Scale a single-precision complex floating-point vector by a single-precision complex floating-point constant.
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> Scale a single-precision complex floating-point vector by a single-precision complex floating-point constant.
``bash`
npm install @stdlib/blas-base-wasm-cscal
`javascript`
var cscal = require( '@stdlib/blas-base-wasm-cscal' );
#### cscal.main( N, ca, cx, strideX )
Scales values from cx by ca.
`javascript
var Complex64Array = require( '@stdlib/array-complex64' );
var Complex64 = require( '@stdlib/complex-float32-ctor' );
// Define a strided array:
var cx = new Complex64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
// Define a scalar constant:
var ca = new Complex64( 2.0, 2.0 );
// Perform operation:
cscal.main( cx.length, ca, cx, 1 );
// cx =>
`
The function has the following parameters:
- N: number of indexed elements.
- ca: scalar [Complex64][@stdlib/complex/float32/ctor] constant.Complex64Array
- cx: input [][@stdlib/array/complex64].cx
- strideX: index increment for .
The N and stride parameters determine which elements in the input strided array are accessed at runtime. For example, to scale every other value in cx by ca,
`javascript
var Complex64Array = require( '@stdlib/array-complex64' );
var Complex64 = require( '@stdlib/complex-float32-ctor' );
// Define a strided array:
var cx = new Complex64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
// Define a scalar constant:
var ca = new Complex64( 2.0, 2.0 );
// Perform operation:
cscal.main( 2, ca, cx, 2 );
// cx =>
`
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 cx0 = new Complex64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 ] );
// Define a scalar constant:
var ca = new Complex64( 2.0, 2.0 );
// Create an offset view:
var cx1 = new Complex64Array( cx0.buffer, cx0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
// Scales every other value from cx1 by ca...`
cscal.main( 3, ca, cx1, 1 );
// cx0 =>
#### cscal.ndarray( N, ca, cx, strideX, offsetX )
Scales values from cx by ca using alternative indexing semantics.
`javascript
var Complex64Array = require( '@stdlib/array-complex64' );
var Complex64 = require( '@stdlib/complex-float32-ctor' );
// Define a strided array:
var cx = new Complex64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
// Define a scalar constant:
var ca = new Complex64( 2.0, 2.0 );
// Perform operation:
cscal.ndarray( cx.length, ca, cx, 1, 0 );
// cx =>
`
The function has the following additional parameters:
- offsetX: starting index for cx.
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 cx = new Complex64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 ] );
var ca = new Complex64( 2.0, 2.0 );
cscal.ndarray( 2, ca, cx, 2, 1 );
// cx =>
`
*
#### cscal.Module( memory )
Returns a new WebAssembly [module wrapper][@stdlib/wasm/module-wrapper] instance which uses the provided WebAssembly [memory][@stdlib/wasm/memory] instance as its underlying memory.
`javascript
var Memory = require( '@stdlib/wasm-memory' );
// Create a new memory instance with an initial size of 10 pages (640KiB) and a maximum size of 100 pages (6.4MiB):
var mem = new Memory({
'initial': 10,
'maximum': 100
});
// Create a BLAS routine:
var mod = new cscal.Module( mem );
// returns
// Initialize the routine:
mod.initializeSync();
`
#### cscal.Module.prototype.main( N, cap, cxp, sx )
Scales values from cx by ca.
`javascript
var Memory = require( '@stdlib/wasm-memory' );
var oneTo = require( '@stdlib/array-one-to' );
var ones = require( '@stdlib/array-ones' );
var zeros = require( '@stdlib/array-zeros' );
var bytesPerElement = require( '@stdlib/ndarray-base-bytes-per-element' );
var Float32Array = require( '@stdlib/array-float32' );
var Complex64Array = require( '@stdlib/array-complex64' );
var reinterpretComplex64 = require( '@stdlib/strided-base-reinterpret-complex64' );
var cscal = require( '@stdlib/blas-base-wasm-cscal' );
// Create a new memory instance with an initial size of 10 pages (320KiB) and a maximum size of 100 pages (6.4MiB):
var mem = new Memory({
'initial': 10,
'maximum': 100
});
// Create a BLAS routine:
var mod = new cscal.Module( mem );
// returns
// Initialize the routine:
mod.initializeSync();
// Define a vector data type:
var dtype = 'complex64';
// Specify a vector length:
var N = 5;
// Define a pointer (i.e., byte offset) for storing the input vector:
var xptr = 0;
// Define a pointer for storing a complex number:
var zptr = N * bytesPerElement( dtype );
// Write vector values to module memory:
var xbuf = oneTo( N*2, 'float32' );
var x = new Complex64Array( xbuf.buffer );
mod.write( xptr, x );
// Write a complex number to module memory:
mod.write( zptr, new Float32Array( [ 2.0, 2.0 ] ) );
// Perform computation:
mod.main( N, zptr, xptr, 1 );
// Read out the results:
var view = zeros( N, dtype );
mod.read( xptr, view );
console.log( reinterpretComplex64( view, 0 ) );
// =>
`
The function has the following parameters:
- N: number of indexed elements.
- cap: pointer (i.e., byte offset) to a scalar [Complex64][@stdlib/complex/float32/ctor] constant.Complex64Array
- cxp: input [][@stdlib/array/complex64] pointer (i.e., byte offset).cx
- sx: index increment for .
#### cscal.Module.prototype.ndarray( N, cap, cxp, sx, ox )
Scales values from cx by ca using alternative indexing semantics.
`javascript
var Memory = require( '@stdlib/wasm-memory' );
var oneTo = require( '@stdlib/array-one-to' );
var ones = require( '@stdlib/array-ones' );
var zeros = require( '@stdlib/array-zeros' );
var bytesPerElement = require( '@stdlib/ndarray-base-bytes-per-element' );
var Float32Array = require( '@stdlib/array-float32' );
var Complex64Array = require( '@stdlib/array-complex64' );
var reinterpretComplex64 = require( '@stdlib/strided-base-reinterpret-complex64' );
var cscal = require( '@stdlib/blas-base-wasm-cscal' );
// Create a new memory instance with an initial size of 10 pages (320KiB) and a maximum size of 100 pages (6.4MiB):
var mem = new Memory({
'initial': 10,
'maximum': 100
});
// Create a BLAS routine:
var mod = new cscal.Module( mem );
// returns
// Initialize the routine:
mod.initializeSync();
// Define a vector data type:
var dtype = 'complex64';
// Specify a vector length:
var N = 5;
// Define a pointer (i.e., byte offset) for storing the input vector:
var xptr = 0;
// Define a pointer for storing a complex number:
var zptr = N * bytesPerElement( dtype );
// Write vector values to module memory:
var xbuf = oneTo( N*2, 'float32' );
var x = new Complex64Array( xbuf.buffer );
mod.write( xptr, x );
// Write a complex number to module memory:
mod.write( zptr, new Float32Array( [ 2.0, 2.0 ] ) );
// Perform computation:
mod.ndarray( N, zptr, xptr, 1, 0 );
// Read out the results:
var view = zeros( N, dtype );
mod.read( xptr, view );
console.log( reinterpretComplex64( view, 0 ) );
// =>
`
The function has the following additional parameters:
- ox: starting index for x.
*
- If N <= 0, cx is left unchanged.cscal
- This package implements routines using WebAssembly. When provided arrays which are not allocated on a module memory instance, data must be explicitly copied to module memory prior to computation. Data movement may entail a performance cost, and, thus, if you are using arrays external to module memory, you should prefer using [@stdlib/blas-base/cscal][@stdlib/blas/base/cscal]. However, if working with arrays which are allocated and explicitly managed on module memory, you can achieve better performance when compared to the pure JavaScript implementations found in [@stdlib/blas/base/cscal][@stdlib/blas/base/cscal]. Beware that such performance gains may come at the cost of additional complexity when having to perform manual memory management. Choosing between implementations depends heavily on the particular needs and constraints of your application, with no one choice universally better than the other.cscal()
- corresponds to the [BLAS][blas] level 1 function [cscal][cscal].
*
`javascript
var oneTo = require( '@stdlib/array-one-to' );
var Complex64 = require( '@stdlib/complex-float32-ctor' );
var Complex64Array = require( '@stdlib/array-complex64' );
var reinterpretComplex64 = require( '@stdlib/strided-base-reinterpret-complex64' );
var cscal = require( '@stdlib/blas-base-wasm-cscal' );
// Specify a vector length:
var N = 5;
// Create an input array:
var xbuf = oneTo( N*2, 'float32' );
var x = new Complex64Array( xbuf.buffer );
// Create a complex number:
var z = new Complex64( 2.0, 2.0 );
// Perform computation:
cscal.ndarray( N, z, x, 1, 0 );
// Print the results:
console.log( reinterpretComplex64( x, 0 ) );
// =>
`
*
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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