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* @license Apache-2.0
*
* Copyright (c) 2025 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 isScalarMostlySafeCompatible = require( '@stdlib/ndarray/base/assert/is-scalar-mostly-safe-compatible' ); // eslint-disable-line id-length
var isMostlySafeCast = require( '@stdlib/ndarray/base/assert/is-mostly-safe-data-type-cast' );
var isndarrayLike = require( '@stdlib/assert/is-ndarray-like' );
var unaryReduceSubarray = require( '@stdlib/ndarray/base/unary-reduce-subarray' );
var ndims = require( '@stdlib/ndarray/ndims' );
var base = require( '@stdlib/ndarray/base/includes' );
var getShape = require( '@stdlib/ndarray/shape' ); // note: non-base accessor is intentional due to the input arrays originating in userland
var getOrder = require( '@stdlib/ndarray/base/order' );
var getDType = require( '@stdlib/ndarray/base/dtype' );
var maybeBroadcastArray = require( '@stdlib/ndarray/base/maybe-broadcast-array' );
var broadcastScalar = require( '@stdlib/ndarray/base/broadcast-scalar' );
var objectAssign = require( '@stdlib/object/assign' );
var zeroTo = require( '@stdlib/array/base/zero-to' );
var format = require( '@stdlib/string/format' );
var defaults = require( './defaults.json' );
var validate = require( './validate.js' );
// MAIN //
/**
* Tests whether an ndarray contains a specified value along one or more dimensions and assigns the results to a provided output ndarray.
*
* @param {ndarray} x - input ndarray
* @param {(ndarray|*)} searchElement - search element
* @param {ndarray} y - output ndarray
* @param {Options} [options] - function options
* @param {IntegerArray} [options.dims] - list of dimensions over which to perform a reduction
* @throws {TypeError} first argument must be an ndarray-like object
* @throws {Error} second argument must be broadcast-compatible with the non-reduced dimensions of the input ndarray
* @throws {TypeError} second argument must have a data type which can be safely cast to the data type of the input ndarray
* @throws {TypeError} third argument must be an ndarray-like object
* @throws {TypeError} options argument must be an object
* @throws {Error} must provide valid options
* @returns {ndarray} output ndarray
*
* @example
* var Float64Array = require( '@stdlib/array/float64' );
* var ndarray = require( '@stdlib/ndarray/ctor' );
* var empty = require( '@stdlib/ndarray/empty' );
*
* // Create a data buffer:
* var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
*
* // Define the shape of the input array:
* var shape = [ 3, 1, 2 ];
*
* // Define the array strides:
* var sx = [ 4, 4, 1 ];
*
* // Define the index offset:
* var ox = 1;
*
* // Create an input ndarray:
* var x = new ndarray( 'float64', xbuf, shape, sx, ox, 'row-major' );
*
* // Create an output ndarray:
* var y = empty( [], {
* 'dtype': 'bool'
* });
*
* // Perform reduction:
* var out = assign( x, 6.0, y );
* // returns <ndarray>
*
* var v = out.get();
* // returns true
*/
function assign( x, searchElement, y, options ) {
var opts;
var err;
var ord;
var dt;
var N;
var v;
if ( !isndarrayLike( x ) ) {
throw new TypeError( format( 'invalid argument. First argument must be an ndarray-like object. Value: `%s`.', x ) );
}
if ( !isndarrayLike( y ) ) {
throw new TypeError( format( 'invalid argument. Third argument must be an ndarray-like object. Value: `%s`.', y ) );
}
N = ndims( x );
opts = objectAssign( {}, defaults );
if ( arguments.length > 3 ) {
err = validate( opts, N, options );
if ( err ) {
throw err;
}
}
if ( opts.dims === null ) {
opts.dims = zeroTo( N );
}
// Resolve input array meta data:
dt = getDType( x );
ord = getOrder( x );
// Determine how to broadcast the search element...
if ( isndarrayLike( searchElement ) ) {
if ( !isMostlySafeCast( getDType( searchElement ), dt ) ) {
throw new TypeError( format( 'invalid argument. Second argument cannot be safely cast to the input array data type. Value: `%s`.', searchElement ) );
}
try {
v = maybeBroadcastArray( searchElement, getShape( y ) );
} catch ( err ) { // eslint-disable-line no-unused-vars
throw new Error( 'invalid argument. Second argument must be broadcast-compatible with the non-reduced dimensions of the input array.' );
}
} else if ( isScalarMostlySafeCompatible( searchElement, dt ) ) {
v = broadcastScalar( searchElement, dt, getShape( y ), ord );
} else {
throw new TypeError( format( 'invalid argument. Second argument cannot be safely cast to the input array data type. Value: `%s`.', searchElement ) );
}
// Perform the reduction:
unaryReduceSubarray( base, [ x, y, v ], opts.dims ); // note: we assume that this lower-level function handles further validation of the output ndarray (e.g., expected shape, etc)
return y;
}
// EXPORTS //
module.exports = assign;
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