dmskmap

Apply a unary function to a double-precision floating-point strided input array according to a strided mask array and assign results to a double-precision floating-point strided output array.

Installation

npm install @stdlib/strided-base-dmskmap

Usage

var dmskmap = require( '@stdlib/strided-base-dmskmap' );

dmskmap( N, x, strideX, mask, strideMask, y, strideY, fcn )

Applies a unary function to a double-precision floating-point strided input array according to a strided mask array and assigns results to a double-precision floating-point strided output array.

var Float64Array = require( '@stdlib/array-float64' );
var Uint8Array = require( '@stdlib/array-uint8' );
var abs = require( '@stdlib/math-base-special-abs' );

var x = new Float64Array( [ -2.0, 1.0, -3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );
var m = new Uint8Array( [ 0, 0, 1, 0, 0, 1, 1, 0 ] );

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

The function accepts the following arguments:

• N: number of indexed elements.
• x: input Float64Array.
• strideX: index increment for x.
• mask: mask Uint8Array.
• strideMask: index increment for mask.
• y: output Float64Array.
• strideY: index increment for y.
• fcn: function to apply.

The N and stride parameters determine which elements in the strided arrays 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 Uint8Array = require( '@stdlib/array-uint8' );
var abs = require( '@stdlib/math-base-special-abs' );

var x = new Float64Array( [ -1.0, -2.0, -3.0, -4.0, -5.0, -6.0 ] );
var m = new Uint8Array( [ 0, 0, 1, 0, 0, 1 ] );
var y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );

dmskmap( 3, x, 2, m, 2, y, -1, abs );
// y => <Float64Array>[ 5.0, 0.0, 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 Uint8Array = require( '@stdlib/array-uint8' );
var abs = require( '@stdlib/math-base-special-abs' );

// Initial arrays...
var x0 = new Float64Array( [ -1.0, -2.0, -3.0, -4.0, -5.0, -6.0 ] );
var m0 = new Uint8Array( [ 0, 0, 1, 0, 0, 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*3 ); // start at 4th element
var y1 = new Float64Array( y0.buffer, y0.BYTES_PER_ELEMENT*3 ); // start at 4th element

dmskmap( 3, x1, -2, m1, 1, y1, 1, abs );
// y0 => <Float64Array>[ 0.0, 0.0, 0.0, 6.0, 4.0, 0.0 ]

dmskmap.ndarray( N, x, strideX, offsetX, mask, strideMask, offsetMask, y, strideY, offsetY, fcn )

Applies a unary function to a double-precision floating-point strided input array according to a strided mask array and assigns results to a double-precision floating-point strided output array using alternative indexing semantics.

var Float64Array = require( '@stdlib/array-float64' );
var Uint8Array = require( '@stdlib/array-uint8' );
var abs = require( '@stdlib/math-base-special-abs' );

var x = new Float64Array( [ -1.0, -2.0, -3.0, -4.0, -5.0 ] );
var m = new Uint8Array( [ 0, 0, 1, 0, 0 ] );
var y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0 ] );

dmskmap.ndarray( x.length, x, 1, 0, m, 1, 0, y, 1, 0, abs );
// y => <Float64Array>[ 1.0, 2.0, 0.0, 4.0, 5.0 ]

The function accepts the following additional arguments:

• offsetX: starting index for x.
• offsetMask: starting index for mask.
• 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 in reverse order,

var Float64Array = require( '@stdlib/array-float64' );
var Uint8Array = require( '@stdlib/array-uint8' );
var abs = require( '@stdlib/math-base-special-abs' );

var x = new Float64Array( [ -1.0, -2.0, -3.0, -4.0, -5.0, -6.0 ] );
var m = new Uint8Array( [ 0, 0, 1, 0, 0, 1 ] );
var y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );

dmskmap.ndarray( 3, x, 2, 1, m, 2, 1, y, -1, y.length-1, abs );
// y => <Float64Array>[ 0.0, 0.0, 0.0, 0.0, 4.0, 2.0 ]

Examples

var round = require( '@stdlib/math-base-special-round' );
var randu = require( '@stdlib/random-base-randu' );
var bernoulli = require( '@stdlib/random-base-bernoulli' );
var Float64Array = require( '@stdlib/array-float64' );
var Uint8Array = require( '@stdlib/array-uint8' );
var dmskmap = require( '@stdlib/strided-base-dmskmap' );

function scale( x ) {
    return x * 10.0;
}

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

var i;
for ( i = 0; i < x.length; i++ ) {
    x[ i ] = round( (randu()*200.0) - 100.0 );
    m[ i ] = bernoulli( 0.2 );
}
console.log( x );
console.log( m );
console.log( y );

dmskmap.ndarray( x.length, x, 1, 0, m, 1, 0, y, -1, y.length-1, scale );
console.log( y );

C APIs

Usage

#include "stdlib/strided/base/dmskmap.h"

stdlib_strided_dmskmap( N, *X, strideX, *Mask, strideMask, *Y, strideY, fcn )

Applies a unary function to a double-precision floating-point strided input array according to a strided mask array and assigns results to a double-precision floating-point strided output array.

#include <stdint.h>

static double scale( const double x ) {
    return x * 10.0;
}

double X[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 };
uint8_t M[] = { 0, 0, 1, 0, 0, 1 };
double Y[] = { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 };

int64_t N = 6;

stdlib_strided_dmskmap( N, X, 1, M, 1, Y, 1, scale );

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.
• fcn: [in] double (*fcn)( double ) unary function to apply.

void stdlib_strided_dmskmap( 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, double (*fcn)( double ) );

Examples

#include "stdlib/strided/base/dmskmap.h"
#include <stdint.h>
#include <stdio.h>
#include <inttypes.h>

// Define a callback:
static double scale( const double x ) {
    return x * 10.0;
}

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

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

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

    // Specify the number of elements:
    int64_t N = 6;

    // Define the strides:
    int64_t strideX = 1;
    int64_t strideM = 1;
    int64_t strideY = -1;

    // Apply the callback:
    stdlib_strided_dmskmap( N, X, strideX, M, strideM, Y, strideY, scale );

    // Print the results:
    for ( int64_t i = 0; i < N; i++ ) {
        printf( "Y[ %"PRId64" ] = %lf\n", i, Y[ i ] );
    }
}

See Also

• @stdlib/strided-base/dmap: apply a unary function to a double-precision floating-point strided input array and assign results to a double-precision floating-point strided output array.
• @stdlib/strided-base/dmskmap2: apply a binary function to double-precision floating-point strided input arrays according to a strided mask array and assign results to a double-precision floating-point strided output array.
• @stdlib/strided-base/mskunary: apply a unary callback to elements in a strided input array according to elements in a strided mask array and assign results to elements in a strided output array.
• @stdlib/strided-base/smskmap: apply a unary function to a single-precision floating-point strided input array according to a strided mask array and assign results to a single-precision floating-point strided output array.

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

License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.