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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.
 * 
 ******************************************************************************/
// @formatter:on

package org.tensorics.core.tensor.lang;

import static com.google.common.base.Preconditions.checkArgument;

import java.util.Arrays;
import java.util.Collection;
import java.util.HashSet;
import java.util.Set;

import org.tensorics.core.tensor.ImmutableTensor;
import org.tensorics.core.tensor.ImmutableTensor.Builder;
import org.tensorics.core.tensor.Position;
import org.tensorics.core.tensor.Tensor;

/**
 * Part of the tensoric fluent API which provides methods to describe misc manipulations on a given tensor.
 * 
 * @author kfuchsbe
 * @param  the type of the values of the tensor
 */
public class OngoingTensorManipulation {

    private final Tensor tensor;

    OngoingTensorManipulation(Tensor tensor) {
        super();
        this.tensor = tensor;
    }

    /**
     * Extracts from the tensor only those elements where the values in the given mask is {@code true}. The resulting
     * tensors will then have the same dimensionality as the original tensor, but will only have that many elements as
     * there are {@code true} elements in the mask tensor.
     * 
     * @param mask the mask which determines which elements shall be present in the new tensor.
     * @return A tensor which will contain only those elements which have {@code true} flags in the mask
     */
    public Tensor extractWhereTrue(Tensor mask) {
        Builder tensorBuilder = ImmutableTensor.builder(tensor.shape().dimensionSet());
        for (java.util.Map.Entry entry : tensor.asMap().entrySet()) {
            if (mask.get(entry.getKey()).booleanValue()) {
                tensorBuilder.at(entry.getKey()).put(entry.getValue());
            }
        }
        return tensorBuilder.build();
    }

    /**
     * Retrieves all the unique coordinates of the given type.
     * 
     * @param coordinateType the type of the coordinate to extract
     * @return a set of extracted coordinates
     */
    public  Set extractCoordinatesOfType(Class coordinateType) {
        Set toReturn = new HashSet<>();
        for (Position position : tensor.shape().positionSet()) {
            toReturn.add(position.getCoordinates().getInstance(coordinateType));
        }
        return toReturn;
    }

    public V get(Position position) {
        return tensor.get(position);
    }

    public V get(Object... coordinates) {
        return tensor.get(coordinates);
    }

    public Tensor extract(Position position) {
        return extractTensor(position.coordinates());
    }

    public Tensor extract(Object... coordinates) {
        return extractTensor(Arrays.asList(coordinates));
    }

    public OngoingEitherGet either(V defaultValue) {
        return new OngoingEitherGet<>(tensor, defaultValue);
    }

    private Tensor extractTensor(Collection coordinates) {
        checkArgument(coordinates != null, "Argument 'coordinates' must not be null!");
        for (Object coordinate : coordinates) {
            checkArgument(coordinate != null, "given coordinate must not be null!");
        }
        Tensor slice = tensor;
        for (Object coordinate : coordinates) {
            slice = slice(slice, coordinate);
        }
        return slice;
    }

    private static final  Tensor slice(Tensor tensor, C coordinate) {
        checkArgument(coordinate != null, "Argument '" + "coordinate" + "' must not be null!");
        checkArgument(!(coordinate instanceof Position), "It is not allowed that a coordinate is of type position! "
                + "Most probably this is a programming mistake ;-)");
        @SuppressWarnings("unchecked")
        Class dimension = (Class) coordinate.getClass();
        return TensorStructurals.from(tensor).reduce(dimension).bySlicingAt(coordinate);
    }

    public  OngoingDimensionReduction reduce(Class dimension) {
        return new OngoingDimensionReduction<>(tensor, dimension);
    }

}