KlarFit data analysis software

KlarFit is a powerful, versatile spectral peak-fitting and data presentation package. This software was specifically designed to handle the large datasets collected by our microscopes, where a map could consist of over a million spectra each composed of thousands of values. Utilizing the massively parallel computing capabilities of Graphical Processing Units (GPUs), this software is capable of processing and presenting those data in minutes.

KlarFit can also analyze data obtained by clients using other systems. Write for details.

Video: Spectral fitting with KlarFit

High-Speed Analysis

KlarFit doesn’t just plot intensity. It can perform least-squares fitting of any number of spectral features, using equations that are best suited to the problem.

The figure below shows a PL map generated by KlarFit. The PL peak was modeled as two overlapping Gaussians (6 parameters total). To benchmark the performance, KlarFit performed the analysis using a CPU and a GPU. The CPU (Intel 6-core i7-8700) did 47 fits/sec. The GPU (NVidia GTX 1080 Ti) did 10,056 fits/sec.

High-Quality Display

KlarFit provides three methods for displaying publication-quality data:

Mapping one equation to a color bar. The user defines an equation that involves the fitted parameter(s). This equation could just be a parameter, like peak width, or something more complex. The result of that equation is plotted using a built-in color bar. Users can also define their own color bars for a customized appearance. The figure shows a plot of one parameter, the peak center.

Color representation of up to three equations. Here, the results of up to three equations are orthogonally mapped onto Red, Green, and Blue intensities. At a glance, different spectral features are made apparent. In the figure, the intensity of the narrow peak is mapped to Red while that of the broad peak is mapped to Green and Blue.

Equations mapped to solid colors. An arbitrary number of conditional (Boolean) equations can be mapped to solid colors. This is great for emphasizing regions that have similar characteristics.