@stdlib/utils-map-arguments NPM

mapArguments

Create a function that applies arguments to a provided function after transforming arguments according to a callback function.

Installation

npm install @stdlib/utils-map-arguments

Usage

var mapArguments = require( '@stdlib/utils-map-arguments' );

mapArguments( fcn, clbk[, thisArg] )

Returns a function that invokes a provided function after transforming arguments according to a callback function.

function foo( a, b, c ) {
    return [ a, b, c ];
}

function clbk( v ) {
    return v * 2;
}

var bar = mapArguments( foo, clbk );

var out = bar( 1, 2, 3 );
// returns [ 2, 4, 6 ]

The clbk function is provided the following arguments:

value: argument value.
index: argument index.

To set the this context when invoking the input function, provide a thisArg.

function clbk( v ) {
    return v * 2;
}

function Foo() {
    this.x = 1;
    this.y = 2;
}

Foo.prototype.scale = function scale( a, b ) {
    return [ this.x*a, this.y*b ];
};

var ctx = {
    'x': 10,
    'y': 20
};

var foo = new Foo();

var bar = mapArguments( foo.scale, clbk, ctx );

var out = bar( 1, 2 );
// returns [ 20, 80 ]

Examples

var filledarrayBy = require( '@stdlib/array-filled-by' );
var add = require( '@stdlib/math-base-ops-add' );
var filterArguments = require( '@stdlib/utils-filter-arguments' );
var mapArguments = require( '@stdlib/utils-map-arguments' );

function fill( i ) {
    return i;
}

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

function factory( i, j ) {
    return predicate;

    function predicate( value, index ) {
        return ( i <= index ) && ( index < j );
    }
}

// Create a data array:
var x = filledarrayBy( 10, 'float64', fill );

// Compute the sum of consecutive squared elements...
var f;
var i;
for ( i = 0; i < x.length-1; i++ ) {
    f = filterArguments( mapArguments( add, scale ), factory( i, i+2 ) );
    console.log( 'sum(x_%d^2, x_%d^2) = %d', i, i+1, f.apply( null, x ) );
}

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.