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* Licensed to the Apache Software Foundation (ASF) under one
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* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you 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
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* KIND, either express or implied. See the License for the
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package org.apache.brooklyn.policy.enricher;
import static org.testng.Assert.assertEquals;
import org.apache.brooklyn.api.entity.EntityLocal;
import org.apache.brooklyn.api.mgmt.SubscriptionContext;
import org.apache.brooklyn.api.sensor.AttributeSensor;
import org.apache.brooklyn.api.sensor.Sensor;
import org.apache.brooklyn.core.entity.AbstractApplication;
import org.apache.brooklyn.core.entity.AbstractEntity;
import org.apache.brooklyn.core.entity.Entities;
import org.apache.brooklyn.core.sensor.BasicAttributeSensor;
import org.testng.annotations.AfterMethod;
import org.testng.annotations.BeforeMethod;
import org.testng.annotations.Test;
public class RollingMeanEnricherTest {
AbstractApplication app;
EntityLocal producer;
Sensor<Integer> intSensor;
AttributeSensor<Integer> deltaSensor;
AttributeSensor<Double> avgSensor;
RollingMeanEnricher<Integer> averager;
@BeforeMethod(alwaysRun=true)
public void before() {
app = new AbstractApplication() {};
producer = new AbstractEntity(app) {};
producer.setParent(app);
Entities.startManagement(app);
intSensor = new BasicAttributeSensor<Integer>(Integer.class, "int sensor");
deltaSensor = new BasicAttributeSensor<Integer>(Integer.class, "delta sensor");
avgSensor = new BasicAttributeSensor<Double>(Double.class, "avg sensor");
producer.enrichers().add(new DeltaEnricher<Integer>(producer, intSensor, deltaSensor));
averager = new RollingMeanEnricher<Integer>(producer, deltaSensor, avgSensor, 4);
producer.enrichers().add(averager);
}
@AfterMethod(alwaysRun=true)
public void tearDown() throws Exception {
if (app != null) Entities.destroyAll(app.getManagementContext());
}
@Test
public void testDefaultAverage() {
assertEquals(averager.getAverage(), null);
}
@Test
public void testZeroWindowSize() {
averager = new RollingMeanEnricher<Integer>(producer, deltaSensor, avgSensor, 0);
producer.enrichers().add(averager);
averager.onEvent(intSensor.newEvent(producer, 10));
assertEquals(averager.getAverage(), null);
}
@Test
public void testSingleValueAverage() {
averager.onEvent(intSensor.newEvent(producer, 10));
assertEquals(averager.getAverage(), 10d);
}
@Test
public void testMultipleValueAverage() {
averager.onEvent(intSensor.newEvent(producer, 10));
averager.onEvent(intSensor.newEvent(producer, 20));
averager.onEvent(intSensor.newEvent(producer, 30));
averager.onEvent(intSensor.newEvent(producer, 40));
assertEquals(averager.getAverage(), (10+20+30+40)/4d);
}
@Test
public void testWindowSizeCulling() {
averager.onEvent(intSensor.newEvent(producer, 10));
averager.onEvent(intSensor.newEvent(producer, 20));
averager.onEvent(intSensor.newEvent(producer, 30));
averager.onEvent(intSensor.newEvent(producer, 40));
averager.onEvent(intSensor.newEvent(producer, 50));
averager.onEvent(intSensor.newEvent(producer, 60));
assertEquals(averager.getAverage(), (30+40+50+60)/4d);
}
}