Package: plib2
The PLIB package contains a number of useful sub-packages and
modules, all within theplibpackage namespace in order to
minimize clutter in the top-level namespace of your Python
installation. Each sub-directory of theplibdirectory
contains a sub-package, except for thetestdirectory, which
contains the PLIB test suite. The source distribution also
contains anexamplesdirectory, which has example programs
using PLIB, and ascriptsdirectory, which has a few
post-install scripts. (TheSetupHelperdirectory in the
source distribution is not installed; it contains a helper
module for PLIB’s setup script. SetupHelper is available as
a separate package from PyPI under the namesetuphelper.)NOTE: This is the “legacy” version of PLIB, with support
for older versions of Python (those that require any of
the “backported” implementations of builtins and standard
library functionality that PLIB provides). This version
will not receive any future updates except for critical
bug fixes, if any.The PLIB Sub-PackagesThe individual sub-packages and modules contain docstrings
with more information about their usage; here they are
briefly listed and described.PLIB.CLASSESEach of the modules in this sub-package contains a single
class with the same name; theModuleProxyclass from
thePLIB.UTILSsub-package is used to make all the classes
appear in theplib.classesnamespace. See the sub-package
andModuleProxydocstrings for more information.The classes in this sub-package are mostly a miscellaneous
group, included for no other reason than that I have found
them useful. The only common theme shared by some of them is
implementation of common “policy” for I/O clients and servers,
on top of the basic mechanisms provided by the I/O channel
classes inplib.stdlib.PLIB.EXTENSIONSThis sub-package provides a namespace for functions (and
possibly, in the future, other objects) exported from an
extension module written using the Python/C API. The general
philosophy of PLIB is to do everything possible in pure
Python, so the only functions that appear in this sub-package
are those which by their very nature cannot be done in pure
Python.PLIB.GUIThis is the largest sub-package, and contains a simple GUI
application framework with two main features:It lets the same high-level code work with a number of
different underlying GUI toolkits. Currently supported:
Qt (versions 3 and 4), KDE (versions 3 and 4), wxWidgets,
and GTK. (The original reason for writing this sub-package
was that wxWidgets doesn’t use Qt and I like the Qt/KDE
widgets better, but I wanted code that would run
cross-platform.)It allows you to express the layout of your GUI in terms
of Python lists and dicts, enabling a much more declarative
and easy to read (and maintain) coding style.Other than selecting the toolkit (which may not be necessary:
the main module of the sub-package can ‘auto-detect’ which
toolkit to use–theplib-setup-guipost-install script
does most of the work to enable this–so you only need to
override if you don’t like the default), you should not have
to worry about any toolkit internal details; the goal of this
sub-package is to make them all look the same to your code.Note that the GTK toolkit support in this sub-package is
“experimental” and may be removed if it proves to be more
trouble than it’s worth. It’s currently included because
wxWidgets’ behavior when using GTK as its underlying GUI
framework has some quirks that I haven’t been able to work
around yet. However, the GTK implementation of a number of
widgets (particularly tables and list/tree views) is much
less capable than the wxWidgets one, so the Python code for
GTK ends up relying much more on ugly hacks.PLIB.INIThis sub-package implements an abstract ‘INI file’ API that
usesConfigParseron POSIX systems, and the Windows registry
on Windows systems. As with thePLIB.GUIsub-package, the
goal is to hide the internal details of the configuration
storage from your code, so all you have to do is define
your configuration structure, again using native Python data
types (lists and dicts).PLIB.STDLIBThis is a namespace for various functions and classes that
extend or emulate the Python standard library. Some,
like thecached_propertydecorator, are implementations of
patterns that have been known for some time, but which don’t
have a “canonical” version in the stdlib yet; rather than
have PLIB depend on some other third-party package, I’ve
simply provided my own implementations here. Others, like
theabstractcontainerclass and its subclasses, are
re-implementations of standard Python data structures,
written to enable PLIB to make as many things as possible
look like those data structures without having to subclass
the built-ins (which has some downsides for the use cases
I’ve had thus far–see the docstrings for more information).Theplib.stdlibnamespace also contains a convenience
function.upgrade_builtins; calling this function adds
equivalents to the__builtin__module namespace for built-in
functions that are not present in the running version of
Python but are present in later versions. This is more
convenient than having to worry about importing such
equivalents fromplib.stdlib; as a consequence, a number
of functions are now removed from theplib.stdlibnamespace and are instead provided by this function when the
built-in equivalents are not present. In this version ofplib, you need to call this function somewhere in your
code (but only once); future versions may automagically
invoke it as long as you import anything fromplib. I
should also note that I have snuck in a few extra
“built-ins” that are not in the Python standard library but
IMHO should be. :-) See the docstrings for more details.There are also five modules visible in this sub-package
namespace:Thecollmodule provides two slightly customized collection
classes,fifoandstack, with a common API. This
module also includes the contents of thecollectionsmodule from the standard library, so you don’t have to import
both modules; in addition, for Python versions where some of
the classes are not yet present (defaultdictand/ornamedtuple), equivalents are provided. (The equivalent
implementation ofnamedtupleis taken from the ActiveState
recipe that led to the Python 2.6 implementation.)Thedecotoolsmodule provides functions and factories for
working with decorators.Thefuncmodule provides an alternate implementation of
thefunctoolsmodule in the Python standard library for
Python versions prior to 2.5 (in 2.5 and later it simply
makes the functools module contents appear in its namespace,
so you can safelyfrom plib.stdlib import funcinstead ofimport functoolsand use the same functionality).Theoptionsmodule provides an easier-to-use overlay for
theoptparsemodule which allows you to express your option
configuration in the form of Python lists, tuples, and dicts,
and also adds some minimal argument checking functionality.
(NOTE: In Python 2.7 and later, this module now uses theargparsemodule, since theoptparsemodule is
deprecated. Theargparsemodule provides additional
functionality for arguments, which can be accessed by making
thearglistparameter to theparse_optionsfunction
a sequence of 2-tuples to include keyword arguments, similar
to what is done for options.)Thetimermodule provides functions for timing code, with
an alternate API to the standard library’stimeitmodule
that is easier to use when timing functions that you already
have as objects, instead of source code strings.PLIB.STDLIB.IOThis sub-package contains classes that encapsulate various forms
of client/server I/O channels. It is organized into sub-packages
itself to make the namespace easier to use; the sub-packages fall
into three categories, and each sub-package in a given category
contains the same basic class names, so they’re easier to remember.
The categories are:Device Types:socketandserial. Each device type has
aBaseClientandBaseServerclass; thesockettype
also has aBaseRequestclass. These will usually not need to
be used directly; they are used by the I/O mode classes, and are
factored out so that each I/O mode sees the same API for a given
device type.I/O modes:asyncandblocking(the latter does not just
mean synchronous: it includes a forking TCP socket server). Each
I/O mode has a client and server class for both device types, and
a request class for thesocketdevice type: the class names areSerialClient,SerialServer,SocketClient,SocketServer,
andBaseRequestHandler. Theasynctype also has “persistent”
classes, which support full-duplex asynchronous communication; these
are thePersistentSerial,PersistentSocket, andPersistentRequestHandlerclasses. Mixin versions of these classes
(class names withMixinat the end) are also provided, for use
if alternate data handling is desired (see next bullet), but it is
normally not necessary to use these “by hand”–see “automatic mixins”
below.Data Handling: the I/O mode classes given above include basic
data handling, but it isverybasic: the only way it can detect
that a “message” has been fully received is to detect a closed
channel. For some applications this is enough, but often more
sophisticated and robust data handling is needed. Thedatasub-package provides three mixin classes for this purpose,ShutdownReadWrite,TerminatorReadWriteandReadWrite.
The first of these detects the end of a received message by a
shutdown of the other end of the data channel, but keeps the channel
open to allow further writes (all the other classes default to
closing the channel when the other end closes). The other two
classes allow the detection of multiple “messages” in the data
stream, either by detecting a “terminator” string or by having
each message include its length at the beginning. These classes
also format outgoing messages the same way.Automatic Mixins: To derive your own client or server classes with
alternate data handling “by hand”, you would need to use the “mixin”
versions of the appropriate I/O mode classes, and splice the data
handling class into the middle of the base class list; for example:from plib.stdlib.io.async import SerialClientMixin, SerialBase
from plib.stdlib.io.data import TerminatorReadWrite

class AsyncSerialClientWithTerminator(SerialClientMixin,
    TerminatorReadWrite, SerialBase): passThis is a bit clumsy, but necessary since the read/write handling has
to bebeforethe client/server class in the MRO, butafterthe
base device type, for the cooperativesupercalls that underlie
the functionality to work properly. However, since the pattern is the
same in each case, it can be automated, and this has been done in theasyncandblockingsub-package namespaces, so that instead of
doing the above class construction “by hand”, you can just append a
suffix to your desired class name, thus:from plib.stdlib.io.async import SerialClientWithTerminatorTheWithTerminatorsuffix (or, alternately,WithShutdownorWithReadWrite) will cause the equivalent of the above class
definition to occur on the fly, so that the resulting class appears
in theplib.stdlib.io.asyncnamespace (of course theplib.stdlib.io.blockingnamespace has the same capability).
Once this has happened the first time, however, the class definition
is stored in the appropriate namespace, so additional imports of the
same class name (in different modules of your application) will not
re-do the “on the fly” construction; they will just retrieve the
same class object that was previously constructed.The above machinery is also made available for use with your own custom
read/write handling classes; theasyncandblockingsub-packages
each export aget_readwrite_classfunction that does the same
on-the-fly class definition as above, but with your custom read/write
class instead of one of the built-in ones. All you have to do is pass
the function the name of your desired I/O class and your custom
read/write class object:from plib.stdlib.io import async

class CustomReadWrite(object):
    # class definition

MyAsyncSerialClient = async.get_readwrite_class('SerialClient',
    CustomReadWrite)API Notes: One of the goals of this sub-package is to provide a
common, consistent API for all the different types of I/O, so that
switching one specific implementation of a certain functionality
for another can be done transparently to the rest of your application’s
code. Thus, all of the usable classes follow the same basic pattern of
mixing in the various pieces of functionality: from left to right
in a class’s MRO, one finds the type of endpoint (a client or
server mixin class, which may be specialized to the type of I/O),
the type of data formatting, if any (a mixin class from theReadWritemodule), and the type of I/O, including device type
(socket, serial port, etc.), mode (non-blocking/asynchronous vs.
blocking), and basic data handling. Also, each endpoint type has
a common API independent of the specific type of I/O and mode; a
client can always use theclient_communicatemethod to send
data to the server and receive a response; a server can always use
theserve_forevermethod to start itself; and all I/O objects
override the same methods to implement application-specific
functionality:process_data, to deal with data as it comes in,
andquery_done, to determine when the I/O channel should be
closed. (To see examples of all this in action, look at the test
suite intest_stdlib_io.pyand the library module for it,stdlib_io_testlib.py; the library module can use the same
mixin classes to implement test functionality forallof the
different mixes of I/O classes in the test suite.)PLIB.UTILSThis sub-package contains some miscellaneous useful
functions and modules, and also theModuleProxyclass
referred to above.PLIB.XMLThis sub-package requires thelxmlextension, which uses
the very fastlibxml2library but provides a Pythonic API
similar to ElementTree. The reason for usinglxmlinstead
of ElementTree itself is thatlxmlhas two key additional
features:Custom element classes: theclassesmodule in this
sub-package builds on this feature by using metaclasses
to automate DTD generation and validation, but the
feature is also great for many XML applications.Full andfastXPATH support: this was key in the XML
application that first prompted me to write this sub-package.
Yes, I know there are plenty of other Python XML packages
that do XPATH; the point is to have itplusthe standard
ElementTree APIplusthe speed oflibxml2.InstallationTo install PLIB, you can simply run:$ python setup.py installat a shell prompt from the directory into which you
unzipped the source tarball (the same directory that this
README file is in). This will install PLIB and then
run each of the post-install scripts in the scripts
directory.Example ProgramsPLIB comes with example programs that illustrate key features
of the package. After installation, these can be found in the$PREFIX/share/plib/examplesdirectory. If you have a
POSIX system (Linux or Mac OSX), theplib-setup-examplespost-install script will install symlinks to the example
programs in the$PREFIX/bindirectory.