brighteyes-ffs 0.0.14

Toolbox for analysing FCS/FFS data with array detectors

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)
• Fitting correlations to various 2D and 3D diffusion models
• Calibration-free FCS/FFS analysis such as circular-scanning FCS and iMSD analysis
• Miscellaneous tools

The fcs_gui module contains libraries for:

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

The dataio 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

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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

or:

pip install -i https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple 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

Getting started

Calculating correlations

from brighteyes_ffs.fcs.fcs2corr import fcs_load_and_corr_split as correlate
list_of_g = ['central', 'sum3', 'sum5']
G, time_trace = correlate(file, list_of_g=list_of_g, accuracy=16, split=10, time_trace=True, algorithm='multipletau')

Fitting correlations

from brighteyes_ffs.fcs.fcs_fit import fcs_fit
fitresults = []
for corr in list_of_g:
	Gsingle = getattr(G, corr + '_average')
	Gexp = Gsingle[1:,1]
	tau = Gsingle[1:,0]
	fitfun = 'fitfun_2c'
	fit_info = np.asarray([1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0]) # fit N, tauD, and offset (we fit with one component)
	param = np.asarray([1, 1, 1, 1, 1, 0, 0, 3, 0, 0, 0]) # starting values for all parameters
	lBounds = np.asarray([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])*(-1e6) # lower bounds for all parameters
	uBounds = np.asarray([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])*(1e6) # upper bounds for all parameters
	fitresult = fcs_fit(Gexp, tau, fitfun, fit_info, param, lBounds, uBounds, plotInfo=-1)
	fitresults.append(fitresult)

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!