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map

Apply a callback function to elements in an input ndarray and assign results to elements in an output ndarray.

Installation

npm install @stdlib/ndarray-base-map

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
• If you are using Deno, visit the deno branch (see README for usage intructions).
• For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (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.

Usage

var map = require( '@stdlib/ndarray-base-map' );

map( arrays, fcn[, thisArg] )

Applies a callback function to elements in an input ndarray and assigns results to elements in an output ndarray.

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

function scale( x ) {
    return x * 10.0;
}

// Create data buffers:
var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var ybuf = new Float64Array( 6 );

// Define the shape of the input and output arrays:
var shape = [ 3, 2 ];

// Define the array strides:
var sx = [ 2, 1 ];
var sy = [ 2, 1 ];

// Define the index offsets:
var ox = 0;
var oy = 0;

// Create the input and output ndarray-like objects:
var x = {
    'ref': null,
    'dtype': 'float64',
    'data': xbuf,
    'shape': shape,
    'strides': sx,
    'offset': ox,
    'order': 'row-major'
};
var y = {
    'dtype': 'float64',
    'data': ybuf,
    'shape': shape,
    'strides': sy,
    'offset': oy,
    'order': 'row-major'
};

// Apply the map function:
map( [ x, y ], scale );

console.log( y.data );
// => <Float64Array>[ 10.0, 20.0, 30.0, 40.0, 50.0, 60.0 ]

The function accepts the following arguments:

• arrays: array-like object containing one input ndarray and one output ndarray.
• fcn: callback to apply.
• thisArg: callback execution context.

The callback function is provided the following arguments:

• values: current array element.
• indices: current array element indices.
• arr: the input ndarray.

Notes

• Each provided ndarray should be an object with the following properties:

  • dtype: data type.
  • data: data buffer.
  • shape: dimensions.
  • strides: stride lengths.
  • offset: index offset.
  • order: specifies whether an ndarray is row-major (C-style) or column major (Fortran-style).

• For very high-dimensional ndarrays which are non-contiguous, one should consider copying the underlying data to contiguous memory before applying a callback function in order to achieve better performance.

Examples

var discreteUniform = require( '@stdlib/random-base-discrete-uniform' ).factory;
var filledarray = require( '@stdlib/array-filled' );
var filledarrayBy = require( '@stdlib/array-filled-by' );
var abs = require( '@stdlib/math-base-special-abs' );
var shape2strides = require( '@stdlib/ndarray-base-shape2strides' );
var ndarray2array = require( '@stdlib/ndarray-base-to-array' );
var naryFunction = require( '@stdlib/utils-nary-function' );
var map = require( '@stdlib/ndarray-base-map' );

var N = 10;
var x = {
    'dtype': 'generic',
    'data': filledarrayBy( N, 'generic', discreteUniform( -100, 100 ) ),
    'shape': [ 5, 2 ],
    'strides': [ 2, 1 ],
    'offset': 0,
    'order': 'row-major'
};
var y = {
    'dtype': 'generic',
    'data': filledarray( 0, N, 'generic' ),
    'shape': x.shape.slice(),
    'strides': shape2strides( x.shape, 'column-major' ),
    'offset': 0,
    'order': 'column-major'
};

map( [ x, y ], naryFunction( abs, 1 ) );
console.log( ndarray2array( x.data, x.shape, x.strides, x.offset, x.order ) );
console.log( ndarray2array( y.data, y.shape, y.strides, y.offset, y.order ) );

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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

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.