ml-spectra-fitting - npm Package Compare versions

Comparing version 0.7.1 to 0.8.0

CHANGELOG.md

 # Changelog
 
+## [0.8.0](https://www.github.com/mljs/spectra-fitting/compare/v0.7.1...v0.8.0) (2020-11-18)
+
+
+### Features
+
+* first make a copy of the peaks to keep all attributes ([466278e](https://www.github.com/mljs/spectra-fitting/commit/466278e22628f3d0e73b6e112b42c71fbd30f91f))
+
 ### [0.7.1](https://www.github.com/mljs/spectra-fitting/compare/v0.7.0...v0.7.1) (2020-11-14)
@@ -4,0 +11,0 @@

lib/index.js

@@ -127,3 +127,3 @@ 'use strict';
  * @param {object} data - An object containing the x and y data to be fitted.
- * @param {array} peakList - A list of initial parameters to be optimized. e.g. coming from a peak picking [{x, y, width}].
+ * @param {array} peaks - A list of initial parameters to be optimized. e.g. coming from a peak picking [{x, y, width}].
  * @param {object} [options = {}]
@@ -137,3 +137,3 @@ * @param {object} [options.shape={}] - it's specify the kind of shape used to fitting.
  */
-function optimize(data, peakList, options = {}) {
+function optimize(data, peaks, options = {}) {
   let {
@@ -146,2 +146,4 @@ shape = { kind: 'gaussian' },
 
+  peaks = JSON.parse(JSON.stringify(peaks));
+
   let kind = mlPeakShapeGenerator.getKind(shape.kind);
@@ -152,6 +154,2 @@
   let y = data.y.map((e) => (e /= maxY));
-  let peaks = peakList.map((peak) => {
-    peak.y /= maxY;
-    return peak;
-  });
 
@@ -177,14 +175,13 @@ let nbParams;
 
-  let nL = peaks.length;
-  let pInit = new Float64Array(nL * nbParams);
-  let pMin = new Float64Array(nL * nbParams);
-  let pMax = new Float64Array(nL * nbParams);
-  let dt = Math.abs(data.x[0] - data.x[1]);
+  let pInit = new Float64Array(peaks.length * nbParams);
+  let pMin = new Float64Array(peaks.length * nbParams);
+  let pMax = new Float64Array(peaks.length * nbParams);
+  let deltaX = Math.abs(data.x[0] - data.x[1]);
 
-  for (let i = 0; i < nL; i++) {
+  for (let i = 0; i < peaks.length; i++) {
     let peak = peaks[i];
     for (let s = 0; s < nbParams; s++) {
-      pInit[i + s * nL] = getValue(s, peak, STATE_INIT, dt);
-      pMin[i + s * nL] = getValue(s, peak, STATE_MIN, dt);
-      pMax[i + s * nL] = getValue(s, peak, STATE_MAX, dt);
+      pInit[i + s * peaks.length] = getValue(s, peak, STATE_INIT, deltaX);
+      pMin[i + s * peaks.length] = getValue(s, peak, STATE_MIN, deltaX);
+      pMax[i + s * peaks.length] = getValue(s, peak, STATE_MAX, deltaX);
     }
@@ -202,10 +199,9 @@ }
   let { parameterError: error, iterations } = pFit;
-  let result = { error, iterations, peaks: [] };
-  for (let i = 0; i < nL; i++) {
-    let peak = {};
-    pFit.parameterValues[i + nL] *= maxY;
+  let result = { error, iterations, peaks };
+  for (let i = 0; i < peaks.length; i++) {
+    pFit.parameterValues[i + peaks.length] *= maxY;
     for (let s = 0; s < nbParams; s++) {
-      peak[keys[s]] = pFit.parameterValues[i + s * nL];
+      // we modify the optimized parameters
+      peaks[i][keys[s]] = pFit.parameterValues[i + s * peaks.length];
     }
-    result.peaks.push(peak);
   }
@@ -212,0 +208,0 @@ return result;

package.json

 {
   "name": "ml-spectra-fitting",
-  "version": "0.7.1",
+  "version": "0.8.0",
   "description": "Fit spectra using gaussian or lorentzian",
@@ -5,0 +5,0 @@ "main": "lib/index.js",

README.md

@@ -44,3 +44,3 @@ [![NPM version][npm-image]][npm-url] [![npm download][download-image]][download-url]
 //the approximate values to be optimized, It could come from a peak picking with ml-gsd
-let peakList = [
+let peaks = [
   {
@@ -61,3 +61,3 @@ x: -0.5,
 
-let fittedPeaks = optimize(data, peakList);
+let fittedPeaks = optimize(data, peaks);
 console.log(fittedPeaks);
@@ -117,3 +117,3 @@ /**
 //the approximate values to be optimized, It could coming from a peak picking with ml-gsd
-let peakList = [
+let peaks = [
   {
@@ -131,5 +131,5 @@ x: -0.5,
 
-// the function recive a peaklist with {x, y, width} as a guess
+// the function receive an array of peak with {x, y, width} as a guess
 // and return a list of objects
-let fittedParams = optimize(data, peakList, { shape: { kind: 'pseudovoigt' } });
+let fittedParams = optimize(data, peaks, { shape: { kind: 'pseudovoigt' } });
 
@@ -136,0 +136,0 @@ console.log(fittedParams);

src/index.js

@@ -17,3 +17,3 @@ import getMaxValue from 'ml-array-max';
  * @param {object} data - An object containing the x and y data to be fitted.
- * @param {array} peakList - A list of initial parameters to be optimized. e.g. coming from a peak picking [{x, y, width}].
+ * @param {array} peaks - A list of initial parameters to be optimized. e.g. coming from a peak picking [{x, y, width}].
  * @param {object} [options = {}]
@@ -27,3 +27,3 @@ * @param {object} [options.shape={}] - it's specify the kind of shape used to fitting.
  */
-export function optimize(data, peakList, options = {}) {
+export function optimize(data, peaks, options = {}) {
   let {
@@ -36,2 +36,4 @@ shape = { kind: 'gaussian' },
 
+  peaks = JSON.parse(JSON.stringify(peaks));
+
   let kind = getKind(shape.kind);
@@ -42,6 +44,2 @@
   let y = data.y.map((e) => (e /= maxY));
-  let peaks = peakList.map((peak) => {
-    peak.y /= maxY;
-    return peak;
-  });
 
@@ -67,14 +65,13 @@ let nbParams;
 
-  let nL = peaks.length;
-  let pInit = new Float64Array(nL * nbParams);
-  let pMin = new Float64Array(nL * nbParams);
-  let pMax = new Float64Array(nL * nbParams);
-  let dt = Math.abs(data.x[0] - data.x[1]);
+  let pInit = new Float64Array(peaks.length * nbParams);
+  let pMin = new Float64Array(peaks.length * nbParams);
+  let pMax = new Float64Array(peaks.length * nbParams);
+  let deltaX = Math.abs(data.x[0] - data.x[1]);
 
-  for (let i = 0; i < nL; i++) {
+  for (let i = 0; i < peaks.length; i++) {
     let peak = peaks[i];
     for (let s = 0; s < nbParams; s++) {
-      pInit[i + s * nL] = getValue(s, peak, STATE_INIT, dt);
-      pMin[i + s * nL] = getValue(s, peak, STATE_MIN, dt);
-      pMax[i + s * nL] = getValue(s, peak, STATE_MAX, dt);
+      pInit[i + s * peaks.length] = getValue(s, peak, STATE_INIT, deltaX);
+      pMin[i + s * peaks.length] = getValue(s, peak, STATE_MIN, deltaX);
+      pMax[i + s * peaks.length] = getValue(s, peak, STATE_MAX, deltaX);
     }
@@ -92,10 +89,9 @@ }
   let { parameterError: error, iterations } = pFit;
-  let result = { error, iterations, peaks: [] };
-  for (let i = 0; i < nL; i++) {
-    let peak = {};
-    pFit.parameterValues[i + nL] *= maxY;
+  let result = { error, iterations, peaks };
+  for (let i = 0; i < peaks.length; i++) {
+    pFit.parameterValues[i + peaks.length] *= maxY;
     for (let s = 0; s < nbParams; s++) {
-      peak[keys[s]] = pFit.parameterValues[i + s * nL];
+      // we modify the optimized parameters
+      peaks[i][keys[s]] = pFit.parameterValues[i + s * peaks.length];
     }
-    result.peaks.push(peak);
   }
@@ -102,0 +98,0 @@ return result;

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Total package byte prevSize

28931

increased by1.25%

Worsened metrics

Lines of code

514

decreased by-1.91%

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