feat: add C ndarray interface and refactor implementation for stats/base/dnanmskmin

lib/node_modules/@stdlib/stats/base/dnanmskmin/README.md

@@ -18,6 +18,8 @@ limitations under the License.
 
 -->
 
+<!-- lint disable maximum-heading-length -->
+
 # dnanmskmin
 
 > Calculate the minimum value of a double-precision floating-point strided array according to a mask, ignoring `NaN` values.
@@ -38,7 +40,7 @@ var dnanmskmin = require( '@stdlib/stats/base/dnanmskmin' );
 
 #### dnanmskmin( N, x, strideX, mask, strideMask )
 
-Computes the minimum value of a double-precision floating-point strided array `x` according to a `mask`, ignoring `NaN` values.
+Computes the minimum value of a double-precision floating-point strided array according to a `mask`, ignoring `NaN` values.
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
@@ -55,22 +57,20 @@ The function has the following parameters:
 
 -   **N**: number of indexed elements.
 -   **x**: input [`Float64Array`][@stdlib/array/float64].
--   **strideX**: index increment for `x`.
+-   **strideX**: stride length for `x`.
 -   **mask**: mask [`Uint8Array`][@stdlib/array/uint8]. If a `mask` array element is `0`, the corresponding element in `x` is considered valid and **included** in computation. If a `mask` array element is `1`, the corresponding element in `x` is considered invalid/missing and **excluded** from computation.
--   **strideMask**: index increment for `mask`.
+-   **strideMask**: stride length for `mask`.
 
-The `N` and `stride` parameters determine which elements are accessed at runtime. For example, to compute the minimum value of every other element in `x`,
+The `N` and stride parameters determine which elements in the strided array are accessed at runtime. For example, to compute the minimum value of every other element in `x`,
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
 var Uint8Array = require( '@stdlib/array/uint8' );
-var floor = require( '@stdlib/math/base/special/floor' );
 
 var x = new Float64Array( [ 1.0, 2.0, 7.0, -2.0, -4.0, 3.0, -5.0, -6.0 ] );
 var mask = new Uint8Array( [ 0, 0, 0, 0, 0, 0, 1, 1 ] );
-var N = floor( x.length / 2 );
 
-var v = dnanmskmin( N, x, 2, mask, 2 );
+var v = dnanmskmin( 4, x, 2, mask, 2 );
 // returns -4.0
 ```
 
@@ -81,17 +81,14 @@ Note that indexing is relative to the first index. To introduce offsets, use [`t
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
 var Uint8Array = require( '@stdlib/array/uint8' );
-var floor = require( '@stdlib/math/base/special/floor' );
 
 var x0 = new Float64Array( [ 2.0, 1.0, -2.0, -2.0, 3.0, 4.0, -5.0, -6.0 ] );
 var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
 
 var mask0 = new Uint8Array( [ 0, 0, 0, 0, 0, 0, 1, 1 ] );
 var mask1 = new Uint8Array( mask0.buffer, mask0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
 
-var N = floor( x0.length / 2 );
-
-var v = dnanmskmin( N, x1, 2, mask1, 2 );
+var v = dnanmskmin( 4, x1, 2, mask1, 2 );
 // returns -2.0
 ```
 
@@ -115,18 +112,16 @@ The function has the following additional parameters:
 -   **offsetX**: starting index for `x`.
 -   **offsetMask**: starting index for `mask`.
 
-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 minimum value for every other value in `x` starting from the second value
+While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying buffer, offset parameters support indexing semantics based on starting indices. For example, to calculate the minimum value for every other element in `x` starting from the second element
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
 var Uint8Array = require( '@stdlib/array/uint8' );
-var floor = require( '@stdlib/math/base/special/floor' );
 
 var x = new Float64Array( [ 2.0, 1.0, -2.0, -2.0, 3.0, 4.0, -5.0, -6.0 ] );
 var mask = new Uint8Array( [ 0, 0, 0, 0, 0, 0, 1, 1 ] );
-var N = floor( x.length / 2 );
 
-var v = dnanmskmin.ndarray( N, x, 2, 1, mask, 2, 1 );
+var v = dnanmskmin.ndarray( 4, x, 2, 1, mask, 2, 1 );
 // returns -2.0
 ```
 
@@ -151,31 +146,22 @@ var v = dnanmskmin.ndarray( N, x, 2, 1, mask, 2, 1 );
 <!-- eslint no-undef: "error" -->
 
 ```javascript
-var randu = require( '@stdlib/random/base/randu' );
-var round = require( '@stdlib/math/base/special/round' );
-var Float64Array = require( '@stdlib/array/float64' );
-var Uint8Array = require( '@stdlib/array/uint8' );
+var uniform = require( '@stdlib/random/base/uniform' );
+var bernoulli = require( '@stdlib/random/base/bernoulli' );
+var filledarrayBy = require( '@stdlib/array/filled-by' );
 var dnanmskmin = require( '@stdlib/stats/base/dnanmskmin' );
 
-var mask;
-var x;
-var i;
-
-x = new Float64Array( 10 );
-mask = new Uint8Array( x.length );
-for ( i = 0; i < x.length; i++ ) {
-    if ( randu() < 0.2 ) {
-        mask[ i ] = 1;
-    } else {
-        mask[ i ] = 0;
-    }
-    if ( randu() < 0.1 ) {
-        x[ i ] = NaN;
-    } else {
-        x[ i ] = round( (randu()*100.0) - 50.0 );
+function rand() {
+    if ( bernoulli( 0.8 ) < 1 ) {
+        return NaN;
     }
+    return uniform( -50.0, 50.0 );
 }
+
+var x = filledarrayBy( 10, 'float64', rand );
 console.log( x );
+
+var mask = filledarrayBy( x.length, 'uint8', bernoulli.factory( 0.2 ) );
 console.log( mask );
 
 var v = dnanmskmin( x.length, x, 1, mask, 1 );
@@ -186,6 +172,145 @@ console.log( v );
 
 <!-- /.examples -->
 
+<!-- C interface documentation. -->
+
+* * *
+
+<section class="c">
+
+## C APIs
+
+<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->
+
+<section class="intro">
+
+</section>
+
+<!-- /.intro -->
+
+<!-- C usage documentation. -->
+
+<section class="usage">
+
+### Usage
+
+```c
+#include "stdlib/stats/base/dnanmskmin.h"
+```
+
+#### stdlib_strided_dnanmskmin( N, \*X, strideX, \*Mask, strideMask )
+
+Computes the minimum value of a double-precision floating-point strided array according to a `mask`, ignoring `NaN` values.
+
+```c
+#include <stdint.h>
+
+const double x[] = { 1.0, -2.0, 4.0, 2.0, 0.0/0.0 };
+const uint8_t mask[] = { 0, 0, 1, 0, 0 };
+
+double v = stdlib_strided_dnanmskmin( 5, x, 1, mask, 1 );
+// returns -2.0
+```
+
+The function accepts the following arguments:
+
+-   **N**: `[in] CBLAS_INT` number of indexed elements.
+-   **X**: `[in] double*` input array.
+-   **strideX**: `[in] CBLAS_INT` stride length for `X`.
+-   **Mask**: `[in] uint8_t*` mask array. If a `Mask` array element is `0`, the corresponding element in `X` is considered valid and included in computation. If a `Mask` array element is `1`, the corresponding element in `X` is considered invalid/missing and excluded from computation.
+-   **strideMask**: `[in] CBLAS_INT` stride length for `Mask`.
+
+```c
+double stdlib_strided_dnanmskmin( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, const uint8_t *Mask, const CBLAS_INT strideMask );
+```
+
+#### stdlib_strided_dnanmskmin_ndarray( N, \*X, strideX, offsetX, \*Mask, strideMask, offsetMask )
+
+Computes the minimum value of a double-precision floating-point strided array according to a `mask`, ignoring `NaN` values and using alternative indexing semantics.
+
+```c
+#include <stdint.h>
+
+const double x[] = { 1.0, -2.0, 4.0, 2.0, 0.0/0.0 };
+const uint8_t mask[] = { 0, 0, 1, 0, 0 };
+
+double v = stdlib_strided_dnanmskmin_ndarray( 5, x, 1, 0, mask, 1, 0 );
+// returns -2.0
+```
+
+The function accepts the following arguments:
+
+-   **N**: `[in] CBLAS_INT` number of indexed elements.
+-   **X**: `[in] double*` input array.
+-   **strideX**: `[in] CBLAS_INT` stride length for `X`.
+-   **offsetX**: `[in] CBLAS_INT` starting index for `X`.
+-   **Mask**: `[in] uint8_t*` mask array. If a `Mask` array element is `0`, the corresponding element in `X` is considered valid and included in computation. If a `Mask` array element is `1`, the corresponding element in `X` is considered invalid/missing and excluded from computation.
+-   **strideMask**: `[in] CBLAS_INT` stride length for `Mask`.
+-   **offsetMask**: `[in] CBLAS_INT` starting index for `Mask`.
+
+```c
+double stdlib_strided_dnanmskmin_ndarray( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX, const uint8_t *Mask, const CBLAS_INT strideMask, const CBLAS_INT offsetMask );
+```
+
+</section>
+
+<!-- /.usage -->
+
+<!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
+
+<section class="notes">
+
+</section>
+
+<!-- /.notes -->
+
+<!-- C API usage examples. -->
+
+<section class="examples">
+
+### Examples
+
+```c
+#include "stdlib/stats/base/dnanmskmin.h"
+#include <stdint.h>
+#include <stdio.h>
+
+int main( void ) {
+    // Create a strided array:
+    const double x[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 0.0/0.0, 0.0/0.0 };
+
+    // Create a mask array:
+    const uint8_t mask[] = { 0, 0, 0, 0, 0, 0, 0, 0, 1, 1 };
+
+    // Specify the number of elements:
+    const int N = 5;
+
+    // Specify the stride lengths:
+    const int strideX = 2;
+    const int strideMask = 2;
+
+    // Compute the minimum value:
+    double v = stdlib_strided_dnanmskmin( N, x, strideX, mask, strideMask );
+
+    // Print the result:
+    printf( "min: %lf\n", v );
+}
+```
+
+</section>
+
+<!-- /.examples -->
+
+</section>
+
+<!-- /.c -->
+
+<section class="references">
+
+</section>
+
+<!-- /.references -->
+
 <!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
 
 <section class="related">

lib/node_modules/@stdlib/stats/base/dnanmskmin/benchmark/benchmark.js

@@ -21,17 +21,30 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-var randu = require( '@stdlib/random/base/randu' );
+var uniform = require( '@stdlib/random/base/uniform' );
+var bernoulli = require( '@stdlib/random/base/bernoulli' );
+var filledarrayBy = require( '@stdlib/array/filled-by' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
 var pow = require( '@stdlib/math/base/special/pow' );
-var Float64Array = require( '@stdlib/array/float64' );
-var Uint8Array = require( '@stdlib/array/uint8' );
 var pkg = require( './../package.json' ).name;
 var dnanmskmin = require( './../lib/dnanmskmin.js' );
 
 
 // FUNCTIONS //
 
+/**
+* Returns a random value or `NaN`.
+*
+* @private
+* @returns {number} random number or `NaN`
+*/
+function rand() {
+	if ( bernoulli( 0.8 ) < 1 ) {
+		return NaN;
+	}
+	return uniform( -10.0, 10.0 );
+}
+
 /**
 * Creates a benchmark function.
 *
@@ -42,18 +55,9 @@ var dnanmskmin = require( './../lib/dnanmskmin.js' );
 function createBenchmark( len ) {
 	var mask;
 	var x;
-	var i;
 
-	x = new Float64Array( len );
-	mask = new Uint8Array( len );
-	for ( i = 0; i < x.length; i++ ) {
-		if ( randu() < 0.2 ) {
-			mask[ i ] = 1;
-		} else {
-			mask[ i ] = 0;
-		}
-		x[ i ] = ( randu()*20.0 ) - 10.0;
-	}
+	x = filledarrayBy( len, 'float64', rand );
+	mask = filledarrayBy( len, 'uint8', bernoulli.factory( 0.2 ) );
 	return benchmark;
 
 	function benchmark( b ) {

lib/node_modules/@stdlib/stats/base/dnanmskmin/benchmark/benchmark.native.js

@@ -22,11 +22,11 @@
 
 var resolve = require( 'path' ).resolve;
 var bench = require( '@stdlib/bench' );
-var randu = require( '@stdlib/random/base/randu' );
+var uniform = require( '@stdlib/random/base/uniform' );
+var bernoulli = require( '@stdlib/random/base/bernoulli' );
+var filledarrayBy = require( '@stdlib/array/filled-by' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
 var pow = require( '@stdlib/math/base/special/pow' );
-var Float64Array = require( '@stdlib/array/float64' );
-var Uint8Array = require( '@stdlib/array/uint8' );
 var tryRequire = require( '@stdlib/utils/try-require' );
 var pkg = require( './../package.json' ).name;
 
@@ -41,6 +41,19 @@ var opts = {
 
 // FUNCTIONS //
 
+/**
+* Returns a random value or `NaN`.
+*
+* @private
+* @returns {number} random number or `NaN`
+*/
+function rand() {
+	if ( bernoulli( 0.8 ) < 1 ) {
+		return NaN;
+	}
+	return uniform( -10.0, 10.0 );
+}
+
 /**
 * Creates a benchmark function.
 *
@@ -51,18 +64,9 @@ var opts = {
 function createBenchmark( len ) {
 	var mask;
 	var x;
-	var i;
 
-	x = new Float64Array( len );
-	mask = new Uint8Array( len );
-	for ( i = 0; i < x.length; i++ ) {
-		if ( randu() < 0.2 ) {
-			mask[ i ] = 1;
-		} else {
-			mask[ i ] = 0;
-		}
-		x[ i ] = ( randu()*20.0 ) - 10.0;
-	}
+	x = filledarrayBy( len, 'float64', rand );
+	mask = filledarrayBy( len, 'uint8', bernoulli.factory( 0.2 ) );
 	return benchmark;
 
 	function benchmark( b ) {