pyfli-lib 0.1.0

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.

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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.

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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()

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