Swectral Documentation

Swectral streamlines the batch testing and optimization of plant hyperspectral analysis workflows. It provides a structured and extensible framework to apply and assess various image processing techniques (calibration, baseline correction, denoising, feature engineering, etc.) in combination with various machine learning models. The framework employs a comprehensive full-factorial design to evaluate all method combinations on user spectral dataset and generates standard reports on performance metrics, comparative statistical tests, residual analysis, influence anlaysis and visualizations.

Core features

• Batch processing: Automate numerous data processing and modeling workflows in a single batch operation.

• File-based: A resumable, file-based processing pipeline with full-scale auditability and break tolerance.

• High-performance: Optimized for hyperspectral images with minimal memory consumption and options of GPU acceleration and pipeline-level multiprocessing.

• Simple extensible integration: Intuitive data management and straightforward integration for custom processing functions and Scikit-learn-style models.

Contents

• Installation
• Usage
  • 1. Data preparation
  • 2. Data configuration
  • 3. Design testing pipelines
  • 4 Execute pipelines
  • 5 Generated reports
  • 6 Regression demonstration
  • 7 Feature engineering fittable tests
• Tutorials / Demos
• API Reference
  • Core pipeline API
  • Spectral processing functions
  • Statistical measures
  • Denoisers
  • Spectral indices
  • Model combiners
  • Data augmentation
  • Raster operations
  • I/O utilities
  • Demo data tools
• Contributing