如何在子图中绘制多个Seaborn关节图 [英] How to plot multiple Seaborn Jointplot in Subplot

问题描述

我在将Seaborn Jointplot放在多列subplot内时遇到问题.

I'm having problem placing Seaborn Jointplot inside a multicolumn subplot.

import pandas as pd
import seaborn as sns

df = pd.DataFrame({'C1': {'a': 1,'b': 15,'c': 9,'d': 7,'e': 2,'f': 2,'g': 6,'h': 5,'k': 5,'l': 8},
          'C2': {'a': 6,'b': 18,'c': 13,'d': 8,'e': 6,'f': 6,'g': 8,'h': 9,'k': 13,'l': 15}})

fig = plt.figure();   
ax1 = fig.add_subplot(121);  
ax2 = fig.add_subplot(122);

sns.jointplot("C1", "C2", data=df, kind='reg', ax=ax1)
sns.jointplot("C1", "C2", data=df, kind='kde', ax=ax2)

请注意，jointplot的仅一部分如何放置在子图内，而其余部分如何留在另外两个图框内.我想要的是同时将distributions插入到subplots内.

Notice how only a portion of the jointplot is placed inside the subplot and the rest left inside another two plot frames. What I'd want is to have both the distributions also inserted inside the subplots.

任何人都可以帮忙吗?

推荐答案

在matplotlib中移动轴并不像以前的版本中那样容易.以下适用于最新版本的matplotlib.

正如在多个地方所指出的那样(此问题，也此问题)，一些seaborn命令会自动创建自己的图形.这被硬编码到seaborn代码中，因此当前无法在现有图形中生成此类图.这些是PairGrid，FacetGrid，JointGrid，pairplot，jointplot和lmplot.

As has been pointed out at several places (this question, also this issue) several of the seaborn commands create their own figure automatically. This is hardcoded into the seaborn code, so there is currently no way to produce such plots in existing figures. Those are PairGrid, FacetGrid, JointGrid, pairplot, jointplot and lmplot.

有一个 seaborn fork ，它可以为子图网格提供子图网格.相应的类，以便在预先存在的图形中创建图.要使用此功能，您需要将axisgrid.py从叉子复制到seaborn文件夹.请注意，目前仅限将其与matplotlib 2.1(也可能是2.0)一起使用.

There is a seaborn fork available which would allow to supply a subplot grid to the respective classes such that the plot is created in a preexisting figure. To use this, you would need to copy the axisgrid.py from the fork to the seaborn folder. Note that this is currently restricted to be used with matplotlib 2.1 (possibly 2.0 as well).

另一种选择是创建一个海底人像并将轴复制到另一个人像. 此答案中显示了此原理，并且可以扩展到Searborn图.实现比我最初预期的要复杂一些.以下是可以通过Seaborn网格实例(返回上述任何命令)，matplotlib图形和subplot_spec(即gridspec网格的位置)调用的类SeabornFig2Grid.

An alternative could be to create a seaborn figure and copy the axes to another figure. The principle of this is shown in this answer and could be extended to Searborn plots. The implementation is a bit more complicated that I had initially expected. The following is a class SeabornFig2Grid that can be called with a seaborn grid instance (the return of any of the above commands), a matplotlib figure and a subplot_spec, which is a position of a gridspec grid.

注意:这是一个概念证明，它可能适用于大多数简单情况，但我不建议在生产代码中使用它.

Note: This is a proof of concept, it may work for most easy cases, but I would not recommend using it in production code.

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import seaborn as sns
import numpy as np

class SeabornFig2Grid():

    def __init__(self, seaborngrid, fig,  subplot_spec):
        self.fig = fig
        self.sg = seaborngrid
        self.subplot = subplot_spec
        if isinstance(self.sg, sns.axisgrid.FacetGrid) or \
            isinstance(self.sg, sns.axisgrid.PairGrid):
            self._movegrid()
        elif isinstance(self.sg, sns.axisgrid.JointGrid):
            self._movejointgrid()
        self._finalize()

    def _movegrid(self):
        """ Move PairGrid or Facetgrid """
        self._resize()
        n = self.sg.axes.shape[0]
        m = self.sg.axes.shape[1]
        self.subgrid = gridspec.GridSpecFromSubplotSpec(n,m, subplot_spec=self.subplot)
        for i in range(n):
            for j in range(m):
                self._moveaxes(self.sg.axes[i,j], self.subgrid[i,j])

    def _movejointgrid(self):
        """ Move Jointgrid """
        h= self.sg.ax_joint.get_position().height
        h2= self.sg.ax_marg_x.get_position().height
        r = int(np.round(h/h2))
        self._resize()
        self.subgrid = gridspec.GridSpecFromSubplotSpec(r+1,r+1, subplot_spec=self.subplot)

        self._moveaxes(self.sg.ax_joint, self.subgrid[1:, :-1])
        self._moveaxes(self.sg.ax_marg_x, self.subgrid[0, :-1])
        self._moveaxes(self.sg.ax_marg_y, self.subgrid[1:, -1])

    def _moveaxes(self, ax, gs):
        #https://stackoverflow.com/a/46906599/4124317
        ax.remove()
        ax.figure=self.fig
        self.fig.axes.append(ax)
        self.fig.add_axes(ax)
        ax._subplotspec = gs
        ax.set_position(gs.get_position(self.fig))
        ax.set_subplotspec(gs)

    def _finalize(self):
        plt.close(self.sg.fig)
        self.fig.canvas.mpl_connect("resize_event", self._resize)
        self.fig.canvas.draw()

    def _resize(self, evt=None):
        self.sg.fig.set_size_inches(self.fig.get_size_inches())

该类的用法如下:

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import seaborn as sns; sns.set()
import SeabornFig2Grid as sfg


iris = sns.load_dataset("iris")
tips = sns.load_dataset("tips")

# An lmplot
g0 = sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips, 
                palette=dict(Yes="g", No="m"))
# A PairGrid
g1 = sns.PairGrid(iris, hue="species")
g1.map(plt.scatter, s=5)
# A FacetGrid
g2 = sns.FacetGrid(tips, col="time",  hue="smoker")
g2.map(plt.scatter, "total_bill", "tip", edgecolor="w")
# A JointGrid
g3 = sns.jointplot("sepal_width", "petal_length", data=iris,
                   kind="kde", space=0, color="g")


fig = plt.figure(figsize=(13,8))
gs = gridspec.GridSpec(2, 2)

mg0 = sfg.SeabornFig2Grid(g0, fig, gs[0])
mg1 = sfg.SeabornFig2Grid(g1, fig, gs[1])
mg2 = sfg.SeabornFig2Grid(g2, fig, gs[3])
mg3 = sfg.SeabornFig2Grid(g3, fig, gs[2])

gs.tight_layout(fig)
#gs.update(top=0.7)

plt.show()

请注意，复制轴可能会有一些缺点，并且尚未对上述内容进行彻底的测试.

Note that there might be several drawbacks from copying axes and the above is not (yet) tested thoroughly.

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