Measurement data is often noisy. SpectraFox can apply different smoothing procedures to the data.

To smooth the data proceed as follows:

1. Select the column to be smoothed.
2. Select the smoothing method to be applied. You should choose an appropriate smoothing filter settings, depending on the amount of data points your measurement consists of.
3. Apply the smoothing procedure, and compare the output at the bottom of the window, which shows the smoothed data on top of the unsmoothed counterpart.

SpectraFox support several smoothing methods.

• Adjacent Average Smoothing: Takes the simple average value between the given number of adjacent datapoints. This filter often flattens the height and width of sharp features. E.g., for Kondo-resonances you should chose a different method.
• Savitzky–Golay Smoothing: The Savitzky–Golay smoothing filter is a type of filter first described in 1964 by Abraham Savitzky and Marcel J. E. Golay. The Savitzky–Golay method performs a local polynomial regression on a series of values to determine the smoothed value for each point. Its main advantage is that it preserve sharp features of the source data, such as relative maxima, minima and the width of these features. SpectraFox uses a second order polynomial with a moving average window.