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!

dnansumors

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

> Calculate the sum of double-precision floating-point strided array elements, ignoring NaN values and using ordinary recursive summation.

Installation

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

`Usage`

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

#### dnansumors( N, x, strideX )

Computes the sum of double-precision floating-point strided array elements, ignoring NaN values and using ordinary recursive summation.

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

var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );

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

The function has the following parameters:

- N: number of indexed elements. - x: input [Float64Array][@stdlib/array/float64]. - strideX: stride length forx.

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

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

var x = new Float64Array( [ 1.0, 2.0, NaN, -7.0, NaN, 3.0, 4.0, 2.0 ] );

var v = dnansumors( 4, 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, NaN, -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 = dnansumors( 4, x1, 2 ); // returns 5.0`

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

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

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

var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );

var v = dnansumors.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 Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );

var v = dnansumors.ndarray( 4, x, 2, 1 ); // returns 5.0`

`Notes`

- If N <= 0, both functions return 0.0. - 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.

`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 dnansumors = require( '@stdlib/blas-ext-base-dnansumors' );

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

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

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

*

`C APIs`

`$3`

`c #include "stdlib/blas/ext/base/dnansumors.h"`

#### stdlib_strided_dnansumors( N, \*X, strideX )

Computes the sum of double-precision floating-point strided array elements, ignoring NaN values and using ordinary recursive summation.

`c const double x[] = { 1.0, 2.0, 0.0/0.0, 4.0 };

double v = stdlib_strided_dnansumors( 4, x, 1 ); // returns 7.0`

The function accepts the following arguments:

- N: [in] CBLAS_INTnumber of indexed elements. - X:[in] double*input array. - strideX:[in] CBLAS_INT stride length for X.

`c double stdlib_strided_dnansumors( const CBLAS_INT N, const double *X, const CBLAS_INT strideX );`

#### stdlib_strided_dnansumors_ndarray( N, \*X, strideX, offsetX )

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

`c const double x[] = { 1.0, 2.0, 0.0/0.0, 4.0 };

double v = stdlib_strided_dnansumors_ndarray( 4, x, 1, 0 ); // returns 7.0`

The function accepts the following arguments:

- N: [in] CBLAS_INTnumber of indexed elements. - X:[in] double*input array. - strideX:[in] CBLAS_INT stride length for X. - offsetX:[in] CBLAS_INT starting index for X.

`c double stdlib_strided_dnansumors_ndarray( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX );`

`$3`

`c #include "stdlib/blas/ext/base/dnansumors.h" #include

int main( void ) { // Create a strided array: const double x[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 0.0/0.0, 0.0/0.0 };

// Specify the number of elements: const int N = 5;

// Specify the stride length: const int strideX = 2;

// Compute the sum: double v = stdlib_strided_dnansumors( N, x, strideX );

// Print the result: printf( "sum: %lf\n", 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-dnansumors.svg [npm-url]: https://npmjs.org/package/@stdlib/blas-ext-base-dnansumors

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

[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/blas-ext-base-dnansumors/main.svg [coverage-url]: https://codecov.io/github/stdlib-js/blas-ext-base-dnansumors?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-dnansumors/tree/deno [deno-readme]: https://github.com/stdlib-js/blas-ext-base-dnansumors/blob/deno/README.md [umd-url]: https://github.com/stdlib-js/blas-ext-base-dnansumors/tree/umd [umd-readme]: https://github.com/stdlib-js/blas-ext-base-dnansumors/blob/umd/README.md [esm-url]: https://github.com/stdlib-js/blas-ext-base-dnansumors/tree/esm [esm-readme]: https://github.com/stdlib-js/blas-ext-base-dnansumors/blob/esm/README.md [branches-url]: https://github.com/stdlib-js/blas-ext-base-dnansumors/blob/main/branches.md

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

[@stdlib/array/float64]: https://www.npmjs.com/package/@stdlib/array-float64

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

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

[@stdlib/blas/ext/base/dnansumkbn2]: https://www.npmjs.com/package/@stdlib/blas-ext-base-dnansumkbn2

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

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

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

[@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!

`dnansumors`

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

> Calculate the sum of double-precision floating-point strided array elements, ignoring NaN values and using ordinary recursive summation.

`Installation`

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

`Usage`

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

#### dnansumors( N, x, strideX )

Computes the sum of double-precision floating-point strided array elements, ignoring NaN values and using ordinary recursive summation.

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

var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );

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

The function has the following parameters:

- N: number of indexed elements. - x: input [Float64Array][@stdlib/array/float64]. - strideX: stride length for x.

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

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

var x = new Float64Array( [ 1.0, 2.0, NaN, -7.0, NaN, 3.0, 4.0, 2.0 ] );

var v = dnansumors( 4, 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, NaN, -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 = dnansumors( 4, x1, 2 ); // returns 5.0 `

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

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

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

var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );

var v = dnansumors.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 Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );

var v = dnansumors.ndarray( 4, x, 2, 1 ); // returns 5.0 `

`Notes`

- If N <= 0, both functions return 0.0. - 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.

`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 dnansumors = require( '@stdlib/blas-ext-base-dnansumors' );

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

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

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

*

`C APIs`

`$3`

`c #include "stdlib/blas/ext/base/dnansumors.h" `

#### stdlib_strided_dnansumors( N, \*X, strideX )

Computes the sum of double-precision floating-point strided array elements, ignoring NaN values and using ordinary recursive summation.

`c const double x[] = { 1.0, 2.0, 0.0/0.0, 4.0 };

double v = stdlib_strided_dnansumors( 4, x, 1 ); // returns 7.0 `

The function accepts the following arguments:

- N: [in] CBLAS_INT number of indexed elements. - X: [in] double* input array. - strideX: [in] CBLAS_INT stride length for X.

`c double stdlib_strided_dnansumors( const CBLAS_INT N, const double *X, const CBLAS_INT strideX ); `

#### stdlib_strided_dnansumors_ndarray( N, \*X, strideX, offsetX )

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

`c const double x[] = { 1.0, 2.0, 0.0/0.0, 4.0 };

double v = stdlib_strided_dnansumors_ndarray( 4, x, 1, 0 ); // returns 7.0 `

The function accepts the following arguments:

- N: [in] CBLAS_INT number of indexed elements. - X: [in] double* input array. - strideX: [in] CBLAS_INT stride length for X. - offsetX: [in] CBLAS_INT starting index for X.

`c double stdlib_strided_dnansumors_ndarray( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX ); `

`$3`

`c #include "stdlib/blas/ext/base/dnansumors.h" #include

int main( void ) { // Create a strided array: const double x[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 0.0/0.0, 0.0/0.0 };

// Specify the number of elements: const int N = 5;

// Specify the stride length: const int strideX = 2;

// Compute the sum: double v = stdlib_strided_dnansumors( N, x, strideX );

// Print the result: printf( "sum: %lf\n", v ); } `