2.編寫安裝腳本?

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

一個簡單的例子 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='https://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 一個簡單的例子: 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 Additional meta-data.

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.

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'))

2.1.Listing whole packages?

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 anyway.

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.)

2.2.Listing individual modules?

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 一個簡單的例子; 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.

2.3.Describing extension modules?

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

ext_modules option. ext_modules 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.

2.3.1.擴展名和軟件包?

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.

2.3.2.Extension source files?

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.

This warning notwithstanding, options to SWIG can be currently passed like

this:

setup(...,

ext_modules=[Extension('_foo', ['foo.i'],

swig_opts=['-modern', '-I../include'])],

py_modules=['foo'],

)

Or on the commandline like this:

> python setup.py build_ext --swig-opts="-modern -I../include"

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.

2.3.3.Preprocessor options?

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

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_headers 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

If you must put the Numerical include directory right into your header

search path, though, you can find that directory using the Distutils

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.

例如

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

2.3.4.Library options?

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.)

2.3.5.其他選項?

There are still some other options which can be used to handle special cases.

The optional option is a boolean; if it is true,

a build failure in the extension will not abort the build process, but

instead simply not install the failing extension.

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.

The depends option is a list of files that the extension depends on

(for example header files). The build command will call the compiler on the

sources to rebuild extension if any on this files has been modified since the

previous build.

2.4.Relationships between Distributions and Packages?

A distribution may relate to packages in three specific ways:

It can require packages or modules.

It can provide packages or modules.

It can obsolete packages or modules.

These relationships can be specified using keyword arguments to the

distutils.core.setup() function.

Dependencies on other Python modules and packages can be specified by supplying

the requires keyword argument to setup(). The value must be a list of

strings. Each string specifies a package that is required, and optionally what

versions are sufficient.

To specify that any version of a module or package is required, the string

should consist entirely of the module or package name. Examples include

'mymodule' and 'xml.parsers.expat'.

If specific versions are required, a sequence of qualifiers can be supplied in

parentheses. Each qualifier may consist of a comparison operator and a version

number. The accepted comparison operators are:

< > ==

<= >= !=

These can be combined by using multiple qualifiers separated by commas (and

optional whitespace). In this case, all of the qualifiers must be matched; a

logical AND is used to combine the evaluations.

Let’s look at a bunch of examples:

Requires Expression

解釋

==1.0

Only version 1.0 is compatible

>1.0, !=1.5.1, <2.0

Any version after 1.0 and before 2.0

is compatible, except 1.5.1

Now that we can specify dependencies, we also need to be able to specify what we

provide that other distributions can require. This is done using the provides

keyword argument to setup(). The value for this keyword is a list of

strings, each of which names a Python module or package, and optionally

identifies the version. If the version is not specified, it is assumed to match

that of the distribution.

Some examples:

Provides Expression

解釋

mypkg

Provide mypkg, using the distribution

version

mypkg (1.1)

Provide mypkg version 1.1, regardless of

the distribution version

A package can declare that it obsoletes other packages using the obsoletes

keyword argument. The value for this is similar to that of the requires

keyword: a list of strings giving module or package specifiers. Each specifier

consists of a module or package name optionally followed by one or more version

qualifiers. Version qualifiers are given in parentheses after the module or

package name.

The versions identified by the qualifiers are those that are obsoleted by the

distribution being described. If no qualifiers are given, all versions of the

named module or package are understood to be obsoleted.

2.5.Installing Scripts?

So far we have been dealing with pure and non-pure Python modules, which are

usually not run by themselves but imported by scripts.

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. By default, it is replaced with

the current interpreter location. The --executable (or -e)

option will allow the interpreter path to be explicitly overridden.

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']

)

在 3.1 版更改:All the scripts will also be added to the MANIFEST file if no template is

provided. See Specifying the files to distribute.

2.6.Installing Package Data?

Often, additional files need to be installed into a package. These files are

often data that’s closely related to the package’s implementation, or text files

containing documentation that might be of interest to programmers using the

package. These files are called package data.

Package data can be added to packages using the package_data keyword

argument to the setup() function. The value must be a mapping from

package name to a list of relative path names that should be copied into the

package. The paths are interpreted as relative to the directory containing the

package (information from the package_dir mapping is used if appropriate);

that is, the files are expected to be part of the package in the source

directories. They may contain glob patterns as well.

The path names may contain directory portions; any necessary directories will be

created in the installation.

For example, if a package should contain a subdirectory with several data files,

the files can be arranged like this in the source tree:

setup.py

src/

mypkg/

__init__.py

module.py

data/

tables.dat

spoons.dat

forks.dat

The corresponding call to setup() might be:

setup(...,

packages=['mypkg'],

package_dir={'mypkg': 'src/mypkg'},

package_data={'mypkg': ['data/*.dat']},

)

在 3.1 版更改:All the files that match package_data will be added to the MANIFEST

file if no template is provided. See Specifying the files to distribute.

2.7.Installing Additional Files?

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.

在 3.1 版更改:All the files that match data_files will be added to the MANIFEST

file if no template is provided. See Specifying the files to distribute.

2.8.Additional meta-data?

The setup script may include additional meta-data beyond the name and version.

This information includes:

元數據

描述

值

注釋

name

包名稱

短字符串

(1)

version

此發布的版本

短字符串

(1)(2)

author

軟件包作者的姓名

短字符串

(3)

author_email

軟件包的作者的電子郵件地址

電子郵件地址

(3)

maintainer

軟件包維護者的名字

短字符串

(3)

maintainer_email

軟件包維護者的電子郵件地址

電子郵件地址

(3)

url

軟件包的網址

網址

(1)

description

軟件包的簡短摘要說明

短字符串

long_description

軟件包的詳細說明

長字符串

(5)

download_url

可以下載軟件包的網址

網址

(4)

classifiers

分類列表

字符串列表

(4)

platforms

平臺清單

字符串列表

license

軟件包許可證

短字符串

(6)

注釋:

These fields are required.

It is recommended that versions take the form major.minor[.patch[.sub]].

Either the author or the maintainer must be identified. If maintainer is

provided, distutils lists it as the author in PKG-INFO.

These fields should not be used if your package is to be compatible with Python

versions prior to 2.2.3 or 2.3. The list is available from the PyPI website.

The long_description field is used by PyPI when you are

registering a package, to

build its home page.

The license field is a text indicating the license covering the

package where the license is not a selection from the “License” Trove

classifiers. See the Classifier field. Notice that

there’s a licence distribution option which is deprecated but still

acts as an alias for license.

‘short string’A single line of text, not more than 200 characters.

‘long string’Multiple lines of plain text in reStructuredText format (see

http://docutils.sourceforge.net/).

‘list of strings’See below.

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:

0.1.0the first, experimental release of a package

1.0.1a2the second alpha release of the first patch version of 1.0

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',

],

)

2.9.Debugging the setup script?

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 about what it is doing, dump the full traceback when an exception

occurs, and print the whole command line when an external program (like a C

compiler) fails.

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