BrightEyes-FFS

A toolbox for analysing Fluorescence Correlation Spectroscopy (FCS) and Fluorescence Fluctuation Spectroscopy (FFS) data with array detectors. The fcs module contains libraries for:

• Calculating autocorrelations and cross-correlations of raw FCS/FFS data (i.e. photon counts vs. time or photon arrival time traces). Supported file types include .h5, .ptu, and .czi.
• Fitting correlations to various 2D and 3D diffusion models
• Calibration-free FCS/FFS analysis such as circular-scanning FCS and pair-correlation analysis
• Miscellaneous tools

The fcs_gui module contains libraries for:

• Storing and loading FCS/FFS analysis sessions, as used in the GUI

The pch module contains libraries for:

• Calculating photon counting histograms
• Fitting histograms with Fluorescence Intensity Distribution Analysis (FIDA)

The tools module contains libraries for:

• Fitting various models to data (polynomial, Gaussian, power law, etc.)
• Stokes-Einstein relation
• Save/load 2D arrays to/from .csv files
• Save data to .tiff file
• Miscellaneous tools

Installation

You can install brighteyes-ffs via [pip] directly from [PyPI]:

pip install brighteyes-ffs

or using the version on GitHub:

pip install git+https://github.com/VicidominiLab/BrightEyes-FFS

It requires the following Python packages

h5py
joblib
matplotlib>=3.3.2
multipletau>=0.3.3
numpy>=1.19.4
pandas>=1.1.4
scipy
tifffile>=2020.9.29
seaborn
imutils
PyQt5
qdarkstyle
nbformat
ome_types
czifile
brighteyes_ism
notebook
ptufile
phconvert

GUI

For quick and common types of analysis, you can use the GUI (https://github.com/VicidominiLab/BrightEyes-FFS-GUI), which contains most of the basic features. In addition, there is an automatic Jupyter Notebook writing tool to convert an analysis session started in the GUI to a Notebook.

License

Distributed under the terms of the [GNU GPL v3.0] license, "BrightEyes-FFS" is free and open source software

Contributing

You want to contribute? Great! Contributing works best if you creat a pull request with your changes.

1. Fork the project.
2. Create a branch for your feature: git checkout -b my-new-feature
3. Commit your changes: git commit -am 'My new feature'
4. Push to the branch: git push origin my-new-feature
5. Submit a pull request!