/*
* Redberry: symbolic tensor computations.
*
* Copyright (c) 2010-2012:
* Stanislav Poslavsky <stvlpos@mail.ru>
* Bolotin Dmitriy <bolotin.dmitriy@gmail.com>
*
* 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 <http://www.gnu.org/licenses/>.
*/
package cc.redberry.transformation.substitutions;
import cc.redberry.concurrent.OutputPortUnsafe;
import cc.redberry.core.tensor.Derivative;
import cc.redberry.core.tensor.Product;
import cc.redberry.core.tensor.SimpleTensor;
import cc.redberry.core.tensor.Tensor;
import cc.redberry.core.tensor.TensorNumber;
import cc.redberry.core.tensor.TensorWrapper;
import cc.redberry.core.indexmapping.IndexMappingUtils;
import cc.redberry.core.indexmapping.IndexMappingBuffer;
import cc.redberry.core.indexmapping.IndexMappings;
import cc.redberry.core.tensor.iterators.TensorFirstTreeIterator;
import cc.redberry.transformation.Transformation;
import cc.redberry.transformation.Transformations;
import cc.redberry.core.utils.TensorUtils;
/**
*
* @author Dmitry Bolotin
* @author Stanislav Poslavsky
*/
public abstract class AbstractSimpleSubstitution<T extends SimpleTensor> extends AbstractSubstitution<T> {
private int level = 0;
private int indicesLevel = -1;
private Tensor firstProduct = null;
private boolean derivativeVarChangedSignum = false;
public AbstractSimpleSubstitution(T from, Tensor to, boolean allowDiffStates) {
super(from, to, allowDiffStates);
}
public AbstractSimpleSubstitution(T from, Tensor to) {
super(from, to);
}
@Override
public Tensor transform(Tensor tensor) {
//TODO review
Tensor parent = tensor.getParent();
TensorWrapper wrapper = new TensorWrapper(tensor);
TensorFirstTreeIterator iterator = new TensorFirstTreeIterator(wrapper, new OnLeaving());
Tensor current;
while (iterator.hasNext()) {
current = iterator.next();
level++;
if (indicesLevel == -1 && current instanceof Product) {
firstProduct = current;
indicesLevel = level;
}
if (current.getClass() != getFromClasss())
continue;
T _current = (T) current;
if (_current.getName() != from.getName())
continue;
OutputPortUnsafe<IndexMappingBuffer> opu = IndexMappings.createPortForSimpleTensor(from, _current, allowDiffStates);
IndexMappingBuffer buffer;
//TODO refactor throw off if (i.e. spread this code in inheritors)
if (allowDiffStates)
buffer = IndexMappingUtils.tryGetPositiveWithoutDiffStates(opu);
else
buffer = IndexMappingUtils.tryGetPositive(opu);
if (buffer == null)
continue;
Tensor newTo = getNewTo(_current, from, to);
if (newTo == null)
continue;
if (allowDiffStates)
if (!IndexMappingUtils.containsDiffStates(buffer))
newTo = ApplyIndexMappingUtils.applyIndexMappingWithoutDiffStates(newTo, buffer, getUsedIndices());
else
newTo = ApplyIndexMappingUtils.applyIndexMappingWithDiffStates(newTo, buffer, getUsedIndices());
else
newTo = ApplyIndexMappingUtils.applyIndexMappingWithDiffStates(newTo, buffer, getUsedIndices());
//TODO discuss with Dmitry
if (buffer.getSignum())
if (Derivative.onVarsIndicator.is(iterator)) {
derivativeVarChangedSignum ^= true;
iterator.set(Transformations.contractMetrics(newTo));
} else
iterator.set(new Product(TensorNumber.createMINUSONE(), newTo));
else if (Derivative.onVarsIndicator.is(iterator))
iterator.set(Transformations.contractMetrics(newTo));
else
iterator.set(newTo);
}
Tensor result = wrapper.getInnerTensor();
result.setParent(parent);
return result;
}
private int[] getUsedIndices() {
if (indicesLevel == -1)
return new int[0];
return TensorUtils.getAllIndicesNames(firstProduct);
}
public void derivativeVarCangeSignum() {
derivativeVarChangedSignum ^= true;
}
private class OnLeaving implements Transformation {
@Override
public Tensor transform(Tensor tensor) {
if (indicesLevel == level)
indicesLevel = -1;
level--;
if (derivativeVarChangedSignum) {
derivativeVarChangedSignum = false;
return new Product(TensorNumber.createMINUSONE(), tensor);
}
return tensor;
}
}
protected abstract Tensor getNewTo(T current, T from, Tensor to);
}