About stdlib...

We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.

The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.

When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.

To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

cscal

Scales a single-precision complex floating-point vector by a single-precision complex floating-point constant.

Installation

npm install @stdlib/blas-base-cscal

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
• If you are using Deno, visit the deno branch (see README for usage intructions).
• For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.

Usage

var cscal = require( '@stdlib/blas-base-cscal' );

cscal( N, alpha, x, strideX )

Scales values from x by alpha.

var Complex64Array = require( '@stdlib/array-complex64' );
var Complex64 = require( '@stdlib/complex-float32-ctor' );

var x = new Complex64Array( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] );
var alpha = new Complex64( 2.0, 0.0 );

cscal( 3, alpha, x, 1 );
// x => <Complex64Array>[ 2.0, 2.0, 2.0, 2.0, 2.0, 2.0 ]

The function has the following parameters:

• N: number of indexed elements.
• alpha: scalar Complex64 constant.
• x: input Complex64Array.
• strideX: index increment for x.

The N and stride parameters determine how values from x are scaled by alpha. For example, to scale every other value in x by alpha,

var Complex64Array = require( '@stdlib/array-complex64' );
var Complex64 = require( '@stdlib/complex-float32-ctor' );

var x = new Complex64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 ] );
var alpha = new Complex64( 2.0, 0.0 );

cscal( 2, alpha, x, 2 );
// x => <Complex64Array>[ 2.0, 4.0, 3.0, 4.0, 10.0, 12.0, 7.0, 8.0 ]

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

var Complex64Array = require( '@stdlib/array-complex64' );
var Complex64 = require( '@stdlib/complex-float32-ctor' );

// Initial array:
var x0 = new Complex64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 ] );

// Define a scalar constant:
var alpha = new Complex64( 2.0, 2.0 );

// Create an offset view:
var x1 = new Complex64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

// Scales every other value from `x1` by `alpha`...
cscal( 3, alpha, x1, 1 );
// x0 => <Complex64Array>[ 1.0, 2.0, -2.0, 14.0, -2.0, 22.0, -2.0, 30.0 ]

cscal.ndarray( N, alpha, x, strideX, offsetX )

Scales values from x by alpha using alternative indexing semantics.

var Complex64Array = require( '@stdlib/array-complex64' );
var Complex64 = require( '@stdlib/complex-float32-ctor' );

var x = new Complex64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var alpha = new Complex64( 2.0, 2.0 );

cscal.ndarray( 3, alpha, x, 1, 0 );
// x => <Complex64Array>[ -2.0, 6.0, -2.0, 14.0, -2.0, 22.0 ]

The function has the following additional parameters:

• offsetX: starting index for x.

While typed array views mandate a view offset based on the underlying buffer, the offset parameter supports indexing semantics based on a starting index. For example, to scale every other value in the input strided array starting from the second element,

var Complex64Array = require( '@stdlib/array-complex64' );
var Complex64 = require( '@stdlib/complex-float32-ctor' );

var x = new Complex64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 ] );
var alpha = new Complex64( 2.0, 2.0 );

cscal.ndarray( 2, alpha, x, 2, 1 );
// x => <Complex64Array>[ 1.0, 2.0, -2.0, 14.0, 5.0, 6.0, -2.0, 30.0 ]

Notes

• If N <= 0 or strideX <= 0, both functions return x unchanged.
• cscal() corresponds to the BLAS level 1 function cscal.

Examples

var discreteUniform = require( '@stdlib/random-base-discrete-uniform' );
var filledarrayBy = require( '@stdlib/array-filled-by' );
var Complex64 = require( '@stdlib/complex-float32-ctor' );
var cscal = require( '@stdlib/blas-base-cscal' );

function rand() {
    return new Complex64( discreteUniform( 0, 10 ), discreteUniform( -5, 5 ) );
}

var x = filledarrayBy( 10, 'complex64', rand );
console.log( x.toString() );

var alpha = new Complex64( 2.0, 2.0 );
console.log( alpha.toString() );

// Scale elements from `x` by `alpha`:
cscal( x.length, alpha, x, 1 );
console.log( x.get( x.length-1 ).toString() );

---

C APIs

Usage

#include "stdlib/blas/base/cscal.h"

c_cscal( N, alpha, *X, strideX )

Scales values from X by alpha.

#include "stdlib/complex/float32/ctor.h"

float x[] = { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f };
const stdlib_complex64_t alpha = stdlib_complex64( 2.0f, 2.0f );

c_cscal( 4, alpha, (void *)x, 1 );

The function accepts the following arguments:

• N: [in] CBLAS_INT number of indexed elements.
• alpha: [in] stdlib_complex64_t scalar constant.
• X: [inout] void* input array.
• strideX: [in] CBLAS_INT index increment for X.

void c_cscal( const CBLAS_INT N, const stdlib_complex64_t alpha, void *X, const CBLAS_INT strideX );

c_cscal_ndarray( N, alpha, *X, strideX, offsetX )

Scales values from X by alpha using alternative indexing semantics.

#include "stdlib/complex/float32/ctor.h"

float x[] = { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f };
const stdlib_complex64_t alpha = stdlib_complex64( 2.0f, 2.0f );

c_cscal_ndarray( 4, alpha, (void *)x, 1, 0 );

The function accepts the following arguments:

• N: [in] CBLAS_INT number of indexed elements.
• alpha: [in] stdlib_complex64_t scalar constant.
• X: [inout] void* input array.
• strideX: [in] CBLAS_INT index increment for X.
• offsetX: [in] CBLAS_INT starting index for X.

void c_cscal_ndarray( const CBLAS_INT N, const stdlib_complex64_t alpha, void *X, const CBLAS_INT strideX, const CBLAS_INT offsetX );

Examples

#include "stdlib/blas/base/cscal.h"
#include "stdlib/complex/float32/ctor.h"
#include <stdio.h>

int main( void ) {
    // Create a strided array of interleaved real and imaginary components:
    float x[] = { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f };

    // Create a complex scalar:
    const stdlib_complex64_t alpha = stdlib_complex64( 2.0f, 2.0f );

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

    // Specify stride length:
    const int strideX = 1;

    // Scale the elements of the array:
    c_cscal( N, alpha, (void *)x, strideX );

    // Print the result:
    for ( int i = 0; i < N; i++ ) {
        printf( "x[ %i ] = %f + %fj\n", i, x[ i*2 ], x[ (i*2)+1 ] );
    }

    // Scale the elements of the array using alternative indexing semantics:
    c_cscal_ndarray( N, alpha, (void *)x, -strideX, 3 );

    // Print the result:
    for ( int i = 0; i < N; i++ ) {
        printf( "x[ %i ] = %f + %fj\n", i, x[ i*2 ], x[ (i*2)+1 ] );
    }
}

---

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 © 2016-2025. The Stdlib Authors.