IRC-SPHERE/HyperStream

# The MIT License (MIT) # Copyright (c) 2014-2017 University of Bristol
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from hyperstream.stream import StreamInstance
from hyperstream.tool import AggregateTool
from hyperstream.utils import MIN_DATE


class Aggregate(AggregateTool):
    """
    This tool aggregates over a given plate, for example, if the input is all the streams in a node on plate A.B,
    and the aggregation is over plate B, the results will live on plate A alone.
    This can also be thought of as marginalising one dimension of a tensor over the plates
    """
    def __init__(self, func, aggregation_meta_data):
        super(Aggregate, self).__init__(func=func, aggregation_meta_data=aggregation_meta_data)
        self.func = func

    def _execute(self, sources, alignment_stream, interval):

        # Put all of the data in a dict of sorted lists (inefficient!)
        data = dict((source.stream_id,
                     sorted(source.window(interval, force_calculation=True), key=lambda x: x.timestamp))
                    for source in sources)

        # Create a set of all of the timestamps available (also inefficient!)
        timestamps = sorted(set(item.timestamp for d in data.values() for item in d))

        # maintain dict of indices where the timestamps appear
        last_timestamps = dict((stream_id, MIN_DATE) for stream_id in data)

        # Now loop through the timestamps, and aggregate over the aggregation plate
        for ts in timestamps:
            values = []
            for stream_id in data:
                for item in data[stream_id]:
                    if item.timestamp < last_timestamps[stream_id]:
                        continue
                    if item.timestamp < ts:
                        continue
                    if item.timestamp == ts:
                        values.append(item.value)
                    last_timestamps[stream_id] = item.timestamp
                    break
            yield StreamInstance(ts, self.func(values))