.. _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` (XXX reference: ../lib/module-repr.html) 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` (XXX reference: ../lib/module-pprint.html) 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` (XXX reference: ../lib/module-textwrap.html) 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` (XXX reference: ../lib/module-locale.html) 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` (XXX reference: ../lib/module-string.html) 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 '%s --> %s' % (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` (XXX reference: ../lib/module-struct.html) 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 (with pack codes ``"H"`` and ``"L"`` representing two
and four byte unsigned numbers respectively)::

   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('LLLHH', 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` (XXX reference:
../lib/module-threading.html) 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` (XXX reference: ../lib/module-Queue.html) module to feed that
thread with requests from other threads.  Applications using :class:`Queue`
objects for inter-thread communication and coordination are easier to design,
more readable, and more reliable.


.. _tut-logging:

Logging
=======

The :mod:`logging` (XXX reference: ../lib/module-logging.html) 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
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` (XXX reference: ../lib/module-weakref.html) 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 "<pyshell#108>", line 1, in -toplevel-
       d['primary']                # entry was automatically removed
     File "C:/PY24/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` (XXX reference: ../lib/module-array.html) module provides an
:class:`array()` object that is like a list that stores only homogenous 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` (XXX reference: ../lib/module-collections.html) 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` (XXX reference: ../lib/module-bisect.html)
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` (XXX reference: ../lib/module-heapq.html) 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` (XXX reference: ../lib/module-decimal.html) 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")


