231 lines
7.0 KiB
Python
231 lines
7.0 KiB
Python
"""A sensor that monitors trends in other components."""
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from __future__ import annotations
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from collections import deque
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import logging
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import math
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import numpy as np
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import voluptuous as vol
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from homeassistant.components.binary_sensor import (
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DEVICE_CLASSES_SCHEMA,
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ENTITY_ID_FORMAT,
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PLATFORM_SCHEMA,
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BinarySensorEntity,
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)
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from homeassistant.const import (
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ATTR_ENTITY_ID,
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ATTR_FRIENDLY_NAME,
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CONF_ATTRIBUTE,
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CONF_DEVICE_CLASS,
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CONF_ENTITY_ID,
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CONF_FRIENDLY_NAME,
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CONF_SENSORS,
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STATE_UNAVAILABLE,
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STATE_UNKNOWN,
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)
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from homeassistant.core import HomeAssistant, callback
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import homeassistant.helpers.config_validation as cv
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from homeassistant.helpers.entity import generate_entity_id
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from homeassistant.helpers.entity_platform import AddEntitiesCallback
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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 homeassistant.helpers.typing import ConfigType, DiscoveryInfoType
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from homeassistant.util.dt import utcnow
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from . import PLATFORMS
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from .const import (
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ATTR_GRADIENT,
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ATTR_INVERT,
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ATTR_MIN_GRADIENT,
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ATTR_SAMPLE_COUNT,
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ATTR_SAMPLE_DURATION,
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CONF_INVERT,
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CONF_MAX_SAMPLES,
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CONF_MIN_GRADIENT,
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CONF_SAMPLE_DURATION,
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DOMAIN,
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)
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_LOGGER = logging.getLogger(__name__)
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SENSOR_SCHEMA = vol.Schema(
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{
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vol.Required(CONF_ENTITY_ID): cv.entity_id,
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vol.Optional(CONF_ATTRIBUTE): cv.string,
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vol.Optional(CONF_DEVICE_CLASS): DEVICE_CLASSES_SCHEMA,
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vol.Optional(CONF_FRIENDLY_NAME): cv.string,
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vol.Optional(CONF_INVERT, default=False): cv.boolean,
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vol.Optional(CONF_MAX_SAMPLES, default=2): cv.positive_int,
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vol.Optional(CONF_MIN_GRADIENT, default=0.0): vol.Coerce(float),
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vol.Optional(CONF_SAMPLE_DURATION, default=0): cv.positive_int,
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}
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)
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PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend(
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{vol.Required(CONF_SENSORS): cv.schema_with_slug_keys(SENSOR_SCHEMA)}
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)
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async def async_setup_platform(
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hass: HomeAssistant,
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config: ConfigType,
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async_add_entities: AddEntitiesCallback,
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discovery_info: DiscoveryInfoType | None = None,
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) -> None:
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"""Set up the trend sensors."""
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await async_setup_reload_service(hass, DOMAIN, PLATFORMS)
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sensors = []
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for device_id, device_config in config[CONF_SENSORS].items():
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entity_id = device_config[ATTR_ENTITY_ID]
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attribute = device_config.get(CONF_ATTRIBUTE)
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device_class = device_config.get(CONF_DEVICE_CLASS)
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friendly_name = device_config.get(ATTR_FRIENDLY_NAME, device_id)
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invert = device_config[CONF_INVERT]
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max_samples = device_config[CONF_MAX_SAMPLES]
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min_gradient = device_config[CONF_MIN_GRADIENT]
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sample_duration = device_config[CONF_SAMPLE_DURATION]
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sensors.append(
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SensorTrend(
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hass,
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device_id,
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friendly_name,
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entity_id,
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attribute,
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device_class,
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invert,
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max_samples,
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min_gradient,
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sample_duration,
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)
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)
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if not sensors:
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_LOGGER.error("No sensors added")
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return
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async_add_entities(sensors)
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class SensorTrend(BinarySensorEntity):
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"""Representation of a trend Sensor."""
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_attr_should_poll = False
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def __init__(
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self,
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hass,
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device_id,
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friendly_name,
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entity_id,
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attribute,
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device_class,
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invert,
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max_samples,
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min_gradient,
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sample_duration,
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):
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"""Initialize the sensor."""
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self._hass = hass
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self.entity_id = generate_entity_id(ENTITY_ID_FORMAT, device_id, hass=hass)
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self._name = friendly_name
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self._entity_id = entity_id
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self._attribute = attribute
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self._device_class = device_class
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self._invert = invert
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self._sample_duration = sample_duration
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self._min_gradient = min_gradient
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self._gradient = None
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self._state = None
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self.samples = deque(maxlen=max_samples)
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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 is_on(self):
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"""Return true if sensor is on."""
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return self._state
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@property
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def device_class(self):
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"""Return the sensor class of the sensor."""
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return self._device_class
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@property
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def extra_state_attributes(self):
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"""Return the state attributes of the sensor."""
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return {
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ATTR_ENTITY_ID: self._entity_id,
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ATTR_FRIENDLY_NAME: self._name,
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ATTR_GRADIENT: self._gradient,
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ATTR_INVERT: self._invert,
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ATTR_MIN_GRADIENT: self._min_gradient,
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ATTR_SAMPLE_COUNT: len(self.samples),
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ATTR_SAMPLE_DURATION: self._sample_duration,
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}
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async def async_added_to_hass(self) -> None:
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"""Complete device setup after being added to hass."""
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@callback
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def trend_sensor_state_listener(event):
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"""Handle state changes on the observed device."""
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if (new_state := event.data.get("new_state")) is None:
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return
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try:
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if self._attribute:
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state = new_state.attributes.get(self._attribute)
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else:
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state = new_state.state
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if state not in (STATE_UNKNOWN, STATE_UNAVAILABLE):
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sample = (new_state.last_updated.timestamp(), float(state))
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self.samples.append(sample)
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self.async_schedule_update_ha_state(True)
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except (ValueError, TypeError) as ex:
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_LOGGER.error(ex)
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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_id], trend_sensor_state_listener
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)
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)
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async def async_update(self) -> None:
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"""Get the latest data and update the states."""
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# Remove outdated samples
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if self._sample_duration > 0:
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cutoff = utcnow().timestamp() - self._sample_duration
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while self.samples and self.samples[0][0] < cutoff:
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self.samples.popleft()
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if len(self.samples) < 2:
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return
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# Calculate gradient of linear trend
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await self.hass.async_add_executor_job(self._calculate_gradient)
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# Update state
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self._state = (
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abs(self._gradient) > abs(self._min_gradient)
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and math.copysign(self._gradient, self._min_gradient) == self._gradient
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)
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if self._invert:
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self._state = not self._state
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def _calculate_gradient(self):
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"""Compute the linear trend gradient of the current samples.
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This need run inside executor.
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"""
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timestamps = np.array([t for t, _ in self.samples])
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values = np.array([s for _, s in self.samples])
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coeffs = np.polyfit(timestamps, values, 1)
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self._gradient = coeffs[0]
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