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from collections.abc import Mapping, Hashable
from itertools import chain
from pyrsistent._pvector import pvector
from pyrsistent._transformations import transform
class PMapView:
"""View type for the persistent map/dict type `PMap`.
Provides an equivalent of Python's built-in `dict_values` and `dict_items`
types that result from expreessions such as `{}.values()` and
`{}.items()`. The equivalent for `{}.keys()` is absent because the keys are
instead represented by a `PSet` object, which can be created in `O(1)` time.
The `PMapView` class is overloaded by the `PMapValues` and `PMapItems`
classes which handle the specific case of values and items, respectively
Parameters
----------
m : mapping
The mapping/dict-like object of which a view is to be created. This
should generally be a `PMap` object.
"""
# The public methods that use the above.
def __init__(self, m):
# Make sure this is a persistnt map
if not isinstance(m, PMap):
# We can convert mapping objects into pmap objects, I guess (but why?)
if isinstance(m, Mapping):
m = pmap(m)
else:
raise TypeError("PViewMap requires a Mapping object")
object.__setattr__(self, '_map', m)
def __len__(self):
return len(self._map)
def __setattr__(self, k, v):
raise TypeError("%s is immutable" % (type(self),))
def __reversed__(self):
raise TypeError("Persistent maps are not reversible")
class PMapValues(PMapView):
"""View type for the values of the persistent map/dict type `PMap`.
Provides an equivalent of Python's built-in `dict_values` type that result
from expreessions such as `{}.values()`. See also `PMapView`.
Parameters
----------
m : mapping
The mapping/dict-like object of which a view is to be created. This
should generally be a `PMap` object.
"""
def __iter__(self):
return self._map.itervalues()
def __contains__(self, arg):
return arg in self._map.itervalues()
# The str and repr methods imitate the dict_view style currently.
def __str__(self):
return f"pmap_values({list(iter(self))})"
def __repr__(self):
return f"pmap_values({list(iter(self))})"
def __eq__(self, x):
# For whatever reason, dict_values always seem to return False for ==
# (probably it's not implemented), so we mimic that.
if x is self: return True
else: return False
class PMapItems(PMapView):
"""View type for the items of the persistent map/dict type `PMap`.
Provides an equivalent of Python's built-in `dict_items` type that result
from expreessions such as `{}.items()`. See also `PMapView`.
Parameters
----------
m : mapping
The mapping/dict-like object of which a view is to be created. This
should generally be a `PMap` object.
"""
def __iter__(self):
return self._map.iteritems()
def __contains__(self, arg):
try: (k,v) = arg
except Exception: return False
return k in self._map and self._map[k] == v
# The str and repr methods mitate the dict_view style currently.
def __str__(self):
return f"pmap_items({list(iter(self))})"
def __repr__(self):
return f"pmap_items({list(iter(self))})"
def __eq__(self, x):
if x is self: return True
elif not isinstance(x, type(self)): return False
else: return self._map == x._map
class PMap(object):
"""
Persistent map/dict. Tries to follow the same naming conventions as the built in dict where feasible.
Do not instantiate directly, instead use the factory functions :py:func:`m` or :py:func:`pmap` to
create an instance.
Was originally written as a very close copy of the Clojure equivalent but was later rewritten to closer
re-assemble the python dict. This means that a sparse vector (a PVector) of buckets is used. The keys are
hashed and the elements inserted at position hash % len(bucket_vector). Whenever the map size exceeds 2/3 of
the containing vectors size the map is reallocated to a vector of double the size. This is done to avoid
excessive hash collisions.
This structure corresponds most closely to the built in dict 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 and deletion of values.
PMap implements the Mapping protocol and is Hashable. It also supports dot-notation for
element access.
Random access and insert is log32(n) where n is the size of the map.
The following are examples of some common operations on persistent maps
>>> m1 = m(a=1, b=3)
>>> m2 = m1.set('c', 3)
>>> m3 = m2.remove('a')
>>> m1 == {'a': 1, 'b': 3}
True
>>> m2 == {'a': 1, 'b': 3, 'c': 3}
True
>>> m3 == {'b': 3, 'c': 3}
True
>>> m3['c']
3
>>> m3.c
3
"""
__slots__ = ('_size', '_buckets', '__weakref__', '_cached_hash')
def __new__(cls, size, buckets):
self = super(PMap, cls).__new__(cls)
self._size = size
self._buckets = buckets
return self
@staticmethod
def _get_bucket(buckets, key):
index = hash(key) % len(buckets)
bucket = buckets[index]
return index, bucket
@staticmethod
def _getitem(buckets, key):
_, bucket = PMap._get_bucket(buckets, key)
if bucket:
for k, v in bucket:
if k == key:
return v
raise KeyError(key)
def __getitem__(self, key):
return PMap._getitem(self._buckets, key)
@staticmethod
def _contains(buckets, key):
_, bucket = PMap._get_bucket(buckets, key)
if bucket:
for k, _ in bucket:
if k == key:
return True
return False
return False
def __contains__(self, key):
return self._contains(self._buckets, key)
get = Mapping.get
def __iter__(self):
return self.iterkeys()
# If this method is not defined, then reversed(pmap) will attempt to reverse
# the map using len() and getitem, usually resulting in a mysterious
# KeyError.
def __reversed__(self):
raise TypeError("Persistent maps are not reversible")
def __getattr__(self, key):
try:
return self[key]
except KeyError as e:
raise AttributeError(
"{0} has no attribute '{1}'".format(type(self).__name__, key)
) from e
def iterkeys(self):
for k, _ in self.iteritems():
yield k
# These are more efficient implementations compared to the original
# methods that are based on the keys iterator and then calls the
# accessor functions to access the value for the corresponding key
def itervalues(self):
for _, v in self.iteritems():
yield v
def iteritems(self):
for bucket in self._buckets:
if bucket:
for k, v in bucket:
yield k, v
def values(self):
return PMapValues(self)
def keys(self):
from ._pset import PSet
return PSet(self)
def items(self):
return PMapItems(self)
def __len__(self):
return self._size
def __repr__(self):
return 'pmap({0})'.format(str(dict(self)))
def __eq__(self, other):
if self is other:
return True
if not isinstance(other, Mapping):
return NotImplemented
if len(self) != len(other):
return False
if isinstance(other, PMap):
if (hasattr(self, '_cached_hash') and hasattr(other, '_cached_hash')
and self._cached_hash != other._cached_hash):
return False
if self._buckets == other._buckets:
return True
return dict(self.iteritems()) == dict(other.iteritems())
elif isinstance(other, dict):
return dict(self.iteritems()) == other
return dict(self.iteritems()) == dict(other.items())
__ne__ = Mapping.__ne__
def __lt__(self, other):
raise TypeError('PMaps are not orderable')
__le__ = __lt__
__gt__ = __lt__
__ge__ = __lt__
def __str__(self):
return self.__repr__()
def __hash__(self):
if not hasattr(self, '_cached_hash'):
self._cached_hash = hash(frozenset(self.iteritems()))
return self._cached_hash
def set(self, key, val):
"""
Return a new PMap with key and val inserted.
>>> m1 = m(a=1, b=2)
>>> m2 = m1.set('a', 3)
>>> m3 = m1.set('c' ,4)
>>> m1 == {'a': 1, 'b': 2}
True
>>> m2 == {'a': 3, 'b': 2}
True
>>> m3 == {'a': 1, 'b': 2, 'c': 4}
True
"""
return self.evolver().set(key, val).persistent()
def remove(self, key):
"""
Return a new PMap without the element specified by key. Raises KeyError if the element
is not present.
>>> m1 = m(a=1, b=2)
>>> m1.remove('a')
pmap({'b': 2})
"""
return self.evolver().remove(key).persistent()
def discard(self, key):
"""
Return a new PMap without the element specified by key. Returns reference to itself
if element is not present.
>>> m1 = m(a=1, b=2)
>>> m1.discard('a')
pmap({'b': 2})
>>> m1 is m1.discard('c')
True
"""
try:
return self.remove(key)
except KeyError:
return self
def update(self, *maps):
"""
Return a new PMap with the items in Mappings inserted. If the same key is present in multiple
maps the rightmost (last) value is inserted.
>>> m1 = m(a=1, b=2)
>>> m1.update(m(a=2, c=3), {'a': 17, 'd': 35}) == {'a': 17, 'b': 2, 'c': 3, 'd': 35}
True
"""
return self.update_with(lambda l, r: r, *maps)
def update_with(self, update_fn, *maps):
"""
Return a new PMap with the items in Mappings maps inserted. If the same key is present in multiple
maps the values will be merged using merge_fn going from left to right.
>>> from operator import add
>>> m1 = m(a=1, b=2)
>>> m1.update_with(add, m(a=2)) == {'a': 3, 'b': 2}
True
The reverse behaviour of the regular merge. Keep the leftmost element instead of the rightmost.
>>> m1 = m(a=1)
>>> m1.update_with(lambda l, r: l, m(a=2), {'a':3})
pmap({'a': 1})
"""
evolver = self.evolver()
for map in maps:
for key, value in map.items():
evolver.set(key, update_fn(evolver[key], value) if key in evolver else value)
return evolver.persistent()
def __add__(self, other):
return self.update(other)
__or__ = __add__
def __reduce__(self):
# Pickling support
return pmap, (dict(self),)
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
"""
return transform(self, transformations)
def copy(self):
return self
class _Evolver(object):
__slots__ = ('_buckets_evolver', '_size', '_original_pmap')
def __init__(self, original_pmap):
self._original_pmap = original_pmap
self._buckets_evolver = original_pmap._buckets.evolver()
self._size = original_pmap._size
def __getitem__(self, key):
return PMap._getitem(self._buckets_evolver, key)
def __setitem__(self, key, val):
self.set(key, val)
def set(self, key, val):
kv = (key, val)
index, bucket = PMap._get_bucket(self._buckets_evolver, key)
reallocation_required = len(self._buckets_evolver) < 0.67 * self._size
if bucket:
for k, v in bucket:
if k == key:
if v is not val:
new_bucket = [(k2, v2) if k2 != k else (k2, val) for k2, v2 in bucket]
self._buckets_evolver[index] = new_bucket
return self
# Only check and perform reallocation if not replacing an existing value.
# This is a performance tweak, see #247.
if reallocation_required:
self._reallocate()
return self.set(key, val)
new_bucket = [kv]
new_bucket.extend(bucket)
self._buckets_evolver[index] = new_bucket
self._size += 1
else:
if reallocation_required:
self._reallocate()
return self.set(key, val)
self._buckets_evolver[index] = [kv]
self._size += 1
return self
def _reallocate(self):
new_size = 2 * len(self._buckets_evolver)
new_list = new_size * [None]
buckets = self._buckets_evolver.persistent()
for k, v in chain.from_iterable(x for x in buckets if x):
index = hash(k) % new_size
if new_list[index]:
new_list[index].append((k, v))
else:
new_list[index] = [(k, v)]
# A reallocation should always result in a dirty buckets evolver to avoid
# possible loss of elements when doing the reallocation.
self._buckets_evolver = pvector().evolver()
self._buckets_evolver.extend(new_list)
def is_dirty(self):
return self._buckets_evolver.is_dirty()
def persistent(self):
if self.is_dirty():
self._original_pmap = PMap(self._size, self._buckets_evolver.persistent())
return self._original_pmap
def __len__(self):
return self._size
def __contains__(self, key):
return PMap._contains(self._buckets_evolver, key)
def __delitem__(self, key):
self.remove(key)
def remove(self, key):
index, bucket = PMap._get_bucket(self._buckets_evolver, key)
if bucket:
new_bucket = [(k, v) for (k, v) in bucket if k != key]
if len(bucket) > len(new_bucket):
self._buckets_evolver[index] = new_bucket if new_bucket else None
self._size -= 1
return self
raise KeyError('{0}'.format(key))
def evolver(self):
"""
Create a new evolver for this pmap. For a discussion on evolvers in general see the
documentation for the pvector evolver.
Create the evolver and perform various mutating updates to it:
>>> m1 = m(a=1, b=2)
>>> e = m1.evolver()
>>> e['c'] = 3
>>> len(e)
3
>>> del e['a']
The underlying pmap remains the same:
>>> m1 == {'a': 1, 'b': 2}
True
The changes are kept in the evolver. An updated pmap can be created using the
persistent() function on the evolver.
>>> m2 = e.persistent()
>>> m2 == {'b': 2, 'c': 3}
True
The new pmap will share data with the original pmap in the same way that would have
been done if only using operations on the pmap.
"""
return self._Evolver(self)
Mapping.register(PMap)
Hashable.register(PMap)
def _turbo_mapping(initial, pre_size):
if pre_size:
size = pre_size
else:
try:
size = 2 * len(initial) or 8
except Exception:
# Guess we can't figure out the length. Give up on length hinting,
# we can always reallocate later.
size = 8
buckets = size * [None]
if not isinstance(initial, Mapping):
# Make a dictionary of the initial data if it isn't already,
# that will save us some job further down since we can assume no
# key collisions
initial = dict(initial)
for k, v in initial.items():
h = hash(k)
index = h % size
bucket = buckets[index]
if bucket:
bucket.append((k, v))
else:
buckets[index] = [(k, v)]
return PMap(len(initial), pvector().extend(buckets))
_EMPTY_PMAP = _turbo_mapping({}, 0)
def pmap(initial={}, pre_size=0):
"""
Create new persistent map, inserts all elements in initial into the newly created map.
The optional argument pre_size may be used to specify an initial size of the underlying bucket vector. This
may have a positive performance impact in the cases where you know beforehand that a large number of elements
will be inserted into the map eventually since it will reduce the number of reallocations required.
>>> pmap({'a': 13, 'b': 14}) == {'a': 13, 'b': 14}
True
"""
if not initial and pre_size == 0:
return _EMPTY_PMAP
return _turbo_mapping(initial, pre_size)
def m(**kwargs):
"""
Creates a new persistent map. Inserts all key value arguments into the newly created map.
>>> m(a=13, b=14) == {'a': 13, 'b': 14}
True
"""
return pmap(kwargs)