@stdlib/ndarray-base-for-each NPM

forEach

Invoke a callback function once for each ndarray element.

Installation

npm install @stdlib/ndarray-base-for-each

Usage

var forEach = require( '@stdlib/ndarray-base-for-each' );

forEach( arrays, fcn[, thisArg] )

Invokes a callback function once for each ndarray element.

var Float64Array = require( '@stdlib/array-float64' );
var naryFunction = require( '@stdlib/utils-nary-function' );
var log = require( '@stdlib/console-log' );

// Create data buffers:
var xbuf = new Float64Array( 12 );

// Define the shape of the array:
var shape = [ 3, 1, 2 ];

// Define the array strides:
var sx = [ 4, 4, 1 ];

// Define the index offset:
var ox = 1;

// Create an ndarray-like object:
var x = {
    'dtype': 'float64',
    'data': xbuf,
    'shape': shape,
    'strides': sx,
    'offset': ox,
    'order': 'row-major'
};

// Apply the callback function:
forEach( [ x ], naryFunction( log, 1 ) );

The function accepts the following arguments:

arrays: array-like object containing an ndarray.
fcn: callback to apply.
thisArg: callback execution context.

The callback function is provided the following arguments:

value: current array element.
indices: current array element indices.
arr: the input ndarray.

Notes

The 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 ndarray2array = require( '@stdlib/ndarray-base-to-array' );
var zeroTo = require( '@stdlib/array-base-zero-to' );
var naryFunction = require( '@stdlib/utils-nary-function' );
var log = require( '@stdlib/console-log' );
var forEach = require( '@stdlib/ndarray-base-for-each' );

var x = {
    'dtype': 'generic',
    'data': zeroTo( 10 ),
    'shape': [ 5, 2 ],
    'strides': [ -2, 1 ],
    'offset': 8,
    'order': 'row-major'
};

log( ndarray2array( x.data, x.shape, x.strides, x.offset, x.order ) );
forEach( [ x ], naryFunction( log, 2 ) );

x = {
    'dtype': 'generic',
    'data': zeroTo( 10 ),
    'shape': [ 5, 2 ],
    'strides': [ 1, -5 ],
    'offset': 5,
    'order': 'column-major'
};

log( ndarray2array( x.data, x.shape, x.strides, x.offset, x.order ) );
forEach( [ x ], naryFunction( log, 2 ) );

x = {
    'dtype': 'generic',
    'data': zeroTo( 18 ),
    'shape': [ 2, 3, 3 ],
    'strides': [ 9, 3, 1 ],
    'offset': 0,
    'order': 'row-major'
};

log( ndarray2array( x.data, x.shape, x.strides, x.offset, x.order ) );
forEach( [ x ], naryFunction( log, 2 ) );

x = {
    'dtype': 'generic',
    'data': zeroTo( 18 ),
    'shape': [ 2, 3, 3 ],
    'strides': [ -1, -2, -6 ],
    'offset': 17,
    'order': 'column-major'
};

log( ndarray2array( x.data, x.shape, x.strides, x.offset, x.order ) );
forEach( [ x ], naryFunction( log, 2 ) );

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.

@stdlib/ndarray-base-for-each v0.1.0