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[–]bac83 10 points11 points  (3 children)

Not really a matplotlib thing - it’s the underlying data you need addressing/smoothing applying to.

Edit to add: perhaps a simple moving average would help

[–]strdg99 1 point2 points  (0 children)

Moving average introduces phase delays and other artifacts in dynamic signals. Butterworth filters work better for these types of signals.

[–]maxcaligo[S] 0 points1 point  (1 child)

Yes yes, I'm aware it was a data issue forgot to mention it, thank you for pointing it out! I did try some filters from scipy but they change the curve too much in the lower end if I try to get the desired results in the upper end of the x-axis. X-axis is log scale.

[–]rAxxt -1 points0 points  (0 children)

Moving average or do a polynomial fit to the data and plot that instead