""" Support for statistics for sensor values. For more details about this platform, please refer to the documentation at https://home-assistant.io/components/sensor.statistics/ """ import asyncio import logging import statistics from collections import deque import voluptuous as vol import homeassistant.helpers.config_validation as cv from homeassistant.components.sensor import PLATFORM_SCHEMA from homeassistant.const import ( CONF_NAME, CONF_ENTITY_ID, STATE_UNKNOWN, ATTR_UNIT_OF_MEASUREMENT) from homeassistant.core import callback from homeassistant.helpers.entity import Entity from homeassistant.helpers.event import async_track_state_change _LOGGER = logging.getLogger(__name__) ATTR_MIN_VALUE = 'min_value' ATTR_MAX_VALUE = 'max_value' ATTR_COUNT = 'count' ATTR_MEAN = 'mean' ATTR_MEDIAN = 'median' ATTR_VARIANCE = 'variance' ATTR_STANDARD_DEVIATION = 'standard_deviation' ATTR_SAMPLING_SIZE = 'sampling_size' ATTR_TOTAL = 'total' CONF_SAMPLING_SIZE = 'sampling_size' DEFAULT_NAME = 'Stats' DEFAULT_SIZE = 20 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): cv.positive_int, }) @asyncio.coroutine def async_setup_platform(hass, config, async_add_devices, discovery_info=None): """Set up the Statistics sensor.""" entity_id = config.get(CONF_ENTITY_ID) name = config.get(CONF_NAME) sampling_size = config.get(CONF_SAMPLING_SIZE) yield from async_add_devices( [StatisticsSensor(hass, entity_id, name, sampling_size)], True) return True class StatisticsSensor(Entity): """Representation of a Statistics sensor.""" def __init__(self, hass, entity_id, name, sampling_size): """Initialize the Statistics sensor.""" self._hass = hass self._entity_id = entity_id self.is_binary = True if self._entity_id.split('.')[0] == \ 'binary_sensor' else False if not self.is_binary: self._name = '{} {}'.format(name, ATTR_MEAN) else: self._name = '{} {}'.format(name, ATTR_COUNT) self._sampling_size = sampling_size self._unit_of_measurement = None if self._sampling_size == 0: self.states = deque() else: self.states = deque(maxlen=self._sampling_size) self.median = self.mean = self.variance = self.stdev = 0 self.min = self.max = self.total = self.count = 0 @callback # pylint: disable=invalid-name def async_stats_sensor_state_listener(entity, old_state, new_state): """Called when the sensor changes state.""" self._unit_of_measurement = new_state.attributes.get( ATTR_UNIT_OF_MEASUREMENT) try: self.states.append(float(new_state.state)) self.count = self.count + 1 except ValueError: self.count = self.count + 1 hass.async_add_job(self.async_update_ha_state, True) async_track_state_change( hass, entity_id, async_stats_sensor_state_listener) @property def name(self): """Return the name of the sensor.""" return self._name @property def state(self): """Return the state of the sensor.""" return self.mean if not self.is_binary else self.count @property def 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 state_attributes(self): """Return the state attributes of the sensor.""" if not self.is_binary: return { ATTR_MEAN: self.mean, ATTR_COUNT: self.count, ATTR_MAX_VALUE: self.max, ATTR_MEDIAN: self.median, ATTR_MIN_VALUE: self.min, ATTR_SAMPLING_SIZE: 'unlimited' if self._sampling_size is 0 else self._sampling_size, ATTR_STANDARD_DEVIATION: self.stdev, ATTR_TOTAL: self.total, ATTR_VARIANCE: self.variance, } @property def icon(self): """Return the icon to use in the frontend, if any.""" return ICON @asyncio.coroutine def async_update(self): """Get the latest data and updates the states.""" if not self.is_binary: try: self.mean = round(statistics.mean(self.states), 2) self.median = round(statistics.median(self.states), 2) self.stdev = round(statistics.stdev(self.states), 2) self.variance = round(statistics.variance(self.states), 2) except statistics.StatisticsError as err: _LOGGER.warning(err) self.mean = self.median = STATE_UNKNOWN self.stdev = self.variance = STATE_UNKNOWN if self.states: self.total = round(sum(self.states), 2) self.min = min(self.states) self.max = max(self.states) else: self.min = self.max = self.total = STATE_UNKNOWN