Zoomed inset in matplotlib without re-plotting data

I think the following does what you want. Note that you use the returned
handle to the first

imshow

and add it to the axis for the insert. You need
to make a copy so you have a separate handle for each figure,

import matplotlib.pyplot as pltfrom mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axesfrom mpl_toolkits.axes_grid1.inset_locator import mark_insetimport numpy as npimport copydef get_demo_image():    from matplotlib.cbook import get_sample_data    import numpy as np    f = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False)    z = np.load(f)    # z is a numpy array of 15x15    return z, (-3,4,-4,3)fig, ax = plt.subplots(figsize=[5,4])# prepare the demo imageZ, extent = get_demo_image()Z2 = np.zeros([150, 150], dtype="d")ny, nx = Z.shapeZ2[30:30+ny, 30:30+nx] = Z# extent = [-3, 4, -4, 3]im = ax.imshow(Z2, extent=extent, interpolation="nearest",          origin="lower")#Without copy, image is shown in insert onlyimcopy = copy.copy(im)axins = zoomed_inset_axes(ax, 6, loc=1) # zoom = 6axins.add_artist(imcopy)# sub region of the original imagex1, x2, y1, y2 = -1.5, -0.9, -2.5, -1.9axins.set_xlim(x1, x2)axins.set_ylim(y1, y2)plt.xticks(visible=False)plt.yticks(visible=False)# draw a bbox of the region of the inset axes in the parent axes and# connecting lines between the bbox and the inset axes areamark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")plt.draw()plt.show()

For your wrapper function, this would be something like,

def plot_with_zoom(*args, **kwargs):    im = ax.imshow(*args, **kwargs)    imcopy = copy.copy(im)    axins.add_artist(imcopy)

However, as

imshow

just displays the data stored in array

Z

as an image, I
would think this solution would actually be slower than two separate calls to

imshow

. For plots which take more time, e.g. a

contour

plot or

pcolormesh

, this approach may be sensible…

EDIT:

Beyond a single

imshow

, and for multiple plots of different types. Plotting
functions all return different handles (e.g. plot returns a list of lines,
imshow returns a matplotlib.image.AxesImage, etc). You could keep adding these
handles to a list (or dict) as you plot (or use a
collection if they are
similar enough). Then you could write a general function which adds them to an
axis using add_artist or add_patch methods from the zoomed axis, probably with
if type checking to deal with the various types used in the plot. A simpler
method may be to loop over

ax.get_children()

and reuse anything which isn’t
an element of the axis itself.

Another option may be to look into blitting techniques,
rasterization
or other techniques used to speed up animation, for example using

fig.canvas.copy_from_bbox

or

fig.canvas.tostring_rgb

to copy the entire
figure as an image (see why is plotting with Matplotlib so
slow?‌​low). You could also draw the figure, save it to a non-
vector graphic (with

savefig

or to a StringIO
buffer), read back in and plot a zoomed
in version.