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* @license Apache-2.0
*
* Copyright (c) 2023 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 blockedaccessornullary2d = require( './2d_blocked_accessors.js' );
var blockedaccessornullary3d = require( './3d_blocked_accessors.js' );
var blockedaccessornullary4d = require( './4d_blocked_accessors.js' );
var blockedaccessornullary5d = require( './5d_blocked_accessors.js' );
var blockedaccessornullary6d = require( './6d_blocked_accessors.js' );
var blockedaccessornullary7d = require( './7d_blocked_accessors.js' );
var blockedaccessornullary8d = require( './8d_blocked_accessors.js' );
var blockedaccessornullary9d = require( './9d_blocked_accessors.js' );
var blockedaccessornullary10d = require( './10d_blocked_accessors.js' );
var blockednullary2d = require( './2d_blocked.js' );
var blockednullary3d = require( './3d_blocked.js' );
var blockednullary4d = require( './4d_blocked.js' );
var blockednullary5d = require( './5d_blocked.js' );
var blockednullary6d = require( './6d_blocked.js' );
var blockednullary7d = require( './7d_blocked.js' );
var blockednullary8d = require( './8d_blocked.js' );
var blockednullary9d = require( './9d_blocked.js' );
var blockednullary10d = require( './10d_blocked.js' );
var accessornullary0d = require( './0d_accessors.js' );
var accessornullary1d = require( './1d_accessors.js' );
var accessornullary2d = require( './2d_accessors.js' );
var accessornullary3d = require( './3d_accessors.js' );
var accessornullary4d = require( './4d_accessors.js' );
var accessornullary5d = require( './5d_accessors.js' );
var accessornullary6d = require( './6d_accessors.js' );
var accessornullary7d = require( './7d_accessors.js' );
var accessornullary8d = require( './8d_accessors.js' );
var accessornullary9d = require( './9d_accessors.js' );
var accessornullary10d = require( './10d_accessors.js' );
var accessornullarynd = require( './nd_accessors.js' );
var nullary0d = require( './0d.js' );
var nullary1d = require( './1d.js' );
var nullary2d = require( './2d.js' );
var nullary3d = require( './3d.js' );
var nullary4d = require( './4d.js' );
var nullary5d = require( './5d.js' );
var nullary6d = require( './6d.js' );
var nullary7d = require( './7d.js' );
var nullary8d = require( './8d.js' );
var nullary9d = require( './9d.js' );
var nullary10d = require( './10d.js' );
var nullarynd = require( './nd.js' );
// VARIABLES //
var NULLARY = [
nullary0d,
nullary1d,
nullary2d,
nullary3d,
nullary4d,
nullary5d,
nullary6d,
nullary7d,
nullary8d,
nullary9d,
nullary10d
];
var ACCESSOR_NULLARY = [
accessornullary0d,
accessornullary1d,
accessornullary2d,
accessornullary3d,
accessornullary4d,
accessornullary5d,
accessornullary6d,
accessornullary7d,
accessornullary8d,
accessornullary9d,
accessornullary10d
];
var BLOCKED_NULLARY = [
blockednullary2d, // 0
blockednullary3d,
blockednullary4d,
blockednullary5d,
blockednullary6d,
blockednullary7d,
blockednullary8d,
blockednullary9d,
blockednullary10d // 8
];
var BLOCKED_ACCESSOR_NULLARY = [
blockedaccessornullary2d, // 0
blockedaccessornullary3d,
blockedaccessornullary4d,
blockedaccessornullary5d,
blockedaccessornullary6d,
blockedaccessornullary7d,
blockedaccessornullary8d,
blockedaccessornullary9d,
blockedaccessornullary10d // 8
];
var MAX_DIMS = NULLARY.length - 1;
// MAIN //
/**
* Applies a nullary function and assigns results to elements in an output ndarray.
*
* ## Notes
*
* - A 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 one output array
* @param {Callback} fcn - nullary callback
* @returns {void}
*
* @example
* var Float64Array = require( '@stdlib/array/float64' );
*
* function fcn() {
* return 10.0;
* }
*
* // Create a data buffer:
* var xbuf = new Float64Array( 12 );
*
* // Define the shape of the output array:
* var shape = [ 3, 1, 2 ];
*
* // Define the array strides:
* var sx = [ 4, 4, 1 ];
*
* // Define the index offset:
* var ox = 1;
*
* // Create the output ndarray-like object:
* var x = {
* 'dtype': 'float64',
* 'data': xbuf,
* 'shape': shape,
* 'strides': sx,
* 'offset': ox,
* 'order': 'row-major'
* };
*
* // Apply the nullary function:
* nullary( [ x ], fcn );
*
* console.log( x.data );
* // => <Float64Array>[ 0.0, 10.0, 10.0, 0.0, 0.0, 10.0, 10.0, 0.0, 0.0, 10.0, 10.0, 0.0 ]
*/
function nullary( arrays, fcn ) {
var ndims;
var xmmv;
var shx;
var iox;
var len;
var sx;
var ox;
var ns;
var x;
var d;
var i;
// Unpack the ndarray and standardize ndarray meta data:
x = ndarray2object( arrays[ 0 ] );
shx = x.shape;
ndims = shx.length;
// Determine whether we can avoid iteration altogether...
if ( ndims === 0 ) {
if ( x.accessorProtocol ) {
return ACCESSOR_NULLARY[ ndims ]( x, fcn );
}
return NULLARY[ ndims ]( x, fcn );
}
// Compute the number of elements and the number of singleton dimensions...
len = 1; // number of elements
ns = 0; // number of singleton dimensions
for ( i = 0; i < ndims; i++ ) {
d = shx[ i ];
// 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 an empty ndarray...
if ( len === 0 ) {
return;
}
// Determine whether the ndarray is one-dimensional and thus readily translates to a one-dimensional strided array...
if ( ndims === 1 ) {
if ( x.accessorProtocol ) {
return ACCESSOR_NULLARY[ ndims ]( x, fcn );
}
return NULLARY[ ndims ]( x, fcn );
}
sx = x.strides;
// Determine whether the ndarray has only **one** non-singleton dimension (e.g., ndims=4, shape=[10,1,1,1]) so that we can treat an ndarray as being equivalent to a one-dimensional strided array...
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] ];
x.strides = [ sx[i] ];
if ( x.accessorProtocol ) {
return ACCESSOR_NULLARY[ 1 ]( x, fcn );
}
return NULLARY[ 1 ]( x, fcn );
}
iox = iterationOrder( sx ); // +/-1
// Determine whether we can avoid blocked iteration...
if ( iox !== 0 ) {
// Determine the minimum and maximum linear indices which are accessible by the array view:
xmmv = minmaxViewBufferIndex( shx, sx, x.offset );
// Determine whether we can ignore shape (and strides) and treat the ndarray as a linear one-dimensional strided array...
if ( len === ( xmmv[1]-xmmv[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 ];
}
x.shape = [ len ];
x.strides = [ iox ];
x.offset = ox;
if ( x.accessorProtocol ) {
return ACCESSOR_NULLARY[ 1 ]( x, fcn );
}
return NULLARY[ 1 ]( x, fcn );
}
// The 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 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 ( x.accessorProtocol ) {
return ACCESSOR_NULLARY[ ndims ]( x, fcn );
}
return NULLARY[ ndims ]( x, fcn );
}
// Fall-through to blocked iteration...
}
// At this point, we're either dealing with a non-contiguous n-dimensional array or a high dimensional n-dimensional array, so our only hope is that we can still perform blocked iteration...
// Determine whether we can perform blocked iteration...
if ( ndims <= MAX_DIMS ) {
if ( x.accessorProtocol ) {
return BLOCKED_ACCESSOR_NULLARY[ ndims-2 ]( x, fcn );
}
return BLOCKED_NULLARY[ ndims-2 ]( x, fcn );
}
// Fall-through to linear view iteration without regard for how data is stored in memory (i.e., take the slow path)...
if ( x.accessorProtocol ) {
return accessornullarynd( x, fcn );
}
nullarynd( x, fcn );
}
// EXPORTS //
module.exports = nullary;
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