pysampled 1.0.0

:py:mod:`pysampled` provides tools for working with uniformly sampled (time series) data.

pysampled

A toolkit for working with uniformly sampled time series data.

pysampled streamlines the exploration of time series data and the development of signal processing pipelines. It enables researchers and engineers to analyze time series data—including audio signals and physiological data—efficiently and intuitively. With its user-friendly interface and well-documented examples, the package makes signal processing accessible for both basic manipulations and analyses like filtering, resampling, trend extraction, and spectral analysis.

Installation

1. Installing from PyPI (Recommended)

pip install pysampled && download-airpls

You can optionally use pip install pysampled[minimal] to skip installing scikit-learn and matplotlib.

Note: The download-airpls command is defined in pyproject.toml and ensures that the required airPLS.py file is properly downloaded. More information on airPLS here.

2. Installing from the GitHub Repository

pip install git+https://github.com/praneethnamburi/pysampled.git && download-airpls

Alternatively, you can clone the repository locally and set up your environment using the requirements.yml file. If you do this, download airPLS.py manually from here and add it to the pysampled folder inside the cloned repository.

git clone https://github.com/praneethnamburi/pysampled.git
cd pysampled
conda env create -n pysampled -f requirements.yml

Quickstart

import pysampled as sampled

# Generate a 10 Hz signal sampled at 100 Hz. Sum of three sine waves (1, 3, and 5 Hz).
sig = sampled.generate_signal("three_sine_waves")[:5.0] 

# Only keep first 5 seconds of the signal
sig = sig[:5.0]

# visualize the signal, before and after applying a bandpass filter between 2 and 4 Hz
sampled.plot([sig, sig.bandpass(2, 4)])

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

Praneeth Namburi

Project Link: https://github.com/praneethnamburi/pysampled

Acknowledgments

This tool was developed as part of the ImmersionToolbox initiative at the MIT.nano Immersion Lab. Thanks to NCSOFT for supporting this initiative.