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Asencio","email":"maasencioh@gmail.com"},"license":"MIT","homepage":"https://github.com/mljs/savitzky-golay-generalized","keywords":["signal","filtering","transformation","savitzky","Golay","derivative"],"repository":{"type":"git","url":"git+https://github.com/mljs/savitzky-golay-generalized.git"},"description":"Savitzky–Golay filter in Javascript","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":"andcastillo","email":"andcastillo@gmail.com"}],"readme":"# ml-savitzky-golay-generalized\n\nGeneral Least-Squares Smoothing and Differentiation by the Convolution (Savitzky-Golay) Method, after Peter A. Gorry.\n\nPretty much the same as the savitzky-golay method, but without border problems, and without inventing points.\n\n## Installation\n\n```console\nnpm i ml-savitzky-golay-generalized\n```\n\nThis package is ESM-only. CommonJS consumers need Node.js >= 22.12 (or any 24.x\nor later), where `require()` of a synchronous ES module works out of the box;\notherwise use `import`.\n\n## Usage\n\n```js\nimport { sgg } from 'ml-savitzky-golay-generalized';\n\nconst result = sgg(ys, deltaX, options);\n```\n\n`sgg` returns a `Float64Array` of the same length as `ys`.\n\nWhen you need two derivative orders of the same data — peak picking wants the\nfirst and the second — `sggPair` computes both in one pass:\n\n```js\nimport { sggPair } from 'ml-savitzky-golay-generalized';\n\nconst [dY, ddY] = sggPair(ys, deltaX, options);\n```\n\n## Parameters\n\n### ys\n\nThe data to be filtered.\n\n### deltaX | xs\n\n`deltaX` specifies the difference between 2 consecutive points of the independent variable: `deltaX = xs[i + 1] - xs[i]`. Specifying a `deltaX` supposes that all your points are equally spaced on the independent variable.\n\nIf your points are not equally spaced, you have to provide your `xs` values explicitly. The algorithm will use the average `deltaX` within each bin of `windowSize` points to approximate the derivatives. This fast approximation only works if the `xs` are almost locally equally spaced.\n\n### options\n\n#### windowSize\n\nThe odd number of points used to approximate the regression polynomial. Must be an odd integer of at least 5. Default `9`.\n\n#### derivative\n\nThe order of the derivative. `0` (smoothing) by default.\n\n#### polynomial\n\nThe order of the regression polynomial. Default `3`.\n\n## sggPair\n\n`sggPair(ys, deltaX | xs, options)` returns `[Float64Array, Float64Array]`, one\narray per requested order.\n\nIt is measurably faster than calling `sgg` twice: each `ys` value is read once\nand multiplied into both accumulators, and the window spacing is measured once\nand raised twice. On 40 real mass spectra of 4436 points each, the pair of\nderivatives `ml-gsd` asks for went from 33.3 to 17.4 ns per point.\n\nThe results are bit-identical to the two separate `sgg` calls they replace.\n\n### options\n\nTakes `windowSize` and `polynomial` exactly as `sgg` does, and `derivatives` in\nplace of `derivative`.\n\n#### derivatives\n\nThe two orders, in the order they are returned. Default `[1, 2]`.\n\nTwo and not a list: an accumulator per order behind a loop costs more than\nsharing the reads saves, so a general N-derivative version measured _slower_\nthan calling `sgg` repeatedly. For a third order, call `sgg` for it.\n\n## Examples\n\n### Smoothing\n\n```js\nimport { sgg } from 'ml-savitzky-golay-generalized';\n\nconst noiseLevel = 0.1;\nconst data = new Array(200);\nfor (let i = 0; i < data.length; i++) {\n  data[i] =\n    Math.sin((i * Math.PI * 2) / data.length) +\n    (Math.random() - 0.5) * noiseLevel;\n}\n\nconst answer = sgg(data, (Math.PI * 2) / data.length, {\n  windowSize: 15,\n  derivative: 0,\n  polynomial: 3,\n});\nconsole.log(answer); // Float64Array(200), the smoothed signal\n```\n\n### First derivative, equally spaced x\n\n```js\nimport { sgg } from 'ml-savitzky-golay-generalized';\n\nconst noiseLevel = 0.1;\nconst data = new Array(200);\nfor (let i = 0; i < data.length; i++) {\n  data[i] =\n    Math.sin((i * Math.PI * 2) / data.length) +\n    (Math.random() - 0.5) * noiseLevel;\n}\n\nconst answer = sgg(data, (Math.PI * 2) / data.length, {\n  windowSize: 45,\n  derivative: 1,\n  polynomial: 3,\n});\nconsole.log(answer); // Float64Array(200), starts near 1 (the cosine at 0)\n```\n\n### First derivative, x as a vector (it could be non-equally spaced)\n\n```js\nimport { sgg } from 'ml-savitzky-golay-generalized';\n\nconst noiseLevel = 0.1;\nconst data = new Array(200);\nconst x = new Array(200);\nfor (let i = 0; i < data.length; i++) {\n  data[i] =\n    Math.sin((i * Math.PI * 2) / data.length) +\n    (Math.random() - 0.5) * noiseLevel;\n  x[i] = (i * Math.PI * 2) / data.length;\n}\n\nconst options = { windowSize: 47, derivative: 1, polynomial: 3 };\nconst fromDeltaX = sgg(data, (Math.PI * 2) / data.length, options);\nconst fromX = sgg(data, x, options);\n```\n\n### First and second derivative in one pass\n\n```js\nimport { sgg, sggPair } from 'ml-savitzky-golay-generalized';\n\nconst data = new Array(200);\nconst x = new Array(200);\nfor (let i = 0; i < data.length; i++) {\n  data[i] = Math.sin((i * Math.PI * 2) / data.length);\n  x[i] = (i * Math.PI * 2) / data.length;\n}\n\nconst [dY, ddY] = sggPair(data, x, { windowSize: 45, polynomial: 3 });\n\n// the same answer as, and faster than:\nconst options = { windowSize: 45, polynomial: 3 };\nconst alsoDY = sgg(data, x, { ...options, derivative: 1 });\nconst alsoDdY = sgg(data, x, { ...options, derivative: 2 });\n```\n\n## License\n\n[MIT](./LICENSE)\n","readmeFilename":"README.md"}