feat!: homogenize and generalize output

.github/workflows/nodejs.yml

@@ -24,7 +24,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        node-version: [10.x, 12.x, 14.x, 15.x]
+        node-version: [12.x, 14.x, 16.x]
       fail-fast: false
     steps:
       - uses: actions/checkout@v2

package.json

@@ -20,6 +20,8 @@
     "eslint-fix": "npm run eslint -- --fix",
     "compile": "rollup -c",
     "prepack": "npm run compile",
+    "prettier": "prettier --check src",
+    "prettier-write": "prettier --write src",
     "test": "npm run test-coverage && npm run eslint",
     "test-only": "jest",
     "test-coverage": "jest --coverage"
@@ -77,7 +79,7 @@
     "cheminfo-types": "^0.5.0",
     "ml-peak-shape-generator": "^2.0.2",
     "ml-savitzky-golay-generalized": "2.0.3",
-    "ml-spectra-fitting": "^1.0.0",
+    "ml-spectra-fitting": "^2.0.0",
     "ml-spectra-processing": "^6.8.0"
   }
 }

src/__tests__/bigLowS2NSpectrum.js → src/__tests__/bigLowS2NSpectrum.test.js

@@ -23,7 +23,7 @@ describe('Global spectra deconvolution NMR spectra', () => {
       },
     );
 
-    joinBroadPeaks(pp, { width: 0.25 });
+    pp = joinBroadPeaks(pp, { width: 0.25 });
 
     expect(pp).toHaveLength(91);
   });

src/__tests__/broadNMR.js → src/__tests__/broadNMR.test.js

@@ -21,11 +21,11 @@ describe('Global spectra deconvolution NMR spectra', () => {
         },
       },
     );
-    joinBroadPeaks(result, { width: 0.25, shape: { kind: 'lorentzian' } });
-    expect(result).toHaveLength(14);
-    result.forEach((peak) => {
+    const newResult = joinBroadPeaks(result, { width: 0.25, shape: { kind: 'lorentzian' } });
+    expect(newResult).toHaveLength(14);
+    newResult.forEach((peak) => {
       if (Math.abs(peak.x - 4.31) < 0.01) {
-        expect(peak.width).toBeCloseTo(0.39, 2);
+        expect(peak.shape.width).toBeCloseTo(0.39, 2);
       }
     });
   });

src/__tests__/massPeakPicking.js → src/__tests__/massPeakPicking.test.js

@@ -43,26 +43,26 @@ describe('Check the peak picking of a simulated mass spectrum', () => {
     });
     expect(result[0].x).toBeCloseTo(69.938, 1);
     expect(result[0].y).toBeCloseTo(max, 2);
-    expect(result[0].width).toBeCloseTo(0.01, 4);
+    expect(result[0].shape.width).toBeCloseTo(0.01, 4);
 
     expect(result[1].x).toBeCloseTo(71.935, 2);
     expect(result[1].y).toBeCloseTo((63.99155 * max) / 100, 3);
-    expect(result[1].width).toBeCloseTo(0.01, 4);
+    expect(result[1].shape.width).toBeCloseTo(0.01, 4);
 
     expect(result[2].x).toBeCloseTo(73.932, 1);
     expect(result[2].y).toBeCloseTo((10.2373 * max) / 100, 2);
-    expect(result[2].width).toBeCloseTo(0.01, 4);
+    expect(result[2].shape.width).toBeCloseTo(0.01, 4);
 
     expect(result[3].x).toBeCloseTo(157.837, 1);
     expect(result[3].y).toBeCloseTo((51.39931 * max) / 100, 2);
-    expect(result[3].width).toBeCloseTo(0.01, 4);
+    expect(result[3].shape.width).toBeCloseTo(0.01, 4);
 
     expect(result[4].x).toBeCloseTo(159.835, 1);
     expect(result[4].y).toBeCloseTo(max, 2);
-    expect(result[4].width).toBeCloseTo(0.01, 4);
+    expect(result[4].shape.width).toBeCloseTo(0.01, 4);
 
     expect(result[5].x).toBeCloseTo(161.833, 1);
     expect(result[5].y).toBeCloseTo((48.63878 * max) / 100, 2);
-    expect(result[5].width).toBeCloseTo(0.01, 4);
+    expect(result[5].shape.width).toBeCloseTo(0.01, 4);
   });
 });

src/__tests__/simple.js → src/__tests__/simple.test.js

@@ -1,4 +1,5 @@
 import { toMatchCloseTo } from 'jest-matcher-deep-close-to';
+import { getShape1D } from 'ml-peak-shape-generator';
 
 import { gsd } from '..';
 
@@ -25,6 +26,8 @@ describe('Simple test cases', () => {
     negY.push(0);
   }
 
+  let widthToFWHM = getShape1D('gaussian').widthToFWHM;
+
   it('gsd not realtop', () => {
     let peaks = gsd(
       { x, y },
@@ -39,8 +42,8 @@ describe('Simple test cases', () => {
     );
 
     expect(peaks[0].y).toBeCloseTo(4.657, 3);
-    expect(peaks[0].base).toBeCloseTo(1.1956, 3);
-    expect(peaks[0].x).toStrictEqual(15);
+    expect(peaks[0].shape.noiseLevel).toBeCloseTo(1.1956, 3);
+    expect(peaks[0].x).toBeCloseTo(15, 2);
   });
 
   it('gsd negative peak', () => {
@@ -57,8 +60,8 @@ describe('Simple test cases', () => {
       },
     );
     expect(peaks[0].y).toBeCloseTo(-4.657, 3);
-    expect(peaks[0].base).toBeCloseTo(1.1956, 3);
-    expect(peaks[0].x).toStrictEqual(15);
+    expect(peaks[0].shape.noiseLevel).toBeCloseTo(1.1956, 3);
+    expect(peaks[0].x).toBeCloseTo(15,2);
   });
 
   it('gsd not realtop asymetric', () => {
@@ -79,18 +82,11 @@ describe('Simple test cases', () => {
     expect(peaks).toMatchCloseTo(
       [
         {
-          base: 1.2434539324230613,
-          index: 15,
-          left: {
-            index: 13,
-            x: 13,
-          },
-          right: {
-            index: 16,
-            x: 16,
+          shape: {
+            noiseLevel: 1.2434539324230613,
+            soft: false,
+            width: widthToFWHM(3),
           },
-          soft: false,
-          width: 3,
           x: 15,
           y: 5,
         },
@@ -116,18 +112,7 @@ describe('Simple test cases', () => {
     expect(peaks).toMatchCloseTo(
       [
         {
-          base: 1.2434539324230613,
-          index: 15,
-          left: {
-            index: 13,
-            x: 13,
-          },
-          right: {
-            index: 16,
-            x: 16,
-          },
-          soft: false,
-          width: 3,
+          shape: { noiseLevel: 1.2434539324230613, soft: false, width: widthToFWHM(3) },
           x: 14.5,
           y: 4.006546067576939,
         },

src/__tests__/simpleShifted.js → src/__tests__/simpleShifted.test.js

@@ -1,4 +1,5 @@
 import { toMatchCloseTo } from 'jest-matcher-deep-close-to';
+import { getShape1D } from 'ml-peak-shape-generator';
 
 import { gsd } from '..';
 
@@ -37,16 +38,16 @@ describe('Simple shifted baseline test cases', () => {
         },
       },
     );
+    let widthToFWHM = getShape1D('gaussian').widthToFWHM;
     expect(peaks).toHaveLength(1);
     expect(peaks[0]).toMatchCloseTo({
-      index: 15,
       x: 15,
       y: 4.657142857142857,
-      width: 2,
-      soft: false,
-      left: { x: 14, index: 14 },
-      right: { x: 16, index: 16 },
-      base: 0.6695521174585716,
+      shape: {
+        width: widthToFWHM(2),
+        soft: false,
+        noiseLevel: 0.6695521174585716,
+      },
     });
   });
 
@@ -63,16 +64,16 @@ describe('Simple shifted baseline test cases', () => {
         },
       },
     );
+    let widthToFWHM = getShape1D('gaussian').widthToFWHM;
     expect(peaks).toHaveLength(1);
     expect(peaks[0]).toMatchCloseTo({
-      index: 15,
       x: 15,
       y: -4.657142857142857,
-      width: 2,
-      soft: false,
-      left: { x: 14, index: 14 },
-      right: { x: 16, index: 16 },
-      base: 0.6695521174585716,
+      shape: {
+        width: widthToFWHM(2),
+        soft: false,
+        noiseLevel: 0.6695521174585716,
+      },
     });
   });
 });

src/__tests__/simulated.js → src/__tests__/simulated.test.js

@@ -32,19 +32,18 @@ describe('Global spectra deconvolution with simulated spectra', () => {
 
     expect(peakList[0].x).toBeCloseTo(-0.1, 2);
     expect(peakList[0].y).toBeCloseTo(0.2, 2);
-    expect(peakList[0].width).toBeCloseTo(0.03, 2);
+    expect(peakList[0].shape.width).toBeCloseTo(0.03, 2);
     expect(peakList[1].x).toBeCloseTo(0.1, 2);
     expect(peakList[1].y).toBeCloseTo(0.2, 2);
-    expect(peakList[1].width).toBeCloseTo(0.01, 2);
+    expect(peakList[1].shape.width).toBeCloseTo(0.01, 2);
+
 
     expect(optimizedPeaks[0].x).toBeCloseTo(-0.1, 2);
     expect(optimizedPeaks[0].y).toBeCloseTo(0.2, 2);
-    expect(optimizedPeaks[0].width).toBeCloseTo(0.03, 2);
-    expect(optimizedPeaks[0].group).toBe(0);
+    expect(optimizedPeaks[0].shape.width).toBeCloseTo(0.03, 2);
     expect(optimizedPeaks[1].x).toBeCloseTo(0.1, 2);
     expect(optimizedPeaks[1].y).toBeCloseTo(0.2, 2);
-    expect(optimizedPeaks[1].width).toBeCloseTo(0.01, 2);
-    expect(optimizedPeaks[1].group).toBe(1);
+    expect(optimizedPeaks[1].shape.width).toBeCloseTo(0.01, 2);
   });
 
   it('Check gaussian shapes with shape specification', () => {
@@ -62,62 +61,24 @@ describe('Global spectra deconvolution with simulated spectra', () => {
       realTopDetection: false,
       smoothY: false,
       heightFactor: 1,
-      shape: { kind: 'gaussian' }, // we specifiy we are expecting a gaussian shape
-    });
-
-    expect(peakList[0].x).toBeCloseTo(-0.5, 2);
-    expect(peakList[0].y).toBeCloseTo(1, 2);
-    expect(peakList[0].width).toBeCloseTo(0.2, 2); // inflection points in gaussian are higher tha FWHM
-    expect(peakList[1].x).toBeCloseTo(0.5, 2);
-    expect(peakList[1].y).toBeCloseTo(1, 2);
-    expect(peakList[1].width).toBeCloseTo(0.1, 2);
-
-    let optimizedPeaks = optimizePeaks(data, peakList);
-
-    expect(optimizedPeaks[0].x).toBeCloseTo(-0.5, 2);
-    expect(optimizedPeaks[0].y).toBeCloseTo(1, 2);
-    expect(optimizedPeaks[0].width).toBeCloseTo(0.2, 2); // optimization by default expect a gaussian shape
-    expect(optimizedPeaks[0].group).toBe(0);
-    expect(optimizedPeaks[1].x).toBeCloseTo(0.5, 2);
-    expect(optimizedPeaks[1].y).toBeCloseTo(1, 2);
-    expect(optimizedPeaks[1].width).toBeCloseTo(0.1, 2);
-    expect(optimizedPeaks[1].group).toBe(1);
-  });
-
-  it('Check gaussian shapes without specifying shape', () => {
-    const peaks = [
-      { x: -0.5, y: 1, width: 0.2 },
-      { x: 0.5, y: 1, width: 0.1 },
-    ];
-
-    const data = generateSpectrum(peaks, {
-      generator: { from: -1, to: 1, nbPoints: 10001 },
-    });
-
-    let peakList = gsd(data, {
-      minMaxRatio: 0,
-      realTopDetection: false,
-      smoothY: false,
-      heightFactor: 1,
+      shape: { kind: 'gaussian' },
     });
 
     expect(peakList[0].x).toBeCloseTo(-0.5, 2);
     expect(peakList[0].y).toBeCloseTo(1, 2);
-    expect(peakList[0].width).toBeCloseTo(0.17, 2); // inflection points in gaussian are higher tha FWHM
+    expect(peakList[0].shape.width).toBeCloseTo(0.2, 2); // inflection points in gaussian are higher tha FWHM
     expect(peakList[1].x).toBeCloseTo(0.5, 2);
     expect(peakList[1].y).toBeCloseTo(1, 2);
-    expect(peakList[1].width).toBeCloseTo(0.085, 2);
+    expect(peakList[1].shape.width).toBeCloseTo(0.1, 2);
 
     let optimizedPeaks = optimizePeaks(data, peakList);
 
     expect(optimizedPeaks[0].x).toBeCloseTo(-0.5, 2);
     expect(optimizedPeaks[0].y).toBeCloseTo(1, 2);
-    expect(optimizedPeaks[0].width).toBeCloseTo(0.2, 2); // optimization by default expect a gaussian shape
-    expect(optimizedPeaks[0].group).toBe(0);
+    expect(optimizedPeaks[0].shape.width).toBeCloseTo(0.2, 2); // optimization by default expect a gaussian shape
     expect(optimizedPeaks[1].x).toBeCloseTo(0.5, 2);
     expect(optimizedPeaks[1].y).toBeCloseTo(1, 2);
-    expect(optimizedPeaks[1].width).toBeCloseTo(0.1, 2);
-    expect(optimizedPeaks[1].group).toBe(1);
+    expect(optimizedPeaks[1].shape.width).toBeCloseTo(0.1, 2);
   });
 
   it('Should provide 1 peak', () => {
@@ -145,6 +106,6 @@ describe('Global spectra deconvolution with simulated spectra', () => {
     expect(peakList).toHaveLength(1);
     expect(peakList[0].x).toBeCloseTo(0, 2);
     expect(peakList[0].y).toBeCloseTo(1, 2);
-    expect(peakList[0].width).toBeCloseTo(0.12, 3);
+    expect(peakList[0].shape.width).toBeCloseTo(0.12, 3);
   });
 });

src/gsd.js

@@ -9,7 +9,6 @@ import SG from 'ml-savitzky-golay-generalized';
  * @param {object} [options={}] - Options object
  * @param {object} [options.shape={}] - Object that specified the kind of shape to calculate the FWHM instead of width between inflection points. see https://mljs.github.io/peak-shape-generator/#inflectionpointswidthtofwhm
  * @param {object} [options.shape.kind='gaussian']
- * @param {object} [options.shape.options={}]
  * @param {object} [options.sgOptions] - Options object for Savitzky-Golay filter. See https://github.com/mljs/savitzky-golay-generalized#options
  * @param {number} [options.sgOptions.windowSize = 9] - points to use in the approximations
  * @param {number} [options.sgOptions.polynomial = 3] - degree of the polynomial to use in the approximations
@@ -32,7 +31,7 @@ export function gsd(data, options = {}) {
       windowSize: 9,
       polynomial: 3,
     },
-    shape = {},
+    shape = { kind: 'gaussian' },
     smoothY = true,
     heightFactor = 0,
     broadRatio = 0.0,
@@ -170,9 +169,7 @@ export function gsd(data, options = {}) {
     }
   }
 
-  let widthProcessor = shape.kind
-    ? getShape1D(shape.kind, shape.options).widthToFWHM
-    : (x) => x;
+  let widthProcessor = getShape1D(shape.kind, shape.options).widthToFWHM;
 
   let signals = [];
   let lastK = -1;
@@ -209,17 +206,17 @@ export function gsd(data, options = {}) {
           y: maxCriteria
             ? yData[minddY[j]] + noiseLevel
             : -yData[minddY[j]] - noiseLevel,
-          width: widthProcessor(width),
-          soft: broadMask[j],
+          shape: {
+            kind: shape.kind,
+            width: widthProcessor(width),
+            soft: broadMask[j],
+          },
         });
 
-        signals[signals.length - 1].left = intervalL[possible];
-        signals[signals.length - 1].right = intervalR[possible];
-
         if (heightFactor) {
           let yLeft = yData[intervalL[possible].index];
           let yRight = yData[intervalR[possible].index];
-          signals[signals.length - 1].height =
+          signals[signals.length - 1].shape.height =
             heightFactor *
             (signals[signals.length - 1].y - (yLeft + yRight) / 2);
         }
@@ -233,7 +230,7 @@ export function gsd(data, options = {}) {
 
   // Correct the values to fit the original spectra data
   for (let j = 0; j < signals.length; j++) {
-    signals[j].base = noiseLevel;
+    signals[j].shape.noiseLevel = noiseLevel;
   }
 
   signals.sort((a, b) => {