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>)[source]

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)[source]

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)[source]

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.