在子图中的matplotlib轴上旋转变换 [英] Rotate transformation on matplotlib axis in subplot

问题描述

我正在尝试创建一个四面板的图形，其中左下面板包含散点图，其他三个面板包含直方图.左上角将是散点图x维度的标准直方图，右下角将是y维度的90°旋转直方图.这两个都可以在matplotlib中轻松完成.

I am trying to create a four-panel figure where the bottom-left panel contains a scatter plot, and the other three panels contain histograms. The top-left will be a standard histogram across the x-dimension of the scatter, the bottom-right will be a 90° rotated histogram for the y-dimension. Both of these are easy to do in matplotlib.

我遇到了第三个直方图的问题，该直方图是该图右上方的45°旋转图，给出了x点和y点之间的差异分布.我之前通过在Illustrator中手动旋转和缩放轴来制作了这样的图形，但是看起来matplotlib 应该能够生成已经使用子图坐标轴上的转换方法旋转的图形.

I am running into problems with the third histogram, which is to be a 45° rotated plot in the top-right of the figure giving the distribution of the differences between the x and y points. I have made such figures before by manually rotating and rescaling the axes in Illustrator, but it seems like matplotlib should be able to produce figures that are already rotated using the transformation methods on the subplot axes.

我认为类似以下的内容可能会起作用:

I thought something like the following might work:

import matplotlib.pyplot as plt
from matplotlib.transforms import Affine2D

fig, ax = plt.subplots(nrows=2, ncols=2, squeeze=True, sharex=False, 
                       sharey=False, figsize=(8,8))
ax[0,1].text(0.5,0.5,'I should be rotated',ha='center',va='center')
t = ax[0,1].get_transform()
ax[0,1].set_transform(t.transform(Affine2D().rotate_deg(45)))

plt.show()

在这里，我尝试从轴获取变换，对其进行修改，然后将其替换回到轴对象中.但是此代码无效.任何帮助将不胜感激.

Here I am attempting to get the transform from the axis, modify it, and then replace it back into the axis object. This code has no effect however. Any help would be greatly appreciated.

根据ImportanceOfBeingErnest在评论中的建议进行了

我看了一下浮动轴演示，现在有这个:

I have taken a look at the Floating Axes demo, and now have this:

from matplotlib.transforms import Affine2D
import mpl_toolkits.axisartist.floating_axes as floating_axes
import matplotlib.pyplot as plt

def setup_axes(fig, rect, rotation, axisScale):
    tr = Affine2D().scale(axisScale[0], axisScale[1]).rotate_deg(rotation)

    grid_helper = floating_axes.GridHelperCurveLinear(tr, extremes=(-0.5, 3.5, 0, 4))

    ax = floating_axes.FloatingSubplot(fig, rect, grid_helper=grid_helper)
    fig.add_subplot(ax)
    aux_ax = ax.get_aux_axes(tr)

    return ax, aux_ax

fig  = plt.figure(1, figsize=(8, 8))
axes = []
axisOrientation = [0, 0, 270, -45]
axisScale = [[1,1],[2,1],[2,1],[2,1]]
axisPosition = [223,221,224,222]

for i in range(0, len(axisOrientation)):
    ax, aux_ax = setup_axes(fig, axisPosition[i], axisOrientation[i], axisScale[i])
    axes.append(aux_ax)
fig.subplots_adjust(wspace=-0.2, hspace=-0.2, left=0.00, right=0.99, top=0.99, bottom=0.0)
plt.show()

这使我更接近想要的东西:

This has me closer to what I want:

我将尝试在这些轴上添加散点图和直方图.

I will take a shot at adding in the scatter plot and histograms to these axes.

推荐答案

以下代码实现了我最初想要的功能，除了，我正在寻找一种方法来转换右上角的图形以使其更接近到左下角的散点图.不过，这是一个较小的问题，因此我可以将其发布为新问题.

The following code achieves what I originally wanted, except I am looking for a way to translate the top-right figure to be closer to the scatter plot in the bottom-left. This is a smaller problem though, so I may post it as a new question.

from matplotlib.transforms import Affine2D
import mpl_toolkits.axisartist.floating_axes as floating_axes
import matplotlib.pyplot as plt

def setup_axes(fig, rect, rotation, axisScale, axisLimits, doShift):
    tr_rot = Affine2D().scale(axisScale[0], axisScale[1]).rotate_deg(rotation)

    # This seems to do nothing
    if doShift:
        tr_trn = Affine2D().translate(-90,-5)
    else:
        tr_trn = Affine2D().translate(0,0)

    tr = tr_rot + tr_trn

    grid_helper = floating_axes.GridHelperCurveLinear(tr, extremes=axisLimits)

    ax = floating_axes.FloatingSubplot(fig, rect, grid_helper=grid_helper)
    fig.add_subplot(ax)
    aux_ax = ax.get_aux_axes(tr)

    return ax, aux_ax

fig  = plt.figure(1, figsize=(8, 8))
axes = []
axisOrientation = [0, 0, 270, -45]
axisScale = [[1,1],[6,1],[6,1],[6,1]]
axisPosition = [223,221,224,222]
axisLimits = [(-0.5, 4.5, -0.5, 4.5),
              (-0.5, 4.5, 0, 12),
              (-0.5, 4.5, 0, 12),
              (-3.5, 3.5, 0, 12)]
doShift = [False, False, False, True]

label_axes = []
for i in range(0, len(axisOrientation)):
    ax, aux_ax = setup_axes(fig, axisPosition[i], axisOrientation[i], 
                            axisScale[i], axisLimits[i], doShift[i])
    axes.append(aux_ax)
    label_axes.append(ax)

numPoints = 100
x = []
y = []
for i in range(0,numPoints):
    x.append(np.random.rand() + i/100.0)
    y.append(np.random.rand() + i/100.0 + np.mod(i,2)*2)

axes[0].plot(x,y,ls='none',marker='x')
label_axes[0].axis["bottom"].label.set_text('Variable 1')
label_axes[0].axis["left"].label.set_text('Variable 2')

b = np.linspace(-0.5,4.5,50)
axes[1].hist(x, bins = b)
axes[2].hist(y, bins = b)
b = np.linspace(-3.5,3.5,50)
axes[3].hist(np.array(x)-np.array(y), bins=b)

for i in range(1,len(label_axes)):
    for axisLoc in ['top','left','right']:
        label_axes[i].axis[axisLoc].set_visible(False)
    label_axes[i].axis['bottom'].toggle(ticklabels=False)    

fig.subplots_adjust(wspace=-0.30, hspace=-0.30, left=0.00, right=0.99, top=0.99, bottom=0.0)
plt.show()

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