在Python中反转轴刻度方向

Question

我具有从ycopin-GitHub修改的此功能。

#!/usr/bin/env python
# Copyright: This document has been placed in the public domain.

"""
Taylor diagram (Taylor, 2001) test implementation.
http://www-pcmdi.llnl.gov/about/staff/Taylor/CV/Taylor_diagram_primer.htm
"""

__version__ = "Time-stamp: <2012-02-17 20:59:35 ycopin>"
__author__ = "Yannick Copin <yannick.copin@laposte.net>"

import numpy as NP
import matplotlib.pyplot as PLT

class TaylorDiagram(object):
    """Taylor diagram: plot model standard deviation and correlation
    to reference (data) sample in a single-quadrant polar plot, with
    r=stddev and theta=arccos(correlation).
    """

    def __init__(self, refstd, fig=None, rect=111, label='_'):
        """Set up Taylor diagram axes, i.e. single quadrant polar
        plot, using mpl_toolkits.axisartist.floating_axes. refstd is
        the reference standard deviation to be compared to.
        """

        from matplotlib.projections import PolarAxes
        import mpl_toolkits.axisartist.floating_axes as FA
        import mpl_toolkits.axisartist.grid_finder as GF

        self.refstd = refstd            # Reference standard deviation

        tr = PolarAxes.PolarTransform()

        # Correlation positive labels
        rlocs = NP.concatenate(([-0.99,-0.9],NP.arange(-0.8,0,0.2),
                                NP.arange(0,0.9,0.2),[0.9,0.99]))
        tlocs = NP.arccos(rlocs)        # Conversion to polar angles
        gl1 = GF.FixedLocator(tlocs)    # Positions
        tf1 = GF.DictFormatter(dict(zip(tlocs, map(str,rlocs))))

        # Standard deviation axis extent
        self.smin = 0
        self.smax = 2*self.refstd/self.refstd

        ghelper = FA.GridHelperCurveLinear(tr,
                                           extremes=(0,NP.pi, # 1st quadrant
                                                     self.smin,self.smax),
                                           grid_locator1=gl1,
                                           tick_formatter1=tf1,
                                           )                          


        if fig is None:
            fig = PLT.figure()

        ax = FA.FloatingSubplot(fig, rect, grid_helper=ghelper)
        fig.add_subplot(ax)

        # Adjust axes
        ax.axis["top"].set_axis_direction("bottom")  # "Angle axis"
        ax.axis["top"].toggle(ticklabels=True, label=True)
        ax.axis["top"].major_ticklabels.set_axis_direction("top")
        ax.axis["top"].label.set_axis_direction("top")
        ax.axis["top"].label.set_text("Correlation")


        ax.axis["left"].set_axis_direction("right") # "X axis"
        ax.axis["right"].toggle(ticklabels=True)
        ax.axis["right"].major_ticklabels.set_axis_direction("bottom")

        #ax.axis["bottom"].label.set_text("Standard deviation")

        ax.axis["right"].set_axis_direction("left")   # "Y axis"
        #ax.axis["right"].toggle(ticklabels=True)
        ax.axis["right"].major_ticklabels.set_axis_direction("right")

        ax.axis["bottom"].set_visible(False)         # Useless

        # Contours along standard deviations
        ax.grid(False)

        self._ax = ax                   # Graphical axes
        self.ax = ax.get_aux_axes(tr)   # Polar coordinates

        # Add reference point and stddev contour
        print "Reference std:", self.refstd/self.refstd
        l, = self.ax.plot([0], self.refstd/self.refstd, 'k*',
                          ls='', ms=10, label=label)
        t = NP.linspace(0, NP.pi)
        r = NP.zeros_like(t) + self.refstd/self.refstd
        self.ax.plot(t,r, 'k--', label='_')

        # Collect sample points for latter use (e.g. legend)
        self.samplePoints = [l]

    def add_sample(self, stddev, corrcoef, *args, **kwargs):
        """Add sample (stddev,corrcoeff) to the Taylor diagram. args
        and kwargs are directly propagated to the Figure.plot
        command."""

        l, = self.ax.plot(NP.arccos(corrcoef), stddev/self.refstd,
                          *args, **kwargs) # (theta,radius)
        self.samplePoints.append(l)

        return l

    def add_contours(self, levels=5, **kwargs):
        """Add constant centered RMS difference contours."""

        rs,ts = NP.meshgrid(NP.linspace(self.smin,self.smax),
                            NP.linspace(0,NP.pi))
        # Compute centered RMS difference
        rms = NP.sqrt((self.refstd/self.refstd)**2 + rs**2 - 2*(self.refstd/self.refstd)*rs*NP.cos(ts))

        contours = self.ax.contour(ts, rs, rms, levels, **kwargs)

        return contours


if __name__=='__main__':

    # Reference dataset
    x = NP.linspace(0,4*NP.pi,100)
    data = NP.sin(x)
    refstd = data.std(ddof=1)           # Reference standard deviation

    # Models
    m1 = data + 0.2*NP.random.randn(len(x))    # Model 1
    m2 = 0.8*data + .1*NP.random.randn(len(x)) # Model 2
    m3 = NP.sin(x-NP.pi/10)                    # Model 3

    # Compute stddev and correlation coefficient of models
    samples = NP.array([ [m.std(ddof=1), NP.corrcoef(data, m)[0,1]]
                         for m in (m1,m2,m3)])

    fig = PLT.figure(figsize=(10,4))

    ax1 = fig.add_subplot(1,2,1, xlabel='X', ylabel='Y')
    # Taylor diagram
    dia = TaylorDiagram(refstd, fig=fig, rect=122, label="Reference")

    colors = PLT.matplotlib.cm.jet(NP.linspace(0,1,len(samples)))

    ax1.plot(x,data,'ko', label='Data')
    for i,m in enumerate([m1,m2,m3]):
        ax1.plot(x,m, c=colors[i], label='Model %d' % (i+1))
    ax1.legend(numpoints=1, prop=dict(size='small'), loc='best')

    # Add samples to Taylor diagram
    for i,(stddev,corrcoef) in enumerate(samples):
        dia.add_sample(stddev, corrcoef, marker='s', ls='', c=colors[i],
                       label="Model %d" % (i+1))

    # Add RMS contours, and label them
    contours = dia.add_contours(colors='0.5')
    PLT.clabel(contours, inline=1, fontsize=10)

    # Add a figure legend
    fig.legend(dia.samplePoints,
               [ p.get_label() for p in dia.samplePoints ],
               numpoints=1, prop=dict(size='small'), loc='upper right')


    PLT.show()

运行时，显示此图1

我想要的是减少x，y（极轴）刻度的数量...在每个标准偏差轴上可能是五个（5个，包括参考数据），并将它们水平对齐。 任何想法？

编辑：

从上图传递到此：

减少壁虱数

Answer 1

目前，我还没有办法对此进行测试，但是基于此链接 ，似乎以下内容（或其某些变体）可能有效：

在TaylorDiagram.__init__内部TaylorDiagram.__init__ ：

        ax.axis["right"].major_ticklabels.set_axis_direction("right")

此行（或类似的行）应更改为：

        ax.axis["right"].major_ticklabels.set_axis_direction("bottom")

至于滴答声和东西的数量，我现在很难仅通过阅读文档来弄清楚，但是尝试做一个help(ax.axis["right"].major_ticklabels)并查看滴答声标签的含义。 至少有一个地方可以手动编辑或设置列表。