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1 change: 1 addition & 0 deletions.gitattributes
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*text=auto
76 changes: 76 additions & 0 deletions.gitignore
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# Editor temporary/working/backup files #
#########################################
.#*
[#]*#
*~
*$
*.bak
*.diff
*.org
.project
*.rej
.settings/
.*.sw[nop]
.sw[nop]
*.tmp

# Compiled source #
###################
*.a
*.com
*.class
*.dll
*.exe
*.o
*.py[ocd]
*.so

# Packages #
############
# it's better to unpack these files and commit the raw source
# git has its own built in compression methods
*.7z
*.bz2
*.bzip2
*.dmg
*.iso
*.jar
*.rar
*.tar
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# Python files #
################
# setup.py working directory
build
# sphinx build directory
_build
# setup.py dist directory
dist
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doc/cdoc/build
# Egg metadata
*.egg-info
# The shelf plugin uses this dir
./.shelf

# Logs and databases #
######################
*.log
*.sql
*.sqlite

# OS generated files #
######################
.gdb_history
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# Things specific to this project #
###################################
lib/matplotlib/mpl-data/matplotlib.conf
lib/matplotlib/mpl-data/matplotlibrc
268 changes: 134 additions & 134 deletionsdoc/devel/add_new_projection.rst
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.. _adding-new-scales:
***********************************************
Adding new scales and projections to matplotlib
***********************************************
.. ::author Michael Droettboom
Matplotlib supports the addition of custom procedures that transform
the data before it is displayed.
There is an important distinction between two kinds of
transformations. Separable transformations, working on a single
dimension, are called "scales", and non-separable transformations,
that handle data in two or more dimensions at a time, are called
"projections".
From the user's perspective, the scale of a plot can be set with
:meth:`~matplotlib.axes.Axes.set_xscale` and
:meth:`~matplotlib.axes.Axes.set_xscale`. Projections can be chosen
using the ``projection`` keyword argument to the
:func:`~matplotlib.pylab.plot` or :func:`~matplotlib.pylab.subplot`
functions, e.g.::
plot(x, y, projection="custom")
This document is intended for developers and advanced users who need
to create new scales and projections for matplotlib. The necessary
code for scales and projections can be included anywhere: directly
within a plot script, in third-party code, or in the matplotlib source
tree itself.
.. _creating-new-scale:
Creating a new scale
====================
Adding a new scale consists of defining a subclass of
:class:`matplotlib.scale.ScaleBase`, that includes the following
elements:
- A transformation from data coordinates into display coordinates.
- An inverse of that transformation. This is used, for example, to
convert mouse positions from screen space back into data space.
- A function to limit the range of the axis to acceptable values
(``limit_range_for_scale()``). A log scale, for instance, would
prevent the range from including values less than or equal to
zero.
- Locators (major and minor) that determine where to place ticks in
the plot, and optionally, how to adjust the limits of the plot to
some "good" values. Unlike ``limit_range_for_scale()``, which is
always enforced, the range setting here is only used when
automatically setting the range of the plot.
- Formatters (major and minor) that specify how the tick labels
should be drawn.
Once the class is defined, it must be registered with matplotlib so
that the user can select it.
A full-fledged and heavily annotated example is in
:file:`examples/api/custom_scale_example.py`. There are also some classes
in :mod:`matplotlib.scale` that may be used as starting points.
.. _creating-new-projection:
Creating a new projection
=========================
Adding a new projection consists of defining a subclass of
:class:`matplotlib.axes.Axes`, that includes the following elements:
- A transformation from data coordinates into display coordinates.
- An inverse of that transformation. This is used, for example, to
convert mouse positions from screen space back into data space.
- Transformations for the gridlines, ticks and ticklabels. Custom
projections will often need to place these elements in special
locations, and matplotlib has a facility to help with doing so.
- Setting up default values (overriding
:meth:`~matplotlib.axes.Axes.cla`), since the defaults for a
rectilinear axes may not be appropriate.
- Defining the shape of the axes, for example, an elliptical axes,
that will be used to draw the background of the plot and for
clipping any data elements.
- Defining custom locators and formatters for the projection. For
example, in a geographic projection, it may be more convenient to
display the grid in degrees, even if the data is in radians.
- Set up interactive panning and zooming. This is left as an
"advanced" feature left to the reader, but there is an example of
this for polar plots in :mod:`matplotlib.projections.polar`.
- Any additional methods for additional convenience or features.
Once the class is defined, it must be registered with matplotlib
so that the user can select it.
A full-fledged and heavily annotated example is in
:file:`examples/api/custom_projection_example.py`. The polar plot
functionality in :mod:`matplotlib.projections.polar` may also be of
interest.
API documentation
=================
matplotlib.scale
----------------
.. automodule:: matplotlib.scale
:members:
:show-inheritance:
matplotlib.projections
----------------------
.. automodule:: matplotlib.projections
:members:
:show-inheritance:
matplotlib.projections.polar
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. automodule:: matplotlib.projections.polar
:members:
:show-inheritance:
.. _adding-new-scales:

***********************************************
Adding new scales and projections to matplotlib
***********************************************

.. ::author Michael Droettboom

Matplotlib supports the addition of custom procedures that transform
the data before it is displayed.

There is an important distinction between two kinds of
transformations. Separable transformations, working on a single
dimension, are called "scales", and non-separable transformations,
that handle data in two or more dimensions at a time, are called
"projections".

From the user's perspective, the scale of a plot can be set with
:meth:`~matplotlib.axes.Axes.set_xscale` and
:meth:`~matplotlib.axes.Axes.set_xscale`. Projections can be chosen
using the ``projection`` keyword argument to the
:func:`~matplotlib.pylab.plot` or :func:`~matplotlib.pylab.subplot`
functions, e.g.::

plot(x, y, projection="custom")

This document is intended for developers and advanced users who need
to create new scales and projections for matplotlib. The necessary
code for scales and projections can be included anywhere: directly
within a plot script, in third-party code, or in the matplotlib source
tree itself.

.. _creating-new-scale:

Creating a new scale
====================

Adding a new scale consists of defining a subclass of
:class:`matplotlib.scale.ScaleBase`, that includes the following
elements:

- A transformation from data coordinates into display coordinates.

- An inverse of that transformation. This is used, for example, to
convert mouse positions from screen space back into data space.

- A function to limit the range of the axis to acceptable values
(``limit_range_for_scale()``). A log scale, for instance, would
prevent the range from including values less than or equal to
zero.

- Locators (major and minor) that determine where to place ticks in
the plot, and optionally, how to adjust the limits of the plot to
some "good" values. Unlike ``limit_range_for_scale()``, which is
always enforced, the range setting here is only used when
automatically setting the range of the plot.

- Formatters (major and minor) that specify how the tick labels
should be drawn.

Once the class is defined, it must be registered with matplotlib so
that the user can select it.

A full-fledged and heavily annotated example is in
:file:`examples/api/custom_scale_example.py`. There are also some classes
in :mod:`matplotlib.scale` that may be used as starting points.


.. _creating-new-projection:

Creating a new projection
=========================

Adding a new projection consists of defining a subclass of
:class:`matplotlib.axes.Axes`, that includes the following elements:

- A transformation from data coordinates into display coordinates.

- An inverse of that transformation. This is used, for example, to
convert mouse positions from screen space back into data space.

- Transformations for the gridlines, ticks and ticklabels. Custom
projections will often need to place these elements in special
locations, and matplotlib has a facility to help with doing so.

- Setting up default values (overriding
:meth:`~matplotlib.axes.Axes.cla`), since the defaults for a
rectilinear axes may not be appropriate.

- Defining the shape of the axes, for example, an elliptical axes,
that will be used to draw the background of the plot and for
clipping any data elements.

- Defining custom locators and formatters for the projection. For
example, in a geographic projection, it may be more convenient to
display the grid in degrees, even if the data is in radians.

- Set up interactive panning and zooming. This is left as an
"advanced" feature left to the reader, but there is an example of
this for polar plots in :mod:`matplotlib.projections.polar`.

- Any additional methods for additional convenience or features.

Once the class is defined, it must be registered with matplotlib
so that the user can select it.

A full-fledged and heavily annotated example is in
:file:`examples/api/custom_projection_example.py`. The polar plot
functionality in :mod:`matplotlib.projections.polar` may also be of
interest.

API documentation
=================

matplotlib.scale
----------------

.. automodule:: matplotlib.scale
:members:
:show-inheritance:

matplotlib.projections
----------------------

.. automodule:: matplotlib.projections
:members:
:show-inheritance:

matplotlib.projections.polar
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. automodule:: matplotlib.projections.polar
:members:
:show-inheritance:
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