ml-spectra-fitting - npm Package Compare versions

Comparing version 1.0.0 to 2.0.0

CHANGELOG.md

 # Changelog
 
+## [2.0.0](https://www.github.com/mljs/spectra-fitting/compare/v1.0.0...v2.0.0) (2021-10-11)
+
+
+### ⚠ BREAKING CHANGES
+
+* homogenize and generalize output (#58)
+
+### Features
+
+* fixing wrong order in object assignment. close [#52](https://www.github.com/mljs/spectra-fitting/issues/52) ([#53](https://www.github.com/mljs/spectra-fitting/issues/53)) ([ce3d47f](https://www.github.com/mljs/spectra-fitting/commit/ce3d47f4a9f8da60a58c030a98ac5acf5fda3fa0))
+* homogenize and generalize output ([#58](https://www.github.com/mljs/spectra-fitting/issues/58)) ([1b3970c](https://www.github.com/mljs/spectra-fitting/commit/1b3970c992f45e5c497507b8b0fa500464d73387))
+* update ml-peak-shape-generator to 2.0.1 ([#54](https://www.github.com/mljs/spectra-fitting/issues/54)) ([73782cc](https://www.github.com/mljs/spectra-fitting/commit/73782cc0a5291f0dd7b618861164f3881e89014a))
+
 ## [1.0.0](https://www.github.com/mljs/spectra-fitting/compare/v0.13.0...v1.0.0) (2021-03-24)
@@ -4,0 +17,0 @@

lib/index.js

@@ -5,3 +5,2 @@ 'use strict';
 
-var assignDeep = require('assign-deep');
 var getMaxValue = require('ml-array-max');
@@ -13,3 +12,2 @@ var mlPeakShapeGenerator = require('ml-peak-shape-generator');
 
-var assignDeep__default = /*#__PURE__*/_interopDefaultLegacy(assignDeep);
 var getMaxValue__default = /*#__PURE__*/_interopDefaultLegacy(getMaxValue);
@@ -26,4 +24,6 @@ var LM__default = /*#__PURE__*/_interopDefaultLegacy(LM);
 
+const { fct: pseudoVoigtFct } = mlPeakShapeGenerator.pseudoVoigt;
+
 function sumOfGaussianLorentzians(p) {
-  return function (t) {
+  return (t) => {
     let nL = p.length / 4;
@@ -33,3 +33,3 @@ let result = 0;
       result +=
-        p[i + nL] * mlPeakShapeGenerator.PseudoVoigt.fct(t - p[i], p[i + nL * 2], p[i + nL * 3]);
+        p[i + nL] * pseudoVoigtFct(t - p[i], p[i + nL * 2], p[i + nL * 3]);
     }
@@ -48,8 +48,10 @@ return result;
 
+const { fct: gaussianFct } = mlPeakShapeGenerator.gaussian;
+
 function sumOfGaussians(p) {
-  return function (t) {
+  return (t) => {
     let nL = p.length / 3;
     let result = 0;
     for (let i = 0; i < nL; i++) {
-      result += p[i + nL] * mlPeakShapeGenerator.Gaussian.fct(t - p[i], p[i + nL * 2]);
+      result += p[i + nL] * gaussianFct(t - p[i], p[i + nL * 2]);
     }
@@ -68,8 +70,10 @@ return result;
 
+const { fct: lorentzianFct } = mlPeakShapeGenerator.lorentzian;
+
 function sumOfLorentzians(p) {
-  return function (t) {
+  return (t) => {
     let nL = p.length / 3;
     let result = 0;
     for (let i = 0; i < nL; i++) {
-      result += p[i + nL] * mlPeakShapeGenerator.Lorentzian.fct(t - p[i], p[i + nL * 2]);
+      result += p[i + nL] * lorentzianFct(t - p[i], p[i + nL * 2]);
     }
@@ -80,2 +84,31 @@ return result;
 
+const isValidKey = (key) => {
+  return key !== '__proto__' && key !== 'constructor' && key !== 'prototype';
+};
+
+const isObject = (val) => {
+  return typeof val === 'object';
+};
+const isPrimitive = (val) => {
+  return typeof val === 'object' ? val === null : typeof val !== 'function';
+};
+
+function assignDeep(target, ...args) {
+  let index = 0;
+  if (isPrimitive(target)) target = args[index++];
+  if (!target) target = {};
+  for (; index < args.length; index++) {
+    if (!isObject(args[index])) continue;
+    for (const key in args[index]) {
+      if (!isValidKey(key)) continue;
+      if (isObject(target[key]) && isObject(args[index][key])) {
+        assignDeep(target[key], args[index][key]);
+      } else {
+        target[key] = args[index][key];
+      }
+    }
+  }
+  return target;
+}
+
 function checkInput(data, peaks, options) {
@@ -103,5 +136,5 @@ let {
           init: (peak) => peak.x,
-          max: (peak) => peak.x + peak.width * 2,
-          min: (peak) => peak.x - peak.width * 2,
-          gradientDifference: (peak) => peak.width * 2e-3,
+          max: (peak) => peak.x + peak.shape.width * 2,
+          min: (peak) => peak.x - peak.shape.width * 2,
+          gradientDifference: (peak) => peak.shape.width * 2e-3,
         },
@@ -115,6 +148,6 @@ y: {
         width: {
-          init: (peak) => peak.width,
-          max: (peak) => peak.width * 4,
-          min: (peak) => peak.width * 0.25,
-          gradientDifference: (peak) => peak.width * 2e-3,
+          init: (peak) => peak.shape.width,
+          max: (peak) => peak.shape.width * 4,
+          min: (peak) => peak.shape.width * 0.25,
+          gradientDifference: (peak) => peak.shape.width * 2e-3,
         },
@@ -128,5 +161,5 @@ };
           init: (peak) => peak.x,
-          max: (peak) => peak.x + peak.width * 2,
-          min: (peak) => peak.x - peak.width * 2,
-          gradientDifference: (peak) => peak.width * 2e-3,
+          max: (peak) => peak.x + peak.shape.width * 2,
+          min: (peak) => peak.x - peak.shape.width * 2,
+          gradientDifference: (peak) => peak.shape.width * 2e-3,
         },
@@ -140,6 +173,6 @@ y: {
         width: {
-          init: (peak) => peak.width,
-          max: (peak) => peak.width * 4,
-          min: (peak) => peak.width * 0.25,
-          gradientDifference: (peak) => peak.width * 2e-3,
+          init: (peak) => peak.shape.width,
+          max: (peak) => peak.shape.width * 4,
+          min: (peak) => peak.shape.width * 0.25,
+          gradientDifference: (peak) => peak.shape.width * 2e-3,
         },
@@ -153,5 +186,5 @@ };
           init: (peak) => peak.x,
-          max: (peak) => peak.x + peak.width * 2,
-          min: (peak) => peak.x - peak.width * 2,
-          gradientDifference: (peak) => peak.width * 2e-3,
+          max: (peak) => peak.x + peak.shape.width * 2,
+          min: (peak) => peak.x - peak.shape.width * 2,
+          gradientDifference: (peak) => peak.shape.width * 2e-3,
         },
@@ -165,9 +198,9 @@ y: {
         width: {
-          init: (peak) => peak.width,
-          max: (peak) => peak.width * 4,
-          min: (peak) => peak.width * 0.25,
-          gradientDifference: (peak) => peak.width * 2e-3,
+          init: (peak) => peak.shape.width,
+          max: (peak) => peak.shape.width * 4,
+          min: (peak) => peak.shape.width * 0.25,
+          gradientDifference: (peak) => peak.shape.width * 2e-3,
         },
         mu: {
-          init: (peak) => (peak.mu !== undefined ? peak.mu : 0.5),
+          init: (peak) => (peak.shape.mu !== undefined ? peak.shape.mu : 0.5),
           min: () => 0,
@@ -184,3 +217,3 @@ max: () => 1,
   let x = data.x;
-  let maxY = getMaxValue__default['default'](data.y);
+  let maxY = getMaxValue__default["default"](data.y);
   let y = new Array(x.length);
@@ -195,3 +228,3 @@ for (let i = 0; i < x.length; i++) {
 
-  let parameters = assignDeep__default['default']({}, optimization.parameters, defaultParameters);
+  let parameters = assignDeep({}, defaultParameters, optimization.parameters);
 
@@ -238,3 +271,3 @@ for (let key in parameters) {
       return {
-        algorithm: LM__default['default'],
+        algorithm: LM__default["default"],
         optimizationOptions: checkOptions(kind, options),
@@ -358,6 +391,11 @@ };
   for (let i = 0; i < nbShapes; i++) {
-    pFit.parameterValues[i + nbShapes] *= maxY;
     for (let k = 0; k < parameterKey.length; k++) {
+      const key = parameterKey[k];
+      const value = pFit.parameterValues[i + k * nbShapes];
       // we modify the optimized parameters
-      peaks[i][parameterKey[k]] = pFit.parameterValues[i + k * nbShapes];
+      if (key === 'x' || key === 'y') {
+        peaks[i][parameterKey[k]] = key === 'y' ? value * maxY : value;
+      } else {
+        peaks[i].shape[parameterKey[k]] = value;
+      }
     }
@@ -364,0 +402,0 @@ }

package.json

 {
   "name": "ml-spectra-fitting",
-  "version": "1.0.0",
+  "version": "2.0.0",
   "description": "Fit spectra using gaussian or lorentzian",
@@ -18,2 +18,4 @@ "main": "lib/index.js",
     "test": "npm run test-coverage && npm run eslint",
+    "prettier": "prettier --check src",
+    "prettier-write": "prettier --write src",
     "test-coverage": "jest --coverage",
@@ -34,2 +36,5 @@ "test-only": "jest",
   "author": "Andres Castillo",
+  "contributors": [
+    "J. Alejandro Bolaños A. <jobo322>"
+  ],
   "license": "MIT",
@@ -51,19 +56,18 @@ "bugs": {
   "devDependencies": {
-    "@babel/plugin-transform-modules-commonjs": "^7.13.8",
-    "@types/jest": "^26.0.21",
-    "cheminfo-build": "^1.1.10",
-    "eslint": "^7.22.0",
-    "eslint-config-cheminfo": "^5.2.3",
+    "@babel/plugin-transform-modules-commonjs": "^7.15.4",
+    "@types/jest": "^27.0.2",
+    "cheminfo-build": "^1.1.11",
+    "eslint": "^7.32.0",
+    "eslint-config-cheminfo": "^5.6.0",
     "esm": "^3.2.25",
-    "jest": "^26.6.3",
-    "prettier": "^2.2.1",
-    "rollup": "^2.42.3",
-    "spectrum-generator": "^4.7.0"
+    "jest": "^27.2.5",
+    "prettier": "^2.4.1",
+    "rollup": "^2.58.0",
+    "spectrum-generator": "^5.4.0"
   },
   "dependencies": {
-    "assign-deep": "^1.0.1",
-    "ml-array-max": "^1.2.2",
-    "ml-levenberg-marquardt": "^3.1.0",
-    "ml-peak-shape-generator": "^1.0.0"
+    "ml-array-max": "^1.2.3",
+    "ml-levenberg-marquardt": "^3.1.1",
+    "ml-peak-shape-generator": "^2.0.2"
   }
 }

src/__tests__/optimize.test.js

@@ -13,4 +13,4 @@ import { optimize } from '../index';
 
-describe('Optimize sum of Lorentzian', function () {
-  it('group of two GL', function () {
+describe('Optimize sum of Lorentzian', () => {
+  it('group of two GL', () => {
     let pTrue = [-0.5, 0.5, 0.001, 0.001, 0.31, 0.31];
@@ -21,4 +21,4 @@ let yData = sumOfLorentzians(pTrue);
       [
-        { x: -0.5, y: 0.0009, width: (xFactor * nbPoints) / 8 },
-        { x: 0.52, y: 0.0009, width: (xFactor * nbPoints) / 8 },
+        { x: -0.5, y: 0.0009, shape: { width: (xFactor * nbPoints) / 8 } },
+        { x: 0.52, y: 0.0009, shape: { width: (xFactor * nbPoints) / 8 } },
       ],
@@ -32,3 +32,3 @@ { shape: { kind: 'lorentzian' } },
       expect(pFit.y).toBeCloseTo(pTrue[i + nL], 2);
-      expect(pFit.width).toBeCloseTo(pTrue[i + nL * 2], 2);
+      expect(pFit.shape.width).toBeCloseTo(pTrue[i + nL * 2], 2);
     }
@@ -38,4 +38,4 @@ });
 
-describe('Optimize sum of Gaussians', function () {
-  it('group of two GL', function () {
+describe('Optimize sum of Gaussians', () => {
+  it('group of two GL', () => {
     let pTrue = [-0.5, 0.5, 0.001, 0.001, 0.31, 0.31];
@@ -46,4 +46,4 @@ let yData = sumOfGaussians(pTrue);
       [
-        { x: -0.5, y: 0.0009, width: (xFactor * nbPoints) / 8 },
-        { x: 0.52, y: 0.0009, width: (xFactor * nbPoints) / 8 },
+        { x: -0.5, y: 0.0009, shape: { width: (xFactor * nbPoints) / 8 } },
+        { x: 0.52, y: 0.0009, shape: { width: (xFactor * nbPoints) / 8 } },
       ],
@@ -57,3 +57,3 @@ { shape: { kind: 'gaussian' } },
       expect(pFit.y).toBeCloseTo(pTrue[i + nL], 2);
-      expect(pFit.width).toBeCloseTo(pTrue[i + nL * 2], 2);
+      expect(pFit.shape.width).toBeCloseTo(pTrue[i + nL * 2], 2);
     }
@@ -63,4 +63,4 @@ });
 
-describe('Optimize 4 parameters of a linear combination of gaussian and lorentzians', function () {
-  it('group of two GL', function () {
+describe('Optimize 4 parameters of a linear combination of gaussian and lorentzians', () => {
+  it('group of two GL', () => {
     let pTrue = [
@@ -80,4 +80,4 @@ 0,
       [
-        { x: 0.1, y: 0.0009, width: (xFactor * nbPoints) / 6 },
-        { x: 0.1, y: 0.0009, width: (xFactor * nbPoints) / 6 },
+        { x: 0.1, y: 0.0009, shape: { width: (xFactor * nbPoints) / 6 } },
+        { x: 0.1, y: 0.0009, shape: { width: (xFactor * nbPoints) / 6 } },
       ],
@@ -98,4 +98,4 @@ {
       expect(pFit.y).toBeCloseTo(pTrue[i + nL], 3);
-      expect(pFit.width).toBeCloseTo(pTrue[i + nL * 2], 3);
-      expect(pFit.mu).toBeCloseTo(pTrue[i + nL * 3], 3);
+      expect(pFit.shape.width).toBeCloseTo(pTrue[i + nL * 2], 3);
+      expect(pFit.shape.mu).toBeCloseTo(pTrue[i + nL * 3], 3);
     }
@@ -102,0 +102,0 @@ });

src/index.js

@@ -1,3 +0,3 @@
-import { checkInput } from './checkInput';
-import { selectMethod } from './selectMethod';
+import { checkInput } from './util/checkInput';
+import { selectMethod } from './util/selectMethod';
 
@@ -90,6 +90,11 @@ // const STATE_INIT = 0;
   for (let i = 0; i < nbShapes; i++) {
-    pFit.parameterValues[i + nbShapes] *= maxY;
     for (let k = 0; k < parameterKey.length; k++) {
+      const key = parameterKey[k];
+      const value = pFit.parameterValues[i + k * nbShapes];
       // we modify the optimized parameters
-      peaks[i][parameterKey[k]] = pFit.parameterValues[i + k * nbShapes];
+      if (key === 'x' || key === 'y') {
+        peaks[i][parameterKey[k]] = key === 'y' ? value * maxY : value;
+      } else {
+        peaks[i].shape[parameterKey[k]] = value;
+      }
     }
@@ -96,0 +101,0 @@ }

src/shapes/sumOfGaussianLorentzians.js

@@ -1,2 +0,2 @@
-import { PseudoVoigt } from 'ml-peak-shape-generator';
+import { pseudoVoigt } from 'ml-peak-shape-generator';
 
@@ -11,4 +11,6 @@ /**
 
+const { fct: pseudoVoigtFct } = pseudoVoigt;
+
 export function sumOfGaussianLorentzians(p) {
-  return function (t) {
+  return (t) => {
     let nL = p.length / 4;
@@ -18,3 +20,3 @@ let result = 0;
       result +=
-        p[i + nL] * PseudoVoigt.fct(t - p[i], p[i + nL * 2], p[i + nL * 3]);
+        p[i + nL] * pseudoVoigtFct(t - p[i], p[i + nL * 2], p[i + nL * 3]);
     }
@@ -21,0 +23,0 @@ return result;

src/shapes/sumOfGaussians.js

@@ -1,2 +0,3 @@
-import { Gaussian } from 'ml-peak-shape-generator';
+import { gaussian } from 'ml-peak-shape-generator';
+
 /**
@@ -10,8 +11,10 @@ * This function calculates the spectrum as a sum of gaussian functions. The Gaussian
 
+const { fct: gaussianFct } = gaussian;
+
 export function sumOfGaussians(p) {
-  return function (t) {
+  return (t) => {
     let nL = p.length / 3;
     let result = 0;
     for (let i = 0; i < nL; i++) {
-      result += p[i + nL] * Gaussian.fct(t - p[i], p[i + nL * 2]);
+      result += p[i + nL] * gaussianFct(t - p[i], p[i + nL * 2]);
     }
@@ -18,0 +21,0 @@ return result;

src/shapes/sumOfLorentzians.js

@@ -1,2 +0,2 @@
-import { Lorentzian } from 'ml-peak-shape-generator';
+import { lorentzian } from 'ml-peak-shape-generator';
 
@@ -11,8 +11,10 @@ /**
 
+const { fct: lorentzianFct } = lorentzian;
+
 export function sumOfLorentzians(p) {
-  return function (t) {
+  return (t) => {
     let nL = p.length / 3;
     let result = 0;
     for (let i = 0; i < nL; i++) {
-      result += p[i + nL] * Lorentzian.fct(t - p[i], p[i + nL * 2]);
+      result += p[i + nL] * lorentzianFct(t - p[i], p[i + nL * 2]);
     }
@@ -19,0 +21,0 @@ return result;

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Improved metrics

Total package byte prevSize

46287

increased by9.51%

Dependency count

3

decreased by-25%

Number of package files

15

increased by15.38%

Lines of code

824

increased by11.8%

Dependency changes

• + Addedml-peak-shape-generator@2.0.2(transitive)

• - Removedassign-deep@^1.0.1

• - Removedassign-deep@1.0.1(transitive)

• - Removedassign-symbols@2.0.2(transitive)

• - Removedml-peak-shape-generator@1.0.0(transitive)

• Updatedml-array-max@^1.2.3

• Updatedml-levenberg-marquardt@^3.1.1

• Updatedml-peak-shape-generator@^2.0.2