.. _tut-brieftourtwo:

*********************************************

Brief Tour of the Standard Library -- Part II

*********************************************

This second tour covers more advanced modules that support professional

programming needs.  These modules rarely occur in small scripts.

.. _tut-output-formatting:

Output Formatting

=================

The :mod:`repr` module provides a version of :func:`repr` customized for

abbreviated displays of large or deeply nested containers::

   >>> import repr

   >>> repr.repr(set('supercalifragilisticexpialidocious'))

   "set(['a', 'c', 'd', 'e', 'f', 'g', ...])"

The :mod:`pprint` module offers more sophisticated control over printing both

built-in and user defined objects in a way that is readable by the interpreter.

When the result is longer than one line, the "pretty printer" adds line breaks

and indentation to more clearly reveal data structure::

   >>> import pprint

   >>> t = [[[['black', 'cyan'], 'white', ['green', 'red']], [['magenta',

   ...     'yellow'], 'blue']]]

   ...

   >>> pprint.pprint(t, width=30)

   [[[['black', 'cyan'],

      'white',

      ['green', 'red']],

     [['magenta', 'yellow'],

      'blue']]]

The :mod:`textwrap` module formats paragraphs of text to fit a given screen

width::

   >>> import textwrap

   >>> doc = """The wrap() method is just like fill() except that it returns

   ... a list of strings instead of one big string with newlines to separate

   ... the wrapped lines."""

   ...

   >>> print textwrap.fill(doc, width=40)

   The wrap() method is just like fill()

   except that it returns a list of strings

   instead of one big string with newlines

   to separate the wrapped lines.

The :mod:`locale` module accesses a database of culture specific data formats.

The grouping attribute of locale's format function provides a direct way of

formatting numbers with group separators::

   >>> import locale

   >>> locale.setlocale(locale.LC_ALL, 'English_United States.1252')

   'English_United States.1252'

   >>> conv = locale.localeconv()          # get a mapping of conventions

   >>> x = 1234567.8

   >>> locale.format("%d", x, grouping=True)

   '1,234,567'

   >>> locale.format("%s%.*f", (conv['currency_symbol'],

   ...	      conv['frac_digits'], x), grouping=True)

   '$1,234,567.80'

.. _tut-templating:

Templating

==========

The :mod:`string` module includes a versatile :class:`Template` class with a

simplified syntax suitable for editing by end-users.  This allows users to

customize their applications without having to alter the application.

The format uses placeholder names formed by ``$`` with valid Python identifiers

(alphanumeric characters and underscores).  Surrounding the placeholder with

braces allows it to be followed by more alphanumeric letters with no intervening

spaces.  Writing ``$$`` creates a single escaped ``$``::

   >>> from string import Template

   >>> t = Template('${village}folk send $$10 to $cause.')

   >>> t.substitute(village='Nottingham', cause='the ditch fund')

   'Nottinghamfolk send $10 to the ditch fund.'

The :meth:`substitute` method raises a :exc:`KeyError` when a placeholder is not

supplied in a dictionary or a keyword argument. For mail-merge style

applications, user supplied data may be incomplete and the

:meth:`safe_substitute` method may be more appropriate --- it will leave

placeholders unchanged if data is missing::

   >>> t = Template('Return the $item to $owner.')

   >>> d = dict(item='unladen swallow')

   >>> t.substitute(d)

   Traceback (most recent call last):

     . . .

   KeyError: 'owner'

   >>> t.safe_substitute(d)

   'Return the unladen swallow to $owner.'

Template subclasses can specify a custom delimiter.  For example, a batch

renaming utility for a photo browser may elect to use percent signs for

placeholders such as the current date, image sequence number, or file format::

   >>> import time, os.path

   >>> photofiles = ['img_1074.jpg', 'img_1076.jpg', 'img_1077.jpg']

   >>> class BatchRename(Template):

   ...     delimiter = '%'

   >>> fmt = raw_input('Enter rename style (%d-date %n-seqnum %f-format):  ')

   Enter rename style (%d-date %n-seqnum %f-format):  Ashley_%n%f

   >>> t = BatchRename(fmt)

   >>> date = time.strftime('%d%b%y')

   >>> for i, filename in enumerate(photofiles):

   ...     base, ext = os.path.splitext(filename)

   ...     newname = t.substitute(d=date, n=i, f=ext)

   ...     print '{0} --> {1}'.format(filename, newname)

   img_1074.jpg --> Ashley_0.jpg

   img_1076.jpg --> Ashley_1.jpg

   img_1077.jpg --> Ashley_2.jpg

Another application for templating is separating program logic from the details

of multiple output formats.  This makes it possible to substitute custom

templates for XML files, plain text reports, and HTML web reports.

.. _tut-binary-formats:

Working with Binary Data Record Layouts

=======================================

The :mod:`struct` module provides :func:`pack` and :func:`unpack` functions for

working with variable length binary record formats.  The following example shows

how to loop through header information in a ZIP file without using the

:mod:`zipfile` module.  Pack codes ``"H"`` and ``"I"`` represent two and four

byte unsigned numbers respectively.  The ``"<"`` indicates that they are

standard size and in little-endian byte order::

   import struct

   data = open('myfile.zip', 'rb').read()

   start = 0

   for i in range(3):                      # show the first 3 file headers

       start += 14

       fields = struct.unpack('<IIIHH', data[start:start+16])

       crc32, comp_size, uncomp_size, filenamesize, extra_size = fields

       start += 16

       filename = data[start:start+filenamesize]

       start += filenamesize

       extra = data[start:start+extra_size]

       print filename, hex(crc32), comp_size, uncomp_size

       start += extra_size + comp_size     # skip to the next header

.. _tut-multi-threading:

Multi-threading

===============

Threading is a technique for decoupling tasks which are not sequentially

dependent.  Threads can be used to improve the responsiveness of applications

that accept user input while other tasks run in the background.  A related use

case is running I/O in parallel with computations in another thread.

The following code shows how the high level :mod:`threading` module can run

tasks in background while the main program continues to run::

   import threading, zipfile

   class AsyncZip(threading.Thread):

       def __init__(self, infile, outfile):

           threading.Thread.__init__(self)

           self.infile = infile

           self.outfile = outfile

       def run(self):

           f = zipfile.ZipFile(self.outfile, 'w', zipfile.ZIP_DEFLATED)

           f.write(self.infile)

           f.close()

           print 'Finished background zip of: ', self.infile

   background = AsyncZip('mydata.txt', 'myarchive.zip')

   background.start()

   print 'The main program continues to run in foreground.'

   background.join()    # Wait for the background task to finish

   print 'Main program waited until background was done.'

The principal challenge of multi-threaded applications is coordinating threads

that share data or other resources.  To that end, the threading module provides

a number of synchronization primitives including locks, events, condition

variables, and semaphores.

While those tools are powerful, minor design errors can result in problems that

are difficult to reproduce.  So, the preferred approach to task coordination is

to concentrate all access to a resource in a single thread and then use the

:mod:`Queue` module to feed that thread with requests from other threads.

Applications using :class:`Queue.Queue` objects for inter-thread communication

and coordination are easier to design, more readable, and more reliable.

.. _tut-logging:

Logging

=======

The :mod:`logging` module offers a full featured and flexible logging system.

At its simplest, log messages are sent to a file or to ``sys.stderr``::

   import logging

   logging.debug('Debugging information')

   logging.info('Informational message')

   logging.warning('Warning:config file %s not found', 'server.conf')

   logging.error('Error occurred')

   logging.critical('Critical error -- shutting down')

This produces the following output::

   WARNING:root:Warning:config file server.conf not found

   ERROR:root:Error occurred

   CRITICAL:root:Critical error -- shutting down

By default, informational and debugging messages are suppressed and the output

is sent to standard error.  Other output options include routing messages

through email, datagrams, sockets, or to an HTTP Server.  New filters can select

different routing based on message priority: :const:`DEBUG`, :const:`INFO`,

:const:`WARNING`, :const:`ERROR`, and :const:`CRITICAL`.

The logging system can be configured directly from Python or can be loaded from

a user editable configuration file for customized logging without altering the

application.

.. _tut-weak-references:

Weak References

===============

Python does automatic memory management (reference counting for most objects and

:term:`garbage collection` to eliminate cycles).  The memory is freed shortly

after the last reference to it has been eliminated.

This approach works fine for most applications but occasionally there is a need

to track objects only as long as they are being used by something else.

Unfortunately, just tracking them creates a reference that makes them permanent.

The :mod:`weakref` module provides tools for tracking objects without creating a

reference.  When the object is no longer needed, it is automatically removed

from a weakref table and a callback is triggered for weakref objects.  Typical

applications include caching objects that are expensive to create::

   >>> import weakref, gc

   >>> class A:

   ...     def __init__(self, value):

   ...             self.value = value

   ...     def __repr__(self):

   ...             return str(self.value)

   ...

   >>> a = A(10)                   # create a reference

   >>> d = weakref.WeakValueDictionary()

   >>> d['primary'] = a            # does not create a reference

   >>> d['primary']                # fetch the object if it is still alive

   10

   >>> del a                       # remove the one reference

   >>> gc.collect()                # run garbage collection right away

   0

   >>> d['primary']                # entry was automatically removed

   Traceback (most recent call last):

     File "<stdin>", line 1, in <module>

       d['primary']                # entry was automatically removed

     File "C:/python26/lib/weakref.py", line 46, in __getitem__

       o = self.data[key]()

   KeyError: 'primary'

.. _tut-list-tools:

Tools for Working with Lists

============================

Many data structure needs can be met with the built-in list type. However,

sometimes there is a need for alternative implementations with different

performance trade-offs.

The :mod:`array` module provides an :class:`array()` object that is like a list

that stores only homogeneous data and stores it more compactly.  The following

example shows an array of numbers stored as two byte unsigned binary numbers

(typecode ``"H"``) rather than the usual 16 bytes per entry for regular lists of

python int objects::

   >>> from array import array

   >>> a = array('H', [4000, 10, 700, 22222])

   >>> sum(a)

   26932

   >>> a[1:3]

   array('H', [10, 700])

The :mod:`collections` module provides a :class:`deque()` object that is like a

list with faster appends and pops from the left side but slower lookups in the

middle. These objects are well suited for implementing queues and breadth first

tree searches::

   >>> from collections import deque

   >>> d = deque(["task1", "task2", "task3"])

   >>> d.append("task4")

   >>> print "Handling", d.popleft()

   Handling task1

   unsearched = deque([starting_node])

   def breadth_first_search(unsearched):

       node = unsearched.popleft()

       for m in gen_moves(node):

           if is_goal(m):

               return m

           unsearched.append(m)

In addition to alternative list implementations, the library also offers other

tools such as the :mod:`bisect` module with functions for manipulating sorted

lists::

   >>> import bisect

   >>> scores = [(100, 'perl'), (200, 'tcl'), (400, 'lua'), (500, 'python')]

   >>> bisect.insort(scores, (300, 'ruby'))

   >>> scores

   [(100, 'perl'), (200, 'tcl'), (300, 'ruby'), (400, 'lua'), (500, 'python')]

The :mod:`heapq` module provides functions for implementing heaps based on

regular lists.  The lowest valued entry is always kept at position zero.  This

is useful for applications which repeatedly access the smallest element but do

not want to run a full list sort::

   >>> from heapq import heapify, heappop, heappush

   >>> data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]

   >>> heapify(data)                      # rearrange the list into heap order

   >>> heappush(data, -5)                 # add a new entry

   >>> [heappop(data) for i in range(3)]  # fetch the three smallest entries

   [-5, 0, 1]

.. _tut-decimal-fp:

Decimal Floating Point Arithmetic

=================================

The :mod:`decimal` module offers a :class:`Decimal` datatype for decimal

floating point arithmetic.  Compared to the built-in :class:`float`

implementation of binary floating point, the new class is especially helpful for

financial applications and other uses which require exact decimal

representation, control over precision, control over rounding to meet legal or

regulatory requirements, tracking of significant decimal places, or for

applications where the user expects the results to match calculations done by

hand.

For example, calculating a 5% tax on a 70 cent phone charge gives different

results in decimal floating point and binary floating point. The difference

becomes significant if the results are rounded to the nearest cent::

   >>> from decimal import *

   >>> Decimal('0.70') * Decimal('1.05')

   Decimal("0.7350")

   >>> .70 * 1.05

   0.73499999999999999

The :class:`Decimal` result keeps a trailing zero, automatically inferring four

place significance from multiplicands with two place significance.  Decimal

reproduces mathematics as done by hand and avoids issues that can arise when

binary floating point cannot exactly represent decimal quantities.

Exact representation enables the :class:`Decimal` class to perform modulo

calculations and equality tests that are unsuitable for binary floating point::

   >>> Decimal('1.00') % Decimal('.10')

   Decimal("0.00")

   >>> 1.00 % 0.10

   0.09999999999999995

   >>> sum([Decimal('0.1')]*10) == Decimal('1.0')

   True

   >>> sum([0.1]*10) == 1.0

   False

The :mod:`decimal` module provides arithmetic with as much precision as needed::

   >>> getcontext().prec = 36

   >>> Decimal(1) / Decimal(7)

   Decimal("0.142857142857142857142857142857142857")