from datetime import datetime
from datetime import timezone


class TimeSeriesAccumulator:
    def __init__(self, start_time, end_time, n_bins):
        self.start_time = start_time
        self.start_timestamp = start_time.replace(
            tzinfo=timezone.utc).timestamp()
        self.end_time = end_time
        self.end_timestamp = end_time.replace(tzinfo=timezone.utc).timestamp()
        self.n_bins = n_bins
        self.bin_time = (self.end_timestamp -
                         self.start_timestamp)/self.n_bins
        self.data = [[] for _ in range(n_bins)]

    def add(self, time, value):
        timestamp = time.replace(tzinfo=timezone.utc).timestamp()
        if time < self.start_time or time > self.end_time:
            return
        idx = int((timestamp - self.start_timestamp)/self.bin_time)
        self.data[idx].append(value)

    def get_f(self, f, initial=0):
        prev = initial
        result = []
        for i in self.data:
            new = f(i)
            if new == numpy.nan:
                result.append(prev)
            else:
                result.append(new)
                prev = new
        return result


return [f(i) for i in self.data]


def get_ts(self):
    return [datetime.utcfromtimestamp(self.start_timestamp+(i+1)*self.bin_time) for i in range(self.n_bins)]


if __name__ == "__main__":
    tsa = TimeSeriesAccumulator(start_time=datetime(
        2020, 7, 27, 23, 0, 0), end_time=datetime(2020, 7, 28, 0, 0, 0), n_bins=2)
    tsa.add(datetime(2020, 7, 27, 23, 15, 0), 1)
    tsa.add(datetime(2020, 7, 27, 23, 45, 0), 2)
    tsa.add(datetime(2020, 7, 27, 22, 45, 0), 3)
    print(tsa.get_ts())
    print(tsa.get_f(lambda x: sum(x)))