Welcome to py3dinterpolations’s documentation!

This is a python package to compute quick 3D interpolations of spatial data.

Supports deterministic and interpolation methods:

• Ordinary 3D Kriging : pykrige

• Inverse distance weighting (IDW)

Features parameters estimation:

• GridSearchCV for Kriging : execute a exahustive search over specified parameter values for an estimator. See scikit-learn

Supports preprocessing of data:

• Downsampling - reduce the number of points by using statistical methods by blocks

• Normalization of X,Y,Z coordinates reducing effect of magnitude of coordinates.

• Standardization of signal - standard distribution of signal, reducing effect of magnitude of signal.

Visualizations in 2D and 3D:

• 2D with matplotlib

• 3D with plotly

Examples

• Quick start
  • GridData: core object optimized for grid data.
  • Interpolate
  • Visualization with Plotly and Matplotlib
• Preprocessing data
  • Downsampling
  • Normalization of XYZ and standardization of V
  • Preprocessor class
• Parameter estimation

Models

• Kriging
• Inverse Distance Weighting (IDW)
  • Mathematical formulation
  • Examples

Code reference

py3dinterpolations

quick 3D interpolation with python

Indices and tables

• Index

• Module Index

• Search Page