npm package: @stdlib/blas-ext-base-ndarray-dnansumors
Description: Compute the sum of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values and using ordinary recursive summation.
Author: The Stdlib Authors
Latest version: 0.1.1

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# dnansumors

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

> Compute the sum of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values and using ordinary recursive summation.

<section class="intro">

</section>

<!-- /.intro -->

<section class="installation">

## Installation

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

</section>

<section class="usage">

## Usage

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

#### dnansumors( arrays )

Computes the sum of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values and using ordinary recursive summation.

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

var xbuf = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
var x = new ndarray( 'float64', xbuf, [ 4 ], [ 1 ], 0, 'row-major' );

var v = dnansumors( [ x ] );
// returns 1.0
```

The function has the following parameters:

-   **arrays**: array-like object containing a one-dimensional input ndarray.

</section>

<!-- /.usage -->

<section class="notes">

## Notes

-   If provided an empty one-dimensional ndarray, the function returns `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.

</section>

<!-- /.notes -->

<section class="examples">

## Examples

<!-- eslint no-undef: "error" -->

```javascript
var bernoulli = require( '@stdlib/random-base-bernoulli' );
var discreteUniform = require( '@stdlib/random-base-discrete-uniform' );
var filledarrayBy = require( '@stdlib/array-filled-by' );
var ndarray = require( '@stdlib/ndarray-base-ctor' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );
var dnansumors = require( '@stdlib/blas-ext-base-ndarray-dnansumors' );

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

var xbuf = filledarrayBy( 10, 'float64', clbk );
var x = new ndarray( 'float64', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
console.log( ndarray2array( x ) );

var v = dnansumors( [ x ] );
console.log( v );
```

</section>

<!-- /.examples -->

<section class="references">

</section>

<!-- /.references -->

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<section class="related">

</section>

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<section class="main-repo" >

* * *

## 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

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---

## License

See [LICENSE][stdlib-license].


## Copyright

Copyright &copy; 2016-2026. The Stdlib [Authors][stdlib-authors].

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[umd]: https://github.com/umdjs/umd
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[deno-url]: https://github.com/stdlib-js/blas-ext-base-ndarray-dnansumors/tree/deno
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[esm-url]: https://github.com/stdlib-js/blas-ext-base-ndarray-dnansumors/tree/esm
[esm-readme]: https://github.com/stdlib-js/blas-ext-base-ndarray-dnansumors/blob/esm/README.md
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