https://github.com/jgmotta98/pyterp
A high-performance Python library for parallel k-NN search and IDW on scattered data, powered by a C++ core.
https://github.com/jgmotta98/pyterp
cpp idw interpolation knn nanoflann
Last synced: 4 months ago
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A high-performance Python library for parallel k-NN search and IDW on scattered data, powered by a C++ core.
- Host: GitHub
- URL: https://github.com/jgmotta98/pyterp
- Owner: jgmotta98
- Created: 2025-06-09T03:05:20.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-06-09T03:52:51.000Z (4 months ago)
- Last Synced: 2025-06-09T04:36:39.045Z (4 months ago)
- Topics: cpp, idw, interpolation, knn, nanoflann
- Language: C++
- Homepage:
- Size: 23.4 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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:
```bash
pip install pyterp
```
---
### GitHub
#### 1. Clone the repository:
```bash
git clone https://github.com/jgmotta98/PyTerp.git
cd PyTerp
```
#### 2. Create and activate a virtual environment:
```bash
# 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:
```bash
pip install -r requirements.txt
```
#### 4. Install the package:
```bash
pip install .
```
## Usage Example
For a complete and runnable example, including the creation and preparation of input data, please see the scripts in the [examples](examples/basic_usage.py) 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](https://github.com/jlblancoc/nanoflann)