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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 isndarrayLike = require( '@stdlib/assert/is-ndarray-like' );
var unaryReduceStrided1d = require( '@stdlib/ndarray/base/unary-reduce-strided1d' );
var spreadDimensions = require( '@stdlib/ndarray/base/spread-dimensions' );
var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
var broadcastScalar = require( '@stdlib/ndarray/base/broadcast-scalar' );
var ndarray = require( '@stdlib/ndarray/ctor' );
var indicesComplement = require( '@stdlib/array/base/indices-complement' );
var takeIndexed = require( '@stdlib/array/base/take-indexed' );
var zeroTo = require( '@stdlib/array/base/zero-to' );
var gjoinBetween = require( '@stdlib/blas/ext/base/ndarray/gjoin-between' );
var getShape = require( '@stdlib/ndarray/shape' );
var getOrder = require( '@stdlib/ndarray/order' );
var empty = require( '@stdlib/ndarray/empty' );
var format = require( '@stdlib/string/format' );
var validate = require( './validate.js' );
// MAIN //
/**
* Returns an ndarray created by joining elements using specified separators for each pair of consecutive elements along one or more ndarray dimensions.
*
* @param {ndarrayLike} x - input ndarray
* @param {Options} [options] - function options
* @param {string} [options.prefix=''] - prefix to prepend to each joined string
* @param {string} [options.suffix=''] - suffix to append to each joined string
* @param {ArrayLikeObject} [options.separators=[',']] - separators
* @param {IntegerArray} [options.dims] - list of dimensions over which to perform operation
* @param {boolean} [options.keepdims=false] - boolean indicating whether the reduced dimensions should be included in the returned ndarray as singleton dimensions
* @throws {TypeError} first argument must be an ndarray-like object
* @throws {TypeError} options argument must be an object
* @throws {RangeError} dimension indices must not exceed input ndarray bounds
* @throws {RangeError} number of dimension indices must not exceed the number of input ndarray dimensions
* @throws {Error} must provide valid options
* @returns {ndarray} output ndarray
*
* @example
* var array = require( '@stdlib/ndarray/array' );
*
* // Create an input ndarray:
* var x = array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
* // returns <ndarray>[ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ]
*
* // Perform operation:
* var out = joinBetween( x );
* // returns <ndarray>[ '1,2,3,4,5,6' ]
*/
function joinBetween( x, options ) {
var separators;
var prefix;
var suffix;
var order;
var opts;
var err;
var idx;
var shy;
var shx;
var N;
var y;
if ( !isndarrayLike( x ) ) {
throw new TypeError( format( 'invalid argument. First argument must be an ndarray-like object. Value: `%s`.', x ) );
}
shx = getShape( x );
order = getOrder( x );
N = shx.length;
// Resolve options:
opts = {
'prefix': '',
'suffix': '',
'separators': [ ',' ],
'dims': zeroTo( N ),
'keepdims': false
};
if ( arguments.length > 1 ) {
err = validate( opts, N, options );
if ( err ) {
throw err;
}
}
// Resolve the list of non-reduced dimensions and the output shape:
idx = indicesComplement( N, opts.dims );
shy = takeIndexed( shx, idx );
// Create the output ndarray:
y = empty( shy, {
'dtype': 'generic',
'order': order
});
// Resolve prefix and suffix:
prefix = scalar2ndarray( opts.prefix, {
'dtype': 'generic',
'order': order
});
suffix = scalar2ndarray( opts.suffix, {
'dtype': 'generic',
'order': order
});
// Resolve separators as a one-dimensional ndarray. When the separators array has a single element, the same separator is used for all consecutive pairs regardless of core size:
if ( opts.separators.length === 1 ) {
separators = broadcastScalar( opts.separators[ 0 ], 'generic', [ 1 ], order );
} else {
separators = new ndarray( 'generic', opts.separators, [ opts.separators.length ], [ 1 ], 0, order );
}
// Perform the reduction:
unaryReduceStrided1d( dispatch, [ x, y ], opts.dims );
// Check whether we need to reinsert singleton dimensions...
if ( opts.keepdims ) {
y = spreadDimensions( N, y, idx, true );
}
return y;
/**
* Dispatch function which joins elements of a one-dimensional ndarray slice.
*
* @private
* @param {Array<Object>} arrays - ndarrays
* @returns {string} joined string
*/
function dispatch( arrays ) {
return gjoinBetween( [ arrays[ 0 ], prefix, suffix, separators ] );
}
}
// EXPORTS //
module.exports = joinBetween;
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