Change to module function in statistics (#132648)

homeassistant/components/statistics/config_flow.py

@@ -57,9 +57,9 @@ async def get_state_characteristics(handler: SchemaCommonFlowHandler) -> vol.Sch
         split_entity_id(handler.options[CONF_ENTITY_ID])[0] == BINARY_SENSOR_DOMAIN
     )
     if is_binary:
-        options = STATS_BINARY_SUPPORT
+        options = list(STATS_BINARY_SUPPORT)
     else:
-        options = STATS_NUMERIC_SUPPORT
+        options = list(STATS_NUMERIC_SUPPORT)
 
     return vol.Schema(
         {

homeassistant/components/statistics/sensor.py

@@ -53,7 +53,7 @@ from homeassistant.helpers.event import (
     async_track_state_report_event,
 )
 from homeassistant.helpers.reload import async_setup_reload_service
-from homeassistant.helpers.typing import ConfigType, DiscoveryInfoType, StateType
+from homeassistant.helpers.typing import ConfigType, DiscoveryInfoType
 from homeassistant.util import dt as dt_util
 from homeassistant.util.enum import try_parse_enum
 
@@ -97,47 +97,379 @@ STAT_VALUE_MAX = "value_max"
 STAT_VALUE_MIN = "value_min"
 STAT_VARIANCE = "variance"
 
+
+def _callable_characteristic_fn(
+    characteristic: str, binary: bool
+) -> Callable[
+    [deque[bool | float], deque[datetime], int], float | int | datetime | None
+]:
+    """Return the function callable of one characteristic function."""
+    Callable[[deque[bool | float], deque[datetime], int], datetime | int | float | None]
+    if binary:
+        return STATS_BINARY_SUPPORT[characteristic]
+    return STATS_NUMERIC_SUPPORT[characteristic]
+
+
+# Statistics for numeric sensor
+
+
+def _stat_average_linear(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) == 1:
+        return states[0]
+    if len(states) >= 2:
+        area: float = 0
+        for i in range(1, len(states)):
+            area += (
+                0.5
+                * (states[i] + states[i - 1])
+                * (ages[i] - ages[i - 1]).total_seconds()
+            )
+        age_range_seconds = (ages[-1] - ages[0]).total_seconds()
+        return area / age_range_seconds
+    return None
+
+
+def _stat_average_step(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) == 1:
+        return states[0]
+    if len(states) >= 2:
+        area: float = 0
+        for i in range(1, len(states)):
+            area += states[i - 1] * (ages[i] - ages[i - 1]).total_seconds()
+        age_range_seconds = (ages[-1] - ages[0]).total_seconds()
+        return area / age_range_seconds
+    return None
+
+
+def _stat_average_timeless(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    return _stat_mean(states, ages, percentile)
+
+
+def _stat_change(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) > 0:
+        return states[-1] - states[0]
+    return None
+
+
+def _stat_change_sample(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) > 1:
+        return (states[-1] - states[0]) / (len(states) - 1)
+    return None
+
+
+def _stat_change_second(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) > 1:
+        age_range_seconds = (ages[-1] - ages[0]).total_seconds()
+        if age_range_seconds > 0:
+            return (states[-1] - states[0]) / age_range_seconds
+    return None
+
+
+def _stat_count(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> int | None:
+    return len(states)
+
+
+def _stat_datetime_newest(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> datetime | None:
+    if len(states) > 0:
+        return ages[-1]
+    return None
+
+
+def _stat_datetime_oldest(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> datetime | None:
+    if len(states) > 0:
+        return ages[0]
+    return None
+
+
+def _stat_datetime_value_max(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> datetime | None:
+    if len(states) > 0:
+        return ages[states.index(max(states))]
+    return None
+
+
+def _stat_datetime_value_min(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> datetime | None:
+    if len(states) > 0:
+        return ages[states.index(min(states))]
+    return None
+
+
+def _stat_distance_95_percent_of_values(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) >= 1:
+        return (
+            2 * 1.96 * cast(float, _stat_standard_deviation(states, ages, percentile))
+        )
+    return None
+
+
+def _stat_distance_99_percent_of_values(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) >= 1:
+        return (
+            2 * 2.58 * cast(float, _stat_standard_deviation(states, ages, percentile))
+        )
+    return None
+
+
+def _stat_distance_absolute(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) > 0:
+        return max(states) - min(states)
+    return None
+
+
+def _stat_mean(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) > 0:
+        return statistics.mean(states)
+    return None
+
+
+def _stat_mean_circular(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) > 0:
+        sin_sum = sum(math.sin(math.radians(x)) for x in states)
+        cos_sum = sum(math.cos(math.radians(x)) for x in states)
+        return (math.degrees(math.atan2(sin_sum, cos_sum)) + 360) % 360
+    return None
+
+
+def _stat_median(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) > 0:
+        return statistics.median(states)
+    return None
+
+
+def _stat_noisiness(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) == 1:
+        return 0.0
+    if len(states) >= 2:
+        return cast(float, _stat_sum_differences(states, ages, percentile)) / (
+            len(states) - 1
+        )
+    return None
+
+
+def _stat_percentile(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) == 1:
+        return states[0]
+    if len(states) >= 2:
+        percentiles = statistics.quantiles(states, n=100, method="exclusive")
+        return percentiles[percentile - 1]
+    return None
+
+
+def _stat_standard_deviation(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) == 1:
+        return 0.0
+    if len(states) >= 2:
+        return statistics.stdev(states)
+    return None
+
+
+def _stat_sum(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) > 0:
+        return sum(states)
+    return None
+
+
+def _stat_sum_differences(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) == 1:
+        return 0.0
+    if len(states) >= 2:
+        return sum(
+            abs(j - i) for i, j in zip(list(states), list(states)[1:], strict=False)
+        )
+    return None
+
+
+def _stat_sum_differences_nonnegative(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) == 1:
+        return 0.0
+    if len(states) >= 2:
+        return sum(
+            (j - i if j >= i else j - 0)
+            for i, j in zip(list(states), list(states)[1:], strict=False)
+        )
+    return None
+
+
+def _stat_total(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    return _stat_sum(states, ages, percentile)
+
+
+def _stat_value_max(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) > 0:
+        return max(states)
+    return None
+
+
+def _stat_value_min(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) > 0:
+        return min(states)
+    return None
+
+
+def _stat_variance(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) == 1:
+        return 0.0
+    if len(states) >= 2:
+        return statistics.variance(states)
+    return None
+
+
+# Statistics for binary sensor
+
+
+def _stat_binary_average_step(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) == 1:
+        return 100.0 * int(states[0] is True)
+    if len(states) >= 2:
+        on_seconds: float = 0
+        for i in range(1, len(states)):
+            if states[i - 1] is True:
+                on_seconds += (ages[i] - ages[i - 1]).total_seconds()
+        age_range_seconds = (ages[-1] - ages[0]).total_seconds()
+        return 100 / age_range_seconds * on_seconds
+    return None
+
+
+def _stat_binary_average_timeless(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    return _stat_binary_mean(states, ages, percentile)
+
+
+def _stat_binary_count(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> int | None:
+    return len(states)
+
+
+def _stat_binary_count_on(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> int | None:
+    return states.count(True)
+
+
+def _stat_binary_count_off(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> int | None:
+    return states.count(False)
+
+
+def _stat_binary_datetime_newest(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> datetime | None:
+    return _stat_datetime_newest(states, ages, percentile)
+
+
+def _stat_binary_datetime_oldest(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> datetime | None:
+    return _stat_datetime_oldest(states, ages, percentile)
+
+
+def _stat_binary_mean(
+    states: deque[bool | float], ages: deque[datetime], percentile: int
+) -> float | None:
+    if len(states) > 0:
+        return 100.0 / len(states) * states.count(True)
+    return None
+
+
 # Statistics supported by a sensor source (numeric)
 STATS_NUMERIC_SUPPORT = {
-    STAT_AVERAGE_LINEAR,
-    STAT_AVERAGE_STEP,
-    STAT_AVERAGE_TIMELESS,
-    STAT_CHANGE_SAMPLE,
-    STAT_CHANGE_SECOND,
-    STAT_CHANGE,
-    STAT_COUNT,
-    STAT_DATETIME_NEWEST,
-    STAT_DATETIME_OLDEST,
-    STAT_DATETIME_VALUE_MAX,
-    STAT_DATETIME_VALUE_MIN,
-    STAT_DISTANCE_95P,
-    STAT_DISTANCE_99P,
-    STAT_DISTANCE_ABSOLUTE,
-    STAT_MEAN,
-    STAT_MEAN_CIRCULAR,
-    STAT_MEDIAN,
-    STAT_NOISINESS,
-    STAT_PERCENTILE,
-    STAT_STANDARD_DEVIATION,
-    STAT_SUM,
-    STAT_SUM_DIFFERENCES,
-    STAT_SUM_DIFFERENCES_NONNEGATIVE,
-    STAT_TOTAL,
-    STAT_VALUE_MAX,
-    STAT_VALUE_MIN,
-    STAT_VARIANCE,
+    STAT_AVERAGE_LINEAR: _stat_average_linear,
+    STAT_AVERAGE_STEP: _stat_average_step,
+    STAT_AVERAGE_TIMELESS: _stat_average_timeless,
+    STAT_CHANGE_SAMPLE: _stat_change_sample,
+    STAT_CHANGE_SECOND: _stat_change_second,
+    STAT_CHANGE: _stat_change,
+    STAT_COUNT: _stat_count,
+    STAT_DATETIME_NEWEST: _stat_datetime_newest,
+    STAT_DATETIME_OLDEST: _stat_datetime_oldest,
+    STAT_DATETIME_VALUE_MAX: _stat_datetime_value_max,
+    STAT_DATETIME_VALUE_MIN: _stat_datetime_value_min,
+    STAT_DISTANCE_95P: _stat_distance_95_percent_of_values,
+    STAT_DISTANCE_99P: _stat_distance_99_percent_of_values,
+    STAT_DISTANCE_ABSOLUTE: _stat_distance_absolute,
+    STAT_MEAN: _stat_mean,
+    STAT_MEAN_CIRCULAR: _stat_mean_circular,
+    STAT_MEDIAN: _stat_median,
+    STAT_NOISINESS: _stat_noisiness,
+    STAT_PERCENTILE: _stat_percentile,
+    STAT_STANDARD_DEVIATION: _stat_standard_deviation,
+    STAT_SUM: _stat_sum,
+    STAT_SUM_DIFFERENCES: _stat_sum_differences,
+    STAT_SUM_DIFFERENCES_NONNEGATIVE: _stat_sum_differences_nonnegative,
+    STAT_TOTAL: _stat_total,
+    STAT_VALUE_MAX: _stat_value_max,
+    STAT_VALUE_MIN: _stat_value_min,
+    STAT_VARIANCE: _stat_variance,
 }
 
 # Statistics supported by a binary_sensor source
 STATS_BINARY_SUPPORT = {
-    STAT_AVERAGE_STEP,
-    STAT_AVERAGE_TIMELESS,
-    STAT_COUNT,
-    STAT_COUNT_BINARY_ON,
-    STAT_COUNT_BINARY_OFF,
-    STAT_DATETIME_NEWEST,
-    STAT_DATETIME_OLDEST,
-    STAT_MEAN,
+    STAT_AVERAGE_STEP: _stat_binary_average_step,
+    STAT_AVERAGE_TIMELESS: _stat_binary_average_timeless,
+    STAT_COUNT: _stat_binary_count,
+    STAT_COUNT_BINARY_ON: _stat_binary_count_on,
+    STAT_COUNT_BINARY_OFF: _stat_binary_count_off,
+    STAT_DATETIME_NEWEST: _stat_binary_datetime_newest,
+    STAT_DATETIME_OLDEST: _stat_binary_datetime_oldest,
+    STAT_MEAN: _stat_binary_mean,
 }
 
 STATS_NOT_A_NUMBER = {
@@ -366,9 +698,10 @@ class StatisticsSensor(SensorEntity):
         self.ages: deque[datetime] = deque(maxlen=self._samples_max_buffer_size)
         self._attr_extra_state_attributes = {}
 
-        self._state_characteristic_fn: Callable[[], float | int | datetime | None] = (
-            self._callable_characteristic_fn(self._state_characteristic)
-        )
+        self._state_characteristic_fn: Callable[
+            [deque[bool | float], deque[datetime], int],
+            float | int | datetime | None,
+        ] = _callable_characteristic_fn(self._state_characteristic, self.is_binary)
 
         self._update_listener: CALLBACK_TYPE | None = None
         self._preview_callback: Callable[[str, Mapping[str, Any]], None] | None = None
@@ -754,7 +1087,7 @@ class StatisticsSensor(SensorEntity):
         One of the _stat_*() functions is represented by self._state_characteristic_fn().
         """
 
-        value = self._state_characteristic_fn()
+        value = self._state_characteristic_fn(self.states, self.ages, self._percentile)
         _LOGGER.debug(
             "Updating value: states: %s, ages: %s => %s", self.states, self.ages, value
         )
@@ -764,225 +1097,3 @@ class StatisticsSensor(SensorEntity):
                 if self._precision == 0:
                     value = int(value)
         self._attr_native_value = value
-
-    def _callable_characteristic_fn(
-        self, characteristic: str
-    ) -> Callable[[], float | int | datetime | None]:
-        """Return the function callable of one characteristic function."""
-        function: Callable[[], float | int | datetime | None] = getattr(
-            self,
-            f"_stat_binary_{characteristic}"
-            if self.is_binary
-            else f"_stat_{characteristic}",
-        )
-        return function
-
-    # Statistics for numeric sensor
-
-    def _stat_average_linear(self) -> StateType:
-        if len(self.states) == 1:
-            return self.states[0]
-        if len(self.states) >= 2:
-            area: float = 0
-            for i in range(1, len(self.states)):
-                area += (
-                    0.5
-                    * (self.states[i] + self.states[i - 1])
-                    * (self.ages[i] - self.ages[i - 1]).total_seconds()
-                )
-            age_range_seconds = (self.ages[-1] - self.ages[0]).total_seconds()
-            return area / age_range_seconds
-        return None
-
-    def _stat_average_step(self) -> StateType:
-        if len(self.states) == 1:
-            return self.states[0]
-        if len(self.states) >= 2:
-            area: float = 0
-            for i in range(1, len(self.states)):
-                area += (
-                    self.states[i - 1]
-                    * (self.ages[i] - self.ages[i - 1]).total_seconds()
-                )
-            age_range_seconds = (self.ages[-1] - self.ages[0]).total_seconds()
-            return area / age_range_seconds
-        return None
-
-    def _stat_average_timeless(self) -> StateType:
-        return self._stat_mean()
-
-    def _stat_change(self) -> StateType:
-        if len(self.states) > 0:
-            return self.states[-1] - self.states[0]
-        return None
-
-    def _stat_change_sample(self) -> StateType:
-        if len(self.states) > 1:
-            return (self.states[-1] - self.states[0]) / (len(self.states) - 1)
-        return None
-
-    def _stat_change_second(self) -> StateType:
-        if len(self.states) > 1:
-            age_range_seconds = (self.ages[-1] - self.ages[0]).total_seconds()
-            if age_range_seconds > 0:
-                return (self.states[-1] - self.states[0]) / age_range_seconds
-        return None
-
-    def _stat_count(self) -> StateType:
-        return len(self.states)
-
-    def _stat_datetime_newest(self) -> datetime | None:
-        if len(self.states) > 0:
-            return self.ages[-1]
-        return None
-
-    def _stat_datetime_oldest(self) -> datetime | None:
-        if len(self.states) > 0:
-            return self.ages[0]
-        return None
-
-    def _stat_datetime_value_max(self) -> datetime | None:
-        if len(self.states) > 0:
-            return self.ages[self.states.index(max(self.states))]
-        return None
-
-    def _stat_datetime_value_min(self) -> datetime | None:
-        if len(self.states) > 0:
-            return self.ages[self.states.index(min(self.states))]
-        return None
-
-    def _stat_distance_95_percent_of_values(self) -> StateType:
-        if len(self.states) >= 1:
-            return 2 * 1.96 * cast(float, self._stat_standard_deviation())
-        return None
-
-    def _stat_distance_99_percent_of_values(self) -> StateType:
-        if len(self.states) >= 1:
-            return 2 * 2.58 * cast(float, self._stat_standard_deviation())
-        return None
-
-    def _stat_distance_absolute(self) -> StateType:
-        if len(self.states) > 0:
-            return max(self.states) - min(self.states)
-        return None
-
-    def _stat_mean(self) -> StateType:
-        if len(self.states) > 0:
-            return statistics.mean(self.states)
-        return None
-
-    def _stat_mean_circular(self) -> StateType:
-        if len(self.states) > 0:
-            sin_sum = sum(math.sin(math.radians(x)) for x in self.states)
-            cos_sum = sum(math.cos(math.radians(x)) for x in self.states)
-            return (math.degrees(math.atan2(sin_sum, cos_sum)) + 360) % 360
-        return None
-
-    def _stat_median(self) -> StateType:
-        if len(self.states) > 0:
-            return statistics.median(self.states)
-        return None
-
-    def _stat_noisiness(self) -> StateType:
-        if len(self.states) == 1:
-            return 0.0
-        if len(self.states) >= 2:
-            return cast(float, self._stat_sum_differences()) / (len(self.states) - 1)
-        return None
-
-    def _stat_percentile(self) -> StateType:
-        if len(self.states) == 1:
-            return self.states[0]
-        if len(self.states) >= 2:
-            percentiles = statistics.quantiles(self.states, n=100, method="exclusive")
-            return percentiles[self._percentile - 1]
-        return None
-
-    def _stat_standard_deviation(self) -> StateType:
-        if len(self.states) == 1:
-            return 0.0
-        if len(self.states) >= 2:
-            return statistics.stdev(self.states)
-        return None
-
-    def _stat_sum(self) -> StateType:
-        if len(self.states) > 0:
-            return sum(self.states)
-        return None
-
-    def _stat_sum_differences(self) -> StateType:
-        if len(self.states) == 1:
-            return 0.0
-        if len(self.states) >= 2:
-            return sum(
-                abs(j - i)
-                for i, j in zip(list(self.states), list(self.states)[1:], strict=False)
-            )
-        return None
-
-    def _stat_sum_differences_nonnegative(self) -> StateType:
-        if len(self.states) == 1:
-            return 0.0
-        if len(self.states) >= 2:
-            return sum(
-                (j - i if j >= i else j - 0)
-                for i, j in zip(list(self.states), list(self.states)[1:], strict=False)
-            )
-        return None
-
-    def _stat_total(self) -> StateType:
-        return self._stat_sum()
-
-    def _stat_value_max(self) -> StateType:
-        if len(self.states) > 0:
-            return max(self.states)
-        return None
-
-    def _stat_value_min(self) -> StateType:
-        if len(self.states) > 0:
-            return min(self.states)
-        return None
-
-    def _stat_variance(self) -> StateType:
-        if len(self.states) == 1:
-            return 0.0
-        if len(self.states) >= 2:
-            return statistics.variance(self.states)
-        return None
-
-    # Statistics for binary sensor
-
-    def _stat_binary_average_step(self) -> StateType:
-        if len(self.states) == 1:
-            return 100.0 * int(self.states[0] is True)
-        if len(self.states) >= 2:
-            on_seconds: float = 0
-            for i in range(1, len(self.states)):
-                if self.states[i - 1] is True:
-                    on_seconds += (self.ages[i] - self.ages[i - 1]).total_seconds()
-            age_range_seconds = (self.ages[-1] - self.ages[0]).total_seconds()
-            return 100 / age_range_seconds * on_seconds
-        return None
-
-    def _stat_binary_average_timeless(self) -> StateType:
-        return self._stat_binary_mean()
-
-    def _stat_binary_count(self) -> StateType:
-        return len(self.states)
-
-    def _stat_binary_count_on(self) -> StateType:
-        return self.states.count(True)
-
-    def _stat_binary_count_off(self) -> StateType:
-        return self.states.count(False)
-
-    def _stat_binary_datetime_newest(self) -> datetime | None:
-        return self._stat_datetime_newest()
-
-    def _stat_binary_datetime_oldest(self) -> datetime | None:
-        return self._stat_datetime_oldest()
-
-    def _stat_binary_mean(self) -> StateType:
-        if len(self.states) > 0:
-            return 100.0 / len(self.states) * self.states.count(True)
-        return None