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# Binary

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

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</section>

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<section class="usage">

## Usage

```javascript
var binary = require( '@stdlib/ndarray/base/binary' );
```

#### binary( arrays, fcn )

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

```javascript
var Float64Array = require( '@stdlib/array/float64' );
var add = require( '@stdlib/number/float64/base/add' );

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

// Define the shape of the input and output arrays:
var shape = [ 3, 1, 2 ];

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

// Define the index offsets:
var ox = 0;
var oy = 0;
var oz = 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'
};
var z = {
    'dtype': 'float64',
    'data': zbuf,
    'shape': shape,
    'strides': sz,
    'offset': oz,
    'order': 'row-major'
};

// Apply the binary function:
binary( [ x, y, z ], add );

console.log( z.data );
// => <Float64Array>[ 2.0, 3.0, 4.0, 5.0, 6.0, 7.0 ]
```

The function accepts the following arguments:

-   **arrays**: array-like object containing two input ndarrays and one output ndarray.
-   **fcn**: binary function to apply.

</section>

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## 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 binary function in order to achieve better performance.

</section>

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## Examples

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

```javascript
var discreteUniform = require( '@stdlib/random/base/discrete-uniform' ).factory;
var filledarray = require( '@stdlib/array/filled' );
var filledarrayBy = require( '@stdlib/array/filled-by' );
var add = require( '@stdlib/number/float64/base/add' );
var shape2strides = require( '@stdlib/ndarray/base/shape2strides' );
var ndarray2array = require( '@stdlib/ndarray/base/to-array' );
var binary = require( '@stdlib/ndarray/base/binary' );

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'
};
console.log( ndarray2array( x.data, x.shape, x.strides, x.offset, x.order ) );

var y = {
    'dtype': 'generic',
    'data': filledarrayBy( N, 'generic', discreteUniform( -100, 100 ) ),
    'shape': x.shape.slice(),
    'strides': shape2strides( x.shape, 'column-major' ),
    'offset': 0,
    'order': 'column-major'
};
console.log( ndarray2array( y.data, y.shape, y.strides, y.offset, y.order ) );

var z = {
    'dtype': 'generic',
    'data': filledarray( 0, N, 'generic' ),
    'shape': x.shape.slice(),
    'strides': shape2strides( x.shape, 'column-major' ),
    'offset': 0,
    'order': 'column-major'
};

binary( [ x, y, z ], add );
console.log( ndarray2array( z.data, z.shape, z.strides, z.offset, z.order ) );
```

</section>

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</section>

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