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Alejandro Bolaños A.","email":"jobo322"}],"maintainers":[{"name":"stropitek","email":"kostro.d@gmail.com"},{"name":"targos","email":"npm@secmail.targos.dev"},{"name":"lpatiny","email":"luc@patiny.com"},{"name":"mljs-bot","email":"bot+npm-mljs@zakodium.com"}],"readme":"# ml-spectra-fitting\n\n[![NPM version](https://img.shields.io/npm/v/ml-spectra-fitting.svg)](https://www.npmjs.com/package/ml-spectra-fitting)\n[![npm download](https://img.shields.io/npm/dm/ml-spectra-fitting.svg)](https://www.npmjs.com/package/ml-spectra-fitting)\n[![test coverage](https://img.shields.io/codecov/c/github/mljs/spectra-fitting.svg)](https://codecov.io/gh/mljs/spectra-fitting)\n[![license](https://img.shields.io/npm/l/ml-spectra-fitting.svg)](https://github.com/mljs/spectra-fitting/blob/main/LICENSE)\n\nThis is a spectra fitting package to optimize the position (x), max intensity (y),\nfull width at half-maximum (FWHM = width) and the ratio of gaussian contribution (mu) if it's required.\nIt supports three kinds of shapes:\n\n| Name         |                                                                                                                            Equation                                                                                                                             |\n| ------------ | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |\n| Gaussian     |                                                                 <img src=\"https://tex.cheminfo.org/?tex=y%20%5Ccdot%20exp%20%5Cleft%5B%5Cfrac%7B%5Cdelta%7D%7B2%20%5Csigma%5E2%7D%5Cright%5D\"/>                                                                 |\n| Lorentzian   |                                                                             <img src=\"https://tex.cheminfo.org/?tex=y%5Ccdot%5Cfrac%7B%5Cgamma%7D%7B%5Cdelta%20%2B%20%5Cgamma%7D\"/>                                                                             |\n| Pseudo Voigt | <img src=\"https://tex.cheminfo.org/?tex=y%20*%20%5Cleft%5B%5Cmu%20%5Ccdot%20exp%20%5Cleft%5B%5Cfrac%7B%5Cdelta%7D%7B2%20%5Csigma%5E2%7D%5Cright%5D%20%2B%20(1%20-%20%5Cmu)%20%5Ccdot%20%5Cfrac%7B%5Cgamma%7D%7B%5Cdelta%20%2B%20%5Cgamma%7D%20%5Cright%5D%0A\"/> |\n\nwhere\n\n| <img src=\"https://tex.cheminfo.org/?tex=%5Cdelta%20%3D%20%5Cleft(t%20-%20x%5Cright)%5E2%0A\"/> | <img src=\"https://tex.cheminfo.org/?tex=%5Csigma%20%3D%20%5Cfrac%7BFWHM%7D%7B2%5Csqrt%7B2%20%5Ccdot%20Ln(2)%7D%7D\"/> | <img src=\"https://tex.cheminfo.org/?tex=%5Cgamma%3D%5Cleft(FWHM%5Cright)%5E2\"/> |\n| --------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------ |\n\nIt is a wrapper of [ml-levenberg-marquardt](https://github.com/mljs/levenberg-marquardt)\n\n## [API Documentation](https://mljs.github.io/spectra-fitting/)\n\n## Installation\n\n```console\nnpm i ml-spectra-fitting\n```\n\n## Example\n\n```js\nimport { optimize } from 'ml-spectra-fitting';\nimport { SpectrumGenerator } from 'spectrum-generator';\n\nconst generator = new SpectrumGenerator({\n  nbPoints: 101,\n  from: -1,\n  to: 1,\n});\n\n// by default the kind of shape is gaussian;\ngenerator.addPeak({ x: 0.5, y: 0.2 }, { fwhm: 0.2 });\ngenerator.addPeak(\n  { x: -0.5, y: 0.2 },\n  {\n    shape: {\n      kind: 'lorentzian',\n      fwhm: 0.1,\n    },\n  },\n);\n\n//points to fit {x, y};\nlet data = generator.getSpectrum();\nconsole.log(JSON.stringify({ x: Array.from(data.x), y: Array.from(data.y) }));\n//the approximate values to be optimized, It could coming from a peak picking with ml-gsd\nlet peaks = [\n  {\n    x: -0.5,\n    y: 0.22,\n    shape: {\n      kind: 'gaussian',\n      fwhm: 0.25,\n    },\n  },\n  {\n    x: 0.52,\n    y: 0.18,\n    shape: {\n      kind: 'gaussian',\n      fwhm: 0.18,\n    },\n  },\n];\n\n// the function receive an array of peak with {x, y, fwhm} as a guess\n// and return a list of objects\nlet fittedParams = optimize(data, peaks, { shape: { kind: 'pseudoVoigt' } });\n\nconsole.log(fittedParams);\nconst result = {\n  error: 0.12361588652854476,\n  iterations: 100,\n  peaks: [\n    {\n      x: -0.5000014532421942,\n      y: 0.19995307937326137,\n      shape: {\n        kind: 'pseudoVoigt',\n        fwhm: 0.10007670374735196,\n        mu: 0.004731136777288483,\n      },\n    },\n    {\n      x: 0.5001051783652894,\n      y: 0.19960010175400406,\n      shape: {\n        kind: 'pseudoVoigt',\n        fwhm: 0.19935932346969124,\n        mu: 1,\n      },\n    },\n  ],\n};\n```\n\n## Linked parameters\n\nYou can link one parameter across multiple peaks so they share one optimization\nvariable.\n\nFor each linked peak, the actual parameter value is reconstructed as:\n\n```text\nactualValue = sharedVariable * factor + offset\n```\n\nBy default, `factor = 1` and `offset = 0`.\n\n```js\nimport { optimize } from 'ml-spectra-fitting';\n\nconst result = optimize(data, peaks, {\n  parameters: {\n    x: { optimize: false },\n  },\n  linkedParameters: [\n    {\n      parameter: 'fwhm',\n      peaks: [{ id: 'left' }, { id: 'right' }],\n    },\n    {\n      parameter: 'y',\n      peaks: [\n        { id: 'left', factor: 1 },\n        { id: 'right', factor: 2 },\n      ],\n    },\n  ],\n});\n```\n\n`linkedParameters.peaks[].id` accepts either the peak index or peak `id`.\n\nIf the linked parameter is `y`, `offset` is interpreted in the original Y scale.\n\n## License\n\n[MIT](./LICENSE)\n","readmeFilename":"README.md"}