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)

    def preparePlot(self):
        """Prepare the plotting window"""
        plt.hot()
        self.figure=plt.figure(self.figNr)
        self.figure.clear()
        # this is black magic that makes the legend work with two axes
        if self.hasSubplotHost:
            self.axis1=SubplotHost(self.figure,111)
            self.figure.add_subplot(self.axis1)
        else:
            self.axis1=self.figure.add_subplot(111)
        self.axis1.set_xlabel(self.xlabel)
        self.axis1.set_ylabel(self.ylabel)

        if len(self.alternate)>0:
            self.axis2=self.axis1.twinx()
            self.axis2.set_ylabel(self.ylabel2)

        try:
            if self.spec.logscale:
                self.axis1.set_yscale("log")
                if self.axis2:
                    self.axis2.set_yscale("log")
        except AttributeError:
            pass

def setup_rot_axes(fig, rect):
	tr = Affine2D().rotate_deg(90.0)
	grid_helper = gh.GridHelperCurveLinear(tr)
	ax1 = SubplotHost(fig, rect, grid_helper=grid_helper)
	fig.add_subplot(ax1)
	ax2 = ParasiteAxesAuxTrans(ax1, tr, "equal")
	ax1.set_ylim([end, start])
	ax1.set_xlim([-8, 4])
	ax2 = ax1.get_aux_axes(tr)
	ax1.set_aspect('auto')
	ax1.axis['top', 'right', 'left', 'bottom'].set_visible(False)
	return ax1, ax2

def get_axis_two_scales(fig, scale_x, scale_y, \
                        ax2_xlabel = None, ax2_ylabel = None, \
                        subplot = 111,
                        sharex = None,
                        sharey = None):
    kargs = {}
    if (sharex != None):
        kargs['sharex'] = sharex
    if (sharey != None):
        kargs['sharey'] = sharey
    ax1 = SubplotHost(fig, subplot, **kargs)
    ax1_to_2 = mtransforms.Affine2D().scale(1.0/scale_x, 1.0/scale_y)
    ax2 = ax1.twin(ax1_to_2)
    ax2.set_viewlim_mode("transform")
    fig.add_subplot(ax1)
    if (ax2_xlabel != None):
        ax2.set_xlabel(ax2_xlabel)
    if (ax2_ylabel != None):
        ax2.set_ylabel(ax2_ylabel)
    if (scale_x == 1.0):
        ax2.get_xaxis().set_visible(False)
    if (scale_y == 1.0):
        ax2.get_yaxis().set_visible(False)
    return ax1, ax2

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)

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()

class MatplotlibTimelines(GeneralPlotTimelines):
    """This class opens a matplotlib window and plots a timelines-collection in it"""

    figureNr=1

    def __init__(self,
                 timelines,
                 custom,
                 showWindow=True,
                 registry=None):
        """@param timelines: The timelines object
        @type timelines: TimeLineCollection
        @param custom: A CustomplotInfo-object. Values in this object usually override the
        other options
        """

        self.hasSubplotHost=True
        try:
            global plt,matplotlib,firstTimeImport,SubplotHost
            import matplotlib
            if not showWindow and firstTimeImport:
#                matplotlib.use("MacOSX")
                matplotlib.use("agg")
                firstTimeImport=False
            import matplotlib.pyplot as plt
            try:
                from mpl_toolkits.axes_grid.parasite_axes import SubplotHost
            except ImportError:
                self.hasSubplotHost=False
                warning("Matplotlib-Version does not support SubplotHost")
        except ImportError:
            error("Matplotlib not installed.")

        GeneralPlotTimelines.__init__(self,timelines,custom,showWindow=showWindow,registry=registry)

        self.figNr=MatplotlibTimelines.figureNr
        MatplotlibTimelines.figureNr+=1

        self.figure=None
        self.title=""

        self.xlabel=""
        self.ylabel=""
        self.ylabel2=""
        try:
            if self.spec.xlabel:
                self.setXLabel(self.spec.xlabel)
        except AttributeError:
            pass
        try:
            if self.spec.ylabel:
                self.setYLabel(self.spec.ylabel)
        except AttributeError:
            pass
        try:
            if self.spec.y2label:
                self.setYLabel2(self.spec.y2label)
        except AttributeError:
            pass

        self.axis1=None
        self.axis2=None

        self.setTitle(self.spec.theTitle)

        self.with_=self.spec.with_
        if not self.with_ in ['lines','points','dots','steps','linespoints']:
            warning("'with'-style",self.with_,"not implemented, using 'lines'")
            self.with_='lines'
        self.redo()

    def buildData(self,times,name,title,lastValid):
        """Build the implementation specific data
        @param times: The vector of times for which data exists
        @param name: the name under which the data is stored in the timeline
        @param title: the title under which this will be displayed"""

        a=self.axis1
        if name in self.alternate:
            a=self.axis2
        data=self.data.getValues(name)
        tm=times
        if len(tm)>0 and not lastValid:
            tm=tm[:-1]
            data=data[:-1]
        plotIt=True
        try:
            if self.spec.logscale and min(data)<=0:
                plotIt=False
        except AttributeError:
            pass

        if self.spec.start!=None or self.spec.end!=None:
            start=self.spec.start
            end=self.spec.end
            if start==None:
                start=tm[0]
            if end==None:
                end=tm[-1]
            self.axis1.set_xbound(lower=start,upper=end)
#.........这里部分代码省略.........

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)

from scipy.constants import physical_constants
kB=physical_constants['Boltzmann constant in eV/K'][0]

import numpy as np
arange=np.arange
exp=np.exp

tt=arange(18.,501.)
vv=exp(-0.02/(kB*tt))

import matplotlib.pylab as plt
from mpl_toolkits.axes_grid.parasite_axes import SubplotHost

fig=plt.figure(1)
ax1=SubplotHost(fig, 111)
fig.add_subplot(ax1)

ax1.plot(1./tt,vv)
ax1.set_yscale('log')
ax1.set_xlabel('Reciprocal temperature (1/K)')

ax2=ax1.twin() # ax2 is responsible for "top" axis and "right" axis
tticks=np.array([20.,30.,50.,100.,300.])
ax2.set_xticks( [ 1/t for t in tticks ] )
ax2.set_xticklabels(tticks)
ax2.axis["top"].label.set_visible(True)
ax2.set_xlabel('Temperature (K)')
ax2.set_yticks([])

plt.show()

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)

from mpl_toolkits.axes_grid.parasite_axes import SubplotHost
import matplotlib.pyplot as plt

fig = plt.figure(1)

host = SubplotHost(fig, 111)
fig.add_subplot(host)

par = host.twinx()

host.set_xlabel("Distance")
host.set_ylabel("Density")
par.set_ylabel("Temperature")

p1, = host.plot([0, 1, 2], [0, 1, 2], label="Density")
p2, = par.plot([0, 1, 2], [0, 3, 2], label="Temperature")

host.axis["left"].label.set_color(p1.get_color())
par.axis["right"].label.set_color(p2.get_color())

host.legend()

plt.show()

import matplotlib.transforms as mtransforms
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid.parasite_axes import SubplotHost

obs = [["01_S1", 3.88, 0.14, 1970, 63],
       ["01_S4", 5.6, 0.82, 1622, 150],
       ["02_S1", 2.4, 0.54, 1570, 40],
       ["03_S1", 4.1, 0.62, 2380, 170]]


fig = plt.figure()

ax_kms = SubplotHost(fig, 1,1,1, aspect=1.)

# angular proper motion("/yr) to linear velocity(km/s) at distance=2.3kpc
pm_to_kms = 1./206265.*2300*3.085e18/3.15e7/1.e5

aux_trans = mtransforms.Affine2D().scale(pm_to_kms, 1.)
ax_pm = ax_kms.twin(aux_trans)
ax_pm.set_viewlim_mode("transform")

fig.add_subplot(ax_kms)

for n, ds, dse, w, we in obs:
    time = ((2007+(10. + 4/30.)/12)-1988.5)
    v = ds / time * pm_to_kms
    ve = dse / time * pm_to_kms
    ax_kms.errorbar([v], [w], xerr=[ve], yerr=[we], color="k")


ax_kms.axis["bottom"].set_label("Linear velocity at 2.3 kpc [km/s]")

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

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)

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)

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