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/8165/?format=api

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

{
    "ob_id": 8165,
    "computationComponent": [
        {
            "ob_id": 6545,
            "uuid": "b4bc7f45b32c40c8b610675b7e2c2ced",
            "title": "Satellite deployed on Envisat",
            "abstract": "Envisat.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 19732,
            "uuid": "42024d3f3b9f477ca8464e8e0a214674",
            "title": "Level 2 University of Leicester Land Surface Temperature (LST) Product (UOL_LST_2P)",
            "abstract": "This product is the Land Surface Temperature (LST) Level 2 data product (UOL_LST_2P) from the Advanced Along Track Scanning Radiometer (AATSR).\r\n\r\nThe Level 2 LST product provides 1km resolution AATSR-based land surface temperature and its associated uncertainties, as well as auxiliarly information. It has been produced by the University of Leicester under funding from ESA and the NCEO. The data is included as part of the official ESA (A)ATSR multimission product from the 3rd version of that dataset.\r\n\r\nThe algorithm applied for computing LST is based on the methodology originally developed by Prata (2002) in that it uses a split-window approach with model-derived regression coefficients which implicitly include the effects of emissivity. However, the updated methodology that was used here applies significantly improved auxiliary datasets for land cover, green vegetation fraction, and total column water vapour and as such is able to reduce or eliminate a wide variety of issues that were observed with the original LST product. Further details about the algorithm used for producing this dataset can be found in Ghent (in preparation).",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        }
    ],
    "acquisitionComponent": [
        {
            "ob_id": 8164,
            "independentInstrument": [],
            "instrumentplatformpair_set": [
                {
                    "ob_id": 2557,
                    "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/846/?format=api",
                    "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/847/?format=api",
                    "relatedTo": {
                        "ob_id": 8164,
                        "uuid": "37702f39823e49efa1f711fb68430dca",
                        "short_code": "acq"
                    }
                }
            ]
        }
    ],
    "identifier_set": [],
    "responsiblepartyinfo_set": []
}