如何在matplotlib中将多个图绘制成一个图形？

Question

我有16个文件，它们是.xlsx（a1.xlsx…a16.xlsx），每个文件包含54列和n行。我想在每个文件中选择相同的两列，将这些对绘制为散点图，并对这些对实施简单的线性回归。而且，我想在与4 x 4子图相同的图上绘制这些单独的16个图形（plot1-X1vsY1，Plot2-X2vsY2，…，plot16-X16vsY16）。

我在python中有一个脚本，可以一次绘制一个文件的图（脚本1）。另外，我还有另一个脚本可以将我的文件名放在当前文件夹的列表中（脚本2）。如何组合这些脚本以在同一图形上绘制所有文件？

脚本1：

# Importing modules
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
#
def my_plotter(ax, data1, data2):
    """
    A helper function to make a scatter graph

    Parameters
    ----------
    ax : Axes
        The axes to draw to
    data1 : array
       The x data
    data2 : array
       The y data

    Returns
    -------
    out : list
        list of artists added
    """
    stdev = data2.std()
    out = ax.errorbar(data1, data2, yerr=stdev, ls='', marker='o',
             markerfacecolor='w', markeredgecolor='k', capsize=0.5, capthick=0, ecolor='darkgray')
    ax.set_xlim(xmin=0, xmax=220)
    ax.set_ylim(ymin=0, ymax= 0.16)
    return out
#
def my_fit(ax, data1, data2):
    """
    A helper function to fit a linear model which is formed y = m*x + n

    Parameters
    ----------
    ax : Axes
        The axes to draw to
    data1 : array
       The x data
    data2 : array
       The y data

    Returns
    -------
    out : list
        list of artists added
    """
    # Firstly, return a copy of the array collapsed into one dimension.
    data1 = data1.flatten()
    data2 = data2.flatten()
## Using scipy to be able to conduct a regression analysis on two arrays (x and y)
## in order to determine the intercept and slope, along with the p-value, 
## R-Squared value, and standard error.
    slope, intercept, r_value, p_value, std_err = stats.linregress(data1,data2)
    line = slope * data1 + intercept
    out = ax.plot(data1, line, c=(0, 0, 0), lw=2, label= r'$\kappa(r)$' ' = {:.3E} x $r$ + {:.3f}'.format(slope,intercept))
    plt.xlim(0, 220)
    plt.ylim(0, 0.16)
    return out
#
fig, ax = plt.subplots(nrows=1, ncols=4, sharex=True, sharey=True, squeeze=False, figsize=(16,4))

my_plotter(ax[0,0], x, y1)
my_fit(ax[0,0], x, y1)
my_plotter(ax[0,1], x, y2)
my_fit(ax[0,1], x, y2)
my_plotter(ax[0,2], x, y3)
my_fit(ax[0,2], x, y3)
my_plotter(ax[0,3], x, y4)
my_fit(ax[0,3], x, y4)

for i, ax in enumerate(ax.flat):
...

脚本2：

for f in sorted(glob.glob('*.xlsx')):
   df = pd.read_excel(f, sheetname=1)
# Select variables for drawing scattering plots
   x = df[['epi.(km)']].values
   y = df[['kh']].values
   plt.scatter(x,y)
...
   fig, axes = plt.subplots(4,4, figsize=(10,6), sharey=True, dpi=120)
# Plot each axes
   for i, ax in enumerate(axes.ravel()):
       ax.scatter(x, y)