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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"},{"name":"cheminfo-bot","email":"bot+npm-cheminfo@zakodium.com"}],"readme":"# ml-peak-shape-generator\n\n[![NPM version][npm-image]][npm-url]\n[![build status][ci-image]][ci-url]\n[![Test coverage][codecov-image]][codecov-url]\n[![npm download][download-image]][download-url]\n\nGenerate various peak shapes.\n\nThe current supported kinds of shapes:\n\n| Name                        | `kind`                  |                                                                                                                           Equation                                                                                                                            |\n| --------------------------- | ----------------------- | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |\n| Gaussian                    | `gaussian`              |                                                          <img src=\"https://tex.cheminfo.org/?tex=y%5Ccdot%20exp%5Cleft%5B-%5Cfrac%7B1%7D%7B2%7D%5Cfrac%7B%5Cdelta%7D%7B%5Csigma%5E2%7D%5Cright%5D\"/>                                                          |\n| Lorentzian                  | `lorentzian`            |                                                                       <img src=\"https://tex.cheminfo.org/?tex=y%5Ccdot%5Cfrac%7B%5Comega%5E2%7D%7B4%5Cdelta%20%2B%20%5Comega%5E2%7D\"/>                                                                        |\n| Lorentzian Dispersive       | `lorentzianDispersive`  |                                                                       <img src=\"https://tex.cheminfo.org/?tex=y%5Ccdot%5Cfrac%7B2%5Comega%5Cdelta%7D%7B4%5Cdelta%5E2%20%2B%20%5Comega%5E2%7D\"/>                                                                        |\n| Generalized Lorentzian      | `generalizedLorentzian` | <img src=\"https://tex.cheminfo.org/?tex=y%5Ccdot%5Cleft%5B%281-%5Cgamma%29%5Ccdot%5Cfrac%7B1%7D%7B1%2Bu%7D%20%2B%20%5Cgamma%5Ccdot%5Cfrac%7B1%2B%5Cfrac%7Bu%7D%7B2%7D%7D%7B1%2Bu%2Bu%5E2%7D%5Cright%5D\"/> |\n| Pseudo Voigt                | `pseudoVoigt`           | <img src=\"https://tex.cheminfo.org/v1/?tex=y%20%5Ccdot%5Cleft%5Bmu%5Ccdot%20exp%5Cleft%5B-%5Cfrac%7B1%7D%7B2%7D%5Cfrac%7B%5Cdelta%7D%7B%5Csigma%5E2%7D%5Cright%5D%20%2B%20%5Cleft(1%20-%20mu%5Cright)%5Ccdot%5Cfrac%7B%5Comega%5E2%7D%7B4%5Cdelta%20%2B%20%5Comega%5E2%7D%5Cright%5D\"/> |\n| Pseudo Voigt (TCH)          | `pseudoVoigtTCH`        | The pseudo Voigt above, with independent gaussian and lorentzian widths `fwhmG` and `fwhmL`. The effective `fwhm` and `mu` are derived from them through the Thompson–Cox–Hastings approximation. |\n| Split Gaussian (asymmetric) | `splitGaussian`         | Two gaussian halves sharing the apex: the lower-x half (`t ≤ x`) uses `fwhmLow`, the higher-x half (`t > x`) uses `fwhmHigh`. |\n\nThe only 2D shape is `gaussian`, whose widths are set per axis.\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=%5Comega%20%3D%20FWHM\"/> |<img src=\"https://tex.cheminfo.org/v1/?tex=u%20%3D%20%5Cleft(%5Cfrac%7B2%5Cdelta%7D%7B%5Comega%7D%5Cright)%5E2\"/> |\n|---------------------: | :---------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------- |\n\n## Installation\n\n`$ npm i ml-peak-shape-generator`\n\nThis package allows to calculate various shapes. By default they will have a height of 1.\n\n![demo.png](demo.png)\n\nYou see the resulting functions using this [playground](https://codesandbox.io/s/lorentzian-u1upg0?file=/Example.tsx)\n\n## Usage\n\n```js\nimport {\n  getGaussianData,\n  getLorentzianData,\n  getPseudoVoigtData,\n} from 'ml-peak-shape-generator';\n\n// It's possible to specify the windows size with factor option\nlet data = getGaussianData({ sd: 500 }, { factor: 3.5 });\n// or fix the number of points as Full Width at Half Maximum\nlet data = getGaussianData({ fwhm: 500 }, { factor: 3.5 });\n\n// It's possible to specify the windows size with factor option\nlet data = getLorentzianData({ fwhm: 500 }, { factor: 5 });\n\n// It's possible to specify the windows size with factor option\nlet data = getPseudoVoigtData({ fwhm: 500 }, { factor: 5 });\n```\n\nIt is also possible to take an instance of each kind of shape:\n\n```js\nimport { Gaussian, gaussianFct, Gaussian2D } from 'ml-peak-shape-generator';\n\nconst gaussianShape = new Gaussian({ fwhm: 500 });\n// It is possible to set a new value for fwhm\ngaussianShape.fwhm = 300;\n\n// By default the height value ensure a volume equal 1.\nconst symmetric2DShape = new Gaussian2D({ fwhm: 500 });\n\n// It is possible to set values for sd, fwhm and factor for each axes.\nconst gaussian2DShape = new Gaussian2D({ fwhm: { x: 300, y: 500 } });\n\n// It is possible to set new value for fwhm by:\ngaussian2DShape.fwhm = { x: 300, y: 500 };\n// or set the same value for both axes.\ngaussian2DShape.fwhm = 400;\n\n// An instance of any shape has the same methods accessible for each\n// shape e.g. fct or getData, but these use the internal parameters. e.g:\n\ngaussianShape.fct(5);\ngaussianFct(5, 500);\n// getData\ngaussianShape.getData({ factor: 3.5 });\n```\n\n```js\nimport { getShape1D, getShape2D } from 'ml-peak-shape-generator';\n\n// If you want to dynamically select a shape you can use `getShape1D` /\n// `getShape2D`. They return an instance of the required kind of shape.\n\nconst lorentzian = getShape1D({ kind: 'lorentzian', fwhm: 500 });\nconst gaussian2D = getShape2D({ kind: 'gaussian', sd: 500 });\n```\n\n## Descriptors, instances and serialization\n\nA shape exists in two forms: a **descriptor** — a plain object such as\n`{ kind: 'gaussian', fwhm: 500 }` — and an **instance**, the class that computes\nthe curve. `getShape1D` / `getShape2D` turn a descriptor into an instance.\n\nAn instance carries its own `kind` and serializes back to a descriptor, so a\nshape survives a trip through JSON:\n\n```js\nimport { getShape1D } from 'ml-peak-shape-generator';\n\nconst shape = getShape1D({ kind: 'pseudoVoigt', fwhm: 500, mu: 0.3 });\n\nshape.kind; // 'pseudoVoigt'\nJSON.stringify(shape); // '{\"kind\":\"pseudoVoigt\",\"fwhm\":500,\"mu\":0.3}'\n\nconst restored = getShape1D(JSON.parse(JSON.stringify(shape)));\nrestored.fct(5) === shape.fct(5); // true\n```\n\n`toJSON` emits the parameters the shape is defined by — those `getParameters()`\nreports — so a round trip preserves both the curve and its analytical\nderivatives. A `splitGaussian` therefore emits `fwhmLow` and `fwhmHigh` rather\nthan its mean `fwhm`, and a `pseudoVoigtTCH` emits its component widths `fwhmG`\nand `fwhmL`. Options that are alternative ways to express a width, such as `sd`,\nare resolved first and emitted as the resulting `fwhm`.\n\nBecause an instance is itself a valid descriptor, handing one back to the\nfactory copies it:\n\n```js\nconst copy = getShape1D(shape); // a new instance with the same parameters\n```\n\nThe `kind` strings are exported as types:\n\n```ts\nimport type { Shape1DKind, Shape2DKind } from 'ml-peak-shape-generator';\n\n// Shape1DKind: 'gaussian' | 'lorentzian' | 'lorentzianDispersive' |\n//              'pseudoVoigt' | 'pseudoVoigtTCH' | 'generalizedLorentzian' |\n//              'splitGaussian'\n// Shape2DKind: 'gaussian'\n```\n\nIt is also possible to get a function that allows to calculate y for any x\n\n```js\nimport { gaussianFct } from 'ml-peak-shape-generator';\nconst func = gaussianFct(x - mean, fwhm);\n```\n\n## [API Documentation](https://mljs.github.io/peak-shape-generator/)\n\n## License\n\n[MIT](./LICENSE)\n\n[npm-image]: https://img.shields.io/npm/v/ml-peak-shape-generator.svg\n[npm-url]: https://www.npmjs.com/package/ml-peak-shape-generator\n[ci-image]: https://github.com/mljs/peak-shape-generator/workflows/Node.js%20CI/badge.svg?branch=main\n[ci-url]: https://github.com/mljs/peak-shape-generator/actions?query=workflow%3A%22Node.js+CI%22\n[codecov-image]: https://img.shields.io/codecov/c/github/mljs/peak-shape-generator.svg\n[codecov-url]: https://codecov.io/gh/mljs/peak-shape-generator\n[download-image]: https://img.shields.io/npm/dm/ml-peak-shape-generator.svg\n[download-url]: https://www.npmjs.com/package/ml-peak-shape-generator\n","readmeFilename":"README.md"}