python - 如何使用多部分seaborn图迭代填充matplotlib gridspec？

Question

这个问题已经有答案了:

How to plot multiple Seaborn Jointplot in Subplot (4 个回答)

已关闭 5 年前。

我正在处理的一些最小代码。有些参数可能看起来多余，但我没有费心删除所有参数。

import matplotlib
import matplotlib.gridspec as gridspec
matplotlib.use("macosx")
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

def plot_overlaid_2d_hist(data,
                          plot_axis_x,
                          plot_axis_y,
                          plot_axis_x_lab,
                          plot_axis_y_lab,
                          group_by = "group_name"):

    # don't mind this for now
    df = data

    # Figure aspect
    w, h = plt.figaspect(1)
    fig = plt.figure(figsize = (w, h))

    # Count the number of groups to make plots for
    n_groups = len(df.groupby(group_by))
    gs = gridspec.GridSpec(nrows = n_groups, ncols = 1)
    subplot_id = 0

    # Reshape data to make it work
    for name, group in df.groupby(group_by, sort = False):

        # Initialize subplot
        fig.add_subplot(gs[subplot_id, 0])

        # Check if we get subplots with pyplot
        if subplot_id == 0:
            col = "red"
        else:
            col = "blue"

        plt.plot(x, y, color = col)


        # instantiate JointGrid
        # g = sns.JointGrid(group[plot_axis_x],
        #                   group[plot_axis_y],
        #                   data = group,
        #                   space = 0,
        #                   xlim = (0, 1.2),
        #                   ylim = (0, 1))
        # 
        # # Fix labels
        # g = g.set_axis_labels(xlabel = str(plot_axis_x_lab),
        #                   ylabel = str(plot_axis_y_lab))
        # 
        # # center scatter plot on top
        # g = g.plot_joint(plt.scatter,
        #              s = 0.5,
        #              alpha = 1,
        #              linewidth = 1)
        # 
        # # marginals plot
        # g = g.plot_marginals(sns.distplot,
        #                  kde = True,
        #                  kde_kws = dict(linewidth = 2,
        #                                 alpha = 1,
        #                                 bw = "Scott"),
        #                  hist_kws = dict(alpha = 1))


        # Next plot in row +1
        subplot_id += 1

    # Output
    plt.tight_layout()  # Attempts to fix alignment of subplot layout and axis titles

    plt.show()

# quick data to check if the plots end up where they should
x = [0.5, 0.5, 0.4, 0.4]
y = [0.6, 0.4, 0.3, 0.4]
grp = ["a", "a", "b", "b"]


df = pd.DataFrame({"x":x,
                   "y":y,
                   "grp": grp})

plot_overlaid_2d_hist(data = df,
                      group_by = "grp",
                      plot_axis_x_lab = "x",
                      plot_axis_y_lab = "x",
                      plot_axis_y = "x",
                      plot_axis_x = "x")

在所有seaborn图(g)注释掉的情况下运行代码表明它对于 native pyplot工作正常，但是当我添加多部分seaborn图时，它们显示在单独的图中。我想要的是让每个 2D-histogram-with-marginals-and-scatter 填充自己的 gridspec 行/列。

Answer 1

看到这个问题已经被问过here我将此答案移至较旧的问题。我想在这里删除它，但无法这样做，因为它已经被接受。

正如在多个地方所指出的( this question ，以及 this issue )，一些 seaborn 命令会自动创建自己的图形。这是硬编码到seaborn代码中的，因此目前无法在现有的图形中生成这样的图。这些是 PairGrid、FacetGrid、JointGrid、pairplot、jointplot 和 lmplot。

有一个seaborn fork available这将允许向相应的类提供子图网格，以便在预先存在的图形中创建图。要使用它，您需要将axisgrid.py从fork复制到seaborn文件夹。请注意，当前仅限与 matplotlib 2.1(也可能是 2.0)一起使用。

另一种方法是创建一个seaborn图形并将轴复制到另一个图形。其原理见this answer并且可以扩展到 Searborn 地 block 。实现比我最初预期的要复杂一些。下面是一个 SeabornFig2Grid 类，可以使用 Seaborn 网格实例(上述任何命令的返回)、一个 matplotlib 图和一个 subplot_spec 来调用，它是一个gridspec 网格的位置。

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())

这个类的用法如下:

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，复制轴可能存在一些缺点，并且上述内容尚未经过彻底测试。