testbed/matplotlib__matplotlib/galleries/users_explain/figure/figure_intro.rst

.. redirect-from:: /users/explain/figure

.. _figure_explanation:

+++++++++++++++++++++++
Introduction to Figures
+++++++++++++++++++++++

.. plot::
    :include-source:

    fig = plt.figure(figsize=(2, 2), facecolor='lightskyblue',
                     layout='constrained')
    fig.suptitle('Figure')
    ax = fig.add_subplot()
    ax.set_title('Axes', loc='left', fontstyle='oblique', fontsize='medium')

When looking at Matplotlib visualization, you are almost always looking at
Artists placed on a `~.Figure`.  In the example above, the figure is the
blue region and `~.Figure.add_subplot` has added an `~.axes.Axes` artist to the
`~.Figure` (see :ref:`figure_parts`).  A more complicated visualization can add
multiple Axes to the Figure, colorbars, legends, annotations, and the Axes
themselves can have multiple Artists added to them
(e.g. ``ax.plot`` or ``ax.imshow``).

.. contents:: :local:


.. _viewing_figures:

Viewing Figures
================

We will discuss how to create Figures in more detail below, but first it is
helpful to understand how to view a Figure.  This varies based on how you are
using Matplotlib, and what :ref:`Backend <what-is-a-backend>` you are using.

Notebooks and IDEs
------------------

.. figure:: /_static/FigureInline.png
    :alt: Image of figure generated in Jupyter Notebook with inline backend.
    :width: 400

    Screenshot of a `Jupyter Notebook <https://jupyter.org>`_, with a figure
    generated via the default `inline
    <https://github.com/ipython/matplotlib-inline>`_ backend.


If you are using a Notebook (e.g. `Jupyter <https://jupyter.org>`_) or an IDE
that renders Notebooks (PyCharm, VSCode, etc), then they have a backend that
will render the Matplotlib Figure when a code cell is executed.  One thing to
be aware of is that the default Jupyter backend (``%matplotlib inline``) will
by default trim or expand the figure size to have a tight box around Artists
added to the Figure (see :ref:`saving_figures`, below).  If you use a backend
other than the default "inline" backend, you will likely need to use an ipython
"magic" like ``%matplotlib notebook`` for the Matplotlib :ref:`notebook
<jupyter_notebooks_jupyterlab>` or ``%matplotlib widget`` for the  `ipympl
<https://matplotlib.org/ipympl/>`_ backend.

.. figure:: /_static/FigureNotebook.png
    :alt: Image of figure generated in Jupyter Notebook with notebook
          backend, including a toolbar.
    :width: 400

    Screenshot of a  Jupyter Notebook with an interactive figure generated via
    the ``%matplotlib notebook`` magic.  Users should also try the similar
    `widget <https://matplotlib.org/ipympl/>`_ backend if using `JupyterLab
    <https://jupyterlab.readthedocs.io/en/stable/>`_.


.. seealso::
    :ref:`interactive_figures`.

Standalone scripts and interactive use
--------------------------------------

If the user is on a client with a windowing system, there are a number of
:ref:`Backends <what-is-a-backend>` that can be used to render the Figure to
the screen, usually using a Python Qt, Tk, or Wx toolkit, or the native MacOS
backend.  These are typically chosen either in the user's :ref:`matplotlibrc
<customizing-with-matplotlibrc-files>`, or by calling, for example,
``matplotlib.use('QtAgg')`` at the beginning of a session or script.

.. figure:: /_static/FigureQtAgg.png
    :alt: Image of figure generated from a script via the QtAgg backend.
    :width: 370

    Screenshot of a Figure generated via a python script and shown using the
    QtAgg backend.

When run from a script, or interactively (e.g. from an
`iPython shell <https://https://ipython.readthedocs.io/en/stable/>`_) the Figure
will not be shown until we call ``plt.show()``. The Figure will appear in
a new GUI window, and usually will have a toolbar with Zoom, Pan, and other tools
for interacting with the Figure.  By default, ``plt.show()`` blocks
further interaction from the script or shell until the Figure window is closed,
though that can be toggled off for some purposes.  For more details, please see
:ref:`controlling-interactive`.

Note that if you are on a client that does not have access to a windowing
system, the Figure will fallback to being drawn using the "Agg" backend, and
cannot be viewed, though it can be :ref:`saved <saving_figures>`.

.. seealso::
    :ref:`interactive_figures`.

.. _creating_figures:

Creating Figures
================

By far the most common way to create a figure is using the
:ref:`pyplot <pyplot_tutorial>` interface. As noted in
:ref:`api_interfaces`, the pyplot interface serves two purposes.  One is to spin
up the Backend and keep track of GUI windows. The other is a global state for
Axes and Artists that allow a short-form API to plotting methods. In the
example above, we use pyplot for the first purpose, and create the Figure object,
``fig``. As a side effect ``fig`` is also added to pyplot's global state, and
can be accessed via `~.pyplot.gcf`.

Users typically want an Axes or a grid of Axes when they create a Figure, so in
addition to `~.pyplot.figure`, there are convenience methods that return both
a Figure and some Axes.  A simple grid of Axes can be achieved with
`.pyplot.subplots` (which
simply wraps `.Figure.subplots`):

.. plot::
    :include-source:

    fig, axs = plt.subplots(2, 2, figsize=(4, 3), layout='constrained')

More complex grids can be achieved with `.pyplot.subplot_mosaic` (which wraps
`.Figure.subplot_mosaic`):

.. plot::
    :include-source:

    fig, axs = plt.subplot_mosaic([['A', 'right'], ['B', 'right']],
                                  figsize=(4, 3), layout='constrained')
    for ax_name in axs:
        axs[ax_name].text(0.5, 0.5, ax_name, ha='center', va='center')

Sometimes we want to have a nested layout in a Figure, with two or more sets of
Axes that do not share the same subplot grid.
We can use `~.Figure.add_subfigure` or `~.Figure.subfigures` to create virtual
figures inside a parent Figure; see
:doc:`/gallery/subplots_axes_and_figures/subfigures` for more details.

.. plot::
    :include-source:

    fig = plt.figure(layout='constrained', facecolor='lightskyblue')
    fig.suptitle('Figure')
    figL, figR = fig.subfigures(1, 2)
    figL.set_facecolor('thistle')
    axL = figL.subplots(2, 1, sharex=True)
    axL[1].set_xlabel('x [m]')
    figL.suptitle('Left subfigure')
    figR.set_facecolor('paleturquoise')
    axR = figR.subplots(1, 2, sharey=True)
    axR[0].set_title('Axes 1')
    figR.suptitle('Right subfigure')

It is possible to directly instantiate a `.Figure` instance without using the
pyplot interface.  This is usually only necessary if you want to create your
own GUI application or service that you do not want carrying the pyplot global
state.  See the embedding examples in :ref:`user_interfaces` for examples of
how to do this.

Figure options
--------------

There are a few options available when creating figures.  The Figure size on
the screen is set by *figsize* and *dpi*.  *figsize* is the ``(width, height)``
of the Figure in inches (or, if preferred, units of 72 typographic points).  *dpi*
are how many pixels per inch the figure will be rendered at.  To make your Figures
appear on the screen at the physical size you requested, you should set *dpi*
to the same *dpi* as your graphics system.  Note that many graphics systems now use
a "dpi ratio" to specify how many screen pixels are used to represent a graphics
pixel.  Matplotlib applies the dpi ratio to the *dpi* passed to the figure to make
it have higher resolution, so you should pass the lower number to the figure.

The *facecolor*, *edgecolor*, *linewidth*, and *frameon* options all change the appearance of the
figure in expected ways, with *frameon* making the figure transparent if set to *False*.

Finally, the user can specify a layout engine for the figure with the *layout*
parameter.  Currently Matplotlib supplies
:ref:`"constrained" <constrainedlayout_guide>`,
:ref:`"compressed" <compressed_layout>` and
:ref:`"tight" <tight_layout_guide>` layout engines.  These
rescale axes inside the Figure to prevent overlap of ticklabels, and try and align
axes, and can save significant manual adjustment of artists on a Figure for many
common cases.

Adding Artists
--------------

The `~.FigureBase` class has a number of methods to add artists to a `~.Figure` or
a `~.SubFigure`.  By far the most common are to add Axes of various configurations
(`~.FigureBase.add_axes`, `~.FigureBase.add_subplot`, `~.FigureBase.subplots`,
`~.FigureBase.subplot_mosaic`) and subfigures (`~.FigureBase.subfigures`).  Colorbars
are added to Axes or group of Axes at the Figure level (`~.FigureBase.colorbar`).
It is also possible to have a Figure-level legend (`~.FigureBase.legend`).
Other Artists include figure-wide labels (`~.FigureBase.suptitle`,
`~.FigureBase.supxlabel`, `~.FigureBase.supylabel`) and text (`~.FigureBase.text`).
Finally, low-level Artists can be added directly using `~.FigureBase.add_artist`
usually with care being taken to use the appropriate transform.  Usually these
include ``Figure.transFigure`` which ranges from 0 to 1 in each direction, and
represents the fraction of the current Figure size, or ``Figure.dpi_scale_trans``
which will be in physical units of inches from the bottom left corner of the Figure
(see :ref:`transforms_tutorial` for more details).


.. _saving_figures:

Saving Figures
==============

Finally, Figures can be saved to disk using the `~.Figure.savefig` method.
``fig.savefig('MyFigure.png', dpi=200)`` will save a PNG formatted figure to
the file ``MyFigure.png`` in the current directory on disk with 200 dots-per-inch
resolution.  Note that the filename can include a relative or absolute path to
any place on the file system.

Many types of output are supported, including raster formats like PNG, GIF, JPEG,
TIFF and vector formats like PDF, EPS, and SVG.

By default, the size of the saved Figure is set by the Figure size (in inches) and, for the raster
formats, the *dpi*.  If *dpi* is not set, then the *dpi* of the Figure is used.
Note that *dpi* still has meaning for vector formats like PDF if the Figure includes
Artists that have been :doc:`rasterized </gallery/misc/rasterization_demo>`; the
*dpi* specified will be the resolution of the rasterized objects.

It is possible to change the size of the Figure using the *bbox_inches* argument
to savefig.  This can be specified manually, again in inches.  However, by far
the most common use is ``bbox_inches='tight'``.  This option "shrink-wraps", trimming
or expanding as needed, the size of the figure so that it is tight around all the artists
in a figure, with a small pad that can be specified by *pad_inches*, which defaults to
0.1 inches.  The dashed box in the plot below shows the portion of the figure that
would be saved if ``bbox_inches='tight'`` were used in savefig.

.. plot::

    import matplotlib.pyplot as plt
    from matplotlib.patches import FancyBboxPatch

    fig, ax = plt.subplots(figsize=(4, 2), facecolor='lightskyblue')
    ax.set_position([0.1, 0.2, 0.8, 0.7])
    ax.set_aspect(1)
    bb = ax.get_tightbbox()
    bb = bb.padded(10)
    bb = bb.transformed(fig.dpi_scale_trans.inverted())
    fancy = FancyBboxPatch(bb.p0, bb.width, bb.height, fc='none',
                           ec=(0, 0.0, 0, 0.5), lw=2, linestyle='--',
                           transform=fig.dpi_scale_trans,
                           clip_on=False, boxstyle='Square, pad=0')
    ax.add_patch(fancy)