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feat: add blas/ext/base/ndarray/ddiff
PR-URL: #12870 Co-authored-by: Athan Reines <kgryte@gmail.com> Reviewed-by: Athan Reines <kgryte@gmail.com> Signed-off-by: Athan Reines <kgryte@gmail.com> Closes: stdlib-js/metr-issue-tracker#667
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‎lib/node_modules/@stdlib/blas/ext/base/ndarray/ddiff/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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# ddiff
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> Calculate the k-th discrete forward difference of a one-dimensional double-precision floating-point ndarray.
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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 ddiff = require( '@stdlib/blas/ext/base/ndarray/ddiff' );
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```
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#### ddiff( arrays )
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Calculates the k-th discrete forward difference of a one-dimensional double-precision floating-point ndarray.
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```javascript
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var Float64Vector = require( '@stdlib/ndarray/vector/float64' );
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var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
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var x = new Float64Vector( [ 2.0, 4.0, 6.0, 8.0, 10.0 ] );
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var prepend = new Float64Vector( [ 1.0 ] );
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var append = new Float64Vector( [ 11.0 ] );
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var out = new Float64Vector( 6 );
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var workspace = new Float64Vector( 6 );
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var k = scalar2ndarray( 1, {
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'dtype': 'generic'
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});
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var y = ddiff( [ x, prepend, append, out, workspace, k ] );
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// returns <ndarray>[ 1.0, 2.0, 2.0, 2.0, 2.0, 1.0 ]
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```
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The function has the following parameters:
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- **arrays**: array-like object containing the following ndarrays:
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- a one-dimensional input ndarray.
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- a one-dimensional ndarray containing values to prepend prior to computing differences.
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- a one-dimensional ndarray containing values to append prior to computing differences.
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- a one-dimensional output ndarray. Must have `N + N1 + N2 - k` elements, where `N` is the number of elements in the input ndarray, `N1` is the number of elements to prepend, `N2` is the number of elements to append, and `k` is the number of times to recursively compute differences.
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- a one-dimensional workspace ndarray. Must have `N + N1 + N2 - 1` elements.
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- a zero-dimensional ndarray specifying the number of times to recursively compute differences.
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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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- When `k <= 1`, the workspace ndarray is unused.
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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/discrete-uniform' );
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var zeros = require( '@stdlib/ndarray/zeros' );
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var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
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var ndarray2array = require( '@stdlib/ndarray/to-array' );
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var ddiff = require( '@stdlib/blas/ext/base/ndarray/ddiff' );
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var N = 10;
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var N1 = 2;
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var N2 = 2;
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var k = 4;
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var opts = {
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'dtype': 'float64'
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};
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var x = discreteUniform( [ N ], -100, 100, opts );
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var p = discreteUniform( [ N1 ], -100, 100, opts );
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var a = discreteUniform( [ N2 ], -100, 100, opts );
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var out = zeros( [ N + N1 + N2 - k ], opts );
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var w = zeros( [ N + N1 + N2 - 1 ], opts );
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var knd = scalar2ndarray( k, {
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'dtype': 'generic'
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});
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console.log( 'x: ', ndarray2array( x ) );
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console.log( 'prepend: ', ndarray2array( p ) );
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console.log( 'append: ', ndarray2array( a ) );
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ddiff( [ x, p, a, out, w, knd ] );
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console.log( 'out: ', ndarray2array( out ) );
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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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</section>
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<!-- /.links -->

‎lib/node_modules/@stdlib/blas/ext/base/ndarray/ddiff/benchmark/benchmark.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 uniform = require( '@stdlib/random/uniform' );
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var zeros = require( '@stdlib/ndarray/zeros' );
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var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
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var pow = require( '@stdlib/math/base/special/pow' );
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var format = require( '@stdlib/string/format' );
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var pkg = require( './../package.json' ).name;
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var ddiff = require( './../lib' );
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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 workspace;
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var prepend;
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var append;
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var opts;
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var out;
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var k;
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var x;
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opts = {
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'dtype': 'float64'
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};
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x = uniform( [ len ], -100.0, 100.0, opts );
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prepend = uniform( [ 1 ], -100.0, 100.0, opts );
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append = uniform( [ 1 ], -100.0, 100.0, opts );
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out = zeros( [ len + 1 ], opts );
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workspace = zeros( [ len + 1 ], opts );
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k = scalar2ndarray( 1, {
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'dtype': 'generic'
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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 v;
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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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v = ddiff( [ x, prepend, append, out, workspace, k ] );
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if ( typeof v !== '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 ( typeof v !== 'object' ) {
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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 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:len=%d', pkg, len ), f );
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}
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}
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main();

‎lib/node_modules/@stdlib/blas/ext/base/ndarray/ddiff/docs/repl.txt‎

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{{alias}}( arrays )
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Calculates the k-th discrete forward difference of a one-dimensional double-
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precision floating-point ndarray.
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The output ndarray must have `N + N1 + N2 - k` elements, where `N` is the
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number of elements in the input ndarray, `N1` is the number of elements to
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prepend, `N2` is the number of elements to append, and `k` is the number
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of times to recursively compute differences.
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The workspace ndarray must have `N + N1 + N2 - 1` elements.
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When `k <= 1`, the workspace ndarray is unused.
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Parameters
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----------
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arrays: ArrayLikeObject<ndarray>
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Array-like object containing the following ndarrays:
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- a one-dimensional input ndarray.
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- a one-dimensional ndarray containing values to prepend.
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- a one-dimensional ndarray containing values to append.
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- a one-dimensional output ndarray.
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- a one-dimensional workspace ndarray.
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- a zero-dimensional ndarray specifying the number of times to
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recursively compute differences.
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Returns
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-------
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out: ndarray
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Output ndarray.
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Examples
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--------
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> var x = new {{alias:@stdlib/ndarray/vector/float64}}( [ 2.0, 4.0, 6.0, 8.0, 10.0 ] );
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> var p = new {{alias:@stdlib/ndarray/vector/float64}}( [ 1.0 ] );
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> var a = new {{alias:@stdlib/ndarray/vector/float64}}( [ 11.0 ] );
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> var out = new {{alias:@stdlib/ndarray/vector/float64}}( 6 );
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> var w = new {{alias:@stdlib/ndarray/vector/float64}}( 6 );
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> var k = {{alias:@stdlib/ndarray/from-scalar}}( 1, { 'dtype': 'generic' } );
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> {{alias}}( [ x, p, a, out, w, k ] )
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<ndarray>[ 1.0, 2.0, 2.0, 2.0, 2.0, 1.0 ]
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See Also
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--------

‎lib/node_modules/@stdlib/blas/ext/base/ndarray/ddiff/docs/types/index.d.ts‎

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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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// TypeScript Version: 4.1
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/// <reference types="@stdlib/types"/>
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import { typedndarray, float64ndarray } from '@stdlib/types/ndarray';
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/**
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* Calculates the k-th discrete forward difference of a one-dimensional double-precision floating-point ndarray.
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*
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* ## Notes
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*
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* - The function expects the following ndarrays:
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*
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* - a one-dimensional input ndarray.
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* - a one-dimensional ndarray containing values to prepend.
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* - a one-dimensional ndarray containing values to append.
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* - a one-dimensional output ndarray.
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* - a one-dimensional workspace ndarray.
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* - a zero-dimensional ndarray specifying the number of times to recursively compute differences.
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*
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* @param arrays - array-like object containing ndarrays
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* @returns output ndarray
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*
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* @example
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* var Float64Vector = require( '@stdlib/ndarray/vector/float64' );
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* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
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*
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* var x = new Float64Vector( [ 2.0, 4.0, 6.0, 8.0, 10.0 ] );
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* var prepend = new Float64Vector( [ 1.0 ] );
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* var append = new Float64Vector( [ 11.0 ] );
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* var out = new Float64Vector( 6 );
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* var workspace = new Float64Vector( 6 );
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* var k = scalar2ndarray( 1, {
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* 'dtype': 'generic'
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* });
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*
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* var y = ddiff( [ x, prepend, append, out, workspace, k ] );
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* // returns <ndarray>[ 1.0, 2.0, 2.0, 2.0, 2.0, 1.0 ]
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*/
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declare function ddiff( arrays: [ float64ndarray, float64ndarray, float64ndarray, float64ndarray, float64ndarray, typedndarray<number> ] ): float64ndarray;
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// EXPORTS //
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export = ddiff;

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