.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tutorials/introductory/customizing.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here <sphx_glr_download_tutorials_introductory_customizing.py>`
to download the full example code
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_tutorials_introductory_customizing.py:
Customizing Matplotlib with style sheets and rcParams
=====================================================
Tips for customizing the properties and default styles of Matplotlib.
Using style sheets
------------------
The :mod:`.style` package adds support for easy-to-switch plotting
"styles" with the same parameters as a :ref:`matplotlib rc
<customizing-with-matplotlibrc-files>` file (which is read at startup to
configure Matplotlib).
There are a number of pre-defined styles `provided by Matplotlib`_. For
example, there's a pre-defined style called "ggplot", which emulates the
aesthetics of ggplot_ (a popular plotting package for R_). To use this style,
just add:
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.. code-block:: default
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from cycler import cycler
plt.style.use('ggplot')
data = np.random.randn(50)
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To list all available styles, use:
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.. code-block:: default
print(plt.style.available)
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
['Solarize_Light2', '_classic_test_patch', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']
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Defining your own style
-----------------------
You can create custom styles and use them by calling `.style.use` with
the path or URL to the style sheet.
For example, you might want to create
``./images/presentation.mplstyle`` with the following::
axes.titlesize : 24
axes.labelsize : 20
lines.linewidth : 3
lines.markersize : 10
xtick.labelsize : 16
ytick.labelsize : 16
Then, when you want to adapt a plot designed for a paper to one that looks
good in a presentation, you can just add::
>>> import matplotlib.pyplot as plt
>>> plt.style.use('./images/presentation.mplstyle')
Alternatively, you can make your style known to Matplotlib by placing
your ``<style-name>.mplstyle`` file into ``mpl_configdir/stylelib``. You
can then load your custom style sheet with a call to
``style.use(<style-name>)``. By default ``mpl_configdir`` should be
``~/.config/matplotlib``, but you can check where yours is with
`matplotlib.get_configdir()`; you may need to create this directory. You
also can change the directory where Matplotlib looks for the stylelib/
folder by setting the :envvar:`MPLCONFIGDIR` environment variable, see
:ref:`locating-matplotlib-config-dir`.
Note that a custom style sheet in ``mpl_configdir/stylelib`` will override a
style sheet defined by Matplotlib if the styles have the same name.
Once your ``<style-name>.mplstyle`` file is in the appropriate
``mpl_configdir`` you can specify your style with::
>>> import matplotlib.pyplot as plt
>>> plt.style.use(<style-name>)
Composing styles
----------------
Style sheets are designed to be composed together. So you can have a style
sheet that customizes colors and a separate style sheet that alters element
sizes for presentations. These styles can easily be combined by passing
a list of styles::
>>> import matplotlib.pyplot as plt
>>> plt.style.use(['dark_background', 'presentation'])
Note that styles further to the right will overwrite values that are already
defined by styles on the left.
Temporary styling
-----------------
If you only want to use a style for a specific block of code but don't want
to change the global styling, the style package provides a context manager
for limiting your changes to a specific scope. To isolate your styling
changes, you can write something like the following:
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.. code-block:: default
with plt.style.context('dark_background'):
plt.plot(np.sin(np.linspace(0, 2 * np.pi)), 'r-o')
plt.show()
.. image:: /tutorials/introductory/images/sphx_glr_customizing_001.png
:alt: customizing
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 104-118
.. _matplotlib-rcparams:
Matplotlib rcParams
===================
.. _customizing-with-dynamic-rc-settings:
Dynamic rc settings
-------------------
You can also dynamically change the default rc settings in a python script or
interactively from the python shell. All of the rc settings are stored in a
dictionary-like variable called :data:`matplotlib.rcParams`, which is global to
the matplotlib package. rcParams can be modified directly, for example:
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.. code-block:: default
mpl.rcParams['lines.linewidth'] = 2
mpl.rcParams['lines.linestyle'] = '--'
plt.plot(data)
.. image:: /tutorials/introductory/images/sphx_glr_customizing_002.png
:alt: customizing
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
[<matplotlib.lines.Line2D object at 0x7f73b55ea2b0>]
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Note, that in order to change the usual `~.Axes.plot` color you have to
change the *prop_cycle* property of *axes*:
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.. code-block:: default
mpl.rcParams['axes.prop_cycle'] = cycler(color=['r', 'g', 'b', 'y'])
plt.plot(data) # first color is red
.. image:: /tutorials/introductory/images/sphx_glr_customizing_003.png
:alt: customizing
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
[<matplotlib.lines.Line2D object at 0x7f73b5304250>]
.. GENERATED FROM PYTHON SOURCE LINES 131-134
Matplotlib also provides a couple of convenience functions for modifying rc
settings. `matplotlib.rc` can be used to modify multiple
settings in a single group at once, using keyword arguments:
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.. code-block:: default
mpl.rc('lines', linewidth=4, linestyle='-.')
plt.plot(data)
.. image:: /tutorials/introductory/images/sphx_glr_customizing_004.png
:alt: customizing
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
[<matplotlib.lines.Line2D object at 0x7f73c00c4040>]
.. GENERATED FROM PYTHON SOURCE LINES 139-206
`matplotlib.rcdefaults` will restore the standard Matplotlib
default settings.
There is some degree of validation when setting the values of rcParams, see
:mod:`matplotlib.rcsetup` for details.
.. _customizing-with-matplotlibrc-files:
The :file:`matplotlibrc` file
-----------------------------
Matplotlib uses :file:`matplotlibrc` configuration files to customize all
kinds of properties, which we call 'rc settings' or 'rc parameters'. You can
control the defaults of almost every property in Matplotlib: figure size and
DPI, line width, color and style, axes, axis and grid properties, text and
font properties and so on. When a URL or path is not specified with a call to
``style.use('<path>/<style-name>.mplstyle')``, Matplotlib looks for
:file:`matplotlibrc` in four locations, in the following order:
1. :file:`matplotlibrc` in the current working directory, usually used for
specific customizations that you do not want to apply elsewhere.
2. :file:`$MATPLOTLIBRC` if it is a file, else
:file:`$MATPLOTLIBRC/matplotlibrc`.
3. It next looks in a user-specific place, depending on your platform:
- On Linux and FreeBSD, it looks in
:file:`.config/matplotlib/matplotlibrc` (or
:file:`$XDG_CONFIG_HOME/matplotlib/matplotlibrc`) if you've customized
your environment.
- On other platforms, it looks in :file:`.matplotlib/matplotlibrc`.
See :ref:`locating-matplotlib-config-dir`.
4. :file:`{INSTALL}/matplotlib/mpl-data/matplotlibrc`, where
:file:`{INSTALL}` is something like
:file:`/usr/lib/python3.7/site-packages` on Linux, and maybe
:file:`C:\\Python37\\Lib\\site-packages` on Windows. Every time you
install matplotlib, this file will be overwritten, so if you want
your customizations to be saved, please move this file to your
user-specific matplotlib directory.
Once a :file:`matplotlibrc` file has been found, it will *not* search any of
the other paths.
To display where the currently active :file:`matplotlibrc` file was
loaded from, one can do the following::
>>> import matplotlib
>>> matplotlib.matplotlib_fname()
'/home/foo/.config/matplotlib/matplotlibrc'
See below for a sample :ref:`matplotlibrc file<matplotlibrc-sample>`.
.. _matplotlibrc-sample:
A sample matplotlibrc file
~~~~~~~~~~~~~~~~~~~~~~~~~~
.. literalinclude:: ../../../matplotlibrc.template
.. _ggplot: https://ggplot2.tidyverse.org/
.. _R: https://www.r-project.org/
.. _provided by Matplotlib: https://github.com/matplotlib/matplotlib/tree/master/lib/matplotlib/mpl-data/stylelib
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 3.146 seconds)
.. _sphx_glr_download_tutorials_introductory_customizing.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: customizing.py <customizing.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: customizing.ipynb <customizing.ipynb>`
.. only:: html
.. rst-class:: sphx-glr-signature
Keywords: matplotlib code example, codex, python plot, pyplot
`Gallery generated by Sphinx-Gallery
<https://sphinx-gallery.readthedocs.io>`_