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feat: add ndarray/map
PR-URL: #3314 Ref: #2656 Co-authored-by: Athan Reines <kgryte@gmail.com> Reviewed-by: Athan Reines <kgryte@gmail.com> Signed-off-by: Muhammad Haris <101793258+headlessNode@users.noreply.github.com>
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‎lib/node_modules/@stdlib/ndarray/map/README.md‎

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<!--
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@license Apache-2.0
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Copyright (c) 2024 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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# map
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> Apply a callback function to elements in an input [ndarray][@stdlib/ndarray/ctor] and assign results to elements in a new output [ndarray][@stdlib/ndarray/ctor].
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<section class="intro">
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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 map = require( '@stdlib/ndarray/map' );
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```
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#### map( x\[, options], fcn\[, thisArg] )
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Applies a callback function to elements in an input [ndarray][@stdlib/ndarray/ctor] and assigns results to elements in a new output [ndarray][@stdlib/ndarray/ctor].
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<!-- eslint-disable max-len -->
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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var ndarray = require( '@stdlib/ndarray/ctor' );
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var ndarray2array = require( '@stdlib/ndarray/to-array' );
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function scale( z ) {
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return z * 10.0;
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}
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var buffer = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
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var shape = [ 2, 3 ];
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var strides = [ 6, 1 ];
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var offset = 1;
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var x = ndarray( 'float64', buffer, shape, strides, offset, 'row-major' );
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// returns <ndarray>
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var y = map( x, scale );
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// returns <ndarray>
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var arr = ndarray2array( y );
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// returns [ [ 20.0, 30.0, 40.0 ], [ 80.0, 90.0, 100.0 ] ]
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```
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The function accepts the following arguments:
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- **x**: input [ndarray][@stdlib/ndarray/ctor].
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- **options**: function options.
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- **fcn**: callback to apply.
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- **thisArg**: callback execution context.
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The function accepts the following options:
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- **dtype**: output ndarray [data type][@stdlib/ndarray/dtypes]. If not specified, the output ndarray [data type][@stdlib/ndarray/dtypes] is inferred from the input [ndarray][@stdlib/ndarray/ctor].
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By default, the output ndarray [data type][@stdlib/ndarray/dtypes] is inferred from the input [ndarray][@stdlib/ndarray/ctor]. To return an ndarray with a different [data type][@stdlib/ndarray/dtypes], specify the `dtype` option.
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<!-- eslint-disable max-len -->
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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var ndarray = require( '@stdlib/ndarray/ctor' );
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var dtype = require( '@stdlib/ndarray/dtype' );
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var ndarray2array = require( '@stdlib/ndarray/to-array' );
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function scale( z ) {
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return z * 10.0;
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}
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var buffer = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
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var shape = [ 2, 3 ];
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var strides = [ 6, 1 ];
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var offset = 1;
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var x = ndarray( 'float64', buffer, shape, strides, offset, 'row-major' );
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// returns <ndarray>
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var opts = {
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'dtype': 'float32'
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};
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var y = map( x, opts, scale );
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// returns <ndarray>
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var dt = dtype( y );
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// returns 'float32'
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var arr = ndarray2array( y );
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// returns [ [ 20.0, 30.0, 40.0 ], [ 80.0, 90.0, 100.0 ] ]
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```
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The callback function is provided the following arguments:
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- **values**: current array element.
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- **indices**: current array element indices.
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- **arr**: the 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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- The function does **not** perform explicit casting (e.g., from a real-valued floating-point number to a complex floating-point number). Any such casting should be performed by a provided callback function.
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<!-- eslint-disable max-len -->
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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var ndarray = require( '@stdlib/ndarray/ctor' );
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var Complex128 = require( '@stdlib/complex/float64/ctor' );
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var ndarray2array = require( '@stdlib/ndarray/to-array' );
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function toComplex( z ) {
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return new Complex128( z, 0.0 );
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}
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var buffer = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
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var shape = [ 2, 3 ];
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var strides = [ 6, 1 ];
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var offset = 1;
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var x = ndarray( 'float64', buffer, shape, strides, offset, 'row-major' );
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// returns <ndarray>
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var opts = {
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'dtype': 'complex128'
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};
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var y = map( x, opts, toComplex );
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// returns <ndarray>
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```
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- For very high-dimensional ndarrays which are non-contiguous, one should consider copying the underlying data to contiguous memory before applying a callback function in order to achieve better performance.
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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 discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
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var abs = require( '@stdlib/math/base/special/abs' );
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var ndarray2array = require( '@stdlib/ndarray/to-array' );
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var naryFunction = require( '@stdlib/utils/nary-function' );
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var ndarray = require( '@stdlib/ndarray/ctor' );
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var map = require( '@stdlib/ndarray/map' );
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var buffer = discreteUniform( 10, -100, 100, {
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'dtype': 'generic'
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});
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var shape = [ 5, 2 ];
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var strides = [ 2, 1 ];
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var offset = 0;
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var x = ndarray( 'generic', buffer, shape, strides, offset, 'row-major' );
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console.log( ndarray2array( x ) );
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var y = map( x, naryFunction( abs, 1 ) );
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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 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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<!-- <related-links> -->
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<!-- </related-links> -->
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</section>
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<!-- /.links -->

‎lib/node_modules/@stdlib/ndarray/map/benchmark/benchmark.1d.js‎

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2024 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 isndarrayLike = require( '@stdlib/assert/is-ndarray-like' );
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var identity = require( '@stdlib/math/base/special/identity' );
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var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
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var shape2strides = require( '@stdlib/ndarray/base/shape2strides' );
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var ndarray = require( '@stdlib/ndarray/ctor' );
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var pkg = require( './../package.json' ).name;
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var map = require( './../lib' );
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// VARIABLES //
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var xtypes = [ 'generic' ];
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var ytypes = [ 'float64' ];
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var orders = [ 'row-major', 'column-major' ];
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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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* @param {NonNegativeIntegerArray} shape - ndarray shape
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* @param {string} xtype - input ndarray data type
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* @param {string} ytype - output ndarray data type
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* @param {string} order - ndarray memory layout
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* @returns {Function} benchmark function
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*/
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function createBenchmark( len, shape, xtype, ytype, order ) {
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var strides;
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var opts;
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var xbuf;
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var x;
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xbuf = discreteUniform( len, -100, 100, {
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'dtype': xtype
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});
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strides = shape2strides( shape, order );
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x = ndarray( xtype, xbuf, shape, strides, 0, order );
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opts = {
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'dtype': ytype
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};
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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 y;
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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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y = map( x, opts, identity );
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if ( isnan( y.data[ i%len ] ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( !isndarrayLike( y ) ) {
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b.fail( 'should return an ndarray' );
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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 ord;
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var sh;
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var t1;
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var t2;
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var f;
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var i;
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var j;
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var k;
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min = 1; // 10^min
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max = 6; // 10^max
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for ( k = 0; k < orders.length; k++ ) {
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ord = orders[ k ];
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for ( j = 0; j < xtypes.length; j++ ) {
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t1 = xtypes[ j ];
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t2 = ytypes[ j ];
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for ( i = min; i <= max; i++ ) {
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len = pow( 10, i );
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sh = [ len ];
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f = createBenchmark( len, sh, t1, t2, ord );
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bench( pkg+':ndims='+sh.length+',len='+len+',shape=['+sh.join(',')+'],xorder='+ord+',yorder='+ord+',xtype='+t1+',ytype='+t2, f );
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}
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}
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}
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}
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main();

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