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stdlib-js/utils-flatten-array

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flattenArray

NPM version Build Status Coverage Status

Flatten an array.

Installation

npm install @stdlib/utils-flatten-array

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 flattenArray = require( '@stdlib/utils-flatten-array' );

flattenArray( arr[, options] )

Flattens an array.

var arr = [ 1, [2, [3, [4, [ 5 ], 6], 7], 8], 9 ];

var out = flattenArray( arr );
// returns [ 1, 2, 3, 4, 5, 6, 7, 8, 9 ]

The function accepts the following options:

  • depth: maximum depth to flatten.
  • copy: boolean indicating whether to deep copy array elements. Default: false.

To flatten to a specified depth, set the depth option.

var arr = [ 1, [2, [3, [4, [ 5 ], 6], 7], 8], 9 ];

var out = flattenArray( arr, {
    'depth': 2
});
// returns [ 1, 2, 3, [4, [5], 6], 7, 8, 9 ]

var bool = ( arr[1][1][1] === out[3] );
// returns true

To deep copy array elements, set the copy option to true.

var arr = [ 1, [2, [3, [4, [ 5 ], 6], 7], 8], 9 ];

var out = flattenArray( arr, {
    'depth': 2,
    'copy': true
});
// returns [ 1, 2, 3, [4, [5], 6], 7, 8, 9 ]

var bool = ( arr[1][1][1] === out[3] );
// returns false

flattenArray.factory( dims[, options] )

Returns a function optimized for flattening arrays having specified dimensions.

var flatten = flattenArray.factory( [ 3, 3 ] );

var arr = [
    [ 1, 2, 3 ],
    [ 4, 5, 6 ],
    [ 7, 8, 9 ]
];

var out = flatten( arr );
// returns [ 1, 2, 3, 4, 5, 6, 7, 8, 9 ]

arr = [
    [ 11, 12, 13 ],
    [ 14, 15, 16 ],
    [ 17, 18, 19 ]
];

out = flatten( arr );
// returns [ 11, 12, 13, 14, 15, 16, 17, 18, 19 ]

The function accepts the following options:

  • copy: boolean indicating whether to deep copy array elements. Default: false.

To deep copy array elements, set the copy option to true.

var flatten = flattenArray.factory( [ 3, 3 ], {
    'copy': true
});

var arr = [
    [ 1, 2, 3 ],
    [ 4, { 'x': 5 }, 6 ],
    [ 7, 8, 9 ]
];

var out = flatten( arr );
// returns [ 1, 2, 3, 4, {'x':5}, 6, 7, 8, 9 ]

var bool = ( arr[1][1] === out[4] );
// returns false

Notes

  • A flatten function returned by the factory method does not validate that input arrays actually have the specified dimensions.
  • The factory method uses code evaluation, which may be problematic in browser contexts enforcing a strict content security policy (CSP).

Examples

var flattenArray = require( '@stdlib/utils-flatten-array' );

function tensor( N, M, L ) {
    var tmp1;
    var tmp2;
    var out;
    var i;
    var j;
    var k;

    out = [];
    for ( i = 0; i < N; i++ ) {
        tmp1 = [];
        for ( j = 0; j < M; j++ ) {
            tmp2 = [];
            for ( k = 0; k < L; k++ ) {
                tmp2.push( (M*L*i) + (j*L) + k + 1 );
            }
            tmp1.push( tmp2 );
        }
        out.push( tmp1 );
    }
    return out;
}

// Define array dimensions:
var N = 1000;
var M = 100;
var L = 10;

// Create a 3-dimensional nested array:
var data = tensor( N, M, L );

// Create a flattened (strided) array from a 3-dimensional nested array:
var arr = flattenArray( data );

// To access the data[4][20][2] element...
var xStride = M * L;
var yStride = L;
var zStride = 1;
var v = arr[ (4*xStride) + (20*yStride) + (2*zStride) ];
// returns 4203

var bool = ( data[4][20][2] === v );
// returns true

See Also


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.

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License

See LICENSE.

Copyright

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