Python SubplotHost.grid方法代码示例

# 需要导入模块: from mpl_toolkits.axes_grid.parasite_axes import SubplotHost [as 别名]
# 或者: from mpl_toolkits.axes_grid.parasite_axes.SubplotHost import grid [as 别名]
def test_polar_box():
    fig = plt.figure(figsize=(5, 5))

    # PolarAxes.PolarTransform takes radian. However, we want our coordinate
    # system in degree
    tr = Affine2D().scale(np.pi / 180., 1.) + PolarAxes.PolarTransform()

    # polar projection, which involves cycle, and also has limits in
    # its coordinates, needs a special method to find the extremes
    # (min, max of the coordinate within the view).
    extreme_finder = angle_helper.ExtremeFinderCycle(20, 20,
                                                     lon_cycle=360,
                                                     lat_cycle=None,
                                                     lon_minmax=None,
                                                     lat_minmax=(0, np.inf))

    grid_locator1 = angle_helper.LocatorDMS(12)
    tick_formatter1 = angle_helper.FormatterDMS()

    grid_helper = GridHelperCurveLinear(tr,
                                        extreme_finder=extreme_finder,
                                        grid_locator1=grid_locator1,
                                        tick_formatter1=tick_formatter1)

    ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)

    ax1.axis["right"].major_ticklabels.set_visible(True)
    ax1.axis["top"].major_ticklabels.set_visible(True)

    # let right axis shows ticklabels for 1st coordinate (angle)
    ax1.axis["right"].get_helper().nth_coord_ticks = 0
    # let bottom axis shows ticklabels for 2nd coordinate (radius)
    ax1.axis["bottom"].get_helper().nth_coord_ticks = 1

    fig.add_subplot(ax1)

    ax1.axis["lat"] = axis = grid_helper.new_floating_axis(0, 45, axes=ax1)
    axis.label.set_text("Test")
    axis.label.set_visible(True)
    axis.get_helper()._extremes = 2, 12

    ax1.axis["lon"] = axis = grid_helper.new_floating_axis(1, 6, axes=ax1)
    axis.label.set_text("Test 2")
    axis.get_helper()._extremes = -180, 90

    # A parasite axes with given transform
    ax2 = ParasiteAxesAuxTrans(ax1, tr, "equal")
    assert ax2.transData == tr + ax1.transData
    # Anything you draw in ax2 will match the ticks and grids of ax1.
    ax1.parasites.append(ax2)
    ax2.plot(np.linspace(0, 30, 50), np.linspace(10, 10, 50))

    ax1.set_aspect(1.)
    ax1.set_xlim(-5, 12)
    ax1.set_ylim(-5, 10)

    ax1.grid(True)

# 需要导入模块: from mpl_toolkits.axes_grid.parasite_axes import SubplotHost [as 别名]
# 或者: from mpl_toolkits.axes_grid.parasite_axes.SubplotHost import grid [as 别名]
def curvelinear_test2(fig):
    """
    polar projection, but in a rectangular box.
    """
    global ax1
    import numpy as np
    import  mpl_toolkits.axes_grid.angle_helper as angle_helper
    from matplotlib.projections import PolarAxes
    from matplotlib.transforms import Affine2D

    from mpl_toolkits.axes_grid.parasite_axes import SubplotHost

    from mpl_toolkits.axes_grid.grid_helper_curvelinear import GridHelperCurveLinear

    # see demo_curvelinear_grid.py for details
    tr = Affine2D().scale(np.pi/180., 1.) + PolarAxes.PolarTransform()

    extreme_finder = angle_helper.ExtremeFinderCycle(20, 20,
                                                     lon_cycle = 360,
                                                     lat_cycle = None,
                                                     lon_minmax = None,
                                                     lat_minmax = (0, np.inf),
                                                     )

    grid_locator1 = angle_helper.LocatorDMS(12)

    tick_formatter1 = angle_helper.FormatterDMS()

    grid_helper = GridHelperCurveLinear(tr,
                                        extreme_finder=extreme_finder,
                                        grid_locator1=grid_locator1,
                                        tick_formatter1=tick_formatter1
                                        )


    ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)

    fig.add_subplot(ax1)

    # Now creates floating axis

    #grid_helper = ax1.get_grid_helper()
    # floating axis whose first coordinate (theta) is fixed at 60
    ax1.axis["lat"] = axis = ax1.new_floating_axis(0, 60)
    axis.label.set_text(r"$\theta = 60^{\circ}$")
    axis.label.set_visible(True)

    # floating axis whose second coordinate (r) is fixed at 6
    ax1.axis["lon"] = axis = ax1.new_floating_axis(1, 6)
    axis.label.set_text(r"$r = 6$")

    ax1.set_aspect(1.)
    ax1.set_xlim(-5, 12)
    ax1.set_ylim(-5, 10)

    ax1.grid(True)

# 需要导入模块: from mpl_toolkits.axes_grid.parasite_axes import SubplotHost [as 别名]
# 或者: from mpl_toolkits.axes_grid.parasite_axes.SubplotHost import grid [as 别名]
def curvelinear_test3(fig):
    """
    polar projection, but in a rectangular box.
    """
    global ax1, axis
    import numpy as np
    from . import angle_helper
    from matplotlib.projections import PolarAxes
    from matplotlib.transforms import Affine2D

    from mpl_toolkits.axes_grid.parasite_axes import SubplotHost

    # PolarAxes.PolarTransform takes radian. However, we want our coordinate
    # system in degree
    tr = Affine2D().scale(np.pi / 180., 1.) + PolarAxes.PolarTransform()

    # polar projection, which involves cycle, and also has limits in
    # its coordinates, needs a special method to find the extremes
    # (min, max of the coordinate within the view).

    # 20, 20 : number of sampling points along x, y direction
    extreme_finder = angle_helper.ExtremeFinderCycle(
        20,
        20,
        lon_cycle=360,
        lat_cycle=None,
        lon_minmax=None,
        lat_minmax=(0, np.inf),
    )

    grid_locator1 = angle_helper.LocatorDMS(12)
    # Find a grid values appropriate for the coordinate (degree,
    # minute, second).

    tick_formatter1 = angle_helper.FormatterDMS()
    # And also uses an appropriate formatter.  Note that,the
    # acceptable Locator and Formatter class is a bit different than
    # that of mpl's, and you cannot directly use mpl's Locator and
    # Formatter here (but may be possible in the future).

    grid_helper = GridHelperCurveLinear(
        tr,
        extreme_finder=extreme_finder,
        grid_locator1=grid_locator1,
        tick_formatter1=tick_formatter1)

    ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)

    for axis in list(six.itervalues(ax1.axis)):
        axis.set_visible(False)

    fig.add_subplot(ax1)

    grid_helper = ax1.get_grid_helper()
    ax1.axis["lat1"] = axis = grid_helper.new_floating_axis(
        0, 130, axes=ax1, axis_direction="left")
    axis.label.set_text("Test")
    axis.label.set_visible(True)
    axis.get_helper()._extremes = 0.001, 10

    grid_helper = ax1.get_grid_helper()
    ax1.axis["lat2"] = axis = grid_helper.new_floating_axis(
        0, 50, axes=ax1, axis_direction="right")
    axis.label.set_text("Test")
    axis.label.set_visible(True)
    axis.get_helper()._extremes = 0.001, 10

    ax1.axis["lon"] = axis = grid_helper.new_floating_axis(
        1, 10, axes=ax1, axis_direction="bottom")
    axis.label.set_text("Test 2")
    axis.get_helper()._extremes = 50, 130
    axis.major_ticklabels.set_axis_direction("top")
    axis.label.set_axis_direction("top")

    grid_helper.grid_finder.grid_locator1.den = 5
    grid_helper.grid_finder.grid_locator2._nbins = 5

    #     # A parasite axes with given transform
    #     ax2 = ParasiteAxesAuxTrans(ax1, tr, "equal")
    #     # note that ax2.transData == tr + ax1.transData
    #     # Anthing you draw in ax2 will match the ticks and grids of ax1.
    #     ax1.parasites.append(ax2)
    #     intp = cbook.simple_linear_interpolation
    #     ax2.plot(intp(np.array([0, 30]), 50),
    #              intp(np.array([10., 10.]), 50))

    ax1.set_aspect(1.)
    ax1.set_xlim(-5, 12)
    ax1.set_ylim(-5, 10)

    ax1.grid(True)

# 需要导入模块: from mpl_toolkits.axes_grid.parasite_axes import SubplotHost [as 别名]
# 或者: from mpl_toolkits.axes_grid.parasite_axes.SubplotHost import grid [as 别名]
def test3():

    import numpy as np
    from matplotlib.transforms import Transform
    from matplotlib.path import Path

    class MyTransform(Transform):
        input_dims = 2
        output_dims = 2
        is_separable = False

        def __init__(self, resolution):
            """
            Create a new Aitoff transform.  Resolution is the number of steps
            to interpolate between each input line segment to approximate its
            path in curved Aitoff space.
            """
            Transform.__init__(self)
            self._resolution = resolution

        def transform(self, ll):
            x = ll[:, 0:1]
            y = ll[:, 1:2]

            return np.concatenate((x, y - x), 1)

        transform.__doc__ = Transform.transform.__doc__

        transform_non_affine = transform
        transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__

        def transform_path(self, path):
            vertices = path.vertices
            ipath = path.interpolated(self._resolution)
            return Path(self.transform(ipath.vertices), ipath.codes)

        transform_path.__doc__ = Transform.transform_path.__doc__

        transform_path_non_affine = transform_path
        transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__

        def inverted(self):
            return MyTransformInv(self._resolution)

        inverted.__doc__ = Transform.inverted.__doc__

    class MyTransformInv(Transform):
        input_dims = 2
        output_dims = 2
        is_separable = False

        def __init__(self, resolution):
            Transform.__init__(self)
            self._resolution = resolution

        def transform(self, ll):
            x = ll[:, 0:1]
            y = ll[:, 1:2]

            return np.concatenate((x, y + x), 1)

        transform.__doc__ = Transform.transform.__doc__

        def inverted(self):
            return MyTransform(self._resolution)

        inverted.__doc__ = Transform.inverted.__doc__

    import matplotlib.pyplot as plt
    fig = plt.figure(1)
    fig.clf()
    tr = MyTransform(1)
    grid_helper = GridHelperCurveLinear(tr)

    from mpl_toolkits.axes_grid1.parasite_axes import host_subplot_class_factory
    from .axislines import Axes

    SubplotHost = host_subplot_class_factory(Axes)

    ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)

    fig.add_subplot(ax1)

    ax2 = ParasiteAxesAuxTrans(ax1, tr, "equal")
    ax1.parasites.append(ax2)
    ax2.plot([3, 6], [5.0, 10.])

    ax1.set_aspect(1.)
    ax1.set_xlim(0, 10)
    ax1.set_ylim(0, 10)

    ax1.grid(True)
    plt.draw()

# 需要导入模块: from mpl_toolkits.axes_grid.parasite_axes import SubplotHost [as 别名]
# 或者: from mpl_toolkits.axes_grid.parasite_axes.SubplotHost import grid [as 别名]
def test_custom_transform():
    class MyTransform(Transform):
        input_dims = 2
        output_dims = 2
        is_separable = False

        def __init__(self, resolution):
            """
            Resolution is the number of steps to interpolate between each input
            line segment to approximate its path in transformed space.
            """
            Transform.__init__(self)
            self._resolution = resolution

        def transform(self, ll):
            x = ll[:, 0:1]
            y = ll[:, 1:2]

            return np.concatenate((x, y - x), 1)

        transform_non_affine = transform

        def transform_path(self, path):
            vertices = path.vertices
            ipath = path.interpolated(self._resolution)
            return Path(self.transform(ipath.vertices), ipath.codes)

        transform_path_non_affine = transform_path

        def inverted(self):
            return MyTransformInv(self._resolution)

    class MyTransformInv(Transform):
        input_dims = 2
        output_dims = 2
        is_separable = False

        def __init__(self, resolution):
            Transform.__init__(self)
            self._resolution = resolution

        def transform(self, ll):
            x = ll[:, 0:1]
            y = ll[:, 1:2]

            return np.concatenate((x, y+x), 1)

        def inverted(self):
            return MyTransform(self._resolution)

    fig = plt.figure()

    SubplotHost = host_subplot_class_factory(Axes)

    tr = MyTransform(1)
    grid_helper = GridHelperCurveLinear(tr)
    ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)
    fig.add_subplot(ax1)

    ax2 = ParasiteAxesAuxTrans(ax1, tr, "equal")
    ax1.parasites.append(ax2)
    ax2.plot([3, 6], [5.0, 10.])

    ax1.set_aspect(1.)
    ax1.set_xlim(0, 10)
    ax1.set_ylim(0, 10)

    ax1.grid(True)

# 需要导入模块: from mpl_toolkits.axes_grid.parasite_axes import SubplotHost [as 别名]
# 或者: from mpl_toolkits.axes_grid.parasite_axes.SubplotHost import grid [as 别名]
def test_axis_direction():
    fig = plt.figure(figsize=(5, 5))

    # PolarAxes.PolarTransform takes radian. However, we want our coordinate
    # system in degree
    tr = Affine2D().scale(np.pi / 180., 1.) + PolarAxes.PolarTransform()

    # polar projection, which involves cycle, and also has limits in
    # its coordinates, needs a special method to find the extremes
    # (min, max of the coordinate within the view).

    # 20, 20 : number of sampling points along x, y direction
    extreme_finder = angle_helper.ExtremeFinderCycle(20, 20,
                                                     lon_cycle=360,
                                                     lat_cycle=None,
                                                     lon_minmax=None,
                                                     lat_minmax=(0, np.inf),
                                                     )

    grid_locator1 = angle_helper.LocatorDMS(12)
    tick_formatter1 = angle_helper.FormatterDMS()

    grid_helper = GridHelperCurveLinear(tr,
                                        extreme_finder=extreme_finder,
                                        grid_locator1=grid_locator1,
                                        tick_formatter1=tick_formatter1)

    ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)

    for axis in ax1.axis.values():
        axis.set_visible(False)

    fig.add_subplot(ax1)

    ax1.axis["lat1"] = axis = grid_helper.new_floating_axis(
        0, 130,
        axes=ax1, axis_direction="left")
    axis.label.set_text("Test")
    axis.label.set_visible(True)
    axis.get_helper()._extremes = 0.001, 10

    ax1.axis["lat2"] = axis = grid_helper.new_floating_axis(
        0, 50,
        axes=ax1, axis_direction="right")
    axis.label.set_text("Test")
    axis.label.set_visible(True)
    axis.get_helper()._extremes = 0.001, 10

    ax1.axis["lon"] = axis = grid_helper.new_floating_axis(
        1, 10,
        axes=ax1, axis_direction="bottom")
    axis.label.set_text("Test 2")
    axis.get_helper()._extremes = 50, 130
    axis.major_ticklabels.set_axis_direction("top")
    axis.label.set_axis_direction("top")

    grid_helper.grid_finder.grid_locator1.den = 5
    grid_helper.grid_finder.grid_locator2._nbins = 5

    ax1.set_aspect(1.)
    ax1.set_xlim(-8, 8)
    ax1.set_ylim(-4, 12)

    ax1.grid(True)

# 需要导入模块: from mpl_toolkits.axes_grid.parasite_axes import SubplotHost [as 别名]
# 或者: from mpl_toolkits.axes_grid.parasite_axes.SubplotHost import grid [as 别名]
def plotcomplexpolar(metaAry, size = (10, 7.5), dpi = 75, grid = True, legend = 0, fontsize = 15):
    """
    metaArray function to do a simple 1D plot of complex array as magnitude and phase angle.
    
    legend:
        'best'  0
        'upper right'   1
        'upper left'    2
        'lower left'    3
        'lower right'   4
        'right'         5
        'center left'   6
        'center right'  7
        'lower center'  8
        'upper center'  9
        'center'        10
    """
    
    if legend is None:
        legend = 0
    
    axis = metaAry['range']    
    mag = abs(metaAry.data)
    pha = angle(metaAry.data, deg=True)
    
    # Load the plotting ranges and units
    x0 = axis['begin'][0]
    x1 = axis['end'][0]
    my0 = min(mag)
    my1 = max(mag)
    py0 = min(pha)
    py1 = max(pha)
    xunit = axis['unit'][0]
    myunit = metaAry['unit']
    pyunit = 'Degree'
    
    # Leave 10% margin in the y axis
    mmean = average((my0, my1))
    mreach = abs(my0-my1) / 2 / 0.9
    my0 = sign(my0-mmean) * mreach + mmean
    my1 = sign(my1-mmean) * mreach + mmean
    
    pmean = average((py0, py1))
    preach = abs(py0-py1) / 2 / 0.9
    py0 = sign(py0-pmean) * preach + pmean
    py1 = sign(py1-pmean) * preach + pmean
    
    # Apply unit prefix if unit is defined
    xunit, x0, x1, xscale = prettyunit(xunit, x0, x1)
    myunit, my0, my1, myscale = prettyunit(myunit, my0, my1)
    pyunit, py0, py1, pyscale = prettyunit(pyunit, py0, py1)
    
    if myscale != 1:
        mag = mag * myscale
        
    if pyscale != 1:
        pha = pha.copy() * pyscale
        
    xlabl = lbl_repr(axis['label'][0], xunit)
    mylabl = lbl_repr(metaAry['label'], myunit, "Magnitude")
    pylabl = lbl_repr(metaAry['label'], pyunit, "Phase angle")
    
    title = metaAry['name']
    
    fig = figure(figsize=size, dpi = dpi)
    host = SubplotHost(fig, 111)
    
    fig.add_subplot(host)
    par = host.twinx()
    
    if axis['log'][0] is False:
        x = linspace(x0, x1, len(metaAry))
    else:
        raise NotImplemented, "Log axis is not yet implemented."
    
    host.plot(x, mag, 'b-', label=lbl_repr(axis['label'][0], '', "Magnitude"))
    par.plot(x, pha, 'r--', label=lbl_repr(axis['label'][0], '', "Phase"))
    
    host.grid(grid)
    
    host.set_xlabel(xlabl, fontsize=fontsize)
    host.set_ylabel(mylabl, fontsize=fontsize)
    par.set_ylabel(pylabl, fontsize=fontsize)
    
    host.set_xlim([x0, x1])
    host.set_ylim([my0, my1])
    par.set_ylim([py0, py1])
    
    if fontsize is not None:
        host.set_title(title, fontsize=int(fontsize*1.3))
    else:
        host.set_title(title)
        
    if legend >= 0:
        host.legend(loc=legend)
    
    return fig, host, par

# 需要导入模块: from mpl_toolkits.axes_grid.parasite_axes import SubplotHost [as 别名]
# 或者: from mpl_toolkits.axes_grid.parasite_axes.SubplotHost import grid [as 别名]
def plotcomplex(metaAry, size = (10, 7.5), dpi = 75, grid = True, legend = 0, fontsize = 15):
    """
    metaArray function to do a simple 1D plot of complex array as real and imaginary parts.
    
    legend:
        'best'  0
        'upper right'   1
        'upper left'    2
        'lower left'    3
        'lower right'   4
        'right'         5
        'center left'   6
        'center right'  7
        'lower center'  8
        'upper center'  9
        'center'        10
    """
    
    if legend is None:
        legend = 0
    
    axis = metaAry['range']
    rdata = metaAry.data.real
    idata = metaAry.data.imag
    
    # Load the plotting ranges and units
    x0 = axis['begin'][0]
    x1 = axis['end'][0]
    ry0 = min(rdata)
    ry1 = max(rdata)
    iy0 = min(idata)
    iy1 = max(idata)
    xunit = axis['unit'][0]
    ryunit = metaAry['unit']
    iyunit = metaAry['unit']
    
    # Leave 10% margin in the y axis
    rmean = average((ry0, ry1))
    rreach = abs(ry0-ry1) / 2 / 0.9
    ry0 = sign(ry0-rmean) * rreach + rmean
    ry1 = sign(ry1-rmean) * rreach + rmean
    
    imean = average((iy0, iy1))
    ireach = abs(iy0-iy1) / 2 / 0.9
    iy0 = sign(iy0-imean) * ireach + imean
    iy1 = sign(iy1-imean) * ireach + imean
    
    # Apply unit prefix if unit is defined
    xunit, x0, x1, xscale = prettyunit(xunit, x0, x1)
    ryunit, ry0, ry1, ryscale = prettyunit(ryunit, ry0, ry1)
    iyunit, iy0, iy1, iyscale = prettyunit(iyunit, iy0, iy1)
    
    if ryscale != 1:
        rdata = rdata.copy() * ryscale
        
    if iyscale != 1:
        idata = idata.copy() * iyscale
        
    xlabl = lbl_repr(axis['label'][0], xunit)
    rylabl = lbl_repr(metaAry['label'], ryunit, "Real part")
    iylabl = lbl_repr(metaAry['label'], iyunit, "Imaginary part")
    
    title = metaAry['name']
    
    fig = figure(figsize=size, dpi = dpi)
    host = SubplotHost(fig, 111)
    
    fig.add_subplot(host)
    par = host.twinx()
    
    if axis['log'][0] is False:
        x = linspace(x0, x1, len(metaAry))
    else:
        raise NotImplemented, "Log axis is not yet implemented."
    
    host.plot(x, rdata, 'b-', label=lbl_repr(axis['label'][0], '', "Real"))
    par.plot(x, idata, 'r--', label=lbl_repr(axis['label'][0], '', "Imaginary"))
    
    host.grid(grid)
    
    host.set_xlabel(xlabl, fontsize=fontsize)
    host.set_ylabel(rylabl, fontsize=fontsize)
    par.set_ylabel(iylabl, fontsize=fontsize)
    
    host.set_xlim([x0, x1])
    host.set_ylim([ry0, ry1])
    par.set_ylim([iy0, iy1])
    
    if fontsize is not None:
        host.set_title(title, fontsize=int(fontsize*1.3))
    else:
        host.set_title(title)
        
    if legend >= 0:
        host.legend(loc=legend)
    
    return fig, host, par

# 需要导入模块: from mpl_toolkits.axes_grid.parasite_axes import SubplotHost [as 别名]
# 或者: from mpl_toolkits.axes_grid.parasite_axes.SubplotHost import grid [as 别名]
def curvelinear_test2(fig):
    """
    polar projection, but in a rectangular box.
    """

    # PolarAxes.PolarTransform takes radian. However, we want our coordinate
    # system in degree
    tr = Affine2D().scale(np.pi/180., 1.) + PolarAxes.PolarTransform()

    # polar projection, which involves cycle, and also has limits in
    # its coordinates, needs a special method to find the extremes
    # (min, max of the coordinate within the view).

    # 20, 20 : number of sampling points along x, y direction
    extreme_finder = angle_helper.ExtremeFinderCycle(20, 20,
                                                     lon_cycle = 360,
                                                     lat_cycle = None,
                                                     lon_minmax = None,
                                                     lat_minmax = (0, np.inf),
                                                     )

    grid_locator1 = angle_helper.LocatorDMS(12)
    # Find a grid values appropriate for the coordinate (degree,
    # minute, second).

    tick_formatter1 = angle_helper.FormatterDMS()
    # And also uses an appropriate formatter.  Note that,the
    # acceptable Locator and Formatter class is a bit different than
    # that of mpl's, and you cannot directly use mpl's Locator and
    # Formatter here (but may be possible in the future).

    grid_helper = GridHelperCurveLinear(tr,
                                        extreme_finder=extreme_finder,
                                        grid_locator1=grid_locator1,
                                        tick_formatter1=tick_formatter1
                                        )


    ax1 = SubplotHost(fig, 1, 2, 2, grid_helper=grid_helper)

    # make ticklabels of right and top axis visible.
    ax1.axis["right"].major_ticklabels.set_visible(True)
    ax1.axis["top"].major_ticklabels.set_visible(True)

    # let right axis shows ticklabels for 1st coordinate (angle)
    ax1.axis["right"].get_helper().nth_coord_ticks=0
    # let bottom axis shows ticklabels for 2nd coordinate (radius)
    ax1.axis["bottom"].get_helper().nth_coord_ticks=1

    fig.add_subplot(ax1)


    # A parasite axes with given transform
    ax2 = ParasiteAxesAuxTrans(ax1, tr, "equal")
    # note that ax2.transData == tr + ax1.transData
    # Anthing you draw in ax2 will match the ticks and grids of ax1.
    ax1.parasites.append(ax2)
    intp = cbook.simple_linear_interpolation
    ax2.plot(intp(np.array([0, 30]), 50),
             intp(np.array([10., 10.]), 50))

    ax1.set_aspect(1.)
    ax1.set_xlim(-5, 12)
    ax1.set_ylim(-5, 10)

    ax1.grid(True)