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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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 * This file is part of tensorics.
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 * 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
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// @formatter:on

package org.tensorics.core.quantity.operations;

import org.tensorics.core.lang.Tensorics;
import org.tensorics.core.quantity.QuantifiedValue;
import org.tensorics.core.quantity.options.QuantificationStrategy;
import org.tensorics.core.quantity.options.QuantityEnvironment;
import org.tensorics.core.units.Unit;

/**
 * An unary operation describing the multiplicative inversion of a physical quantity (aka '1/x').
 * 
 * @author kfuchsbe
 * @param  the type of the scalars (field elements) on which all the operations are based
 */
public class QuantityMultiplicativeInversion extends QuantityUnaryOperation {

    public QuantityMultiplicativeInversion(QuantityEnvironment environment) {
        super(environment);
    }

    @Override
    public QuantifiedValue perform(QuantifiedValue scalar) {
        S newValue = environment().field().multiplicativeInversion().perform(scalar.value());
        QuantificationStrategy quant = environment().quantification();
        Unit newUnit = quant.divide(quant.one(), scalar.unit());
        /* XXX is the error propagated correctly here? */
        return Tensorics.quantityOf(newValue, newUnit).withError(scalar.error()).withValidity(scalar.validity());
    }

}