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README.md

gnansum

Calculate the sum of strided array elements, ignoring NaN values.

Usage

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

gnansum( N, x, strideX )

Computes the sum of strided array elements, ignoring NaN values.

var x = [ 1.0, -2.0, NaN, 2.0 ];

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

The function has the following parameters:

  • N: number of indexed elements.
  • x: input Array or typed array.
  • 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 x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN, NaN ];

var v = gnansum( 5, 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 = gnansum( 4, x1, 2 );
// returns 5.0

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

Computes the sum of strided array elements, ignoring NaN values and using alternative indexing semantics.

var x = [ 1.0, -2.0, NaN, 2.0 ];

var v = gnansum.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 x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ];

var v = gnansum.ndarray( 5, x, 2, 1 );
// returns 5.0

Notes

  • If N <= 0, both functions return 0.0.
  • Both functions support array-like objects having getter and setter accessors for array element access (e.g., @stdlib/array/base/accessor).
  • Depending on the environment, the typed versions (dnansum, snansum, etc.) are likely to be significantly more performant.

Examples

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

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

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

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

See Also