Procedure Composite Process Instance – Django REST framework

Get a list of ProcedureComputation objects. ProcedureComputations have a 1:1 mapping with Observations where used.
These may have a number of 2 or more components made up of combinations of Computation and Acquisition records.
The details of the underlying records have been serialised.

### 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:

None
### How to use filters:

None

GET /api/v2/composites/31819/?format=api

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

{
    "ob_id": 31819,
    "computationComponent": [
        {
            "ob_id": 31818,
            "uuid": "98042648cc55470f8f96ea704107181e",
            "title": "Met Office Unified Model United Kingdom Variable resolution (UKV) version 8.2, run xkyib",
            "abstract": "Control experiment run of version version 8.2 of the Met Office Unified Model,  United Kingdom Variable resolution (UKV) following operational configuration of the Unified Model.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 31820,
            "uuid": "d18395020fed4679a60e4d7c167917e5",
            "title": "Met Office Unified Model United Kingdom Variable resolution (UKV) version 8.2, run xldef",
            "abstract": "Perturbation experiment run of version 8.2 of the Met Office Unified Model,  United Kingdom Variable resolution (UKV) following operational configuration of the Unified Model.\r\n\r\nHere the Gaussian potential temperature perturbations are added into the model. Full details of the perturbation method are described in Flack et al. (2018) Convective-\r\nScale Perturbation Growth Across the Spectrum of Convective Regimes, Monthly Weather Review, 146, 387-405, however I give a brief overview here:\r\n\r\nA Gaussian distribution (defined using random numbers between +/- 1 at each grid point, with the seed determined by the time the model is ran) is created at every grid point in the domain. A superposition is created and rescaled to 0.1 K so as to be an appropriate amplitude for boundary layer noise. Each of the Gaussian distributions have a standard deviation of 9km so as to be added onto an appropriate scale for the model. The perturbations are added in at a model hybrid height of 261.6 m (appro\r\nximately the 8th model level).",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        }
    ],
    "acquisitionComponent": [],
    "identifier_set": [],
    "responsiblepartyinfo_set": [
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/140345/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/140349/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/140350/?format=api"
    ]
}