A prototype for the extraction, storage, validation and distribution of public epidemiological data for analysis.

Prerequisite:

pip install -e . # Ensure you are in the root repository

Examples:

python osprey/client/client.py -list_sources # Lists all the sources

python osprey/client/client.py -create_source -n <name> -u <url> -v <verifier-function-uuid> # Read instructions for creating verifier uuid

python osprey/client/client.py -get_file --source_id <source_id>

# From /osprey/client/example.py

# Example of the verfier/modifier function

# NOTE:

# The function must:
#   1.    Take (*args, **kwargs)
#   2.    Return args, kwargs
#   3.    For verifier: raise `Exception` if the verifier is failed
#   4.    For modifier: Before returning, kwargs['file']  = modified_file


def verifier(*args, **kwargs):

    # Your downloaded file will be found in kwargs['file']
    # Ensure that you replace the `modified` file to kwargs['file']

    return args, kwargs

from client import source_file, register_function

try:
    response = register_function(verifier)
    assert response.get('code') == 200
    function = response.get('function_id')
except Exception as e:
    print(e)

All the server components are made available within a series of dockerfiles provided in Dockerfile. To install the components, the following steps can be followed:

# Running the script to create necessary volumes, and build projects
export DSAAS_EMAIL_PASSWORD="<password>" GCS_ROOT_DIR="<dir>"
source scripts/prepare_start.sh

docker compose up

flask_sqlalchemy is the library of choice for handling migrations for tables. Docs can be found at https://flask-migrate.readthedocs.io/en/latest/

1. When creating a new model, create in osprey/server. Ensure that the new model inherits from db.Model, so that the flask_sqlalchemy can automatically detect the changes to the model variables.
2. When a new column is added, run flask db migrate -m <-message-> to commit the changes. The command automatically creates a migration file, which can be reused to recreate the database schema.
3. Docker Exec into the WEB application container and then run flask db upgrade to run the migrations. Ensure that the database docker container is running, so the migration can be successful
4. All models in osprey/worker serve just extensions. So you can load the same values that server models have created, and write custom worker related functions

1. You can add editable configs in docker-compose.yml to the respective application
2. Non-editable configs for the worker/server can be added to osprey/<-service-name->/config.py