private void calcMinMaxAvg(List<MeasurementDataNumericHighLowComposite> metricData) {
int averageCount = 0;
for (MeasurementDataNumericHighLowComposite measurement : metricData) {
if (!Double.isNaN(measurement.getLowValue())) {
min = Math.min(min, measurement.getLowValue());
}
if (!Double.isNaN(measurement.getHighValue())) {
max = Math.max(max, measurement.getHighValue());
}
if (!Double.isNaN(measurement.getValue())) {
average = average + measurement.getValue();
averageCount++;
}
}
average = average / averageCount;
}
/**
* When the scale is adjusted down to a level where bars no longer have highs and lows different
* from the avg value then have probably hit a scale where aggregates no longer exist and we are
* looking the individual values. At the very least the data is less interesting and a trendline
* connecting the points has less meaning because we are essentially a scatterplot. A different
* algorithm could be used here but this will suffice.
*
* @return true if the graphs should show the bar avg line meaning there is aggregates in the data
* @see StackedBarMetricGraphImpl
*/
public boolean showBarAvgTrendLine() {
int numberOfAggBars = 0;
for (MeasurementDataNumericHighLowComposite measurement : metricData) {
boolean noValuesInCurrentBarUndefined =
(!Double.isNaN(measurement.getValue())
&& !Double.isNaN(measurement.getHighValue())
&& !Double.isNaN(measurement.getLowValue()));
boolean foundAggregateBar =
(measurement.getValue() != measurement.getHighValue()
|| measurement.getHighValue() != measurement.getLowValue());
// if there exists a even one aggregate bar then I can short circuit this and exit
if (noValuesInCurrentBarUndefined && foundAggregateBar) {
numberOfAggBars++;
if (numberOfAggBars > 4) {
return true;
}
}
}
return false;
}
@Override
/**
* Format the json for the front JSNI(javascript) UI to consume.
*
* @todo: future: this should really use GSON or some Json marshaller
*/
public String getJsonMetrics() {
StringBuilder sb = new StringBuilder();
if (null != metricData) {
sb = new StringBuilder("[");
long firstBarTime = metricData.get(0).getTimestamp();
long secondBarTime = metricData.get(1).getTimestamp();
long barDuration = secondBarTime - firstBarTime;
String barDurationString =
MeasurementConverterClient.format(
(double) barDuration, MeasurementUnits.MILLISECONDS, true);
calculateOOB();
// find the lowest value and use it's UOM to translated everything else into
MeasurementDataNumericHighLowComposite lowestValue = null;
for (MeasurementDataNumericHighLowComposite measurement : metricData) {
if (!Double.isNaN(measurement.getValue())) {
if (null == lowestValue) {
lowestValue = measurement;
}
if (measurement.getLowValue() < lowestValue.getLowValue()) {
lowestValue = measurement;
}
}
}
MeasurementNumericValueAndUnits adjustedMeasurementUnitsAndValue =
MeasurementConverterClient.fit(lowestValue.getLowValue(), definition.getUnits());
adjustedMeasurementUnits = adjustedMeasurementUnitsAndValue.getUnits();
for (MeasurementDataNumericHighLowComposite measurement : metricData) {
sb.append("{ \"x\":" + measurement.getTimestamp() + ",");
if (null != lastOOB) {
sb.append(" \"baselineMin\":" + lastOOB.getBlMin() + ", ");
sb.append(" \"baselineMax\":" + lastOOB.getBlMax() + ", ");
}
if (!Double.isNaN(measurement.getValue())) {
Double newHigh =
normalizeUnitsAndValues(
new MeasurementNumericValueAndUnits(
measurement.getHighValue(), definition.getUnits()),
adjustedMeasurementUnits);
Double newAvg =
normalizeUnitsAndValues(
new MeasurementNumericValueAndUnits(
measurement.getValue(), definition.getUnits()),
adjustedMeasurementUnits);
Double newLow =
normalizeUnitsAndValues(
new MeasurementNumericValueAndUnits(
measurement.getLowValue(), definition.getUnits()),
adjustedMeasurementUnits);
sb.append(" \"barDuration\": \"" + barDurationString + "\", ");
sb.append(" \"high\":" + newHigh + ",");
sb.append(
" \"highFormatted\":\""
+ MeasurementConverterClient.format(
measurement.getHighValue(), definition.getUnits(), true, 0, 3)
+ "\",");
sb.append(" \"low\":" + cleanseLow(newLow, newAvg, newHigh) + ",");
sb.append(
" \"lowFormatted\":\""
+ MeasurementConverterClient.format(
cleanseLow(
measurement.getLowValue(),
measurement.getValue(),
measurement.getHighValue()),
definition.getUnits(),
true,
0,
3)
+ "\",");
sb.append(" \"avg\":" + newAvg + ",");
sb.append(
" \"avgFormatted\":\""
+ MeasurementConverterClient.format(
measurement.getValue(), definition.getUnits(), true, 0, 3)
+ "\"},");
} else {
// give it some values so that we dont have NaN
sb.append(" \"high\":0,");
sb.append(" \"low\":0,");
sb.append(" \"avg\":0,");
sb.append(" \"nodata\":true },");
}
if (!sb.toString().endsWith("},")) {
sb.append(" },");
}
}
sb.setLength(sb.length() - 1); // delete the last ','
sb.append("]");
}
Log.debug("Json data for: " + getChartTitle());
Log.debug(sb.toString());
return sb.toString();
}