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returned=1 errno=0 peeraddr=140.82.121.5:443 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"],"created_at":"2025-02-11T09:51:39.446Z","updated_at":"2026-03-06T04:32:12.420Z","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# accumulateUnary\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 elements in an input 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-accumulate\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 accumulateUnary = require( '@stdlib/ndarray-base-unary-accumulate' );\n```\n\n#### accumulateUnary( arrays, initial, clbk )\n\nPerforms a reduction over elements in an input ndarray.\n\n\u003c!-- eslint-disable max-len --\u003e\n\n```javascript\nvar Float64Array = require( '@stdlib/array-float64' );\n\nfunction add( acc, x ) {\n    return acc + x;\n}\n\n// Create a data buffer:\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 ] );\n\n// Define the shape of the input array:\nvar shape = [ 3, 1, 2 ];\n\n// Define the array strides:\nvar sx = [ 4, 4, 1 ];\n\n// Define the index offset:\nvar ox = 1;\n\n// Create the input ndarray-like object:\nvar x = {\n    'dtype': 'float64',\n    'data': xbuf,\n    'shape': shape,\n    'strides': sx,\n    'offset': ox,\n    'order': 'row-major'\n};\n\n// Compute the sum:\nvar v = accumulateUnary( [ x ], 0.0, add );\n// returns 39.0\n```\n\nThe function accepts the following arguments:\n\n-   **arrays**: array-like object containing one input ndarray.\n-   **initial**: initial value.\n-   **clbk**: callback 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 callback is invoked with two arguments:\n\n-   **acc**: the current accumulated value. The first time the callback is invoked, `acc` is equal to the initial value.\n-   **value**: the current element.\n\nAfter each callback invocation, the callback return value is subsequently used as the accumulated value for the next callback invocation.\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 an accumulator 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 ndarray2array = require( '@stdlib/ndarray-base-to-array' );\nvar add = require( '@stdlib/number-float64-base-add' );\nvar accumulateUnary = require( '@stdlib/ndarray-base-unary-accumulate' );\n\nvar N = 10;\nvar x = {\n    'dtype': 'generic',\n    'data': discreteUniform( N, -100, 100, {\n        'dtype': 'generic'\n    }),\n    'shape': [ 5, 2 ],\n    'strides': [ 2, 1 ],\n    'offset': 0,\n    'order': 'row-major'\n};\n\nvar sum = accumulateUnary( [ x ], 0.0, add );\nconsole.log( ndarray2array( x.data, x.shape, x.strides, x.offset, x.order ) );\n\nconsole.log( 'sum: %d', sum );\n```\n\n\u003c/section\u003e\n\n\u003c!-- /.examples --\u003e\n\n\u003c!-- C interface documentation. --\u003e\n\n* * *\n\n\u003csection class=\"c\"\u003e\n\n## C APIs\n\n\u003c!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. --\u003e\n\n\u003csection class=\"intro\"\u003e\n\nCharacter codes for data types:\n\n\u003c!-- The following is auto-generated. Do not manually edit. See scripts/loops.js. --\u003e\n\n\u003c!-- charcodes --\u003e\n\n-   **x**: `bool` (boolean).\n-   **c**: `complex64` (single-precision floating-point complex number).\n-   **z**: `complex128` (double-precision floating-point complex number).\n-   **f**: `float32` (single-precision floating-point number).\n-   **d**: `float64` (double-precision floating-point number).\n-   **k**: `int16` (signed 16-bit integer).\n-   **i**: `int32` (signed 32-bit integer).\n-   **s**: `int8` (signed 8-bit integer).\n-   **t**: `uint16` (unsigned 16-bit integer).\n-   **u**: `uint32` (unsigned 32-bit integer).\n-   **b**: `uint8` (unsigned 8-bit integer).\n\n\u003c!-- ./charcodes --\u003e\n\nFunction name suffix naming convention:\n\n```text\nstdlib_ndarray_\u003caccumulation_data_type\u003e\u003cinput_data_type\u003e_\u003coutput_data_type\u003e[_as_\u003ccallback_arg1_data_type\u003e\u003ccallback_arg2_data_type\u003e_\u003ccallback_return_data_type\u003e]\n```\n\nFor example,\n\n\u003c!-- run-disable --\u003e\n\n```c\nvoid stdlib_ndarray_accumulate_dd_d(...) {...}\n```\n\nis a function which performs accumulation in double-precision and accepts one double-precision floating-point input ndarray and one double-precision floating-point output ndarray. In other words, the suffix encodes the function type signature.\n\nTo support callbacks whose input arguments and/or return values are of a different data type than the input and/or output ndarray data types, the naming convention supports appending an `as` suffix. For example,\n\n\u003c!-- run-disable --\u003e\n\n```c\nvoid stdlib_ndarray_accumulate_ff_f_as_dd_d(...) {...}\n```\n\nis a function which performs accumulation in single-precision and accepts one single-precision floating-point input ndarray and one single-precision floating-point output ndarray. However, the callback accepts and returns double-precision floating-point numbers. Accordingly, the input and output values need to be cast using the following conversion sequence\n\n```c\n// Convert the current accumulated value to double-precision:\ndouble curr = (double)acc;\n\n// Convert each input array element to double-precision:\ndouble in1 = (double)x[ i ];\n\n// Evaluate the callback:\ndouble out = f( curr, in1 );\n\n// Convert the callback return value to single-precision:\nacc = (float)out;\n```\n\nThe accumulation data type and the output ndarray data type should **always** be the same.\n\nThe callback is invoked with two arguments:\n\n-   **acc**: the current accumulated value. The first time the callback is invoked, this argument is equal to the initial value.\n-   **value**: the current element.\n\nAfter each callback invocation, the callback return value is subsequently used as the accumulated value for the next callback invocation.\n\n\u003c/section\u003e\n\n\u003c!-- /.intro --\u003e\n\n\u003c!-- C usage documentation. --\u003e\n\n\u003csection class=\"usage\"\u003e\n\n### Usage\n\n```c\n#include \"stdlib/ndarray/base/unary_accumulate.h\"\n```\n\n\u003c!-- The following is auto-generated. Do not manually edit. See scripts/loops.js. --\u003e\n\n\u003c!-- loops --\u003e\n\n\u003c!-- ./loops --\u003e\n\n\u003c!-- macros --\u003e\n\n\u003c!-- TODO: consider documenting macros --\u003e\n\n\u003c!-- ./macros --\u003e\n\n\u003c/section\u003e\n\n\u003c!-- /.usage --\u003e\n\n\u003c!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. --\u003e\n\n\u003csection class=\"notes\"\u003e\n\n* * *\n\n### Notes\n\n-   The initial value and output ndarrays are assumed to be zero-dimensional ndarrays.\n\n\u003c/section\u003e\n\n\u003c!-- /.notes --\u003e\n\n\u003c!-- C API usage examples. --\u003e\n\n* * *\n\n\u003csection class=\"examples\"\u003e\n\n### Examples\n\n```c\n#include \"stdlib/ndarray/base/unary_accumulate.h\"\n#include \"stdlib/ndarray/dtypes.h\"\n#include \"stdlib/ndarray/index_modes.h\"\n#include \"stdlib/ndarray/orders.h\"\n#include \"stdlib/ndarray/ctor.h\"\n#include \u003cstdint.h\u003e\n#include \u003cstdlib.h\u003e\n#include \u003cstdio.h\u003e\n#include \u003cinttypes.h\u003e\n\nstatic void print_ndarray_contents( const struct ndarray *x ) {\n    int64_t i;\n    int8_t s;\n    double v;\n\n    for ( i = 0; i \u003c stdlib_ndarray_length( x ); i++ ) {\n        s = stdlib_ndarray_iget_float64( x, i, \u0026v );\n        if ( s != 0 ) {\n            fprintf( stderr, \"Unable to resolve data element.\\n\" );\n            exit( EXIT_FAILURE );\n        }\n        fprintf( stdout, \"data[%\"PRId64\"] = %lf\\n\", i, v );\n    }\n}\n\nstatic double add( const double acc, const double x ) {\n    return acc + x;\n}\n\nint main( void ) {\n    // Define the ndarray data type:\n    enum STDLIB_NDARRAY_DTYPE dtype = STDLIB_NDARRAY_FLOAT64;\n\n    // Create underlying byte arrays:\n    double xvalues[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 };\n    double ivalues[] = { 0.0 };\n    double ovalues[] = { 0.0 };\n\n    uint8_t *xbuf = (uint8_t *)xvalues;\n    uint8_t *ibuf = (uint8_t *)ivalues;\n    uint8_t *obuf = (uint8_t *)ovalues;\n\n    // Define the number of dimensions:\n    int64_t ndims = 3;\n\n    // Define the array shapes:\n    int64_t xsh[] = { 2, 2, 2 };\n    int64_t ish[] = {};\n    int64_t osh[] = {};\n\n    // Define the strides:\n    int64_t sx[] = { 32, 16, 8 };\n    int64_t si[] = { 0 };\n    int64_t so[] = { 0 };\n\n    // Define the offsets:\n    int64_t ox = 0;\n    int64_t oi = 0;\n    int64_t oo = 0;\n\n    // Define the array order:\n    enum STDLIB_NDARRAY_ORDER order = STDLIB_NDARRAY_ROW_MAJOR;\n\n    // Specify the index mode:\n    enum STDLIB_NDARRAY_INDEX_MODE imode = STDLIB_NDARRAY_INDEX_ERROR;\n\n    // Specify the subscript index modes:\n    int8_t submodes[] = { imode };\n    int64_t nsubmodes = 1;\n\n    // Create an input ndarray:\n    struct ndarray *x = stdlib_ndarray_allocate( dtype, xbuf, ndims, xsh, sx, ox, order, imode, nsubmodes, submodes );\n    if ( x == NULL ) {\n        fprintf( stderr, \"Error allocating memory.\\n\" );\n        exit( EXIT_FAILURE );\n    }\n\n    // Create an initial value zero-dimensional ndarray:\n    struct ndarray *initial = stdlib_ndarray_allocate( dtype, ibuf, ndims, ish, si, oi, order, imode, nsubmodes, submodes );\n    if ( initial == NULL ) {\n        fprintf( stderr, \"Error allocating memory.\\n\" );\n        exit( EXIT_FAILURE );\n    }\n\n    // Create an output zero-dimensional ndarray:\n    struct ndarray *out = stdlib_ndarray_allocate( dtype, obuf, ndims, osh, so, oo, order, imode, nsubmodes, submodes );\n    if ( out == NULL ) {\n        fprintf( stderr, \"Error allocating memory.\\n\" );\n        exit( EXIT_FAILURE );\n    }\n\n    // Define an array containing the ndarrays:\n    struct ndarray *arrays[] = { x, initial, out };\n\n    // Apply the callback:\n    int8_t status = stdlib_ndarray_accumulate_dd_d( arrays, (void *)add );\n    if ( status != 0 ) {\n        fprintf( stderr, \"Error during computation.\\n\" );\n        exit( EXIT_FAILURE );\n    }\n\n    // Print the results:\n    print_ndarray_contents( out );\n    fprintf( stdout, \"\\n\" );\n\n    // Free allocated memory:\n    stdlib_ndarray_free( x );\n    stdlib_ndarray_free( initial );\n    stdlib_ndarray_free( out );\n}\n```\n\n\u003c/section\u003e\n\n\u003c!-- /.examples --\u003e\n\n\u003c/section\u003e\n\n\u003c!-- /.c --\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; 2016-2025. 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-accumulate.svg\n[npm-url]: https://npmjs.org/package/@stdlib/ndarray-base-unary-accumulate\n\n[test-image]: https://github.com/stdlib-js/ndarray-base-unary-accumulate/actions/workflows/test.yml/badge.svg?branch=main\n[test-url]: https://github.com/stdlib-js/ndarray-base-unary-accumulate/actions/workflows/test.yml?query=branch:main\n\n[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/ndarray-base-unary-accumulate/main.svg\n[coverage-url]: https://codecov.io/github/stdlib-js/ndarray-base-unary-accumulate?branch=main\n\n\u003c!--\n\n[dependencies-image]: https://img.shields.io/david/stdlib-js/ndarray-base-unary-accumulate.svg\n[dependencies-url]: https://david-dm.org/stdlib-js/ndarray-base-unary-accumulate/main\n\n--\u003e\n\n[chat-image]: https://img.shields.io/gitter/room/stdlib-js/stdlib.svg\n[chat-url]: https://app.gitter.im/#/room/#stdlib-js_stdlib:gitter.im\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-accumulate/tree/deno\n[deno-readme]: https://github.com/stdlib-js/ndarray-base-unary-accumulate/blob/deno/README.md\n[umd-url]: https://github.com/stdlib-js/ndarray-base-unary-accumulate/tree/umd\n[umd-readme]: https://github.com/stdlib-js/ndarray-base-unary-accumulate/blob/umd/README.md\n[esm-url]: https://github.com/stdlib-js/ndarray-base-unary-accumulate/tree/esm\n[esm-readme]: https://github.com/stdlib-js/ndarray-base-unary-accumulate/blob/esm/README.md\n[branches-url]: https://github.com/stdlib-js/ndarray-base-unary-accumulate/blob/main/branches.md\n\n[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/ndarray-base-unary-accumulate/main/LICENSE\n\n\u003c!-- \u003crelated-links\u003e --\u003e\n\n\u003c!-- \u003c/related-links\u003e --\u003e\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-accumulate","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fstdlib-js%2Fndarray-base-unary-accumulate","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstdlib-js%2Fndarray-base-unary-accumulate/lists"}