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returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: 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":["accumulate","accumulation","array","base","javascript","ndarray","node","node-js","nodejs","reduce","reduction","stdlib","strided","unary","vector"],"created_at":"2025-06-09T20:09:26.066Z","updated_at":"2026-04-27T18:34:15.798Z","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) 2025 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# unaryReduceStrided1dBy\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 Perform a reduction over a list of specified dimensions in an input ndarray via a one-dimensional strided array reduction function accepting a callback and assign results to a provided 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-reduce-strided1d-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 unaryReduceStrided1dBy = require( '@stdlib/ndarray-base-unary-reduce-strided1d-by' );\n```\n\n#### unaryReduceStrided1dBy( fcn, arrays, dims\\[, options], clbk\\[, thisArg] )\n\nPerforms a reduction over a list of specified dimensions in an input ndarray via a one-dimensional strided array reduction function accepting a callback and assigns results to a provided output ndarray.\n\n\u003c!-- eslint-disable max-len --\u003e\n\n```javascript\nvar Float64Array = require( '@stdlib/array-float64' );\nvar ndarray2array = require( '@stdlib/ndarray-base-to-array' );\nvar maxBy = require( '@stdlib/stats-base-ndarray-max-by' );\n\n// Define a callback function:\nfunction clbk( value ) {\n    return value * 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( [ 0.0, 0.0, 0.0 ] );\n\n// Define the array shapes:\nvar xsh = [ 1, 3, 2, 2 ];\nvar ysh = [ 1, 3 ];\n\n// Define the array strides:\nvar sx = [ 12, 4, 2, 1 ];\nvar sy = [ 3, 1 ];\n\n// Define the index offsets:\nvar ox = 0;\nvar oy = 0;\n\n// Create an input ndarray-like object:\nvar x = {\n    'dtype': 'float64',\n    'data': xbuf,\n    'shape': xsh,\n    'strides': sx,\n    'offset': ox,\n    'order': 'row-major'\n};\n\n// Create an output ndarray-like object:\nvar y = {\n    'dtype': 'float64',\n    'data': ybuf,\n    'shape': ysh,\n    'strides': sy,\n    'offset': oy,\n    'order': 'row-major'\n};\n\n// Perform a reduction:\nunaryReduceStrided1dBy( maxBy, [ x, y ], [ 2, 3 ], clbk );\n\nvar arr = ndarray2array( y.data, y.shape, y.strides, y.offset, y.order );\n// returns [ [ 8.0, 16.0, 24.0 ] ]\n```\n\nThe function accepts the following arguments:\n\n-   **fcn**: function which will be applied to a one-dimensional subarray and should reduce the subarray to a single scalar value.\n-   **arrays**: array-like object containing one input ndarray and one output ndarray, followed by any additional ndarray arguments.\n-   **dims**: list of dimensions over which to perform a reduction.\n-   **options**: function options which are passed through to `fcn` (_optional_).\n-   **clbk**: callback function.\n-   **thisArg**: callback execution context (_optional_).\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 the following arguments:\n\n-   **value**: current array element.\n-   **indices**: current array element indices.\n-   **arr**: the input ndarray.\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' );\nvar ndarray2array = require( '@stdlib/ndarray-base-to-array' );\nvar maxBy = require( '@stdlib/stats-base-ndarray-max-by' );\n\n// Define a callback function:\nfunction clbk( value ) {\n    this.count += 1;\n    return value * 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( [ 0.0, 0.0, 0.0 ] );\n\n// Define the array shapes:\nvar xsh = [ 1, 3, 2, 2 ];\nvar ysh = [ 1, 3 ];\n\n// Define the array strides:\nvar sx = [ 12, 4, 2, 1 ];\nvar sy = [ 3, 1 ];\n\n// Define the index offsets:\nvar ox = 0;\nvar oy = 0;\n\n// Create an input ndarray-like object:\nvar x = {\n    'dtype': 'float64',\n    'data': xbuf,\n    'shape': xsh,\n    'strides': sx,\n    'offset': ox,\n    'order': 'row-major'\n};\n\n// Create an output ndarray-like object:\nvar y = {\n    'dtype': 'float64',\n    'data': ybuf,\n    'shape': ysh,\n    'strides': sy,\n    'offset': oy,\n    'order': 'row-major'\n};\n\n// Define callback execution context:\nvar ctx = {\n    'count': 0\n};\n\n// Perform a reduction:\nunaryReduceStrided1dBy( maxBy, [ x, y ], [ 2, 3 ], clbk, ctx );\n\nvar arr = ndarray2array( y.data, y.shape, y.strides, y.offset, y.order );\n// returns [ [ 8.0, 16.0, 24.0 ] ]\n\nvar count = ctx.count;\n// returns 12\n```\n\n#### TODO: document factory method\n\n\u003c/section\u003e\n\n\u003c!-- /.usage --\u003e\n\n\u003csection class=\"notes\"\u003e\n\n## Notes\n\n-   The output ndarray is expected to have the same dimensions as the non-reduced dimensions of the input ndarray.\n\n-   Any additional ndarray arguments are expected to have the same leading dimensions as the non-reduced dimensions of the input ndarray.\n\n-   When calling the reduction function, any additional ndarray arguments are provided as k-dimensional subarrays, where `k = M - N` with `M` being the number of dimensions in an ndarray argument and `N` being the number of non-reduced dimensions in the input ndarray. For example, if an input ndarray has three dimensions, the number of reduced dimensions is two, and an additional ndarray argument has one dimension, thus matching the number of non-reduced dimensions in the input ndarray, the reduction function is provided a zero-dimensional subarray as an additional ndarray argument. In the same scenario but where an additional ndarray argument has two dimensions, thus exceeding the number of non-reduced dimensions in the input ndarray, the reduction function is provided a one-dimensional subarray as an additional ndarray argument.\n\n-   The reduction function is expected to have the following signature:\n\n    ```text\n    fcn( arrays[, options], clbk[, thisArg ] )\n    ```\n\n    where\n\n    -   **arrays**: array containing a one-dimensional subarray of the input ndarray and any additional ndarray arguments as subarrays.\n    -   **options**: function options (_optional_).\n    -   **clbk**: callback function.\n    -   **thisArg**: callback execution context (_optional_).\n\n-   For very high-dimensional ndarrays which are non-contiguous, one should consider copying the underlying data to contiguous memory before performing a reduction in order to achieve better performance.\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-array-discrete-uniform' );\nvar zeros = require( '@stdlib/array-base-zeros' );\nvar ndarray2array = require( '@stdlib/ndarray-base-to-array' );\nvar maxBy = require( '@stdlib/stats-base-ndarray-max-by' );\nvar unaryReduceStrided1dBy = require( '@stdlib/ndarray-base-unary-reduce-strided1d-by' );\n\nfunction clbk( value ) {\n    return value * 2.0;\n}\n\nvar N = 10;\nvar x = {\n    'dtype': 'generic',\n    'data': discreteUniform( N, -5, 5, {\n        'dtype': 'generic'\n    }),\n    'shape': [ 1, 5, 2 ],\n    'strides': [ 10, 2, 1 ],\n    'offset': 0,\n    'order': 'row-major'\n};\nvar y = {\n    'dtype': 'generic',\n    'data': zeros( 2 ),\n    'shape': [ 1, 5 ],\n    'strides': [ 5, 1 ],\n    'offset': 0,\n    'order': 'row-major'\n};\n\nunaryReduceStrided1dBy( maxBy, [ x, y ], [ 2 ], clbk );\n\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## 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-reduce-strided1d-by.svg\n[npm-url]: https://npmjs.org/package/@stdlib/ndarray-base-unary-reduce-strided1d-by\n\n[test-image]: https://github.com/stdlib-js/ndarray-base-unary-reduce-strided1d-by/actions/workflows/test.yml/badge.svg?branch=main\n[test-url]: https://github.com/stdlib-js/ndarray-base-unary-reduce-strided1d-by/actions/workflows/test.yml?query=branch:main\n\n[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/ndarray-base-unary-reduce-strided1d-by/main.svg\n[coverage-url]: https://codecov.io/github/stdlib-js/ndarray-base-unary-reduce-strided1d-by?branch=main\n\n\u003c!--\n\n[dependencies-image]: https://img.shields.io/david/stdlib-js/ndarray-base-unary-reduce-strided1d-by.svg\n[dependencies-url]: https://david-dm.org/stdlib-js/ndarray-base-unary-reduce-strided1d-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-reduce-strided1d-by/tree/deno\n[deno-readme]: https://github.com/stdlib-js/ndarray-base-unary-reduce-strided1d-by/blob/deno/README.md\n[umd-url]: https://github.com/stdlib-js/ndarray-base-unary-reduce-strided1d-by/tree/umd\n[umd-readme]: https://github.com/stdlib-js/ndarray-base-unary-reduce-strided1d-by/blob/umd/README.md\n[esm-url]: https://github.com/stdlib-js/ndarray-base-unary-reduce-strided1d-by/tree/esm\n[esm-readme]: https://github.com/stdlib-js/ndarray-base-unary-reduce-strided1d-by/blob/esm/README.md\n[branches-url]: https://github.com/stdlib-js/ndarray-base-unary-reduce-strided1d-by/blob/main/branches.md\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-reduce-strided1d-by","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fstdlib-js%2Fndarray-base-unary-reduce-strided1d-by","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstdlib-js%2Fndarray-base-unary-reduce-strided1d-by/lists"}