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dmsa-0.5.8
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مشاهده بیشتر
تبلیغات ما
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تبلیغات ما
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مشاهده بیشترتوضیحات
SQLAlchemy models and DDL and ERD generation from chop-dbhi/data-models style JSON endpoints.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | dmsa-0.5.8 |
| نام | dmsa |
| نسخه کتابخانه | 0.5.8 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | The Children's Hospital of Philadelphia |
| ایمیل نویسنده | cbmisupport@email.chop.edu |
| آدرس صفحه اصلی | https://github.com/chop-dbhi/data-models-sqlalchemy |
| آدرس اینترنتی | https://pypi.org/project/dmsa/ |
| مجوز | Other/Proprietary |
# Data Models SQLAlchemy
[](https://circleci.com/gh/chop-dbhi/data-models-sqlalchemy/tree/master) [](https://coveralls.io/github/chop-dbhi/data-models-sqlalchemy?branch=master)
SQLAlchemy models and DDL and ERD generation for [chop-dbhi/data-models-service](https://github.com/chop-dbhi/data-models-service) 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 sqlalchemy import MetaData
from dmsa import make_model_from_service
metadata = MetaData()
metadata = make_model_from_service('omop', '5.0.0',
'http://data-models.origins.link/',
metadata)
for tbl in metadata.sorted_tables:
print tbl.name
```
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. Any data model stored there can be converted into SQLAlchemy models. At the time of writing, the following are available.
CAVEAT: The models are currently "Classical"-style and therefore un-mapped. See more information [here](https://github.com/chop-dbhi/data-models-sqlalchemy/issues/22).
- i2b2
- 1.7.0
- i2b2 for PEDSnet
- 2.0.1
- OMOP
- 4.0.0
- 5.0.0
- PCORnet
- 1.0.0
- 2.0.0
- 3.0.0
- PEDSnet
- 1.0.0
- 2.0.0
- 2.1.0
- 2.2.0
## 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
- MySQL
- MS SQL Server
- Oracle
### With Docker:
Retrieve the image:
```sh
docker pull dbhi/data-models-sqlalchemy
```
Usage Message:
```sh
docker run --rm dbhi/data-models-sqlalchemy dmsa -h
```
Generate OMOP V5 creation DDL for Oracle:
```sh
docker run --rm dbhi/data-models-sqlalchemy dmsa ddl -tci omop 5.0.0 oracle
```
Generate OMOP V5 drop DDL for Oracle:
```sh
docker run --rm dbhi/data-models-sqlalchemy dmsa ddl -tci --drop omop 5.0.0 oracle
```
Generate OMOP V5 data deletion DML for Oracle:
```sh
docker run --rm dbhi/data-models-sqlalchemy dmsa ddl --delete-data omop 5.0.0 oracle
```
Generate i2b2 PEDSnet V2 ERD (the image will land at `./erd/i2b2_pedsnet_2.0.0_erd.png`):
```sh
docker 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
```
The `graphviz` graphing package supports a number of other output formats, 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 Message:
```sh
dmsa -h
```
Generate OMOP V5 creation DDL for Oracle:
```sh
dmsa ddl -tci omop 5.0.0 oracle
```
Generate OMOP V5 drop DDL for Oracle:
```sh
dmsa ddl -tci --drop omop 5.0.0 oracle
```
Generate OMOP V5 data deletion DML for Oracle:
```sh
dmsa ddl --delete-data omop 5.0.0 oracle
```
Generate i2b2 PEDSnet V2 ERD (the image will land at `./erd/i2b2_pedsnet_2.0.0_erd.png`):
```sh
mkdir -p erd
dmsa erd -o ./erd/i2b2_pedsnet_2.0.0_erd.png i2b2_pedsnet 2.0.0
```
## Web Service
The web service uses a [Gunicorn](http://gunicorn.org/) server in the Docker container and the Flask debug server locally. 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/`
- Oracle logging scripts at `/<model>/<version>/logging/oracle/`
- Oracle logging scripts for only `table` or `index` elements at `/<model>/<version>/logging/oracle/<elements>/`
- Oracle nologging scripts at `/<model>/<version>/nologging/oracle/`
- Oracle nologging scripts for only `table` or `index` elements at `/<model>/<version>/logging/oracle/<elements>/`
### With Docker:
Usage:
```sh
docker run dbhi/data-models-sqlalchemy gunicorn -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 Message:
```sh
dmsa -h
```
Run:
```sh
dmsa serve # Uses Flask defaults of 127.0.0.1:5000
```
نیازمندی
| مقدار | نام |
|---|---|
| ==0.0.28 | ERAlchemy |
| ==0.10.1 | Flask |
| ==1.0.5 | SQLAlchemy |
| ==0.6.2 | docopt |
| ==2.7.0 | requests |
نحوه نصب
نصب پکیج whl dmsa-0.5.8:
pip install dmsa-0.5.8.whl
نصب پکیج tar.gz dmsa-0.5.8:
pip install dmsa-0.5.8.tar.gz