dor-py

Python code for the DOR project.

Setup

1. Clone the repo

git clone https://github.com/mlibrary/dor-py.git
cd dor-py

2. In the terminal, run the init.sh

./init.sh

This will:

• set up the initial environment variables file
• build the docker image
• install the python dependencies

./init.sh can be run more than once.

1. Edit .env with actual environment variables

2. In the app container, use poetry shell to enable the virtual environment. Otherwise use:

 docker compose run --rm app poetry run YOUR_COMMAND

Behave

Behavior driven development with behave

Advanced Guide to Behavior-Driven Development with Behave in Python

docker compose run --rm app poetry run behave

Generating Samples

To generate sample packages, on the command line run:

docker compose run --rm app poetry run dor samples generate --collid xyzzy --num-scans 5 --total 1 --versions 1

This will generate a submission package in BagIt format for the collection xyzzy for one item made up of 5 page scans.

The complete list of options:

--collid: str -- required
--action: store|stage|purge
--num_scans: int = None -- if unspecified, a random number of scans will be generated
--total: int = 1 -- number of items to generate
--versions: int = 1 --- number of versions to generate
--output_pathname: str -- default is the "output" directory at the root of this repository

How does the versions option work? The first version of the item will contain all the scans. The next versions will only contain a random subset of updated scans.

Can you validate the METS2 documents? The schemas for METS2 and PREMIS3 are in the etc/xsd directory. With xmllint --schema you can only use one of these schemas, so validating a METS2 document will always complain about the PREMIS:object type.