galleries/users_explain/figure/backends.rst
.. redirect-from:: /users/explain/backends
.. _backends:
.. _what-is-a-backend:
Backends are used for displaying Matplotlib figures (see :ref:figure-intro),
on the screen, or for writing to files. A lot of documentation on the website
and in the mailing lists refers to the "backend" and many new users are
confused by this term. Matplotlib targets many different use cases and output
formats. Some people use Matplotlib interactively from the Python shell and
have plotting windows pop up when they type commands. Some people run Jupyter <https://jupyter.org>_ notebooks and draw inline plots for quick data
analysis. Others embed Matplotlib into graphical user interfaces like PyQt or
PyGObject to build rich applications. Some people use Matplotlib in batch
scripts to generate postscript images from numerical simulations, and still
others run web application servers to dynamically serve up graphs.
To support all of these use cases, Matplotlib can target different outputs, and each of these capabilities is called a backend; the "frontend" is the user facing code, i.e., the plotting code, whereas the "backend" does all the hard work behind-the-scenes to make the figure. There are two types of backends: user interface backends (for use in PyQt/PySide, PyGObject, Tkinter, wxPython, or macOS/Cocoa); also referred to as "interactive backends") and hardcopy backends to make image files (PNG, SVG, PDF, PS; also referred to as "non-interactive backends").
There are three ways to configure your backend:
backend parameter in your :file:matplotlibrc fileMPLBACKEND environment variablematplotlib.useBelow is a more detailed description.
If there is more than one configuration present, the last one from the
list takes precedence; e.g. calling :func:matplotlib.use() will override
the setting in your :file:matplotlibrc.
Without a backend explicitly set, Matplotlib automatically detects a usable backend based on what is available on your system and on whether a GUI event loop is already running. The first usable backend in the following list is selected: MacOSX, QtAgg, GTK4Agg, Gtk3Agg, TkAgg, WxAgg, Agg. The last, Agg, is a non-interactive backend that can only write to files. It is used on Linux, if Matplotlib cannot connect to either an X display or a Wayland display.
Here is a detailed description of the configuration methods:
#. Setting :rc:backend in your :file:matplotlibrc file::
backend : qtagg # use pyqt with antigrain (agg) rendering
See also :ref:customizing.
#. Setting the :envvar:MPLBACKEND environment variable:
You can set the environment variable either for your current shell or for a single script.
On Unix::
> export MPLBACKEND=qtagg
> python simple_plot.py
> MPLBACKEND=qtagg python simple_plot.py
On Windows, only the former is possible::
> set MPLBACKEND=qtagg
> python simple_plot.py
Setting this environment variable will override the backend parameter
in any :file:matplotlibrc, even if there is a :file:matplotlibrc in
your current working directory. Therefore, setting :envvar:MPLBACKEND
globally, e.g. in your :file:.bashrc or :file:.profile, is discouraged
as it might lead to counter-intuitive behavior.
#. If your script depends on a specific backend you can use the function
:func:matplotlib.use::
import matplotlib
matplotlib.use('qtagg')
This should be done before any figure is created, otherwise Matplotlib may fail to switch the backend and raise an ImportError.
Using ~matplotlib.use will require changes in your code if users want to
use a different backend. Therefore, you should avoid explicitly calling
~matplotlib.use unless absolutely necessary.
.. _the-builtin-backends:
By default, Matplotlib should automatically select a default backend which
allows both interactive work and plotting from scripts, with output to the
screen and/or to a file, so at least initially, you will not need to worry
about the backend. The most common exception is if your Python distribution
comes without :mod:tkinter and you have no other GUI toolkit installed.
This happens with certain Linux distributions, where you need to install a
Linux package named python-tk (or similar).
If, however, you want to write graphical user interfaces, or a web
application server
(:doc:/gallery/user_interfaces/web_application_server_sgskip), or need a
better understanding of what is going on, read on. To make things easily
more customizable for graphical user interfaces, Matplotlib separates
the concept of the renderer (the thing that actually does the drawing)
from the canvas (the place where the drawing goes). The canonical
renderer for user interfaces is Agg which uses the Anti-Grain Geometry_ C++ library to make a raster (pixel) image of the figure; it
is used by the QtAgg, GTK4Agg, GTK3Agg, wxAgg, TkAgg, and
macosx backends. An alternative renderer is based on the Cairo library,
used by QtCairo, etc.
For the rendering engines, users can also distinguish between vector <https://en.wikipedia.org/wiki/Vector_graphics>_ or raster <https://en.wikipedia.org/wiki/Raster_graphics>_ renderers. Vector
graphics languages issue drawing commands like "draw a line from this
point to this point" and hence are scale free. Raster backends
generate a pixel representation of the line whose accuracy depends on a
DPI setting.
Static backends ^^^^^^^^^^^^^^^
Here is a summary of the Matplotlib renderers (there is an eponymous backend for each; these are non-interactive backends, capable of writing to a file):
======== ========= =======================================================
Renderer Filetypes Description
======== ========= =======================================================
AGG png raster_ graphics -- high quality images using the
Anti-Grain Geometry_ engine.
PDF pdf vector_ graphics -- Portable Document Format_ output.
PS ps, eps vector_ graphics -- PostScript_ output.
SVG svg vector_ graphics -- Scalable Vector Graphics_ output.
PGF pgf, pdf vector_ graphics -- using the pgf_ package.
Cairo png, ps, raster_ or vector_ graphics -- using the Cairo_ library
pdf, svg (requires pycairo_ or cairocffi_).
======== ========= =======================================================
To save plots using the non-interactive backends, use the
matplotlib.pyplot.savefig('filename') method.
Interactive backends ^^^^^^^^^^^^^^^^^^^^
These are the user interfaces and renderer combinations supported; these are interactive backends, capable of displaying to the screen and using appropriate renderers from the table above to write to a file:
========= ================================================================
Backend Description
========= ================================================================
QtAgg Agg rendering in a Qt_ canvas (requires PyQt_ or Qt for Python,
a.k.a. PySide). This backend can be activated in IPython with
%matplotlib qt. The Qt binding can be selected via the
:envvar:QT_API environment variable; see :ref:QT_bindings for
more details.
ipympl Agg rendering embedded in a Jupyter widget (requires ipympl).
This backend can be enabled in a Jupyter notebook with
%matplotlib ipympl or %matplotlib widget. Works with
Jupyter lab and notebook>=7.
GTK3Agg Agg rendering to a GTK_ 3.x canvas (requires PyGObject_ and
pycairo_). This backend can be activated in IPython with
%matplotlib gtk3.
GTK4Agg Agg rendering to a GTK_ 4.x canvas (requires PyGObject_ and
pycairo_). This backend can be activated in IPython with
%matplotlib gtk4.
macosx Agg rendering into a Cocoa canvas in macOS. This backend can be
activated in IPython with %matplotlib osx.
TkAgg Agg rendering to a Tk_ canvas (requires TkInter_). This
backend can be activated in IPython with %matplotlib tk.
nbAgg Embed an interactive figure in a Jupyter classic notebook. This
backend can be enabled in Jupyter notebooks via
%matplotlib notebook or %matplotlib nbagg. Works with
Jupyter notebook<7 and nbclassic.
WebAgg On show() will start a tornado server with an interactive
figure.
GTK3Cairo Cairo rendering to a GTK_ 3.x canvas (requires PyGObject_ and
pycairo_).
GTK4Cairo Cairo rendering to a GTK_ 4.x canvas (requires PyGObject_ and
pycairo_).
wxAgg Agg rendering to a wxWidgets_ canvas (requires wxPython_ 4).
This backend can be activated in IPython with %matplotlib wx.
========= ================================================================
.. note:: The names of builtin backends are case-insensitive; e.g., 'QtAgg' and 'qtagg' are equivalent.
.. _Anti-Grain Geometry: http://agg.sourceforge.net/antigrain.com/
.. _Portable Document Format: https://en.wikipedia.org/wiki/Portable_Document_Format
.. _Postscript: https://en.wikipedia.org/wiki/PostScript
.. _Scalable Vector Graphics: https://en.wikipedia.org/wiki/Scalable_Vector_Graphics
.. _pgf: https://ctan.org/pkg/pgf
.. _Cairo: https://www.cairographics.org
.. _PyGObject: https://pygobject.gnome.org/
.. _pycairo: https://www.cairographics.org/pycairo/
.. _cairocffi: https://doc.courtbouillon.org/cairocffi/stable/
.. _wxPython: https://www.wxpython.org/
.. _TkInter: https://docs.python.org/3/library/tk.html
.. _PyQt: https://riverbankcomputing.com/software/pyqt/intro
.. _Qt for Python: https://doc.qt.io/qtforpython/
.. _Qt: https://qt.io/
.. _GTK: https://www.gtk.org/
.. _Tk: https://www.tcl.tk/
.. _wxWidgets: https://www.wxwidgets.org/
.. _ipympl: https://www.matplotlib.org/ipympl
.. _ipympl_install:
ipympl ^^^^^^
The ipympl backend is in a separate package that must be explicitly installed if you wish to use it, for example:
.. code-block:: bash
pip install ipympl
or
.. code-block:: bash
conda install ipympl -c conda-forge
See installing ipympl <https://matplotlib.org/ipympl/installing.html>__ for more details.
More generally, any importable backend can be selected by using any of the
methods above. If name.of.the.backend is the module containing the
backend, use module://name.of.the.backend as the backend name, e.g.
matplotlib.use('module://name.of.the.backend').
Information for backend implementers is available at :ref:writing_backend_interface.
.. _figures-not-showing:
Sometimes things do not work as expected, usually during an install.
If you are using a Notebook or integrated development environment (see :ref:notebooks-and-ides),
please consult their documentation for debugging figures not working in their
environments.
If you are using one of Matplotlib's graphics backends (see :ref:standalone-scripts-and-interactive-use), make sure you know which
one is being used:
.. code-block:: python3
import matplotlib
print(matplotlib.get_backend())
Try a simple plot to see if the GUI opens:
.. code-block:: python3
import matplotlib import matplotlib.pyplot as plt
print(matplotlib.get_backend()) plt.plot((1, 4, 6)) plt.show()
If it does not, you perhaps have an installation problem. A good step at this point is to ensure that your GUI toolkit is installed properly, taking Matplotlib out of the testing. Almost all GUI toolkits have a small test program that can be run to test basic functionality. If this test fails, try re-installing.
QtAgg, QtCairo, Qt5Agg, and Qt5Cairo ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Test PyQt5.
If you have PySide or PyQt6 installed rather than PyQt5, just change the import
accordingly:
.. code-block:: bash
python -c "from PyQt5.QtWidgets import *; app = QApplication([]); win = QMainWindow(); win.show(); app.exec()"
TkAgg and TkCairo ^^^^^^^^^^^^^^^^^
Test tkinter:
.. code-block:: bash
python3 -c "from tkinter import Tk; Tk().mainloop()"
GTK3Agg, GTK4Agg, GTK3Cairo, GTK4Cairo ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Test Gtk:
.. code-block:: bash
python3 -c "from gi.repository import Gtk; win = Gtk.Window(); win.connect('destroy', Gtk.main_quit); win.show(); Gtk.main()"
wxAgg and wxCairo ^^^^^^^^^^^^^^^^^
Test wx:
.. code-block:: python3
import wx
app = wx.App(False) # Create a new app, don't redirect stdout/stderr to a window. frame = wx.Frame(None, wx.ID_ANY, "Hello World") # A Frame is a top-level window. frame.Show(True) # Show the frame. app.MainLoop()
If the test works for your desired backend but you still cannot get Matplotlib to display a figure, then contact us (see
:ref:get-help).