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

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# Ternary

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

<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->

<section class="intro">

</section>

<!-- /.intro -->

<!-- Package usage documentation. -->

<section class="installation">

## Installation

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

</section>

<section class="usage">

## Usage

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

#### ternary( arrays, fcn )

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

```javascript
var Float64Array = require( '@stdlib/array-float64' );
var add3 = require( '@stdlib/number-float64-base-add3' );

// 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( [ 0.5, 0.5, 0.5, 0.5, 0.5, 0.5 ] );
var wbuf = 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 ];
var sw = [ 2, 2, 1 ];

// Define the index offsets:
var ox = 0;
var oy = 0;
var oz = 0;
var ow = 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'
};
var w = {
    'dtype': 'float64',
    'data': wbuf,
    'shape': shape,
    'strides': sw,
    'offset': ow,
    'order': 'row-major'
};

// Apply the ternary function:
ternary( [ x, y, z, w ], add3 );

console.log( w.data );
// => <Float64Array>[ 2.5, 3.5, 4.5, 5.5, 6.5, 7.5 ]
```

The function accepts the following arguments:

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

</section>

<!-- /.usage -->

<!-- Package usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->

<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 ternary function in order to achieve better performance.

</section>

<!-- /.notes -->

<!-- Package usage examples. -->

<section class="examples">

## 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 add3 = require( '@stdlib/number-float64-base-add3' );
var shape2strides = require( '@stdlib/ndarray-base-shape2strides' );
var ndarray2array = require( '@stdlib/ndarray-base-to-array' );
var ternary = require( '@stdlib/ndarray-base-ternary' );

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': filledarrayBy( N, 'generic', discreteUniform( -100, 100 ) ),
    'shape': x.shape.slice(),
    'strides': shape2strides( x.shape, 'row-major' ),
    'offset': 0,
    'order': 'row-major'
};
console.log( ndarray2array( z.data, z.shape, z.strides, z.offset, z.order ) );

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

ternary( [ x, y, z, w ], add3 );
console.log( ndarray2array( w.data, w.shape, w.strides, w.offset, w.order ) );
```

</section>

<!-- /.examples -->

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* * *

## 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].

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---

## License

See [LICENSE][stdlib-license].


## Copyright

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

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