broadbean
Package for easily generating and manipulating signal pulses.
Decision gist · record as of 2026-08-14
Yes, if you work with pulse sequences or arbitrary waveform generation—especially in a QCoDeS lab environment or with Tektronix AWGs. The package is actively maintained, has no known vulnerabilities, low install friction, and a permissive license. The Python 3.12+ requirement is a hard constraint; if you're on an earlier version, you cannot use it.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.12 or later; earlier versions are not supported.
- Low install friction with a pure-wheel distribution.
- Active maintenance with recent commits and a stable release cycle.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; you may use, modify, and distribute the package freely provided you include the license notice.
last release 2026-05-11 (95 days) · last repo commit 2026-08-12 · 10 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 75,688 downloads/mo, #14,686 on PyPI
Alternatives
Verify before relying
pip install broadbean
import broadbean
# Compose a pulse sequence: square wave, then ramp, then sine
pulse = broadbean.Pulse(...) # See docs for full API- Whether ripasso (the frequency filtering submodule) is independently useful outside pulse building workflows
- Performance characteristics when composing very large or complex pulse sequences
- Compatibility details with specific Tektronix AWG models beyond the 5000/7000 series
What it is and what it does
Broadbean is a pulse-building library that lets you compose and manipulate signal sequences declaratively—specify a square wave, a ramp, a sine, and a delay as easily as describing them in words, then modify parameters like frequency without rebuilding from scratch. It integrates with QCoDeS for laboratory automation and works with Tektronix 5000/7000 series arbitrary waveform generators, but also functions as a standalone signal-processing tool.
The package wraps numpy and matplotlib for numerical computation and visualization, with schema for validation and versioningit for version management. It targets Python 3.12+ and is actively maintained. A companion module called ripasso handles frequency filtering and compensation, factored out for potential reuse in other contexts.
Use it for
- Design complex pulse sequences for quantum computing or condensed-matter experiments with QCoDeS integration
- Generate and visualize arbitrary waveforms for Tektronix AWG instruments without manual low-level specification
- Compose repeatable pulse patterns and modify them parametrically (e.g., sweep frequency) without rewriting sequences
- Apply frequency filtering and compensation to pulse signals via the ripasso submodule
- Prototype signal-processing workflows in Python before deployment to hardware
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with pulse sequences or arbitrary waveform generation—especially in a QCoDeS lab environment or with Tektronix AWGs.
The package is actively maintained, has no known vulnerabilities, low install friction, and a permissive license. The Python 3.12+ requirement is a hard constraint; if you're on an earlier version, you cannot use it.
Install
broadbean on PyPI
Before you install
Low install friction with a pure-wheel distribution. Active maintenance with recent commits and a stable release cycle. Requires Python 3.12 or later.
Requires Python 3.12 or later; earlier versions are not supported.
License in practice
MIT license permits commercial and private use with minimal restrictions; you may use, modify, and distribute the package freely provided you include the license notice.
Quickstart
pip install broadbean
import broadbean
# Compose a pulse sequence: square wave, then ramp, then sine
pulse = broadbean.Pulse(...) # See docs for full API
Verify before relying
- Whether ripasso (the frequency filtering submodule) is independently useful outside pulse building workflows
- Performance characteristics when composing very large or complex pulse sequences
- Compatibility details with specific Tektronix AWG models beyond the 5000/7000 series
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.12 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesnumpymatplotlibschemaversioningit |
| Maintenance | Actively maintained 95 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 75,688 / month, #14,686 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering |
Evidence: broadbean-0.15.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “pulse sequence builder”
- broadbeanBroadbean composes and manipulates pulse sequences for arbitrary…
- openpulseOpenPulse parses and represents pulse grammar for quantum computing…
- mermaid-builderProgrammatically generates MermaidJS diagram markup (flowcharts,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also qcodes · qualang-tools · attoworld · oqpy · openpulse · wavedrom · doppler-dsp · pyPPG · wfdb · pyjls