gempy.core.data.InterpolationOptions

class gempy.core.data.InterpolationOptions(*, kernel_options: ~gempy_engine.core.data.options.kernel_options.KernelOptions, evaluation_options: ~gempy_engine.core.data.options.evaluation_options.EvaluationOptions, debug: bool, cache_mode: ~gempy_engine.core.data.options.interpolation_options.InterpolationOptions.CacheMode, cache_model_name: str, block_solutions_type: ~gempy_engine.core.data.raw_arrays_solution.RawArraysSolution.BlockSolutionType, sigmoid_slope: int, debug_water_tight: bool = False, temp_interpolation_values: ~gempy_engine.core.data.options.temp_interpolation_values.TempInterpolationValues = <factory>)

Methods

__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
construct([_fields_set])
copy(*[, include, exclude, update, deep])	Returns a copy of the model.
dict(*[, include, exclude, by_alias, ...])
from_args(range, c_o[, uni_degree, i_res, ...])
from_orm(obj)
init_dense_grid_options()
init_octree_options([range, c_o, refinement])
json(*[, include, exclude, by_alias, ...])
model_construct([_fields_set])	Creates a new instance of the Model class with validated data.
model_copy(*[, update, deep])	!!! abstract "Usage Documentation"
model_dump(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
model_dump_json(*[, indent, include, ...])	!!! abstract "Usage Documentation"
model_json_schema([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
model_parametrized_name(params)	Compute the class name for parametrizations of generic classes.
model_post_init(context, /)	Override this method to perform additional initialization after __init__ and model_construct.
model_rebuild(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, ...])	Validate a pydantic model instance.
model_validate_json(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
model_validate_strings(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
parse_file(path, *[, content_type, ...])
parse_obj(obj)
parse_raw(b, *[, content_type, encoding, ...])
probabilistic_options()
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
update_forward_refs(**localns)
update_options(**kwargs)	Updates the options of the class based on the provided keyword arguments.
validate(value)

Attributes

c_o
compute_corners
compute_scalar_gradient
evaluation_chunk_size
gi_res
i_res
is_last_octree_level
kernel_function
mesh_extraction
model_computed_fields
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extra	Get extra fields set during validation.
model_fields
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
n_uni_eq
number_dimensions
number_octree_levels
number_octree_levels_surface
range
uni_degree
kernel_options
evaluation_options
debug
cache_mode
cache_model_name
block_solutions_type
sigmoid_slope
debug_water_tight
temp_interpolation_values

class CacheMode(value)

Cache mode for the interpolation

NO_CACHE: int = 1

No cache at all even during the interpolation computation. This is quite expensive for no good reason.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': False, 'json_encoders': {<enum 'AvailableKernelFunctions'>: <function InterpolationOptions.<lambda>>, <enum 'CacheMode'>: <function InterpolationOptions.<lambda>>}, 'use_enum_values': False}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

update_options(**kwargs)

Updates the options of the class based on the provided keyword arguments.

Kwargs:

kernel_options (KernelOptions, optional): Options for the kernel. Default is None. number_octree_levels (int, optional): Number of octree levels. Default is 1. current_octree_level (int, optional): Current octree level. Default is 0. compute_scalar_gradient (bool, optional): Whether to compute the scalar gradient. Default is False. dual_contouring (bool, optional): Whether to use dual contouring. Default is True. mesh_extraction_masking_options (MeshExtractionMaskingOptions, optional): Options for dual contouring masking. evalution_options.mesh_extraction_fancy (bool, optional): Fancy version of dual contouring. Default is True. debug (bool, optional): Debug mode status. Default is derived from config. debug_water_tight (bool, optional): Debug mode for water-tight conditions. Default is False. tensor_dtype (str, optional): Data type for tensors. Default is derived from config.

Returns:

None

Raises:

Warning – If a provided keyword is not a recognized attribute.