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Commit 4aa5110d authored by Dylan Trotter's avatar Dylan Trotter
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Add a few more libraries to the stdlib.

parent bb8a3187
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lib/_collections.py 0 → 100644
View file @ 4aa5110d
class deque(object):
def __init__(self, *_):
raise NotImplementedError
lib/errno.py 0 → 100644
View file @ 4aa5110d
EINVAL = 22
lib/operator.py 0 → 100644
View file @ 4aa5110d
def eq(a, b):
return a == b
def le(a, b):
return a <= b
def lt(a, b):
return a < b
def ge(a, b):
return a >= b
def gt(a, b):
return a > b
def itemgetter(*items):
if len(items) == 1:
item = items[0]
def g(obj):
return obj[item]
else:
def g(obj):
return tuple(obj[item] for item in items)
return g
def ne(a, b):
return a != b
lib/thread.py 0 → 100644
View file @ 4aa5110d
def get_ident():
f = __frame__()
while f.f_back:
f = f.f_back
return id(f)
third_party/stdlib/StringIO.py 0 → 100644
View file @ 4aa5110d
r"""File-like objects that read from or write to a string buffer.
This implements (nearly) all stdio methods.
f = StringIO() # ready for writing
f = StringIO(buf) # ready for reading
f.close() # explicitly release resources held
flag = f.isatty() # always false
pos = f.tell() # get current position
f.seek(pos) # set current position
f.seek(pos, mode) # mode 0: absolute; 1: relative; 2: relative to EOF
buf = f.read() # read until EOF
buf = f.read(n) # read up to n bytes
buf = f.readline() # read until end of line ('\n') or EOF
list = f.readlines()# list of f.readline() results until EOF
f.truncate([size]) # truncate file at to at most size (default: current pos)
f.write(buf) # write at current position
f.writelines(list) # for line in list: f.write(line)
f.getvalue() # return whole file's contents as a string
Notes:
- Using a real file is often faster (but less convenient).
- There's also a much faster implementation in C, called cStringIO, but
it's not subclassable.
- fileno() is left unimplemented so that code which uses it triggers
an exception early.
- Seeking far beyond EOF and then writing will insert real null
bytes that occupy space in the buffer.
- There's a simple test set (see end of this file).
"""
try:
import errno
EINVAL = errno.EINVAL
except ImportError:
EINVAL = 22
__all__ = ["StringIO"]
def _complain_ifclosed(closed):
if closed:
raise ValueError, "I/O operation on closed file"
class StringIO(object):
"""class StringIO([buffer])
When a StringIO object is created, it can be initialized to an existing
string by passing the string to the constructor. If no string is given,
the StringIO will start empty.
The StringIO object can accept either Unicode or 8-bit strings, but
mixing the two may take some care. If both are used, 8-bit strings that
cannot be interpreted as 7-bit ASCII (that use the 8th bit) will cause
a UnicodeError to be raised when getvalue() is called.
"""
def __init__(self, buf = ''):
# Force self.buf to be a string or unicode
if not isinstance(buf, basestring):
buf = str(buf)
self.buf = buf
self.len = len(buf)
self.buflist = []
self.pos = 0
self.closed = False
self.softspace = 0
def __iter__(self):
return self
def next(self):
"""A file object is its own iterator, for example iter(f) returns f
(unless f is closed). When a file is used as an iterator, typically
in a for loop (for example, for line in f: print line), the next()
method is called repeatedly. This method returns the next input line,
or raises StopIteration when EOF is hit.
"""
_complain_ifclosed(self.closed)
r = self.readline()
if not r:
raise StopIteration
return r
def close(self):
"""Free the memory buffer.
"""
if not self.closed:
self.closed = True
del self.buf, self.pos
def isatty(self):
"""Returns False because StringIO objects are not connected to a
tty-like device.
"""
_complain_ifclosed(self.closed)
return False
def seek(self, pos, mode = 0):
"""Set the file's current position.
The mode argument is optional and defaults to 0 (absolute file
positioning); other values are 1 (seek relative to the current
position) and 2 (seek relative to the file's end).
There is no return value.
"""
_complain_ifclosed(self.closed)
if self.buflist:
self.buf += ''.join(self.buflist)
self.buflist = []
if mode == 1:
pos += self.pos
elif mode == 2:
pos += self.len
self.pos = max(0, pos)
def tell(self):
"""Return the file's current position."""
_complain_ifclosed(self.closed)
return self.pos
def read(self, n = -1):
"""Read at most size bytes from the file
(less if the read hits EOF before obtaining size bytes).
If the size argument is negative or omitted, read all data until EOF
is reached. The bytes are returned as a string object. An empty
string is returned when EOF is encountered immediately.
"""
_complain_ifclosed(self.closed)
if self.buflist:
self.buf += ''.join(self.buflist)
self.buflist = []
if n is None or n < 0:
newpos = self.len
else:
newpos = min(self.pos+n, self.len)
r = self.buf[self.pos:newpos]
self.pos = newpos
return r
def readline(self, length=None):
r"""Read one entire line from the file.
A trailing newline character is kept in the string (but may be absent
when a file ends with an incomplete line). If the size argument is
present and non-negative, it is a maximum byte count (including the
trailing newline) and an incomplete line may be returned.
An empty string is returned only when EOF is encountered immediately.
Note: Unlike stdio's fgets(), the returned string contains null
characters ('\0') if they occurred in the input.
"""
_complain_ifclosed(self.closed)
if self.buflist:
self.buf += ''.join(self.buflist)
self.buflist = []
i = self.buf.find('\n', self.pos)
if i < 0:
newpos = self.len
else:
newpos = i+1
if length is not None and length >= 0:
if self.pos + length < newpos:
newpos = self.pos + length
r = self.buf[self.pos:newpos]
self.pos = newpos
return r
def readlines(self, sizehint = 0):
"""Read until EOF using readline() and return a list containing the
lines thus read.
If the optional sizehint argument is present, instead of reading up
to EOF, whole lines totalling approximately sizehint bytes (or more
to accommodate a final whole line).
"""
total = 0
lines = []
line = self.readline()
while line:
lines.append(line)
total += len(line)
if 0 < sizehint <= total:
break
line = self.readline()
return lines
def truncate(self, size=None):
"""Truncate the file's size.
If the optional size argument is present, the file is truncated to
(at most) that size. The size defaults to the current position.
The current file position is not changed unless the position
is beyond the new file size.
If the specified size exceeds the file's current size, the
file remains unchanged.
"""
_complain_ifclosed(self.closed)
if size is None:
size = self.pos
elif size < 0:
raise IOError(EINVAL, "Negative size not allowed")
elif size < self.pos:
self.pos = size
self.buf = self.getvalue()[:size]
self.len = size
def write(self, s):
"""Write a string to the file.
There is no return value.
"""
_complain_ifclosed(self.closed)
if not s: return
# Force s to be a string or unicode
if not isinstance(s, basestring):
s = str(s)
spos = self.pos
slen = self.len
if spos == slen:
self.buflist.append(s)
self.len = self.pos = spos + len(s)
return
if spos > slen:
self.buflist.append('\0'*(spos - slen))
slen = spos
newpos = spos + len(s)
if spos < slen:
if self.buflist:
self.buf += ''.join(self.buflist)
self.buflist = [self.buf[:spos], s, self.buf[newpos:]]
self.buf = ''
if newpos > slen:
slen = newpos
else:
self.buflist.append(s)
slen = newpos
self.len = slen
self.pos = newpos
def writelines(self, iterable):
"""Write a sequence of strings to the file. The sequence can be any
iterable object producing strings, typically a list of strings. There
is no return value.
(The name is intended to match readlines(); writelines() does not add
line separators.)
"""
write = self.write
for line in iterable:
write(line)
def flush(self):
"""Flush the internal buffer
"""
_complain_ifclosed(self.closed)
def getvalue(self):
"""
Retrieve the entire contents of the "file" at any time before
the StringIO object's close() method is called.
The StringIO object can accept either Unicode or 8-bit strings,
but mixing the two may take some care. If both are used, 8-bit
strings that cannot be interpreted as 7-bit ASCII (that use the
8th bit) will cause a UnicodeError to be raised when getvalue()
is called.
"""
_complain_ifclosed(self.closed)
if self.buflist:
self.buf += ''.join(self.buflist)
self.buflist = []
return self.buf
# A little test suite
def test():
import sys
if sys.argv[1:]:
file = sys.argv[1]
else:
file = '/etc/passwd'
lines = open(file, 'r').readlines()
text = open(file, 'r').read()
f = StringIO()
for line in lines[:-2]:
f.write(line)
f.writelines(lines[-2:])
if f.getvalue() != text:
raise RuntimeError, 'write failed'
length = f.tell()
print 'File length =', length
f.seek(len(lines[0]))
f.write(lines[1])
f.seek(0)
print 'First line =', repr(f.readline())
print 'Position =', f.tell()
line = f.readline()
print 'Second line =', repr(line)
f.seek(-len(line), 1)
line2 = f.read(len(line))
if line != line2:
raise RuntimeError, 'bad result after seek back'
f.seek(len(line2), 1)
list = f.readlines()
line = list[-1]
f.seek(f.tell() - len(line))
line2 = f.read()
if line != line2:
raise RuntimeError, 'bad result after seek back from EOF'
print 'Read', len(list), 'more lines'
print 'File length =', f.tell()
if f.tell() != length:
raise RuntimeError, 'bad length'
f.truncate(length/2)
f.seek(0, 2)
print 'Truncated length =', f.tell()
if f.tell() != length/2:
raise RuntimeError, 'truncate did not adjust length'
f.close()
if __name__ == '__main__':
test()
third_party/stdlib/collections.py 0 → 100644
View file @ 4aa5110d
'''This module implements specialized container datatypes providing
alternatives to Python's general purpose built-in containers, dict,
list, set, and tuple.
* namedtuple factory function for creating tuple subclasses with named fields
* deque list-like container with fast appends and pops on either end
* Counter dict subclass for counting hashable objects
* OrderedDict dict subclass that remembers the order entries were added
* defaultdict dict subclass that calls a factory function to supply missing values
'''
__all__ = ['Counter', 'namedtuple', 'OrderedDict'] # 'deque', 'defaultdict',
# For bootstrapping reasons, the collection ABCs are defined in _abcoll.py.
# They should however be considered an integral part of collections.py.
import _abcoll
# TODO: Support from foo import * syntax.
for name in _abcoll.__all__:
globals()[name] = getattr(_abcoll, name)
import _collections
deque = _collections.deque
#defaultdict = _collections.defaultdict
import operator
_itemgetter = operator.itemgetter
_eq = operator.eq
import keyword
_iskeyword = keyword.iskeyword
import sys as _sys
import heapq as _heapq
import itertools
_repeat = itertools.repeat
_chain = itertools.chain
_starmap = itertools.starmap
_imap = itertools.imap
#try:
import thread
_get_ident = thread.get_ident
#except ImportError:
# from dummy_thread import get_ident as _get_ident
################################################################################
### OrderedDict
################################################################################
class OrderedDict(dict):
'Dictionary that remembers insertion order'
# An inherited dict maps keys to values.
# The inherited dict provides __getitem__, __len__, __contains__, and get.
# The remaining methods are order-aware.
# Big-O running times for all methods are the same as regular dictionaries.
# The internal self.__map dict maps keys to links in a doubly linked list.
# The circular doubly linked list starts and ends with a sentinel element.
# The sentinel element never gets deleted (this simplifies the algorithm).
# Each link is stored as a list of length three: [PREV, NEXT, KEY].
def __init__(*args, **kwds):
'''Initialize an ordered dictionary. The signature is the same as
regular dictionaries, but keyword arguments are not recommended because
their insertion order is arbitrary.
'''
if not args:
raise TypeError("descriptor '__init__' of 'OrderedDict' object "
"needs an argument")
self = args[0]
args = args[1:]
if len(args) > 1:
raise TypeError('expected at most 1 arguments, got %d' % len(args))
try:
self.__root
except AttributeError:
self.__root = root = [] # sentinel node
root[:] = [root, root, None]
self.__map = {}
self.__update(*args, **kwds)
def __setitem__(self, key, value, dict_setitem=dict.__setitem__):
'od.__setitem__(i, y) <==> od[i]=y'
# Setting a new item creates a new link at the end of the linked list,
# and the inherited dictionary is updated with the new key/value pair.
if key not in self:
root = self.__root
last = root[0]
last[1] = root[0] = self.__map[key] = [last, root, key]
return dict_setitem(self, key, value)
def __delitem__(self, key, dict_delitem=dict.__delitem__):
'od.__delitem__(y) <==> del od[y]'
# Deleting an existing item uses self.__map to find the link which gets
# removed by updating the links in the predecessor and successor nodes.
dict_delitem(self, key)
link_prev, link_next, _ = self.__map.pop(key)
link_prev[1] = link_next # update link_prev[NEXT]
link_next[0] = link_prev # update link_next[PREV]
def __iter__(self):
'od.__iter__() <==> iter(od)'
# Traverse the linked list in order.
root = self.__root
curr = root[1] # start at the first node
while curr is not root:
yield curr[2] # yield the curr[KEY]
curr = curr[1] # move to next node
def __reversed__(self):
'od.__reversed__() <==> reversed(od)'
# Traverse the linked list in reverse order.
root = self.__root
curr = root[0] # start at the last node
while curr is not root:
yield curr[2] # yield the curr[KEY]
curr = curr[0] # move to previous node
def clear(self):
'od.clear() -> None. Remove all items from od.'
root = self.__root
root[:] = [root, root, None]
self.__map.clear()
dict.clear(self)
# -- the following methods do not depend on the internal structure --
def keys(self):
'od.keys() -> list of keys in od'
return list(self)
def values(self):
'od.values() -> list of values in od'
return [self[key] for key in self]
def items(self):
'od.items() -> list of (key, value) pairs in od'
return [(key, self[key]) for key in self]
def iterkeys(self):
'od.iterkeys() -> an iterator over the keys in od'
return iter(self)
def itervalues(self):
'od.itervalues -> an iterator over the values in od'
for k in self:
yield self[k]
def iteritems(self):
'od.iteritems -> an iterator over the (key, value) pairs in od'
for k in self:
yield (k, self[k])
update = MutableMapping.update
__update = update # let subclasses override update without breaking __init__
__marker = object()
def pop(self, key, default=__marker):
'''od.pop(k[,d]) -> v, remove specified key and return the corresponding
value. If key is not found, d is returned if given, otherwise KeyError
is raised.
'''
if key in self:
result = self[key]
del self[key]
return result
if default is self.__marker:
raise KeyError(key)
return default
def setdefault(self, key, default=None):
'od.setdefault(k[,d]) -> od.get(k,d), also set od[k]=d if k not in od'
if key in self:
return self[key]
self[key] = default
return default
def popitem(self, last=True):
'''od.popitem() -> (k, v), return and remove a (key, value) pair.
Pairs are returned in LIFO order if last is true or FIFO order if false.
'''
if not self:
raise KeyError('dictionary is empty')
key = next(reversed(self) if last else iter(self))
value = self.pop(key)
return key, value
def __repr__(self, _repr_running={}):
'od.__repr__() <==> repr(od)'
call_key = id(self), _get_ident()
if call_key in _repr_running:
return '...'
_repr_running[call_key] = 1
try:
if not self:
return '%s()' % (self.__class__.__name__,)
return '%s(%r)' % (self.__class__.__name__, self.items())
finally:
del _repr_running[call_key]
def __reduce__(self):
'Return state information for pickling'
items = [[k, self[k]] for k in self]
inst_dict = vars(self).copy()
for k in vars(OrderedDict()):
inst_dict.pop(k, None)
if inst_dict:
return (self.__class__, (items,), inst_dict)
return self.__class__, (items,)
def copy(self):
'od.copy() -> a shallow copy of od'
return self.__class__(self)
@classmethod
def fromkeys(cls, iterable, value=None):
'''OD.fromkeys(S[, v]) -> New ordered dictionary with keys from S.
If not specified, the value defaults to None.
'''
self = cls()
for key in iterable:
self[key] = value
return self
def __eq__(self, other):
'''od.__eq__(y) <==> od==y. Comparison to another OD is order-sensitive
while comparison to a regular mapping is order-insensitive.
'''
if isinstance(other, OrderedDict):
return dict.__eq__(self, other) and all(_imap(_eq, self, other))
return dict.__eq__(self, other)
def __ne__(self, other):
'od.__ne__(y) <==> od!=y'
return not self == other
# -- the following methods support python 3.x style dictionary views --
def viewkeys(self):
"od.viewkeys() -> a set-like object providing a view on od's keys"
return KeysView(self)
def viewvalues(self):
"od.viewvalues() -> an object providing a view on od's values"
return ValuesView(self)
def viewitems(self):
"od.viewitems() -> a set-like object providing a view on od's items"
return ItemsView(self)
################################################################################
### namedtuple
################################################################################
_class_template = '''\
class {typename}(tuple):
'{typename}({arg_list})'
__slots__ = ()
_fields = {field_names!r}
def __new__(_cls, {arg_list}):
'Create new instance of {typename}({arg_list})'
return _tuple.__new__(_cls, ({arg_list}))
@classmethod
def _make(cls, iterable, new=tuple.__new__, len=len):
'Make a new {typename} object from a sequence or iterable'
result = new(cls, iterable)
if len(result) != {num_fields:d}:
raise TypeError('Expected {num_fields:d} arguments, got %d' % len(result))
return result
def __repr__(self):
'Return a nicely formatted representation string'
return '{typename}({repr_fmt})' % self
def _asdict(self):
'Return a new OrderedDict which maps field names to their values'
return OrderedDict(zip(self._fields, self))
def _replace(_self, **kwds):
'Return a new {typename} object replacing specified fields with new values'
result = _self._make(map(kwds.pop, {field_names!r}, _self))
if kwds:
raise ValueError('Got unexpected field names: %r' % kwds.keys())
return result
def __getnewargs__(self):
'Return self as a plain tuple. Used by copy and pickle.'
return tuple(self)
__dict__ = _property(_asdict)
def __getstate__(self):
'Exclude the OrderedDict from pickling'
pass
{field_defs}
'''
_repr_template = '{name}=%r'
_field_template = '''\
{name} = _property(_itemgetter({index:d}), doc='Alias for field number {index:d}')
'''
#def namedtuple(typename, field_names, verbose=False, rename=False):
# """Returns a new subclass of tuple with named fields.
#
# >>> Point = namedtuple('Point', ['x', 'y'])
# >>> Point.__doc__ # docstring for the new class
# 'Point(x, y)'
# >>> p = Point(11, y=22) # instantiate with positional args or keywords
# >>> p[0] + p[1] # indexable like a plain tuple
# 33
# >>> x, y = p # unpack like a regular tuple
# >>> x, y
# (11, 22)
# >>> p.x + p.y # fields also accessible by name
# 33
# >>> d = p._asdict() # convert to a dictionary
# >>> d['x']
# 11
# >>> Point(**d) # convert from a dictionary
# Point(x=11, y=22)
# >>> p._replace(x=100) # _replace() is like str.replace() but targets named fields
# Point(x=100, y=22)
#
# """
#
# # Validate the field names. At the user's option, either generate an error
# # message or automatically replace the field name with a valid name.
# if isinstance(field_names, basestring):
# field_names = field_names.replace(',', ' ').split()
# field_names = map(str, field_names)
# typename = str(typename)
# if rename:
# seen = set()
# for index, name in enumerate(field_names):
# if (not all(c.isalnum() or c=='_' for c in name)
# or _iskeyword(name)
# or not name
# or name[0].isdigit()
# or name.startswith('_')
# or name in seen):
# field_names[index] = '_%d' % index
# seen.add(name)
# for name in [typename] + field_names:
# if type(name) != str:
# raise TypeError('Type names and field names must be strings')
# if not all(c.isalnum() or c=='_' for c in name):
# raise ValueError('Type names and field names can only contain '
# 'alphanumeric characters and underscores: %r' % name)
# if _iskeyword(name):
# raise ValueError('Type names and field names cannot be a '
# 'keyword: %r' % name)
# if name[0].isdigit():
# raise ValueError('Type names and field names cannot start with '
# 'a number: %r' % name)
# seen = set()
# for name in field_names:
# if name.startswith('_') and not rename:
# raise ValueError('Field names cannot start with an underscore: '
# '%r' % name)
# if name in seen:
# raise ValueError('Encountered duplicate field name: %r' % name)
# seen.add(name)
#
# # Fill-in the class template
# class_definition = _class_template.format(
# typename = typename,
# field_names = tuple(field_names),
# num_fields = len(field_names),
# arg_list = repr(tuple(field_names)).replace("'", "")[1:-1],
# repr_fmt = ', '.join(_repr_template.format(name=name)
# for name in field_names),
# field_defs = '\n'.join(_field_template.format(index=index, name=name)
# for index, name in enumerate(field_names))
# )
# if verbose:
# print class_definition
#
# # Execute the template string in a temporary namespace and support
# # tracing utilities by setting a value for frame.f_globals['__name__']
# namespace = dict(_itemgetter=_itemgetter, __name__='namedtuple_%s' % typename,
# OrderedDict=OrderedDict, _property=property, _tuple=tuple)
# try:
# exec class_definition in namespace
# except SyntaxError as e:
# raise SyntaxError(e.message + ':\n' + class_definition)
# result = namespace[typename]
#
# # For pickling to work, the __module__ variable needs to be set to the frame
# # where the named tuple is created. Bypass this step in environments where
# # sys._getframe is not defined (Jython for example) or sys._getframe is not
# # defined for arguments greater than 0 (IronPython).
# try:
# result.__module__ = _sys._getframe(1).f_globals.get('__name__', '__main__')
# except (AttributeError, ValueError):
# pass
#
# return result
########################################################################
### Counter
########################################################################
class Counter(dict):
'''Dict subclass for counting hashable items. Sometimes called a bag
or multiset. Elements are stored as dictionary keys and their counts
are stored as dictionary values.
>>> c = Counter('abcdeabcdabcaba') # count elements from a string
>>> c.most_common(3) # three most common elements
[('a', 5), ('b', 4), ('c', 3)]
>>> sorted(c) # list all unique elements
['a', 'b', 'c', 'd', 'e']
>>> ''.join(sorted(c.elements())) # list elements with repetitions
'aaaaabbbbcccdde'
>>> sum(c.values()) # total of all counts
15
>>> c['a'] # count of letter 'a'
5
>>> for elem in 'shazam': # update counts from an iterable
... c[elem] += 1 # by adding 1 to each element's count
>>> c['a'] # now there are seven 'a'
7
>>> del c['b'] # remove all 'b'
>>> c['b'] # now there are zero 'b'
0
>>> d = Counter('simsalabim') # make another counter
>>> c.update(d) # add in the second counter
>>> c['a'] # now there are nine 'a'
9
>>> c.clear() # empty the counter
>>> c
Counter()
Note: If a count is set to zero or reduced to zero, it will remain
in the counter until the entry is deleted or the counter is cleared:
>>> c = Counter('aaabbc')
>>> c['b'] -= 2 # reduce the count of 'b' by two
>>> c.most_common() # 'b' is still in, but its count is zero
[('a', 3), ('c', 1), ('b', 0)]
'''
# References:
# http://en.wikipedia.org/wiki/Multiset
# http://www.gnu.org/software/smalltalk/manual-base/html_node/Bag.html
# http://www.demo2s.com/Tutorial/Cpp/0380__set-multiset/Catalog0380__set-multiset.htm
# http://code.activestate.com/recipes/259174/
# Knuth, TAOCP Vol. II section 4.6.3
def __init__(*args, **kwds):
'''Create a new, empty Counter object. And if given, count elements
from an input iterable. Or, initialize the count from another mapping
of elements to their counts.
>>> c = Counter() # a new, empty counter
>>> c = Counter('gallahad') # a new counter from an iterable
>>> c = Counter({'a': 4, 'b': 2}) # a new counter from a mapping
>>> c = Counter(a=4, b=2) # a new counter from keyword args
'''
if not args:
raise TypeError("descriptor '__init__' of 'Counter' object "
"needs an argument")
self = args[0]
args = args[1:]
if len(args) > 1:
raise TypeError('expected at most 1 arguments, got %d' % len(args))
super(Counter, self).__init__()
self.update(*args, **kwds)
def __missing__(self, key):
'The count of elements not in the Counter is zero.'
# Needed so that self[missing_item] does not raise KeyError
return 0
def most_common(self, n=None):
'''List the n most common elements and their counts from the most
common to the least. If n is None, then list all element counts.
>>> Counter('abcdeabcdabcaba').most_common(3)
[('a', 5), ('b', 4), ('c', 3)]
'''
# Emulate Bag.sortedByCount from Smalltalk
if n is None:
return sorted(self.iteritems(), key=_itemgetter(1), reverse=True)
return _heapq.nlargest(n, self.iteritems(), key=_itemgetter(1))
def elements(self):
'''Iterator over elements repeating each as many times as its count.
>>> c = Counter('ABCABC')
>>> sorted(c.elements())
['A', 'A', 'B', 'B', 'C', 'C']
# Knuth's example for prime factors of 1836: 2**2 * 3**3 * 17**1
>>> prime_factors = Counter({2: 2, 3: 3, 17: 1})
>>> product = 1
>>> for factor in prime_factors.elements(): # loop over factors
... product *= factor # and multiply them
>>> product
1836
Note, if an element's count has been set to zero or is a negative
number, elements() will ignore it.
'''
# Emulate Bag.do from Smalltalk and Multiset.begin from C++.
return _chain.from_iterable(_starmap(_repeat, self.iteritems()))
# Override dict methods where necessary
@classmethod
def fromkeys(cls, iterable, v=None):
# There is no equivalent method for counters because setting v=1
# means that no element can have a count greater than one.
raise NotImplementedError(
'Counter.fromkeys() is undefined. Use Counter(iterable) instead.')
def update(*args, **kwds):
'''Like dict.update() but add counts instead of replacing them.
Source can be an iterable, a dictionary, or another Counter instance.
>>> c = Counter('which')
>>> c.update('witch') # add elements from another iterable
>>> d = Counter('watch')
>>> c.update(d) # add elements from another counter
>>> c['h'] # four 'h' in which, witch, and watch
4
'''
# The regular dict.update() operation makes no sense here because the
# replace behavior results in the some of original untouched counts
# being mixed-in with all of the other counts for a mismash that
# doesn't have a straight-forward interpretation in most counting
# contexts. Instead, we implement straight-addition. Both the inputs
# and outputs are allowed to contain zero and negative counts.
if not args:
raise TypeError("descriptor 'update' of 'Counter' object "
"needs an argument")
self = args[0]
args = args[1:]
if len(args) > 1:
raise TypeError('expected at most 1 arguments, got %d' % len(args))
iterable = args[0] if args else None
if iterable is not None:
if isinstance(iterable, Mapping):
if self:
self_get = self.get
for elem, count in iterable.iteritems():
self[elem] = self_get(elem, 0) + count
else:
super(Counter, self).update(iterable) # fast path when counter is empty
else:
self_get = self.get
for elem in iterable:
self[elem] = self_get(elem, 0) + 1
if kwds:
self.update(kwds)
def subtract(*args, **kwds):
'''Like dict.update() but subtracts counts instead of replacing them.
Counts can be reduced below zero. Both the inputs and outputs are
allowed to contain zero and negative counts.
Source can be an iterable, a dictionary, or another Counter instance.
>>> c = Counter('which')
>>> c.subtract('witch') # subtract elements from another iterable
>>> c.subtract(Counter('watch')) # subtract elements from another counter
>>> c['h'] # 2 in which, minus 1 in witch, minus 1 in watch
0
>>> c['w'] # 1 in which, minus 1 in witch, minus 1 in watch
-1
'''
if not args:
raise TypeError("descriptor 'subtract' of 'Counter' object "
"needs an argument")
self = args[0]
args = args[1:]
if len(args) > 1:
raise TypeError('expected at most 1 arguments, got %d' % len(args))
iterable = args[0] if args else None
if iterable is not None:
self_get = self.get
if isinstance(iterable, Mapping):
for elem, count in iterable.items():
self[elem] = self_get(elem, 0) - count
else:
for elem in iterable:
self[elem] = self_get(elem, 0) - 1
if kwds:
self.subtract(kwds)
def copy(self):
'Return a shallow copy.'
return self.__class__(self)
def __reduce__(self):
return self.__class__, (dict(self),)
def __delitem__(self, elem):
'Like dict.__delitem__() but does not raise KeyError for missing values.'
if elem in self:
super(Counter, self).__delitem__(elem)
def __repr__(self):
if not self:
return '%s()' % self.__class__.__name__
items = ', '.join(map('%r: %r'.__mod__, self.most_common()))
return '%s({%s})' % (self.__class__.__name__, items)
# Multiset-style mathematical operations discussed in:
# Knuth TAOCP Volume II section 4.6.3 exercise 19
# and at http://en.wikipedia.org/wiki/Multiset
#
# Outputs guaranteed to only include positive counts.
#
# To strip negative and zero counts, add-in an empty counter:
# c += Counter()
def __add__(self, other):
'''Add counts from two counters.
>>> Counter('abbb') + Counter('bcc')
Counter({'b': 4, 'c': 2, 'a': 1})
'''
if not isinstance(other, Counter):
return NotImplemented
result = Counter()
for elem, count in self.items():
newcount = count + other[elem]
if newcount > 0:
result[elem] = newcount
for elem, count in other.items():
if elem not in self and count > 0:
result[elem] = count
return result
def __sub__(self, other):
''' Subtract count, but keep only results with positive counts.
>>> Counter('abbbc') - Counter('bccd')
Counter({'b': 2, 'a': 1})
'''
if not isinstance(other, Counter):
return NotImplemented
result = Counter()
for elem, count in self.items():
newcount = count - other[elem]
if newcount > 0:
result[elem] = newcount
for elem, count in other.items():
if elem not in self and count < 0:
result[elem] = 0 - count
return result
def __or__(self, other):
'''Union is the maximum of value in either of the input counters.
>>> Counter('abbb') | Counter('bcc')
Counter({'b': 3, 'c': 2, 'a': 1})
'''
if not isinstance(other, Counter):
return NotImplemented
result = Counter()
for elem, count in self.items():
other_count = other[elem]
newcount = other_count if count < other_count else count
if newcount > 0:
result[elem] = newcount
for elem, count in other.items():
if elem not in self and count > 0:
result[elem] = count
return result
def __and__(self, other):
''' Intersection is the minimum of corresponding counts.
>>> Counter('abbb') & Counter('bcc')
Counter({'b': 1})
'''
if not isinstance(other, Counter):
return NotImplemented
result = Counter()
for elem, count in self.items():
other_count = other[elem]
newcount = count if count < other_count else other_count
if newcount > 0:
result[elem] = newcount
return result
if __name__ == '__main__':
pass
# # verify that instances can be pickled
# from cPickle import loads, dumps
# Point = namedtuple('Point', 'x, y', True)
# p = Point(x=10, y=20)
# assert p == loads(dumps(p))
#
# # test and demonstrate ability to override methods
# class Point(namedtuple('Point', 'x y')):
# __slots__ = ()
# @property
# def hypot(self):
# return (self.x ** 2 + self.y ** 2) ** 0.5
# def __str__(self):
# return 'Point: x=%6.3f y=%6.3f hypot=%6.3f' % (self.x, self.y, self.hypot)
#
# for p in Point(3, 4), Point(14, 5/7.):
# print p
#
# class Point(namedtuple('Point', 'x y')):
# 'Point class with optimized _make() and _replace() without error-checking'
# __slots__ = ()
# _make = classmethod(tuple.__new__)
# def _replace(self, _map=map, **kwds):
# return self._make(_map(kwds.get, ('x', 'y'), self))
#
# print Point(11, 22)._replace(x=100)
#
# Point3D = namedtuple('Point3D', Point._fields + ('z',))
# print Point3D.__doc__
#
# import doctest
# TestResults = namedtuple('TestResults', 'failed attempted')
# print TestResults(*doctest.testmod())
third_party/stdlib/heapq.py 0 → 100644
View file @ 4aa5110d
# -*- coding: latin-1 -*-
"""Heap queue algorithm (a.k.a. priority queue).
Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for
all k, counting elements from 0. For the sake of comparison,
non-existing elements are considered to be infinite. The interesting
property of a heap is that a[0] is always its smallest element.
Usage:
heap = [] # creates an empty heap
heappush(heap, item) # pushes a new item on the heap
item = heappop(heap) # pops the smallest item from the heap
item = heap[0] # smallest item on the heap without popping it
heapify(x) # transforms list into a heap, in-place, in linear time
item = heapreplace(heap, item) # pops and returns smallest item, and adds
# new item; the heap size is unchanged
Our API differs from textbook heap algorithms as follows:
- We use 0-based indexing. This makes the relationship between the
index for a node and the indexes for its children slightly less
obvious, but is more suitable since Python uses 0-based indexing.
- Our heappop() method returns the smallest item, not the largest.
These two make it possible to view the heap as a regular Python list
without surprises: heap[0] is the smallest item, and heap.sort()
maintains the heap invariant!
"""
# Original code by Kevin O'Connor, augmented by Tim Peters and Raymond Hettinger
__about__ = """Heap queues
[explanation by François Pinard]
Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for
all k, counting elements from 0. For the sake of comparison,
non-existing elements are considered to be infinite. The interesting
property of a heap is that a[0] is always its smallest element.
The strange invariant above is meant to be an efficient memory
representation for a tournament. The numbers below are `k', not a[k]:
0
1 2
3 4 5 6
7 8 9 10 11 12 13 14
15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
In the tree above, each cell `k' is topping `2*k+1' and `2*k+2'. In
a usual binary tournament we see in sports, each cell is the winner
over the two cells it tops, and we can trace the winner down the tree
to see all opponents s/he had. However, in many computer applications
of such tournaments, we do not need to trace the history of a winner.
To be more memory efficient, when a winner is promoted, we try to
replace it by something else at a lower level, and the rule becomes
that a cell and the two cells it tops contain three different items,
but the top cell "wins" over the two topped cells.
If this heap invariant is protected at all time, index 0 is clearly
the overall winner. The simplest algorithmic way to remove it and
find the "next" winner is to move some loser (let's say cell 30 in the
diagram above) into the 0 position, and then percolate this new 0 down
the tree, exchanging values, until the invariant is re-established.
This is clearly logarithmic on the total number of items in the tree.
By iterating over all items, you get an O(n ln n) sort.
A nice feature of this sort is that you can efficiently insert new
items while the sort is going on, provided that the inserted items are
not "better" than the last 0'th element you extracted. This is
especially useful in simulation contexts, where the tree holds all
incoming events, and the "win" condition means the smallest scheduled
time. When an event schedule other events for execution, they are
scheduled into the future, so they can easily go into the heap. So, a
heap is a good structure for implementing schedulers (this is what I
used for my MIDI sequencer :-).
Various structures for implementing schedulers have been extensively
studied, and heaps are good for this, as they are reasonably speedy,
the speed is almost constant, and the worst case is not much different
than the average case. However, there are other representations which
are more efficient overall, yet the worst cases might be terrible.
Heaps are also very useful in big disk sorts. You most probably all
know that a big sort implies producing "runs" (which are pre-sorted
sequences, which size is usually related to the amount of CPU memory),
followed by a merging passes for these runs, which merging is often
very cleverly organised[1]. It is very important that the initial
sort produces the longest runs possible. Tournaments are a good way
to that. If, using all the memory available to hold a tournament, you
replace and percolate items that happen to fit the current run, you'll
produce runs which are twice the size of the memory for random input,
and much better for input fuzzily ordered.
Moreover, if you output the 0'th item on disk and get an input which
may not fit in the current tournament (because the value "wins" over
the last output value), it cannot fit in the heap, so the size of the
heap decreases. The freed memory could be cleverly reused immediately
for progressively building a second heap, which grows at exactly the
same rate the first heap is melting. When the first heap completely
vanishes, you switch heaps and start a new run. Clever and quite
effective!
In a word, heaps are useful memory structures to know. I use them in
a few applications, and I think it is good to keep a `heap' module
around. :-)
--------------------
[1] The disk balancing algorithms which are current, nowadays, are
more annoying than clever, and this is a consequence of the seeking
capabilities of the disks. On devices which cannot seek, like big
tape drives, the story was quite different, and one had to be very
clever to ensure (far in advance) that each tape movement will be the
most effective possible (that is, will best participate at
"progressing" the merge). Some tapes were even able to read
backwards, and this was also used to avoid the rewinding time.
Believe me, real good tape sorts were quite spectacular to watch!
From all times, sorting has always been a Great Art! :-)
"""
__all__ = ['heappush', 'heappop', 'heapify', 'heapreplace', 'merge',
'nlargest', 'nsmallest', 'heappushpop']
import itertools
islice = itertools.islice
count = itertools.count
imap = itertools.imap
izip = itertools.izip
tee = itertools.tee
chain = itertools.chain
import operator
itemgetter = operator.itemgetter
def cmp_lt(x, y):
# Use __lt__ if available; otherwise, try __le__.
# In Py3.x, only __lt__ will be called.
return (x < y) if hasattr(x, '__lt__') else (not y <= x)
def heappush(heap, item):
"""Push item onto heap, maintaining the heap invariant."""
heap.append(item)
_siftdown(heap, 0, len(heap)-1)
def heappop(heap):
"""Pop the smallest item off the heap, maintaining the heap invariant."""
lastelt = heap.pop() # raises appropriate IndexError if heap is empty
if heap:
returnitem = heap[0]
heap[0] = lastelt
_siftup(heap, 0)
else:
returnitem = lastelt
return returnitem
def heapreplace(heap, item):
"""Pop and return the current smallest value, and add the new item.
This is more efficient than heappop() followed by heappush(), and can be
more appropriate when using a fixed-size heap. Note that the value
returned may be larger than item! That constrains reasonable uses of
this routine unless written as part of a conditional replacement:
if item > heap[0]:
item = heapreplace(heap, item)
"""
returnitem = heap[0] # raises appropriate IndexError if heap is empty
heap[0] = item
_siftup(heap, 0)
return returnitem
def heappushpop(heap, item):
"""Fast version of a heappush followed by a heappop."""
if heap and cmp_lt(heap[0], item):
item, heap[0] = heap[0], item
_siftup(heap, 0)
return item
def heapify(x):
"""Transform list into a heap, in-place, in O(len(x)) time."""
n = len(x)
# Transform bottom-up. The largest index there's any point to looking at
# is the largest with a child index in-range, so must have 2*i + 1 < n,
# or i < (n-1)/2. If n is even = 2*j, this is (2*j-1)/2 = j-1/2 so
# j-1 is the largest, which is n//2 - 1. If n is odd = 2*j+1, this is
# (2*j+1-1)/2 = j so j-1 is the largest, and that's again n//2-1.
for i in reversed(xrange(n//2)):
_siftup(x, i)
def _heappushpop_max(heap, item):
"""Maxheap version of a heappush followed by a heappop."""
if heap and cmp_lt(item, heap[0]):
item, heap[0] = heap[0], item
_siftup_max(heap, 0)
return item
def _heapify_max(x):
"""Transform list into a maxheap, in-place, in O(len(x)) time."""
n = len(x)
for i in reversed(range(n//2)):
_siftup_max(x, i)
def nlargest(n, iterable):
"""Find the n largest elements in a dataset.
Equivalent to: sorted(iterable, reverse=True)[:n]
"""
if n < 0:
return []
it = iter(iterable)
result = list(islice(it, n))
if not result:
return result
heapify(result)
_heappushpop = heappushpop
for elem in it:
_heappushpop(result, elem)
result.sort(reverse=True)
return result
def nsmallest(n, iterable):
"""Find the n smallest elements in a dataset.
Equivalent to: sorted(iterable)[:n]
"""
if n < 0:
return []
it = iter(iterable)
result = list(islice(it, n))
if not result:
return result
_heapify_max(result)
_heappushpop = _heappushpop_max
for elem in it:
_heappushpop(result, elem)
result.sort()
return result
# 'heap' is a heap at all indices >= startpos, except possibly for pos. pos
# is the index of a leaf with a possibly out-of-order value. Restore the
# heap invariant.
def _siftdown(heap, startpos, pos):
newitem = heap[pos]
# Follow the path to the root, moving parents down until finding a place
# newitem fits.
while pos > startpos:
parentpos = (pos - 1) >> 1
parent = heap[parentpos]
if cmp_lt(newitem, parent):
heap[pos] = parent
pos = parentpos
continue
break
heap[pos] = newitem
# The child indices of heap index pos are already heaps, and we want to make
# a heap at index pos too. We do this by bubbling the smaller child of
# pos up (and so on with that child's children, etc) until hitting a leaf,
# then using _siftdown to move the oddball originally at index pos into place.
#
# We *could* break out of the loop as soon as we find a pos where newitem <=
# both its children, but turns out that's not a good idea, and despite that
# many books write the algorithm that way. During a heap pop, the last array
# element is sifted in, and that tends to be large, so that comparing it
# against values starting from the root usually doesn't pay (= usually doesn't
# get us out of the loop early). See Knuth, Volume 3, where this is
# explained and quantified in an exercise.
#
# Cutting the # of comparisons is important, since these routines have no
# way to extract "the priority" from an array element, so that intelligence
# is likely to be hiding in custom __cmp__ methods, or in array elements
# storing (priority, record) tuples. Comparisons are thus potentially
# expensive.
#
# On random arrays of length 1000, making this change cut the number of
# comparisons made by heapify() a little, and those made by exhaustive
# heappop() a lot, in accord with theory. Here are typical results from 3
# runs (3 just to demonstrate how small the variance is):
#
# Compares needed by heapify Compares needed by 1000 heappops
# -------------------------- --------------------------------
# 1837 cut to 1663 14996 cut to 8680
# 1855 cut to 1659 14966 cut to 8678
# 1847 cut to 1660 15024 cut to 8703
#
# Building the heap by using heappush() 1000 times instead required
# 2198, 2148, and 2219 compares: heapify() is more efficient, when
# you can use it.
#
# The total compares needed by list.sort() on the same lists were 8627,
# 8627, and 8632 (this should be compared to the sum of heapify() and
# heappop() compares): list.sort() is (unsurprisingly!) more efficient
# for sorting.
def _siftup(heap, pos):
endpos = len(heap)
startpos = pos
newitem = heap[pos]
# Bubble up the smaller child until hitting a leaf.
childpos = 2*pos + 1 # leftmost child position
while childpos < endpos:
# Set childpos to index of smaller child.
rightpos = childpos + 1
if rightpos < endpos and not cmp_lt(heap[childpos], heap[rightpos]):
childpos = rightpos
# Move the smaller child up.
heap[pos] = heap[childpos]
pos = childpos
childpos = 2*pos + 1
# The leaf at pos is empty now. Put newitem there, and bubble it up
# to its final resting place (by sifting its parents down).
heap[pos] = newitem
_siftdown(heap, startpos, pos)
def _siftdown_max(heap, startpos, pos):
'Maxheap variant of _siftdown'
newitem = heap[pos]
# Follow the path to the root, moving parents down until finding a place
# newitem fits.
while pos > startpos:
parentpos = (pos - 1) >> 1
parent = heap[parentpos]
if cmp_lt(parent, newitem):
heap[pos] = parent
pos = parentpos
continue
break
heap[pos] = newitem
def _siftup_max(heap, pos):
'Maxheap variant of _siftup'
endpos = len(heap)
startpos = pos
newitem = heap[pos]
# Bubble up the larger child until hitting a leaf.
childpos = 2*pos + 1 # leftmost child position
while childpos < endpos:
# Set childpos to index of larger child.
rightpos = childpos + 1
if rightpos < endpos and not cmp_lt(heap[rightpos], heap[childpos]):
childpos = rightpos
# Move the larger child up.
heap[pos] = heap[childpos]
pos = childpos
childpos = 2*pos + 1
# The leaf at pos is empty now. Put newitem there, and bubble it up
# to its final resting place (by sifting its parents down).
heap[pos] = newitem
_siftdown_max(heap, startpos, pos)
# If available, use C implementation
#try:
# import _heapq
#except ImportError:
# pass
def merge(*iterables):
'''Merge multiple sorted inputs into a single sorted output.
Similar to sorted(itertools.chain(*iterables)) but returns a generator,
does not pull the data into memory all at once, and assumes that each of
the input streams is already sorted (smallest to largest).
>>> list(merge([1,3,5,7], [0,2,4,8], [5,10,15,20], [], [25]))
[0, 1, 2, 3, 4, 5, 5, 7, 8, 10, 15, 20, 25]
'''
_heappop, _heapreplace, _StopIteration = heappop, heapreplace, StopIteration
_len = len
h = []
h_append = h.append
for itnum, it in enumerate(map(iter, iterables)):
try:
next = it.next
h_append([next(), itnum, next])
except _StopIteration:
pass
heapify(h)
while _len(h) > 1:
try:
while 1:
v, itnum, next = s = h[0]
yield v
s[0] = next() # raises StopIteration when exhausted
_heapreplace(h, s) # restore heap condition
except _StopIteration:
_heappop(h) # remove empty iterator
if h:
# fast case when only a single iterator remains
v, itnum, next = h[0]
yield v
for v in next.__self__:
yield v
# Extend the implementations of nsmallest and nlargest to use a key= argument
_nsmallest = nsmallest
def nsmallest(n, iterable, key=None):
"""Find the n smallest elements in a dataset.
Equivalent to: sorted(iterable, key=key)[:n]
"""
# Short-cut for n==1 is to use min() when len(iterable)>0
if n == 1:
it = iter(iterable)
head = list(islice(it, 1))
if not head:
return []
if key is None:
return [min(chain(head, it))]
return [min(chain(head, it), key=key)]
# When n>=size, it's faster to use sorted()
try:
size = len(iterable)
except (TypeError, AttributeError):
pass
else:
if n >= size:
return sorted(iterable, key=key)[:n]
# When key is none, use simpler decoration
if key is None:
it = izip(iterable, count()) # decorate
result = _nsmallest(n, it)
return map(itemgetter(0), result) # undecorate
# General case, slowest method
in1, in2 = tee(iterable)
it = izip(imap(key, in1), count(), in2) # decorate
result = _nsmallest(n, it)
return map(itemgetter(2), result) # undecorate
_nlargest = nlargest
def nlargest(n, iterable, key=None):
"""Find the n largest elements in a dataset.
Equivalent to: sorted(iterable, key=key, reverse=True)[:n]
"""
# Short-cut for n==1 is to use max() when len(iterable)>0
if n == 1:
it = iter(iterable)
head = list(islice(it, 1))
if not head:
return []
if key is None:
return [max(chain(head, it))]
return [max(chain(head, it), key=key)]
# When n>=size, it's faster to use sorted()
try:
size = len(iterable)
except (TypeError, AttributeError):
pass
else:
if n >= size:
return sorted(iterable, key=key, reverse=True)[:n]
# When key is none, use simpler decoration
if key is None:
it = izip(iterable, count(0,-1)) # decorate
result = _nlargest(n, it)
return map(itemgetter(0), result) # undecorate
# General case, slowest method
in1, in2 = tee(iterable)
it = izip(imap(key, in1), count(0,-1), in2) # decorate
result = _nlargest(n, it)
return map(itemgetter(2), result) # undecorate
#if __name__ == "__main__":
# # Simple sanity test
# heap = []
# data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
# for item in data:
# heappush(heap, item)
# sort = []
# while heap:
# sort.append(heappop(heap))
# print sort
#
# import doctest
# doctest.testmod()
third_party/stdlib/keyword.py 0 → 100755
View file @ 4aa5110d
#! /usr/bin/env python
"""Keywords (from "graminit.c")
This file is automatically generated; please don't muck it up!
To update the symbols in this file, 'cd' to the top directory of
the python source tree after building the interpreter and run:
./python Lib/keyword.py
"""
__all__ = ["iskeyword", "kwlist"]
kwlist = [
#--start keywords--
'and',
'as',
'assert',
'break',
'class',
'continue',
'def',
'del',
'elif',
'else',
'except',
'exec',
'finally',
'for',
'from',
'global',
'if',
'import',
'in',
'is',
'lambda',
'not',
'or',
'pass',
'print',
'raise',
'return',
'try',
'while',
'with',
'yield',
#--end keywords--
]
iskeyword = frozenset(kwlist).__contains__
def main():
import sys, re
args = sys.argv[1:]
iptfile = args and args[0] or "Python/graminit.c"
if len(args) > 1: optfile = args[1]
else: optfile = "Lib/keyword.py"
# scan the source file for keywords
fp = open(iptfile)
strprog = re.compile('"([^"]+)"')
lines = []
for line in fp:
if '{1, "' in line:
match = strprog.search(line)
if match:
lines.append(" '" + match.group(1) + "',\n")
fp.close()
lines.sort()
# load the output skeleton from the target
fp = open(optfile)
format = fp.readlines()
fp.close()
# insert the lines of keywords
try:
start = format.index("#--start keywords--\n") + 1
end = format.index("#--end keywords--\n")
format[start:end] = lines
except ValueError:
sys.stderr.write("target does not contain format markers\n")
sys.exit(1)
# write the output file
fp = open(optfile, 'w')
fp.write(''.join(format))
fp.close()
if __name__ == "__main__":
main()
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