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colorbar.py
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''' Colorbar toolkit with two classes and a function: :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` the derived class for use with images or contour plots. :func:`make_axes` a function for resizing an axes and adding a second axes suitable for a colorbar The :meth:`~matplotlib.figure.Figure.colorbar` method uses :func:`make_axes` and :class:`Colorbar`; the :func:`~matplotlib.pyplot.colorbar` function is a thin wrapper over :meth:`~matplotlib.figure.Figure.colorbar`. ''' import warnings import numpy as np import matplotlib as mpl import matplotlib.colors as colors import matplotlib.cm as cm from matplotlib import docstring import matplotlib.ticker as ticker import matplotlib.cbook as cbook import matplotlib.lines as lines import matplotlib.patches as patches import matplotlib.collections as collections import matplotlib.contour as contour import matplotlib.artist as martist make_axes_kw_doc = ''' ============= ==================================================== Property Description ============= ==================================================== *orientation* vertical or horizontal *fraction* 0.15; fraction of original axes to use for colorbar *pad* 0.05 if vertical, 0.15 if horizontal; fraction of original axes between colorbar and new image axes *shrink* 1.0; fraction by which to shrink the colorbar *aspect* 20; ratio of long to short dimensions ============= ==================================================== ''' 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. *spacing* [ 'uniform' | 'proportional' ] Uniform spacing gives each discrete color the same space; proportional makes the space proportional to the data interval. *ticks* [ None | list of ticks | Locator object ] If None, ticks are determined automatically from the input. *format* [ None | format string | Formatter object ] If None, the :class:`~matplotlib.ticker.ScalarFormatter` is used. If a format string is given, e.g. '%.3f', that is used. An alternative :class:`~matplotlib.ticker.Formatter` object may be given instead. *drawedges* [ False | True ] If true, draw lines at color boundaries. =========== ==================================================== 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 :meth:`~matplotlib.figure.Figure.colorbar` method:: colorbar(**kwargs) colorbar(mappable, **kwargs) colorbar(mappable, cax=cax, **kwargs) colorbar(mappable, ax=ax, **kwargs) arguments: *mappable* the :class:`~matplotlib.image.Image`, :class:`~matplotlib.contour.ContourSet`, etc. to which the colorbar applies; this argument is mandatory for the :meth:`~matplotlib.figure.Figure.colorbar` method but optional for the :func:`~matplotlib.pyplot.colorbar` function, which sets the default to the current image. keyword arguments: *cax* None | axes object into which the colorbar will be drawn *ax* None | parent axes object from which space for a new colorbar axes will be stolen Additional keyword arguments are of two kinds: axes properties: %s colorbar properties: %s If *mappable* is a :class:`~matplotlib.contours.ContourSet`, its *extend* kwarg is included automatically. Note that the *shrink* kwarg provides a simple way to keep a vertical colorbar, for example, from being taller than the axes of the mappable to which the colorbar is attached; but it is a manual method requiring some trial and error. If the colorbar is too tall (or a horizontal colorbar is too wide) use a smaller value of *shrink*. 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. returns: :class:`~matplotlib.colorbar.Colorbar` instance; see also its base class, :class:`~matplotlib.colorbar.ColorbarBase`. Call the :meth:`~matplotlib.colorbar.ColorbarBase.set_label` method to label the colorbar. ''' % (make_axes_kw_doc, colormap_kw_doc) docstring.interpd.update(colorbar_doc=colorbar_doc) class ColorbarBase(cm.ScalarMappable): ''' Draw a colorbar in an existing axes. This is a base class for the :class:`Colorbar` class, which is the basis for the :func:`~matplotlib.pyplot.colorbar` function and the :meth:`~matplotlib.figure.Figure.colorbar` method, which are the usual ways of creating a colorbar. It is also useful by itself for showing a colormap. 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:: colors.Normalize(clip=False) To show the colors versus index instead of on the 0-1 scale, use:: norm=colors.NoNorm. Useful attributes: :attr:`ax` the Axes instance in which the colorbar is drawn :attr:`lines` a LineCollection if lines were drawn, otherwise None :attr:`dividers` a LineCollection if *drawedges* is True, otherwise None Useful public methods are :meth:`set_label` and :meth:`add_lines`. ''' _slice_dict = {'neither': slice(0,1000000), 'both': slice(1,-1), 'min': slice(1,1000000), 'max': slice(0,-1)} def __init__(self, ax, cmap=None, norm=None, alpha=None, values=None, boundaries=None, orientation='vertical', extend='neither', spacing='uniform', # uniform or proportional ticks=None, format=None, drawedges=False, filled=True, ): self.ax = ax self._patch_ax() if cmap is None: cmap = cm.get_cmap() if norm is None: norm = colors.Normalize() self.alpha = alpha cm.ScalarMappable.__init__(self, cmap=cmap, norm=norm) self.values = values self.boundaries = boundaries self.extend = extend self._inside = self._slice_dict[extend] self.spacing = spacing self.orientation = orientation self.drawedges = drawedges self.filled = filled self.solids = None self.lines = None self.outline = None self.patch = None self.dividers = None self.set_label('') if cbook.iterable(ticks): self.locator = ticker.FixedLocator(ticks, nbins=len(ticks)) else: self.locator = ticks # Handle default in _ticker() if format is None: if isinstance(self.norm, colors.LogNorm): self.formatter = ticker.LogFormatter() else: self.formatter = ticker.ScalarFormatter() elif cbook.is_string_like(format): self.formatter = ticker.FormatStrFormatter(format) else: self.formatter = format # Assume it is a Formatter # The rest is in a method so we can recalculate when clim changes. self.config_axis() self.draw_all() def _patch_ax(self): def _warn(*args, **kw): warnings.warn("Use the colorbar set_ticks() method instead.") self.ax.set_xticks = _warn self.ax.set_yticks = _warn def draw_all(self): ''' Calculate any free parameters based on the current cmap and norm, and do all the drawing. ''' self._process_values() self._find_range() X, Y = self._mesh() C = self._values[:,np.newaxis] self._config_axes(X, Y) if self.filled: self._add_solids(X, Y, C) def config_axis(self): ax = self.ax if self.orientation == 'vertical': ax.xaxis.set_ticks([]) ax.yaxis.set_label_position('right') ax.yaxis.set_ticks_position('right') else: ax.yaxis.set_ticks([]) ax.xaxis.set_label_position('bottom') self._set_label() 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 ticks, ticklabels, offset_string = self._ticker() if self.orientation == 'vertical': ax.yaxis.set_ticks(ticks) ax.set_yticklabels(ticklabels) ax.yaxis.get_major_formatter().set_offset_string(offset_string) else: ax.xaxis.set_ticks(ticks) ax.set_xticklabels(ticklabels) ax.xaxis.get_major_formatter().set_offset_string(offset_string) def set_ticks(self, ticks, update_ticks=True): """ set tick locations. Tick locations are updated immediately unless update_ticks is *False*. To manually update the ticks, call *update_ticks* method explicitly. """ if cbook.iterable(ticks): self.locator = ticker.FixedLocator(ticks, nbins=len(ticks)) else: self.locator = ticks if update_ticks: self.update_ticks() def set_ticklabels(self, ticklabels, update_ticks=True): """ set tick labels. Tick labels are updated immediately unless update_ticks is *False*. To manually update the ticks, call *update_ticks* method explicitly. """ if isinstance(self.locator, ticker.FixedLocator): self.formatter = ticker.FixedFormatter(ticklabels) if update_ticks: self.update_ticks() else: warnings.warn("set_ticks() must have been called.") def _config_axes(self, X, Y): ''' Make an axes patch and outline. ''' ax = self.ax ax.set_frame_on(False) ax.set_navigate(False) xy = self._outline(X, Y) ax.update_datalim(xy) ax.set_xlim(*ax.dataLim.intervalx) ax.set_ylim(*ax.dataLim.intervaly) if self.outline is not None: self.outline.remove() self.outline = lines.Line2D(xy[:, 0], xy[:, 1], color=mpl.rcParams['axes.edgecolor'], linewidth=mpl.rcParams['axes.linewidth']) ax.add_artist(self.outline) self.outline.set_clip_box(None) self.outline.set_clip_path(None) c = mpl.rcParams['axes.facecolor'] if self.patch is not None: self.patch.remove() self.patch = patches.Polygon(xy, edgecolor=c, facecolor=c, linewidth=0.01, zorder=-1) ax.add_artist(self.patch) self.update_ticks() 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) def set_label(self, label, **kw): ''' Label the long axis of the colorbar ''' self._label = label self._labelkw = kw 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 = np.take(np.ravel(np.transpose(X)), ii) y = np.take(np.ravel(np.transpose(Y)), ii) x = x.reshape((len(x), 1)) y = y.reshape((len(y), 1)) if self.orientation == 'horizontal': return np.hstack((y, x)) return np.hstack((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 [zip(X[i], Y[i]) for i in range(1, N-1)] else: return [zip(Y[i], X[i]) for i in range(1, N-1)] def _add_solids(self, X, Y, C): ''' Draw the colors using :meth:`~matplotlib.axes.Axes.pcolor`; optionally add separators. ''' if self.orientation == 'vertical': args = (X, Y, C) else: args = (np.transpose(Y), np.transpose(X), np.transpose(C)) kw = {'cmap':self.cmap, 'norm':self.norm, 'alpha':self.alpha,} # Save, set, and restore hold state to keep pcolor from # clearing the axes. Ordinarily this will not be needed, # since the axes object should already have hold set. _hold = self.ax.ishold() self.ax.hold(True) col = self.ax.pcolormesh(*args, **kw) self.ax.hold(_hold) #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: 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 add_lines(self, levels, colors, linewidths): ''' Draw lines on the colorbar. ''' N = len(levels) dummy, y = self._locate(levels) if len(y) <> N: raise ValueError("levels are outside colorbar range") x = np.array([0.0, 1.0]) X, Y = np.meshgrid(x,y) if self.orientation == 'vertical': xy = [zip(X[i], Y[i]) for i in range(N)] else: xy = [zip(Y[i], X[i]) for i in range(N)] col = collections.LineCollection(xy, linewidths=linewidths) if self.lines: self.lines.remove() self.lines = col col.set_color(colors) self.ax.add_collection(col) def _ticker(self): ''' Return two sequences: ticks (colorbar data locations) and ticklabels (strings). ''' 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 = ticker.LogLocator() else: locator = ticker.MaxNLocator() else: b = self._boundaries[self._inside] locator = ticker.FixedLocator(b, nbins=10) if isinstance(self.norm, colors.NoNorm): intv = self._values[0], self._values[-1] else: intv = self.vmin, self.vmax locator.create_dummy_axis() formatter.create_dummy_axis() locator.set_view_interval(*intv) locator.set_data_interval(*intv) formatter.set_view_interval(*intv) formatter.set_data_interval(*intv) # the dummy axis is expecting a minpos locator.axis.get_minpos = lambda : intv[0] formatter.axis.get_minpos = lambda : intv[0] b = np.array(locator()) b, ticks = self._locate(b) formatter.set_locs(b) ticklabels = [formatter(t, i) for i, t in enumerate(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) return 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, 'd') 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 in ('both', 'min'): v[0] = -1 if self.extend in ('both', 'max'): 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 in ('both', 'min'): b = [b[0]-1] + b if self.extend in ('both', 'max'): b = b + [b[-1] + 1] b = np.array(b) v = np.zeros((len(b)-1,), dtype=float) bi = self.norm.boundaries v[self._inside] = 0.5*(bi[:-1] + bi[1:]) if self.extend in ('both', 'min'): v[0] = b[0] - 1 if self.extend in ('both', 'max'): 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 b = self.norm.inverse(self._uniform_y(self.cmap.N+1)) if self.extend in ('both', 'min'): b[0] = b[0] - 1 if self.extend in ('both', 'max'): 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): '''number of boundaries **before** extension of ends''' 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 _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: if self.extend == 'both': y = np.zeros(N + 2, 'd') y[0] = -0.05 y[-1] = 1.05 elif self.extend == 'min': y = np.zeros(N + 1, 'd') y[0] = -0.05 else: y = np.zeros(N + 1, 'd') y[-1] = 1.05 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): b = self._boundaries[self._inside] y = (self._boundaries - self._boundaries[0]) y = y / (self._boundaries[-1] - self._boundaries[0]) else: y = self.norm(self._boundaries.copy()) if self.extend in ('both', 'min'): y[0] = -0.05 if self.extend in ('both', 'max'): y[-1] = 1.05 yi = y[self._inside] norm = colors.Normalize(yi[0], yi[-1]) y[self._inside] = norm(yi) 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. ''' x = np.array([0.0, 1.0]) if self.spacing == 'uniform': y = self._uniform_y(self._central_N()) else: y = self._proportional_y() self._y = y X, Y = np.meshgrid(x,y) if self.extend in ('min', 'both'): X[0,:] = 0.5 if self.extend in ('max', 'both'): X[-1,:] = 0.5 return X, Y def _locate(self, x): ''' Given a possible set of color data values, return the ones within range, together with their corresponding colorbar data coordinates. ''' if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)): b = self._boundaries xn = x xout = x else: # Do calculations using normalized coordinates so # as to make the interpolation more accurate. b = self.norm(self._boundaries, clip=False).filled() # We do our own clipping so that we can allow a tiny # bit of slop in the end point ticks to allow for # floating point errors. xn = self.norm(x, clip=False).filled() in_cond = (xn > -0.001) & (xn < 1.001) xn = np.compress(in_cond, xn) xout = np.compress(in_cond, x) # The rest is linear interpolation with clipping. y = self._y N = len(b) ii = np.minimum(np.searchsorted(b, xn), N-1) i0 = np.maximum(ii - 1, 0) #db = b[ii] - b[i0] db = np.take(b, ii) - np.take(b, i0) db = np.where(i0==ii, 1.0, db) #dy = y[ii] - y[i0] dy = np.take(y, ii) - np.take(y, i0) z = np.take(y, i0) + (xn-np.take(b,i0))*dy/db return xout, z def set_alpha(self, alpha): self.alpha = alpha class Colorbar(ColorbarBase): """ This class connects a :class:`ColorbarBase` to a :class:`~matplotlib.cm.ScalarMappable` such as a :class:`~matplotlib.image.AxesImage` generated via :meth:`~matplotlib.axes.Axes.imshow`. It is not intended to be instantiated directly; instead, use :meth:`~matplotlib.figure.Figure.colorbar` or :func:`~matplotlib.pyplot.colorbar` to make your colorbar. """ def __init__(self, ax, mappable, **kw): mappable.autoscale_None() # Ensure mappable.norm.vmin, vmax # are set when colorbar is called, # even if mappable.draw has not yet # been called. This will not change # vmin, vmax if they are already set. self.mappable = mappable kw['cmap'] = mappable.cmap kw['norm'] = mappable.norm if isinstance(mappable, contour.ContourSet): CS = mappable kw['alpha'] = mappable.get_alpha() kw['boundaries'] = CS._levels kw['values'] = CS.cvalues kw['extend'] = CS.extend #kw['ticks'] = CS._levels kw.setdefault('ticks', ticker.FixedLocator(CS.levels, nbins=10)) kw['filled'] = CS.filled ColorbarBase.__init__(self, ax, **kw) if not CS.filled: self.add_lines(CS) else: if isinstance(mappable, martist.Artist): kw['alpha'] = mappable.get_alpha() ColorbarBase.__init__(self, ax, **kw) def add_lines(self, CS): ''' Add the lines from a non-filled :class:`~matplotlib.contour.ContourSet` to the colorbar. ''' 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] #print 'tlinewidths:', tlinewidths ColorbarBase.add_lines(self, CS.levels, tcolors, tlinewidths) def update_normal(self, mappable): ''' update solid, lines, etc. Unlike update_bruteforce, it does not clear the axes. This is meant to be called when the image or contour plot to which this colorbar belongs is changed. ''' self.draw_all() if isinstance(self.mappable, contour.ContourSet): CS = self.mappable if not CS.filled: self.add_lines(CS) 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() # clearing the axes will delete outline, patch, solids, and lines: self.outline = None self.patch = None self.solids = None self.lines = None self.dividers = None self.set_alpha(mappable.get_alpha()) self.cmap = mappable.cmap self.norm = mappable.norm self.config_axis() 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. @docstring.Substitution(make_axes_kw_doc) def make_axes(parent, **kw): ''' Resize and reposition a parent axes, and return a child axes suitable for a colorbar:: cax, kw = make_axes(parent, **kw) Keyword arguments may include the following (with defaults): *orientation* 'vertical' or 'horizontal' %s All but the first of these are stripped from the input kw set. Returns (cax, kw), the child axes and the reduced kw dictionary. ''' orientation = kw.setdefault('orientation', 'vertical') fraction = kw.pop('fraction', 0.15) shrink = kw.pop('shrink', 1.0) aspect = kw.pop('aspect', 20) #pb = transforms.PBox(parent.get_position()) pb = parent.get_position(original=True).frozen() if orientation == 'vertical': pad = kw.pop('pad', 0.05) x1 = 1.0-fraction pb1, pbx, pbcb = pb.splitx(x1-pad, x1) pbcb = pbcb.shrunk(1.0, shrink).anchored('C', pbcb) anchor = (0.0, 0.5) panchor = (1.0, 0.5) else: pad = kw.pop('pad', 0.15) pbcb, pbx, pb1 = pb.splity(fraction, fraction+pad) pbcb = pbcb.shrunk(shrink, 1.0).anchored('C', pbcb) aspect = 1.0/aspect anchor = (0.5, 1.0) panchor = (0.5, 0.0) parent.set_position(pb1) parent.set_anchor(panchor) fig = parent.get_figure() cax = fig.add_axes(pbcb) cax.set_aspect(aspect, anchor=anchor, adjustable='box') return cax, kw