tests/org.eclipse.stem.tests.diseasemodels/src/org/eclipse/stem/diseasemodels/standard/tests/DeterministicSIScenarioTest.java

// DeterministicSIScenarioTest.java
package org.eclipse.stem.diseasemodels.standard.tests;

/*******************************************************************************
 * Copyright (c) 2006 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.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

import org.eclipse.stem.core.graph.LabelValue;
import org.eclipse.stem.core.model.NodeDecorator;
import org.eclipse.stem.diseasemodels.standard.SILabelValue;
import org.eclipse.stem.diseasemodels.standard.impl.SIImpl;
import org.eclipse.stem.diseasemodels.standard.impl.SILabelValueImpl;

/**
 * This class is a JUnit test case for a Deterministic SI disease model
 * scenario.
 * 
 * <ul>
 * <li>S - The number of <code>Susceptible</code> population members. Members
 * enter this state by being born or by "recovering" from being
 * <code>Infectious</code>. They leave this state either by death or by
 * entering the <code>Infectious</code> state.
 * {@link SITest#TRANSMISSION_RATE} = 0.1, {@link SITest#RECOVERY_RATE} = 0.1
 * 
 * Initialized to {@link DiseaseModelTestUtil#TEST_POPULATION_COUNT} = 100 </li>
 * <li>I - The number of <code>Infectious</code> population members.
 * Initialized to {@link SIDiseaseModelScenarioTest#NUMBER_TO_INFECT} = 1 </li>
 * 
 * <li>B - The number of <code>Births</code> of new (Susceptible) population
 * members.
 * 
 * 
  * </ul>
 * <ul>
 * <li>&mu; = {@link DiseaseModelTest#MORTALITY_RATE} = 0.01</li>
 * <li>&beta; = {@link SITest#TRANSMISSION_RATE} = 0.1</li>
 * <li>&sigma; = {@link SITest#RECOVERY_RATE} = 0.01</li>
 * <li>x = {@link SITest#INFECTIOUS_MORTALITY} = 0.1 </li>
 * <li>&mu;<sub>i</sub> = {@link SITest#INFECTIOUS_MORTAILY_RATE} = 0.1</li>
 * <li>Area<sub>l</sub> = 1.0</li>
 * <li>Area = 1.0</li>
 * <li>P = S + I = {@link DiseaseModelTestUtil#TEST_POPULATION_COUNT} = 100</li>
 * </ul>
 * <h2>1x1 Deterministic SI Scenario</h2>
 * 
 * @see DiseaseModelTestUtil#TEST_POPULATION_COUNT
 * @see SIDiseaseModelScenarioTest#NUMBER_TO_INFECT
 * @see DiseaseModelTestUtil#TEST_AREA
 * @see SITest#INFECTIOUS_MORTALITY
 * @see SITest#TRANSMISSION_RATE
 * @see SITest#NON_LINEARITY_COEFFICIENT
 * @see SITest#RECOVERY_RATE
 * @see SIImpl
 */
public class DeterministicSIScenarioTest extends SIDiseaseModelScenarioTest {

	private static final String DISEASE_URI_PREFIX = "DeterministicSI";

	private static Map<String, Integer> expectedNumberOfLabelsToUpdate = new HashMap<String, Integer>();

	private static Map<String, SILabelValue[][][]> expectedDiseaseModelStates = new HashMap<String, SILabelValue[][][]>();

	static {

		// 1x1
		expectedDiseaseModelStates.put(TEST_SCENARIO1x1_KEY,
				new SILabelValue[][][] {
				// Step 0
						{ {
						// N[0,0]
						new SILabelValueImpl(98.91, 0.99, 0.0, 0.1) } },

						// Step 1
						{ {
						// N[0,0]
						new SILabelValueImpl(98.82, 0.98, 0.0, 0.2) } } } 
				// SILabelValue

				); // put(TEST_SCENARIO1x1_KEY)

		// 1x2
		expectedDiseaseModelStates.put(TEST_SCENARIO1x2_KEY,
				new SILabelValue[][][] {
				// Step 0
						{ {
						// N[0,0]
								new SILabelValueImpl(100, 0, 0,  0),
								// N[0,1]
								new SILabelValueImpl(100, 0, 0,  0) } },

						// Step 1
						{ {
						// N[0,0]
								new SILabelValueImpl(100, 0, 0,  0),
								// N[0,1]
								new SILabelValueImpl(100, 0, 0,  0) } },
						// Step 2
						{ {
						// N[0,0]
								new SILabelValueImpl(100, 0, 0,  0),
								// N[0,1]
								new SILabelValueImpl(100, 0, 0,  0) } } } // new
				// SILabelValue
				); // put(TEST_SCENARIO1x2_KEY)

		// 1x3
		expectedDiseaseModelStates.put(TEST_SCENARIO1x3_KEY,
				new SILabelValue[][][] {
				// Step 0
						{ {

						// N[0,0]
								new SILabelValueImpl(100, 0, 0,  0),
								// N[0,1]
								new SILabelValueImpl(100, 0, 0,  0),
								// N[0,2]
								new SILabelValueImpl(100, 0, 0, 0)

						} },

						// Step 1
						{ {
						// N[0,0]
								new SILabelValueImpl(100, 0, 0,  0),
								// N[0,1]
								new SILabelValueImpl(100, 0, 0,  0),
								// N[0,2]
								new SILabelValueImpl(100, 0, 0,  0)

						} },

						// Step 2
						{ {
						// N[0,0]
								new SILabelValueImpl(100, 0, 0,  0),
								// N[0,1]
								new SILabelValueImpl(100, 0, 0,  0),
								// N[0,2]
								new SILabelValueImpl(100, 0, 0,  0)

						} } } // new SILabelValue

				); // put(TEST_SCENARIO1x3_KEY)

		// 2x2
		expectedDiseaseModelStates.put(TEST_SCENARIO2x2_KEY,
				new SILabelValue[][][] {
				// Step 0
						{ {

						// N[0,0]
								new SILabelValueImpl(100, 0, 0,  0),
								// N[0,1]
								new SILabelValueImpl(100, 0, 0,  0)

						}, {

						// N[1,0]
								new SILabelValueImpl(100, 0, 0,  0),
								// N[1,1]
								new SILabelValueImpl(100, 0, 0,  0)

						} },

						// Step 1
						{ {
						// N[0,0]
								new SILabelValueImpl(100, 0, 0,  0),
								// N[0,1]
								new SILabelValueImpl(100, 0, 0,  0)

						}, {

						// N[1,0]
								new SILabelValueImpl(100, 0, 0,  0),
								// N[1,1]
								new SILabelValueImpl(100, 0, 0,  0)

						} },

						// Step 2
						{ {
						// N[0,0]
								new SILabelValueImpl(100, 0, 0,  0),
								// N[0,1]
								new SILabelValueImpl(100, 0, 0,  0)

						}, {

						// N[1,0]
								new SILabelValueImpl(100, 0, 0,  0),
								// N[1,1]
								new SILabelValueImpl(100, 0, 0,  0)

						} } } // new SILabelValue

				); // put(TEST_SCENARIO2x2_KEY)

		// 3x3
		expectedDiseaseModelStates.put(TEST_SCENARIO3x3_KEY,
				new SILabelValue[][][] {
				// Step 0
						{ {

						// N[0,0]
								new SILabelValueImpl(100, 0, 0, 0),
								// N[0,1]
								new SILabelValueImpl(100, 0, 0, 0),
								// N[0,2]
								new SILabelValueImpl(100, 0, 0, 0)

						}, {

						// N[1,0]
								new SILabelValueImpl(100, 0, 0, 0),
								// N[1,1]
								new SILabelValueImpl(100, 0, 0,  0),
								// N[1,2]
								new SILabelValueImpl(100, 0, 0, 0)

						}, {

						// N[2,0]
								new SILabelValueImpl(100, 0, 0, 0),
								// N[2,1]
								new SILabelValueImpl(100, 0, 0, 0),
								// N[2,2]
								new SILabelValueImpl(100, 0, 0, 0)

						} },

						// Step 1
						{ {
						// N[0,0]
								new SILabelValueImpl(100, 0, 0, 0),
								// N[0,1]
								new SILabelValueImpl(100, 0, 0, 0),
								// N[0,2]
								new SILabelValueImpl(100, 0, 0, 0)

						}, {

						// N[1,0]
								new SILabelValueImpl(100, 0, 0, 0),
								// N[1,1]
								new SILabelValueImpl(100, 0, 0, 0),
								// N[1,2]
								new SILabelValueImpl(100, 0, 0, 0)

						}, {

						// N[2,0]
								new SILabelValueImpl(100, 0, 0, 0),
								// N[2,1]
								new SILabelValueImpl(100, 0, 0, 0),
								// N[2,2]
								new SILabelValueImpl(100, 0, 0, 0)

						} },

						// Step 2
						{ {
						// N[0,0]
								new SILabelValueImpl(100, 0, 0, 0),
								// N[0,1]
								new SILabelValueImpl(100, 0, 0, 0),
								// N[0,2]
								new SILabelValueImpl(100, 0, 0,0)

						}, {

						// N[1,0]
								new SILabelValueImpl(100, 0, 0, 0),
								// N[1,1]
								new SILabelValueImpl(100, 0, 0, 0),
								// N[1,2]
								new SILabelValueImpl(100, 0, 0, 0)

						}, {

						// N[2,0]
								new SILabelValueImpl(100, 0, 0, 0),
								// N[2,1]
								new SILabelValueImpl(100, 0, 0, 0),
								// N[2,2]
								new SILabelValueImpl(100, 0, 0, 0)

						} } } // new SILabelValue

				); // put(TEST_SCENARIO3x3_KEY)

		// Fill out the map that specifies how many labels should be updated by
		// a disease model for each test.
		for (TestSpec testSpec : testSpecifications) {
			expectedNumberOfLabelsToUpdate
					.put(
							testSpec.scenarioDiseaseKey,
							new Integer(
									computeExpectedNumberOfLabels(expectedDiseaseModelStates
											.get(testSpec.scenarioDiseaseKey))));
		} // for each test specification

	} // static

	/**
	 * @see org.eclipse.stem.diseasemodels.standard.tests.DiseaseModelScenarioTest#getDiseaseModelsToTest()
	 */
	@Override
	public List<NodeDecorator> getDiseaseModelsToTest() {
		final List<NodeDecorator> retValue = new ArrayList<NodeDecorator>();
		retValue.add(DeterministicSIDiseaseModelTest.createFixture());
		return retValue;
	} // getDiseaseModelsToTest

	@Override
	protected int getNumberOfSteps(final String diseaseScenarioKey) {
		SILabelValue[][][] temp = expectedDiseaseModelStates
				.get(diseaseScenarioKey);
		return temp.length;
	} // getNumberOfSteps

	@Override
	protected int getExpectedNumberOfLabelsToUpdate(
			final String diseaseScenarioKey) {
		Integer temp = expectedNumberOfLabelsToUpdate.get(diseaseScenarioKey);
		return temp.intValue();
	} // getExpectedNumberOfLabelsToUpdate

	@Override
	protected LabelValue[][] getExpectedDiseaseModelState(
			final String diseaseScenarioKey, final int step) {
		final SILabelValue[][][] siLabelValue = expectedDiseaseModelStates
				.get(diseaseScenarioKey);
		return siLabelValue[step];
	} // getExpectedDiseaseModelState

	protected String getDiseaseURIPrefix() {
		return DISEASE_URI_PREFIX;
	} // getDiseaseURIPrefix
} // DeterministicSIScenarioTest