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ddiff

Calculate the k-th discrete forward difference of a one-dimensional double-precision floating-point ndarray.

Installation

npm install @stdlib/blas-ext-base-ndarray-ddiff

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 ddiff = require( '@stdlib/blas-ext-base-ndarray-ddiff' );

ddiff( arrays )

Calculates the k-th discrete forward difference of a one-dimensional double-precision floating-point ndarray.

var Float64Vector = require( '@stdlib/ndarray-vector-float64' );
var scalar2ndarray = require( '@stdlib/ndarray-from-scalar' );

var x = new Float64Vector( [ 2.0, 4.0, 6.0, 8.0, 10.0 ] );
var prepend = new Float64Vector( [ 1.0 ] );
var append = new Float64Vector( [ 11.0 ] );
var out = new Float64Vector( 6 );
var workspace = new Float64Vector( 6 );
var k = scalar2ndarray( 1, {
    'dtype': 'generic'
});

var y = ddiff( [ x, prepend, append, out, workspace, k ] );
// returns <ndarray>[ 1.0, 2.0, 2.0, 2.0, 2.0, 1.0 ]

The function has the following parameters:

• arrays: array-like object containing the following ndarrays:

  • a one-dimensional input ndarray.
  • a one-dimensional ndarray containing values to prepend prior to computing differences.
  • a one-dimensional ndarray containing values to append prior to computing differences.
  • a one-dimensional output ndarray. Must have N + N1 + N2 - k elements, where N is the number of elements in the input ndarray, N1 is the number of elements to prepend, N2 is the number of elements to append, and k is the number of times to recursively compute differences.
  • a one-dimensional workspace ndarray. Must have N + N1 + N2 - 1 elements.
  • a zero-dimensional ndarray specifying the number of times to recursively compute differences.

Notes

• When k <= 1, the workspace ndarray is unused.

Examples

var discreteUniform = require( '@stdlib/random-discrete-uniform' );
var zeros = require( '@stdlib/ndarray-zeros' );
var scalar2ndarray = require( '@stdlib/ndarray-from-scalar' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );
var ddiff = require( '@stdlib/blas-ext-base-ndarray-ddiff' );

var N = 10;
var N1 = 2;
var N2 = 2;
var k = 4;
var opts = {
    'dtype': 'float64'
};

var x = discreteUniform( [ N ], -100, 100, opts );
var p = discreteUniform( [ N1 ], -100, 100, opts );
var a = discreteUniform( [ N2 ], -100, 100, opts );
var out = zeros( [ N + N1 + N2 - k ], opts );
var w = zeros( [ N + N1 + N2 - 1 ], opts );
var knd = scalar2ndarray( k, {
    'dtype': 'generic'
});

console.log( 'x: ', ndarray2array( x ) );
console.log( 'prepend: ', ndarray2array( p ) );
console.log( 'append: ', ndarray2array( a ) );

ddiff( [ x, p, a, out, w, knd ] );
console.log( 'out: ', ndarray2array( out ) );

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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 © 2016-2026. The Stdlib Authors.