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binary_sensor.py
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binary_sensor.py
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"""A sensor that monitors trends in other components."""
from __future__ import annotations
from collections import deque
from collections.abc import Mapping
import logging
import math
from typing import Any
import numpy as np
import voluptuous as vol
from homeassistant.components.binary_sensor import (
DEVICE_CLASSES_SCHEMA,
ENTITY_ID_FORMAT,
PLATFORM_SCHEMA as BINARY_SENSOR_PLATFORM_SCHEMA,
BinarySensorDeviceClass,
BinarySensorEntity,
)
from homeassistant.config_entries import ConfigEntry
from homeassistant.const import (
ATTR_ENTITY_ID,
ATTR_FRIENDLY_NAME,
CONF_ATTRIBUTE,
CONF_DEVICE_CLASS,
CONF_ENTITY_ID,
CONF_FRIENDLY_NAME,
CONF_SENSORS,
STATE_ON,
STATE_UNAVAILABLE,
STATE_UNKNOWN,
)
from homeassistant.core import Event, EventStateChangedData, HomeAssistant, callback
from homeassistant.helpers import device_registry as dr
import homeassistant.helpers.config_validation as cv
from homeassistant.helpers.device import async_device_info_to_link_from_entity
from homeassistant.helpers.entity import generate_entity_id
from homeassistant.helpers.entity_platform import AddEntitiesCallback
from homeassistant.helpers.event import async_track_state_change_event
from homeassistant.helpers.reload import async_setup_reload_service
from homeassistant.helpers.restore_state import RestoreEntity
from homeassistant.helpers.typing import ConfigType, DiscoveryInfoType
from homeassistant.util.dt import utcnow
from . import PLATFORMS
from .const import (
ATTR_GRADIENT,
ATTR_INVERT,
ATTR_MIN_GRADIENT,
ATTR_SAMPLE_COUNT,
ATTR_SAMPLE_DURATION,
CONF_INVERT,
CONF_MAX_SAMPLES,
CONF_MIN_GRADIENT,
CONF_MIN_SAMPLES,
CONF_SAMPLE_DURATION,
DEFAULT_MAX_SAMPLES,
DEFAULT_MIN_GRADIENT,
DEFAULT_MIN_SAMPLES,
DEFAULT_SAMPLE_DURATION,
DOMAIN,
)
_LOGGER = logging.getLogger(__name__)
def _validate_min_max(data: dict[str, Any]) -> dict[str, Any]:
if (
CONF_MIN_SAMPLES in data
and CONF_MAX_SAMPLES in data
and data[CONF_MAX_SAMPLES] < data[CONF_MIN_SAMPLES]
):
raise vol.Invalid("min_samples must be smaller than or equal to max_samples")
return data
SENSOR_SCHEMA = vol.All(
vol.Schema(
{
vol.Required(CONF_ENTITY_ID): cv.entity_id,
vol.Optional(CONF_ATTRIBUTE): cv.string,
vol.Optional(CONF_DEVICE_CLASS): DEVICE_CLASSES_SCHEMA,
vol.Optional(CONF_FRIENDLY_NAME): cv.string,
vol.Optional(CONF_INVERT, default=False): cv.boolean,
vol.Optional(CONF_MAX_SAMPLES, default=2): cv.positive_int,
vol.Optional(CONF_MIN_GRADIENT, default=0.0): vol.Coerce(float),
vol.Optional(CONF_SAMPLE_DURATION, default=0): cv.positive_int,
vol.Optional(CONF_MIN_SAMPLES, default=2): cv.positive_int,
}
),
_validate_min_max,
)
PLATFORM_SCHEMA = BINARY_SENSOR_PLATFORM_SCHEMA.extend(
{vol.Required(CONF_SENSORS): cv.schema_with_slug_keys(SENSOR_SCHEMA)}
)
async def async_setup_platform(
hass: HomeAssistant,
config: ConfigType,
async_add_entities: AddEntitiesCallback,
discovery_info: DiscoveryInfoType | None = None,
) -> None:
"""Set up the trend sensors."""
await async_setup_reload_service(hass, DOMAIN, PLATFORMS)
entities = []
for sensor_name, sensor_config in config[CONF_SENSORS].items():
entities.append(
SensorTrend(
name=sensor_config.get(CONF_FRIENDLY_NAME, sensor_name),
entity_id=sensor_config[CONF_ENTITY_ID],
attribute=sensor_config.get(CONF_ATTRIBUTE),
invert=sensor_config[CONF_INVERT],
sample_duration=sensor_config[CONF_SAMPLE_DURATION],
min_gradient=sensor_config[CONF_MIN_GRADIENT],
min_samples=sensor_config[CONF_MIN_SAMPLES],
max_samples=sensor_config[CONF_MAX_SAMPLES],
device_class=sensor_config.get(CONF_DEVICE_CLASS),
sensor_entity_id=generate_entity_id(
ENTITY_ID_FORMAT, sensor_name, hass=hass
),
)
)
async_add_entities(entities)
async def async_setup_entry(
hass: HomeAssistant,
entry: ConfigEntry,
async_add_entities: AddEntitiesCallback,
) -> None:
"""Set up trend sensor from config entry."""
device_info = async_device_info_to_link_from_entity(
hass,
entry.options[CONF_ENTITY_ID],
)
async_add_entities(
[
SensorTrend(
name=entry.title,
entity_id=entry.options[CONF_ENTITY_ID],
attribute=entry.options.get(CONF_ATTRIBUTE),
invert=entry.options[CONF_INVERT],
sample_duration=entry.options.get(
CONF_SAMPLE_DURATION, DEFAULT_SAMPLE_DURATION
),
min_gradient=entry.options.get(CONF_MIN_GRADIENT, DEFAULT_MIN_GRADIENT),
min_samples=entry.options.get(CONF_MIN_SAMPLES, DEFAULT_MIN_SAMPLES),
max_samples=entry.options.get(CONF_MAX_SAMPLES, DEFAULT_MAX_SAMPLES),
unique_id=entry.entry_id,
device_info=device_info,
)
]
)
class SensorTrend(BinarySensorEntity, RestoreEntity):
"""Representation of a trend Sensor."""
_attr_should_poll = False
_gradient = 0.0
_state: bool | None = None
def __init__(
self,
name: str,
entity_id: str,
attribute: str | None,
invert: bool,
sample_duration: int,
min_gradient: float,
min_samples: int,
max_samples: int,
unique_id: str | None = None,
device_class: BinarySensorDeviceClass | None = None,
sensor_entity_id: str | None = None,
device_info: dr.DeviceInfo | None = None,
) -> None:
"""Initialize the sensor."""
self._entity_id = entity_id
self._attribute = attribute
self._invert = invert
self._sample_duration = sample_duration
self._min_gradient = min_gradient
self._min_samples = min_samples
self.samples: deque = deque(maxlen=int(max_samples))
self._attr_name = name
self._attr_device_class = device_class
self._attr_unique_id = unique_id
self._attr_device_info = device_info
if sensor_entity_id:
self.entity_id = sensor_entity_id
@property
def is_on(self) -> bool | None:
"""Return true if sensor is on."""
return self._state
@property
def extra_state_attributes(self) -> Mapping[str, Any]:
"""Return the state attributes of the sensor."""
return {
ATTR_ENTITY_ID: self._entity_id,
ATTR_FRIENDLY_NAME: self._attr_name,
ATTR_GRADIENT: self._gradient,
ATTR_INVERT: self._invert,
ATTR_MIN_GRADIENT: self._min_gradient,
ATTR_SAMPLE_COUNT: len(self.samples),
ATTR_SAMPLE_DURATION: self._sample_duration,
}
async def async_added_to_hass(self) -> None:
"""Complete device setup after being added to hass."""
@callback
def trend_sensor_state_listener(
event: Event[EventStateChangedData],
) -> None:
"""Handle state changes on the observed device."""
if (new_state := event.data["new_state"]) is None:
return
try:
if self._attribute:
state = new_state.attributes.get(self._attribute)
else:
state = new_state.state
if state not in (STATE_UNKNOWN, STATE_UNAVAILABLE):
sample = (new_state.last_updated.timestamp(), float(state)) # type: ignore[arg-type]
self.samples.append(sample)
self.async_schedule_update_ha_state(True)
except (ValueError, TypeError) as ex:
_LOGGER.error(ex)
self.async_on_remove(
async_track_state_change_event(
self.hass, [self._entity_id], trend_sensor_state_listener
)
)
if not (state := await self.async_get_last_state()):
return
if state.state == STATE_UNKNOWN:
return
self._state = state.state == STATE_ON
async def async_update(self) -> None:
"""Get the latest data and update the states."""
# Remove outdated samples
if self._sample_duration > 0:
cutoff = utcnow().timestamp() - self._sample_duration
while self.samples and self.samples[0][0] < cutoff:
self.samples.popleft()
if len(self.samples) < self._min_samples:
return
# Calculate gradient of linear trend
await self.hass.async_add_executor_job(self._calculate_gradient)
# Update state
self._state = (
abs(self._gradient) > abs(self._min_gradient)
and math.copysign(self._gradient, self._min_gradient) == self._gradient
)
if self._invert:
self._state = not self._state
def _calculate_gradient(self) -> None:
"""Compute the linear trend gradient of the current samples.
This need run inside executor.
"""
timestamps = np.array([t for t, _ in self.samples])
values = np.array([s for _, s in self.samples])
coeffs = np.polyfit(timestamps, values, 1)
self._gradient = coeffs[0]