PyFli#

PyFli is a unified platform for Fluorescence Lifetime Imaging (FLI) data analysis, simulation and visulalization. It streamlines the workflow for handling diverse file formats from different hardware manufacturers, and provides a standardized pipeline for both traditional analytical and deep-learning-based lifetime inference.

Streamlined Processing Pipeline

Simplifies handling data acquired by different imaging systems. consistent loading and processing interface.

Enhanced FLI Simulator

A robust simulation engine adaptable to specific camera hardware parameters and noise models, for method development and testing.

Standardized Inference

One unified interface for time-resolved data across modalities (microscopy - FLIM, mesoscopy- m-FLI and macroscopic - MFLI) FLI data .

Overview#

pyfli sits between raw instrument output and lifetime results: it loads and pre-processes decay data from a given acquisition system, then hands it to one of several interchangeable analytical or deep-learning fitting backends. The simulator can be used to generate the FLI/FLIM data for model training etc.

Supported Acquisition Methods

ICCD — Intensified Charge-Coupled Device cameras for fast-gated, wide-field imaging.
SPAD — High-speed SPAD (Single-Photon Avalanche Diode) architectures for high-resolution photon counting.
TCSPC — Standardized processing for Time-Correlated Single Photon Counting microscopy data.

Data Processing & Analysis

Non-linear Least Squares Fitting (NLSF) — robust exponential decay modeling.
Phasor Plot Analysis — graphical, model-free transformation of fluorescence decay into a 2D polar plot for species separation.
Maximum Likelihood Estimation (MLE) — statistical estimator optimized for low-photon regimes.
Rapid Lifetime Determination (RLD) — computationally efficient method for fast inference.
Laguerre Method — model-free IRF deconvolution followed by multi-exponential lifetime extraction on a per-pixel basis.