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Apply a unary callback to elements in a three-dimensional nested input array according to elements in a three-dimensional nested mask array and assign results to elements in a three-dimensional nested output array.
npm install @stdlib/array-base-mskunary3d
var mskunary3d = require( '@stdlib/array-base-mskunary3d' );
Applies a unary callback to elements in a three-dimensional nested input array according to elements in a three-dimensional nested mask array and assigns results to elements in a three-dimensional nested output array.
var abs = require( '@stdlib/math-base-special-abs' );
var x = [ [ [ -1.0, -2.0 ], [ -3.0, -4.0 ] ] ];
var mask = [ [ [ 0, 1 ], [ 0, 0 ] ] ];
var shape = [ 1, 2, 2 ];
// Compute the absolute values in-place:
mskunary3d( [ x, mask, x ], shape, abs );
// x => [ [ [ 1.0, -2.0 ], [ 3.0, 4.0 ] ] ]
The function accepts the following arguments:
- arrays: array-like object containing one input nested array, an input nested mask array, and one output nested array.
- shape: array shape.
- fcn: unary function to apply.
- The function assumes that the input and output arrays have the same shape.
- An element in an input array is "masked" if the corresponding element in the mask array is non-zero.
var discreteUniform = require( '@stdlib/random-base-discrete-uniform' ).factory;
var bernoulli = require( '@stdlib/random-base-bernoulli' ).factory;
var filled3dBy = require( '@stdlib/array-base-filled3d-by' );
var zeros3d = require( '@stdlib/array-base-zeros3d' );
var abs = require( '@stdlib/math-base-special-abs' );
var mskunary3d = require( '@stdlib/array-base-mskunary3d' );
var shape = [ 3, 3, 3 ];
var x = filled3dBy( shape, discreteUniform( -100, 100 ) );
console.log( x );
var mask = filled3dBy( shape, bernoulli( 0.5 ) );
console.log( mask );
var y = zeros3d( shape );
console.log( y );
mskunary3d( [ x, mask, y ], shape, abs );
console.log( y );
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.
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