README.rst

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ParamRF, or pmrf, is an open-source radio frequency (RF) design and modeling framework. It provides a declarative syntax for creating microwave circuits and surrogate models using JAX <https://github.com/jax-ml/jax>_.

The library provides tools for frequency-domain simulation, optimization, fitting, statistical analysis, and Bayesian inference.

:Version: |version_badge_text| :Author: Gary Allen :GitHub: https://github.com/gvcallen/paramrf :Docs: https://gvcallen.github.io/paramrf :Paper: https://doi.org/10.48550/arXiv.2510.15881

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Features

• Declarative: Models can be composed and defined using an intuitive, object-oriented syntax which feels similar to CAD circuit software.
• Hierarchical: By nesting models within models, it is easy to create deep, hierarchical structures.
• Differentiable: Since the library is built on jax (as opposed to numpy), derivatives are available via auto-differentiation, enabling faster optimization and new design opportunities.
• Parameter-driven: Parameters are first-class citizens, allowing the easy definition and propagation of constraints/distributions through models.
• Hardware flexible: Functions are compiled just-in-time (JIT), reducing overhead and allowing computation on high-performance hardware (CPU, GPU, TPU).
• Extensible: Power users can easily add additional models and algorithms by extending the library's built-in classes and interfaces.

Installation

ParamRF can be installed directly using pip (requires Python 3.11+):

.. code-block:: bash

$ pip install paramrf

Example

The code below demonstrate how to define and optimize an RLC model to satisfy a given goal function. See the documentation <https://gvcallen.github.io/paramrf>_ for more examples.

.. code-block:: python

import pmrf as prf from pmrf.models import Resistor, Inductor, Capacitor

R = prf.Unconstrained(50.0, name='R') L = prf.Bounded(0.0, 100.0, scale=1e-9, name='L') C = prf.Bounded(0.0, 100.0, scale=1e-12, name='C')

model = Resistor(R) ** Inductor(L) ** Capacitor(C) goal = prf.evaluators.Goal('s11_db', '<', -20) passband = prf.Frequency(2, 5, 101, 'GHz')

result = prf.optimize.minimize(goal, model, passband, solver=prf.optimize.NelderMead())

plot_freq = prf.Frequency(1, 6, 101, 'GHz') model.plot_s_db(plot_freq, m=0, n=0, label='initial') result.model.plot_s_db(plot_freq, m=0, n=0, label='optimized')

print(result.model.named_params())

Next steps

• For an overview of the library's features, see the examples <https://gvcallen.github.io/paramrf/examples/index.html>_ page.
• For step-by-step guides that you can follow, check out the tutorials <https://gvcallen.github.io/paramrf/tutorials/index.html>_.
• To delve a bit deeper into the library's core building blocks and philosophy, head off to core concepts <https://gvcallen.github.io/paramrf/core_concepts/index.html>_.

Optional dependencies

Several additional dependencies are required/recommended for more advanced use-cases.

For Bayesian inference, you may need this fork of distreqx:

.. code-block:: bash

$ pip install git+https://github.com/gvcallen/distreqx

For BlackJAX's Bayesian solvers:

.. code-block:: bash

$ pip install git+https://github.com/handley-lab/blackjax.git@v0.1.0-beta

For the PolyChord solver:

.. code-block:: bash

$ pip install git+https://github.com/PolyChord/PolyChordLite.git anesthetic mpi4py

Development Warning

ParamRF is an active research project with an evolving API. Although a large portion of the API has stabilized, until V1.0.0 is reached, minor version bumps (0.X.0) will indicate breaking changes, while patch versions (0.x.Y) will imply new features and/or bug fixes.

Citation

If you have used ParamRF for academic work, please cite the arXiv preprint (https://doi.org/10.48550/arXiv.2510.15881) as: ::

G.V.C. Allen, D.I.L. de Villiers, (2025). ParamRF: A JAX-native Framework for Declarative Circuit Modelling. arXiv, https://doi.org/10.48550/arXiv.2510.15881.

or using the BibTeX:

.. code:: bibtex

@article{paramrf, doi = {10.48550/arXiv.2510.15881}, url = {https://doi.org/10.48550/arXiv.2510.15881}, year = {2025}, month = {Oct}, title = {ParamRF: A JAX-native Framework for Declarative Circuit Modelling}, author = {Gary V. C. Allen and Dirk I. L. de Villiers}, eprint = {2510.15881}, archivePrefix = {arXiv}, primaryClass = {cs.OH}, }