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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["accessor","apply","array","base","foreach","javascript","map","ndarray","node","node-js","nodejs","stdlib","strided","transform","unary"],"created_at":"2026-02-12T09:04:24.333Z","updated_at":"2026-02-12T09:04:25.076Z","avatar_url":"https://github.com/stdlib-js.png","language":"JavaScript","funding_links":["https://github.com/sponsors/stdlib-js","https://opencollective.com/stdlib","https://tidelift.com/funding/github/npm/@stdlib/stdlib"],"categories":[],"sub_categories":[],"readme":"\u003c!--\n\n@license Apache-2.0\n\nCopyright (c) 2022 The Stdlib Authors.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n   http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\n--\u003e\n\n\n\u003cdetails\u003e\n  \u003csummary\u003e\n    About stdlib...\n  \u003c/summary\u003e\n  \u003cp\u003eWe believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.\u003c/p\u003e\n  \u003cp\u003eThe library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.\u003c/p\u003e\n  \u003cp\u003eWhen you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.\u003c/p\u003e\n  \u003cp\u003eTo join us in bringing numerical computing to the web, get started by checking us out on \u003ca href=\"https://github.com/stdlib-js/stdlib\"\u003eGitHub\u003c/a\u003e, and please consider \u003ca href=\"https://opencollective.com/stdlib\"\u003efinancially supporting stdlib\u003c/a\u003e. We greatly appreciate your continued support!\u003c/p\u003e\n\u003c/details\u003e\n\n# unaryBy\n\n[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url] \u003c!-- [![dependencies][dependencies-image]][dependencies-url] --\u003e\n\n\u003e Apply a unary function to each element in an input ndarray according to a callback function and assign results to elements in an output ndarray.\n\n\u003csection class=\"intro\"\u003e\n\n\u003c/section\u003e\n\n\u003c!-- /.intro --\u003e\n\n\u003csection class=\"installation\"\u003e\n\n## Installation\n\n```bash\nnpm install @stdlib/ndarray-base-unary-by\n```\n\nAlternatively,\n\n-   To load the package in a website via a `script` tag without installation and bundlers, use the [ES Module][es-module] available on the [`esm`][esm-url] branch (see [README][esm-readme]).\n-   If you are using Deno, visit the [`deno`][deno-url] branch (see [README][deno-readme] for usage intructions).\n-   For use in Observable, or in browser/node environments, use the [Universal Module Definition (UMD)][umd] build available on the [`umd`][umd-url] branch (see [README][umd-readme]).\n\nThe [branches.md][branches-url] file summarizes the available branches and displays a diagram illustrating their relationships.\n\nTo view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.\n\n\u003c/section\u003e\n\n\u003csection class=\"usage\"\u003e\n\n## Usage\n\n```javascript\nvar unaryBy = require( '@stdlib/ndarray-base-unary-by' );\n```\n\n#### unaryBy( arrays, fcn, clbk\\[, thisArg] )\n\nApplies a unary function to each element retrieved from an input ndarray according to a callback function and assigns results to elements in an output ndarray.\n\n\u003c!-- eslint-disable max-len --\u003e\n\n```javascript\nvar Float64Array = require( '@stdlib/array-float64' );\n\nfunction scale( x ) {\n    return x * 10.0;\n}\n\nfunction accessor( v ) {\n    return v * 2.0;\n}\n\n// Create data buffers:\nvar xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );\nvar ybuf = new Float64Array( 6 );\n\n// Define the shape of the input and output arrays:\nvar shape = [ 3, 1, 2 ];\n\n// Define the array strides:\nvar sx = [ 4, 4, 1 ];\nvar sy = [ 2, 2, 1 ];\n\n// Define the index offsets:\nvar ox = 1;\nvar oy = 0;\n\n// Create the input and output ndarray-like objects:\nvar x = {\n    'dtype': 'float64',\n    'data': xbuf,\n    'shape': shape,\n    'strides': sx,\n    'offset': ox,\n    'order': 'row-major'\n};\nvar y = {\n    'dtype': 'float64',\n    'data': ybuf,\n    'shape': shape,\n    'strides': sy,\n    'offset': oy,\n    'order': 'row-major'\n};\n\n// Apply the unary function:\nunaryBy( [ x, y ], scale, accessor );\n\nconsole.log( y.data );\n// =\u003e \u003cFloat64Array\u003e[ 40.0, 60.0, 120.0, 140.0, 200.0, 220.0 ]\n```\n\nThe function accepts the following arguments:\n\n-   **arrays**: array-like object containing one input ndarray and one output ndarray.\n-   **fcn**: unary function to apply.\n\nEach provided ndarray should be an object with the following properties:\n\n-   **dtype**: data type.\n-   **data**: data buffer.\n-   **shape**: dimensions.\n-   **strides**: stride lengths.\n-   **offset**: index offset.\n-   **order**: specifies whether an ndarray is row-major (C-style) or column major (Fortran-style).\n\nThe invoked callback function is provided four arguments:\n\n-   **value**: input array element.\n-   **idx**: iteration index (zero-based).\n-   **indices**: input and output ndarray data buffer indices `[ix, iy]`.\n-   **arrays**: input and output ndarrays `[x, y]`.\n\nTo set the callback execution context, provide a `thisArg`.\n\n\u003c!-- eslint-disable max-len --\u003e\n\n```javascript\nvar Float64Array = require( '@stdlib/array-float64' );\n\nfunction scale( x ) {\n    return x * 10.0;\n}\n\nfunction accessor( v ) {\n    this.count += 1;\n    return v * 2.0;\n}\n\n// Create data buffers:\nvar xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );\nvar ybuf = new Float64Array( 6 );\n\n// Define the shape of the input and output arrays:\nvar shape = [ 3, 1, 2 ];\n\n// Define the array strides:\nvar sx = [ 4, 4, 1 ];\nvar sy = [ 2, 2, 1 ];\n\n// Define the index offsets:\nvar ox = 1;\nvar oy = 0;\n\n// Create the input and output ndarray-like objects:\nvar x = {\n    'dtype': 'float64',\n    'data': xbuf,\n    'shape': shape,\n    'strides': sx,\n    'offset': ox,\n    'order': 'row-major'\n};\nvar y = {\n    'dtype': 'float64',\n    'data': ybuf,\n    'shape': shape,\n    'strides': sy,\n    'offset': oy,\n    'order': 'row-major'\n};\n\n// Apply the unary function:\nvar context = {\n    'count': 0\n};\nunaryBy( [ x, y ], scale, accessor, context );\n\nvar cnt = context.count;\n// returns 6\n```\n\n\u003c/section\u003e\n\n\u003c!-- /.usage --\u003e\n\n\u003csection class=\"notes\"\u003e\n\n## Notes\n\n-   For very high-dimensional ndarrays which are non-contiguous, one should consider copying the underlying data to contiguous memory before applying a unary function in order to achieve better performance.\n\n-   If a provided callback function does not return any value (or equivalently, explicitly returns `undefined`), the value is **ignored**.\n\n    \u003c!-- eslint-disable max-len --\u003e\n\n    ```javascript\n    var Float64Array = require( '@stdlib/array-float64' );\n\n    function scale( x ) {\n        return x * 10.0;\n    }\n\n    function accessor() {\n        // No-op...\n    }\n\n    // Create data buffers:\n    var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );\n    var ybuf = new Float64Array( 6 );\n\n    // Define the shape of the input and output arrays:\n    var shape = [ 3, 1, 2 ];\n\n    // Define the array strides:\n    var sx = [ 4, 4, 1 ];\n    var sy = [ 2, 2, 1 ];\n\n    // Define the index offsets:\n    var ox = 1;\n    var oy = 0;\n\n    // Create the input and output ndarray-like objects:\n    var x = {\n        'dtype': 'float64',\n        'data': xbuf,\n        'shape': shape,\n        'strides': sx,\n        'offset': ox,\n        'order': 'row-major'\n    };\n    var y = {\n        'dtype': 'float64',\n        'data': ybuf,\n        'shape': shape,\n        'strides': sy,\n        'offset': oy,\n        'order': 'row-major'\n    };\n\n    // Apply the unary function:\n    unaryBy( [ x, y ], scale, accessor );\n\n    console.log( y.data );\n    // =\u003e \u003cFloat64Array\u003e[ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]\n    ```\n\n\u003c/section\u003e\n\n\u003c!-- /.notes --\u003e\n\n\u003csection class=\"examples\"\u003e\n\n## Examples\n\n\u003c!-- eslint no-undef: \"error\" --\u003e\n\n```javascript\nvar discreteUniform = require( '@stdlib/random-base-discrete-uniform' ).factory;\nvar filledarray = require( '@stdlib/array-filled' );\nvar filledarrayBy = require( '@stdlib/array-filled-by' );\nvar abs = require( '@stdlib/math-base-special-abs' );\nvar sqrt = require( '@stdlib/math-base-special-sqrt' );\nvar naryFunction = require( '@stdlib/utils-nary-function' );\nvar shape2strides = require( '@stdlib/ndarray-base-shape2strides' );\nvar ndarray2array = require( '@stdlib/ndarray-base-to-array' );\nvar unaryBy = require( '@stdlib/ndarray-base-unary-by' );\n\nvar N = 10;\nvar x = {\n    'dtype': 'generic',\n    'data': filledarrayBy( N, 'generic', discreteUniform( -100, 100 ) ),\n    'shape': [ 5, 2 ],\n    'strides': [ 2, 1 ],\n    'offset': 0,\n    'order': 'row-major'\n};\nvar y = {\n    'dtype': 'generic',\n    'data': filledarray( 0, N, 'generic' ),\n    'shape': x.shape.slice(),\n    'strides': shape2strides( x.shape, 'column-major' ),\n    'offset': 0,\n    'order': 'column-major'\n};\n\nunaryBy( [ x, y ], sqrt, naryFunction( abs, 1 ) );\nconsole.log( ndarray2array( x.data, x.shape, x.strides, x.offset, x.order ) );\nconsole.log( ndarray2array( y.data, y.shape, y.strides, y.offset, y.order ) );\n```\n\n\u003c/section\u003e\n\n\u003c!-- /.examples --\u003e\n\n\u003c!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. --\u003e\n\n\u003csection class=\"related\"\u003e\n\n\u003c/section\u003e\n\n\u003c!-- /.related --\u003e\n\n\n\u003csection class=\"main-repo\" \u003e\n\n* * *\n\n## Notice\n\nThis package is part of [stdlib][stdlib], a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.\n\nFor more information on the project, filing bug reports and feature requests, and guidance on how to develop [stdlib][stdlib], see the main project [repository][stdlib].\n\n#### Community\n\n[![Chat][chat-image]][chat-url]\n\n---\n\n## License\n\nSee [LICENSE][stdlib-license].\n\n\n## Copyright\n\nCopyright \u0026copy; 2016-2026. The Stdlib [Authors][stdlib-authors].\n\n\u003c/section\u003e\n\n\u003c!-- /.stdlib --\u003e\n\n\u003c!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. --\u003e\n\n\u003csection class=\"links\"\u003e\n\n[npm-image]: http://img.shields.io/npm/v/@stdlib/ndarray-base-unary-by.svg\n[npm-url]: https://npmjs.org/package/@stdlib/ndarray-base-unary-by\n\n[test-image]: https://github.com/stdlib-js/ndarray-base-unary-by/actions/workflows/test.yml/badge.svg?branch=v0.2.3\n[test-url]: https://github.com/stdlib-js/ndarray-base-unary-by/actions/workflows/test.yml?query=branch:v0.2.3\n\n[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/ndarray-base-unary-by/main.svg\n[coverage-url]: https://codecov.io/github/stdlib-js/ndarray-base-unary-by?branch=main\n\n\u003c!--\n\n[dependencies-image]: https://img.shields.io/david/stdlib-js/ndarray-base-unary-by.svg\n[dependencies-url]: https://david-dm.org/stdlib-js/ndarray-base-unary-by/main\n\n--\u003e\n\n[chat-image]: https://img.shields.io/badge/zulip-join_chat-brightgreen.svg\n[chat-url]: https://stdlib.zulipchat.com\n\n[stdlib]: https://github.com/stdlib-js/stdlib\n\n[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors\n\n[umd]: https://github.com/umdjs/umd\n[es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules\n\n[deno-url]: https://github.com/stdlib-js/ndarray-base-unary-by/tree/deno\n[deno-readme]: https://github.com/stdlib-js/ndarray-base-unary-by/blob/deno/README.md\n[umd-url]: https://github.com/stdlib-js/ndarray-base-unary-by/tree/umd\n[umd-readme]: https://github.com/stdlib-js/ndarray-base-unary-by/blob/umd/README.md\n[esm-url]: https://github.com/stdlib-js/ndarray-base-unary-by/tree/esm\n[esm-readme]: https://github.com/stdlib-js/ndarray-base-unary-by/blob/esm/README.md\n[branches-url]: https://github.com/stdlib-js/ndarray-base-unary-by/blob/main/branches.md\n\n[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/ndarray-base-unary-by/main/LICENSE\n\n\u003c/section\u003e\n\n\u003c!-- /.links --\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstdlib-js%2Fndarray-base-unary-by","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fstdlib-js%2Fndarray-base-unary-by","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstdlib-js%2Fndarray-base-unary-by/lists"}