openeo_udf.server.data_model package

Submodules

openeo_udf.server.data_model.bounding_box_schema module

class openeo_udf.server.data_model.bounding_box_schema.SpatialBoundingBoxModel

Bases: pydantic.main.BaseModel

Spatial bounding box definitions

openeo_udf.server.data_model.data_collection_schema module

class openeo_udf.server.data_model.data_collection_schema.DataCollectionModel

Bases: pydantic.main.BaseModel

Data collection

class openeo_udf.server.data_model.data_collection_schema.ObjectCollectionModel

Bases: pydantic.main.BaseModel

Object collection that contains data cubes and simple feature collections

class openeo_udf.server.data_model.data_collection_schema.TimeStampsModel

Bases: pydantic.main.BaseModel

The time stamps of the data collections

openeo_udf.server.data_model.datacube_schema module

class openeo_udf.server.data_model.datacube_schema.DataCubeModel

Bases: pydantic.main.BaseModel

A multidimensional representation of a data cube

class openeo_udf.server.data_model.datacube_schema.DimensionModel

Bases: pydantic.main.BaseModel

Description of a data cube dimension. See the STAC dimension definition for more details and examples.

openeo_udf.server.data_model.machine_learn_schema module

class openeo_udf.server.data_model.machine_learn_schema.MachineLearnModel

Bases: pydantic.main.BaseModel

A machine learn model that should be applied to the UDF data.

class Config

Bases: object

schema_extra = {'examples': [{'framework': 'sklearn', 'name': 'random_forest', 'description': 'A random forest model', 'path': '/tmp/model.pkl.xz'}]}

openeo_udf.server.data_model.metadata_schema module

class openeo_udf.server.data_model.metadata_schema.MetadataModel

Bases: pydantic.main.BaseModel

Metadata description of the topological data collection

openeo_udf.server.data_model.model_example_creator module

openeo_udf.server.data_model.model_example_creator.create_data_collection_model_example() → openeo_udf.server.data_model.data_collection_schema.DataCollectionModel

openeo_udf.server.data_model.model_example_creator.create_datacube_model_example() → Tuple[openeo_udf.server.data_model.datacube_schema.DataCubeModel, openeo_udf.server.data_model.variables_collection_schema.VariablesCollectionModel]

openeo_udf.server.data_model.model_example_creator.create_dimension_model_example()

openeo_udf.server.data_model.model_example_creator.create_machine_learn_model_example() → openeo_udf.server.data_model.machine_learn_schema.MachineLearnModel

openeo_udf.server.data_model.model_example_creator.create_metadata_model_example() → openeo_udf.server.data_model.metadata_schema.MetadataModel

openeo_udf.server.data_model.model_example_creator.create_object_collection_model_example() → openeo_udf.server.data_model.data_collection_schema.ObjectCollectionModel

openeo_udf.server.data_model.model_example_creator.create_simple_feature_collection_model_example() → Tuple[[openeo_udf.server.data_model.simple_feature_collection_schema.SimpleFeatureCollectionModel, openeo_udf.server.data_model.variables_collection_schema.VariablesCollectionModel], List[str]]

openeo_udf.server.data_model.model_example_creator.create_structured_data_model_example() → openeo_udf.server.data_model.structured_data_schema.StructuredDataModel

openeo_udf.server.data_model.model_example_creator.create_timestamp_model_example() → openeo_udf.server.data_model.data_collection_schema.TimeStampsModel

openeo_udf.server.data_model.model_example_creator.create_udf_data_model_example() → openeo_udf.server.data_model.udf_schemas.UdfDataModel

openeo_udf.server.data_model.model_example_creator.create_variable_collection_model_example()

openeo_udf.server.data_model.simple_feature_collection_schema module

class openeo_udf.server.data_model.simple_feature_collection_schema.SimpleFeatureCollectionModel

Bases: pydantic.main.BaseModel

Simple feature collection: (multi)points, (multi)lines or (multi)polygons

class openeo_udf.server.data_model.simple_feature_collection_schema.SimpleFeatureModel

Bases: pydantic.main.BaseModel

A simple feature definition that may contain (multi)points, (multi)lines or (multi)polygons

openeo_udf.server.data_model.structured_data_schema module

class openeo_udf.server.data_model.structured_data_schema.StructuredDataModel

Bases: pydantic.main.BaseModel

This model represents structured data that can not be represented as a RasterCollectionTile or FeatureCollectionTile. For example the result of a statistical computation. The data is self descriptive and supports the basic types dict/map, list and table. This data structure can also be used to provide contextual data from the user to the UDF, like kernel size, resampling pixel size and so on.

openeo_udf.server.data_model.udf_schemas module

class openeo_udf.server.data_model.udf_schemas.ErrorResponseModel

Bases: pydantic.main.BaseModel

The error message. This class is not part of the UDF API but used to create the UDF test server."

class openeo_udf.server.data_model.udf_schemas.UdfCodeModel

Bases: pydantic.main.BaseModel

The object that stores the UDF code and language specification. This class is not part of the UDF API but used to create the UDF test server."

class openeo_udf.server.data_model.udf_schemas.UdfDataModel

Bases: pydantic.main.BaseModel

The UDF data object that stores data cubes, simple feature collection and machine learn models. This object is argument for the UDF as well as their return value.

class openeo_udf.server.data_model.udf_schemas.UdfRequestModel

Bases: pydantic.main.BaseModel

The udf request JSON specification. This class is not part of the UDF API but used to create the UDF test server.

openeo_udf.server.data_model.variables_collection_schema module

class openeo_udf.server.data_model.variables_collection_schema.VariableModel

Bases: pydantic.main.BaseModel

This represents a variable definition with values and labels

class openeo_udf.server.data_model.variables_collection_schema.VariablesCollectionModel

Bases: pydantic.main.BaseModel

A collection of variables that all have the same size

Module contents