core/homeassistant/components/sensor/statistics.py

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"""
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