Adding upstream version 110.0.1.upstream/110.0.1upstream

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

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+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!