"""Support for statistics for sensor values."""
from collections import deque
import logging
import statistics
import voluptuous as vol
from homeassistant.components.recorder.models import States
from homeassistant.components.recorder.util import execute, session_scope
from homeassistant.components.sensor import PLATFORM_SCHEMA, SensorEntity
from homeassistant.const import (
ATTR_UNIT_OF_MEASUREMENT,
CONF_ENTITY_ID,
CONF_NAME,
EVENT_HOMEASSISTANT_START,
STATE_UNAVAILABLE,
STATE_UNKNOWN,
)
from homeassistant.core import callback
from homeassistant.helpers import config_validation as cv
from homeassistant.helpers.event import (
async_track_point_in_utc_time,
async_track_state_change_event,
)
from homeassistant.helpers.reload import async_setup_reload_service
from homeassistant.util import dt as dt_util
from . import DOMAIN, PLATFORMS
_LOGGER = logging.getLogger(__name__)
ATTR_AVERAGE_CHANGE = "average_change"
ATTR_CHANGE = "change"
ATTR_CHANGE_RATE = "change_rate"
ATTR_COUNT = "count"
ATTR_MAX_AGE = "max_age"
ATTR_MAX_VALUE = "max_value"
ATTR_MEAN = "mean"
ATTR_MEDIAN = "median"
ATTR_MIN_AGE = "min_age"
ATTR_MIN_VALUE = "min_value"
ATTR_QUANTILES = "quantiles"
ATTR_SAMPLING_SIZE = "sampling_size"
ATTR_STANDARD_DEVIATION = "standard_deviation"
ATTR_TOTAL = "total"
ATTR_VARIANCE = "variance"
CONF_SAMPLING_SIZE = "sampling_size"
CONF_MAX_AGE = "max_age"
CONF_PRECISION = "precision"
CONF_QUANTILE_INTERVALS = "quantile_intervals"
CONF_QUANTILE_METHOD = "quantile_method"
DEFAULT_NAME = "Stats"
DEFAULT_SIZE = 20
DEFAULT_PRECISION = 2
DEFAULT_QUANTILE_INTERVALS = 4
DEFAULT_QUANTILE_METHOD = "exclusive"
ICON = "mdi:calculator"
PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend(
{
vol.Required(CONF_ENTITY_ID): cv.entity_id,
vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string,
vol.Optional(CONF_SAMPLING_SIZE, default=DEFAULT_SIZE): vol.All(
vol.Coerce(int), vol.Range(min=1)
),
vol.Optional(CONF_MAX_AGE): cv.time_period,
vol.Optional(CONF_PRECISION, default=DEFAULT_PRECISION): vol.Coerce(int),
vol.Optional(
CONF_QUANTILE_INTERVALS, default=DEFAULT_QUANTILE_INTERVALS
): vol.All(vol.Coerce(int), vol.Range(min=2)),
vol.Optional(CONF_QUANTILE_METHOD, default=DEFAULT_QUANTILE_METHOD): vol.In(
["exclusive", "inclusive"]
),
}
)
async def async_setup_platform(hass, config, async_add_entities, discovery_info=None):
"""Set up the Statistics sensor."""
await async_setup_reload_service(hass, DOMAIN, PLATFORMS)
entity_id = config.get(CONF_ENTITY_ID)
name = config.get(CONF_NAME)
sampling_size = config.get(CONF_SAMPLING_SIZE)
max_age = config.get(CONF_MAX_AGE)
precision = config.get(CONF_PRECISION)
quantile_intervals = config.get(CONF_QUANTILE_INTERVALS)
quantile_method = config.get(CONF_QUANTILE_METHOD)
async_add_entities(
[
StatisticsSensor(
entity_id,
name,
sampling_size,
max_age,
precision,
quantile_intervals,
quantile_method,
)
],
True,
)
return True
class StatisticsSensor(SensorEntity):
"""Representation of a Statistics sensor."""
def __init__(
self,
entity_id,
name,
sampling_size,
max_age,
precision,
quantile_intervals,
quantile_method,
):
"""Initialize the Statistics sensor."""
self._entity_id = entity_id
self.is_binary = self._entity_id.split(".")[0] == "binary_sensor"
self._name = name
self._sampling_size = sampling_size
self._max_age = max_age
self._precision = precision
self._quantile_intervals = quantile_intervals
self._quantile_method = quantile_method
self._unit_of_measurement = None
self.states = deque(maxlen=self._sampling_size)
self.ages = deque(maxlen=self._sampling_size)
self.count = 0
self.mean = self.median = self.quantiles = self.stdev = self.variance = None
self.total = self.min = self.max = None
self.min_age = self.max_age = None
self.change = self.average_change = self.change_rate = None
self._update_listener = None
async def async_added_to_hass(self):
"""Register callbacks."""
@callback
def async_stats_sensor_state_listener(event):
"""Handle the sensor state changes."""
if (new_state := event.data.get("new_state")) is None:
return
self._unit_of_measurement = new_state.attributes.get(
ATTR_UNIT_OF_MEASUREMENT
)
self._add_state_to_queue(new_state)
self.async_schedule_update_ha_state(True)
@callback
def async_stats_sensor_startup(_):
"""Add listener and get recorded state."""
_LOGGER.debug("Startup for %s", self.entity_id)
self.async_on_remove(
async_track_state_change_event(
self.hass, [self._entity_id], async_stats_sensor_state_listener
)
)
if "recorder" in self.hass.config.components:
# Only use the database if it's configured
self.hass.async_create_task(self._async_initialize_from_database())
self.hass.bus.async_listen_once(
EVENT_HOMEASSISTANT_START, async_stats_sensor_startup
)
def _add_state_to_queue(self, new_state):
"""Add the state to the queue."""
if new_state.state in (STATE_UNKNOWN, STATE_UNAVAILABLE):
return
try:
if self.is_binary:
self.states.append(new_state.state)
else:
self.states.append(float(new_state.state))
self.ages.append(new_state.last_updated)
except ValueError:
_LOGGER.error(
"%s: parsing error, expected number and received %s",
self.entity_id,
new_state.state,
)
@property
def name(self):
"""Return the name of the sensor."""
return self._name
@property
def native_value(self):
"""Return the state of the sensor."""
return self.mean if not self.is_binary else self.count
@property
def native_unit_of_measurement(self):
"""Return the unit the value is expressed in."""
return self._unit_of_measurement if not self.is_binary else None
@property
def should_poll(self):
"""No polling needed."""
return False
@property
def extra_state_attributes(self):
"""Return the state attributes of the sensor."""
if not self.is_binary:
return {
ATTR_SAMPLING_SIZE: self._sampling_size,
ATTR_COUNT: self.count,
ATTR_MEAN: self.mean,
ATTR_MEDIAN: self.median,
ATTR_QUANTILES: self.quantiles,
ATTR_STANDARD_DEVIATION: self.stdev,
ATTR_VARIANCE: self.variance,
ATTR_TOTAL: self.total,
ATTR_MIN_VALUE: self.min,
ATTR_MAX_VALUE: self.max,
ATTR_MIN_AGE: self.min_age,
ATTR_MAX_AGE: self.max_age,
ATTR_CHANGE: self.change,
ATTR_AVERAGE_CHANGE: self.average_change,
ATTR_CHANGE_RATE: self.change_rate,
}
@property
def icon(self):
"""Return the icon to use in the frontend, if any."""
return ICON
def _purge_old(self):
"""Remove states which are older than self._max_age."""
now = dt_util.utcnow()
_LOGGER.debug(
"%s: purging records older then %s(%s)",
self.entity_id,
dt_util.as_local(now - self._max_age),
self._max_age,
)
while self.ages and (now - self.ages[0]) > self._max_age:
_LOGGER.debug(
"%s: purging record with datetime %s(%s)",
self.entity_id,
dt_util.as_local(self.ages[0]),
(now - self.ages[0]),
)
self.ages.popleft()
self.states.popleft()
def _next_to_purge_timestamp(self):
"""Find the timestamp when the next purge would occur."""
if self.ages and self._max_age:
# Take the oldest entry from the ages list and add the configured max_age.
# If executed after purging old states, the result is the next timestamp
# in the future when the oldest state will expire.
return self.ages[0] + self._max_age
return None
async def async_update(self):
"""Get the latest data and updates the states."""
_LOGGER.debug("%s: updating statistics", self.entity_id)
if self._max_age is not None:
self._purge_old()
self.count = len(self.states)
if not self.is_binary:
try: # require only one data point
self.mean = round(statistics.mean(self.states), self._precision)
self.median = round(statistics.median(self.states), self._precision)
except statistics.StatisticsError as err:
_LOGGER.debug("%s: %s", self.entity_id, err)
self.mean = self.median = STATE_UNKNOWN
try: # require at least two data points
self.stdev = round(statistics.stdev(self.states), self._precision)
self.variance = round(statistics.variance(self.states), self._precision)
if self._quantile_intervals < self.count:
self.quantiles = [
round(quantile, self._precision)
for quantile in statistics.quantiles(
self.states,
n=self._quantile_intervals,
method=self._quantile_method,
)
]
except statistics.StatisticsError as err:
_LOGGER.debug("%s: %s", self.entity_id, err)
self.stdev = self.variance = self.quantiles = STATE_UNKNOWN
if self.states:
self.total = round(sum(self.states), self._precision)
self.min = round(min(self.states), self._precision)
self.max = round(max(self.states), self._precision)
self.min_age = self.ages[0]
self.max_age = self.ages[-1]
self.change = self.states[-1] - self.states[0]
self.average_change = self.change
self.change_rate = 0
if len(self.states) > 1:
self.average_change /= len(self.states) - 1
time_diff = (self.max_age - self.min_age).total_seconds()
if time_diff > 0:
self.change_rate = self.change / time_diff
self.change = round(self.change, self._precision)
self.average_change = round(self.average_change, self._precision)
self.change_rate = round(self.change_rate, self._precision)
else:
self.total = self.min = self.max = STATE_UNKNOWN
self.min_age = self.max_age = dt_util.utcnow()
self.change = self.average_change = STATE_UNKNOWN
self.change_rate = STATE_UNKNOWN
# If max_age is set, ensure to update again after the defined interval.
next_to_purge_timestamp = self._next_to_purge_timestamp()
if next_to_purge_timestamp:
_LOGGER.debug(
"%s: scheduling update at %s", self.entity_id, next_to_purge_timestamp
)
if self._update_listener:
self._update_listener()
self._update_listener = None
@callback
def _scheduled_update(now):
"""Timer callback for sensor update."""
_LOGGER.debug("%s: executing scheduled update", self.entity_id)
self.async_schedule_update_ha_state(True)
self._update_listener = None
self._update_listener = async_track_point_in_utc_time(
self.hass, _scheduled_update, next_to_purge_timestamp
)
async def _async_initialize_from_database(self):
"""Initialize the list of states from the database.
The query will get the list of states in DESCENDING order so that we
can limit the result to self._sample_size. Afterwards reverse the
list so that we get it in the right order again.
If MaxAge is provided then query will restrict to entries younger then
current datetime - MaxAge.
"""
_LOGGER.debug("%s: initializing values from the database", self.entity_id)
with session_scope(hass=self.hass) as session:
query = session.query(States).filter(
States.entity_id == self._entity_id.lower()
)
if self._max_age is not None:
records_older_then = dt_util.utcnow() - self._max_age
_LOGGER.debug(
"%s: retrieve records not older then %s",
self.entity_id,
records_older_then,
)
query = query.filter(States.last_updated >= records_older_then)
else:
_LOGGER.debug("%s: retrieving all records", self.entity_id)
query = query.order_by(States.last_updated.desc()).limit(
self._sampling_size
)
states = execute(query, to_native=True, validate_entity_ids=False)
for state in reversed(states):
self._add_state_to_queue(state)
self.async_schedule_update_ha_state(True)
_LOGGER.debug("%s: initializing from database completed", self.entity_id)