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Fix links
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‎galleries/users_explain/artists/artist_intro.rst

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@@ -5,9 +5,8 @@ Introduction to Artists
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Almost all objects you interact with on a Matplotlib plot are called "Artist"
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(and are subclasses of the `.Artist` class). :doc:`Figure <../figure/index>`
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and :doc:`Axes <../axes/index>` are Artists, and generally contain :doc:`Axis
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<../axis/index>` Artists and Artists that contain data or annotation
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information.
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and :doc:`Axes <../axes/index>` are Artists, and generally contain
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`~.axis.Axis` Artists and Artists that contain data or annotation information.
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Creating Artists

‎galleries/users_explain/artists/index.rst

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Almost all objects you interact with on a Matplotlib plot are called "Artist"
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(and are subclasses of the `.Artist` class). :doc:`Figure <../figure/index>`
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and :doc:`Axes <../axes/index>` are Artists, and generally contain :doc:`Axis
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<../axis/index>` Artists and Artists that contain data or annotation
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information.
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and :doc:`Axes <../axes/index>` are Artists, and generally contain
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`~.axis.Axis` Artists and Artists that contain data or annotation information.
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.. toctree::
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:maxdepth: 2

‎galleries/users_explain/axes/axes_intro.rst

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@@ -5,7 +5,7 @@ Introduction to Axes (or Subplots)
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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 :doc:`Axis <../axis/index>`
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add data to the Axes. An Axes typically has a pair of `~.axis.Axis`
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Artists that define the data coordinate 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 the API at `~.axis.Axis`. 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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labelcolor='green')
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More fine-grained control on ticks, setting scales, and controlling the Axis can be highly customized beyond these Axes-level helpers. An introduction to these methods can be found in :ref:`users_axis`, or the API reference for `.axis.Axis`.
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More fine-grained control on ticks, setting scales, and controlling the Axis can be highly customized beyond these Axes-level helpers.
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Axes layout
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-----------

‎galleries/users_explain/axes/scales.py renamed to ‎galleries/users_explain/axes/axes_scales.py

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# =====================
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#
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# There are other scales that can be used. The list of registered
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# scales can be returned from `.scales.get_scale_names`:
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# scales can be returned from `.scale.get_scale_names`:
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print(mscale.get_scale_names())
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#
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# Some of the default scales have optional arguments. These are
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# documented in the API reference for the respective scales at
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# `~.matplotlib.scales`. One can change the base of the logarithm
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# `~.matplotlib.scale`. One can change the base of the logarithm
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# being plotted (eg 2 below) or the linear threshold range
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# for ``'symlog'``.
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# ==================
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#
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# A scale is an object that gets attached to an axis. The class documentation
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# is at `~.scale`. `~.axes.Axes.set_xscale` and `~.axes.Axes.set_yscale` are
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# helper methods for `~.axis.Axis.set_scale`. You can also determine the scale
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# on an axis with:
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# is at `~matplotlib.scale`. `~.axes.Axes.set_xscale` and `~.axes.Axes.set_yscale`
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# set the scale on the respective Axis objects. You can determine the scale
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# on an axis with `~.axis.Axis.get_scale`:
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fig, ax = plt.subplots(layout='constrained',
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figsize=(3.2, 3))

‎galleries/users_explain/axes/ticks.py renamed to ‎galleries/users_explain/axes/axes_ticks.py

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# ===============================
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#
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# The appearance of ticks can be controlled at a low level by finding the
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# individual `~.ticker.Ticks` on the axis. However, usually it is simplest to
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# individual `~.axis.Tick` on the axis. However, usually it is simplest to
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# use `~.axes.Axes.tick_params` to change all the objects at once.
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#
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# The ``tick_params`` method can change the properties of ticks:

‎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 :doc:`Axis <../axis/index>`
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add data to the Axes. An Axes typically has a pair of `~.axis.Axis`
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Artists that define the data coordinate system, and include methods to add
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annotations like x- and y-labels, titles, and legends.
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:maxdepth: 2
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:includehidden:
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scales
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ticks
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axes_scales
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axes_ticks
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Legends <legend_guide>
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Subplot mosaic <mosaic>
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‎lib/matplotlib/axis.py

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self.stale = True
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def get_transform(self):
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"""Return the transform used in the Axis' scale"""
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return self._scale.get_transform()
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def get_scale(self):

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