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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 meta-data, 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.
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 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 right 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:
Extension("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++ extensions, these are normally C/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 of 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 your code to include
(e.g.) <X11/Xlib.h>.
If you need to include header files from some other Python extension,
you can take advantage of the fact that the Distutils install extension
header files in a consistent way. 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 include (e.g.)
<Numerical/arrayobject.h>. If you insist on putting the
Numerical include directory right into your header search path,
though, you can find that directory using the Distutils
sysconfig module:
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 compiler resp. the linker command line.
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: the initmodule
function will automatically be added to the exported symbols list
by Distutils.
Scripts are files containing Python source code, indended to be started
from the command line.
Distutils doesn't provide much functionality for the scripts: the only
support Distutils gives is to adjust the first line of the script
if it starts with #! and contains the word ``python'' to refer
to the current interpreter location.
The scripts option simply is a list of files to be handled in this way.
The data_files option can be used to specify additional files needed by the module distribution: configuration files, data files, anything which does not fit in the previous categories.
data_files specify a sequence of (directory, files)
pairs in the following way:
setup(...
data_files=[('bitmaps', ['bm/b1.gif', 'bm/b2.gif']),
('config', ['cfg/data.cfg'])])
Note that you can specify the directory names where the data files will be installed, but you cannot rename the data files themselves.
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.
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Distributing Python Modules | |
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