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Apply a mask to two provided input arrays in a single pass.
npm install @stdlib/array-base-mskfilter2Alternatively,
- To load the package in a website via a
scripttag without installation and bundlers, use the ES Module available on theesmbranch (see README). - If you are using Deno, visit the
denobranch (see README for usage intructions). - For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the
umdbranch (see README).
The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.
To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.
var mskfilter2 = require( '@stdlib/array-base-mskfilter2' );Returns new arrays by applying a mask to two provided input arrays in a single pass.
var x = [ 1, 2, 3, 4 ];
var y = [ 0, 1, 2, 3 ];
var out = mskfilter2( x, y, [ 0, 1, 0, 1 ] );
// returns [ [ 2, 4 ], [ 1, 3 ] ]The function supports the following parameters:
- x: first input array.
- y: second input array.
- mask: mask array.
The function always returns new "generic" arrays.
- If a
maskarray element is truthy, the corresponding elements in the input arrays are included in the respective output arrays; otherwise, the corresponding elements in the input arrays are "masked" and thus excluded from the respective output arrays.
var zeroTo = require( '@stdlib/array-base-zero-to' );
var bernoulli = require( '@stdlib/random-array-bernoulli' );
var mskfilter2 = require( '@stdlib/array-base-mskfilter2' );
// Generate linearly spaced arrays:
var x = zeroTo( 20 );
console.log( x );
var y = zeroTo( x.length );
console.log( y );
// Generate a random mask:
var mask = bernoulli( x.length, 0.5, {
'dtype': 'generic'
});
console.log( mask );
// Filter both arrays using the mask:
var out = mskfilter2( x, y, mask );
console.log( out );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.
See LICENSE.
Copyright © 2016-2025. The Stdlib Authors.
