在matplotlib中变换整个轴（或散点图）

Question

我正在用以下代码绘制一些数据的均值和方差的变化

import matplotlib.pyplot as pyplot
import numpy

vis_mv(data, ax = None):
    if ax is None: ax = pyplot.gca()
    cmap = pyplot.get_cmap()
    colors = cmap(numpy.linspace(0, 1, len(data)))

    xs = numpy.arange(len(data)) + 1
    means = numpy.array([ numpy.mean(x) for x in data ])
    varis = numpy.array([ numpy.var(x) for x in data ])
    vlim = max(1, numpy.amax(varis))

    # variance
    ax.imshow([[0.,1.],[0.,1.]],
        cmap = cmap, interpolation = 'bicubic',
        extent = (1, len(data), -vlim, vlim), aspect = 'auto'
    )
    ax.fill_between(xs, -vlim, -varis, color = 'white')
    ax.fill_between(xs, varis, vlim, color = 'white')

    # mean
    ax.plot(xs, means, color = 'white', zorder = 1)
    ax.scatter(xs, means, color = colors, edgecolor = 'white', zorder = 2)

    return ax

这工作得很好： 但是现在，我希望能够以垂直方式使用此可视化效果，因为某种高级彩条在另一块绘图旁边很麻烦。我希望有可能旋转整个轴及其所有内容，但是我只能找到这个问题，这个问题也没有真正的答案。因此，我尝试自己按以下步骤进行操作：

from matplotlib.transforms import Affine2D

ax = vis_mv()
r = Affine2D().rotate_deg(90) + ax.transData

for x in ax.images + ax.lines + ax.collections:
    x.set_transform(r)

old = ax.axis()
ax.axis(old[2:4] + old[0:2])

这几乎可以解决问题（请注意，过去沿白线分布的散乱点是如何被炸毁而不会按预期旋转的）。 不幸的是，PathCollection保留结果的结果scatter与预期的不同。在尝试了一些东西之后，我发现散点图具有某种偏移量转换，这似乎与其他集合中的常规转换等效。

x = numpy.arange(5)
ax = pyplot.gca()
p0, = ax.plot(x)
p1 = ax.scatter(x,x)

ax.transData == p0.get_transform()           # True
ax.transData == p1.get_offset_transform()    # True

似乎我可能想更改散点图的偏移变换，但是我没有找到能使我在上更改该变换的方法PathCollection。而且，这将使我真正想做的事情变得更加不便。

有人知道是否有可能更改偏移量转换吗？

提前致谢

Answer 1

不幸的是，PathCollection该.set_offset_transform()方法没有方法，但是可以访问私有_transOffset属性并为其设置旋转变换。

import matplotlib.pyplot as plt
from matplotlib.transforms import Affine2D
from matplotlib.collections import PathCollection
import numpy as np; np.random.seed(3)

def vis_mv(data, ax = None):
    if ax is None: ax = plt.gca()
    cmap = plt.get_cmap()
    colors = cmap(np.linspace(0, 1, len(data)))

    xs = np.arange(len(data)) + 1
    means = np.array([ np.mean(x) for x in data ])
    varis = np.array([ np.var(x) for x in data ])
    vlim = max(1, np.amax(varis))

    # variance
    ax.imshow([[0.,1.],[0.,1.]],
        cmap = cmap, interpolation = 'bicubic',
        extent = (1, len(data), -vlim, vlim), aspect = 'auto'  )
    ax.fill_between(xs, -vlim, -varis, color = 'white')
    ax.fill_between(xs, varis, vlim, color = 'white')

    # mean
    ax.plot(xs, means, color = 'white', zorder = 1)
    ax.scatter(xs, means, color = colors, edgecolor = 'white', zorder = 2)

    return ax

data = np.random.normal(size=(9, 9))
ax  = vis_mv(data)


r = Affine2D().rotate_deg(90)

for x in ax.images + ax.lines + ax.collections:
    trans = x.get_transform()
    x.set_transform(r+trans)
    if isinstance(x, PathCollection):
        transoff = x.get_offset_transform()
        x._transOffset = r+transoff

old = ax.axis()
ax.axis(old[2:4] + old[0:2])


plt.show()