From 458120dd40db6b4df55a4e96b650e16798ef06a0 Mon Sep 17 00:00:00 2001
From: cinap_lenrek <cinap_lenrek@localhost>
Date: Tue, 3 May 2011 11:25:13 +0000
Subject: add hg and python

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+\section{\module{itertools} ---
+         Functions creating iterators for efficient looping}
+
+\declaremodule{standard}{itertools}
+\modulesynopsis{Functions creating iterators for efficient looping.}
+\moduleauthor{Raymond Hettinger}{python@rcn.com}
+\sectionauthor{Raymond Hettinger}{python@rcn.com}
+\versionadded{2.3}
+
+
+This module implements a number of iterator building blocks inspired
+by constructs from the Haskell and SML programming languages.  Each
+has been recast in a form suitable for Python.
+
+The module standardizes a core set of fast, memory efficient tools
+that are useful by themselves or in combination.  Standardization helps
+avoid the readability and reliability problems which arise when many
+different individuals create their own slightly varying implementations,
+each with their own quirks and naming conventions.
+
+The tools are designed to combine readily with one another.  This makes
+it easy to construct more specialized tools succinctly and efficiently
+in pure Python.
+
+For instance, SML provides a tabulation tool: \code{tabulate(f)}
+which produces a sequence \code{f(0), f(1), ...}.  This toolbox
+provides \function{imap()} and \function{count()} which can be combined
+to form \code{imap(f, count())} and produce an equivalent result.
+
+Likewise, the functional tools are designed to work well with the
+high-speed functions provided by the \refmodule{operator} module.
+
+The module author welcomes suggestions for other basic building blocks
+to be added to future versions of the module.
+
+Whether cast in pure python form or compiled code, tools that use iterators
+are more memory efficient (and faster) than their list based counterparts.
+Adopting the principles of just-in-time manufacturing, they create
+data when and where needed instead of consuming memory with the
+computer equivalent of ``inventory''.
+
+The performance advantage of iterators becomes more acute as the number
+of elements increases -- at some point, lists grow large enough to
+severely impact memory cache performance and start running slowly.
+
+\begin{seealso}
+  \seetext{The Standard ML Basis Library,
+           \citetitle[http://www.standardml.org/Basis/]
+           {The Standard ML Basis Library}.}
+
+  \seetext{Haskell, A Purely Functional Language,
+           \citetitle[http://www.haskell.org/definition/]
+           {Definition of Haskell and the Standard Libraries}.}
+\end{seealso}
+
+
+\subsection{Itertool functions \label{itertools-functions}}
+
+The following module functions all construct and return iterators.
+Some provide streams of infinite length, so they should only be accessed
+by functions or loops that truncate the stream.
+
+\begin{funcdesc}{chain}{*iterables}
+  Make an iterator that returns elements from the first iterable until
+  it is exhausted, then proceeds to the next iterable, until all of the
+  iterables are exhausted.  Used for treating consecutive sequences as
+  a single sequence.  Equivalent to:
+
+  \begin{verbatim}
+     def chain(*iterables):
+         for it in iterables:
+             for element in it:
+                 yield element
+  \end{verbatim}
+\end{funcdesc}
+
+\begin{funcdesc}{count}{\optional{n}}
+  Make an iterator that returns consecutive integers starting with \var{n}.
+  If not specified \var{n} defaults to zero.  
+  Does not currently support python long integers.  Often used as an
+  argument to \function{imap()} to generate consecutive data points.
+  Also, used with \function{izip()} to add sequence numbers.  Equivalent to:
+
+  \begin{verbatim}
+     def count(n=0):
+         while True:
+             yield n
+             n += 1
+  \end{verbatim}
+
+  Note, \function{count()} does not check for overflow and will return
+  negative numbers after exceeding \code{sys.maxint}.  This behavior
+  may change in the future.
+\end{funcdesc}
+
+\begin{funcdesc}{cycle}{iterable}
+  Make an iterator returning elements from the iterable and saving a
+  copy of each.  When the iterable is exhausted, return elements from
+  the saved copy.  Repeats indefinitely.  Equivalent to:
+
+  \begin{verbatim}
+     def cycle(iterable):
+         saved = []
+         for element in iterable:
+             yield element
+             saved.append(element)
+         while saved:
+             for element in saved:
+                   yield element
+  \end{verbatim}
+
+  Note, this member of the toolkit may require significant
+  auxiliary storage (depending on the length of the iterable).
+\end{funcdesc}
+
+\begin{funcdesc}{dropwhile}{predicate, iterable}
+  Make an iterator that drops elements from the iterable as long as
+  the predicate is true; afterwards, returns every element.  Note,
+  the iterator does not produce \emph{any} output until the predicate
+  is true, so it may have a lengthy start-up time.  Equivalent to:
+
+  \begin{verbatim}
+     def dropwhile(predicate, iterable):
+         iterable = iter(iterable)
+         for x in iterable:
+             if not predicate(x):
+                 yield x
+                 break
+         for x in iterable:
+             yield x
+  \end{verbatim}
+\end{funcdesc}
+
+\begin{funcdesc}{groupby}{iterable\optional{, key}}
+  Make an iterator that returns consecutive keys and groups from the
+  \var{iterable}. The \var{key} is a function computing a key value for each
+  element.  If not specified or is \code{None}, \var{key} defaults to an
+  identity function and returns  the element unchanged.  Generally, the
+  iterable needs to already be sorted on the same key function.
+
+  The returned group is itself an iterator that shares the underlying
+  iterable with \function{groupby()}.  Because the source is shared, when
+  the \function{groupby} object is advanced, the previous group is no
+  longer visible.  So, if that data is needed later, it should be stored
+  as a list:
+
+  \begin{verbatim}
+    groups = []
+    uniquekeys = []
+    for k, g in groupby(data, keyfunc):
+        groups.append(list(g))      # Store group iterator as a list
+        uniquekeys.append(k)
+  \end{verbatim}
+
+  \function{groupby()} is equivalent to:
+
+  \begin{verbatim}
+    class groupby(object):
+        def __init__(self, iterable, key=None):
+            if key is None:
+                key = lambda x: x
+            self.keyfunc = key
+            self.it = iter(iterable)
+            self.tgtkey = self.currkey = self.currvalue = xrange(0)
+        def __iter__(self):
+            return self
+        def next(self):
+            while self.currkey == self.tgtkey:
+                self.currvalue = self.it.next() # Exit on StopIteration
+                self.currkey = self.keyfunc(self.currvalue)
+            self.tgtkey = self.currkey
+            return (self.currkey, self._grouper(self.tgtkey))
+        def _grouper(self, tgtkey):
+            while self.currkey == tgtkey:
+                yield self.currvalue
+                self.currvalue = self.it.next() # Exit on StopIteration
+                self.currkey = self.keyfunc(self.currvalue)
+  \end{verbatim}
+  \versionadded{2.4}
+\end{funcdesc}
+
+\begin{funcdesc}{ifilter}{predicate, iterable}
+  Make an iterator that filters elements from iterable returning only
+  those for which the predicate is \code{True}.
+  If \var{predicate} is \code{None}, return the items that are true.
+  Equivalent to:
+
+  \begin{verbatim}
+     def ifilter(predicate, iterable):
+         if predicate is None:
+             predicate = bool
+         for x in iterable:
+             if predicate(x):
+                 yield x
+  \end{verbatim}
+\end{funcdesc}
+
+\begin{funcdesc}{ifilterfalse}{predicate, iterable}
+  Make an iterator that filters elements from iterable returning only
+  those for which the predicate is \code{False}.
+  If \var{predicate} is \code{None}, return the items that are false.
+  Equivalent to:
+
+  \begin{verbatim}
+     def ifilterfalse(predicate, iterable):
+         if predicate is None:
+             predicate = bool
+         for x in iterable:
+             if not predicate(x):
+                 yield x
+  \end{verbatim}
+\end{funcdesc}
+
+\begin{funcdesc}{imap}{function, *iterables}
+  Make an iterator that computes the function using arguments from
+  each of the iterables.  If \var{function} is set to \code{None}, then
+  \function{imap()} returns the arguments as a tuple.  Like
+  \function{map()} but stops when the shortest iterable is exhausted
+  instead of filling in \code{None} for shorter iterables.  The reason
+  for the difference is that infinite iterator arguments are typically
+  an error for \function{map()} (because the output is fully evaluated)
+  but represent a common and useful way of supplying arguments to
+  \function{imap()}.
+  Equivalent to:
+
+  \begin{verbatim}
+     def imap(function, *iterables):
+         iterables = map(iter, iterables)
+         while True:
+             args = [i.next() for i in iterables]
+             if function is None:
+                 yield tuple(args)
+             else:
+                 yield function(*args)
+  \end{verbatim}
+\end{funcdesc}
+
+\begin{funcdesc}{islice}{iterable, \optional{start,} stop \optional{, step}}
+  Make an iterator that returns selected elements from the iterable.
+  If \var{start} is non-zero, then elements from the iterable are skipped
+  until start is reached.  Afterward, elements are returned consecutively
+  unless \var{step} is set higher than one which results in items being
+  skipped.  If \var{stop} is \code{None}, then iteration continues until
+  the iterator is exhausted, if at all; otherwise, it stops at the specified
+  position.  Unlike regular slicing,
+  \function{islice()} does not support negative values for \var{start},
+  \var{stop}, or \var{step}.  Can be used to extract related fields
+  from data where the internal structure has been flattened (for
+  example, a multi-line report may list a name field on every
+  third line).  Equivalent to:
+
+  \begin{verbatim}
+     def islice(iterable, *args):
+         s = slice(*args)
+         it = iter(xrange(s.start or 0, s.stop or sys.maxint, s.step or 1))
+         nexti = it.next()
+         for i, element in enumerate(iterable):
+             if i == nexti:
+                 yield element
+                 nexti = it.next()          
+  \end{verbatim}
+
+  If \var{start} is \code{None}, then iteration starts at zero.
+  If \var{step} is \code{None}, then the step defaults to one.
+  \versionchanged[accept \code{None} values for default \var{start} and
+                  \var{step}]{2.5}
+\end{funcdesc}
+
+\begin{funcdesc}{izip}{*iterables}
+  Make an iterator that aggregates elements from each of the iterables.
+  Like \function{zip()} except that it returns an iterator instead of
+  a list.  Used for lock-step iteration over several iterables at a
+  time.  Equivalent to:
+
+  \begin{verbatim}
+     def izip(*iterables):
+         iterables = map(iter, iterables)
+         while iterables:
+             result = [it.next() for it in iterables]
+             yield tuple(result)
+  \end{verbatim}
+
+  \versionchanged[When no iterables are specified, returns a zero length
+                  iterator instead of raising a \exception{TypeError}
+		  exception]{2.4}
+
+  Note, the left-to-right evaluation order of the iterables is guaranteed.
+  This makes possible an idiom for clustering a data series into n-length
+  groups using \samp{izip(*[iter(s)]*n)}.  For data that doesn't fit
+  n-length groups exactly, the last tuple can be pre-padded with fill
+  values using \samp{izip(*[chain(s, [None]*(n-1))]*n)}.
+         
+  Note, when \function{izip()} is used with unequal length inputs, subsequent
+  iteration over the longer iterables cannot reliably be continued after
+  \function{izip()} terminates.  Potentially, up to one entry will be missing
+  from each of the left-over iterables. This occurs because a value is fetched
+  from each iterator in-turn, but the process ends when one of the iterators
+  terminates.  This leaves the last fetched values in limbo (they cannot be
+  returned in a final, incomplete tuple and they are cannot be pushed back
+  into the iterator for retrieval with \code{it.next()}).  In general,
+  \function{izip()} should only be used with unequal length inputs when you
+  don't care about trailing, unmatched values from the longer iterables.
+\end{funcdesc}
+
+\begin{funcdesc}{repeat}{object\optional{, times}}
+  Make an iterator that returns \var{object} over and over again.
+  Runs indefinitely unless the \var{times} argument is specified.
+  Used as argument to \function{imap()} for invariant parameters
+  to the called function.  Also used with \function{izip()} to create
+  an invariant part of a tuple record.  Equivalent to:
+
+  \begin{verbatim}
+     def repeat(object, times=None):
+         if times is None:
+             while True:
+                 yield object
+         else:
+             for i in xrange(times):
+                 yield object
+  \end{verbatim}
+\end{funcdesc}
+
+\begin{funcdesc}{starmap}{function, iterable}
+  Make an iterator that computes the function using arguments tuples
+  obtained from the iterable.  Used instead of \function{imap()} when
+  argument parameters are already grouped in tuples from a single iterable
+  (the data has been ``pre-zipped'').  The difference between
+  \function{imap()} and \function{starmap()} parallels the distinction
+  between \code{function(a,b)} and \code{function(*c)}.
+  Equivalent to:
+
+  \begin{verbatim}
+     def starmap(function, iterable):
+         iterable = iter(iterable)
+         while True:
+             yield function(*iterable.next())
+  \end{verbatim}
+\end{funcdesc}
+
+\begin{funcdesc}{takewhile}{predicate, iterable}
+  Make an iterator that returns elements from the iterable as long as
+  the predicate is true.  Equivalent to:
+
+  \begin{verbatim}
+     def takewhile(predicate, iterable):
+         for x in iterable:
+             if predicate(x):
+                 yield x
+             else:
+                 break
+  \end{verbatim}
+\end{funcdesc}
+
+\begin{funcdesc}{tee}{iterable\optional{, n=2}}
+  Return \var{n} independent iterators from a single iterable.
+  The case where \code{n==2} is equivalent to:
+
+  \begin{verbatim}
+     def tee(iterable):
+         def gen(next, data={}, cnt=[0]):
+             for i in count():
+                 if i == cnt[0]:
+                     item = data[i] = next()
+                     cnt[0] += 1
+                 else:
+                     item = data.pop(i)
+                 yield item
+         it = iter(iterable)
+         return (gen(it.next), gen(it.next))
+  \end{verbatim}
+
+  Note, once \function{tee()} has made a split, the original \var{iterable}
+  should not be used anywhere else; otherwise, the \var{iterable} could get
+  advanced without the tee objects being informed.
+
+  Note, this member of the toolkit may require significant auxiliary
+  storage (depending on how much temporary data needs to be stored).
+  In general, if one iterator is going to use most or all of the data before
+  the other iterator, it is faster to use \function{list()} instead of
+  \function{tee()}.
+  \versionadded{2.4}
+\end{funcdesc}
+
+
+\subsection{Examples \label{itertools-example}}
+
+The following examples show common uses for each tool and
+demonstrate ways they can be combined.
+
+\begin{verbatim}
+
+>>> amounts = [120.15, 764.05, 823.14]
+>>> for checknum, amount in izip(count(1200), amounts):
+...     print 'Check %d is for $%.2f' % (checknum, amount)
+...
+Check 1200 is for $120.15
+Check 1201 is for $764.05
+Check 1202 is for $823.14
+
+>>> import operator
+>>> for cube in imap(operator.pow, xrange(1,5), repeat(3)):
+...    print cube
+...
+1
+8
+27
+64
+
+>>> reportlines = ['EuroPython', 'Roster', '', 'alex', '', 'laura',
+                  '', 'martin', '', 'walter', '', 'mark']
+>>> for name in islice(reportlines, 3, None, 2):
+...    print name.title()
+...
+Alex
+Laura
+Martin
+Walter
+Mark
+
+# Show a dictionary sorted and grouped by value
+>>> from operator import itemgetter
+>>> d = dict(a=1, b=2, c=1, d=2, e=1, f=2, g=3)
+>>> di = sorted(d.iteritems(), key=itemgetter(1))
+>>> for k, g in groupby(di, key=itemgetter(1)):
+...     print k, map(itemgetter(0), g)
+...
+1 ['a', 'c', 'e']
+2 ['b', 'd', 'f']
+3 ['g']
+
+# Find runs of consecutive numbers using groupby.  The key to the solution
+# is differencing with a range so that consecutive numbers all appear in
+# same group.
+>>> data = [ 1,  4,5,6, 10, 15,16,17,18, 22, 25,26,27,28]
+>>> for k, g in groupby(enumerate(data), lambda (i,x):i-x):
+...     print map(operator.itemgetter(1), g)
+... 
+[1]
+[4, 5, 6]
+[10]
+[15, 16, 17, 18]
+[22]
+[25, 26, 27, 28]
+
+\end{verbatim}
+
+
+\subsection{Recipes \label{itertools-recipes}}
+
+This section shows recipes for creating an extended toolset using the
+existing itertools as building blocks.
+
+The extended tools offer the same high performance as the underlying
+toolset.  The superior memory performance is kept by processing elements one
+at a time rather than bringing the whole iterable into memory all at once.
+Code volume is kept small by linking the tools together in a functional style
+which helps eliminate temporary variables.  High speed is retained by
+preferring ``vectorized'' building blocks over the use of for-loops and
+generators which incur interpreter overhead.
+
+
+\begin{verbatim}
+def take(n, seq):
+    return list(islice(seq, n))
+
+def enumerate(iterable):
+    return izip(count(), iterable)
+
+def tabulate(function):
+    "Return function(0), function(1), ..."
+    return imap(function, count())
+
+def iteritems(mapping):
+    return izip(mapping.iterkeys(), mapping.itervalues())
+
+def nth(iterable, n):
+    "Returns the nth item or raise IndexError"
+    return list(islice(iterable, n, n+1))[0]
+
+def all(seq, pred=None):
+    "Returns True if pred(x) is true for every element in the iterable"
+    for elem in ifilterfalse(pred, seq):
+        return False
+    return True
+
+def any(seq, pred=None):
+    "Returns True if pred(x) is true for at least one element in the iterable"
+    for elem in ifilter(pred, seq):
+        return True
+    return False
+
+def no(seq, pred=None):
+    "Returns True if pred(x) is false for every element in the iterable"
+    for elem in ifilter(pred, seq):
+        return False
+    return True
+
+def quantify(seq, pred=None):
+    "Count how many times the predicate is true in the sequence"
+    return sum(imap(pred, seq))
+
+def padnone(seq):
+    """Returns the sequence elements and then returns None indefinitely.
+
+    Useful for emulating the behavior of the built-in map() function.
+    """
+    return chain(seq, repeat(None))
+
+def ncycles(seq, n):
+    "Returns the sequence elements n times"
+    return chain(*repeat(seq, n))
+
+def dotproduct(vec1, vec2):
+    return sum(imap(operator.mul, vec1, vec2))
+
+def flatten(listOfLists):
+    return list(chain(*listOfLists))
+
+def repeatfunc(func, times=None, *args):
+    """Repeat calls to func with specified arguments.
+    
+    Example:  repeatfunc(random.random)
+    """
+    if times is None:
+        return starmap(func, repeat(args))
+    else:
+        return starmap(func, repeat(args, times))
+
+def pairwise(iterable):
+    "s -> (s0,s1), (s1,s2), (s2, s3), ..."
+    a, b = tee(iterable)
+    try:
+        b.next()
+    except StopIteration:
+        pass
+    return izip(a, b)
+
+def grouper(n, iterable, padvalue=None):
+    "grouper(3, 'abcdefg', 'x') --> ('a','b','c'), ('d','e','f'), ('g','x','x')"
+    return izip(*[chain(iterable, repeat(padvalue, n-1))]*n)
+
+def reverse_map(d):
+    "Return a new dict with swapped keys and values"
+    return dict(izip(d.itervalues(), d))
+
+\end{verbatim}
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