dsum

Calculate the sum of double-precision floating-point strided array elements.

Installation

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

Usage

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

dsum( N, x, strideX )

Computes the sum of double-precision floating-point strided array elements.

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

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

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

The function has the following parameters:

• N: number of indexed elements.
• x: input Float64Array.
• strideX: 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 Float64Array = require( '@stdlib/array-float64' );

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

var v = dsum( 4, x, 2 );
// returns 5.0

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

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

var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -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 = dsum( 4, x1, 2 );
// returns 5.0

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

Computes the sum of double-precision floating-point strided array elements using alternative indexing semantics.

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

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

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

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

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

Notes

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

Examples

var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var dsum = require( '@stdlib/blas-ext-base-dsum' );

var x = discreteUniform( 10, -100, 100, {
    'dtype': 'float64'
});
console.log( x );

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

C APIs

Usage

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

stdlib_strided_dsum( N, *X, strideX )

Computes the sum of double-precision floating-point strided array elements.

const double x[] = { 1.0, 2.0, 3.0, 4.0 };

double v = stdlib_strided_dsum( 4, x, 1 );
// returns 10.0

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.

double stdlib_strided_dsum( const CBLAS_INT N, const double *X, const CBLAS_INT strideX );

stdlib_strided_dsum_ndarray( N, *X, strideX, offsetX )

Computes the sum of double-precision floating-point strided array elements using alternative indexing semantics.

const double x[] = { 1.0, 2.0, 3.0, 4.0 };

double v = stdlib_strided_dsum_ndarray( 4, x, 1, 0 );
// returns 10.0

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.

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

Examples

#include "stdlib/blas/ext/base/dsum.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 };

    // Specify the number of elements:
    const int N = 4;

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

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

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

See Also

• @stdlib/blas-base/dasum: compute the sum of absolute values (L1 norm).
• @stdlib/stats-strided/dmean: calculate the arithmetic mean of a double-precision floating-point strided array.
• @stdlib/blas-ext/base/dnansum: calculate the sum of double-precision floating-point strided array elements, ignoring NaN values.
• @stdlib/blas-ext/base/ssum: calculate the sum of single-precision floating-point strided array elements.
• @stdlib/blas-ext/base/gsum: calculate the sum of strided array elements.

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.