# 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.
## 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**: index increment 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**: index increment for `out`.
The `N` and stride parameters determine which elements are accessed at runtime. For example, to compute the sum of every other element in `x`,
```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 and using ordinary recursive summation with extended accumulation and alternative indexing semantics.
```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` parameter supports indexing semantics based on a starting index. For example, to calculate the sum of every other value in `x` starting from the second value
```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 );
```
[@stdlib/array/float32]: https://www.npmjs.com/package/@stdlib/array-float32
[@stdlib/array/float64]: https://www.npmjs.com/package/@stdlib/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/tree/main/ext/base/dnannsumors
[@stdlib/blas/ext/base/dsnansumors]: https://github.com/stdlib-js/blas/tree/main/ext/base/dsnansumors
[@stdlib/blas/ext/base/dssumors]: https://github.com/stdlib-js/blas/tree/main/ext/base/dssumors