Coding Guidelines¶

Warning

This page is not fully adopted from astropy

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 3.4 and later, as well as 2.7. The use of six for writing code that is portable between Python 2.7 and 3.x is required.

Code that uses six should use the following preamble:
from __future__ import (absolute_import, division, print_function,
                        unicode_literals)
Additional information on writing code using six that is compatible with both Python 2.x and 3.x is in the section Writing portable code for Python 2 and 3.

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 Scikit-beam core, the Python Standard Library, and NumPy 1.10 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(ScikitbeamWarning('opdep is not present, so <functionality below> will not work.'))
    class SuperClass(object): pass

class Whatever(Superclass):
    ...
```
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 scikit-beam.utils module when the package is integrated into the core package. If a utility is already present in scikit-beam.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.

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 scikit-beam.utils.data.get_pkg_data_fileobj() or scikit-beam.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 scikit-beam data server. In either case, it should always be downloaded using the scikit-beam.utils.data.get_pkg_data_fileobj() or scikit-beam.utils.data.get_pkg_data_filename() functions. If a specific version of a data file is needed, the hash mechanism described in scikit-beam.utils.data should be used.
All persistent configuration should use the scikit-beam.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, warning_class). These get redirected to log.warn by default, but one can still use the standard warning-catching mechanism and custom warning classes. The warning class should be either Scikit-beamUserWarning 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 scikit-beam import logger

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 Scikit-beam, 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.
Scikit-beam 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 Scikit-beam source code. This line should say:
```
# Licensed under a 3-clause BSD style license - see LICENSE.rst
```
Warning

Many files currently have the full 3-clause BSD, this may need some discussion.
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. 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.
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.

Unicode guidelines¶

Warning

Top level utilities are not implemented yet.

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, scikit-beam.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__, __unicode__, __bytes__, and __format__). In the bullets below, the phrase “unicode instance” is used to refer to unicode on Python 2 and str on Python 3. The phrase “bytes instance” is used to refer to str on Python 2 and bytes on Python 3.

__repr__: Return a “unicode instance” (for historical reasons, could also be a “bytes instance” on Python 2, though not preferred) containing only 7-bit characters.
__str__ on Python 2 / __bytes__ on Python 3: Return a “bytes instance” containing only 7-bit characters.
__unicode__ on Python 2 / __str__ on Python 3: Return a “unicode instance”. If scikit-beam.conf.unicode_output is False, it must contain only 7-bit characters. If scikit-beam.conf.unicode_output is True, it may contain non-ascii characters when applicable.
__format__: Return a “unicode instance”. If scikit-beam.UNICODE_OUTPUT is False, it must contain only 7-bit characters. If scikit-beam.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 either the output of __str__ or __unicode__ unambiguously. 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 (using six for Python 2 and 3 cross-version compatibility:

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

from __future__ import unicode_literals

from scikit-beam.extern import six
from scikit-beam import conf

class FloatList(object):
    def __init__(self, init):
        if isinstance(init, six.text_type):
            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(
            six.text_type(x) for x in self.x))

    def __bytes__(self):
        return b'|'.join(bytes(x) for x in self.x)
    if six.PY2:
        __str__ = __bytes__

    def __unicode__(self):
        if scikit-beam.conf.unicode_output:
            return '‖'.join(six.text_type(x) for x in self.x)
        else:
            return self.__bytes__().decode('ascii')
    if six.PY3:
        __str__ = __unicode__

Additionally, there is a test helper, scikit-beam.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 scikit-beam.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.

Warning

We should discuss if we want to commit the .c or not.
If a C extension has a dependency on an external C library, the source code for the library should be bundled with the Scikit-beam core, provided the license for the C library is compatible with the Scikit-beam license. Additionally, the package must be compatible with using a system-installed library in place of the library included in Scikit-beam.
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.

Writing portable code for Python 2 and 3¶

As of scikit-beam 0.3, the six library is included to allow supporting Python 2 and 3 from a single code base. The use of the 2to3 tool has been phased out in favor of using six.

This section is mainly about moving existing code that works with 2to3 to using six. It is not a complete guide to Python 2 and 3 compatibility.

Many more details and tools can be found at python-future

Welcome to the `future`¶

The top of every .py file should include the following:

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

This will make the Python 2 interpreter behave as close to Python 3 as possible.

All files should also import six, whether they are using it or not, just to make moving code between modules easier, as six gets used a lot:

import six

Finding places to use six¶

Unfortunately, the only way to be certain that code works on both Python 2 and 3 is to make sure it is covered by unit tests.

The six documentation serves as a good reference for the sorts of things that need to be updated.

Not so fast on that Unicode thing¶

By importing unicode_literals from __future__, many things that were once byte strings on Python 2 will now by unicode strings. This is mostly a good thing, as the behavior of the code will be more consistent between Python 2 and 3. However, certain third-party libraries still assume certain values will be byte strings on Python 2.

For example, when specifying Numpy structured dtypes, all strings must be byte strings on Python 2 and unicode strings on Python 3. The easiest way to handle this is to force cast them using str(), for example:

x = np.array([1.0, 2.0, 3.0], dtype=[(str('name'), '>f8')])

pytest.mark.skipif also requires a “native” string, i.e.:

@pytest.mark.skipif(str('CONDITIONAL'))

Iteration¶

The behavior of the methods for iterating over the items, values and keys of a dictionary has changed in Python 3. Additionally, other built-in functions such as zip(), range() and map() have changed to return iterators rather than temporary lists.

In many cases, the performance implications of iterating vs. creating a temporary list won’t matter, so it’s tempting to use the form that is simplest to read. However, that results in code that behaves differently on Python 2 and 3, leading to subtle bugs that may not be detected by the regression tests. Therefore, unless the loop in question is provably simple and doesn’t call into other code, the six versions that ensure the same behavior on both Python 2 and 3 should be used. The following table shows the mapping of equivalent semantics between Python 2, 3 and six for dict.items():

Python 2	Python 3	six
`d.items()`	`list(d.items())`	`list(six.iteritems(d))`
`d.iteritems()`	`d.items()`	`six.iteritems(d)`

The above table holds true, analogously, for values, keys, zip, range and map.

Note that for keys only, list(d) is an acceptable shortcut to list(six.iterkeys(d)).

Issues with `\u` escapes¶

When from __future__ import unicode_literals is used, all string literals (not preceded with a 'b') will become unicode literals.

Normally, one would use “raw” string literals to encode strings that contain a lot of slashes that we don’t want Python to interpret as special characters. Unfortunately, on Python 2, there is no way to represent '\u' in a raw unicode string literal, since it will always be interpreted as the start of a unicode character escape, such as '\u20af'. The only solution is to use a regular (non-raw) string literal and repeat all slashes, e.g. "\\usepackage{foo}".

The following shows the problem on Python 2:

>>> ur'\u'
Traceback (most recent call last):
...
SyntaxError: (unicode error) 'rawunicodeescape' codec can't decode bytes in
position 0-1: truncated \uXXXX
>>> ur'\\u'
u'\\\\u'
>>> u'\u'
Traceback (most recent call last):
...
SyntaxError: (unicode error) 'unicodeescape' codec can't decode bytes in
position 0-1: truncated \uXXXX escape
>>> u'\\u'
u'\\u'

This bug has been fixed in Python 3, however, we can’t take advantage of that and still support Python 2:

>>> r'\u'
'\\u'
>>> r'\\u'
'\\\\u'
>>> '\u'
  File "<stdin>", line 1
SyntaxError: (unicode error) 'unicodeescape' codec can't decode bytes in
position 0-1: truncated \uXXXX escape
>>> '\\u'
'\\u'

Compatibility between versions of Numpy¶

Warning

This does not exist yet

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 scikit-beam.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.
Affiliated packages must be registered on the Python Package Index, with proper metadata for downloading and installing the source package.
The scikit-beam 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 awscikit-beam.packagename (“affiliated with Scikit-beam”).

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 ':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!
>>> 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 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(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.

Note

For more information on the the benefits of super(), see http://rhettinger.wordpress.com/2011/05/26/super-considered-super/

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'.

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.

Emacs setup for following coding guidelines

Coding Guidelines¶

Interface and Dependencies¶

Documentation and Testing¶

Data and Configuration¶

Standard output, warnings, and errors¶

Coding Style/Conventions¶

Unicode guidelines¶

Including C Code¶

Writing portable code for Python 2 and 3¶

Welcome to the `future`¶

Finding places to use six¶

Not so fast on that Unicode thing¶

Iteration¶

Issues with `\u` escapes¶

Compatibility between versions of Numpy¶

Requirements Specific to Affiliated Packages¶

Examples¶

Properties vs. get_/set_¶

super() vs. Direct Calling¶

Acceptable use of `from module import *`¶

try…except block “as” syntax¶

Additional Resources¶

scikit-beam

Navigation

Related Topics

Coding Guidelines¶

Interface and Dependencies¶

Documentation and Testing¶

Data and Configuration¶

Standard output, warnings, and errors¶

Coding Style/Conventions¶

Unicode guidelines¶

Including C Code¶

Writing portable code for Python 2 and 3¶

Welcome to the __future__¶

Finding places to use six¶

Not so fast on that Unicode thing¶

Iteration¶

Issues with \u escapes¶

Compatibility between versions of Numpy¶

Requirements Specific to Affiliated Packages¶

Examples¶

Properties vs. get_/set_¶

super() vs. Direct Calling¶

Acceptable use of from module import *¶

try…except block “as” syntax¶

Additional Resources¶

Welcome to the `future`¶

Issues with `\u` escapes¶

Acceptable use of `from module import *`¶