'''
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 numpy as np
import matplotlib as mpl
import matplotlib.colors as colors
import matplotlib.cm as cm
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

make_axes_kw_doc = '''

    ==========   ====================================================
    Property     Description
    ==========   ====================================================
    *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)



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` method and pylab
    function.

    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=1.0,
                           values=None,
                           boundaries=None,
                           orientation='vertical',
                           extend='neither',
                           spacing='uniform',  # uniform or proportional
                           ticks=None,
                           format=None,
                           drawedges=False,
                           filled=True,
                           ):
        self.ax = 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.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.draw_all()

    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)
        self._set_label()

    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)
        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']
        self.patch = patches.Polygon(xy, edgecolor=c,
                 facecolor=c,
                 linewidth=0.01,
                 zorder=-1)
        ax.add_artist(self.patch)
        ticks, ticklabels, offset_string = self._ticker()
        if self.orientation == 'vertical':
            ax.set_xticks([])
            ax.yaxis.set_label_position('right')
            ax.yaxis.set_ticks_position('right')
            ax.set_yticks(ticks)
            ax.set_yticklabels(ticklabels)
            ax.yaxis.get_major_formatter().set_offset_string(offset_string)

        else:
            ax.set_yticks([])
            ax.xaxis.set_label_position('bottom')
            ax.set_xticks(ticks)
            ax.set_xticklabels(ticklabels)
            ax.xaxis.get_major_formatter().set_offset_string(offset_string)

    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.
        '''
        ## Change to pcolorfast after fixing bugs in some backends...
        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,
                    'shading':'flat', '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.pcolor(*args, **kw)
        self.ax.hold(_hold)
        #self.add_observer(col) # We should observe, not be observed...
        self.solids = col
        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)
        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)
        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):
    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
        kw['alpha'] = mappable.get_alpha()
        if isinstance(mappable, contour.ContourSet):
            CS = mappable
            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:
            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_bruteforce(self, mappable):
        '''
        Manually change any contour line colors.  This is called
        when the image or contour plot to which this colorbar belongs
        is changed.
        '''
        # 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.draw_all()
        #if self.vmin != self.norm.vmin or self.vmax != self.norm.vmax:
        #    self.ax.cla()
        #    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 make_axes(parent, **kw):
    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
make_axes.__doc__ ='''
    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.
    '''  % make_axes_kw_doc


