Structures

subsurface.structs.errors Module

subsurface.structs.structured_elements Module

For regularly gridded datasets like rasters and tensor meshes.

Classes

OctreeMesh(data)

TODO: implement as Dom discussed with data frames to track the levels.

StructuredSurface(structured_data)

StructuredGrid(structured_data)

Container for curvilinear mesh grids.

subsurface.structs.unstructured_elements Module

These are classes that are point based and managed via Pandas DataFrames.

All data is tracked within internal DataFrames that we validate.

This is for holding general “mesh”-like data structures: point clouds, linesets, triangulated surfaces, tetrahedralized volumes, octree grids, etc.

Regularly gridded dataset will NOT be managed by these classes but will use xarray under the hood.

Classes

PointSet(data)

Class for pointset based data structures.

TriSurf(mesh, texture, **kwargs)

PointSet with triangle cells.

LineSet(data, radius)

PointSet with line cells.

TetraMesh(data)

PointSet with tetrahedron cells.

subsurface.structs.base_structures.common_data_utils Module

Functions

replace_outliers(base_data[, dim, perc, …])

@Edoardo Guerreiro https://stackoverflow.com/questions/60816533/ is-there-a-built-in-function-in-xarray-to-remove-outliers-from-a-dataset

to_netcdf(base_data[, path, file])

default_path_and_name(path[, name])

subsurface.structs.base_structures.structured_data Module

Classes

StructuredData(data, data_array_name)

subsurface.structs.base_structures.unstructured_data Module

Classes

UnstructuredData(data, cells_attr_name, …)