npm package: @stdlib/math-strided-special-dmsksqrt
Description: Compute the principal square root for each element in a double-precision floating-point strided array according to a strided mask array.
Author: The Stdlib Authors
Latest version: 0.2.3

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# dmsksqrt

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url] <!-- [![dependencies][dependencies-image]][dependencies-url] -->

> Compute the principal [square root][@stdlib/math/base/special/sqrt] for each element in a double-precision floating-point strided array according to a strided mask array.

<section class="intro">

</section>

<!-- /.intro -->

<section class="installation">

## Installation

```bash
npm install @stdlib/math-strided-special-dmsksqrt
```

</section>

<section class="usage">

## Usage

```javascript
var dmsksqrt = require( '@stdlib/math-strided-special-dmsksqrt' );
```

#### dmsksqrt( N, x, sx, m, sm, y, sy )

Computes the principal [square root][@stdlib/math/base/special/sqrt] for each element in a double-precision floating-point strided array `x` according to a strided mask array and assigns the results to elements in a double-precision floating-point strided array `y`.

```javascript
var Float64Array = require( '@stdlib/array-float64' );
var Uint8Array = require( '@stdlib/array-uint8' );

var x = new Float64Array( [ 0.0, 4.0, 9.0, 12.0, 24.0 ] );
var m = new Uint8Array( [ 0, 0, 1, 0, 1 ] );
var y = new Float64Array( x.length );

dmsksqrt( x.length, x, 1, m, 1, y, 1 );
// y => <Float64Array>[ 0.0, 2.0, 0.0, ~3.464, 0.0 ]
```

The function accepts the following arguments:

-   **N**: number of indexed elements.
-   **x**: input [`Float64Array`][@stdlib/array/float64].
-   **sx**: index increment for `x`.
-   **m**: mask [`Uint8Array`][@stdlib/array/uint8].
-   **sm**: index increment for `m`.
-   **y**: output [`Float64Array`][@stdlib/array/float64].
-   **sy**: index increment for `y`.

The `N` and stride parameters determine which strided array elements are accessed at runtime. For example, to index every other value in `x` and to index the first `N` elements of `y` in reverse order,

```javascript
var Float64Array = require( '@stdlib/array-float64' );
var Uint8Array = require( '@stdlib/array-uint8' );

var x = new Float64Array( [ 0.0, 4.0, 9.0, 12.0, 24.0, 64.0 ] );
var m = new Uint8Array( [ 0, 0, 1, 0, 1, 1 ] );
var y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );

dmsksqrt( 3, x, 2, m, 2, y, -1 );
// y => <Float64Array>[ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]
```

Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][@stdlib/array/float64] views.

```javascript
var Float64Array = require( '@stdlib/array-float64' );
var Uint8Array = require( '@stdlib/array-uint8' );

// Initial arrays...
var x0 = new Float64Array( [ 0.0, 4.0, 9.0, 12.0, 24.0, 64.0 ] );
var m0 = new Uint8Array( [ 0, 0, 1, 0, 1, 1 ] );
var y0 = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );

// Create offset views...
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var m1 = new Uint8Array( m0.buffer, m0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var y1 = new Float64Array( y0.buffer, y0.BYTES_PER_ELEMENT*3 ); // start at 4th element

dmsksqrt( 3, x1, -2, m1, -2, y1, 1 );
// y0 => <Float64Array>[ 0.0, 0.0, 0.0, 0.0, ~3.464, 2.0 ]
```

#### dmsksqrt.ndarray( N, x, sx, ox, m, sm, om, y, sy, oy )

Computes the principal [square root][@stdlib/math/base/special/sqrt] for each element in a double-precision floating-point strided array `x` according to a strided mask array and assigns the results to elements in a double-precision floating-point strided array `y` using alternative indexing semantics.

```javascript
var Float64Array = require( '@stdlib/array-float64' );
var Uint8Array = require( '@stdlib/array-uint8' );

var x = new Float64Array( [ 0.0, 4.0, 9.0, 12.0, 24.0 ] );
var m = new Uint8Array( [ 0, 0, 1, 0, 1 ] );
var y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0 ] );

dmsksqrt.ndarray( x.length, x, 1, 0, m, 1, 0, y, 1, 0 );
// y => <Float64Array>[ 0.0, 2.0, 0.0, ~3.464, 0.0 ]
```

The function accepts the following additional arguments:

-   **ox**: starting index for `x`.
-   **om**: starting index for `m`.
-   **oy**: starting index for `y`.

While [`typed array`][@stdlib/array/float64] views mandate a view offset based on the underlying `buffer`, the offset parameters support indexing semantics based on starting indices. For example, to index every other value in `x` starting from the second value and to index the last `N` elements in `y`,

```javascript
var Float64Array = require( '@stdlib/array-float64' );
var Uint8Array = require( '@stdlib/array-uint8' );

var x = new Float64Array( [ 0.0, 4.0, 9.0, 12.0, 24.0, 64.0 ] );
var m = new Uint8Array( [ 0, 0, 1, 0, 1, 1 ] );
var y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );

dmsksqrt.ndarray( 3, x, 2, 1, m, 2, 1, y, -1, y.length-1 );
// y => <Float64Array>[ 0.0, 0.0, 0.0, 0.0, ~3.464, 2.0 ]
```

</section>

<!-- /.usage -->

<section class="notes">

</section>

<!-- /.notes -->

<section class="examples">

## Examples

<!-- eslint no-undef: "error" -->

```javascript
var uniform = require( '@stdlib/random-base-uniform' );
var Float64Array = require( '@stdlib/array-float64' );
var Uint8Array = require( '@stdlib/array-uint8' );
var dmsksqrt = require( '@stdlib/math-strided-special-dmsksqrt' );

var x = new Float64Array( 10 );
var m = new Uint8Array( 10 );
var y = new Float64Array( 10 );

var i;
for ( i = 0; i < x.length; i++ ) {
    x[ i ] = uniform( 0.0, 200.0 );
    if ( uniform( 0.0, 1.0 ) < 0.5 ) {
        m[ i ] = 1;
    }
}
console.log( x );
console.log( m );
console.log( y );

dmsksqrt.ndarray( x.length, x, 1, 0, m, 1, 0, y, -1, y.length-1 );
console.log( y );
```

</section>

<!-- /.examples -->

<!-- C interface documentation. -->

* * *

<section class="c">

## C APIs

<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->

<section class="intro">

</section>

<!-- /.intro -->

<!-- C usage documentation. -->

<section class="usage">

### Usage

```c
#include "stdlib/math/strided/special/dmsksqrt.h"
```

#### stdlib_strided_dmsksqrt( N, \*X, strideX, \*Mask, strideMask, \*Y, strideY )

Computes the principal [square root][@stdlib/math/base/special/sqrt] for each element in a double-precision floating-point strided array `X` according to a strided mask array and assigns the results to elements in a double-precision floating-point strided array `Y`.

```c
#include <stdint.h>

const double X[] = { 0.0, 4.0, 9.0, 12.0, 24.0, 64.0, 81.0, 101.0 };
const uint8_t Mask[] = { 0, 0, 1, 0, 1, 1, 0, 0 };
double Y[] = { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 };

const int64_t N = 4;

stdlib_strided_dmsksqrt( N, X, 2, Mask, 2, Y, 2 );
```

The function accepts the following arguments:

-   **N**: `[in] int64_t` number of indexed elements.
-   **X**: `[in] double*` input array.
-   **strideX**: `[in] int64_t` index increment for `X`.
-   **Mask**: `[in] uint8_t*` mask array.
-   **strideMask**: `[in] int64_t` index increment for `Mask`.
-   **Y**: `[out] double*` output array.
-   **strideY**: `[in] int64_t` index increment for `Y`.

```c
void stdlib_strided_dmsksqrt( const int64_t N, const double *X, const int64_t strideX, const uint8_t *Mask, const int64_t strideMask, double *Y, const int64_t strideY );
```

</section>

<!-- /.usage -->

<!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->

<section class="notes">

</section>

<!-- /.notes -->

<!-- C API usage examples. -->

<section class="examples">

### Examples

```c
#include "stdlib/math/strided/special/dmsksqrt.h"
#include <stdint.h>
#include <stdio.h>

int main( void ) {
    // Create an input strided array:
    const double X[] = { 0.0, 4.0, 9.0, 12.0, 24.0, 64.0, 81.0, 101.0 };

    // Create a mask strided array:
    const uint8_t M[] = { 0, 0, 1, 0, 1, 1, 0, 0 };

    // Create an output strided array:
    double Y[] = { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 };

    // Specify the number of elements:
    const int64_t N = 4;

    // Specify the stride lengths:
    const int64_t strideX = 2;
    const int64_t strideM = 2;
    const int64_t strideY = 2;

    // Compute the results:
    stdlib_strided_dmsksqrt( N, X, strideX, M, strideM, Y, strideY );

    // Print the results:
    for ( int i = 0; i < 8; i++ ) {
        printf( "Y[ %i ] = %lf\n", i, Y[ i ] );
    }
}
```

</section>

<!-- /.examples -->

</section>

<!-- /.c -->

<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->

<section class="related">

* * *

## See Also

-   <span class="package-name">[`@stdlib/math-strided/special/dmskcbrt`][@stdlib/math/strided/special/dmskcbrt]</span><span class="delimiter">: </span><span class="description">compute the cube root for each element in a double-precision floating-point strided array according to a strided mask array.</span>
-   <span class="package-name">[`@stdlib/math-strided/special/dmskrsqrt`][@stdlib/math/strided/special/dmskrsqrt]</span><span class="delimiter">: </span><span class="description">compute the reciprocal square root for each element in a double-precision floating-point strided array according to a strided mask array.</span>
-   <span class="package-name">[`@stdlib/math-strided/special/dsqrt`][@stdlib/math/strided/special/dsqrt]</span><span class="delimiter">: </span><span class="description">compute the principal square root for each element in a double-precision floating-point strided array.</span>
-   <span class="package-name">[`@stdlib/math-strided/special/smsksqrt`][@stdlib/math/strided/special/smsksqrt]</span><span class="delimiter">: </span><span class="description">compute the principal square root for each element in a single-precision floating-point strided array according to a strided mask array.</span>

</section>

<!-- /.related -->

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<section class="main-repo" >

* * *

## Notice

This package is part of [stdlib][stdlib], a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop [stdlib][stdlib], see the main project [repository][stdlib].

#### Community

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---

## License

See [LICENSE][stdlib-license].


## Copyright

Copyright &copy; 2016-2026. The Stdlib [Authors][stdlib-authors].

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<!-- /.stdlib -->

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[@stdlib/math/strided/special/smsksqrt]: https://www.npmjs.com/package/@stdlib/math-strided-special-smsksqrt

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