How to use the Fourier transform function of JMP to fit a list of curve data to the most suitable orthodox curve?

[#1]

For example, in the attachment, I have obtained the "smooth" data of the second column by drawing the smooth line formula of the first column.
The effect of increased "smooth" is to determine the most appropriate sine wave period by repeatedly crossing the two curves.
Of course, the original curve change is completely random, so it can only be required that the fitted sine wave can achieve the maximum satisfaction (sine wave is just a fixed pattern).
Like a sine wave in the picture.
(This image is implemented in python.)

Thanks Experts!

[#2]

[#3]

That is, the number of the high point of the fitted fixed sine wave and the high point of the original "num" curve stage coincide as much as possible, and the number of the low point of the same sine wave and the low point of the original "num" curve stage coincide as much as possible

Thanks!

[#4]

JMP has FFT and some other things related to Fourier

Time Series Analysis could be helpful or if you have access to JMP Pro, you might have more options with Functional Data Explorer

[#5]

Hi @lala,

I'm not completely sure to have understood your objective and needs (get a smoother curve than the original, probably noisy, curve data ?), but as @jthi mentioned, you have a lot of options in Functional Data Explorer to fit a curve and extract the fitted "smooth" model. I'm thinking B-/P-Splines and Fourier Basis models could work well on your use case, and there are pre-processing options that could help smoothen your curve data even further if needed : Savitzky-Golay filtering and extraction of derivatives, baseline correction, Standard Normal Variate method, Multiplicative Scatter Correction, etc...

Alternatively, you could also use the platform Fit Curve, that enable to fit a large amount of curve models.

You could also create a formula to specify your model equation, and use the platform Nonlinear Regression to estimate the different parameters of your model.

Hope this answer will help you in the meantime,