eogrow.pipelines.zipmap

pydantic model eogrow.pipelines.zipmap.InputFeatureSchema[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

Fields:

field feature: Tuple[FeatureType, str] [Required]: Which features to load from folder.

field folder_key: str [Required]: The storage manager key pointing to the folder from which to load data.

class eogrow.pipelines.zipmap.ZipMapPipeline(config, raw_config=None)[source]

Parameters:

config (Schema) – A dictionary with configuration parameters
raw_config (RawConfig | None) – The configuration parameters pre-validation, for logging purposes only

pydantic model Schema[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

Fields:

field input_features: List[InputFeatureSchema] [Required]: The specification for all the features to be loaded.

field output_folder_key: str [Required]: The storage manager key pointing to the output folder for the algorithm pipeline.

field params: Dict[str, Any] [Optional]

Any keyword arguments to be passed to the zipmap function.

Validated by:

field params_model: str | None = None: Optional import path for the pydantic model class, with which to parse and validate the parameters for the callable. The model will be used to parse the params and then unpacked back into a dictionary, which is passed to the callable as **params.

field zipmap_import_path: str [Required]: Import path of the callable with which to process the loaded features.

filter_patch_list(patch_list)[source]

EOPatches are filtered according to existence of new features

get_load_nodes()[source]

Prepare all nodes with load tasks.

get_zipmap_node(previous_node)[source]

Adds all algorithm and dataframe-saving nodes and returns the endnode.

build_workflow()[source]

Builds the workflow