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