Distlib’s design

This is the section containing some discussion of how distlib’s design was arrived at, as and when time permits.

The locators API

This section describes the design of the distlib API relating to accessing distribution metadata, whether stored locally or in indexes like PyPI.

The problem we’re trying to solve

People who use distributions need to locate, download and install them. Distributions can be found in a number of places, such as:

  • An Internet index such as The Python Packages Index (PyPI), or a mirror thereof.
  • Other Internet resources, such as the developer’s website, or a source code repository such as GitHub, BitBucket, Google Code or similar.
  • File systems, whether local to one computer or shared between several.
  • Distributions which have already been installed, and are available in the sys.path of a running Python interpreter.

When we’re looking for distributions, we don’t always know exactly what we want: often, we just want the latest version, but it’s not uncommon to want a specific older version, or perhaps the most recent version that meets some constraints on the version. Since we need to be concerned with matching versions, we need to consider the version schemes in use (see The version API).

It’s useful to separate the notion of a project from a distribution: The project is the version-independent part of the distribution, i.e. it’s described by the name of the distribution and encompasses all released distributions which use that name.

We often don’t just want a single distribution, either: a common requirement, when installing a distribution, is to locate all distributions that it relies on, which aren’t already installed. So we need a dependency finder, which itself needs to locate depended-upon distributions, and recursively search for dependencies until all that are available have been found.

We may need to distinguish between different types of dependencies:

  • Post-installation dependencies. These are needed by the distribution after it has been installed, and is in use.
  • Build dependencies. These are needed for building and/or installing the distribution, but are not needed by the distribution itself after installation.
  • Test dependencies. These are only needed for testing the distribution, but are not needed by the distribution itself after installation.

When testing a distribution, we need all three types of dependencies. When installing a distribution, we need the first two, but not the third.

A minimal solution

Locating distributions

It seems that the simplest API to locate a distribution would look like locate(requirement), where requirement is a string giving the distribution name and optional version constraints. Given that we know that distributions can be found in different places, it’s best to consider a Locator class which has a locate() method with a corresponding signature, with subclasses for each of the different types of location that distributions inhabit. It’s also reasonable to provide a default locator in a module attribute default_locator, and a module-level locate() function which calls the locate() method on the default locator.

Since we’ll often need to locate all the versions of a project before picking one, we can imagine that a locator would need a get_project() method for fetching all versions of a project; and since we will be likely to want to use caching, we can assume there will be a _get_project() method to do the actual work of fetching the version data, which the higher-level get_project() will call (and probably cache). So our locator base class will look something like this:

class Locator(object):
    Locate distributions.

    def __init__(self, version_scheme='default'):
        Initialise a locator with the specified version scheme.

    def locate(self, requirement):
        Locate the highest-version distribution which satisfies
        the constraints in ``requirement``, and return a
        ``Distribution`` instance if found, or else ``None``.

    def get_project(self, name):
        Return all known distributions for a project named ``name``,
        returning a dictionary mapping version to ``Distribution``
        instance, or an empty dictionary if nothing was found.
        Use _get_project to do the actual work, and cache the results for
        future use.

    def _get_project(self, name):
        Return all known distributions for a project named ``name``,
        returning a dictionary mapping version to ``Distribution``
        instance, or an empty dictionary if nothing was found.

When attempting to locate(), it would be useful to pass requirement information to get_project() / _get_project(). This can be done in a matcher attribute which is normally None but set to a distlib.version.Matcher instance when a locate() call is in progress.

Finding dependencies

A dependency finder will depend on a locator to locate dependencies. A simple approach will be to consider a DependencyFinder class which takes a locator as a constructor argument. It might look something like this:

class DependencyFinder(object):
    Locate dependencies for distributions.

    def __init__(self, locator):
        Initialise an instance, using the specified locator
        to locate distributions.

    def find(self, requirement, meta_extras=None, prereleases=False):
        Find a distribution matching requirement and all distributions
        it depends on. Use the ``meta_extras`` argument to determine
        whether distributions used only for build, test etc. should be
        included in the results. Allow ``requirement`` to be either a
        :class:`Distribution` instance or a string expressing a
        requirement. If ``prereleases`` is True, treat pre-releases as
        normal releases; otherwise only return pre-releases if they're
        all that's available.

        Return a set of :class:`Distribution` instances and a set of

        The distributions returned should be such that they have the
        :attr:`required` attribute set to ``True`` if they were
        from the ``requirement`` passed to ``find()``, and they have the
        :attr:`build_time_dependency` attribute set to ``True`` unless they
        are post-installation dependencies of the ``requirement``.

        The problems should be a tuple consisting of the string
        ``'unsatisfied'`` and the requirement which couldn't be satisfied
        by any distribution known to the locator.

The index API

This section describes the design of the distlib API relating to performing certain operations on Python package indexes like PyPI. Note that this API does not support finding distributions - the locators API is used for that.

The problem we’re trying to solve

Operations on a package index that are commonly performed by distribution developers are:

  • Register projects on the index.
  • Upload distributions relating to projects on the index, with support for signed distributions.
  • Upload documentation relating to projects.

Less common operations are:

  • Find a list of hosts which mirror the index.
  • Save a default .pypirc file with default username and password to use.

A minimal solution

The distutils approach was to have several separate command classes called register, upload and upload_doc, where really all that was needed was some methods. That’s the approach distlib takes, by implementing a PackageIndex class with register(), upload_file() and upload_documentation() methods. The PackageIndex class contains no user interface code whatsoever: that’s assumed to be the domain of the packaging tool. The packaging tool is expected to get the required information from a user using whatever means the developers of that tool deem to be the most appropriate; the required attributes are then set on the PackageIndex instance. (Examples of this kind of information: user name, password, whether the user wants to save a default configuration, where the signing program and its keys live.)

The minimal interface to provide the required functionality thus looks like this:

class PackageIndex(object):
    def __init__(self, url=None, mirror_host=None):
        Initialise an instance using a specific index URL, and
        a DNS name for a mirror host which can be used to
        determine available mirror hosts for the index.

    def save_configuration(self):
        Save the username and password attributes of this
        instance in a default .pypirc file.
    def register(self, metadata):
        Register a project on the index, using the specified metadata.

    def upload_file(self, metadata, filename, signer=None,
                    sign_password=None, filetype='sdist',
        Upload a distribution file to the index using the
        specified metadata to identify it, with options
        for signing and for binary distributions which are
        specific to Python versions.

    def upload_documentation(self, metadata, doc_dir):
        Upload documentation files in a specified directory
        using the specified metadata to identify it, after
        archiving the directory contents into a .zip file.

The following additional attributes can be identified on PackageIndex instances:

  • username - the username to use for authentication.
  • password - the password to use for authentication.
  • mirrors (read-only) - a list of hostnames of known mirrors.

The resources API

This section describes the design of the distlib API relating to accessing ‘resources’, which is a convenient label for data files associated with Python packages.

The problem we’re trying to solve

Developers often have a need to co-locate data files with their Python packages. Examples of these might be:

  • Templates, commonly used in web applications
  • Translated messages used in internationalisation/localisation

The stdlib does not provide a uniform API to access these resources. A common approach is to use __file__ like this:

base = os.path.dirname(__file__)
data_filename = os.path.join(base, 'data.bin')
with open(data_filename, 'rb') as f:
    # read the data from f

However, this approach fails if the package is deployed in a .zip file.

To consider how to provide a minimal uniform API to access resources in Python packages, we’ll assume that the requirements are as follows:

  • All resources are regarded as binary. The consuming application is expected to know how to convert resources to text, where appropriate.
  • All resources are read-only.
  • It should be possible to access resources either as streams, or as their entire data as a byte-string.
  • Resources will have a unique, identifying name which is text. Resources will be hierarchical and named using filesystem-like paths using ‘/’ as a separator. The library will be responsible for converting resource names to the names of the underlying representations (e.g. encoding of file names corresponding to resource names).
  • Some resources are containers of other resources, some are not. For example, a resource nested/nested_resource.bin in a package would not contain other resources, but implies the existence of a resource nested, which contains nested_resource.bin.
  • Resources can only be associated with packages, not with modules. That’s because with peer modules a.py and b.py, there’s no obvious location for data associated only with a: both a and b are in the same directory. With a package, there’s no ambiguity, as a package is associated with a specific directory, and no other package can be associated with that directory.
  • Support should be provided for access to data deployed in the file system or in packages contained in .zip files, and third parties should be able to extend the facilities to work with other storage formats which support import of Python packages.
  • It should be possible to access the contents of any resource through a file on the file system. This is to cater for any external APIs which need to access the resource data as files (examples would be a shared library for linking using dlopen() on POSIX, or any APIs which need access to resource data via OS-level file handles rather than Python streams).

A minimal solution

We know that we will have to deal with resources, so it seems natural that there would be a Resource class in the solution. From the requirements, we can see that a Resource would have the following:

  • A name property identifying the resource.
  • A as_stream method allowing access to the resource data as a binary stream. This is not a property, because a new stream is returned each time this method is called. The returned stream should be closed by the caller.
  • A bytes property returning the entire contents of the resource as a byte string.
  • A size property indicating the size of the resource (in bytes).
  • An is_container property indicating whether the resource is a container of other resources.
  • A resources property returning the names of resources contained within the resource.

The Resource class would be the logical place to perform sanity checks which relate to all resources. For example:

  • It doesn’t make sense to ask for the bytes or size properties or call the as_stream method of a container resource.
  • It doesn’t make sense to ask for the resources property of a resource which is not a container.

It seems reasonable to raise exceptions for incorrect property or method accesses.

We know that we need to support resource access in the file system as well as .zip files, and to support other sources of storage which might be used to import Python packages. Since import and loading of Python packages happens through PEP 302 importers and loaders, we can deduce that the mechanism used to find resources in a package will be closely tied to the loader for that package.

We could consider an API for finding resources in a package like this:

def find_resource(pkg, resource_name):
    # return a Resource instance for the resource

and then use it like this:

r1 = find_resource(pkg, 'foo')
r2 = find_resource(pkg, 'bar')

However, we’ll often have situations where we will want to get multiple resources from a package, and in certain applications we might want to implement caching or other processing of resources before returning them. The above API doesn’t facilitate this, so let’s consider delegating the finding of resources in a package to a finder for that package. Once we get a finder, we can hang on to it and ask it to find multiple resources. Finders can be extended to provide whatever caching and preprocessing an application might need.

To get a finder for a package, let’s assume there’s a finder function:

def finder(pkg):
    # return a finder for the specified package

We can use it like this:

f = finder(pkg)
r1 = f.find('foo')
r2 = f.find('bar')

The finder function knows what kind of finder to return for a particular package through the use of a registry. Given a package, finder can determine the loader for that package, and based on the type of loader, it can instantiate the right kind of finder. The registry maps loader types to callables that return finders. The callable is called with a single argument – the Python module object for the package.

Given that we have finders in the design, we can identify ResourceFinder and ZipResourceFinder classes for the two import systems we’re going to support. We’ll make ResourceFinder a concrete class rather than an interface - it’ll implement getting resources from packages stored in the file system. ZipResourceFinder will be a subclass of ResourceFinder.

Since there is no loader for file system packages when the C-based import system is used, the registry will come with the following mappings:

  • type(None) -> ResourceFinder
  • _frozen_importlib.SourceFileLoader -> ``ResourceFinder
  • zipimport.zipimporter -> ZipResourceFinder

Users of the API can add new or override existing mappings using the following function:

def register_finder(loader, finder_maker):
    # register ``finder_maker`` to make finders for packages with a loader
    # of the same type as ``loader``.

Typically, the finder_maker will be a class like ResourceFinder or ZipResourceFinder, but it can be any callable which takes the Python module object for a package and returns a finder.

Let’s consider in more detail what finders look like and how they interact with the Resource class. We’ll keep the Resource class minimal; API users never instantiate Resource directly, but call a finder’s find method to return a Resource instance. A finder could return an instance of a Resource subclass if really needed, though it shouldn’t be necessary in most cases. If a finder can’t find a resource, it should return None.

The Resource constructor will look like this:

def __init__(self, finder, name):
    self.finder = finder
    self.name = name
    # other initialisation, not specified

and delegate as much work as possible to its finder. That way, new import loader types can be supported just by implementing a suitable XXXResourceFinder for that loader type.

What a finder needs to do can be exemplified by the following skeleton for ResourceFinder:

class ResourceFinder(object):
    def __init__(self, module):
        "Initialise finder for the specified package"

    def find(self, resource_name):
        "Find and return a ``Resource`` instance or ``None``"

    def is_container(self, resource):
        "Return whether resource is a container"

    def get_bytes(self, resource):
        "Return the resource's data as bytes"

    def get_size(self, resource):
        "Return the size of the resource's data in bytes"

    def get_stream(self, resource):
        "Return the resource's data as a binary stream"

    def get_resources(self, resource):
        Return the resources contained in this resource as a set of
        (relative) resource names

Dealing with the requirement for access via file system files

To cater for the requirement that the contents of some resources be made available via a file on the file system, we’ll assume a simple caching solution that saves any such resources to a local file system cache, and returns the filename of the resource in the cache. We need to divide the work between the finder and the cache. We’ll deliver the cache function through a Cache class, which will have the following methods:

  • A constructor which takes an optional base directory for the cache. If none is provided, we’ll construct a base directory of the form:


    where <rootdir> is the user’s home directory. On Windows, if the environment specifies a variable named LOCALAPPDATA, its value will be used as <rootdir> – otherwise, the user’s home directory will be used.

  • A get() method which takes a Resource and returns a file system filename, such that the contents of that named file will be the contents of the resource.

  • An is_stale() method which takes a Resource and its corresponding file system filename, and returns whether the file system file is stale when compared with the resource. Knowing that cache invalidation is hard, the default implementation just returns True.

  • A prefix_to_dir() method which converts a prefix to a directory name. We’ll assume that for the cache, a resource path can be divided into two parts: the prefix and the subpath. For resources in a .zip file, the prefix would be the pathname of the archive, while the subpath would be the path inside the archive. For a file system resource, since it is already in the file system, the prefix would be None and the subpath would be the absolute path name of the resource. The prefix_to_dir() method’s job is to convert a prefix (if not None) to a subdirectory in the cache that holds the cached files for all resources with that prefix. We’ll delegate the determination of a resource’s prefix and subpath to its finder, using a get_cache_info() method on finders, which takes a Resource and returns a (prefix, subpath) tuple.

    The default implementation will use os.splitdrive() to see if there’s a Windows drive, if present, and convert its ':' to '---'. The rest of the prefix will be converted by replacing '/' by '--', and appending '.cache' to the result.

The cache will be activated when the file_path property of a Resource is accessed. This will be a cached property, and will call the cache’s get() method to obtain the file system path.

The scripts API

This section describes the design of the distlib API relating to installing scripts.

The problem we’re trying to solve

Installing scripts is slightly more involved than simply copying files from source to target, for the following reasons:

  • On POSIX systems, scripts need to be made executable. To cater for scenarios where there are multiple Python versions installed on a computer, scripts need to have their shebang lines adjusted to point to the correct interpreter. This requirement is commonly found when virtual environments (venvs) are in use, but also in other multiple-interpreter scenarios.
  • On Windows systems, which don’t support shebang lines natively, some alternate means of finding the correct interpreter need to be provided. Following the acceptance and implementation of PEP 397, a shebang- interpreting launcher will be available in Python 3.3 and later and a standalone version of it for use with earlier Python versions is also available. However, where this can’t be used, an alternative approach using executable launchers installed with the scripts may be necessary. (That is the approach taken by setuptools.) Windows also has two types of launchers - console applications and Windows applications. The appropriate launcher needs to be used for scripts.
  • Some scripts are effectively just callable code in a Python package, with boilerplate for importing that code, calling it and returning its return value as the script’s return code. It would be useful to have the boilerplate standardised, so that developers need just specify which callables to expose as scripts, and under what names, using e.g. a name = callable syntax. (This is the approach taken by setuptools for the popular console_scripts feature).

A minimal solution

Script handling in distutils and setuptools is done in two phases: ‘build’ and ‘install’. Whether a particular packaging tool chooses to do the ‘heavy lifting’ of script creation (i.e. the things referred to above, beyond simple copying) in ‘build’ or ‘install’ phases, the job is the same. To abstract out just the functionality relating to scripts, in an extensible way, we can just delegate the work to a class, unimaginatively called ScriptMaker. Given the above requirements, together with the more basic requirement of being able to do ‘dry-run’ installation, we need to provide a ScriptMaker with the following items of information:

  • Where source scripts are to be found.
  • Where scripts are to be installed.
  • Whether, on Windows, executable launchers should be added.
  • Whether a dry-run mode is in effect.

These dictate the form that ScriptMaker.__init__() will take.

In addition, other methods suggest themselves for ScriptMaker:

  • A make() method, which takes a specification, which is either a filename or a ‘wrap me a callable’ indicator which looks like this:

    name = some_package.some_module:some_callable [ flag(=value) ... ]

    The name would need to be a valid filename for a script, and the some_package.some_module part would indicate the module where the callable resides. The some_callable part identifies the callable, and optionally you can have flags, which the ScriptMaker instance must know how to interpret. One flag would be 'gui', indicating that the launcher should be a Windows application rather than a console application, for GUI-based scripts which shouldn’t show a console window.

    The above specification is used by setuptools for the ‘console_scripts’ feature. See Flag formats for more information about flags.


    Both setuptools and PEP 426 interpret flags as a single value, which represents an extra (a set of optional dependencies needed for optional features of a distribution).

    It seems sensible for this method to return a list of absolute paths of files that were installed (or would have been installed, but for the dry-run mode being in effect).

  • A make_multiple() method, which takes an iterable of specifications and just runs calls make() on each item iterated over, aggregating the results to return a list of absolute paths of all files that were installed (or would have been installed, but for the dry-run mode being in effect).

    One advantage of having this method is that you can override it in a subclass for post-processing, e.g. to run a tool like 2to3, or an analysis tool, over all the installed files.

  • The details of the callable specification can be encapsulated in a utility function, get_exports_entry(). This would take a specification and return None, if the specification didn’t match the callable format, or an instance of ExportEntry if it did match.

In addition, the following attributes on a ScriptMaker could be further used to refine its behaviour:

  • force to indicate when scripts should be copied from source to target even when timestamps show the target is up to date.
  • set_mode to indicate whether, on Posix, the execute mode bits should be set on the target script.

Flag formats

Flags, if present, are enclosed by square brackets. Each flag can have the format of just an alphanumeric string, optionally followed by an ‘=’ and a value (with no intervening spaces). Multiple flags can be separated by ‘,’ and whitespace. The following would be valid flag sections:

[a, b, c]
[a=b, c=d, e, f=g, 9=8]

whereas the following would be invalid:



Both setuptools and PEP 426 restrict flag formats to a single value, without an =. This value represents an extra (a set of optional dependencies needed for optional features of a distribution).

The version API

This section describes the design of the distlib API relating to versions.

The problem we’re trying to solve

Distribution releases are named by versions and versions have two principal uses:

  • Identifying a particular release and determining whether or not it is earlier or later than some other release.
  • When specifying other distributions that a distribution release depends on, specifying constraints governing the releases of those distributions that are depended upon.

In addition, qualitative information may be given by the version format about the quality of the release: e.g. alpha versions, beta versions, stable releases, hot-fixes following a stable release. The following excerpt from PEP 386 defines the requirements for versions:

  • It should be possible to express more than one versioning level (usually this is expressed as major and minor revision and, sometimes, also a micro revision).
  • A significant number of projects need special meaning versions for “pre-releases” (such as “alpha”, “beta”, “rc”), and these have widely used aliases (“a” stands for “alpha”, “b” for “beta” and “c” for “rc”). And these pre-release versions make it impossible to use a simple alphanumerical ordering of the version string components. (e.g. 3.1a1 < 3.1)
  • Some projects also need “post-releases” of regular versions, mainly for maintenance purposes, which can’t be clearly expressed otherwise.
  • Development versions allow packagers of unreleased work to avoid version clashes with later stable releases.

There are a number of version schemes in use. The ones of most interest in the Python ecosystem are:

  • Loose versioning in distutils. Any version number is allowed, with lexicographical ordering. No support exists for pre- and post-releases, and lexicographical ordering can be unintuitive (e.g. ‘1.10’ < ‘1.2.1’)
  • Strict versioning in distutils, which supports slightly more structure. It allows for up to three dot-separated numeric components, and support for multiple alpha and beta releases. However, there is no support for release candidates, nor for post-release versions.
  • Versioning in setuptools/distribute. This is described in PEP 386 in this section – it’s perhaps the most widely used Python version scheme, but since it tries to be very flexible and work with a wide range of conventions, it ends up allowing a very chaotic mess of version conventions in the Python community as a whole.
  • The proposed versioning scheme described in PEP 440.
  • Semantic versioning, which is rational, simple and well-regarded in the software community in general.

Although the new versioning scheme mentioned in PEP 386 was implemented in distutils2 and that code has been copied over to distlib, there are many projects on PyPI which do not conform to it, but rather to the “legacy” versioning schemes in distutils/setuptools/distribute. These schemes are deserving of some support not because of their intrinsic qualities, but due to their ubiquity in projects registered on PyPI. Below are some results from testing actual projects on PyPI:

Packages processed: 24891
Packages with no versions: 217
Packages with versions: 24674
Number of packages clean for all schemes: 19010 (77%)
Number of packages clean for PEP 386: 21072 (85%)
Number of packages clean for PEP 386 + suggestion: 23685 (96%)
Number of packages clean for legacy: 24674 (100%, by you would expect)
Number of packages clean for semantic: 19278 (78%)

where “+ suggestion” refers to using the suggested version algorithm to derive a version from a version which would otherwise be incompatible with PEP 386.

A minimal solution

Since distlib is a low-level library which might be used by tools which work with existing projects, the internal implementation of versions has changed slightly from distutils2 to allow better support for legacy version numbering. Since the re-implementation facilitated adding semantic version support at minimal cost, this has also been provided.


The basic scheme is as follows. The differences between versioning schemes is catered for by having a single function for each scheme which converts a string version to an appropriate tuple which acts as a key for sorting and comparison of versions. We have a base class, Version, which defines any common code. Then we can have subclasses NormalizedVersion (PEP-386), LegacyVersion (distribute/setuptools) and SemanticVersion.

To compare versions, we just check type compatibility and then compare the corresponding tuples.


Matchers take a name followed by a set of constraints in parentheses. Each constraint is an operation together with a version string which needs to be converted to the corresponding version instance.

In summary, the following attributes can be identified for Version and Matcher:

  • version string passed in to constructor (stripped)
  • parser to convert string string to tuple
  • compare functions to compare with other versions of same type
  • version string passed in to constructor (stripped)
  • name of distribution
  • list of constraints
  • parser to convert string to name and set of constraints, using the same function as for Version to convert the version strings in the constraints to version instances
  • method to match version to constraints and return True/False

Given the above, it appears that all the functionality could be provided with a single class per versioning scheme, with the only difference between them being the function to convert from version string to tuple. Any instance would act as either version or predicate, would display itself differently according to which it is, and raise exceptions if the wrong type of operation is performed on it (matching only allowed for predicate instances; <=, <, >=, > comparisons only allowed for version instances; and == and != allowed for either.

However, the use of the same class to implement versions and predicates leads to ambiguity, because of the very loose project naming and versioning schemes allowed by PyPI. For example, “Hello 2.0” could be a valid project name, and “5” is a project name actually registered on PyPI. If distribution names can look like versions, it’s hard to discern the developer’s intent when creating an instance with the string “5”. So, we make separate classes for Version and Matcher.

For ease of testing, the module will define, for each of the supported schemes, a function to do the parsing (as no information is needed other than the string), and the parse method of the class will call that function:

def normalized_key(s):
    "parse using PEP-386 logic"

def legacy_key(s):
    "parse using distribute/setuptools logic"

def semantic_key(s):
    "parse using semantic versioning logic"

class Version:
    # defines all common code

    def parse(self, s):
        raise NotImplementedError('Please implement in a subclass')

and then:

class NormalizedVersion(Version):
    def parse(self, s): return normalized_key(s)

class LegacyVersion(Version):
    def parse(self, s): return legacy_key(s)

class SemanticVersion(Version):
    def parse(self, s): return semantic_key(s)

And a custom versioning scheme can be devised to work in the same way:

def custom_key(s):
   convert s to tuple using custom logic, raise UnsupportedVersionError
   on problems

class CustomVersion(Version):
    def parse(self, s): return custom_key(s)

The matcher classes are pretty minimal, too:

class Matcher(object):
    version_class = None

    def match(self, string_or_version):
        If passed a string, convert to version using version_class,
        then do matching in a way independent of version scheme in use

and then:

class NormalizedMatcher(Matcher):
    version_class = NormalizedVersion

class LegacyMatcher(Matcher):
    version_class = LegacyVersion

class SemanticMatcher(Matcher):
    version_class = SemanticVersion

Version schemes

Ideally one would want to work with the PEP 386 scheme, but there might be times when one needs to work with the legacy scheme (for example, when investigating dependency graphs of existing PyPI projects). Hence, the important aspects of each scheme are bundled into a simple VersionScheme class:

class VersionScheme(object):
    def __init__(self, key, matcher):
        self.key = key          # version string -> tuple converter
        self.matcher = matcher  # Matcher subclass for the scheme

Of course, the version class is also available through the matcher’s version_class attribute.

VersionScheme makes it easier to work with alternative version schemes. For example, say we decide to experiment with an “adaptive” version scheme, which is based on the PEP 386 scheme, but when handed a non-conforming version, automatically tries to convert it to a normalized version using suggest_normalized_version(). Then, code which has to deal with version schemes just has to pick the appropriate scheme by name.

Creating the adaptive scheme is easy:

def adaptive_key(s):
        result = normalized_key(s, False)
    except UnsupportedVersionError:
        s = suggest_normalized_version(s)
        if s is None:
        result = normalized_key(s, False)
    return result

class AdaptiveVersion(NormalizedVersion):
    def parse(self, s): return adaptive_key(s)

class AdaptiveMatcher(Matcher):
    version_class = AdaptiveVersion

The appropriate scheme can be fetched by using the get_scheme() function, which is defined thus:

def get_scheme(scheme_name):
    "Get a VersionScheme for the given scheme_name."

Allowed names are 'normalized', 'legacy', 'semantic', 'adaptive' and 'default' (which points to the same as 'adaptive'). If an unrecognised name is passed in, a ValueError is raised.

The reimplemented distlib.version module is shorter than the corresponding module in distutils2, but the entire test suite passes and there is support for working with three versioning schemes as opposed to just one. However, the concept of “final” versions, which is not in the PEP but which was in the distutils2 implementation, has been removed because it appears of little value (there’s no way to determine the “final” status of versions for many of the project releases registered on PyPI).

The wheel API

This section describes the design of the wheel API which facilitates building and installing from wheels, the new binary distribution format for Python described in PEP 427.

The problem we’re trying to solve

There are basically two operations which need to be performed on wheels:

  • Building a wheel from a source distribution.
  • Installing a distribution which has been packaged as a wheel.

A minimal solution

Since we’re talking about wheels, it seems likely that a Wheel class would be part of the design. This allows for extensibility over a purely function-based API. The Wheel would be expected to have methods that support the required operations:

class Wheel(object):
    def __init__(self, spec):
        Initialise an instance from a specification. This can either be a
        valid filename for a wheel (for when you want to work with an
        existing wheel), or just the ``name-version-buildver`` portion of
        a wheel's filename (for when you're going to build a wheel for a
        known version and build of a named project).

    def build(self, paths, tags=None):
        Build a wheel. The ``name`, ``version`` and ``buildver`` should
        already have been set correctly. The ``paths`` should be a
        dictionary with keys 'prefix', 'scripts', 'headers', 'data' and one
        of 'purelib' and 'platlib'. These must point to valid paths if
        they are to be included in the wheel. The optional ``tags``
        argument should, if specified, be a dictionary with optional keys
        'pyver', 'abi' and 'arch' indicating lists of tags which
        indicate environments with which the wheel is compatible.

    def install(self, paths, maker, **kwargs):
        Install from a wheel. The ``paths`` should be a dictionary with
        keys 'prefix', 'scripts', 'headers', 'data', 'purelib' and
        'platlib'. These must point to valid paths to which files may
        be written if they are in the wheel. Only one of the 'purelib'
        and 'platlib' paths will be used (in the case where they are
        different), depending on whether the wheel is for a pure-
        Python distribution.

        The ``maker`` argument should be a suitably configured
        :class:`ScriptMaker` instance. The ``source_dir`` and
        ``target_dir`` arguments can be set to ``None`` when creating the
        instance - these will be set to appropriate values inside this

        The following keyword arguments are recognised:

        * ``warner``, if specified, should be a callable
          that will be called with (software_wheel_ver, file_wheel_ver)
          if they differ. They will both be in the form of tuples
          (major_ver, minor_ver). The ``warner`` defaults to ``None``.

        * It's conceivable that one might want to install only the library
          portion of a package -- not installing scripts, headers data and
          so on. If ``lib_only`` is specified as ``True``, only the
          ``site-packages`` contents will be installed. The default value
          is ``False`` (meaning everything will be installed).

In addition to the above, the following attributes can be identified for a Wheel instance:

  • name – the name of the distribution
  • version – the version of the distribution
  • buildver – the build tag for the distribution
  • pyver – a list of Python versions with which the wheel is compatible
  • abi – a list of application binary interfaces (ABIs) with which the wheel is compatible
  • arch – a list of architectures with which the wheel is compatible
  • dirname – The directory in which a wheel file is found/to be created
  • filename – The filename of the wheel (computed from the other attributes)

Next steps

You might find it helpful to look at the API Reference.

Comments powered by Disqus