export fwhm instead of width between the inflection points if kind of shape is specified (#32)

* feat: add shape and lmOptions as options of joinBroadPeaks method

* feat: export fwhm instead of width between the inflection points if kind of shape is specified
Default behavior: the fwhm depends of the kind of shape in options. By default it returns the width between the inflection points

Author: jobo322

package.json

@@ -15,7 +15,8 @@
     "build": "rollup -c && cheminfo-build --entry src/index.js --root GSD",
     "eslint": "eslint src --cache",
     "eslint-fix": "npm run eslint -- --fix",
-    "prepublishOnly": "rollup -c",
+    "compile": "rollup -c",
+    "prepublishOnly": "npm run compile",
     "test": "npm run test-coverage && npm run eslint",
     "test-only": "jest",
     "test-coverage": "jest --coverage"
@@ -61,6 +62,7 @@
     "eslint-plugin-prettier": "^3.1.4",
     "esm": "^3.2.25",
     "jest": "^26.6.3",
+    "jest-matcher-deep-close-to": "^2.0.1",
     "mf-global": "^1.3.0",
     "ml-stat": "^1.3.3",
     "prettier": "^2.1.2",
@@ -70,6 +72,7 @@
   },
   "dependencies": {
     "ml-savitzky-golay-generalized": "2.0.2",
-    "ml-spectra-fitting": "^0.5.0"
+    "ml-spectra-fitting": "^0.6.0",
+    "ml-peak-shape-generator": "^0.9.0"
   }
 }

src/__tests__/broadNMR.js

@@ -21,7 +21,7 @@ describe('Global spectra deconvolution NMR spectra', () => {
         },
       },
     );
-    joinBroadPeaks(result, { width: 0.25 });
+    joinBroadPeaks(result, { width: 0.25, shape: { kind: 'lorentzian' } });
     expect(result).toHaveLength(14);
     result.forEach((peak) => {
       if (Math.abs(peak.x - 4.31) < 0.01) {

src/__tests__/simple.js

@@ -1,5 +1,9 @@
+import { toMatchCloseTo } from 'jest-matcher-deep-close-to';
+
 import { gsd } from '..';
 
+expect.extend({ toMatchCloseTo });
+
 describe('Simple test cases', () => {
   let x = [];
   let y = [];
@@ -49,24 +53,27 @@ describe('Simple test cases', () => {
       },
     );
 
-    expect(peaks).toStrictEqual([
-      {
-        base: 1.2434539324230613,
-        index: 15,
-        left: {
-          index: 13,
-          x: 13,
+    expect(peaks).toMatchCloseTo(
+      [
+        {
+          base: 1.2434539324230613,
+          index: 15,
+          left: {
+            index: 13,
+            x: 13,
+          },
+          right: {
+            index: 16,
+            x: 16,
+          },
+          soft: false,
+          width: 3,
+          x: 15,
+          y: 5,
         },
-        right: {
-          index: 16,
-          x: 16,
-        },
-        soft: false,
-        width: 3,
-        x: 15,
-        y: 5,
-      },
-    ]);
+      ],
+      2,
+    );
   });
 
   it('gsd realtop', () => {
@@ -83,23 +90,26 @@ describe('Simple test cases', () => {
         },
       },
     );
-    expect(peaks).toStrictEqual([
-      {
-        base: 1.2434539324230613,
-        index: 15,
-        left: {
-          index: 13,
-          x: 13,
-        },
-        right: {
-          index: 16,
-          x: 16,
+    expect(peaks).toMatchCloseTo(
+      [
+        {
+          base: 1.2434539324230613,
+          index: 15,
+          left: {
+            index: 13,
+            x: 13,
+          },
+          right: {
+            index: 16,
+            x: 16,
+          },
+          soft: false,
+          width: 3,
+          x: 14.5,
+          y: 4.006546067576939,
         },
-        soft: false,
-        width: 3,
-        x: 14.5,
-        y: 4.006546067576939,
-      },
-    ]);
+      ],
+      2,
+    );
   });
 });

src/gsd.js

@@ -1,3 +1,4 @@
+import { inflectionPointsWidthToFWHM } from 'ml-peak-shape-generator';
 import SG from 'ml-savitzky-golay-generalized';
 
 /**
@@ -28,6 +29,7 @@ export function gsd(data, options = {}) {
       windowSize: 9,
       polynomial: 3,
     },
+    shape = {},
     smoothY = true,
     heightFactor = 0,
     broadRatio = 0.0,
@@ -198,11 +200,14 @@ export function gsd(data, options = {}) {
 
     if (possible !== -1) {
       if (Math.abs(yData[minddY[j]]) > minMaxRatio * maxY) {
+        let width = Math.abs(intervalR[possible].x - intervalL[possible].x);
         signals.push({
           index: minddY[j],
           x: frequency,
           y: (yData[minddY[j]] + yCorrection.b) / yCorrection.m,
-          width: Math.abs(intervalR[possible].x - intervalL[possible].x), // widthCorrection
+          width: shape.kind
+            ? inflectionPointsWidthToFWHM(width, shape.kind, shape.options)
+            : width, // widthCorrection
           soft: broadMask[j],
         });
 

src/post/joinBroadPeaks.js

@@ -4,10 +4,10 @@ import { optimize } from 'ml-spectra-fitting';
  * This function try to join the peaks that seems to belong to a broad signal in a single broad peak.
  * @param {Array} peakList - A list of initial parameters to be optimized. e.g. coming from a peak picking [{x, y, width}].
  * @param {object} [options = {}] -
- * @param {number} [options.width] - width limit to join peaks.
+ * @param {number} options.width - width limit to join peaks.
  */
 export function joinBroadPeaks(peakList, options = {}) {
-  let width = options.width;
+  let { shape = { kind: 'gaussian' }, width, lmOptions = {} } = options;
   let broadLines = [];
   // Optimize the possible broad lines
   let max = 0;
@@ -48,7 +48,7 @@ export function joinBroadPeaks(peakList, options = {}) {
               ),
             },
           ],
-          { kind: 'lorentzian' },
+          { shape, lmOptions },
         );
         let { peaks: peak } = fitted;
         peak[0].index = Math.floor(

src/post/optimizePeaks.js

@@ -1,7 +1,5 @@
 import { optimize } from 'ml-spectra-fitting';
 
-const kindSupported = ['gaussian', 'lorentzian', 'pseudovoigt'];
-
 /**
  * Optimize the position (x), max intensity (y), full width at half maximum (width)
  * and the ratio of gaussian contribution (mu) if it's required. It supports three kind of shapes: gaussian, lorentzian and pseudovoigt
@@ -16,9 +14,11 @@ const kindSupported = ['gaussian', 'lorentzian', 'pseudovoigt'];
 
 export function optimizePeaks(data, peakList, options = {}) {
   const {
-    functionName = 'gaussian',
     factorWidth = 1,
     joinPeaks = true,
+    shape = {
+      kind: 'gaussian',
+    },
     optimizationOptions = {
       damping: 1.5,
       maxIterations: 100,
@@ -28,8 +28,6 @@ export function optimizePeaks(data, peakList, options = {}) {
 
   let { x, y } = data;
 
-  checkFuncName(functionName, optimizationOptions);
-
   let lastIndex = [0];
   let groups = groupPeaks(peakList, factorWidth, joinPeaks);
 
@@ -48,7 +46,7 @@ export function optimizePeaks(data, peakList, options = {}) {
       );
       if (sampling.x.length > 5) {
         let { peaks: optPeaks } = optimize(sampling, peaks, {
-          kind: functionName,
+          shape,
           lmOptions: optimizationOptions,
         });
         for (let j = 0; j < optPeaks.length; j++) {
@@ -69,7 +67,7 @@ export function optimizePeaks(data, peakList, options = {}) {
 
       if (sampling.x.length > 5) {
         let fitResult = optimize(sampling, [peaks], {
-          kind: functionName,
+          shape,
           lmOptions: optimizationOptions,
         });
         let { peaks: optPeaks } = fitResult;
@@ -177,17 +175,3 @@ function groupPeaks(peakList, nL, joinPeaks) {
   }
   return groups;
 }
-
-function checkFuncName(functionName, optimizationOptions) {
-  let kind = functionName.toLowerCase().replace(/[^a-z]/g, '');
-  let isSupported = kindSupported.some((ks) => ks === kind);
-  if (isSupported) {
-    optimizationOptions.kind = kind;
-  } else {
-    throw new Error(
-      `Kind of function unsupported. Just these kind are supported: ${kindSupported.join(
-        ', ',
-      )}`,
-    );
-  }
-}