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feat: add stats/mskrange
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‎lib/node_modules/@stdlib/stats/mskrange/README.md‎

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<!--
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@license Apache-2.0
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Copyright (c) 2026 The Stdlib Authors.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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# mskrange
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> Compute the [range][range] along one or more [ndarray][@stdlib/ndarray/ctor] dimensions according to a mask.
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<section class="intro">
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The [**range**][range] is defined as the difference between the maximum and minimum values.
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</section>
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<!-- /.intro -->
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<section class="usage">
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## Usage
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```javascript
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var mskrange = require( '@stdlib/stats/mskrange' );
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```
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#### mskrange( x, mask\[, options] )
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Computes the [range][range] along one or more [ndarray][@stdlib/ndarray/ctor] dimensions according to a mask.
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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var Uint8Array = require( '@stdlib/array/uint8' );
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var array = require( '@stdlib/ndarray/array' );
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var x = array( new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] ), {
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'shape': [ 3, 2 ]
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});
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var mask = array( new Uint8Array( [ 0, 0, 1, 0, 0, 1 ] ), {
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'shape': [ 3, 2 ]
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});
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var y = mskrange( x, mask );
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// returns <ndarray>[ 4.0 ]
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```
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The function has the following parameters:
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- **x**: input [ndarray][@stdlib/ndarray/ctor]. Must have a real-valued or "generic" [data type][@stdlib/ndarray/dtypes].
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- **mask**: mask [ndarray][@stdlib/ndarray/ctor]. Must have a data type that can be interpreted as a mask.
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- **options**: function options (_optional_).
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The function accepts the following options:
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- **dims**: list of dimensions over which to perform a reduction. If not provided, the function performs a reduction over all elements in a provided input [ndarray][@stdlib/ndarray/ctor].
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- **dtype**: output ndarray [data type][@stdlib/ndarray/dtypes]. Must be a real-valued or "generic" [data type][@stdlib/ndarray/dtypes].
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- **keepdims**: boolean indicating whether the reduced dimensions should be included in the returned [ndarray][@stdlib/ndarray/ctor] as singleton dimensions. Default: `false`.
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By default, the function performs a reduction over all elements in a provided input [ndarray][@stdlib/ndarray/ctor]. To perform a reduction over specific dimensions, provide a `dims` option.
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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var Uint8Array = require( '@stdlib/array/uint8' );
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var array = require( '@stdlib/ndarray/array' );
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var x = array( new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] ), {
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'shape': [ 3, 2 ],
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'order': 'row-major'
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});
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var mask = array( new Uint8Array( [ 0, 0, 1, 1, 0, 0 ] ), {
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'shape': [ 3, 2 ],
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'order': 'row-major'
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});
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var y = mskrange( x, mask, {
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'dims': [ 0 ]
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});
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// returns <ndarray>[ 4.0, 4.0 ]
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```
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By default, the function excludes reduced dimensions from the output [ndarray][@stdlib/ndarray/ctor]. To include the reduced dimensions as singleton dimensions, set the `keepdims` option to `true`.
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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var Uint8Array = require( '@stdlib/array/uint8' );
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var array = require( '@stdlib/ndarray/array' );
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var x = array( new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] ), {
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'shape': [ 3, 2 ],
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'order': 'row-major'
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});
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var mask = array( new Uint8Array( [ 0, 0, 1, 1, 0, 0 ] ), {
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'shape': [ 3, 2 ],
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'order': 'row-major'
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});
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var y = mskrange( x, mask, {
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'dims': [ 0 ],
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'keepdims': true
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});
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// returns <ndarray>[ [ 4.0, 4.0 ] ]
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```
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By default, the function returns an [ndarray][@stdlib/ndarray/ctor] having a [data type][@stdlib/ndarray/dtypes] determined by the function's output data type [policy][@stdlib/ndarray/output-dtype-policies]. To override the default behavior, set the `dtype` option.
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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var Uint8Array = require( '@stdlib/array/uint8' );
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var getDType = require( '@stdlib/ndarray/dtype' );
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var array = require( '@stdlib/ndarray/array' );
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var x = array( new Float64Array( [ 1.0, 2.0, 3.0 ] ), {
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'dtype': 'generic'
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});
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var mask = array( new Uint8Array( [ 0, 0, 0 ] ), {
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'dtype': 'uint8'
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});
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var y = mskrange( x, mask, {
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'dtype': 'float64'
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});
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// returns <ndarray>[ 2.0 ]
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var dt = String( getDType( y ) );
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// returns 'float64'
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```
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#### mskrange.assign( x, mask, out\[, options] )
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Computes the [range][range] along one or more [ndarray][@stdlib/ndarray/ctor] dimensions according to a mask and assigns results to a provided output [ndarray][@stdlib/ndarray/ctor].
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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var Uint8Array = require( '@stdlib/array/uint8' );
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var array = require( '@stdlib/ndarray/array' );
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var zeros = require( '@stdlib/ndarray/zeros' );
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var x = array( new Float64Array( [ 1.0, 2.0, 3.0 ] ) );
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var mask = array( new Uint8Array( [ 0, 0, 0 ] ) );
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var y = zeros( [] );
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var out = mskrange.assign( x, mask, y );
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// returns <ndarray>[ 2.0 ]
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var bool = ( out === y );
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// returns true
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```
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The method has the following parameters:
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- **x**: input [ndarray][@stdlib/ndarray/ctor]. Must have a real-valued or "generic" [data type][@stdlib/ndarray/dtypes].
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- **mask**: mask [ndarray][@stdlib/ndarray/ctor]. Must have a data type that can be interpreted as a mask.
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- **out**: output [ndarray][@stdlib/ndarray/ctor].
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- **options**: function options (_optional_).
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The method accepts the following options:
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- **dims**: list of dimensions over which to perform a reduction. If not provided, the function performs a reduction over all elements in a provided input [ndarray][@stdlib/ndarray/ctor].
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</section>
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<!-- /.usage -->
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<section class="notes">
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## Notes
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- Setting the `keepdims` option to `true` can be useful when wanting to ensure that the output [ndarray][@stdlib/ndarray/ctor] is [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with ndarrays having the same shape as the input [ndarray][@stdlib/ndarray/ctor].
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- The output data type [policy][@stdlib/ndarray/output-dtype-policies] only applies to the main function and specifies that, by default, the function must return an [ndarray][@stdlib/ndarray/ctor] having the same [data type][@stdlib/ndarray/dtypes] as the input [ndarray][@stdlib/ndarray/ctor] (or a promoted data type if the input data types differ). For the `assign` method, the output [ndarray][@stdlib/ndarray/ctor] is allowed to have any supported output [data type][@stdlib/ndarray/dtypes].
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</section>
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<!-- /.notes -->
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<section class="examples">
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## Examples
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<!-- eslint no-undef: "error" -->
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```javascript
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var uniform = require( '@stdlib/random/uniform' );
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var bernoulli = require( '@stdlib/random/bernoulli' );
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var getDType = require( '@stdlib/ndarray/dtype' );
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var ndarray2array = require( '@stdlib/ndarray/to-array' );
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var mskrange = require( '@stdlib/stats/mskrange' );
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// Generate an array of random numbers:
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var x = uniform( [ 5, 5 ], 0.0, 20.0 );
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console.log( 'x:' );
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console.log( ndarray2array( x ) );
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// Generate a random mask:
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var mask = bernoulli( [ 5, 5 ], 0.2, {
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'dtype': 'uint8'
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});
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console.log( 'mask:' );
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console.log( ndarray2array( mask ) );
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// Perform a reduction:
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var y = mskrange( x, mask, {
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'dims': [ 0 ]
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});
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// Resolve the output array data type:
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var dt = getDType( y );
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console.log( 'Output dtype: %s', dt );
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// Print the results:
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console.log( 'y:' );
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console.log( ndarray2array( y ) );
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```
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</section>
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<!-- /.examples -->
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<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
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<section class="related">
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</section>
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<!-- /.related -->
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<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
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<section class="links">
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[@stdlib/ndarray/ctor]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/ctor
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[@stdlib/ndarray/dtypes]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/dtypes
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[@stdlib/ndarray/output-dtype-policies]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/output-dtype-policies
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[@stdlib/ndarray/base/broadcast-shapes]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/base/broadcast-shapes
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[range]: https://en.wikipedia.org/wiki/Range_%28statistics%29
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</section>
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<!-- /.links -->

‎lib/node_modules/@stdlib/stats/mskrange/benchmark/benchmark.assign.js‎

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2026 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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// MODULES //
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var bench = require( '@stdlib/bench' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var pow = require( '@stdlib/math/base/special/pow' );
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var uniform = require( '@stdlib/random/array/uniform' );
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var format = require( '@stdlib/string/format' );
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var bernoulli = require( '@stdlib/random/array/bernoulli' );
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var zeros = require( '@stdlib/array/zeros' );
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var ndarray = require( '@stdlib/ndarray/base/ctor' );
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var pkg = require( './../package.json' ).name;
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var mskrange = require( './../lib' );
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// VARIABLES //
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var options = {
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'dtype': 'float64'
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};
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// FUNCTIONS //
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/**
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* Creates a benchmark function.
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*
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* @private
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* @param {PositiveInteger} len - array length
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* @returns {Function} benchmark function
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*/
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function createBenchmark( len ) {
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var mask;
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var out;
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var x;
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x = uniform( len, -50.0, 50.0, options );
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x = new ndarray( options.dtype, x, [ len ], [ 1 ], 0, 'row-major' );
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mask = bernoulli( len, 0.2, {
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'dtype': 'uint8'
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});
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mask = new ndarray( 'uint8', mask, [ len ], [ 1 ], 0, 'row-major' );
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out = new ndarray( options.dtype, zeros( 1, options.dtype ), [], [ 0 ], 0, 'row-major' );
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return benchmark;
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/**
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* Benchmark function.
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*
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* @private
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* @param {Benchmark} b - benchmark instance
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*/
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function benchmark( b ) {
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var o;
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var i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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o = mskrange.assign( x, mask, out );
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if ( typeof o !== 'object' ) {
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b.fail( 'should return an ndarray' );
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}
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}
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b.toc();
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if ( isnan( o.get() ) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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}
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}
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// MAIN //
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/**
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* Main execution sequence.
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*
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* @private
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*/
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function main() {
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var len;
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var min;
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var max;
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var f;
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var i;
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min = 1; // 10^min
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max = 6; // 10^max
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for ( i = min; i <= max; i++ ) {
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len = pow( 10, i );
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f = createBenchmark( len );
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bench( format( '%s:assign:dtype=%s,len=%d', pkg, options.dtype, len ), f );
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}
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}
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main();

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