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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 hasOwnProp = require( '@stdlib/assert/has-own-property' );
var isPlainObject = require( '@stdlib/assert/is-plain-object' );
var isInteger = require( '@stdlib/assert/is-integer' ).isPrimitive;
var isndarrayLike = require( '@stdlib/assert/is-ndarray-like' );
var broadcastArray = require( '@stdlib/ndarray/base/broadcast-array' );
var nonCoreShape = require( '@stdlib/ndarray/base/complement-shape' );
var normalizeIndex = require( '@stdlib/ndarray/base/normalize-index' );
var empty = require( '@stdlib/ndarray/base/empty' );
var getDType = require( '@stdlib/ndarray/dtype' );
var getShape = require( '@stdlib/ndarray/shape' );
var getOrder = require( '@stdlib/ndarray/order' );
var format = require( '@stdlib/string/format' );
var normalizeIndices = require( './normalize_indices.js' );
var broadcastIndex = require( './broadcast_index.js' );
var base = require( './base.js' );
// MAIN //
/**
* Performs an in-place copy of elements within an ndarray along an ndarray dimension.
*
* @param {ndarrayLike} x - input ndarray
* @param {(ndarrayLike|integer)} target - target index
* @param {(ndarrayLike|integer)} start - source start index (inclusive)
* @param {(ndarrayLike|integer)} [end] - source end index (exclusive)
* @param {Options} [options] - function options
* @param {integer} [options.dim=-1] - dimension over which to perform operation
* @throws {TypeError} first argument must be an ndarray-like object
* @throws {TypeError} index arguments must be either ndarray-like objects or integers
* @throws {TypeError} options argument must be an object
* @throws {RangeError} dimension index must not exceed input ndarray bounds
* @throws {RangeError} first argument must have at least one dimension
* @throws {TypeError} must provide valid options
* @returns {ndarray} input ndarray
*
* @example
* var Float64Array = require( '@stdlib/array/float64' );
* var ndarray = require( '@stdlib/ndarray/ctor' );
*
* // Create a data buffer:
* var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
*
* // Define the shape of the input array:
* var sh = [ 6 ];
*
* // Define the array strides:
* var sx = [ 1 ];
*
* // Define the index offset:
* var ox = 0;
*
* // Create an input ndarray:
* var x = new ndarray( 'float64', xbuf, sh, sx, ox, 'row-major' );
*
* // Perform operation:
* var out = copyWithin( x, 3, 1, 4 );
* // returns <ndarray>[ 1.0, 2.0, 3.0, 2.0, 3.0, 4.0 ]
*
* var bool = ( out === x );
* // returns true
*/
function copyWithin( x, target, start ) {
var hasOptions;
var options;
var hasEnd;
var nargs;
var opts;
var end;
var lsh;
var len;
var ord;
var sh;
var dt;
var d;
var e;
var s;
var t;
var w;
nargs = arguments.length;
if ( !isndarrayLike( x ) ) {
throw new TypeError( format( 'invalid argument. First argument must be an ndarray. Value: `%s`.', x ) );
}
if ( !isInteger( target ) && !isndarrayLike( target ) ) {
throw new TypeError( format( 'invalid argument. Second argument must be either an ndarray or an integer. Value: `%s`.', target ) );
}
if ( !isInteger( start ) && !isndarrayLike( start ) ) {
throw new TypeError( format( 'invalid argument. Third argument must be either an ndarray or an integer. Value: `%s`.', start ) );
}
// Resolve input ndarray meta data:
dt = getDType( x );
ord = getOrder( x );
// Initialize an options object:
opts = {
'dims': [ -1 ] // default behavior is to perform the operation over the last dimension
};
// Initialize flags indicating whether `end` and `options` arguments were provided:
hasOptions = false;
hasEnd = false;
// Case: copyWithin( x, target, start, end, options )
if ( nargs > 4 ) {
end = arguments[ 3 ];
if ( !isInteger( end ) && !isndarrayLike( end ) ) {
throw new TypeError( format( 'invalid argument. Fourth argument must be either an ndarray or an integer. Value: `%s`.', end ) );
}
hasEnd = true;
options = arguments[ 4 ];
hasOptions = true;
}
// Case: copyWithin( x, target, start, ??? )
else if ( nargs === 4 ) {
// Case: copyWithin( x, target, start, end )
if ( isInteger( arguments[ 3 ] ) || isndarrayLike( arguments[ 3 ] ) ) {
end = arguments[ 3 ];
hasEnd = true;
}
// Case: copyWithin( x, target, start, options )
else {
options = arguments[ 3 ];
hasOptions = true;
}
}
if ( hasOptions ) {
if ( !isPlainObject( options ) ) {
throw new TypeError( format( 'invalid argument. Options argument must be an object. Value: `%s`.', options ) );
}
// Resolve provided options...
if ( hasOwnProp( options, 'dim' ) ) {
if ( !isInteger( options.dim ) ) {
throw new TypeError( format( 'invalid option. `%s` option must be an integer. Option: `%s`.', 'dim', options.dim ) );
}
opts.dims[ 0 ] = options.dim;
}
}
// Resolve the shape of the non-core dimensions and the number of indexed elements...
sh = getShape( x );
if ( sh.length < 1 ) {
throw new RangeError( 'invalid argument. First argument must have at least one dimension.' );
}
d = normalizeIndex( opts.dims[ 0 ], sh.length-1 );
if ( d === -1 ) {
throw new RangeError( format( 'invalid option. Dimension index exceeds the number of dimensions. Number of dimensions: %d. Value: `%d`.', sh.length, opts.dims[ 0 ] ) );
}
opts.dims[ 0 ] = d;
lsh = nonCoreShape( sh, opts.dims );
len = sh[ d ];
// Normalize the provided indices...
t = normalizeIndices( target, len );
s = normalizeIndices( start, len );
e = ( hasEnd ) ? normalizeIndices( end, len ) : len;
// Broadcast the indices to match the shape of the non-core dimensions...
t = broadcastIndex( t, lsh, ord );
s = broadcastIndex( s, lsh, ord );
e = broadcastIndex( e, lsh, ord );
// Allocate a workspace ndarray which is broadcast across the non-core dimensions, thus allowing each one-dimensional slice to reuse the same workspace buffer:
w = broadcastArray( empty( dt, [ len ], ord ), lsh.concat( [ len ] ) );
// Perform operation:
return base( x, t, s, e, w, opts );
}
// EXPORTS //
module.exports = copyWithin;
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