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        "author": "Vincent Aranega",
        "author_email": "vincent.aranega@gmail.com",
        "bugtrack_url": null,
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            "Development Status :: 4 - Beta",
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            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Programming Language :: Python :: 3.9",
            "Topic :: Software Development",
            "Topic :: Software Development :: Libraries"
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        "description": "====================================================================\nPyEcore: A Pythonic Implementation of the Eclipse Modeling Framework\n====================================================================\n\n|pypi-version| |master-build| |coverage| |code-quality| |license|\n\n.. |master-build| image:: https://travis-ci.org/pyecore/pyecore.svg?branch=master\n    :target: https://travis-ci.org/pyecore/pyecore\n\n.. |develop-build| image:: https://travis-ci.org/pyecore/pyecore.svg?branch=develop\n    :target: https://travis-ci.org/pyecore/pyecore\n\n.. |pypi-version| image:: https://badge.fury.io/py/pyecore.svg\n    :target: https://badge.fury.io/py/pyecore\n\n.. |coverage| image:: https://coveralls.io/repos/github/pyecore/pyecore/badge.svg?branch=master\n    :target: https://coveralls.io/github/pyecore/pyecore?branch=master\n\n.. |license| image:: https://img.shields.io/badge/license-New%20BSD-blue.svg\n    :target: https://raw.githubusercontent.com/pyecore/pyecore/master/LICENSE\n\n.. |code-quality| image:: https://api.codacy.com/project/badge/Grade/ed038354821f43e7a4579c6a14185cdf\n    :target: https://www.codacy.com/app/aranega/pyecore\n\nPyEcore is a Model Driven Engineering (MDE) framework written for Python.\nPrecisely, it is an implementation of `EMF/Ecore\n<https://www.eclipse.org/modeling/emf/>`_ for Python, and it tries to give an\nAPI which is compatible with the original EMF Java implementation.\n\nPyEcore allows you to handle models and metamodels (structured data model), and\ngives the key you need for building MDE-based tools and other applications based\non a structured data model. It supports out-of-the-box:\n\n* Data inheritance,\n* Two-ways relationship management (opposite references),\n* XMI (de)serialization,\n* JSON (de)serialization,\n* Notification system,\n* Reflexive API...\n\nLet see how to create on a very simple \"dynamic\" metamodel (in opposite to\nstatic ones, see the `documentation <https://pyecore.readthedocs.io/en/latest/>`_\nfor more details):\n\n.. code-block:: python\n\n    >>> from pyecore.ecore import EClass, EAttribute, EString, EObject\n    >>> Graph = EClass('Graph')  # We create a 'Graph' concept\n    >>> Node = EClass('Node')  # We create a 'Node' concept\n    >>>\n    >>> # We add a \"name\" attribute to the Graph concept\n    >>> Graph.eStructuralFeatures.append(EAttribute('name', EString,\n                                                    default_value='new_name'))\n    >>> # And one on the 'Node' concept\n    >>> Node.eStructuralFeatures.append(EAttribute('name', EString))\n    >>>\n    >>> # We now introduce a containment relation between Graph and Node\n    >>> contains_nodes = EReference('nodes', Node, upper=-1, containment=True)\n    >>> Graph.eStructuralFeatures.append(contains_nodes)\n    >>> # We add an opposite relation between Graph and Node\n    >>> Node.eStructuralFeatures.append(EReference('owned_by', Graph, eOpposite=contains_nodes))\n\nWith this code, we have defined two concepts: ``Graph`` and ``Node``. Both have\na ``name``, and it exists a containment relationship between them. This relation\nis bi-directionnal, which means that each time a ``Node`` object is added to the\n``nodes`` relationship of a ``Graph``, the ``owned_by`` relation of the ``Node``\nis updated also (it also work in the other way).\n\nLet's create some instances of our freshly created metamodel:\n\n.. code-block:: python\n\n    >>> # We create a Graph\n    >>> g1 = Graph(name='Graph 1')\n    >>> g1\n    <pyecore.ecore.Graph at 0x7f0055554dd8>\n    >>>\n    >>> # And two node instances\n    >>> n1 = Node(name='Node 1')\n    >>> n2 = Node(name='Node 2')\n    >>> n1, n2\n    (<pyecore.ecore.Node at 0x7f0055550588>,\n     <pyecore.ecore.Node at 0x7f00555502b0>)\n    >>>\n    >>> # We add them to the Graph\n    >>> g1.nodes.extend([n1, n2])\n    >>> g1.nodes\n    EOrderedSet([<pyecore.ecore.Node object at 0x7f0055550588>,\n                 <pyecore.ecore.Node object at 0x7f00555502b0>])\n    >>>\n    >>> # bi-directional references are updated\n    >>> n1.owned_by\n    <pyecore.ecore.Graph at 0x7f0055554dd8>\n\n\nThis example gives a quick overview of some of the features you get for free\nwhen using PyEcore.\n\n*The project slowly grows and it still requires more love.*\n\nInstallation\n============\n\nPyEcore is available on ``pypi``, you can simply install it using ``pip``:\n\n.. code-block:: bash\n\n    $ pip install pyecore\n\nThe installation can also be performed manually (better in a virtualenv):\n\n.. code-block:: bash\n\n    $ python setup.py install\n\n\nDocumentation\n=============\n\nYou can read the documentation at this address:\n\nhttps://pyecore.readthedocs.io/en/latest/\n\n\nDependencies\n============\n\nThe dependencies required by pyecore are:\n\n* ordered-set which is used for the ``ordered`` and ``unique`` collections expressed in the metamodel,\n* lxml which is used for the XMI parsing.\n\nThese dependencies are directly installed if you choose to use ``pip``.\n\n\nRun the Tests\n=============\n\nTests uses `py.test` and 'coverage'. Everything is driven by `Tox`, so in order\nto run the tests simply run:\n\n.. code-block:: bash\n\n    $ tox\n\n\nLiberty Regarding the Java EMF Implementation\n=============================================\n\n* There is some meta-property that could be missing inside PyEcore. If you see one missing, please open a new ticket!\n* Proxies are not \"removed\" once resolved as in the the Java version, instead they acts as transparent proxies and redirect each calls to the 'proxied' object.\n* PyEcore is able to automatically load some model/metamodel dependencies on its own.\n\nState\n=====\n\nIn the current state, the project implements:\n\n* the dynamic/static metamodel definitions,\n* reflexive API,\n* inheritance,\n* enumerations,\n* abstract metaclasses,\n* runtime typechecking,\n* attribute/reference creations,\n* collections (attribute/references with upper bound set to ``-1``),\n* reference eopposite,\n* containment reference,\n* introspection,\n* select/reject on collections,\n* Eclipse XMI import (partially, only single root models),\n* Eclipse XMI export (partially, only single root models),\n* simple notification/Event system,\n* EOperations support,\n* code generator for the static part,\n* EMF proxies (first version),\n* object deletion (first version),\n* EMF commands (first version),\n* EMF basic command stack,\n* EMF very basic Editing Domain,\n* JSON import (simple JSON format),\n* JSON export (simple JSON format),\n* introduce behavior @runtime,\n* resources auto-load for some cross-references,\n* derived collections,\n* multiple roots ressources,\n* ``xsi:schemaLocation`` support for XMI resources,\n* URI mapper like,\n* ``EGeneric`` support (first simple version),\n* URI converter like\n\nThe things that are in the roadmap:\n\n* new implementation of ``EOrderedSet``, ``EList``, ``ESet`` and ``EBag``,\n* new implementation of ``EStringToStringMapEntry`` and ``EFeatureMapEntry``,\n* improve documentation,\n* copy/paste (?).\n\nExisting Projects\n=================\n\nThere is not so much projects proposing to handle model and metamodel in Python.\nThe only projects I found are:\n\n* PyEMOF (http://www.lifl.fr/~marvie/software/pyemof.html)\n* EMF4CPP (https://github.com/catedrasaes-umu/emf4cpp)\n* PyEMOFUC (http://www.istr.unican.es/pyemofuc/index_En.html)\n\nPyEMOF proposes an implementation of the OMG's EMOF in Python. The project\ntargets Python2, only supports Class/Primitive Types (no Enumeration), XMI\nimport/export and does not provide a reflexion layer. The project didn't move\nsince 2005.\n\nEMF4CPP proposes a C++ implementation of EMF. This implementation also\nintroduces Python scripts to call the generated C++ code from a Python\nenvironment. It seems that the EMF4CPP does not provide a reflexive layer\neither.\n\nPyEMOFUC proposes, like PyEMOF, a pure Python implementation of the OMG's EMOF.\nIf we stick to a kind of EMF terminology, PyEMOFUC only supports dynamic\nmetamodels and seems to provide a reflexive layer. The project does not appear\nseems to have moved since a while.\n\nContributors\n============\n\nThanks for making PyEcore better!\n\n* Mike Pagel (`@moltob <https://github.com/moltob>`_), which is also the author\n  of `pyecoregen <https://github.com/pyecore/pyecoregen>`_ and `pymultigen <https://github.com/moltob/pymultigen>`_ (on which pyecoregen is based)\n* Terry Kingston (`@TerryKingston <https://github.com/TerryKingston>`_)\n* Afonso Pinto (`@afonsobspinto <https://github.com/afonsobspinto>`_)\n* Andy (`@CFAndy <https://github.com/CFAndy>`_)\n* annighoefer (`@annighoefer <https://github.com/annighoefer>`_)\n* Rodriguez Facundo (`@rodriguez-facundo <https://github.com/rodriguez-facundo>`_)\n* Filippo Ledda (`@filippometacell <https://github.com/filippometacell>`_)\n* Ewoud Werkman (`@ewoudwerkman <https://github.com/ewoudwerkman>`_)\n* Eugene (`@4ekin <https://github.com/4ekin>`_)\n* Alexandre Acebedo (`@aacebedo <https://github.com/aacebedo>`_)\n* Jinhu (`@jinhu <https://github.com/jinhu>`_)\n\n\n\nAdditional Resources\n====================\n\n* `This article <http://modeling-languages.com/pyecore-python-eclipse-modeling-framework>`_\n  on the blog of the Professor Jordi Cabot gives more information and\n  implementations details about PyEcore.\n\n\n",
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