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# map

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

<section class="intro">

</section>

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<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>

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<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="links">

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</section>

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