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refactor: add function to resolve a reshape strategy
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‎lib/node_modules/@stdlib/ndarray/base/unary-reduce-strided1d-to-struct/lib/reshape_strategy.js

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2025 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 iterationOrder = require( '@stdlib/ndarray/base/iteration-order' );
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var minmaxViewBufferIndex = require( '@stdlib/ndarray/base/minmax-view-buffer-index' ).assign;
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var ndarraylike2object = require( '@stdlib/ndarray/base/ndarraylike2object' );
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var assign = require( '@stdlib/ndarray/base/assign' );
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var ndarraylike2ndarray = require( '@stdlib/ndarray/base/ndarraylike2ndarray' );
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var emptyLike = require( '@stdlib/ndarray/base/empty-like' );
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// FUNCTIONS //
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/**
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* Returns an input ndarray.
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*
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* @private
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* @param {ndarrayLike} x - input ndarray
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* @returns {ndarrayLike} input ndarray
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*/
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function identity( x ) {
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return x;
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}
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/**
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* Broadcasts a zero-dimensional ndarray to a one-dimensional ndarray view containing a single element.
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*
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* @private
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* @param {ndarrayLike} x - input ndarray
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* @returns {ndarrayLike} broadcasted ndarray view
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*/
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function broadcast( x ) {
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// NOTE: the following properties must be set in the exact same order as in `x` in order to ensure that the returned object has the same hidden shape as the input ndarray-like object...
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return {
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'dtype': x.dtype,
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'data': x.data,
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'shape': [ 1 ],
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'strides': [ 0 ],
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'offset': x.offset,
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'order': x.order
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};
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}
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/**
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* Returns a function which returns an ndarray view in which the singleton dimensions are removed from an input ndarray having only a single non-singleton dimension.
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*
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* @private
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* @param {ndarrayLike} arr - original ndarray
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* @param {NonNegativeInteger} index - index of the non-singleton dimension
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* @returns {Function} function for returning an ndarray view
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*/
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function squeeze( arr, index ) {
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var sh = [ arr.shape[ index ] ];
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var sx = [ arr.strides[ index ] ];
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return reshape;
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/**
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* Returns an ndarray view in which the singleton dimensions are removed from an input ndarray having only a single non-singleton dimension.
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*
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* @private
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* @param {ndarrayLike} x - input ndarray
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* @returns {ndarrayLike} a squeezed ndarray view
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*/
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function reshape( x ) {
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// NOTE: the following properties must be set in the exact same order as in `arr` in order to ensure that the returned object has the same hidden shape as the input ndarray-like object...
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return {
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'dtype': x.dtype,
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'data': x.data,
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'shape': sh,
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'strides': sx,
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'offset': x.offset,
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'order': x.order
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};
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}
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}
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/**
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* Returns a function which returns a one-dimensional ndarray view of a contiguous input ndarray having more than one dimension.
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*
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* @private
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* @param {NonNegativeInteger} len - number of elements in an ndarray
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* @param {integer} iox - iteration order
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* @returns {Function} function for returning a one-dimensional ndarray view
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*/
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function contiguous( len, iox ) {
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var xmmv;
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var ind;
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var sh;
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var sx;
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// Resolve the index of the min/max view buffer element which is the first indexed element...
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if ( iox === 1 ) {
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ind = 0;
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} else {
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ind = 1;
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}
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// Initialize an array for storing the min/max view buffer elements:
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xmmv = [ 0, 0 ]; // [ min, max ]
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// Initialize the output one-dimensional view's shape and strides:
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sh = [ len ];
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sx = [ iox ];
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return reshape;
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/**
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* Returns a one-dimensional ndarray view of a contiguous input ndarray having more than one dimension.
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*
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* @private
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* @param {ndarrayLike} x - input ndarray
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* @returns {ndarrayLike} a one-dimensional ndarray view
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*/
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function reshape( x ) {
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// Resolve the minimum and maximum linear indices in the underlying data buffer which are accessible to the input ndarray view:
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minmaxViewBufferIndex( x.shape, x.strides, x.offset, xmmv );
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// NOTE: the following properties must be set in the exact same order as in `x` in order to ensure that the returned object has the same hidden shape as the input ndarray-like object...
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return {
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'dtype': x.dtype,
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'data': x.data,
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'shape': sh,
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'strides': sx,
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'offset': xmmv[ ind ], // the index of the first indexed element
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'order': x.order
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};
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}
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}
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/**
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* Returns a function which copies an input ndarray to a contiguous ndarray workspace.
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*
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* @private
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* @param {NonNegativeInteger} len - number of elements in an ndarray
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* @param {ndarrayLike} workspace - ndarray workspace
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* @returns {Function} function which copies an input ndarray to a contiguous ndarray workspace
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*/
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function copy( len, workspace ) {
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// NOTE: the following properties must be set in the exact same order as in the input ndarray-like object in order to ensure that the returned object has the same hidden shape...
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var view = {
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'dtype': workspace.dtype,
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'data': workspace.data,
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'shape': [ len ],
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'strides': [ 1 ],
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'offset': workspace.offset,
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'order': workspace.order
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};
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return reshape;
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/**
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* Copies an input ndarray to a contiguous ndarray workspace and returns a one-dimensional workspace view.
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*
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* @private
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* @param {ndarrayLike} x - input ndarray
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* @returns {ndarrayLike} one-dimensional workspace view
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*/
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function reshape( x ) {
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assign( [ x, workspace ] );
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return view;
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}
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}
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// MAIN //
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/**
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* Returns a function for reshaping input ndarrays which have the same data type, shape, and strides as a provided ndarray.
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*
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* @private
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* @param {ndarrayLike} x - input ndarray
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* @param {string} x.dtype - input ndarray data type
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* @param {Collection} x.data - input ndarray data buffer
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* @param {NonNegativeIntegerArray} x.shape - input ndarray shape
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* @param {IntegerArray} x.strides - input ndarray strides
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* @param {NonNegativeInteger} x.offset - input ndarray index offset
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* @param {string} x.order - input ndarray memory layout
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* @returns {Function} function implementing a reshape strategy
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*/
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function strategy( x ) {
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var ndims;
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var xmmv;
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var len;
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var iox;
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var sh;
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var ns;
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var i;
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// Resolve the number of array dimensions:
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sh = x.shape;
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ndims = sh.length;
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// Check whether the ndarray is zero-dimensional...
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if ( ndims === 0 ) {
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return broadcast;
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}
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// Check whether the ndarray is already one-dimensional...
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if ( ndims === 1 ) {
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return identity;
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}
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// Determine the number of singleton dimensions...
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len = 1; // number of elements
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ns = 0; // number of singleton dimensions
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for ( i = 0; i < ndims; i++ ) {
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// Check whether the current dimension is a singleton dimension...
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if ( sh[ i ] === 1 ) {
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ns += 1;
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}
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len *= sh[ i ];
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}
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// Determine whether the ndarray has only **one** non-singleton dimension (e.g., ndims=4, shape=[10,1,1,1]) so that we can simply create an ndarray view without the singleton dimensions...
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if ( ns === ndims-1 ) {
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// Get the index of the non-singleton dimension...
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for ( i = 0; i < ndims; i++ ) {
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if ( sh[ i ] !== 1 ) {
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break;
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}
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}
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return squeeze( x, i );
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}
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iox = iterationOrder( x.strides ); // +/-1
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// Determine whether we can avoid copying data...
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if ( iox !== 0 ) {
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// Determine the minimum and maximum linear indices which are accessible by the ndarray view:
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xmmv = minmaxViewBufferIndex( sh, x.strides, x.offset, [ 0, 0 ] );
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// Determine whether we can ignore shape (and strides) and create a new one-dimensional ndarray view...
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if ( len === ( xmmv[1]-xmmv[0]+1 ) ) {
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return contiguous( len, iox );
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}
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// The ndarray is non-contiguous, so we cannot directly interpret as a one-dimensional ndarray...
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// Fall-through to copying to a workspace ndarray...
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}
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// At this point, we're dealing with a non-contiguous multi-dimensional ndarray, so we need to copy to a contiguous workspace:
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return copy( len, ndarraylike2object( emptyLike( ndarraylike2ndarray( x ) ) ) ); // eslint-disable-line max-len
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}
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// EXPORTS //
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module.exports = strategy;

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