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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 ndarray2object = require( '@stdlib/ndarray/base/ndarraylike2object' );
var numel = require( '@stdlib/ndarray/base/numel' );
var accessorfind0d = require( './0d_accessors.js' );
var accessorfind1d = require( './1d_accessors.js' );
var accessorfind2d = require( './2d_accessors.js' );
var accessorfind3d = require( './3d_accessors.js' );
var accessorfind4d = require( './4d_accessors.js' );
var accessorfind5d = require( './5d_accessors.js' );
var accessorfind6d = require( './6d_accessors.js' );
var accessorfind7d = require( './7d_accessors.js' );
var accessorfind8d = require( './8d_accessors.js' );
var accessorfind9d = require( './9d_accessors.js' );
var accessorfind10d = require( './10d_accessors.js' );
var accessorfindnd = require( './nd_accessors.js' );
var find0d = require( './0d.js' );
var find1d = require( './1d.js' );
var find2d = require( './2d.js' );
var find3d = require( './3d.js' );
var find4d = require( './4d.js' );
var find5d = require( './5d.js' );
var find6d = require( './6d.js' );
var find7d = require( './7d.js' );
var find8d = require( './8d.js' );
var find9d = require( './9d.js' );
var find10d = require( './10d.js' );
var findnd = require( './nd.js' );
// VARIABLES //
var FIND = [
find0d,
find1d,
find2d,
find3d,
find4d,
find5d,
find6d,
find7d,
find8d,
find9d,
find10d
];
var ACCESSOR_FIND = [
accessorfind0d,
accessorfind1d,
accessorfind2d,
accessorfind3d,
accessorfind4d,
accessorfind5d,
accessorfind6d,
accessorfind7d,
accessorfind8d,
accessorfind9d,
accessorfind10d
];
var MAX_DIMS = FIND.length - 1;
// MAIN //
/**
* Returns the first element in an ndarray which passes a test implemented by a predicate function.
*
* ## Notes
*
* - A provided ndarray should be an object with the following properties:
*
* - **dtype**: data type.
* - **data**: data buffer.
* - **shape**: dimensions.
* - **strides**: stride lengths.
* - **offset**: index offset.
* - **order**: specifies whether an ndarray is row-major (C-style) or column major (Fortran-style).
*
* @param {ArrayLikeObject<Object>} arrays - array-like object containing an input ndarray and a zero-dimensional ndarray containing a sentinel value
* @param {Function} predicate - predicate function
* @param {thisArg} [thisArg] - predicate function execution context
* @returns {*} result
*
* @example
* var Float64Array = require( '@stdlib/array/float64' );
*
* function predicate( value ) {
* return value % 2.0 === 0.0;
* }
*
* // 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 the input ndarray-like object:
* var x = {
* 'dtype': 'float64',
* 'data': xbuf,
* 'shape': shape,
* 'strides': sx,
* 'offset': ox,
* 'order': 'row-major'
* };
*
* // Create an ndarray-like object containing a sentinel value:
* var sentinelValue = {
* 'dtype': 'float64',
* 'data': new Float64Array( [ NaN ] ),
* 'shape': [],
* 'strides': [ 0 ],
* 'offset': 0,
* 'order': 'row-major'
* };
*
* // Perform operation:
* var out = find( [ x, sentinelValue ], predicate );
* // returns 2.0
*/
function find( arrays, predicate, thisArg ) { // eslint-disable-line stdlib/no-redeclare
var ndims;
var shx;
var sv;
var x;
// Unpack the ndarrays and standardize ndarray meta data:
x = ndarray2object( arrays[ 0 ] );
sv = ndarray2object( arrays[ 1 ] );
shx = x.shape;
ndims = shx.length;
// Resolve the sentinel value:
sv = sv.accessors[ 0 ]( sv.data, sv.offset );
// Determine whether we can avoid iteration altogether...
if ( ndims === 0 ) {
if ( x.accessorProtocol ) {
return ACCESSOR_FIND[ ndims ]( x, sv, predicate, thisArg );
}
return FIND[ ndims ]( x, sv, predicate, thisArg );
}
// Check whether we were provided an empty ndarray...
if ( numel( shx ) === 0 ) {
return sv;
}
// Determine whether we can avoid linear view iteration...
if ( ndims <= MAX_DIMS ) {
// So long as iteration always moves in the same direction (i.e., no mixed sign strides), we can leverage cache-optimal (i.e., normal) nested loops; however, for mixed signed strides, iteration will not be cache-optimal, but we don't have much of a choice as loop tiling could lead to different results than cache-optimal iteration, so we just use normal nested loops for all iteration...
if ( x.accessorProtocol ) {
return ACCESSOR_FIND[ ndims ]( x, sv, predicate, thisArg );
}
return FIND[ ndims ]( x, sv, predicate, thisArg );
}
// Fall-through to linear view iteration without regard for how data is stored in memory (i.e., take the slow path)...
if ( x.accessorProtocol ) {
return accessorfindnd( x, sv, predicate, thisArg );
}
return findnd( x, sv, predicate, thisArg );
}
// EXPORTS //
module.exports = find;
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