复制坐标轴

这里的初始答案不起作用，我们保留它以供将来参考，并了解为什么需要更复杂的方法.

#There are some pitfalls on the way with the initial approach. 
#Adding an `axes` to a figure can be done via `fig.add_axes(axes)`. However, at this point, 
#the axes' figure needs to be the figure the axes should be added to. 
#This may sound a bit like running in circles but we can actually set the axes' 
#figure as `axes.figure = fig2` and hence break out of this.

#One might then also position the axes in the new figure to take the usual dimensions. 
#For this a dummy axes can be added first, the axes can change its position to the position 
#of the dummy axes and then the dummy axes is removed again. In total, this would look as follows.

import matplotlib.pyplot as plt
import numpy as np

num_rows = 10
num_cols = 1
fig, axs = plt.subplots(num_rows, num_cols, sharex=True)
for i in xrange(num_rows):
     ax = axs[i]
     ax.plot(np.arange(10), np.arange(10)**i)
     
     
def on_click(event):
    axes = event.inaxes
    if not axes: return   
    fig2 = plt.figure()
    axes.figure=fig2
    fig2.axes.append(axes)
    fig2.add_axes(axes)
    
    dummy = fig2.add_subplot(111)
    axes.set_position(dummy.get_position())
    dummy.remove()
    fig2.show()

fig.canvas.mpl_connect('button_press_event', on_click)


plt.show()

#So far so good, however, be aware that now after a click the axes is somehow 
#residing in both figures, which can cause all sorts of problems, e.g. if you
# want to resize or save the initial figure.

相反，以下内容将起作用:

问题是无法复制轴(即使 deepcopy 也会失败).因此，要获得轴的真实副本，您可能需要使用 pickle.以下将起作用.它腌制了完整的图形并删除了除一个轴之外的所有要显示的轴.

The problem is that axes cannot be copied (even deepcopy will fail). Hence to obtain a true copy of an axes, you may need to use pickle. The following will work. It pickles the complete figure and removes all but the one axes to show.

import matplotlib.pyplot as plt
import numpy as np
import pickle
import io

num_rows = 10
num_cols = 1
fig, axs = plt.subplots(num_rows, num_cols, sharex=True)
for i in range(num_rows):
     ax = axs[i]
     ax.plot(np.arange(10), np.arange(10)**i)

def on_click(event):

    if not event.inaxes: return
    inx = list(fig.axes).index(event.inaxes)
    buf = io.BytesIO()
    pickle.dump(fig, buf)
    buf.seek(0)
    fig2 = pickle.load(buf) 

    for i, ax in enumerate(fig2.axes):
        if i != inx:
            fig2.delaxes(ax)
        else:
            axes=ax

    axes.change_geometry(1,1,1)
    fig2.show()

fig.canvas.mpl_connect('button_press_event', on_click)

plt.show()

重新创建绘图

上述替代方案当然是每次单击轴时在新图形中重新创建绘图.为此，可以使用一个函数，该函数在指定的轴上创建一个绘图，并使用指定的索引作为输入.在图形创建期间以及稍后在另一个图形中复制图形时使用此函数可确保在所有情况下都具有相同的图形.

Recreate plots

The alternative to the above is of course to recreate the plot in a new figure each time the axes is clicked. To this end one may use a function that creates a plot on a specified axes and with a specified index as input. Using this function during figure creation as well as later for replicating the plot in another figure ensures to have the same plot in all cases.

import matplotlib.pyplot as plt
import numpy as np

num_rows = 10
num_cols = 1
colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
labels = ["Label {}".format(i+1) for i in range(num_rows)]

def myplot(i, ax):
    ax.plot(np.arange(10), np.arange(10)**i, color=colors[i])
    ax.set_ylabel(labels[i])


fig, axs = plt.subplots(num_rows, num_cols, sharex=True)
for i in xrange(num_rows):
     myplot(i, axs[i])


def on_click(event):
    axes = event.inaxes
    if not axes: return
    inx = list(fig.axes).index(axes)
    fig2 = plt.figure()
    ax = fig2.add_subplot(111)
    myplot(inx, ax)
    fig2.show()

fig.canvas.mpl_connect('button_press_event', on_click)

plt.show()