ml-savitzky-golay-generalized - npm Package Compare versions

Comparing version 1.0.3 to 1.0.4

package.json

 {
   "name": "ml-savitzky-golay-generalized",
-  "version": "1.0.3",
+  "version": "1.0.4",
   "description": "Savitzky–Golay filter in Javascript",
@@ -41,4 +41,4 @@ "main": "src/index.js",
     "extend": "^3.0.0",
-    "ml-stat":"1.1.0"
+    "ml-stat": "^1.3.3"
   }
 }

README.md

@@ -8,3 +8,24 @@ # savitzky-golay-generalized
 I'll try an automatic parameter tunning based on the SNR or in the entropy of the signal.
+#Usage
+```js
+npm i ml-savitzky-golay-generalized
+const SG = var SG = require("ml-savitzky-golay-generalized");
+SG(dataY, deltaX|X, options)
+```
+##Parameters
+### dataY
+The data to be filtered.
 
+###deltaX | X
+deltaX specifies the difference between 2 consecutive points of the independent: deltaX = X[i+1] - X[i]. Specficiying a deltaX suppose that all your points are equally spaced on the independent variable.
+If your points are not equally spaced in the ordinate variable, then you have to provide explicitelly your X values. The algorithm will use the average deltaX within each bin of 'windowSize' points to approximate the derivatives. This fast approximation only works if the X is almost locally equally spaced.
+
+###options
+####windowSize: 
+The odd number of points to approximate the regresion polynomial. Default 9
+####derivative: 
+The grade of the derivative. 0 by defualt(Smoothing)
+####polynomial: 
+The order of the regresion polynomial. Default 3
+
 ## Examples
@@ -11,0 +32,0 @@

src/index.js

@@ -6,5 +6,5 @@ //Code translate from Pascal source in http://pubs.acs.org/doi/pdf/10.1021/ac00205a007
 var defaultOptions = {
-    windowSize: 11,
+    windowSize: 9,
     derivative: 0,
-    polynomial: 2,
+    polynomial: 3,
 };
@@ -11,0 +11,0 @@

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Dependency changes

• + Addedml-stat@1.3.3(transitive)

• - Removedml-stat@1.1.0(transitive)

• Updatedml-stat@^1.3.3