Problem

Often, I will create figures using matplotlib using theFigure class explicitly, because either I want to make a figure and repeatedly tweak it to see how it looks, or I am creating a lot of figures and don't need / want to deal with the pyplot interface. However, as far as I can tell, there is no native matplotlib way to show those figures since they weren't created with the pyplot interface.

An example of what I would like to do:

fig =. Figure()ax = fig.subplots()ax.plot([1, 2, 3, 4])fig.show() # This gives an error (below)

Error:

Traceback (most recent call last):  File "<stdin>", line 1, in <module>  File "/Users/jordan.e/opt/miniconda3/lib/python3.7/site-packages/matplotlib/figure.py", line 408, in show    "Figure.show works only for figures managed by pyplot, "AttributeError: Figure.show works only for figures managed by pyplot, normally created by pyplot.figure()

The reason I don't want to useplt.subplots() followed byplt.show(), is, as mentioned above, because I often want to close and re-show the same figure. It seems really odd to me that matplotlib has a way of creating figures via a pure-OOP interface, but no easy way to display them. This is very frustrating and basically leads me to either repeatedly re-create the same plot with the pyplot interface instead of just tweaking the one I already have. I wrote a very hacky method for showing created figures based on a StackOverflow answer to this question which is below, and does not work reliably (the figure doubles in size every time I show it):

def show_figure(fig):    """    create a dummy figure and use its manager to display "fig"    """    dummy = plt.figure()    new_manager = dummy.canvas.manager    new_manager.canvas.figure = fig    fig.set_canvas(new_manager.canvas)    plt.show()

Proposed Solution

Either supportfig.show() directly for figures created using the OOP-interface, or provide a method likepyplot.show_figure() which takes as an argument an existing figure and prints it to the screen (doing the exact same thing that aplt.plot() followed byplt.show() would do)