pyterp 0.1.0

A package for performing optimized k-NN IDW interpolation using C++.

PyTerp

A 3D interpolator for Python designed for maximum speed on large datasets. It accelerates the IDW algorithm with a parallelized C++ core (OpenMP) and optimized k-NN searches (nanoflann).

Theoretical Summary

The interpolation is performed in a two-step process that combines the k-NN and IDW algorithms.

1. Neighbor Selection (k-NN): For each point where a value is to be estimated, the k-Nearest Neighbors algorithm first finds the k closest known source points in space. The efficiency of this search is ensured by an optimized data structure (k-d tree).

2. Value Calculation (IDW): Next, the Inverse Distance Weighting method calculates the final value as a weighted average of the k found neighbors. The weight of each neighbor is inversely proportional to its distance (weight = 1/distanceᵖ, where p is a power parameter), causing closer points to have a much greater influence on the result.

Prerequisites

Before you begin, ensure you have the following software installed:

• Python 3.10+
• Git
• A C++ compiler: This package contains C++ code that needs to be compiled during installation.
  • Windows: Install Visual Studio Build Tools (select the "Desktop development with C++" workload).
  • Linux (Debian/Ubuntu): Install build-essential with: sudo apt-get install build-essential.

Installation

PyPI

Install the package:

pip install pyterp

---

GitHub

1. Clone the repository:

git clone https://github.com/jgmotta98/PyTerp.git
cd PyTerp

2. Create and activate a virtual environment:

# Create the environment
python -m venv .venv

# Activate the environment
# On Windows (cmd.exe):
.venv\Scripts\activate
# On macOS/Linux (bash/zsh):
source .venv/bin/activate

3. Install the requirements:

pip install -r requirements.txt

4. Install the package:

pip install .

Usage Example

import numpy as np
import pyterp as pt

# Assuming 'source_points', 'source_values', and 'target_points'
# are properly prepared NumPy arrays.
interpolated_values = pt.interpolate(
    source_points=source_points,
    source_values=source_values,
    target_points=target_points,
    k_neighbors=10,
    power=2
)

print("Interpolated values:", interpolated_values)

For a complete and runnable example, including the creation and preparation of input data, please see the script in the examples folder.

Acknowledgements

This project uses nanoflann, a high-performance C++ library for the k-Nearest Neighbors algorithm. The efficiency of nanoflann's k-d tree implementation is fundamental to this interpolator's performance.

• Official Repository: Nanoflann