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Update Lib/operator.py and add Lib/heapq.py from cpython

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coolreader18 committed 2019-05-30 23:04:09 -05:00
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Lib/heapq.py
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"""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']
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)
return returnitem
return lastelt
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 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(range(n//2)):
_siftup(x, i)
def _heappop_max(heap):
"""Maxheap version of a heappop."""
lastelt = heap.pop() # raises appropriate IndexError if heap is empty
if heap:
returnitem = heap[0]
heap[0] = lastelt
_siftup_max(heap, 0)
return returnitem
return lastelt
def _heapreplace_max(heap, item):
"""Maxheap version of a heappop followed by a heappush."""
returnitem = heap[0] # raises appropriate IndexError if heap is empty
heap[0] = item
_siftup_max(heap, 0)
return returnitem
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)
# '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 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 comparison 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 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 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 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)
def merge(*iterables, key=None, reverse=False):
'''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]
If *key* is not None, applies a key function to each element to determine
its sort order.
>>> list(merge(['dog', 'horse'], ['cat', 'fish', 'kangaroo'], key=len))
['dog', 'cat', 'fish', 'horse', 'kangaroo']
'''
h = []
h_append = h.append
if reverse:
_heapify = _heapify_max
_heappop = _heappop_max
_heapreplace = _heapreplace_max
direction = -1
else:
_heapify = heapify
_heappop = heappop
_heapreplace = heapreplace
direction = 1
if key is None:
for order, it in enumerate(map(iter, iterables)):
try:
next = it.__next__
h_append([next(), order * direction, next])
except StopIteration:
pass
_heapify(h)
while len(h) > 1:
try:
while True:
value, order, next = s = h[0]
yield value
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
value, order, next = h[0]
yield value
yield from next.__self__
return
for order, it in enumerate(map(iter, iterables)):
try:
next = it.__next__
value = next()
h_append([key(value), order * direction, value, next])
except StopIteration:
pass
_heapify(h)
while len(h) > 1:
try:
while True:
key_value, order, value, next = s = h[0]
yield value
value = next()
s[0] = key(value)
s[2] = value
_heapreplace(h, s)
except StopIteration:
_heappop(h)
if h:
key_value, order, value, next = h[0]
yield value
yield from next.__self__
# Algorithm notes for nlargest() and nsmallest()
# ==============================================
#
# Make a single pass over the data while keeping the k most extreme values
# in a heap. Memory consumption is limited to keeping k values in a list.
#
# Measured performance for random inputs:
#
# number of comparisons
# n inputs k-extreme values (average of 5 trials) % more than min()
# ------------- ---------------- --------------------- -----------------
# 1,000 100 3,317 231.7%
# 10,000 100 14,046 40.5%
# 100,000 100 105,749 5.7%
# 1,000,000 100 1,007,751 0.8%
# 10,000,000 100 10,009,401 0.1%
#
# Theoretical number of comparisons for k smallest of n random inputs:
#
# Step Comparisons Action
# ---- -------------------------- ---------------------------
# 1 1.66 * k heapify the first k-inputs
# 2 n - k compare remaining elements to top of heap
# 3 k * (1 + lg2(k)) * ln(n/k) replace the topmost value on the heap
# 4 k * lg2(k) - (k/2) final sort of the k most extreme values
#
# Combining and simplifying for a rough estimate gives:
#
# comparisons = n + k * (log(k, 2) * log(n/k) + log(k, 2) + log(n/k))
#
# Computing the number of comparisons for step 3:
# -----------------------------------------------
# * For the i-th new value from the iterable, the probability of being in the
# k most extreme values is k/i. For example, the probability of the 101st
# value seen being in the 100 most extreme values is 100/101.
# * If the value is a new extreme value, the cost of inserting it into the
# heap is 1 + log(k, 2).
# * The probability times the cost gives:
# (k/i) * (1 + log(k, 2))
# * Summing across the remaining n-k elements gives:
# sum((k/i) * (1 + log(k, 2)) for i in range(k+1, n+1))
# * This reduces to:
# (H(n) - H(k)) * k * (1 + log(k, 2))
# * Where H(n) is the n-th harmonic number estimated by:
# gamma = 0.5772156649
# H(n) = log(n, e) + gamma + 1 / (2 * n)
# http://en.wikipedia.org/wiki/Harmonic_series_(mathematics)#Rate_of_divergence
# * Substituting the H(n) formula:
# comparisons = k * (1 + log(k, 2)) * (log(n/k, e) + (1/n - 1/k) / 2)
#
# Worst-case for step 3:
# ----------------------
# In the worst case, the input data is reversed sorted so that every new element
# must be inserted in the heap:
#
# comparisons = 1.66 * k + log(k, 2) * (n - k)
#
# Alternative Algorithms
# ----------------------
# Other algorithms were not used because they:
# 1) Took much more auxiliary memory,
# 2) Made multiple passes over the data.
# 3) Made more comparisons in common cases (small k, large n, semi-random input).
# See the more detailed comparison of approach at:
# http://code.activestate.com/recipes/577573-compare-algorithms-for-heapqsmallest
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()
if n == 1:
it = iter(iterable)
sentinel = object()
result = min(it, default=sentinel, key=key)
return [] if result is sentinel else [result]
# 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 = iter(iterable)
# put the range(n) first so that zip() doesn't
# consume one too many elements from the iterator
result = [(elem, i) for i, elem in zip(range(n), it)]
if not result:
return result
_heapify_max(result)
top = result[0][0]
order = n
_heapreplace = _heapreplace_max
for elem in it:
if elem < top:
_heapreplace(result, (elem, order))
top, _order = result[0]
order += 1
result.sort()
return [elem for (elem, order) in result]
# General case, slowest method
it = iter(iterable)
result = [(key(elem), i, elem) for i, elem in zip(range(n), it)]
if not result:
return result
_heapify_max(result)
top = result[0][0]
order = n
_heapreplace = _heapreplace_max
for elem in it:
k = key(elem)
if k < top:
_heapreplace(result, (k, order, elem))
top, _order, _elem = result[0]
order += 1
result.sort()
return [elem for (k, order, elem) in result]
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()
if n == 1:
it = iter(iterable)
sentinel = object()
result = max(it, default=sentinel, key=key)
return [] if result is sentinel else [result]
# 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 = iter(iterable)
result = [(elem, i) for i, elem in zip(range(0, -n, -1), it)]
if not result:
return result
heapify(result)
top = result[0][0]
order = -n
_heapreplace = heapreplace
for elem in it:
if top < elem:
_heapreplace(result, (elem, order))
top, _order = result[0]
order -= 1
result.sort(reverse=True)
return [elem for (elem, order) in result]
# General case, slowest method
it = iter(iterable)
result = [(key(elem), i, elem) for i, elem in zip(range(0, -n, -1), it)]
if not result:
return result
heapify(result)
top = result[0][0]
order = -n
_heapreplace = heapreplace
for elem in it:
k = key(elem)
if top < k:
_heapreplace(result, (k, order, elem))
top, _order, _elem = result[0]
order -= 1
result.sort(reverse=True)
return [elem for (k, order, elem) in result]
# If available, use C implementation
try:
from _heapq import *
except ImportError:
pass
try:
from _heapq import _heapreplace_max
except ImportError:
pass
try:
from _heapq import _heapify_max
except ImportError:
pass
try:
from _heapq import _heappop_max
except ImportError:
pass
if __name__ == "__main__":
import doctest
print(doctest.testmod())
Lib/operator.py
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@@ -1,15 +1,464 @@
"""
Operator Interface
This module exports a set of functions corresponding to the intrinsic
operators of Python. For example, operator.add(x, y) is equivalent
to the expression x+y. The function names are those used for special
methods; variants without leading and trailing '__' are also provided
for convenience.
This is the pure Python implementation of the module.
"""
__all__ = ['abs', 'add', 'and_', 'attrgetter', 'concat', 'contains', 'countOf',
'delitem', 'eq', 'floordiv', 'ge', 'getitem', 'gt', 'iadd', 'iand',
'iconcat', 'ifloordiv', 'ilshift', 'imatmul', 'imod', 'imul',
'index', 'indexOf', 'inv', 'invert', 'ior', 'ipow', 'irshift',
'is_', 'is_not', 'isub', 'itemgetter', 'itruediv', 'ixor', 'le',
'length_hint', 'lshift', 'lt', 'matmul', 'methodcaller', 'mod',
'mul', 'ne', 'neg', 'not_', 'or_', 'pos', 'pow', 'rshift',
'setitem', 'sub', 'truediv', 'truth', 'xor']
from builtins import abs as _abs
# Comparison operations:
# Comparison Operations *******************************************************#
def lt(a, b):
"Same as a < b."
return a < b
def le(a, b):
"Same as a <= b."
return a <= b
def eq(a, b):
"Same as a == b."
return a == b
def ne(a, b):
"Same as a != b."
return a != b
def ge(a, b):
"Same as a >= b."
return a >= b
def gt(a, b):
"Same as a > b."
return a > b
# Logical Operations **********************************************************#
def not_(a):
"Same as not a."
return not a
def truth(a):
"Return True if a is true, False otherwise."
return True if a else False
def is_(a, b):
"Same as a is b."
return a is b
def is_not(a, b):
"Same as a is not b."
return a is not b
# Mathematical/Bitwise Operations *********************************************#
def abs(a):
"Same as abs(a)."
return _abs(a)
def add(a, b):
"Same as a + b."
return a + b
def and_(a, b):
"Same as a & b."
return a & b
def floordiv(a, b):
"Same as a // b."
return a // b
def index(a):
"Same as a.__index__()."
return a.__index__()
def inv(a):
"Same as ~a."
return ~a
invert = inv
def lshift(a, b):
"Same as a << b."
return a << b
def mod(a, b):
"Same as a % b."
return a % b
def mul(a, b):
"Same as a * b."
return a * b
def matmul(a, b):
"Same as a @ b."
return a @ b
def neg(a):
"Same as -a."
return -a
def or_(a, b):
"Same as a | b."
return a | b
def pos(a):
"Same as +a."
return +a
def pow(a, b):
"Same as a ** b."
return a ** b
def rshift(a, b):
"Same as a >> b."
return a >> b
def sub(a, b):
"Same as a - b."
return a - b
def truediv(a, b):
"Same as a / b."
return a / b
def xor(a, b):
"Same as a ^ b."
return a ^ b
# Sequence Operations *********************************************************#
def concat(a, b):
"Same as a + b, for a and b sequences."
if not hasattr(a, '__getitem__'):
msg = "'%s' object can't be concatenated" % type(a).__name__
raise TypeError(msg)
return a + b
def contains(a, b):
"Same as b in a (note reversed operands)."
return b in a
def countOf(a, b):
"Return the number of times b occurs in a."
count = 0
for i in a:
if i == b:
count += 1
return count
def delitem(a, b):
"Same as del a[b]."
del a[b]
def getitem(a, b):
"Same as a[b]."
return a[b]
def indexOf(a, b):
"Return the first index of b in a."
for i, j in enumerate(a):
if j == b:
return i
else:
raise ValueError('sequence.index(x): x not in sequence')
def setitem(a, b, c):
"Same as a[b] = c."
a[b] = c
def length_hint(obj, default=0):
"""
Return an estimate of the number of items in obj.
This is useful for presizing containers when building from an iterable.
If the object supports len(), the result will be exact. Otherwise, it may
over- or under-estimate by an arbitrary amount. The result will be an
integer >= 0.
"""
if not isinstance(default, int):
msg = ("'%s' object cannot be interpreted as an integer" %
type(default).__name__)
raise TypeError(msg)
try:
return len(obj)
except TypeError:
pass
try:
hint = type(obj).__length_hint__
except AttributeError:
return default
try:
val = hint(obj)
except TypeError:
return default
if val is NotImplemented:
return default
if not isinstance(val, int):
msg = ('__length_hint__ must be integer, not %s' %
type(val).__name__)
raise TypeError(msg)
if val < 0:
msg = '__length_hint__() should return >= 0'
raise ValueError(msg)
return val
# Generalized Lookup Objects **************************************************#
class attrgetter:
"""
Return a callable object that fetches the given attribute(s) from its operand.
After f = attrgetter('name'), the call f(r) returns r.name.
After g = attrgetter('name', 'date'), the call g(r) returns (r.name, r.date).
After h = attrgetter('name.first', 'name.last'), the call h(r) returns
(r.name.first, r.name.last).
"""
__slots__ = ('_attrs', '_call')
def __init__(self, attr, *attrs):
if not attrs:
if not isinstance(attr, str):
raise TypeError('attribute name must be a string')
self._attrs = (attr,)
names = attr.split('.')
def func(obj):
for name in names:
obj = getattr(obj, name)
return obj
self._call = func
else:
self._attrs = (attr,) + attrs
getters = tuple(map(attrgetter, self._attrs))
def func(obj):
return tuple(getter(obj) for getter in getters)
self._call = func
def __call__(self, obj):
return self._call(obj)
def __repr__(self):
return '%s.%s(%s)' % (self.__class__.__module__,
self.__class__.__qualname__,
', '.join(map(repr, self._attrs)))
def __reduce__(self):
return self.__class__, self._attrs
class itemgetter:
"""
Return a callable object that fetches the given item(s) from its operand.
After f = itemgetter(2), the call f(r) returns r[2].
After g = itemgetter(2, 5, 3), the call g(r) returns (r[2], r[5], r[3])
"""
__slots__ = ('_items', '_call')
def __init__(self, item, *items):
if not items:
self._items = (item,)
def func(obj):
return obj[item]
self._call = func
else:
self._items = items = (item,) + items
def func(obj):
return tuple(obj[i] for i in items)
self._call = func
def __call__(self, obj):
return self._call(obj)
def __repr__(self):
return '%s.%s(%s)' % (self.__class__.__module__,
self.__class__.__name__,
', '.join(map(repr, self._items)))
def __reduce__(self):
return self.__class__, self._items
class methodcaller:
"""
Return a callable object that calls the given method on its operand.
After f = methodcaller('name'), the call f(r) returns r.name().
After g = methodcaller('name', 'date', foo=1), the call g(r) returns
r.name('date', foo=1).
"""
__slots__ = ('_name', '_args', '_kwargs')
def __init__(*args, **kwargs):
if len(args) < 2:
msg = "methodcaller needs at least one argument, the method name"
raise TypeError(msg)
self = args[0]
self._name = args[1]
if not isinstance(self._name, str):
raise TypeError('method name must be a string')
self._args = args[2:]
self._kwargs = kwargs
def __call__(self, obj):
return getattr(obj, self._name)(*self._args, **self._kwargs)
def __repr__(self):
args = [repr(self._name)]
args.extend(map(repr, self._args))
args.extend('%s=%r' % (k, v) for k, v in self._kwargs.items())
return '%s.%s(%s)' % (self.__class__.__module__,
self.__class__.__name__,
', '.join(args))
def __reduce__(self):
if not self._kwargs:
return self.__class__, (self._name,) + self._args
else:
from functools import partial
return partial(self.__class__, self._name, **self._kwargs), self._args
# In-place Operations *********************************************************#
def iadd(a, b):
"Same as a += b."
a += b
return a
def iand(a, b):
"Same as a &= b."
a &= b
return a
def iconcat(a, b):
"Same as a += b, for a and b sequences."
if not hasattr(a, '__getitem__'):
msg = "'%s' object can't be concatenated" % type(a).__name__
raise TypeError(msg)
a += b
return a
def ifloordiv(a, b):
"Same as a //= b."
a //= b
return a
def ilshift(a, b):
"Same as a <<= b."
a <<= b
return a
def imod(a, b):
"Same as a %= b."
a %= b
return a
def imul(a, b):
"Same as a *= b."
a *= b
return a
def imatmul(a, b):
"Same as a @= b."
a @= b
return a
def ior(a, b):
"Same as a |= b."
a |= b
return a
def ipow(a, b):
"Same as a **= b."
a **=b
return a
def irshift(a, b):
"Same as a >>= b."
a >>= b
return a
def isub(a, b):
"Same as a -= b."
a -= b
return a
def itruediv(a, b):
"Same as a /= b."
a /= b
return a
def ixor(a, b):
"Same as a ^= b."
a ^= b
return a
try:
from _operator import *
except ImportError:
pass
else:
from _operator import __doc__
# All of these "__func__ = func" assignments have to happen after importing
# from _operator to make sure they're set to the right function
__lt__ = lt
__le__ = le
__eq__ = eq
__ne__ = ne
__ge__ = ge
__gt__ = gt
__not__ = not_
__abs__ = abs
__add__ = add
__and__ = and_
__floordiv__ = floordiv
__index__ = index
__inv__ = inv
__invert__ = invert
__lshift__ = lshift
__mod__ = mod
__mul__ = mul
__matmul__ = matmul
__neg__ = neg
__or__ = or_
__pos__ = pos
__pow__ = pow
__rshift__ = rshift
__sub__ = sub
__truediv__ = truediv
__xor__ = xor
__concat__ = concat
__contains__ = contains
__delitem__ = delitem
__getitem__ = getitem
__setitem__ = setitem
__iadd__ = iadd
__iand__ = iand
__iconcat__ = iconcat
__ifloordiv__ = ifloordiv
__ilshift__ = ilshift
__imod__ = imod
__imul__ = imul
__imatmul__ = imatmul
__ior__ = ior
__ipow__ = ipow
__irshift__ = irshift
__isub__ = isub
__itruediv__ = itruediv
__ixor__ = ixor