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    "info": {
        "author": "The Children's Hospital of Philadelphia",
        "author_email": "cbmisupport@email.chop.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Healthcare Industry",
            "Intended Audience :: Science/Research",
            "License :: Other/Proprietary License",
            "Natural Language :: English",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Topic :: Database",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Scientific/Engineering :: Visualization",
            "Topic :: Software Development :: Code Generators",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# Data Models SQLAlchemy\n\n[![Circle CI](https://circleci.com/gh/chop-dbhi/data-models-sqlalchemy/tree/master.svg?style=svg)](https://circleci.com/gh/chop-dbhi/data-models-sqlalchemy/tree/master) [![Coverage Status](https://coveralls.io/repos/chop-dbhi/data-models-sqlalchemy/badge.svg?branch=master&service=github)](https://coveralls.io/github/chop-dbhi/data-models-sqlalchemy?branch=master)\n\nSQLAlchemy models and DDL and ERD generation for [chop-dbhi/data-models-service](https://github.com/chop-dbhi/data-models-service) style JSON endpoints.\n\nWeb service available at http://dmsa.a0b.io/\n\n## SQLAlchemy Models\n\nIn your shell, hopefully within a virtualenv:\n\n```sh\npip install dmsa\n```\n\nIn python:\n\n```python\nfrom sqlalchemy import MetaData\nfrom dmsa import make_model_from_service\n\nmetadata = MetaData()\nmetadata = make_model_from_service('omop', '5.0.0',\n                                   'http://data-models.origins.link/',\n                                   metadata)\n\nfor tbl in metadata.sorted_tables:\n    print tbl.name\n```\n\nThese models are dynamically generated at runtime from JSON endpoints provided by chop-dbhi/data-models-service, which reads data stored in chop-dbhi/data-models. Any data model stored there can be converted into SQLAlchemy models. At the time of writing, the following are available.\n\nCAVEAT: The models are currently \"Classical\"-style and therefore un-mapped. See more information [here](https://github.com/chop-dbhi/data-models-sqlalchemy/issues/22).\n\n- i2b2\n    - 1.7.0\n- i2b2 for PEDSnet\n    - 2.0.1\n- OMOP\n    - 4.0.0\n    - 5.0.0\n- PCORnet\n    - 1.0.0\n    - 2.0.0\n    - 3.0.0\n- PEDSnet\n    - 1.0.0\n    - 2.0.0\n    - 2.1.0\n    - 2.2.0\n\n## DDL and ERD Generation\n\nUse of the included Dockerfile is highly recommended to avoid installing DBMS and graphing specific system requirements.\n\nThe following DBMS dialects are supported when generating DDL:\n\n- PostgreSQL\n- MySQL\n- MS SQL Server\n- Oracle\n\n### With Docker:\n\nRetrieve the image:\n\n```sh\ndocker pull dbhi/data-models-sqlalchemy\n```\n\nUsage Message:\n\n```sh\ndocker run --rm dbhi/data-models-sqlalchemy dmsa -h\n```\n\nGenerate OMOP V5 creation DDL for Oracle:\n\n```sh\ndocker run --rm dbhi/data-models-sqlalchemy dmsa ddl -tci omop 5.0.0 oracle\n```\n\nGenerate OMOP V5 drop DDL for Oracle:\n\n```sh\ndocker run --rm dbhi/data-models-sqlalchemy dmsa ddl -tci --drop omop 5.0.0 oracle\n```\n\nGenerate OMOP V5 data deletion DML for Oracle:\n\n```sh\ndocker run --rm dbhi/data-models-sqlalchemy dmsa ddl --delete-data omop 5.0.0 oracle\n```\n\nGenerate i2b2 PEDSnet V2 ERD (the image will land at `./erd/i2b2_pedsnet_2.0.0_erd.png`):\n\n```sh\ndocker run --rm -v $(pwd)/erd:/erd dbhi/data-models-sqlalchemy dmsa erd -o /erd/i2b2_pedsnet_2.0.0_erd.png i2b2_pedsnet 2.0.0\n```\n\nThe `graphviz` graphing package supports a number of other output formats, which are interpreted from the passed extension.\n\n### Without Docker:\n\nInstall the system requirements (see Dockerfile for details):\n\n- Python 2.7\n- `graphviz` for ERD generation\n- Oracle `instantclient-basic` and `-sdk` and `libaio1` for Oracle DDL generation\n- `libpq-dev` for PostgreSQL DDL generation\n- `unixodbc-dev` for MS SQL Server DDL generation\n\nInstall the python requirements, hopefully within a virtualenv (see Dockerfile for details):\n\n```sh\npip install cx-Oracle            # for Oracle DDL generation\npip install psycopg2             # for PostgreSQL DDL generation\npip install PyMySQL              # for MySQL DDL generation\npip install pyodbc               # for MS SQL Server DDL generation\n```\n\nInstall the data-models-sqlalchemy python package:\n\n```sh\npip install dmsa\n```\n\nUsage Message:\n\n```sh\ndmsa -h\n```\n\nGenerate OMOP V5 creation DDL for Oracle:\n\n```sh\ndmsa ddl -tci omop 5.0.0 oracle\n```\n\nGenerate OMOP V5 drop DDL for Oracle:\n\n```sh\ndmsa ddl -tci --drop omop 5.0.0 oracle\n```\n\nGenerate OMOP V5 data deletion DML for Oracle:\n\n```sh\ndmsa ddl --delete-data omop 5.0.0 oracle\n```\n\nGenerate i2b2 PEDSnet V2 ERD (the image will land at `./erd/i2b2_pedsnet_2.0.0_erd.png`):\n\n```sh\nmkdir -p erd\ndmsa erd -o ./erd/i2b2_pedsnet_2.0.0_erd.png i2b2_pedsnet 2.0.0\n```\n\n## Web Service\n\nThe web service uses a [Gunicorn](http://gunicorn.org/) server in the Docker container and the Flask debug server locally. It exposes the following endpoints:\n\n- Creation DDL at `/<model>/<version>/ddl/<dialect>/`\n- Creation DDL for only `table`, `constraint`, or `index` elements at `/<model>/<version>/ddl/<dialect>/<elements>`\n- Drop DDL at `/<model>/<version>/drop/<dialect>/`\n- Drop DDL for only `table`, `constraint`, or `index` elements at `/<model>/<version>/drop/<dialect>/<elements>`\n- Data deletion DML at `/<model>/<version>/delete/<dialect>/`\n- ERDs at `/<model>/<version>/erd/`\n- Oracle logging scripts at `/<model>/<version>/logging/oracle/`\n- Oracle logging scripts for only `table` or `index` elements at `/<model>/<version>/logging/oracle/<elements>/`\n- Oracle nologging scripts at `/<model>/<version>/nologging/oracle/`\n- Oracle nologging scripts for only `table` or `index` elements at `/<model>/<version>/logging/oracle/<elements>/`\n\n### With Docker:\n\nUsage:\n\n```sh\ndocker run dbhi/data-models-sqlalchemy gunicorn -h\n```\n\nRun:\n\n```sh\ndocker run dbhi/data-models-sqlalchemy  # Uses Dockerfile defaults of 0.0.0.0:80\n```\n\n### Without Docker:\n\nInstall Flask:\n\n```sh\npip install Flask\n```\n\nUsage Message:\n\n```sh\ndmsa -h\n```\n\nRun:\n\n```sh\ndmsa serve                              # Uses Flask defaults of 127.0.0.1:5000\n```",
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