import enum
import warnings
from pydantic import BaseModel, ConfigDict, Field, model_validator, PrivateAttr
import gempy_engine.config
from .evaluation_options import MeshExtractionMaskingOptions, EvaluationOptions
from .temp_interpolation_values import TempInterpolationValues
from ..kernel_classes.kernel_functions import AvailableKernelFunctions
from .kernel_options import KernelOptions
from ..raw_arrays_solution import RawArraysSolution
[docs]
class InterpolationOptions(BaseModel):
[docs]
class CacheMode(enum.Enum):
""" Cache mode for the interpolation"""
NO_CACHE: int = enum.auto() #: No cache at all even during the interpolation computation. This is quite expensive for no good reason.
CACHE = enum.auto()
IN_MEMORY_CACHE = enum.auto()
CLEAR_CACHE = enum.auto()
model_config = ConfigDict(
arbitrary_types_allowed=False,
use_enum_values=False,
json_encoders={
CacheMode: lambda e: e.value,
AvailableKernelFunctions: lambda e: e.name
}
)
# @off
kernel_options: KernelOptions = Field(init=True, exclude=False) # * This is the compression of the fields above and the way to go in the future
evaluation_options: EvaluationOptions = Field(init=True, exclude= False)
debug: bool
cache_mode: CacheMode
cache_model_name: str # : Model name for the cache
block_solutions_type: RawArraysSolution.BlockSolutionType
sigmoid_slope: int
debug_water_tight: bool = False
# region Volatile
temp_interpolation_values: TempInterpolationValues = Field(
default_factory=TempInterpolationValues,
exclude=True,
repr=False
)
# endregion
@classmethod
def from_args(
cls,
range: int | float,
c_o: float,
uni_degree: int = 1,
i_res: float = 4.,
gi_res: float = 2., # ! This should be DEP
number_dimensions: int = 3, # ? This probably too
number_octree_levels: int = 1,
kernel_function: AvailableKernelFunctions = AvailableKernelFunctions.cubic,
mesh_extraction: bool = True,
compute_scalar_gradient: bool = False,
compute_condition_number: bool = False,
):
kernel_options = KernelOptions(
range=range,
c_o=c_o,
uni_degree=uni_degree,
i_res=i_res,
gi_res=gi_res,
number_dimensions=number_dimensions,
kernel_function=kernel_function,
compute_condition_number=compute_condition_number
)
evaluation_options = EvaluationOptions(
_number_octree_levels=number_octree_levels,
_number_octree_levels_surface=4,
mesh_extraction=mesh_extraction,
mesh_extraction_masking_options=MeshExtractionMaskingOptions.INTERSECT,
mesh_extraction_fancy=True,
compute_scalar_gradient=compute_scalar_gradient
)
temp_interpolation_values = TempInterpolationValues()
debug = gempy_engine.config.DEBUG_MODE
cache_mode = InterpolationOptions.CacheMode.IN_MEMORY_CACHE
cache_model_name = ""
block_solutions_type = RawArraysSolution.BlockSolutionType.OCTREE
sigmoid_slope = 5_000_000
return InterpolationOptions(
kernel_options=kernel_options,
evaluation_options=evaluation_options,
# temp_interpolation_values=temp_interpolation_values,
debug=debug,
cache_mode=cache_mode,
cache_model_name=cache_model_name,
block_solutions_type=block_solutions_type,
sigmoid_slope=sigmoid_slope,
debug_water_tight=False,
)
# @on
@classmethod
def init_octree_options(cls, range=1.7, c_o=10., refinement: int = 1):
return InterpolationOptions.from_args(
range=range,
c_o=c_o,
mesh_extraction=True,
number_octree_levels=refinement,
)
@classmethod
def init_dense_grid_options(cls):
options = InterpolationOptions.from_args(
range=1.7,
c_o=10.,
mesh_extraction=False,
number_octree_levels=1
)
options.block_solutions_type = RawArraysSolution.BlockSolutionType.DENSE_GRID
return options
@classmethod
def probabilistic_options(cls):
# TODO: This should have the sigmoid slope different
raise NotImplementedError("Probabilistic interpolation is not yet implemented.")
# def __repr__(self):
# return f"InterpolationOptions.from_args({', '.join(f'{k}={v}' for k, v in asdict(self).items())})"
# def _repr_html_(self):
# html = f"""
# <table>
# <tr><td colspan='2' style='text-align:center'><b>InterpolationOptions</b></td></tr>
# {''.join(f'<tr><td>{k}</td><td>{v._repr_html_() if isinstance(v, KernelOptions) else v}</td></tr>' for k, v in asdict(self).items())}
# </table>
# """
# return html
[docs]
def update_options(self, **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.
"""
for key, value in kwargs.items():
if hasattr(self, key): # checks if the attribute exists
setattr(self, key, value) # sets the attribute to the provided value
else:
warnings.warn(f"{key} is not a recognized attribute and will be ignored.")
@property
def number_octree_levels(self):
return self.evaluation_options.number_octree_levels
@number_octree_levels.setter
def number_octree_levels(self, value):
warnings.warn("The number_octree_levels attribute is deprecated and will be removed in the future. Use"
"evaluation_options.number_octree_levels instead.", DeprecationWarning)
self.evaluation_options.number_octree_levels = value
@property
def mesh_extraction(self):
return self.evaluation_options.mesh_extraction
@mesh_extraction.setter
def mesh_extraction(self, value):
warnings.warn(
"The mesh_extraction attribute is deprecated and will be removed in the future. Use"
"evaluation_options.mesh_extraction instead.", DeprecationWarning)
self.evaluation_options.mesh_extraction = value
@property
def compute_corners(self):
is_not_last_octree = (self.is_last_octree_level is False)
is_dual_contouring = self.mesh_extraction
is_octree_for_surfaces = self.temp_interpolation_values.current_octree_level + 1 == self.number_octree_levels_surface
is_dual_contouring_and_octree_is_for_surfaces = is_dual_contouring and is_octree_for_surfaces
compute_corners = is_dual_contouring_and_octree_is_for_surfaces or is_not_last_octree
return compute_corners
@property
def compute_scalar_gradient(self):
return self.evaluation_options.compute_scalar_gradient
@compute_scalar_gradient.setter
def compute_scalar_gradient(self, value):
warnings.warn("The compute_scalar_gradient attribute is deprecated and will be removed in the future. Use"
"evaluation_options.compute_scalar_gradient instead.", DeprecationWarning)
self.evaluation_options.compute_scalar_gradient = value
@property
def is_last_octree_level(self) -> bool:
return self.temp_interpolation_values.current_octree_level == self.number_octree_levels - 1
@property
def range(self):
return self.kernel_options.range
@property
def c_o(self):
return self.kernel_options.c_o
@property
def uni_degree(self):
return self.kernel_options.uni_degree
@uni_degree.setter
def uni_degree(self, value):
warnings.warn("The uni_degree attribute is deprecated and will be removed in the future. ", DeprecationWarning)
self.kernel_options.uni_degree = value
@property
def i_res(self):
return self.kernel_options.i_res
@i_res.setter
def i_res(self, value):
warnings.warn("The i_res attribute is deprecated and will be removed in the future. ", DeprecationWarning)
self.kernel_options.i_res = value
@property
def gi_res(self):
return self.kernel_options.gi_res
@gi_res.setter
def gi_res(self, value):
warnings.warn("The gi_res attribute is deprecated and will be removed in the future. ", DeprecationWarning)
self.kernel_options.gi_res = value
@property
def number_dimensions(self):
return self.kernel_options.number_dimensions
@property
def kernel_function(self):
return self.kernel_options.kernel_function
@kernel_function.setter
def kernel_function(self, value):
warnings.warn("The kernel_function attribute is deprecated and will be removed in the future. ", DeprecationWarning)
self.kernel_options.kernel_function = value
@property
def n_uni_eq(self):
return self.kernel_options.n_uni_eq
@property
def number_octree_levels_surface(self):
return self.evaluation_options.number_octree_levels_surface
@number_octree_levels_surface.setter
def number_octree_levels_surface(self, value):
warnings.warn("The number_octree_levels_surface attribute is deprecated and will be removed in the future. Use"
"evaluation_options.number_octree_levels_surface instead."
, DeprecationWarning)
self.evaluation_options.number_octree_levels_surface = value
@property
def evaluation_chunk_size(self):
return self.evaluation_options.evaluation_chunk_size
