Reduce noise without distorting peaks. Savitzky-Golay for peak-preserving smoothing, FIR for targeted frequency rejection.
A good filter removes random fluctuation without softening the peaks you care about. A bad filter broadens peaks and shifts positions.
Studio offers two filters. Savitzky-Golay for peak-preserving smoothing, and FIR for targeted frequency rejection (50 or 60 Hz line pickup, for example).
Savitzky-Golay fits a polynomial to a sliding window of points and replaces the centre point with the fitted value. The window width sets how aggressively noise is averaged out; the polynomial order sets how much peak shape is preserved.
The FIR (finite impulse response) filter convolves the data with a pre-designed kernel. Studio ships notch kernels for 50 Hz and 60 Hz and a configurable low-pass.
Filtering
Screenshot coming
Filter type
Typical: Savitzky-Golay
Savitzky-Golay or FIR. SG for general smoothing; FIR for line-noise rejection or sharp low-pass cut-offs.
Window (SG)
Typical: 11 points
Number of points per fit. Must be odd. Larger windows smooth more but broaden peaks. Start near 5 % of your peak width in points.
Polynomial order (SG)
Typical: 3
2 or 3 for most scans. Order 3 preserves peak shape better; order 2 is more aggressive.
FIR kernel
Notch (50 / 60 Hz) or low-pass. Cutoff is user-configurable for the low-pass.
Cutoff (FIR low-pass)
Stop-band frequency in Hz. Choose above your signal bandwidth but below the noise you want to reject.
Walkthrough
Smooth a noisy DPV trace before peak finding
Result
A cleaner trace where peak positions and widths are unchanged but baseline noise is cut by roughly a factor of two.
Filters run in Analyse → Filter. The filtered trace overlays the raw one so you can see whether the filter preserved what you care about. The raw data stays in the file.
Step-by-step in docs
Filter a signal
Other analysis tools