dnannsum

Calculate the sum of double-precision floating-point strided array elements, ignoring NaN values.

Usage

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

dnannsum( N, x, strideX, out, strideOut )

Computes the sum of double-precision floating-point strided array elements, ignoring NaN values.

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

var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
var out = new Float64Array( 2 );

var v = dnannsum( x.length, x, 1, out, 1 );
// returns <Float64Array>[ 1.0, 3 ]

The function has the following parameters:

N: number of indexed elements.
x: input Float64Array.
strideX: index increment for x.
out: output Float64Array whose first element is the sum and whose second element is the number of non-NaN elements.
strideOut: index increment for out.

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 in the strided array,

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 out = new Float64Array( 2 );

var v = dnannsum( 4, x, 2, out, 1 );
// returns <Float64Array>[ 5.0, 2 ]

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, 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 out0 = new Float64Array( 4 );
var out1 = new Float64Array( out0.buffer, out0.BYTES_PER_ELEMENT*2 ); // start at 3rd element

var v = dnannsum( 4, x1, 2, out1, 1 );
// returns <Float64Array>[ 5.0, 4 ]

dnannsum.ndarray( N, x, strideX, offsetX, out, strideOut, offsetOut )

Computes the sum of double-precision floating-point strided array elements, ignoring NaN values and using alternative indexing semantics.

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

var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
var out = new Float64Array( 2 );

var v = dnannsum.ndarray( x.length, x, 1, 0, out, 1, 0 );
// returns <Float64Array>[ 1.0, 3 ]

The function has the following additional parameters:

offsetX: starting index for x.
offsetOut: starting index for out.

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 the strided array starting from the second value

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 out = new Float64Array( 4 );

var v = dnannsum.ndarray( 4, x, 2, 1, out, 2, 1 );
// returns <Float64Array>[ 0.0, 5.0, 0.0, 4 ]

Notes

If N <= 0, both functions return a sum equal to 0.0.

Examples

var bernoulli = require( '@stdlib/random/base/bernoulli' );
var discreteUniform = require( '@stdlib/random/base/discrete-uniform' );
var filledarrayBy = require( '@stdlib/array/filled-by' );
var Float64Array = require( '@stdlib/array/float64' );
var dnannsum = require( '@stdlib/blas/ext/base/dnannsum' );

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

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

var out = new Float64Array( 2 );
dnannsum( x.length, x, 1, out, 1 );
console.log( out );