impulsox-mna 0.1

An advanced package for the simulation of analog electronic circuits using Modified Nodal Analysis

impulsox-mna

impulsox-mna is an advanced Python package for circuit simulation based on Modified Nodal Analysis (MNA). It is designed as a platform for more demanding use cases and ongoing development of advanced simulation techniques.

The package is installed as impulsox-mna and imported as:

import impulsox as imp

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✨ Features

Currently implemented

• Modified Nodal Analysis (MNA) engine

• Unified framework for:

  • DC operating point analysis
  • AC small-signal analysis
  • Transient simulation

• Solver improvements over impulso-mna:

  • Reuse of previous solution vectors across iterations
  • Option to re-stamp only nonlinear components (avoiding unnecessary re-stamping of linear components)

These features primarily target performance and convergence efficiency in iterative solving.

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Planned / future features

The project is intended as a foundation for more advanced capabilities, which may include:

• Advanced semiconductor device models (e.g. detailed BJT/MOSFET models)
• Enhanced nonlinear convergence strategies
• Sparse matrix and high-performance solver backends
• RF and high-frequency analysis tools
• Noise analysis
• More sophisticated simulation workflows

These features are not yet implemented but represent the intended direction of the project.

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📦 Installation

pip install impulsox-mna

For development:

git clone https://github.com/<your-username>/impulsox-mna.git
cd impulsox-mna
pip install -e .

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🚀 Quick Example

import impulsox as imp

ckt = imp.Circuit()

ckt.add(imp.Resistor("R1", n1=1, n2=0, value=1e3))
ckt.add(imp.VoltageSource("V1", n_plus=1, n_minus=0, value=5.0))

result = ckt.solve_transient(t_stop=1e-3, dt=1e-6)

print(result.node_voltages)

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📊 Example Simulations

The repository includes examples demonstrating more advanced simulation workflows and solver behavior.

1. Nonlinear Device Characterization

• Demonstrates improved iteration efficiency using solution reuse

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2. Transistor-Level Circuit Analysis

• Multi-device circuits benefiting from selective re-stamping

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3. Time-Domain Signal Processing Circuits

• Transient simulations with iterative solver improvements

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4. High-Fidelity Transient Response

• Focus on stability and efficiency rather than new physical models

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🧠 Design Philosophy

• Performance-oriented: Focus on improving solver efficiency
• Experimental: A space for developing and testing advanced ideas
• Flexible: Designed for extension and modification
• Self-contained: No dependency on impulso-mna

While it shares conceptual similarities with impulso-mna, this package is implemented independently and is free to evolve in more complex directions.

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🔗 Relationship to impulso-mna

impulso-mna   → minimal, stable, educational core
impulsox-mna  → experimental, advanced, independently evolving implementation

• No dependency between the packages
• Similar conceptual structure and API philosophy
• Different design goals and complexity levels

Users can choose the package that best fits their needs:

• Use impulso-mna for simplicity and clarity
• Use impulsox-mna for experimentation and advanced solver behavior

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Good idea — a comparison table makes the distinction immediately clear.

Here’s a section you can drop right after “Relationship to impulso-mna”:

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📊 impulso-mna vs impulsox-mna

Aspect	impulso-mna	impulsox-mna
Purpose	Minimal, stable MNA core	Experimental, advanced development platform
Complexity	Low	Moderate → High
Dependencies	Minimal	Minimal (but more open to change)
Architecture	Simple, explicit	Flexible, evolving
Solver behavior	Standard iteration	Improved iteration (solution reuse, selective stamping)
Performance focus	Secondary	Primary focus
Advanced features	Not included	In development / planned
Stability	High (intentionally stable)	Evolving
Use case	Learning, prototyping, small circuits	Research, experimentation, larger/complex circuits
Dependency relation	—	Independent (no dependency on impulso-mna)

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🧩 Component Model

Components follow the same general MNA principles:

• Matrix stamping for linear elements
• Augmented variables for sources and dynamic components
• Linearization of nonlinear devices

Current focus is on solver-level improvements, with future work potentially expanding into more advanced component models.

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🔧 Development Workflow

pip install -e .[dev]

Typical structure:

.
├── impulsox/
├── examples/
├── tests/
└── pyproject.toml

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📈 Performance Notes

• Improved efficiency through reduced re-stamping
• Faster convergence via solution reuse
• Designed as a testbed for further performance enhancements

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🤝 Contributing

Contributions are highly encouraged.

This project is explicitly intended as a collaborative space for advanced features, and many planned capabilities are not yet implemented.

Areas where contributions would be especially valuable:

• Advanced device modeling
• Numerical methods and convergence strategies
• Sparse matrix and solver optimizations
• RF / frequency-domain extensions
• Noise analysis
• Benchmarking and validation

If you are interested in circuit simulation, numerical methods, or MNA-based solvers, your contributions can have a direct impact on the direction of this project.

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📄 License

MIT License (or your chosen license)

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🏁 Roadmap

impulsox-mna is expected to evolve actively.

Near-term focus:

• Further solver performance improvements
• Refinement of current iteration strategies

Long-term direction:

• Expansion into advanced modeling and simulation techniques
• Development of high-performance solver infrastructure

📦 Requirements

▶️ Core (runtime)

The core of impulsox-mna is intentionally lightweight and depends only on a small set of packages:

• numpy — numerical computations and matrix operations

These are the only dependencies required to use the simulator itself.

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📊 Optional (examples & visualization)

The following packages are used for running examples and visualizing results:

• matplotlib — plotting simulation results
• quantiphy — formatted physical quantities

matplotlib brings in several supporting dependencies (e.g. cycler, kiwisolver, pillow, etc.).

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🧪 Development & testing

For development, testing, and contributing:

• pytest — test framework
• pluggy, iniconfig, Pygments — pytest dependencies

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🛠 Build system

• setuptools
• wheel

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🔗 Development note on impulso-mna

During development, you may encounter setups that include:

-e git+https://github.com/kvdijken/impulso-mna.git@<commit>#egg=impulso

This is only for development and comparison purposes.

• impulsox-mna does not depend on impulso-mna
• Both packages are fully independent implementations

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⚙️ Installation

Minimal installation

pip install impulsox-mna

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Development environment

To reproduce the full development setup:

pip install -r requirements.txt

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📝 Notes

• Runtime dependencies are intentionally minimal
• Additional packages are only required for examples, testing, and development
• Future versions may formalize this split via optional dependency groups (e.g. extras_require)