geotda

Geotda is a computational geometry library for Python. It provides tools for topological data analysis, specifically focusing on persistent homology in the context of geometric data. The library aims to make these advanced analytical techniques accessible to researchers and practitioners working with point clouds, meshes, and other geometric representations of data.

At its core, Geotda allows users to compute persistent homology barcodes and diagrams. These outputs represent the topological features of a dataset, such as connected components, loops, and voids, at different scales. This information can reveal underlying structures and relationships within data that might not be apparent through traditional geometric or statistical methods.

The library is designed to be efficient and scalable, enabling the analysis of large and complex datasets. It integrates with other popular Python scientific libraries, such as NumPy and SciPy, facilitating seamless incorporation into existing data analysis workflows. Geotda supports various distance metrics and filtration methods, offering flexibility in how topological features are extracted.

Applications of Geotda span diverse fields including computer vision, machine learning, computational biology, and materials science. For instance, it can be used to characterize the shape of proteins, analyze the structure of neural networks, or identify patterns in 3D scanned objects. The library contributes to a deeper understanding of data by providing a robust framework for exploring its intrinsic topological properties.