ParamRF: Parametric Microwave Circuit Modelling, Fitting and Sampling

ParamRF, or pmrf, is a declarative circuit modelling framework catering for the frequency-domain fitting and simulation of circuit models in an efficient, parametric, object-orientated manner. This documentation serves as an introduction into the framework and its features with some basic examples, and also provides an overall API reference.

Version

0.5.0

Author

Gary Allen

Homepage

https://github.com/paramrf/paramrf

Docs

https://paramrf.github.io/paramrf

Key Features

  • Declarative and Composable Modelling: Allows for the definition of models using either a self-documenting, declarative syntax or via compositional techniques such as cascading. Since models can consist of a mix of Parameter objects as well as other Model’s, this allows for a natural means of building complex, hierarchial models from both equations and other sub-models.

  • Unified Fitting Engine: Provides a number of commonly available fitting algorithms with a unified interface, catering for both classical frequentist optimization and statistical Bayesian inference.

  • JAX Backend: Leverages JAX for Just-In-Time (JIT) compilation of models to high-performance hardware (CPU, GPU, TPU). This removes python overhead due to interpreter context switching; enables better vectorization and parallelization; and provides automatic differentiation through the entire model structure, enabling new analysis and more efficient gradient-based optimization.

  • Extensibility: Designed to be extendable, such that additional models, fitting algorithms, cost functions, sampling routines etc. can easily be implemented.

  • scikit-rf Integration: Designed for seamless interoperability with scikit-rf, pmrf models can be evaluated and converted to skrf.Network objects, providing access to scikit-rf’s library of analysis and plotting tools.

Documentation