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* @license Apache-2.0
*
* Copyright (c) 2026 The Stdlib Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
'use strict';
// MODULES //
var ndarraylike2scalar = require( '@stdlib/ndarray/base/ndarraylike2scalar' );
var numelDimension = require( '@stdlib/ndarray/base/numel-dimension' );
var getStride = require( '@stdlib/ndarray/base/stride' );
var getOffset = require( '@stdlib/ndarray/base/offset' );
var getData = require( '@stdlib/ndarray/base/data-buffer' );
var strided = require( '@stdlib/blas/ext/base/ddiff' ).ndarray;
// MAIN //
/**
* Calculates the k-th discrete forward difference of a one-dimensional double-precision floating-point ndarray.
*
* ## Notes
*
* - The function expects the following ndarrays:
*
* - a one-dimensional input ndarray.
* - a one-dimensional ndarray containing values to prepend.
* - a one-dimensional ndarray containing values to append.
* - a one-dimensional output ndarray.
* - a one-dimensional workspace ndarray.
* - a zero-dimensional ndarray specifying the number of times to recursively compute differences.
*
* @param {ArrayLikeObject<Object>} arrays - array-like object containing ndarrays
* @returns {ndarray} output ndarray
*
* @example
* 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 ]
*/
function ddiff( arrays ) {
var workspace;
var prepend;
var append;
var out;
var wo;
var ws;
var wd;
var po;
var ps;
var pd;
var oo;
var os;
var od;
var ao;
var as;
var ad;
var N2;
var N1;
var xo;
var xs;
var xd;
var N;
var x;
var k;
x = arrays[ 0 ];
prepend = arrays[ 1 ];
append = arrays[ 2 ];
out = arrays[ 3 ];
workspace = arrays[ 4 ];
k = ndarraylike2scalar( arrays[ 5 ] );
// Resolve number of indexed elements...
N = numelDimension( x, 0 );
N1 = numelDimension( prepend, 0 );
N2 = numelDimension( append, 0 );
// Resolve the array data...
xd = getData( x );
pd = getData( prepend );
ad = getData( append );
od = getData( out );
wd = getData( workspace );
// Resolve the array strides...
xs = getStride( x, 0 );
ps = getStride( prepend, 0 );
as = getStride( append, 0 );
os = getStride( out, 0 );
ws = getStride( workspace, 0 );
// Resolve the array offsets...
xo = getOffset( x );
po = getOffset( prepend );
ao = getOffset( append );
oo = getOffset( out );
wo = getOffset( workspace );
strided( N, k, xd, xs, xo, N1, pd, ps, po, N2, ad, as, ao, od, os, oo, wd, ws, wo ); // eslint-disable-line max-len
return out;
}
// EXPORTS //
module.exports = ddiff;
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