KrigingResult

GitHub source

KrigingResult

evo.compute.tasks.kriging.KrigingResult

message

message: str

A message describing what happened in the task.

target_name

target_name: str

The name of the target object.

target_reference

target_reference: str

Reference URL to the target object.

attribute_name

attribute_name: str

The name of the attribute that was created/updated.

schema

schema: ObjectSchema

The schema type of the target object (e.g., 'regular-masked-3d-grid').

Uses ObjectSchema.from_id to parse the schema ID. Falls back to the raw schema_id string when it cannot be parsed.

get_target_object

get_target_object() -> BaseObject

Load and return the target geoscience object.

Args: context: Optional context to use. If not provided, uses the context from when the task was run.

Returns: The typed geoscience object (e.g., Regular3DGrid, RegularMasked3DGrid, BlockModel)

Example: >>> result = await run(manager, params) >>> target = await result.get_target_object() >>> target # Pretty-prints with Portal/Viewer links

to_dataframe

to_dataframe(*columns: str) -> pd.DataFrame

Get the task results as a DataFrame.

This is the simplest way to access the task output data. It loads the target object and returns its data as a pandas DataFrame.

Args: context: Optional context to use. If not provided, uses the context from when the task was run. columns: Optional list of column names to include. If None, includes all columns. Use ["*"] to explicitly request all columns.

Returns: A pandas DataFrame containing the task results.

Example: >>> result = await run(manager, params) >>> df = await result.to_dataframe() >>> df.head()

__str__

__str__() -> str

String representation.