A comprehensive GNU Octave development environment with Jupyter Lab integration, featuring advanced numerical computing implementations, parallel processing capabilities, and real-world data science applications.
OctaveMasterPro is a complete numerical computing and data analysis framework built around GNU Octave. It provides a production-ready development environment with comprehensive implementations of advanced algorithms, parallel computing demonstrations, and practical applications across multiple domains including signal processing, image analysis, financial modeling, and industrial data analytics.
- Dockerized Octave + Jupyter Lab - Consistent development environment across platforms
- Pre-configured packages - Statistics, Signal Processing, Image Processing, Optimization, Parallel Computing
- Interactive notebooks - Live code execution with rich visualizations
- Command-line interface - Direct Octave CLI access for scripting
- Advanced Linear Algebra - Eigenvalue decompositions, matrix factorizations, numerical stability analysis
- Optimization Algorithms - Newton-Raphson, BFGS, genetic algorithms, simulated annealing, constrained optimization
- Statistical Analysis - Hypothesis testing, regression analysis, time series modeling, Monte Carlo methods
- Signal Processing - Filter design, spectral analysis, real-time processing simulation
- Parallel Computing - Multi-core processing, distributed task execution, performance benchmarking
- Financial Analysis - Portfolio optimization, risk modeling, technical indicators
- Image Processing - Filtering, morphological operations, batch processing pipelines
- Industrial Analytics - Sensor data analysis, predictive maintenance, failure detection
- Scientific Computing - Numerical methods, data visualization, algorithm benchmarking
- Docker and Docker Compose
- Git
- 8GB RAM recommended for parallel computing demos
# Clone repository
git clone https://github.com/SatvikPraveen/OctaveMasterPro.git
cd OctaveMasterPro
# Initialize environment
chmod +x init_project.sh
./init_project.sh
# Launch Jupyter Lab
docker-compose up
# Access at http://localhost:8888Octave CLI Only:
docker-compose --profile cli run octave-cliLocal Installation:
sudo apt install octave jupyter-notebook python3-pip
pip3 install octave_kernel
python3 -m octave_kernel install
jupyter labOctaveMasterPro/
├── notebooks/ # Core computational notebooks
│ ├── 00_environment_check.ipynb
│ ├── 01_basics_intro.ipynb
│ ├── 02_vectors_matrices.ipynb
│ ├── 03_indexing_logic.ipynb
│ ├── 04_scripts_functions.ipynb
│ ├── 05_data_handling_files.ipynb
│ ├── 06_plotting_2d_3d.ipynb
│ ├── 07_linear_algebra.ipynb
│ ├── 08_statistics_analysis.ipynb
│ ├── 09_optimization_roots.ipynb
│ ├── 10_advanced_programming.ipynb
│ ├── 11_expert_topics.ipynb
│ └── 12_parallel_computing.ipynb
├── mini_projects/ # Focused application demonstrations
│ ├── signal_processing_simulation/
│ ├── image_processing_basics/
│ ├── stock_market_analysis/
│ └── parallel_image_batch_processing/
├── flagship_project/ # Industrial data analytics pipeline
│ ├── datasets/
│ ├── project_scripts/
│ └── report/
├── cheatsheets/ # Quick reference materials
├── utils/ # Shared utility functions
├── datasets/ # Sample data for demonstrations
└── docs/ # Technical documentation
- Linear Algebra - Matrix decompositions (LU, QR, SVD, Eigenvalue), numerical stability, condition numbers
- Optimization - Root-finding algorithms, unconstrained/constrained optimization, global optimization methods
- Statistics - Descriptive statistics, hypothesis testing, regression analysis, time series modeling
- Data Handling - Large dataset processing, file I/O optimization, memory management
- Visualization - 2D/3D plotting, publication-quality figures, interactive visualizations
- Advanced Programming - Object-oriented programming, GUI development, event-driven systems
- Parallel Computing - Multi-core processing, performance optimization, distributed computing
Real-time signal analysis framework with:
- Digital filter design and implementation
- Spectral analysis and FFT processing
- Noise reduction and signal enhancement
- Performance benchmarking
Complete image analysis toolkit featuring:
- Basic filtering operations (Gaussian, median, edge detection)
- Morphological operations (erosion, dilation, opening, closing)
- Histogram analysis and enhancement
- Batch processing capabilities
Quantitative finance applications including:
- Technical indicator calculations (RSI, MACD, Bollinger Bands)
- Portfolio optimization using modern portfolio theory
- Risk analysis and VaR calculations
- Statistical arbitrage strategies
High-performance computing demonstrations:
- Multi-core image processing pipeline
- Performance scaling analysis
- Memory optimization techniques
- Distributed task management
A comprehensive predictive maintenance system featuring:
Data Pipeline:
- Multi-sensor data ingestion and preprocessing
- Real-time anomaly detection algorithms
- Machine learning model training and validation
- Automated reporting and visualization
Key Components:
- Sensor Network Analysis - Time series processing of equipment telemetry
- Failure Prediction - Statistical models for equipment health assessment
- Maintenance Optimization - Cost-benefit analysis for maintenance scheduling
- Performance Dashboard - Interactive visualization of system metrics
- Linear Algebra - BLAS/LAPACK optimized operations
- Optimization - Multiple solvers with convergence analysis
- Statistics - Comprehensive statistical testing suite
- Parallel Processing - Multi-core task distribution
- File Formats - CSV, MAT, JSON, binary data support
- Large Datasets - Memory-efficient processing strategies
- Real-time Processing - Streaming data analysis capabilities
- 2D/3D Plotting - Publication-quality figure generation
- Interactive Plots - Dynamic data exploration
- Custom Visualizations - Domain-specific plotting functions
The parallel computing implementations demonstrate significant performance improvements:
- Image Processing Pipeline - Up to 4x speedup on quad-core systems
- Matrix Operations - Optimized BLAS operations for large matrices
- Statistical Analysis - Vectorized implementations for large datasets
- Optimization Algorithms - Convergence analysis and performance comparison
- Modular Design - Reusable utility functions
- Documentation - Comprehensive inline documentation
- Testing - Validation scripts for critical algorithms
- Version Control - Git workflow with pre-commit hooks
- Algorithm Validation - Comparison with reference implementations
- Numerical Stability - Condition number analysis and error propagation
- Performance Monitoring - Execution time and memory usage tracking
- GNU Octave 6.0+
- Docker and Docker Compose
- Jupyter Lab with Octave kernel
- statistics (statistical analysis)
- signal (signal processing)
- image (image processing)
- optimization (numerical optimization)
- parallel (parallel computing)
This is a personal project, but contributions are welcome:
- Fork the repository
- Create a feature branch
- Implement improvements with proper documentation
- Add validation tests
- Submit a pull request
- Follow existing code style and documentation standards
- Add comprehensive comments for complex algorithms
- Include performance benchmarks for new implementations
- Validate numerical accuracy against reference solutions
MIT License - see LICENSE for details.
- Check setup_instructions.md for detailed installation guide
- Review docs/troubleshooting.md for common problems
- Consult cheatsheets/parallel_computing_quickref.md
- Review benchmark results in individual project directories
- Detailed mathematical documentation available in notebook implementations
- Reference academic papers cited in algorithm comments
Advanced numerical computing with GNU Octave - from mathematical foundations to industrial applications.