I have a problem using pyplot. I am new to Python so sorry if I am doing some obvious mistake.
After I have plotted something using pyplot it shows the graph, but when I then try and add e.g. ylabel it will not update the current graph. It results in a new graph with only the ylabel, not previously entered information. So to me it seems to be a problem with recognizing the current graph/axis, but the ishold delivers a True statement.
My setup is Python 2.7 in Python(x,y). The problem occurs both in the Spyder IDE and the IPython Qt Console. It does however not occur in the regular IPython console (which, by constrast, is not interactive, but everything is included when using show(). When I turn off interactive in Spyder/Qt console it does not show anything after using the show() command).
import matplotlib.pyplot as plt
plt.plot([1,2,3,4])
Out[2]: [<matplotlib.lines.Line2D at 0x78ca370>]

plt.ylabel('test')
Out[3]: <matplotlib.text.Text at 0x5bd5990>

plt.ishold()
Out[4]: True
matplotlib.get_backend()
Out[6]: 'module://IPython.kernel.zmq.pylab.backend_inline'
Hope any of you have some input. Thanks.
This is one of the things were InlineBackend have to behave differently from other backend or you would have sort of a memory leak. You have to keep explicit handle to matplotlib figure and/or set close_figure to False in config. Usually pyplot is a compatibility layer for matlab for convenience, try to learn to do using the Object Oriented way.
fig,ax = subplots()
ax.plot(range(4))
ax.set_ylabel('my label')
...
Related
Running following code inside python interpretor displays a figure with random values
>>>fig = plt.figure();ax1 = fig.add_subplot(111);plt.ion();ax1 = ax1.imshow(np.random.rand(256,256))
while running the following script as a file does not display any output/figure.
import numpy as np
import matplotlib.pyplot as plt
import time
fig = plt.figure()
ax1 = fig.add_subplot(111)
plt.ion()
ax1 =ax1.imshow(np.random.rand(256,256))
what is the reason for difference in behaviour?
I suspect what is going on is that
matplotlib.rcParams['interactive'] == True
and this is set in your .matplotlibrc file.
which means that plt.show is non-blocking (so that you get a figure that you can interact with and an command prompt you can type more code at). However, in the case of a script the (implicit) plt.show does not block so the script exits, taking the figure with it.
I suggest the setting the interactive rcparam to False and then either explitily setting it to true in the repl or (the preferred method) use IPython and the %matplotlib magic.
I am using PyCharm 2016.1 and Python 2.7 on Windows 10 and imported the matplotlib module.
As the matplotlib module ist very extensive and I am relatively new to Python, I hoped the Auto Complete function in PyCharm could help me to get an overview of the existent properties/ functions of an object. It would be more convenient as digging through the api documentation every time, not knowing what to look for an where to find it.
For example:
from matplotlib import pyplot as plt
fig, ax = plt.subplots()
When I type ax. there ist no auto completion for the properties, functions etc. of the axis, I only get the suggestions list.
I already tried this and imported the axis module directly with:
import matplotlib.axis as axis
or
from matplotlib.axis import Axis as axis
Smart Auto Completion and 'Collect run-time types information' is already enabled.
Is there a way to enable the auto completion like described or is there another IDE that supports that?
I believe your problem is highlighted here:
https://intellij-support.jetbrains.com/hc/en-us/community/posts/205816499-Improving-collecting-run-time-type-information-for-code-insight?sort_by=votes
Tldr return types can vary, so it cant be figured out at compile time.
Most accepted way is to use a type hint, since it can only figure out what type it as run time :
import matplotlib.axes._axes as axes
fig = plt.figure(figsize=(5,10))
ax1 = fig.add_subplot(3,1,1) # type:axes.Axes
ax1.set_xlabel('Test') <- now autocompletes
You can also try an assert isinstance:
import matplotlib.axes._axes as axes
fig = plt.figure(figsize=(5,10))
ax1 = fig.add_subplot(3,1,1)
assert isinstance(ax1, axes.Axes)
ax1.set_xlabel('Test')
It wont find the autocomplete if you do it after the method you are looking for:
ax1.set_xlabel('Test')
assert isinstance(ax1, axes.Axes)
With this, you shouldnt let isinstance dictate the control flow of your code, if you are trying to run a method that doesnt exist on an object, it should crash, however, if your different object has a method of the same name (!) then you have inadvertently reached that goal without annotations being there. So I like it better, since you want it to crash early and in the correct place. YMMV
From the doc:
Assertions should not be used to test for failure cases that can
occur because of bad user input or operating system/environment
failures, such as a file not being found. Instead, you should raise an
exception, or print an error message, or whatever is appropriate. One
important reason why assertions should only be used for self-tests of
the program is that assertions can be disabled at compile time.
If Python is started with the -O option, then assertions will be
stripped out and not evaluated. So if code uses assertions heavily,
but is performance-critical, then there is a system for turning them
off in release builds. (But don't do this unless it's really
necessary.
https://wiki.python.org/moin/UsingAssertionsEffectively
Alternatively, if you dont want to add to your code in this fashion, and have Ipython/jupyter installed through anoconda, you can get the code completion from the console by right clicking the code to be ran and choosing "execute selection in console"
In addition to Paul's answer. If you are using fig, ax = plt.subplots() , you could use figure type hint. See below example:
from matplotlib import pyplot as plt
import matplotlib.axes._axes as axes
import matplotlib.figure as figure
fig, ax = plt.subplots() # type:figure.Figure, axes.Axes
ax.
fig.
Introduction
As I am coming from matlab, I am used to an interactive interface where a script can update figures while it is running. During the processing each figure can be re-sized or even closed. This probably means that each figure is running in its own thread which is obviously not the case with matplotlib.
IPython can imitate the Matlab behavior using the magic command %pylab or %matplotlib which does something that I don't understand yet and which is the very point of my question.
My goal is then to allow standalone Python scripts to work as Matlab does (or as IPython with %matplotlib does). In other words, I would like this script to be executed from the command line. I am expecting a new figure that pop-up every 3 seconds. During the execution I would be able to zoom, resize or even close the figure.
#!/usr/bin/python
import matplotlib.pyplot as plt
import time
def do_some_work():
time.sleep(3)
for i in range(10):
plt.plot([1,2,3,4])
plt.show() # this is way too boilerplate, I'd like to avoid it too.
do_some_work()
What alternative to %matplotlib I can use to manipulate figures while a script is running in Python (not IPython)?
What solutions I've already investigated?
I currently found 3 way to get a plot show.
1. %pylab / %matplotlib
As tom said, the use of %pylab should be avoided to prevent the namespace to be polluted.
>>> %pylab
>>> plot([1,2,3,4])
This solution is sweet, the plot is non-blocking, there is no need for an additionnal show(), I can still add a grid with grid() afterwards and I can close, resize or zoom on my figure with no additional issues.
Unfortunately the %matplotlib command is only available on IPython.
2. from pylab import * or from matplotlib.pyplot import plt
>>> from pylab import *
>>> plot([1,2,3,4])
Things are quite different here. I need to add the command show() to display my figure which is blocking. I cannot do anything but closing the figure to execute the next command such as grid() which will have no effect since the figure is now closed...
** 3. from pylab import * or from matplotlib.pyplot import plt + ion()**
Some suggestions recommend to use the ion() command as follow:
>>> from pylab import *
>>> ion()
>>> plot([1,2,3,4])
>>> draw()
>>> pause(0.0001)
Unfortunately, even if the plot shows, I cannot close the figure manually. I will need to execute close() on the terminal which is not very convenient. Moreover the need for two additional commands such as draw(); pause(0.0001) is not what I am expecting.
Summary
With %pylab, everything is wonderful, but I cannot use it outside of IPython
With from pylab import * followed by a plot, I get a blocking behavior and all the power of IPython is wasted.
from pylab import * followed by ion offers a nice alternative to the previous one, but I have to use the weird pause(0.0001) command that leads to a window that I cannot close manually (I know that the pause is not needed with some backends. I am using WxAgg which is the only one that works well on Cygwin x64.
This question advices to use matplotlib.interactive(True). Unfortunately it does not work and gives the same behavior as ion() does.
Change your do_some_work function to the following and it should work.
def do_some_work():
plt.pause(3)
For interactive backends plt.pause(3) starts the event loop for 3 seconds so that it can process your resize events. Note that the documentation says that it is an experimental function and that for complex animations you should use the animation module.
The, %pylab and %matplotlib magic commands also start an event loop, which is why user interaction with the plots is possible. Alternatively, you can start the event loop with %gui wx, and turn it off with %gui. You can use the IPython.lib.guisupport.is_event_loop_running_wx() function to test if it is running.
The reason for using ion() or ioff() is very well explained in the 'What is interactive mode' page. In principle, user interaction is possible without IPython. However, I could not get the interactive-example from that page to work with the Qt4Agg backend, only with the MacOSX backend (on my Mac). I didn't try with the WX backend.
Edit
I did manage to get the interactive-example to work with the Qt4Agg backend by using PyQt4 instead of PySide (so by setting backend.qt4 : PyQt4 in my ~/.config/matplotlibrc file). I think the example doesn't work with all backends. I submitted an issue here.
Edit 2
I'm afraid I can't think of a way of manipulating the figure while a long calculation is running, without using threads. As you mentioned: Matplotlib doesn't start a thread, and neither does IPython. The %pylab and %matplotlib commands alternate between processing commands from the read-eval-print loop and letting the GUI processing events for a short time. They do this sequentially.
In fact, I'm unable to reproduce your behavior, even with the %matplotlib or %pylab magic. (Just to be clear: in ipython I first call %matplotlib and then %run yourscript.py). The %matplotlib magic puts Matplotlib in interactive-mode, which makes the plt.show() call non-blocking so that the do_some_work function is executed immediately. However, during the time.sleep(3) call, the figure is unresponsive (this becomes even more apparent if I increase the sleeping period). I don't understand how this can work at your end.
Unless I'm wrong you'll have to break up your calculation in smaller parts and use plt.pause (or even better, the animation module) to update the figures.
My advice would be to keep using IPython, since it manages the GUI event loop for you (that's what pylab/pylot does).
I tried interactive plotting in a normal interpreter and it worked the way it is expected, even without calling ion() (Debian unstable, Python 3.4.3+, Matplotlib 1.4.2-3.1). If I recall it right, it's a fairly new feature in Matplotlib.
Alternatively, you can also use Matplotlib's animation capabilities to update a plot periodically:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import time
plt.ion()
tt = np.linspace(0, 1, 200)
freq = 1 # parameter for sine
t0 = time.time() # for measuring ellapsed time
fig, ax = plt.subplots()
def draw_func(i):
""" This function gets called repeated times """
global freq # needed because freq is changed in this function
xx = np.sin(2*np.pi*freq*tt)/freq
ax.set_title("Passed Time: %.1f s, " % (time.time()-t0) +
"Parameter i=%d" % i)
ax.plot(tt, xx, label="$f=%d$ Hz" % freq)
ax.legend()
freq += 1
# call draw_func every 3 seconds 1 + 4 times (first time is initialization):
ani = animation.FuncAnimation(fig, draw_func, np.arange(4), interval=3000,
repeat=False)
# plt.show()
Checkout matplotlib.animation.FuncAnimation for details. You'll find further examples in the examples section.
I am using the anaconda distribution of ipython/Qt console. I want to plot things inline so I type the following from the ipython console:
%pylab inline
Next I type the tutorial at (http://pandas.pydata.org/pandas-docs/dev/visualization.html) into ipython...
import matplotlib.pyplot as plt
import pandas as pd
ts = pd.Series(randn(1000), index = pd.date_range('1/1/2000', periods=1000))
ts = ts.cumsum()
ts.plot()
... and this is all that i get back:
<matplotlib.axes.AxesSubplot at 0x109253410>
But there is no plot. What could be wrong? Is there another command that I need to supply? The tutorial suggests that that is all that I need to type.
Plots are not displayed until you run
plt.show()
There could be 2 ways to approach this problem:
1) Either invoke the inline/osx/qt/gtk/gtk3/tk backend. Depends on the ipython console that you have been using. So, simply do:
%matplotlib inline #Here the inline backend is invoked, which removes the necessity of calling show after each plot.
or for ipython/qt console, do:
%matplotlib qt #This one works for me, thus, depends on the ipython console you use.
#
2) Or, do the traditional way as aforementioned (already answered above on this page):
plt.show() #However, you will have to call this show function each time.
[I originally posted this in serverfault, but was advised there to post it here instead.]
Matplotlib is a python library for data visualization. When I attempt to display a graph on the screen, I get the following error/warnings:
2012-12-21 16:40:05.532 python[9705:903] *** __NSAutoreleaseNoPool(): Object 0x103e25d80 of class NSCFArray autoreleased with no pool in place - just leaking
2012-12-21 16:40:05.534 python[9705:903] *** __NSAutoreleaseNoPool(): Object 0x103e26820 of class __NSFastEnumerationEnumerator autoreleased with no pool in place - just leaking
2012-12-21 16:40:05.535 python[9705:903] *** __NSAutoreleaseNoPool(): Object 0x103e9f080 of class NSObject autoreleased with no pool in place - just leaking
FWIW, one way to produce these results is shown below; all the steps shown (including the call to ipython) are taken from a matplotlib tutorial:
% ipython
...
In [1]: import matplotlib.pyplot as plt
In [2]: plt.plot([1, 3, 2, 4])
Out[3]: [<matplotlib.lines.Line2D at 0x106aabd90>]
In [3]: plt.show()
ALso, FWIW, I've observed exactly the same behavior with multiple styles of installation (on the same machine) of python+numpy+matplotlib+ipython, including installs that use the system-supplied python, those that use the python installed by homebrew, or those that use a python installed directly from source into a location off my home directory.
Any ideas of what may be going on, or what I could do about it?
I am having the same problem, one solution I found is to add the line:
plt.ion()
before the first plotting command. This turns on the interactive plotting mode and the error messages go away. This has only worked for me when plotting on the command line, if I do ion() and then show() in a script the plots don't show up at all, and if I leave the ion() out, I can see my plots, but I get the error messages. This has only happened since updated to version 1.2.0.
It's trying to do something with Cocoa, but Cocoa hasn't really been initialized or anything. You may be able to silence the errors and fix the problems by running this before:
from Foundation import NSAutoreleasePool
pool = NSAutoreleasePool()
And this after:
from AppKit import NSApplication
NSApplication.sharedApplication().run()
This requires PyObjC. Unfortunately, this may only allow for displaying one plot per IPython session. You may wish to try the IPython notebook instead, which removes the dependency on Cocoa.