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About stdlib...

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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!

dlacpy

NPM version Build Status Coverage Status

Copy all or part of a matrix A to another matrix B.

Usage

import dlacpy from 'https://cdn.jsdelivr.net/gh/stdlib-js/lapack-base-dlacpy@esm/index.mjs';

dlacpy( order, uplo, M, N, A, LDA, B, LDB )

Copies all or part of a matrix A to another matrix B.

import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@esm/index.mjs';

var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );
var B = new Float64Array( 4 );

dlacpy( 'row-major', 'all', 2, 2, A, 2, B, 2 );
// B => <Float64Array>[ 1.0, 2.0, 3.0, 4.0 ]

The function has the following parameters:

  • order: storage layout.
  • uplo: specifies whether to copy the upper or lower triangular/trapezoidal part of a matrix A.
  • M: number of rows in A.
  • N: number of columns in A.
  • A: input Float64Array.
  • LDA: stride of the first dimension of A (a.k.a., leading dimension of the matrix A).
  • B: output Float64Array.
  • LDB: stride of the first dimension of B (a.k.a., leading dimension of the matrix B).

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

import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@esm/index.mjs';

// Initial arrays...
var A0 = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
var B0 = new Float64Array( 5 );

// Create offset views...
var A1 = new Float64Array( A0.buffer, A0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var B1 = new Float64Array( B0.buffer, B0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

dlacpy( 'row-major', 'all', 2, 2, A1, 2, B1, 2 );
// B0 => <Float64Array>[ 0.0, 2.0, 3.0, 4.0, 5.0 ]

dlacpy.ndarray( uplo, M, N, A, sa1, sa2, oa, B, sb1, sb2, ob )

Copies all or part of a matrix A to another matrix B using alternative indexing semantics.

import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@esm/index.mjs';

var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );
var B = new Float64Array( [ 0.0, 0.0, 0.0, 0.0 ] );

dlacpy.ndarray( 'all', 2, 2, A, 2, 1, 0, B, 2, 1, 0 );
// B => <Float64Array>[ 1.0, 2.0, 3.0, 4.0 ]

The function has the following parameters:

  • uplo: specifies whether to copy the upper or lower triangular/trapezoidal part of a matrix A.
  • M: number of rows in A.
  • N: number of columns in A.
  • A: input Float64Array.
  • sa1: stride of the first dimension of A.
  • sa2: stride of the second dimension of A.
  • oa: starting index for A.
  • B: output Float64Array.
  • sb1: stride of the first dimension of B.
  • sb2: stride of the second dimension of B.
  • ob: starting index for B.

While typed array views mandate a view offset based on the underlying buffer, the offset parameters support indexing semantics based on starting indices. For example,

import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@esm/index.mjs';

var A = new Float64Array( [ 0.0, 1.0, 2.0, 3.0, 4.0 ] );
var B = new Float64Array( [ 0.0, 0.0, 11.0, 312.0, 53.0, 412.0 ] );

dlacpy.ndarray( 'all', 2, 2, A, 2, 1, 1, B, 2, 1, 2 );
// B => <Float64Array>[ 0.0, 0.0, 1.0, 2.0, 3.0, 4.0 ]

Notes

Examples

<!DOCTYPE html>
<html lang="en">
<body>
<script type="module">

import ndarray2array from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-to-array@esm/index.mjs';
import uniform from 'https://cdn.jsdelivr.net/gh/stdlib-js/random-array-discrete-uniform@esm/index.mjs';
import numel from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-numel@esm/index.mjs';
import shape2strides from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-shape2strides@esm/index.mjs';
import dlacpy from 'https://cdn.jsdelivr.net/gh/stdlib-js/lapack-base-dlacpy@esm/index.mjs';

var shape = [ 5, 8 ];
var order = 'row-major';
var strides = shape2strides( shape, order );

var N = numel( shape );

var A = uniform( N, -10, 10, {
    'dtype': 'float64'
});
console.log( ndarray2array( A, shape, strides, 0, order ) );

var B = uniform( N, -10, 10, {
    'dtype': 'float64'
});
console.log( ndarray2array( B, shape, strides, 0, order ) );

dlacpy( order, 'all', shape[ 0 ], shape[ 1 ], A, strides[ 0 ], B, strides[ 0 ] );
console.log( ndarray2array( B, shape, strides, 0, order ) );

</script>
</body>
</html>

Notice

This package is part of stdlib, a standard library 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.

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.