birfi 0.1.1

Blind instrument response function identification from fluorescence decays.

BIRFI

Blind Instrument Response Function Identification (BIRFI) from fluorescence decays. This is a Python re-implementation of the algorithm described in: Adrián Gómez-Sánchez et al., Blind instrument response function identification from fluorescence decays, Biophysical Reports, 2024.

It works with single-channel and multi-channel (e.g. ISM) datasets.

Installation

You can install the stable version from PyPI:

pip install birfi

or the latest version directly from GitHub:

pip install git+https://github.com/VicidominiLab/birfi

It requires the following Python packages

numpy
scipy
matplotlib
torch

Documentation

The algorithm calculates the IRFs from a single-channel or multi-channel fluorescence decay dataset, assuming that the fluorescence decays are mono-exponential, and they share the same lifetime. The dataset should be in the shape of (n_time_bins,) or (n_time_bins, n_channels). The algorithm is sensitive to noise, so we recommend acquiring calibration data with the highest possible signal-to-noise ratio. In case this is not possible, we provide a simple regularization tool to minimize noise overfitting.

You can find examples of usage here:

https://github.com/VicidominiLab/birfi/tree/main/demo

License

Distributed under the terms of the GNU GPL v3.0 license. "birfi" is free and open source software