ml-spectra-fitting - npm Package Compare versions

Comparing version 0.10.0 to 0.11.0

CHANGELOG.md

		# Changelog

		## [0.11.0](https://www.github.com/mljs/spectra-fitting/compare/v0.10.0...v0.11.0) (2020-12-20)


		### Features

		* add timeout option ([7743e81](https://www.github.com/mljs/spectra-fitting/commit/7743e811232e842927a05f2ead48c5bf86e56c8e))
		* update ml-peak-shape-generator 0.12.0 ([de1313e](https://www.github.com/mljs/spectra-fitting/commit/de1313e26083fd0c96ae3ba90650364b1dee185b))

		## [0.10.0](https://www.github.com/mljs/spectra-fitting/compare/v0.9.0...v0.10.0) (2020-12-10)
		@@ -4,0 +12,0 @@

lib/index.js

		@@ -128,2 +128,3 @@ 'use strict';
		* @param {object} [options.optimization.kind = 'lm'] - kind of algorithm. By default it's levenberg-marquardt.
		* @param {number} [options.optimization.options.timeout] - maximum time running before break in seconds.
		* @param {object} [options.optimization.options = {}] - options for the specific kind of algorithm.
		@@ -130,0 +131,0 @@ * @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.

package.json

		{
		"name": "ml-spectra-fitting",
		"version": "0.10.0",
		"version": "0.11.0",
		"description": "Fit spectra using gaussian or lorentzian",
		@@ -65,5 +65,5 @@ "main": "lib/index.js",
		"ml-array-max": "^1.2.0",
		"ml-levenberg-marquardt": "^3.0.1",
		"ml-peak-shape-generator": "^0.11.0"
		"ml-levenberg-marquardt": "3.1.0",
		"ml-peak-shape-generator": "^0.12.0"
		}
		}

src/index.js

		@@ -23,2 +23,3 @@ import getMaxValue from 'ml-array-max';
		* @param {object} [options.optimization.kind = 'lm'] - kind of algorithm. By default it's levenberg-marquardt.
		* @param {number} [options.optimization.options.timeout] - maximum time running before break in seconds.
		* @param {object} [options.optimization.options = {}] - options for the specific kind of algorithm.
		@@ -25,0 +26,0 @@ * @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.

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