@stdlib/ndarray-base-minmax-view-buffer-index

Compute the minimum and maximum linear indices in an underlying data buffer which are accessible to an array view.

Usage no npm install needed!

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README

Min and Max View Buffer Indices

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Compute the minimum and maximum linear indices in an underlying data buffer which are accessible to an array view.

Installation

npm install @stdlib/ndarray-base-minmax-view-buffer-index

Usage

var minmaxViewBufferIndex = require( '@stdlib/ndarray-base-minmax-view-buffer-index' );

minmaxViewBufferIndex( shape, strides, offset )

Computes the minimum and maximum linear indices in an underlying data buffer which are accessible to an array view.

// Array shape:
var shape = [ 2, 2 ];

// Stride array:
var strides = [ 2, 1 ];

// Index offset which specifies the location of the first indexed value:
var offset = 0;

var idx = minmaxViewBufferIndex( shape, strides, offset );
// returns [ 0, 3 ]

minmaxViewBufferIndex.assign( shape, strides, offset, out )

Computes the minimum and maximum linear indices in an underlying data buffer which are accessible to an array view and assigns results to a provided output array.

var shape = [ 2, 2 ];
var strides = [ -1, -2 ];
var offset = 3;

var out = [ 0, 0 ];
var idx = minmaxViewBufferIndex.assign( shape, strides, offset, out );
// returns [ 0, 3 ]

var bool = ( idx === out );
// returns true

Examples

var discreteUniform = require( '@stdlib/random-base-discrete-uniform' );
var shape2strides = require( '@stdlib/ndarray-base-shape2strides' );
var strides2offset = require( '@stdlib/ndarray-base-strides2offset' );
var randu = require( '@stdlib/random-base-randu' );
var minmaxViewBufferIndex = require( '@stdlib/ndarray-base-minmax-view-buffer-index' );

var strides;
var offset;
var shape;
var idx;
var i;
var j;

shape = [ 0, 0, 0 ];

for ( i = 0; i < 100; i++ ) {
    // Generate a random array shape:
    shape[ 0 ] = discreteUniform( 1, 10 );
    shape[ 1 ] = discreteUniform( 1, 10 );
    shape[ 2 ] = discreteUniform( 1, 10 );

    // Generate strides:
    if ( randu() < 0.5 ) {
        strides = shape2strides( shape, 'row-major' );
    } else {
        strides = shape2strides( shape, 'column-major' );
    }
    j = discreteUniform( 0, shape.length-1 );
    strides[ j ] *= ( randu() < 0.5 ) ? -1 : 1;

    // Compute the index offset:
    offset = strides2offset( shape, strides ) + 25; // include a view offset

    // Compute the minimum and maximum linear indices:
    idx = minmaxViewBufferIndex( shape, strides, offset );
    console.log( 'Shape: %s. Strides: %s. Offset: %d. Min idx: %d. Max idx: %d.', shape.join( 'x' ), strides.join( ',' ), offset, idx[ 0 ], idx[ 1 ] );
}

C APIs

Usage

#include "stdlib/ndarray/base/minmax_view_buffer_index.h"

stdlib_ndarray_minmax_view_buffer_index( ndims, *shape, *strides, offset, *out )

Computes the minimum and maximum linear indices (in bytes) in an underlying data buffer accessible to an array view.

int64_t ndims = 2;
int64_t shape[] = { 10, 10 };
int64_t strides[] = { 10, 1 };
int64_t offset = 0;
int64_t out[ 2 ];

stdlib_ndarray_minmax_view_buffer_index( ndims, shape, strides, offset, out );

int64_t min = out[ 0 ];
// returns 0

int64_t max = out[ 1 ];
// returns 99

The function accepts the following arguments:

  • ndims: [in] int64_t number of dimensions.
  • shape: [in] int64_t* array shape (dimensions).
  • strides: [in] int64_t* array strides (in bytes).
  • offset: [in] int64_t index offset.
  • out: [out] int64_t* two-element output array.
int8_t stdlib_ndarray_minmax_view_buffer_index( int64_t ndims, int64_t *shape, int64_t *strides, int64_t offset, int64_t *out );

Examples

#include "stdlib/ndarray/base/minmax_view_buffer_index.h"
#include <stdio.h>
#include <inttypes.h>

int main() {
    int64_t ndims = 2;
    int64_t shape[] = { 10, 10 };
    int64_t strides[] = { 10, 1 };
    int64_t offset = 0;
    int64_t out[ 2 ];

    stdlib_ndarray_minmax_view_buffer_index( ndims, shape, strides, offset, out );

    printf( "min: %"PRId64"\n", out[ 0 ] );
    printf( "max: %"PRId64"\n", out[ 1 ] );
}

Notice

This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.

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License

See LICENSE.

Copyright

Copyright © 2016-2022. The Stdlib Authors.