gdf.pc.random_field() or PointCloud.random_field()#
- PointCloudBase.random_field(error_structure, *, predictors=None, n_fields=1, random_state=None, chunksizes=None, mp_config=None, backend='gpytorch', gpytorch_inducing_points='auto')[source]#
Generate one or more error fields at every point in this point cloud.
- Parameters:
error_structure (
ErrorStructure) – Error model defined by magnitude and correlation components.predictors (
Mapping[str,Any] |None) – Named magnitude predictors or point column names.n_fields (
int) – Number of independent fields.random_state (
int|Generator|None) – Seed or generator used for reproducible fields.chunksizes (
int|None) – Target point rows per Dask partition for a Dask GeoDataFrame.mp_config (
MultiprocConfig|None) – Worker, row partition and output file settings for multiprocessing fields.backend (
Literal['gstools','gpytorch']) – Library used to draw correlated errors. Chunked GPyTorch fields use an inducing grid.gpytorch_inducing_points (
int|Literal['auto'] |None) – Target grid size for approximate GPyTorch fields. “auto” uses 256 points for eager and chunked point clouds; None requests an exact eager draw.
- Return type:
- Returns:
One result of the same point cloud type, or a list when n_fields is greater than one.