paramrf

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.

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

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

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+):

$ 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 for more examples.

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 page.
For step-by-step guides that you can follow, check out the tutorials.
To delve a bit deeper into the library’s core building blocks and philosophy, head off to core concepts.

Optional dependencies

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

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

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

For BlackJAX’s Bayesian solvers:

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

For the PolyChord solver:

$ 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:

@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},
}

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

paramrf-0.32.0.tar.gz (5.5 MB view details)

Uploaded Aug 17, 2026 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

paramrf-0.32.0-py3-none-any.whl (191.1 kB view details)

Uploaded Aug 17, 2026 Python 3

File details

Details for the file paramrf-0.32.0.tar.gz.

File metadata

Download URL: paramrf-0.32.0.tar.gz
Upload date: Aug 17, 2026
Size: 5.5 MB
Tags: Source
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for paramrf-0.32.0.tar.gz
Algorithm	Hash digest
SHA256	`9697bc9833fef04292684fb7b2218c6090620f09b1b0e2b9b102773d05ba0844`
MD5	`38d6aaea9d5c37149b6981aeb10bbeef`
BLAKE2b-256	`966a1e38c190776f110fa564dde8fb2835cf521ac45309f46e41afb3048521a2`

See more details on using hashes here.

Provenance

The following attestation bundles were made for paramrf-0.32.0.tar.gz:

Publisher: publish.yml on gvcallen/paramrf

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Statement:
- Statement type: https://in-toto.io/Statement/v1
- Predicate type: https://docs.pypi.org/attestations/publish/v1
- Subject name: paramrf-0.32.0.tar.gz
- Subject digest: 9697bc9833fef04292684fb7b2218c6090620f09b1b0e2b9b102773d05ba0844
- Sigstore transparency entry: 2497673863
- Sigstore integration time: Aug 17, 2026
Source repository:
- Permalink: gvcallen/paramrf@eedde84d2b868fad87946cd6d123b435fc4358ba
- Branch / Tag: refs/tags/v0.32.0
- Owner: https://github.com/gvcallen
- Access: public
Publication detail:
- Token Issuer: https://token.actions.githubusercontent.com
- Runner Environment: github-hosted
- Publication workflow: publish.yml@eedde84d2b868fad87946cd6d123b435fc4358ba
- Trigger Event: push