Press n or j to go to the next uncovered block, b, p or k for the previous block.
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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 numelDimension = require( '@stdlib/ndarray/base/numel-dimension' );
var getStride = require( '@stdlib/ndarray/base/stride' );
var getOffset = require( '@stdlib/ndarray/base/offset' );
var getData = require( '@stdlib/ndarray/base/data-buffer' );
var strided = require( '@stdlib/blas/ext/base/cindex-of-falsy' ).ndarray;
var ndarraylike2scalar = require( '@stdlib/ndarray/base/ndarraylike2scalar' );
// MAIN //
/**
* Returns the index of the first falsy element in a one-dimensional single-precision complex floating-point ndarray.
*
* ## Notes
*
* - The function expects the following ndarrays:
*
* - a one-dimensional input ndarray.
* - a zero-dimensional ndarray containing the index from which to begin searching.
*
* @param {ArrayLikeObject<Object>} arrays - array-like object containing ndarrays
* @returns {integer} index
*
* @example
* var Complex64Vector = require( '@stdlib/ndarray/vector/complex64' );
* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
*
* var x = new Complex64Vector( [ 1.0, 2.0, 0.0, 0.0, 3.0, 0.0, 2.0, 0.0 ] );
*
* var fromIndex = scalar2ndarray( 0, {
* 'dtype': 'generic'
* });
*
* var v = cindexOfFalsy( [ x, fromIndex ] );
* // returns 1
*/
function cindexOfFalsy( arrays ) {
var fromIndex;
var stride;
var offset;
var idx;
var N;
var x;
x = arrays[ 0 ];
N = numelDimension( x, 0 );
fromIndex = ndarraylike2scalar( arrays[ 1 ] );
if ( fromIndex < 0 ) {
fromIndex += N;
if ( fromIndex < 0 ) {
fromIndex = 0;
}
} else if ( fromIndex >= N ) {
return -1;
}
N -= fromIndex;
stride = getStride( x, 0 );
offset = getOffset( x ) + ( stride*fromIndex );
idx = strided( N, getData( x ), stride, offset );
if ( idx >= 0 ) {
idx += fromIndex;
}
return idx;
}
// EXPORTS //
module.exports = cindexOfFalsy;
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