matplotlib has a config file and IPython has its own. Which one has precedence when it comes to setting things like matplotlib backends?
For example, say my config file for matplotlib says to use a specific backend, but then I modify my IPython startup or config files to use a different one. Which one would be used when I start IPython and import matplotlib?
More generally, what is the right way to set things up so that different profiles use different matplotlib backends or matplotlib configurations?
IPython configuration is used, as IPython itself chooses the matplotlib backend.
For reference, see IPython:core/pylabtools.py:activate_matplotlib and notice how matplotlib.use(backend) is called explicitly.
Related
I just started using Codespaces. In my python file I have this code:
import matplotlib.pyplot as plt
import pandas as pd
print("Hello")
titanic_data = pd.read_csv("https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv")
titanic_data = titanic_data[titanic_data['Age'].notnull()]
titanic_data['Fare'] = titanic_data['Fare'].fillna(titanic_data['Fare'].mean())
titanic_data = titanic_data.drop_duplicates()
plt.scatter(titanic_data['Age'], titanic_data['Fare'])
plt.show()
print("Goodbye")
When I run this on my local machine, this works perfectly. I can see the console logs, and the figure appears as a new window:
However, when I run this in Codespaces, I can see all of the code running without any errors, but it does not show the figure. Is this a known limitation or a feature that is not yet supported? Is there another way I can plot figures in Codespaces?
They mention this in the docs docs:
The default container image that's used by GitHub Codespaces includes a set of machine learning libraries that are preinstalled in your codespace. For example, Numpy, pandas, SciPy, Matplotlib, seaborn, scikit-learn, Keras, PyTorch, Requests, and Plotly.
It sounds like it should be supported out of the box. Is additional configuration required?
Based on the experimentation I have done thus far, plotting these diagrams as one would do in a local dev environment is not (yet?) possible.
For this specific case, the next best solution was to create a new GitHub Codespace from this repo: https://github.com/education/codespaces-teaching-template-py
Once the repo has been cloned into the Codespace, navigate to an existing .ipynb file or create your own.
Inside there you'll be able to run chunks of custom code and plot figures.
The big limitation I see is that the figure cannot be interacted with the same way that one would be able to on a local machine (zooming, panning, etc).
As always, don't forget to shut your Codespace down when you're done using it!
Qt5Agg is necessary to use the mayavi 3D visualization package. I have installed PyQt5 and mayavi using pip in a separate copied conda environment. The default backend then changes from TkAgg to QtAgg. This is a bit weird because in an earlier installation in a different PC the default changed directly to Qt5Agg. I always check the backend using the following commands from the python console :
import matplotlib
matplotlib.get_backend()
Even with the backend being 'QtAgg', I am able to use mayavi from the terminal without any issue but not when I do so in Pycharm. Here I get a non-responsive empty window (image below) :
Image of the non-responsive window
I have been able to get rid of this issue by explicitly using Qt5Agg instead of QtAgg before the plt call :
import matplotlib
matplotlib.use('Qt5Agg')
import matplotlib.pyplot as plt
But I would prefer a better way than using the above in every script that I write. As I had mentioned earlier, I already have mayavi installed and have used it successfully it in Pycharm in a different PC and there the default backend is 'Qt5Agg' and hence there is no need to change the backend explicitly.
Is there anything obvious that I'm overlooking ? Can you please let me know of a way to change the default backend for matplotlib from QtAgg to Qt5Agg after PyQt5 installation using pip ?
Thanks in advance !!
Thanks to #PaulH's comment, I was able to solve the issue. Owing to #mx0's suggestion, I shall now explicitly mention the fix below so that others can also benefit from it.
In a particular conda environment, if matplotlib package is installed, then there will be a 'matplotlibrc' file stored somewhere that defines what the default backend will be whenever matplotlib is imported from that conda environment. The location of this 'matplotlibrc' can be found using the following commands :
import matplotlib
matplotlib.matplotlib_fname()
Please look into the following link if there's any deprecation issue with the above commands :
https://matplotlib.org/stable/tutorials/introductory/customizing.html#customizing-with-matplotlibrc-files
Once the location of the 'matplotlibrc' file is known, open it and simply uncomment one line inside this file. Just change the backend from :
##backend: Agg
to :
backend: Qt5Agg
And that's it. All the plot window troubles in PyCharm will be solved as far as the mayavi 3D visualization package is concerned. For any other use, where a specific backend is necessary, you can also set the default to any other backend of choice.
I often plot data that I do not want to leave my personal computer. I notice that when using mpld3 to generate plots in the jupyter notebook, https://mpld3.github.io is accessed. I think its just pulling plotting scripts, but is there any risk of any of my plotted data being sent off of my computer when using mpld3? Is there an "offline mode" I could use mpld3 with?
Try to pass local=True either to mpld3.enable_notebook() or mpld3.display(). As FAQ claims:
Setting this to True will copy the mpld3 and d3 JavaScript libraries to the notebook directory, and will use the appropriate path within IPython (/files/*.js) to load the libraries
Exactly what the title says. Is there a way to use the matplotlib library without installing TCL? Please don't tell me to bite the bullet and install TCL - I know how to do it but for my own (ok maybe silly) reasons I don't want to.
I don't care about displaying the plots, I only want to be able to output them in a png. I tried various things (using different backends etc) but matplotlib always wanted to find tcl to work :( Why is TCL so essential for matplotlib?
Also, please notice that I am using windows -- I have installed everything that could be required (numpy, pandas, matplotlib) using pip.
#gerrit's solution is the correct one (I was trying to change the backends but I was doing it after loading pyplot -- the important thing seems to be that you need to change the backend immediately after imporing matplotlib). Here's a small example using it:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
fig, ax = plt.subplots( nrows=1, ncols=1 )
ax.plot([0,1,2], [10,20,3])
fig.savefig('foo.png')
plt.close(fig)
This will output a file named 'foo.png' without using TCL \o/
Matplotlib 3.0 and newer
(Added to answer in October 2018)
Starting with Matplotlib 3, released on 19 September 2018, the problem described in the question should not occur. From the what's new part of the documentation:
The default backend no longer must be set as part of the build process. Instead, at run time, the builtin backends are tried in sequence until one of them imports.
Headless linux servers (identified by the DISPLAY env not being defined) will not select a GUI backend.
So, as long as you make sure DISPLAY is not defined, you should not run into any problems with the backend when running in a script on a headless Linux server.
Matplotlib 2.2 and older
(Original answer May 2016)
Immediately after loading matplotlib, enter
matplotlib.use('Agg')
Do this before loading pyplot, if at all.
By default, Matplotlib uses the TkAgg backend, which requires Tcl. If you don't want to display the plots, Agg is fine. Other alternatives include WX and QTAgg, but both require the installation of additional libraries.
Alternately, you can set this directive in your matplotlibrc file:
backend : Agg
For details, see the Matplotlib Usage FAQ on What is a backend?.
I am writing a basic Python module which contains a function that plots some data from a Pandas DataFrame. The problem is that I expect users to call this function from both the interactive shell and IPython notebooks. Unfortunately, our interactive shell environment does not have any valid DISPLAY options for matplotlib to use, so I want my function (or the full module) to behave slightly different depending on which environment it is loaded into. (E.g., the default is show the plot without saving in IPython, and save the plot without showing in the shell).
This question gives me a partial answer, in that I can explicitly tell users to write matplotlib.use('Agg') before they ever import my module, but surely there is a more automated backend way to handle this?
You could try checking if you are running into the situation (empty $DISPLAY) and default users to the Agg backend, rather than asking them to do so.
def configure_matplotlib():
import os
if 'DISPLAY' not in os.environ:
if matplotlib.get_backend() != 'Agg':
matplotlib.use('Agg')
import matplotlib
configure_matplotlib()
import matplotlib.pyplot as plt
...
Now matplotlib.use has no effect once pyplot has been imported, but if your users have already done so, they probably have a working configuration anyway.