ml-spectra-fitting - npm Package Compare versions

Comparing version 3.0.4 to 3.1.0

lib/index.js

@@ -1,390 +0,68 @@
-'use strict';
-
-Object.defineProperty(exports, '__esModule', { value: true });
-
-var getMaxValue = require('ml-array-max');
-var mlPeakShapeGenerator = require('ml-peak-shape-generator');
-var mlLevenbergMarquardt = require('ml-levenberg-marquardt');
-
-function _interopDefaultLegacy (e) { return e && typeof e === 'object' && 'default' in e ? e : { 'default': e }; }
-
-var getMaxValue__default = /*#__PURE__*/_interopDefaultLegacy(getMaxValue);
-
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+exports.optimize = void 0;
+const checkInput_1 = require("./util/checkInput");
+const selectMethod_1 = require("./util/selectMethod");
 /**
- * This function calculates the spectrum as a sum of linear combination of gaussian and lorentzian functions. The pseudo voigt
- * parameters are divided in 4 batches. 1st: centers; 2nd: heights; 3th: widths; 4th: mu's ;
- * @param t Ordinate value
- * @param p Lorentzian parameters
- * @returns {*}
+ * Fits a set of points to the sum of a set of bell functions.
+ *
+ * @param data - An object containing the x and y data to be fitted.
+ * @param peakList - A list of initial parameters to be optimized. e.g. coming from a peak picking [{x, y, width}].
+ * @param options - Options.
+ * @returns - An object with fitting error and the list of optimized parameters { parameters: [ {x, y, width} ], error } if the kind of shape is pseudoVoigt mu parameter is optimized.
  */
-
-// const pseudoVoigtFct = PseudoVoigt.fct;
-
-function sumOfGaussianLorentzians(p) {
-  const pseudoVoigt = new mlPeakShapeGenerator.PseudoVoigt();
-  return (t) => {
-    let nL = p.length / 4;
-    let result = 0;
-    for (let i = 0; i < nL; i++) {
-      pseudoVoigt.fwhm = p[i + nL * 2];
-      pseudoVoigt.mu = p[i + nL * 3];
-      result += p[i + nL] * pseudoVoigt.fct(t - p[i]);
+function optimize(data, peakList, options = {}) {
+    if (!options.shape) {
+        options = { ...options, ...{ shape: { kind: 'gaussian' } } };
     }
-    return result;
-  };
-}
-
-/**
- * This function calculates the spectrum as a sum of gaussian functions. The Gaussian
- * parameters are divided in 3 batches. 1st: centers; 2nd: height; 3th: widths;
- * @param t Ordinate values
- * @param p Gaussian parameters
- * @returns {*}
- */
-
-function sumOfGaussians(p) {
-  const nL = p.length / 3;
-  const gaussian = new mlPeakShapeGenerator.Gaussian();
-  return (t) => {
-    let result = 0;
-    for (let i = 0; i < nL; i++) {
-      gaussian.fwhm = p[i + nL * 2];
-      result += p[i + nL] * gaussian.fct(t - p[i]);
+    const { y, x, maxY, minY, peaks, paramsFunc, optimization } = (0, checkInput_1.checkInput)(data, peakList, options);
+    let parameters = optimization.parameters;
+    let nbShapes = peaks.length;
+    let parameterKey = Object.keys(parameters);
+    let nbParams = nbShapes * parameterKey.length;
+    let pMin = new Float64Array(nbParams);
+    let pMax = new Float64Array(nbParams);
+    let pInit = new Float64Array(nbParams);
+    let gradientDifference = new Float64Array(nbParams);
+    for (let i = 0; i < nbShapes; i++) {
+        let peak = peaks[i];
+        for (let k = 0; k < parameterKey.length; k++) {
+            let key = parameterKey[k];
+            let init = parameters[key].init;
+            let min = parameters[key].min;
+            let max = parameters[key].max;
+            let gradientDifferenceValue = parameters[key].gradientDifference;
+            pInit[i + k * nbShapes] = init[i % init.length](peak);
+            pMin[i + k * nbShapes] = min[i % min.length](peak);
+            pMax[i + k * nbShapes] = max[i % max.length](peak);
+            gradientDifference[i + k * nbShapes] =
+                gradientDifferenceValue[i % gradientDifferenceValue.length](peak);
+        }
     }
-    return result;
-  };
-}
-
-/**
- * This function calculates the spectrum as a sum of lorentzian functions. The Lorentzian
- * parameters are divided in 3 batches. 1st: centers; 2nd: heights; 3th: widths;
- * @param t Ordinate values
- * @param p Lorentzian parameters
- * @returns {*}
- */
-
-// const lorentzianFct = Lorentzian.fct;
-
-function sumOfLorentzians(p) {
-  const lorentzian = new mlPeakShapeGenerator.Lorentzian();
-  return (t) => {
-    let nL = p.length / 3;
-    let result = 0;
-    for (let i = 0; i < nL; i++) {
-      lorentzian.fwhm = p[i + nL * 2];
-      result += p[i + nL] * lorentzian.fct(t - p[i]);
+    let { algorithm, optimizationOptions } = (0, selectMethod_1.selectMethod)(optimization);
+    optimizationOptions.minValues = pMin;
+    optimizationOptions.maxValues = pMax;
+    optimizationOptions.initialValues = pInit;
+    optimizationOptions.gradientDifference = gradientDifference;
+    let pFit = algorithm({ x, y }, paramsFunc, optimizationOptions);
+    let { parameterError: error, iterations } = pFit;
+    let result = { error, iterations, peaks };
+    for (let i = 0; i < nbShapes; i++) {
+        for (let k = 0; k < parameterKey.length; k++) {
+            const key = parameterKey[k];
+            const value = pFit.parameterValues[i + k * nbShapes];
+            if (key === 'x' || key === 'fwhm') {
+                peaks[i][key] = value;
+            }
+            else if (key === 'y') {
+                peaks[i][key] = value * maxY + minY;
+            }
+            else {
+                peaks[i].shape[key] = value;
+            }
+        }
     }
     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, peakList, options) {
-  let {
-    shape = { kind: 'gaussian' },
-    optimization = {
-      kind: 'lm',
-    },
-  } = options;
-
-  let peaks = JSON.parse(JSON.stringify(peakList));
-
-  if (typeof shape.kind !== 'string') {
-    throw new Error('kind should be a string');
-  }
-
-  let kind = shape.kind.toLowerCase().replace(/[^a-z]/g, '');
-
-  let paramsFunc;
-  let defaultParameters;
-  switch (kind) {
-    case 'gaussian':
-      paramsFunc = sumOfGaussians;
-      defaultParameters = {
-        x: {
-          init: (peak) => peak.x,
-          max: (peak) => peak.x + peak.fwhm * 2,
-          min: (peak) => peak.x - peak.fwhm * 2,
-          gradientDifference: (peak) => peak.fwhm * 2e-3,
-        },
-        y: {
-          init: (peak) => peak.y,
-          max: () => 1.5,
-          min: () => 0,
-          gradientDifference: () => 1e-3,
-        },
-        fwhm: {
-          init: (peak) => peak.fwhm,
-          max: (peak) => peak.fwhm * 4,
-          min: (peak) => peak.fwhm * 0.25,
-          gradientDifference: (peak) => peak.fwhm * 2e-3,
-        },
-      };
-      break;
-    case 'lorentzian':
-      paramsFunc = sumOfLorentzians;
-      defaultParameters = {
-        x: {
-          init: (peak) => peak.x,
-          max: (peak) => peak.x + peak.fwhm * 2,
-          min: (peak) => peak.x - peak.fwhm * 2,
-          gradientDifference: (peak) => peak.fwhm * 2e-3,
-        },
-        y: {
-          init: (peak) => peak.y,
-          max: () => 1.5,
-          min: () => 0,
-          gradientDifference: () => 1e-3,
-        },
-        fwhm: {
-          init: (peak) => peak.fwhm,
-          max: (peak) => peak.fwhm * 4,
-          min: (peak) => peak.fwhm * 0.25,
-          gradientDifference: (peak) => peak.fwhm * 2e-3,
-        },
-      };
-      break;
-    case 'pseudovoigt':
-      paramsFunc = sumOfGaussianLorentzians;
-      defaultParameters = {
-        x: {
-          init: (peak) => peak.x,
-          max: (peak) => peak.x + peak.fwhm * 2,
-          min: (peak) => peak.x - peak.fwhm * 2,
-          gradientDifference: (peak) => peak.fwhm * 2e-3,
-        },
-        y: {
-          init: (peak) => peak.y,
-          max: () => 1.5,
-          min: () => 0,
-          gradientDifference: () => 1e-3,
-        },
-        fwhm: {
-          init: (peak) => peak.fwhm,
-          max: (peak) => peak.fwhm * 4,
-          min: (peak) => peak.fwhm * 0.25,
-          gradientDifference: (peak) => peak.fwhm * 2e-3,
-        },
-        mu: {
-          init: (peak) =>
-            peak.shape && peak.shape.mu !== undefined ? peak.shape.mu : 0.5,
-          min: () => 0,
-          max: () => 1,
-          gradientDifference: () => 0.01,
-        },
-      };
-      break;
-    default:
-      throw new Error('kind of shape is not supported');
-  }
-
-  let x = data.x;
-  let maxY = getMaxValue__default["default"](data.y);
-  let y = new Array(x.length);
-  for (let i = 0; i < x.length; i++) {
-    y[i] = data.y[i] / maxY;
-  }
-
-  for (let i = 0; i < peaks.length; i++) {
-    peaks[i].y /= maxY;
-    peaks[i].shape = {
-      kind: shape.kind,
-      ...peaks[i].shape,
-    };
-  }
-
-  let parameters = assignDeep({}, defaultParameters, optimization.parameters);
-
-  for (let key in parameters) {
-    for (let par in parameters[key]) {
-      if (!Array.isArray(parameters[key][par])) {
-        parameters[key][par] = [parameters[key][par]];
-      }
-      if (
-        parameters[key][par].length !== 1 &&
-        parameters[key][par].length !== peaks.length
-      ) {
-        throw new Error(`The length of ${key}-${par} is not correct`);
-      }
-      for (let index = 0; index < parameters[key][par].length; index++) {
-        if (typeof parameters[key][par][index] === 'number') {
-          let value = parameters[key][par][index];
-          parameters[key][par][index] = () => value;
-        }
-      }
-    }
-  }
-
-  optimization.parameters = parameters;
-
-  return {
-    y,
-    x,
-    maxY,
-    peaks,
-    paramsFunc,
-    optimization,
-  };
-}
-
-const LEVENBERG_MARQUARDT = 1;
-
-function selectMethod(optimizationOptions = {}) {
-  let { kind, options } = optimizationOptions;
-  kind = getKind(kind);
-  switch (kind) {
-    case LEVENBERG_MARQUARDT:
-      return {
-        algorithm: mlLevenbergMarquardt.levenbergMarquardt,
-        optimizationOptions: checkOptions(kind, options),
-      };
-    default:
-      throw new Error(`Unknown kind algorithm`);
-  }
-}
-
-function checkOptions(kind, options = {}) {
-  // eslint-disable-next-line default-case
-  switch (kind) {
-    case LEVENBERG_MARQUARDT:
-      return Object.assign({}, lmOptions, options);
-  }
-}
-
-function getKind(kind) {
-  if (typeof kind !== 'string') return kind;
-  switch (kind.toLowerCase().replace(/[^a-z]/g, '')) {
-    case 'lm':
-    case 'levenbergmarquardt':
-      return LEVENBERG_MARQUARDT;
-    default:
-      throw new Error(`Unknown kind algorithm`);
-  }
-}
-
-const lmOptions = {
-  damping: 1.5,
-  maxIterations: 100,
-  errorTolerance: 1e-8,
-};
-
-/**
- * Fits a set of points to the sum of a set of bell functions.
- * @param {object} data - An object containing the x and y data to be fitted.
- * @param {array} peaks - A list of initial parameters to be optimized. e.g. coming from a peak picking [{x, y, width}].
- * @param {object} [options = {}]
- * @param {object} [options.shape={}] - it's specify the kind of shape used to fitting.
- * @param {string} [options.shape.kind = 'gaussian'] - kind of shape; lorentzian, gaussian and pseudovoigt are supported.
- * @param {object} [options.optimization = {}] - it's specify the kind and options of the algorithm use to optimize parameters.
- * @param {object} [options.optimization.kind = 'lm'] - kind of algorithm. By default it's levenberg-marquardt.
- * @param {object} [options.optimization.parameters] - options of each parameter to be optimized e.g. For a gaussian shape
- *  it could have x, y and with properties, each of which could contain init, min, max and gradientDifference, those options will define the guess,
- *  the min and max value of the parameter (search space) and the step size to approximate the jacobian matrix respectively. Those options could be a number,
- *  array of numbers, callback, or array of callbacks. Each kind of shape has default parameters so it could be undefined.
- * @param {object} [options.optimization.parameters.x] - options for x parameter.
- * @param {number|callback|array<number|callback>} [options.optimization.parameters.x.init] - definition of the starting point of the parameter (the guess),
- *  if it is a callback the method pass the peak as the unique input, if it is an array the first element define the guess of the first peak and so on.
- * @param {number|callback|array<number|callback>} [options.optimization.parameters.x.min] - definition of the lower limit of the parameter,
- *  if it is a callback the method pass the peak as the unique input, if it is an array the first element define the min of the first peak and so on.
- * @param {number|callback|array<number|callback>} [options.optimization.parameters.x.max] - definition of the upper limit of the parameter,
- *  if it is a callback the method pass the peak as the unique input, if it is an array the first element define the max of the first peak and so on.
- * @param {number|callback|array<number|callback>} [options.optimization.parameters.x.gradientDifference] - definition of  the step size to approximate the jacobian matrix of the parameter,
- *  if it is a callback the method pass the peak as the unique input, if it is an array the first element define the gradientDifference of the first peak and so on.
- * @param {object} [options.optimization.options = {}] - options for the specific kind of algorithm.
- * @param {number} [options.optimization.options.timeout] - maximum time running before break in seconds.
- * @param {number} [options.optimization.options.damping=1.5]
- * @param {number} [options.optimization.options.maxIterations=100]
- * @param {number} [options.optimization.options.errorTolerance=1e-8]
- * @returns {object} - A object with fitting error and the list of optimized parameters { parameters: [ {x, y, width} ], error } if the kind of shape is pseudoVoigt mu parameter is optimized.
- */
-function optimize(data, peakList, options = {}) {
-  const { y, x, maxY, peaks, paramsFunc, optimization } = checkInput(
-    data,
-    peakList,
-    options,
-  );
-
-  let parameters = optimization.parameters;
-
-  let nbShapes = peaks.length;
-  let parameterKey = Object.keys(parameters);
-  let nbParams = nbShapes * parameterKey.length;
-  let pMin = new Float64Array(nbParams);
-  let pMax = new Float64Array(nbParams);
-  let pInit = new Float64Array(nbParams);
-  let gradientDifference = new Float64Array(nbParams);
-
-  for (let i = 0; i < nbShapes; i++) {
-    let peak = peaks[i];
-    for (let k = 0; k < parameterKey.length; k++) {
-      let key = parameterKey[k];
-      let init = parameters[key].init;
-      let min = parameters[key].min;
-      let max = parameters[key].max;
-      let gradientDifferenceValue = parameters[key].gradientDifference;
-      pInit[i + k * nbShapes] = init[i % init.length](peak);
-      pMin[i + k * nbShapes] = min[i % min.length](peak);
-      pMax[i + k * nbShapes] = max[i % max.length](peak);
-      gradientDifference[i + k * nbShapes] =
-        gradientDifferenceValue[i % gradientDifferenceValue.length](peak);
-    }
-  }
-
-  let { algorithm, optimizationOptions } = selectMethod(optimization);
-
-  optimizationOptions.minValues = pMin;
-  optimizationOptions.maxValues = pMax;
-  optimizationOptions.initialValues = pInit;
-  optimizationOptions.gradientDifference = gradientDifference;
-
-  let pFit = algorithm({ x, y }, paramsFunc, optimizationOptions);
-
-  let { parameterError: error, iterations } = pFit;
-  let result = { error, iterations, peaks };
-  for (let i = 0; i < nbShapes; i++) {
-    for (let k = 0; k < parameterKey.length; k++) {
-      const key = parameterKey[k];
-      const value = pFit.parameterValues[i + k * nbShapes];
-      // we modify the optimized parameters
-      if (key === 'x' || key === 'fwhm') {
-        peaks[i][parameterKey[k]] = value;
-      } else if (key === 'y') {
-        peaks[i][parameterKey[k]] = value * maxY;
-      } else {
-        peaks[i].shape[parameterKey[k]] = value;
-      }
-    }
-  }
-
-  return result;
-}
-
 exports.optimize = optimize;
+//# sourceMappingURL=index.js.map

package.json

 {
   "name": "ml-spectra-fitting",
-  "version": "3.0.4",
+  "version": "3.1.0",
   "description": "Fit spectra using gaussian or lorentzian",
-  "main": "lib/index.js",
-  "module": "src/index.js",
-  "types": "src/spectra-fitting.d.ts",
+  "main": "./lib/index.js",
+  "module": "./lib-esm/index.js",
+  "types": "./lib/index.d.ts",
   "files": [
     "lib",
-    "src"
+    "src",
+    "lib-esm"
   ],
   "scripts": {
-    "build": "cheminfo-build --entry src/index.js --root SpectraFitting",
+    "build": "npm run tsc && cheminfo-build --entry lib-esm/index.js --root SpectraFitting",
+    "check-types": "tsc --noEmit",
+    "clean": "rimraf lib lib-esm",
     "eslint": "eslint src",
     "eslint-fix": "npm run eslint -- --fix",
-    "compile": "rollup -c",
-    "prepublishOnly": "npm run compile",
-    "test": "npm run test-coverage && npm run eslint",
+    "prepack": "npm run tsc",
+    "test": "npm run test-only && npm run eslint && npm run check-types",
     "prettier": "prettier --check src",
     "prettier-write": "prettier --write src",
     "test-coverage": "jest --coverage",
-    "test-only": "jest",
+    "test-only": "jest --coverage",
+    "tsc": "npm run clean && npm run tsc-cjs && npm run tsc-esm",
+    "tsc-cjs": "tsc --project tsconfig.cjs.json",
+    "tsc-esm": "tsc --project tsconfig.esm.json",
     "debug": "npm run prepublishOnly && node src/debug.js"
@@ -44,23 +49,23 @@ },
   "homepage": "https://github.com/mljs/spectra-fitting",
-  "jest": {
-    "testEnvironment": "node"
-  },
   "devDependencies": {
     "@babel/plugin-transform-modules-commonjs": "^7.16.8",
-    "@types/jest": "^27.4.0",
+    "@types/jest": "^27.4.1",
     "cheminfo-build": "^1.1.11",
-    "eslint": "^8.7.0",
-    "eslint-config-cheminfo": "^7.2.1",
+    "eslint": "^8.9.0",
+    "eslint-config-cheminfo": "^7.2.2",
+    "eslint-config-cheminfo-typescript": "^10.3.0",
     "esm": "^3.2.25",
-    "jest": "^27.4.7",
+    "jest": "^27.5.1",
+    "jest-matcher-deep-close-to": "^3.0.2",
     "prettier": "^2.5.1",
-    "rollup": "^2.66.0",
-    "spectrum-generator": "^6.0.2"
+    "spectrum-generator": "^7.0.1",
+    "ts-jest": "^27.1.3",
+    "typescript": "^4.5.5"
   },
   "dependencies": {
-    "cheminfo-types": "^0.9.1",
+    "cheminfo-types": "^1.0.0",
     "ml-array-max": "^1.2.4",
     "ml-levenberg-marquardt": "^4.0.0",
-    "ml-peak-shape-generator": "^4.0.1"
+    "ml-peak-shape-generator": "^4.1.1"
   }
 }

README.md

@@ -134,3 +134,3 @@ # ml-spectra-fitting
       y: 0.19995307937326137,
-      width: 0.10007670374735196,
+      fwhm: 0.10007670374735196,
       mu: 0.004731136777288483
@@ -141,3 +141,3 @@ },
       y: 0.19960010175400406,
-      width: 0.19935932346969124,
+      fwhm: 0.19935932346969124,
       mu: 1
@@ -144,0 +144,0 @@ }

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increased by158.48%

Number of package files

78

increased by420%

Lines of code

1895

increased by119.33%

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increased by30%

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Dependency changes

• - Removedcheminfo-types@0.9.1(transitive)

• Updatedcheminfo-types@^1.0.0

• Updatedml-peak-shape-generator@^4.1.1