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Optimize fetching statistics by avoiding timestamp conversions (#87845)
* Optimize fetching statistics by avoiding timestamp conversions * remove accidential unrelated change * fix test * recreate so we handle timezone changes
This commit is contained in:
J. Nick Koston
GitHub
parent
6a1cd75a67
commit
c35661947a
@ -4,7 +4,7 @@ from __future__ import annotations
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import asyncio
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from collections import defaultdict
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from collections.abc import Awaitable, Callable
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from datetime import datetime, timedelta
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from datetime import timedelta
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import functools
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from itertools import chain
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from types import ModuleType
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@ -278,9 +278,9 @@ async def ws_get_fossil_energy_consumption(
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def _combine_sum_statistics(
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stats: dict[str, list[dict[str, Any]]], statistic_ids: list[str]
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) -> dict[datetime, float]:
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) -> dict[float, float]:
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"""Combine multiple statistics, returns a dict indexed by start time."""
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result: defaultdict[datetime, float] = defaultdict(float)
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result: defaultdict[float, float] = defaultdict(float)
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for statistics_id, stat in stats.items():
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if statistics_id not in statistic_ids:
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@ -292,9 +292,9 @@ async def ws_get_fossil_energy_consumption(
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return {key: result[key] for key in sorted(result)}
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def _calculate_deltas(sums: dict[datetime, float]) -> dict[datetime, float]:
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def _calculate_deltas(sums: dict[float, float]) -> dict[float, float]:
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prev: float | None = None
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result: dict[datetime, float] = {}
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result: dict[float, float] = {}
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for period, sum_ in sums.items():
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if prev is not None:
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result[period] = sum_ - prev
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@ -303,8 +303,8 @@ async def ws_get_fossil_energy_consumption(
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def _reduce_deltas(
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stat_list: list[dict[str, Any]],
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same_period: Callable[[datetime, datetime], bool],
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period_start_end: Callable[[datetime], tuple[datetime, datetime]],
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same_period: Callable[[float, float], bool],
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period_start_end: Callable[[float], tuple[float, float]],
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period: timedelta,
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) -> list[dict[str, Any]]:
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"""Reduce hourly deltas to daily or monthly deltas."""
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@ -316,14 +316,14 @@ async def ws_get_fossil_energy_consumption(
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# Loop over the hourly deltas + a fake entry to end the period
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for statistic in chain(
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stat_list, ({"start": stat_list[-1]["start"] + period},)
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stat_list, ({"start": stat_list[-1]["start"] + period.total_seconds()},)
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):
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if not same_period(prev_stat["start"], statistic["start"]):
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start, _ = period_start_end(prev_stat["start"])
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# The previous statistic was the last entry of the period
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result.append(
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{
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"start": start.isoformat(),
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"start": dt_util.utc_from_timestamp(start).isoformat(),
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"delta": sum(deltas),
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}
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)
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@ -351,24 +351,30 @@ async def ws_get_fossil_energy_consumption(
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if msg["period"] == "hour":
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reduced_fossil_energy = [
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{"start": period["start"].isoformat(), "delta": period["delta"]}
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{
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"start": dt_util.utc_from_timestamp(period["start"]).isoformat(),
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"delta": period["delta"],
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}
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for period in fossil_energy
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]
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elif msg["period"] == "day":
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_same_day, _day_start_end = recorder.statistics.reduce_day_factory()
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_same_day_ts, _day_start_end_ts = recorder.statistics.reduce_day_ts_factory()
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reduced_fossil_energy = _reduce_deltas(
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fossil_energy,
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_same_day,
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_day_start_end,
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_same_day_ts,
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_day_start_end_ts,
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timedelta(days=1),
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)
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else:
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_same_month, _month_start_end = recorder.statistics.reduce_month_factory()
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(
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_same_month_ts,
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_month_start_end_ts,
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) = recorder.statistics.reduce_month_ts_factory()
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reduced_fossil_energy = _reduce_deltas(
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fossil_energy,
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_same_month,
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_month_start_end,
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_same_month_ts,
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_month_start_end_ts,
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timedelta(days=1),
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)
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@ -1028,13 +1028,14 @@ def list_statistic_ids(
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def _reduce_statistics(
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stats: dict[str, list[dict[str, Any]]],
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same_period: Callable[[datetime, datetime], bool],
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period_start_end: Callable[[datetime], tuple[datetime, datetime]],
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same_period: Callable[[float, float], bool],
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period_start_end: Callable[[float], tuple[float, float]],
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period: timedelta,
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types: set[Literal["last_reset", "max", "mean", "min", "state", "sum"]],
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) -> dict[str, list[dict[str, Any]]]:
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"""Reduce hourly statistics to daily or monthly statistics."""
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result: dict[str, list[dict[str, Any]]] = defaultdict(list)
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period_seconds = period.total_seconds()
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for statistic_id, stat_list in stats.items():
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max_values: list[float] = []
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mean_values: list[float] = []
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@ -1043,7 +1044,7 @@ def _reduce_statistics(
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# Loop over the hourly statistics + a fake entry to end the period
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for statistic in chain(
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stat_list, ({"start": stat_list[-1]["start"] + period},)
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stat_list, ({"start": stat_list[-1]["start"] + period_seconds},)
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):
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if not same_period(prev_stat["start"], statistic["start"]):
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start, end = period_start_end(prev_stat["start"])
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@ -1080,35 +1081,39 @@ def _reduce_statistics(
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return result
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def reduce_day_factory() -> (
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def reduce_day_ts_factory() -> (
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tuple[
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Callable[[datetime, datetime], bool],
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Callable[[datetime], tuple[datetime, datetime]],
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Callable[[float, float], bool],
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Callable[[float], tuple[float, float]],
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]
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):
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"""Return functions to match same day and day start end."""
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# We have to recreate _local_from_timestamp in the closure in case the timezone changes
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_local_from_timestamp = partial(
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datetime.fromtimestamp, tz=dt_util.DEFAULT_TIME_ZONE
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)
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# We create _as_local_cached in the closure in case the timezone changes
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_as_local_cached = lru_cache(maxsize=6)(dt_util.as_local)
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_as_local_cached = lru_cache(maxsize=6)(_local_from_timestamp)
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def _as_local_date(time: datetime) -> date:
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def _as_local_date(time: float) -> date:
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"""Return the local date of a datetime."""
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return dt_util.as_local(time).date()
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return _local_from_timestamp(time).date()
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_as_local_date_cached = lru_cache(maxsize=6)(_as_local_date)
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def _same_day(time1: datetime, time2: datetime) -> bool:
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def _same_day_ts(time1: float, time2: float) -> bool:
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"""Return True if time1 and time2 are in the same date."""
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return _as_local_date_cached(time1) == _as_local_date_cached(time2)
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def _day_start_end(time: datetime) -> tuple[datetime, datetime]:
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def _day_start_end_ts(time: float) -> tuple[float, float]:
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"""Return the start and end of the period (day) time is within."""
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start = dt_util.as_utc(
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_as_local_cached(time).replace(hour=0, minute=0, second=0, microsecond=0)
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)
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end = start + timedelta(days=1)
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return (start, end)
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return (start.timestamp(), end.timestamp())
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return _same_day, _day_start_end
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return _same_day_ts, _day_start_end_ts
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def _reduce_statistics_per_day(
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@ -1116,37 +1121,41 @@ def _reduce_statistics_per_day(
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types: set[Literal["last_reset", "max", "mean", "min", "state", "sum"]],
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) -> dict[str, list[dict[str, Any]]]:
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"""Reduce hourly statistics to daily statistics."""
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_same_day, _day_start_end = reduce_day_factory()
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_same_day_ts, _day_start_end_ts = reduce_day_ts_factory()
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return _reduce_statistics(
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stats, _same_day, _day_start_end, timedelta(days=1), types
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stats, _same_day_ts, _day_start_end_ts, timedelta(days=1), types
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)
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def reduce_week_factory() -> (
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def reduce_week_ts_factory() -> (
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tuple[
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Callable[[datetime, datetime], bool],
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Callable[[datetime], tuple[datetime, datetime]],
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Callable[[float, float], bool],
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Callable[[float], tuple[float, float]],
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]
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):
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"""Return functions to match same week and week start end."""
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# We have to recreate _local_from_timestamp in the closure in case the timezone changes
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_local_from_timestamp = partial(
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datetime.fromtimestamp, tz=dt_util.DEFAULT_TIME_ZONE
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)
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# We create _as_local_cached in the closure in case the timezone changes
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_as_local_cached = lru_cache(maxsize=6)(dt_util.as_local)
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_as_local_cached = lru_cache(maxsize=6)(_local_from_timestamp)
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def _as_local_isocalendar(
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time: datetime,
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time: float,
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) -> tuple: # Need python3.11 for isocalendar typing
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"""Return the local isocalendar of a datetime."""
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return dt_util.as_local(time).isocalendar()
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return _local_from_timestamp(time).isocalendar()
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_as_local_isocalendar_cached = lru_cache(maxsize=6)(_as_local_isocalendar)
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def _same_week(time1: datetime, time2: datetime) -> bool:
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def _same_week_ts(time1: float, time2: float) -> bool:
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"""Return True if time1 and time2 are in the same year and week."""
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date1 = _as_local_isocalendar_cached(time1)
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date2 = _as_local_isocalendar_cached(time2)
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return (date1.year, date1.week) == (date2.year, date2.week) # type: ignore[attr-defined]
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def _week_start_end(time: datetime) -> tuple[datetime, datetime]:
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def _week_start_end_ts(time: float) -> tuple[float, float]:
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"""Return the start and end of the period (week) time is within."""
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time_local = _as_local_cached(time)
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start_local = time_local.replace(
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@ -1154,9 +1163,9 @@ def reduce_week_factory() -> (
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) - timedelta(days=time_local.weekday())
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start = dt_util.as_utc(start_local)
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end = dt_util.as_utc(start_local + timedelta(days=7))
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return (start, end)
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return (start.timestamp(), end.timestamp())
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return _same_week, _week_start_end
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return _same_week_ts, _week_start_end_ts
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def _reduce_statistics_per_week(
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@ -1164,34 +1173,33 @@ def _reduce_statistics_per_week(
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types: set[Literal["last_reset", "max", "mean", "min", "state", "sum"]],
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) -> dict[str, list[dict[str, Any]]]:
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"""Reduce hourly statistics to weekly statistics."""
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_same_week, _week_start_end = reduce_week_factory()
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_same_week_ts, _week_start_end_ts = reduce_week_ts_factory()
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return _reduce_statistics(
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stats, _same_week, _week_start_end, timedelta(days=7), types
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stats, _same_week_ts, _week_start_end_ts, timedelta(days=7), types
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)
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def reduce_month_factory() -> (
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def reduce_month_ts_factory() -> (
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tuple[
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Callable[[datetime, datetime], bool],
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Callable[[datetime], tuple[datetime, datetime]],
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Callable[[float, float], bool],
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Callable[[float], tuple[float, float]],
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]
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):
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"""Return functions to match same month and month start end."""
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# We have to recreate _local_from_timestamp in the closure in case the timezone changes
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_local_from_timestamp = partial(
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datetime.fromtimestamp, tz=dt_util.DEFAULT_TIME_ZONE
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)
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# We create _as_local_cached in the closure in case the timezone changes
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_as_local_cached = lru_cache(maxsize=6)(dt_util.as_local)
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_as_local_cached = lru_cache(maxsize=6)(_local_from_timestamp)
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def _same_month(time1: datetime, time2: datetime) -> bool:
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def _same_month_ts(time1: float, time2: float) -> bool:
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"""Return True if time1 and time2 are in the same year and month."""
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if 2 < time1.day < 26 and 2 < time2.day < 26:
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# No need to convert to local time if both dates are far
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# enough from possible start or end of the month as time zones
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# can't change more than 24 hours in a month.
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return (time1.year, time1.month) == (time1.year, time1.month)
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date1 = _as_local_cached(time1)
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date2 = _as_local_cached(time2)
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return (date1.year, date1.month) == (date2.year, date2.month)
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def _month_start_end(time: datetime) -> tuple[datetime, datetime]:
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def _month_start_end_ts(time: float) -> tuple[float, float]:
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"""Return the start and end of the period (month) time is within."""
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start_local = _as_local_cached(time).replace(
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day=1, hour=0, minute=0, second=0, microsecond=0
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@ -1199,9 +1207,9 @@ def reduce_month_factory() -> (
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start = dt_util.as_utc(start_local)
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end_local = (start_local + timedelta(days=31)).replace(day=1)
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end = dt_util.as_utc(end_local)
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return (start, end)
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return (start.timestamp(), end.timestamp())
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return _same_month, _month_start_end
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return _same_month_ts, _month_start_end_ts
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def _reduce_statistics_per_month(
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@ -1209,9 +1217,9 @@ def _reduce_statistics_per_month(
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types: set[Literal["last_reset", "max", "mean", "min", "state", "sum"]],
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) -> dict[str, list[dict[str, Any]]]:
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"""Reduce hourly statistics to monthly statistics."""
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_same_month, _month_start_end = reduce_month_factory()
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_same_month_ts, _month_start_end_ts = reduce_month_ts_factory()
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return _reduce_statistics(
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stats, _same_month, _month_start_end, timedelta(days=31), types
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stats, _same_month_ts, _month_start_end_ts, timedelta(days=31), types
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)
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@ -2028,7 +2036,6 @@ def _sorted_statistics_to_dict(
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# Append all statistic entries, and optionally do unit conversion
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table_duration_seconds = table.duration.total_seconds()
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timestamp_to_datetime = dt_util.utc_from_timestamp
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for meta_id, stats_list in stats_by_meta_id.items():
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metadata_by_id = metadata[meta_id]
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statistic_id = metadata_by_id["statistic_id"]
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@ -2043,8 +2050,8 @@ def _sorted_statistics_to_dict(
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for db_state in stats_list:
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start_ts = db_state.start_ts
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row: dict[str, Any] = {
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"start": timestamp_to_datetime(start_ts),
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"end": timestamp_to_datetime(start_ts + table_duration_seconds),
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"start": start_ts,
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"end": start_ts + table_duration_seconds,
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}
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if "mean" in types:
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row["mean"] = convert(db_state.mean) if convert else db_state.mean
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@ -2053,9 +2060,7 @@ def _sorted_statistics_to_dict(
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if "max" in types:
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row["max"] = convert(db_state.max) if convert else db_state.max
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if "last_reset" in types:
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row["last_reset"] = timestamp_to_datetime_or_none(
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db_state.last_reset_ts
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)
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row["last_reset"] = db_state.last_reset_ts
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if "state" in types:
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row["state"] = convert(db_state.state) if convert else db_state.state
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if "sum" in types:
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@ -2509,8 +2514,10 @@ def _validate_db_schema(
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schema_errors,
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stored_statistic[0],
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{
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"last_reset": statistics["last_reset"],
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"start": statistics["start"],
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"last_reset": datetime_to_timestamp_or_none(
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statistics["last_reset"]
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),
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"start": datetime_to_timestamp_or_none(statistics["start"]),
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},
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("start", "last_reset"),
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table.__tablename__,
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@ -169,11 +169,11 @@ def _ws_get_statistics_during_period(
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for statistic_id in result:
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for item in result[statistic_id]:
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if (start := item.get("start")) is not None:
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item["start"] = int(start.timestamp() * 1000)
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item["start"] = int(start * 1000)
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if (end := item.get("end")) is not None:
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item["end"] = int(end.timestamp() * 1000)
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item["end"] = int(end * 1000)
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if (last_reset := item.get("last_reset")) is not None:
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item["last_reset"] = int(last_reset.timestamp() * 1000)
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item["last_reset"] = int(last_reset * 1000)
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return JSON_DUMP(messages.result_message(msg_id, result))
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@ -369,6 +369,13 @@ def _last_reset_as_utc_isoformat(last_reset_s: Any, entity_id: str) -> str | Non
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return dt_util.as_utc(last_reset).isoformat()
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def _timestamp_to_isoformat_or_none(timestamp: float | None) -> str | None:
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"""Convert a timestamp to ISO format or return None."""
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if timestamp is None:
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return None
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return dt_util.utc_from_timestamp(timestamp).isoformat()
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def compile_statistics(
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hass: HomeAssistant, start: datetime.datetime, end: datetime.datetime
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) -> statistics.PlatformCompiledStatistics:
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@ -526,9 +533,9 @@ def _compile_statistics( # noqa: C901
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if entity_id in last_stats:
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# We have compiled history for this sensor before,
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# use that as a starting point.
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last_reset = old_last_reset = last_stats[entity_id][0]["last_reset"]
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if old_last_reset is not None:
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last_reset = old_last_reset = old_last_reset.isoformat()
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last_reset = old_last_reset = _timestamp_to_isoformat_or_none(
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last_stats[entity_id][0]["last_reset"]
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)
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||||
new_state = old_state = last_stats[entity_id][0]["state"]
|
||||
_sum = last_stats[entity_id][0]["sum"] or 0.0
|
||||
|
||||
|
||||
@ -14,7 +14,10 @@ from homeassistant.components import recorder
|
||||
from homeassistant.components.recorder import history, statistics
|
||||
from homeassistant.components.recorder.const import SQLITE_URL_PREFIX
|
||||
from homeassistant.components.recorder.db_schema import StatisticsShortTerm
|
||||
from homeassistant.components.recorder.models import process_timestamp
|
||||
from homeassistant.components.recorder.models import (
|
||||
datetime_to_timestamp_or_none,
|
||||
process_timestamp,
|
||||
)
|
||||
from homeassistant.components.recorder.statistics import (
|
||||
STATISTIC_UNIT_TO_UNIT_CONVERTER,
|
||||
_statistics_during_period_with_session,
|
||||
@ -91,8 +94,8 @@ def test_compile_hourly_statistics(hass_recorder):
|
||||
do_adhoc_statistics(hass, start=four)
|
||||
wait_recording_done(hass)
|
||||
expected_1 = {
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(14.915254237288135),
|
||||
"min": pytest.approx(10.0),
|
||||
"max": pytest.approx(20.0),
|
||||
@ -101,8 +104,8 @@ def test_compile_hourly_statistics(hass_recorder):
|
||||
"sum": None,
|
||||
}
|
||||
expected_2 = {
|
||||
"start": process_timestamp(four),
|
||||
"end": process_timestamp(four + timedelta(minutes=5)),
|
||||
"start": process_timestamp(four).timestamp(),
|
||||
"end": process_timestamp(four + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(20.0),
|
||||
"min": pytest.approx(20.0),
|
||||
"max": pytest.approx(20.0),
|
||||
@ -275,8 +278,8 @@ def test_compile_periodic_statistics_exception(
|
||||
do_adhoc_statistics(hass, start=now + timedelta(minutes=5))
|
||||
wait_recording_done(hass)
|
||||
expected_1 = {
|
||||
"start": process_timestamp(now),
|
||||
"end": process_timestamp(now + timedelta(minutes=5)),
|
||||
"start": process_timestamp(now).timestamp(),
|
||||
"end": process_timestamp(now + timedelta(minutes=5)).timestamp(),
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"max": None,
|
||||
@ -285,8 +288,8 @@ def test_compile_periodic_statistics_exception(
|
||||
"sum": None,
|
||||
}
|
||||
expected_2 = {
|
||||
"start": process_timestamp(now + timedelta(minutes=5)),
|
||||
"end": process_timestamp(now + timedelta(minutes=10)),
|
||||
"start": process_timestamp(now + timedelta(minutes=5)).timestamp(),
|
||||
"end": process_timestamp(now + timedelta(minutes=10)).timestamp(),
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"max": None,
|
||||
@ -345,8 +348,8 @@ def test_rename_entity(hass_recorder):
|
||||
do_adhoc_statistics(hass, start=zero)
|
||||
wait_recording_done(hass)
|
||||
expected_1 = {
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(14.915254237288135),
|
||||
"min": pytest.approx(10.0),
|
||||
"max": pytest.approx(20.0),
|
||||
@ -411,8 +414,8 @@ def test_rename_entity_collision(hass_recorder, caplog):
|
||||
do_adhoc_statistics(hass, start=zero)
|
||||
wait_recording_done(hass)
|
||||
expected_1 = {
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(14.915254237288135),
|
||||
"min": pytest.approx(10.0),
|
||||
"max": pytest.approx(20.0),
|
||||
@ -544,22 +547,22 @@ async def test_import_statistics(
|
||||
assert stats == {
|
||||
statistic_id: [
|
||||
{
|
||||
"start": process_timestamp(period1),
|
||||
"end": process_timestamp(period1 + timedelta(hours=1)),
|
||||
"start": process_timestamp(period1).timestamp(),
|
||||
"end": process_timestamp(period1 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": last_reset_utc,
|
||||
"last_reset": datetime_to_timestamp_or_none(last_reset_utc),
|
||||
"state": pytest.approx(0.0),
|
||||
"sum": pytest.approx(2.0),
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(period2),
|
||||
"end": process_timestamp(period2 + timedelta(hours=1)),
|
||||
"start": process_timestamp(period2).timestamp(),
|
||||
"end": process_timestamp(period2 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": last_reset_utc,
|
||||
"last_reset": datetime_to_timestamp_or_none(last_reset_utc),
|
||||
"state": pytest.approx(1.0),
|
||||
"sum": pytest.approx(3.0),
|
||||
},
|
||||
@ -602,12 +605,12 @@ async def test_import_statistics(
|
||||
assert last_stats == {
|
||||
statistic_id: [
|
||||
{
|
||||
"start": process_timestamp(period2),
|
||||
"end": process_timestamp(period2 + timedelta(hours=1)),
|
||||
"start": process_timestamp(period2).timestamp(),
|
||||
"end": process_timestamp(period2 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": last_reset_utc,
|
||||
"last_reset": datetime_to_timestamp_or_none(last_reset_utc),
|
||||
"state": pytest.approx(1.0),
|
||||
"sum": pytest.approx(3.0),
|
||||
},
|
||||
@ -627,8 +630,8 @@ async def test_import_statistics(
|
||||
assert stats == {
|
||||
statistic_id: [
|
||||
{
|
||||
"start": process_timestamp(period1),
|
||||
"end": process_timestamp(period1 + timedelta(hours=1)),
|
||||
"start": process_timestamp(period1).timestamp(),
|
||||
"end": process_timestamp(period1 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -637,12 +640,12 @@ async def test_import_statistics(
|
||||
"sum": pytest.approx(6.0),
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(period2),
|
||||
"end": process_timestamp(period2 + timedelta(hours=1)),
|
||||
"start": process_timestamp(period2).timestamp(),
|
||||
"end": process_timestamp(period2 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": last_reset_utc,
|
||||
"last_reset": datetime_to_timestamp_or_none(last_reset_utc),
|
||||
"state": pytest.approx(1.0),
|
||||
"sum": pytest.approx(3.0),
|
||||
},
|
||||
@ -693,22 +696,22 @@ async def test_import_statistics(
|
||||
assert stats == {
|
||||
statistic_id: [
|
||||
{
|
||||
"start": process_timestamp(period1),
|
||||
"end": process_timestamp(period1 + timedelta(hours=1)),
|
||||
"start": process_timestamp(period1).timestamp(),
|
||||
"end": process_timestamp(period1 + timedelta(hours=1)).timestamp(),
|
||||
"max": pytest.approx(1.0),
|
||||
"mean": pytest.approx(2.0),
|
||||
"min": pytest.approx(3.0),
|
||||
"last_reset": last_reset_utc,
|
||||
"last_reset": datetime_to_timestamp_or_none(last_reset_utc),
|
||||
"state": pytest.approx(4.0),
|
||||
"sum": pytest.approx(5.0),
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(period2),
|
||||
"end": process_timestamp(period2 + timedelta(hours=1)),
|
||||
"start": process_timestamp(period2).timestamp(),
|
||||
"end": process_timestamp(period2 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": last_reset_utc,
|
||||
"last_reset": datetime_to_timestamp_or_none(last_reset_utc),
|
||||
"state": pytest.approx(1.0),
|
||||
"sum": pytest.approx(3.0),
|
||||
},
|
||||
@ -734,22 +737,22 @@ async def test_import_statistics(
|
||||
assert stats == {
|
||||
statistic_id: [
|
||||
{
|
||||
"start": process_timestamp(period1),
|
||||
"end": process_timestamp(period1 + timedelta(hours=1)),
|
||||
"start": process_timestamp(period1).timestamp(),
|
||||
"end": process_timestamp(period1 + timedelta(hours=1)).timestamp(),
|
||||
"max": pytest.approx(1.0),
|
||||
"mean": pytest.approx(2.0),
|
||||
"min": pytest.approx(3.0),
|
||||
"last_reset": last_reset_utc,
|
||||
"last_reset": datetime_to_timestamp_or_none(last_reset_utc),
|
||||
"state": pytest.approx(4.0),
|
||||
"sum": pytest.approx(5.0),
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(period2),
|
||||
"end": process_timestamp(period2 + timedelta(hours=1)),
|
||||
"start": process_timestamp(period2).timestamp(),
|
||||
"end": process_timestamp(period2 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": last_reset_utc,
|
||||
"last_reset": datetime_to_timestamp_or_none(last_reset_utc),
|
||||
"state": pytest.approx(1.0),
|
||||
"sum": pytest.approx(1000 * 1000 + 3.0),
|
||||
},
|
||||
@ -993,8 +996,8 @@ def test_weekly_statistics(hass_recorder, caplog, timezone):
|
||||
assert stats == {
|
||||
"test:total_energy_import": [
|
||||
{
|
||||
"start": week1_start,
|
||||
"end": week1_end,
|
||||
"start": week1_start.timestamp(),
|
||||
"end": week1_end.timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -1003,8 +1006,8 @@ def test_weekly_statistics(hass_recorder, caplog, timezone):
|
||||
"sum": 3.0,
|
||||
},
|
||||
{
|
||||
"start": week2_start,
|
||||
"end": week2_end,
|
||||
"start": week2_start.timestamp(),
|
||||
"end": week2_end.timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -1024,8 +1027,8 @@ def test_weekly_statistics(hass_recorder, caplog, timezone):
|
||||
assert stats == {
|
||||
"test:total_energy_import": [
|
||||
{
|
||||
"start": week1_start,
|
||||
"end": week1_end,
|
||||
"start": week1_start.timestamp(),
|
||||
"end": week1_end.timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -1034,8 +1037,8 @@ def test_weekly_statistics(hass_recorder, caplog, timezone):
|
||||
"sum": 3.0,
|
||||
},
|
||||
{
|
||||
"start": week2_start,
|
||||
"end": week2_end,
|
||||
"start": week2_start.timestamp(),
|
||||
"end": week2_end.timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -1127,8 +1130,8 @@ def test_monthly_statistics(hass_recorder, caplog, timezone):
|
||||
assert stats == {
|
||||
"test:total_energy_import": [
|
||||
{
|
||||
"start": sep_start,
|
||||
"end": sep_end,
|
||||
"start": sep_start.timestamp(),
|
||||
"end": sep_end.timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -1137,8 +1140,8 @@ def test_monthly_statistics(hass_recorder, caplog, timezone):
|
||||
"sum": pytest.approx(3.0),
|
||||
},
|
||||
{
|
||||
"start": oct_start,
|
||||
"end": oct_end,
|
||||
"start": oct_start.timestamp(),
|
||||
"end": oct_end.timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -1162,8 +1165,8 @@ def test_monthly_statistics(hass_recorder, caplog, timezone):
|
||||
assert stats == {
|
||||
"test:total_energy_import": [
|
||||
{
|
||||
"start": sep_start,
|
||||
"end": sep_end,
|
||||
"start": sep_start.timestamp(),
|
||||
"end": sep_end.timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -1172,8 +1175,8 @@ def test_monthly_statistics(hass_recorder, caplog, timezone):
|
||||
"sum": pytest.approx(3.0),
|
||||
},
|
||||
{
|
||||
"start": oct_start,
|
||||
"end": oct_end,
|
||||
"start": oct_start.timestamp(),
|
||||
"end": oct_end.timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -1570,11 +1573,11 @@ async def test_validate_db_schema_fix_float_issue(
|
||||
statistics = {
|
||||
"recorder.db_test": [
|
||||
{
|
||||
"last_reset": precise_time,
|
||||
"last_reset": precise_time.timestamp(),
|
||||
"max": precise_number,
|
||||
"mean": precise_number,
|
||||
"min": precise_number,
|
||||
"start": precise_time,
|
||||
"start": precise_time.timestamp(),
|
||||
"state": precise_number,
|
||||
"sum": precise_number,
|
||||
}
|
||||
|
||||
@ -2446,8 +2446,8 @@ async def test_import_statistics(
|
||||
assert stats == {
|
||||
statistic_id: [
|
||||
{
|
||||
"start": period1,
|
||||
"end": (period1 + timedelta(hours=1)),
|
||||
"start": period1.timestamp(),
|
||||
"end": (period1 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -2456,8 +2456,8 @@ async def test_import_statistics(
|
||||
"sum": pytest.approx(2.0),
|
||||
},
|
||||
{
|
||||
"start": period2,
|
||||
"end": period2 + timedelta(hours=1),
|
||||
"start": period2.timestamp(),
|
||||
"end": (period2 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -2504,8 +2504,8 @@ async def test_import_statistics(
|
||||
assert last_stats == {
|
||||
statistic_id: [
|
||||
{
|
||||
"start": period2,
|
||||
"end": period2 + timedelta(hours=1),
|
||||
"start": period2.timestamp(),
|
||||
"end": (period2 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -2541,8 +2541,8 @@ async def test_import_statistics(
|
||||
assert stats == {
|
||||
statistic_id: [
|
||||
{
|
||||
"start": period1,
|
||||
"end": period1 + timedelta(hours=1),
|
||||
"start": period1.timestamp(),
|
||||
"end": (period1 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -2551,8 +2551,8 @@ async def test_import_statistics(
|
||||
"sum": pytest.approx(6.0),
|
||||
},
|
||||
{
|
||||
"start": period2,
|
||||
"end": period2 + timedelta(hours=1),
|
||||
"start": period2.timestamp(),
|
||||
"end": (period2 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -2591,8 +2591,8 @@ async def test_import_statistics(
|
||||
assert stats == {
|
||||
statistic_id: [
|
||||
{
|
||||
"start": period1,
|
||||
"end": period1 + timedelta(hours=1),
|
||||
"start": period1.timestamp(),
|
||||
"end": (period1 + timedelta(hours=1)).timestamp(),
|
||||
"max": pytest.approx(1.0),
|
||||
"mean": pytest.approx(2.0),
|
||||
"min": pytest.approx(3.0),
|
||||
@ -2601,8 +2601,8 @@ async def test_import_statistics(
|
||||
"sum": pytest.approx(5.0),
|
||||
},
|
||||
{
|
||||
"start": period2,
|
||||
"end": period2 + timedelta(hours=1),
|
||||
"start": period2.timestamp(),
|
||||
"end": (period2 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -2673,8 +2673,8 @@ async def test_adjust_sum_statistics_energy(
|
||||
assert stats == {
|
||||
statistic_id: [
|
||||
{
|
||||
"start": period1,
|
||||
"end": period1 + timedelta(hours=1),
|
||||
"start": period1.timestamp(),
|
||||
"end": (period1 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -2683,8 +2683,8 @@ async def test_adjust_sum_statistics_energy(
|
||||
"sum": pytest.approx(2.0),
|
||||
},
|
||||
{
|
||||
"start": period2,
|
||||
"end": period2 + timedelta(hours=1),
|
||||
"start": period2.timestamp(),
|
||||
"end": (period2 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -2741,8 +2741,8 @@ async def test_adjust_sum_statistics_energy(
|
||||
assert stats == {
|
||||
statistic_id: [
|
||||
{
|
||||
"start": period1,
|
||||
"end": period1 + timedelta(hours=1),
|
||||
"start": period1.timestamp(),
|
||||
"end": (period1 + timedelta(hours=1)).timestamp(),
|
||||
"max": pytest.approx(None),
|
||||
"mean": pytest.approx(None),
|
||||
"min": pytest.approx(None),
|
||||
@ -2751,8 +2751,8 @@ async def test_adjust_sum_statistics_energy(
|
||||
"sum": pytest.approx(2.0),
|
||||
},
|
||||
{
|
||||
"start": period2,
|
||||
"end": period2 + timedelta(hours=1),
|
||||
"start": period2.timestamp(),
|
||||
"end": (period2 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -2782,8 +2782,8 @@ async def test_adjust_sum_statistics_energy(
|
||||
assert stats == {
|
||||
statistic_id: [
|
||||
{
|
||||
"start": period1,
|
||||
"end": period1 + timedelta(hours=1),
|
||||
"start": period1.timestamp(),
|
||||
"end": (period1 + timedelta(hours=1)).timestamp(),
|
||||
"max": pytest.approx(None),
|
||||
"mean": pytest.approx(None),
|
||||
"min": pytest.approx(None),
|
||||
@ -2792,8 +2792,8 @@ async def test_adjust_sum_statistics_energy(
|
||||
"sum": pytest.approx(2.0),
|
||||
},
|
||||
{
|
||||
"start": period2,
|
||||
"end": period2 + timedelta(hours=1),
|
||||
"start": period2.timestamp(),
|
||||
"end": (period2 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -2864,8 +2864,8 @@ async def test_adjust_sum_statistics_gas(
|
||||
assert stats == {
|
||||
statistic_id: [
|
||||
{
|
||||
"start": period1,
|
||||
"end": period1 + timedelta(hours=1),
|
||||
"start": period1.timestamp(),
|
||||
"end": (period1 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -2874,8 +2874,8 @@ async def test_adjust_sum_statistics_gas(
|
||||
"sum": pytest.approx(2.0),
|
||||
},
|
||||
{
|
||||
"start": period2,
|
||||
"end": period2 + timedelta(hours=1),
|
||||
"start": period2.timestamp(),
|
||||
"end": (period2 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -2932,8 +2932,8 @@ async def test_adjust_sum_statistics_gas(
|
||||
assert stats == {
|
||||
statistic_id: [
|
||||
{
|
||||
"start": period1,
|
||||
"end": period1 + timedelta(hours=1),
|
||||
"start": period1.timestamp(),
|
||||
"end": (period1 + timedelta(hours=1)).timestamp(),
|
||||
"max": pytest.approx(None),
|
||||
"mean": pytest.approx(None),
|
||||
"min": pytest.approx(None),
|
||||
@ -2942,8 +2942,8 @@ async def test_adjust_sum_statistics_gas(
|
||||
"sum": pytest.approx(2.0),
|
||||
},
|
||||
{
|
||||
"start": period2,
|
||||
"end": period2 + timedelta(hours=1),
|
||||
"start": period2.timestamp(),
|
||||
"end": (period2 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -2973,8 +2973,8 @@ async def test_adjust_sum_statistics_gas(
|
||||
assert stats == {
|
||||
statistic_id: [
|
||||
{
|
||||
"start": period1,
|
||||
"end": period1 + timedelta(hours=1),
|
||||
"start": period1.timestamp(),
|
||||
"end": (period1 + timedelta(hours=1)).timestamp(),
|
||||
"max": pytest.approx(None),
|
||||
"mean": pytest.approx(None),
|
||||
"min": pytest.approx(None),
|
||||
@ -2983,8 +2983,8 @@ async def test_adjust_sum_statistics_gas(
|
||||
"sum": pytest.approx(2.0),
|
||||
},
|
||||
{
|
||||
"start": period2,
|
||||
"end": period2 + timedelta(hours=1),
|
||||
"start": period2.timestamp(),
|
||||
"end": (period2 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -3070,8 +3070,8 @@ async def test_adjust_sum_statistics_errors(
|
||||
assert stats == {
|
||||
statistic_id: [
|
||||
{
|
||||
"start": period1,
|
||||
"end": period1 + timedelta(hours=1),
|
||||
"start": period1.timestamp(),
|
||||
"end": (period1 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -3080,8 +3080,8 @@ async def test_adjust_sum_statistics_errors(
|
||||
"sum": pytest.approx(2.0 * factor),
|
||||
},
|
||||
{
|
||||
"start": period2,
|
||||
"end": period2 + timedelta(hours=1),
|
||||
"start": period2.timestamp(),
|
||||
"end": (period2 + timedelta(hours=1)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
|
||||
@ -162,8 +162,8 @@ def test_compile_hourly_statistics(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(mean),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -236,8 +236,8 @@ def test_compile_hourly_statistics_purged_state_changes(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(mean),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -345,8 +345,8 @@ def test_compile_hourly_statistics_wrong_unit(hass_recorder, caplog, attributes)
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(13.050847),
|
||||
"min": pytest.approx(-10.0),
|
||||
"max": pytest.approx(30.0),
|
||||
@ -357,8 +357,8 @@ def test_compile_hourly_statistics_wrong_unit(hass_recorder, caplog, attributes)
|
||||
],
|
||||
"sensor.test2": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": 13.05084745762712,
|
||||
"min": -10.0,
|
||||
"max": 30.0,
|
||||
@ -369,8 +369,8 @@ def test_compile_hourly_statistics_wrong_unit(hass_recorder, caplog, attributes)
|
||||
],
|
||||
"sensor.test3": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": 13.05084745762712,
|
||||
"min": -10.0,
|
||||
"max": 30.0,
|
||||
@ -381,8 +381,8 @@ def test_compile_hourly_statistics_wrong_unit(hass_recorder, caplog, attributes)
|
||||
],
|
||||
"sensor.test6": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(13.050847),
|
||||
"min": pytest.approx(-10.0),
|
||||
"max": pytest.approx(30.0),
|
||||
@ -393,8 +393,8 @@ def test_compile_hourly_statistics_wrong_unit(hass_recorder, caplog, attributes)
|
||||
],
|
||||
"sensor.test7": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(13.050847),
|
||||
"min": pytest.approx(-10.0),
|
||||
"max": pytest.approx(30.0),
|
||||
@ -503,32 +503,32 @@ async def test_compile_hourly_sum_statistics_amount(
|
||||
expected_stats = {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(period0),
|
||||
"end": process_timestamp(period0_end),
|
||||
"start": process_timestamp(period0).timestamp(),
|
||||
"end": process_timestamp(period0_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": process_timestamp(period0),
|
||||
"last_reset": process_timestamp(period0).timestamp(),
|
||||
"state": pytest.approx(factor * seq[2]),
|
||||
"sum": pytest.approx(factor * 10.0),
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(period1),
|
||||
"end": process_timestamp(period1_end),
|
||||
"start": process_timestamp(period1).timestamp(),
|
||||
"end": process_timestamp(period1_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": process_timestamp(four),
|
||||
"last_reset": process_timestamp(four).timestamp(),
|
||||
"state": pytest.approx(factor * seq[5]),
|
||||
"sum": pytest.approx(factor * 40.0),
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(period2),
|
||||
"end": process_timestamp(period2_end),
|
||||
"start": process_timestamp(period2).timestamp(),
|
||||
"end": process_timestamp(period2_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": process_timestamp(four),
|
||||
"last_reset": process_timestamp(four).timestamp(),
|
||||
"state": pytest.approx(factor * seq[8]),
|
||||
"sum": pytest.approx(factor * 70.0),
|
||||
},
|
||||
@ -695,22 +695,22 @@ def test_compile_hourly_sum_statistics_amount_reset_every_state_change(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": process_timestamp(dt_util.as_local(one)),
|
||||
"last_reset": process_timestamp(dt_util.as_local(one)).timestamp(),
|
||||
"state": pytest.approx(factor * seq[7]),
|
||||
"sum": pytest.approx(factor * (sum(seq) - seq[0])),
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"end": process_timestamp(zero + timedelta(minutes=10)),
|
||||
"start": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=10)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": process_timestamp(dt_util.as_local(two)),
|
||||
"last_reset": process_timestamp(dt_util.as_local(two)).timestamp(),
|
||||
"state": pytest.approx(factor * seq[7]),
|
||||
"sum": pytest.approx(factor * (2 * sum(seq) - seq[0])),
|
||||
},
|
||||
@ -794,12 +794,12 @@ def test_compile_hourly_sum_statistics_amount_invalid_last_reset(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": process_timestamp(dt_util.as_local(one)),
|
||||
"last_reset": process_timestamp(dt_util.as_local(one)).timestamp(),
|
||||
"state": pytest.approx(factor * seq[7]),
|
||||
"sum": pytest.approx(factor * (sum(seq) - seq[0] - seq[3])),
|
||||
},
|
||||
@ -880,12 +880,12 @@ def test_compile_hourly_sum_statistics_nan_inf_state(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": process_timestamp(one),
|
||||
"last_reset": process_timestamp(one).timestamp(),
|
||||
"state": pytest.approx(factor * seq[7]),
|
||||
"sum": pytest.approx(
|
||||
factor * (seq[2] + seq[3] + seq[4] + seq[6] + seq[7])
|
||||
@ -1008,8 +1008,8 @@ def test_compile_hourly_sum_statistics_negative_state(
|
||||
stats = statistics_during_period(hass, zero, period="5minute")
|
||||
assert stats[entity_id] == [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -1099,8 +1099,8 @@ def test_compile_hourly_sum_statistics_total_no_reset(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(period0),
|
||||
"end": process_timestamp(period0_end),
|
||||
"start": process_timestamp(period0).timestamp(),
|
||||
"end": process_timestamp(period0_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -1109,8 +1109,8 @@ def test_compile_hourly_sum_statistics_total_no_reset(
|
||||
"sum": pytest.approx(factor * 10.0),
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(period1),
|
||||
"end": process_timestamp(period1_end),
|
||||
"start": process_timestamp(period1).timestamp(),
|
||||
"end": process_timestamp(period1_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -1119,8 +1119,8 @@ def test_compile_hourly_sum_statistics_total_no_reset(
|
||||
"sum": pytest.approx(factor * 30.0),
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(period2),
|
||||
"end": process_timestamp(period2_end),
|
||||
"start": process_timestamp(period2).timestamp(),
|
||||
"end": process_timestamp(period2_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -1201,8 +1201,8 @@ def test_compile_hourly_sum_statistics_total_increasing(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(period0),
|
||||
"end": process_timestamp(period0_end),
|
||||
"start": process_timestamp(period0).timestamp(),
|
||||
"end": process_timestamp(period0_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -1211,8 +1211,8 @@ def test_compile_hourly_sum_statistics_total_increasing(
|
||||
"sum": pytest.approx(factor * 10.0),
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(period1),
|
||||
"end": process_timestamp(period1_end),
|
||||
"start": process_timestamp(period1).timestamp(),
|
||||
"end": process_timestamp(period1_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -1221,8 +1221,8 @@ def test_compile_hourly_sum_statistics_total_increasing(
|
||||
"sum": pytest.approx(factor * 50.0),
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(period2),
|
||||
"end": process_timestamp(period2_end),
|
||||
"start": process_timestamp(period2).timestamp(),
|
||||
"end": process_timestamp(period2_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -1315,8 +1315,8 @@ def test_compile_hourly_sum_statistics_total_increasing_small_dip(
|
||||
"sensor.test1": [
|
||||
{
|
||||
"last_reset": None,
|
||||
"start": process_timestamp(period0),
|
||||
"end": process_timestamp(period0_end),
|
||||
"start": process_timestamp(period0).timestamp(),
|
||||
"end": process_timestamp(period0_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -1325,8 +1325,8 @@ def test_compile_hourly_sum_statistics_total_increasing_small_dip(
|
||||
},
|
||||
{
|
||||
"last_reset": None,
|
||||
"start": process_timestamp(period1),
|
||||
"end": process_timestamp(period1_end),
|
||||
"start": process_timestamp(period1).timestamp(),
|
||||
"end": process_timestamp(period1_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -1335,8 +1335,8 @@ def test_compile_hourly_sum_statistics_total_increasing_small_dip(
|
||||
},
|
||||
{
|
||||
"last_reset": None,
|
||||
"start": process_timestamp(period2),
|
||||
"end": process_timestamp(period2_end),
|
||||
"start": process_timestamp(period2).timestamp(),
|
||||
"end": process_timestamp(period2_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
@ -1408,32 +1408,32 @@ def test_compile_hourly_energy_statistics_unsupported(hass_recorder, caplog):
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(period0),
|
||||
"end": process_timestamp(period0_end),
|
||||
"start": process_timestamp(period0).timestamp(),
|
||||
"end": process_timestamp(period0_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": process_timestamp(period0),
|
||||
"last_reset": process_timestamp(period0).timestamp(),
|
||||
"state": pytest.approx(20.0),
|
||||
"sum": pytest.approx(10.0),
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(period1),
|
||||
"end": process_timestamp(period1_end),
|
||||
"start": process_timestamp(period1).timestamp(),
|
||||
"end": process_timestamp(period1_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": process_timestamp(four),
|
||||
"last_reset": process_timestamp(four).timestamp(),
|
||||
"state": pytest.approx(40.0),
|
||||
"sum": pytest.approx(40.0),
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(period2),
|
||||
"end": process_timestamp(period2_end),
|
||||
"start": process_timestamp(period2).timestamp(),
|
||||
"end": process_timestamp(period2_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": process_timestamp(four),
|
||||
"last_reset": process_timestamp(four).timestamp(),
|
||||
"state": pytest.approx(70.0),
|
||||
"sum": pytest.approx(70.0),
|
||||
},
|
||||
@ -1520,96 +1520,96 @@ def test_compile_hourly_energy_statistics_multiple(hass_recorder, caplog):
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(period0),
|
||||
"end": process_timestamp(period0_end),
|
||||
"start": process_timestamp(period0).timestamp(),
|
||||
"end": process_timestamp(period0_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": process_timestamp(period0),
|
||||
"last_reset": process_timestamp(period0).timestamp(),
|
||||
"state": pytest.approx(20.0),
|
||||
"sum": pytest.approx(10.0),
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(period1),
|
||||
"end": process_timestamp(period1_end),
|
||||
"start": process_timestamp(period1).timestamp(),
|
||||
"end": process_timestamp(period1_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": process_timestamp(four),
|
||||
"last_reset": process_timestamp(four).timestamp(),
|
||||
"state": pytest.approx(40.0),
|
||||
"sum": pytest.approx(40.0),
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(period2),
|
||||
"end": process_timestamp(period2_end),
|
||||
"start": process_timestamp(period2).timestamp(),
|
||||
"end": process_timestamp(period2_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": process_timestamp(four),
|
||||
"last_reset": process_timestamp(four).timestamp(),
|
||||
"state": pytest.approx(70.0),
|
||||
"sum": pytest.approx(70.0),
|
||||
},
|
||||
],
|
||||
"sensor.test2": [
|
||||
{
|
||||
"start": process_timestamp(period0),
|
||||
"end": process_timestamp(period0_end),
|
||||
"start": process_timestamp(period0).timestamp(),
|
||||
"end": process_timestamp(period0_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": process_timestamp(period0),
|
||||
"last_reset": process_timestamp(period0).timestamp(),
|
||||
"state": pytest.approx(130.0),
|
||||
"sum": pytest.approx(20.0),
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(period1),
|
||||
"end": process_timestamp(period1_end),
|
||||
"start": process_timestamp(period1).timestamp(),
|
||||
"end": process_timestamp(period1_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": process_timestamp(four),
|
||||
"last_reset": process_timestamp(four).timestamp(),
|
||||
"state": pytest.approx(45.0),
|
||||
"sum": pytest.approx(-65.0),
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(period2),
|
||||
"end": process_timestamp(period2_end),
|
||||
"start": process_timestamp(period2).timestamp(),
|
||||
"end": process_timestamp(period2_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": process_timestamp(four),
|
||||
"last_reset": process_timestamp(four).timestamp(),
|
||||
"state": pytest.approx(75.0),
|
||||
"sum": pytest.approx(-35.0),
|
||||
},
|
||||
],
|
||||
"sensor.test3": [
|
||||
{
|
||||
"start": process_timestamp(period0),
|
||||
"end": process_timestamp(period0_end),
|
||||
"start": process_timestamp(period0).timestamp(),
|
||||
"end": process_timestamp(period0_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": process_timestamp(period0),
|
||||
"last_reset": process_timestamp(period0).timestamp(),
|
||||
"state": pytest.approx(5.0),
|
||||
"sum": pytest.approx(5.0),
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(period1),
|
||||
"end": process_timestamp(period1_end),
|
||||
"start": process_timestamp(period1).timestamp(),
|
||||
"end": process_timestamp(period1_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": process_timestamp(four),
|
||||
"last_reset": process_timestamp(four).timestamp(),
|
||||
"state": pytest.approx(50.0),
|
||||
"sum": pytest.approx(60.0),
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(period2),
|
||||
"end": process_timestamp(period2_end),
|
||||
"start": process_timestamp(period2).timestamp(),
|
||||
"end": process_timestamp(period2_end).timestamp(),
|
||||
"max": None,
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"last_reset": process_timestamp(four),
|
||||
"last_reset": process_timestamp(four).timestamp(),
|
||||
"state": pytest.approx(90.0),
|
||||
"sum": pytest.approx(100.0),
|
||||
},
|
||||
@ -1665,8 +1665,8 @@ def test_compile_hourly_statistics_unchanged(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(four),
|
||||
"end": process_timestamp(four + timedelta(minutes=5)),
|
||||
"start": process_timestamp(four).timestamp(),
|
||||
"end": process_timestamp(four + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(value),
|
||||
"min": pytest.approx(value),
|
||||
"max": pytest.approx(value),
|
||||
@ -1697,8 +1697,8 @@ def test_compile_hourly_statistics_partially_unavailable(hass_recorder, caplog):
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(21.1864406779661),
|
||||
"min": pytest.approx(10.0),
|
||||
"max": pytest.approx(25.0),
|
||||
@ -1766,8 +1766,8 @@ def test_compile_hourly_statistics_unavailable(
|
||||
assert stats == {
|
||||
"sensor.test2": [
|
||||
{
|
||||
"start": process_timestamp(four),
|
||||
"end": process_timestamp(four + timedelta(minutes=5)),
|
||||
"start": process_timestamp(four).timestamp(),
|
||||
"end": process_timestamp(four + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(value),
|
||||
"min": pytest.approx(value),
|
||||
"max": pytest.approx(value),
|
||||
@ -1988,8 +1988,8 @@ def test_compile_hourly_statistics_changing_units_1(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(mean),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -2023,8 +2023,8 @@ def test_compile_hourly_statistics_changing_units_1(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(mean),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -2166,8 +2166,8 @@ def test_compile_hourly_statistics_changing_units_3(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(mean),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -2201,8 +2201,8 @@ def test_compile_hourly_statistics_changing_units_3(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(mean),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -2274,8 +2274,8 @@ def test_compile_hourly_statistics_convert_units_1(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(mean),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -2317,8 +2317,8 @@ def test_compile_hourly_statistics_convert_units_1(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(mean * factor),
|
||||
"min": pytest.approx(min * factor),
|
||||
"max": pytest.approx(max * factor),
|
||||
@ -2327,8 +2327,8 @@ def test_compile_hourly_statistics_convert_units_1(
|
||||
"sum": None,
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(zero + timedelta(minutes=10)),
|
||||
"end": process_timestamp(zero + timedelta(minutes=15)),
|
||||
"start": process_timestamp(zero + timedelta(minutes=10)).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=15)).timestamp(),
|
||||
"mean": pytest.approx(mean),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -2406,8 +2406,8 @@ def test_compile_hourly_statistics_equivalent_units_1(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(mean),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -2437,8 +2437,8 @@ def test_compile_hourly_statistics_equivalent_units_1(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(mean),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -2447,8 +2447,8 @@ def test_compile_hourly_statistics_equivalent_units_1(
|
||||
"sum": None,
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(zero + timedelta(minutes=10)),
|
||||
"end": process_timestamp(zero + timedelta(minutes=15)),
|
||||
"start": process_timestamp(zero + timedelta(minutes=10)).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=15)).timestamp(),
|
||||
"mean": pytest.approx(mean2),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -2521,8 +2521,10 @@ def test_compile_hourly_statistics_equivalent_units_2(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero + timedelta(seconds=30 * 5)),
|
||||
"end": process_timestamp(zero + timedelta(seconds=30 * 15)),
|
||||
"start": process_timestamp(
|
||||
zero + timedelta(seconds=30 * 5)
|
||||
).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(seconds=30 * 15)).timestamp(),
|
||||
"mean": pytest.approx(mean),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -2590,8 +2592,8 @@ def test_compile_hourly_statistics_changing_device_class_1(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(mean1),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -2635,8 +2637,8 @@ def test_compile_hourly_statistics_changing_device_class_1(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(mean1),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -2645,8 +2647,8 @@ def test_compile_hourly_statistics_changing_device_class_1(
|
||||
"sum": None,
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(zero + timedelta(minutes=10)),
|
||||
"end": process_timestamp(zero + timedelta(minutes=15)),
|
||||
"start": process_timestamp(zero + timedelta(minutes=10)).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=15)).timestamp(),
|
||||
"mean": pytest.approx(mean2),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -2690,8 +2692,8 @@ def test_compile_hourly_statistics_changing_device_class_1(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(mean1),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -2700,8 +2702,8 @@ def test_compile_hourly_statistics_changing_device_class_1(
|
||||
"sum": None,
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(zero + timedelta(minutes=10)),
|
||||
"end": process_timestamp(zero + timedelta(minutes=15)),
|
||||
"start": process_timestamp(zero + timedelta(minutes=10)).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=15)).timestamp(),
|
||||
"mean": pytest.approx(mean2),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -2710,8 +2712,8 @@ def test_compile_hourly_statistics_changing_device_class_1(
|
||||
"sum": None,
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(zero + timedelta(minutes=20)),
|
||||
"end": process_timestamp(zero + timedelta(minutes=25)),
|
||||
"start": process_timestamp(zero + timedelta(minutes=20)).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=25)).timestamp(),
|
||||
"mean": pytest.approx(mean2),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -2780,8 +2782,8 @@ def test_compile_hourly_statistics_changing_device_class_2(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(mean),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -2825,8 +2827,8 @@ def test_compile_hourly_statistics_changing_device_class_2(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(zero),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)),
|
||||
"start": process_timestamp(zero).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=5)).timestamp(),
|
||||
"mean": pytest.approx(mean),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -2835,8 +2837,8 @@ def test_compile_hourly_statistics_changing_device_class_2(
|
||||
"sum": None,
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(zero + timedelta(minutes=10)),
|
||||
"end": process_timestamp(zero + timedelta(minutes=15)),
|
||||
"start": process_timestamp(zero + timedelta(minutes=10)).timestamp(),
|
||||
"end": process_timestamp(zero + timedelta(minutes=15)).timestamp(),
|
||||
"mean": pytest.approx(mean2),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -2954,8 +2956,8 @@ def test_compile_hourly_statistics_changing_state_class(
|
||||
assert stats == {
|
||||
"sensor.test1": [
|
||||
{
|
||||
"start": process_timestamp(period0),
|
||||
"end": process_timestamp(period0_end),
|
||||
"start": process_timestamp(period0).timestamp(),
|
||||
"end": process_timestamp(period0_end).timestamp(),
|
||||
"mean": pytest.approx(mean),
|
||||
"min": pytest.approx(min),
|
||||
"max": pytest.approx(max),
|
||||
@ -2964,8 +2966,8 @@ def test_compile_hourly_statistics_changing_state_class(
|
||||
"sum": None,
|
||||
},
|
||||
{
|
||||
"start": process_timestamp(period1),
|
||||
"end": process_timestamp(period1_end),
|
||||
"start": process_timestamp(period1).timestamp(),
|
||||
"end": process_timestamp(period1_end).timestamp(),
|
||||
"mean": None,
|
||||
"min": None,
|
||||
"max": None,
|
||||
@ -3204,8 +3206,8 @@ def test_compile_statistics_hourly_daily_monthly_summary(hass_recorder, caplog):
|
||||
)
|
||||
expected_stats[entity_id].append(
|
||||
{
|
||||
"start": process_timestamp(start),
|
||||
"end": process_timestamp(end),
|
||||
"start": process_timestamp(start).timestamp(),
|
||||
"end": process_timestamp(end).timestamp(),
|
||||
"mean": pytest.approx(expected_average),
|
||||
"min": pytest.approx(expected_minimum),
|
||||
"max": pytest.approx(expected_maximum),
|
||||
@ -3261,8 +3263,8 @@ def test_compile_statistics_hourly_daily_monthly_summary(hass_recorder, caplog):
|
||||
)
|
||||
expected_stats[entity_id].append(
|
||||
{
|
||||
"start": process_timestamp(start),
|
||||
"end": process_timestamp(end),
|
||||
"start": process_timestamp(start).timestamp(),
|
||||
"end": process_timestamp(end).timestamp(),
|
||||
"mean": pytest.approx(expected_average),
|
||||
"min": pytest.approx(expected_minimum),
|
||||
"max": pytest.approx(expected_maximum),
|
||||
@ -3318,8 +3320,8 @@ def test_compile_statistics_hourly_daily_monthly_summary(hass_recorder, caplog):
|
||||
)
|
||||
expected_stats[entity_id].append(
|
||||
{
|
||||
"start": process_timestamp(start),
|
||||
"end": process_timestamp(end),
|
||||
"start": process_timestamp(start).timestamp(),
|
||||
"end": process_timestamp(end).timestamp(),
|
||||
"mean": pytest.approx(expected_average),
|
||||
"min": pytest.approx(expected_minimum),
|
||||
"max": pytest.approx(expected_maximum),
|
||||
@ -3375,8 +3377,8 @@ def test_compile_statistics_hourly_daily_monthly_summary(hass_recorder, caplog):
|
||||
)
|
||||
expected_stats[entity_id].append(
|
||||
{
|
||||
"start": process_timestamp(start),
|
||||
"end": process_timestamp(end),
|
||||
"start": process_timestamp(start).timestamp(),
|
||||
"end": process_timestamp(end).timestamp(),
|
||||
"mean": pytest.approx(expected_average),
|
||||
"min": pytest.approx(expected_minimum),
|
||||
"max": pytest.approx(expected_maximum),
|
||||
|
||||
@ -43,7 +43,7 @@ async def test_async_setup_entry(recorder_mock, hass):
|
||||
assert len(stats[statistic_id]) == 3
|
||||
_sum = 0
|
||||
for k, stat in enumerate(stats[statistic_id]):
|
||||
assert stat["start"] == dt_util.parse_datetime(data[k]["from"])
|
||||
assert stat["start"] == dt_util.parse_datetime(data[k]["from"]).timestamp()
|
||||
assert stat["state"] == data[k][key]
|
||||
assert stat["mean"] is None
|
||||
assert stat["min"] is None
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user