256 lines
7.7 KiB
Python
256 lines
7.7 KiB
Python
"""Support for displaying minimal, maximal, mean or median values."""
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import logging
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import voluptuous as vol
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from homeassistant.components.sensor import PLATFORM_SCHEMA
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from homeassistant.const import (
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ATTR_UNIT_OF_MEASUREMENT,
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CONF_NAME,
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CONF_TYPE,
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STATE_UNAVAILABLE,
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STATE_UNKNOWN,
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)
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from homeassistant.core import callback
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import homeassistant.helpers.config_validation as cv
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from homeassistant.helpers.entity import Entity
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from homeassistant.helpers.event import async_track_state_change_event
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from homeassistant.helpers.reload import async_setup_reload_service
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from . import DOMAIN, PLATFORMS
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_LOGGER = logging.getLogger(__name__)
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ATTR_MIN_VALUE = "min_value"
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ATTR_MIN_ENTITY_ID = "min_entity_id"
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ATTR_MAX_VALUE = "max_value"
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ATTR_MAX_ENTITY_ID = "max_entity_id"
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ATTR_COUNT_SENSORS = "count_sensors"
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ATTR_MEAN = "mean"
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ATTR_MEDIAN = "median"
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ATTR_LAST = "last"
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ATTR_LAST_ENTITY_ID = "last_entity_id"
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ATTR_TO_PROPERTY = [
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ATTR_COUNT_SENSORS,
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ATTR_MAX_VALUE,
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ATTR_MAX_ENTITY_ID,
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ATTR_MEAN,
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ATTR_MEDIAN,
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ATTR_MIN_VALUE,
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ATTR_MIN_ENTITY_ID,
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ATTR_LAST,
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ATTR_LAST_ENTITY_ID,
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]
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CONF_ENTITY_IDS = "entity_ids"
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CONF_ROUND_DIGITS = "round_digits"
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ICON = "mdi:calculator"
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SENSOR_TYPES = {
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ATTR_MIN_VALUE: "min",
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ATTR_MAX_VALUE: "max",
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ATTR_MEAN: "mean",
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ATTR_MEDIAN: "median",
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ATTR_LAST: "last",
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}
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PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend(
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{
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vol.Optional(CONF_TYPE, default=SENSOR_TYPES[ATTR_MAX_VALUE]): vol.All(
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cv.string, vol.In(SENSOR_TYPES.values())
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),
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vol.Optional(CONF_NAME): cv.string,
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vol.Required(CONF_ENTITY_IDS): cv.entity_ids,
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vol.Optional(CONF_ROUND_DIGITS, default=2): vol.Coerce(int),
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}
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)
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async def async_setup_platform(hass, config, async_add_entities, discovery_info=None):
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"""Set up the min/max/mean sensor."""
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entity_ids = config.get(CONF_ENTITY_IDS)
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name = config.get(CONF_NAME)
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sensor_type = config.get(CONF_TYPE)
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round_digits = config.get(CONF_ROUND_DIGITS)
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await async_setup_reload_service(hass, DOMAIN, PLATFORMS)
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async_add_entities([MinMaxSensor(entity_ids, name, sensor_type, round_digits)])
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def calc_min(sensor_values):
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"""Calculate min value, honoring unknown states."""
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val = None
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entity_id = None
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for sensor_id, sensor_value in sensor_values:
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if sensor_value not in [STATE_UNKNOWN, STATE_UNAVAILABLE]:
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if val is None or val > sensor_value:
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entity_id, val = sensor_id, sensor_value
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return entity_id, val
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def calc_max(sensor_values):
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"""Calculate max value, honoring unknown states."""
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val = None
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entity_id = None
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for sensor_id, sensor_value in sensor_values:
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if sensor_value not in [STATE_UNKNOWN, STATE_UNAVAILABLE]:
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if val is None or val < sensor_value:
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entity_id, val = sensor_id, sensor_value
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return entity_id, val
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def calc_mean(sensor_values, round_digits):
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"""Calculate mean value, honoring unknown states."""
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result = []
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for _, sensor_value in sensor_values:
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if sensor_value not in [STATE_UNKNOWN, STATE_UNAVAILABLE]:
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result.append(sensor_value)
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if len(result) == 0:
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return None
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return round(sum(result) / len(result), round_digits)
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def calc_median(sensor_values, round_digits):
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"""Calculate median value, honoring unknown states."""
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result = []
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for _, sensor_value in sensor_values:
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if sensor_value not in [STATE_UNKNOWN, STATE_UNAVAILABLE]:
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result.append(sensor_value)
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if len(result) == 0:
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return None
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result.sort()
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if len(result) % 2 == 0:
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median1 = result[len(result) // 2]
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median2 = result[len(result) // 2 - 1]
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median = (median1 + median2) / 2
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else:
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median = result[len(result) // 2]
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return round(median, round_digits)
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class MinMaxSensor(Entity):
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"""Representation of a min/max sensor."""
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def __init__(self, entity_ids, name, sensor_type, round_digits):
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"""Initialize the min/max sensor."""
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self._entity_ids = entity_ids
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self._sensor_type = sensor_type
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self._round_digits = round_digits
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if name:
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self._name = name
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else:
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self._name = f"{next(v for k, v in SENSOR_TYPES.items() if self._sensor_type == v)} sensor".capitalize()
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self._unit_of_measurement = None
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self._unit_of_measurement_mismatch = False
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self.min_value = self.max_value = self.mean = self.last = self.median = None
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self.min_entity_id = self.max_entity_id = self.last_entity_id = None
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self.count_sensors = len(self._entity_ids)
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self.states = {}
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async def async_added_to_hass(self):
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"""Handle added to Hass."""
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self.async_on_remove(
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async_track_state_change_event(
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self.hass, self._entity_ids, self._async_min_max_sensor_state_listener
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)
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)
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self._calc_values()
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@property
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def name(self):
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"""Return the name of the sensor."""
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return self._name
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@property
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def state(self):
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"""Return the state of the sensor."""
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if self._unit_of_measurement_mismatch:
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return None
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return getattr(
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self, next(k for k, v in SENSOR_TYPES.items() if self._sensor_type == v)
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)
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@property
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def unit_of_measurement(self):
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"""Return the unit the value is expressed in."""
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if self._unit_of_measurement_mismatch:
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return "ERR"
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return self._unit_of_measurement
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@property
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def should_poll(self):
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"""No polling needed."""
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return False
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@property
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def device_state_attributes(self):
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"""Return the state attributes of the sensor."""
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return {
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attr: getattr(self, attr)
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for attr in ATTR_TO_PROPERTY
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if getattr(self, attr) is not None
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}
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@property
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def icon(self):
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"""Return the icon to use in the frontend, if any."""
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return ICON
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@callback
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def _async_min_max_sensor_state_listener(self, event):
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"""Handle the sensor state changes."""
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new_state = event.data.get("new_state")
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entity = event.data.get("entity_id")
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if new_state.state is None or new_state.state in [
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STATE_UNKNOWN,
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STATE_UNAVAILABLE,
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]:
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self.states[entity] = STATE_UNKNOWN
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self._calc_values()
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self.async_write_ha_state()
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return
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if self._unit_of_measurement is None:
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self._unit_of_measurement = new_state.attributes.get(
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ATTR_UNIT_OF_MEASUREMENT
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)
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if self._unit_of_measurement != new_state.attributes.get(
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ATTR_UNIT_OF_MEASUREMENT
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):
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_LOGGER.warning(
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"Units of measurement do not match for entity %s", self.entity_id
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)
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self._unit_of_measurement_mismatch = True
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try:
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self.states[entity] = float(new_state.state)
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self.last = float(new_state.state)
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self.last_entity_id = entity
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except ValueError:
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_LOGGER.warning(
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"Unable to store state. Only numerical states are supported"
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)
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self._calc_values()
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self.async_write_ha_state()
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@callback
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def _calc_values(self):
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"""Calculate the values."""
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sensor_values = [
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(entity_id, self.states[entity_id])
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for entity_id in self._entity_ids
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if entity_id in self.states
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]
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self.min_entity_id, self.min_value = calc_min(sensor_values)
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self.max_entity_id, self.max_value = calc_max(sensor_values)
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self.mean = calc_mean(sensor_values, self._round_digits)
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self.median = calc_median(sensor_values, self._round_digits)
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