About stdlib...

We 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.

The 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.

When 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.

To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

gnansumors

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url]

> Calculate the sum of strided array elements, ignoring NaN values and using ordinary recursive summation.

Installation

``bash npm install @stdlib/blas-ext-base-gnansumors`

`Usage`

`javascript var gnansumors = require( '@stdlib/blas-ext-base-gnansumors' );`

#### gnansumors( N, x, strideX )

Computes the sum of strided array elements, ignoring NaN values and using ordinary recursive summation.

`javascript var x = [ 1.0, -2.0, NaN, 2.0 ];

var v = gnansumors( x.length, x, 1 ); // returns 1.0`

The function has the following parameters:

- N: number of indexed elements. - x: input [Array][mdn-array] or [typed array][mdn-typed-array]. - strideX: stride length forx.

The N and stride parameters determine which elements in the strided array are accessed at runtime. For example, to compute the sum of every other element:

`javascript var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN, NaN ];

var v = gnansumors( 5, x, 2 ); // returns 5.0`

Note that indexing is relative to the first index. To introduce an offset, use [typed array][mdn-typed-array] views.

`javascript var Float64Array = require( '@stdlib/array-float64' );

var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] ); var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

var v = gnansumors( 4, x1, 2 ); // returns 5.0`

#### gnansumors.ndarray( N, x, strideX, offsetX )

Computes the sum of strided array elements, ignoring NaN values and using ordinary recursive summation and alternative indexing semantics.

`javascript var x = [ 1.0, -2.0, NaN, 2.0 ];

var v = gnansumors.ndarray( x.length, x, 1, 0 ); // returns 1.0`

The function has the following additional parameters:

- offsetX: starting index for x.

While [typed array][mdn-typed-array] views mandate a view offset based on the underlying buffer, the offset parameter supports indexing semantics based on a starting index. For example, to calculate the sum of every other element starting from the second element:

`javascript var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ];

var v = gnansumors.ndarray( 5, x, 2, 1 ); // returns 5.0`

`Notes`

- If N <= 0, both functions return 0.0. - Both functions support array-like objects having getter and setter accessors for array element access (e.g., [@stdlib/array-base/accessor][@stdlib/array/base/accessor]). - Ordinary recursive summation (i.e., a "simple" sum) is performant, but can incur significant numerical error. If performance is paramount and error tolerated, using ordinary recursive summation is acceptable; in all other cases, exercise due caution. - Depending on the environment, the typed versions ([dnansumors][@stdlib/blas/ext/base/dnansumors], [snansumors][@stdlib/blas/ext/base/snansumors], etc.) are likely to be significantly more performant.

`Examples`

`javascript var discreteUniform = require( '@stdlib/random-base-discrete-uniform' ); var bernoulli = require( '@stdlib/random-base-bernoulli' ); var filledarrayBy = require( '@stdlib/array-filled-by' ); var gnansumors = require( '@stdlib/blas-ext-base-gnansumors' );

function rand() { if ( bernoulli( 0.7 ) > 0 ) { return discreteUniform( 0, 100 ); } return NaN; }

var x = filledarrayBy( 10, 'float64', rand ); console.log( x );

var v = gnansumors( x.length, x, 1 ); console.log( v );`

*

`Notice`

This 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.

For 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].

#### Community

[![Chat][chat-image]][chat-url]

---

`License`

See [LICENSE][stdlib-license].

`Copyright`

[npm-image]: http://img.shields.io/npm/v/@stdlib/blas-ext-base-gnansumors.svg [npm-url]: https://npmjs.org/package/@stdlib/blas-ext-base-gnansumors

[test-image]: https://github.com/stdlib-js/blas-ext-base-gnansumors/actions/workflows/test.yml/badge.svg?branch=v0.3.1 [test-url]: https://github.com/stdlib-js/blas-ext-base-gnansumors/actions/workflows/test.yml?query=branch:v0.3.1

[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/blas-ext-base-gnansumors/main.svg [coverage-url]: https://codecov.io/github/stdlib-js/blas-ext-base-gnansumors?branch=main

[chat-image]: https://img.shields.io/badge/zulip-join_chat-brightgreen.svg [chat-url]: https://stdlib.zulipchat.com

[stdlib]: https://github.com/stdlib-js/stdlib

[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors

[umd]: https://github.com/umdjs/umd [es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules

[deno-url]: https://github.com/stdlib-js/blas-ext-base-gnansumors/tree/deno [deno-readme]: https://github.com/stdlib-js/blas-ext-base-gnansumors/blob/deno/README.md [umd-url]: https://github.com/stdlib-js/blas-ext-base-gnansumors/tree/umd [umd-readme]: https://github.com/stdlib-js/blas-ext-base-gnansumors/blob/umd/README.md [esm-url]: https://github.com/stdlib-js/blas-ext-base-gnansumors/tree/esm [esm-readme]: https://github.com/stdlib-js/blas-ext-base-gnansumors/blob/esm/README.md [branches-url]: https://github.com/stdlib-js/blas-ext-base-gnansumors/blob/main/branches.md

[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/blas-ext-base-gnansumors/main/LICENSE

[mdn-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array

[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray

[@stdlib/array/base/accessor]: https://www.npmjs.com/package/@stdlib/array-base-accessor

[@stdlib/blas/ext/base/dnansumors]: https://www.npmjs.com/package/@stdlib/blas-ext-base-dnansumors

[@stdlib/blas/ext/base/gnansum]: https://www.npmjs.com/package/@stdlib/blas-ext-base-gnansum

[@stdlib/blas/ext/base/gnansumkbn2]: https://www.npmjs.com/package/@stdlib/blas-ext-base-gnansumkbn2

[@stdlib/blas/ext/base/gnansumpw]: https://www.npmjs.com/package/@stdlib/blas-ext-base-gnansumpw

[@stdlib/blas/ext/base/gsumors]: https://www.npmjs.com/package/@stdlib/blas-ext-base-gsumors

[@stdlib/blas/ext/base/snansumors]: https://www.npmjs.com/package/@stdlib/blas-ext-base-snansumors


  
    About stdlib...
  

  We 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.

  The 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.

  When 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.

  To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

`gnansumors`

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url]

> Calculate the sum of strided array elements, ignoring NaN values and using ordinary recursive summation.

`Installation`

``bash npm install @stdlib/blas-ext-base-gnansumors `

`Usage`

`javascript var gnansumors = require( '@stdlib/blas-ext-base-gnansumors' ); `

#### gnansumors( N, x, strideX )

Computes the sum of strided array elements, ignoring NaN values and using ordinary recursive summation.

`javascript var x = [ 1.0, -2.0, NaN, 2.0 ];

var v = gnansumors( x.length, x, 1 ); // returns 1.0 `

The function has the following parameters:

- N: number of indexed elements. - x: input [Array][mdn-array] or [typed array][mdn-typed-array]. - strideX: stride length for x.

The N and stride parameters determine which elements in the strided array are accessed at runtime. For example, to compute the sum of every other element:

`javascript var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN, NaN ];

var v = gnansumors( 5, x, 2 ); // returns 5.0 `

Note that indexing is relative to the first index. To introduce an offset, use [typed array][mdn-typed-array] views.

`javascript var Float64Array = require( '@stdlib/array-float64' );

var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] ); var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

var v = gnansumors( 4, x1, 2 ); // returns 5.0 `

#### gnansumors.ndarray( N, x, strideX, offsetX )

Computes the sum of strided array elements, ignoring NaN values and using ordinary recursive summation and alternative indexing semantics.

`javascript var x = [ 1.0, -2.0, NaN, 2.0 ];

var v = gnansumors.ndarray( x.length, x, 1, 0 ); // returns 1.0 `

The function has the following additional parameters:

- offsetX: starting index for x.

While [typed array][mdn-typed-array] views mandate a view offset based on the underlying buffer, the offset parameter supports indexing semantics based on a starting index. For example, to calculate the sum of every other element starting from the second element:

`javascript var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ];

var v = gnansumors.ndarray( 5, x, 2, 1 ); // returns 5.0 `

`Notes`

- If N <= 0, both functions return 0.0. - Both functions support array-like objects having getter and setter accessors for array element access (e.g., [@stdlib/array-base/accessor][@stdlib/array/base/accessor]). - Ordinary recursive summation (i.e., a "simple" sum) is performant, but can incur significant numerical error. If performance is paramount and error tolerated, using ordinary recursive summation is acceptable; in all other cases, exercise due caution. - Depending on the environment, the typed versions ([dnansumors][@stdlib/blas/ext/base/dnansumors], [snansumors][@stdlib/blas/ext/base/snansumors], etc.) are likely to be significantly more performant.

`Examples`

`javascript var discreteUniform = require( '@stdlib/random-base-discrete-uniform' ); var bernoulli = require( '@stdlib/random-base-bernoulli' ); var filledarrayBy = require( '@stdlib/array-filled-by' ); var gnansumors = require( '@stdlib/blas-ext-base-gnansumors' );

function rand() { if ( bernoulli( 0.7 ) > 0 ) { return discreteUniform( 0, 100 ); } return NaN; }

var x = filledarrayBy( 10, 'float64', rand ); console.log( x );

var v = gnansumors( x.length, x, 1 ); console.log( v ); `