dsnansumpw

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

Installation

npm install @stdlib/blas-ext-base-dsnansumpw

Usage

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

dsnansumpw( N, x, strideX )

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

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

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

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

The function has the following parameters:

• N: number of indexed elements.
• x: input Float32Array.
• stride: 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:

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

var x = new Float32Array( [ 1.0, 2.0, NaN, -7.0, NaN, 3.0, 4.0, 2.0 ] );

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

dsnansumpw.ndarray( N, x, strideX, offsetX )

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

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

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

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

The function has the following additional parameters:

• offsetX: 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 element starting from the second element:

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

var x = new Float32Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );

var v = dsnansumpw.ndarray( 4, 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 discreteUniform = require( '@stdlib/random-base-discrete-uniform' );
var bernoulli = require( '@stdlib/random-base-bernoulli' );
var filledarrayBy = require( '@stdlib/array-filled-by' );
var dsnansumpw = require( '@stdlib/blas-ext-base-dsnansumpw' );

function rand() {
    if ( bernoulli( 0.8 ) > 0 ) {
        return NaN;
    }
    return discreteUniform( 0, 100 );
}

var x = filledarrayBy( 10, 'float32', rand );
console.log( x );

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

C APIs

Usage

#include "stdlib/blas/ext/base/dsnansumpw.h"

stdlib_strided_dsnansumpw( N, *X, strideX )

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

const float x[] = { 1.0f, -2.0f, 0.0f/0.0f, 2.0f };

double v = stdlib_strided_dsnansumpw( 4, x, 1 );
// returns 1.0

The function accepts the following arguments:

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

double stdlib_strided_dsnansumpw( const CBLAS_INT N, const float *X, const CBLAS_INT strideX );

stdlib_strided_dsnansumpw_ndarray( N, *X, strideX, offsetX )

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

const float x[] = { 1.0f, -2.0f, 0.0f/0.0f, 2.0f };

double v = stdlib_strided_dsnansumpw_ndarray( 4, x, 1, 0 );
// returns 1.0

The function accepts the following arguments:

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

double stdlib_strided_dsnansumpw_ndarray( const CBLAS_INT N, const float *X, const CBLAS_INT strideX, const CBLAS_INT offsetX );

Examples

#include "stdlib/blas/ext/base/dsnansumpw.h"
#include <stdio.h>

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

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

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

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

    // Print the result:
    printf( "sum: %lf\n", 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.

See Also

• @stdlib/blas-ext/base/dnansumpw: calculate the sum of double-precision floating-point strided array elements, ignoring NaN values and using pairwise summation.
• @stdlib/blas-ext/base/dssum: calculate the sum of single-precision floating-point strided array elements using extended accumulation and returning an extended precision result.
• @stdlib/blas-ext/base/dssumpw: calculate the sum of single-precision floating-point strided array elements using pairwise summation with extended accumulation and returning an extended precision result.
• @stdlib/blas-ext/base/snansumpw: calculate the sum of single-precision floating-point strided array elements, ignoring NaN values and using pairwise summation.

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.

Community

License

See LICENSE.

Copyright

Copyright © 2016-2026. The Stdlib Authors.