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 2.6, 2.7, as well as 3.1 and later. All files should include the preamble:

    from __future__ import print_function, division
    

    and therefore use the print() function from Python 3. In addition, the new Python 3 formatting style should be used (i.e. "{0:s}".format("spam") instead of "%s" % "spam"), although when using positional arguments, the position should always be specified (i.e. "{:s}" is not compatible with Python 2.6). Astropy automatically runs the 2to3 tool on the source code, so in cases where syntax is different between Python 2 and 3, the Python 2 syntax should be used.

  • 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 1.4 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.

  • General utilities necessary for but not specific to the package or sub-package should be placed in the packagename.utils. 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.

Documentation and Testing

  • Docstrings must be present for all public classes/methods/functions, and must follow the form outlined in the Documentation Guidelines document.
  • 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.

Data and Configuration

  • Packages can include data in a directory named data inside a subpackage source directory as long as it is less than about 100 kb. These data should always be accessed via the astropy.utils.data.get_pkg_data_fileobj() or astropy.utils.data.get_pkg_data_filename() functions. If the data exceeds this size, it should be hosted outside the source code repository, either at a third-party location on the internet or the astropy data server. In either case, it should always be downloaded using the astropy.utils.data.get_pkg_data_fileobj() or astropy.utils.data.get_pkg_data_filename() functions. If a specific version of a data file is needed, the hash mechanism described in astropy.utils.data should be used.
  • All persistent configuration should use the astropy.config.ConfigurationItem mechanism. Such configuration items should be placed at the top of the module or package that makes use of them, and supply a description sufficient for users to understand what the setting changes.

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). These get redirected to log.warn by default, but one can still use the standard warning-catching mechanism and custom warning classes.
  • 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.

  • 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 suggets) 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.

  • All new classes should be new-style classes inheriting from object (in Python 3 this is a non-issue as all classes are new-style by default). The one exception to this rule is older classes in third-party libraries such the Python standard library or numpy.

  • 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. Mulitple 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 simple be a single file, rather than a directory with an __init__.py file.

  • When try...except blocks are used to catch exceptions, the as syntax should always be used, because this is available in all supported versions of python and is less ambiguous syntax (see try...except block “as” syntax).

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

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.

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.
  • Affiliated packages must be registered on the Python Package Index, with proper metadata for downloading and installing the source package.
  • 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”).

Examples

This section shows a few examples (not all of which are correct!) to illustrate points from the guidelines. These will be moved into the template project once it has been written.

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 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!
>>> print s.get_color()  #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 inherts 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(B, self).method()


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

class D(C, B):
    def method(self):
        print 'Doing D'
        super(D, self).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 foo() and AClass, but not numpy.array or numpy.linspace().

try...except block “as” syntax

Catching of exceptions should always use this syntax:

try:
    ... some code that might produce a variety of exceptions ...
except ImportError as e:
    if 'somemodule' in e.args[0]"
        #for whatever reason, failed import of somemodule is ok
        pass
    else:
        raise
except ValueError, TypeError as e:
    msg = 'Hit an input problem, which is ok,'
    msg2 = 'but we're printing it here just so you know:'
    print msg, msg2, e

This avoids the old style syntax of except ImportError, e or except (ValueError,TypeError), e, which is dangerous because it’s easy to instead accidentally do something like except ValueError,TypeError, which won’t catch TypeError.

Additional Resources

Further tips and hints relating to the coding guidelines are included below.