Checks an oparl server for compliance with the oparl specification
We offer a continually updated Docker container on Docker Hub which is the easiest way to use the validator. You don't even need to clone the repository that way.
First you need to install Docker CE.
You can run the validator with Docker, saving the results in a plain text file
named result.txt with
docker run --rm oparl/validator [entrypoint]To build the Docker container on your system, you need to clone the repository. You can then build with
docker build . -t oparl-validatorAnd run the local container with
docker run --rm oparl-validator [entrypoint]on the system
- liboparl and liboparl requirements
- redis
- Python >= 3.5
Python specific
You can install all python dependencies with pip install -r requirements.txt.
- pygobject
- requests
- redis
- tqdm
- beautifultable
./validate https://my.oparl.endpoint/You can also use the OParl Validator in your Python projects by simply importing it and passing it url and desired options:
from src.validator import Validator
result = Validator(url='https://my.oparl.endpoint/', options = { 'validate_schema': False, 'print_result': False })Mark this: It is quite important to either redirect stdout/stderr or pass the print_result
option unless you explicitly want to have the validation progress and results passed to stdout.
You might also want to choose json formatting instead of plain text output.
The OParl Validator supports a --porcelain output mode which will output all messages and progress reports
in the Json Patch Format. However, in porcelain mode the output will never include
the validation result. Instead, you must set an explicit output file.
All output the validator produces while running is sent to stdout.
This repository uses git hooks which are being tracked in the repository under the .hooks directory.
To configure these hooks on your clone, please run
git config include.path ../.gitconfig
in the repository root directory after cloning.