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Tensorics is a java framework which uses a tensor as a central object. A tensor represents a set of values placed in an N-dimensional space. Wherever you are tempted to use maps of maps, a tensor might be a good choice ;-) Tensorics provides methods to create, transform and performing calculations with those tensors.

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// @formatter:off
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 *
 * This file is part of tensorics.
 * 
 * Copyright (c) 2008-2011, CERN. All rights reserved.
 *
 * 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.
 * 
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// @formatter:on
package org.tensorics.core.tensor.lang;

import static java.util.Collections.singleton;

import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.Map;

import org.tensorics.core.lang.Tensorics;
import org.tensorics.core.tensor.Position;
import org.tensorics.core.tensor.Tensor;
import org.tensorics.core.tensor.Tensor.Entry;
import org.tensorics.core.tensor.operations.TensorInternals;

import com.google.common.collect.ImmutableListMultimap;
import com.google.common.collect.ListMultimap;
import com.google.common.collect.Multimaps;
import com.google.common.collect.Sets;
import com.google.common.collect.Sets.SetView;

public class OngoingOrderedFlattening> {

    private final Tensor tensor;
    private final Class dimensionToFlatten;

    public OngoingOrderedFlattening(Tensor tensor, Class dimension) {
        super();
        this.tensor = tensor;
        this.dimensionToFlatten = dimension;
    }

    public Tensor> intoTensorOfLists() {
        return Tensorics.fromMap(remainingDimensions(), Multimaps.asMap(intoListMultimap()));
    }

    private ListMultimap intoListMultimap() {
        ImmutableListMultimap.Builder builder = ImmutableListMultimap.builder();
        Tensor> maps = TensorInternals.mapOut(tensor).inDirectionOf(dimensionToFlatten);
        for (Entry> entry : maps.entrySet()) {
            Position newPosition = entry.getPosition();
            Map map = entry.getValue();
            List keys = new ArrayList<>(map.keySet());
            Collections.sort(keys);
            for (C key : keys) {
                builder.put(newPosition, map.get(key));
            }
        }
        return builder.build();
    }
    
    public Tensor> intoTensorOfKeyLists() {
        return Tensorics.fromMap(remainingDimensions(), Multimaps.asMap(intoKeyListMultimap()));
    }

    private ListMultimap intoKeyListMultimap() {
        ImmutableListMultimap.Builder builder = ImmutableListMultimap.builder();
        Tensor> maps = TensorInternals.mapOut(tensor).inDirectionOf(dimensionToFlatten);
        for (Entry> entry : maps.entrySet()) {
            Position newPosition = entry.getPosition();
            Map map = entry.getValue();
            List keys = new ArrayList<>(map.keySet());
            Collections.sort(keys);
            builder.putAll(newPosition, keys);
        }
        return builder.build();
    }

    private SetView> remainingDimensions() {
        return Sets.difference(tensor.shape().dimensionSet(), singleton(dimensionToFlatten));
    }
}