ml-spectra-fitting - npm Package Compare versions

Comparing version 4.0.2 to 4.1.0-pre.1661357854

lib-esm/index.d.ts

@@ -16,2 +16,3 @@ import { DataXY, DoubleArray } from 'cheminfo-types';
 export interface Peak {
+    id?: string;
     x: number;
@@ -32,2 +33,7 @@ y: number;
 }
+declare type OptimizedPeakIDOrNot<T extends Peak> = T extends {
+    id: string;
+} ? OptimizedPeak & {
+    id: string;
+} : OptimizedPeak;
 declare type OptimizationParameter = number | ((peak: Peak) => number);
@@ -59,2 +65,6 @@ export interface OptimizationOptions {
     /**
+     * baseline value to shift the intensity of data and peak
+     */
+    baseline?: number;
+    /**
      * Kind of shape used for fitting.
@@ -83,5 +93,5 @@ **/
  */
-export declare function optimize(data: DataXY<DoubleArray>, peaks: Peak[], options?: OptimizeOptions): {
+export declare function optimize<T extends Peak>(data: DataXY<DoubleArray>, peaks: T[], options?: OptimizeOptions): {
     error: number;
-    peaks: OptimizedPeak[];
+    peaks: OptimizedPeakIDOrNot<T>[];
     iterations: number;
@@ -88,0 +98,0 @@ };

lib-esm/index.js

@@ -19,5 +19,6 @@ import { xMinMaxValues } from 'ml-spectra-processing';
     // need to rescale what is related to Y
+    const { baseline: shiftValue = minMaxY.min } = options;
     let normalizedY = new Float64Array(data.y.length);
     for (let i = 0; i < data.y.length; i++) {
-        normalizedY[i] = (data.y[i] - minMaxY.min) / minMaxY.range;
+        normalizedY[i] = (data.y[i] - shiftValue) / minMaxY.range;
     }
@@ -51,12 +52,12 @@ const nbParams = internalPeaks[internalPeaks.length - 1].toIndex + 1;
     for (let peak of internalPeaks) {
-        const newPeak = {
-            x: 0,
-            y: 0,
-            shape: peak.shape,
-        };
-        newPeak.x = fittedValues[peak.fromIndex];
-        newPeak.y = fittedValues[peak.fromIndex + 1] * minMaxY.range + minMaxY.min;
-        for (let i = 2; i < peak.parameters.length; i++) {
+        const { id, shape, parameters, fromIndex } = peak;
+        let newPeak = { x: 0, y: 0, shape };
+        if (id) {
+            newPeak = { ...newPeak, id };
+        }
+        newPeak.x = fittedValues[fromIndex];
+        newPeak.y = fittedValues[fromIndex + 1] * minMaxY.range + shiftValue;
+        for (let i = 2; i < parameters.length; i++) {
             //@ts-expect-error should be fixed once
-            newPeak.shape[peak.parameters[i]] = fittedValues[peak.fromIndex + i];
+            newPeak.shape[parameters[i]] = fittedValues[fromIndex + i];
         }
@@ -63,0 +64,0 @@ newPeaks.push(newPeak);

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

@@ -12,4 +12,4 @@ import { Shape1DInstance, PseudoVoigt } from 'ml-peak-shape-generator';
         init: (peak: Peak) => number;
-        min: () => number;
-        max: () => number;
+        min: (peak: Peak) => 0 | -1.1;
+        max: (peak: Peak) => 0 | 1.1;
         gradientDifference: () => number;
@@ -16,0 +16,0 @@ };

lib-esm/util/internalPeaks/DefaultParameters.js

@@ -10,4 +10,4 @@ export const DefaultParameters = {
         init: (peak) => peak.y,
-        min: () => 0,
-        max: () => 1.5,
+        min: (peak) => (peak.y < 0 ? -1.1 : 0),
+        max: (peak) => (peak.y < 0 ? 0 : 1.1),
         gradientDifference: () => 1e-3,
@@ -14,0 +14,0 @@ },

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

@@ -6,2 +6,3 @@ import { Shape1D, Shape1DInstance } from 'ml-peak-shape-generator';
 export interface InternalPeak {
+    id?: string;
     shape: Shape1D;
@@ -8,0 +9,0 @@ shapeFct: Shape1DInstance;

lib-esm/util/internalPeaks/getInternalPeaks.js

@@ -14,10 +14,12 @@ import { getShape1D } from 'ml-peak-shape-generator';
     let internalPeaks = [];
-    for (const peak of peaks) {
-        const shape = peak.shape
-            ? peak.shape
-            : options.shape
-                ? options.shape
-                : { kind: 'gaussian' };
+    const { baseline: shiftValue = minMaxY.min } = options;
+    const normalizedPeaks = peaks.map((peak) => {
+        return {
+            ...peak,
+            y: (peak.y - shiftValue) / minMaxY.range,
+        };
+    });
+    for (const peak of normalizedPeaks) {
+        const { id, shape = options.shape ? options.shape : { kind: 'gaussian' } } = peak;
         const shapeFct = getShape1D(shape);
-        //@ts-expect-error Should disappear with next release of peak-shape-generator
         const parameters = ['x', 'y', ...shapeFct.getParameters()];
@@ -33,7 +35,5 @@ const propertiesValues = {
                 // check if the property is specified in the peak
-                let propertyValue = peak.parameters &&
-                    peak.parameters[parameter] &&
-                    peak.parameters[parameter][property];
+                let propertyValue = peak?.parameters?.[parameter]?.[property];
                 if (propertyValue) {
-                    propertyValue = getNormalizedValue(propertyValue, parameter, property, minMaxY);
+                    propertyValue = getNormalizedValue(propertyValue, parameter, property, minMaxY, options.baseline);
                     propertiesValues[property].push(propertyValue);
@@ -43,8 +43,6 @@ continue;
                 // check if there are some global option, it could be a number or a callback
-                let generalParameterValue = options.parameters &&
-                    options.parameters[parameter] &&
-                    options.parameters[parameter][property];
+                let generalParameterValue = options?.parameters?.[parameter]?.[property];
                 if (generalParameterValue) {
                     if (typeof generalParameterValue === 'number') {
-                        generalParameterValue = getNormalizedValue(generalParameterValue, parameter, property, minMaxY);
+                        generalParameterValue = getNormalizedValue(generalParameterValue, parameter, property, minMaxY, options.baseline);
                         propertiesValues[property].push(generalParameterValue);
@@ -55,3 +53,3 @@ continue;
                         let value = generalParameterValue(peak);
-                        value = getNormalizedValue(value, parameter, property, minMaxY);
+                        value = getNormalizedValue(value, parameter, property, minMaxY, options.baseline);
                         propertiesValues[property].push(value);
@@ -72,2 +70,3 @@ continue;
         internalPeaks.push({
+            id,
             shape,
@@ -83,9 +82,11 @@ shapeFct,
 }
-function getNormalizedValue(value, parameter, property, minMaxY) {
+function getNormalizedValue(value, parameter, property, minMaxY, baseline) {
     if (parameter === 'y') {
         if (property === 'gradientDifference') {
-            return value / minMaxY.range;
+            return value;
         }
         else {
-            return (value - minMaxY.min) / minMaxY.range;
+            return baseline !== undefined
+                ? (value - baseline) / minMaxY.range
+                : (value - minMaxY.min) / minMaxY.range;
         }
@@ -92,0 +93,0 @@ }

lib/index.d.ts

@@ -16,2 +16,3 @@ import { DataXY, DoubleArray } from 'cheminfo-types';
 export interface Peak {
+    id?: string;
     x: number;
@@ -32,2 +33,7 @@ y: number;
 }
+declare type OptimizedPeakIDOrNot<T extends Peak> = T extends {
+    id: string;
+} ? OptimizedPeak & {
+    id: string;
+} : OptimizedPeak;
 declare type OptimizationParameter = number | ((peak: Peak) => number);
@@ -59,2 +65,6 @@ export interface OptimizationOptions {
     /**
+     * baseline value to shift the intensity of data and peak
+     */
+    baseline?: number;
+    /**
      * Kind of shape used for fitting.
@@ -83,5 +93,5 @@ **/
  */
-export declare function optimize(data: DataXY<DoubleArray>, peaks: Peak[], options?: OptimizeOptions): {
+export declare function optimize<T extends Peak>(data: DataXY<DoubleArray>, peaks: T[], options?: OptimizeOptions): {
     error: number;
-    peaks: OptimizedPeak[];
+    peaks: OptimizedPeakIDOrNot<T>[];
     iterations: number;
@@ -88,0 +98,0 @@ };

lib/index.js

@@ -22,5 +22,6 @@ "use strict";
     // need to rescale what is related to Y
+    const { baseline: shiftValue = minMaxY.min } = options;
     let normalizedY = new Float64Array(data.y.length);
     for (let i = 0; i < data.y.length; i++) {
-        normalizedY[i] = (data.y[i] - minMaxY.min) / minMaxY.range;
+        normalizedY[i] = (data.y[i] - shiftValue) / minMaxY.range;
     }
@@ -54,12 +55,12 @@ const nbParams = internalPeaks[internalPeaks.length - 1].toIndex + 1;
     for (let peak of internalPeaks) {
-        const newPeak = {
-            x: 0,
-            y: 0,
-            shape: peak.shape,
-        };
-        newPeak.x = fittedValues[peak.fromIndex];
-        newPeak.y = fittedValues[peak.fromIndex + 1] * minMaxY.range + minMaxY.min;
-        for (let i = 2; i < peak.parameters.length; i++) {
+        const { id, shape, parameters, fromIndex } = peak;
+        let newPeak = { x: 0, y: 0, shape };
+        if (id) {
+            newPeak = { ...newPeak, id };
+        }
+        newPeak.x = fittedValues[fromIndex];
+        newPeak.y = fittedValues[fromIndex + 1] * minMaxY.range + shiftValue;
+        for (let i = 2; i < parameters.length; i++) {
             //@ts-expect-error should be fixed once
-            newPeak.shape[peak.parameters[i]] = fittedValues[peak.fromIndex + i];
+            newPeak.shape[parameters[i]] = fittedValues[fromIndex + i];
         }
@@ -66,0 +67,0 @@ newPeaks.push(newPeak);

lib/util/internalPeaks/DefaultParameters.d.ts

@@ -12,4 +12,4 @@ import { Shape1DInstance, PseudoVoigt } from 'ml-peak-shape-generator';
         init: (peak: Peak) => number;
-        min: () => number;
-        max: () => number;
+        min: (peak: Peak) => 0 | -1.1;
+        max: (peak: Peak) => 0 | 1.1;
         gradientDifference: () => number;
@@ -16,0 +16,0 @@ };

lib/util/internalPeaks/DefaultParameters.js

@@ -13,4 +13,4 @@ "use strict";
         init: (peak) => peak.y,
-        min: () => 0,
-        max: () => 1.5,
+        min: (peak) => (peak.y < 0 ? -1.1 : 0),
+        max: (peak) => (peak.y < 0 ? 0 : 1.1),
         gradientDifference: () => 1e-3,
@@ -17,0 +17,0 @@ },

lib/util/internalPeaks/getInternalPeaks.d.ts

@@ -6,2 +6,3 @@ import { Shape1D, Shape1DInstance } from 'ml-peak-shape-generator';
 export interface InternalPeak {
+    id?: string;
     shape: Shape1D;
@@ -8,0 +9,0 @@ shapeFct: Shape1DInstance;

lib/util/internalPeaks/getInternalPeaks.js

@@ -17,10 +17,12 @@ "use strict";
     let internalPeaks = [];
-    for (const peak of peaks) {
-        const shape = peak.shape
-            ? peak.shape
-            : options.shape
-                ? options.shape
-                : { kind: 'gaussian' };
+    const { baseline: shiftValue = minMaxY.min } = options;
+    const normalizedPeaks = peaks.map((peak) => {
+        return {
+            ...peak,
+            y: (peak.y - shiftValue) / minMaxY.range,
+        };
+    });
+    for (const peak of normalizedPeaks) {
+        const { id, shape = options.shape ? options.shape : { kind: 'gaussian' } } = peak;
         const shapeFct = (0, ml_peak_shape_generator_1.getShape1D)(shape);
-        //@ts-expect-error Should disappear with next release of peak-shape-generator
         const parameters = ['x', 'y', ...shapeFct.getParameters()];
@@ -36,7 +38,5 @@ const propertiesValues = {
                 // check if the property is specified in the peak
-                let propertyValue = peak.parameters &&
-                    peak.parameters[parameter] &&
-                    peak.parameters[parameter][property];
+                let propertyValue = peak?.parameters?.[parameter]?.[property];
                 if (propertyValue) {
-                    propertyValue = getNormalizedValue(propertyValue, parameter, property, minMaxY);
+                    propertyValue = getNormalizedValue(propertyValue, parameter, property, minMaxY, options.baseline);
                     propertiesValues[property].push(propertyValue);
@@ -46,8 +46,6 @@ continue;
                 // check if there are some global option, it could be a number or a callback
-                let generalParameterValue = options.parameters &&
-                    options.parameters[parameter] &&
-                    options.parameters[parameter][property];
+                let generalParameterValue = options?.parameters?.[parameter]?.[property];
                 if (generalParameterValue) {
                     if (typeof generalParameterValue === 'number') {
-                        generalParameterValue = getNormalizedValue(generalParameterValue, parameter, property, minMaxY);
+                        generalParameterValue = getNormalizedValue(generalParameterValue, parameter, property, minMaxY, options.baseline);
                         propertiesValues[property].push(generalParameterValue);
@@ -58,3 +56,3 @@ continue;
                         let value = generalParameterValue(peak);
-                        value = getNormalizedValue(value, parameter, property, minMaxY);
+                        value = getNormalizedValue(value, parameter, property, minMaxY, options.baseline);
                         propertiesValues[property].push(value);
@@ -75,2 +73,3 @@ continue;
         internalPeaks.push({
+            id,
             shape,
@@ -87,9 +86,11 @@ shapeFct,
 exports.getInternalPeaks = getInternalPeaks;
-function getNormalizedValue(value, parameter, property, minMaxY) {
+function getNormalizedValue(value, parameter, property, minMaxY, baseline) {
     if (parameter === 'y') {
         if (property === 'gradientDifference') {
-            return value / minMaxY.range;
+            return value;
         }
         else {
-            return (value - minMaxY.min) / minMaxY.range;
+            return baseline !== undefined
+                ? (value - baseline) / minMaxY.range
+                : (value - minMaxY.min) / minMaxY.range;
         }
@@ -96,0 +97,0 @@ }

package.json

 {
   "name": "ml-spectra-fitting",
-  "version": "4.0.2",
+  "version": "4.1.0-pre.1661357854",
   "description": "Fit spectra using gaussian or lorentzian",
@@ -65,5 +65,5 @@ "main": "./lib/index.js",
     "ml-levenberg-marquardt": "^4.1.0",
-    "ml-peak-shape-generator": "^4.1.1",
+    "ml-peak-shape-generator": "^4.1.2",
     "ml-spectra-processing": "^11.5.0"
   }
 }

src/__tests__/specifyShapeInOptimize.test.ts

@@ -12,4 +12,14 @@ import type { DataXY } from 'cheminfo-types';
     const peaks = [
-      { x: -0.5, y: 1, shape: { kind: 'gaussian' as const, fwhm: 0.05 } },
-      { x: 0.5, y: 1, shape: { kind: 'gaussian' as const, fwhm: 0.05 } },
+      {
+        id: 'first',
+        x: -0.5,
+        y: 1,
+        shape: { kind: 'gaussian' as const, fwhm: 0.05 },
+      },
+      {
+        id: 'second',
+        x: 0.5,
+        y: 1,
+        shape: { kind: 'gaussian' as const, fwhm: 0.05 },
+      },
     ];
@@ -27,4 +37,14 @@
     let result = optimize(data, [
-      { x: -0.55, y: 0.9, shape: { kind: 'gaussian' as const, fwhm: 0.08 } },
-      { x: 0.55, y: 0.9, shape: { kind: 'gaussian' as const, fwhm: 0.08 } },
+      {
+        id: 'first',
+        x: -0.55,
+        y: 0.9,
+        shape: { kind: 'gaussian' as const, fwhm: 0.08 },
+      },
+      {
+        id: 'second',
+        x: 0.55,
+        y: 0.9,
+        shape: { kind: 'gaussian' as const, fwhm: 0.08 },
+      },
     ]);
@@ -31,0 +51,0 @@

src/index.ts

@@ -23,2 +23,3 @@ import { DataXY, DoubleArray } from 'cheminfo-types';
 export interface Peak {
+  id?: string;
   x: number;
@@ -39,2 +40,6 @@ y: number;
 
+type OptimizedPeakIDOrNot<T extends Peak> = T extends { id: string }
+  ? OptimizedPeak & { id: string }
+  : OptimizedPeak;
+
 type OptimizationParameter = number | ((peak: Peak) => number);
@@ -69,2 +74,6 @@
   /**
+   * baseline value to shift the intensity of data and peak
+   */
+  baseline?: number;
+  /**
    * Kind of shape used for fitting.
@@ -94,9 +103,9 @@ **/
  */
-export function optimize(
+export function optimize<T extends Peak>(
   data: DataXY<DoubleArray>,
-  peaks: Peak[],
+  peaks: T[],
   options: OptimizeOptions = {},
 ): {
   error: number;
-  peaks: OptimizedPeak[];
+  peaks: OptimizedPeakIDOrNot<T>[];
   iterations: number;
@@ -109,6 +118,8 @@ } {
   const internalPeaks = getInternalPeaks(peaks, minMaxY, options);
+
   // need to rescale what is related to Y
+  const { baseline: shiftValue = minMaxY.min } = options;
   let normalizedY = new Float64Array(data.y.length);
   for (let i = 0; i < data.y.length; i++) {
-    normalizedY[i] = (data.y[i] - minMaxY.min) / minMaxY.range;
+    normalizedY[i] = (data.y[i] - shiftValue) / minMaxY.range;
   }
@@ -143,16 +154,19 @@
   const fittedValues = fitted.parameterValues;
-  let newPeaks: OptimizedPeak[] = [];
+
+  let newPeaks = [];
   for (let peak of internalPeaks) {
-    const newPeak = {
-      x: 0,
-      y: 0,
-      shape: peak.shape,
-    };
-    newPeak.x = fittedValues[peak.fromIndex];
-    newPeak.y = fittedValues[peak.fromIndex + 1] * minMaxY.range + minMaxY.min;
-    for (let i = 2; i < peak.parameters.length; i++) {
+    const { id, shape, parameters, fromIndex } = peak;
+
+    let newPeak = { x: 0, y: 0, shape } as OptimizedPeakIDOrNot<T>;
+
+    if (id) {
+      newPeak = { ...newPeak, id } as OptimizedPeakIDOrNot<T>;
+    }
+
+    newPeak.x = fittedValues[fromIndex];
+    newPeak.y = fittedValues[fromIndex + 1] * minMaxY.range + shiftValue;
+    for (let i = 2; i < parameters.length; i++) {
       //@ts-expect-error should be fixed once
-      newPeak.shape[peak.parameters[i]] = fittedValues[peak.fromIndex + i];
+      newPeak.shape[parameters[i]] = fittedValues[fromIndex + i];
     }
-
     newPeaks.push(newPeak);
@@ -159,0 +173,0 @@ }

src/util/internalPeaks/__tests__/getInternalPeaks.test.ts

@@ -19,3 +19,3 @@ import { toBeDeepCloseTo, toMatchCloseTo } from 'jest-matcher-deep-close-to';
           min: [-1000, 0, 125],
-          max: [1000, 1.5, 2000],
+          max: [1000, 1.1, 2000],
           init: [0, 1, 500],
@@ -43,3 +43,3 @@ gradientDifference: [1, 0.001, 1],
         min: [-999, 0, 125, 0],
-        max: [1001, 1.5, 2000, 1],
+        max: [1001, 1.1, 2000, 1],
         init: [1, 2, 500, 0.5],
@@ -69,3 +69,3 @@ gradientDifference: [1, 0.001, 1, 0.01],
           min: [-1000, 0, 125],
-          max: [1000, 1.5, 2000],
+          max: [1000, 1.1, 2000],
           init: [0, 1, 500],
@@ -100,3 +100,3 @@ gradientDifference: [1, 0.001, 1],
           min: [-1, -2, 125],
-          max: [1, 1.5, 2000],
+          max: [1, 1.1, 2000],
           init: [0, 1, 500],
@@ -103,0 +103,0 @@ gradientDifference: [1, 0.001, 1],

src/util/internalPeaks/DefaultParameters.ts

@@ -17,4 +17,4 @@ import { Shape1DInstance, PseudoVoigt } from 'ml-peak-shape-generator';
     init: (peak: Peak) => peak.y,
-    min: () => 0,
-    max: () => 1.5,
+    min: (peak: Peak) => (peak.y < 0 ? -1.1 : 0),
+    max: (peak: Peak) => (peak.y < 0 ? 0 : 1.1),
     gradientDifference: () => 1e-3,
@@ -21,0 +21,0 @@ },

src/util/internalPeaks/getInternalPeaks.ts

@@ -13,2 +13,3 @@ import { getShape1D, Shape1D, Shape1DInstance } from 'ml-peak-shape-generator';
 export interface InternalPeak {
+  id?: string;
   shape: Shape1D;
@@ -35,12 +36,17 @@ shapeFct: Shape1DInstance;
   let internalPeaks: InternalPeak[] = [];
-  for (const peak of peaks) {
-    const shape: Shape1D = peak.shape
-      ? peak.shape
-      : options.shape
-      ? options.shape
-      : { kind: 'gaussian' };
+  const { baseline: shiftValue = minMaxY.min } = options;
 
+  const normalizedPeaks = peaks.map((peak) => {
+    return {
+      ...peak,
+      y: (peak.y - shiftValue) / minMaxY.range,
+    };
+  });
+
+  for (const peak of normalizedPeaks) {
+    const { id, shape = options.shape ? options.shape : { kind: 'gaussian' } } =
+      peak;
+
     const shapeFct: Shape1DInstance = getShape1D(shape);
 
-    //@ts-expect-error Should disappear with next release of peak-shape-generator
     const parameters: Parameter[] = ['x', 'y', ...shapeFct.getParameters()];
@@ -58,6 +64,3 @@
         // check if the property is specified in the peak
-        let propertyValue =
-          peak.parameters &&
-          peak.parameters[parameter] &&
-          peak.parameters[parameter][property];
+        let propertyValue = peak?.parameters?.[parameter]?.[property];
         if (propertyValue) {
@@ -69,2 +72,3 @@ propertyValue = getNormalizedValue(
             minMaxY,
+            options.baseline,
           );
@@ -78,5 +82,3 @@
         let generalParameterValue =
-          options.parameters &&
-          options.parameters[parameter] &&
-          options.parameters[parameter][property];
+          options?.parameters?.[parameter]?.[property];
         if (generalParameterValue) {
@@ -89,2 +91,3 @@ if (typeof generalParameterValue === 'number') {
               minMaxY,
+              options.baseline,
             );
@@ -95,3 +98,9 @@ propertiesValues[property].push(generalParameterValue);
             let value = generalParameterValue(peak);
-            value = getNormalizedValue(value, parameter, property, minMaxY);
+            value = getNormalizedValue(
+              value,
+              parameter,
+              property,
+              minMaxY,
+              options.baseline,
+            );
             propertiesValues[property].push(value);
@@ -118,2 +127,3 @@ continue;
     internalPeaks.push({
+      id,
       shape,
@@ -135,8 +145,11 @@ shapeFct,
   minMaxY: { min: number; max: number; range: number },
+  baseline?: number,
 ): number {
   if (parameter === 'y') {
     if (property === 'gradientDifference') {
-      return value / minMaxY.range;
+      return value;
     } else {
-      return (value - minMaxY.min) / minMaxY.range;
+      return baseline !== undefined
+        ? (value - baseline) / minMaxY.range
+        : (value - minMaxY.min) / minMaxY.range;
     }
@@ -143,0 +156,0 @@ }

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• Updatedml-peak-shape-generator@^4.1.2