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DOC: scales - built in options and custom scale usefulness #29404

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Merged
merged 1 commit into from
Jan 11, 2025
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DOC: scales - built in options and custom scale usefulness #29404

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24 changes: 18 additions & 6 deletions 24 galleries/examples/scales/custom_scale.py
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Custom scale
============

Create a custom scale, by implementing the scaling use for latitude data in a
Mercator Projection.
Custom scales can be created in two ways

#. For simple cases, use `~.scale.FuncScale` and the ``'function'`` option of
`~.Axes.set_xscale` and `~.Axes.set_yscale`. See the last example in
:doc:`/gallery/scales/scales`.

#. Create a custom scale class such as the one in this example, which implements
the scaling use for latitude data in a Mercator Projection. This more complicated
approach is useful when

* You are making special use of the `.Transform` class, such as the special
handling of values beyond the threshold in ``MercatorLatitudeTransform``
below.

* You want to override the default locators and formatters for the axis
(``set_default_locators_and_formatters`` below).

* You want to limit the range of the the axis (``limit_range_for_scale`` below).

Unless you are making special use of the `.Transform` class, you probably
don't need to use this verbose method, and instead can use `~.scale.FuncScale`
and the ``'function'`` option of `~.Axes.set_xscale` and `~.Axes.set_yscale`.
See the last example in :doc:`/gallery/scales/scales`.
"""

import numpy as np
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25 changes: 7 additions & 18 deletions 25 lib/matplotlib/axis.py
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Expand Up @@ -760,26 +760,15 @@ def _set_axes_scale(self, value, **kwargs):

Parameters
----------
value : {"linear", "log", "symlog", "logit", ...} or `.ScaleBase`
The axis scale type to apply.
value : str or `.ScaleBase`
The axis scale type to apply. Valid string values are the names of scale
classes ("linear", "log", "function",...). These may be the names of any
of the :ref:`built-in scales<builtin_scales>` or of any custom scales
registered using `matplotlib.scale.register_scale`.

**kwargs
Different keyword arguments are accepted, depending on the scale.
See the respective class keyword arguments:

- `matplotlib.scale.LinearScale`
- `matplotlib.scale.LogScale`
- `matplotlib.scale.SymmetricalLogScale`
- `matplotlib.scale.LogitScale`
- `matplotlib.scale.FuncScale`
- `matplotlib.scale.AsinhScale`

Notes
-----
By default, Matplotlib supports the above-mentioned scales.
Additionally, custom scales may be registered using
`matplotlib.scale.register_scale`. These scales can then also
be used here.
If *value* is a string, keywords are passed to the instantiation method of
the respective class.
"""
name = self._get_axis_name()
old_default_lims = (self.get_major_locator()
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4 changes: 3 additions & 1 deletion 4 lib/matplotlib/scale.py
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The mapping is implemented through `.Transform` subclasses.

The following scales are builtin:
The following scales are built-in:
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Merriam-Webster says this is hyphenated, though we seem to have both ways all over the docs...

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ChatGPT says:

The correct spelling depends on how the term is used in a sentence:

  1. "Built-in" (with a hyphen) is an adjective. It is used to describe something that is an integral or permanent part of something else.

    • Example: "The phone has a built-in camera."

  2. "Builtin" (no hyphen) is not a standard English word, but it is sometimes used as a term in programming or technical contexts, especially in filenames, identifiers, or codebases. For instance, Python has "builtin functions," though even here, "built-in" is more widely accepted in official documentation.

To summarize:

  • For general usage, "built-in" with a hyphen is correct.
  • For programming or technical contexts, check the convention of the specific language or framework being used.


.. _builtin_scales:

============= ===================== ================================ =================================
Name Class Transform Inverted transform
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