npm package: @stdlib/ndarray-base-unary-by
Description: Apply a unary function to each element retrieved from a input ndarray according to a callback function and assign results to elements in an output ndarray.
Author: The Stdlib Authors
Latest version: 0.2.3

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# unaryBy

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url] <!-- [![dependencies][dependencies-image]][dependencies-url] -->

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

<section class="intro">

</section>

<!-- /.intro -->

<section class="installation">

## Installation

```bash
npm install @stdlib/ndarray-base-unary-by
```

</section>

<section class="usage">

## Usage

```javascript
var unaryBy = require( '@stdlib/ndarray-base-unary-by' );
```

#### unaryBy( arrays, fcn, clbk\[, thisArg] )

Applies a unary function to each element retrieved from an input ndarray according to a callback function and assigns results to elements in an output ndarray.

<!-- eslint-disable max-len -->

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

function scale( x ) {
    return x * 10.0;
}

function accessor( v ) {
    return v * 2.0;
}

// Create data buffers:
var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
var ybuf = new Float64Array( 6 );

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

// Define the array strides:
var sx = [ 4, 4, 1 ];
var sy = [ 2, 2, 1 ];

// Define the index offsets:
var ox = 1;
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 the unary function:
unaryBy( [ x, y ], scale, accessor );

console.log( y.data );
// => <Float64Array>[ 40.0, 60.0, 120.0, 140.0, 200.0, 220.0 ]
```

The function accepts the following arguments:

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

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).

The invoked callback function is provided four arguments:

-   **value**: input array element.
-   **idx**: iteration index (zero-based).
-   **indices**: input and output ndarray data buffer indices `[ix, iy]`.
-   **arrays**: input and output ndarrays `[x, y]`.

To set the callback execution context, provide a `thisArg`.

<!-- eslint-disable max-len -->

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

function scale( x ) {
    return x * 10.0;
}

function accessor( v ) {
    this.count += 1;
    return v * 2.0;
}

// Create data buffers:
var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
var ybuf = new Float64Array( 6 );

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

// Define the array strides:
var sx = [ 4, 4, 1 ];
var sy = [ 2, 2, 1 ];

// Define the index offsets:
var ox = 1;
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 the unary function:
var context = {
    'count': 0
};
unaryBy( [ x, y ], scale, accessor, context );

var cnt = context.count;
// returns 6
```

</section>

<!-- /.usage -->

<section class="notes">

## Notes

-   For very high-dimensional ndarrays which are non-contiguous, one should consider copying the underlying data to contiguous memory before applying a unary function in order to achieve better performance.

-   If a provided callback function does not return any value (or equivalently, explicitly returns `undefined`), the value is **ignored**.

    <!-- eslint-disable max-len -->

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

    function scale( x ) {
        return x * 10.0;
    }

    function accessor() {
        // No-op...
    }

    // Create data buffers:
    var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
    var ybuf = new Float64Array( 6 );

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

    // Define the array strides:
    var sx = [ 4, 4, 1 ];
    var sy = [ 2, 2, 1 ];

    // Define the index offsets:
    var ox = 1;
    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 the unary function:
    unaryBy( [ x, y ], scale, accessor );

    console.log( y.data );
    // => <Float64Array>[ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]
    ```

</section>

<!-- /.notes -->

<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 abs = require( '@stdlib/math-base-special-abs' );
var sqrt = require( '@stdlib/math-base-special-sqrt' );
var naryFunction = require( '@stdlib/utils-nary-function' );
var shape2strides = require( '@stdlib/ndarray-base-shape2strides' );
var ndarray2array = require( '@stdlib/ndarray-base-to-array' );
var unaryBy = require( '@stdlib/ndarray-base-unary-by' );

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

unaryBy( [ x, y ], sqrt, 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 -->

<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->

<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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