====================================
A Guide to NumPy/SciPy Documentation
====================================

.. Contents::

.. Note::

   For an accompanying example, see `example.py
   <http://github.com/numpy/numpy/blob/master/doc/example.py>`_.

   When using `Sphinx <http://sphinx.pocoo.org/>`__ in combination with the
   numpy conventions, you should use the ``numpydoc`` extension so that your
   docstrings will be handled correctly. For example, Sphinx will extract the
   ``Parameters`` section from your docstring and convert it into a field
   list.  Using numpydoc will also avoid the reStructuredText errors produced
   by plain Sphinx when it encounters numpy docstring conventions like
   section headers (e.g. ``-------------``) that sphinx does not expect to
   find in docstrings.

   It is available from:

   * `numpydoc on PyPI <http://pypi.python.org/pypi/numpydoc>`_
   * `numpydoc on GitHub (in main numpy repository)
     <https://github.com/numpy/numpy/blob/master/doc/sphinxext/numpydoc.py>`_

   Details of how to use it can be found `here
   <https://github.com/numpy/numpy/blob/master/doc/sphinxext/README.txt>`__ and
   `here
   <https://github.com/numpy/numpy/blob/master/doc/HOWTO_BUILD_DOCS.rst.txt>`__

Overview
--------
In general, we follow the standard Python style conventions as described here:
 * `Style Guide for C Code <http://www.python.org/peps/pep-0007.html>`_
 * `Style Guide for Python Code <http://www.python.org/peps/pep-0008.html>`_
 * `Docstring Conventions <http://www.python.org/peps/pep-0257.html>`_

Additional PEPs of interest regarding documentation of code:
 * `Docstring Processing Framework <http://www.python.org/peps/pep-0256.html>`_
 * `Docutils Design Specification <http://www.python.org/peps/pep-0258.html>`_

Use a code checker:
 * `pylint <http://www.logilab.org/857>`_
 * `pyflakes` easy_install pyflakes
 * `pep8.py <http://svn.browsershots.org/trunk/devtools/pep8/pep8.py>`_

The following import conventions are used throughout the NumPy source
and documentation::

   import numpy as np
   import matplotlib as mpl
   import matplotlib.pyplot as plt

Do not abbreviate ``scipy``. There is no motivating use case to
abbreviate it in the real world, so we avoid it in the documentation
to avoid confusion.

It is not necessary to do ``import numpy as np`` at the beginning of
an example.  However, some sub-modules, such as ``fft``, are not
imported by default, and you have to include them explicitly::

  import numpy.fft

after which you may use it::

  np.fft.fft2(...)

Docstring Standard
------------------
A documentation string (docstring) is a string that describes a module,
function, class, or method definition.  The docstring is a special attribute
of the object (``object.__doc__``) and, for consistency, is surrounded by
triple double quotes, i.e.::

   """This is the form of a docstring.

   It can be spread over several lines.

   """

NumPy, SciPy_, and the scikits follow a common convention for
docstrings that provides for consistency, while also allowing our
toolchain to produce well-formatted reference guides.  This document
describes the current community consensus for such a standard.  If you
have suggestions for improvements, post them on the `numpy-discussion
list`_.

Our docstring standard uses `re-structured text (reST)
<http://docutils.sourceforge.net/rst.html>`_ syntax and is rendered
using Sphinx_ (a pre-processor that understands the particular
documentation style we are using).  While a rich set of
markup is available, we limit ourselves to a very basic subset, in
order to provide docstrings that are easy to read on text-only
terminals.

A guiding principle is that human readers of the text are given
precedence over contorting docstrings so our tools produce nice
output.  Rather than sacrificing the readability of the docstrings, we
have written pre-processors to assist Sphinx_ in its task.

The length of docstring lines should be kept to 75 characters to
facilitate reading the docstrings in text terminals.

Status
------
We are busy converting existing docstrings to the new format,
expanding them where they are lacking, as well as writing new ones for
undocumented functions.  Volunteers are welcome to join the effort on
our new documentation system (see the `Documentation Editor
<http://docs.scipy.org/doc/>`_ and the `Developer Zone
<http://www.scipy.org/Developer_Zone/DocMarathon2008>`_).

Sections
--------
The sections of the docstring are:

1. **Short summary**

   A one-line summary that does not use variable names or the function
   name, e.g.

   ::

     def add(a, b):
        """The sum of two numbers.

        """

   The function signature is normally found by introspection and
   displayed by the help function.  For some functions (notably those
   written in C) the signature is not available, so we have to specify
   it as the first line of the docstring::

     """
     add(a, b)

     The sum of two numbers.

     """

2. **Deprecation warning**

   A section (use if applicable) to warn users that the object is deprecated.
   Section contents should include:

   * In what Numpy version the object was deprecated, and when it will be
     removed.

   * Reason for deprecation if this is useful information (e.g., object
     is superseded, duplicates functionality found elsewhere, etc.).

   * New recommended way of obtaining the same functionality.

   This section should use the note Sphinx directive instead of an
   underlined section header.

   ::

     .. note:: Deprecated in Numpy 1.6
               `ndobj_old` will be removed in Numpy 2.0, it is replaced by
               `ndobj_new` because the latter works also with array subclasses.

3. **Extended summary**

   A few sentences giving an extended description.  This section
   should be used to clarify *functionality*, not to discuss
   implementation detail or background theory, which should rather be
   explored in the **notes** section below.  You may refer to the
   parameters and the function name, but parameter descriptions still
   belong in the **parameters** section.

4. **Parameters**

   Description of the function arguments, keywords and their
   respective types.

   ::

     Parameters
     ----------
     x : type
         Description of parameter `x`.

   Enclose variables in single backticks.  The colon must be preceded
   by a space, or omitted if the type is absent.

   For the parameter types, be as precise as possible.  Below are a
   few examples of parameters and their types.

   ::

     Parameters
     ----------
     filename : str
     copy : bool
     dtype : data-type
     iterable : iterable object
     shape : int or tuple of int
     files : list of str

   If it is not necessary to specify a keyword argument, use
   ``optional``::

     x : int, optional

   Optional keyword parameters have default values, which are
   displayed as part of the function signature.  They can also be
   detailed in the description::

     Description of parameter `x` (the default is -1, which implies summation
     over all axes).

   When a parameter can only assume one of a fixed set of values,
   those values can be listed in braces, with the default appearing first::

     order : {'C', 'F', 'A'}
         Description of `order`.

   When two or more input parameters have exactly the same type, shape and
   description, they can be combined::

     x1, x2 : array_like
         Input arrays, description of `x1`, `x2`.

5. **Returns**

   Explanation of the returned values and their types. Similar to the
   **parameters** section, except the name of each return value is optional.
   The type of each return value is always required::

     Returns
     -------
     int
         Description of anonymous integer return value.

   If both the name and type are specified, the **returns** section takes the
   same form as the **parameters** section::

     Returns
     -------
     err_code : int
         Non-zero value indicates error code, or zero on success.
     err_msg : str or None
         Human readable error message, or None on success.

6. **Other parameters**

   An optional section used to describe infrequently used parameters.
   It should only be used if a function has a large number of keyword
   parameters, to prevent cluttering the **parameters** section.

7. **Raises**

   An optional section detailing which errors get raised and under
   what conditions::

     Raises
     ------
     LinAlgException
         If the matrix is not numerically invertible.

   This section should be used judiciously, i.e only for errors
   that are non-obvious or have a large chance of getting raised.

8. **See Also**

   An optional section used to refer to related code.  This section
   can be very useful, but should be used judiciously.  The goal is to
   direct users to other functions they may not be aware of, or have
   easy means of discovering (by looking at the module docstring, for
   example).  Routines whose docstrings further explain parameters
   used by this function are good candidates.

   As an example, for ``numpy.mean`` we would have::

     See Also
     --------
     average : Weighted average

   When referring to functions in the same sub-module, no prefix is
   needed, and the tree is searched upwards for a match.

   Prefix functions from other sub-modules appropriately.  E.g.,
   whilst documenting the ``random`` module, refer to a function in
   ``fft`` by

   ::

     fft.fft2 : 2-D fast discrete Fourier transform

   When referring to an entirely different module::

     scipy.random.norm : Random variates, PDFs, etc.

   Functions may be listed without descriptions, and this is
   preferable if the functionality is clear from the function name::

     See Also
     --------
     func_a : Function a with its description.
     func_b, func_c_, func_d
     func_e

9. **Notes**

   An optional section that provides additional information about the
   code, possibly including a discussion of the algorithm. This
   section may include mathematical equations, written in
   `LaTeX <http://www.latex-project.org/>`_ format::

     The FFT is a fast implementation of the discrete Fourier transform:

     .. math:: X(e^{j\omega } ) = x(n)e^{ - j\omega n}

   Equations can also be typeset underneath the math directive::

     The discrete-time Fourier time-convolution property states that

     .. math::

          x(n) * y(n) \Leftrightarrow X(e^{j\omega } )Y(e^{j\omega } )\\
          another equation here

   Math can furthermore be used inline, i.e.

   ::

     The value of :math:`\omega` is larger than 5.

   Variable names are displayed in typewriter font, obtained by using
   ``\mathtt{var}``::

     We square the input parameter `alpha` to obtain
     :math:`\mathtt{alpha}^2`.

   Note that LaTeX is not particularly easy to read, so use equations
   sparingly.

   Images are allowed, but should not be central to the explanation;
   users viewing the docstring as text must be able to comprehend its
   meaning without resorting to an image viewer.  These additional
   illustrations are included using::

     .. image:: filename

   where filename is a path relative to the reference guide source
   directory.

10. **References**

   References cited in the **notes** section may be listed here,
   e.g. if you cited the article below using the text ``[1]_``,
   include it as in the list as follows::

     .. [1] O. McNoleg, "The integration of GIS, remote sensing,
        expert systems and adaptive co-kriging for environmental habitat
        modelling of the Highland Haggis using object-oriented, fuzzy-logic
        and neural-network techniques," Computers & Geosciences, vol. 22,
        pp. 585-588, 1996.

   which renders as

   .. [1] O. McNoleg, "The integration of GIS, remote sensing,
      expert systems and adaptive co-kriging for environmental habitat
      modelling of the Highland Haggis using object-oriented, fuzzy-logic
      and neural-network techniques," Computers & Geosciences, vol. 22,
      pp. 585-588, 1996.

   Referencing sources of a temporary nature, like web pages, is
   discouraged.  References are meant to augment the docstring, but
   should not be required to understand it.  References are numbered, starting
   from one, in the order in which they are cited.

11. **Examples**

   An optional section for examples, using the `doctest
   <http://docs.python.org/library/doctest.html>`_ format.
   This section is meant to illustrate usage, not to provide a
   testing framework -- for that, use the ``tests/`` directory.
   While optional, this section is very strongly encouraged.

   When multiple examples are provided, they should be separated by
   blank lines. Comments explaining the examples should have blank
   lines both above and below them::

     >>> np.add(1, 2)
     3

     Comment explaining the second example

     >>> np.add([1, 2], [3, 4])
     array([4, 6])

   For tests with a result that is random or platform-dependent, mark the
   output as such::

     >>> import numpy.random
     >>> np.random.rand(2)
     array([ 0.35773152,  0.38568979])  #random

   You can run examples as doctests using::

     >>> np.test(doctests=True)
     >>> np.linalg.test(doctests=True)  # for a single module

   In IPython it is also possible to run individual examples simply by
   copy-pasting them in doctest mode::

     In [1]: %doctest_mode
     Exception reporting mode: Plain
     Doctest mode is: ON
     >>> %paste
      import numpy.random
      np.random.rand(2)
     ## -- End pasted text --
     array([ 0.8519522 ,  0.15492887])


   It is not necessary to use the doctest markup ``<BLANKLINE>`` to
   indicate empty lines in the output. Note that the option to run
   the examples through ``numpy.test`` is provided for checking if the
   examples work, not for making the examples part of the testing framework.

   The examples may assume that ``import numpy as np`` is executed before
   the example code in *numpy*. Additional examples may make use of
   *matplotlib* for plotting, but should import it explicitly, e.g.,
   ``import matplotlib.pyplot as plt``.


Documenting classes
-------------------

Class docstring
```````````````
Use the same sections as outlined above (all except ``Returns`` are
applicable).  The constructor (``__init__``) should also be documented
here, the **parameters** section of the docstring details the constructors
parameters.

An **Attributes** section, located below the **parameters** section,
may be used to describe class variables::

  Attributes
  ----------
  x : float
      The X coordinate.
  y : float
      The Y coordinate.

Attributes that are properties and have their own docstrings can be
simply listed by name::

  Attributes
  ----------
  real
  imag
  x : float
      The X coordinate
  y : float
      The Y coordinate

In general, it is not necessary to list class methods.  Those that are
not part of the public API have names that start with an underscore.
In some cases, however, a class may have a great many methods, of
which only a few are relevant (e.g., subclasses of ndarray).  Then, it
becomes useful to have an additional **methods** section::

  class Photo(ndarray):
      """
      Array with associated photographic information.

      ...

      Attributes
      ----------
      exposure : float
          Exposure in seconds.

      Methods
      -------
      colorspace(c='rgb')
          Represent the photo in the given colorspace.
      gamma(n=1.0)
          Change the photo's gamma exposure.

      """

If it is necessary to explain a private method (use with care!), it can
be referred to in the **extended summary** or the **notes**. Do not
list private methods in the **methods** section.

Note that `self` is *not* listed as the first parameter of methods.

Method docstrings
`````````````````
Document these as you would any other function.  Do not include
``self`` in the list of parameters.  If a method has an equivalent function
(which is the case for many ndarray methods for example), the function
docstring should contain the detailed documentation, and the method docstring
should refer to it.  Only put brief summary and **See Also** sections in the
method docstring.


Documenting class instances
---------------------------
Instances of classes that are part of the Numpy API (for example `np.r_`
`np,c_`, `np.index_exp`, etc.) may require some care. To give these
instances a useful docstring, we do the following:

* Single instance: If only a single instance of a class is exposed,
  document the class. Examples can use the instance name.

* Multiple instances: If multiple instances are exposed, docstrings
  for each instance are written and assigned to the instances'
  ``__doc__`` attributes at run time. The class is documented as usual, and
  the exposed instances can be mentioned in the **Notes** and **See Also**
  sections.


Documenting constants
---------------------
Use the same sections as outlined for functions where applicable::

   1. summary
   2. extended summary (optional)
   3. see also (optional)
   4. references (optional)
   5. examples (optional)

Docstrings for constants will not be visible in text terminals
(constants are of immutable type, so docstrings can not be assigned
to them like for for class instances), but will appear in the
documentation built with Sphinx.


Documenting modules
-------------------
Each module should have a docstring with at least a summary line. Other
sections are optional, and should be used in the same order as for documenting
functions when they are appropriate::

    1. summary
    2. extended summary
    3. routine listings
    4. see also
    5. notes
    6. references
    7. examples

Routine listings are encouraged, especially for large modules, for which it is
hard to get a good overview of all functionality provided by looking at the
source file(s) or the ``__all__`` dict.

Note that license and author info, while often included in source files, do not
belong in docstrings.


Other points to keep in mind
----------------------------
* Equations : as discussed in the **Notes** section above, LaTeX formatting
  should be kept to a minimum.  Often it's possible to show equations as
  Python code or pseudo-code instead, which is much more readable in a
  terminal.  For inline display use double backticks (like ``y = np.sin(x)``).
  For display with blank lines above and below, use a double colon and indent
  the code, like::

    end of previous sentence::

        y = np.sin(x)

* Notes and Warnings : If there are points in the docstring that deserve
  special emphasis, the reST directives for a note or warning can be used
  in the vicinity of the context of the warning (inside a section). Syntax::

    .. warning:: Warning text.

    .. note:: Note text.

  Use these sparingly, as they do not look very good in text terminals
  and are not often necessary. One situation in which a warning can
  be useful is for marking a known bug that is not yet fixed.

* Questions and Answers : For general questions on how to write docstrings
  that are not answered in this document, refer to
  `<http://docs.scipy.org/numpy/Questions+Answers/>`_.

* array_like : For functions that take arguments which can have not only
  a type `ndarray`, but also types that can be converted to an ndarray
  (i.e. scalar types, sequence types), those arguments can be documented
  with type `array_like`.

Common reST concepts
--------------------
For paragraphs, indentation is significant and indicates indentation in the
output. New paragraphs are marked with a blank line.

Use *italics*, **bold**, and ``monospace`` if needed in any explanations
(but not for variable names and doctest code or multi-line code).
Variable, module, function, and class names should be written between
single back-ticks (```numpy```).

A more extensive example of reST markup can be found in `this example
document <http://docutils.sourceforge.net/docs/user/rst/demo.txt>`_;
the `quick reference
<http://docutils.sourceforge.net/docs/user/rst/quickref.html>`_ is
useful while editing.

Line spacing and indentation are significant and should be carefully
followed.

Conclusion
----------

`An example <http://github.com/numpy/numpy/blob/master/doc/example.py>`_ of the
format shown here is available.  Refer to `How to Build API/Reference
Documentation
<http://github.com/numpy/numpy/blob/master/doc/HOWTO_BUILD_DOCS.rst.txt>`_
on how to use Sphinx_ to build the manual.

This document itself was written in ReStructuredText, and may be converted to
HTML using::

  $ rst2html HOWTO_DOCUMENT.txt HOWTO_DOCUMENT.html

.. _SciPy: http://www.scipy.org
.. _numpy-discussion list: http://www.scipy.org/Mailing_Lists
.. _Sphinx: http://sphinx.pocoo.org
