base

py3dinterpolations.modelling.models.base

Abstract base class for all interpolation models.

BaseModel

Bases: ABC

Interface for interpolation models.

All models must implement fit() and predict() with consistent signatures.

name abstractmethod property

Human-readable model name.

fit(x, y, z, v) abstractmethod

Fit the model to training data.

Parameters:

Name	Type	Description	Default
x	ndarray	X coordinates of training points.	required
y	ndarray	Y coordinates of training points.	required
z	ndarray	Z coordinates of training points.	required
v	ndarray	Values at training points.	required

Source code in py3dinterpolations/modelling/models/base.py

@abstractmethod
def fit(self, x: np.ndarray, y: np.ndarray, z: np.ndarray, v: np.ndarray) -> None:
    """Fit the model to training data.

    Args:
        x: X coordinates of training points.
        y: Y coordinates of training points.
        z: Z coordinates of training points.
        v: Values at training points.
    """
    ...

predict(grid_x, grid_y, grid_z, **kwargs) abstractmethod

Predict on 1D grid arrays.

Parameters:

Name	Type	Description	Default
grid_x	ndarray	1D array of X grid coordinates.	required
grid_y	ndarray	1D array of Y grid coordinates.	required
grid_z	ndarray	1D array of Z grid coordinates.	required

Returns:

Type	Description
InterpolationResult	Interpolation result with at least the interpolated field.

Source code in py3dinterpolations/modelling/models/base.py

@abstractmethod
def predict(
    self,
    grid_x: np.ndarray,
    grid_y: np.ndarray,
    grid_z: np.ndarray,
    **kwargs: object,
) -> InterpolationResult:
    """Predict on 1D grid arrays.

    Args:
        grid_x: 1D array of X grid coordinates.
        grid_y: 1D array of Y grid coordinates.
        grid_z: 1D array of Z grid coordinates.

    Returns:
        Interpolation result with at least the interpolated field.
    """
    ...