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Distributing Python Modules |  |
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Distributing Python Modules | |
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The setup script is the centre of all activity in building, distributing, and installing modules using the Distutils. The main purpose of the setup script is to describe your module distribution to the Distutils, so that the various commands that operate on your modules do the right thing. As we saw in section 2.1 above, the setup script consists mainly of a call to setup(), and most information supplied to the Distutils by the module developer is supplied as keyword arguments to setup().
Here's a slightly more involved example, which we'll follow for the next couple of sections: the Distutils' own setup script. (Keep in mind that although the Distutils are included with Python 1.6 and later, they also have an independent existence so that Python 1.5.2 users can use them to install other module distributions. The Distutils' own setup script, shown here, is used to install the package into Python 1.5.2.)
#!/usr/bin/env python
from distutils.core import setup
setup(name="Distutils",
version="1.0",
description="Python Distribution Utilities",
author="Greg Ward",
author_email="gward@python.net",
url="http://www.python.org/sigs/distutils-sig/",
packages=['distutils', 'distutils.command'],
)
There are only two differences between this and the trivial one-file distribution presented in section 2.1: more metadata, and the specification of pure Python modules by package, rather than by module. This is important since the Distutils consist of a couple of dozen modules split into (so far) two packages; an explicit list of every module would be tedious to generate and difficult to maintain. For more information on the additional meta-data, see section 3.7.
Note that any pathnames (files or directories) supplied in the setup script should be written using the Unix convention, i.e. slash-separated. The Distutils will take care of converting this platform-neutral representation into whatever is appropriate on your current platform before actually using the pathname. This makes your setup script portable across operating systems, which of course is one of the major goals of the Distutils. In this spirit, all pathnames in this document are slash-separated. (MacOS programmers should keep in mind that the absence of a leading slash indicates a relative path, the opposite of the MacOS convention with colons.)
This, of course, only applies to pathnames given to Distutils functions. If you, for example, use standard Python functions such as glob.glob() or os.listdir() to specify files, you should be careful to write portable code instead of hardcoding path separators:
glob.glob(os.path.join('mydir', 'subdir', '*.html'))
os.listdir(os.path.join('mydir', 'subdir'))
The packages option tells the Distutils to process (build,
distribute, install, etc.) all pure Python modules found in each package
mentioned in the packages list. In order to do this, of
course, there has to be a correspondence between package names and
directories in the filesystem. The default correspondence is the most
obvious one, i.e. package distutils is found in the directory
distutils relative to the distribution root. Thus, when you say
packages = ['foo'] in your setup script, you are promising that
the Distutils will find a file foo/__init__.py (which might
be spelled differently on your system, but you get the idea) relative to
the directory where your setup script lives. If you break this
promise, the Distutils will issue a warning but still process the broken
package anyways.
If you use a different convention to lay out your source directory, that's no problem: you just have to supply the package_dir option to tell the Distutils about your convention. For example, say you keep all Python source under lib, so that modules in the ``root package'' (i.e., not in any package at all) are in lib, modules in the foo package are in lib/foo, and so forth. Then you would put
package_dir = {'': 'lib'}
in your setup script. The keys to this dictionary are package names,
and an empty package name stands for the root package. The values are
directory names relative to your distribution root. In this case, when
you say packages = ['foo'], you are promising that the file
lib/foo/__init__.py exists.
Another possible convention is to put the foo package right in lib, the foo.bar package in lib/bar, etc. This would be written in the setup script as
package_dir = {'foo': 'lib'}
A package: dir entry in the package_dir
dictionary implicitly applies to all packages below package, so
the foo.bar case is automatically handled here. In this
example, having packages = ['foo', 'foo.bar'] tells the Distutils
to look for lib/__init__.py and
lib/bar/__init__.py. (Keep in mind that although
package_dir applies recursively, you must explicitly list all
packages in packages: the Distutils will not recursively
scan your source tree looking for any directory with an
__init__.py file.)
For a small module distribution, you might prefer to list all modules rather than listing packages--especially the case of a single module that goes in the ``root package'' (i.e., no package at all). This simplest case was shown in section 2.1; here is a slightly more involved example:
py_modules = ['mod1', 'pkg.mod2']
This describes two modules, one of them in the ``root'' package, the other in the pkg package. Again, the default package/directory layout implies that these two modules can be found in mod1.py and pkg/mod2.py, and that pkg/__init__.py exists as well. And again, you can override the package/directory correspondence using the package_dir option.
Just as writing Python extension modules is a bit more complicated than writing pure Python modules, describing them to the Distutils is a bit more complicated. Unlike pure modules, it's not enough just to list modules or packages and expect the Distutils to go out and find the right files; you have to specify the extension name, source file(s), and any compile/link requirements (include directories, libraries to link with, etc.).
All of this is done through another keyword argument to setup(), the extensions option. extensions is just a list of Extension instances, each of which describes a single extension module. Suppose your distribution includes a single extension, called foo and implemented by foo.c. If no additional instructions to the compiler/linker are needed, describing this extension is quite simple:
uExtension("foo", ["foo.c"])
The Extension class can be imported from distutils.core along with setup(). Thus, the setup script for a module distribution that contains only this one extension and nothing else might be:
from distutils.core import setup, Extension
setup(name="foo", version="1.0",
ext_modules=[Extension("foo", ["foo.c"])])
The Extension class (actually, the underlying extension-building
machinery implemented by the build_ext command) supports a
great deal of flexibility in describing Python extensions, which is
explained in the following sections.
The first argument to the Extension constructor is always the name of the extension, including any package names. For example,
Extension("foo", ["src/foo1.c", "src/foo2.c"])
describes an extension that lives in the root package, while
Extension("pkg.foo", ["src/foo1.c", "src/foo2.c"])
describes the same extension in the pkg package. The source files and resulting object code are identical in both cases; the only difference is where in the filesystem (and therefore where in Python's namespace hierarchy) the resulting extension lives.
If you have a number of extensions all in the same package (or all under the same base package), use the ext_package keyword argument to setup(). For example,
setup(...
ext_package="pkg",
ext_modules=[Extension("foo", ["foo.c"]),
Extension("subpkg.bar", ["bar.c"])]
)
will compile foo.c to the extension pkg.foo, and bar.c to pkg.subpkg.bar.
The second argument to the Extension constructor is a list of source files. Since the Distutils currently only support C, C++, and Objective-C extensions, these are normally C/C++/Objective-C source files. (Be sure to use appropriate extensions to distinguish C++ source files: .cc and .cpp seem to be recognized by both Unix and Windows compilers.)
However, you can also include SWIG interface (.i) files in the
list; the build_ext command knows how to deal with SWIG
extensions: it will run SWIG on the interface file and compile the
resulting C/C++ file into your extension.
** SWIG support is rough around the edges and largely untested; especially SWIG support for C++ extensions! Explain in more detail here when the interface firms up. **
On some platforms, you can include non-source files that are processed by the compiler and included in your extension. Currently, this just means Windows message text (.mc) files and resource definition (.rc) files for Visual C++. These will be compiled to binary resource (.res) files and linked into the executable.
Three optional arguments to Extension will help if you need to
specify include directories to search or preprocessor macros to
define/undefine: include_dirs, define_macros, and
undef_macros.
For example, if your extension requires header files in the
include directory under your distribution root, use the
include_dirs option:
Extension("foo", ["foo.c"], include_dirs=["include"])
You can specify absolute directories there; if you know that your extension will only be built on Unix systems with X11R6 installed to /usr, you can get away with
Extension("foo", ["foo.c"], include_dirs=["/usr/include/X11"])
You should avoid this sort of non-portable usage if you plan to distribute your code: it's probably better to write C code like
#include <X11/Xlib.h>
If you need to include header files from some other Python extension,
you can take advantage of the fact that header files are installed in a
consistent way by the Distutils install_header command. For
example, the Numerical Python header files are installed (on a standard
Unix installation) to /usr/local/include/python1.5/Numerical.
(The exact location will differ according to your platform and Python
installation.) Since the Python include
directory--/usr/local/include/python1.5 in this case--is always
included in the search path when building Python extensions, the best
approach is to write C code like
#include <Numerical/arrayobject.h>
from distutils.sysconfig import get_python_inc
incdir = os.path.join(get_python_inc(plat_specific=1), "Numerical")
setup(...,
Extension(..., include_dirs=[incdir]))
Even though this is quite portable--it will work on any Python installation, regardless of platform--it's probably easier to just write your C code in the sensible way.
You can define and undefine pre-processor macros with the
define_macros and undef_macros options.
define_macros takes a list of (name, value) tuples, where
name is the name of the macro to define (a string) and
value is its value: either a string or None. (Defining a
macro FOO to None is the equivalent of a bare
#define FOO in your C source: with most compilers, this sets
FOO to the string 1.) undef_macros is just
a list of macros to undefine.
For example:
Extension(...,
define_macros=[('NDEBUG', '1'),
('HAVE_STRFTIME', None)],
undef_macros=['HAVE_FOO', 'HAVE_BAR'])
is the equivalent of having this at the top of every C source file:
#define NDEBUG 1 #define HAVE_STRFTIME #undef HAVE_FOO #undef HAVE_BAR
You can also specify the libraries to link against when building your
extension, and the directories to search for those libraries. The
libraries option is a list of libraries to link against,
library_dirs is a list of directories to search for libraries at
link-time, and runtime_library_dirs is a list of directories to
search for shared (dynamically loaded) libraries at run-time.
For example, if you need to link against libraries known to be in the standard library search path on target systems
Extension(...,
libraries=["gdbm", "readline"])
If you need to link with libraries in a non-standard location, you'll
have to include the location in library_dirs:
Extension(...,
library_dirs=["/usr/X11R6/lib"],
libraries=["X11", "Xt"])
(Again, this sort of non-portable construct should be avoided if you intend to distribute your code.)
** Should mention clib libraries here or somewhere else! **
There are still some other options which can be used to handle special cases.
The extra_objects option is a list of object files to be passed to the linker. These files must not have extensions, as the default extension for the compiler is used.
extra_compile_args and extra_link_args can be used to specify additional command line options for the respective compiler and linker command lines.
export_symbols is only useful on Windows. It can contain a list
of symbols (functions or variables) to be exported. This option
is not needed when building compiled extensions: Distutils
will automatically add initmodule
to the list of exported symbols.
Scripts are files containing Python source code, intended to be
started from the command line. Scripts don't require Distutils to do
anything very complicated. The only clever feature is that if the
first line of the script starts with #! and contains the word
``python'', the Distutils will adjust the first line to refer to the
current interpreter location.
The scripts option simply is a list of files to be handled in this way. From the PyXML setup script:
setup (...
scripts = ['scripts/xmlproc_parse', 'scripts/xmlproc_val']
)
The data_files option can be used to specify additional files needed by the module distribution: configuration files, message catalogs, data files, anything which doesn't fit in the previous categories.
data_files specifies a sequence of (directory, files) pairs in the following way:
setup(...
data_files=[('bitmaps', ['bm/b1.gif', 'bm/b2.gif']),
('config', ['cfg/data.cfg']),
('/etc/init.d', ['init-script'])]
)
Note that you can specify the directory names where the data files will be installed, but you cannot rename the data files themselves.
Each (directory, files) pair in the sequence specifies the
installation directory and the files to install there. If
directory is a relative path, it is interpreted relative to the
installation prefix (Python's sys.prefix for pure-Python
packages, sys.exec_prefix for packages that contain extension
modules). Each file name in files is interpreted relative to
the setup.py script at the top of the package source
distribution. No directory information from files is used to
determine the final location of the installed file; only the name of
the file is used.
You can specify the data_files options as a simple sequence
of files without specifying a target directory, but this is not recommended,
and the install command will print a warning in this case.
To install data files directly in the target directory, an empty
string should be given as the directory.
The setup script may include additional meta-data beyond the name and version. This information includes:
| Meta-Data | Description | Value | Notes |
|---|---|---|---|
name |
name of the package | short string | (1) |
version |
version of this release | short string | (1)(2) |
author |
package author's name | short string | (3) |
author_email |
email address of the package author | email address | (3) |
maintainer |
package maintainer's name | short string | (3) |
maintainer_email |
email address of the package maintainer | email address | (3) |
url |
home page for the package | URL | (1) |
description |
short, summary description of the package | short string | |
long_description |
longer description of the package | long string | |
download_url |
location where the package may be downloaded | URL | (4) |
classifiers |
a list of Trove classifiers | list of strings | (4) |
Notes:
None of the string values may be Unicode.
Encoding the version information is an art in itself. Python packages generally adhere to the version format major.minor[.patch][sub]. The major number is 0 for initial, experimental releases of software. It is incremented for releases that represent major milestones in a package. The minor number is incremented when important new features are added to the package. The patch number increments when bug-fix releases are made. Additional trailing version information is sometimes used to indicate sub-releases. These are "a1,a2,...,aN" (for alpha releases, where functionality and API may change), "b1,b2,...,bN" (for beta releases, which only fix bugs) and "pr1,pr2,...,prN" (for final pre-release release testing). Some examples:
classifiers are specified in a python list:
setup(...
classifiers = [
'Development Status :: 4 - Beta',
'Environment :: Console',
'Environment :: Web Environment',
'Intended Audience :: End Users/Desktop',
'Intended Audience :: Developers',
'Intended Audience :: System Administrators',
'License :: OSI Approved :: Python Software Foundation License',
'Operating System :: MacOS :: MacOS X',
'Operating System :: Microsoft :: Windows',
'Operating System :: POSIX',
'Programming Language :: Python',
'Topic :: Communications :: Email',
'Topic :: Office/Business',
'Topic :: Software Development :: Bug Tracking',
],
)
If you wish to include classifiers in your setup.py file and also
wish to remain backwards-compatible with Python releases prior to 2.2.3,
then you can include the following code fragment in your setup.py
before the setup() call.
# patch distutils if it can't cope with the "classifiers" or
# "download_url" keywords
if sys.version < '2.2.3':
from distutils.dist import DistributionMetadata
DistributionMetadata.classifiers = None
DistributionMetadata.download_url = None
Sometimes things go wrong, and the setup script doesn't do what the developer wants.
Distutils catches any exceptions when running the setup script, and print a simple error message before the script is terminated. The motivation for this behaviour is to not confuse administrators who don't know much about Python and are trying to install a package. If they get a big long traceback from deep inside the guts of Distutils, they may think the package or the Python installation is broken because they don't read all the way down to the bottom and see that it's a permission problem.
On the other hand, this doesn't help the developer to find the cause of the failure. For this purpose, the DISTUTILS_DEBUG environment variable can be set to anything except an empty string, and distutils will now print detailed information what it is doing, and prints the full traceback in case an exception occurs.
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Distributing Python Modules | |
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