Examples with Interval org.joda.time.Interval used on opensource projects

Example 91 with Interval

use of org.joda.time.Interval in project head by mifos.

the class DecliningBalanceWithInterestCalculatedDailyFormula method calculate.

@Override
public Money calculate(Money principalOutstanding, Double aprInterestRate, LocalDate interestPeriodStartDate, LocalDate interestPeriodEndDate) {
    Interval installmentPeriod = null;
    if (interestPeriodStartDate.isAfter(interestPeriodEndDate)) {
        installmentPeriod = new Interval(interestPeriodEndDate.toDateMidnight().toDateTime(), interestPeriodStartDate.toDateMidnight().toDateTime());
    } else {
        installmentPeriod = new Interval(interestPeriodStartDate.toDateMidnight().toDateTime(), interestPeriodEndDate.toDateMidnight().toDateTime());
    }
    Integer installmentPeriodDuration = Days.daysIn(installmentPeriod).getDays();
    BigDecimal periodicInterestRate = BigDecimal.valueOf(aprInterestRate / Double.valueOf("365.0"));
    BigDecimal interestDue = principalOutstanding.getAmount().divide(BigDecimal.valueOf(Long.valueOf("100"))).multiply(periodicInterestRate).multiply(BigDecimal.valueOf(installmentPeriodDuration.doubleValue()));
    return new Money(principalOutstanding.getCurrency(), interestDue);
}

Example 92 with Interval

use of org.joda.time.Interval in project pinot by linkedin.

the class BackwardAnomalyFunctionUtils method splitSetsOfTimeSeries.

/**
   * Splits a MetricTimeSeries to current (observed) time series and baselines. The list of time
   * series is sorted by the start time of their interval in the reversed natural order. Therefore,
   * the current time series is located at the beginning of the returned list.
   *
   * @param metricTimeSeries the metric time series that contains current and baseline time series.
   * @param metricName the metric name to retrieve the value from the given metric time series.
   * @param timeSeriesIntervals the intervals of the split time series.
   * @return a list of time series, which are split from the metric time series.
   */
public static List<TimeSeries> splitSetsOfTimeSeries(MetricTimeSeries metricTimeSeries, String metricName, List<Interval> timeSeriesIntervals) {
    List<TimeSeries> timeSeriesList = new ArrayList<>(timeSeriesIntervals.size());
    for (Interval interval : timeSeriesIntervals) {
        TimeSeries timeSeries = new TimeSeries();
        timeSeries.setTimeSeriesInterval(interval);
        timeSeriesList.add(timeSeries);
    }
    // Sort time series by their start time in reversed natural order, i.e., the latest time series
    // is arranged in the front of the list
    Collections.sort(timeSeriesList, new TimeSeriesStartTimeComparator().reversed());
    // the timestamp and its value could be inserted to multiple time series.
    for (long timestamp : metricTimeSeries.getTimeWindowSet()) {
        for (TimeSeries timeSeries : timeSeriesList) {
            if (timeSeries.getTimeSeriesInterval().contains(timestamp)) {
                double value = metricTimeSeries.get(timestamp, metricName).doubleValue();
                timeSeries.set(timestamp, value);
            }
        }
    }
    return timeSeriesList;
}

Example 93 with Interval

use of org.joda.time.Interval in project pinot by linkedin.

the class BackwardAnomalyFunctionUtils method buildAnomalyDetectionContext.

/**
   * Returns an anomaly detection context from the given information.
   *
   * @param anomalyFunction the anomaly function for anomaly detection.
   * @param timeSeries the given time series.
   * @param metric the metric name of the given time series.
   * @param exploredDimensions the dimension map of the given time series.
   * @param windowStart the start of the interval of the time series.
   * @param windowEnd the end of the interval of the time series.
   *
   * @return an anomaly detection context from the given information.
   */
public static AnomalyDetectionContext buildAnomalyDetectionContext(AnomalyDetectionFunction anomalyFunction, MetricTimeSeries timeSeries, String metric, DimensionMap exploredDimensions, int bucketSize, TimeUnit bucketUnit, DateTime windowStart, DateTime windowEnd) {
    // Create the anomaly detection context for the new modularized anomaly function
    AnomalyDetectionContext anomalyDetectionContext = new AnomalyDetectionContext();
    anomalyDetectionContext.setBucketSizeInMS(AnomalyDetectionUtils.getBucketInMillis(bucketSize, bucketUnit));
    anomalyDetectionContext.setAnomalyDetectionFunction(anomalyFunction);
    // Construct TimeSeriesKey
    TimeSeriesKey timeSeriesKey = new TimeSeriesKey();
    timeSeriesKey.setDimensionMap(exploredDimensions);
    timeSeriesKey.setMetricName(metric);
    anomalyDetectionContext.setTimeSeriesKey(timeSeriesKey);
    // Split time series to observed time series and baselines for each metric
    for (String metricName : anomalyFunction.getSpec().getMetrics()) {
        List<Interval> intervals = anomalyFunction.getTimeSeriesIntervals(windowStart.getMillis(), windowEnd.getMillis());
        List<TimeSeries> timeSeriesList = BackwardAnomalyFunctionUtils.splitSetsOfTimeSeries(timeSeries, metricName, intervals);
        anomalyDetectionContext.setCurrent(metricName, timeSeriesList.get(0));
        timeSeriesList.remove(0);
        anomalyDetectionContext.setBaselines(metricName, timeSeriesList);
    }
    return anomalyDetectionContext;
}

Example 94 with Interval

use of org.joda.time.Interval in project pinot by linkedin.

the class NoopDataModel method getAllDataIntervals.

@Override
public List<Interval> getAllDataIntervals(long monitoringWindowStartTime, long monitoringWindowEndTime) {
    Interval interval = new Interval(monitoringWindowStartTime, monitoringWindowEndTime);
    List<Interval> ret = new ArrayList<>();
    ret.add(interval);
    return ret;
}

Example 95 with Interval

use of org.joda.time.Interval in project pinot by linkedin.

the class SeasonalDataModel method getAllDataIntervals.

@Override
public List<Interval> getAllDataIntervals(long monitoringWindowStartTime, long monitoringWindowEndTime) {
    List<Interval> intervals = getTrainingDataIntervals(monitoringWindowStartTime, monitoringWindowEndTime);
    Interval currentInterval = new Interval(monitoringWindowStartTime, monitoringWindowEndTime);
    intervals.add(0, currentInterval);
    return intervals;
}