# @stdlib/blas-ext-base-sdsnansumpw

Calculate the sum of single-precision floating-point strided array elements, ignoring NaN values and using pairwise summation with extended accumulation.

## Usage no npm install needed!

``````<script type="module">
import stdlibBlasExtBaseSdsnansumpw from 'https://cdn.skypack.dev/@stdlib/blas-ext-base-sdsnansumpw';
</script>``````

# sdsnansumpw

Calculate the sum of single-precision floating-point strided array elements, ignoring `NaN` values and using pairwise summation with extended accumulation.

## Installation

``````npm install @stdlib/blas-ext-base-sdsnansumpw
``````

## Usage

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

#### sdsnansumpw( N, x, stride )

Computes the sum of single-precision floating-point strided array elements, ignoring `NaN` values and using pairwise summation with extended accumulation.

``````var Float32Array = require( '@stdlib/array-float32' );

var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
var N = x.length;

var v = sdsnansumpw( N, x, 1 );
// returns 1.0
``````

The function has the following parameters:

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

``````var Float32Array = require( '@stdlib/array-float32' );
var floor = require( '@stdlib/math-base-special-floor' );

var x = new Float32Array( [ 1.0, 2.0, NaN, -7.0, NaN, 3.0, 4.0, 2.0 ] );
var N = floor( x.length / 2 );

var v = sdsnansumpw( N, x, 2 );
// returns 5.0
``````

Note that indexing is relative to the first index. To introduce an offset, use `typed array` views.

``````var Float32Array = require( '@stdlib/array-float32' );
var floor = require( '@stdlib/math-base-special-floor' );

var x0 = new Float32Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

var N = floor( x0.length / 2 );

var v = sdsnansumpw( N, x1, 2 );
// returns 5.0
``````

#### sdsnansumpw.ndarray( N, x, stride, offset )

Computes the sum of single-precision floating-point strided array elements, ignoring `NaN` values and using pairwise summation with extended accumulation and alternative indexing semantics.

``````var Float32Array = require( '@stdlib/array-float32' );

var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
var N = x.length;

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

The function has the following additional parameters:

• offset: starting index for `x`.

While `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 value in `x` starting from the second value

``````var Float32Array = require( '@stdlib/array-float32' );
var floor = require( '@stdlib/math-base-special-floor' );

var x = new Float32Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var N = floor( x.length / 2 );

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

## Notes

• If `N <= 0`, both functions return `0.0`.
• Accumulated intermediate values are stored as double-precision floating-point numbers.

## Examples

``````var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var Float32Array = require( '@stdlib/array-float32' );
var sdsnansumpw = require( '@stdlib/blas-ext-base-sdsnansumpw' );

var x;
var i;

x = new Float32Array( 10 );
for ( i = 0; i < x.length; i++ ) {
if ( randu() < 0.2 ) {
x[ i ] = NaN;
} else {
x[ i ] = round( randu()*100.0 );
}
}
console.log( x );

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

## References

• Higham, Nicholas J. 1993. "The Accuracy of Floating Point Summation." SIAM Journal on Scientific Computing 14 (4): 783–99. doi:10.1137/0914050.

## Notice

This package is part of 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, see the main project repository.