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712 lines
22 KiB
712 lines
22 KiB
from abc import abstractmethod, ABCMeta
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from collections.abc import Sequence, Hashable
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from numbers import Integral
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import operator
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from pyrsistent._transformations import transform
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def _bitcount(val):
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return bin(val).count("1")
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BRANCH_FACTOR = 32
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BIT_MASK = BRANCH_FACTOR - 1
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SHIFT = _bitcount(BIT_MASK)
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def compare_pvector(v, other, operator):
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return operator(v.tolist(), other.tolist() if isinstance(other, PVector) else other)
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def _index_or_slice(index, stop):
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if stop is None:
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return index
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return slice(index, stop)
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class PythonPVector(object):
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"""
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Support structure for PVector that implements structural sharing for vectors using a trie.
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"""
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__slots__ = ('_count', '_shift', '_root', '_tail', '_tail_offset', '__weakref__')
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def __new__(cls, count, shift, root, tail):
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self = super(PythonPVector, cls).__new__(cls)
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self._count = count
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self._shift = shift
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self._root = root
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self._tail = tail
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# Derived attribute stored for performance
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self._tail_offset = self._count - len(self._tail)
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return self
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def __len__(self):
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return self._count
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def __getitem__(self, index):
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if isinstance(index, slice):
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# There are more conditions than the below where it would be OK to
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# return ourselves, implement those...
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if index.start is None and index.stop is None and index.step is None:
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return self
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# This is a bit nasty realizing the whole structure as a list before
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# slicing it but it is the fastest way I've found to date, and it's easy :-)
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return _EMPTY_PVECTOR.extend(self.tolist()[index])
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if index < 0:
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index += self._count
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return PythonPVector._node_for(self, index)[index & BIT_MASK]
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def __add__(self, other):
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return self.extend(other)
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def __repr__(self):
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return 'pvector({0})'.format(str(self.tolist()))
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def __str__(self):
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return self.__repr__()
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def __iter__(self):
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# This is kind of lazy and will produce some memory overhead but it is the fasted method
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# by far of those tried since it uses the speed of the built in python list directly.
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return iter(self.tolist())
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def __ne__(self, other):
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return not self.__eq__(other)
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def __eq__(self, other):
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return self is other or (hasattr(other, '__len__') and self._count == len(other)) and compare_pvector(self, other, operator.eq)
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def __gt__(self, other):
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return compare_pvector(self, other, operator.gt)
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def __lt__(self, other):
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return compare_pvector(self, other, operator.lt)
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def __ge__(self, other):
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return compare_pvector(self, other, operator.ge)
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def __le__(self, other):
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return compare_pvector(self, other, operator.le)
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def __mul__(self, times):
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if times <= 0 or self is _EMPTY_PVECTOR:
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return _EMPTY_PVECTOR
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if times == 1:
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return self
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return _EMPTY_PVECTOR.extend(times * self.tolist())
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__rmul__ = __mul__
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def _fill_list(self, node, shift, the_list):
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if shift:
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shift -= SHIFT
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for n in node:
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self._fill_list(n, shift, the_list)
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else:
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the_list.extend(node)
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def tolist(self):
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"""
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The fastest way to convert the vector into a python list.
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"""
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the_list = []
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self._fill_list(self._root, self._shift, the_list)
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the_list.extend(self._tail)
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return the_list
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def _totuple(self):
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"""
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Returns the content as a python tuple.
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"""
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return tuple(self.tolist())
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def __hash__(self):
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# Taking the easy way out again...
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return hash(self._totuple())
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def transform(self, *transformations):
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return transform(self, transformations)
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def __reduce__(self):
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# Pickling support
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return pvector, (self.tolist(),)
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def mset(self, *args):
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if len(args) % 2:
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raise TypeError("mset expected an even number of arguments")
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evolver = self.evolver()
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for i in range(0, len(args), 2):
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evolver[args[i]] = args[i+1]
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return evolver.persistent()
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class Evolver(object):
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__slots__ = ('_count', '_shift', '_root', '_tail', '_tail_offset', '_dirty_nodes',
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'_extra_tail', '_cached_leafs', '_orig_pvector')
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def __init__(self, v):
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self._reset(v)
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def __getitem__(self, index):
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if not isinstance(index, Integral):
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raise TypeError("'%s' object cannot be interpreted as an index" % type(index).__name__)
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if index < 0:
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index += self._count + len(self._extra_tail)
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if self._count <= index < self._count + len(self._extra_tail):
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return self._extra_tail[index - self._count]
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return PythonPVector._node_for(self, index)[index & BIT_MASK]
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def _reset(self, v):
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self._count = v._count
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self._shift = v._shift
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self._root = v._root
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self._tail = v._tail
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self._tail_offset = v._tail_offset
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self._dirty_nodes = {}
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self._cached_leafs = {}
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self._extra_tail = []
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self._orig_pvector = v
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def append(self, element):
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self._extra_tail.append(element)
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return self
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def extend(self, iterable):
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self._extra_tail.extend(iterable)
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return self
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def set(self, index, val):
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self[index] = val
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return self
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def __setitem__(self, index, val):
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if not isinstance(index, Integral):
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raise TypeError("'%s' object cannot be interpreted as an index" % type(index).__name__)
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if index < 0:
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index += self._count + len(self._extra_tail)
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if 0 <= index < self._count:
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node = self._cached_leafs.get(index >> SHIFT)
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if node:
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node[index & BIT_MASK] = val
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elif index >= self._tail_offset:
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if id(self._tail) not in self._dirty_nodes:
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self._tail = list(self._tail)
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self._dirty_nodes[id(self._tail)] = True
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self._cached_leafs[index >> SHIFT] = self._tail
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self._tail[index & BIT_MASK] = val
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else:
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self._root = self._do_set(self._shift, self._root, index, val)
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elif self._count <= index < self._count + len(self._extra_tail):
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self._extra_tail[index - self._count] = val
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elif index == self._count + len(self._extra_tail):
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self._extra_tail.append(val)
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else:
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raise IndexError("Index out of range: %s" % (index,))
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def _do_set(self, level, node, i, val):
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if id(node) in self._dirty_nodes:
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ret = node
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else:
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ret = list(node)
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self._dirty_nodes[id(ret)] = True
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if level == 0:
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ret[i & BIT_MASK] = val
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self._cached_leafs[i >> SHIFT] = ret
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else:
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sub_index = (i >> level) & BIT_MASK # >>>
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ret[sub_index] = self._do_set(level - SHIFT, node[sub_index], i, val)
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return ret
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def delete(self, index):
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del self[index]
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return self
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def __delitem__(self, key):
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if self._orig_pvector:
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# All structural sharing bets are off, base evolver on _extra_tail only
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l = PythonPVector(self._count, self._shift, self._root, self._tail).tolist()
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l.extend(self._extra_tail)
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self._reset(_EMPTY_PVECTOR)
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self._extra_tail = l
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del self._extra_tail[key]
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def persistent(self):
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result = self._orig_pvector
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if self.is_dirty():
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result = PythonPVector(self._count, self._shift, self._root, self._tail).extend(self._extra_tail)
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self._reset(result)
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return result
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def __len__(self):
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return self._count + len(self._extra_tail)
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def is_dirty(self):
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return bool(self._dirty_nodes or self._extra_tail)
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def evolver(self):
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return PythonPVector.Evolver(self)
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def set(self, i, val):
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# This method could be implemented by a call to mset() but doing so would cause
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# a ~5 X performance penalty on PyPy (considered the primary platform for this implementation
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# of PVector) so we're keeping this implementation for now.
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if not isinstance(i, Integral):
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raise TypeError("'%s' object cannot be interpreted as an index" % type(i).__name__)
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if i < 0:
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i += self._count
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if 0 <= i < self._count:
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if i >= self._tail_offset:
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new_tail = list(self._tail)
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new_tail[i & BIT_MASK] = val
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return PythonPVector(self._count, self._shift, self._root, new_tail)
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return PythonPVector(self._count, self._shift, self._do_set(self._shift, self._root, i, val), self._tail)
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if i == self._count:
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return self.append(val)
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raise IndexError("Index out of range: %s" % (i,))
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def _do_set(self, level, node, i, val):
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ret = list(node)
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if level == 0:
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ret[i & BIT_MASK] = val
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else:
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sub_index = (i >> level) & BIT_MASK # >>>
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ret[sub_index] = self._do_set(level - SHIFT, node[sub_index], i, val)
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return ret
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@staticmethod
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def _node_for(pvector_like, i):
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if 0 <= i < pvector_like._count:
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if i >= pvector_like._tail_offset:
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return pvector_like._tail
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node = pvector_like._root
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for level in range(pvector_like._shift, 0, -SHIFT):
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node = node[(i >> level) & BIT_MASK] # >>>
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return node
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raise IndexError("Index out of range: %s" % (i,))
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def _create_new_root(self):
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new_shift = self._shift
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# Overflow root?
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if (self._count >> SHIFT) > (1 << self._shift): # >>>
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new_root = [self._root, self._new_path(self._shift, self._tail)]
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new_shift += SHIFT
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else:
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new_root = self._push_tail(self._shift, self._root, self._tail)
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return new_root, new_shift
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def append(self, val):
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if len(self._tail) < BRANCH_FACTOR:
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new_tail = list(self._tail)
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new_tail.append(val)
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return PythonPVector(self._count + 1, self._shift, self._root, new_tail)
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# Full tail, push into tree
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new_root, new_shift = self._create_new_root()
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return PythonPVector(self._count + 1, new_shift, new_root, [val])
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def _new_path(self, level, node):
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if level == 0:
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return node
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return [self._new_path(level - SHIFT, node)]
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def _mutating_insert_tail(self):
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self._root, self._shift = self._create_new_root()
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self._tail = []
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def _mutating_fill_tail(self, offset, sequence):
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max_delta_len = BRANCH_FACTOR - len(self._tail)
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delta = sequence[offset:offset + max_delta_len]
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self._tail.extend(delta)
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delta_len = len(delta)
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self._count += delta_len
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return offset + delta_len
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def _mutating_extend(self, sequence):
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offset = 0
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sequence_len = len(sequence)
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while offset < sequence_len:
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offset = self._mutating_fill_tail(offset, sequence)
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if len(self._tail) == BRANCH_FACTOR:
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self._mutating_insert_tail()
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self._tail_offset = self._count - len(self._tail)
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def extend(self, obj):
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# Mutates the new vector directly for efficiency but that's only an
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# implementation detail, once it is returned it should be considered immutable
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l = obj.tolist() if isinstance(obj, PythonPVector) else list(obj)
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if l:
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new_vector = self.append(l[0])
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new_vector._mutating_extend(l[1:])
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return new_vector
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return self
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def _push_tail(self, level, parent, tail_node):
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"""
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if parent is leaf, insert node,
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else does it map to an existing child? ->
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node_to_insert = push node one more level
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else alloc new path
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return node_to_insert placed in copy of parent
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"""
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ret = list(parent)
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if level == SHIFT:
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ret.append(tail_node)
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return ret
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sub_index = ((self._count - 1) >> level) & BIT_MASK # >>>
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if len(parent) > sub_index:
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ret[sub_index] = self._push_tail(level - SHIFT, parent[sub_index], tail_node)
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return ret
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ret.append(self._new_path(level - SHIFT, tail_node))
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return ret
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def index(self, value, *args, **kwargs):
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return self.tolist().index(value, *args, **kwargs)
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def count(self, value):
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return self.tolist().count(value)
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def delete(self, index, stop=None):
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l = self.tolist()
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del l[_index_or_slice(index, stop)]
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return _EMPTY_PVECTOR.extend(l)
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def remove(self, value):
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l = self.tolist()
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l.remove(value)
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return _EMPTY_PVECTOR.extend(l)
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class PVector(metaclass=ABCMeta):
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"""
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Persistent vector implementation. Meant as a replacement for the cases where you would normally
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use a Python list.
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Do not instantiate directly, instead use the factory functions :py:func:`v` and :py:func:`pvector` to
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create an instance.
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Heavily influenced by the persistent vector available in Clojure. Initially this was more or
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less just a port of the Java code for the Clojure vector. It has since been modified and to
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some extent optimized for usage in Python.
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The vector is organized as a trie, any mutating method will return a new vector that contains the changes. No
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updates are done to the original vector. Structural sharing between vectors are applied where possible to save
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space and to avoid making complete copies.
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This structure corresponds most closely to the built in list type and is intended as a replacement. Where the
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semantics are the same (more or less) the same function names have been used but for some cases it is not possible,
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for example assignments.
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The PVector implements the Sequence protocol and is Hashable.
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Inserts are amortized O(1). Random access is log32(n) where n is the size of the vector.
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The following are examples of some common operations on persistent vectors:
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>>> p = v(1, 2, 3)
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>>> p2 = p.append(4)
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>>> p3 = p2.extend([5, 6, 7])
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>>> p
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pvector([1, 2, 3])
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>>> p2
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pvector([1, 2, 3, 4])
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>>> p3
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pvector([1, 2, 3, 4, 5, 6, 7])
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>>> p3[5]
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6
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>>> p.set(1, 99)
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pvector([1, 99, 3])
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>>>
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"""
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@abstractmethod
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def __len__(self):
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"""
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>>> len(v(1, 2, 3))
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3
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"""
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@abstractmethod
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def __getitem__(self, index):
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"""
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Get value at index. Full slicing support.
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>>> v1 = v(5, 6, 7, 8)
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>>> v1[2]
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7
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>>> v1[1:3]
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pvector([6, 7])
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"""
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@abstractmethod
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def __add__(self, other):
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"""
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>>> v1 = v(1, 2)
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>>> v2 = v(3, 4)
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>>> v1 + v2
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pvector([1, 2, 3, 4])
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"""
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@abstractmethod
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def __mul__(self, times):
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"""
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>>> v1 = v(1, 2)
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>>> 3 * v1
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pvector([1, 2, 1, 2, 1, 2])
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"""
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@abstractmethod
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def __hash__(self):
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"""
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>>> v1 = v(1, 2, 3)
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>>> v2 = v(1, 2, 3)
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>>> hash(v1) == hash(v2)
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True
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"""
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@abstractmethod
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def evolver(self):
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"""
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Create a new evolver for this pvector. The evolver acts as a mutable view of the vector
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with "transaction like" semantics. No part of the underlying vector i updated, it is still
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fully immutable. Furthermore multiple evolvers created from the same pvector do not
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interfere with each other.
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You may want to use an evolver instead of working directly with the pvector in the
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following cases:
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* Multiple updates are done to the same vector and the intermediate results are of no
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interest. In this case using an evolver may be a more efficient and easier to work with.
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* You need to pass a vector into a legacy function or a function that you have no control
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over which performs in place mutations of lists. In this case pass an evolver instance
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instead and then create a new pvector from the evolver once the function returns.
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The following example illustrates a typical workflow when working with evolvers. It also
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displays most of the API (which i kept small by design, you should not be tempted to
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use evolvers in excess ;-)).
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Create the evolver and perform various mutating updates to it:
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>>> v1 = v(1, 2, 3, 4, 5)
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>>> e = v1.evolver()
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>>> e[1] = 22
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>>> _ = e.append(6)
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>>> _ = e.extend([7, 8, 9])
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>>> e[8] += 1
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>>> len(e)
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9
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The underlying pvector remains the same:
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>>> v1
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pvector([1, 2, 3, 4, 5])
|
|
|
|
The changes are kept in the evolver. An updated pvector can be created using the
|
|
persistent() function on the evolver.
|
|
|
|
>>> v2 = e.persistent()
|
|
>>> v2
|
|
pvector([1, 22, 3, 4, 5, 6, 7, 8, 10])
|
|
|
|
The new pvector will share data with the original pvector in the same way that would have
|
|
been done if only using operations on the pvector.
|
|
"""
|
|
|
|
@abstractmethod
|
|
def mset(self, *args):
|
|
"""
|
|
Return a new vector with elements in specified positions replaced by values (multi set).
|
|
|
|
Elements on even positions in the argument list are interpreted as indexes while
|
|
elements on odd positions are considered values.
|
|
|
|
>>> v1 = v(1, 2, 3)
|
|
>>> v1.mset(0, 11, 2, 33)
|
|
pvector([11, 2, 33])
|
|
"""
|
|
|
|
@abstractmethod
|
|
def set(self, i, val):
|
|
"""
|
|
Return a new vector with element at position i replaced with val. The original vector remains unchanged.
|
|
|
|
Setting a value one step beyond the end of the vector is equal to appending. Setting beyond that will
|
|
result in an IndexError.
|
|
|
|
>>> v1 = v(1, 2, 3)
|
|
>>> v1.set(1, 4)
|
|
pvector([1, 4, 3])
|
|
>>> v1.set(3, 4)
|
|
pvector([1, 2, 3, 4])
|
|
>>> v1.set(-1, 4)
|
|
pvector([1, 2, 4])
|
|
"""
|
|
|
|
@abstractmethod
|
|
def append(self, val):
|
|
"""
|
|
Return a new vector with val appended.
|
|
|
|
>>> v1 = v(1, 2)
|
|
>>> v1.append(3)
|
|
pvector([1, 2, 3])
|
|
"""
|
|
|
|
@abstractmethod
|
|
def extend(self, obj):
|
|
"""
|
|
Return a new vector with all values in obj appended to it. Obj may be another
|
|
PVector or any other Iterable.
|
|
|
|
>>> v1 = v(1, 2, 3)
|
|
>>> v1.extend([4, 5])
|
|
pvector([1, 2, 3, 4, 5])
|
|
"""
|
|
|
|
@abstractmethod
|
|
def index(self, value, *args, **kwargs):
|
|
"""
|
|
Return first index of value. Additional indexes may be supplied to limit the search to a
|
|
sub range of the vector.
|
|
|
|
>>> v1 = v(1, 2, 3, 4, 3)
|
|
>>> v1.index(3)
|
|
2
|
|
>>> v1.index(3, 3, 5)
|
|
4
|
|
"""
|
|
|
|
@abstractmethod
|
|
def count(self, value):
|
|
"""
|
|
Return the number of times that value appears in the vector.
|
|
|
|
>>> v1 = v(1, 4, 3, 4)
|
|
>>> v1.count(4)
|
|
2
|
|
"""
|
|
|
|
@abstractmethod
|
|
def transform(self, *transformations):
|
|
"""
|
|
Transform arbitrarily complex combinations of PVectors and PMaps. A transformation
|
|
consists of two parts. One match expression that specifies which elements to transform
|
|
and one transformation function that performs the actual transformation.
|
|
|
|
>>> from pyrsistent import freeze, ny
|
|
>>> news_paper = freeze({'articles': [{'author': 'Sara', 'content': 'A short article'},
|
|
... {'author': 'Steve', 'content': 'A slightly longer article'}],
|
|
... 'weather': {'temperature': '11C', 'wind': '5m/s'}})
|
|
>>> short_news = news_paper.transform(['articles', ny, 'content'], lambda c: c[:25] + '...' if len(c) > 25 else c)
|
|
>>> very_short_news = news_paper.transform(['articles', ny, 'content'], lambda c: c[:15] + '...' if len(c) > 15 else c)
|
|
>>> very_short_news.articles[0].content
|
|
'A short article'
|
|
>>> very_short_news.articles[1].content
|
|
'A slightly long...'
|
|
|
|
When nothing has been transformed the original data structure is kept
|
|
|
|
>>> short_news is news_paper
|
|
True
|
|
>>> very_short_news is news_paper
|
|
False
|
|
>>> very_short_news.articles[0] is news_paper.articles[0]
|
|
True
|
|
"""
|
|
|
|
@abstractmethod
|
|
def delete(self, index, stop=None):
|
|
"""
|
|
Delete a portion of the vector by index or range.
|
|
|
|
>>> v1 = v(1, 2, 3, 4, 5)
|
|
>>> v1.delete(1)
|
|
pvector([1, 3, 4, 5])
|
|
>>> v1.delete(1, 3)
|
|
pvector([1, 4, 5])
|
|
"""
|
|
|
|
@abstractmethod
|
|
def remove(self, value):
|
|
"""
|
|
Remove the first occurrence of a value from the vector.
|
|
|
|
>>> v1 = v(1, 2, 3, 2, 1)
|
|
>>> v2 = v1.remove(1)
|
|
>>> v2
|
|
pvector([2, 3, 2, 1])
|
|
>>> v2.remove(1)
|
|
pvector([2, 3, 2])
|
|
"""
|
|
|
|
|
|
_EMPTY_PVECTOR = PythonPVector(0, SHIFT, [], [])
|
|
PVector.register(PythonPVector)
|
|
Sequence.register(PVector)
|
|
Hashable.register(PVector)
|
|
|
|
def python_pvector(iterable=()):
|
|
"""
|
|
Create a new persistent vector containing the elements in iterable.
|
|
|
|
>>> v1 = pvector([1, 2, 3])
|
|
>>> v1
|
|
pvector([1, 2, 3])
|
|
"""
|
|
return _EMPTY_PVECTOR.extend(iterable)
|
|
|
|
try:
|
|
# Use the C extension as underlying trie implementation if it is available
|
|
import os
|
|
if os.environ.get('PYRSISTENT_NO_C_EXTENSION'):
|
|
pvector = python_pvector
|
|
else:
|
|
from pvectorc import pvector
|
|
PVector.register(type(pvector()))
|
|
except ImportError:
|
|
pvector = python_pvector
|
|
|
|
|
|
def v(*elements):
|
|
"""
|
|
Create a new persistent vector containing all parameters to this function.
|
|
|
|
>>> v1 = v(1, 2, 3)
|
|
>>> v1
|
|
pvector([1, 2, 3])
|
|
"""
|
|
return pvector(elements)
|