EVOLUTION-MANAGER

Edit File: colorbar.py

""" Colorbars are a visualization of the mapping from scalar values to colors. In Matplotlib they are drawn into a dedicated `~.axes.Axes`. .. note:: Colorbars are typically created through `.Figure.colorbar` or its pyplot wrapper `.pyplot.colorbar`, which use `.make_axes` and `.Colorbar` internally. As an end-user, you most likely won't have to call the methods or instantiate the classes in this module explicitly. :class:`ColorbarBase` The base class with full colorbar drawing functionality. It can be used as-is to make a colorbar for a given colormap; a mappable object (e.g., image) is not needed. :class:`Colorbar` On top of `.ColorbarBase` this connects the colorbar with a `.ScalarMappable` such as an image or contour plot. :class:`ColorbarPatch` A specialized `.Colorbar` to support hatched contour plots. :func:`make_axes` Create an `~.axes.Axes` suitable for a colorbar. This functions can be used with figures containing a single axes or with freely placed axes. :func:`make_axes_gridspec` Create a `~.SubplotBase` suitable for a colorbar. This function should be used for adding a colorbar to a `.GridSpec`. """ import copy import logging import numpy as np import matplotlib as mpl import matplotlib.artist as martist import matplotlib.cbook as cbook import matplotlib.collections as collections import matplotlib.colors as colors import matplotlib.contour as contour import matplotlib.cm as cm import matplotlib.gridspec as gridspec import matplotlib.patches as mpatches import matplotlib.path as mpath import matplotlib.ticker as ticker import matplotlib.transforms as mtransforms import matplotlib._layoutbox as layoutbox import matplotlib._constrained_layout as constrained_layout from matplotlib import docstring _log = logging.getLogger(__name__) _make_axes_param_doc = """ fraction : float, default: 0.15 Fraction of original axes to use for colorbar. shrink : float, default: 1.0 Fraction by which to multiply the size of the colorbar. aspect : float, default: 20 Ratio of long to short dimensions. """ _make_axes_other_param_doc = """ pad : float, default: 0.05 if vertical, 0.15 if horizontal Fraction of original axes between colorbar and new image axes. anchor : (float, float), optional The anchor point of the colorbar axes. Defaults to (0.0, 0.5) if vertical; (0.5, 1.0) if horizontal. panchor : (float, float), or *False*, optional The anchor point of the colorbar parent axes. If *False*, the parent axes' anchor will be unchanged. Defaults to (1.0, 0.5) if vertical; (0.5, 0.0) if horizontal. """ make_axes_kw_doc = _make_axes_param_doc + _make_axes_other_param_doc colormap_kw_doc = """ ============ ==================================================== Property Description ============ ==================================================== *extend* {'neither', 'both', 'min', 'max'} If not 'neither', make pointed end(s) for out-of- range values. These are set for a given colormap using the colormap set_under and set_over methods. *extendfrac* {*None*, 'auto', length, lengths} If set to *None*, both the minimum and maximum triangular colorbar extensions with have a length of 5% of the interior colorbar length (this is the default setting). If set to 'auto', makes the triangular colorbar extensions the same lengths as the interior boxes (when *spacing* is set to 'uniform') or the same lengths as the respective adjacent interior boxes (when *spacing* is set to 'proportional'). If a scalar, indicates the length of both the minimum and maximum triangular colorbar extensions as a fraction of the interior colorbar length. A two-element sequence of fractions may also be given, indicating the lengths of the minimum and maximum colorbar extensions respectively as a fraction of the interior colorbar length. *extendrect* bool If *False* the minimum and maximum colorbar extensions will be triangular (the default). If *True* the extensions will be rectangular. *spacing* {'uniform', 'proportional'} Uniform spacing gives each discrete color the same space; proportional makes the space proportional to the data interval. *ticks* *None* or list of ticks or Locator If None, ticks are determined automatically from the input. *format* None or str or Formatter If None, `~.ticker.ScalarFormatter` is used. If a format string is given, e.g., '%.3f', that is used. An alternative `~.ticker.Formatter` may be given instead. *drawedges* bool Whether to draw lines at color boundaries. *label* str The label on the colorbar's long axis. ============ ==================================================== The following will probably be useful only in the context of indexed colors (that is, when the mappable has norm=NoNorm()), or other unusual circumstances. ============ =================================================== Property Description ============ =================================================== *boundaries* None or a sequence *values* None or a sequence which must be of length 1 less than the sequence of *boundaries*. For each region delimited by adjacent entries in *boundaries*, the color mapped to the corresponding value in values will be used. ============ =================================================== """ colorbar_doc = """ Add a colorbar to a plot. Function signatures for the :mod:`~matplotlib.pyplot` interface; all but the first are also method signatures for the `~.Figure.colorbar` method:: colorbar(**kwargs) colorbar(mappable, **kwargs) colorbar(mappable, cax=cax, **kwargs) colorbar(mappable, ax=ax, **kwargs) Parameters ---------- mappable The `matplotlib.cm.ScalarMappable` (i.e., `~matplotlib.image.AxesImage`, `~matplotlib.contour.ContourSet`, etc.) described by this colorbar. This argument is mandatory for the `.Figure.colorbar` method but optional for the `.pyplot.colorbar` function, which sets the default to the current image. Note that one can create a `.ScalarMappable` "on-the-fly" to generate colorbars not attached to a previously drawn artist, e.g. :: fig.colorbar(cm.ScalarMappable(norm=norm, cmap=cmap), ax=ax) cax : `~matplotlib.axes.Axes`, optional Axes into which the colorbar will be drawn. ax : `~matplotlib.axes.Axes`, list of Axes, optional Parent axes from which space for a new colorbar axes will be stolen. If a list of axes is given they will all be resized to make room for the colorbar axes. use_gridspec : bool, optional If *cax* is ``None``, a new *cax* is created as an instance of Axes. If *ax* is an instance of Subplot and *use_gridspec* is ``True``, *cax* is created as an instance of Subplot using the :mod:`~.gridspec` module. Returns ------- colorbar : `~matplotlib.colorbar.Colorbar` See also its base class, `~matplotlib.colorbar.ColorbarBase`. Notes ----- Additional keyword arguments are of two kinds: axes properties: %s colorbar properties: %s If *mappable* is a `~.contour.ContourSet`, its *extend* kwarg is included automatically. The *shrink* kwarg provides a simple way to scale the colorbar with respect to the axes. Note that if *cax* is specified, it determines the size of the colorbar and *shrink* and *aspect* kwargs are ignored. For more precise control, you can manually specify the positions of the axes objects in which the mappable and the colorbar are drawn. In this case, do not use any of the axes properties kwargs. It is known that some vector graphics viewers (svg and pdf) renders white gaps between segments of the colorbar. This is due to bugs in the viewers, not Matplotlib. As a workaround, the colorbar can be rendered with overlapping segments:: cbar = colorbar() cbar.solids.set_edgecolor("face") draw() However this has negative consequences in other circumstances, e.g. with semi-transparent images (alpha < 1) and colorbar extensions; therefore, this workaround is not used by default (see issue #1188). """ % (make_axes_kw_doc, colormap_kw_doc) docstring.interpd.update(colorbar_doc=colorbar_doc) def _set_ticks_on_axis_warn(*args, **kw): # a top level function which gets put in at the axes' # set_xticks and set_yticks by ColorbarBase.__init__. cbook._warn_external("Use the colorbar set_ticks() method instead.") class _ColorbarAutoLocator(ticker.MaxNLocator): """ AutoLocator for Colorbar This locator is just a `.MaxNLocator` except the min and max are clipped by the norm's min and max (i.e. vmin/vmax from the image/pcolor/contour object). This is necessary so ticks don't extrude into the "extend regions". """ def __init__(self, colorbar): """ This ticker needs to know the *colorbar* so that it can access its *vmin* and *vmax*. Otherwise it is the same as `~.ticker.AutoLocator`. """ self._colorbar = colorbar nbins = 'auto' steps = [1, 2, 2.5, 5, 10] super().__init__(nbins=nbins, steps=steps) def tick_values(self, vmin, vmax): # flip if needed: if vmin > vmax: vmin, vmax = vmax, vmin vmin = max(vmin, self._colorbar.norm.vmin) vmax = min(vmax, self._colorbar.norm.vmax) ticks = super().tick_values(vmin, vmax) rtol = (vmax - vmin) * 1e-10 return ticks[(ticks >= vmin - rtol) & (ticks <= vmax + rtol)] class _ColorbarAutoMinorLocator(ticker.AutoMinorLocator): """ AutoMinorLocator for Colorbar This locator is just a `.AutoMinorLocator` except the min and max are clipped by the norm's min and max (i.e. vmin/vmax from the image/pcolor/contour object). This is necessary so that the minorticks don't extrude into the "extend regions". """ def __init__(self, colorbar, n=None): """ This ticker needs to know the *colorbar* so that it can access its *vmin* and *vmax*. """ self._colorbar = colorbar self.ndivs = n super().__init__(n=None) def __call__(self): vmin = self._colorbar.norm.vmin vmax = self._colorbar.norm.vmax ticks = super().__call__() rtol = (vmax - vmin) * 1e-10 return ticks[(ticks >= vmin - rtol) & (ticks <= vmax + rtol)] class _ColorbarLogLocator(ticker.LogLocator): """ LogLocator for Colorbarbar This locator is just a `.LogLocator` except the min and max are clipped by the norm's min and max (i.e. vmin/vmax from the image/pcolor/contour object). This is necessary so ticks don't extrude into the "extend regions". """ def __init__(self, colorbar, *args, **kwargs): """ This ticker needs to know the *colorbar* so that it can access its *vmin* and *vmax*. Otherwise it is the same as `~.ticker.LogLocator`. The ``*args`` and ``**kwargs`` are the same as `~.ticker.LogLocator`. """ self._colorbar = colorbar super().__init__(*args, **kwargs) def tick_values(self, vmin, vmax): if vmin > vmax: vmin, vmax = vmax, vmin vmin = max(vmin, self._colorbar.norm.vmin) vmax = min(vmax, self._colorbar.norm.vmax) ticks = super().tick_values(vmin, vmax) rtol = (np.log10(vmax) - np.log10(vmin)) * 1e-10 ticks = ticks[(np.log10(ticks) >= np.log10(vmin) - rtol) & (np.log10(ticks) <= np.log10(vmax) + rtol)] return ticks class ColorbarBase: r""" Draw a colorbar in an existing axes. There are only some rare cases in which you would work directly with a `.ColorbarBase` as an end-user. Typically, colorbars are used with `.ScalarMappable`\s such as an `.AxesImage` generated via `~.axes.Axes.imshow`. For these cases you will use `.Colorbar` and likely create it via `.pyplot.colorbar` or `.Figure.colorbar`. The main application of using a `.ColorbarBase` explicitly is drawing colorbars that are not associated with other elements in the figure, e.g. when showing a colormap by itself. If the *cmap* kwarg is given but *boundaries* and *values* are left as None, then the colormap will be displayed on a 0-1 scale. To show the under- and over-value colors, specify the *norm* as:: norm=colors.Normalize(clip=False) To show the colors versus index instead of on the 0-1 scale, use:: norm=colors.NoNorm() Useful public methods are :meth:`set_label` and :meth:`add_lines`. Attributes ---------- ax : `~matplotlib.axes.Axes` The `~.axes.Axes` instance in which the colorbar is drawn. lines : list A list of `.LineCollection` if lines were drawn, otherwise an empty list. dividers : `.LineCollection` A LineCollection if *drawedges* is ``True``, otherwise ``None``. Parameters ---------- ax : `~matplotlib.axes.Axes` The `~.axes.Axes` instance in which the colorbar is drawn. cmap : `~matplotlib.colors.Colormap`, default: :rc:`image.cmap` The colormap to use. norm : `~matplotlib.colors.Normalize` alpha : float The colorbar transparency between 0 (transparent) and 1 (opaque). values boundaries orientation : {'vertical', 'horizontal'} ticklocation : {'auto', 'left', 'right', 'top', 'bottom'} extend : {'neither', 'both', 'min', 'max'} spacing : {'uniform', 'proportional'} ticks : `~matplotlib.ticker.Locator` or array-like of float format : str or `~matplotlib.ticker.Formatter` drawedges : bool filled : bool extendfrac extendrec label : str """ n_rasterize = 50 # rasterize solids if number of colors >= n_rasterize @cbook._make_keyword_only("3.3", "cmap") def __init__(self, ax, cmap=None, norm=None, alpha=None, values=None, boundaries=None, orientation='vertical', ticklocation='auto', extend=None, spacing='uniform', # uniform or proportional ticks=None, format=None, drawedges=False, filled=True, extendfrac=None, extendrect=False, label='', ): cbook._check_isinstance([colors.Colormap, None], cmap=cmap) cbook._check_in_list( ['vertical', 'horizontal'], orientation=orientation) cbook._check_in_list( ['auto', 'left', 'right', 'top', 'bottom'], ticklocation=ticklocation) cbook._check_in_list( ['uniform', 'proportional'], spacing=spacing) self.ax = ax # Bind some methods to the axes to warn users against using them. ax.set_xticks = ax.set_yticks = _set_ticks_on_axis_warn ax.set(frame_on=False, navigate=False) if cmap is None: cmap = cm.get_cmap() if norm is None: norm = colors.Normalize() if extend is None: if hasattr(norm, 'extend'): extend = norm.extend else: extend = 'neither' self.alpha = alpha self.cmap = cmap self.norm = norm self.values = values self.boundaries = boundaries self.extend = extend self._inside = cbook._check_getitem( {'neither': slice(0, None), 'both': slice(1, -1), 'min': slice(1, None), 'max': slice(0, -1)}, extend=extend) self.spacing = spacing self.orientation = orientation self.drawedges = drawedges self.filled = filled self.extendfrac = extendfrac self.extendrect = extendrect self.solids = None self.lines = [] self.outline = mpatches.Polygon( np.empty((0, 2)), edgecolor=mpl.rcParams['axes.edgecolor'], facecolor='none', linewidth=mpl.rcParams['axes.linewidth'], closed=True, zorder=2) ax.add_artist(self.outline) self.outline.set(clip_box=None, clip_path=None) self.patch = mpatches.Polygon( np.empty((0, 2)), color=mpl.rcParams['axes.facecolor'], linewidth=0.01, zorder=-1) ax.add_artist(self.patch) self.dividers = None self.locator = None self.formatter = None self._manual_tick_data_values = None self.__scale = None # linear, log10 for now. Hopefully more? if ticklocation == 'auto': ticklocation = 'bottom' if orientation == 'horizontal' else 'right' self.ticklocation = ticklocation self.set_label(label) self._reset_locator_formatter_scale() if np.iterable(ticks): self.locator = ticker.FixedLocator(ticks, nbins=len(ticks)) else: self.locator = ticks # Handle default in _ticker() if isinstance(format, str): self.formatter = ticker.FormatStrFormatter(format) else: self.formatter = format # Assume it is a Formatter or None self.draw_all() def _extend_lower(self): """Return whether the lower limit is open ended.""" return self.extend in ('both', 'min') def _extend_upper(self): """Return whether the upper limit is open ended.""" return self.extend in ('both', 'max') def draw_all(self): """ Calculate any free parameters based on the current cmap and norm, and do all the drawing. """ # sets self._boundaries and self._values in real data units. # takes into account extend values: self._process_values() # sets self.vmin and vmax in data units, but just for the part of the # colorbar that is not part of the extend patch: self._find_range() # returns the X and Y mesh, *but* this was/is in normalized units: X, Y = self._mesh() C = self._values[:, np.newaxis] self._config_axis() # Inline it after deprecation elapses. # Configure axes limits, patch, and outline. xy = self._outline(X, Y) xmin, ymin = xy.min(axis=0) xmax, ymax = xy.max(axis=0) self.ax.set(xlim=(xmin, xmax), ylim=(ymin, ymax)) self.outline.set_xy(xy) self.patch.set_xy(xy) self.update_ticks() if self.filled: self._add_solids(X, Y, C) @cbook.deprecated("3.3") def config_axis(self): self._config_axis() def _config_axis(self): """Set up long and short axis.""" ax = self.ax if self.orientation == 'vertical': long_axis, short_axis = ax.yaxis, ax.xaxis if mpl.rcParams['ytick.minor.visible']: self.minorticks_on() else: long_axis, short_axis = ax.xaxis, ax.yaxis if mpl.rcParams['xtick.minor.visible']: self.minorticks_on() long_axis.set(label_position=self.ticklocation, ticks_position=self.ticklocation) short_axis.set_ticks([]) short_axis.set_ticks([], minor=True) self._set_label() def _get_ticker_locator_formatter(self): """ Return the ``locator`` and ``formatter`` of the colorbar. If they have not been defined (i.e. are *None*), suitable formatter and locator instances will be created, attached to the respective attributes and returned. """ locator = self.locator formatter = self.formatter if locator is None: if self.boundaries is None: if isinstance(self.norm, colors.NoNorm): nv = len(self._values) base = 1 + int(nv / 10) locator = ticker.IndexLocator(base=base, offset=0) elif isinstance(self.norm, colors.BoundaryNorm): b = self.norm.boundaries locator = ticker.FixedLocator(b, nbins=10) elif isinstance(self.norm, colors.LogNorm): locator = _ColorbarLogLocator(self) elif isinstance(self.norm, colors.SymLogNorm): # The subs setting here should be replaced # by logic in the locator. locator = ticker.SymmetricalLogLocator( subs=np.arange(1, 10), linthresh=self.norm.linthresh, base=10) else: if mpl.rcParams['_internal.classic_mode']: locator = ticker.MaxNLocator() else: locator = _ColorbarAutoLocator(self) else: b = self._boundaries[self._inside] locator = ticker.FixedLocator(b, nbins=10) if formatter is None: if isinstance(self.norm, colors.LogNorm): formatter = ticker.LogFormatterSciNotation() elif isinstance(self.norm, colors.SymLogNorm): formatter = ticker.LogFormatterSciNotation( linthresh=self.norm.linthresh) else: formatter = ticker.ScalarFormatter() else: formatter = self.formatter self.locator = locator self.formatter = formatter _log.debug('locator: %r', locator) return locator, formatter def _use_auto_colorbar_locator(self): """ Return if we should use an adjustable tick locator or a fixed one. (check is used twice so factored out here...) """ contouring = self.boundaries is not None and self.spacing == 'uniform' return (type(self.norm) in [colors.Normalize, colors.LogNorm] and not contouring) def _reset_locator_formatter_scale(self): """ Reset the locator et al to defaults. Any user-hardcoded changes need to be re-entered if this gets called (either at init, or when the mappable normal gets changed: Colorbar.update_normal) """ self.locator = None self.formatter = None if isinstance(self.norm, colors.LogNorm): # *both* axes are made log so that determining the # mid point is easier. self.ax.set_xscale('log') self.ax.set_yscale('log') self.minorticks_on() self.__scale = 'log' else: self.ax.set_xscale('linear') self.ax.set_yscale('linear') if type(self.norm) is colors.Normalize: self.__scale = 'linear' else: self.__scale = 'manual' def update_ticks(self): """ Force the update of the ticks and ticklabels. This must be called whenever the tick locator and/or tick formatter changes. """ ax = self.ax # Get the locator and formatter; defaults to self.locator if not None. locator, formatter = self._get_ticker_locator_formatter() long_axis = ax.yaxis if self.orientation == 'vertical' else ax.xaxis if self._use_auto_colorbar_locator(): _log.debug('Using auto colorbar locator %r on colorbar', locator) long_axis.set_major_locator(locator) long_axis.set_major_formatter(formatter) else: _log.debug('Using fixed locator on colorbar') ticks, ticklabels, offset_string = self._ticker(locator, formatter) long_axis.set_ticks(ticks) long_axis.set_ticklabels(ticklabels) long_axis.get_major_formatter().set_offset_string(offset_string) def set_ticks(self, ticks, update_ticks=True): """ Set tick locations. Parameters ---------- ticks : array-like or `~matplotlib.ticker.Locator` or None The tick positions can be hard-coded by an array of values; or they can be defined by a `.Locator`. Setting to *None* reverts to using a default locator. update_ticks : bool, default: True If True, tick locations are updated immediately. If False, the user has to call `update_ticks` later to update the ticks. """ if np.iterable(ticks): self.locator = ticker.FixedLocator(ticks, nbins=len(ticks)) else: self.locator = ticks if update_ticks: self.update_ticks() self.stale = True def get_ticks(self, minor=False): """Return the x ticks as a list of locations.""" if self._manual_tick_data_values is None: ax = self.ax long_axis = ( ax.yaxis if self.orientation == 'vertical' else ax.xaxis) return long_axis.get_majorticklocs() else: # We made the axes manually, the old way, and the ylim is 0-1, # so the majorticklocs are in those units, not data units. return self._manual_tick_data_values def set_ticklabels(self, ticklabels, update_ticks=True): """ Set tick labels. Tick labels are updated immediately unless *update_ticks* is *False*, in which case one should call `.update_ticks` explicitly. """ if isinstance(self.locator, ticker.FixedLocator): self.formatter = ticker.FixedFormatter(ticklabels) if update_ticks: self.update_ticks() else: cbook._warn_external("set_ticks() must have been called.") self.stale = True def minorticks_on(self): """ Turn the minor ticks of the colorbar on without extruding into the "extend regions". """ ax = self.ax long_axis = ax.yaxis if self.orientation == 'vertical' else ax.xaxis if long_axis.get_scale() == 'log': long_axis.set_minor_locator(_ColorbarLogLocator(self, base=10., subs='auto')) long_axis.set_minor_formatter(ticker.LogFormatterSciNotation()) else: long_axis.set_minor_locator(_ColorbarAutoMinorLocator(self)) def minorticks_off(self): """Turn the minor ticks of the colorbar off.""" ax = self.ax long_axis = ax.yaxis if self.orientation == 'vertical' else ax.xaxis long_axis.set_minor_locator(ticker.NullLocator()) def _set_label(self): if self.orientation == 'vertical': self.ax.set_ylabel(self._label, **self._labelkw) else: self.ax.set_xlabel(self._label, **self._labelkw) self.stale = True def set_label(self, label, *, loc=None, **kwargs): """Add a label to the long axis of the colorbar.""" _pos_xy = 'y' if self.orientation == 'vertical' else 'x' _protected_kw = [_pos_xy, 'horizontalalignment', 'ha'] if any([k in kwargs for k in _protected_kw]): if loc is not None: raise TypeError(f'Specifying *loc* is disallowed when any of ' f'its corresponding low level keyword ' f'arguments {_protected_kw} are also supplied') loc = 'center' else: if loc is None: loc = mpl.rcParams['%saxis.labellocation' % _pos_xy] if self.orientation == 'vertical': cbook._check_in_list(('bottom', 'center', 'top'), loc=loc) else: cbook._check_in_list(('left', 'center', 'right'), loc=loc) if loc in ['right', 'top']: kwargs[_pos_xy] = 1. kwargs['horizontalalignment'] = 'right' elif loc in ['left', 'bottom']: kwargs[_pos_xy] = 0. kwargs['horizontalalignment'] = 'left' self._label = label self._labelkw = kwargs self._set_label() def _outline(self, X, Y): """ Return *x*, *y* arrays of colorbar bounding polygon, taking orientation into account. """ N = X.shape[0] ii = [0, 1, N - 2, N - 1, 2 * N - 1, 2 * N - 2, N + 1, N, 0] x = X.T.reshape(-1)[ii] y = Y.T.reshape(-1)[ii] return (np.column_stack([y, x]) if self.orientation == 'horizontal' else np.column_stack([x, y])) def _edges(self, X, Y): """Return the separator line segments; helper for _add_solids.""" N = X.shape[0] # Using the non-array form of these line segments is much # simpler than making them into arrays. if self.orientation == 'vertical': return [list(zip(X[i], Y[i])) for i in range(1, N - 1)] else: return [list(zip(Y[i], X[i])) for i in range(1, N - 1)] def _add_solids(self, X, Y, C): """ Draw the colors using `~.axes.Axes.pcolormesh`; optionally add separators. """ if self.orientation == 'vertical': args = (X, Y, C) else: args = (np.transpose(Y), np.transpose(X), np.transpose(C)) kw = dict(cmap=self.cmap, norm=self.norm, alpha=self.alpha, edgecolors='None') _log.debug('Setting pcolormesh') col = self.ax.pcolormesh(*args, **kw, shading='flat') # self.add_observer(col) # We should observe, not be observed... if self.solids is not None: self.solids.remove() self.solids = col if self.dividers is not None: self.dividers.remove() self.dividers = None if self.drawedges: linewidths = (0.5 * mpl.rcParams['axes.linewidth'],) self.dividers = collections.LineCollection( self._edges(X, Y), colors=(mpl.rcParams['axes.edgecolor'],), linewidths=linewidths) self.ax.add_collection(self.dividers) elif len(self._y) >= self.n_rasterize: self.solids.set_rasterized(True) def add_lines(self, levels, colors, linewidths, erase=True): """ Draw lines on the colorbar. The lines are appended to the list :attr:`lines`. Parameters ---------- levels : array-like The positions of the lines. colors : color or list of colors Either a single color applying to all lines or one color value for each line. linewidths : float or array-like Either a single linewidth applying to all lines or one linewidth for each line. erase : bool, default: True Whether to remove any previously added lines. """ y = self._locate(levels) rtol = (self._y[-1] - self._y[0]) * 1e-10 igood = (y < self._y[-1] + rtol) & (y > self._y[0] - rtol) y = y[igood] if np.iterable(colors): colors = np.asarray(colors)[igood] if np.iterable(linewidths): linewidths = np.asarray(linewidths)[igood] X, Y = np.meshgrid([self._y[0], self._y[-1]], y) if self.orientation == 'vertical': xy = np.stack([X, Y], axis=-1) else: xy = np.stack([Y, X], axis=-1) col = collections.LineCollection(xy, linewidths=linewidths) if erase and self.lines: for lc in self.lines: lc.remove() self.lines = [] self.lines.append(col) col.set_color(colors) self.ax.add_collection(col) self.stale = True def _ticker(self, locator, formatter): """ Return the sequence of ticks (colorbar data locations), ticklabels (strings), and the corresponding offset string. """ if isinstance(self.norm, colors.NoNorm) and self.boundaries is None: intv = self._values[0], self._values[-1] else: intv = self.vmin, self.vmax locator.create_dummy_axis(minpos=intv[0]) formatter.create_dummy_axis(minpos=intv[0]) locator.set_view_interval(*intv) locator.set_data_interval(*intv) formatter.set_view_interval(*intv) formatter.set_data_interval(*intv) b = np.array(locator()) if isinstance(locator, ticker.LogLocator): eps = 1e-10 b = b[(b <= intv[1] * (1 + eps)) & (b >= intv[0] * (1 - eps))] else: eps = (intv[1] - intv[0]) * 1e-10 b = b[(b <= intv[1] + eps) & (b >= intv[0] - eps)] self._manual_tick_data_values = b ticks = self._locate(b) ticklabels = formatter.format_ticks(b) offset_string = formatter.get_offset() return ticks, ticklabels, offset_string def _process_values(self, b=None): """ Set the :attr:`_boundaries` and :attr:`_values` attributes based on the input boundaries and values. Input boundaries can be *self.boundaries* or the argument *b*. """ if b is None: b = self.boundaries if b is not None: self._boundaries = np.asarray(b, dtype=float) if self.values is None: self._values = 0.5 * (self._boundaries[:-1] + self._boundaries[1:]) if isinstance(self.norm, colors.NoNorm): self._values = (self._values + 0.00001).astype(np.int16) else: self._values = np.array(self.values) return if self.values is not None: self._values = np.array(self.values) if self.boundaries is None: b = np.zeros(len(self.values) + 1) b[1:-1] = 0.5 * (self._values[:-1] + self._values[1:]) b[0] = 2.0 * b[1] - b[2] b[-1] = 2.0 * b[-2] - b[-3] self._boundaries = b return self._boundaries = np.array(self.boundaries) return # Neither boundaries nor values are specified; # make reasonable ones based on cmap and norm. if isinstance(self.norm, colors.NoNorm): b = self._uniform_y(self.cmap.N + 1) * self.cmap.N - 0.5 v = np.zeros(len(b) - 1, dtype=np.int16) v[self._inside] = np.arange(self.cmap.N, dtype=np.int16) if self._extend_lower(): v[0] = -1 if self._extend_upper(): v[-1] = self.cmap.N self._boundaries = b self._values = v return elif isinstance(self.norm, colors.BoundaryNorm): b = list(self.norm.boundaries) if self._extend_lower(): b = [b[0] - 1] + b if self._extend_upper(): b = b + [b[-1] + 1] b = np.array(b) v = np.zeros(len(b) - 1) bi = self.norm.boundaries v[self._inside] = 0.5 * (bi[:-1] + bi[1:]) if self._extend_lower(): v[0] = b[0] - 1 if self._extend_upper(): v[-1] = b[-1] + 1 self._boundaries = b self._values = v return else: if not self.norm.scaled(): self.norm.vmin = 0 self.norm.vmax = 1 self.norm.vmin, self.norm.vmax = mtransforms.nonsingular( self.norm.vmin, self.norm.vmax, expander=0.1) b = self.norm.inverse(self._uniform_y(self.cmap.N + 1)) if isinstance(self.norm, (colors.PowerNorm, colors.LogNorm)): # If using a lognorm or powernorm, ensure extensions don't # go negative if self._extend_lower(): b[0] = 0.9 * b[0] if self._extend_upper(): b[-1] = 1.1 * b[-1] else: if self._extend_lower(): b[0] = b[0] - 1 if self._extend_upper(): b[-1] = b[-1] + 1 self._process_values(b) def _find_range(self): """ Set :attr:`vmin` and :attr:`vmax` attributes to the first and last boundary excluding extended end boundaries. """ b = self._boundaries[self._inside] self.vmin = b[0] self.vmax = b[-1] def _central_N(self): """Return the number of boundaries excluding end extensions.""" nb = len(self._boundaries) if self.extend == 'both': nb -= 2 elif self.extend in ('min', 'max'): nb -= 1 return nb def _extended_N(self): """ Based on the colormap and extend variable, return the number of boundaries. """ N = self.cmap.N + 1 if self.extend == 'both': N += 2 elif self.extend in ('min', 'max'): N += 1 return N def _get_extension_lengths(self, frac, automin, automax, default=0.05): """ Return the lengths of colorbar extensions. This is a helper method for _uniform_y and _proportional_y. """ # Set the default value. extendlength = np.array([default, default]) if isinstance(frac, str): cbook._check_in_list(['auto'], extendfrac=frac.lower()) # Use the provided values when 'auto' is required. extendlength[:] = [automin, automax] elif frac is not None: try: # Try to set min and max extension fractions directly. extendlength[:] = frac # If frac is a sequence containing None then NaN may # be encountered. This is an error. if np.isnan(extendlength).any(): raise ValueError() except (TypeError, ValueError) as err: # Raise an error on encountering an invalid value for frac. raise ValueError('invalid value for extendfrac') from err return extendlength def _uniform_y(self, N): """ Return colorbar data coordinates for *N* uniformly spaced boundaries, plus ends if required. """ if self.extend == 'neither': y = np.linspace(0, 1, N) else: automin = automax = 1. / (N - 1.) extendlength = self._get_extension_lengths(self.extendfrac, automin, automax, default=0.05) if self.extend == 'both': y = np.zeros(N + 2, 'd') y[0] = 0. - extendlength[0] y[-1] = 1. + extendlength[1] elif self.extend == 'min': y = np.zeros(N + 1, 'd') y[0] = 0. - extendlength[0] else: y = np.zeros(N + 1, 'd') y[-1] = 1. + extendlength[1] y[self._inside] = np.linspace(0, 1, N) return y def _proportional_y(self): """ Return colorbar data coordinates for the boundaries of a proportional colorbar. """ if isinstance(self.norm, colors.BoundaryNorm): y = (self._boundaries - self._boundaries[0]) y = y / (self._boundaries[-1] - self._boundaries[0]) else: y = self.norm(self._boundaries.copy()) y = np.ma.filled(y, np.nan) if self.extend == 'min': # Exclude leftmost interval of y. clen = y[-1] - y[1] automin = (y[2] - y[1]) / clen automax = (y[-1] - y[-2]) / clen elif self.extend == 'max': # Exclude rightmost interval in y. clen = y[-2] - y[0] automin = (y[1] - y[0]) / clen automax = (y[-2] - y[-3]) / clen elif self.extend == 'both': # Exclude leftmost and rightmost intervals in y. clen = y[-2] - y[1] automin = (y[2] - y[1]) / clen automax = (y[-2] - y[-3]) / clen if self.extend in ('both', 'min', 'max'): extendlength = self._get_extension_lengths(self.extendfrac, automin, automax, default=0.05) if self.extend in ('both', 'min'): y[0] = 0. - extendlength[0] if self.extend in ('both', 'max'): y[-1] = 1. + extendlength[1] yi = y[self._inside] norm = colors.Normalize(yi[0], yi[-1]) y[self._inside] = np.ma.filled(norm(yi), np.nan) return y def _mesh(self): """ Return ``(X, Y)``, the coordinate arrays for the colorbar pcolormesh. These are suitable for a vertical colorbar; swapping and transposition for a horizontal colorbar are done outside this function. These are scaled between vmin and vmax. """ # copy the norm and change the vmin and vmax to the vmin and # vmax of the colorbar, not the norm. This allows the situation # where the colormap has a narrower range than the colorbar, to # accommodate extra contours: norm = copy.copy(self.norm) norm.vmin = self.vmin norm.vmax = self.vmax x = np.array([0.0, 1.0]) if self.spacing == 'uniform': y = self._uniform_y(self._central_N()) else: y = self._proportional_y() xmid = np.array([0.5]) if self.__scale != 'manual': y = norm.inverse(y) x = norm.inverse(x) xmid = norm.inverse(xmid) else: # if a norm doesn't have a named scale, or # we are not using a norm dv = self.vmax - self.vmin x = x * dv + self.vmin y = y * dv + self.vmin xmid = xmid * dv + self.vmin self._y = y X, Y = np.meshgrid(x, y) if self._extend_lower() and not self.extendrect: X[0, :] = xmid if self._extend_upper() and not self.extendrect: X[-1, :] = xmid return X, Y def _locate(self, x): """ Given a set of color data values, return their corresponding colorbar data coordinates. """ if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)): b = self._boundaries xn = x else: # Do calculations using normalized coordinates so # as to make the interpolation more accurate. b = self.norm(self._boundaries, clip=False).filled() xn = self.norm(x, clip=False).filled() bunique = b yunique = self._y # trim extra b values at beginning and end if they are # not unique. These are here for extended colorbars, and are not # wanted for the interpolation. if b[0] == b[1]: bunique = bunique[1:] yunique = yunique[1:] if b[-1] == b[-2]: bunique = bunique[:-1] yunique = yunique[:-1] z = np.interp(xn, bunique, yunique) return z def set_alpha(self, alpha): """Set the transparency between 0 (transparent) and 1 (opaque).""" self.alpha = alpha def remove(self): """Remove this colorbar from the figure.""" self.ax.remove() def _add_disjoint_kwargs(d, **kwargs): """ Update dict *d* with entries in *kwargs*, which must be absent from *d*. """ for k, v in kwargs.items(): if k in d: cbook.warn_deprecated( "3.3", message=f"The {k!r} parameter to Colorbar has no " "effect because it is overridden by the mappable; it is " "deprecated since %(since)s and will be removed %(removal)s.") d[k] = v class Colorbar(ColorbarBase): """ This class connects a `ColorbarBase` to a `~.cm.ScalarMappable` such as an `~.image.AxesImage` generated via `~.axes.Axes.imshow`. .. note:: This class is not intended to be instantiated directly; instead, use `.Figure.colorbar` or `.pyplot.colorbar` to create a colorbar. """ def __init__(self, ax, mappable, **kwargs): # Ensure the given mappable's norm has appropriate vmin and vmax set # even if mappable.draw has not yet been called. if mappable.get_array() is not None: mappable.autoscale_None() self.mappable = mappable _add_disjoint_kwargs(kwargs, cmap=mappable.cmap, norm=mappable.norm) if isinstance(mappable, contour.ContourSet): cs = mappable _add_disjoint_kwargs( kwargs, alpha=cs.get_alpha(), boundaries=cs._levels, values=cs.cvalues, extend=cs.extend, filled=cs.filled, ) kwargs.setdefault( 'ticks', ticker.FixedLocator(cs.levels, nbins=10)) ColorbarBase.__init__(self, ax, **kwargs) if not cs.filled: self.add_lines(cs) else: if getattr(mappable.cmap, 'colorbar_extend', False) is not False: kwargs.setdefault('extend', mappable.cmap.colorbar_extend) if isinstance(mappable, martist.Artist): _add_disjoint_kwargs(kwargs, alpha=mappable.get_alpha()) ColorbarBase.__init__(self, ax, **kwargs) @cbook.deprecated("3.3", alternative="update_normal") def on_mappable_changed(self, mappable): """ Update this colorbar to match the mappable's properties. Typically this is automatically registered as an event handler by :func:`colorbar_factory` and should not be called manually. """ _log.debug('colorbar mappable changed') self.update_normal(mappable) def add_lines(self, CS, erase=True): """ Add the lines from a non-filled `~.contour.ContourSet` to the colorbar. Parameters ---------- CS : `~.contour.ContourSet` The line positions are taken from the ContourSet levels. The ContourSet must not be filled. erase : bool, default: True Whether to remove any previously added lines. """ if not isinstance(CS, contour.ContourSet) or CS.filled: raise ValueError('add_lines is only for a ContourSet of lines') tcolors = [c[0] for c in CS.tcolors] tlinewidths = [t[0] for t in CS.tlinewidths] # The following was an attempt to get the colorbar lines # to follow subsequent changes in the contour lines, # but more work is needed: specifically, a careful # look at event sequences, and at how # to make one object track another automatically. #tcolors = [col.get_colors()[0] for col in CS.collections] #tlinewidths = [col.get_linewidth()[0] for lw in CS.collections] ColorbarBase.add_lines(self, CS.levels, tcolors, tlinewidths, erase=erase) def update_normal(self, mappable): """ Update solid patches, lines, etc. This is meant to be called when the norm of the image or contour plot to which this colorbar belongs changes. If the norm on the mappable is different than before, this resets the locator and formatter for the axis, so if these have been customized, they will need to be customized again. However, if the norm only changes values of *vmin*, *vmax* or *cmap* then the old formatter and locator will be preserved. """ _log.debug('colorbar update normal %r %r', mappable.norm, self.norm) self.mappable = mappable self.set_alpha(mappable.get_alpha()) self.cmap = mappable.cmap if mappable.norm != self.norm: self.norm = mappable.norm self._reset_locator_formatter_scale() self.draw_all() if isinstance(self.mappable, contour.ContourSet): CS = self.mappable if not CS.filled: self.add_lines(CS) self.stale = True @cbook.deprecated("3.3", alternative="update_normal") def update_bruteforce(self, mappable): """ Destroy and rebuild the colorbar. This is intended to become obsolete, and will probably be deprecated and then removed. It is not called when the pyplot.colorbar function or the Figure.colorbar method are used to create the colorbar. """ # We are using an ugly brute-force method: clearing and # redrawing the whole thing. The problem is that if any # properties have been changed by methods other than the # colorbar methods, those changes will be lost. self.ax.cla() self.locator = None self.formatter = None # clearing the axes will delete outline, patch, solids, and lines: self.outline = mpatches.Polygon( np.empty((0, 2)), edgecolor=mpl.rcParams['axes.edgecolor'], facecolor='none', linewidth=mpl.rcParams['axes.linewidth'], closed=True, zorder=2) self.ax.add_artist(self.outline) self.outline.set(clip_box=None, clip_path=None) self.patch = mpatches.Polygon( np.empty((0, 2)), color=mpl.rcParams['axes.facecolor'], linewidth=0.01, zorder=-1) self.ax.add_artist(self.patch) self.solids = None self.lines = [] self.dividers = None self.update_normal(mappable) self.draw_all() if isinstance(self.mappable, contour.ContourSet): CS = self.mappable if not CS.filled: self.add_lines(CS) #if self.lines is not None: # tcolors = [c[0] for c in CS.tcolors] # self.lines.set_color(tcolors) #Fixme? Recalculate boundaries, ticks if vmin, vmax have changed. #Fixme: Some refactoring may be needed; we should not # be recalculating everything if there was a simple alpha # change. def remove(self): """ Remove this colorbar from the figure. If the colorbar was created with ``use_gridspec=True`` the previous gridspec is restored. """ ColorbarBase.remove(self) self.mappable.callbacksSM.disconnect(self.mappable.colorbar_cid) self.mappable.colorbar = None self.mappable.colorbar_cid = None try: ax = self.mappable.axes except AttributeError: return try: gs = ax.get_subplotspec().get_gridspec() subplotspec = gs.get_topmost_subplotspec() except AttributeError: # use_gridspec was False pos = ax.get_position(original=True) ax._set_position(pos) else: # use_gridspec was True ax.set_subplotspec(subplotspec) @docstring.Substitution(_make_axes_param_doc, _make_axes_other_param_doc) def make_axes(parents, location=None, orientation=None, fraction=0.15, shrink=1.0, aspect=20, **kw): """ Create an `~.axes.Axes` suitable for a colorbar. The axes is placed in the figure of the *parents* axes, by resizing and repositioning *parents*. Parameters ---------- parents : `~.axes.Axes` or list of `~.axes.Axes` The Axes to use as parents for placing the colorbar. location : None or {'left', 'right', 'top', 'bottom'} The position, relative to *parents*, where the colorbar axes should be created. If None, the value will either come from the given ``orientation``, else it will default to 'right'. orientation : None or {'vertical', 'horizontal'} The orientation of the colorbar. Typically, this keyword shouldn't be used, as it can be derived from the ``location`` keyword. %s Returns ------- cax : `~.axes.Axes` The child axes. kw : dict The reduced keyword dictionary to be passed when creating the colorbar instance. Other Parameters ---------------- %s """ locations = ["left", "right", "top", "bottom"] if orientation is not None and location is not None: raise TypeError('position and orientation are mutually exclusive. ' 'Consider setting the position to any of {}' .format(', '.join(locations))) # provide a default location if location is None and orientation is None: location = 'right' # allow the user to not specify the location by specifying the # orientation instead if location is None: location = 'right' if orientation == 'vertical' else 'bottom' cbook._check_in_list(locations, location=location) default_location_settings = {'left': {'anchor': (1.0, 0.5), 'panchor': (0.0, 0.5), 'pad': 0.10, 'orientation': 'vertical'}, 'right': {'anchor': (0.0, 0.5), 'panchor': (1.0, 0.5), 'pad': 0.05, 'orientation': 'vertical'}, 'top': {'anchor': (0.5, 0.0), 'panchor': (0.5, 1.0), 'pad': 0.05, 'orientation': 'horizontal'}, 'bottom': {'anchor': (0.5, 1.0), 'panchor': (0.5, 0.0), 'pad': 0.15, # backwards compat 'orientation': 'horizontal'}, } loc_settings = default_location_settings[location] # put appropriate values into the kw dict for passing back to # the Colorbar class kw['orientation'] = loc_settings['orientation'] kw['ticklocation'] = location anchor = kw.pop('anchor', loc_settings['anchor']) parent_anchor = kw.pop('panchor', loc_settings['panchor']) parents_iterable = np.iterable(parents) # turn parents into a list if it is not already. We do this w/ np # because `plt.subplots` can return an ndarray and is natural to # pass to `colorbar`. parents = np.atleast_1d(parents).ravel() # check if using constrained_layout: try: gs = parents[0].get_subplotspec().get_gridspec() using_constrained_layout = (gs._layoutbox is not None) except AttributeError: using_constrained_layout = False # defaults are not appropriate for constrained_layout: pad0 = loc_settings['pad'] if using_constrained_layout: pad0 = 0.02 pad = kw.pop('pad', pad0) fig = parents[0].get_figure() if not all(fig is ax.get_figure() for ax in parents): raise ValueError('Unable to create a colorbar axes as not all ' 'parents share the same figure.') # take a bounding box around all of the given axes parents_bbox = mtransforms.Bbox.union( [ax.get_position(original=True).frozen() for ax in parents]) pb = parents_bbox if location in ('left', 'right'): if location == 'left': pbcb, _, pb1 = pb.splitx(fraction, fraction + pad) else: pb1, _, pbcb = pb.splitx(1 - fraction - pad, 1 - fraction) pbcb = pbcb.shrunk(1.0, shrink).anchored(anchor, pbcb) else: if location == 'bottom': pbcb, _, pb1 = pb.splity(fraction, fraction + pad) else: pb1, _, pbcb = pb.splity(1 - fraction - pad, 1 - fraction) pbcb = pbcb.shrunk(shrink, 1.0).anchored(anchor, pbcb) # define the aspect ratio in terms of y's per x rather than x's per y aspect = 1.0 / aspect # define a transform which takes us from old axes coordinates to # new axes coordinates shrinking_trans = mtransforms.BboxTransform(parents_bbox, pb1) # transform each of the axes in parents using the new transform for ax in parents: new_posn = shrinking_trans.transform(ax.get_position(original=True)) new_posn = mtransforms.Bbox(new_posn) ax._set_position(new_posn) if parent_anchor is not False: ax.set_anchor(parent_anchor) cax = fig.add_axes(pbcb, label="<colorbar>") # OK, now make a layoutbox for the cb axis. Later, we will use this # to make the colorbar fit nicely. if not using_constrained_layout: # no layout boxes: lb = None lbpos = None # and we need to set the aspect ratio by hand... cax.set_aspect(aspect, anchor=anchor, adjustable='box') else: if not parents_iterable: # this is a single axis... ax = parents[0] lb, lbpos = constrained_layout.layoutcolorbarsingle( ax, cax, shrink, aspect, location, pad=pad) else: # there is more than one parent, so lets use gridspec # the colorbar will be a sibling of this gridspec, so the # parent is the same parent as the gridspec. Either the figure, # or a subplotspec. lb, lbpos = constrained_layout.layoutcolorbargridspec( parents, cax, shrink, aspect, location, pad) cax._layoutbox = lb cax._poslayoutbox = lbpos return cax, kw @docstring.Substitution(_make_axes_param_doc, _make_axes_other_param_doc) def make_axes_gridspec(parent, *, fraction=0.15, shrink=1.0, aspect=20, **kw): """ Create a `~.SubplotBase` suitable for a colorbar. The axes is placed in the figure of the *parent* axes, by resizing and repositioning *parent*. This function is similar to `.make_axes`. Primary differences are - `.make_axes_gridspec` only handles the *orientation* keyword and cannot handle the *location* keyword. - `.make_axes_gridspec` should only be used with a `.SubplotBase` parent. - `.make_axes` creates an `~.axes.Axes`; `.make_axes_gridspec` creates a `.SubplotBase`. - `.make_axes` updates the position of the parent. `.make_axes_gridspec` replaces the ``grid_spec`` attribute of the parent with a new one. While this function is meant to be compatible with `.make_axes`, there could be some minor differences. Parameters ---------- parent : `~.axes.Axes` The Axes to use as parent for placing the colorbar. %s Returns ------- cax : `~.axes.SubplotBase` The child axes. kw : dict The reduced keyword dictionary to be passed when creating the colorbar instance. Other Parameters ---------------- orientation : {'vertical', 'horizontal'}, default: 'vertical' The orientation of the colorbar. %s """ orientation = kw.setdefault('orientation', 'vertical') kw['ticklocation'] = 'auto' x1 = 1 - fraction # for shrinking pad_s = (1 - shrink) * 0.5 wh_ratios = [pad_s, shrink, pad_s] # we need to none the tree of layoutboxes because # constrained_layout can't remove and replace the tree # hierarchy w/o a seg fault. gs = parent.get_subplotspec().get_gridspec() layoutbox.nonetree(gs._layoutbox) gs_from_subplotspec = gridspec.GridSpecFromSubplotSpec if orientation == 'vertical': pad = kw.pop('pad', 0.05) wh_space = 2 * pad / (1 - pad) gs = gs_from_subplotspec(1, 2, subplot_spec=parent.get_subplotspec(), wspace=wh_space, width_ratios=[x1 - pad, fraction]) gs2 = gs_from_subplotspec(3, 1, subplot_spec=gs[1], hspace=0., height_ratios=wh_ratios) anchor = (0.0, 0.5) panchor = (1.0, 0.5) else: pad = kw.pop('pad', 0.15) wh_space = 2 * pad / (1 - pad) gs = gs_from_subplotspec(2, 1, subplot_spec=parent.get_subplotspec(), hspace=wh_space, height_ratios=[x1 - pad, fraction]) gs2 = gs_from_subplotspec(1, 3, subplot_spec=gs[1], wspace=0., width_ratios=wh_ratios) aspect = 1 / aspect anchor = (0.5, 1.0) panchor = (0.5, 0.0) parent.set_subplotspec(gs[0]) parent.update_params() parent._set_position(parent.figbox) parent.set_anchor(panchor) fig = parent.get_figure() cax = fig.add_subplot(gs2[1], label="<colorbar>") cax.set_aspect(aspect, anchor=anchor, adjustable='box') return cax, kw class ColorbarPatch(Colorbar): """ A Colorbar that uses a list of `~.patches.Patch` instances rather than the default `~.collections.PatchCollection` created by `~.axes.Axes.pcolor`, because the latter does not allow the hatch pattern to vary among the members of the collection. """ def __init__(self, ax, mappable, **kw): # we do not want to override the behaviour of solids # so add a new attribute which will be a list of the # colored patches in the colorbar self.solids_patches = [] Colorbar.__init__(self, ax, mappable, **kw) def _add_solids(self, X, Y, C): """ Draw the colors using `~matplotlib.patches.Patch`; optionally add separators. """ n_segments = len(C) # ensure there are sufficient hatches hatches = self.mappable.hatches * n_segments patches = [] for i in range(len(X) - 1): val = C[i][0] hatch = hatches[i] xy = np.array([[X[i][0], Y[i][0]], [X[i][1], Y[i][0]], [X[i + 1][1], Y[i + 1][0]], [X[i + 1][0], Y[i + 1][1]]]) if self.orientation == 'horizontal': # if horizontal swap the xs and ys xy = xy[..., ::-1] patch = mpatches.PathPatch(mpath.Path(xy), facecolor=self.cmap(self.norm(val)), hatch=hatch, linewidth=0, antialiased=False, alpha=self.alpha) self.ax.add_patch(patch) patches.append(patch) if self.solids_patches: for solid in self.solids_patches: solid.remove() self.solids_patches = patches if self.dividers is not None: self.dividers.remove() self.dividers = None if self.drawedges: self.dividers = collections.LineCollection( self._edges(X, Y), colors=(mpl.rcParams['axes.edgecolor'],), linewidths=(0.5 * mpl.rcParams['axes.linewidth'],)) self.ax.add_collection(self.dividers) def colorbar_factory(cax, mappable, **kwargs): """ Create a colorbar on the given axes for the given mappable. .. note:: This is a low-level function to turn an existing axes into a colorbar axes. Typically, you'll want to use `~.Figure.colorbar` instead, which automatically handles creation and placement of a suitable axes as well. Parameters ---------- cax : `~matplotlib.axes.Axes` The `~.axes.Axes` to turn into a colorbar. mappable : `~matplotlib.cm.ScalarMappable` The mappable to be described by the colorbar. **kwargs Keyword arguments are passed to the respective colorbar class. Returns ------- `.Colorbar` or `.ColorbarPatch` The created colorbar instance. `.ColorbarPatch` is only used if *mappable* is a `.ContourSet` with hatches. """ # if the given mappable is a contourset with any hatching, use # ColorbarPatch else use Colorbar if (isinstance(mappable, contour.ContourSet) and any(hatch is not None for hatch in mappable.hatches)): cb = ColorbarPatch(cax, mappable, **kwargs) else: cb = Colorbar(cax, mappable, **kwargs) cid = mappable.callbacksSM.connect('changed', cb.update_normal) mappable.colorbar = cb mappable.colorbar_cid = cid return cb