object WindowSlider

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Type Members

  1. type Window[T] = TSEntry[Queue[TSEntry[T]]]

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  6. def dynamicWindow[T, A](entries: Stream[TSEntry[T]], start: (TSEntry[T]) => Boolean, stop: (TSEntry[T]) => Boolean, aggregator: => Aggregator[T, A]): Stream[TSEntry[A]]

    Slides windows over the given TSEntries that are dynamically defined by a start and a stop condition.

    Slides windows over the given TSEntries that are dynamically defined by a start and a stop condition. Once the start condition is true on an entry, the window extends from this entry up to but not including the entry where the stop condition returns true. (If both conditions are true, no window is started.)

    The returned entries contain the value of the aggregator after the last entry of the window has been added. Their validity is given by the loose domain of the entries in the window. A new aggregator is taken for each window.

    entries

    over which to slide windows

    start

    defines the start of the windows

    stop

    defines the end of the windows

    aggregator

    a by-name aggregator to use for each window

    returns

    the value of the aggregator for each window

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  21. def window[T, A](in: Stream[TSEntry[T]], windowWidth: Long, aggregator: TimeAwareReversibleAggregator[T, A], sampleRate: Long, useClosestInWindow: Boolean = true): Stream[(Window[T], Option[A])]

    See window() above.

    See window() above. This function slides a window and uses a time-aware aggregator. Therefore it samples the entries first.

    sampleRate

    to resample the entries

    useClosestInWindow

    whether to sample strictly or not (see TimeSeries.sample())

    returns

    a stream of entries representing the content of the window in a time-interval, along with the aggregated value

  22. def window[T, A](in: Stream[TSEntry[T]], windowWidth: Long, aggregator: TimeUnawareReversibleAggregator[T, A]): Stream[(Window[T], Option[A])]

    Slides a window of size 'windowWidth' on the entries present in 'in'.

    Slides a window of size 'windowWidth' on the entries present in 'in'. And calculate some aggregate that does not depend on the time of validity of the entries.

    Each returned entry E contains the entries of the original time series that intersect with any window that ends in the domain of E. The returned stream can be seen as a time series that, when queried for time 't', answers the question "All the entries that have a domain that is at least partly contained in 't - window' and 't', along with the aggregated value for those entries.

    in

    the entries over which to slide a window

    windowWidth

    width of the window

    aggregator

    a reversible aggregator to efficiently compute aggregations over the window

    returns

    a stream of entries representing the content of the window in a time-interval, along with the aggregated value

  23. def window[T](in: Stream[TSEntry[T]], windowWidth: Long): Stream[Window[T]]

    See A]()

    See A]()

    in

    the entries over which to slide a window

    windowWidth

    width of the window

    returns

    a stream of entries representing the content of the window in a time-interval

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