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/32698/?format=api

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

{
    "ob_id": 32698,
    "computationComponent": [
        {
            "ob_id": 32697,
            "uuid": "29ac5e1fed1243ee8ad453ba74c5d6a4",
            "title": "Derivation of Gravimetric Mass Balance products from the Antarctic Ice Sheet CCI  (v3.0)",
            "abstract": "TU Dresden has generated a Gravimetric Mass Balance (GMB) product for the Antarctic Ice Sheet (AIS) based on monthly snapshots of the Earth’s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through July 2020. The GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended by means of GRACE-FO data starting from June 2018, thus including 187 monthly solutions. The mass change estimation is based on the tailored sensitivity kernel approach developed at TU Dresden.\r\n\r\nThe methodology is described in: Groh, A. & Horwath, M. (2021). Antarctic Ice Mass Change Products from GRACE/GRACE-FO Using Tailored Sensitivity Kernels. Remote Sens., 13(9), 1736. doi:10.3390/rs13091736",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        }
    ],
    "acquisitionComponent": [
        {
            "ob_id": 32696,
            "independentInstrument": [],
            "instrumentplatformpair_set": [
                {
                    "ob_id": 12581,
                    "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32693/?format=api",
                    "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32695/?format=api",
                    "relatedTo": {
                        "ob_id": 32696,
                        "uuid": "8a71f0acff9e45199e34e879671f2b29",
                        "short_code": "acq"
                    }
                },
                {
                    "ob_id": 12582,
                    "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32694/?format=api",
                    "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32695/?format=api",
                    "relatedTo": {
                        "ob_id": 32696,
                        "uuid": "8a71f0acff9e45199e34e879671f2b29",
                        "short_code": "acq"
                    }
                }
            ]
        }
    ],
    "identifier_set": [],
    "responsiblepartyinfo_set": [
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/145783/?format=api"
    ]
}