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

Comparing version 1.1.1 to 2.0.0

History.md

@@ -0,1 +1,10 @@
+# [2.0.0](https://github.com/mljs/savitzky-golay-generalized/compare/v1.1.1...v2.0.0) (2020-02-26)
+
+
+### Bug Fixes
+
+* spelling ([aa11ca3](https://github.com/mljs/savitzky-golay-generalized/commit/aa11ca304fc307619b1e7f3cb978e68ea4caa9a5))
+
+
+
 <a name="1.1.1"></a>
@@ -2,0 +11,0 @@ ## [1.1.1](https://github.com/mljs/savitzky-golay-generalized/compare/v1.0.4...v1.1.1) (2017-07-24)

package.json

 {
   "name": "ml-savitzky-golay-generalized",
-  "version": "1.1.1",
+  "version": "2.0.0",
   "description": "Savitzky–Golay filter in Javascript",
-  "main": "src/index.js",
-  "directories": {
-    "lib": "src",
-    "test": "test"
-  },
+  "main": "lib/index.js",
+  "module": "src/index.js",
+  "files": [
+    "lib",
+    "src"
+  ],
   "scripts": {
-    "test": "mocha --require should --reporter mocha-better-spec-reporter --recursive"
+    "eslint": "eslint src",
+    "eslint-fix": "npm run eslint -- --fix",
+    "prepublishOnly": "rollup -c",
+    "test": "npm run test-coverage && npm run eslint",
+    "test-coverage": "jest --coverage",
+    "test-only": "jest"
   },
@@ -35,10 +41,14 @@ "repository": {
   "devDependencies": {
-    "mocha": "latest",
-    "mocha-better-spec-reporter": "latest",
-    "should": "latest"
+    "@babel/plugin-transform-modules-commonjs": "^7.8.3",
+    "@types/jest": "^25.1.3",
+    "eslint": "^6.8.0",
+    "eslint-config-cheminfo": "^2.0.4",
+    "eslint-plugin-import": "^2.20.1",
+    "eslint-plugin-jest": "^23.8.0",
+    "eslint-plugin-prettier": "^3.1.2",
+    "jest": "^25.1.0",
+    "prettier": "^1.19.1",
+    "rollup": "^1.31.1"
   },
-  "dependencies": {
-    "extend": "^3.0.0",
-    "ml-stat": "^1.3.3"
-  }
+  "dependencies": {}
 }

README.md

 # savitzky-golay-generalized
+
 General Least-Squares Smoothing and Differentiation by the Convolution (Savitzky-Golay) Method Peter A. Gorry
@@ -8,37 +9,50 @@
 I'll try an automatic parameter tunning based on the SNR or in the entropy of the signal.
-#Usage
+
+# Usage
+
 ```js
 npm i ml-savitzky-golay-generalized
-const SG = var SG = require("ml-savitzky-golay-generalized");
+const SG = require("ml-savitzky-golay-generalized");
 SG(dataY, deltaX|X, options)
 ```
-##Parameters
+
+## Parameters
+
 ### dataY
+
 The data to be filtered.
 
-###deltaX | X
+### 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.
+If your points are not equally spaced in the ordinate variable, then you have to provide explicitly 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
+### options
 
+#### windowSize:
+
+The odd number of points to approximate the regression polynomial. Default 9
+
+#### derivative:
+
+The grade of the derivative. 0 by default (Smoothing)
+
+#### polynomial:
+
+The order of the regression polynomial. Default 3
+
 ## Examples
 
 ```js
-var SG = require("ml-savitzky-golay-generalized");
+var SG = require('ml-savitzky-golay-generalized');
 ```
 
 ### Smoothing example
+
 ```js
 var options = {
-            windowSize: 15,
-            derivative: 0,
-            polynomial: 3
-        };
+  windowSize: 15,
+  derivative: 0,
+  polynomial: 3,
+};
 
@@ -48,4 +62,6 @@ var noiseLevel = 0.1;
 for (var i = 0; i < data.length; i++)
-     data[i] = Math.sin(i*Math.PI*2/data.length)+(Math.random()-0.5)*noiseLevel;
-var ans = SG(data, Math.PI*2/data.length, options);
+  data[i] =
+    Math.sin((i * Math.PI * 2) / data.length) +
+    (Math.random() - 0.5) * noiseLevel;
+var ans = SG(data, (Math.PI * 2) / data.length, options);
 console.log(ans);
@@ -58,6 +74,6 @@ ```
 var options = {
-    windowSize: 47,
-    derivative: 1,
-    polynomial: 3
-    };
+  windowSize: 45,
+  derivative: 1,
+  polynomial: 3,
+};
 
@@ -67,6 +83,7 @@ var noiseLevel = 0.1;
 for (var i = 0; i < data.length; i++)
-     data[i] = Math.sin(i*Math.PI*2/data.length)+(Math.random()-0.5)*noiseLevel;
-var ans = SG(data, Math.PI*2/data.length, options);
+  data[i] =
+    Math.sin((i * Math.PI * 2) / data.length) +
+    (Math.random() - 0.5) * noiseLevel;
+var ans = SG(data, (Math.PI * 2) / data.length, options);
 console.log(ans);
-
 ```
@@ -78,6 +95,6 @@
 var options = {
-            windowSize: 47,
-            derivative: 1,
-            polynomial: 3
-    };
+  windowSize: 47,
+  derivative: 1,
+  polynomial: 3,
+};
 
@@ -87,8 +104,10 @@ var noiseLevel = 0.1;
 var x = new Array(200);
-for (var i = 0; i < data.length; i++){
-    data[i] = Math.sin(i*Math.PI*2/data.length)+(Math.random()-0.5)*noiseLevel;
-    x[i]=i*Math.PI*2/data.length;
+for (var i = 0; i < data.length; i++) {
+  data[i] =
+    Math.sin((i * Math.PI * 2) / data.length) +
+    (Math.random() - 0.5) * noiseLevel;
+  x[i] = (i * Math.PI * 2) / data.length;
 }
 
-var ans = SG(data, Math.PI*2/data.length, options);
+var ans = SG(data, (Math.PI * 2) / data.length, options);
 var ans2 = SG(data, x, options);
@@ -95,0 +114,0 @@

src/index.js

@@ -1,124 +0,123 @@
-//Code translate from Pascal source in http://pubs.acs.org/doi/pdf/10.1021/ac00205a007
-var extend = require('extend');
-var stat = require('ml-stat');
+export default function SavitzkyGolay(data, h, options = {}) {
+  let { windowSize = 9, derivative = 0, polynomial = 3 } = options;
 
-var defaultOptions = {
-    windowSize: 9,
-    derivative: 0,
-    polynomial: 3,
-};
+  if (windowSize % 2 === 0 || windowSize < 5 || !Number.isInteger(windowSize)) {
+    throw new RangeError(
+      'Invalid window size (should be odd and at least 5 integer number)',
+    );
+  }
+  if (windowSize > data.length) {
+    throw new RangeError(
+      `Window size is higher than the data length ${windowSize}>${data.length}`,
+    );
+  }
+  if (derivative < 0 || !Number.isInteger(derivative)) {
+    throw new RangeError('Derivative should be a positive integer');
+  }
+  if (polynomial < 1 || !Number.isInteger(polynomial)) {
+    throw new RangeError('Polynomial should be a positive integer');
+  }
+  if (polynomial >= 6) {
+    // eslint-disable-next-line no-console
+    console.warn(
+      'You should not use polynomial grade higher than 5 if you are' +
+        ' not sure that your data arises from such a model. Possible polynomial oscillation problems',
+    );
+  }
 
+  let half = Math.floor(windowSize / 2);
+  let np = data.length;
+  let ans = new Array(np);
+  let weights = fullWeights(windowSize, polynomial, derivative);
+  let hs = 0;
+  let constantH = true;
+  if (Array.isArray(h)) {
+    constantH = false;
+  } else {
+    hs = Math.pow(h, derivative);
+  }
 
-function SavitzkyGolay(data, h, options) {
-    options = extend({}, defaultOptions, options);
-
-    if ((options.windowSize % 2 === 0) || (options.windowSize < 5) || !(Number.isInteger(options.windowSize)))
-            throw new RangeError('Invalid window size (should be odd and at least 5 integer number)')
-
-
-    if (options.windowSize>data.length)
-        throw new RangeError('Window size is higher than the data length '+options.windowSize+">"+data.length);
-    if ((options.derivative < 0) || !(Number.isInteger(options.derivative)))
-        throw new RangeError('Derivative should be a positive integer');
-    if ((options.polynomial < 1) || !(Number.isInteger(options.polynomial)))
-        throw new RangeError('Polynomial should be a positive integer');
-    if (options.polynomial >= 6)
-        console.warn('You should not use polynomial grade higher than 5 if you are' +
-            ' not sure that your data arises from such a model. Possible polynomial oscillation problems');
-
-    var windowSize = options.windowSize;
-
-    var half = Math.floor(windowSize/2);
-    var np = data.length;
-    var ans = new Array(np);
-    var weights = fullWeights(windowSize,options.polynomial,options.derivative);
-    var hs = 0;
-    var constantH = true;
-    if( Object.prototype.toString.call( h ) === '[object Array]' ) {
-        constantH = false;
+  //For the borders
+  for (let i = 0; i < half; i++) {
+    let wg1 = weights[half - i - 1];
+    let wg2 = weights[half + i + 1];
+    let d1 = 0;
+    let d2 = 0;
+    for (let l = 0; l < windowSize; l++) {
+      d1 += wg1[l] * data[l];
+      d2 += wg2[l] * data[np - windowSize + l - 1];
     }
-    else{
-        hs = Math.pow(h, options.derivative);
+    if (constantH) {
+      ans[half - i - 1] = d1 / hs;
+      ans[np - half + i] = d2 / hs;
+    } else {
+      hs = getHs(h, half - i - 1, half, derivative);
+      ans[half - i - 1] = d1 / hs;
+      hs = getHs(h, np - half + i, half, derivative);
+      ans[np - half + i] = d2 / hs;
     }
-    //console.log("Constant h: "+constantH);
-    //For the borders
-    for(var i=0;i<half;i++){
-        var wg1=weights[half-i-1];
-        var wg2=weights[half+i+1];
-        var d1 = 0,d2=0;
-        for (var l = 0; l < windowSize; l++){
-            d1 += wg1[l] * data[l];
-            d2 += wg2[l] * data[np-windowSize+l-1];
-        }
-        if(constantH){
-            ans[half-i-1] = d1/hs;
-            ans[np-half+i] = d2/hs;
-        }
-        else{
-            hs = getHs(h,half-i-1,half, options.derivative);
-            ans[half-i-1] = d1/hs;
-            hs = getHs(h,np-half+i,half, options.derivative);
-            ans[np-half+i] = d2/hs;
-        }
-    }
-    //For the internal points
-    var wg = weights[half];
-    for(var i=windowSize;i<np+1;i++){
-        var d = 0;
-        for (var l = 0; l < windowSize; l++)
-            d += wg[l] * data[l+i-windowSize];
-        if(!constantH)
-            hs = getHs(h,i-half-1,half, options.derivative);
-        ans[i-half-1] = d/hs;
-    }
-    return ans;
+  }
+
+  //For the internal points
+  let wg = weights[half];
+  for (let i = windowSize; i <= np; i++) {
+    let d = 0;
+    for (let l = 0; l < windowSize; l++) d += wg[l] * data[l + i - windowSize];
+    if (!constantH) hs = getHs(h, i - half - 1, half, derivative);
+    ans[i - half - 1] = d / hs;
+  }
+  return ans;
 }
 
-function getHs(h,center,half,derivative){
-    var hs = 0;
-    var count = 0;
-    for(var i=center-half;i<center+half;i++){
-        if(i>=0 && i < h.length-1){
-            hs+= (h[i+1]-h[i]);
-            count++;
-        }
+function getHs(h, center, half, derivative) {
+  let hs = 0;
+  let count = 0;
+  for (let i = center - half; i < center + half; i++) {
+    if (i >= 0 && i < h.length - 1) {
+      hs += h[i + 1] - h[i];
+      count++;
     }
-    return Math.pow(hs/count,derivative);
+  }
+  return Math.pow(hs / count, derivative);
 }
 
-function GramPoly(i,m,k,s){
-    var Grampoly = 0;
-    if(k>0){
-        Grampoly = (4*k-2)/(k*(2*m-k+1))*(i*GramPoly(i,m,k-1,s) +
-            s*GramPoly(i,m,k-1,s-1)) - ((k-1)*(2*m+k))/(k*(2*m-k+1))*GramPoly(i,m,k-2,s);
+function GramPoly(i, m, k, s) {
+  let Grampoly = 0;
+  if (k > 0) {
+    Grampoly =
+      ((4 * k - 2) / (k * (2 * m - k + 1))) *
+        (i * GramPoly(i, m, k - 1, s) + s * GramPoly(i, m, k - 1, s - 1)) -
+      (((k - 1) * (2 * m + k)) / (k * (2 * m - k + 1))) *
+        GramPoly(i, m, k - 2, s);
+  } else {
+    if (k === 0 && s === 0) {
+      Grampoly = 1;
+    } else {
+      Grampoly = 0;
     }
-    else{
-        if(k==0&&s==0){
-            Grampoly=1;
-        }
-        else{
-            Grampoly=0;
-        }
-    }
-    //console.log(Grampoly);
-    return Grampoly;
+  }
+  return Grampoly;
 }
 
-function GenFact(a,b){
-    var gf=1;
-    if(a>=b){
-        for(var j=a-b+1;j<=a;j++){
-            gf*=j;
-        }
+function GenFact(a, b) {
+  let gf = 1;
+  if (a >= b) {
+    for (let j = a - b + 1; j <= a; j++) {
+      gf *= j;
     }
-    return gf;
+  }
+  return gf;
 }
 
-function Weight(i,t,m,n,s){
-    var sum=0;
-    for(var k=0;k<=n;k++){
-        //console.log(k);
-        sum+=(2*k+1)*(GenFact(2*m,k)/GenFact(2*m+k+1,k+1))*GramPoly(i,m,k,0)*GramPoly(t,m,k,s)
-    }
-    return sum;
+function Weight(i, t, m, n, s) {
+  let sum = 0;
+  for (let k = 0; k <= n; k++) {
+    //console.log(k);
+    sum +=
+      (2 * k + 1) *
+      (GenFact(2 * m, k) / GenFact(2 * m + k + 1, k + 1)) *
+      GramPoly(i, m, k, 0) *
+      GramPoly(t, m, k, s);
+  }
+  return sum;
 }
@@ -132,12 +131,12 @@
  */
-function fullWeights(m,n,s){
-    var weights = new Array(m);
-    var np = Math.floor(m/2);
-    for(var t=-np;t<=np;t++){
-        weights[t+np] = new Array(m);
-        for(var j=-np;j<=np;j++){
-            weights[t+np][j+np]=Weight(j,t,np,n,s);
-        }
+function fullWeights(m, n, s) {
+  let weights = new Array(m);
+  let np = Math.floor(m / 2);
+  for (let t = -np; t <= np; t++) {
+    weights[t + np] = new Array(m);
+    for (let j = -np; j <= np; j++) {
+      weights[t + np][j + np] = Weight(j, t, np, n, s);
     }
-    return weights;
+  }
+  return weights;
 }
@@ -170,2 +169,1 @@
 module.exports = SavitzkyGolay;
- 

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increased by29.14%

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decreased by-100%

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381

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Number of lines in readme file

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Dependency changes

• - Removedextend@^3.0.0

• - Removedml-stat@^1.3.3

• - Removedextend@3.0.2(transitive)

• - Removedml-stat@1.3.3(transitive)