Metadata-Version: 1.1
Name: dmsa
Version: 0.4.4
Summary: SQLAlchemy models and DDL and ERD generation from chop-dbhi/data-models style JSON endpoints.
Home-page: https://github.com/chop-dbhi/data-models-sqlalchemy
Author: The Children's Hospital of Philadelphia
Author-email: cbmisupport@email.chop.edu
License: Other/Proprietary
Download-URL: https://github.com/chop-dbhi/data-models-sqlalchemy/tarball/0.4.4
Description: # Data Models SQLAlchemy
        
        [![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)
        
        SQLAlchemy models and DDL and ERD generation for chop-dbhi/data-models style JSON endpoints.
        
        Web service available at http://dmsa.a0b.io/
        
        ## SQLAlchemy Models
        
        In your shell, hopefully within a virtualenv:
        
        ```sh
        pip install dmsa
        ```
        
        In python:
        
        ```python
        from dmsa.omop.v5.models import Base
        
        for tbl in Base.metadata.sorted_tables():
            print tbl.name
        ```
        
        Or:
        
        ```python
        from dmsa.pedsnet.v2.models import Person, VisitPayer
        
        print VisitPayer.columns
        ```
        
        These 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. It should be simple to add modules for any additional data models that become available, but the currently provided ones are:
        
        - **OMOP V4** at `omop.v4.models`
        - **OMOP V5** at `omop.v5.models`
        - **PEDSnet V1** at `pedsnet.v1.models`
        - **PEDSnet V2** at `pedsnet.v2.models`
        - **i2b2 V1.7** at `i2b2.v1_7.models`
        - **i2b2 PEDSnet V2** at `i2b2.pedsnet.v2.models`
        - **PCORnet V1** at `pcornet.v1.models`
        - **PCORnet V2** at `pcornet.v2.models`
        - **PCORnet V3** at `pcornet.v3.models`
        
        ## DDL and ERD Generation
        
        Use of the included Dockerfile is highly recommended to avoid installing DBMS and graphing specific system requirements.
        
        The following DBMS dialects are supported when generating DDL:
        
        - **PostgreSQL** called as `postgresql`
        - **MySQL** called as `mysql`
        - **MS SQL Server** called as `mssql`
        - **Oracle** called as `oracle`
        
        ### With Docker:
        
        Retrieve the image:
        
        ```sh
        docker pull dbhi/data-models-sqlalchemy
        ```
        
        Usage for DDL generation:
        
        ```sh
        docker run --rm dbhi/data-models-sqlalchemy ddl -h
        ```
        
        Generate OMOP V5 creation DDL for Oracle:
        
        ```sh
        docker run --rm dbhi/data-models-sqlalchemy ddl omop v5 oracle
        ```
        
        Generate OMOP V5 drop DDL for Oracle:
        
        ```sh
        docker run --rm dbhi/data-models-sqlalchemy ddl -d omop v5 oracle
        ```
        
        Generate OMOP V5 data deletion DML for Oracle:
        
        ```sh
        docker run --rm dbhi/data-models-sqlalchemy ddl -x omop v5 oracle
        ```
        
        Usage for ERD generation:
        
        ```sh
        docker run --rm dbhi/data-models-sqlalchemy erd -h
        ```
        
        Generate i2b2 PEDSnet V2 ERD (the image will land at `./erd/i2b2_pedsnet_v2_erd.png`):
        
        ```sh
        docker run --rm -v $(pwd)/erd:/erd dbhi/data-models-sqlalchemy erd i2b2_pedsnet v2 /erd/i2b2_pedsnet_v2_erd.png
        ```
        
        The `graphviz` graphing package supports a number of other output formats, listed here (link pending), which are interpreted from the passed extension.
        
        ### Without Docker:
        
        Install the system requirements (see Dockerfile for details):
        
        - **Python 2.7**
        - `graphviz` for ERD generation
        - Oracle `instantclient-basic` and `-sdk` and `libaio1` for Oracle DDL generation
        - `libpq-dev` for PostgreSQL DDL generation
        - `unixodbc-dev` for MS SQL Server DDL generation
        
        Install the python requirements, hopefully within a virtualenv (see Dockerfile for details):
        
        ```sh
        pip install cx-Oracle            # for Oracle DDL generation
        pip install psycopg2             # for PostgreSQL DDL generation
        pip install PyMySQL              # for MySQL DDL generation
        pip install pyodbc               # for MS SQL Server DDL generation
        ```
        
        Install the data-models-sqlalchemy python package:
        
        ```sh
        pip install dmsa
        ```
        
        Usage for DDL generation:
        
        ```sh
        dmsa ddl -h
        ```
        
        Generate OMOP V5 creation DDL for Oracle:
        
        ```sh
        dmsa ddl omop v5 oracle
        ```
        
        Generate OMOP V5 drop DDL for Oracle:
        
        ```sh
        dmsa ddl -d omop v5 oracle
        ```
        
        Generate OMOP V5 data deletion DML for Oracle:
        
        ```sh
        dmsa ddl -x omop v5 oracle
        ```
        
        Usage for ERD generation:
        
        ```sh
        dmsa erd -h
        ```
        
        Generate i2b2 PEDSnet V2 ERD (the image will land at `./erd/i2b2_pedsnet_v2_erd.png`):
        
        ```sh
        mkdir erd
        dmsa erd i2b2_pedsnet v2 ./erd/i2b2_pedsnet_v2_erd.png
        ```
        
        ## Web Service
        
        The web service uses a simple Flask debug server for now. It exposes the following endpoints:
        
        - Creation DDL at `/<model>/<version>/ddl/<dialect>/`
        - Creation DDL for only `table`, `constraint`, or `index` elements at `/<model>/<version>/ddl/<dialect>/<elements>`
        - Drop DDL at `/<model>/<version>/drop/<dialect>/`
        - Drop DDL for only `table`, `constraint`, or `index` elements at `/<model>/<version>/drop/<dialect>/<elements>`
        - Data deletion DML at `/<model>/<version>/delete/<dialect>/`
        - ERDs at `/<model>/<version>/erd/`
        
        ### With Docker:
        
        Usage:
        
        ```sh
        docker run  dbhi/data-models-sqlalchemy start -h
        ```
        
        Run:
        
        ```sh
        docker run dbhi/data-models-sqlalchemy  # Uses Dockerfile defaults of 0.0.0.0:80
        ```
        
        ### Without Docker:
        
        Install Flask:
        
        ```sh
        pip install Flask
        ```
        
        Usage:
        
        ```sh
        dmsa start -h
        ```
        
        Run:
        
        ```sh
        dmsa start                              # Uses Flask defaults of 127.0.0.1:5000
        ```
        
Keywords: healthcare,data models,SQLAlchemy,DDL,ERD
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Healthcare Industry
Classifier: License :: Other/Proprietary License
Classifier: Topic :: Database
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Software Development :: Code Generators
Classifier: Natural Language :: English
