Procedure Computation Instance – Django REST framework

Get a list of ProcedureComputation objects. ProcedureComputations have a 1:1 mapping with Observations.

### Available end points:

- `/ProcedureComputations/` - Will list all ProcedureComputations in the database
- `/ProcedureComputations.json` - Will return all ProcedureComputations in json format
- `/ProcedureComputations/<object_id>/` - Returns ProcedureComputations object with that id

### Available Methods:

- `GET`
- `HEAD`

### Available filters:

- `uuid`
- `title`
- `keywords`
- `abstract`

### How to use filters:

These filters can be used like django query filters using __ for related model relationships.

- `/computations/?uuid=d594d53df2612bbd89c2e0e770b5c1a0`
- `/computations/?title__startswith!=DETAILS NEEDED - COMPUTATION CREATED FOR SATELLITE COMPOSITE`
- `/computations/?abstract__contains=HadCM3 model`

GET /api/v2/computations/43887/?format=api

HTTP 200 OK
Allow: GET, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

{
    "ob_id": 43887,
    "uuid": "81e8949519894720abd964b782e13275",
    "title": "Machine-Learning-Based Prediction and Aggregation of Air Pollution Estimates into \"Typical Day\" Profiles",
    "abstract": "The dataset was created using a supervised machine-learning pipeline. The pipeline generates air pollution concentration predictions across a 1 km^2^ grid over England, subsequently aggregated to form representative \"typical\" hourly cycles for each day of the week and month. This approach simplifies downstream use cases such as policy assessment and public communication. The underlying methodology is implemented in the accompanying open-source Python package Environmental Insights, available at https://github.com/berrli/Environmental-Insights",
    "keywords": "",
    "inputDescription": null,
    "outputDescription": null,
    "softwareReference": null,
    "identifier_set": []
}