pyFDN Python versions License Test coverage

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Overview

pyFDN provides building blocks for designing, simulating, and analysing Feedback Delay Networks (FDNs). The package focuses on reusable, tested helper functions that simplify typical FDN workflows such as creating orthogonal feedback matrices, designing loop filters, and inspecting pole locations. Using flamo as a dependency, pyFDN allows modular design of advanced FDN structure with DSP operations in time and frequency domain.

The source code is MIT-licensed. Small packaged tutorial resources retain the separate terms recorded in src/pyFDN/resources/ATTRIBUTION.md; in particular, the Pori concert-hall impulse response is limited to noncommercial use.

Highlights

  • Matrix polynomial helpers for evaluating, differentiating, and convolving FIR/IIR blocks.

  • Loop analysis utilities including pole boundary estimation and curve bounding checks.

  • Acoustic absorption design tools that translate RT targets into one-pole loop filters.

  • Echo density (Abel & Huang 2006) for analysing reverberation and mixing time.

  • Random orthogonal matrix generation to prototype energy-preserving feedback networks.

Installation

Install the current release from PyPI:

pip install pyFDN

For local development, create a virtual environment and install the package in editable mode together with the optional tooling:

python -m venv .venv
source .venv/bin/activate
pip install -e .

Quick start

All main functions are accessible directly from the top-level pyFDN namespace:

import numpy as np
import pyFDN

fs = 48_000
delays = np.array([331, 347, 359, 373], dtype=int)

# energy-preserving feedback matrix
feedback = pyFDN.random_orthogonal(len(delays))

# one-pole absorption filters targeting RT of 1.2 s at DC and 0.9 s at Nyquist
absorption = pyFDN.one_pole_absorption(1.2, 0.9, delays, fs)

# convert delay state-space to standard state-space (A_ss, b, c, d)
A_ss, b, c, d = pyFDN.dss_to_ss(delays, feedback)

Alternatively, import specific functions directly:

from pyFDN import random_orthogonal, one_pole_absorption, lin_to_db

feedback = random_orthogonal(4)
absorption = one_pole_absorption(1.2, 0.9, [100, 150, 200, 250], 48_000)

Development

Run the test suite (the configuration mirrors CI and emits coverage details):

tox -e py311

Or, inside an activated virtual environment:

pytest --cov=src/pyFDN --cov-report=term-missing

For linting and packaging helpers see Makefile (make lint/make docs) and tox.ini for multi-environment testing.

Project links

Data verified by PyPI on Aug 10, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (0.4.2)

1 maintainer

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Avatar for SebastianSchlecht from gravatar.com SebastianSchlecht

Credits

Author: Sebastian J. Schlecht, Facundo Franchino, Jeremy Bai

Maintainer: Facundo Franchino, Sebastian J. Schlecht

GitHub Statistics

Data verified by PyPI on Aug 10, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.10

Provides Extra

examples test dev docs

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