pyplot - copy an axes content and show it in a new figure

Copying the axes

The inital answer here does not work, we keep it for future reference and also to see why a more sophisticated approach is needed.

#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.

Instead, the following will work:

Pickling the figure

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()

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()