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‎lib/node_modules/@stdlib/stats/mskmax/README.md‎

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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 the same shape as `x`. If a mask element is `0`, the corresponding element in `x` is considered valid. If a mask element is non-zero, the corresponding element in `x` is ignored.
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- **mask**: mask [ndarray][@stdlib/ndarray/ctor]. Must be [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with `x`. If a mask element is `0`, the corresponding element in `x` is considered valid. If a mask element is non-zero, the corresponding element in `x` is ignored.
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- **options**: function options (_optional_).
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The function accepts the following options:
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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 the same shape as `x`. If a mask element is `0`, the corresponding element in `x` is considered valid. If a mask element is non-zero, the corresponding element in `x` is ignored.
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- **mask**: mask [ndarray][@stdlib/ndarray/ctor]. Must be [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with `x`. If a mask element is `0`, the corresponding element in `x` is considered valid. If a mask element is non-zero, the corresponding element in `x` is ignored.
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- **out**: output [ndarray][@stdlib/ndarray/ctor].
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- **options**: function options (_optional_).
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‎lib/node_modules/@stdlib/stats/mskmax/lib/base.js‎

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2025 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 dtypes = require( '@stdlib/ndarray/dtypes' );
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var gmskmax = require( '@stdlib/stats/base/ndarray/mskmax' );
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var dmskmax = require( '@stdlib/stats/base/ndarray/dmskmax' );
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var smskmax = require( '@stdlib/stats/base/ndarray/smskmax' );
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var factory = require( '@stdlib/ndarray/base/binary-reduce-strided1d-dispatch-factory' );
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// VARIABLES //
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var idtypes = dtypes( 'real_and_generic' );
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var mdtypes = dtypes( 'mask_index_and_generic' );
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var odtypes = dtypes( 'real_and_generic' );
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var policies = {
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'output': 'promoted',
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'casting': 'none'
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};
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var table = {
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'types': [
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'float64',
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'uint8', // dmskmax: x, mask
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'float32',
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'uint8' // smskmax: x, mask
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],
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'fcns': [
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dmskmax,
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smskmax
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],
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'default': gmskmax
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};
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// MAIN //
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/**
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* Base implementation for computing the maximum value along one or more ndarray dimensions according to a mask.
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*
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* @name base
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* @type {Function}
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* @param {ndarray} x - input ndarray
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* @param {ndarray} mask - mask ndarray
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* @param {Options} [options] - function options
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* @param {IntegerArray} [options.dims] - list of dimensions over which to perform a reduction
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* @param {boolean} [options.keepdims=false] - boolean indicating whether the reduced dimensions should be included in the returned ndarray as singleton dimensions
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* @param {string} [options.dtype] - output ndarray data type
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* @throws {TypeError} first argument must be an ndarray-like object
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* @throws {TypeError} second argument must be an ndarray-like object
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* @throws {TypeError} options argument must be an object
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* @throws {RangeError} dimension indices must not exceed input ndarray bounds
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* @throws {RangeError} number of dimension indices must not exceed the number of input ndarray dimensions
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* @throws {Error} must provide valid options
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* @returns {ndarray} output ndarray
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*/
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var base = factory( table, [ idtypes, mdtypes ], odtypes, policies );
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// EXPORTS //
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module.exports = base;

‎lib/node_modules/@stdlib/stats/mskmax/lib/main.js‎

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// MODULES //
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var dtypes = require( '@stdlib/ndarray/dtypes' );
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var gmskmax = require( '@stdlib/stats/base/ndarray/mskmax' );
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var dmskmax = require( '@stdlib/stats/base/ndarray/dmskmax' );
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var smskmax = require( '@stdlib/stats/base/ndarray/smskmax' );
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var factory = require( '@stdlib/ndarray/base/binary-reduce-strided1d-dispatch-factory' );
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// VARIABLES //
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var idtypes = dtypes( 'real_and_generic' );
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var mdtypes = dtypes( 'integer' );
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var odtypes = dtypes( 'real_and_generic' );
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var policies = {
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'output': 'promoted',
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'casting': 'none'
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};
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var table = {
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'types': [
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'float64',
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'uint8', // dmskmax: x, mask
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'float32',
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'uint8' // smskmax: x, mask
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],
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'fcns': [
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dmskmax,
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smskmax
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],
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'default': gmskmax
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};
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var isNdarrayLike = require( '@stdlib/assert/is-ndarray-like' );
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var format = require( '@stdlib/string/format' );
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var maybeBroadcastArrays = require( '@stdlib/ndarray/base/maybe-broadcast-arrays' );
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var base = require( './base.js' );
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// MAIN //
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/**
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* Computes the maximum value along one or more ndarray dimensions according to a mask.
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*
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* @name max
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* @type {Function}
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* @param {ndarray} x - input ndarray
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* @param {ndarray} mask - mask ndarray
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* @param {Options} [options] - function options
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* var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
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*
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* // Create a mask buffer:
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* var mbuf = new Uint8Array( [ 0, 0, 0, 1, 0, 1 ] ); // eslint-disable-line id-length
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* var mbuf = new Uint8Array( [ 0, 0, 0, 1, 0, 1 ] );
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*
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* // Define the shape of the input array:
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* var sh = [ 3, 2 ];
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* var v = out.get();
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* // returns 5.0
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*/
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var mskmax = factory( table, [ idtypes, mdtypes ], odtypes, policies );
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function mskmax( x, mask ) {
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var arrs;
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if ( !isNdarrayLike( x ) ) {
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throw new TypeError( format( 'invalid argument. First argument must be an ndarray-like object. Value: `%s`.', x ) );
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}
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if ( !isNdarrayLike( mask ) ) {
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throw new TypeError( format( 'invalid argument. Second argument must be an ndarray-like object. Value: `%s`.', mask ) );
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}
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arrs = maybeBroadcastArrays( [ x, mask ] );
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if ( arguments.length > 2 ) {
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return base( arrs[ 0 ], arrs[ 1 ], arguments[ 2 ] );
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}
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return base( arrs[ 0 ], arrs[ 1 ] );
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}
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/**
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* Computes the maximum value along one or more ndarray dimensions according to a mask and assigns the results to a provided output ndarray.
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*
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* @name assign
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* @memberof mskmax
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* @type {Function}
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* @param {ndarray} x - input ndarray
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* @param {ndarray} mask - mask ndarray
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* @param {ndarray} out - output ndarray
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* @param {Options} [options] - function options
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* @param {IntegerArray} [options.dims] - list of dimensions over which to perform a reduction
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* @param {boolean} [options.keepdims=false] - boolean indicating whether the reduced dimensions should be included in the returned ndarray as singleton dimensions
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* @throws {TypeError} first argument must be an ndarray-like object
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* @throws {TypeError} second argument must be an ndarray-like object
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* @throws {TypeError} third argument must be an ndarray-like object
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* @throws {TypeError} options argument must be an object
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* @throws {RangeError} dimension indices must not exceed input ndarray bounds
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* @throws {RangeError} number of dimension indices must not exceed the number of input ndarray dimensions
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* @throws {Error} must provide valid options
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* @returns {ndarray} output ndarray
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*
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* @example
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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 ndarray = require( '@stdlib/ndarray/ctor' );
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*
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* // Create a data buffer:
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* var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
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*
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* // Create a mask buffer:
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* var mbuf = new Uint8Array( [ 0, 0, 0, 1, 0, 1 ] );
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*
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* // Create an output buffer:
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* var obuf = new Float64Array( [ 0.0 ] );
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*
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* // Define the shape of the input array:
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* var sh = [ 3, 2 ];
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*
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* // Define the array strides:
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* var sx = [ 2, 1 ];
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* var sm = [ 2, 1 ];
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*
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* // Define the index offset:
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* var ox = 0;
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* var om = 0;
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*
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* // Create the input ndarray:
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* var x = new ndarray( 'float64', xbuf, sh, sx, ox, 'row-major' );
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*
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* // Create the mask ndarray:
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* var mask = new ndarray( 'uint8', mbuf, sh, sm, om, 'row-major' ); // cspell:disable-line
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*
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* // Create the output ndarray:
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* var out = new ndarray( 'float64', obuf, [], [ 0 ], 0, 'row-major' );
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*
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* // Perform reduction:
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* var res = mskmax.assign( x, mask, out );
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* // returns <ndarray>
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*
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* var v = res.get();
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* // returns 5.0
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*/
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function assign( x, mask, out ) {
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var arrs;
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if ( !isNdarrayLike( x ) ) {
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throw new TypeError( format( 'invalid argument. First argument must be an ndarray-like object. Value: `%s`.', x ) );
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}
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if ( !isNdarrayLike( mask ) ) {
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throw new TypeError( format( 'invalid argument. Second argument must be an ndarray-like object. Value: `%s`.', mask ) );
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}
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if ( !isNdarrayLike( out ) ) {
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throw new TypeError( format( 'invalid argument. Third argument must be an ndarray-like object. Value: `%s`.', out ) );
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}
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arrs = maybeBroadcastArrays( [ x, mask ] );
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if ( arguments.length > 3 ) {
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return base.assign( arrs[ 0 ], arrs[ 1 ], out, arguments[ 3 ] );
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}
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return base.assign( arrs[ 0 ], arrs[ 1 ], out );
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}
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mskmax.assign = assign;
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// EXPORTS //

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