Scale a single-precision complex floating-point vector by a single-precision complex floating-point constant.
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> Scales a single-precision complex floating-point vector by a single-precision complex floating-point constant.
``bash`
npm install @stdlib/blas-base-cscal
`javascript`
var cscal = require( '@stdlib/blas-base-cscal' );
#### cscal( N, alpha, x, strideX )
Scales values from x by alpha.
`javascript
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 =>
`
The function has the following parameters:
- N: number of indexed elements.
- alpha: scalar [Complex64][@stdlib/complex/float32/ctor] constant.Complex64Array
- x: input [][@stdlib/array/complex64].x
- strideX: index increment for .
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,
`javascript
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 =>
`
Note that indexing is relative to the first index. To introduce an offset, use [typed array][mdn-typed-array] views.
`javascript
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 =>
#### cscal.ndarray( N, alpha, x, strideX, offsetX )
Scales values from x by alpha using alternative indexing semantics.
`javascript
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 =>
`
The function has the following additional parameters:
- offsetX: starting index for x.
While [typed array][mdn-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,
`javascript
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 =>
`
- If N <= 0 or strideX <= 0, both functions return x unchanged.cscal()
- corresponds to the [BLAS][blas] level 1 function [cscal][cscal].
`javascript
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`
#include "stdlib/blas/base/cscal.h"
#### c_cscal( N, alpha, \*X, strideX )
Scales values from X by alpha.
`c
#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.[in] stdlib_complex64_t
- alpha: scalar constant.[inout] void*
- X: input array.[in] CBLAS_INT
- strideX: index increment for X.
`c`
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.
`c
#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.[in] stdlib_complex64_t
- alpha: scalar constant.[inout] void*
- X: input array.[in] CBLAS_INT
- strideX: index increment for X.[in] CBLAS_INT
- offsetX: starting index for X.
`c`
void c_cscal_ndarray( const CBLAS_INT N, const stdlib_complex64_t alpha, void *X, const CBLAS_INT strideX, const CBLAS_INT offsetX );
`c
#include "stdlib/blas/base/cscal.h"
#include "stdlib/complex/float32/ctor.h"
#include
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[ i2 ], x[ (i2)+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[ i2 ], x[ (i2)+1 ] );
}
}
`
*
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].
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