py3dinterpolations.modelling.modeler.Modeler

class py3dinterpolations.modelling.modeler.Modeler(griddata: GridData, grid3d: Grid3D, model_name: str = 'ordinary_kriging', model_params: dict = {})

Bases: object

modeler class for 3d modelling

This class applies a model defined within the ModelWrapper class to a Grid3D instance

Currently supports:

Statistical:

Ordinary Kriging : ordinary_kriging (pykrige)

Deterministic:

Inverse Distance Weighting : idw

Parameters

griddata (GridData) – GridData istance
grid3d (Grid3D) – Grid3D istance
model_name (str) – model name, default ordinary_kriging
model_params (dict) – model parameters

griddata

GridData istance

Type: GridData

grid3d

Grid3D istance

Type: Grid3D

model

model object

Type: object

results

dictionary with interpolated and variance grids

Type: dict

Examples

>>> # modeler
>>> modeler = Modeler(griddata, grid3d)
>>> # predict
>>> interpolated = modeler.predict()

Methods

`__init__`(griddata, grid3d[, model_name, ...])
`predict`(**kwargs)	makes predictions considering all past preprocessing

Attributes

`model`
`results`

predict(**kwargs) → ndarray

makes predictions considering all past preprocessing

if normalization was applied, predict on normalized grid
if standardized data, reverse standardization
reshape from zxy to xyz (pykrige output)

Parameters: grids_arrays (dict) – dictionary with x, y, z grids 1d np.ndarray
Returns: interpolated grid
Return type: interpolated (np.ndarray)