Matplotlib offers these functions:
cla() # Clear axis
clf() # Clear figure
close() # Close a figure window
When should I use each function and what exactly does it do?
They all do different things, since matplotlib uses a hierarchical order in which a figure window contains a figure which may consist of many axes. Additionally, there are functions from the pyplot interface and there are methods on the Figure class. I will discuss both cases below.
pyplot interface
pyplot is a module that collects a couple of functions that allow matplotlib to be used in a functional manner. I here assume that pyplot has been imported as import matplotlib.pyplot as plt.
In this case, there are three different commands that remove stuff:
See matplotlib.pyplot Functions:
plt.cla() clears an axis, i.e. the currently active axis in the current figure. It leaves the other axes untouched.
plt.clf() clears the entire current figure with all its axes, but leaves the window opened, such that it may be reused for other plots.
plt.close() closes a window, which will be the current window, if not specified otherwise.
Which functions suits you best depends thus on your use-case.
The close() function furthermore allows one to specify which window should be closed. The argument can either be a number or name given to a window when it was created using figure(number_or_name) or it can be a figure instance fig obtained, i.e., usingfig = figure(). If no argument is given to close(), the currently active window will be closed. Furthermore, there is the syntax close('all'), which closes all figures.
methods of the Figure class
Additionally, the Figure class provides methods for clearing figures.
I'll assume in the following that fig is an instance of a Figure:
fig.clf() clears the entire figure. This call is equivalent to plt.clf() only if fig is the current figure.
fig.clear() is a synonym for fig.clf()
Note that even del fig will not close the associated figure window. As far as I know the only way to close a figure window is using plt.close(fig) as described above.
There is just a caveat that I discovered today.
If you have a function that is calling a plot a lot of times you better use plt.close(fig) instead of fig.clf() somehow the first does not accumulate in memory. In short if memory is a concern use plt.close(fig) (Although it seems that there are better ways, go to the end of this comment for relevant links).
So the the following script will produce an empty list:
for i in range(5):
fig = plot_figure()
plt.close(fig)
# This returns a list with all figure numbers available
print(plt.get_fignums())
Whereas this one will produce a list with five figures on it.
for i in range(5):
fig = plot_figure()
fig.clf()
# This returns a list with all figure numbers available
print(plt.get_fignums())
From the documentation above is not clear to me what is the difference between closing a figure and closing a window. Maybe that will clarify.
If you want to try a complete script there you have:
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(1000)
y = np.sin(x)
for i in range(5):
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(x, y)
plt.close(fig)
print(plt.get_fignums())
for i in range(5):
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(x, y)
fig.clf()
print(plt.get_fignums())
If memory is a concern somebody already posted a work-around in SO see:
Create a figure that is reference counted
plt.cla() means clear current axis
plt.clf() means clear current figure
also, there's plt.gca() (get current axis) and plt.gcf() (get current figure)
Read more here: Matplotlib, Pyplot, Pylab etc: What's the difference between these and when to use each?
Related
The following example code generates a simple plot, then saves it to 'fig1.pdf', then displays it, then saves it again to 'fig2.pdf'. The first image looks as expected, but the second one is blank (contains a white square). What's actually going on here? The line plt.show() apparently messes something up, but I can't figure out what/how!
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-1, 1, 100)
y = x**2
plt.plot(x,y)
plt.savefig('fig1.pdf')
plt.show()
plt.savefig('fig2.pdf')
If you want to save the figure after displaying it, you'll need to hold on to the figure instance. The reason that plt.savefig doesn't work after calling show is that the current figure has been reset.
pyplot keeps track of which figures, axes, etc are "current" (i.e. have not yet been displayed with show) behind-the-scenes. gcf and gca get the current figure and current axes instances, respectively. plt.savefig (and essentially any other pyplot method) just does plt.gcf().savefig(...). In other words, get the current figure instance and call its savefig method. Similarly plt.plot basically does plt.gca().plot(...).
After show is called, the list of "current" figures and axes is empty.
In general, you're better off directly using the figure and axes instances to plot/save/show/etc, rather than using plt.plot, etc, to implicitly get the current figure/axes and plot on it. There's nothing wrong with using pyplot for everything (especially interactively), but it makes it easier to shoot yourself in the foot.
Use pyplot for plt.show() and to generate a figure and an axes object(s), but then use the figure or axes methods directly. (e.g. ax.plot(x, y) instead of plt.plot(x, y), etc) The main advantage of this is that it's explicit. You know what objects you're plotting on, and don't have to reason about what the pyplot state-machine does (though it's not that hard to understand the state-machine interface, either).
As an example of the "recommended" way of doing things, do something like:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-1, 1, 100)
y = x**2
fig, ax = plt.subplots()
ax.plot(x, y)
fig.savefig('fig1.pdf')
plt.show()
fig.savefig('fig2.pdf')
If you'd rather use the pyplot interface for everything, then just grab the figure instance before you call show. For example:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-1, 1, 100)
y = x**2
plt.plot(x, y)
fig = plt.gcf()
fig.savefig('fig1.pdf')
plt.show()
fig.savefig('fig2.pdf')
pyplot.show blocks and destroys the plot upon closing. You can use
plt.show(block=False)
after which the save to fig2.pdf will work or you can plot it again before saving
plt.plot(x,y)
plt.savefig('fig2.pdf')
I had to run plt.cla() and plt.clf() before plotting the second one. Clear current axes and clear current plot, respectively.
If you just want to see the figure before saving, you can call
plt.ion()
before plotting, which starts interactive mode, and shows all figures as they are drawn. This mostly removes the need to call plt.show(). You no longer need to close the figures to continue.
To disable interactive mode again, call plt.ioff().
If I put pyplot.show() before I try to save an image, the file doesn't actually contain the image.
At first I thought it was a bug but then I switched pyplot.show() and pyplot.savefig('foo5.png') and it worked.
Here is an example code piece.
def plot(embeddings, labels):
assert embeddings.shape[0] >= len(labels), 'More labels than embeddings'
pyplot.figure(figsize=(20, 20)) # in inches
for i, label in enumerate(labels):
x, y = embeddings[i,:]
pyplot.scatter(x, y)
pyplot.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',
ha='right', va='bottom')
pyplot.savefig('foo4.png')
pyplot.show()
pyplot.savefig('foo5.png')
books = [bookDictionary[i] for i in range(1, num_points2+1)]
plot(two_d_embeddings, books)
print( os.listdir() )
foo4.png is just fine, but foo5.png is blank.
As you found out yourself, using pyplot you need to save the figure before it is shown.
This is in fact only true for non-interactive mode (ioff), but that is the default and probably most common use case.
What happens is that once pyplot.show() is called, the figure is shown and an event loop is started, taking over the python even loop. Hence any command after pyplot.show() is delayed until the figure is closed. This means that pyplot.savefig(), which comes after show is not executed until the figure is closed. Once the figure is closed, there is no figure to save present inside the pyplot state machine anymore.
You may however save a particular figure, in case that is desired. E.g.
import matplotlib.pyplot as plt
plt.plot([1,2,3])
fig = plt.gcf()
plt.show()
fig.savefig("foo5.png")
Here, we call the savefig method of a particular figure (in this case the only one present) for which we need to obtain a handle (fig) to that figure.
Note that in such cases, where you need fine control over how matplotlib works it's always useful to not use pyplot, but rely mostly on the object oriented interface.
Hence,
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([1,2,3])
plt.show()
fig.savefig("foo5.png")
would be the more natural way to save a figure after having shown it.
The following example code generates a simple plot, then saves it to 'fig1.pdf', then displays it, then saves it again to 'fig2.pdf'. The first image looks as expected, but the second one is blank (contains a white square). What's actually going on here? The line plt.show() apparently messes something up, but I can't figure out what/how!
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-1, 1, 100)
y = x**2
plt.plot(x,y)
plt.savefig('fig1.pdf')
plt.show()
plt.savefig('fig2.pdf')
If you want to save the figure after displaying it, you'll need to hold on to the figure instance. The reason that plt.savefig doesn't work after calling show is that the current figure has been reset.
pyplot keeps track of which figures, axes, etc are "current" (i.e. have not yet been displayed with show) behind-the-scenes. gcf and gca get the current figure and current axes instances, respectively. plt.savefig (and essentially any other pyplot method) just does plt.gcf().savefig(...). In other words, get the current figure instance and call its savefig method. Similarly plt.plot basically does plt.gca().plot(...).
After show is called, the list of "current" figures and axes is empty.
In general, you're better off directly using the figure and axes instances to plot/save/show/etc, rather than using plt.plot, etc, to implicitly get the current figure/axes and plot on it. There's nothing wrong with using pyplot for everything (especially interactively), but it makes it easier to shoot yourself in the foot.
Use pyplot for plt.show() and to generate a figure and an axes object(s), but then use the figure or axes methods directly. (e.g. ax.plot(x, y) instead of plt.plot(x, y), etc) The main advantage of this is that it's explicit. You know what objects you're plotting on, and don't have to reason about what the pyplot state-machine does (though it's not that hard to understand the state-machine interface, either).
As an example of the "recommended" way of doing things, do something like:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-1, 1, 100)
y = x**2
fig, ax = plt.subplots()
ax.plot(x, y)
fig.savefig('fig1.pdf')
plt.show()
fig.savefig('fig2.pdf')
If you'd rather use the pyplot interface for everything, then just grab the figure instance before you call show. For example:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-1, 1, 100)
y = x**2
plt.plot(x, y)
fig = plt.gcf()
fig.savefig('fig1.pdf')
plt.show()
fig.savefig('fig2.pdf')
pyplot.show blocks and destroys the plot upon closing. You can use
plt.show(block=False)
after which the save to fig2.pdf will work or you can plot it again before saving
plt.plot(x,y)
plt.savefig('fig2.pdf')
I had to run plt.cla() and plt.clf() before plotting the second one. Clear current axes and clear current plot, respectively.
If you just want to see the figure before saving, you can call
plt.ion()
before plotting, which starts interactive mode, and shows all figures as they are drawn. This mostly removes the need to call plt.show(). You no longer need to close the figures to continue.
To disable interactive mode again, call plt.ioff().
The following example code generates a simple plot, then saves it to 'fig1.pdf', then displays it, then saves it again to 'fig2.pdf'. The first image looks as expected, but the second one is blank (contains a white square). What's actually going on here? The line plt.show() apparently messes something up, but I can't figure out what/how!
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-1, 1, 100)
y = x**2
plt.plot(x,y)
plt.savefig('fig1.pdf')
plt.show()
plt.savefig('fig2.pdf')
If you want to save the figure after displaying it, you'll need to hold on to the figure instance. The reason that plt.savefig doesn't work after calling show is that the current figure has been reset.
pyplot keeps track of which figures, axes, etc are "current" (i.e. have not yet been displayed with show) behind-the-scenes. gcf and gca get the current figure and current axes instances, respectively. plt.savefig (and essentially any other pyplot method) just does plt.gcf().savefig(...). In other words, get the current figure instance and call its savefig method. Similarly plt.plot basically does plt.gca().plot(...).
After show is called, the list of "current" figures and axes is empty.
In general, you're better off directly using the figure and axes instances to plot/save/show/etc, rather than using plt.plot, etc, to implicitly get the current figure/axes and plot on it. There's nothing wrong with using pyplot for everything (especially interactively), but it makes it easier to shoot yourself in the foot.
Use pyplot for plt.show() and to generate a figure and an axes object(s), but then use the figure or axes methods directly. (e.g. ax.plot(x, y) instead of plt.plot(x, y), etc) The main advantage of this is that it's explicit. You know what objects you're plotting on, and don't have to reason about what the pyplot state-machine does (though it's not that hard to understand the state-machine interface, either).
As an example of the "recommended" way of doing things, do something like:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-1, 1, 100)
y = x**2
fig, ax = plt.subplots()
ax.plot(x, y)
fig.savefig('fig1.pdf')
plt.show()
fig.savefig('fig2.pdf')
If you'd rather use the pyplot interface for everything, then just grab the figure instance before you call show. For example:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-1, 1, 100)
y = x**2
plt.plot(x, y)
fig = plt.gcf()
fig.savefig('fig1.pdf')
plt.show()
fig.savefig('fig2.pdf')
pyplot.show blocks and destroys the plot upon closing. You can use
plt.show(block=False)
after which the save to fig2.pdf will work or you can plot it again before saving
plt.plot(x,y)
plt.savefig('fig2.pdf')
I had to run plt.cla() and plt.clf() before plotting the second one. Clear current axes and clear current plot, respectively.
If you just want to see the figure before saving, you can call
plt.ion()
before plotting, which starts interactive mode, and shows all figures as they are drawn. This mostly removes the need to call plt.show(). You no longer need to close the figures to continue.
To disable interactive mode again, call plt.ioff().
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()
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))
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).
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.
Perhaps you need to read about interactive usage of Matplotlib. However, if you are going to build an app, you should be using the API and embedding the figures in the windows of your chosen GUI toolkit (see examples/embedding_in_tk.py, etc).
None of the above solutions seems to work in my case, with matplotlib 3.1.0 and Python 3.7.3. Either both the figures show up on calling show() or none show up in different answers posted above.
Building upon #Ivan's answer, and taking hint from here, the following seemed to work well for me:
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.
# plt.close(fig) # For not showing fig
plt.close(fig2) # For not showing fig2
plt.show()
As #arpanmangal, the solutions above do not work for me (matplotlib 3.0.3, python 3.5.2).
It seems that using .show() in a figure, e.g., figure.show(), is not recommended, because this method does not manage a GUI event loop and therefore the figure is just shown briefly. (See figure.show() documentation). However, I do not find any another way to show only a figure.
In my solution I get to prevent the figure for instantly closing by using click events. We do not have to close the figure — closing the figure deletes it.
I present two options:
- waitforbuttonpress(timeout=-1) will close the figure window when clicking on the figure, so we cannot use some window functions like zooming.
- ginput(n=-1,show_clicks=False) will wait until we close the window, but it releases an error :-.
Example:
import matplotlib.pyplot as plt
fig1, ax1 = plt.subplots(1) # Creates figure fig1 and add an axes, ax1
fig2, ax2 = plt.subplots(1) # Another figure fig2 and add an axes, ax2
ax1.plot(range(20),c='red') #Add a red straight line to the axes of fig1.
ax2.plot(range(100),c='blue') #Add a blue straight line to the axes of fig2.
#Option1: This command will hold the window of fig2 open until you click on the figure
fig2.waitforbuttonpress(timeout=-1) #Alternatively, use fig1
#Option2: This command will hold the window open until you close the window, but
#it releases an error.
#fig2.ginput(n=-1,show_clicks=False) #Alternatively, use fig1
#We show only fig2
fig2.show() #Alternatively, use fig1
As of November 2020, in order to show one figure at a time, the following works:
import matplotlib.pyplot as plt
f1, ax1 = plt.subplots()
ax1.plot(range(0,10))
f1.show()
input("Close the figure and press a key to continue")
f2, ax2 = plt.subplots()
ax2.plot(range(10,20))
f2.show()
input("Close the figure and press a key to continue")
The call to input() prevents the figure from opening and closing immediately.