npm package: @stdlib/ndarray-base-map
Description: Apply a callback to elements in an input ndarray and assign results to elements in an output ndarray.
Author: The Stdlib Authors
Latest version: 0.1.1

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# map

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

<section class="intro">

</section>

<!-- /.intro -->

<section class="installation">

## Installation

```bash
npm install @stdlib/ndarray-base-map
```

</section>

<section class="usage">

## Usage

```javascript
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.

```javascript
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 = {
    '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 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:

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

</section>

<!-- /.usage -->

<section class="notes">

## 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.

</section>

<!-- /.notes -->

<section class="examples">

## Examples

<!-- eslint no-undef: "error" -->

```javascript
var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var zeros = require( '@stdlib/array-zeros' );
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': discreteUniform( N, -100, 100, {
        'dtype': 'generic'
    }),
    'shape': [ 5, 2 ],
    'strides': [ 2, 1 ],
    'offset': 0,
    'order': 'row-major'
};
var y = {
    'dtype': 'generic',
    'data': zeros( 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 ) );
```

</section>

<!-- /.examples -->

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<section class="related">

</section>

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<section class="main-repo" >

* * *

## Notice

This package is part of [stdlib][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][stdlib], see the main project [repository][stdlib].

#### Community

[![Chat][chat-image]][chat-url]

---

## License

See [LICENSE][stdlib-license].


## Copyright

Copyright &copy; 2016-2026. The Stdlib [Authors][stdlib-authors].

</section>

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