ml-spectra-fitting - npm Package Compare versions

Comparing version 4.2.3 to 4.2.4

lib-esm/util/internalPeaks/DefaultParameters.d.ts

@@ -1,2 +0,2 @@
-import { Shape1DInstance, PseudoVoigt } from 'ml-peak-shape-generator';
+import { Shape1DInstance, PseudoVoigt, GeneralizedLorentzian } from 'ml-peak-shape-generator';
 import { Peak } from '../../index';
@@ -28,3 +28,9 @@ export declare const DefaultParameters: {
     };
+    gamma: {
+        init: (peak: Peak, peakShape: GeneralizedLorentzian) => number;
+        min: () => number;
+        max: () => number;
+        gradientDifference: () => number;
+    };
 };
 //# sourceMappingURL=DefaultParameters.d.ts.map

lib-esm/util/internalPeaks/DefaultParameters.js

@@ -26,3 +26,9 @@ export const DefaultParameters = {
     },
+    gamma: {
+        init: (peak, peakShape) => peakShape.gamma || 0.5,
+        min: () => -1,
+        max: () => 2,
+        gradientDifference: () => 0.01,
+    },
 };
 //# sourceMappingURL=DefaultParameters.js.map

lib-esm/util/internalPeaks/getInternalPeaks.d.ts

 import { Shape1D, Shape1DInstance } from 'ml-peak-shape-generator';
 import { Peak, OptimizeOptions } from '../../index';
-type Parameter = 'x' | 'y' | 'fwhm' | 'mu';
+type Parameter = 'x' | 'y' | 'fwhm' | 'mu' | 'gamma';
 type Property = 'init' | 'min' | 'max' | 'gradientDifference';
@@ -5,0 +5,0 @@ export interface InternalPeak {

lib-esm/util/selectMethod.d.ts

@@ -15,5 +15,5 @@ import { levenbergMarquardt } from 'ml-levenberg-marquardt';
     } | {
-        epsilon?: number | undefined;
-        tolerance?: number | undefined;
-        tolerance2?: number | undefined;
+        epsilon?: number;
+        tolerance?: number;
+        tolerance2?: number;
         initialState?: any;
@@ -24,3 +24,3 @@ maxIterations: number;
     } | {
-        timeout?: number | undefined;
+        timeout?: number;
         damping: number;
@@ -43,9 +43,9 @@ errorTolerance: number;
         initialState: any;
-        maxIterations?: number | undefined;
+        maxIterations?: number;
         iterations: number;
     } | {
-        timeout?: number | undefined;
-        damping?: number | undefined;
-        errorTolerance?: number | undefined;
-        maxIterations?: number | undefined;
+        timeout?: number;
+        damping?: number;
+        errorTolerance?: number;
+        maxIterations?: number;
         iterations: number;
@@ -52,0 +52,0 @@ epsilon: number;

lib/index.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
-exports.optimize = void 0;
+exports.optimize = optimize;
 const ml_spectra_processing_1 = require("ml-spectra-processing");
@@ -73,3 +73,2 @@ const getSumOfShapes_1 = require("./shapes/getSumOfShapes");
 }
-exports.optimize = optimize;
 //# sourceMappingURL=index.js.map

lib/shapes/getSumOfShapes.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
-exports.getSumOfShapes = void 0;
+exports.getSumOfShapes = getSumOfShapes;
 /**
@@ -26,3 +26,2 @@ * This function returns the sumOfShapes function
 }
-exports.getSumOfShapes = getSumOfShapes;
 //# sourceMappingURL=getSumOfShapes.js.map

lib/util/assert.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
-exports.assert = void 0;
+exports.assert = assert;
 /**
@@ -15,3 +15,2 @@ * Asserts that value is truthy.
 }
-exports.assert = assert;
 //# sourceMappingURL=assert.js.map

lib/util/internalPeaks/DefaultParameters.d.ts

@@ -1,2 +0,2 @@
-import { Shape1DInstance, PseudoVoigt } from 'ml-peak-shape-generator';
+import { Shape1DInstance, PseudoVoigt, GeneralizedLorentzian } from 'ml-peak-shape-generator';
 import { Peak } from '../../index';
@@ -28,3 +28,9 @@ export declare const DefaultParameters: {
     };
+    gamma: {
+        init: (peak: Peak, peakShape: GeneralizedLorentzian) => number;
+        min: () => number;
+        max: () => number;
+        gradientDifference: () => number;
+    };
 };
 //# sourceMappingURL=DefaultParameters.d.ts.map

lib/util/internalPeaks/DefaultParameters.js

@@ -29,3 +29,9 @@ "use strict";
     },
+    gamma: {
+        init: (peak, peakShape) => peakShape.gamma || 0.5,
+        min: () => -1,
+        max: () => 2,
+        gradientDifference: () => 0.01,
+    },
 };
 //# sourceMappingURL=DefaultParameters.js.map

lib/util/internalPeaks/getInternalPeaks.d.ts

 import { Shape1D, Shape1DInstance } from 'ml-peak-shape-generator';
 import { Peak, OptimizeOptions } from '../../index';
-type Parameter = 'x' | 'y' | 'fwhm' | 'mu';
+type Parameter = 'x' | 'y' | 'fwhm' | 'mu' | 'gamma';
 type Property = 'init' | 'min' | 'max' | 'gradientDifference';
@@ -5,0 +5,0 @@ export interface InternalPeak {

lib/util/internalPeaks/getInternalPeaks.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
-exports.getInternalPeaks = void 0;
+exports.getInternalPeaks = getInternalPeaks;
 const ml_peak_shape_generator_1 = require("ml-peak-shape-generator");
@@ -80,3 +80,2 @@ const assert_1 = require("../assert");
 }
-exports.getInternalPeaks = getInternalPeaks;
 function getNormalizedValue(value, parameter, property, minMaxY, baseline) {
@@ -83,0 +82,0 @@ if (parameter === 'y') {

lib/util/selectMethod.d.ts

@@ -15,5 +15,5 @@ import { levenbergMarquardt } from 'ml-levenberg-marquardt';
     } | {
-        epsilon?: number | undefined;
-        tolerance?: number | undefined;
-        tolerance2?: number | undefined;
+        epsilon?: number;
+        tolerance?: number;
+        tolerance2?: number;
         initialState?: any;
@@ -24,3 +24,3 @@ maxIterations: number;
     } | {
-        timeout?: number | undefined;
+        timeout?: number;
         damping: number;
@@ -43,9 +43,9 @@ errorTolerance: number;
         initialState: any;
-        maxIterations?: number | undefined;
+        maxIterations?: number;
         iterations: number;
     } | {
-        timeout?: number | undefined;
-        damping?: number | undefined;
-        errorTolerance?: number | undefined;
-        maxIterations?: number | undefined;
+        timeout?: number;
+        damping?: number;
+        errorTolerance?: number;
+        maxIterations?: number;
         iterations: number;
@@ -52,0 +52,0 @@ epsilon: number;

lib/util/selectMethod.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
-exports.selectMethod = void 0;
+exports.selectMethod = selectMethod;
 const ml_levenberg_marquardt_1 = require("ml-levenberg-marquardt");
@@ -41,3 +41,2 @@ const directOptimization_1 = require("./wrappers/directOptimization");
 }
-exports.selectMethod = selectMethod;
 //# sourceMappingURL=selectMethod.js.map

lib/util/wrappers/directOptimization.js

@@ -6,3 +6,3 @@ "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
-exports.directOptimization = void 0;
+exports.directOptimization = directOptimization;
 const ml_direct_1 = __importDefault(require("ml-direct"));
@@ -26,3 +26,2 @@ function directOptimization(data, sumOfShapes, options) {
 }
-exports.directOptimization = directOptimization;
 function getObjectiveFunction(data, sumOfShapes) {
@@ -29,0 +28,0 @@ const { x, y } = data;

package.json

 {
   "name": "ml-spectra-fitting",
-  "version": "4.2.3",
+  "version": "4.2.4",
   "description": "Fit spectra using gaussian or lorentzian",
@@ -66,5 +66,5 @@ "main": "./lib/index.js",
     "ml-levenberg-marquardt": "^4.1.3",
-    "ml-peak-shape-generator": "^4.1.2",
+    "ml-peak-shape-generator": "^4.1.4",
     "ml-spectra-processing": "^14.2.0"
   }
 }

src/__tests__/mixShapes.test.ts

@@ -115,4 +115,4 @@ import type { DataXY } from 'cheminfo-types';
 
-    const guess = JSON.parse(JSON.stringify(peaks));
-    guess.forEach((peak: any) => (peak.x += Math.random() / 10));
+    const guess = structuredClone(peaks);
+    guess.forEach((peak: { x: number }) => (peak.x += Math.random() / 10));
 
@@ -119,0 +119,0 @@ const result = optimize(data, guess, {

src/__tests__/oneShape.test.ts

@@ -112,2 +112,50 @@ import type { DataXY } from 'cheminfo-types';
   });
+  it('generalized Lorentzian', () => {
+    const peaks = [
+      {
+        x: 0,
+        y: 0.001,
+        shape: { kind: 'lorentzian' as const, fwhm: 0.31 },
+      },
+    ];
+
+    const data: DataXY = generateSpectrum(peaks, {
+      generator: {
+        from: -10,
+        to: 10,
+        nbPoints: 256,
+      },
+    });
+
+    const result = optimize(
+      data,
+      [
+        {
+          x: 0.001,
+          y: 0.0009,
+          shape: {
+            kind: 'generalizedLorentzian',
+            fwhm: 0.25,
+            gamma: 0.5,
+          },
+        },
+      ],
+      {
+        optimization: {
+          kind: 'lm',
+          options: {
+            damping: 0.1,
+            maxIterations: 10,
+            errorTolerance: 1e-8,
+          },
+        },
+      },
+    );
+    expect(result.peaks[0].shape.fwhm).toBeCloseTo(0.31, 4);
+    expect(result.peaks[0].x).toBeCloseTo(0, 5);
+    expect(result.peaks[0].y).toBeCloseTo(0.001, 5);
+    expect(result.peaks[0].shape.fwhm).toBeCloseTo(0.31, 1);
+    //@ts-expect-error should exists
+    expect(result.peaks[0].shape.gamma).toBeCloseTo(0, 1);
+  });
 });

src/__tests__/specifyShapeInOptimize.test.ts

@@ -139,8 +139,5 @@ import type { DataXY } from 'cheminfo-types';
     for (let i = 0; i < 2; i++) {
-      expect(result.peaks[i]).toMatchCloseTo(
-        JSON.parse(JSON.stringify(peaks[i])),
-        3,
-      );
+      expect(result.peaks[i]).toMatchCloseTo(structuredClone(peaks[i]), 3);
     }
   });
 });

src/__tests__/specifyShapeInPeaks.test.ts

@@ -53,3 +53,3 @@ import type { DataXY } from 'cheminfo-types';
   it('shifted baseline up by two', () => {
-    const shiftedPeaks = JSON.parse(JSON.stringify(peaks));
+    const shiftedPeaks = structuredClone(peaks);
     for (const shiftedPeak of shiftedPeaks) {
@@ -80,3 +80,3 @@ shiftedPeak.y = shiftedPeak.y + 2;
   it('negative maxima peaks', () => {
-    const shiftedPeaks = JSON.parse(JSON.stringify(peaks));
+    const shiftedPeaks = structuredClone(peaks);
     for (const shiftedPeak of shiftedPeaks) {
@@ -147,3 +147,3 @@ shiftedPeak.y = shiftedPeak.y - 2;
   it('negative maxima peaks', () => {
-    const shiftedPeaks = JSON.parse(JSON.stringify(peaks));
+    const shiftedPeaks = structuredClone(peaks);
     for (const shiftedPeak of shiftedPeaks) {
@@ -150,0 +150,0 @@ shiftedPeak.y = shiftedPeak.y - 2;

src/util/internalPeaks/DefaultParameters.ts

@@ -1,2 +0,6 @@
-import { Shape1DInstance, PseudoVoigt } from 'ml-peak-shape-generator';
+import {
+  Shape1DInstance,
+  PseudoVoigt,
+  GeneralizedLorentzian,
+} from 'ml-peak-shape-generator';
 
@@ -34,2 +38,9 @@ import { Peak } from '../../index';
   },
+  gamma: {
+    init: (peak: Peak, peakShape: GeneralizedLorentzian) =>
+      peakShape.gamma || 0.5,
+    min: () => -1,
+    max: () => 2,
+    gradientDifference: () => 0.01,
+  },
 };

src/util/internalPeaks/getInternalPeaks.ts

@@ -8,3 +8,3 @@ import { getShape1D, Shape1D, Shape1DInstance } from 'ml-peak-shape-generator';
 
-type Parameter = 'x' | 'y' | 'fwhm' | 'mu';
+type Parameter = 'x' | 'y' | 'fwhm' | 'mu' | 'gamma';
 
@@ -11,0 +11,0 @@ type Property = 'init' | 'min' | 'max' | 'gradientDifference';

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Improved metrics

Total package byte prevSize

120518

increased by1.8%

Lines of code

2442

increased by3.04%

Dependency changes

• Updatedml-peak-shape-generator@^4.1.4