python - 如何在 Subplot 中绘制带有注释的多个 Seaborn Jointplot

Question

目标是为带有相关注释的联合图创建子图。但是，当 plt.show() 时，图形会单独显示。

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

在此基础上构建answer您应该通过组合自定义 SeabornFig2Grid 类和 matplotlib GridSpec 来获得成功。这是您的玩具示例:

• 首先定义SeabornFig2Grid类

  SeabornFig2Grid() 类:

   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绘图

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？