Examples with SeriesValueTime com.serotonin.m2m2.view.stats.SeriesValueTime used on opensource projects

Example 1 with SeriesValueTime

use of com.serotonin.m2m2.view.stats.SeriesValueTime in project ma-modules-public by infiniteautomation.

the class AggregateValueMapper method apply.

@Override
public StreamingPointValueTimeModel apply(SeriesValueTime<? extends AggregateValue> value) {
    DataPointVO point = lookupPoint(value.getSeriesId());
    RollupEnum rollup = rollup(point);
    AggregateValue aggregate = value.getValue();
    StreamingPointValueTimeModel model = new StreamingPointValueTimeModel(point.getXid(), value.getTime());
    if (rollup == RollupEnum.ALL) {
        AllStatisticsModel allStatisticsModel = getAllRollup(point, aggregate);
        model.setAllStatistics(allStatisticsModel);
    } else {
        ValueTimeModel rollupValue = getRollupValue(point, aggregate, rollup);
        model.setValueModel(rollupValue);
    }
    if (fields.contains(PointValueField.CACHED)) {
        model.setCached(false);
    }
    if (fields.contains(PointValueField.BOOKEND)) {
        model.setBookend(false);
    }
    return copyPointPropertiesToModel(point, model);
}

Example 2 with SeriesValueTime

use of com.serotonin.m2m2.view.stats.SeriesValueTime in project ma-core-public by infiniteautomation.

the class AggregateDao method aggregate.

/**
 * Aggregate a stream of raw point values into aggregate statistics. Mango statistics rely on knowing the initial
 * value of the point before the "from" time, you must include an initial start value in the stream (if one exists).
 * The timestamp of this start value should be less than the "from" time.
 *
 * @param point data point
 * @param from from time (inclusive)
 * @param to to time (exclusive)
 * @param pointValues stream of point values, must include a start value (at time < from) for accurate statistics
 * @param aggregationPeriod aggregation period (bucket/window size)
 * @return stream of aggregates
 */
default Stream<SeriesValueTime<AggregateValue>> aggregate(DataPointVO point, ZonedDateTime from, ZonedDateTime to, Stream<? extends PointValueTime> pointValues, TemporalAmount aggregationPeriod) {
    BucketCalculator bucketCalc = new TemporalAmountBucketCalculator(from, to, aggregationPeriod);
    AbstractPointValueTimeQuantizer<?> quantizer;
    switch(point.getPointLocator().getDataType()) {
        case BINARY:
        case MULTISTATE:
            quantizer = new StartsAndRuntimeListQuantizer(bucketCalc);
            break;
        case NUMERIC:
            quantizer = new AnalogStatisticsQuantizer(bucketCalc);
            break;
        case ALPHANUMERIC:
            quantizer = new ValueChangeCounterQuantizer(bucketCalc);
            break;
        default:
            throw new IllegalStateException("Unknown data type: " + point.getPointLocator().getDataType());
    }
    Stream<AggregateValue> aggregateStream = StatisticsAggregator.aggregate(pointValues, quantizer).filter(v -> v instanceof AggregateValue).map(v -> (AggregateValue) v);
    return aggregateStream.map(v -> new DefaultSeriesValueTime<>(point.getSeriesId(), v.getPeriodStartTime(), v));
}