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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 hasOwnProp = require( '@stdlib/assert/has-own-property' );
var isNumber = require( '@stdlib/assert/is-number' ).isPrimitive;
var isComplexLike = require( '@stdlib/assert/is-complex-like' );
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 getDType = require( '@stdlib/ndarray/dtype' );
var getShape = require( '@stdlib/ndarray/shape' );
var getOrder = require( '@stdlib/ndarray/order' );
var format = require( '@stdlib/string/format' );
var base = require( './base.js' ).assign;
// MAIN //
/**
* Computes the cumulative sum along one or more ndarray dimensions and assigns the results to a provided output ndarray.
*
* @param {ndarrayLike} x - input ndarray
* @param {(ndarrayLike|number|ComplexLike)} [initial] - initial value
* @param {ndarrayLike} out - output ndarray
* @param {Options} [options] - function options
* @param {integer} [options.dims] - list of dimensions over which to perform operation
* @throws {TypeError} first argument must be an ndarray-like object
* @throws {TypeError} initial value argument must be either an ndarray-like object or a numeric value
* @throws {TypeError} output 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 Float64Array = require( '@stdlib/array/float64' );
* var ndarray = require( '@stdlib/ndarray/ctor' );
*
* // Create data buffers:
* var xbuf = new Float64Array( [ 1.0, 2.0, -3.0, 4.0, -5.0, 6.0 ] );
* var ybuf = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
*
* // Define the shape of the input array:
* var shape = [ 3, 1, 2 ];
*
* // Define the array strides:
* var strides = [ 2, 2, 1 ];
*
* // Define the index offset:
* var offset = 0;
*
* // Create an input ndarray:
* var x = new ndarray( 'float64', xbuf, shape, strides, offset, 'row-major' );
*
* // Create an output ndarray:
* var y = new ndarray( 'float64', ybuf, shape, strides, offset, 'row-major' );
*
* // Perform operation:
* var out = assign( x, y );
* // returns <ndarray>[ [ [ 1.0, 3.0 ] ], [ [ 0.0, 4.0 ] ], [ [ -1.0, 5.0 ] ] ]
*
* var bool = ( out === y );
* // returns true
*/
function assign( x ) {
var nargs;
var opts;
var ord;
var out;
var dt;
var sh;
var v;
nargs = arguments.length;
// Resolve input ndarray meta data:
dt = getDType( x );
ord = getOrder( x );
// Case: assign( x, out )
if ( nargs < 3 ) {
return base( x, broadcastScalar( 0.0, dt, [], ord ), arguments[ 1 ] );
}
v = arguments[ 1 ];
out = arguments[ 2 ];
// Case: assign( x, ???, ??? )
if ( nargs === 3 ) {
// Case: assign( x, initial, out )
if ( isndarrayLike( out ) ) {
// Case: assign( x, initial_ndarray, out )
if ( isndarrayLike( v ) ) {
// As the operation is performed across all dimensions, `v` is assumed to be a zero-dimensional ndarray...
return base( x, v, out );
}
// When computing the sum, initial values must be numeric...
if ( !isNumber( v ) && !isComplexLike( v ) ) {
throw new TypeError( format( 'invalid argument. Second argument must be either an ndarray or a numeric scalar value. Value: `%s`.', v ) );
}
// Case: assign( x, initial_scalar, out )
return base( x, broadcastScalar( v, dt, [], ord ), out );
}
// Case: assign( x, out, opts )
opts = out;
out = v;
v = 0.0;
// Intentionally fall through...
}
// Case: assign( x, initial, out, opts )
else { // nargs > 3
opts = arguments[ 3 ];
}
// Case: assign( x, initial_ndarray, out, opts )
if ( isndarrayLike( v ) ) {
// When not provided `dims`, the operation is performed across all dimensions and `v` is assumed to be a zero-dimensional ndarray; when `dims` is provided, we need to broadcast `v` to match the shape of the non-core dimensions...
if ( hasOwnProp( opts, 'dims' ) ) {
v = maybeBroadcastArray( v, nonCoreShape( getShape( x ), opts.dims ) ); // eslint-disable-line max-len
}
}
// Case: assign( x, initial_scalar, out, opts )
else if ( isNumber( v ) || isComplexLike( v ) ) {
if ( hasOwnProp( opts, 'dims' ) ) {
sh = nonCoreShape( getShape( x ), opts.dims );
} else {
sh = [];
}
v = broadcastScalar( v, dt, sh, getOrder( x ) );
} else {
throw new TypeError( format( 'invalid argument. Second argument must be either an ndarray or a numeric scalar value. Value: `%s`.', v ) );
}
return base( x, v, out, opts );
}
// EXPORTS //
module.exports = assign;
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