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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';
// MAIN //
/**
* Applies a one-dimensional strided array function to a list of specified dimensions in two input ndarrays and assigns results to a provided output ndarray.
*
* ## Notes
*
* - The `views`, `shape`, `stridesX`, `stridesY`, and `stridesZ` parameters are unused. Providing empty arrays for these parameters is recommended in order to ensure a monomorphic API.
*
* @private
* @param {Function} fcn - wrapper for a one-dimensional strided array function
* @param {Array<Object>} arrays - ndarray descriptors
* @param {Array<Object>} views - initialized ndarray descriptors representing subarray views
* @param {Array<integer>} shape - loop dimensions
* @param {Array<integer>} stridesX - loop dimension strides for the first input ndarray
* @param {Array<integer>} stridesY - loop dimension strides for the second input ndarray
* @param {Array<integer>} stridesZ - loop dimension strides for the output ndarray
* @param {Object} strategyX - strategy for marshaling data to and from a first input ndarray view
* @param {Object} strategyY - strategy for marshaling data to and from a second input ndarray view
* @param {Object} strategyZ - strategy for marshaling data to and from an output ndarray view
* @param {Options} opts - function options
* @returns {void}
*
* @example
* var Float64Array = require( '@stdlib/array/float64' );
* var ndarray2array = require( '@stdlib/ndarray/base/to-array' );
* var gwxpy = require( '@stdlib/blas/ext/base/ndarray/gwxpy' );
*
* // Create data buffers:
* var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );
* var ybuf = new Float64Array( [ 2.0, 3.0, 4.0, 5.0 ] );
* var zbuf = new Float64Array( [ 0.0, 0.0, 0.0, 0.0 ] );
*
* // Define the array shapes:
* var xsh = [ 2, 2 ];
* var ysh = [ 2, 2 ];
* var zsh = [ 2, 2 ];
*
* // Define the array strides:
* var sx = [ 2, 1 ];
* var sy = [ 2, 1 ];
* var sz = [ 2, 1 ];
*
* // Define the index offsets:
* var ox = 0;
* var oy = 0;
* var oz = 0;
*
* // Create the input ndarray descriptors:
* var x = {
* 'dtype': 'float64',
* 'data': xbuf,
* 'shape': xsh,
* 'strides': sx,
* 'offset': ox,
* 'order': 'row-major'
* };
*
* var y = {
* 'dtype': 'float64',
* 'data': ybuf,
* 'shape': ysh,
* 'strides': sy,
* 'offset': oy,
* 'order': 'row-major'
* };
*
* // Create an output ndarray descriptor:
* var z = {
* 'dtype': 'float64',
* 'data': zbuf,
* 'shape': zsh,
* 'strides': sz,
* 'offset': oz,
* 'order': 'row-major'
* };
*
* // Define an input strategy:
* function inputStrategy( x ) {
* return {
* 'dtype': x.dtype,
* 'data': x.data,
* 'shape': [ 4 ],
* 'strides': [ 1 ],
* 'offset': x.offset,
* 'order': x.order
* };
* }
*
* // Define an output strategy:
* function outputStrategy( x ) {
* return x;
* }
*
* var strategy = {
* 'input': inputStrategy,
* 'output': outputStrategy
* };
*
* // Apply strided function:
* kernel( gwxpy, [ x, y, z ], [], [], [], [], [], strategy, strategy, strategy, {} );
*
* var v = z.data;
* // returns <Float64Array>[ 3.0, 5.0, 7.0, 9.0 ]
*/
function kernel( fcn, arrays, views, shape, stridesX, stridesY, stridesZ, strategyX, strategyY, strategyZ, opts ) { // eslint-disable-line max-len, max-params
arrays[ 0 ] = strategyX.input( arrays[ 0 ] );
arrays[ 1 ] = strategyY.input( arrays[ 1 ] );
arrays[ 2 ] = strategyZ.input( arrays[ 2 ] );
fcn( arrays, opts );
strategyZ.output( arrays[ 2 ] );
}
// EXPORTS //
module.exports = kernel;
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