How can I show figures separately in matplotlib?

Say that I have two figures in matplotlib, with one plot per figure:

import matplotlib.pyplot as plt

f1 = plt.figure()
plt.plot(range(0,10))
f2 = plt.figure()
plt.plot(range(10,20))

Then I show both in one shot

plt.show()

Is there a way to show them separately, i.e. to show just f1?

Or better: how can I manage the figures separately like in the following 'wishful' code (that doesn't work):

f1 = plt.figure()
f1.plot(range(0,10))
f1.show()

Solution 1:

Sure. Add an Axes using add_subplot. (Edited import.) (Edited show.)

import matplotlib.pyplot as plt
f1 = plt.figure()
f2 = plt.figure()
ax1 = f1.add_subplot(111)
ax1.plot(range(0,10))
ax2 = f2.add_subplot(111)
ax2.plot(range(10,20))
plt.show()

Alternatively, use add_axes.

ax1 = f1.add_axes([0.1,0.1,0.8,0.8])
ax1.plot(range(0,10))
ax2 = f2.add_axes([0.1,0.1,0.8,0.8])
ax2.plot(range(10,20))

Solution 2:

With Matplotlib prior to version 1.0.1, show() should only be called once per program, even if it seems to work within certain environments (some backends, on some platforms, etc.).

The relevant drawing function is actually draw():

import matplotlib.pyplot as plt

plt.plot(range(10))  # Creates the plot.  No need to save the current figure.
plt.draw()  # Draws, but does not block
raw_input()  # This shows the first figure "separately" (by waiting for "enter").

plt.figure()  # New window, if needed.  No need to save it, as pyplot uses the concept of current figure
plt.plot(range(10, 20))
plt.draw()
# raw_input()  # If you need to wait here too...

# (...)

# Only at the end of your program:
plt.show()  # blocks

It is important to recognize that show() is an infinite loop, designed to handle events in the various figures (resize, etc.). Note that in principle, the calls to draw() are optional if you call matplotlib.ion() at the beginning of your script (I have seen this fail on some platforms and backends, though).

I don't think that Matplotlib offers a mechanism for creating a figure and optionally displaying it; this means that all figures created with figure() will be displayed. If you only need to sequentially display separate figures (either in the same window or not), you can do like in the above code.

Now, the above solution might be sufficient in simple cases, and for some Matplotlib backends. Some backends are nice enough to let you interact with the first figure even though you have not called show(). But, as far as I understand, they do not have to be nice. The most robust approach would be to launch each figure drawing in a separate thread, with a final show() in each thread. I believe that this is essentially what IPython does.

The above code should be sufficient most of the time.

PS: now, with Matplotlib version 1.0.1+, show() can be called multiple times (with most backends).

Solution 3:

I think I am a bit late to the party but... In my opinion, what you need is the object oriented API of matplotlib. In matplotlib 1.4.2 and using IPython 2.4.1 with Qt4Agg backend, I can do the following:

import matplotlib.pyplot as plt
fig, ax = plt.subplots(1) # Creates figure fig and add an axes, ax.
fig2, ax2 = plt.subplots(1) # Another figure

ax.plot(range(20)) #Add a straight line to the axes of the first figure.
ax2.plot(range(100)) #Add a straight line to the axes of the first figure.

fig.show() #Only shows figure 1 and removes it from the "current" stack.
fig2.show() #Only shows figure 2 and removes it from the "current" stack.
plt.show() #Does not show anything, because there is nothing in the "current" stack.
fig.show() # Shows figure 1 again. You can show it as many times as you want.

In this case plt.show() shows anything in the "current" stack. You can specify figure.show() ONLY if you are using a GUI backend (e.g. Qt4Agg). Otherwise, I think you will need to really dig down into the guts of matplotlib to monkeypatch a solution.

Remember that most (all?) plt.* functions are just shortcuts and aliases for figure and axes methods. They are very useful for sequential programing, but you will find blocking walls very soon if you plan to use them in a more complex way.