Press n or j to go to the next uncovered block, b, p or k for the previous block.
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 | 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x | /**
* @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 ndarray2object = require( '@stdlib/ndarray/base/ndarraylike2object' );
var normalizeIndices = require( '@stdlib/ndarray/base/to-unique-normalized-indices' );
var indicesComplement = require( '@stdlib/array/base/indices-complement' );
var takeIndexed2 = require( '@stdlib/array/base/take-indexed2' );
var iterationOrder = require( '@stdlib/ndarray/base/iteration-order' );
var strides2order = require( '@stdlib/ndarray/base/strides2order' );
var numel = require( '@stdlib/ndarray/base/numel' );
var join = require( '@stdlib/array/base/join' );
var format = require( '@stdlib/string/format' );
var initializeViews = require( './initialize_array_views.js' );
var strategy = require( './strategy.js' );
var defaults = require( './defaults.js' );
var validate = require( './validate.js' );
var blockednullary2d = require( './2d_blocked.js' );
var blockednullary3d = require( './3d_blocked.js' );
var nullary0d = require( './0d.js' );
var nullary1d = require( './1d.js' );
var nullary2d = require( './2d.js' );
var nullary3d = require( './3d.js' );
var nullarynd = require( './nd.js' );
// VARIABLES //
var NULLARY = [
nullary0d,
nullary1d,
nullary2d,
nullary3d
];
var BLOCKED_NULLARY = [
blockednullary2d, // 0
blockednullary3d
];
var MAX_DIMS = NULLARY.length - 1;
// MAIN //
/**
* Returns a function for applying a one-dimensional strided array function to a list of specified dimensions in an ndarray.
*
* @private
* @param {Options} [options] - function options
* @param {boolean} [options.strictTraversalOrder=false] - boolean specifying whether to require that element traversal match the memory layout of the target ndarray
* @throws {TypeError} options argument must be an object
* @throws {TypeError} must provide valid options
* @returns {Function} function for applying a strided array function
*
* @example
* var ndarray2array = require( '@stdlib/ndarray/base/to-array' );
* var getStride = require( '@stdlib/ndarray/base/stride' );
* var getOffset = require( '@stdlib/ndarray/base/offset' );
* var getData = require( '@stdlib/ndarray/base/data-buffer' );
* var numelDimension = require( '@stdlib/ndarray/base/numel-dimension' );
* var ndarraylike2scalar = require( '@stdlib/ndarray/base/ndarraylike2scalar' );
* var gsorthp = require( '@stdlib/blas/ext/base/gsorthp' ).ndarray;
*
* function wrapper( arrays ) {
* var x = arrays[ 0 ];
* var o = arrays[ 1 ];
* return gsorthp( numelDimension( x, 0 ), ndarraylike2scalar( o ), getData( x ), getStride( x, 0 ), getOffset( x ) );
* }
*
* // Create data buffers:
* var xbuf = [ 12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0 ];
*
* // Define the array shapes:
* var xsh = [ 1, 3, 2, 2 ];
*
* // Define the array strides:
* var sx = [ 12, 4, 2, 1 ];
*
* // Define the index offsets:
* var ox = 0;
*
* // Create an ndarray-like object:
* var x = {
* 'dtype': 'generic',
* 'data': xbuf,
* 'shape': xsh,
* 'strides': sx,
* 'offset': ox,
* 'order': 'row-major'
* };
*
* // Create an ndarray-like object for the sort order:
* var sortOrder = {
* 'dtype': 'generic',
* 'data': [ 1.0 ],
* 'shape': [ 1, 3 ],
* 'strides': [ 0, 0 ],
* 'offset': 0,
* 'order': 'row-major'
* };
*
* // Apply strided function:
* var f = factory();
* f( wrapper, [ x, sortOrder ], [ 2, 3 ] );
*
* var arr = ndarray2array( x.data, x.shape, x.strides, x.offset, x.order );
* // returns [ [ [ [ 9.0, 10.0 ], [ 11.0, 12.0 ] ], [ [ 5.0, 6.0 ], [ 7.0, 8.0 ] ], [ [ 1.0, 2.0 ], [ 3.0, 4.0 ] ] ] ]
*
* @example
* var ndarray2array = require( '@stdlib/ndarray/base/to-array' );
* var getStride = require( '@stdlib/ndarray/base/stride' );
* var getOffset = require( '@stdlib/ndarray/base/offset' );
* var getData = require( '@stdlib/ndarray/base/data-buffer' );
* var numelDimension = require( '@stdlib/ndarray/base/numel-dimension' );
* var ndarraylike2scalar = require( '@stdlib/ndarray/base/ndarraylike2scalar' );
* var gsorthp = require( '@stdlib/blas/ext/base/gsorthp' ).ndarray;
*
* function wrapper( arrays ) {
* var x = arrays[ 0 ];
* var o = arrays[ 1 ];
* return gsorthp( numelDimension( x, 0 ), ndarraylike2scalar( o ), getData( x ), getStride( x, 0 ), getOffset( x ) );
* }
*
* // Create data buffers:
* var xbuf = [ 12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0 ];
*
* // Define the array shapes:
* var xsh = [ 1, 3, 2, 2 ];
*
* // Define the array strides:
* var sx = [ 12, 4, 2, 1 ];
*
* // Define the index offsets:
* var ox = 0;
*
* // Create an ndarray-like object:
* var x = {
* 'dtype': 'generic',
* 'data': xbuf,
* 'shape': xsh,
* 'strides': sx,
* 'offset': ox,
* 'order': 'row-major'
* };
*
* // Create an ndarray-like object for the sort order:
* var sortOrder = {
* 'dtype': 'generic',
* 'data': [ 1.0 ],
* 'shape': [],
* 'strides': [ 0 ],
* 'offset': 0,
* 'order': 'row-major'
* };
*
* // Apply strided function:
* var f = factory();
* f( wrapper, [ x, sortOrder ], [ 0, 1, 2, 3 ] );
*
* var arr = ndarray2array( x.data, x.shape, x.strides, x.offset, x.order );
* // returns [ [ [ [ 1.0, 2.0 ], [ 3.0, 4.0 ] ], [ [ 5.0, 6.0 ], [ 7.0, 8.0 ] ], [ [ 9.0, 10.0 ], [ 11.0, 12.0 ] ] ] ]
*/
function factory( options ) {
var OPTS;
var err;
OPTS = defaults();
if ( arguments.length ) {
err = validate( OPTS, options );
if ( err ) {
throw err;
}
}
return nullaryStrided1d;
/**
* Applies a one-dimensional strided array function to a list of specified dimensions in an ndarray.
*
* @private
* @param {Function} fcn - wrapper for a one-dimensional strided array function
* @param {ArrayLikeObject<Object>} arrays - array-like object containing ndarrays
* @param {IntegerArray} dims - list of dimensions to which to apply a strided array function
* @param {Options} [options] - function options
* @throws {Error} arrays must have the expected number of dimensions
* @throws {RangeError} dimension indices must not exceed target ndarray bounds
* @throws {RangeError} number of dimension indices must not exceed the number of target ndarray dimensions
* @throws {Error} must provide unique dimension indices
* @throws {Error} arrays must have the same loop dimension sizes
* @returns {void}
*/
function nullaryStrided1d( fcn, arrays, dims, options ) {
var strategyX;
var views;
var ndims;
var ldims;
var opts;
var arr;
var tmp;
var len;
var shl;
var shc;
var shx;
var iox;
var scx;
var slx;
var ord;
var ns;
var d;
var s;
var N;
var M;
var K;
var x;
var i;
var j;
if ( arguments.length > 3 ) {
opts = options;
} else {
opts = {};
}
// Standardize ndarray meta data...
N = arrays.length;
arr = [];
for ( i = 0; i < N; i++ ) {
arr.push( ndarray2object( arrays[ i ] ) );
}
// Cache references to the input array:
x = arr[ 0 ];
// Resolve the number of input array dimensions:
shx = x.shape;
ndims = shx.length;
// Verify that we've been provided a list of unique dimension indices...
M = dims.length;
d = normalizeIndices( dims, ndims-1 );
if ( d === null ) {
throw new RangeError( format( 'invalid argument. Third argument contains an out-of-bounds dimension index. Value: [%s].', join( dims, ',' ) ) );
}
d.sort();
if ( d.length !== M ) {
throw new Error( format( 'invalid argument. Third argument must contain a list of unique dimension indices. Value: [%s].', join( dims, ',' ) ) );
}
// Check whether we've been provided a valid number of dimensions...
if ( M > ndims ) {
throw new RangeError( format( 'invalid argument. Number of specified dimensions cannot exceed the number of dimensions in the input array. Number of dimensions: %d. Value: [%s].', ndims, join( dims, ',' ) ) );
}
// Verify that provided ancillary ndarrays have the expected number of dimensions...
K = ndims - M;
for ( i = 1; i < N; i++ ) {
if ( arr[ i ].shape.length !== K ) {
throw new Error( format( 'invalid argument. Array arguments after the first array must have the same number of loop dimensions. Input array shape: [%s]. Number of loop dimensions: %d. Array shape: [%s] (index: %d).', join( shx, ',' ), K, join( arr[ i ].shape, ',' ), i ) );
}
}
// Resolve the loop dimensions and associated strides:
ldims = indicesComplement( shx.length, d );
tmp = takeIndexed2( shx, x.strides, ldims );
shl = tmp[ 0 ];
slx = tmp[ 1 ];
// Resolve the core dimensions and associated strides:
tmp = takeIndexed2( shx, x.strides, d );
shc = tmp[ 0 ];
scx = tmp[ 1 ];
// Verify that provided ancillary arrays have the same loop dimensions...
len = 1; // number of elements
ns = 0; // number of singleton dimensions
for ( i = 0; i < K; i++ ) {
s = shl[ i ];
for ( j = 2; j < N; j++ ) {
if ( s !== arr[ j ].shape[ i ] ) {
throw new Error( format( 'invalid argument. Loop dimensions must be consistent across all provided arrays. Input array shape: [%s]. Loop dimension indices: [%s]. Loop dimensions: [%s]. Array shape: [%s] (index: %d).', join( shx, ',' ), join( ldims, ',' ), join( shl, ',' ), join( arr[ j ].shape, ',' ), j ) );
}
}
// Note that, if one of the dimensions is `0`, the length will be `0`...
len *= s;
// Check whether the current dimension is a singleton dimension...
if ( s === 1 ) {
ns += 1;
}
}
// Check whether we were provided empty ndarrays...
if ( len === 0 || ( shc.length && numel( shc ) === 0 ) ) {
return;
}
// Initialize ndarray-like objects for representing sub-array views...
views = [
{
'dtype': x.dtype,
'data': x.data,
'shape': shc,
'strides': scx,
'offset': x.offset,
'order': x.order
}
];
initializeViews( arr, views );
// Determine the strategy for marshaling data to and from sub-array views of the input array before and after performing an operation:
strategyX = strategy( views[ 0 ] );
// Determine whether we can avoid iteration altogether...
if ( K === 0 ) {
return NULLARY[ K ]( fcn, arr, strategyX, opts );
}
// Determine whether we only have one loop dimension and can thus readily perform one-dimensional iteration...
if ( K === 1 ) {
return NULLARY[ K ]( fcn, arr, views, shl, slx, strategyX, opts );
}
// Determine whether the loop dimensions have only **one** non-singleton dimension (e.g., shape=[10,1,1,1]) so that we can treat loop iteration as being equivalent to one-dimensional iteration...
if ( ns === K-1 ) {
// Get the index of the non-singleton dimension...
for ( i = 0; i < K; i++ ) {
if ( shl[ i ] !== 1 ) {
break;
}
}
for ( j = 0; j < N; j++ ) {
arr[ j ].strides = [ arr[j].strides[i] ];
}
slx = [ slx[i] ];
return NULLARY[ 1 ]( fcn, arr, views, [ shl[i] ], slx, strategyX, opts ); // eslint-disable-line max-len
}
iox = iterationOrder( slx ); // +/-1
// Determine whether we can avoid blocked iteration...
ord = strides2order( slx );
if ( iox !== 0 && K <= MAX_DIMS ) {
// So long as iteration for each respective array always moves in the same direction (i.e., no mixed sign strides) and the memory layouts are the same, we can leverage cache-optimal (i.e., normal) nested loops without resorting to blocked iteration...
return NULLARY[ K ]( fcn, arr, views, shl, slx, ord === 1, strategyX, opts ); // eslint-disable-line max-len
}
// Check whether blocked iteration is prohibited due to a requirement that the order of element traversal match the memory layout of a provided ndarray...
if ( OPTS.strictTraversalOrder && K <= MAX_DIMS ) {
// We have two choices here: (1) we could copy to contiguous memory or (2) we can perform normal nested loop iteration, even though this is not cache-optimal based on the assumption that, while this may hurt performance, for many cases (i.e., smaller ndarrays), this should be fine and likely better than performing a complete copy...
return NULLARY[ K ]( fcn, arr, views, shl, slx, ord === 1, strategyX, opts ); // eslint-disable-line max-len
}
// 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 ( K <= MAX_DIMS ) {
return BLOCKED_NULLARY[ K-2 ]( fcn, arr, views, shl, slx, strategyX, opts ); // eslint-disable-line max-len
}
// Perform linear view iteration without regard for how data is stored in memory (i.e., take the slow path)...
nullarynd( fcn, arr, views, shl, slx, strategyX, opts );
}
}
// EXPORTS //
module.exports = factory;
|