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diff --git a/third_party/python/pyrsistent/PKG-INFO b/third_party/python/pyrsistent/PKG-INFO new file mode 100644 index 0000000000..1d1c159034 --- /dev/null +++ b/third_party/python/pyrsistent/PKG-INFO @@ -0,0 +1,742 @@ +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 |