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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 isndarrayLike = require( '@stdlib/assert/is-ndarray-like' );
var broadcastScalar = require( '@stdlib/ndarray/base/broadcast-scalar' );
var maybeBroadcastArray = require( '@stdlib/ndarray/base/maybe-broadcast-array' );
var nonCoreShape = require( '@stdlib/ndarray/base/complement-shape' );
var getShape = require( '@stdlib/ndarray/shape' );
var getOrder = require( '@stdlib/ndarray/order' );
var ndims = require( '@stdlib/ndarray/base/ndims' );
var numel = require( '@stdlib/ndarray/base/numel' );
var format = require( '@stdlib/string/format' );
var base = require( './base.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 {ndarrayLike} separators - separators
* @param {Options} [options] - function options
* @param {(ndarrayLike|string)} [options.prefix=''] - prefix to prepend to each joined string
* @param {(ndarrayLike|string)} [options.suffix=''] - suffix to append to each joined string
* @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} second argument must be an ndarray-like object
* @throws {TypeError} options argument must be an object
* @throws {TypeError} a provided `prefix` or `suffix` ndarray must be zero-dimensional when not specifying reduction dimensions
* @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} a provided `prefix` or `suffix` ndarray must be broadcast compatible with the complement of the shape defined by `options.dims`
* @throws {Error} a provided `separators` ndarray must be broadcast compatible with the shape consisting of the complement of the shape defined by `options.dims` followed by `N-1` where `N` is the number of elements along the reduced dimensions
* @throws {Error} must provide valid options
* @returns {ndarray} output ndarray
*
* @example
* var array = require( '@stdlib/ndarray/array' );
* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
*
* // 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 ]
*
* // Create a separators ndarray:
* var sep = scalar2ndarray( ',', {
* 'dtype': 'generic'
* });
*
* // Perform operation:
* var out = joinBetween( x, sep );
* // returns <ndarray>[ '1,2,3,4,5,6' ]
*/
function joinBetween( x, separators, options ) {
var prefix;
var suffix;
var opts;
var sep;
var shx;
var sh;
var M;
var p;
var s;
if ( !isndarrayLike( x ) ) {
throw new TypeError( format( 'invalid argument. First argument must be an ndarray-like object. Value: `%s`.', x ) );
}
if ( !isndarrayLike( separators ) ) {
throw new TypeError( format( 'invalid argument. Second argument must be an ndarray-like object. Value: `%s`.', separators ) );
}
// Initialize prefix, suffix, and options:
p = '';
s = '';
opts = {
'dtype': 'generic',
'keepdims': false
};
if ( arguments.length > 2 ) {
if ( !isPlainObject( options ) ) {
throw new TypeError( format( 'invalid argument. Options argument must be an object. Value: `%s`.', options ) );
}
if ( hasOwnProp( options, 'prefix' ) ) {
p = options.prefix;
}
if ( hasOwnProp( options, 'suffix' ) ) {
s = options.suffix;
}
if ( hasOwnProp( options, 'dims' ) ) {
opts.dims = options.dims;
}
if ( hasOwnProp( options, 'keepdims' ) ) {
opts.keepdims = options.keepdims;
}
}
shx = getShape( x );
// Broadcast prefix to the complement shape...
if ( isndarrayLike( p ) ) {
if ( hasOwnProp( opts, 'dims' ) ) {
prefix = maybeBroadcastArray( p, nonCoreShape( shx, opts.dims ) );
} else if ( ndims( p ) === 0 ) {
prefix = p;
} else {
throw new TypeError( format( 'invalid option. `%s` option must be a zero-dimensional ndarray when not specifying dimensions. Value: `%s`.', 'prefix', p ) );
}
} else {
if ( hasOwnProp( opts, 'dims' ) ) {
sh = nonCoreShape( shx, opts.dims );
} else {
sh = [];
}
prefix = broadcastScalar( p, 'generic', sh, getOrder( x ) );
}
// Broadcast suffix to the complement shape...
if ( isndarrayLike( s ) ) {
if ( hasOwnProp( opts, 'dims' ) ) {
suffix = maybeBroadcastArray( s, nonCoreShape( shx, opts.dims ) );
} else if ( ndims( s ) === 0 ) {
suffix = s;
} else {
throw new TypeError( format( 'invalid option. `%s` option must be a zero-dimensional ndarray when not specifying dimensions. Value: `%s`.', 'suffix', s ) );
}
} else {
if ( hasOwnProp( opts, 'dims' ) ) {
sh = nonCoreShape( shx, opts.dims );
} else {
sh = [];
}
suffix = broadcastScalar( s, 'generic', sh, getOrder( x ) );
}
// Broadcast separators to [ ...complementShape, M-1 ] where M is the number of elements along the reduced dimensions...
if ( hasOwnProp( opts, 'dims' ) ) {
sh = nonCoreShape( shx, opts.dims );
M = numel( shx ) / numel( sh );
} else {
sh = [];
M = numel( shx );
}
sh.push( M - 1 );
sep = maybeBroadcastArray( separators, sh );
return base( x, prefix, suffix, sep, opts );
}
// EXPORTS //
module.exports = joinBetween;
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