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  r59324 | georg.brandl | 2007-12-04 17:10:02 +0100 (Tue, 04 Dec 2007) | 3 lines

  Add "Python on Unix" document, mostly written for GHOP
  by Shriphani Palakodety.
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  r59325 | facundo.batista | 2007-12-04 17:31:53 +0100 (Tue, 04 Dec 2007) | 3 lines


  fma speedup by avoiding to create a Context. Thanks Mark Dickinson.
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  r59326 | christian.heimes | 2007-12-04 17:36:20 +0100 (Tue, 04 Dec 2007) | 2 lines

  Added warning that make install may overwrite or masquerade the default python binary. Use make altinstall instead.
  A native English speaker may want to rephrase the paragraph. ;)
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  r59327 | georg.brandl | 2007-12-04 17:50:28 +0100 (Tue, 04 Dec 2007) | 2 lines

  Fix duplicate label and a typo.
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  r59329 | georg.brandl | 2007-12-04 18:46:27 +0100 (Tue, 04 Dec 2007) | 2 lines

  Add tutorial and examples to logging docs, from GHOP student "oscar8thegrouch".
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  r59332 | christian.heimes | 2007-12-04 19:43:19 +0100 (Tue, 04 Dec 2007) | 1 line

  These optimizations create smaller and a bit faster code on my machine. I've also disabled an optimization that may be dangerous. Intrinsic functions conflict with errno.
........
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605
Doc/library/logging.rst
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@ -9,10 +9,6 @@
.. sectionauthor:: Vinay Sajip <vinay_sajip@red-dove.com>
.. % These apply to all modules, and may be given more than once:
.. index:: pair: Errors; logging
This module defines functions and classes which implement a flexible error
@ -34,6 +30,400 @@ importance of a logged message by calling an appropriate method of
constrained to use these levels: you can specify your own and use a more general
:class:`Logger` method, :meth:`log`, which takes an explicit level argument.
Logging tutorial
----------------
The key benefit of having the logging API provided by a standard library module
is that all Python modules can participate in logging, so your application log
can include messages from third-party modules.
It is, of course, possible to log messages with different verbosity levels or to
different destinations. Support for writing log messages to files, HTTP
GET/POST locations, email via SMTP, generic sockets, or OS-specific logging
mechnisms are all supported by the standard module. You can also create your
own log destination class if you have special requirements not met by any of the
built-in classes.
Simple examples
^^^^^^^^^^^^^^^
.. sectionauthor:: Doug Hellmann
.. (see <http://blog.doughellmann.com/2007/05/pymotw-logging.html>)
Most applications are probably going to want to log to a file, so let's start
with that case. Using the :func:`basicConfig` function, we can set up the
default handler so that debug messages are written to a file::
import logging
LOG_FILENAME = '/tmp/logging_example.out'
logging.basicConfig(filename=LOG_FILENAME,level=logging.DEBUG,)
logging.debug('This message should go to the log file')
And now if we open the file and look at what we have, we should find the log
message::
DEBUG:root:This message should go to the log file
If you run the script repeatedly, the additional log messages are appended to
the file. To create a new file each time, you can pass a filemode argument to
:func:`basicConfig` with a value of ``'w'``. Rather than managing the file size
yourself, though, it is simpler to use a :class:`RotatingFileHandler`::
import glob
import logging
import logging.handlers
LOG_FILENAME = '/tmp/logging_rotatingfile_example.out'
# Set up a specific logger with our desired output level
my_logger = logging.getLogger('MyLogger')
my_logger.setLevel(logging.DEBUG)
# Add the log message handler to the logger
handler = logging.handlers.RotatingFileHandler(
LOG_FILENAME, maxBytes=20, backupCount=5)
my_logger.addHandler(handler)
# Log some messages
for i in range(20):
my_logger.debug('i = %d' % i)
# See what files are created
logfiles = glob.glob('%s*' % LOG_FILENAME)
for filename in logfiles:
print filename
The result should be 6 separate files, each with part of the log history for the
application::
/tmp/logging_rotatingfile_example.out
/tmp/logging_rotatingfile_example.out.1
/tmp/logging_rotatingfile_example.out.2
/tmp/logging_rotatingfile_example.out.3
/tmp/logging_rotatingfile_example.out.4
/tmp/logging_rotatingfile_example.out.5
The most current file is always :file:`/tmp/logging_rotatingfile_example.out`,
and each time it reaches the size limit it is renamed with the suffix
``.1``. Each of the existing backup files is renamed to increment the suffix
(``.1`` becomes ``.2``, etc.) and the ``.5`` file is erased.
Obviously this example sets the log length much much too small as an extreme
example. You would want to set *maxBytes* to an appropriate value.
Another useful feature of the logging API is the ability to produce different
messages at different log levels. This allows you to instrument your code with
debug messages, for example, but turning the log level down so that those debug
messages are not written for your production system. The default levels are
``CRITICAL``, ``ERROR``, ``WARNING``, ``INFO``, ``DEBUG`` and ``UNSET``.
The logger, handler, and log message call each specify a level. The log message
is only emitted if the handler and logger are configured to emit messages of
that level or lower. For example, if a message is ``CRITICAL``, and the logger
is set to ``ERROR``, the message is emitted. If a message is a ``WARNING``, and
the logger is set to produce only ``ERROR``\s, the message is not emitted::
import logging
import sys
LEVELS = {'debug': logging.DEBUG,
'info': logging.INFO,
'warning': logging.WARNING,
'error': logging.ERROR,
'critical': logging.CRITICAL}
if len(sys.argv) > 1:
level_name = sys.argv[1]
level = LEVELS.get(level_name, logging.NOTSET)
logging.basicConfig(level=level)
logging.debug('This is a debug message')
logging.info('This is an info message')
logging.warning('This is a warning message')
logging.error('This is an error message')
logging.critical('This is a critical error message')
Run the script with an argument like 'debug' or 'warning' to see which messages
show up at different levels::
$ python logging_level_example.py debug
DEBUG:root:This is a debug message
INFO:root:This is an info message
WARNING:root:This is a warning message
ERROR:root:This is an error message
CRITICAL:root:This is a critical error message
$ python logging_level_example.py info
INFO:root:This is an info message
WARNING:root:This is a warning message
ERROR:root:This is an error message
CRITICAL:root:This is a critical error message
You will notice that these log messages all have ``root`` embedded in them. The
logging module supports a hierarchy of loggers with different names. An easy
way to tell where a specific log message comes from is to use a separate logger
object for each of your modules. Each new logger "inherits" the configuration
of its parent, and log messages sent to a logger include the name of that
logger. Optionally, each logger can be configured differently, so that messages
from different modules are handled in different ways. Let's look at a simple
example of how to log from different modules so it is easy to trace the source
of the message::
import logging
logging.basicConfig(level=logging.WARNING)
logger1 = logging.getLogger('package1.module1')
logger2 = logging.getLogger('package2.module2')
logger1.warning('This message comes from one module')
logger2.warning('And this message comes from another module')
And the output::
$ python logging_modules_example.py
WARNING:package1.module1:This message comes from one module
WARNING:package2.module2:And this message comes from another module
There are many more options for configuring logging, including different log
message formatting options, having messages delivered to multiple destinations,
and changing the configuration of a long-running application on the fly using a
socket interface. All of these options are covered in depth in the library
module documentation.
Loggers
^^^^^^^
The logging library takes a modular approach and offers the several categories
of components: loggers, handlers, filters, and formatters. Loggers expose the
interface that application code directly uses. Handlers send the log records to
the appropriate destination. Filters provide a finer grained facility for
determining which log records to send on to a handler. Formatters specify the
layout of the resultant log record.
:class:`Logger` objects have a threefold job. First, they expose several
methods to application code so that applications can log messages at runtime.
Second, logger objects determine which log messages to act upon based upon
severity (the default filtering facility) or filter objects. Third, logger
objects pass along relevant log messages to all interested log handlers.
The most widely used methods on logger objects fall into two categories:
configuration and message sending.
* :meth:`Logger.setLevel` specifies the lowest-severity log message a logger
will handle, where debug is the lowest built-in severity level and critical is
the highest built-in severity. For example, if the severity level is info,
the logger will handle only info, warning, error, and critical messages and
will ignore debug messages.
* :meth:`Logger.addFilter` and :meth:`Logger.removeFilter` add and remove filter
objects from the logger object. This tutorial does not address filters.
With the logger object configured, the following methods create log messages:
* :meth:`Logger.debug`, :meth:`Logger.info`, :meth:`Logger.warning`,
:meth:`Logger.error`, and :meth:`Logger.critical` all create log records with
a message and a level that corresponds to their respective method names. The
message is actually a format string, which may contain the standard string
substitution syntax of :const:`%s`, :const:`%d`, :const:`%f`, and so on. The
rest of their arguments is a list of objects that correspond with the
substitution fields in the message. With regard to :const:`**kwargs`, the
logging methods care only about a keyword of :const:`exc_info` and use it to
determine whether to log exception information.
* :meth:`Logger.exception` creates a log message similar to
:meth:`Logger.error`. The difference is that :meth:`Logger.exception` dumps a
stack trace along with it. Call this method only from an exception handler.
* :meth:`Logger.log` takes a log level as an explicit argument. This is a
little more verbose for logging messages than using the log level convenience
methods listed above, but this is how to log at custom log levels.
:func:`getLogger` returns a reference to a logger instance with a name of name
if a name is provided, or root if not. The names are period-separated
hierarchical structures. Multiple calls to :func:`getLogger` with the same name
will return a reference to the same logger object. Loggers that are further
down in the hierarchical list are children of loggers higher up in the list.
For example, given a logger with a name of ``foo``, loggers with names of
``foo.bar``, ``foo.bar.baz``, and ``foo.bam`` are all children of ``foo``.
Child loggers propagate messages up to their parent loggers. Because of this,
it is unnecessary to define and configure all the loggers an application uses.
It is sufficient to configure a top-level logger and create child loggers as
needed.
Handlers
^^^^^^^^
:class:`Handler` objects are responsible for dispatching the appropriate log
messages (based on the log messages' severity) to the handler's specified
destination. Logger objects can add zero or more handler objects to themselves
with an :func:`addHandler` method. As an example scenario, an application may
want to send all log messages to a log file, all log messages of error or higher
to stdout, and all messages of critical to an email address. This scenario
requires three individual handlers where each hander is responsible for sending
messages of a specific severity to a specific location.
The standard library includes quite a few handler types; this tutorial uses only
:class:`StreamHandler` and :class:`FileHandler` in its examples.
There are very few methods in a handler for application developers to concern
themselves with. The only handler methods that seem relevant for application
developers who are using the built-in handler objects (that is, not creating
custom handlers) are the following configuration methods:
* The :meth:`Handler.setLevel` method, just as in logger objects, specifies the
lowest severity that will be dispatched to the appropriate destination. Why
are there two :func:`setLevel` methods? The level set in the logger
determines which severity of messages it will pass to its handlers. The level
set in each handler determines which messages that handler will send on.
:func:`setFormatter` selects a Formatter object for this handler to use.
* :func:`addFilter` and :func:`removeFilter` respectively configure and
deconfigure filter objects on handlers.
Application code should not directly instantiate and use handlers. Instead, the
:class:`Handler` class is a base class that defines the interface that all
Handlers should have and establishes some default behavior that child classes
can use (or override).
Formatters
^^^^^^^^^^
Formatter objects configure the final order, structure, and contents of the log
message. Unlike the base logging.Handler class, application code may
instantiate formatter classes, although you could likely subclass the formatter
if your application needs special behavior. The constructor takes two optional
arguments: a message format string and a date format string. If there is no
message format string, the default is to use the raw message. If there is no
date format string, the default date format is::
%Y-%m-%d %H:%M:%S
with the milliseconds tacked on at the end.
The message format string uses ``%(<dictionary key>)s`` styled string
substitution; the possible keys are documented in :ref:`formatter-objects`.
The following message format string will log the time in a human-readable
format, the severity of the message, and the contents of the message, in that
order::
"%(asctime)s - %(levelname)s - %(message)s"
Configuring Logging
^^^^^^^^^^^^^^^^^^^
Programmers can configure logging either by creating loggers, handlers, and
formatters explicitly in a main module with the configuration methods listed
above (using Python code), or by creating a logging config file. The following
code is an example of configuring a very simple logger, a console handler, and a
simple formatter in a Python module::
import logging
# create logger
logger = logging.getLogger("simple_example")
logger.setLevel(logging.DEBUG)
# create console handler and set level to debug
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
# create formatter
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
# add formatter to ch
ch.setFormatter(formatter)
# add ch to logger
logger.addHandler(ch)
# "application" code
logger.debug("debug message")
logger.info("info message")
logger.warn("warn message")
logger.error("error message")
logger.critical("critical message")
Running this module from the command line produces the following output::
$ python simple_logging_module.py
2005-03-19 15:10:26,618 - simple_example - DEBUG - debug message
2005-03-19 15:10:26,620 - simple_example - INFO - info message
2005-03-19 15:10:26,695 - simple_example - WARNING - warn message
2005-03-19 15:10:26,697 - simple_example - ERROR - error message
2005-03-19 15:10:26,773 - simple_example - CRITICAL - critical message
The following Python module creates a logger, handler, and formatter nearly
identical to those in the example listed above, with the only difference being
the names of the objects::
import logging
import logging.config
logging.config.fileConfig("logging.conf")
# create logger
logger = logging.getLogger("simpleExample")
# "application" code
logger.debug("debug message")
logger.info("info message")
logger.warn("warn message")
logger.error("error message")
logger.critical("critical message")
Here is the logging.conf file::
[loggers]
keys=root,simpleExample
[handlers]
keys=consoleHandler
[formatters]
keys=simpleFormatter
[logger_root]
level=DEBUG
handlers=consoleHandler
[logger_simpleExample]
level=DEBUG
handlers=consoleHandler
qualname=simpleExample
propagate=0
[handler_consoleHandler]
class=StreamHandler
level=DEBUG
formatter=simpleFormatter
args=(sys.stdout,)
[formatter_simpleFormatter]
format=%(asctime)s - %(name)s - %(levelname)s - %(message)s
datefmt=
The output is nearly identical to that of the non-config-file-based example::
$ python simple_logging_config.py
2005-03-19 15:38:55,977 - simpleExample - DEBUG - debug message
2005-03-19 15:38:55,979 - simpleExample - INFO - info message
2005-03-19 15:38:56,054 - simpleExample - WARNING - warn message
2005-03-19 15:38:56,055 - simpleExample - ERROR - error message
2005-03-19 15:38:56,130 - simpleExample - CRITICAL - critical message
You can see that the config file approach has a few advantages over the Python
code approach, mainly separation of configuration and code and the ability of
noncoders to easily modify the logging properties.
Logging Levels
--------------
The numeric values of logging levels are given in the following table. These are
primarily of interest if you want to define your own levels, and need them to
have specific values relative to the predefined levels. If you define a level
@ -1446,6 +1836,8 @@ supports sending logging messages to a Web server, using either ``GET`` or
Sends the record to the Web server as an URL-encoded dictionary.
.. _formatter-objects:
Formatter Objects
-----------------
@ -1654,15 +2046,17 @@ in :mod:`logging` itself) and defining handlers which are declared either in
sent as a file suitable for processing by :func:`fileConfig`. Returns a
:class:`Thread` instance on which you can call :meth:`start` to start the
server, and which you can :meth:`join` when appropriate. To stop the server,
call :func:`stopListening`. To send a configuration to the socket, read in the
configuration file and send it to the socket as a string of bytes preceded by a
four-byte length packed in binary using struct.\ ``pack('>L', n)``.
call :func:`stopListening`.
To send a configuration to the socket, read in the configuration file and
send it to the socket as a string of bytes preceded by a four-byte length
string packed in binary using ``struct.pack('>L', n)``.
.. function:: stopListening()
Stops the listening server which was created with a call to :func:`listen`. This
is typically called before calling :meth:`join` on the return value from
Stops the listening server which was created with a call to :func:`listen`.
This is typically called before calling :meth:`join` on the return value from
:func:`listen`.
@ -1671,8 +2065,6 @@ in :mod:`logging` itself) and defining handlers which are declared either in
Configuration file format
^^^^^^^^^^^^^^^^^^^^^^^^^
.. %
The configuration file format understood by :func:`fileConfig` is based on
ConfigParser functionality. The file must contain sections called ``[loggers]``,
``[handlers]`` and ``[formatters]`` which identify by name the entities of each
@ -1825,3 +2217,194 @@ The ``class`` entry is optional. It indicates the name of the formatter's class
:class:`Formatter` subclass. Subclasses of :class:`Formatter` can present
exception tracebacks in an expanded or condensed format.
Configuration server example
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Here is an example of a module using the logging configuration server::
import logging
import logging.config
import time
import os
# read initial config file
logging.config.fileConfig("logging.conf")
# create and start listener on port 9999
t = logging.config.listen(9999)
t.start()
logger = logging.getLogger("simpleExample")
try:
# loop through logging calls to see the difference
# new configurations make, until Ctrl+C is pressed
while True:
logger.debug("debug message")
logger.info("info message")
logger.warn("warn message")
logger.error("error message")
logger.critical("critical message")
time.sleep(5)
except KeyboardInterrupt:
# cleanup
logging.config.stopListening()
t.join()
And here is a script that takes a filename and sends that file to the server,
properly preceded with the binary-encoded length, as the new logging
configuration::
#!/usr/bin/env python
import socket, sys, struct
data_to_send = open(sys.argv[1], "r").read()
HOST = 'localhost'
PORT = 9999
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
print "connecting..."
s.connect((HOST, PORT))
print "sending config..."
s.send(struct.pack(">L", len(data_to_send)))
s.send(data_to_send)
s.close()
print "complete"
More examples
-------------
Multiple handlers and formatters
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Loggers are plain Python objects. The :func:`addHandler` method has no minimum
or maximum quota for the number of handlers you may add. Sometimes it will be
beneficial for an application to log all messages of all severities to a text
file while simultaneously logging errors or above to the console. To set this
up, simply configure the appropriate handlers. The logging calls in the
application code will remain unchanged. Here is a slight modification to the
previous simple module-based configuration example::
import logging
logger = logging.getLogger("simple_example")
logger.setLevel(logging.DEBUG)
# create file handler which logs even debug messages
fh = logging.FileHandler("spam.log")
fh.setLevel(logging.DEBUG)
# create console handler with a higher log level
ch = logging.StreamHandler()
ch.setLevel(logging.ERROR)
# create formatter and add it to the handlers
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
ch.setFormatter(formatter)
fh.setFormatter(formatter)
# add the handlers to logger
logger.addHandler(ch)
logger.addHandler(fh)
# "application" code
logger.debug("debug message")
logger.info("info message")
logger.warn("warn message")
logger.error("error message")
logger.critical("critical message")
Notice that the "application" code does not care about multiple handlers. All
that changed was the addition and configuration of a new handler named *fh*.
The ability to create new handlers with higher- or lower-severity filters can be
very helpful when writing and testing an application. Instead of using many
``print`` statements for debugging, use ``logger.debug``: Unlike the print
statements, which you will have to delete or comment out later, the logger.debug
statements can remain intact in the source code and remain dormant until you
need them again. At that time, the only change that needs to happen is to
modify the severity level of the logger and/or handler to debug.
Using logging in multiple modules
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
It was mentioned above that multiple calls to
``logging.getLogger('someLogger')`` return a reference to the same logger
object. This is true not only within the same module, but also across modules
as long as it is in the same Python interpreter process. It is true for
references to the same object; additionally, application code can define and
configure a parent logger in one module and create (but not configure) a child
logger in a separate module, and all logger calls to the child will pass up to
the parent. Here is a main module::
import logging
import auxiliary_module
# create logger with "spam_application"
logger = logging.getLogger("spam_application")
logger.setLevel(logging.DEBUG)
# create file handler which logs even debug messages
fh = logging.FileHandler("spam.log")
fh.setLevel(logging.DEBUG)
# create console handler with a higher log level
ch = logging.StreamHandler()
ch.setLevel(logging.ERROR)
# create formatter and add it to the handlers
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
fh.setFormatter(formatter)
ch.setFormatter(formatter)
# add the handlers to the logger
logger.addHandler(fh)
logger.addHandler(ch)
logger.info("creating an instance of auxiliary_module.Auxiliary")
a = auxiliary_module.Auxiliary()
logger.info("created an instance of auxiliary_module.Auxiliary")
logger.info("calling auxiliary_module.Auxiliary.do_something")
a.do_something()
logger.info("finished auxiliary_module.Auxiliary.do_something")
logger.info("calling auxiliary_module.some_function()")
auxiliary_module.some_function()
logger.info("done with auxiliary_module.some_function()")
Here is the auxiliary module::
import logging
# create logger
module_logger = logging.getLogger("spam_application.auxiliary")
class Auxiliary:
def __init__(self):
self.logger = logging.getLogger("spam_application.auxiliary.Auxiliary")
self.logger.info("creating an instance of Auxiliary")
def do_something(self):
self.logger.info("doing something")
a = 1 + 1
self.logger.info("done doing something")
def some_function():
module_logger.info("received a call to \"some_function\"")
The output looks like this::
2005-03-23 23:47:11,663 - spam_application - INFO -
creating an instance of auxiliary_module.Auxiliary
2005-03-23 23:47:11,665 - spam_application.auxiliary.Auxiliary - INFO -
creating an instance of Auxiliary
2005-03-23 23:47:11,665 - spam_application - INFO -
created an instance of auxiliary_module.Auxiliary
2005-03-23 23:47:11,668 - spam_application - INFO -
calling auxiliary_module.Auxiliary.do_something
2005-03-23 23:47:11,668 - spam_application.auxiliary.Auxiliary - INFO -
doing something
2005-03-23 23:47:11,669 - spam_application.auxiliary.Auxiliary - INFO -
done doing something
2005-03-23 23:47:11,670 - spam_application - INFO -
finished auxiliary_module.Auxiliary.do_something
2005-03-23 23:47:11,671 - spam_application - INFO -
calling auxiliary_module.some_function()
2005-03-23 23:47:11,672 - spam_application.auxiliary - INFO -
received a call to "some_function"
2005-03-23 23:47:11,673 - spam_application - INFO -
done with auxiliary_module.some_function()

1
Doc/using/index.rst
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@ -13,6 +13,7 @@ interpreter and things that make working with Python easier.
.. toctree::
cmdline.rst
unix.rst
windows.rst
mac.rst

151
Doc/using/unix.rst Normal file
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@ -0,0 +1,151 @@
.. highlightlang:: none
.. _using-on-unix:
********************************
Using Python on Unix platforms
********************************
.. sectionauthor:: Shriphani Palakodety
Getting and installing the latest version of Python
===================================================
On Linux
--------
Python comes preinstalled on most Linux distributions, and is available as a
package on all others. However there are certain features you might want to use
that are not available on your distro's package. You can easily compile the
latest version of Python from source.
In the event Python doesn't come preinstalled and isn't in the repositories as
well, you can easily make packages for your own distro. Have a look at the
following links:
.. seealso::
http://www.linux.com/articles/60383
for Debian users
http://linuxmafia.com/pub/linux/suse-linux-internals/chapter35.html
for OpenSuse users
http://docs.fedoraproject.org/drafts/rpm-guide-en/ch-creating-rpms.html
for Fedora users
http://www.slackbook.org/html/package-management-making-packages.html
for Slackware users
On FreeBSD and OpenBSD
----------------------
* FreeBSD users, to add the package use::
pkg_add -r python
* OpenBSD users use::
pkg_add ftp://ftp.openbsd.org/pub/OpenBSD/4.2/packages/<insert your architecture here>/python-<version>.tgz
For example i386 users get the 2.5.1 version of Python using::
pkg_add ftp://ftp.openbsd.org/pub/OpenBSD/4.2/packages/i386/python-2.5.1p2.tgz
On OpenSolaris
--------------
To install the newest Python versions on OpenSolaris, install blastwave
(http://www.blastwave.org/howto.html) and type "pkg_get -i python" at the
prompt.
Building Python
===============
If you want to compile CPython yourself, first thing you should do is get the
`source <http://python.org/download/source/>`_. You can download either the
latest release's source or just grab a fresh `checkout
<http://www.python.org/dev/faq/#how-do-i-get-a-checkout-of-the-repository-read-only-and-read-write>`_.
The build process consists the usual ::
./configure
make
make install
invocations. Configuration options and caveats for specific Unix platforms are
extensively documented in the :file:`README` file in the root of the Python
source tree.
.. warning::
``make install`` can overwrite or masquerade the :file:`python` binary.
``make altinstall`` is therefore recommended instead of ``make install``
since it only installs :file:`{exec_prefix}/bin/python{version}`.
Python-related paths and files
==============================
These are subject to difference depending on local installation conventions;
:envvar:`prefix` (``${prefix}``) and :envvar:`exec_prefix` (``${exec_prefix}``)
are installation-dependent and should be interpreted as for GNU software; they
may be the same.
For example, on most Linux systems, the default for both is :file:`/usr`.
+-----------------------------------------------+------------------------------------------+
| File/directory | Meaning |
+===============================================+==========================================+
| :file:`{exec_prefix}/bin/python` | Recommended location of the interpreter. |
+-----------------------------------------------+------------------------------------------+
| :file:`{prefix}/lib/python{version}`, | Recommended locations of the directories |
| :file:`{exec_prefix}/lib/python{version}` | containing the standard modules. |
+-----------------------------------------------+------------------------------------------+
| :file:`{prefix}/include/python{version}`, | Recommended locations of the directories |
| :file:`{exec_prefix}/include/python{version}` | containing the include files needed for |
| | developing Python extensions and |
| | embedding the interpreter. |
+-----------------------------------------------+------------------------------------------+
| :file:`~/.pythonrc.py` | User-specific initialization file loaded |
| | by the user module; not used by default |
| | or by most applications. |
+-----------------------------------------------+------------------------------------------+
Miscellaneous
=============
To easily use Python scripts on Unix, you need to make them executable,
e.g. with ::
$ chmod +x script
and put an appropriate Shebang line at the top of the script. A good choice is
usually ::
#!/usr/bin/env python
which searches for the Python interpreter in the whole :envvar:`PATH`. However,
some Unices may not have the :program:`env` command, so you may need to hardcode
``/usr/bin/python`` as the interpreter path.
To use shell commands in your python scripts, look at the :mod:`subprocess` module.
Editors
=======
Vim and Emacs are excellent editors which support Python very well. For more
information on how to code in python in these editors, look at:
http://www.vim.org/scripts/script.php?script_id=790
http://sourceforge.net/projects/python-mode
Geany is an excellent IDE with support for a lot of languages. For more
information, read: http://geany.uvena.de/
Komodo edit is another extremely good IDE. It also has support for a lot of
languages. For more information, read:
http://www.activestate.com/store/productdetail.aspx?prdGuid=20f4ed15-6684-4118-a78b-d37ff4058c5f

3
Doc/using/windows.rst
View file

@ -260,8 +260,7 @@ Compiling Python on Windows
If you want to compile CPython yourself, first thing you should do is get the
`source <http://python.org/download/source/>`_. You can download either the
latest release's source or just grab a fresh `checkout
<http://www.python.org/dev/faq/
#how-do-i-get-a-checkout-of-the-repository-read-only-and-read-write>`_.
<http://www.python.org/dev/faq/#how-do-i-get-a-checkout-of-the-repository-read-only-and-read-write>`_.
For Microsoft Visual C++, which is the compiler with which official Python
releases are built, the source tree contains solutions/project files. View the

49
Lib/decimal.py
View file

@ -1646,24 +1646,39 @@ class Decimal(object):
"""
other = _convert_other(other, raiseit=True)
# compute product; raise InvalidOperation if either operand is
# a signaling NaN or if the product is zero times infinity.
if self._is_special or other._is_special:
if context is None:
context = getcontext()
if self._exp == 'N':
return context._raise_error(InvalidOperation, 'sNaN',
1, self)
if other._exp == 'N':
return context._raise_error(InvalidOperation, 'sNaN',
1, other)
if self._exp == 'n':
product = self
elif other._exp == 'n':
product = other
elif self._exp == 'F':
if not other:
return context._raise_error(InvalidOperation,
'INF * 0 in fma')
product = Infsign[self._sign ^ other._sign]
elif other._exp == 'F':
if not self:
return context._raise_error(InvalidOperation,
'0 * INF in fma')
product = Infsign[self._sign ^ other._sign]
else:
product = _dec_from_triple(self._sign ^ other._sign,
str(int(self._int) * int(other._int)),
self._exp + other._exp)
third = _convert_other(third, raiseit=True)
if context is None:
context = getcontext()
# do self*other in fresh context with no traps and no rounding
mul_context = Context(traps=[], flags=[],
_rounding_decision=NEVER_ROUND)
product = self.__mul__(other, mul_context)
if mul_context.flags[InvalidOperation]:
# reraise in current context
return context._raise_error(InvalidOperation,
'invalid multiplication in fma',
1, product)
ans = product.__add__(third, context)
return ans
return product.__add__(third, context)
def _power_modulo(self, other, modulo, context=None):
"""Three argument version of __pow__"""