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* @license Apache-2.0
*
* Copyright (c) 2025 The Stdlib Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
'use strict';
// MODULES //
var iterationOrder = require( '@stdlib/ndarray/base/iteration-order' );
var minmaxViewBufferIndex = require( '@stdlib/ndarray/base/minmax-view-buffer-index' );
var ndarray2object = require( '@stdlib/ndarray/base/ndarraylike2object' );
var strides2order = require( '@stdlib/ndarray/base/strides2order' );
var anyIsEntryIn = require( '@stdlib/array/base/any-is-entry-in' );
var format = require( '@stdlib/string/format' );
var blockedaccessorbinary2d = require( './2d_blocked_accessors.js' );
var blockedaccessorbinary3d = require( './3d_blocked_accessors.js' );
var blockedaccessorbinary4d = require( './4d_blocked_accessors.js' );
var blockedaccessorbinary5d = require( './5d_blocked_accessors.js' );
var blockedaccessorbinary6d = require( './6d_blocked_accessors.js' );
var blockedaccessorbinary7d = require( './7d_blocked_accessors.js' );
var blockedaccessorbinary8d = require( './8d_blocked_accessors.js' );
var blockedaccessorbinary9d = require( './9d_blocked_accessors.js' );
var blockedaccessorbinary10d = require( './10d_blocked_accessors.js' );
var blockedbinary2d = require( './2d_blocked.js' );
var blockedbinary3d = require( './3d_blocked.js' );
var blockedbinary4d = require( './4d_blocked.js' );
var blockedbinary5d = require( './5d_blocked.js' );
var blockedbinary6d = require( './6d_blocked.js' );
var blockedbinary7d = require( './7d_blocked.js' );
var blockedbinary8d = require( './8d_blocked.js' );
var blockedbinary9d = require( './9d_blocked.js' );
var blockedbinary10d = require( './10d_blocked.js' );
var accessorbinary0d = require( './0d_accessors.js' );
var accessorbinary1d = require( './1d_accessors.js' );
var accessorbinary2d = require( './2d_accessors.js' );
var accessorbinary3d = require( './3d_accessors.js' );
var accessorbinary4d = require( './4d_accessors.js' );
var accessorbinary5d = require( './5d_accessors.js' );
var accessorbinary6d = require( './6d_accessors.js' );
var accessorbinary7d = require( './7d_accessors.js' );
var accessorbinary8d = require( './8d_accessors.js' );
var accessorbinary9d = require( './9d_accessors.js' );
var accessorbinary10d = require( './10d_accessors.js' );
var accessorbinarynd = require( './nd_accessors.js' );
var binary0d = require( './0d.js' );
var binary1d = require( './1d.js' );
var binary2d = require( './2d.js' );
var binary3d = require( './3d.js' );
var binary4d = require( './4d.js' );
var binary5d = require( './5d.js' );
var binary6d = require( './6d.js' );
var binary7d = require( './7d.js' );
var binary8d = require( './8d.js' );
var binary9d = require( './9d.js' );
var binary10d = require( './10d.js' );
var binarynd = require( './nd.js' );
// VARIABLES //
var BINARY = [
binary0d,
binary1d,
binary2d,
binary3d,
binary4d,
binary5d,
binary6d,
binary7d,
binary8d,
binary9d,
binary10d
];
var ACCESSOR_BINARY = [
accessorbinary0d,
accessorbinary1d,
accessorbinary2d,
accessorbinary3d,
accessorbinary4d,
accessorbinary5d,
accessorbinary6d,
accessorbinary7d,
accessorbinary8d,
accessorbinary9d,
accessorbinary10d
];
var BLOCKED_BINARY = [
blockedbinary2d, // 0
blockedbinary3d,
blockedbinary4d,
blockedbinary5d,
blockedbinary6d,
blockedbinary7d,
blockedbinary8d,
blockedbinary9d,
blockedbinary10d // 8
];
var BLOCKED_ACCESSOR_BINARY = [
blockedaccessorbinary2d, // 0
blockedaccessorbinary3d,
blockedaccessorbinary4d,
blockedaccessorbinary5d,
blockedaccessorbinary6d,
blockedaccessorbinary7d,
blockedaccessorbinary8d,
blockedaccessorbinary9d,
blockedaccessorbinary10d // 8
];
var MAX_DIMS = BINARY.length - 1;
// FUNCTIONS //
/**
* Returns a boolean indicating if at least one ndarray data buffer implements the accessor protocol.
*
* @private
* @param {ndarrayLike} x - first ndarray
* @param {ndarrayLike} y - second ndarray
* @param {ndarrayLike} z - third ndarray
* @returns {boolean} boolean indicating whether an ndarray data buffer implements the accessor protocol
*/
function hasAccessors( x, y, z ) {
return anyIsEntryIn( [ x, y, z ], 'accessorProtocol', true );
}
// MAIN //
/**
* Applies a binary callback to elements in input ndarrays and assigns results to elements in an output ndarray.
*
* ## Notes
*
* - Each provided ndarray should be an object with the following properties:
*
* - **dtype**: data type.
* - **data**: data buffer.
* - **shape**: dimensions.
* - **strides**: stride lengths.
* - **offset**: index offset.
* - **order**: specifies whether an ndarray is row-major (C-style) or column major (Fortran-style).
*
* @param {ArrayLikeObject<Object>} arrays - array-like object containing two input arrays and one output array
* @param {Callback} fcn - binary callback
* @throws {Error} arrays must have the same number of dimensions
* @throws {Error} arrays must have the same shape
* @returns {void}
*
* @example
* var Float64Array = require( '@stdlib/array/float64' );
* var ndarray = require( '@stdlib/ndarray/ctor' );
* var getData = require( '@stdlib/ndarray/data-buffer' );
*
* function add( a, b ) {
* return a + b;
* }
*
* // Create data buffers:
* var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
* var ybuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
* var zbuf = new Float64Array( 6 );
*
* // Define the shape of the input and output arrays:
* var shape = [ 3, 1, 2 ];
*
* // Define the array strides:
* var sx = [ 2, 2, 1 ];
* var sy = [ 2, 2, 1 ];
* var sz = [ 2, 2, 1 ];
*
* // Define the index offsets:
* var ox = 0;
* var oy = 0;
* var oz = 0;
*
* // Create the input and output ndarrays:
* var x = new ndarray( 'float64', xbuf, shape, sx, ox, 'row-major' );
* var y = new ndarray( 'float64', ybuf, shape, sy, oy, 'row-major' );
* var z = new ndarray( 'float64', zbuf, shape, sz, oz, 'row-major' );
*
* // Apply the binary function:
* binary( [ x, y, z ], add );
*
* console.log( getData( z ) );
* // => <Float64Array>[ 2.0, 4.0, 6.0, 8.0, 10.0, 12.0 ]
*/
function binary( arrays, fcn ) { // eslint-disable-line max-statements
var ndims;
var xmmv;
var ymmv;
var zmmv;
var shx;
var shy;
var shz;
var iox;
var ioy;
var ioz;
var len;
var ord;
var sx;
var sy;
var sz;
var ox;
var oy;
var oz;
var ns;
var x;
var y;
var z;
var d;
var i;
// Unpack the ndarrays and standardize ndarray meta data:
x = ndarray2object( arrays[ 0 ] );
y = ndarray2object( arrays[ 1 ] );
z = ndarray2object( arrays[ 2 ] );
// Verify that the input and output arrays have the same number of dimensions...
shx = x.shape;
shy = y.shape;
shz = z.shape;
ndims = shx.length;
if ( ndims !== shy.length || ndims !== shz.length ) {
throw new Error( format( 'invalid arguments. Arrays must have the same number of dimensions (i.e., same rank). ndims(x) == %d. ndims(y) == %d. ndims(z) == %d.', ndims, shy.length, shz.length ) );
}
// Determine whether we can avoid iteration altogether...
if ( ndims === 0 ) {
if ( hasAccessors( x, y, z ) ) {
return ACCESSOR_BINARY[ ndims ]( x, y, z, fcn );
}
return BINARY[ ndims ]( x, y, z, fcn );
}
// Verify that the input and output arrays have the same dimensions...
len = 1; // number of elements
ns = 0; // number of singleton dimensions
for ( i = 0; i < ndims; i++ ) {
d = shx[ i ];
if ( d !== shy[ i ] || d !== shz[ i ] ) {
throw new Error( 'invalid arguments. Arrays must have the same shape.' );
}
// Note that, if one of the dimensions is `0`, the length will be `0`...
len *= d;
// Check whether the current dimension is a singleton dimension...
if ( d === 1 ) {
ns += 1;
}
}
// Check whether we were provided empty ndarrays...
if ( len === 0 ) {
return;
}
// Determine whether the ndarrays are one-dimensional and thus readily translate to one-dimensional strided arrays...
if ( ndims === 1 ) {
if ( hasAccessors( x, y, z ) ) {
return ACCESSOR_BINARY[ ndims ]( x, y, z, fcn );
}
return BINARY[ ndims ]( x, y, z, fcn );
}
sx = x.strides;
sy = y.strides;
sz = z.strides;
// Determine whether the ndarrays have only **one** non-singleton dimension (e.g., ndims=4, shape=[10,1,1,1]) so that we can treat the ndarrays as being equivalent to one-dimensional strided arrays...
if ( ns === ndims-1 ) {
// Get the index of the non-singleton dimension...
for ( i = 0; i < ndims; i++ ) {
if ( shx[ i ] !== 1 ) {
break;
}
}
x.shape = [ shx[i] ];
y.shape = x.shape;
z.shape = x.shape;
x.strides = [ sx[i] ];
y.strides = [ sy[i] ];
z.strides = [ sz[i] ];
if ( hasAccessors( x, y, z ) ) {
return ACCESSOR_BINARY[ 1 ]( x, y, z, fcn );
}
return BINARY[ 1 ]( x, y, z, fcn );
}
iox = iterationOrder( sx ); // +/-1
ioy = iterationOrder( sy ); // +/-1
ioz = iterationOrder( sz ); // +/-1
// Determine whether we can avoid blocked iteration...
ord = strides2order( sx );
if ( iox !== 0 && ioy !== 0 && ioz !== 0 && ord === strides2order( sy ) && ord === strides2order( sz ) ) { // eslint-disable-line max-len
// Determine the minimum and maximum linear indices which are accessible by the array views:
xmmv = minmaxViewBufferIndex( shx, sx, x.offset );
ymmv = minmaxViewBufferIndex( shy, sy, y.offset );
zmmv = minmaxViewBufferIndex( shz, sz, z.offset );
// Determine whether we can ignore shape (and strides) and treat the ndarrays as linear one-dimensional strided arrays...
if (
len === ( xmmv[1]-xmmv[0]+1 ) &&
len === ( ymmv[1]-ymmv[0]+1 ) &&
len === ( zmmv[1]-zmmv[0]+1 )
) {
// Note: the above is equivalent to @stdlib/ndarray/base/assert/is-contiguous, but in-lined so we can retain computed values...
if ( iox === 1 ) {
ox = xmmv[ 0 ];
} else {
ox = xmmv[ 1 ];
}
if ( ioy === 1 ) {
oy = ymmv[ 0 ];
} else {
oy = ymmv[ 1 ];
}
if ( ioz === 1 ) {
oz = zmmv[ 0 ];
} else {
oz = zmmv[ 1 ];
}
x.shape = [ len ];
y.shape = x.shape;
z.shape = x.shape;
x.strides = [ iox ];
y.strides = [ ioy ];
z.strides = [ ioz ];
x.offset = ox;
y.offset = oy;
z.offset = oz;
if ( hasAccessors( x, y, z ) ) {
return ACCESSOR_BINARY[ 1 ]( x, y, z, fcn );
}
return BINARY[ 1 ]( x, y, z, fcn );
}
// At least one ndarray is non-contiguous, so we cannot directly use one-dimensional array functionality...
// Determine whether we can use simple nested loops...
if ( ndims <= MAX_DIMS ) {
// So long as iteration for each respective array always moves in the same direction (i.e., no mixed sign strides), we can leverage cache-optimal (i.e., normal) nested loops without resorting to blocked iteration...
if ( hasAccessors( x, y, z ) ) {
return ACCESSOR_BINARY[ ndims ]( x, y, z, ord === 1, fcn );
}
return BINARY[ ndims ]( x, y, z, ord === 1, fcn );
}
// Fall-through to blocked iteration...
}
// At this point, we're either dealing with non-contiguous n-dimensional arrays, high dimensional n-dimensional arrays, and/or arrays having differing memory layouts, so our only hope is that we can still perform blocked iteration...
// Determine whether we can perform blocked iteration...
if ( ndims <= MAX_DIMS ) {
if ( hasAccessors( x, y, z ) ) {
return BLOCKED_ACCESSOR_BINARY[ ndims-2 ]( x, y, z, fcn );
}
return BLOCKED_BINARY[ ndims-2 ]( x, y, z, fcn );
}
// Fall-through to linear view iteration without regard for how data is stored in memory (i.e., take the slow path)...
if ( hasAccessors( x, y, z ) ) {
return accessorbinarynd( x, y, z, fcn );
}
binarynd( x, y, z, fcn );
}
// EXPORTS //
module.exports = binary;
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