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
2 maintainers
Metadata
2 maintainers
Release files for birfi 0.1.1
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| File | Size | Uploaded | Provenance | Actions |
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| birfi-0.1.1.tar.gz | 23.3 kB | Without provenance | Details for birfi-0.1.1.tar.gz |
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| File | Interpreter | ABI | Platform | Actions Reset |
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| birfi-0.1.1-py3-none-any.whl | Python 3 | none | any | Details for birfi-0.1.1-py3-none-any.whl |
Total release size: 45.5 kB
Release files / birfi-0.1.1.tar.gz
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| Tags | Python 3 |
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