# map
> Apply a callback function to elements in an input ndarray and assign results to elements in an output ndarray.
## 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 );
// => [ 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.
## 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.
## Examples
```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 ) );
```