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当你退出Python的shell模式然后又重新进入的时候,之前定义的变量,函数等都会没有了. 因此, 推荐的做法是将这些东西写入文件,并在适当的时候调用获取他们. 这就是为人所知的脚本文件. 随着编程的深入,代码的增多,你可能又会将代码存到不同的文件中方便管理. 你会想到去使用之前的编程中已经写好了的一个函数的定义.
Python有自己的方式去实现这些.它会将这些保存了定义的函数,类等的文件(文件夹)称作module; 一个module中的定义的函数 类等可以被导入到另一个module中.(the collection of variables that you have access to in a script executed at the top level and in calculator mode).
module通常是以 .py 结尾的文件. 在module内,我们可以通过全局变量 __name__获取当前module的名字(这会是一个字符串). 接下来的栗子中,你可以用例喜欢的编辑器编辑下面一段代码并存放在当前路径下的 fibo.py 文件中:
# Fibonacci numbers module
def fib(n): # write Fibonacci series up to n
a, b = 0, 1
while b < n:
print b,
a, b = b, a+b
def fib2(n): # return Fibonacci series up to n
result = []
a, b = 0, 1
while b < n:
result.append(b)
a, b = b, a+b
return result
然后,在当前路径下打开命令行窗口,键入下面这段骚气的代码:
>>> import fibo
注意到这里我们并没有使用到文件中定义的函数的名字,而是直接将fibo这个文件(module)导入了,然后通过它,我们就可以使用到其内部定义的一些行函数,就像这样:
>>> fibo.fib(1000)
1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987
>>> fibo.fib2(100)
[1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
>>> fibo.__name__
‘fibo‘
嫌弃名字太长了?那么也可以这样来一发(给它指定一个别名):
>>> fib = fibo.fib
>>> fib(500)
1 1 2 3 5 8 13 21 34 55 89 144 233 377
A module can contain executable statements as well as function definitions. These statements are intended to initialize the module. They are executed only the first time the module name is encountered in an import statement. [1] (They are also run if the file is executed as a script.)
Each module has its own private symbol table, which is used as the global symbol table by all functions defined in the module. Thus, the author of a module can use global variables in the module without worrying about accidental clashes with a user’s global variables. On the other hand, if you know what you are doing you can touch a module’s global variables with the same notation used to refer to its functions, modname.itemname.
Modules can import other modules. It is customary but not required to place all import statements at the beginning of a module (or script, for that matter). The imported module names are placed in the importing module’s global symbol table.
There is a variant of the import statement that imports names from a module directly into the importing module’s symbol table. For example:
>>> from fibo import fib, fib2
>>> fib(500)
1 1 2 3 5 8 13 21 34 55 89 144 233 377
This does not introduce the module name from which the imports are taken in the local symbol table (so in the example, fibo is not defined).
There is even a variant to import all names that a module defines:
>>> from fibo import *
>>> fib(500)
1 1 2 3 5 8 13 21 34 55 89 144 233 377
This imports all names except those beginning with an underscore (_).
Note that in general the practice of importing * from a module or package is frowned upon, since it often causes poorly readable code. However, it is okay to use it to save typing in interactive sessions.
Note
For efficiency reasons, each module is only imported once per interpreter session. Therefore, if you change your modules, you must restart the interpreter – or, if it’s just one module you want to test interactively, use reload(), e.g. reload(modulename).
When you run a Python module with
python fibo.py <arguments>
the code in the module will be executed, just as if you imported it, but with the __name__ set to "__main__". That means that by adding this code at the end of your module:
if __name__ == "__main__":
import sys
fib(int(sys.argv[1]))
you can make the file usable as a script as well as an importable module, because the code that parses the command line only runs if the module is executed as the “main” file:
$ python fibo.py 50
1 1 2 3 5 8 13 21 34
If the module is imported, the code is not run:
>>> import fibo
>>>
This is often used either to provide a convenient user interface to a module, or for testing purposes (running the module as a script executes a test suite).
模版搜索路径的顺序:
Some tips for experts:
When the Python interpreter is invoked with the -O flag, optimized code is generated and stored in .pyo files. The optimizer currently doesn’t help much; it only removes assert statements. When -O is used, all bytecode is optimized; .pyc files are ignored and .py files are compiled to optimized bytecode.
Passing two -O flags to the Python interpreter (-OO) will cause the bytecode compiler to perform optimizations that could in some rare cases result in malfunctioning programs. Currently only __doc__ strings are removed from the bytecode, resulting in more compact .pyo files. Since some programs may rely on having these available, you should only use this option if you know what you’re doing.
A program doesn’t run any faster when it is read from a .pyc or .pyo file than when it is read from a .py file; the only thing that’s faster about .pyc or .pyo files is the speed with which they are loaded.
When a script is run by giving its name on the command line, the bytecode for the script is never written to a .pyc or .pyo file. Thus, the startup time of a script may be reduced by moving most of its code to a module and having a small bootstrap script that imports that module. It is also possible to name a .pyc or .pyo file directly on the command line.
It is possible to have a file called spam.pyc (or spam.pyo when -O is used) without a file spam.py for the same module. This can be used to distribute a library of Python code in a form that is moderately hard to reverse engineer.
The module compileall can create .pyc files (or .pyo files when -O is used) for all modules in a directory.
Python comes with a library of standard modules, described in a separate document, the Python Library Reference (“Library Reference” hereafter). Some modules are built into the interpreter; these provide access to operations that are not part of the core of the language but are nevertheless built in, either for efficiency or to provide access to operating system primitives such as system calls. The set of such modules is a configuration option which also depends on the underlying platform. For example, the winreg module is only provided on Windows systems. One particular module deserves some attention: sys, which is built into every Python interpreter. The variables sys.ps1 and sys.ps2 define the strings used as primary and secondary prompts:
>>> import sys
>>> sys.ps1
‘>>> ‘
>>> sys.ps2
‘... ‘
>>> sys.ps1 = ‘C> ‘
C> print ‘Yuck!‘
Yuck!
C>
These two variables are only defined if the interpreter is in interactive mode.
The variable sys.path is a list of strings that determines the interpreter’s search path for modules. It is initialized to a default path taken from the environment variable PYTHONPATH, or from a built-in default if PYTHONPATH is not set. You can modify it using standard list operations:
>>> import sys
>>> sys.path.append(‘/ufs/guido/lib/python‘)
The built-in function dir() is used to find out which names a module defines. It returns a sorted list of strings:
>>> import fibo, sys
>>> dir(fibo)
[‘__name__‘, ‘fib‘, ‘fib2‘]
>>> dir(sys)
[‘__displayhook__‘, ‘__doc__‘, ‘__excepthook__‘, ‘__name__‘, ‘__package__‘,
‘__stderr__‘, ‘__stdin__‘, ‘__stdout__‘, ‘_clear_type_cache‘,
‘_current_frames‘, ‘_getframe‘, ‘_mercurial‘, ‘api_version‘, ‘argv‘,
‘builtin_module_names‘, ‘byteorder‘, ‘call_tracing‘, ‘callstats‘,
‘copyright‘, ‘displayhook‘, ‘dont_write_bytecode‘, ‘exc_clear‘, ‘exc_info‘,
‘exc_traceback‘, ‘exc_type‘, ‘exc_value‘, ‘excepthook‘, ‘exec_prefix‘,
‘executable‘, ‘exit‘, ‘flags‘, ‘float_info‘, ‘float_repr_style‘,
‘getcheckinterval‘, ‘getdefaultencoding‘, ‘getdlopenflags‘,
‘getfilesystemencoding‘, ‘getobjects‘, ‘getprofile‘, ‘getrecursionlimit‘,
‘getrefcount‘, ‘getsizeof‘, ‘gettotalrefcount‘, ‘gettrace‘, ‘hexversion‘,
‘long_info‘, ‘maxint‘, ‘maxsize‘, ‘maxunicode‘, ‘meta_path‘, ‘modules‘,
‘path‘, ‘path_hooks‘, ‘path_importer_cache‘, ‘platform‘, ‘prefix‘, ‘ps1‘,
‘py3kwarning‘, ‘setcheckinterval‘, ‘setdlopenflags‘, ‘setprofile‘,
‘setrecursionlimit‘, ‘settrace‘, ‘stderr‘, ‘stdin‘, ‘stdout‘, ‘subversion‘,
‘version‘, ‘version_info‘, ‘warnoptions‘]
Without arguments, dir() lists the names you have defined currently:
>>> a = [1, 2, 3, 4, 5]
>>> import fibo
>>> fib = fibo.fib
>>> dir()
[‘__builtins__‘, ‘__name__‘, ‘__package__‘, ‘a‘, ‘fib‘, ‘fibo‘, ‘sys‘]
Note that it lists all types of names: variables, modules, functions, etc.
dir() does not list the names of built-in functions and variables. If you want a list of those, they are defined in the standard module __builtin__:
>>> import __builtin__
>>> dir(__builtin__)
[‘ArithmeticError‘, ‘AssertionError‘, ‘AttributeError‘, ‘BaseException‘,
‘BufferError‘, ‘BytesWarning‘, ‘DeprecationWarning‘, ‘EOFError‘,
‘Ellipsis‘, ‘EnvironmentError‘, ‘Exception‘, ‘False‘, ‘FloatingPointError‘,
‘FutureWarning‘, ‘GeneratorExit‘, ‘IOError‘, ‘ImportError‘, ‘ImportWarning‘,
‘IndentationError‘, ‘IndexError‘, ‘KeyError‘, ‘KeyboardInterrupt‘,
‘LookupError‘, ‘MemoryError‘, ‘NameError‘, ‘None‘, ‘NotImplemented‘,
‘NotImplementedError‘, ‘OSError‘, ‘OverflowError‘,
‘PendingDeprecationWarning‘, ‘ReferenceError‘, ‘RuntimeError‘,
‘RuntimeWarning‘, ‘StandardError‘, ‘StopIteration‘, ‘SyntaxError‘,
‘SyntaxWarning‘, ‘SystemError‘, ‘SystemExit‘, ‘TabError‘, ‘True‘,
‘TypeError‘, ‘UnboundLocalError‘, ‘UnicodeDecodeError‘,
‘UnicodeEncodeError‘, ‘UnicodeError‘, ‘UnicodeTranslateError‘,
‘UnicodeWarning‘, ‘UserWarning‘, ‘ValueError‘, ‘Warning‘,
‘ZeroDivisionError‘, ‘_‘, ‘__debug__‘, ‘__doc__‘, ‘__import__‘,
‘__name__‘, ‘__package__‘, ‘abs‘, ‘all‘, ‘any‘, ‘apply‘, ‘basestring‘,
‘bin‘, ‘bool‘, ‘buffer‘, ‘bytearray‘, ‘bytes‘, ‘callable‘, ‘chr‘,
‘classmethod‘, ‘cmp‘, ‘coerce‘, ‘compile‘, ‘complex‘, ‘copyright‘,
‘credits‘, ‘delattr‘, ‘dict‘, ‘dir‘, ‘divmod‘, ‘enumerate‘, ‘eval‘,
‘execfile‘, ‘exit‘, ‘file‘, ‘filter‘, ‘float‘, ‘format‘, ‘frozenset‘,
‘getattr‘, ‘globals‘, ‘hasattr‘, ‘hash‘, ‘help‘, ‘hex‘, ‘id‘, ‘input‘,
‘int‘, ‘intern‘, ‘isinstance‘, ‘issubclass‘, ‘iter‘, ‘len‘, ‘license‘,
‘list‘, ‘locals‘, ‘long‘, ‘map‘, ‘max‘, ‘memoryview‘, ‘min‘, ‘next‘,
‘object‘, ‘oct‘, ‘open‘, ‘ord‘, ‘pow‘, ‘print‘, ‘property‘, ‘quit‘,
‘range‘, ‘raw_input‘, ‘reduce‘, ‘reload‘, ‘repr‘, ‘reversed‘, ‘round‘,
‘set‘, ‘setattr‘, ‘slice‘, ‘sorted‘, ‘staticmethod‘, ‘str‘, ‘sum‘, ‘super‘,
‘tuple‘, ‘type‘, ‘unichr‘, ‘unicode‘, ‘vars‘, ‘xrange‘, ‘zip‘]
Packages are a way of structuring Python’s module namespace by using “dotted module names”. For example, the module name A.B designates a submodule named B in a package named A. Just like the use of modules saves the authors of different modules from having to worry about each other’s global variable names, the use of dotted module names saves the authors of multi-module packages like NumPy or the Python Imaging Library from having to worry about each other’s module names.
Suppose you want to design a collection of modules (a “package”) for the uniform handling of sound files and sound data. There are many different sound file formats (usually recognized by their extension, for example: .wav, .aiff, .au), so you may need to create and maintain a growing collection of modules for the conversion between the various file formats. There are also many different operations you might want to perform on sound data (such as mixing, adding echo, applying an equalizer function, creating an artificial stereo effect), so in addition you will be writing a never-ending stream of modules to perform these operations. Here’s a possible structure for your package (expressed in terms of a hierarchical filesystem):
sound/ Top-level package
__init__.py Initialize the sound package
formats/ Subpackage for file format conversions
__init__.py
wavread.py
wavwrite.py
aiffread.py
aiffwrite.py
auread.py
auwrite.py
...
effects/ Subpackage for sound effects
__init__.py
echo.py
surround.py
reverse.py
...
filters/ Subpackage for filters
__init__.py
equalizer.py
vocoder.py
karaoke.py
...
When importing the package, Python searches through the directories on sys.path looking for the package subdirectory.
The __init__.py files are required to make Python treat the directories as containing packages; this is done to prevent directories with a common name, such as string, from unintentionally hiding valid modules that occur later on the module search path. In the simplest case, __init__.py can just be an empty file, but it can also execute initialization code for the package or set the __all__ variable, described later.
Users of the package can import individual modules from the package, for example:
import sound.effects.echo
This loads the submodule sound.effects.echo. It must be referenced with its full name.
sound.effects.echo.echofilter(input, output, delay=0.7, atten=4)
An alternative way of importing the submodule is:
from sound.effects import echo
This also loads the submodule echo, and makes it available without its package prefix, so it can be used as follows:
echo.echofilter(input, output, delay=0.7, atten=4)
Yet another variation is to import the desired function or variable directly:
from sound.effects.echo import echofilter
Again, this loads the submodule echo, but this makes its function echofilter() directly available:
echofilter(input, output, delay=0.7, atten=4)
Note that when using from package import item, the item can be either a submodule (or subpackage) of the package, or some other name defined in the package, like a function, class or variable. The import statement first tests whether the item is defined in the package; if not, it assumes it is a module and attempts to load it. If it fails to find it, an ImportError exception is raised.
Contrarily, when using syntax like import item.subitem.subsubitem, each item except for the last must be a package; the last item can be a module or a package but can’t be a class or function or variable defined in the previous item.
Now what happens when the user writes from sound.effects import *? Ideally, one would hope that this somehow goes out to the filesystem, finds which submodules are present in the package, and imports them all. This could take a long time and importing sub-modules might have unwanted side-effects that should only happen when the sub-module is explicitly imported.
The only solution is for the package author to provide an explicit index of the package. The import statement uses the following convention: if a package’s __init__.py code defines a list named __all__, it is taken to be the list of module names that should be imported when from package import * is encountered. It is up to the package author to keep this list up-to-date when a new version of the package is released. Package authors may also decide not to support it, if they don’t see a use for importing * from their package. For example, the file sound/effects/__init__.py could contain the following code:
__all__ = ["echo", "surround", "reverse"]
This would mean that from sound.effects import * would import the three named submodules of the sound package.
If __all__ is not defined, the statement from sound.effects import * does not import all submodules from the package sound.effects into the current namespace; it only ensures that the package sound.effects has been imported (possibly running any initialization code in __init__.py) and then imports whatever names are defined in the package. This includes any names defined (and submodules explicitly loaded) by __init__.py. It also includes any submodules of the package that were explicitly loaded by previous import statements. Consider this code:
import sound.effects.echo
import sound.effects.surround
from sound.effects import *
In this example, the echo and surround modules are imported in the current namespace because they are defined in the sound.effects package when the from...import statement is executed. (This also works when __all__ is defined.)
Although certain modules are designed to export only names that follow certain patterns when you use import *, it is still considered bad practise in production code.
Remember, there is nothing wrong with using from Package import specific_submodule! In fact, this is the recommended notation unless the importing module needs to use submodules with the same name from different packages.
The submodules often need to refer to each other. For example, the surround module might use the echo module. In fact, such references are so common that the import statement first looks in the containing package before looking in the standard module search path. Thus, the surround module can simply use import echo or from echo import echofilter. If the imported module is not found in the current package (the package of which the current module is a submodule), the import statement looks for a top-level module with the given name.
When packages are structured into subpackages (as with the sound package in the example), you can use absolute imports to refer to submodules of siblings packages. For example, if the module sound.filters.vocoder needs to use the echo module in the sound.effects package, it can use from sound.effects import echo.
Starting with Python 2.5, in addition to the implicit relative imports described above, you can write explicit relative imports with the from module import name form of import statement. These explicit relative imports use leading dots to indicate the current and parent packages involved in the relative import. From the surround module for example, you might use:
from . import echo
from .. import formats
from ..filters import equalizer
Note that both explicit and implicit relative imports are based on the name of the current module. Since the name of the main module is always "__main__", modules intended for use as the main module of a Python application should always use absolute imports.
Packages support one more special attribute, __path__. This is initialized to be a list containing the name of the directory holding the package’s __init__.py before the code in that file is executed. This variable can be modified; doing so affects future searches for modules and subpackages contained in the package.
While this feature is not often needed, it can be used to extend the set of modules found in a package.
Footnotes
| [1] | In fact function definitions are also ‘statements’ that are ‘executed’; the execution of a module-level function definition enters the function name in the module’s global symbol table. |
Python Tutorial 学习(六)--Modules
标签：des style blog http io ar os 使用 sp
原文地址：http://www.cnblogs.com/MrWho/p/python-Modules.html
