# Coding Guidelines¶

This section describes requirements and guidelines that should be followed both for the core package and for affiliated packages.

Note

Affiliated packages will only be considered for integration as a module in the core package once these guidelines have been followed.

## Interface and Dependencies¶

• All code must be compatible with Python 3.5 and later. Usage of six, __future__, and 2to3 is not longer acceptable.

• The new Python 3 formatting style should be used (i.e. "{0:s}".format("spam") instead of "%s" % "spam").

• The core package and affiliated packages should be importable with no dependencies other than components already in the Astropy core, the Python Standard Library, and NumPy 1.10.0 or later.

• The package should be importable from the source tree at build time. This means that, for example, if the package relies on C extensions that have yet to be built, the Python code is still importable, even if none of its functionality will work. One way to ensure this is to import the functions in the C extensions only within the functions/methods that require them (see next bullet point).

• Additional dependencies - such as SciPy, Matplotlib, or other third-party packages - are allowed for sub-modules or in function calls, but they must be noted in the package documentation and should only affect the relevant component. In functions and methods, the optional dependency should use a normal import statement, which will raise an ImportError if the dependency is not available.

At the module level, one can subclass a class from an optional dependency like so:

try:
from opdep import Superclass
except ImportError:
warn(AstropyWarning('opdep is not present, so <functionality below> will not work.'))
class SuperClass(object): pass

class Whatever(Superclass):
...


In the astropy core package, such optional dependencies should be recorded in the pip-requirements-dev file in the root of the repository.

• General utilities necessary for but not specific to the package or sub-package should be placed in the packagename.utils module. These utilities will be moved to the astropy.utils module when the package is integrated into the core package. If a utility is already present in astropy.utils, the package should always use that utility instead of re-implementing it in packagename.utils module.

## Documentation and Testing¶

• Docstrings must be present for all public classes/methods/functions, and must follow the form outlined in the Writing Documentation document.
• Write usage examples in the docstrings of all classes and functions whenever possible. These examples should be short and simple to reproduce–users should be able to copy them verbatim and run them. These examples should, whenever possible, be in the doctest format and will be executed as part of the test suite.
• Unit tests should be provided for as many public methods and functions as possible, and should adhere to the standards set in the Testing Guidelines document.

## Standard output, warnings, and errors¶

The built-in print(...) function should only be used for output that is explicitly requested by the user, for example print_header(...) or list_catalogs(...). Any other standard output, warnings, and errors should follow these rules:

• For errors/exceptions, one should always use raise with one of the built-in exception classes, or a custom exception class. The nondescript Exception class 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 log.warning() by default, but one can still use the standard warning-catching mechanism and custom warning classes. The warning class should be either AstropyUserWarning or inherit from it.
• For informational and debugging messages, one should always use log.info(message) and log.debug(message).

The logging system uses the built-in Python logging module. The logger can be imported using:

from astropy import log


## Coding Style/Conventions¶

• 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.

• Follow the existing coding style within a subpackage and avoid making changes that are purely stylistic. In particular, there is variation in the maximum line length for different subpackages (typically either 80 or 100 characters). Please try to maintain the style when adding or modifying code.

• One exception is to be made from the PEP8 style: new style relative imports of the form from . import modname are allowed and required for Astropy, as opposed to absolute (as PEP8 suggests) or the simpler import modname syntax. This is primarily due to improved relative import support since PEP8 was developed, and to simplify the process of moving modules.

Note

There are multiple options for testing PEP8 compliance of code, see Testing Guidelines for more information. See Emacs setup for following coding guidelines for some configuration options for Emacs that helps in ensuring conformance to PEP8.

• Astropy source code should contain a comment at the beginning of the file (or immediately after the #!/usr/bin env python command, if relevant) pointing to the license for the Astropy source code. This line should say:

# Licensed under a 3-clause BSD style license - see LICENSE.rst

• The import numpy as np, import matplotlib as mpl, and import matplotlib.pyplot as plt naming 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. get_value/set_value style 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. Multiple inheritance is complicated to implement well, which is why many object-oriented languages, like Java, do not allow it at all. Python does enable multiple inheritance through use of the C3 Linearization algorithm, which provides a consistent method resolution ordering. Non-trivial multiple-inheritance schemes should not be attempted without good justification, or without understanding how C3 is used to determine method resolution order. However, trivial multiple inheritance using orthogonal base classes, known as the ‘mixin’ pattern, may be used.

• __init__.py files for modules should not contain any significant implementation code. __init__.py can 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 __init__.py file.

• Command-line scripts should follow the form outlined in the Writing Command-Line Scripts document.

## Unicode guidelines¶

For maximum compatibility, we need to assume that writing non-ASCII characters to the console or to files will not work. However, for those that have a correctly configured Unicode environment, we should allow them to opt-in to take advantage of Unicode output when appropriate. Therefore, there is a global configuration option, astropy.conf.unicode_output to enable Unicode output of values, set to False by default.

The following conventions should be used for classes that define the standard string conversion methods (__str__, __repr__, __bytes__, and __format__). In the bullets below, the phrase “string instance” is used to refer to str, while “bytes instance” is used to refer to bytes.

• __repr__: Return a “string instance” containing only 7-bit characters.
• __bytes__: Return a “bytes instance” containing only 7-bit characters.
• __str__: Return a “string instance”. If astropy.conf.unicode_output is False, it must contain only 7-bit characters. If astropy.conf.unicode_output is True, it may contain non-ASCII characters when applicable.
• __format__: Return a “string instance”. If astropy.UNICODE_OUTPUT is False, it must contain only 7-bit characters. If astropy.conf.unicode_output is True, it may contain non-ASCII characters when applicable.

For classes that are expected to roundtrip through strings (unicode or bytes), the parser must accept the output of __str__. Additionally, __repr__ should roundtrip when that makes sense.

This design generally follows Postel’s Law: “Be liberal in what you accept, and conservative in what you send.”

The following example class shows a way to implement this:

# -*- coding: utf-8 -*-

from astropy import conf

class FloatList(object):
def __init__(self, init):
if isinstance(init, str):
init = init.split('‖')
elif isinstance(init, bytes):
init = init.split(b'|')
self.x = [float(x) for x in init]

def __repr__(self):
# Return unicode object containing no non-ASCII characters
return '<FloatList [{0}]>'.format(', '.join(
str(x) for x in self.x))

def __bytes__(self):
return b'|'.join(bytes(x) for x in self.x)

def __str__(self):
if astropy.conf.unicode_output:
return '‖'.join(str(x) for x in self.x)
else:
return self.__bytes__().decode('ascii')


Additionally, there is a test helper, astropy.test.helper.assert_follows_unicode_guidelines to ensure that a class follows the Unicode guidelines outlined above. The following example test will test that our example class above is compliant:

def test_unicode_guidelines():
from astropy.test.helper import assert_follows_unicode_guidelines
assert_follows_unicode_guidelines(FloatList(b'5|4|3|2'), roundtrip=True)


## Including C Code¶

• C extensions are only allowed when they provide a significant performance enhancement over pure Python, or a robust C library already exists to provided the needed functionality. When C extensions are used, the Python interface must meet the aforementioned Python interface guidelines.
• The use of Cython is strongly recommended for C extensions, as per the example in the template package. Cython extensions should store .pyx files in the source code repository, but they should be compiled to .c files that are updated in the repository when important changes are made to the .pyx file.
• If a C extension has a dependency on an external C library, the source code for the library should be bundled with the Astropy core, provided the license for the C library is compatible with the Astropy license. Additionally, the package must be compatible with using a system-installed library in place of the library included in Astropy.
• In cases where C extensions are needed but Cython cannot be used, the PEP 7 Style Guide for C Code is recommended.
• C extensions (Cython or otherwise) should provide the necessary information for building the extension via the mechanisms described in C or Cython Extensions.

## Compatibility between versions of Numpy¶

In general, code should aim to be compatible with the lowest supported version of NumPy. Sometimes, however, it is inefficient to code repeatedly around bugs in earlier versions. For those cases, code can be added to astropy.utils.compat.numpy; see the corresponding instructions for details.

## Requirements Specific to Affiliated Packages¶

• Affiliated packages implementing many classes/functions not relevant to the affiliated package itself (for example leftover code from a previous package) will not be accepted - the package should only include the required functionality and relevant extensions.
• Affiliated packages are required to follow the layout and documentation form of the template package included in the core package source distribution.
• The astropy root package name should not be used by affiliated packages - it is reserved for use by the core package. Recommended naming conventions for an affiliated package are either simply packagename or awastropy.packagename (“affiliated with Astropy”).
• If the affiliated package requires compatibility with Python 2, it should pin to astropy<3 until it is ready to drop Python 2 support. On the other hand, package that only supports Python 3 may still use astropy<3. New affiliated packages created from Astropy’s package template should check with template maintainers regarding Python versions supported if unsure.

## Examples¶

This section shows a few examples (not all of which are correct!) 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 like this:

>>> 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


Rather than like this:

>>> s.set_color(0.4)  # Bad form!
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 example shows why the use of super() leads to a more consistent method resolution order than manually calling methods of the super classes in a multiple inheritance case:

# This is dangerous and bug-prone!

class A(object):
def method(self):
print('Doing A')

class B(A):
def method(self):
print('Doing B')
A.method(self)

class C(A):
def method(self):
print('Doing C')
A.method(self)

class D(C, B):
def method(self):
print('Doing D')
C.method(self)
B.method(self)


if you then do:

>>> b = B()
>>> b.method()


you will see:

Doing B
Doing A


which is what you expect, and similarly for C. However, if you do:

>>> d = D()
>>> d.method()


you might expect to see the methods called in the order D, B, C, A but instead you see:

Doing D
Doing C
Doing A
Doing B
Doing A


because both B.method() and C.method() call A.method() unaware of the fact that they’re being called as part of a chain in a hierarchy. When C.method() is called it is unaware that it’s being called from a subclass that inherits from both B and C, and that B.method() should be called next. By calling super() the entire method resolution order for D is precomputed, enabling each superclass to cooperatively determine which class should be handed control in the next super() call:

# This is safer

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 the hierarchy.

### 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 manner:

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 __all__ variable to specify which modules should be imported. Thus, submodule2.py might read:

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 ':func:foo' and ':class:AClass', but not ':class:numpy.array' or ':func:numpy.linspace'.