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Datalab Backend

This application is the backend server for the PhotonRanch Datalab. It is a django application with a REST API for communicating with the Datalab UI.

Prerequisites

  • Python >= 3.10
  • Django >= 4

Local Development

Bare Metal

Start by creating a virtualenv for this project and entering it:

    python -m venv /path/to/my/virtualenv
    source /path/to/my/virtualenv/bin/activate

Then install the dependencies:

    pip install -e .

The project is configured to use a local sqlite database. You can change that to a postgres one if you want but sqlite is easy for development. Run the migrations to setup the database.

    ./manage.py migrate

Get your auth token from the UI by signing in with your LCO credentials and checking your cookies for an auth-token. Once you have it export it to your dev enviorment like

    export ARCHIVE_API_TOKEN=<your-auth-token>

Start up a Redis Server that will faciliate caching as well as the rabbitmq queue. To do this make sure you have Redis installed and then start a server at port 6379

    redis-server

Start the dramatiq worker threads, here we use a minimal number of processes and threads for size but feel free to run a full dramatiq setup as well.

    ./manage.py rundramatiq --processes 1 --threads 2

Now start your server

    ./manage.py runserver

Nix development

For this mode of development, you must install:

  • nix with flakes support

Then to develop, run these commands:

  • nix develop --impure to start your nix development environment - called anytime you use a new terminal
  • ctlptl apply -f local-registry.yaml -f local-cluster.yaml to start up the registry and cluster - should only need to be called one time within the nix environment
  • skaffold dev -m deps to start the dependencies - run this in a different tab to keep running during development or use 'run' instead of 'dev'
  • Copy ./k8s/envs/local/secrets.env.changeme to a version without .changeme and fill in values for connecting to the appropriate services.
  • skaffold dev -m app --port-forward to start the servers and worker. This will auto-redeploy as you make changes to the code.
  • Once running you will need to initialize your account with the server before using the API - the easiest way to do this is to login one time to the admin interface at http://127.0.0.1:8080/admin

Connecting a frontend

You can also run a local datalab-ui to connect to your datalab. Assuming you've cloned that repo:

  • Change the ./public/config/config.json "datalabApiBaseUrl" to be http://127.0.0.1:8080/api/ or wherever your backend is deployed to
  • npm install to install the libraries
  • npm run serve to run the server at http://127.0.0.1:8081 assuming your backend was already running (otherwise it will try to be :8080)

API Structure

The application has a REST API with the following endpoints you can use. You must pass your user's API token in the request header to access any of the endpoints - the headers looks like {'Authorization': 'Token 123456789abcdefg'} if you are using python's requests library.

Input Data structure

Datasessions can take an input_data parameter, which should contain a list of data objects. The current format is described below, but this is probably something that will evolve as we learn more how we are using it.

session_input_data = [
    {
        'type': 'fitsfile',
        'source': 'archive',
        'basename': 'mrc1-sq005mm-20231114-00010332'
    },
    {
        'type': 'fitsfile',
        'source': 'archive',
        'basename': 'mrc1-sq005mm-20231114-00010333'
    },
]

Data operations can have a varying set of named keys within their input_data that is specific to each operation. For example it would look like this for an operation that just expects a list of files and a threshold value:

operation_input_data = {
    'input_files': [
        {
            'type': 'fitsfile',
            'source': 'archive',
            'basename': 'mrc1-sq005mm-20231114-00010332'
        }
    ],
    'threshold': 255.0
}

Datasessions API

Create a new Datasession

POST /api/datasessions/

post_data = {
    'name': 'My New Session Name',
    'input_data': session_input_data
}

Get all existing Datasessions

GET /api/datasessions/

Get Datasession by id

GET /api/datasessions/datasession_id/

Delete Datasession by id

DELETE /api/datasessions/datasession_id/

Operations API

Available Operations are introspected from the data_operations directory and must implement the BaseDataOperation class. I expect we will add more flesh to those classes when we actually start using them.

Get Operations for a Datasession

GET /api/datasessions/datasession_id/operations/

Create new Operation for a Datasession

POST /api/datasessions/datasession_id/operations/

post_data = {
    'name': 'Median',  # This must match the exact name of an operation
    'input_data': operation_input_data
}

Delete Operation from a Datasession

DELETE /api/datasessions/datasession_id/operations/operation_id/

ROADMAP

  • TBD