Spectroscopy & Analysis Projects

A comprehensive repository for spectroscopic analysis and data processing, featuring Nuclear Magnetic Resonance (NMR), Stellar Spectroscopy, and related computational tools.

📂 Project Structure

🧲 Nuclear Magnetic Resonance (NMR)

Advanced analysis pipeline for NMR spectroscopy data, including:

• 1D NMR Analysis: Hydrogen (¹H) and carbon (¹³C) NMR processing
• 2D NMR Spectroscopy: Complex multi-dimensional NMR techniques
• Peak Assignment: Functional group identification and J-coupling analysis
• Quantum Mechanical Simulation: Spin system modeling and wavefunction evolution
• Data Processing: FFT, peak detection, integration, and visualization

📍 See NMR README

⭐ Stellar Spectroscopy

Zodiac constellation spectral analysis framework with:

• SDSS/SIMBAD Data Integration: Automatic spectrum retrieval from astronomical archives
• Spectral Analysis Pipeline: State estimation, energy models, and tensor analysis
• Zodiac Target Catalog: Complete 12-constellation stellar database
• Results Persistence: SQLite database and CSV output for analysis tracking
• Interactive Notebooks: Phase-by-phase spectral analysis workflows

📍 See Stellar Spectroscopy README

🤖 Machine Learning

Deep learning models for spectroscopy:

• Deep Learning Models: Neural network architectures for spectrum analysis
• Denoising Networks: Physics-informed denoising for spectroscopic data
• Model checkpoints and training utilities

📍 See Machine Learning README

☀️ Solar Project

Solar irradiance and spectral analysis:

• Solar irradiance data sampling and processing for future api integration
• Spectroscopic visualization tools

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🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/Quintinlf/Spectroscopy.git
cd Spectroscopy

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Running Analysis

• NMR Analysis: See NMR Project README
• Stellar Spectroscopy: See Stellar Spectroscopy README
• Machine Learning: See Machine Learning README

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📊 Key Features Across Projects

Feature	NMR	Stellar	ML
Data Import/Processing	✅	✅	✅
Fourier Analysis	✅	✅	✅
Peak Detection	✅	✅	✅
Visualization	✅	✅	✅
Database Storage	✅	✅	-
Quantum Simulation	✅	-	-
Archive Integration	-	✅	-
Deep Learning	-	-	✅

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🛠️ Technology Stack

• Python 3.7+
• Data Processing: NumPy, Pandas, SciPy
• Visualization: Matplotlib, Seaborn
• Machine Learning: PyTorch (for deep learning models)
• Scientific Computing: Quantum mechanics simulation, FFT analysis
• Database: SQLite (for stellar spectroscopy results)
• Notebooks: Jupyter

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📝 Repository Contents

NMR-Project/
├── nuclear_magnetic_resonance_spectrospy/    # NMR analysis pipeline
│   ├── nmr_function.py
│   ├── peak_assignment.py
│   ├── fall_semester_2025/                   # Advanced NMR techniques
│   ├── spring_semester_2025/                 # Basic NMR analysis
│   ├── quantum_mechanics/                    # QM simulations
│   └── README.md
│
├── stellar_spectrospy/                       # Stellar spectroscopy
│   ├── analysis_runner.py
│   ├── zodiac_targets.py
│   ├── spectral_database.py
│   ├── unified_signal_engine.py
│   ├── phase1_spectral_analysis.ipynb
│   └── README.md
│
├── machine_learning/                         # Deep learning models
│   ├── neural_net.py
│   ├── deep_learning_model.ipynb
│   └── checkpoints/
│
├── solar_project/                            # Solar analysis
│   ├── solar_spec.ipynb
│   └── data/
│
└── README.md                                 # This file

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📚 Additional Resources

• NMR Theory: See nuclear_magnetic_resonance_spectrospy/ for technical details
• Stellar Data: Check stellar_spectrospy/ for constellation targets and analysis methods
• ML Models: Review machine_learning/ for model architecture details

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🤝 Contributing

Contributions are welcome! Please ensure that:

• Code follows the existing style conventions
• New features include relevant notebook demonstrations
• Analysis results are documented

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📄 License

See LICENSE file for details.

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✉️ Contact

For questions or collaboration inquiries, please open an issue or reach out through the repository.