如何在子图中使用注释绘制多个 Seaborn Jointplot

Question

目标是为带有相关性的联合图注释创建子图。但是plt.show()的时候，图是分开显示的。

我可以知道如何解决这个问题吗？

import numpy as np
import pandas as pd
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
ncols=['ra','rb','a','b','c','d']

df=pd.DataFrame(np.random.rand(100,len(ncols)),columns=ncols)
nvars=['a','b','c','d']
rt=['a','b']
fig, axs = plt.subplots(len(rt),len(nvars))
for idx_rt, nrt in enumerate(rt):
  for idx_var, nvar  in enumerate(nvars):
    g=sns.jointplot(data=df, y=nrt, x=nvar,kind = 'reg',ax=axs[idx_rt,idx_var])
    r, p = stats.pearsonr(df[nrt], df[nvar])
    g.ax_joint.annotate(f'$\\rho = {r:.3f}, p = {p:.3f}$',
      xy=(0.1, 0.9), xycoords='axes fraction',
      ha='left', va='center',
      bbox={'boxstyle': 'round', 'fc': 'powderblue', 'ec': 'navy'})
    

plt.tight_layout()
plt.show()

Answer 1

在此 answer 之上构建您应该通过组合自定义 SeabornFig2Grid 类和 matplotlib GridSpec 来取得成功。这是您的玩具示例：

• 首先定义SeabornFig2Grid类

  类 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())
  

• 然后在同一个网格中绘制你的地块

import itertools 
import matplotlib.gridspec as gridspec
# import seaborn as sns; sns.set()
import numpy as np
import pandas as pd
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
ncols=['ra','rb','a','b','c','d']

df=pd.DataFrame(np.random.rand(100,len(ncols)),columns=ncols)
nvars=['a','b','c','d']
rt=['a','b']

# A JointGrid
fig = plt.figure(figsize=(13,8))
gs = gridspec.GridSpec(len(rt),len(nvars))

for i, idxs in enumerate(itertools.product(rt, nvars)):
    nrt, nvar = idxs
    g=sns.jointplot(data=df, y=nrt, x=nvar,kind = 'reg')#, space=0,ax=axs[idx_rt,idx_var])
    r, p = stats.pearsonr(df[nrt], df[nvar])
    g.ax_joint.annotate(f'$\\rho = {r:.3f}, p = {p:.3f}$',
      xy=(0.1, 0.9), xycoords='axes fraction',
      ha='left', va='center',
      bbox={'boxstyle': 'round', 'fc': 'powderblue', 'ec': 'navy'})
    mg = SeabornFig2Grid(g, fig, gs[i])

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

plt.show()

结果如下：

这就是你要找的吗？