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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 isndarrayLike = require( '@stdlib/assert/is-ndarray-like' );
var format = require( '@stdlib/string/format' );
var maybeBroadcastArrays = require( '@stdlib/ndarray/base/maybe-broadcast-arrays' );
var base = require( './base.js' );
// MAIN //
/**
* Computes the maximum value along one or more ndarray dimensions according to a mask.
*
* @param {ndarray} x - input ndarray
* @param {ndarray} mask - mask ndarray
* @param {Options} [options] - function options
* @param {IntegerArray} [options.dims] - list of dimensions over which to perform a reduction
* @param {boolean} [options.keepdims=false] - boolean indicating whether the reduced dimensions should be included in the returned ndarray as singleton dimensions
* @param {string} [options.dtype] - output ndarray data type
* @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 {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 Uint8Array = require( '@stdlib/array/uint8' );
* 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 ] );
*
* // Create a mask buffer:
* var mbuf = new Uint8Array( [ 0, 0, 0, 1, 0, 1 ] );
*
* // Define the shape of the input array:
* var sh = [ 3, 2 ];
*
* // Define the array strides:
* var sx = [ 2, 1 ];
* var sm = [ 2, 1 ];
*
* // Define the index offset:
* var ox = 0;
* var om = 0;
*
* // Create the input ndarray:
* var x = new ndarray( 'float64', xbuf, sh, sx, ox, 'row-major' );
*
* // Create the mask ndarray:
* var mask = new ndarray( 'uint8', mbuf, sh, sm, om, 'row-major' ); // cspell:disable-line
*
* // Perform reduction:
* var out = mskmax( x, mask );
* // returns <ndarray>
*
* var v = out.get();
* // returns 5.0
*/
function mskmax( x, mask ) {
var newOpts;
var opts;
var arrs;
var key;
if ( !isndarrayLike( x ) ) {
throw new TypeError( format( 'invalid argument. First argument must be an ndarray-like object. Value: `%s`.', x ) );
}
if ( !isndarrayLike( mask ) ) {
throw new TypeError( format( 'invalid argument. Second argument must be an ndarray-like object. Value: `%s`.', mask ) );
}
arrs = maybeBroadcastArrays( [ x, mask ] );
// Always explicitly specify output dtype to match the data input (first array):
if ( arguments.length > 2 ) {
opts = arguments[ 2 ];
// Only add dtype if opts is an object and doesn't already have dtype specified:
if ( opts !== null && typeof opts === 'object' && !Array.isArray( opts ) && opts.dtype === void 0 ) {
// Create a new options object with all original properties plus dtype:
newOpts = {};
for ( key in opts ) {
if ( hasOwnProp( opts, key ) ) {
newOpts[ key ] = opts[ key ];
}
}
newOpts.dtype = arrs[ 0 ].dtype;
return base( arrs[ 0 ], arrs[ 1 ], newOpts );
}
// Otherwise pass through as-is (will be validated by base):
return base( arrs[ 0 ], arrs[ 1 ], opts );
}
opts = {
'dtype': arrs[ 0 ].dtype
};
return base( arrs[ 0 ], arrs[ 1 ], opts );
}
// EXPORTS //
module.exports = mskmax;
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