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map

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

Installation

npm install @stdlib/ndarray-map

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
• If you are using Deno, visit the deno branch (see README for usage intructions).
• For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.

Usage

var map = require( '@stdlib/ndarray-map' );

map( x[, options], fcn[, thisArg] )

Applies a callback function to elements in an input ndarray and assigns results to elements in a new output ndarray.

var Float64Array = require( '@stdlib/array-float64' );
var ndarray = require( '@stdlib/ndarray-ctor' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );

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

var buffer = 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 shape = [ 2, 3 ];
var strides = [ 6, 1 ];
var offset = 1;

var x = ndarray( 'float64', buffer, shape, strides, offset, 'row-major' );
// returns <ndarray>

var y = map( x, scale );
// returns <ndarray>

var arr = ndarray2array( y );
// returns [ [ 20.0, 30.0, 40.0 ], [ 80.0, 90.0, 100.0 ] ]

The function accepts the following arguments:

• x: input ndarray.
• options: function options (optional).
• fcn: callback to apply.
• thisArg: callback execution context (optional).

The function accepts the following options:

• dtype: output ndarray data type. If not specified, the output ndarray data type is inferred from the input ndarray.

By default, the output ndarray data type is inferred from the input ndarray. To return an ndarray with a different data type, specify the dtype option.

var Float64Array = require( '@stdlib/array-float64' );
var ndarray = require( '@stdlib/ndarray-ctor' );
var dtype = require( '@stdlib/ndarray-dtype' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );

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

var buffer = 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 shape = [ 2, 3 ];
var strides = [ 6, 1 ];
var offset = 1;

var x = ndarray( 'float64', buffer, shape, strides, offset, 'row-major' );
// returns <ndarray>

var opts = {
    'dtype': 'float32'
};
var y = map( x, opts, scale );
// returns <ndarray>

var dt = dtype( y );
// returns 'float32'

var arr = ndarray2array( y );
// returns [ [ 20.0, 30.0, 40.0 ], [ 80.0, 90.0, 100.0 ] ]

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

var Float64Array = require( '@stdlib/array-float64' );
var ndarray = require( '@stdlib/ndarray-ctor' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );

function scale( z ) {
    this.count += 1;
    return z * 10.0;
}

var buffer = 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 shape = [ 2, 3 ];
var strides = [ 6, 1 ];
var offset = 1;

var x = ndarray( 'float64', buffer, shape, strides, offset, 'row-major' );
// returns <ndarray>

var ctx = {
    'count': 0
};
var y = map( x, scale, ctx );
// returns <ndarray>

var arr = ndarray2array( y );
// returns [ [ 20.0, 30.0, 40.0 ], [ 80.0, 90.0, 100.0 ] ]

var count = ctx.count;
// returns 6

The callback function is provided the following arguments:

• value: current array element.
• indices: current array element indices.
• arr: the input ndarray.

Notes

• The function does not perform explicit casting (e.g., from a real-valued floating-point number to a complex floating-point number). Any such casting should be performed by a provided callback function.

  var Float64Array = require( '@stdlib/array-float64' );
  var ndarray = require( '@stdlib/ndarray-ctor' );
  var Complex128 = require( '@stdlib/complex-float64-ctor' );
  var ndarray2array = require( '@stdlib/ndarray-to-array' );
  
  function toComplex( z ) {
      return new Complex128( z, 0.0 );
  }
  
  var buffer = 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 shape = [ 2, 3 ];
  var strides = [ 6, 1 ];
  var offset = 1;
  
  var x = ndarray( 'float64', buffer, shape, strides, offset, 'row-major' );
  // returns <ndarray>
  
  var opts = {
      'dtype': 'complex128'
  };
  var y = map( x, opts, toComplex );
  // returns <ndarray>

• The function always returns an ndarray having the same shape and order as the input ndarray.

• 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

var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var abs = require( '@stdlib/math-base-special-abs' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );
var naryFunction = require( '@stdlib/utils-nary-function' );
var ndarray = require( '@stdlib/ndarray-ctor' );
var map = require( '@stdlib/ndarray-map' );

var buffer = discreteUniform( 10, -100, 100, {
    'dtype': 'generic'
});
var shape = [ 5, 2 ];
var strides = [ 2, 1 ];
var offset = 0;
var x = ndarray( 'generic', buffer, shape, strides, offset, 'row-major' );
console.log( ndarray2array( x ) );

var y = map( x, naryFunction( abs, 1 ) );
console.log( ndarray2array( y ) );

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See Also

• @stdlib/ndarray-filter: return a shallow copy of an ndarray containing only those elements which pass a test implemented by a predicate function.
• @stdlib/ndarray-filter-map: filter and map elements in an input ndarray to elements in a new output ndarray according to a callback function.
• @stdlib/ndarray-slice: return a read-only view of an input ndarray.

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Notice

This package is part of 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, see the main project repository.

Community

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.