PyFDAP

PyFDAP analyzes Fluorescence Decay After Photoconversion (FDAP) datasets by fitting linear and non-linear decay functions to model fluorescence decay processes.

Key Features:

• Decay function fitting: Fits linear and non-linear decay functions to FDAP datasets to model fluorescence decay.
• Multiple fitting algorithms: Supports multiple fitting algorithms to select appropriate models for FDAP data.
• Data structuring and large-dataset handling: Provides robust data structuring and efficient processing of large FDAP datasets.
• Plotting and visualization: Includes plotting options to visualize raw FDAP data and fitted decay models.
• Implementation: Implemented in Python.

Scientific Applications:

• Fluorescence decay modeling: Quantifies decay kinetics from FDAP experiments by fitting decay models to photoconversion data.
• Photoconversion and fluorescence-based studies: Supports interpretation of photoconversion dynamics and other fluorescence-based biological phenomena through fitted decay models.

Methodology:

Computational methods include fitting linear and non-linear decay functions using multiple fitting algorithms, robust data structuring and processing for large FDAP datasets, and plotting of raw data and fitted models; the software is implemented in Python.