- Testing Guidelines
- Writing Command-Line Scripts
- C or Cython Extensions
Python Coding Guidelines¶
This section describes requirements and guidelines.
Interface and Dependencies¶
- All code must be compatible with Python 3.5 and later.
- The new Python 3 formatting style should be used (i.e.
"%s" % "spam").
Documentation and Testing¶
Data and Configuration¶
- All persistent configuration should use python-dotenv.
.envfiles should be placed at the top of the
ska_skeletonmodule and provide a description that is sufficient for users to understand the settings changes.
Standard output, warnings, and errors¶
print(...) function should only be used for output that
is explicitly requested by the user, for example
list_catalogs(...). Any other standard output, warnings, and
errors should follow these rules:
- For errors/exceptions, one should always use
raisewith one of the built-in exception classes, or a custom exception class. The nondescript
Exceptionclass should be avoided as much as possible, in favor of more specific exceptions (IOError, ValueError, etc.).
- For warnings, one should always use
warnings.warn(message, warning_class). These get redirected to
- For informational and debugging messages, one should always use
The logging system should use the built-in Python logging module.
- The code will follow the standard PEP8 Style Guide for Python Code. In particular, this includes using only 4 spaces for indentation, and never tabs.
import numpy as np,
import matplotlib as mpl, and
import matplotlib.pyplot as pltnaming conventions should be used wherever relevant.
from packagename import *should never be used, except as a tool to flatten the namespace of a module. An example of the allowed usage is given in Acceptable use of from module import *.
- Classes should either use direct variable access, or Python’s property
mechanism for setting object instance variables.
set_valuestyle methods should be used only when getting and setting the values requires a computationally-expensive operation. Properties vs. get_/set_ below illustrates this guideline.
- Classes should use the builtin
super()function when making calls to methods in their super-class(es) unless there are specific reasons not to.
super()should be used consistently in all subclasses since it does not work otherwise. super() vs. Direct Calling illustrates why this is important.
- Multiple inheritance should be avoided in general without good reason.
__init__.pyfiles for modules should not contain any significant implementation code.
__init__.pycan contain docstrings and code for organizing the module layout, however (e.g.
from submodule import *in accord with the guideline above). If a module is small enough that it fits in one file, it should simply be a single file, rather than a directory with an
For maximum compatibility, we need to assume that writing non-ASCII characters to the console or to files will not work.
Including C Code¶
- When C extensions are used, the Python interface for those extensions must meet the aforementioned Python interface guidelines.
- The use of Cython is strongly recommended for C extensions. Cython extensions
.pyxfiles in the source code repository, but they should be compiled to
.cfiles that are updated in the repository when important changes are made to the
- In cases where C extensions are needed but Cython cannot be used, the PEP 7 Style Guide for C Code is recommended.
This section shows examples in order to illustrate points from the guidelines.
Properties vs. get_/set_¶
This example shows a sample class illustrating the guideline regarding the use of properties as opposed to getter/setter methods.
Let’s assuming you’ve defined a
':class:`Star`' class and create an instance
>>> s = Star(B=5.48, V=4.83)
You should always use attribute syntax like this:
>>> s.color = 0.4 >>> print(s.color) 0.4
Using Python properties, attribute syntax can still do anything possible with a get/set method. For lengthy or complex calculations, however, use a method:
>>> print(s.compute_color(5800, age=5e9)) 0.4
super() vs. Direct Calling¶
# This is safe class A(object): def method(self): print('Doing A') class B(A): def method(self): print('Doing B') super().method() class C(A): def method(self): print('Doing C') super().method() class D(C, B): def method(self): print('Doing D') super().method()
>>> d = D() >>> d.method() Doing D Doing C Doing B Doing A
As you can see, each superclass’s method is entered only once. For this to
work it is very important that each method in a class that calls its
superclass’s version of that method use
super() instead of calling the
method directly. In the most common case of single-inheritance, using
super() is functionally equivalent to calling the superclass’s method
directly. But as soon as a class is used in a multiple-inheritance
hierarchy it must use
super() in order to cooperate with other classes in
For more information on the the benefits of
Acceptable use of
from module import *¶
from module import * is discouraged in a module that contains
implementation code, as it impedes clarity and often imports unused variables.
It can, however, be used for a package that is laid out in the following
packagename packagename/__init__.py packagename/submodule1.py packagename/submodule2.py
In this case,
packagename/__init__.py may be:
""" A docstring describing the package goes here """ from submodule1 import * from submodule2 import *
This allows functions or classes in the submodules to be used directly as
packagename.foo rather than
packagename.submodule1.foo. If this is
used, it is strongly recommended that the submodules make use of the
variable to specify which modules should be imported. Thus,
from numpy import array, linspace __all__ = ['foo', 'AClass'] def foo(bar): # the function would be defined here pass class AClass(object): # the class is defined here pass
This ensures that
from submodule import * only imports
':class:`AClass', but not