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Merge branch 'v3.7.0-doc' into v3.7.x
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‎doc/_static/FigureInline.png

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‎doc/_static/FigureNotebook.png

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‎doc/_static/FigureQtAgg.png

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‎doc/_static/zenodo_cache/7637593.svg

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‎doc/users/explain/figures.rst

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@@ -34,23 +34,57 @@ using Matplotlib, and what :ref:`Backend <what-is-a-backend>` you are using.
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Notebooks and IDEs
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------------------
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.. figure:: /_static/FigureInline.png
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:alt: Image of figure generated in Jupyter Notebook with inline backend.
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:width: 400
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Screenshot of a `Jupyter Notebook <https://jupyter.org>`_, with a figure
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generated via the default `inline
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<https://github.com/ipython/matplotlib-inline>`_ backend.
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If you are using a Notebook (e.g. `Jupyter <https://jupyter.org>`_) or an IDE
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that renders Notebooks (PyCharm, VSCode, etc), then they have a backend that
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will render the Matplotlib Figure when a code cell is executed. One thing to
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be aware of is that the default Jupyter backend (``%matplotlib inline``) will
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by default trim or expand the figure size to have a tight box around Artists
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added to the Figure (see :ref:`saving_figures`, below).
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added to the Figure (see :ref:`saving_figures`, below). If you use a backend
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other than the default "inline" backend, you will likely need to use an ipython
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"magic" like ``%matplotlib notebook`` for the Matplotlib :ref:`notebook
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<jupyter_notebooks_jupyterlab>` or ``%matplotlib widget`` for the `ipympl
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<https://matplotlib.org/ipympl/>`_ backend.
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.. figure:: /_static/FigureNotebook.png
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:alt: Image of figure generated in Jupyter Notebook with notebook
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backend, including a toolbar.
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:width: 400
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Screenshot of a Jupyter Notebook with an interactive figure generated via
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the ``%matplotlib notebook`` magic. Users should also try the similar
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`widget <https://matplotlib.org/ipympl/>`_ backend if using `JupyterLab
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<https://jupyterlab.readthedocs.io/en/stable/>`_.
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.. seealso::
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:ref:`interactive_figures`.
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Standalone scripts and interactive use
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--------------------------------------
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If the user is on a client with a windowing system, there are a number of
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:ref:`Backends <what-is-a-backend>` that can be used to render the Figure to
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the screen, usually using a Python Qt, Tk, or Wx toolkit, though there is a native
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MacOS backend as well. These are typically chosen either in the user's
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:ref:`matplotlibrc <customizing-with-matplotlibrc-files>`, or by calling
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the screen, usually using a Python Qt, Tk, or Wx toolkit, or the native MacOS
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backend. These are typically chosen either in the user's :ref:`matplotlibrc
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<customizing-with-matplotlibrc-files>`, or by calling, for example,
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``matplotlib.use('QtAgg')`` at the beginning of a session or script.
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.. figure:: /_static/FigureQtAgg.png
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:alt: Image of figure generated from a script via the QtAgg backend.
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:width: 370
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Screenshot of a Figure generated via a python script and shown using the
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QtAgg backend.
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When run from a script, or interactively (e.g. from an
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`iPython shell <https://ipython.readthedocs.io/en/stable/>`_) the Figure
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will not be shown until we call ``plt.show()``. The Figure will appear in
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system, the Figure will fallback to being drawn using the "Agg" backend, and
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cannot be viewed, though it can be :ref:`saved <saving_figures>`.
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.. seealso::
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:ref:`interactive_figures`.
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.. _creating_figures:
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Creating Figures

‎doc/users/explain/interactive.rst

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.. currentmodule:: matplotlib
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.. _mpl-shell:
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.. _interactive_figures:
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===================
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Interactive figures
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mouse-location tools built into the Matplotlib GUI windows are often sufficient, but
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you can also use the event system to build customized data exploration tools.
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.. seealso::
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:ref:`figure_explanation`.
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Matplotlib ships with :ref:`backends <what-is-a-backend>` binding to
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several GUI toolkits (Qt, Tk, Wx, GTK, macOS, JavaScript) and third party
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packages provide bindings to `kivy
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`.pyplot.close`; all open `.Figure`\s can be closed via ``plt.close('all')``.
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For more discussion of Matplotlib's event system and integrated event loops, please read:
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.. seealso::
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For more discussion of Matplotlib's event system and integrated event loops:
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- :ref:`interactive_figures_and_eventloops`
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- :ref:`event-handling-tutorial`
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documentation of your GUI toolkit for details.
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.. _jupyter_notebooks_jupyterlab:
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Jupyter Notebooks / JupyterLab
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------------------------------

‎doc/users/project/citing.rst

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.. START OF AUTOGENERATED
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v3.7.0
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.. image:: ../../_static/zenodo_cache/7637593.svg
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:target: https://doi.org/10.5281/zenodo.7637593
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v3.6.3
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.. image:: ../../_static/zenodo_cache/7527665.svg
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:target: https://doi.org/10.5281/zenodo.7527665

‎doc/users/release_notes.rst

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=============
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.. include from another document so that it's easy to exclude this for releases
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.. include:: release_notes_next.rst
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.. .. include:: release_notes_next.rst
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Version 3.7

‎lib/matplotlib/figure.py

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`SubplotParams`
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Control the default spacing between subplots.
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See :ref:`figure_explanation` for narrative on how figures are used in
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Matplotlib.
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Figures are typically created using pyplot methods `~.pyplot.figure`,
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`~.pyplot.subplots`, and `~.pyplot.subplot_mosaic`.
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.. plot::
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:include-source:
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fig, ax = plt.subplots(figsize=(2, 2), facecolor='lightskyblue',
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layout='constrained')
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fig.suptitle('Figure')
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ax.set_title('Axes', loc='left', fontstyle='oblique', fontsize='medium')
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Some situations call for directly instantiating a `~.figure.Figure` class,
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usually inside an application of some sort (see :ref:`user_interfaces` for a
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list of examples) . More information about Figures can be found at
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:ref:`figure_explanation`.
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"""
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from contextlib import ExitStack

‎tools/cache_zenodo_svg.py

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if __name__ == "__main__":
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data = {
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"v3.7.0": "7637593",
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"v3.6.3": "7527665",
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"v3.6.2": "7275322",
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"v3.6.1": "7162185",

‎tutorials/introductory/quick_start.py

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fig, ax = plt.subplots() # Create a figure containing a single axes.
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ax.plot([1, 2, 3, 4], [1, 4, 2, 3]) # Plot some data on the axes.
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###############################################################################
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# %%
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#
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# Note that to get this Figure to display, you may have to call ``plt.show()``,
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# depending on your backend. For more details of Figures and backends, see
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# :ref:`figure_explanation`.
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#
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# .. _figure_parts:
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#
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# Parts of a Figure
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# fig = plt.figure() # an empty figure with no Axes
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# fig, ax = plt.subplots() # a figure with a single Axes
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# fig, axs = plt.subplots(2, 2) # a figure with a 2x2 grid of Axes
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# # a figure with one axes on the left, and two on the right:
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# fig, axs = plt.subplot_mosaic([['left', 'right-top'],
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# ['left', 'right_bottom]])
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#
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# It is often convenient to create the Axes together with the Figure, but you
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# can also manually add Axes later on. Note that many
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# :doc:`Matplotlib backends </users/explain/backends>` support zooming and
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# panning on figure windows.
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#
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# For more on Figures, see :ref:`figure_explanation`.
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#
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# :class:`~matplotlib.axes.Axes`
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# ------------------------------
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#

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