def _render_on_subplot(self, subplot):
        """
        TESTS::

            sage: P = polygon([(0,0), (1,2), (0,1), (-1,2)])
        """
        import matplotlib.patches as patches
        options = self.options()
        p = patches.Polygon([(self.xdata[i],self.ydata[i]) for i in xrange(len(self.xdata))])
        p.set_linewidth(float(options['thickness']))
        a = float(options['alpha'])
        z = int(options.pop('zorder', 1))
        p.set_alpha(a)
        f = options.pop('fill')
        p.set_fill(f)
        c = to_mpl_color(options['rgbcolor'])
        if f:
            ec = options['edgecolor']
            if ec is None:
                p.set_color(c)
            else:
                p.set_facecolor(c)
                p.set_edgecolor(to_mpl_color(ec))
        else:
            p.set_color(c)
        p.set_label(options['legend_label'])
        p.set_zorder(z)
        subplot.add_patch(p)

    def _render_on_subplot(self, subplot):
        """
        Render this arrow in a subplot.  This is the key function that
        defines how this arrow graphics primitive is rendered in
        matplotlib's library.

        EXAMPLES::

        This function implicitly ends up rendering this arrow on a matplotlib subplot:
            sage: arrow(path=[[(0,1), (2,-1), (4,5)]])
        """
        options = self.options()
        width = float(options['width'])
        head = options.pop('head')
        if head == 0: style = '<|-'
        elif head == 1: style = '-|>'
        elif head == 2: style = '<|-|>'
        else: raise KeyError('head parameter must be one of 0 (start), 1 (end) or 2 (both).')
        arrowsize = float(options.get('arrowsize',5))
        head_width=arrowsize
        head_length=arrowsize*2.0
        color = to_mpl_color(options['rgbcolor'])
        from matplotlib.patches import FancyArrowPatch
        from matplotlib.path import Path
        bpath = Path(self.vertices, self.codes)
        p = FancyArrowPatch(path=bpath,
                            lw=width, arrowstyle='%s,head_width=%s,head_length=%s'%(style,head_width, head_length), 
                            fc=color, ec=color, linestyle=options['linestyle'])
        p.set_zorder(options['zorder'])
        p.set_label(options['legend_label'])
        subplot.add_patch(p)
        return p

    def _render_on_subplot(self, subplot):
        """
        TESTS::

            sage: C = circle((2,pi), 2, edgecolor='black', facecolor='green', fill=True) 
        """
        import matplotlib.patches as patches        
        options = self.options()
        p = patches.Circle((float(self.x), float(self.y)), float(self.r), clip_on=options['clip'])
        if not options['clip']:
            self._bbox_extra_artists=[p]
        p.set_linewidth(float(options['thickness']))
        p.set_fill(options['fill'])
        a = float(options['alpha'])
        p.set_alpha(a)
        ec = to_mpl_color(options['edgecolor'])
        fc = to_mpl_color(options['facecolor'])
        if 'rgbcolor' in options: 
            ec = fc = to_mpl_color(options['rgbcolor'])
        p.set_edgecolor(ec)
        p.set_facecolor(fc)
        p.set_linestyle(options['linestyle'])
        p.set_label(options['legend_label'])
        z = int(options.pop('zorder', 0))
        p.set_zorder(z)
        subplot.add_patch(p)

    def _render_on_subplot(self, subplot):
        """
        TESTS::

            sage: D = disk((2,-1), 2, (0, pi), color='black', thickness=3, fill=False); D

        Save alpha information in pdf (see :trac:`13732`)::

            sage: f = tmp_filename(ext='.pdf')
            sage: p = disk((0,0), 5, (0, pi/4), alpha=0.5)
            sage: p.save(f)

        """
        import matplotlib.patches as patches
        options = self.options()
        deg1 = self.rad1*(180./pi) #convert radians to degrees
        deg2 = self.rad2*(180./pi)
        z = int(options.pop('zorder', 0))
        p = patches.Wedge((float(self.x), float(self.y)), float(self.r), float(deg1),
                            float(deg2), zorder=z)
        a = float(options['alpha'])
        p.set_alpha(a)
        p.set_linewidth(float(options['thickness']))
        p.set_fill(options['fill'])
        c = to_mpl_color(options['rgbcolor'])
        p.set_edgecolor(c)
        p.set_facecolor(c)
        p.set_label(options['legend_label'])
        subplot.add_patch(p)

    def _plot3d_options(self, options=None):
        """
        Translate 2D plot options into 3D plot options.

        EXAMPLES::

            sage: A=point((1,1),size=22)
            sage: a=A[0];a
            Point set defined by 1 point(s)
            sage: b=a.plot3d()
            sage: b.size
            22
            sage: b=a.plot3d(size=3)
            sage: b.size
            3
        """
        if options is None:
            options = dict(self.options())
        options_3d = {}
        if 'size' in options:
            options_3d['size'] = options['size']
            del options['size']
        if options.pop('faceted', False):
            raise NotImplementedError("3D points can not be faceted.")
        for o in ('marker', 'markeredgecolor'): # remove 2D options
            if o in options:
                del options[o]

        options_3d.update(GraphicPrimitive_xydata._plot3d_options(self, options))
        return options_3d

    def _render_on_subplot(self,subplot):
        r"""
        TESTS:

        We check to make sure that \#2076 is fixed by verifying all
        the points are red::

            sage: point(((1,1), (2,2), (3,3)), rgbcolor=hue(1), size=30)
        """
        options = self.options()

        #Convert the color to a hex string so that the scatter
        #method does not interpret it as a list of 3 floating
        #point color specifications when there are
        #three points. This is mentioned in the matplotlib 0.98
        #documentation and fixes \#2076
        from matplotlib.colors import rgb2hex
        c = rgb2hex(to_mpl_color(options['rgbcolor']))

        a = float(options['alpha'])
        z = int(options.pop('zorder', 0))
        s = int(options['size'])
        faceted = options['faceted'] #faceted=True colors the edge of point
        scatteroptions={}
        if not faceted: scatteroptions['edgecolors'] = 'none'
        subplot.scatter(self.xdata, self.ydata, s=s, c=c, alpha=a, zorder=z, label=options['legend_label'], **scatteroptions)

    def _plot3d_options(self, options=None):
        """
        Translate 2d plot options into 3d plot options.

        EXAMPLES::

            sage: P = polygon([(1,1), (1,2), (2,2), (2,1)], alpha=.5)
            sage: p=P[0]; p
            Polygon defined by 4 points
            sage: q=p.plot3d()
            sage: q.texture.opacity
            0.500000000000000
        """
        if options is None:
            options = dict(self.options())
        for o in ['thickness', 'zorder', 'legend_label', 'fill']:
            options.pop(o, None)
        return GraphicPrimitive_xydata._plot3d_options(self, options)

    def _render_on_subplot(self, subplot):
        """
        Render this arrow in a subplot.  This is the key function that
        defines how this arrow graphics primitive is rendered in
        matplotlib's library.

        EXAMPLES::

        This function implicitly ends up rendering this arrow on a matplotlib subplot:
            sage: arrow(path=[[(0,1), (2,-1), (4,5)]])
            Graphics object consisting of 1 graphics primitive
        """
        from sage.plot.misc import get_matplotlib_linestyle

        options = self.options()
        width = float(options["width"])
        head = options.pop("head")
        if head == 0:
            style = "<|-"
        elif head == 1:
            style = "-|>"
        elif head == 2:
            style = "<|-|>"
        else:
            raise KeyError("head parameter must be one of 0 (start), 1 (end) or 2 (both).")
        arrowsize = float(options.get("arrowsize", 5))
        head_width = arrowsize
        head_length = arrowsize * 2.0
        color = to_mpl_color(options["rgbcolor"])
        from matplotlib.patches import FancyArrowPatch
        from matplotlib.path import Path

        bpath = Path(self.vertices, self.codes)
        p = FancyArrowPatch(
            path=bpath,
            lw=width,
            arrowstyle="%s,head_width=%s,head_length=%s" % (style, head_width, head_length),
            fc=color,
            ec=color,
        )
        p.set_linestyle(get_matplotlib_linestyle(options["linestyle"], return_type="long"))
        p.set_zorder(options["zorder"])
        p.set_label(options["legend_label"])
        subplot.add_patch(p)
        return p

    def _render_on_subplot(self, subplot):
        """
        TESTS::

            sage: D = disk((2,-1), 2, (0, pi), color='black', thickness=3, fill=False); D
        """
        import matplotlib.patches as patches        
        options = self.options()
        deg1 = self.rad1*(180./pi) #convert radians to degrees 
        deg2 = self.rad2*(180./pi)
        z = int(options.pop('zorder', 0))
        p = patches.Wedge((float(self.x), float(self.y)), float(self.r), float(deg1),
                            float(deg2), zorder=z)
        p.set_linewidth(float(options['thickness']))
        p.set_fill(options['fill'])
        p.set_alpha(options['alpha'])
        c = to_mpl_color(options['rgbcolor'])
        p.set_edgecolor(c)
        p.set_facecolor(c)
        p.set_label(options['legend_label'])
        subplot.add_patch(p)

#.........这里部分代码省略.........

    The same circle but with a different line width::

        sage: implicit_plot(f, (-3,3), (-3,3), linewidth=6)

    And again the same circle but this time with a dashdot border::

        sage: implicit_plot(f, (-3,3), (-3,3), linestyle='dashdot')

    You can also plot an equation::

        sage: var("x y")
        (x, y)
        sage: implicit_plot(x^2+y^2 == 2, (x,-3,3), (y,-3,3))
        
    You can even change the color of the plot::
    
        sage: implicit_plot(x^2+y^2 == 2, (x,-3,3), (y,-3,3), color="red")

    Here is a beautiful (and long) example which also tests that all 
    colors work with this::

        sage: G = Graphics()
        sage: counter = 0
        sage: for col in colors.keys(): # long time
        ...       G += implicit_plot(x^2+y^2==1+counter*.1, (x,-4,4),(y,-4,4),color=col)
        ...       counter += 1
        sage: G.show(frame=False)

    We can define a level-`n` approximation of the boundary of the 
    Mandelbrot set::

        sage: def mandel(n):
        ...       c = polygen(CDF, 'c')
        ...       z = 0
        ...       for i in range(n):
        ...           z = z*z + c
        ...       def f(x, y):
        ...           val = z(CDF(x, y))
        ...           return val.norm() - 4
        ...       return f

    The first-level approximation is just a circle::

        sage: implicit_plot(mandel(1), (-3, 3), (-3, 3))
        
    A third-level approximation starts to get interesting::

        sage: implicit_plot(mandel(3), (-2, 1), (-1.5, 1.5))

    The seventh-level approximation is a degree 64 polynomial, and 
    ``implicit_plot`` does a pretty good job on this part of the curve.
    (``plot_points=200`` looks even better, but it takes over a second.)

    ::

        sage: implicit_plot(mandel(7), (-0.3, 0.05), (-1.15, -0.9),plot_points=50)

    When making a filled implicit plot using a python function rather than a
    symbolic expression the user should increase the number of plot points to
    avoid artifacts::

        sage: implicit_plot(lambda x,y: x^2+y^2-2, (x,-3,3), (y,-3,3), fill=True, plot_points=500) # long time

    TESTS::

        sage: f(x,y) = x^2 + y^2 - 2
        sage: implicit_plot(f, (-3, 3), (-3, 3),fill=5)
        Traceback (most recent call last):
        ...
        ValueError: fill=5 is not supported
    """
    from sage.symbolic.expression import is_SymbolicEquation
    if is_SymbolicEquation(f):
        if f.operator() != operator.eq:
            raise ValueError, "input to implicit plot must be function or equation"
        f = f.lhs() - f.rhs()
    linewidths = options.pop('linewidth', None)
    linestyles = options.pop('linestyle', None)

    if 'color' in options:
        options['cmap']=[options.pop('color', None)]

    if options['fill'] is True:
        options.pop('fill')
        options.pop('contours',None)
        options.pop('cmap',None)
        from sage.symbolic.expression import is_Expression
        if not is_Expression(f):
            return region_plot(lambda x,y: f(x,y)<0, xrange, yrange,
                               borderwidth=linewidths, borderstyle=linestyles,
                               **options)
        else:
            return region_plot(f<0, xrange, yrange, borderwidth=linewidths,
                               borderstyle=linestyles, **options)
    elif options['fill'] is False:
        return contour_plot(f, xrange, yrange, linewidths=linewidths,
                            linestyles=linestyles, **options)
    else:
        raise ValueError("fill=%s is not supported" % options['fill'])

    def _render_on_subplot(self, subplot):
        r"""
        Render this arrow in a subplot.  This is the key function that
        defines how this arrow graphics primitive is rendered in
        matplotlib's library.

        EXAMPLES:

        This function implicitly ends up rendering this arrow on
        a matplotlib subplot::

            sage: arrow((0,1), (2,-1))

        TESTS:

        The length of the ends (shrinkA and shrinkB) should not depend
        on the width of the arrow, because Matplotlib already takes
        this into account. See :trac:`12836`::

            sage: fig = Graphics().matplotlib()
            sage: sp = fig.add_subplot(1,1,1)
            sage: a = arrow((0,0), (1,1))
            sage: b = arrow((0,0), (1,1), width=20)
            sage: p1 = a[0]._render_on_subplot(sp)
            sage: p2 = b[0]._render_on_subplot(sp)
            sage: p1.shrinkA == p2.shrinkA
            True
            sage: p1.shrinkB == p2.shrinkB
            True

        Dashed arrows should have solid arrowheads,
        :trac:`12852`. This test saves the plot of a dashed arrow to
        an EPS file. Within the EPS file, ``stroke`` will be called
        twice: once to draw the line, and again to draw the
        arrowhead. We check that both calls do not occur while the
        dashed line style is enabled::

            sage: a = arrow((0,0), (1,1), linestyle='dashed')
            sage: filename = tmp_filename(ext='.eps')
            sage: a.save(filename=filename)
            sage: with open(filename, 'r') as f:
            ....:     contents = f.read().replace('\n', ' ')
            sage: two_stroke_pattern = r'setdash.*stroke.*stroke.*setdash'
            sage: import re
            sage: two_stroke_re = re.compile(two_stroke_pattern)
            sage: two_stroke_re.search(contents) is None
            True
        """
        options = self.options()
        head = options.pop('head')
        if head == 0: style = '<|-'
        elif head == 1: style = '-|>'
        elif head == 2: style = '<|-|>'
        else: raise KeyError('head parameter must be one of 0 (start), 1 (end) or 2 (both).')
        width = float(options['width'])
        arrowshorten_end = float(options.get('arrowshorten',0))/2.0
        arrowsize = float(options.get('arrowsize',5))
        head_width=arrowsize
        head_length=arrowsize*2.0
        color = to_mpl_color(options['rgbcolor'])
        from matplotlib.patches import FancyArrowPatch
        p = FancyArrowPatch((self.xtail, self.ytail), (self.xhead, self.yhead),
                            lw=width, arrowstyle='%s,head_width=%s,head_length=%s'%(style,head_width, head_length),
                            shrinkA=arrowshorten_end, shrinkB=arrowshorten_end,
                            fc=color, ec=color, linestyle=options['linestyle'])
        p.set_zorder(options['zorder'])
        p.set_label(options['legend_label'])

        if options['linestyle']!='solid':
            # The next few lines work around a design issue in matplotlib. Currently, the specified
            # linestyle is used to draw both the path and the arrowhead.  If linestyle is 'dashed', this
            # looks really odd.  This code is from Jae-Joon Lee in response to a post to the matplotlib mailing
            # list.  See http://sourceforge.net/mailarchive/forum.php?thread_name=CAG%3DuJ%2Bnw2dE05P9TOXTz_zp-mGP3cY801vMH7yt6vgP9_WzU8w%40mail.gmail.com&forum_name=matplotlib-users

            import matplotlib.patheffects as pe
            class CheckNthSubPath(object):
                def __init__(self, patch, n):
                    """
                    creates an callable object that returns True if the provided
                    path is the n-th path from the patch.
                    """
                    self._patch = patch
                    self._n = n

                def get_paths(self, renderer):
                    self._patch.set_dpi_cor(renderer.points_to_pixels(1.))
                    paths, fillables = self._patch.get_path_in_displaycoord()
                    return paths

                def __call__(self, renderer, gc, tpath, affine, rgbFace):
                    path = self.get_paths(renderer)[self._n]
                    vert1, code1 = path.vertices, path.codes
                    import numpy as np

                    if np.all(vert1 == tpath.vertices) and np.all(code1 == tpath.codes):
                        return True
                    else:
                        return False


#.........这里部分代码省略.........

#.........这里部分代码省略.........
        sage: contour_plot(f, (-2,2), (-2,2), labels=True, label_colors='red')

    We can add a colorbar as well::

        sage: f(x,y)=x^2-y^2
        sage: contour_plot(f, (x,-3,3), (y,-3,3), colorbar=True)

    ::

        sage: contour_plot(f, (x,-3,3), (y,-3,3), colorbar=True,colorbar_orientation='horizontal')

    ::

        sage: contour_plot(f, (x,-3,3), (y,-3,3), contours=[-2,-1,4],colorbar=True)

    ::

        sage: contour_plot(f, (x,-3,3), (y,-3,3), contours=[-2,-1,4],colorbar=True,colorbar_spacing='uniform')

    ::

        sage: contour_plot(f, (x,-3,3), (y,-3,3), contours=[0,2,3,6],colorbar=True,colorbar_format='%.3f')

    ::

        sage: contour_plot(f, (x,-3,3), (y,-3,3), labels=True,label_colors='red',contours=[0,2,3,6],colorbar=True)

    ::

        sage: contour_plot(f, (x,-3,3), (y,-3,3), cmap='winter', contours=20, fill=False, colorbar=True)

    This should plot concentric circles centered at the origin::

        sage: x,y = var('x,y')
        sage: contour_plot(x^2+y^2-2,(x,-1,1), (y,-1,1))

    Extra options will get passed on to show(), as long as they are valid::

        sage: f(x, y) = cos(x) + sin(y)
        sage: contour_plot(f, (0, pi), (0, pi), axes=True)

    One can also plot over a reduced region::

        sage: contour_plot(x**2-y**2, (x,-2, 2), (y,-2, 2),region=x-y,plot_points=300)

    ::

        sage: contour_plot(f, (0, pi), (0, pi)).show(axes=True) # These are equivalent

    Note that with ``fill=False`` and grayscale contours, there is the
    possibility of confusion between the contours and the axes, so use
    ``fill=False`` together with ``axes=True`` with caution::

        sage: contour_plot(f, (-pi, pi), (-pi, pi), fill=False, axes=True)

    TESTS:

    To check that ticket 5221 is fixed, note that this has three curves, not two::

        sage: x,y = var('x,y')
        sage: contour_plot(x-y^2,(x,-5,5),(y,-3,3),contours=[-4,-2,0], fill=False)
    """
    from sage.plot.all import Graphics
    from sage.plot.misc import setup_for_eval_on_grid

    region = options.pop('region')
    ev = [f] if region is None else [f,region]

    F, ranges = setup_for_eval_on_grid(ev, [xrange, yrange], options['plot_points'])
    g = F[0]
    xrange,yrange=[r[:2] for r in ranges]

    xy_data_array = [[g(x, y) for x in xsrange(*ranges[0], include_endpoint=True)]
                              for y in xsrange(*ranges[1], include_endpoint=True)]

    if region is not None:
        import numpy

        xy_data_array = numpy.ma.asarray(xy_data_array,dtype=float)

        m = F[1]

        mask = numpy.asarray([[m(x, y)<=0 for x in xsrange(*ranges[0], include_endpoint=True)]
                                          for y in xsrange(*ranges[1], include_endpoint=True)],dtype=bool)

        xy_data_array[mask] = numpy.ma.masked

    g = Graphics()

    # Reset aspect_ratio to 'automatic' in case scale is 'semilog[xy]'.
    # Otherwise matplotlib complains.
    scale = options.get('scale', None)
    if isinstance(scale, (list, tuple)):
        scale = scale[0]
    if scale == 'semilogy' or scale == 'semilogx':
        options['aspect_ratio'] = 'automatic'

    g._set_extra_kwds(Graphics._extract_kwds_for_show(options, ignore=['xmin', 'xmax']))
    g.add_primitive(ContourPlot(xy_data_array, xrange, yrange, options))
    return g

    def _render_on_subplot(self, subplot):
        """
        TESTS::

            sage: matrix_plot(random_matrix(RDF, 50), cmap='jet')
            Graphics object consisting of 1 graphics primitive
        """
        options = self.options()
        cmap = get_cmap(options.pop('cmap',None))
        origin=options['origin']

        norm=options['norm']

        if norm=='value':
            import matplotlib
            norm=matplotlib.colors.NoNorm()

        if options['subdivisions']:
            subdiv_options=options['subdivision_options']
            if isinstance(subdiv_options['boundaries'], (list, tuple)):
                rowsub,colsub=subdiv_options['boundaries']
            else:
                rowsub=subdiv_options['boundaries']
                colsub=subdiv_options['boundaries']
            if isinstance(subdiv_options['style'], (list, tuple)):
                rowstyle,colstyle=subdiv_options['style']
            else:
                rowstyle=subdiv_options['style']
                colstyle=subdiv_options['style']
            if rowstyle is None:
                rowstyle=dict()
            if colstyle is None:
                colstyle=dict()

            # Make line objects for subdivisions
            from line import line2d
            lim=self.get_minmax_data()
            # First draw horizontal lines representing row subdivisions
            for y in rowsub:
                l=line2d([(lim['xmin'],y-0.5), (lim['xmax'],y-0.5)], **rowstyle)[0]
                l._render_on_subplot(subplot)
            for x in colsub:
                l=line2d([(x-0.5, lim['ymin']), (x-0.5, lim['ymax'])], **colstyle)[0]
                l._render_on_subplot(subplot)

        if hasattr(self.xy_data_array, 'tocoo'):
            # Sparse matrix -- use spy
            opts=options.copy()
            for opt in ['vmin', 'vmax', 'norm', 'origin','subdivisions','subdivision_options',
                        'colorbar','colorbar_options']:
                del opts[opt]
            if origin=='lower':
                subplot.spy(self.xy_data_array.tocsr()[::-1], **opts)
            else:
                subplot.spy(self.xy_data_array, **opts)
        else:
            opts = dict(cmap=cmap, interpolation='nearest', aspect='equal',
                      norm=norm, vmin=options['vmin'], vmax=options['vmax'],
                      origin=origin,zorder=options.get('zorder',None))
            image=subplot.imshow(self.xy_data_array, **opts)

            if options.get('colorbar', False):
                colorbar_options = options['colorbar_options']
                from matplotlib import colorbar
                cax,kwds=colorbar.make_axes_gridspec(subplot,**colorbar_options)
                cb=colorbar.Colorbar(cax,image, **kwds)

        if origin=='upper':
            subplot.xaxis.tick_top()
        elif origin=='lower':
            subplot.xaxis.tick_bottom()
        subplot.xaxis.set_ticks_position('both') #only tick marks, not tick labels

    def _render_on_subplot(self, subplot):
        """
        TESTS::

            sage: matrix_plot(random_matrix(RDF, 50), cmap='jet')
        """
        options = self.options()
        cmap = get_cmap(options.pop("cmap", None))
        origin = options["origin"]

        norm = options["norm"]

        if norm == "value":
            import matplotlib

            norm = matplotlib.colors.NoNorm()

        if options["subdivisions"]:
            subdiv_options = options["subdivision_options"]
            if isinstance(subdiv_options["boundaries"], (list, tuple)):
                rowsub, colsub = subdiv_options["boundaries"]
            else:
                rowsub = subdiv_options["boundaries"]
                colsub = subdiv_options["boundaries"]
            if isinstance(subdiv_options["style"], (list, tuple)):
                rowstyle, colstyle = subdiv_options["style"]
            else:
                rowstyle = subdiv_options["style"]
                colstyle = subdiv_options["style"]
            if rowstyle is None:
                rowstyle = dict()
            if colstyle is None:
                colstyle = dict()

            # Make line objects for subdivisions
            from line import line2d

            lim = self.get_minmax_data()
            # First draw horizontal lines representing row subdivisions
            for y in rowsub:
                l = line2d([(lim["xmin"], y - 0.5), (lim["xmax"], y - 0.5)], **rowstyle)[0]
                l._render_on_subplot(subplot)
            for x in colsub:
                l = line2d([(x - 0.5, lim["ymin"]), (x - 0.5, lim["ymax"])], **colstyle)[0]
                l._render_on_subplot(subplot)

        if hasattr(self.xy_data_array, "tocoo"):
            # Sparse matrix -- use spy
            opts = options.copy()
            for opt in [
                "vmin",
                "vmax",
                "norm",
                "origin",
                "subdivisions",
                "subdivision_options",
                "colorbar",
                "colorbar_options",
            ]:
                del opts[opt]
            if origin == "lower":
                subplot.spy(self.xy_data_array.tocsr()[::-1], **opts)
            else:
                subplot.spy(self.xy_data_array, **opts)
        else:
            opts = dict(
                cmap=cmap,
                interpolation="nearest",
                aspect="equal",
                norm=norm,
                vmin=options["vmin"],
                vmax=options["vmax"],
                origin=origin,
                zorder=options.get("zorder", None),
            )
            image = subplot.imshow(self.xy_data_array, **opts)

            if options.get("colorbar", False):
                colorbar_options = options["colorbar_options"]
                from matplotlib import colorbar

                cax, kwds = colorbar.make_axes_gridspec(subplot, **colorbar_options)
                cb = colorbar.Colorbar(cax, image, **kwds)

        if origin == "upper":
            subplot.xaxis.tick_top()
        elif origin == "lower":
            subplot.xaxis.tick_bottom()
        subplot.xaxis.set_ticks_position("both")  # only tick marks, not tick labels