Standards#

This document defines the code and documentation standards used in this repository.

Code Standards#

The code in this repository conforms to standards set by the following tools:

black for code formatting
flake8 for style checks
isort for import ordering
mypy for static type checking

Documentation Standards#

Docstrings are pre-processed using the Sphinx Napoleon extension. As such, numpydoc-style is considered as standard for this repository. Please use type hints in the function signature for types. For example:

def foo(var1, var2, *args, long_var_name="hi", only_seldom_used_keyword=0, **kwargs):
r"""Summarize the function in one line.

Several sentences providing an extended description. Refer to
variables using back-ticks, e.g. `var`.

Parameters
----------
var1 : array_like
    Array_like means all those objects -- lists, nested lists, etc. --
    that can be converted to an array.  We can also refer to
    variables like `var1`.
var2 : int
    The type above can either refer to an actual Python type
    (e.g. ``int``), or describe the type of the variable in more
    detail, e.g. ``(N,) ndarray`` or ``array_like``.
*args : iterable
    Other arguments.
long_var_name : {'hi', 'ho'}, optional
    Choices in brackets, default first when optional.

Returns
-------
type
    Explanation of anonymous return value of type ``type``.
describe : type
    Explanation of return value named `describe`.
out : type
    Explanation of `out`.
type_without_description

Other Parameters
----------------
only_seldom_used_keyword : int, optional
    Infrequently used parameters can be described under this optional
    section to prevent cluttering the Parameters section.
**kwargs : dict
    Other infrequently used keyword arguments. Note that all keyword
    arguments appearing after the first parameter specified under the
    Other Parameters section, should also be described under this
    section.

Raises
------
BadException
    Because you shouldn't have done that.

See Also
--------
numpy.array : Relationship (optional).
numpy.ndarray : Relationship (optional), which could be fairly long, in
                which case the line wraps here.
numpy.dot, numpy.linalg.norm, numpy.eye

Notes
-----
Notes about the implementation algorithm (if needed).

This can have multiple paragraphs.

You may include some math:

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

And even use a Greek symbol like :math:`\omega` inline.

References
----------
Cite the relevant literature, e.g. [1]_.  You may also cite these
references in the notes section above.

.. [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.

Examples
--------
These are written in doctest format, and should illustrate how to
use the function.

>>> a = [1, 2, 3]
>>> print([x + 3 for x in a])
[4, 5, 6]
>>> print("a\nb")
a
b

"""
pass

Documentation is contained in the docs directory and extracted from docstrings of the API.

Docs follow the underlining convention:

Headling 1 (page title)
=======================

Heading 2
---------

Heading 3
~~~~~~~~~