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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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/**
 * Copyright (c) 2015 European Organisation for Nuclear Research (CERN), All Rights Reserved.
 */

package org.tensorics.core.reduction;

import java.util.List;

import org.tensorics.core.tensor.Tensor;

/**
 * An abstract implementation for the linear interpolation of the {@link Double} {@link Tensor}.
 * 
 * It returns a value based on the ratio in the comparable (ie. time) domain, between the reference point(t_i) and the
 * pair of the one before(t_1) and after(t_2) like: 

 * 

 * ratio = (t_i - t_1)/(t_2 - t_1)
 * 
 * The final interpolated value is: v_i = v(t_1)+(v(t_2)-v(t_1))*ratio.
 * 
 * @author agorzaws
 * @param  type of the coordinate, must be the {@link Comparable}
 */
public abstract class AbstractLinearDoubleInterpolationStrategy> extends
        AbstractInterpolationStrategy {

    @Override
    public Double getInterpolatedValue(Tensor tensorWithTheOnlyOneCoordinateOfC, C coordineteToInterpolate) {

        List orderedList = getOrderedListOfComparableCoodrinate(tensorWithTheOnlyOneCoordinateOfC,
                coordineteToInterpolate);
        C thePreviousComparable = findIndex(orderedList, coordineteToInterpolate, 0);
        C theNextComparable = findIndex(orderedList, coordineteToInterpolate, 1);

        Double firstPoint = tensorWithTheOnlyOneCoordinateOfC.get(thePreviousComparable);
        return firstPoint + (tensorWithTheOnlyOneCoordinateOfC.get(theNextComparable) - firstPoint)
                * ratio(thePreviousComparable, theNextComparable, coordineteToInterpolate);
    }

    public abstract double ratio(C previousComparable, C nextComparable, C value);

}