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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 isnan = require( '@stdlib/math/base/assert/is-nan' );
var isNegativeZero = require( '@stdlib/math/base/assert/is-negative-zero' );
var isPositiveZero = require( '@stdlib/math/base/assert/is-positive-zero' );
// MAIN //
/**
* Computes the mid-range of a double-precision floating-point strided array according to a mask, ignoring `NaN` values.
*
* @param {PositiveInteger} N - number of indexed elements
* @param {Float64Array} x - input array
* @param {integer} strideX - `x` stride length
* @param {NonNegativeInteger} offsetX - `x` starting index
* @param {Uint8Array} mask - mask array
* @param {integer} strideMask - `mask` stride length
* @param {NonNegativeInteger} offsetMask - `mask` starting index
* @returns {number} mid-range
*
* @example
* var Float64Array = require( '@stdlib/array/float64' );
* var Uint8Array = require( '@stdlib/array/uint8' );
*
* var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
* var mask = new Uint8Array( [ 0, 0, 0, 0, 0, 0, 0, 0, 1, 1 ] );
*
* var v = dnanmskmidrange( 5, x, 2, 1, mask, 2, 1 );
* // returns 1.0
*/
function dnanmskmidrange( N, x, strideX, offsetX, mask, strideMask, offsetMask ) {
var max;
var min;
var ix;
var im;
var v;
var i;
if ( N <= 0 ) {
return NaN;
}
ix = offsetX;
im = offsetMask;
for ( i = 0; i < N; i++ ) {
v = x[ ix ];
if ( v === v && mask[ im ] === 0 ) {
break;
}
ix += strideX;
im += strideMask;
}
if ( i === N ) {
return NaN;
}
min = v;
max = min;
i += 1;
for ( i; i < N; i++ ) {
ix += strideX;
im += strideMask;
if ( mask[ im ] ) {
continue;
}
v = x[ ix ];
if ( isnan( v ) ) {
continue;
}
if ( v < min || ( v === min && isNegativeZero( v ) ) ) {
min = v;
} else if ( v > max || ( v === max && isPositiveZero( v ) ) ) {
max = v;
}
}
return ( max + min ) / 2.0;
}
// EXPORTS //
module.exports = dnanmskmidrange;
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