propoptics 0.1.1

Generate noise signal based on PSD measurement and analyze the output

Propoptics

propoptics is a small Python library aimed at providing tools to generate pulse train based on measured noise power spectral densities (PSDs), as done in [1]. Moreover, it provides a limited number of analysis tools:

• Tooling for spectral filtering and integration, allowing users to quickly convert a series of optical spectra into a sequence of values (energy or timing delay).
• A wrapper around scipy.signal.welch to make power spectral density estimations easier.

Supported diagnostics are:

Time series data	Frequency data
energy	relative intensity noise
timing delay	timing jitter, directly related to phase noise

Installation

Run using uv tool

The easiest way to use the command-line interface (CLI) is to install uv

You can use propoptics in your own project by simply installing the package from PyPI.

pip install propoptics

The minimum supported Python version is 3.11.

You can also install from source, in which case you must install the development dependencies and have a Rust compiler installed. rustc 1.86.0 is the only version tested.

pip install -r requirements_dev.txt 
maturin develop

# run tests
cargo test
pytest

Quick start

Generate a pulse train:

import propoptics
import numpy as np

freq, rin_psd = np.load("path/to/measurement")
rin_obj = propoptics.NoiseMeasurement(freq, rin_psd)
t, signal = rin_obj.time_series(nt=32, rng=123456789)

Complete example in ./examples/simple_pulse_train.py.

Welch method

The total number of points must account for 50% overlap when using the Welch method

Each · corresponds to one pulse:

 time -->
 0             nt/2              nt
 ┃·······┃·······┃·······┃·······┃
 ┆   ┃·······┃·······┃·······┃
 ┆   ┆               ┆       ┆
 ┆   ┆              >┆       ┆< = nperseg
>┆   ┆< = nperseg // 2

Reference

[1] CAMENZIND, Sandro L., SIERRO, Benoît, WILLENBERG, Benjamin, et al. Ultra-low noise spectral broadening of two combs in a single ANDi fiber. APL Photonics, 2025, vol. 10, no 3.