https://github.com/stdlib-js/blas-ext-base-dsnannsumors
Calculate the sum of single-precision floating-point strided array elements, ignoring NaN values, using ordinary recursive summation with extended accumulation, and returning an extended precision result.
https://github.com/stdlib-js/blas-ext-base-dsnannsumors
array blas extended float32 javascript math mathematics node node-js nodejs ors statistics stats stdlib strided strided-array sum summation total typed
Last synced: 6 months ago
JSON representation
Calculate the sum of single-precision floating-point strided array elements, ignoring NaN values, using ordinary recursive summation with extended accumulation, and returning an extended precision result.
- Host: GitHub
- URL: https://github.com/stdlib-js/blas-ext-base-dsnannsumors
- Owner: stdlib-js
- License: apache-2.0
- Created: 2021-06-14T16:50:15.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2025-03-17T02:44:15.000Z (8 months ago)
- Last Synced: 2025-05-01T21:33:55.518Z (6 months ago)
- Topics: array, blas, extended, float32, javascript, math, mathematics, node, node-js, nodejs, ors, statistics, stats, stdlib, strided, strided-array, sum, summation, total, typed
- Language: JavaScript
- Homepage: https://github.com/stdlib-js/stdlib
- Size: 808 KB
- Stars: 2
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Security: SECURITY.md
Awesome Lists containing this project
README
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!
# dsnannsumors
[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url]
> Calculate the sum of single-precision floating-point strided array elements, ignoring `NaN` values, using ordinary recursive summation with extended accumulation, and returning an extended precision result.
## Installation
```bash
npm install @stdlib/blas-ext-base-dsnannsumors
```
Alternatively,
- To load the package in a website via a `script` tag without installation and bundlers, use the [ES Module][es-module] available on the [`esm`][esm-url] branch (see [README][esm-readme]).
- If you are using Deno, visit the [`deno`][deno-url] branch (see [README][deno-readme] for usage intructions).
- For use in Observable, or in browser/node environments, use the [Universal Module Definition (UMD)][umd] build available on the [`umd`][umd-url] branch (see [README][umd-readme]).
The [branches.md][branches-url] 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
```javascript
var dsnannsumors = require( '@stdlib/blas-ext-base-dsnannsumors' );
```
#### dsnannsumors( N, x, strideX, out, strideOut )
Computes the sum of single-precision floating-point strided array elements, ignoring `NaN` values, using ordinary recursive summation with extended accumulation, and returning an extended precision result.
```javascript
var Float32Array = require( '@stdlib/array-float32' );
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
var out = new Float64Array( 2 );
var v = dsnannsumors( x.length, x, 1, out, 1 );
// returns [ 1.0, 3 ]
```
The function has the following parameters:
- **N**: number of indexed elements.
- **x**: input [`Float32Array`][@stdlib/array/float32].
- **strideX**: stride length for `x`.
- **out**: output [`Float64Array`][@stdlib/array/float64] whose first element is the sum and whose second element is the number of non-NaN elements.
- **strideOut**: stride length for `out`.
The `N` and stride parameters determine which elements are accessed at runtime. For example, to compute the sum of every other element:
```javascript
var Float32Array = require( '@stdlib/array-float32' );
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float32Array( [ 1.0, 2.0, NaN, -7.0, NaN, 3.0, 4.0, 2.0 ] );
var out = new Float64Array( 2 );
var v = dsnannsumors( 4, x, 2, out, 1 );
// returns [ 5.0, 2 ]
```
Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views.
```javascript
var Float32Array = require( '@stdlib/array-float32' );
var Float64Array = require( '@stdlib/array-float64' );
var x0 = new Float32Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var out0 = new Float64Array( 4 );
var out1 = new Float64Array( out0.buffer, out0.BYTES_PER_ELEMENT*2 ); // start at 3rd element
var v = dsnannsumors( 4, x1, 2, out1, 1 );
// returns [ 5.0, 4 ]
```
#### dsnannsumors.ndarray( N, x, strideX, offsetX, out, strideOut, offsetOut )
Computes the sum of single-precision floating-point strided array elements, ignoring `NaN` values, using ordinary recursive summation with extended accumulation and alternative indexing semantics, and returning an extended precision result.
```javascript
var Float32Array = require( '@stdlib/array-float32' );
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
var out = new Float64Array( 2 );
var v = dsnannsumors.ndarray( x.length, x, 1, 0, out, 1, 0 );
// returns [ 1.0, 3 ]
```
The function has the following additional parameters:
- **offsetX**: starting index for `x`.
- **offsetOut**: starting index for `out`.
While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying buffer, the offset parameters support indexing semantics based on starting indices. For example, to calculate the sum of every other element starting from the second element:
```javascript
var Float32Array = require( '@stdlib/array-float32' );
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float32Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var out = new Float64Array( 4 );
var v = dsnannsumors.ndarray( 4, x, 2, 1, out, 2, 1 );
// returns [ 0.0, 5.0, 0.0, 4 ]
```
## Notes
- If `N <= 0`, both functions return a sum equal to `0.0`.
- Accumulated intermediate values are stored as double-precision floating-point numbers.
## Examples
```javascript
var discreteUniform = require( '@stdlib/random-base-discrete-uniform' );
var bernoulli = require( '@stdlib/random-base-bernoulli' );
var filledarrayBy = require( '@stdlib/array-filled-by' );
var Float32Array = require( '@stdlib/array-float32' );
var Float64Array = require( '@stdlib/array-float64' );
var dsnannsumors = require( '@stdlib/blas-ext-base-dsnannsumors' );
function rand() {
if ( bernoulli( 0.5 ) < 0.2 ) {
return NaN;
}
return discreteUniform( 0, 100 );
}
var x = filledarrayBy( 10, 'float32', rand );
console.log( x );
var out = new Float64Array( 2 );
dsnannsumors( x.length, x, 1, out, 1 );
console.log( out );
```
* * *
## C APIs
### Usage
```c
#include "stdlib/blas/ext/base/dsnannsumors.h"
```
#### stdlib_strided_dsnannsumors( N, \*X, strideX, \*n )
Computes the sum of single-precision floating-point strided array elements, ignoring `NaN` values, using ordinary recursive summation with extended accumulation, and returning an extended precision result.
```c
#include "stdlib/blas/base/shared.h"
const float x[] = { 1.0f, -2.0f, 0.0f/0.0f, 2.0f };
CBLAS_INT n = 0;
double v = stdlib_strided_dsnannsumors( 4, x, 1, &n );
// returns 1.0
```
The function accepts the following arguments:
- **N**: `[in] CBLAS_INT` number of indexed elements.
- **X**: `[in] float*` input array.
- **strideX**: `[in] CBLAS_INT` stride length for `X`.
- **n**: `[out] CBLAS_INT*` pointer for storing the number of non-NaN elements.
```c
double stdlib_strided_dsnannsumors( const CBLAS_INT N, const float *X, const CBLAS_INT strideX, CBLAS_INT *n );
```
#### stdlib_strided_dsnannsumors_ndarray( N, \*X, strideX, offsetX, \*n )
Computes the sum of single-precision floating-point strided array elements, ignoring `NaN` values, using ordinary recursive summation with extended accumulation and alternative indexing semantics, and returning an extended precision result.
```c
#include "stdlib/blas/base/shared.h"
const float x[] = { 1.0f, -2.0f, 0.0f/0.0f, 2.0f };
CBLAS_INT n = 0;
double v = stdlib_strided_dsnannsumors_ndarray( 4, x, 1, 0, &n );
// returns 1.0
```
The function accepts the following arguments:
- **N**: `[in] CBLAS_INT` number of indexed elements.
- **X**: `[in] float*` input array.
- **strideX**: `[in] CBLAS_INT` stride length for `X`.
- **offsetX**: `[in] CBLAS_INT` starting index for `X`.
- **n**: `[out] CBLAS_INT*` pointer for storing the number of non-NaN elements.
```c
double stdlib_strided_dsnannsumors_ndarray( const CBLAS_INT N, const float *X, const CBLAS_INT strideX, const CBLAS_INT offsetX, CBLAS_INT *n );
```
### Examples
```c
#include "stdlib/blas/ext/base/dsnannsumors.h"
#include "stdlib/blas/base/shared.h"
#include
int main( void ) {
// Create a strided array:
const float x[] = { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 0.0f/0.0f, 0.0f/0.0f };
// Specify the number of elements:
const int N = 5;
// Specify the stride length:
const int strideX = 2;
// Initialize a variable for storing the number of non-NaN elements:
CBLAS_INT n = 0;
// Compute the sum:
double v = stdlib_strided_dsnannsumors( N, x, strideX, &n );
// Print the result:
printf( "sum: %lf\n", v );
printf( "n: %"CBLAS_IFMT"\n", n );
}
```
* * *
## See Also
- [`@stdlib/blas-ext/base/dnannsumors`][@stdlib/blas/ext/base/dnannsumors]: calculate the sum of double-precision floating-point strided array elements, ignoring NaN values and using ordinary recursive summation.
- [`@stdlib/blas-ext/base/dsnansumors`][@stdlib/blas/ext/base/dsnansumors]: calculate the sum of single-precision floating-point strided array elements, ignoring NaN values, using ordinary recursive summation with extended accumulation, and returning an extended precision result.
- [`@stdlib/blas-ext/base/dssumors`][@stdlib/blas/ext/base/dssumors]: calculate the sum of single-precision floating-point strided array elements using ordinary recursive summation with extended accumulation and returning an extended precision result.
* * *
## Notice
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].
#### Community
[![Chat][chat-image]][chat-url]
---
## License
See [LICENSE][stdlib-license].
## Copyright
Copyright © 2016-2025. The Stdlib [Authors][stdlib-authors].
[npm-image]: http://img.shields.io/npm/v/@stdlib/blas-ext-base-dsnannsumors.svg
[npm-url]: https://npmjs.org/package/@stdlib/blas-ext-base-dsnannsumors
[test-image]: https://github.com/stdlib-js/blas-ext-base-dsnannsumors/actions/workflows/test.yml/badge.svg?branch=main
[test-url]: https://github.com/stdlib-js/blas-ext-base-dsnannsumors/actions/workflows/test.yml?query=branch:main
[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/blas-ext-base-dsnannsumors/main.svg
[coverage-url]: https://codecov.io/github/stdlib-js/blas-ext-base-dsnannsumors?branch=main
[chat-image]: https://img.shields.io/gitter/room/stdlib-js/stdlib.svg
[chat-url]: https://app.gitter.im/#/room/#stdlib-js_stdlib:gitter.im
[stdlib]: https://github.com/stdlib-js/stdlib
[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors
[umd]: https://github.com/umdjs/umd
[es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules
[deno-url]: https://github.com/stdlib-js/blas-ext-base-dsnannsumors/tree/deno
[deno-readme]: https://github.com/stdlib-js/blas-ext-base-dsnannsumors/blob/deno/README.md
[umd-url]: https://github.com/stdlib-js/blas-ext-base-dsnannsumors/tree/umd
[umd-readme]: https://github.com/stdlib-js/blas-ext-base-dsnannsumors/blob/umd/README.md
[esm-url]: https://github.com/stdlib-js/blas-ext-base-dsnannsumors/tree/esm
[esm-readme]: https://github.com/stdlib-js/blas-ext-base-dsnannsumors/blob/esm/README.md
[branches-url]: https://github.com/stdlib-js/blas-ext-base-dsnannsumors/blob/main/branches.md
[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/blas-ext-base-dsnannsumors/main/LICENSE
[@stdlib/array/float32]: https://github.com/stdlib-js/array-float32
[@stdlib/array/float64]: https://github.com/stdlib-js/array-float64
[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray
[@stdlib/blas/ext/base/dnannsumors]: https://github.com/stdlib-js/blas-ext-base-dnannsumors
[@stdlib/blas/ext/base/dsnansumors]: https://github.com/stdlib-js/blas-ext-base-dsnansumors
[@stdlib/blas/ext/base/dssumors]: https://github.com/stdlib-js/blas-ext-base-dssumors