""" 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 internally use `.Colorbar` together with `.make_axes_gridspec` (for `.GridSpec`-positioned axes) or `.make_axes` (for non-`.GridSpec`-positioned axes). End-users most likely won't need to directly use this module's API. """ import logging import numpy as np import matplotlib as mpl from matplotlib import _api, cbook, collections, cm, colors, contour, ticker import matplotlib.artist as martist import matplotlib.patches as mpatches import matplotlib.path as mpath import matplotlib.spines as mspines import matplotlib.transforms as mtransforms from matplotlib import _docstring _log = logging.getLogger(__name__) _docstring.interpd.update( _make_axes_kw_doc=""" location : None or {'left', 'right', 'top', 'bottom'} The location, relative to the parent axes, where the colorbar axes is created. It also determines the *orientation* of the colorbar (colorbars on the left and right are vertical, colorbars at the top and bottom are horizontal). If None, the location will come from the *orientation* if it is set (vertical colorbars on the right, horizontal ones at the bottom), or default to 'right' if *orientation* is unset. orientation : None or {'vertical', 'horizontal'} The orientation of the colorbar. It is preferable to set the *location* of the colorbar, as that also determines the *orientation*; passing incompatible values for *location* and *orientation* raises an exception. 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. 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.""", _colormap_kw_doc=""" extend : {'neither', 'both', 'min', 'max'} Make pointed end(s) for out-of-range values (unless 'neither'). 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 will 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'} For discrete colorbars (`.BoundaryNorm` or contours), 'uniform' gives each 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. Format strings, e.g., ``"%4.2e"`` or ``"{x:.2e}"``, are supported. 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. boundaries, values : None or a sequence If unset, the colormap will be displayed on a 0-1 scale. If sequences, *values* must have a length 1 less than *boundaries*. For each region delimited by adjacent entries in *boundaries*, the color mapped to the corresponding value in values will be used. Normally only useful for indexed colors (i.e. ``norm=NoNorm()``) or other unusual circumstances.""") def _set_ticks_on_axis_warn(*args, **kwargs): # a top level function which gets put in at the axes' # set_xticks and set_yticks by Colorbar.__init__. _api.warn_external("Use the colorbar set_ticks() method instead.") class _ColorbarSpine(mspines.Spine): def __init__(self, axes): self._ax = axes super().__init__(axes, 'colorbar', mpath.Path(np.empty((0, 2)))) mpatches.Patch.set_transform(self, axes.transAxes) def get_window_extent(self, renderer=None): # This Spine has no Axis associated with it, and doesn't need to adjust # its location, so we can directly get the window extent from the # super-super-class. return mpatches.Patch.get_window_extent(self, renderer=renderer) def set_xy(self, xy): self._path = mpath.Path(xy, closed=True) self._xy = xy self.stale = True def draw(self, renderer): ret = mpatches.Patch.draw(self, renderer) self.stale = False return ret class _ColorbarAxesLocator: """ Shrink the axes if there are triangular or rectangular extends. """ def __init__(self, cbar): self._cbar = cbar self._orig_locator = cbar.ax._axes_locator def __call__(self, ax, renderer): if self._orig_locator is not None: pos = self._orig_locator(ax, renderer) else: pos = ax.get_position(original=True) if self._cbar.extend == 'neither': return pos y, extendlen = self._cbar._proportional_y() if not self._cbar._extend_lower(): extendlen[0] = 0 if not self._cbar._extend_upper(): extendlen[1] = 0 len = sum(extendlen) + 1 shrink = 1 / len offset = extendlen[0] / len # we need to reset the aspect ratio of the axes to account # of the extends... if hasattr(ax, '_colorbar_info'): aspect = ax._colorbar_info['aspect'] else: aspect = False # now shrink and/or offset to take into account the # extend tri/rectangles. if self._cbar.orientation == 'vertical': if aspect: self._cbar.ax.set_box_aspect(aspect*shrink) pos = pos.shrunk(1, shrink).translated(0, offset * pos.height) else: if aspect: self._cbar.ax.set_box_aspect(1/(aspect * shrink)) pos = pos.shrunk(shrink, 1).translated(offset * pos.width, 0) return pos def get_subplotspec(self): # make tight_layout happy.. return ( self._cbar.ax.get_subplotspec() or getattr(self._orig_locator, "get_subplotspec", lambda: None)()) @_docstring.interpd class Colorbar: r""" Draw a colorbar in an existing axes. Typically, colorbars are created using `.Figure.colorbar` or `.pyplot.colorbar` and associated with `.ScalarMappable`\s (such as an `.AxesImage` generated via `~.axes.Axes.imshow`). In order to draw a colorbar not associated with other elements in the figure, e.g. when showing a colormap by itself, one can create an empty `.ScalarMappable`, or directly pass *cmap* and *norm* instead of *mappable* to `Colorbar`. 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` (empty if no lines were drawn). dividers : `.LineCollection` A LineCollection (empty if *drawedges* is ``False``). Parameters ---------- ax : `~matplotlib.axes.Axes` The `~.axes.Axes` instance in which the colorbar is drawn. mappable : `.ScalarMappable` The mappable whose colormap and norm will be used. To show the under- and over- value colors, the mappable's norm should be specified as :: norm = colors.Normalize(clip=False) To show the colors versus index instead of on a 0-1 scale, use:: norm=colors.NoNorm() cmap : `~matplotlib.colors.Colormap`, default: :rc:`image.cmap` The colormap to use. This parameter is ignored, unless *mappable* is None. norm : `~matplotlib.colors.Normalize` The normalization to use. This parameter is ignored, unless *mappable* is None. alpha : float The colorbar transparency between 0 (transparent) and 1 (opaque). orientation : None or {'vertical', 'horizontal'} If None, use the value determined by *location*. If both *orientation* and *location* are None then defaults to 'vertical'. ticklocation : {'auto', 'left', 'right', 'top', 'bottom'} The location of the colorbar ticks. The *ticklocation* must match *orientation*. For example, a horizontal colorbar can only have ticks at the top or the bottom. If 'auto', the ticks will be the same as *location*, so a colorbar to the left will have ticks to the left. If *location* is None, the ticks will be at the bottom for a horizontal colorbar and at the right for a vertical. drawedges : bool Whether to draw lines at color boundaries. %(_colormap_kw_doc)s location : None or {'left', 'right', 'top', 'bottom'} Set the *orientation* and *ticklocation* of the colorbar using a single argument. Colorbars on the left and right are vertical, colorbars at the top and bottom are horizontal. The *ticklocation* is the same as *location*, so if *location* is 'top', the ticks are on the top. *orientation* and/or *ticklocation* can be provided as well and overrides the value set by *location*, but there will be an error for incompatible combinations. .. versionadded:: 3.7 """ n_rasterize = 50 # rasterize solids if number of colors >= n_rasterize def __init__(self, ax, mappable=None, *, cmap=None, norm=None, alpha=None, values=None, boundaries=None, orientation=None, ticklocation='auto', extend=None, spacing='uniform', # uniform or proportional ticks=None, format=None, drawedges=False, extendfrac=None, extendrect=False, label='', location=None, ): if mappable is None: mappable = cm.ScalarMappable(norm=norm, cmap=cmap) self.mappable = mappable cmap = mappable.cmap norm = mappable.norm filled = True if isinstance(mappable, contour.ContourSet): cs = mappable alpha = cs.get_alpha() boundaries = cs._levels values = cs.cvalues extend = cs.extend filled = cs.filled if ticks is None: ticks = ticker.FixedLocator(cs.levels, nbins=10) elif isinstance(mappable, martist.Artist): alpha = mappable.get_alpha() mappable.colorbar = self mappable.colorbar_cid = mappable.callbacks.connect( 'changed', self.update_normal) location_orientation = _get_orientation_from_location(location) _api.check_in_list( [None, 'vertical', 'horizontal'], orientation=orientation) _api.check_in_list( ['auto', 'left', 'right', 'top', 'bottom'], ticklocation=ticklocation) _api.check_in_list( ['uniform', 'proportional'], spacing=spacing) if location_orientation is not None and orientation is not None: if location_orientation != orientation: raise TypeError( "location and orientation are mutually exclusive") else: orientation = orientation or location_orientation or "vertical" self.ax = ax self.ax._axes_locator = _ColorbarAxesLocator(self) if extend is None: if (not isinstance(mappable, contour.ContourSet) and getattr(cmap, 'colorbar_extend', False) is not False): extend = cmap.colorbar_extend elif hasattr(norm, 'extend'): extend = norm.extend else: extend = 'neither' self.alpha = None # Call set_alpha to handle array-like alphas properly self.set_alpha(alpha) self.cmap = cmap self.norm = norm self.values = values self.boundaries = boundaries self.extend = extend self._inside = _api.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._extend_patches = [] self.solids = None self.solids_patches = [] self.lines = [] for spine in self.ax.spines.values(): spine.set_visible(False) self.outline = self.ax.spines['outline'] = _ColorbarSpine(self.ax) self.dividers = collections.LineCollection( [], colors=[mpl.rcParams['axes.edgecolor']], linewidths=[0.5 * mpl.rcParams['axes.linewidth']], clip_on=False) self.ax.add_collection(self.dividers) self._locator = None self._minorlocator = None self._formatter = None self._minorformatter = None if ticklocation == 'auto': ticklocation = _get_ticklocation_from_orientation( orientation) if location is None else location 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 if isinstance(format, str): # Check format between FormatStrFormatter and StrMethodFormatter try: self._formatter = ticker.FormatStrFormatter(format) _ = self._formatter(0) except (TypeError, ValueError): self._formatter = ticker.StrMethodFormatter(format) else: self._formatter = format # Assume it is a Formatter or None self._draw_all() if isinstance(mappable, contour.ContourSet) and not mappable.filled: self.add_lines(mappable) # Link the Axes and Colorbar for interactive use self.ax._colorbar = self # Don't navigate on any of these types of mappables if (isinstance(self.norm, (colors.BoundaryNorm, colors.NoNorm)) or isinstance(self.mappable, contour.ContourSet)): self.ax.set_navigate(False) # These are the functions that set up interactivity on this colorbar self._interactive_funcs = ["_get_view", "_set_view", "_set_view_from_bbox", "drag_pan"] for x in self._interactive_funcs: setattr(self.ax, x, getattr(self, x)) # Set the cla function to the cbar's method to override it self.ax.cla = self._cbar_cla # Callbacks for the extend calculations to handle inverting the axis self._extend_cid1 = self.ax.callbacks.connect( "xlim_changed", self._do_extends) self._extend_cid2 = self.ax.callbacks.connect( "ylim_changed", self._do_extends) @property def locator(self): """Major tick `.Locator` for the colorbar.""" return self._long_axis().get_major_locator() @locator.setter def locator(self, loc): self._long_axis().set_major_locator(loc) self._locator = loc @property def minorlocator(self): """Minor tick `.Locator` for the colorbar.""" return self._long_axis().get_minor_locator() @minorlocator.setter def minorlocator(self, loc): self._long_axis().set_minor_locator(loc) self._minorlocator = loc @property def formatter(self): """Major tick label `.Formatter` for the colorbar.""" return self._long_axis().get_major_formatter() @formatter.setter def formatter(self, fmt): self._long_axis().set_major_formatter(fmt) self._formatter = fmt @property def minorformatter(self): """Minor tick `.Formatter` for the colorbar.""" return self._long_axis().get_minor_formatter() @minorformatter.setter def minorformatter(self, fmt): self._long_axis().set_minor_formatter(fmt) self._minorformatter = fmt def _cbar_cla(self): """Function to clear the interactive colorbar state.""" for x in self._interactive_funcs: delattr(self.ax, x) # We now restore the old cla() back and can call it directly del self.ax.cla self.ax.cla() 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 def _draw_all(self): """ Calculate any free parameters based on the current cmap and norm, and do all the drawing. """ if self.orientation == 'vertical': if mpl.rcParams['ytick.minor.visible']: self.minorticks_on() else: if mpl.rcParams['xtick.minor.visible']: self.minorticks_on() self._long_axis().set(label_position=self.ticklocation, ticks_position=self.ticklocation) self._short_axis().set_ticks([]) self._short_axis().set_ticks([], minor=True) # Set self._boundaries and self._values, including extensions. # self._boundaries are the edges of each square of color, and # self._values are the value to map into the norm to get the # color: self._process_values() # Set self.vmin and self.vmax to first and last boundary, excluding # extensions: self.vmin, self.vmax = self._boundaries[self._inside][[0, -1]] # Compute the X/Y mesh. X, Y = self._mesh() # draw the extend triangles, and shrink the inner axes to accommodate. # also adds the outline path to self.outline spine: self._do_extends() lower, upper = self.vmin, self.vmax if self._long_axis().get_inverted(): # If the axis is inverted, we need to swap the vmin/vmax lower, upper = upper, lower if self.orientation == 'vertical': self.ax.set_xlim(0, 1) self.ax.set_ylim(lower, upper) else: self.ax.set_ylim(0, 1) self.ax.set_xlim(lower, upper) # set up the tick locators and formatters. A bit complicated because # boundary norms + uniform spacing requires a manual locator. self.update_ticks() if self._filled: ind = np.arange(len(self._values)) if self._extend_lower(): ind = ind[1:] if self._extend_upper(): ind = ind[:-1] self._add_solids(X, Y, self._values[ind, np.newaxis]) def _add_solids(self, X, Y, C): """Draw the colors; optionally add separators.""" # Cleanup previously set artists. if self.solids is not None: self.solids.remove() for solid in self.solids_patches: solid.remove() # Add new artist(s), based on mappable type. Use individual patches if # hatching is needed, pcolormesh otherwise. mappable = getattr(self, 'mappable', None) if (isinstance(mappable, contour.ContourSet) and any(hatch is not None for hatch in mappable.hatches)): self._add_solids_patches(X, Y, C, mappable) else: self.solids = self.ax.pcolormesh( X, Y, C, cmap=self.cmap, norm=self.norm, alpha=self.alpha, edgecolors='none', shading='flat') if not self.drawedges: if len(self._y) >= self.n_rasterize: self.solids.set_rasterized(True) self._update_dividers() def _update_dividers(self): if not self.drawedges: self.dividers.set_segments([]) return # Place all *internal* dividers. if self.orientation == 'vertical': lims = self.ax.get_ylim() bounds = (lims[0] < self._y) & (self._y < lims[1]) else: lims = self.ax.get_xlim() bounds = (lims[0] < self._y) & (self._y < lims[1]) y = self._y[bounds] # And then add outer dividers if extensions are on. if self._extend_lower(): y = np.insert(y, 0, lims[0]) if self._extend_upper(): y = np.append(y, lims[1]) X, Y = np.meshgrid([0, 1], y) if self.orientation == 'vertical': segments = np.dstack([X, Y]) else: segments = np.dstack([Y, X]) self.dividers.set_segments(segments) def _add_solids_patches(self, X, Y, C, mappable): hatches = mappable.hatches * (len(C) + 1) # Have enough hatches. if self._extend_lower(): # remove first hatch that goes into the extend patch hatches = hatches[1:] patches = [] for i in range(len(X) - 1): xy = np.array([[X[i, 0], Y[i, 1]], [X[i, 1], Y[i, 0]], [X[i + 1, 1], Y[i + 1, 0]], [X[i + 1, 0], Y[i + 1, 1]]]) patch = mpatches.PathPatch(mpath.Path(xy), facecolor=self.cmap(self.norm(C[i][0])), hatch=hatches[i], linewidth=0, antialiased=False, alpha=self.alpha) self.ax.add_patch(patch) patches.append(patch) self.solids_patches = patches def _do_extends(self, ax=None): """ Add the extend tri/rectangles on the outside of the axes. ax is unused, but required due to the callbacks on xlim/ylim changed """ # Clean up any previous extend patches for patch in self._extend_patches: patch.remove() self._extend_patches = [] # extend lengths are fraction of the *inner* part of colorbar, # not the total colorbar: _, extendlen = self._proportional_y() bot = 0 - (extendlen[0] if self._extend_lower() else 0) top = 1 + (extendlen[1] if self._extend_upper() else 0) # xyout is the outline of the colorbar including the extend patches: if not self.extendrect: # triangle: xyout = np.array([[0, 0], [0.5, bot], [1, 0], [1, 1], [0.5, top], [0, 1], [0, 0]]) else: # rectangle: xyout = np.array([[0, 0], [0, bot], [1, bot], [1, 0], [1, 1], [1, top], [0, top], [0, 1], [0, 0]]) if self.orientation == 'horizontal': xyout = xyout[:, ::-1] # xyout is the path for the spine: self.outline.set_xy(xyout) if not self._filled: return # Make extend triangles or rectangles filled patches. These are # defined in the outer parent axes' coordinates: mappable = getattr(self, 'mappable', None) if (isinstance(mappable, contour.ContourSet) and any(hatch is not None for hatch in mappable.hatches)): hatches = mappable.hatches * (len(self._y) + 1) else: hatches = [None] * (len(self._y) + 1) if self._extend_lower(): if not self.extendrect: # triangle xy = np.array([[0, 0], [0.5, bot], [1, 0]]) else: # rectangle xy = np.array([[0, 0], [0, bot], [1., bot], [1, 0]]) if self.orientation == 'horizontal': xy = xy[:, ::-1] # add the patch val = -1 if self._long_axis().get_inverted() else 0 color = self.cmap(self.norm(self._values[val])) patch = mpatches.PathPatch( mpath.Path(xy), facecolor=color, alpha=self.alpha, linewidth=0, antialiased=False, transform=self.ax.transAxes, hatch=hatches[0], clip_on=False, # Place it right behind the standard patches, which is # needed if we updated the extends zorder=np.nextafter(self.ax.patch.zorder, -np.inf)) self.ax.add_patch(patch) self._extend_patches.append(patch) # remove first hatch that goes into the extend patch hatches = hatches[1:] if self._extend_upper(): if not self.extendrect: # triangle xy = np.array([[0, 1], [0.5, top], [1, 1]]) else: # rectangle xy = np.array([[0, 1], [0, top], [1, top], [1, 1]]) if self.orientation == 'horizontal': xy = xy[:, ::-1] # add the patch val = 0 if self._long_axis().get_inverted() else -1 color = self.cmap(self.norm(self._values[val])) hatch_idx = len(self._y) - 1 patch = mpatches.PathPatch( mpath.Path(xy), facecolor=color, alpha=self.alpha, linewidth=0, antialiased=False, transform=self.ax.transAxes, hatch=hatches[hatch_idx], clip_on=False, # Place it right behind the standard patches, which is # needed if we updated the extends zorder=np.nextafter(self.ax.patch.zorder, -np.inf)) self.ax.add_patch(patch) self._extend_patches.append(patch) self._update_dividers() def add_lines(self, *args, **kwargs): """ 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. Notes ----- Alternatively, this method can also be called with the signature ``colorbar.add_lines(contour_set, erase=True)``, in which case *levels*, *colors*, and *linewidths* are taken from *contour_set*. """ params = _api.select_matching_signature( [lambda self, CS, erase=True: locals(), lambda self, levels, colors, linewidths, erase=True: locals()], self, *args, **kwargs) if "CS" in params: self, cs, erase = params.values() if not isinstance(cs, contour.ContourSet) or cs.filled: raise ValueError("If a single artist is passed to add_lines, " "it must be a ContourSet of lines") # TODO: Make colorbar lines auto-follow changes in contour lines. return self.add_lines( cs.levels, cs.to_rgba(cs.cvalues, cs.alpha), cs.get_linewidths(), erase=erase) else: self, levels, colors, linewidths, erase = params.values() 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([0, 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, colors=colors) if erase and self.lines: for lc in self.lines: lc.remove() self.lines = [] self.lines.append(col) # make a clip path that is just a linewidth bigger than the axes... fac = np.max(linewidths) / 72 xy = np.array([[0, 0], [1, 0], [1, 1], [0, 1], [0, 0]]) inches = self.ax.get_figure().dpi_scale_trans # do in inches: xy = inches.inverted().transform(self.ax.transAxes.transform(xy)) xy[[0, 1, 4], 1] -= fac xy[[2, 3], 1] += fac # back to axes units... xy = self.ax.transAxes.inverted().transform(inches.transform(xy)) col.set_clip_path(mpath.Path(xy, closed=True), self.ax.transAxes) self.ax.add_collection(col) self.stale = True def update_ticks(self): """ Set up the ticks and ticklabels. This should not be needed by users. """ # Get the locator and formatter; defaults to self._locator if not None. self._get_ticker_locator_formatter() self._long_axis().set_major_locator(self._locator) self._long_axis().set_minor_locator(self._minorlocator) self._long_axis().set_major_formatter(self._formatter) def _get_ticker_locator_formatter(self): """ Return the ``locator`` and ``formatter`` of the colorbar. If they have not been defined (i.e. are *None*), the formatter and locator are retrieved from the axis, or from the value of the boundaries for a boundary norm. Called by update_ticks... """ locator = self._locator formatter = self._formatter minorlocator = self._minorlocator if isinstance(self.norm, colors.BoundaryNorm): b = self.norm.boundaries if locator is None: locator = ticker.FixedLocator(b, nbins=10) if minorlocator is None: minorlocator = ticker.FixedLocator(b) elif isinstance(self.norm, colors.NoNorm): if locator is None: # put ticks on integers between the boundaries of NoNorm nv = len(self._values) base = 1 + int(nv / 10) locator = ticker.IndexLocator(base=base, offset=.5) elif self.boundaries is not None: b = self._boundaries[self._inside] if locator is None: locator = ticker.FixedLocator(b, nbins=10) else: # most cases: if locator is None: # we haven't set the locator explicitly, so use the default # for this axis: locator = self._long_axis().get_major_locator() if minorlocator is None: minorlocator = self._long_axis().get_minor_locator() if minorlocator is None: minorlocator = ticker.NullLocator() if formatter is None: formatter = self._long_axis().get_major_formatter() self._locator = locator self._formatter = formatter self._minorlocator = minorlocator _log.debug('locator: %r', locator) def set_ticks(self, ticks, *, labels=None, minor=False, **kwargs): """ Set tick locations. Parameters ---------- ticks : 1D array-like List of tick locations. labels : list of str, optional List of tick labels. If not set, the labels show the data value. minor : bool, default: False If ``False``, set the major ticks; if ``True``, the minor ticks. **kwargs `.Text` properties for the labels. These take effect only if you pass *labels*. In other cases, please use `~.Axes.tick_params`. """ if np.iterable(ticks): self._long_axis().set_ticks(ticks, labels=labels, minor=minor, **kwargs) self._locator = self._long_axis().get_major_locator() else: self._locator = ticks self._long_axis().set_major_locator(self._locator) self.stale = True def get_ticks(self, minor=False): """ Return the ticks as a list of locations. Parameters ---------- minor : boolean, default: False if True return the minor ticks. """ if minor: return self._long_axis().get_minorticklocs() else: return self._long_axis().get_majorticklocs() def set_ticklabels(self, ticklabels, *, minor=False, **kwargs): """ [*Discouraged*] Set tick labels. .. admonition:: Discouraged The use of this method is discouraged, because of the dependency on tick positions. In most cases, you'll want to use ``set_ticks(positions, labels=labels)`` instead. If you are using this method, you should always fix the tick positions before, e.g. by using `.Colorbar.set_ticks` or by explicitly setting a `~.ticker.FixedLocator` on the long axis of the colorbar. Otherwise, ticks are free to move and the labels may end up in unexpected positions. Parameters ---------- ticklabels : sequence of str or of `.Text` Texts for labeling each tick location in the sequence set by `.Colorbar.set_ticks`; the number of labels must match the number of locations. update_ticks : bool, default: True This keyword argument is ignored and will be removed. Deprecated minor : bool If True, set minor ticks instead of major ticks. **kwargs `.Text` properties for the labels. """ self._long_axis().set_ticklabels(ticklabels, minor=minor, **kwargs) def minorticks_on(self): """ Turn on colorbar minor ticks. """ self.ax.minorticks_on() self._short_axis().set_minor_locator(ticker.NullLocator()) def minorticks_off(self): """Turn the minor ticks of the colorbar off.""" self._minorlocator = ticker.NullLocator() self._long_axis().set_minor_locator(self._minorlocator) def set_label(self, label, *, loc=None, **kwargs): """ Add a label to the long axis of the colorbar. Parameters ---------- label : str The label text. loc : str, optional The location of the label. - For horizontal orientation one of {'left', 'center', 'right'} - For vertical orientation one of {'bottom', 'center', 'top'} Defaults to :rc:`xaxis.labellocation` or :rc:`yaxis.labellocation` depending on the orientation. **kwargs Keyword arguments are passed to `~.Axes.set_xlabel` / `~.Axes.set_ylabel`. Supported keywords are *labelpad* and `.Text` properties. """ if self.orientation == "vertical": self.ax.set_ylabel(label, loc=loc, **kwargs) else: self.ax.set_xlabel(label, loc=loc, **kwargs) self.stale = True def set_alpha(self, alpha): """ Set the transparency between 0 (transparent) and 1 (opaque). If an array is provided, *alpha* will be set to None to use the transparency values associated with the colormap. """ self.alpha = None if isinstance(alpha, np.ndarray) else alpha def _set_scale(self, scale, **kwargs): """ Set the colorbar long axis scale. Parameters ---------- scale : {"linear", "log", "symlog", "logit", ...} or `.ScaleBase` The axis scale type to apply. **kwargs Different keyword arguments are accepted, depending on the scale. See the respective class keyword arguments: - `matplotlib.scale.LinearScale` - `matplotlib.scale.LogScale` - `matplotlib.scale.SymmetricalLogScale` - `matplotlib.scale.LogitScale` - `matplotlib.scale.FuncScale` Notes ----- By default, Matplotlib supports the above-mentioned scales. Additionally, custom scales may be registered using `matplotlib.scale.register_scale`. These scales can then also be used here. """ self._long_axis()._set_axes_scale(scale, **kwargs) def remove(self): """ Remove this colorbar from the figure. If the colorbar was created with ``use_gridspec=True`` the previous gridspec is restored. """ if hasattr(self.ax, '_colorbar_info'): parents = self.ax._colorbar_info['parents'] for a in parents: if self.ax in a._colorbars: a._colorbars.remove(self.ax) self.ax.remove() self.mappable.callbacks.disconnect(self.mappable.colorbar_cid) self.mappable.colorbar = None self.mappable.colorbar_cid = None # Remove the extension callbacks self.ax.callbacks.disconnect(self._extend_cid1) self.ax.callbacks.disconnect(self._extend_cid2) 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) def _process_values(self): """ Set `_boundaries` and `_values` based on the self.boundaries and self.values if not None, or based on the size of the colormap and the vmin/vmax of the norm. """ if self.values is not None: # set self._boundaries from the values... self._values = np.array(self.values) if self.boundaries is None: # bracket values by 1/2 dv: 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 # otherwise values are set from the boundaries if isinstance(self.norm, colors.BoundaryNorm): b = self.norm.boundaries elif isinstance(self.norm, colors.NoNorm): # NoNorm has N blocks, so N+1 boundaries, centered on integers: b = np.arange(self.cmap.N + 1) - .5 elif self.boundaries is not None: b = self.boundaries else: # otherwise make the boundaries from the size of the cmap: N = self.cmap.N + 1 b, _ = self._uniform_y(N) # add extra boundaries if needed: if self._extend_lower(): b = np.hstack((b[0] - 1, b)) if self._extend_upper(): b = np.hstack((b, b[-1] + 1)) # transform from 0-1 to vmin-vmax: if self.mappable.get_array() is not None: self.mappable.autoscale_None() if not self.norm.scaled(): # If we still aren't scaled after autoscaling, use 0, 1 as default 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) if (not isinstance(self.norm, colors.BoundaryNorm) and (self.boundaries is None)): b = self.norm.inverse(b) self._boundaries = np.asarray(b, dtype=float) 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) def _mesh(self): """ Return the coordinate arrays for the colorbar pcolormesh/patches. These are scaled between vmin and vmax, and already handle colorbar orientation. """ y, _ = self._proportional_y() # Use the vmin and vmax of the colorbar, which may not be the same # as the norm. There are situations where the colormap has a # narrower range than the colorbar and we want to accommodate the # extra contours. if (isinstance(self.norm, (colors.BoundaryNorm, colors.NoNorm)) or self.boundaries is not None): # not using a norm. y = y * (self.vmax - self.vmin) + self.vmin else: # Update the norm values in a context manager as it is only # a temporary change and we don't want to propagate any signals # attached to the norm (callbacks.blocked). with self.norm.callbacks.blocked(), \ cbook._setattr_cm(self.norm, vmin=self.vmin, vmax=self.vmax): y = self.norm.inverse(y) self._y = y X, Y = np.meshgrid([0., 1.], y) if self.orientation == 'vertical': return (X, Y) else: return (Y, X) def _forward_boundaries(self, x): # map boundaries equally between 0 and 1... b = self._boundaries y = np.interp(x, b, np.linspace(0, 1, len(b))) # the following avoids ticks in the extends: eps = (b[-1] - b[0]) * 1e-6 # map these _well_ out of bounds to keep any ticks out # of the extends region... y[x < b[0]-eps] = -1 y[x > b[-1]+eps] = 2 return y def _inverse_boundaries(self, x): # invert the above... b = self._boundaries return np.interp(x, np.linspace(0, 1, len(b)), b) 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._process_values() self._locator = None self._minorlocator = None self._formatter = None self._minorformatter = None if (isinstance(self.mappable, contour.ContourSet) and isinstance(self.norm, colors.LogNorm)): # if contours have lognorm, give them a log scale... self._set_scale('log') elif (self.boundaries is not None or isinstance(self.norm, colors.BoundaryNorm)): if self.spacing == 'uniform': funcs = (self._forward_boundaries, self._inverse_boundaries) self._set_scale('function', functions=funcs) elif self.spacing == 'proportional': self._set_scale('linear') elif getattr(self.norm, '_scale', None): # use the norm's scale (if it exists and is not None): self._set_scale(self.norm._scale) elif type(self.norm) is colors.Normalize: # plain Normalize: self._set_scale('linear') else: # norm._scale is None or not an attr: derive the scale from # the Norm: funcs = (self.norm, self.norm.inverse) self._set_scale('function', functions=funcs) 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[self._inside] yunique = self._y z = np.interp(xn, bunique, yunique) return z # trivial helpers def _uniform_y(self, N): """ Return colorbar data coordinates for *N* uniformly spaced boundaries, plus extension lengths if required. """ automin = automax = 1. / (N - 1.) extendlength = self._get_extension_lengths(self.extendfrac, automin, automax, default=0.05) y = np.linspace(0, 1, N) return y, extendlength def _proportional_y(self): """ Return colorbar data coordinates for the boundaries of a proportional colorbar, plus extension lengths if required: """ if (isinstance(self.norm, colors.BoundaryNorm) or self.boundaries is not None): y = (self._boundaries - self._boundaries[self._inside][0]) y = y / (self._boundaries[self._inside][-1] - self._boundaries[self._inside][0]) # need yscaled the same as the axes scale to get # the extend lengths. if self.spacing == 'uniform': yscaled = self._forward_boundaries(self._boundaries) else: yscaled = y else: y = self.norm(self._boundaries.copy()) y = np.ma.filled(y, np.nan) # the norm and the scale should be the same... yscaled = y y = y[self._inside] yscaled = yscaled[self._inside] # normalize from 0..1: norm = colors.Normalize(y[0], y[-1]) y = np.ma.filled(norm(y), np.nan) norm = colors.Normalize(yscaled[0], yscaled[-1]) yscaled = np.ma.filled(norm(yscaled), np.nan) # make the lower and upper extend lengths proportional to the lengths # of the first and last boundary spacing (if extendfrac='auto'): automin = yscaled[1] - yscaled[0] automax = yscaled[-1] - yscaled[-2] extendlength = [0, 0] if self._extend_lower() or self._extend_upper(): extendlength = self._get_extension_lengths( self.extendfrac, automin, automax, default=0.05) return y, extendlength 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): _api.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 _extend_lower(self): """Return whether the lower limit is open ended.""" minmax = "max" if self._long_axis().get_inverted() else "min" return self.extend in ('both', minmax) def _extend_upper(self): """Return whether the upper limit is open ended.""" minmax = "min" if self._long_axis().get_inverted() else "max" return self.extend in ('both', minmax) def _long_axis(self): """Return the long axis""" if self.orientation == 'vertical': return self.ax.yaxis return self.ax.xaxis def _short_axis(self): """Return the short axis""" if self.orientation == 'vertical': return self.ax.xaxis return self.ax.yaxis def _get_view(self): # docstring inherited # An interactive view for a colorbar is the norm's vmin/vmax return self.norm.vmin, self.norm.vmax def _set_view(self, view): # docstring inherited # An interactive view for a colorbar is the norm's vmin/vmax self.norm.vmin, self.norm.vmax = view def _set_view_from_bbox(self, bbox, direction='in', mode=None, twinx=False, twiny=False): # docstring inherited # For colorbars, we use the zoom bbox to scale the norm's vmin/vmax new_xbound, new_ybound = self.ax._prepare_view_from_bbox( bbox, direction=direction, mode=mode, twinx=twinx, twiny=twiny) if self.orientation == 'horizontal': self.norm.vmin, self.norm.vmax = new_xbound elif self.orientation == 'vertical': self.norm.vmin, self.norm.vmax = new_ybound def drag_pan(self, button, key, x, y): # docstring inherited points = self.ax._get_pan_points(button, key, x, y) if points is not None: if self.orientation == 'horizontal': self.norm.vmin, self.norm.vmax = points[:, 0] elif self.orientation == 'vertical': self.norm.vmin, self.norm.vmax = points[:, 1] ColorbarBase = Colorbar # Backcompat API def _normalize_location_orientation(location, orientation): if location is None: location = _get_ticklocation_from_orientation(orientation) loc_settings = _api.check_getitem({ "left": {"location": "left", "anchor": (1.0, 0.5), "panchor": (0.0, 0.5), "pad": 0.10}, "right": {"location": "right", "anchor": (0.0, 0.5), "panchor": (1.0, 0.5), "pad": 0.05}, "top": {"location": "top", "anchor": (0.5, 0.0), "panchor": (0.5, 1.0), "pad": 0.05}, "bottom": {"location": "bottom", "anchor": (0.5, 1.0), "panchor": (0.5, 0.0), "pad": 0.15}, }, location=location) loc_settings["orientation"] = _get_orientation_from_location(location) if orientation is not None and orientation != loc_settings["orientation"]: # Allow the user to pass both if they are consistent. raise TypeError("location and orientation are mutually exclusive") return loc_settings def _get_orientation_from_location(location): return _api.check_getitem( {None: None, "left": "vertical", "right": "vertical", "top": "horizontal", "bottom": "horizontal"}, location=location) def _get_ticklocation_from_orientation(orientation): return _api.check_getitem( {None: "right", "vertical": "right", "horizontal": "bottom"}, orientation=orientation) @_docstring.interpd def make_axes(parents, location=None, orientation=None, fraction=0.15, shrink=1.0, aspect=20, **kwargs): """ 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 : `~matplotlib.axes.Axes` or iterable or `numpy.ndarray` of `~.axes.Axes` The Axes to use as parents for placing the colorbar. %(_make_axes_kw_doc)s Returns ------- cax : `~matplotlib.axes.Axes` The child axes. kwargs : dict The reduced keyword dictionary to be passed when creating the colorbar instance. """ loc_settings = _normalize_location_orientation(location, orientation) # put appropriate values into the kwargs dict for passing back to # the Colorbar class kwargs['orientation'] = loc_settings['orientation'] location = kwargs['ticklocation'] = loc_settings['location'] anchor = kwargs.pop('anchor', loc_settings['anchor']) panchor = kwargs.pop('panchor', loc_settings['panchor']) aspect0 = aspect # turn parents into a list if it is not already. Note we cannot # use .flatten or .ravel as these copy the references rather than # reuse them, leading to a memory leak if isinstance(parents, np.ndarray): parents = list(parents.flat) elif np.iterable(parents): parents = list(parents) else: parents = [parents] fig = parents[0].get_figure() pad0 = 0.05 if fig.get_constrained_layout() else loc_settings['pad'] pad = kwargs.pop('pad', pad0) 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 panchor is not False: ax.set_anchor(panchor) cax = fig.add_axes(pbcb, label="") for a in parents: # tell the parent it has a colorbar a._colorbars += [cax] cax._colorbar_info = dict( parents=parents, location=location, shrink=shrink, anchor=anchor, panchor=panchor, fraction=fraction, aspect=aspect0, pad=pad) # and we need to set the aspect ratio by hand... cax.set_anchor(anchor) cax.set_box_aspect(aspect) cax.set_aspect('auto') return cax, kwargs @_docstring.interpd def make_axes_gridspec(parent, *, location=None, orientation=None, fraction=0.15, shrink=1.0, aspect=20, **kwargs): """ Create an `~.axes.Axes` 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` and mostly compatible with it. Primary differences are - `.make_axes_gridspec` requires the *parent* to have a subplotspec. - `.make_axes` positions the axes in figure coordinates; `.make_axes_gridspec` positions it using a subplotspec. - `.make_axes` updates the position of the parent. `.make_axes_gridspec` replaces the parent gridspec with a new one. Parameters ---------- parent : `~matplotlib.axes.Axes` The Axes to use as parent for placing the colorbar. %(_make_axes_kw_doc)s Returns ------- cax : `~matplotlib.axes.Axes` The child axes. kwargs : dict The reduced keyword dictionary to be passed when creating the colorbar instance. """ loc_settings = _normalize_location_orientation(location, orientation) kwargs['orientation'] = loc_settings['orientation'] location = kwargs['ticklocation'] = loc_settings['location'] aspect0 = aspect anchor = kwargs.pop('anchor', loc_settings['anchor']) panchor = kwargs.pop('panchor', loc_settings['panchor']) pad = kwargs.pop('pad', loc_settings["pad"]) wh_space = 2 * pad / (1 - pad) if location in ('left', 'right'): # for shrinking height_ratios = [ (1-anchor[1])*(1-shrink), shrink, anchor[1]*(1-shrink)] if location == 'left': gs = parent.get_subplotspec().subgridspec( 1, 2, wspace=wh_space, width_ratios=[fraction, 1-fraction-pad]) ss_main = gs[1] ss_cb = gs[0].subgridspec( 3, 1, hspace=0, height_ratios=height_ratios)[1] else: gs = parent.get_subplotspec().subgridspec( 1, 2, wspace=wh_space, width_ratios=[1-fraction-pad, fraction]) ss_main = gs[0] ss_cb = gs[1].subgridspec( 3, 1, hspace=0, height_ratios=height_ratios)[1] else: # for shrinking width_ratios = [ anchor[0]*(1-shrink), shrink, (1-anchor[0])*(1-shrink)] if location == 'bottom': gs = parent.get_subplotspec().subgridspec( 2, 1, hspace=wh_space, height_ratios=[1-fraction-pad, fraction]) ss_main = gs[0] ss_cb = gs[1].subgridspec( 1, 3, wspace=0, width_ratios=width_ratios)[1] aspect = 1 / aspect else: gs = parent.get_subplotspec().subgridspec( 2, 1, hspace=wh_space, height_ratios=[fraction, 1-fraction-pad]) ss_main = gs[1] ss_cb = gs[0].subgridspec( 1, 3, wspace=0, width_ratios=width_ratios)[1] aspect = 1 / aspect parent.set_subplotspec(ss_main) if panchor is not False: parent.set_anchor(panchor) fig = parent.get_figure() cax = fig.add_subplot(ss_cb, label="") cax.set_anchor(anchor) cax.set_box_aspect(aspect) cax.set_aspect('auto') cax._colorbar_info = dict( location=location, parents=[parent], shrink=shrink, anchor=anchor, panchor=panchor, fraction=fraction, aspect=aspect0, pad=pad) return cax, kwargs