griddata

py3dinterpolations.core.griddata

Core data container for 3D interpolation.

GridData(data, *, ID='ID', X='X', Y='Y', Z='Z', V='V', preprocessing_params=None)

Container for 3D grid data with spatial coordinates and values.

Standardizes input DataFrames into a canonical format with MultiIndex (ID, X, Y, Z) and a single column V.

Parameters:

Name	Type	Description	Default
data	DataFrame	Source DataFrame with spatial data.	required
ID	str	Column name for point identifier.	'ID'
X	str	Column name for X coordinate.	'X'
Y	str	Column name for Y coordinate.	'Y'
Z	str	Column name for Z coordinate.	'Z'
V	str	Column name for value.	'V'
preprocessing_params	PreprocessingParams | None	Parameters from preprocessing applied to this data.	None

Source code in py3dinterpolations/core/griddata.py

def __init__(
    self,
    data: pd.DataFrame,
    *,
    ID: str = "ID",
    X: str = "X",
    Y: str = "Y",
    Z: str = "Z",
    V: str = "V",
    preprocessing_params: PreprocessingParams | None = None,
):
    self.preprocessing_params = preprocessing_params
    self.columns = {"ID": ID, "X": X, "Y": Y, "Z": Z, "V": V}
    self.data = self._set_data(data)

specs property

Compute spatial and value extent statistics.

numpy_data property

Return X, Y, Z, V as a numpy array.

hull property

Convex hull of XY coordinates as a shapely geometry.

GridDataSpecs(xmin, xmax, ymin, ymax, zmin, zmax, vmin, vmax) dataclass

Precomputed spatial and value extent statistics.

Computed once from a DataFrame rather than re-reading on every access.

from_dataframe(data) classmethod

Compute specs from a canonical GridData DataFrame.

Source code in py3dinterpolations/core/griddata.py

@classmethod
def from_dataframe(cls, data: pd.DataFrame) -> "GridDataSpecs":
    """Compute specs from a canonical GridData DataFrame."""
    df = data.reset_index()
    return cls(
        xmin=float(df["X"].min()),
        xmax=float(df["X"].max()),
        ymin=float(df["Y"].min()),
        ymax=float(df["Y"].max()),
        zmin=float(df["Z"].min()),
        zmax=float(df["Z"].max()),
        vmin=float(df["V"].min()),
        vmax=float(df["V"].max()),
    )