sg data smoothing(polynomial smoothing)
For some data with much the noise, it is usually necessary to smooth the data to reduce the noise.
For example, a set of noisy data as below, use the sg() function to polynomial smooth the data
A 

1 
=file("sgdata.xlsx").xlsimport@w() 
2 
=A1.(~(1)) 
3 
=A1.(~(2)) 
4 
=sg(A3,1,15).conj() 
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=canvas() 
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=A5.plot("NumericAxis","name":"x") 
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=A5.plot("NumericAxis","name":"y","location":2) 
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=A5.plot("Line","markerStyle":0,"axis1":"x","data1":A2,"axis2":"y", "data2":A3) 
9 
=A5.plot("Line","markerStyle":0,"lineColor":65536,"axis1":"x","data1":A2,"axis2":"y", "data2":A4) 
10 
=A5.draw@p(600,600) 
A1 Read the data as a secondary sequence
A2 The sequence number
A3 Original data
A4 Perform 15point linear smoothing on A3 data (n=1,m=15), and return the smoothed data, as the red line in the figure. In sg(A,n,m,d), taking no derivative when the parameter d is omitted means smoothing only; d=1, indicating the smoothing result of the first derivative, which can eliminate the constant term error; d=2 Second derivative smooth, can eliminate the linear error
A5A10 Draw the data before and after smoothing and observe the smoothing result. If the result is not good, the value of n, m, d can be adjusted until it is good.