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doc/users/prev_whats_new/whats_new_3.7.0.rst

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New & Improved Narrative Documentation
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======================================
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* Brand new :doc:`Animations </tutorials/introductory/animation_tutorial>` tutorial.
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* Brand new :ref:`Animations <animations>` tutorial.
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* New grouped and stacked `bar chart <../../gallery/index.html#lines_bars_and_markers>`_ examples.
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* New section for new contributors and reorganized git instructions in the :ref:`contributing guide<contributing>`.
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* Restructured :ref:`annotations` tutorial.

galleries/examples/event_handling/README.txt

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Event handling
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==============
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Matplotlib supports :doc:`event handling</users/explain/event_handling>` with
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Matplotlib supports :ref:`event handling <event_handling>` with
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a GUI neutral event model, so you can connect to Matplotlib events without
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knowledge of what user interface Matplotlib will ultimately be plugged in to.
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This has two advantages: the code you write will be more portable, and

galleries/examples/subplots_axes_and_figures/auto_subplots_adjust.py

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set the subplot parameters accordingly. Its main purpose is to illustrate
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some advanced concepts like reading out text positions, working with
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bounding boxes and transforms and using
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:ref:`events <event-handling-tutorial>`. But it can also serve as a starting
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:ref:`events <event-handling>`. But it can also serve as a starting
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point if you want to automate the layouting and need more flexibility than
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tight layout and constrained layout.
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galleries/users_explain/animations/blitting.py

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"""
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.. redirected-from: /tutorials/advanced/blitting
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.. redirect-from:: /tutorials/advanced/blitting
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.. _blitting:
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galleries/users_explain/artists/index.rst

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In addition to styling properties like *color* and *linewidth*, the Line2D
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object has a *data* property. You can set the data after the line has been
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created using `.Line2D.set_data`. This is often used for Animations, where the
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same line is shown evolving over time (see :doc:`../animations`)
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same line is shown evolving over time (see :doc:`../animations/index`)
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.. plot::
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:include-source:

galleries/users_explain/artists/transforms_tutorial.py

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# This is one good reason why you rarely want to work in *display*
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# space, but you can connect to the ``'on_draw'``
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# :class:`~matplotlib.backend_bases.Event` to update *figure*
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# coordinates on figure draws; see :ref:`event-handling-tutorial`.
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# coordinates on figure draws; see :ref:`event-handling`.
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#
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# When you change the x or y limits of your axes, the data limits are
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# updated so the transformation yields a new display point. Note that

galleries/users_explain/axes/index.rst

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Matplotlib `~.axes.Axes` are the gateway to creating your data visualizations.
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Once an Axes is placed on a figure there are many methods that can be used to
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add data to the Axes. An Axes typically has a pair of :ref:`Axis <../axis/>`
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add data to the Axes. An Axes typically has a pair of :doc:`Axis <../axis/index>`
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Artists that define the data co-ordinate system, and include methods to add
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annotations like x- and y-labels, titles, and legends.
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Axes limits, scales, and ticking
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--------------------------------
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Each Axes has two (or more) `~.axis.Axis` objects, that can be accessed via :attr:`~matplotlib.axes.Axes.xaxis` and :attr:`~matplotlib.axes.Axes.yaxis` properties. These have substantial number of methods on them, and for highly customizable Axis-es it is useful to read more about that API (:doc:`axis/index`). However, the Axes class offers a number of helpers for the most common of these methods. Indeed, the `~.axes.Axes.set_xlabel`, discussed above, is a helper for the `~.Axis.set_label_text`.
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Each Axes has two (or more) `~.axis.Axis` objects, that can be accessed via :attr:`~matplotlib.axes.Axes.xaxis` and :attr:`~matplotlib.axes.Axes.yaxis` properties. These have substantial number of methods on them, and for highly customizable Axis-es it is useful to read more about that API (:doc:`../axis/index`). However, the Axes class offers a number of helpers for the most common of these methods. Indeed, the `~.axes.Axes.set_xlabel`, discussed above, is a helper for the `~.Axis.set_label_text`.
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Other important methods set the extent on the axes (`~.axes.Axes.set_xlim`, `~.axes.Axes.set_ylim`), or more fundamentally the scale of the axes. So for instance, we can make an Axis have a logarithmic scale, and zoom in on a sub-portion of the data:
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galleries/users_explain/axes/legend_guide.py

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"""
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.. redirect-from: /tutorials/intermediate/legend_guide
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.. redirect-from:: /tutorials/intermediate/legend_guide
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.. _legend_guide:
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galleries/users_explain/figure/event_handling.rst

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.. redirect-from:: /users/event_handling
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.. _event-handling-tutorial:
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.. _event-handling:
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.. _event_handling:
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**************************
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Event handling and picking

galleries/users_explain/figure/interactive.rst

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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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- :ref:`event-handling`
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.. _ipython-pylab:

galleries/users_explain/figure/interactive_guide.rst

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This guide is meant to be an introduction to the low-level details of
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how Matplotlib integration with a GUI event loop works. For a more
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practical introduction to the Matplotlib event API see :ref:`event
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handling system <event-handling-tutorial>`, `Interactive Tutorial
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handling system <event-handling>`, `Interactive Tutorial
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<https://github.com/matplotlib/interactive_tutorial>`__, and
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`Interactive Applications using Matplotlib
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<http://www.amazon.com/Interactive-Applications-using-Matplotlib-Benjamin/dp/1783988843>`__.
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details depend on the toolkit. Matplotlib has a :ref:`backend
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<what-is-a-backend>` for each GUI toolkit we support which uses the
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toolkit API to bridge the toolkit UI events into Matplotlib's :ref:`event
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handling system <event-handling-tutorial>`. You can then use
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handling system <event-handling>`. You can then use
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`.FigureCanvasBase.mpl_connect` to connect your function to
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Matplotlib's event handling system. This allows you to directly
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interact with your data and write GUI toolkit agnostic user

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