ml-spectra-fitting - npm Package Compare versions

Comparing version 4.2.1 to 4.2.2

lib-esm/index.d.ts

@@ -1,2 +0,2 @@
-import { DataXY, DoubleArray } from 'cheminfo-types';
+import { DataXY } from 'cheminfo-types';
 import { Shape1D } from 'ml-peak-shape-generator';
@@ -99,5 +99,5 @@ export interface InitialParameter {
  */
-export declare function optimize<T extends Peak>(data: DataXY<DoubleArray>, peaks: T[], options?: OptimizeOptions): {
+export declare function optimize<T extends Peak>(data: DataXY, peaks: T[], options?: OptimizeOptions): {
     error: number;
-    peaks: OptimizedPeakIDOrNot<T>[];
+    peaks: Array<OptimizedPeakIDOrNot<T>>;
     iterations: number;
@@ -104,0 +104,0 @@ };

lib-esm/index.js

@@ -15,3 +15,3 @@ import { xMinMaxValues } from 'ml-spectra-processing';
     // rescale data
-    let temp = xMinMaxValues(data.y);
+    const temp = xMinMaxValues(data.y);
     const minMaxY = { ...temp, range: temp.max - temp.min };
@@ -21,3 +21,3 @@ const internalPeaks = getInternalPeaks(peaks, minMaxY, options);
     const { baseline: shiftValue = minMaxY.min } = options;
-    let normalizedY = new Float64Array(data.y.length);
+    const normalizedY = new Float64Array(data.y.length);
     for (let i = 0; i < data.y.length; i++) {
@@ -41,5 +41,5 @@ normalizedY[i] = (data.y[i] - shiftValue) / minMaxY.range;
     }
-    let { algorithm, optimizationOptions } = selectMethod(options.optimization);
-    let sumOfShapes = getSumOfShapes(internalPeaks);
-    let fitted = algorithm({ x: data.x, y: normalizedY }, sumOfShapes, {
+    const { algorithm, optimizationOptions } = selectMethod(options.optimization);
+    const sumOfShapes = getSumOfShapes(internalPeaks);
+    const fitted = algorithm({ x: data.x, y: normalizedY }, sumOfShapes, {
         minValues,
@@ -52,4 +52,4 @@ maxValues,
     const fittedValues = fitted.parameterValues;
-    let newPeaks = [];
-    for (let peak of internalPeaks) {
+    const newPeaks = [];
+    for (const peak of internalPeaks) {
         const { id, shape, parameters, fromIndex } = peak;
@@ -56,0 +56,0 @@ let newPeak = { x: 0, y: 0, shape };

lib-esm/util/internalPeaks/getInternalPeaks.js

@@ -13,3 +13,3 @@ import { getShape1D } from 'ml-peak-shape-generator';
     let index = 0;
-    let internalPeaks = [];
+    const internalPeaks = [];
     const { baseline: shiftValue = minMaxY.min } = options;
@@ -32,4 +32,4 @@ const normalizedPeaks = peaks.map((peak) => {
         };
-        for (let parameter of parameters) {
-            for (let property of properties) {
+        for (const parameter of parameters) {
+            for (const property of properties) {
                 // check if the property is specified in the peak
@@ -36,0 +36,0 @@ let propertyValue = peak?.parameters?.[parameter]?.[property];

lib-esm/util/selectMethod.js

@@ -8,3 +8,3 @@ import { levenbergMarquardt } from 'ml-levenberg-marquardt';
 export function selectMethod(optimizationOptions = {}) {
-    let { kind = 'lm', options } = optimizationOptions;
+    const { kind = 'lm', options } = optimizationOptions;
     switch (kind) {
@@ -11,0 +11,0 @@ case 'lm':

lib-esm/util/wrappers/directOptimization.js

@@ -26,3 +26,3 @@ import direct from 'ml-direct';
         for (let i = 0; i < nbPoints; i++) {
-            error += Math.pow(y[i] - fct(x[i]), 2);
+            error += (y[i] - fct(x[i])) ** 2;
         }
@@ -29,0 +29,0 @@ return error;

lib/index.d.ts

@@ -1,2 +0,2 @@
-import { DataXY, DoubleArray } from 'cheminfo-types';
+import { DataXY } from 'cheminfo-types';
 import { Shape1D } from 'ml-peak-shape-generator';
@@ -99,5 +99,5 @@ export interface InitialParameter {
  */
-export declare function optimize<T extends Peak>(data: DataXY<DoubleArray>, peaks: T[], options?: OptimizeOptions): {
+export declare function optimize<T extends Peak>(data: DataXY, peaks: T[], options?: OptimizeOptions): {
     error: number;
-    peaks: OptimizedPeakIDOrNot<T>[];
+    peaks: Array<OptimizedPeakIDOrNot<T>>;
     iterations: number;
@@ -104,0 +104,0 @@ };

lib/index.js

@@ -18,3 +18,3 @@ "use strict";
     // rescale data
-    let temp = (0, ml_spectra_processing_1.xMinMaxValues)(data.y);
+    const temp = (0, ml_spectra_processing_1.xMinMaxValues)(data.y);
     const minMaxY = { ...temp, range: temp.max - temp.min };
@@ -24,3 +24,3 @@ const internalPeaks = (0, getInternalPeaks_1.getInternalPeaks)(peaks, minMaxY, options);
     const { baseline: shiftValue = minMaxY.min } = options;
-    let normalizedY = new Float64Array(data.y.length);
+    const normalizedY = new Float64Array(data.y.length);
     for (let i = 0; i < data.y.length; i++) {
@@ -44,5 +44,5 @@ normalizedY[i] = (data.y[i] - shiftValue) / minMaxY.range;
     }
-    let { algorithm, optimizationOptions } = (0, selectMethod_1.selectMethod)(options.optimization);
-    let sumOfShapes = (0, getSumOfShapes_1.getSumOfShapes)(internalPeaks);
-    let fitted = algorithm({ x: data.x, y: normalizedY }, sumOfShapes, {
+    const { algorithm, optimizationOptions } = (0, selectMethod_1.selectMethod)(options.optimization);
+    const sumOfShapes = (0, getSumOfShapes_1.getSumOfShapes)(internalPeaks);
+    const fitted = algorithm({ x: data.x, y: normalizedY }, sumOfShapes, {
         minValues,
@@ -55,4 +55,4 @@ maxValues,
     const fittedValues = fitted.parameterValues;
-    let newPeaks = [];
-    for (let peak of internalPeaks) {
+    const newPeaks = [];
+    for (const peak of internalPeaks) {
         const { id, shape, parameters, fromIndex } = peak;
@@ -59,0 +59,0 @@ let newPeak = { x: 0, y: 0, shape };

lib/util/internalPeaks/getInternalPeaks.js

@@ -16,3 +16,3 @@ "use strict";
     let index = 0;
-    let internalPeaks = [];
+    const internalPeaks = [];
     const { baseline: shiftValue = minMaxY.min } = options;
@@ -35,4 +35,4 @@ const normalizedPeaks = peaks.map((peak) => {
         };
-        for (let parameter of parameters) {
-            for (let property of properties) {
+        for (const parameter of parameters) {
+            for (const property of properties) {
                 // check if the property is specified in the peak
@@ -39,0 +39,0 @@ let propertyValue = peak?.parameters?.[parameter]?.[property];

lib/util/selectMethod.js

@@ -11,3 +11,3 @@ "use strict";
 function selectMethod(optimizationOptions = {}) {
-    let { kind = 'lm', options } = optimizationOptions;
+    const { kind = 'lm', options } = optimizationOptions;
     switch (kind) {
@@ -14,0 +14,0 @@ case 'lm':

lib/util/wrappers/directOptimization.js

@@ -33,3 +33,3 @@ "use strict";
         for (let i = 0; i < nbPoints; i++) {
-            error += Math.pow(y[i] - fct(x[i]), 2);
+            error += (y[i] - fct(x[i])) ** 2;
         }
@@ -36,0 +36,0 @@ return error;

package.json

 {
   "name": "ml-spectra-fitting",
-  "version": "4.2.1",
+  "version": "4.2.2",
   "description": "Fit spectra using gaussian or lorentzian",
@@ -49,22 +49,22 @@ "main": "./lib/index.js",
   "devDependencies": {
-    "@babel/plugin-transform-modules-commonjs": "^7.21.2",
-    "@babel/preset-typescript": "^7.21.0",
-    "@types/jest": "^29.5.0",
+    "@babel/plugin-transform-modules-commonjs": "^7.23.3",
+    "@babel/preset-typescript": "^7.23.3",
+    "@types/jest": "^29.5.12",
     "cheminfo-build": "^1.2.0",
-    "eslint": "^8.36.0",
-    "eslint-config-cheminfo-typescript": "^11.3.1",
-    "jest": "^29.5.0",
+    "eslint": "^8.57.0",
+    "eslint-config-cheminfo-typescript": "^12.2.0",
+    "jest": "^29.7.0",
     "jest-matcher-deep-close-to": "^3.0.2",
-    "prettier": "^2.8.7",
-    "spectrum-generator": "^8.0.6",
-    "typescript": "^5.0.2"
+    "prettier": "^3.2.5",
+    "spectrum-generator": "^8.0.9",
+    "typescript": "^5.3.3"
   },
   "dependencies": {
-    "cheminfo-types": "^1.4.0",
+    "cheminfo-types": "^1.7.2",
     "ml-array-max": "^1.2.4",
-    "ml-direct": "^0.1.1",
-    "ml-levenberg-marquardt": "^4.1.0",
+    "ml-direct": "^0.1.3",
+    "ml-levenberg-marquardt": "^4.1.3",
     "ml-peak-shape-generator": "^4.1.2",
-    "ml-spectra-processing": "^12.0.0"
+    "ml-spectra-processing": "^14.0.0"
   }
 }

src/__tests__/globalOptimization.test.ts

@@ -24,3 +24,3 @@ import type { DataXY } from 'cheminfo-types';
 
-  let result = optimize(
+  const result = optimize(
     data,
@@ -27,0 +27,0 @@ [

src/__tests__/mixShapes.test.ts

@@ -11,3 +11,3 @@ import type { DataXY } from 'cheminfo-types';
   it('2 peaks', () => {
-    let peaks = [
+    const peaks = [
       {
@@ -32,3 +32,3 @@ x: -0.5,
 
-    let result = optimize(
+    const result = optimize(
       data,
@@ -62,3 +62,3 @@ [
   it('2 peaks same position', () => {
-    let peaks = [
+    const peaks = [
       {
@@ -82,3 +82,3 @@ x: 0,
 
-    let result = optimize(data, [
+    const result = optimize(data, [
       {
@@ -103,3 +103,3 @@ x: -0.1,
     const nbPeaks = 5;
-    let peaks = [];
+    const peaks = [];
     for (let i = 0; i < nbPeaks; i++) {
@@ -120,6 +120,6 @@ peaks.push({
 
-    let guess = JSON.parse(JSON.stringify(peaks));
+    const guess = JSON.parse(JSON.stringify(peaks));
     guess.forEach((peak: any) => (peak.x += Math.random() / 10));
 
-    let result = optimize(data, guess, {
+    const result = optimize(data, guess, {
       optimization: { options: { maxIterations: 10 } },
@@ -135,3 +135,3 @@ });
   it('6 peaks', () => {
-    let peaks = [
+    const peaks = [
       {
@@ -169,3 +169,3 @@ x: 0,
 
-    let result = optimize(
+    const result = optimize(
       data,
@@ -172,0 +172,0 @@ [

src/__tests__/oneShape.test.ts

@@ -9,5 +9,5 @@ import type { DataXY } from 'cheminfo-types';
 
-let nbPoints = 31;
-let xFactor = 0.1;
-let x = new Float64Array(nbPoints);
+const nbPoints = 31;
+const xFactor = 0.1;
+const x = new Float64Array(nbPoints);
 for (let i = 0; i < nbPoints; i++) {
@@ -35,3 +35,3 @@ x[i] = (i - nbPoints / 2) * xFactor;
 
-    let result = optimize(data, [
+    const result = optimize(data, [
       {
@@ -47,3 +47,3 @@ x: -0.52,
   it('Lorentzian', () => {
-    let peaks = [
+    const peaks = [
       {
@@ -69,3 +69,3 @@ x: -0.5,
 
-    let result = optimize(data, [
+    const result = optimize(data, [
       {
@@ -82,3 +82,3 @@ x: -0.52,
   it('Pseudo Voigt', () => {
-    let peaks = [
+    const peaks = [
       {
@@ -99,3 +99,3 @@ x: 0,
 
-    let result = optimize(data, [
+    const result = optimize(data, [
       {
@@ -102,0 +102,0 @@ x: 0.001,

src/__tests__/specifyShapeInOptimize.test.ts

@@ -35,3 +35,3 @@ import type { DataXY } from 'cheminfo-types';
 
-    let result = optimize(data, [
+    const result = optimize(data, [
       {
@@ -70,3 +70,3 @@ id: 'first',
 
-    let result = optimize(data, [
+    const result = optimize(data, [
       { x: -0.52, y: 0.9, shape: { kind: 'gaussian', fwhm: 0.2 } },
@@ -96,3 +96,3 @@ { x: 0.52, y: 0.9, shape: { kind: 'lorentzian', fwhm: 0.6 } },
 
-    let result = optimize(
+    const result = optimize(
       data,
@@ -131,3 +131,3 @@ [
 
-    let result = optimize(
+    const result = optimize(
       data,
@@ -134,0 +134,0 @@ [

src/__tests__/specifyShapeInPeaks.test.ts

@@ -9,5 +9,5 @@ import type { DataXY } from 'cheminfo-types';
 
-let nbPoints = 31;
-let xFactor = 0.1;
-let x = new Float64Array(nbPoints);
+const nbPoints = 31;
+const xFactor = 0.1;
+const x = new Float64Array(nbPoints);
 for (let i = 0; i < nbPoints; i++) {
@@ -35,3 +35,3 @@ x[i] = (i - nbPoints / 2) * xFactor;
   it('positive maxima peaks', () => {
-    let initialPeaks: Peak[] = [
+    const initialPeaks: Peak[] = [
       {
@@ -48,3 +48,3 @@ x: -0.52,
     ];
-    let result = optimize(data, initialPeaks);
+    const result = optimize(data, initialPeaks);
     for (let i = 0; i < 2; i++) {
@@ -56,12 +56,11 @@ expect(result.peaks[i]).toMatchCloseTo(peaks[i], 3);
   it('shifted baseline up by two', () => {
-    let shiftedPeaks = JSON.parse(JSON.stringify(peaks));
+    const shiftedPeaks = JSON.parse(JSON.stringify(peaks));
     for (const shiftedPeak of shiftedPeaks) {
-      // eslint-disable-next-line @typescript-eslint/restrict-plus-operands
       shiftedPeak.y = shiftedPeak.y + 2;
     }
-    let yShiftedData = {
+    const yShiftedData = {
       x: data.x,
       y: data.y.map((el: number) => el + 2),
     };
-    let result = optimize(yShiftedData, [
+    const result = optimize(yShiftedData, [
       {
@@ -84,7 +83,7 @@ x: -0.52,
   it('negative maxima peaks', () => {
-    let shiftedPeaks = JSON.parse(JSON.stringify(peaks));
+    const shiftedPeaks = JSON.parse(JSON.stringify(peaks));
     for (const shiftedPeak of shiftedPeaks) {
       shiftedPeak.y = shiftedPeak.y - 2;
     }
-    let yShiftedPeaks = [
+    const yShiftedPeaks = [
       {
@@ -102,3 +101,3 @@ x: -0.52,
 
-    let yShiftedData = {
+    const yShiftedData = {
       x: data.x.slice(),
@@ -108,3 +107,3 @@ y: data.y.map((el: number) => el - 2),
 
-    let result = optimize(yShiftedData, yShiftedPeaks);
+    const result = optimize(yShiftedData, yShiftedPeaks);
     for (let i = 0; i < 2; i++) {
@@ -134,3 +133,3 @@ expect(result.peaks[i]).toMatchCloseTo(shiftedPeaks[i], 3);
   it('positive maxima peaks', () => {
-    let peakList: Peak[] = [
+    const peakList: Peak[] = [
       {
@@ -147,3 +146,3 @@ x: -0.52,
     ];
-    let result = optimize(data, peakList);
+    const result = optimize(data, peakList);
     for (let i = 0; i < 2; i++) {
@@ -155,3 +154,3 @@ expect(result.peaks[i]).toMatchCloseTo(peaks[i], 3);
   it('negative maxima peaks', () => {
-    let shiftedPeaks = JSON.parse(JSON.stringify(peaks));
+    const shiftedPeaks = JSON.parse(JSON.stringify(peaks));
     for (const shiftedPeak of shiftedPeaks) {
@@ -161,3 +160,3 @@ shiftedPeak.y = shiftedPeak.y - 2;
 
-    let yShiftedData = {
+    const yShiftedData = {
       x: data.x.slice(),
@@ -167,3 +166,3 @@ y: data.y.map((el: number) => el - 2),
 
-    let result = optimize(
+    const result = optimize(
       yShiftedData,
@@ -231,3 +230,3 @@ [
   it('positive maxima peaks', () => {
-    let peakList: Peak[] = [
+    const peakList: Peak[] = [
       {
@@ -252,3 +251,3 @@ x: -0.3,
     ];
-    let result = optimize(data, peakList, {
+    const result = optimize(data, peakList, {
       optimization: {
@@ -255,0 +254,0 @@ kind: 'lm',

src/index.ts

@@ -1,2 +0,2 @@
-import { DataXY, DoubleArray } from 'cheminfo-types';
+import { DataXY } from 'cheminfo-types';
 import { Shape1D } from 'ml-peak-shape-generator';
@@ -112,3 +112,3 @@ import { xMinMaxValues } from 'ml-spectra-processing';
 export function optimize<T extends Peak>(
-  data: DataXY<DoubleArray>,
+  data: DataXY,
   peaks: T[],
@@ -118,7 +118,7 @@ options: OptimizeOptions = {},
   error: number;
-  peaks: OptimizedPeakIDOrNot<T>[];
+  peaks: Array<OptimizedPeakIDOrNot<T>>;
   iterations: number;
 } {
   // rescale data
-  let temp = xMinMaxValues(data.y);
+  const temp = xMinMaxValues(data.y);
   const minMaxY = { ...temp, range: temp.max - temp.min };
@@ -130,3 +130,3 @@
   const { baseline: shiftValue = minMaxY.min } = options;
-  let normalizedY = new Float64Array(data.y.length);
+  const normalizedY = new Float64Array(data.y.length);
   for (let i = 0; i < data.y.length; i++) {
@@ -151,7 +151,7 @@ normalizedY[i] = (data.y[i] - shiftValue) / minMaxY.range;
   }
-  let { algorithm, optimizationOptions } = selectMethod(options.optimization);
+  const { algorithm, optimizationOptions } = selectMethod(options.optimization);
 
-  let sumOfShapes = getSumOfShapes(internalPeaks);
+  const sumOfShapes = getSumOfShapes(internalPeaks);
 
-  let fitted = algorithm({ x: data.x, y: normalizedY }, sumOfShapes, {
+  const fitted = algorithm({ x: data.x, y: normalizedY }, sumOfShapes, {
     minValues,
@@ -165,4 +165,4 @@ maxValues,
 
-  let newPeaks = [];
-  for (let peak of internalPeaks) {
+  const newPeaks = [];
+  for (const peak of internalPeaks) {
     const { id, shape, parameters, fromIndex } = peak;
@@ -169,0 +169,0 @@

src/util/internalPeaks/getInternalPeaks.ts

@@ -34,3 +34,3 @@ import { getShape1D, Shape1D, Shape1DInstance } from 'ml-peak-shape-generator';
   let index = 0;
-  let internalPeaks: InternalPeak[] = [];
+  const internalPeaks: InternalPeak[] = [];
   const { baseline: shiftValue = minMaxY.min } = options;
@@ -60,4 +60,4 @@
 
-    for (let parameter of parameters) {
-      for (let property of properties) {
+    for (const parameter of parameters) {
+      for (const property of properties) {
         // check if the property is specified in the peak
@@ -64,0 +64,0 @@ let propertyValue = peak?.parameters?.[parameter]?.[property];

src/util/selectMethod.ts

@@ -12,3 +12,3 @@ import { levenbergMarquardt } from 'ml-levenberg-marquardt';
 export function selectMethod(optimizationOptions: OptimizationOptions = {}) {
-  let { kind = 'lm', options } = optimizationOptions;
+  const { kind = 'lm', options } = optimizationOptions;
 
@@ -15,0 +15,0 @@ switch (kind) {

src/util/wrappers/directOptimization.ts

@@ -46,3 +46,3 @@ import { DataXY } from 'cheminfo-types';
     for (let i = 0; i < nbPoints; i++) {
-      error += Math.pow(y[i] - fct(x[i]), 2);
+      error += (y[i] - fct(x[i])) ** 2;
     }
@@ -49,0 +49,0 @@ return error;

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Improved metrics

Total package byte prevSize

118383

increased by0.31%

Worsened metrics

Lines of code

2370

decreased by-0.04%

Dependency changes

• + Addedml-spectra-processing@14.9.1(transitive)

• + Addedml-xsadd@3.0.1(transitive)

• Updatedcheminfo-types@^1.7.2

• Updatedml-direct@^0.1.3

• Updatedml-levenberg-marquardt@^4.1.3

• Updatedml-spectra-processing@^14.0.0