package org.eclipse.stem.populationmodels.standard.impl;

/*******************************************************************************
 * Copyright (c) 2009 IBM Corporation and others.
 * All rights reserved. This program and the accompanying materials
 * are made available under the terms of the Eclipse Public License v1.0
 * which accompanies this distribution, and is available at
 * http://www.eclipse.org/legal/epl-v10.html
 *
 * Contributors:
 *     IBM Corporation - initial API and implementation
 *******************************************************************************/


import java.util.Random;

import org.eclipse.emf.common.notify.Notification;
import org.eclipse.emf.ecore.EClass;
import org.eclipse.emf.ecore.impl.ENotificationImpl;
import org.eclipse.stem.core.graph.LabelValue;
import org.eclipse.stem.populationmodels.standard.StandardPackage;
import org.eclipse.stem.populationmodels.standard.StandardPopulationModelLabelValue;
import org.eclipse.stem.populationmodels.standard.StochasticStandardPopulationModel;

/**
 * <!-- begin-user-doc -->
 * An implementation of the model object '<em><b>Stochastic Standard Population Model</b></em>'.
 * <!-- end-user-doc -->
 * <p>
 * The following features are implemented:
 * <ul>
 *   <li>{@link org.eclipse.stem.populationmodels.standard.impl.StochasticStandardPopulationModelImpl#getGain <em>Gain</em>}</li>
 * </ul>
 * </p>
 *
 * @generated
 */
public class StochasticStandardPopulationModelImpl extends StandardPopulationModelImpl implements StochasticStandardPopulationModel {
	protected static double MAX_GAIN = 0.02;
	
	/**
	 * The default value of the '{@link #getGain() <em>Gain</em>}' attribute.
	 * <!-- begin-user-doc -->
	 * <!-- end-user-doc -->
	 * @see #getGain()
	 * @generated
	 * @ordered
	 */
	protected static final double GAIN_EDEFAULT = 0.01;

	/**
	 * The cached value of the '{@link #getGain() <em>Gain</em>}' attribute.
	 * <!-- begin-user-doc -->
	 * <!-- end-user-doc -->
	 * @see #getGain()
	 * @generated
	 * @ordered
	 */
	protected double gain = GAIN_EDEFAULT;

	/**
	 * <!-- begin-user-doc -->
	 * <!-- end-user-doc -->
	 * @generated NOT
	 */
	public StochasticStandardPopulationModelImpl() {
		super();
	}

	/**
	 * <!-- begin-user-doc -->
	 * <!-- end-user-doc -->
	 * @generated
	 */
	@Override
	protected EClass eStaticClass() {
		return StandardPackage.Literals.STOCHASTIC_STANDARD_POPULATION_MODEL;
	}

	/**
	 * <!-- begin-user-doc -->
	 * <!-- end-user-doc -->
	 * @generated
	 */
	public double getGain() {
		return gain;
	}

	@Override
	public void doModelSpecificAdjustments(LabelValue label) {
		final StandardPopulationModelLabelValue currentLabelValue = (StandardPopulationModelLabelValue) label;
		//double oldCount = currentLabelValue.getCount();
		double incidence = currentLabelValue.getIncidence();
		double newIncidence = incidence*computeNoise();
		double diff = newIncidence - incidence;
		
		double newCount = currentLabelValue.getCount() + diff;
		double newBirths = currentLabelValue.getBirths() + diff/2.0;
		double newDeaths = currentLabelValue.getDeaths() + diff/2.0;
		
		currentLabelValue.setCount(newCount);
		currentLabelValue.setBirths(newBirths);
		currentLabelValue.setDeaths(newDeaths);
	}
	
	Random rand = new Random();
	protected double computeNoise() {
		return this.computeNoise(this.getGain(), this.rand);
	}
	
	/**
	 * This method returns a double precision random noise variable
	 * r between (1.0+x) and (1.0-x)
	 * where x is a product G* Math.rand(), and G is a gain factor 0<G<max noise
	 * 
	 * @param gain
	 * @param rand
	 * @return noise
	 */
	protected double computeNoise(final double gain, final Random rand) {
     
	    assert (gain <= MAX_GAIN):"STOCHASTIC NOISE must be 'small', therefore gain parameter must be < MAX_GAIN="+MAX_GAIN; // maximum noise should be 1%
		double x = (2.0*rand.nextDouble())-1.0; // +/- 1
		x *= gain;
		x += 1.0;
		assert ((x>0.0)&&(x<2.0)) : "random noise should be a small perturbation Gain factor, g,  must 0.0 < g << 1.0 ";
		return x;
		
	} // getNoise
	
	/**
	 * <!-- begin-user-doc -->
	 * <!-- end-user-doc -->
	 * @generated
	 */
	public void setGain(double newGain) {
		double oldGain = gain;
		gain = newGain;
		if (eNotificationRequired())
			eNotify(new ENotificationImpl(this, Notification.SET, StandardPackage.STOCHASTIC_STANDARD_POPULATION_MODEL__GAIN, oldGain, gain));
	}

	/**
	 * <!-- begin-user-doc -->
	 * <!-- end-user-doc -->
	 * @generated
	 */
	@Override
	public Object eGet(int featureID, boolean resolve, boolean coreType) {
		switch (featureID) {
			case StandardPackage.STOCHASTIC_STANDARD_POPULATION_MODEL__GAIN:
				return getGain();
		}
		return super.eGet(featureID, resolve, coreType);
	}

	/**
	 * <!-- begin-user-doc -->
	 * <!-- end-user-doc -->
	 * @generated
	 */
	@Override
	public void eSet(int featureID, Object newValue) {
		switch (featureID) {
			case StandardPackage.STOCHASTIC_STANDARD_POPULATION_MODEL__GAIN:
				setGain((Double)newValue);
				return;
		}
		super.eSet(featureID, newValue);
	}

	/**
	 * <!-- begin-user-doc -->
	 * <!-- end-user-doc -->
	 * @generated
	 */
	@Override
	public void eUnset(int featureID) {
		switch (featureID) {
			case StandardPackage.STOCHASTIC_STANDARD_POPULATION_MODEL__GAIN:
				setGain(GAIN_EDEFAULT);
				return;
		}
		super.eUnset(featureID);
	}

	/**
	 * <!-- begin-user-doc -->
	 * <!-- end-user-doc -->
	 * @generated
	 */
	@Override
	public boolean eIsSet(int featureID) {
		switch (featureID) {
			case StandardPackage.STOCHASTIC_STANDARD_POPULATION_MODEL__GAIN:
				return gain != GAIN_EDEFAULT;
		}
		return super.eIsSet(featureID);
	}

	/**
	 * <!-- begin-user-doc -->
	 * <!-- end-user-doc -->
	 * @generated
	 */
	@Override
	public String toString() {
		if (eIsProxy()) return super.toString();

		StringBuffer result = new StringBuffer(super.toString());
		result.append(" (gain: ");
		result.append(gain);
		result.append(')');
		return result.toString();
	}

} //StochasticStandardPopulationModelImpl