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