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dcusumors

Compute the cumulative sum of a one-dimensional double-precision floating-point ndarray using ordinary recursive summation.

Installation

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

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

dcusumors( arrays )

Computes the cumulative sum of a one-dimensional double-precision floating-point ndarray using ordinary recursive summation.

var Float64Array = require( '@stdlib/array-float64' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );
var scalar2ndarray = require( '@stdlib/ndarray-base-from-scalar' );
var ndarray = require( '@stdlib/ndarray-base-ctor' );

var xbuf = new Float64Array( [ 1.0, 3.0, 4.0, 2.0 ] );
var x = new ndarray( 'float64', xbuf, [ 4 ], [ 1 ], 0, 'row-major' );

var ybuf = new Float64Array( [ 0.0, 0.0, 0.0, 0.0 ] );
var y = new ndarray( 'float64', ybuf, [ 4 ], [ 1 ], 0, 'row-major' );

var initial = scalar2ndarray( 0.0, 'float64', 'row-major' );

var v = dcusumors( [ x, y, initial ] );
// returns <ndarray>

var bool = ( v === y );
// returns true

var arr = ndarray2array( v );
// returns [ 1.0, 4.0, 8.0, 10.0 ]

The function has the following parameters:

• arrays: array-like object containing a one-dimensional input ndarray, a one-dimensional output ndarray, and a zero-dimensional ndarray containing the initial sum.

Notes

• If provided an empty one-dimensional input ndarray, the function returns the output ndarray unchanged.
• Ordinary recursive summation (i.e., a "simple" sum) is performant, but can incur significant numerical error. If performance is paramount and error tolerated, using ordinary recursive summation is acceptable; in all other cases, exercise due caution.

Examples

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

var xbuf = discreteUniform( 10, -50, 50, {
    'dtype': 'float64'
});
var x = new ndarray( 'float64', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
console.log( ndarray2array( x ) );

var y = zerosLike( x );
console.log( ndarray2array( y ) );

var initial = scalar2ndarray( 100.0, {
    'dtype': 'float64'
});

var v = dcusumors( [ x, y, initial ] );
console.log( ndarray2array( v ) );

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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 © 2016-2025. The Stdlib Authors.