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Support calculating changes between consecutive sum statistics (#92823)

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* Support calculating changes between consecutive sum statistics

* Add support for unit conversion when calculating change

* Don't include sum in WS response unless requested

* Improve tests

* Break out calculating change to its own function

* Improve test coverage
This commit is contained in:
Erik Montnemery 2023-05-11 10:05:58 +02:00 committed by GitHub
parent 8b57d31eba
commit 4568207f9b
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5 changed files with 863 additions and 76 deletions
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97
homeassistant/components/recorder/statistics.py
View File

@ -165,6 +165,7 @@ class StatisticsRow(BaseStatisticsRow, total=False):
min: float | None
max: float | None
mean: float | None
change: float | None
def _get_unit_class(unit: str | None) -> str | None:
@ -1540,6 +1541,57 @@ def _extract_metadata_and_discard_impossible_columns(
return metadata_ids
def _augment_result_with_change(
hass: HomeAssistant,
session: Session,
start_time: datetime,
units: dict[str, str] | None,
_types: set[Literal["change", "last_reset", "max", "mean", "min", "state", "sum"]],
table: type[Statistics | StatisticsShortTerm],
metadata: dict[str, tuple[int, StatisticMetaData]],
result: dict[str, list[StatisticsRow]],
) -> None:
"""Add change to the result."""
drop_sum = "sum" not in _types
prev_sums = {}
if tmp := _statistics_at_time(
session,
{metadata[statistic_id][0] for statistic_id in result},
table,
start_time,
{"sum"},
):
_metadata = dict(metadata.values())
for row in tmp:
metadata_by_id = _metadata[row.metadata_id]
statistic_id = metadata_by_id["statistic_id"]
state_unit = unit = metadata_by_id["unit_of_measurement"]
if state := hass.states.get(statistic_id):
state_unit = state.attributes.get(ATTR_UNIT_OF_MEASUREMENT)
convert = _get_statistic_to_display_unit_converter(unit, state_unit, units)
if convert is not None:
prev_sums[statistic_id] = convert(row.sum)
else:
prev_sums[statistic_id] = row.sum
for statistic_id, rows in result.items():
prev_sum = prev_sums.get(statistic_id) or 0
for statistics_row in rows:
if "sum" not in statistics_row:
continue
if drop_sum:
_sum = statistics_row.pop("sum")
else:
_sum = statistics_row["sum"]
if _sum is None:
statistics_row["change"] = None
continue
statistics_row["change"] = _sum - prev_sum
prev_sum = _sum
def _statistics_during_period_with_session(
hass: HomeAssistant,
session: Session,
@ -1548,7 +1600,7 @@ def _statistics_during_period_with_session(
statistic_ids: set[str] | None,
period: Literal["5minute", "day", "hour", "week", "month"],
units: dict[str, str] | None,
types: set[Literal["last_reset", "max", "mean", "min", "state", "sum"]],
_types: set[Literal["change", "last_reset", "max", "mean", "min", "state", "sum"]],
) -> dict[str, list[StatisticsRow]]:
"""Return statistic data points during UTC period start_time - end_time.
@ -1559,7 +1611,6 @@ def _statistics_during_period_with_session(
# This is for backwards compatibility to avoid a breaking change
# for custom integrations that call this method.
statistic_ids = set(statistic_ids) # type: ignore[unreachable]
metadata = None
# Fetch metadata for the given (or all) statistic_ids
metadata = get_instance(hass).statistics_meta_manager.get_many(
session, statistic_ids=statistic_ids
@ -1567,6 +1618,13 @@ def _statistics_during_period_with_session(
if not metadata:
return {}
types: set[Literal["last_reset", "max", "mean", "min", "state", "sum"]] = set()
for stat_type in _types:
if stat_type == "change":
types.add("sum")
continue
types.add(stat_type)
metadata_ids = None
if statistic_ids is not None:
metadata_ids = _extract_metadata_and_discard_impossible_columns(metadata, types)
@ -1597,17 +1655,22 @@ def _statistics_during_period_with_session(
types,
)
# Return statistics combined with metadata
if period not in ("day", "week", "month"):
return result
if period == "day":
return _reduce_statistics_per_day(result, types)
result = _reduce_statistics_per_day(result, types)
if period == "week":
return _reduce_statistics_per_week(result, types)
result = _reduce_statistics_per_week(result, types)
return _reduce_statistics_per_month(result, types)
if period == "month":
result = _reduce_statistics_per_month(result, types)
if "change" in _types:
_augment_result_with_change(
hass, session, start_time, units, _types, table, metadata, result
)
# Return statistics combined with metadata
return result
def statistics_during_period(
@ -1617,7 +1680,7 @@ def statistics_during_period(
statistic_ids: set[str] | None,
period: Literal["5minute", "day", "hour", "week", "month"],
units: dict[str, str] | None,
types: set[Literal["last_reset", "max", "mean", "min", "state", "sum"]],
types: set[Literal["change", "last_reset", "max", "mean", "min", "state", "sum"]],
) -> dict[str, list[StatisticsRow]]:
"""Return statistic data points during UTC period start_time - end_time.
@ -1886,8 +1949,6 @@ def _sorted_statistics_to_dict( # noqa: C901
assert stats, "stats must not be empty" # Guard against implementation error
result: dict[str, list[StatisticsRow]] = defaultdict(list)
metadata = dict(_metadata.values())
need_stat_at_start_time: set[int] = set()
start_time_ts = start_time.timestamp() if start_time else None
# Identify metadata IDs for which no data was available at the requested start time
field_map: dict[str, int] = {key: idx for idx, key in enumerate(stats[0]._fields)}
metadata_id_idx = field_map["metadata_id"]
@ -1898,9 +1959,6 @@ def _sorted_statistics_to_dict( # noqa: C901
for meta_id, group in groupby(stats, key_func):
stats_list = stats_by_meta_id[meta_id] = list(group)
seen_statistic_ids.add(metadata[meta_id]["statistic_id"])
first_start_time_ts = stats_list[0][start_ts_idx]
if start_time_ts and first_start_time_ts > start_time_ts:
need_stat_at_start_time.add(meta_id)
# Set all statistic IDs to empty lists in result set to maintain the order
if statistic_ids is not None:
@ -1911,15 +1969,6 @@ def _sorted_statistics_to_dict( # noqa: C901
if stat_id in seen_statistic_ids:
result[stat_id] = []
# Fetch last known statistics for the needed metadata IDs
if need_stat_at_start_time:
assert start_time # Can not be None if need_stat_at_start_time is not empty
if tmp := _statistics_at_time(
session, need_stat_at_start_time, table, start_time, types
):
for stat in tmp:
stats_by_meta_id[stat[metadata_id_idx]].insert(0, stat)
# Figure out which fields we need to extract from the SQL result
# and which indices they have in the result so we can avoid the overhead
# of doing a dict lookup for each row

6
homeassistant/components/recorder/websocket_api.py
View File

@ -154,7 +154,7 @@ def _ws_get_statistics_during_period(
statistic_ids: set[str] | None,
period: Literal["5minute", "day", "hour", "week", "month"],
units: dict[str, str],
types: set[Literal["last_reset", "max", "mean", "min", "state", "sum"]],
types: set[Literal["change", "last_reset", "max", "mean", "min", "state", "sum"]],
) -> str:
"""Fetch statistics and convert them to json in the executor."""
result = statistics_during_period(
@ -200,7 +200,7 @@ async def ws_handle_get_statistics_during_period(
end_time = None
if (types := msg.get("types")) is None:
types = {"last_reset", "max", "mean", "min", "state", "sum"}
types = {"change", "last_reset", "max", "mean", "min", "state", "sum"}
connection.send_message(
await get_instance(hass).async_add_executor_job(
_ws_get_statistics_during_period,
@ -225,7 +225,7 @@ async def ws_handle_get_statistics_during_period(
vol.Required("period"): vol.Any("5minute", "hour", "day", "week", "month"),
vol.Optional("units"): UNIT_SCHEMA,
vol.Optional("types"): vol.All(
[vol.Any("last_reset", "max", "mean", "min", "state", "sum")],
[vol.Any("change", "last_reset", "max", "mean", "min", "state", "sum")],
vol.Coerce(set),
),
}

826
tests/components/recorder/test_statistics.py
View File

@ -959,6 +959,151 @@ def test_import_statistics_errors(
assert get_metadata(hass, statistic_ids={"sensor.total_energy_import"}) == {}
@pytest.mark.parametrize("timezone", ["America/Regina", "Europe/Vienna", "UTC"])
@pytest.mark.freeze_time("2022-10-01 00:00:00+00:00")
def test_daily_statistics(
hass_recorder: Callable[..., HomeAssistant],
caplog: pytest.LogCaptureFixture,
timezone,
) -> None:
"""Test daily statistics."""
dt_util.set_default_time_zone(dt_util.get_time_zone(timezone))
hass = hass_recorder()
wait_recording_done(hass)
assert "Compiling statistics for" not in caplog.text
assert "Statistics already compiled" not in caplog.text
zero = dt_util.utcnow()
period1 = dt_util.as_utc(dt_util.parse_datetime("2022-10-03 00:00:00"))
period2 = dt_util.as_utc(dt_util.parse_datetime("2022-10-03 23:00:00"))
period3 = dt_util.as_utc(dt_util.parse_datetime("2022-10-04 00:00:00"))
period4 = dt_util.as_utc(dt_util.parse_datetime("2022-10-04 23:00:00"))
external_statistics = (
{
"start": period1,
"last_reset": None,
"state": 0,
"sum": 2,
},
{
"start": period2,
"last_reset": None,
"state": 1,
"sum": 3,
},
{
"start": period3,
"last_reset": None,
"state": 2,
"sum": 4,
},
{
"start": period4,
"last_reset": None,
"state": 3,
"sum": 5,
},
)
external_metadata = {
"has_mean": False,
"has_sum": True,
"name": "Total imported energy",
"source": "test",
"statistic_id": "test:total_energy_import",
"unit_of_measurement": "kWh",
}
async_add_external_statistics(hass, external_metadata, external_statistics)
wait_recording_done(hass)
stats = statistics_during_period(
hass, zero, period="day", statistic_ids={"test:total_energy_import"}
)
day1_start = dt_util.as_utc(dt_util.parse_datetime("2022-10-03 00:00:00"))
day1_end = dt_util.as_utc(dt_util.parse_datetime("2022-10-04 00:00:00"))
day2_start = dt_util.as_utc(dt_util.parse_datetime("2022-10-04 00:00:00"))
day2_end = dt_util.as_utc(dt_util.parse_datetime("2022-10-05 00:00:00"))
expected_stats = {
"test:total_energy_import": [
{
"start": day1_start.timestamp(),
"end": day1_end.timestamp(),
"last_reset": None,
"state": 1.0,
"sum": 3.0,
},
{
"start": day2_start.timestamp(),
"end": day2_end.timestamp(),
"last_reset": None,
"state": 3.0,
"sum": 5.0,
},
]
}
assert stats == expected_stats
# Get change
stats = statistics_during_period(
hass,
start_time=period1,
statistic_ids={"test:total_energy_import"},
period="day",
types={"change"},
)
assert stats == {
"test:total_energy_import": [
{
"start": day1_start.timestamp(),
"end": day1_end.timestamp(),
"change": 3.0,
},
{
"start": day2_start.timestamp(),
"end": day2_end.timestamp(),
"change": 2.0,
},
]
}
# Get data with start during the first period
stats = statistics_during_period(
hass,
start_time=period1 + timedelta(hours=1),
statistic_ids={"test:total_energy_import"},
period="day",
)
assert stats == expected_stats
# Try to get data for entities which do not exist
stats = statistics_during_period(
hass,
start_time=zero,
statistic_ids={"not", "the", "same", "test:total_energy_import"},
period="day",
)
assert stats == expected_stats
# Use 5minute to ensure table switch works
stats = statistics_during_period(
hass,
start_time=zero,
statistic_ids=["test:total_energy_import", "with_other"],
period="5minute",
)
assert stats == {}
# Ensure future date has not data
future = dt_util.as_utc(dt_util.parse_datetime("2221-11-01 00:00:00"))
stats = statistics_during_period(
hass, start_time=future, end_time=future, period="day"
)
assert stats == {}
dt_util.set_default_time_zone(dt_util.get_time_zone("UTC"))
@pytest.mark.parametrize("timezone", ["America/Regina", "Europe/Vienna", "UTC"])
@pytest.mark.freeze_time("2022-10-01 00:00:00+00:00")
def test_weekly_statistics(
@ -1024,7 +1169,7 @@ def test_weekly_statistics(
week1_end = dt_util.as_utc(dt_util.parse_datetime("2022-10-10 00:00:00"))
week2_start = dt_util.as_utc(dt_util.parse_datetime("2022-10-10 00:00:00"))
week2_end = dt_util.as_utc(dt_util.parse_datetime("2022-10-17 00:00:00"))
assert stats == {
expected_stats = {
"test:total_energy_import": [
{
"start": week1_start.timestamp(),
@ -1042,32 +1187,49 @@ def test_weekly_statistics(
},
]
}
assert stats == expected_stats
# Get change
stats = statistics_during_period(
hass,
start_time=zero,
statistic_ids=["not", "the", "same", "test:total_energy_import"],
start_time=period1,
statistic_ids={"test:total_energy_import"},
period="week",
types={"change"},
)
assert stats == {
"test:total_energy_import": [
{
"start": week1_start.timestamp(),
"end": week1_end.timestamp(),
"last_reset": None,
"state": 1.0,
"sum": 3.0,
"change": 3.0,
},
{
"start": week2_start.timestamp(),
"end": week2_end.timestamp(),
"last_reset": None,
"state": 3.0,
"sum": 5.0,
"change": 2.0,
},
]
}
# Get data with start during the first period
stats = statistics_during_period(
hass,
start_time=period1 + timedelta(days=1),
statistic_ids={"test:total_energy_import"},
period="week",
)
assert stats == expected_stats
# Try to get data for entities which do not exist
stats = statistics_during_period(
hass,
start_time=zero,
statistic_ids={"not", "the", "same", "test:total_energy_import"},
period="week",
)
assert stats == expected_stats
# Use 5minute to ensure table switch works
stats = statistics_during_period(
hass,
@ -1080,7 +1242,7 @@ def test_weekly_statistics(
# Ensure future date has not data
future = dt_util.as_utc(dt_util.parse_datetime("2221-11-01 00:00:00"))
stats = statistics_during_period(
hass, start_time=future, end_time=future, period="month"
hass, start_time=future, end_time=future, period="week"
)
assert stats == {}
@ -1152,7 +1314,7 @@ def test_monthly_statistics(
sep_end = dt_util.as_utc(dt_util.parse_datetime("2021-10-01 00:00:00"))
oct_start = dt_util.as_utc(dt_util.parse_datetime("2021-10-01 00:00:00"))
oct_end = dt_util.as_utc(dt_util.parse_datetime("2021-11-01 00:00:00"))
assert stats == {
expected_stats = {
"test:total_energy_import": [
{
"start": sep_start.timestamp(),
@ -1170,47 +1332,56 @@ def test_monthly_statistics(
},
]
}
assert stats == expected_stats
# Get change
stats = statistics_during_period(
hass,
start_time=zero,
statistic_ids=["not", "the", "same", "test:total_energy_import"],
start_time=period1,
statistic_ids={"test:total_energy_import"},
period="month",
types={"change"},
)
assert stats == {
"test:total_energy_import": [
{
"start": sep_start.timestamp(),
"end": sep_end.timestamp(),
"change": 3.0,
},
{
"start": oct_start.timestamp(),
"end": oct_end.timestamp(),
"change": 2.0,
},
]
}
# Get data with start during the first period
stats = statistics_during_period(
hass,
start_time=period1 + timedelta(days=1),
statistic_ids={"test:total_energy_import"},
period="month",
)
sep_start = dt_util.as_utc(dt_util.parse_datetime("2021-09-01 00:00:00"))
sep_end = dt_util.as_utc(dt_util.parse_datetime("2021-10-01 00:00:00"))
oct_start = dt_util.as_utc(dt_util.parse_datetime("2021-10-01 00:00:00"))
oct_end = dt_util.as_utc(dt_util.parse_datetime("2021-11-01 00:00:00"))
assert stats == {
"test:total_energy_import": [
{
"start": sep_start.timestamp(),
"end": sep_end.timestamp(),
"last_reset": None,
"state": pytest.approx(1.0),
"sum": pytest.approx(3.0),
},
{
"start": oct_start.timestamp(),
"end": oct_end.timestamp(),
"last_reset": None,
"state": pytest.approx(3.0),
"sum": pytest.approx(5.0),
},
]
}
assert stats == expected_stats
# Try to get data for entities which do not exist
stats = statistics_during_period(
hass,
start_time=zero,
statistic_ids=["not", "the", "same", "test:total_energy_import"],
statistic_ids={"not", "the", "same", "test:total_energy_import"},
period="month",
)
assert stats == expected_stats
# Get only sum
stats = statistics_during_period(
hass,
start_time=zero,
statistic_ids={"not", "the", "same", "test:total_energy_import"},
period="month",
types={"sum"},
)
sep_start = dt_util.as_utc(dt_util.parse_datetime("2021-09-01 00:00:00"))
sep_end = dt_util.as_utc(dt_util.parse_datetime("2021-10-01 00:00:00"))
oct_start = dt_util.as_utc(dt_util.parse_datetime("2021-10-01 00:00:00"))
oct_end = dt_util.as_utc(dt_util.parse_datetime("2021-11-01 00:00:00"))
assert stats == {
"test:total_energy_import": [
{
@ -1226,18 +1397,15 @@ def test_monthly_statistics(
]
}
# Get only sum + convert units
stats = statistics_during_period(
hass,
start_time=zero,
statistic_ids=["not", "the", "same", "test:total_energy_import"],
statistic_ids={"not", "the", "same", "test:total_energy_import"},
period="month",
types={"sum"},
units={"energy": "Wh"},
)
sep_start = dt_util.as_utc(dt_util.parse_datetime("2021-09-01 00:00:00"))
sep_end = dt_util.as_utc(dt_util.parse_datetime("2021-10-01 00:00:00"))
oct_start = dt_util.as_utc(dt_util.parse_datetime("2021-10-01 00:00:00"))
oct_end = dt_util.as_utc(dt_util.parse_datetime("2021-11-01 00:00:00"))
assert stats == {
"test:total_energy_import": [
{
@ -1354,3 +1522,571 @@ def test_cache_key_for_generate_statistics_at_time_stmt() -> None:
)
cache_key_3 = stmt3._generate_cache_key()
assert cache_key_1 != cache_key_3
@pytest.mark.parametrize("timezone", ["America/Regina", "Europe/Vienna", "UTC"])
@pytest.mark.freeze_time("2022-10-01 00:00:00+00:00")
def test_change(
hass_recorder: Callable[..., HomeAssistant],
caplog: pytest.LogCaptureFixture,
timezone,
) -> None:
"""Test deriving change from sum statistic."""
dt_util.set_default_time_zone(dt_util.get_time_zone(timezone))
hass = hass_recorder()
wait_recording_done(hass)
assert "Compiling statistics for" not in caplog.text
assert "Statistics already compiled" not in caplog.text
zero = dt_util.utcnow()
period1 = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 00:00:00"))
period2 = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 01:00:00"))
period3 = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 02:00:00"))
period4 = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 03:00:00"))
external_statistics = (
{
"start": period1,
"last_reset": None,
"state": 0,
"sum": 2,
},
{
"start": period2,
"last_reset": None,
"state": 1,
"sum": 3,
},
{
"start": period3,
"last_reset": None,
"state": 2,
"sum": 5,
},
{
"start": period4,
"last_reset": None,
"state": 3,
"sum": 8,
},
)
external_metadata = {
"has_mean": False,
"has_sum": True,
"name": "Total imported energy",
"source": "recorder",
"statistic_id": "sensor.total_energy_import",
"unit_of_measurement": "kWh",
}
async_import_statistics(hass, external_metadata, external_statistics)
wait_recording_done(hass)
# Get change from far in the past
stats = statistics_during_period(
hass,
zero,
period="hour",
statistic_ids={"sensor.total_energy_import"},
types={"change"},
)
hour1_start = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 00:00:00"))
hour1_end = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 01:00:00"))
hour2_start = hour1_end
hour2_end = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 02:00:00"))
hour3_start = hour2_end
hour3_end = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 03:00:00"))
hour4_start = hour3_end
hour4_end = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 04:00:00"))
expected_stats = {
"sensor.total_energy_import": [
{
"start": hour1_start.timestamp(),
"end": hour1_end.timestamp(),
"change": 2.0,
},
{
"start": hour2_start.timestamp(),
"end": hour2_end.timestamp(),
"change": 1.0,
},
{
"start": hour3_start.timestamp(),
"end": hour3_end.timestamp(),
"change": 2.0,
},
{
"start": hour4_start.timestamp(),
"end": hour4_end.timestamp(),
"change": 3.0,
},
]
}
assert stats == expected_stats
# Get change + sum from far in the past
stats = statistics_during_period(
hass,
zero,
period="hour",
statistic_ids={"sensor.total_energy_import"},
types={"change", "sum"},
)
hour1_start = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 00:00:00"))
hour1_end = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 01:00:00"))
hour2_start = hour1_end
hour2_end = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 02:00:00"))
hour3_start = hour2_end
hour3_end = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 03:00:00"))
hour4_start = hour3_end
hour4_end = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 04:00:00"))
expected_stats_change_sum = {
"sensor.total_energy_import": [
{
"start": hour1_start.timestamp(),
"end": hour1_end.timestamp(),
"change": 2.0,
"sum": 2.0,
},
{
"start": hour2_start.timestamp(),
"end": hour2_end.timestamp(),
"change": 1.0,
"sum": 3.0,
},
{
"start": hour3_start.timestamp(),
"end": hour3_end.timestamp(),
"change": 2.0,
"sum": 5.0,
},
{
"start": hour4_start.timestamp(),
"end": hour4_end.timestamp(),
"change": 3.0,
"sum": 8.0,
},
]
}
assert stats == expected_stats_change_sum
# Get change from far in the past with unit conversion
stats = statistics_during_period(
hass,
start_time=hour1_start,
statistic_ids={"sensor.total_energy_import"},
period="hour",
types={"change"},
units={"energy": "Wh"},
)
expected_stats_wh = {
"sensor.total_energy_import": [
{
"start": hour1_start.timestamp(),
"end": hour1_end.timestamp(),
"change": 2.0 * 1000,
},
{
"start": hour2_start.timestamp(),
"end": hour2_end.timestamp(),
"change": 1.0 * 1000,
},
{
"start": hour3_start.timestamp(),
"end": hour3_end.timestamp(),
"change": 2.0 * 1000,
},
{
"start": hour4_start.timestamp(),
"end": hour4_end.timestamp(),
"change": 3.0 * 1000,
},
]
}
assert stats == expected_stats_wh
# Get change from far in the past with implicit unit conversion
hass.states.async_set(
"sensor.total_energy_import", "unknown", {"unit_of_measurement": "MWh"}
)
stats = statistics_during_period(
hass,
start_time=hour1_start,
statistic_ids={"sensor.total_energy_import"},
period="hour",
types={"change"},
)
expected_stats_mwh = {
"sensor.total_energy_import": [
{
"start": hour1_start.timestamp(),
"end": hour1_end.timestamp(),
"change": 2.0 / 1000,
},
{
"start": hour2_start.timestamp(),
"end": hour2_end.timestamp(),
"change": 1.0 / 1000,
},
{
"start": hour3_start.timestamp(),
"end": hour3_end.timestamp(),
"change": 2.0 / 1000,
},
{
"start": hour4_start.timestamp(),
"end": hour4_end.timestamp(),
"change": 3.0 / 1000,
},
]
}
assert stats == expected_stats_mwh
hass.states.async_remove("sensor.total_energy_import")
# Get change from the first recorded hour
stats = statistics_during_period(
hass,
start_time=hour1_start,
statistic_ids={"sensor.total_energy_import"},
period="hour",
types={"change"},
)
assert stats == expected_stats
# Get change from the first recorded hour with unit conversion
stats = statistics_during_period(
hass,
start_time=hour1_start,
statistic_ids={"sensor.total_energy_import"},
period="hour",
types={"change"},
units={"energy": "Wh"},
)
assert stats == expected_stats_wh
# Get change from the first recorded hour with implicit unit conversion
hass.states.async_set(
"sensor.total_energy_import", "unknown", {"unit_of_measurement": "MWh"}
)
stats = statistics_during_period(
hass,
start_time=hour1_start,
statistic_ids={"sensor.total_energy_import"},
period="hour",
types={"change"},
)
assert stats == expected_stats_mwh
hass.states.async_remove("sensor.total_energy_import")
# Get change from the second recorded hour
stats = statistics_during_period(
hass,
start_time=hour2_start,
statistic_ids={"sensor.total_energy_import"},
period="hour",
types={"change"},
)
assert stats == {
"sensor.total_energy_import": expected_stats["sensor.total_energy_import"][1:4]
}
# Get change from the second recorded hour with unit conversion
stats = statistics_during_period(
hass,
start_time=hour2_start,
statistic_ids={"sensor.total_energy_import"},
period="hour",
types={"change"},
units={"energy": "Wh"},
)
assert stats == {
"sensor.total_energy_import": expected_stats_wh["sensor.total_energy_import"][
1:4
]
}
# Get change from the second recorded hour with implicit unit conversion
hass.states.async_set(
"sensor.total_energy_import", "unknown", {"unit_of_measurement": "MWh"}
)
stats = statistics_during_period(
hass,
start_time=hour2_start,
statistic_ids={"sensor.total_energy_import"},
period="hour",
types={"change"},
)
assert stats == {
"sensor.total_energy_import": expected_stats_mwh["sensor.total_energy_import"][
1:4
]
}
hass.states.async_remove("sensor.total_energy_import")
# Get change from the second until the third recorded hour
stats = statistics_during_period(
hass,
start_time=hour2_start,
end_time=hour4_start,
statistic_ids={"sensor.total_energy_import"},
period="hour",
types={"change"},
)
assert stats == {
"sensor.total_energy_import": expected_stats["sensor.total_energy_import"][1:3]
}
# Get change from the fourth recorded hour
stats = statistics_during_period(
hass,
start_time=hour4_start,
statistic_ids={"sensor.total_energy_import"},
period="hour",
types={"change"},
)
assert stats == {
"sensor.total_energy_import": expected_stats["sensor.total_energy_import"][3:4]
}
# Test change with a far future start date
future = dt_util.as_utc(dt_util.parse_datetime("2221-11-01 00:00:00"))
stats = statistics_during_period(
hass,
start_time=future,
statistic_ids={"sensor.total_energy_import"},
period="hour",
types={"change"},
)
assert stats == {}
dt_util.set_default_time_zone(dt_util.get_time_zone("UTC"))
@pytest.mark.parametrize("timezone", ["America/Regina", "Europe/Vienna", "UTC"])
@pytest.mark.freeze_time("2022-10-01 00:00:00+00:00")
def test_change_with_none(
hass_recorder: Callable[..., HomeAssistant],
caplog: pytest.LogCaptureFixture,
timezone,
) -> None:
"""Test deriving change from sum statistic.
This tests the behavior when some record has None sum. The calculated change
is not expected to be correct, but we should not raise on this error.
"""
dt_util.set_default_time_zone(dt_util.get_time_zone(timezone))
hass = hass_recorder()
wait_recording_done(hass)
assert "Compiling statistics for" not in caplog.text
assert "Statistics already compiled" not in caplog.text
zero = dt_util.utcnow()
period1 = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 00:00:00"))
period2 = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 01:00:00"))
period3 = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 02:00:00"))
period4 = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 03:00:00"))
external_statistics = (
{
"start": period1,
"last_reset": None,
"state": 0,
"sum": 2,
},
{
"start": period2,
"last_reset": None,
"state": 1,
"sum": 3,
},
{
"start": period3,
"last_reset": None,
"state": 2,
"sum": None,
},
{
"start": period4,
"last_reset": None,
"state": 3,
"sum": 8,
},
)
external_metadata = {
"has_mean": False,
"has_sum": True,
"name": "Total imported energy",
"source": "test",
"statistic_id": "test:total_energy_import",
"unit_of_measurement": "kWh",
}
async_add_external_statistics(hass, external_metadata, external_statistics)
wait_recording_done(hass)
# Get change from far in the past
stats = statistics_during_period(
hass,
zero,
period="hour",
statistic_ids={"test:total_energy_import"},
types={"change"},
)
hour1_start = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 00:00:00"))
hour1_end = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 01:00:00"))
hour2_start = hour1_end
hour2_end = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 02:00:00"))
hour3_start = hour2_end
hour3_end = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 03:00:00"))
hour4_start = hour3_end
hour4_end = dt_util.as_utc(dt_util.parse_datetime("2023-05-08 04:00:00"))
expected_stats = {
"test:total_energy_import": [
{
"start": hour1_start.timestamp(),
"end": hour1_end.timestamp(),
"change": 2.0,
},
{
"start": hour2_start.timestamp(),
"end": hour2_end.timestamp(),
"change": 1.0,
},
{
"start": hour3_start.timestamp(),
"end": hour3_end.timestamp(),
"change": None,
},
{
"start": hour4_start.timestamp(),
"end": hour4_end.timestamp(),
"change": 5.0,
},
]
}
assert stats == expected_stats
# Get change from far in the past with unit conversion
stats = statistics_during_period(
hass,
start_time=hour1_start,
statistic_ids={"test:total_energy_import"},
period="hour",
types={"change"},
units={"energy": "Wh"},
)
expected_stats_wh = {
"test:total_energy_import": [
{
"start": hour1_start.timestamp(),
"end": hour1_end.timestamp(),
"change": 2.0 * 1000,
},
{
"start": hour2_start.timestamp(),
"end": hour2_end.timestamp(),
"change": 1.0 * 1000,
},
{
"start": hour3_start.timestamp(),
"end": hour3_end.timestamp(),
"change": None,
},
{
"start": hour4_start.timestamp(),
"end": hour4_end.timestamp(),
"change": 5.0 * 1000,
},
]
}
assert stats == expected_stats_wh
# Get change from the first recorded hour
stats = statistics_during_period(
hass,
start_time=hour1_start,
statistic_ids={"test:total_energy_import"},
period="hour",
types={"change"},
)
assert stats == expected_stats
# Get change from the first recorded hour with unit conversion
stats = statistics_during_period(
hass,
start_time=hour1_start,
statistic_ids={"test:total_energy_import"},
period="hour",
types={"change"},
units={"energy": "Wh"},
)
assert stats == expected_stats_wh
# Get change from the second recorded hour
stats = statistics_during_period(
hass,
start_time=hour2_start,
statistic_ids={"test:total_energy_import"},
period="hour",
types={"change"},
)
assert stats == {
"test:total_energy_import": expected_stats["test:total_energy_import"][1:4]
}
# Get change from the second recorded hour with unit conversion
stats = statistics_during_period(
hass,
start_time=hour2_start,
statistic_ids={"test:total_energy_import"},
period="hour",
types={"change"},
units={"energy": "Wh"},
)
assert stats == {
"test:total_energy_import": expected_stats_wh["test:total_energy_import"][1:4]
}
# Get change from the second until the third recorded hour
stats = statistics_during_period(
hass,
start_time=hour2_start,
end_time=hour4_start,
statistic_ids={"test:total_energy_import"},
period="hour",
types={"change"},
)
assert stats == {
"test:total_energy_import": expected_stats["test:total_energy_import"][1:3]
}
# Get change from the fourth recorded hour
stats = statistics_during_period(
hass,
start_time=hour4_start,
statistic_ids={"test:total_energy_import"},
period="hour",
types={"change"},
)
assert stats == {
"test:total_energy_import": [
{
"start": hour4_start.timestamp(),
"end": hour4_end.timestamp(),
"change": 8.0, # Assumed to be 8 because the previous hour has no data
},
]
}
# Test change with a far future start date
future = dt_util.as_utc(dt_util.parse_datetime("2221-11-01 00:00:00"))
stats = statistics_during_period(
hass,
start_time=future,
statistic_ids={"test:total_energy_import"},
period="hour",
types={"change"},
)
assert stats == {}
dt_util.set_default_time_zone(dt_util.get_time_zone("UTC"))

2
tests/components/recorder/test_websocket_api.py
View File

@ -1037,6 +1037,7 @@ async def test_sum_statistics_during_period_unit_conversion(
{
"start": int(now.timestamp() * 1000),
"end": int((now + timedelta(minutes=5)).timestamp() * 1000),
"change": pytest.approx(value),
"last_reset": None,
"state": pytest.approx(value),
"sum": pytest.approx(value),
@ -1062,6 +1063,7 @@ async def test_sum_statistics_during_period_unit_conversion(
{
"start": int(now.timestamp() * 1000),
"end": int((now + timedelta(minutes=5)).timestamp() * 1000),
"change": pytest.approx(converted_value),
"last_reset": None,
"state": pytest.approx(converted_value),
"sum": pytest.approx(converted_value),

8
tests/components/sensor/test_recorder.py
View File

@ -915,11 +915,11 @@ async def test_compile_hourly_sum_statistics_amount(
}
assert stats == expected_stats
# With an offset of 1 minute, we expect to get all periods
# With an offset of 1 minute, we expect to get the 2nd and 3rd periods
stats = statistics_during_period(
hass, period0 + timedelta(minutes=1), period="5minute"
)
assert stats == expected_stats
assert stats == {"sensor.test1": expected_stats["sensor.test1"][1:3]}
# With an offset of 5 minutes, we expect to get the 2nd and 3rd periods
stats = statistics_during_period(
@ -927,11 +927,11 @@ async def test_compile_hourly_sum_statistics_amount(
)
assert stats == {"sensor.test1": expected_stats["sensor.test1"][1:3]}
# With an offset of 6 minutes, we expect to get the 2nd and 3rd periods
# With an offset of 6 minutes, we expect to get the 3rd period
stats = statistics_during_period(
hass, period0 + timedelta(minutes=6), period="5minute"
)
assert stats == {"sensor.test1": expected_stats["sensor.test1"][1:3]}
assert stats == {"sensor.test1": expected_stats["sensor.test1"][2:3]}
assert "Error while processing event StatisticsTask" not in caplog.text
assert "Detected new cycle for sensor.test1, last_reset set to" in caplog.text