pyfli-lib 0.1.16

Unified Fluorescence Lifetime Imaging (FLI) data processing platform using analytical and deep learning methods.

pyfli: A Unified Platform for FLI Data Processing

pyfli is a comprehensive library designed for Fluorescence Lifetime Imaging (FLI) data processing. It streamlines the workflow for handling diverse file formats from various hardware manufacturers and provides a standardized pipeline for both traditional analytical and deep-learning-based inference.

---

Key Features

• Universal Processing Pipeline: Simplifies the handling of multiple FLI file types (ICCD, SPAD, TCSPC).
• Enhanced FLI Simulator: A robust simulation engine adaptable to specific camera hardware parameters and noise models.
• Standardized Inference: Unified interface for time-resolved microscopy and macroscopic FLI data (MFLI).

Supported Data Acquisition Methods

The platform provides native support for several high-end imaging systems:

1. ICCD: Intensified Charge-Coupled Device cameras for fast-gated, wide-field imaging.
2. SwissSPAD2 & SwissSPAD3: High-speed SPAD (Single-Photon Avalanche Diode) architectures for high-resolution photon counting.
3. SPCImage/TCSPC: Standardized processing for Time-Correlated Single Photon Counting microscopy data.

Data Processing & Analysis

pyfli implements industry-standard analytical methods to extract lifetime information:

• Non-linear Least Squares Fitting (NLSF): Robust mathematical approach for exponential decay modeling.
• Phasor Plot Analysis: Graphical, model-free transformation of fluorescence decay into a 2D polar plot for easy species separation.
• Maximum Likelihood Estimation (MLE): Statistical estimator optimized for low-photon regimes.
• Rapid Lifetime Determination (RLD): Computationally efficient method for real-time applications and high-frame-rate data.
• Laguerre Method (LET): Laguerre Expansion Technique for model-free IRF deconvolution followed by multi-exponential lifetime extraction on a per-pixel basis.

---

Installation

Install the stable version directly from PyPI:

pip install pyfli-lib

For users requiring GPU-based processing, install the optional tensor/AI dependencies:

pip install "pyfli-lib[gpu]"

Quick Start

Even though the package is installed as pyfli-lib, you import it as pyfli in your scripts:

from pyfli import DataOperations

loader = DataOperations(    
    data_path = "experimental_data.sdt",
    irf_path = "instrument_data.txt", 
    bg_path = "background_data.tif",   
    mask_path="background_data.png",
    )
decay_data = loader.load_data()
irf_data = loader.load_irf()

Citation

If you use pyfli in your research, please cite this package. If you use the phasor SEPL analysis functionality specifically, please also cite the following paper on which the phasor module is based:

Michalet X. "Continuous and discrete phasor analysis of binned or time-gated periodic decays." AIP Advances 11, 035331 (2021). https://doi.org/10.1063/5.0027834

---

Repository & Issues

The source code is hosted on GitHub. Please report any bugs or feature requests via the issues tracker.

• GitHub: https://github.com/vkp217/pyfli-pkg