npm package: @stdlib/blas-ext-base-dnanasumors
Description: Calculate the sum of absolute values (L1 norm) of double-precision floating-point strided array elements, ignoring NaN values and using ordinary recursive summation.
Author: The Stdlib Authors
Latest version: 0.3.1

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# dnanasumors

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

> Calculate the sum of absolute values ([_L1_ norm][l1norm]) of double-precision floating-point strided array elements, ignoring `NaN` values and using ordinary recursive summation.

<section class="intro">

The [_L1_ norm][l1norm] is defined as

<!-- <equation class="equation" label="eq:l1norm" align="center" raw="\|\mathbf{x}\|_1 = \sum_{i=0}^{n-1} \vert x_i \vert" alt="L1 norm definition."> -->

<div class="equation" align="center" data-raw-text="\|\mathbf{x}\|_1 = \sum_{i=0}^{n-1} \vert x_i \vert" data-equation="eq:l1norm">
    <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@77bf4832b5dc49c733700e76e58555d7e8ccda69/lib/node_modules/@stdlib/blas/ext/base/dnanasumors/docs/img/equation_l1norm.svg" alt="L1 norm definition.">
    <br>
</div>

<!-- </equation> -->

</section>

<!-- /.intro -->

<section class="installation">

## Installation

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

</section>

<section class="usage">

## Usage

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

#### dnanasumors( N, x, strideX )

Computes the sum of absolute values ([_L1_ norm][l1norm]) 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 = dnanasumors( x.length, x, 1 );
// returns 5.0
```

The function has the following parameters:

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

The `N` and stride parameters determine which elements in the strided array are accessed at runtime. For example, to compute the sum of absolute values ([_L1_ norm][l1norm]) for every other element:

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

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

var v = dnanasumors( 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.

<!-- eslint-disable stdlib/capitalized-comments -->

```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 = dnanasumors( 4, x1, 2 );
// returns 9.0
```

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

Computes the sum of absolute values ([_L1_ norm][l1norm]) 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 = dnanasumors.ndarray( x.length, x, 1, 0 );
// returns 5.0
```

The function has the following additional parameters:

-   **offsetX**: starting index.

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 absolute values ([_L1_ norm][l1norm]) for every other value in the strided array starting from the second value:

```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 = dnanasumors.ndarray( 4, x, 2, 1 );
// returns 9.0
```

</section>

<!-- /.usage -->

<section class="notes">

## 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.

</section>

<!-- /.notes -->

<section class="examples">

## Examples

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

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

function rand() {
    if ( bernoulli( 0.5 ) < 1 ) {
        return NaN;
    }
    return discreteUniform( 0, 100 );
}

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

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

</section>

<!-- /.examples -->

<!-- C interface documentation. -->

* * *

<section class="c">

## C APIs

<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->

<section class="intro">

</section>

<!-- /.intro -->

<!-- C usage documentation. -->

<section class="usage">

### Usage

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

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

Computes the sum of absolute values ([_L1_ norm][l1norm]) 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_dnanasumors( 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.

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

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

Computes the sum of absolute values ([_L1_ norm][l1norm]) 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_dnanasumors_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.
-   **offsetX**: `[in] CBLAS_INT` starting index.

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

</section>

<!-- /.usage -->

<!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->

<section class="notes">

</section>

<!-- /.notes -->

<!-- C API usage examples. -->

<section class="examples">

### Examples

```c
#include "stdlib/blas/ext/base/dnanasumors.h"
#include <stdio.h>

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_dnanasumors( N, x, strideX );

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

</section>

<!-- /.examples -->

</section>

<!-- /.c -->

<section class="references">

</section>

<!-- /.references -->

<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->

<section class="related">

* * *

## See Also

-   <span class="package-name">[`@stdlib/blas-ext/base/dnanasum`][@stdlib/blas/ext/base/dnanasum]</span><span class="delimiter">: </span><span class="description">calculate the sum of absolute values (L1 norm) of double-precision floating-point strided array elements, ignoring NaN values.</span>

</section>

<!-- /.related -->

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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

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

---

## License

See [LICENSE][stdlib-license].


## Copyright

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

</section>

<!-- /.stdlib -->

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[deno-url]: https://github.com/stdlib-js/blas-ext-base-dnanasumors/tree/deno
[deno-readme]: https://github.com/stdlib-js/blas-ext-base-dnanasumors/blob/deno/README.md
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[l1norm]: https://en.wikipedia.org/wiki/Norm_%28mathematics%29

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

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