mirror of https://github.com/home-assistant/core
Add support for daily and monthly statistics (#57576)
* Add support for daily and monthly statistics * Remove debug code * Format code * Don't use dateutil package * Remove 2 TODOs * Remove TODO * Add comments
This commit is contained in:
Erik Montnemery
GitHub
parent
6576225c48
commit
9a26a8cfd8
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@ -7,6 +7,7 @@ import dataclasses
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from datetime import datetime, timedelta
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from itertools import chain, groupby
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import logging
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from statistics import mean
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from typing import TYPE_CHECKING, Any, Literal
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from sqlalchemy import bindparam, func
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@ -583,13 +584,107 @@ def _statistics_during_period_query(
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return baked_query # type: ignore[no-any-return]
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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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period: timedelta,
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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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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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min_values: list[float] = []
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prev_stat: dict[str, Any] = stat_list[0]
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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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):
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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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# The previous statistic was the last entry of the period
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result[statistic_id].append(
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{
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"statistic_id": statistic_id,
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"start": start.isoformat(),
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"end": end.isoformat(),
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"mean": mean(mean_values) if mean_values else None,
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"min": min(min_values) if min_values else None,
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"max": max(max_values) if max_values else None,
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"last_reset": prev_stat["last_reset"],
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"state": prev_stat["state"],
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"sum": prev_stat["sum"],
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}
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)
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max_values = []
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mean_values = []
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min_values = []
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if statistic.get("max") is not None:
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max_values.append(statistic["max"])
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if statistic.get("mean") is not None:
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mean_values.append(statistic["mean"])
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if statistic.get("min") is not None:
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min_values.append(statistic["min"])
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prev_stat = statistic
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return result
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def _reduce_statistics_per_day(
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stats: dict[str, list[dict[str, Any]]]
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) -> dict[str, list[dict[str, Any]]]:
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"""Reduce hourly statistics to daily statistics."""
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def same_period(time1: datetime, time2: datetime) -> bool:
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"""Return True if time1 and time2 are in the same date."""
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date1 = dt_util.as_local(time1).date()
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date2 = dt_util.as_local(time2).date()
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return date1 == date2
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def period_start_end(time: datetime) -> tuple[datetime, datetime]:
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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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dt_util.as_local(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 _reduce_statistics(stats, same_period, period_start_end, timedelta(days=1))
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def _reduce_statistics_per_month(
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stats: dict[str, list[dict[str, Any]]]
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) -> dict[str, list[dict[str, Any]]]:
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"""Reduce hourly statistics to monthly statistics."""
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def same_period(time1: datetime, time2: datetime) -> bool:
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"""Return True if time1 and time2 are in the same year and month."""
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date1 = dt_util.as_local(time1).date()
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date2 = dt_util.as_local(time2).date()
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return (date1.year, date1.month) == (date2.year, date2.month)
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def period_start_end(time: datetime) -> tuple[datetime, datetime]:
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"""Return the start and end of the period (month) time is within."""
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start = dt_util.as_utc(
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dt_util.as_local(time).replace(
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day=1, hour=0, minute=0, second=0, microsecond=0
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)
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)
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end = (start + timedelta(days=31)).replace(day=1)
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return (start, end)
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return _reduce_statistics(stats, same_period, period_start_end, timedelta(days=31))
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def statistics_during_period(
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hass: HomeAssistant,
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start_time: datetime,
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end_time: datetime | None = None,
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statistic_ids: list[str] | None = None,
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period: Literal["hour"] | Literal["5minute"] = "hour",
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) -> dict[str, list[dict[str, str]]]:
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period: Literal["5minute", "day", "hour", "month"] = "hour",
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) -> dict[str, list[dict[str, Any]]]:
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"""Return statistics during UTC period start_time - end_time for the statistic_ids.
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If end_time is omitted, returns statistics newer than or equal to start_time.
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@ -606,14 +701,14 @@ def statistics_during_period(
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if statistic_ids is not None:
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metadata_ids = [metadata_id for metadata_id, _ in metadata.values()]
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if period == "hour":
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bakery = STATISTICS_BAKERY
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base_query = QUERY_STATISTICS
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table = Statistics
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else:
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if period == "5minute":
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bakery = STATISTICS_SHORT_TERM_BAKERY
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base_query = QUERY_STATISTICS_SHORT_TERM
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table = StatisticsShortTerm
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else:
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bakery = STATISTICS_BAKERY
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base_query = QUERY_STATISTICS
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table = Statistics
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baked_query = _statistics_during_period_query(
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hass, end_time, statistic_ids, bakery, base_query, table
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@ -627,10 +722,20 @@ def statistics_during_period(
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if not stats:
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return {}
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# Return statistics combined with metadata
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return _sorted_statistics_to_dict(
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hass, session, stats, statistic_ids, metadata, True, table, start_time
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if period not in ("day", "month"):
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return _sorted_statistics_to_dict(
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hass, session, stats, statistic_ids, metadata, True, table, start_time
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)
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result = _sorted_statistics_to_dict(
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hass, session, stats, statistic_ids, metadata, True, table, start_time, True
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)
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if period == "day":
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return _reduce_statistics_per_day(result)
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return _reduce_statistics_per_month(result)
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def get_last_statistics(
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hass: HomeAssistant, number_of_stats: int, statistic_id: str, convert_units: bool
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@ -718,6 +823,7 @@ def _sorted_statistics_to_dict(
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convert_units: bool,
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table: type[Statistics | StatisticsShortTerm],
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start_time: datetime | None,
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start_time_as_datetime: bool = False,
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) -> dict[str, list[dict]]:
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"""Convert SQL results into JSON friendly data structure."""
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result: dict = defaultdict(list)
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@ -765,7 +871,7 @@ def _sorted_statistics_to_dict(
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ent_results.append(
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{
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"statistic_id": statistic_id,
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"start": start.isoformat(),
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"start": start if start_time_as_datetime else start.isoformat(),
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"end": end.isoformat(),
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"mean": convert(db_state.mean, units),
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"min": convert(db_state.min, units),
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@ -2017,16 +2017,19 @@ def test_compile_hourly_statistics_changing_statistics(
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"db_supports_row_number,in_log,not_in_log",
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[(True, "row_number", None), (False, None, "row_number")],
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)
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def test_compile_statistics_hourly_summary(
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def test_compile_statistics_hourly_daily_monthly_summary(
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hass_recorder, caplog, db_supports_row_number, in_log, not_in_log
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):
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"""Test compiling hourly statistics."""
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"""Test compiling hourly statistics + monthly and daily summary."""
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zero = dt_util.utcnow()
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zero = zero.replace(minute=0, second=0, microsecond=0)
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# Travel to the future, recorder gets confused otherwise because states are added
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# before the start of the recorder_run
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zero += timedelta(hours=1)
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hass = hass_recorder()
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# August 31st, 23:00 local time
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zero = zero.replace(
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year=2021, month=9, day=1, hour=5, minute=0, second=0, microsecond=0
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)
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with patch(
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"homeassistant.components.recorder.models.dt_util.utcnow", return_value=zero
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):
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hass = hass_recorder()
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recorder = hass.data[DATA_INSTANCE]
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recorder._db_supports_row_number = db_supports_row_number
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setup_component(hass, "sensor", {})
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@ -2265,6 +2268,123 @@ def test_compile_statistics_hourly_summary(
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start += timedelta(hours=1)
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end += timedelta(hours=1)
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assert stats == expected_stats
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stats = statistics_during_period(hass, zero, period="day")
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expected_stats = {
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"sensor.test1": [],
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"sensor.test2": [],
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"sensor.test3": [],
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"sensor.test4": [],
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}
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start = dt_util.parse_datetime("2021-08-31T06:00:00+00:00")
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end = start + timedelta(days=1)
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for i in range(2):
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for entity_id in [
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"sensor.test1",
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"sensor.test2",
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"sensor.test3",
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"sensor.test4",
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]:
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expected_average = (
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mean(expected_averages[entity_id][i * 12 : (i + 1) * 12])
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if entity_id in expected_averages
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else None
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)
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expected_minimum = (
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min(expected_minima[entity_id][i * 12 : (i + 1) * 12])
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if entity_id in expected_minima
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else None
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)
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expected_maximum = (
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max(expected_maxima[entity_id][i * 12 : (i + 1) * 12])
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if entity_id in expected_maxima
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else None
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)
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expected_state = (
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expected_states[entity_id][(i + 1) * 12 - 1]
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if entity_id in expected_states
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else None
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)
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expected_sum = (
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expected_sums[entity_id][(i + 1) * 12 - 1]
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if entity_id in expected_sums
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else None
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)
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expected_stats[entity_id].append(
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{
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"statistic_id": entity_id,
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"start": process_timestamp_to_utc_isoformat(start),
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"end": process_timestamp_to_utc_isoformat(end),
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"mean": approx(expected_average),
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"min": approx(expected_minimum),
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"max": approx(expected_maximum),
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"last_reset": None,
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"state": expected_state,
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"sum": expected_sum,
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}
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)
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start += timedelta(days=1)
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end += timedelta(days=1)
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assert stats == expected_stats
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stats = statistics_during_period(hass, zero, period="month")
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expected_stats = {
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"sensor.test1": [],
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"sensor.test2": [],
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"sensor.test3": [],
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"sensor.test4": [],
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}
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start = dt_util.parse_datetime("2021-08-01T06:00:00+00:00")
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end = dt_util.parse_datetime("2021-09-01T06:00:00+00:00")
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for i in range(2):
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for entity_id in [
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"sensor.test1",
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"sensor.test2",
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"sensor.test3",
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"sensor.test4",
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]:
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expected_average = (
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mean(expected_averages[entity_id][i * 12 : (i + 1) * 12])
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if entity_id in expected_averages
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else None
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)
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expected_minimum = (
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min(expected_minima[entity_id][i * 12 : (i + 1) * 12])
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if entity_id in expected_minima
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else None
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)
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expected_maximum = (
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max(expected_maxima[entity_id][i * 12 : (i + 1) * 12])
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if entity_id in expected_maxima
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else None
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)
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expected_state = (
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expected_states[entity_id][(i + 1) * 12 - 1]
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if entity_id in expected_states
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else None
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)
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expected_sum = (
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expected_sums[entity_id][(i + 1) * 12 - 1]
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if entity_id in expected_sums
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else None
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)
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expected_stats[entity_id].append(
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{
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"statistic_id": entity_id,
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"start": process_timestamp_to_utc_isoformat(start),
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"end": process_timestamp_to_utc_isoformat(end),
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"mean": approx(expected_average),
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"min": approx(expected_minimum),
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"max": approx(expected_maximum),
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"last_reset": None,
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"state": expected_state,
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"sum": expected_sum,
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}
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)
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start = (start + timedelta(days=31)).replace(day=1)
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end = (end + timedelta(days=31)).replace(day=1)
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assert stats == expected_stats
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assert "Error while processing event StatisticsTask" not in caplog.text
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if in_log:
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assert in_log in caplog.text
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