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!

Policies

List of input ndarray casting policies.

Installation

npm install @stdlib/ndarray-input-casting-policies

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 policies = require( '@stdlib/ndarray-input-casting-policies' );

policies()

Returns a list of input ndarray casting policies.

var out = policies();
// e.g., returns [ 'none', 'promoted', ... ]

The output array contains the following policies:

• none: no guidance on specific casting behavior. An input ndarray may or may not be cast depending on the needs of an implementation.
• promoted: cast an input ndarray to a promoted data type.
• accumulation: cast an input ndarray to a data type amenable to accumulation.
• output: cast an input ndarray to the data type of the output ndarray.

Notes

• The following is some general guidance for the casting policies listed above:

  • none: applies whenever an input ndarray casting behavior should be entirely left up to an implementation. For example, an implementation may choose to cast internally in order to take advantage of specialized algorithms operating on specific data types.
  • promoted: applies whenever an input ndarray should be cast to the data type resolved from applying the rules of type promotion to an implementation's input and output ndarrays. For example, suppose an implementation is computing the sum and the data type of the input ndarray is int32 and the data type of the output ndarray is float32. In this scenario, casting int32 to float32 is not desirable as not all int32 values can be safely represented in float32, thus potentially leading to significant accumulated numerical error. Instead, we can promote int32 to float64, compute the sum, and then downcast the result for more accurate summation.
  • accumulation: applies whenever an input ndarray should be cast to a data type amenable to accumulation, irrespective of the output ndarray or other input ndarrays. For example, suppose an implementation is computing the sum and determining whether the sum passes a threshold, and further suppose that the data type of the input ndarray is int8 and the data type of the output ndarray is bool. In this scenario, as int8 has a small range of values, computing the sum has a high risk of overflow, rendering the results potentially meaningless, and type promotion is not applicable. As such, an implementation may prefer to internally cast int8 to a data type more amenable to accumulation, such as int32.
  • output: applies whenever an input ndarray should always be cast to the data type of the output ndarray. This might apply when an implementation wraps a type homogeneous interface, such as those commonly found in BLAS/LAPACK routines.

• Whether an implementation supports a casting policy depends on the implementation. Supporting casting policies is mainly envisioned for generalized utilities wrapping lower-level APIs and needing to accommodate varied use cases (e.g., @stdlib/ndarray-base/unary-reduce-strided1d-dispatch). Exposing casting policies as part of user-facing APIs is generally not a good idea.

Examples

var indexOf = require( '@stdlib/utils-index-of' );
var policies = require( '@stdlib/ndarray-input-casting-policies' );

var POLICIES = policies();

function isPolicy( str ) {
    if ( indexOf( POLICIES, str ) === -1 ) {
        return false;
    }
    return true;
}

var bool = isPolicy( 'none' );
// returns true

bool = isPolicy( 'output' );
// returns true

bool = isPolicy( 'promoted' );
// returns true

bool = isPolicy( 'beep' );
// returns false

---

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.