defining current axes limits such `bounding_box = (x min, x max, y min, y max)`. tight_layout : bool, optional Whether to invoke the :func:`plt.tight_layout` definition. legend : bool, optional Whether to display the legend. Default is *False*. legend_columns : int, optional Number of columns in the legend. Default is *1*. transparent_background : bool, optional Whether to turn off the background patch. Default is *False*. title : unicode, optional Figure title. wrap_title : unicode, optional Whether to wrap the figure title, the default is to wrap at a number of characters equal to the width of the figure multiplied by 10. x_label : unicode, optional *X* axis label. y_label : unicode, optional *Y* axis label. x_ticker : bool, optional Whether to display the *X* axis ticker. Default is *True*. y_ticker : bool, optional Whether to display the *Y* axis ticker. Default is *True*. Returns ------- tuple Current figure and axes. """ figure = kwargs.get('figure', plt.gcf()) axes = kwargs.get('axes', plt.gca()) settings = Structure( **{ 'filename': None, 'standalone': 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: title = settings.title if settings.wrap_title: title = wrap_title(settings.title, int(plt.rcParams['figure.figsize'][0] * 10)) axes.set_title(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.standalone: if settings.filename is not None: figure.savefig(settings.filename) else: plt.show() return figure, axes
def wrap_title(title, wrap_length=60): """ Wraps given tile at given length. The intent of this definition is to wrap long titles so that they don't overflow the figure. Parameters ---------- title : unicode Title to wrap. wrap_length : int, optional Length at which wrapping should occur. Returns ------- unicode Wrapped title. Examples -------- >>> wrap_title( # doctest: +SKIP ... 'This is a very long figure title that would overflow the figure ' ... 'container if it was not wrapped.') 'This is a very long figure title that would overflow the figure \ container if it\\nwas not wrapped.' """ if wrap_length is not None: return '\n'.join(textwrap.wrap(title, wrap_length)) else: return title
[docs]def label_rectangles(labels, rectangles, rotation='vertical', text_size=10, offset=None, **kwargs): """ Add labels above given rectangles. Parameters ---------- labels : array_like Labels to display. rectangles : object Rectangles to used to set the labels value and position. rotation : unicode, optional **{'horizontal', 'vertical'}**, Labels orientation. text_size : numeric, optional Labels text size. offset : array_like, optional Labels offset as percentages of the largest rectangle dimensions. Other Parameters ---------------- figure : Figure, optional Figure to apply the render elements onto. axes : Axes, optional Axes to apply the render elements onto. Returns ------- tuple Current figure and axes. """ figure = kwargs.get('figure', plt.gcf()) axes = kwargs.get('axes', plt.gca()) if offset is None: offset = (0.0, 0.025) x_m, y_m = 0, 0 for rectangle in rectangles: x_m = max(x_m, rectangle.get_width()) y_m = max(y_m, rectangle.get_height()) for i, rectangle in enumerate(rectangles): x = rectangle.get_x() height = rectangle.get_height() width = rectangle.get_width() ha = 'center' va = 'bottom' axes.text( x + width / 2 + offset[0] * width, height + offset[1] * y_m, labels[i], ha=ha, va=va, rotation=rotation, fontsize=text_size, clip_on=True) return figure, axes
[docs]def uniform_axes3d(**kwargs): """ Sets equal aspect ratio to given 3d axes. Other Parameters ---------------- figure : Figure, optional Figure to apply the render elements onto. axes : Axes, optional Axes to apply the render elements onto. Returns ------- tuple Current figure and axes. """ figure = kwargs.get('figure', plt.gcf()) axes = kwargs.get('axes', plt.gca()) axes.set_aspect('equal') extents = np.array( [getattr(axes, 'get_{}lim'.format(axis))() 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'): getattr(axes, 'set_{}lim'.format(axis))(center - extent / 2, center + extent / 2) return figure, axes
def filter_passthrough(mapping, filterers, anchors=True, allow_non_siblings=True, flags=re.IGNORECASE): """ Returns mapping objects matching given filterers while passing through class instances whose type is one of the mapping element types. This definition allows passing custom but compatible objects to the various plotting definitions that by default expect the key from a dataset element. For example, a typical call to :func:`colour.plotting.\ plot_multi_illuminant_sds` definition is as follows: >>> import colour >>> import colour.plotting >>> colour.plotting.plot_multi_illuminant_sds(['A']) ... # doctest: +SKIP But 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 :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 : dict_like Mapping to filter. filterers : unicode or object or array_like Filterer or object class instance (which is passed through directly if its type is one of the mapping element types) or list of filterers. anchors : bool, optional Whether to use Regex line anchors, i.e. *^* and *$* are added, surrounding the filterers patterns. allow_non_siblings : bool, optional Whether to allow non-siblings to be also passed through. flags : int, optional Regex flags. Returns ------- dict_like Filtered mapping. """ if is_string(filterers): filterers = [filterers] elif not isinstance(filterers, (list, tuple)): filterers = [filterers] string_filterers = [ filterer for filterer in filterers if is_string(filterer) ] object_filterers = [ filterer for filterer in filterers if is_sibling(filterer, mapping) ] if allow_non_siblings: non_siblings = [ filterer for filterer in filterers if filterer not in string_filterers and filterer not in object_filterers ] if non_siblings: runtime_warning( 'Non-sibling elements are passed-through: "{0}"'.format( non_siblings)) object_filterers.extend(non_siblings) filtered_mapping = filter_mapping(mapping, string_filterers, anchors, flags) for filterer in object_filterers: # TODO: Consider using "MutableMapping" here. if isinstance(filterer, (dict, OrderedDict, CaseInsensitiveMapping)): for key, value in filterer.items(): filtered_mapping[key] = value else: try: name = filterer.name except AttributeError: try: name = filterer.__name__ except AttributeError: name = str(id(filterer)) filtered_mapping[name] = filterer return filtered_mapping def filter_RGB_colourspaces(filterers, anchors=True, allow_non_siblings=True, flags=re.IGNORECASE): """ Returns the *RGB* colourspaces matching given filterers. Parameters ---------- filterers : unicode or RGB_Colourspace or array_like Filterer or :class:`colour.RGB_Colourspace` class instance (which is passed through directly if its type is one of the mapping element types) or list of filterers. anchors : bool, optional Whether to use Regex line anchors, i.e. *^* and *$* are added, surrounding the filterers patterns. allow_non_siblings : bool, optional Whether to allow non-siblings to be also passed through. flags : int, optional Regex flags. Returns ------- dict_like Filtered *RGB* colourspaces. """ return filter_passthrough(RGB_COLOURSPACES, filterers, anchors, allow_non_siblings, flags) def filter_cmfs(filterers, anchors=True, allow_non_siblings=True, flags=re.IGNORECASE): """ Returns the colour matching functions matching given filterers. Parameters ---------- filterers : unicode or LMS_ConeFundamentals or \ RGB_ColourMatchingFunctions or XYZ_ColourMatchingFunctions or array_like 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. anchors : bool, optional Whether to use Regex line anchors, i.e. *^* and *$* are added, surrounding the filterers patterns. allow_non_siblings : bool, optional Whether to allow non-siblings to be also passed through. flags : int, optional Regex flags. Returns ------- dict_like Filtered colour matching functions. """ return filter_passthrough(CMFS, filterers, anchors, allow_non_siblings, flags) def filter_illuminants(filterers, anchors=True, allow_non_siblings=True, flags=re.IGNORECASE): """ Returns the illuminants matching given filterers. Parameters ---------- filterers : unicode or SpectralDistribution or array_like Filterer or :class:`colour.SpectralDistribution` class instance (which is passed through directly if its type is one of the mapping element types) or list of filterers. anchors : bool, optional Whether to use Regex line anchors, i.e. *^* and *$* are added, surrounding the filterers patterns. allow_non_siblings : bool, optional Whether to allow non-siblings to be also passed through. flags : int, optional Regex flags. Returns ------- dict_like Filtered illuminants. """ illuminants = OrderedDict() illuminants.update( filter_passthrough(ILLUMINANTS_SDS, filterers, anchors, allow_non_siblings, flags)) illuminants.update( filter_passthrough(LIGHT_SOURCES_SDS, filterers, anchors, allow_non_siblings, flags)) return illuminants def filter_colour_checkers(filterers, anchors=True, allow_non_siblings=True, flags=re.IGNORECASE): """ Returns the colour checkers matching given filterers. Parameters ---------- filterers : unicode or ColourChecker or array_like 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. anchors : bool, optional Whether to use Regex line anchors, i.e. *^* and *$* are added, surrounding the filterers patterns. allow_non_siblings : bool, optional Whether to allow non-siblings to be also passed through. flags : int, optional Regex flags. Returns ------- dict_like Filtered colour checkers. """ return filter_passthrough(COLOURCHECKERS, filterers, anchors, allow_non_siblings, flags)
[docs]@override_style( **{ 'axes.grid': False, 'xtick.bottom': False, 'ytick.left': False, 'xtick.labelbottom': False, 'ytick.labelleft': False, }) def plot_single_colour_swatch(colour_swatch, **kwargs): """ Plots given colour swatch. Parameters ---------- colour_swatch : ColourSwatch ColourSwatch. Other Parameters ---------------- \\**kwargs : dict, optional {:func:`colour.plotting.artist`, :func:`colour.plotting.plot_multi_colour_swatches`, :func:`colour.plotting.render`}, Please refer to the documentation of the previously listed definitions. width : numeric, optional {:func:`colour.plotting.plot_multi_colour_swatches`}, Colour swatch width. height : numeric, optional {:func:`colour.plotting.plot_multi_colour_swatches`}, Colour swatch height. spacing : numeric, optional {:func:`colour.plotting.plot_multi_colour_swatches`}, Colour swatches spacing. columns : int, optional {:func:`colour.plotting.plot_multi_colour_swatches`}, Colour swatches columns count. text_parameters : dict, optional {:func:`colour.plotting.plot_multi_colour_swatches`}, Parameters for the :func:`plt.text` definition, ``offset`` can be set to define the text offset. Returns ------- tuple Current figure and axes. Examples -------- >>> RGB = ColourSwatch(RGB=(0.45620519, 0.03081071, 0.04091952)) >>> plot_single_colour_swatch(RGB) # doctest: +ELLIPSIS (<Figure size ... with 1 Axes>, \ <matplotlib.axes._subplots.AxesSubplot object at 0x...>) .. image:: ../_static/Plotting_Plot_Single_Colour_Swatch.png :align: center :alt: plot_single_colour_swatch """ return plot_multi_colour_swatches((colour_swatch, ), **kwargs)
[docs]@override_style( **{ 'axes.grid': False, 'xtick.bottom': False, 'ytick.left': False, 'xtick.labelbottom': False, 'ytick.labelleft': False, }) def plot_multi_colour_swatches(colour_swatches, width=1, height=1, spacing=0, columns=None, text_parameters=None, background_colour=(1.0, 1.0, 1.0), compare_swatches=None, **kwargs): """ Plots given colours swatches. Parameters ---------- colour_swatches : list Colour swatch sequence. width : numeric, optional Colour swatch width. height : numeric, optional Colour swatch height. spacing : numeric, optional Colour swatches spacing. columns : int, optional Colour swatches columns count, defaults to the colour swatch count or half of it if comparing. text_parameters : dict, optional Parameters for the :func:`plt.text` definition, ``visible`` can be set to make the text visible, ``offset`` can be set to define the text offset. background_colour : array_like or unicode, optional Background colour. compare_swatches : unicode, optional **{None, 'Stacked', 'Diagonal'}**, 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 : dict, optional {:func:`colour.plotting.artist`, :func:`colour.plotting.render`}, Please refer to the documentation of the previously listed definitions. Returns ------- tuple Current figure and axes. Examples -------- >>> RGB_1 = ColourSwatch(RGB=(0.45293517, 0.31732158, 0.26414773)) >>> RGB_2 = ColourSwatch(RGB=(0.77875824, 0.57726450, 0.50453169)) >>> plot_multi_colour_swatches([RGB_1, RGB_2]) # doctest: +ELLIPSIS (<Figure size ... with 1 Axes>, \ <matplotlib.axes._subplots.AxesSubplot object at 0x...>) .. image:: ../_static/Plotting_Plot_Multi_Colour_Swatches.png :align: center :alt: plot_multi_colour_swatches """ _figure, axes = artist(**kwargs) if compare_swatches is not None: assert len(colour_swatches) % 2 == 0, ( 'Cannot compare an odd number of colour swatches!') reference_colour_swatches = colour_swatches[0::2] test_colour_swatches = colour_swatches[1::2] else: reference_colour_swatches = test_colour_swatches = colour_swatches compare_swatches = str(compare_swatches).lower() if columns is None: columns = len(reference_colour_swatches) text_settings = { 'offset': 0.05, 'visible': True, } if text_parameters is not None: text_settings.update(text_parameters) text_offset = text_settings.pop('offset') offset_X = offset_Y = 0 x_min, x_max, y_min, y_max = 0, width, 0, height for i, colour_swatch in enumerate(reference_colour_swatches): if i % columns == 0 and i != 0: offset_X = 0 offset_Y -= height + spacing x_0, x_1 = offset_X, offset_X + width y_0, y_1 = offset_Y, offset_Y + height axes.fill( (x_0, x_1, x_1, x_0), (y_0, y_0, y_1, y_1), color=reference_colour_swatches[i].RGB) 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_0 + margin_Y, y_1 - margin_Y, y_1 - margin_Y, ), color=test_colour_swatches[i].RGB) else: axes.fill( (x_0, x_1, x_1), (y_0, y_0, y_1), color=test_colour_swatches[i].RGB) if colour_swatch.name is not None and text_settings['visible']: axes.text( x_0 + text_offset, y_0 + text_offset, colour_swatch.name, clip_on=True, **text_settings) offset_X += width + spacing x_max = min(len(colour_swatches), columns) x_max = x_max * width + x_max * spacing - spacing y_min = offset_Y axes.patch.set_facecolor(background_colour) bounding_box = (x_min - spacing, x_max + spacing, y_min - spacing, y_max + spacing) settings = { 'axes': axes, 'bounding_box': bounding_box, 'aspect': 'equal', } settings.update(kwargs) return render(**settings)
[docs]@override_style() def plot_single_function(function, samples=None, log_x=None, log_y=None, **kwargs): """ Plots given function. Parameters ---------- function : callable, optional Function to plot. samples : array_like, optional, Samples to evaluate the functions with. log_x : int, optional Log base to use for the *x* axis scale, if *None*, the *x* axis scale will be linear. log_y : int, optional Log base to use for the *y* axis scale, if *None*, the *y* axis scale will be linear. Other Parameters ---------------- \\**kwargs : dict, optional {:func:`colour.plotting.artist`, :func:`colour.plotting.plot_multi_functions`, :func:`colour.plotting.render`}, Please refer to the documentation of the previously listed definitions. Returns ------- tuple Current figure and axes. Examples -------- >>> from colour.models import gamma_function >>> plot_single_function(partial(gamma_function, exponent=1 / 2.2)) ... # doctest: +ELLIPSIS (<Figure size ... with 1 Axes>, \ <matplotlib.axes._subplots.AxesSubplot object at 0x...>) .. image:: ../_static/Plotting_Plot_Single_Function.png :align: center :alt: plot_single_function """ try: name = function.__name__ except AttributeError: name = 'Unnamed' settings = { 'title': '{0} - Function'.format(name), 'legend': False, } settings.update(kwargs) return plot_multi_functions({ name: function }, samples, log_x, log_y, **settings)
[docs]@override_style() def plot_multi_functions(functions, samples=None, log_x=None, log_y=None, **kwargs): """ Plots given functions. Parameters ---------- functions : dict Functions to plot. samples : array_like, optional, Samples to evaluate the functions with. log_x : int, optional Log base to use for the *x* axis scale, if *None*, the *x* axis scale will be linear. log_y : int, optional Log base to use for the *y* axis scale, if *None*, the *y* axis scale will be linear. Other Parameters ---------------- \\**kwargs : dict, optional {:func:`colour.plotting.artist`, :func:`colour.plotting.render`}, Please refer to the documentation of the previously listed definitions. Returns ------- 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 (<Figure size ... with 1 Axes>, \ <matplotlib.axes._subplots.AxesSubplot object at 0x...>) .. image:: ../_static/Plotting_Plot_Multi_Functions.png :align: center :alt: plot_multi_functions """ settings = {} settings.update(kwargs) _figure, axes = artist(**settings) if log_x is not None and log_y is not None: assert log_x >= 2 and log_y >= 2, ( 'Log base must be equal or greater than 2.') plotting_function = partial(axes.loglog, basex=log_x, basey=log_y) elif log_x is not None: assert log_x >= 2, 'Log base must be equal or greater than 2.' plotting_function = partial(axes.semilogx, basex=log_x) elif log_y is not None: assert log_y >= 2, 'Log base must be equal or greater than 2.' plotting_function = partial(axes.semilogy, basey=log_y) else: plotting_function = axes.plot if samples is None: samples = np.linspace(0, 1, 1000) for name, function in functions.items(): plotting_function(samples, function(samples), label='{0}'.format(name)) x_label = ('x - Log Base {0} Scale'.format(log_x) if log_x is not None else 'x - Linear Scale') y_label = ('y - Log Base {0} Scale'.format(log_y) if log_y is not None else 'y - Linear Scale') settings = { 'axes': axes, 'legend': True, 'title': '{0} - Functions'.format(', '.join(functions)), 'x_label': x_label, 'y_label': y_label } settings.update(kwargs) return render(**settings)
[docs]@override_style() def plot_image(image, text_parameters=None, interpolation='nearest', colour_map=matplotlib.cm.Greys_r, **kwargs): """ Plots given image. Parameters ---------- image : array_like Image to plot. text_parameters : dict, optional Parameters for the :func:`plt.text` definition, ``offset`` can be set to define the text offset. interpolation: unicode, optional **{'nearest', None, 'none', 'bilinear', 'bicubic', 'spline16', 'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos'}** Image display interpolation. colour_map: unicode, optional Colour map used to display single channel images. Other Parameters ---------------- \\**kwargs : dict, optional {:func:`colour.plotting.artist`, :func:`colour.plotting.render`}, Please refer to the documentation of the previously listed definitions. Returns ------- tuple Current figure and axes. Examples -------- >>> import os >>> import colour >>> from colour import read_image >>> path = os.path.join( ... colour.__path__[0], '..', 'docs', '_static', 'Logo_Medium_001.png') >>> plot_image(read_image(path)) # doctest: +ELLIPSIS (<Figure size ... with 1 Axes>, \ <matplotlib.axes._subplots.AxesSubplot object at 0x...>) .. image:: ../_static/Plotting_Plot_Image.png :align: center :alt: plot_image """ _figure, axes = artist(**kwargs) text_settings = { 'text': None, 'offset': 0.005, 'color': COLOUR_STYLE_CONSTANTS.colour.brightest, 'alpha': COLOUR_STYLE_CONSTANTS.opacity.high, } if text_parameters is not None: text_settings.update(text_parameters) text_offset = text_settings.pop('offset') image = as_float_array(image) axes.imshow( np.clip(image, 0, 1), interpolation=interpolation, cmap=colour_map) if text_settings['text'] is not None: axes.text( text_offset, text_offset, text_settings['text'], transform=axes.transAxes, ha='left', va='bottom', **text_settings) settings = { 'axes': axes, 'axes_visible': False, } settings.update(kwargs) return render(**settings)