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+Metadata-Version: 1.1
+Name: pyrsistent
+Version: 0.16.0
+Summary: Persistent/Functional/Immutable data structures
+Home-page: http://github.com/tobgu/pyrsistent/
+Author: Tobias Gustafsson
+Author-email: tobias.l.gustafsson@gmail.com
+License: MIT
+Description: Pyrsistent
+ ==========
+ .. image:: https://travis-ci.org/tobgu/pyrsistent.png?branch=master
+ :target: https://travis-ci.org/tobgu/pyrsistent
+
+ .. image:: https://badge.fury.io/py/pyrsistent.svg
+ :target: https://badge.fury.io/py/pyrsistent
+
+ .. image:: https://coveralls.io/repos/tobgu/pyrsistent/badge.svg?branch=master&service=github
+ :target: https://coveralls.io/github/tobgu/pyrsistent?branch=master
+
+
+ .. _Pyrthon: https://www.github.com/tobgu/pyrthon/
+
+ Pyrsistent is a number of persistent collections (by some referred to as functional data structures). Persistent in
+ the sense that they are immutable.
+
+ All methods on a data structure that would normally mutate it instead return a new copy of the structure containing the
+ requested updates. The original structure is left untouched.
+
+ This will simplify the reasoning about what a program does since no hidden side effects ever can take place to these
+ data structures. You can rest assured that the object you hold a reference to will remain the same throughout its
+ lifetime and need not worry that somewhere five stack levels below you in the darkest corner of your application
+ someone has decided to remove that element that you expected to be there.
+
+ Pyrsistent is influenced by persistent data structures such as those found in the standard library of Clojure. The
+ data structures are designed to share common elements through path copying.
+ It aims at taking these concepts and make them as pythonic as possible so that they can be easily integrated into any python
+ program without hassle.
+
+ If you want to go all in on persistent data structures and use literal syntax to define them in your code rather
+ than function calls check out Pyrthon_.
+
+ Examples
+ --------
+ .. _Sequence: collections_
+ .. _Hashable: collections_
+ .. _Mapping: collections_
+ .. _Mappings: collections_
+ .. _Set: collections_
+ .. _collections: https://docs.python.org/3/library/collections.abc.html
+ .. _documentation: http://pyrsistent.readthedocs.org/
+
+ The collection types and key features currently implemented are:
+
+ * PVector_, similar to a python list
+ * PMap_, similar to dict
+ * PSet_, similar to set
+ * PRecord_, a PMap on steroids with fixed fields, optional type and invariant checking and much more
+ * PClass_, a Python class fixed fields, optional type and invariant checking and much more
+ * `Checked collections`_, PVector, PMap and PSet with optional type and invariance checks and more
+ * PBag, similar to collections.Counter
+ * PList, a classic singly linked list
+ * PDeque, similar to collections.deque
+ * Immutable object type (immutable) built on the named tuple
+ * freeze_ and thaw_ functions to convert between pythons standard collections and pyrsistent collections.
+ * Flexible transformations_ of arbitrarily complex structures built from PMaps and PVectors.
+
+ Below are examples of common usage patterns for some of the structures and features. More information and
+ full documentation for all data structures is available in the documentation_.
+
+ .. _PVector:
+
+ PVector
+ ~~~~~~~
+ With full support for the Sequence_ protocol PVector is meant as a drop in replacement to the built in list from a readers
+ point of view. Write operations of course differ since no in place mutation is done but naming should be in line
+ with corresponding operations on the built in list.
+
+ Support for the Hashable_ protocol also means that it can be used as key in Mappings_.
+
+ Appends are amortized O(1). Random access and insert is log32(n) where n is the size of the vector.
+
+ .. code:: python
+
+ >>> from pyrsistent import v, pvector
+
+ # No mutation of vectors once created, instead they
+ # are "evolved" leaving the original untouched
+ >>> v1 = v(1, 2, 3)
+ >>> v2 = v1.append(4)
+ >>> v3 = v2.set(1, 5)
+ >>> v1
+ pvector([1, 2, 3])
+ >>> v2
+ pvector([1, 2, 3, 4])
+ >>> v3
+ pvector([1, 5, 3, 4])
+
+ # Random access and slicing
+ >>> v3[1]
+ 5
+ >>> v3[1:3]
+ pvector([5, 3])
+
+ # Iteration
+ >>> list(x + 1 for x in v3)
+ [2, 6, 4, 5]
+ >>> pvector(2 * x for x in range(3))
+ pvector([0, 2, 4])
+
+ .. _PMap:
+
+ PMap
+ ~~~~
+ With full support for the Mapping_ protocol PMap is meant as a drop in replacement to the built in dict from a readers point
+ of view. Support for the Hashable_ protocol also means that it can be used as key in other Mappings_.
+
+ Random access and insert is log32(n) where n is the size of the map.
+
+ .. code:: python
+
+ >>> from pyrsistent import m, pmap, v
+
+ # No mutation of maps once created, instead they are
+ # "evolved" leaving the original untouched
+ >>> m1 = m(a=1, b=2)
+ >>> m2 = m1.set('c', 3)
+ >>> m3 = m2.set('a', 5)
+ >>> m1
+ pmap({'a': 1, 'b': 2})
+ >>> m2
+ pmap({'a': 1, 'c': 3, 'b': 2})
+ >>> m3
+ pmap({'a': 5, 'c': 3, 'b': 2})
+ >>> m3['a']
+ 5
+
+ # Evolution of nested persistent structures
+ >>> m4 = m(a=5, b=6, c=v(1, 2))
+ >>> m4.transform(('c', 1), 17)
+ pmap({'a': 5, 'c': pvector([1, 17]), 'b': 6})
+ >>> m5 = m(a=1, b=2)
+
+ # Evolve by merging with other mappings
+ >>> m5.update(m(a=2, c=3), {'a': 17, 'd': 35})
+ pmap({'a': 17, 'c': 3, 'b': 2, 'd': 35})
+ >>> pmap({'x': 1, 'y': 2}) + pmap({'y': 3, 'z': 4})
+ pmap({'y': 3, 'x': 1, 'z': 4})
+
+ # Dict-like methods to convert to list and iterate
+ >>> m3.items()
+ pvector([('a', 5), ('c', 3), ('b', 2)])
+ >>> list(m3)
+ ['a', 'c', 'b']
+
+ .. _PSet:
+
+ PSet
+ ~~~~
+ With full support for the Set_ protocol PSet is meant as a drop in replacement to the built in set from a readers point
+ of view. Support for the Hashable_ protocol also means that it can be used as key in Mappings_.
+
+ Random access and insert is log32(n) where n is the size of the set.
+
+ .. code:: python
+
+ >>> from pyrsistent import s
+
+ # No mutation of sets once created, you know the story...
+ >>> s1 = s(1, 2, 3, 2)
+ >>> s2 = s1.add(4)
+ >>> s3 = s1.remove(1)
+ >>> s1
+ pset([1, 2, 3])
+ >>> s2
+ pset([1, 2, 3, 4])
+ >>> s3
+ pset([2, 3])
+
+ # Full support for set operations
+ >>> s1 | s(3, 4, 5)
+ pset([1, 2, 3, 4, 5])
+ >>> s1 & s(3, 4, 5)
+ pset([3])
+ >>> s1 < s2
+ True
+ >>> s1 < s(3, 4, 5)
+ False
+
+ .. _PRecord:
+
+ PRecord
+ ~~~~~~~
+ A PRecord is a PMap with a fixed set of specified fields. Records are declared as python classes inheriting
+ from PRecord. Because it is a PMap it has full support for all Mapping methods such as iteration and element
+ access using subscript notation.
+
+ .. code:: python
+
+ >>> from pyrsistent import PRecord, field
+ >>> class ARecord(PRecord):
+ ... x = field()
+ ...
+ >>> r = ARecord(x=3)
+ >>> r
+ ARecord(x=3)
+ >>> r.x
+ 3
+ >>> r.set(x=2)
+ ARecord(x=2)
+ >>> r.set(y=2)
+ Traceback (most recent call last):
+ AttributeError: 'y' is not among the specified fields for ARecord
+
+ Type information
+ ****************
+ It is possible to add type information to the record to enforce type checks. Multiple allowed types can be specified
+ by providing an iterable of types.
+
+ .. code:: python
+
+ >>> class BRecord(PRecord):
+ ... x = field(type=int)
+ ... y = field(type=(int, type(None)))
+ ...
+ >>> BRecord(x=3, y=None)
+ BRecord(y=None, x=3)
+ >>> BRecord(x=3.0)
+ Traceback (most recent call last):
+ PTypeError: Invalid type for field BRecord.x, was float
+
+
+ Custom types (classes) that are iterable should be wrapped in a tuple to prevent their
+ members being added to the set of valid types. Although Enums in particular are now
+ supported without wrapping, see #83 for more information.
+
+ Mandatory fields
+ ****************
+ Fields are not mandatory by default but can be specified as such. If fields are missing an
+ *InvariantException* will be thrown which contains information about the missing fields.
+
+ .. code:: python
+
+ >>> from pyrsistent import InvariantException
+ >>> class CRecord(PRecord):
+ ... x = field(mandatory=True)
+ ...
+ >>> r = CRecord(x=3)
+ >>> try:
+ ... r.discard('x')
+ ... except InvariantException as e:
+ ... print(e.missing_fields)
+ ...
+ ('CRecord.x',)
+
+ Invariants
+ **********
+ It is possible to add invariants that must hold when evolving the record. Invariants can be
+ specified on both field and record level. If invariants fail an *InvariantException* will be
+ thrown which contains information about the failing invariants. An invariant function should
+ return a tuple consisting of a boolean that tells if the invariant holds or not and an object
+ describing the invariant. This object can later be used to identify which invariant that failed.
+
+ The global invariant function is only executed if all field invariants hold.
+
+ Global invariants are inherited to subclasses.
+
+ .. code:: python
+
+ >>> class RestrictedVector(PRecord):
+ ... __invariant__ = lambda r: (r.y >= r.x, 'x larger than y')
+ ... x = field(invariant=lambda x: (x > 0, 'x negative'))
+ ... y = field(invariant=lambda y: (y > 0, 'y negative'))
+ ...
+ >>> r = RestrictedVector(y=3, x=2)
+ >>> try:
+ ... r.set(x=-1, y=-2)
+ ... except InvariantException as e:
+ ... print(e.invariant_errors)
+ ...
+ ('y negative', 'x negative')
+ >>> try:
+ ... r.set(x=2, y=1)
+ ... except InvariantException as e:
+ ... print(e.invariant_errors)
+ ...
+ ('x larger than y',)
+
+ Invariants may also contain multiple assertions. For those cases the invariant function should
+ return a tuple of invariant tuples as described above. This structure is reflected in the
+ invariant_errors attribute of the exception which will contain tuples with data from all failed
+ invariants. Eg:
+
+ .. code:: python
+
+ >>> class EvenX(PRecord):
+ ... x = field(invariant=lambda x: ((x > 0, 'x negative'), (x % 2 == 0, 'x odd')))
+ ...
+ >>> try:
+ ... EvenX(x=-1)
+ ... except InvariantException as e:
+ ... print(e.invariant_errors)
+ ...
+ (('x negative', 'x odd'),)
+
+
+ Factories
+ *********
+ It's possible to specify factory functions for fields. The factory function receives whatever
+ is supplied as field value and the actual returned by the factory is assigned to the field
+ given that any type and invariant checks hold.
+ PRecords have a default factory specified as a static function on the class, create(). It takes
+ a *Mapping* as argument and returns an instance of the specific record.
+ If a record has fields of type PRecord the create() method of that record will
+ be called to create the "sub record" if no factory has explicitly been specified to override
+ this behaviour.
+
+ .. code:: python
+
+ >>> class DRecord(PRecord):
+ ... x = field(factory=int)
+ ...
+ >>> class ERecord(PRecord):
+ ... d = field(type=DRecord)
+ ...
+ >>> ERecord.create({'d': {'x': '1'}})
+ ERecord(d=DRecord(x=1))
+
+ Collection fields
+ *****************
+ It is also possible to have fields with ``pyrsistent`` collections.
+
+ .. code:: python
+
+ >>> from pyrsistent import pset_field, pmap_field, pvector_field
+ >>> class MultiRecord(PRecord):
+ ... set_of_ints = pset_field(int)
+ ... map_int_to_str = pmap_field(int, str)
+ ... vector_of_strs = pvector_field(str)
+ ...
+
+ Serialization
+ *************
+ PRecords support serialization back to dicts. Default serialization will take keys and values
+ "as is" and output them into a dict. It is possible to specify custom serialization functions
+ to take care of fields that require special treatment.
+
+ .. code:: python
+
+ >>> from datetime import date
+ >>> class Person(PRecord):
+ ... name = field(type=unicode)
+ ... birth_date = field(type=date,
+ ... serializer=lambda format, d: d.strftime(format['date']))
+ ...
+ >>> john = Person(name=u'John', birth_date=date(1985, 10, 21))
+ >>> john.serialize({'date': '%Y-%m-%d'})
+ {'birth_date': '1985-10-21', 'name': u'John'}
+
+
+ .. _instar: https://github.com/boxed/instar/
+
+ .. _PClass:
+
+ PClass
+ ~~~~~~
+ A PClass is a python class with a fixed set of specified fields. PClasses are declared as python classes inheriting
+ from PClass. It is defined the same way that PRecords are and behaves like a PRecord in all aspects except that it
+ is not a PMap and hence not a collection but rather a plain Python object.
+
+ .. code:: python
+
+ >>> from pyrsistent import PClass, field
+ >>> class AClass(PClass):
+ ... x = field()
+ ...
+ >>> a = AClass(x=3)
+ >>> a
+ AClass(x=3)
+ >>> a.x
+ 3
+
+
+ Checked collections
+ ~~~~~~~~~~~~~~~~~~~
+ Checked collections currently come in three flavors: CheckedPVector, CheckedPMap and CheckedPSet.
+
+ .. code:: python
+
+ >>> from pyrsistent import CheckedPVector, CheckedPMap, CheckedPSet, thaw
+ >>> class Positives(CheckedPSet):
+ ... __type__ = (long, int)
+ ... __invariant__ = lambda n: (n >= 0, 'Negative')
+ ...
+ >>> class Lottery(PRecord):
+ ... name = field(type=str)
+ ... numbers = field(type=Positives, invariant=lambda p: (len(p) > 0, 'No numbers'))
+ ...
+ >>> class Lotteries(CheckedPVector):
+ ... __type__ = Lottery
+ ...
+ >>> class LotteriesByDate(CheckedPMap):
+ ... __key_type__ = date
+ ... __value_type__ = Lotteries
+ ...
+ >>> lotteries = LotteriesByDate.create({date(2015, 2, 15): [{'name': 'SuperLotto', 'numbers': {1, 2, 3}},
+ ... {'name': 'MegaLotto', 'numbers': {4, 5, 6}}],
+ ... date(2015, 2, 16): [{'name': 'SuperLotto', 'numbers': {3, 2, 1}},
+ ... {'name': 'MegaLotto', 'numbers': {6, 5, 4}}]})
+ >>> lotteries
+ LotteriesByDate({datetime.date(2015, 2, 15): Lotteries([Lottery(numbers=Positives([1, 2, 3]), name='SuperLotto'), Lottery(numbers=Positives([4, 5, 6]), name='MegaLotto')]), datetime.date(2015, 2, 16): Lotteries([Lottery(numbers=Positives([1, 2, 3]), name='SuperLotto'), Lottery(numbers=Positives([4, 5, 6]), name='MegaLotto')])})
+
+ # The checked versions support all operations that the corresponding
+ # unchecked types do
+ >>> lottery_0215 = lotteries[date(2015, 2, 15)]
+ >>> lottery_0215.transform([0, 'name'], 'SuperDuperLotto')
+ Lotteries([Lottery(numbers=Positives([1, 2, 3]), name='SuperDuperLotto'), Lottery(numbers=Positives([4, 5, 6]), name='MegaLotto')])
+
+ # But also makes asserts that types and invariants hold
+ >>> lottery_0215.transform([0, 'name'], 999)
+ Traceback (most recent call last):
+ PTypeError: Invalid type for field Lottery.name, was int
+
+ >>> lottery_0215.transform([0, 'numbers'], set())
+ Traceback (most recent call last):
+ InvariantException: Field invariant failed
+
+ # They can be converted back to python built ins with either thaw()
+ # or serialize() (which provides possibilities to customize serialization)
+ >>> thaw(lottery_0215)
+ [{'numbers': set([1, 2, 3]), 'name': 'SuperLotto'}, {'numbers': set([4, 5, 6]), 'name': 'MegaLotto'}]
+ >>> lottery_0215.serialize()
+ [{'numbers': set([1, 2, 3]), 'name': 'SuperLotto'}, {'numbers': set([4, 5, 6]), 'name': 'MegaLotto'}]
+
+ .. _transformations:
+
+ Transformations
+ ~~~~~~~~~~~~~~~
+ Transformations are inspired by the cool library instar_ for Clojure. They let you evolve PMaps and PVectors
+ with arbitrarily deep/complex nesting using simple syntax and flexible matching syntax.
+
+ The first argument to transformation is the path that points out the value to transform. The
+ second is the transformation to perform. If the transformation is callable it will be applied
+ to the value(s) matching the path. The path may also contain callables. In that case they are
+ treated as matchers. If the matcher returns True for a specific key it is considered for transformation.
+
+ .. code:: python
+
+ # Basic examples
+ >>> from pyrsistent import inc, freeze, thaw, rex, ny, discard
+ >>> v1 = freeze([1, 2, 3, 4, 5])
+ >>> v1.transform([2], inc)
+ pvector([1, 2, 4, 4, 5])
+ >>> v1.transform([lambda ix: 0 < ix < 4], 8)
+ pvector([1, 8, 8, 8, 5])
+ >>> v1.transform([lambda ix, v: ix == 0 or v == 5], 0)
+ pvector([0, 2, 3, 4, 0])
+
+ # The (a)ny matcher can be used to match anything
+ >>> v1.transform([ny], 8)
+ pvector([8, 8, 8, 8, 8])
+
+ # Regular expressions can be used for matching
+ >>> scores = freeze({'John': 12, 'Joseph': 34, 'Sara': 23})
+ >>> scores.transform([rex('^Jo')], 0)
+ pmap({'Joseph': 0, 'Sara': 23, 'John': 0})
+
+ # Transformations can be done on arbitrarily deep structures
+ >>> 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
+
+ # There is a special transformation that can be used to discard elements. Also
+ # multiple transformations can be applied in one call
+ >>> thaw(news_paper.transform(['weather'], discard, ['articles', ny, 'content'], discard))
+ {'articles': [{'author': 'Sara'}, {'author': 'Steve'}]}
+
+ Evolvers
+ ~~~~~~~~
+ PVector, PMap and PSet all have support for a concept dubbed *evolvers*. An evolver acts like a mutable
+ view of the underlying persistent data structure with "transaction like" semantics. No updates of the original
+ data structure is ever performed, it is still fully immutable.
+
+ The evolvers have a very limited API by design to discourage excessive, and inappropriate, usage as that would
+ take us down the mutable road. In principle only basic mutation and element access functions are supported.
+ Check out the documentation_ of each data structure for specific examples.
+
+ Examples of when you may want to use an evolver instead of working directly with the data structure include:
+
+ * Multiple updates are done to the same data structure 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. In this case pass an evolver instance
+ instead and then create a new pvector from the evolver once the function returns.
+
+ .. code:: python
+
+ >>> from pyrsistent import v
+
+ # In place mutation as when working with the built in counterpart
+ >>> v1 = v(1, 2, 3)
+ >>> e = v1.evolver()
+ >>> e[1] = 22
+ >>> e = e.append(4)
+ >>> e = e.extend([5, 6])
+ >>> e[5] += 1
+ >>> len(e)
+ 6
+
+ # The evolver is considered *dirty* when it contains changes compared to the underlying vector
+ >>> e.is_dirty()
+ True
+
+ # But the underlying pvector still remains untouched
+ >>> v1
+ pvector([1, 2, 3])
+
+ # Once satisfied with the updates you can produce a new pvector containing the updates.
+ # 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.
+ >>> v2 = e.persistent()
+ >>> v2
+ pvector([1, 22, 3, 4, 5, 7])
+
+ # The evolver is now no longer considered *dirty* as it contains no differences compared to the
+ # pvector just produced.
+ >>> e.is_dirty()
+ False
+
+ # You may continue to work with the same evolver without affecting the content of v2
+ >>> e[0] = 11
+
+ # Or create a new evolver from v2. The two evolvers can be updated independently but will both
+ # share data with v2 where possible.
+ >>> e2 = v2.evolver()
+ >>> e2[0] = 1111
+ >>> e.persistent()
+ pvector([11, 22, 3, 4, 5, 7])
+ >>> e2.persistent()
+ pvector([1111, 22, 3, 4, 5, 7])
+
+ .. _freeze:
+ .. _thaw:
+
+ freeze and thaw
+ ~~~~~~~~~~~~~~~
+ These functions are great when your cozy immutable world has to interact with the evil mutable world outside.
+
+ .. code:: python
+
+ >>> from pyrsistent import freeze, thaw, v, m
+ >>> freeze([1, {'a': 3}])
+ pvector([1, pmap({'a': 3})])
+ >>> thaw(v(1, m(a=3)))
+ [1, {'a': 3}]
+
+ Compatibility
+ -------------
+
+ Pyrsistent is developed and tested on Python 2.7, 3.5, 3.6, 3.7 and PyPy (Python 2 and 3 compatible). It will most
+ likely work on all other versions >= 3.4 but no guarantees are given. :)
+
+ Compatibility issues
+ ~~~~~~~~~~~~~~~~~~~~
+
+ .. _27: https://github.com/tobgu/pyrsistent/issues/27
+
+ There is currently one known compatibility issue when comparing built in sets and frozensets to PSets as discussed in 27_.
+ It affects python 2 versions < 2.7.8 and python 3 versions < 3.4.0 and is due to a bug described in
+ http://bugs.python.org/issue8743.
+
+ Comparisons will fail or be incorrect when using the set/frozenset as left hand side of the comparison. As a workaround
+ you need to either upgrade Python to a more recent version, avoid comparing sets/frozensets with PSets or always make
+ sure to convert both sides of the comparison to the same type before performing the comparison.
+
+ Performance
+ -----------
+
+ Pyrsistent is developed with performance in mind. Still, while some operations are nearly on par with their built in,
+ mutable, counterparts in terms of speed, other operations are slower. In the cases where attempts at
+ optimizations have been done, speed has generally been valued over space.
+
+ Pyrsistent comes with two API compatible flavors of PVector (on which PMap and PSet are based), one pure Python
+ implementation and one implemented as a C extension. The latter generally being 2 - 20 times faster than the former.
+ The C extension will be used automatically when possible.
+
+ The pure python implementation is fully PyPy compatible. Running it under PyPy speeds operations up considerably if
+ the structures are used heavily (if JITed), for some cases the performance is almost on par with the built in counterparts.
+
+ Type hints
+ ----------
+
+ PEP 561 style type hints for use with mypy and various editors are available for most types and functions in pyrsistent.
+
+ Type classes for annotating your own code with pyrsistent types are also available under pyrsistent.typing.
+
+ Installation
+ ------------
+
+ pip install pyrsistent
+
+ Documentation
+ -------------
+
+ Available at http://pyrsistent.readthedocs.org/
+
+ Brief presentation available at http://slides.com/tobiasgustafsson/immutability-and-python/
+
+ Contributors
+ ------------
+
+ Tobias Gustafsson https://github.com/tobgu
+
+ Christopher Armstrong https://github.com/radix
+
+ Anders Hovmöller https://github.com/boxed
+
+ Itamar Turner-Trauring https://github.com/itamarst
+
+ Jonathan Lange https://github.com/jml
+
+ Richard Futrell https://github.com/Futrell
+
+ Jakob Hollenstein https://github.com/jkbjh
+
+ David Honour https://github.com/foolswood
+
+ David R. MacIver https://github.com/DRMacIver
+
+ Marcus Ewert https://github.com/sarum90
+
+ Jean-Paul Calderone https://github.com/exarkun
+
+ Douglas Treadwell https://github.com/douglas-treadwell
+
+ Travis Parker https://github.com/teepark
+
+ Julian Berman https://github.com/Julian
+
+ Dennis Tomas https://github.com/dtomas
+
+ Neil Vyas https://github.com/neilvyas
+
+ doozr https://github.com/doozr
+
+ Kamil Galuszka https://github.com/galuszkak
+
+ Tsuyoshi Hombashi https://github.com/thombashi
+
+ nattofriends https://github.com/nattofriends
+
+ agberk https://github.com/agberk
+
+ Waleed Khan https://github.com/arxanas
+
+ Jean-Louis Fuchs https://github.com/ganwell
+
+ Carlos Corbacho https://github.com/ccorbacho
+
+ Felix Yan https://github.com/felixonmars
+
+ benrg https://github.com/benrg
+
+ Jere Lahelma https://github.com/je-l
+
+ Max Taggart https://github.com/MaxTaggart
+
+ Vincent Philippon https://github.com/vphilippon
+
+ Semen Zhydenko https://github.com/ss18
+
+ Till Varoquaux https://github.com/till-varoquaux
+
+ Michal Kowalik https://github.com/michalvi
+
+ ossdev07 https://github.com/ossdev07
+
+ Kerry Olesen https://github.com/qhesz
+
+ johnthagen https://github.com/johnthagen
+
+ Contributing
+ ------------
+
+ Want to contribute? That's great! If you experience problems please log them on GitHub. If you want to contribute code,
+ please fork the repository and submit a pull request.
+
+ Run tests
+ ~~~~~~~~~
+ .. _tox: https://tox.readthedocs.io/en/latest/
+
+ Tests can be executed using tox_.
+
+ Install tox: ``pip install tox``
+
+ Run test for Python 2.7: ``tox -epy27``
+
+ Release
+ ~~~~~~~
+ * Update CHANGES.txt
+ * Update README with any new contributors and potential info needed.
+ * Update _pyrsistent_version.py
+ * python setup.py sdist upload
+ * Commit and tag with new version: git add -u . && git commit -m 'Prepare version vX.Y.Z' && git tag -a vX.Y.Z -m 'vX.Y.Z'
+ * Push commit and tags: git push && git push --tags
+
+ Project status
+ --------------
+ Pyrsistent can be considered stable and mature (who knows, there may even be a 1.0 some day :-)). The project is
+ maintained, bugs fixed, PRs reviewed and merged and new releases made. I currently do not have time for development
+ of new features or functionality which I don't have use for myself. I'm more than happy to take PRs for new
+ functionality though!
+
+ There are a bunch of issues marked with ``enhancement`` and ``help wanted`` that contain requests for new functionality
+ that would be nice to include. The level of difficulty and extend of the issues varies, please reach out to me if you're
+ interested in working on any of them.
+
+ If you feel that you have a grand master plan for where you would like Pyrsistent to go and have the time to put into
+ it please don't hesitate to discuss this with me and submit PRs for it. If all goes well I'd be more than happy to add
+ additional maintainers to the project!
+
+Platform: UNKNOWN
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: Implementation :: PyPy