myems / myems-api

        stdev = None

        variance = None

        current_datetime_utc = start_datetime_utc.replace(minute=0, second=0, microsecond=0, tzinfo=None)

        while current_datetime_utc <= end_datetime_utc:

            sub_total = Decimal(0.0)

            for row in rows_hourly:

                if current_datetime_utc <= row[0] < current_datetime_utc + \

                        timedelta(minutes=config.minutes_to_count):

                    sub_total += row[1]

            result_rows_hourly.append((current_datetime_utc, sub_total))

            sample_data.append(sub_total)

            counter += 1

            if minimum is None:

                minimum = sub_total

            elif minimum > sub_total:

                minimum = sub_total

            if maximum is None:

                maximum = sub_total

            elif maximum < sub_total:

                maximum = sub_total

            current_datetime_utc += timedelta(minutes=config.minutes_to_count)

        if len(sample_data) > 1:

            mean = statistics.mean(sample_data)

        # calculate the start datetime in utc of the first day in local

        start_datetime_local = start_datetime_utc + timedelta(hours=int(config.utc_offset[1:3]))

        current_datetime_utc = start_datetime_local.replace(hour=0) - timedelta(hours=int(config.utc_offset[1:3]))

        while current_datetime_utc <= end_datetime_utc:

            sub_total = Decimal(0.0)

            for row in rows_hourly:

                if current_datetime_utc <= row[0] < current_datetime_utc + timedelta(days=1):

                    sub_total += row[1]

            result_rows_daily.append((current_datetime_utc, sub_total))

            sample_data.append(sub_total)

            counter += 1

            if minimum is None:

                minimum = sub_total

            elif minimum > sub_total:

                minimum = sub_total

            if maximum is None:

                maximum = sub_total

            elif maximum < sub_total:

                maximum = sub_total

            current_datetime_utc += timedelta(days=1)

        if len(sample_data) > 1:

            mean = statistics.mean(sample_data)