cc.redberry.transformation.concurrent.EACScalars Maven / Gradle / Ivy

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/*
 * Redberry: symbolic tensor computations.
 *
 * Copyright (c) 2010-2012:
 *   Stanislav Poslavsky   
 *   Bolotin Dmitriy       
 *
 * This file is part of Redberry.
 *
 * Redberry is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * Redberry is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with Redberry. If not, see .
 */
package cc.redberry.transformation.concurrent;

import java.util.ArrayList;
import java.util.List;
import cc.redberry.core.tensor.Product;
import cc.redberry.core.tensor.Tensor;
import cc.redberry.core.tensor.TensorIterator;
import cc.redberry.core.tensor.testing.TTest;
import cc.redberry.transformation.Transformation;
import cc.redberry.transformation.Transformer;

/**
 * Expands and collects scalars
 * 
 * @author Dmitry Bolotin
 * @author Stanislav Poslavsky
 */
public class EACScalars implements Transformation {
    public static final EACScalars INSTANCE = new EACScalars();

    private EACScalars() {
    }

    @Override
    public Tensor transform(Tensor tensor) {
        if (TTest.testIsSymbol(tensor))
            return ExpandAndCollectTransformation.EXPAND_AND_COLLECT_SCALARS.transform(tensor);
        if (tensor instanceof Product) {
            TensorIterator it = tensor.iterator();
            Tensor c;
            List syms = new ArrayList<>();
            while (it.hasNext())
                if (TTest.testIsSymbol((c = it.next()))) {
                    syms.add(c);
                    it.remove();
                }
            if (syms.isEmpty())
                return tensor;
            if (syms.size() == 1) {
                ((Product) tensor).addFirst(syms.get(0));
                return tensor;
            }
            ((Product) tensor).addFirst(ExpandAndCollectTransformation.EXPAND_AND_COLLECT_SCALARS.transform(new Product(syms)));
            return tensor;
        }
        return tensor;
    }

    public static Transformer getTransformer() {
        return new Transformer(INSTANCE);
    }
}