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# npm

## @stdlib/math-strided-special-dabs 0.0.7 • Public • Published

# dabs

Compute the absolute value for each element in a double-precision floating-point strided array.

## Installation

`npm install @stdlib/math-strided-special-dabs`

## Usage

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

#### dabs( N, x, strideX, y, strideY )

Computes the absolute value for each element in a double-precision floating-point strided array `x` and assigns the results to elements in a double-precision floating-point strided array `y`.

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

var x = new Float64Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );

// Compute the absolute values in-place:
dabs( x.length, x, 1, x, 1 );
// x => <Float64Array>[ 2.0, 1.0, 3.0, 5.0, 4.0, 0.0, 1.0, 3.0 ]```

The function accepts the following arguments:

The `N` and `stride` parameters determine which elements in `x` and `y` 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,

```var Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );

var x = new Float64Array( [ -1.0, -2.0, -3.0, -4.0, -5.0, -6.0 ] );
var y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );

var N = floor( x.length / 2 );

dabs( N, x, 2, y, -1 );
// y => <Float64Array>[ 5.0, 3.0, 1.0, 0.0, 0.0, 0.0 ]```

Note that indexing is relative to the first index. To introduce an offset, use `typed array` views.

```var Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );

// Initial arrays...
var x0 = new Float64Array( [ -1.0, -2.0, -3.0, -4.0, -5.0, -6.0 ] );
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 y1 = new Float64Array( y0.buffer, y0.BYTES_PER_ELEMENT*3 ); // start at 4th element

var N = floor( x0.length / 2 );

dabs( N, x1, -2, y1, 1 );
// y0 => <Float64Array>[ 0.0, 0.0, 0.0, 6.0, 4.0, 2.0 ]```

#### dabs.ndarray( N, x, strideX, offsetX, y, strideY, offsetY )

Computes the absolute value for each element in a double-precision floating-point strided array `x` and assigns the results to elements in a double-precision floating-point strided array `y` using alternative indexing semantics.

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

var x = new Float64Array( [ -1.0, -2.0, -3.0, -4.0, -5.0 ] );
var y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0 ] );

dabs.ndarray( x.length, x, 1, 0, y, 1, 0 );
// y => <Float64Array>[ 1.0, 2.0, 3.0, 4.0, 5.0 ]```

The function accepts the following additional arguments:

• offsetX: starting index for `x`.
• offsetY: starting index for `y`.

While `typed array` views mandate a view offset based on the underlying `buffer`, the `offsetX` and `offsetY` 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`,

```var Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );

var x = new Float64Array( [ -1.0, -2.0, -3.0, -4.0, -5.0, -6.0 ] );
var y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );

var N = floor( x.length / 2 );

dabs.ndarray( N, x, 2, 1, y, -1, y.length-1 );
// y => <Float64Array>[ 0.0, 0.0, 0.0, 6.0, 4.0, 2.0 ]```

## Examples

```var round = require( '@stdlib/math-base-special-round' );
var randu = require( '@stdlib/random-base-randu' );
var Float64Array = require( '@stdlib/array-float64' );
var dabs = require( '@stdlib/math-strided-special-dabs' );

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

var i;
for ( i = 0; i < x.length; i++ ) {
x[ i ] = round( (randu()*200.0) - 100.0 );
}
console.log( x );
console.log( y );

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

## C APIs

### Usage

`#include "stdlib/math/strided/special/dabs.h"`

#### stdlib_strided_dabs( N, *X, strideX, *Y, strideY )

Computes the absolute value for each element in a double-precision floating-point strided array `X` and assigns the results to elements in a double-precision floating-point strided array `Y`.

```#include <stdint.h>

double X[] = { -1.0, -2.0, -3.0, -4.0, -5.0, -6.0, -7.0, -8.0 };
double Y[] = { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 };

int64_t N = 4;

stdlib_strided_dabs( N, X, 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`.
• Y: `[out] double*` output array.
• strideY: `[in] int64_t` index increment for `Y`.
`void stdlib_strided_dabs( const int64_t N, const double *X, const int64_t strideX, double *Y, const int64_t strideY );`

### Examples

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

int main() {
// Create an input strided array:
double X[] = { -1.0, -2.0, -3.0, -4.0, -5.0, -6.0, -7.0, -8.0 };

// 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:
int64_t N = 4;

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

// Compute the absolute value element-wise:
stdlib_strided_dabs( N, X, strideX, Y, strideY );

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

## Notice

This package is part of 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, see the main project repository.

#### Community ### Install

`npm i @stdlib/math-strided-special-dabs`

### Repository

github.com/stdlib-js/math-strided-special-dabs

stdlib.io

26

0.0.7