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/44907/?format=api

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

{
    "ob_id": 44907,
    "computationComponent": [
        {
            "ob_id": 44906,
            "uuid": "7e7cc1392e694b149ae5b4858550a47d",
            "title": "Computation for the Copernicus Land Cover product",
            "abstract": "Pre-processing:\r\n\r\nDeep-learning (DL) based clouds detection: Land Occlusion Score (LOS) product\r\nLOS weighted compositing and timeseries interpolation\r\nLSF-ANNUAL-S2 and LSF-ANNUAL-S1 extraction\r\nAncillary data preparation: AgERA5 climatic regions embeddings processing\r\nModelling: The backbone to produce the LCM-10 layers is EvoNet, a novel algorithm that integrates the strengths of convolutional neural networks (CNNs) and pixel-based classifiers into a unified framework. EvoNet avoids the inefficiencies of conventional approaches that either rely on multiple regional models, requiring complex post-processing, or exclusively use CNNs or pixel classifiers, each of which has limitations. CNNs excel in generalization but struggle with fine spatial details, while pixel classifiers offer high spatial resolution but are prone to noise and overfitting. The core innovation of EvoNet lies in unifying these strengths with its dual architecture: a CNN-based spatial feature extractor and a multi-layer perceptron (MLP) pixel classifier. \r\n\r\nPost-processing: expert rules polishing and tiling of the final product.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        }
    ],
    "acquisitionComponent": [
        {
            "ob_id": 12318,
            "independentInstrument": [],
            "instrumentplatformpair_set": [
                {
                    "ob_id": 4316,
                    "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12319/?format=api",
                    "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api",
                    "relatedTo": {
                        "ob_id": 12318,
                        "uuid": "f95b77f14a554727a1975802b25ad8a7",
                        "short_code": "acq"
                    }
                }
            ]
        },
        {
            "ob_id": 13191,
            "independentInstrument": [],
            "instrumentplatformpair_set": [
                {
                    "ob_id": 4334,
                    "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/13187/?format=api",
                    "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/13182/?format=api",
                    "relatedTo": {
                        "ob_id": 13191,
                        "uuid": "e05a470bb02a4bf5bba845b1fcc3aca6",
                        "short_code": "acq"
                    }
                }
            ]
        },
        {
            "ob_id": 20018,
            "independentInstrument": [],
            "instrumentplatformpair_set": [
                {
                    "ob_id": 10863,
                    "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/20017/?format=api",
                    "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api",
                    "relatedTo": {
                        "ob_id": 20018,
                        "uuid": "c28a3a6627354dd19363ac971116b0d8",
                        "short_code": "acq"
                    }
                }
            ]
        },
        {
            "ob_id": 25439,
            "independentInstrument": [],
            "instrumentplatformpair_set": [
                {
                    "ob_id": 11412,
                    "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/25277/?format=api",
                    "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/13182/?format=api",
                    "relatedTo": {
                        "ob_id": 25439,
                        "uuid": "18f84df32d934058862f2c3990885a4c",
                        "short_code": "acq"
                    }
                }
            ]
        }
    ],
    "identifier_set": [],
    "responsiblepartyinfo_set": [
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/215147/?format=api"
    ]
}