BING - Bayesian INference with Gordon coefficients

Welcome to the BING documentation. BING is a comprehensive Python package for ocean color remote sensing analysis, specializing in bio-optical modeling, spectral fitting, and satellite data processing with a focus on NASA’s PACE (Plankton, Aerosol, Cloud, ocean Ecosystem) mission data.

Features

• Ocean Color Analysis: Process and analyze remote sensing reflectance (Rrs) data

• Model Fitting: Bayesian and least-squares fitting of bio-optical models

• PACE Integration: Native support for PACE OCI data processing

• Uncertainty Quantification: Comprehensive error propagation and uncertainty analysis

Contents:

• Getting Started
  • Overview
  • Core Concepts
  • Basic Workflow
  • Example: Processing PACE Data
  • Working with Matched Data
  • Next Steps
  • Tips and Best Practices
  • Getting Help
• Installation
  • Requirements
  • Installation Methods
  • Dependencies Setup
  • Verification
  • Optional Dependencies
  • Troubleshooting
  • Next Steps
• Tutorials
  • Getting Started
  • Tutorial Format
  • Example Data
  • Jupyter Notebooks
  • Quick Examples
  • Common Issues
  • Support
  • Video Tutorials
  • Contributing Tutorials
• API Reference
  • Core Modules
  • Quick Reference
  • Module Structure
• Bio-optical Models
  • Overview
  • Model Components
  • Implemented Models
  • Model Initialization
  • Model Configuration
  • Model Evaluation
  • Performance Considerations
  • Model Validation
  • Advanced Topics
  • References
• Fitting Algorithms
  • Overview
  • Least-Squares Fitting
  • MCMC Fitting
  • L23 Fitting
  • Custom Fitting Functions
  • Fitting Strategies
  • Error Analysis
  • Convergence Diagnostics
  • Best Practices
  • Performance Tips
• Parameters
  • Overview
  • Standard Parameter Sets
  • Custom Parameter Sets
  • Satellite-Specific Settings
  • Prior Distributions
  • Noise Models
  • Parameter Validation
  • Best Practices
  • Examples
  • References
• Data Processing
  • PACE Data Processing
  • Argo BGC Integration
  • Data Organization
  • Batch Processing
  • Data Formats
  • Best Practices
• Raman Scattering
  • Overview
  • Quick Start
  • Module Contents
  • Physical Background
  • Example: Radiative Transfer Source Term
  • Example: Effect on Remote Sensing Reflectance
  • Limitations and Notes
  • References
• Chlorophyll Fluorescence
  • Overview
  • Quick Start
  • Module Architecture
  • Module Contents
  • Physical Background
  • Example: Full Fluorescence Analysis
  • Comparison with Raman Scattering
  • Limitations and Notes
  • References

Quick Start

import numpy as np
from bing import evaluate
from bing.parameters import standard
from bing.models import utils as model_utils
from bing.fitting import chisq_fit, inference

# Load standard parameters
params = standard.expb_pow(satellite='PACE')

# Initialize models
models = model_utils.init(params.model_names, wavelengths)

# Perform fitting
result = chisq_fit.fit(Rrs_data, Rrs_uncertainty, models)

Indices and tables

• Index

• Module Index

• Search Page

Citation

If you use BING in your research, please cite:

@software{bing2024,
  title={BING: Biogeochemical Index Network Generator},
  author={Your Name},
  year={2024},
  url={https://github.com/yourusername/bing}
}