ml-savitzky-golay-generalized

savitzky-golay-generalized

General Least-Squares Smoothing and Differentiation by the Convolution (Savitzky-Golay) Method Peter A. Gorry

Pretty much the same as the savitzky-golay method, but without border problems, and without inventing points. It can be maybe merged into the savitzky-golay project but by the now this is the version used in the last GSD project.

I'll try an automatic parameter tunning based on the SNR or in the entropy of the signal.

Examples

´´´ var SG = require("savitzky-golay-generalized"); ´´´

Smoothing example

´´´ var options = { windowSize: 15, derivative: 0, polynomial: 3 };

var noiseLevel = 0.1; var data = new Array(200); for (var i = 0; i < data.length; i++) data[i] = Math.sin(iMath.PI2/data.length)+(Math.random()-0.5)noiseLevel; var ans = SG(data, Math.PI2/data.length, options); console.log(ans); ´´´

First derivative test (Equally spaced x)

´´´ var options = { windowSize: 47, derivative: 1, polynomial: 3 };

var noiseLevel = 0.1; var data = new Array(200); for (var i = 0; i < data.length; i++) data[i] = Math.sin(iMath.PI2/data.length)+(Math.random()-0.5)noiseLevel; var ans = SG(data, Math.PI2/data.length, options); console.log(ans);

´´´

First derivative test x as vector(It could be non-equally spaced!!)

´´ var options = { windowSize: 47, derivative: 1, polynomial: 3 };

var noiseLevel = 0.1; var data = new Array(200); var x = new Array(200); for (var i = 0; i < data.length; i++){ data[i] = Math.sin(iMath.PI2/data.length)+(Math.random()-0.5)noiseLevel; x[i]=iMath.PI*2/data.length; }

var ans = SG(data, Math.PI*2/data.length, options); var ans2 = SG(data, x, options);

/for (var j = 0; j < data.length; j++){ console.log(ans[j]+" "+ans2[j]); }/ /* The result should be the approximately the same for (var j = Math.round(options.windowSize/2); j < data.length-Math.round(options.windowSize/2); j++){ ans[j].should.be.approximately(ans2[j], 10e-10); } */ ´´´