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dnannsum
Calculate the sum of double-precision floating-point strided array elements, ignoring
NaNvalues.
Installation
npm install @stdlib/blas-ext-base-dnannsumAlternatively,
- To load the package in a website via a
scripttag without installation and bundlers, use the ES Module available on theesmbranch. - If you are using Deno, visit the
denobranch. - For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the
umdbranch.
The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.
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
Float64Arraywhose 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 are accessed at runtime. For example, to compute the sum of every other element in x,
var Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );
var x = new Float64Array( [ 1.0, 2.0, NaN, -7.0, NaN, 3.0, 4.0, 2.0 ] );
var out = new Float64Array( 2 );
var N = floor( x.length / 2 );
var v = dnannsum( N, 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 floor = require( '@stdlib/math-base-special-floor' );
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 N = floor( x0.length / 2 );
var v = dnannsum( N, 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 x starting from the second value
var Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );
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 N = floor( x.length / 2 );
var v = dnannsum.ndarray( N, 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 to0.0.
Examples
var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var Float64Array = require( '@stdlib/array-float64' );
var dnannsum = require( '@stdlib/blas-ext-base-dnannsum' );
var x;
var i;
x = new Float64Array( 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 out = new Float64Array( 2 );
dnannsum( x.length, x, 1, out, 1 );
console.log( out );See Also
@stdlib/blas-ext/base/dnansum: calculate the sum of double-precision floating-point strided array elements, ignoring NaN values.@stdlib/blas-ext/base/dsum: calculate the sum of double-precision floating-point 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 © 2016-2023. The Stdlib Authors.
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