Welcome to perch’s documentation!

perch is a Python package for applying the topological technique of persistent homology to astronomical images & data cubes.

Persistent homology identifies and characterizes multi-scale structures in your data by tracking topological features (peaks, rings, voids) across all threshold values.

Check out the Usage section for Installation instructions, skim the Conceptual Introduction overview for the theory, then move on to the Quickstart Guide guide to start analyzing your data.

Note

This project is under active development.

Contents

• Usage
  • Installation
• Conceptual Introduction
  • What Can Persistent Homology Do?
  • Homology Dimensions
  • How Perch Works: Superlevel Set Filtration
  • Birth, Death, and Persistence
  • Persistence Diagrams
  • Hierarchical Structure
• Quickstart Guide
  • Basic Workflow Overview
  • Your First Analysis: 2D Example
  • Understanding Your Results
  • Choosing Parameters
  • Saving and Loading Results
  • Next Steps
• Advanced Usage
  • Working with 3D Data
  • Working with FITS Files
  • Advanced Filtering Techniques
  • Hierarchical Analysis
  • Tips for Large Datasets
  • Further Resources
• API
  • perch
  • perch.ph
  • perch.structures
  • perch.structure
  • perch.utils
  • perch.pplot
• Acknowledgements

Getting Started

New to perch? Start here:

1. Usage - Install perch and its dependencies

2. Conceptual Introduction - Understand the theory behind persistent homology

3. Quickstart Guide - Your first 2D analysis

4. Advanced Usage - 3D data, FITS files, and advanced techniques

5. API - Detailed API reference

Indices and tables

• Index

• Module Index

• Search Page