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afc848bf22
* initial support for Bayesian type * Cast bool state of binary_sensor to uint64_t * Rename channels to observations with Bayesian * Improve/standardize comments for all types * Use black to correct sensor.py formatting * Add SUM and BAYESIAN binary sensor map tests * Remove unused variable * Update esphome/components/binary_sensor_map/binary_sensor_map.cpp Co-authored-by: Jesse Hills <3060199+jesserockz@users.noreply.github.com> --------- Co-authored-by: Jesse Hills <3060199+jesserockz@users.noreply.github.com>
107 lines
3.2 KiB
Python
107 lines
3.2 KiB
Python
import esphome.codegen as cg
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import esphome.config_validation as cv
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from esphome.components import sensor, binary_sensor
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from esphome.const import (
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CONF_CHANNELS,
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CONF_VALUE,
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CONF_TYPE,
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ICON_CHECK_CIRCLE_OUTLINE,
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CONF_BINARY_SENSOR,
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CONF_GROUP,
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CONF_SUM,
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)
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DEPENDENCIES = ["binary_sensor"]
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binary_sensor_map_ns = cg.esphome_ns.namespace("binary_sensor_map")
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BinarySensorMap = binary_sensor_map_ns.class_(
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"BinarySensorMap", cg.Component, sensor.Sensor
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)
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SensorMapType = binary_sensor_map_ns.enum("SensorMapType")
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CONF_BAYESIAN = "bayesian"
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CONF_PRIOR = "prior"
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CONF_PROB_GIVEN_TRUE = "prob_given_true"
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CONF_PROB_GIVEN_FALSE = "prob_given_false"
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CONF_OBSERVATIONS = "observations"
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SENSOR_MAP_TYPES = {
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CONF_GROUP: SensorMapType.BINARY_SENSOR_MAP_TYPE_GROUP,
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CONF_SUM: SensorMapType.BINARY_SENSOR_MAP_TYPE_SUM,
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CONF_BAYESIAN: SensorMapType.BINARY_SENSOR_MAP_TYPE_BAYESIAN,
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}
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entry_one_parameter = {
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cv.Required(CONF_BINARY_SENSOR): cv.use_id(binary_sensor.BinarySensor),
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cv.Required(CONF_VALUE): cv.float_,
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}
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entry_bayesian_parameters = {
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cv.Required(CONF_BINARY_SENSOR): cv.use_id(binary_sensor.BinarySensor),
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cv.Required(CONF_PROB_GIVEN_TRUE): cv.float_range(min=0, max=1),
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cv.Required(CONF_PROB_GIVEN_FALSE): cv.float_range(min=0, max=1),
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}
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CONFIG_SCHEMA = cv.typed_schema(
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{
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CONF_GROUP: sensor.sensor_schema(
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BinarySensorMap,
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icon=ICON_CHECK_CIRCLE_OUTLINE,
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accuracy_decimals=0,
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).extend(
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{
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cv.Required(CONF_CHANNELS): cv.All(
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cv.ensure_list(entry_one_parameter), cv.Length(min=1, max=64)
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),
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}
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),
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CONF_SUM: sensor.sensor_schema(
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BinarySensorMap,
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icon=ICON_CHECK_CIRCLE_OUTLINE,
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accuracy_decimals=0,
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).extend(
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{
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cv.Required(CONF_CHANNELS): cv.All(
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cv.ensure_list(entry_one_parameter), cv.Length(min=1, max=64)
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),
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}
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),
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CONF_BAYESIAN: sensor.sensor_schema(
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BinarySensorMap,
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accuracy_decimals=2,
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).extend(
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{
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cv.Required(CONF_PRIOR): cv.float_range(min=0, max=1),
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cv.Required(CONF_OBSERVATIONS): cv.All(
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cv.ensure_list(entry_bayesian_parameters), cv.Length(min=1, max=64)
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),
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}
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),
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},
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lower=True,
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)
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async def to_code(config):
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var = await sensor.new_sensor(config)
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await cg.register_component(var, config)
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constant = SENSOR_MAP_TYPES[config[CONF_TYPE]]
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cg.add(var.set_sensor_type(constant))
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if config[CONF_TYPE] == CONF_BAYESIAN:
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cg.add(var.set_bayesian_prior(config[CONF_PRIOR]))
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for obs in config[CONF_OBSERVATIONS]:
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input_var = await cg.get_variable(obs[CONF_BINARY_SENSOR])
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cg.add(
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var.add_channel(
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input_var, obs[CONF_PROB_GIVEN_TRUE], obs[CONF_PROB_GIVEN_FALSE]
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)
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)
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else:
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for ch in config[CONF_CHANNELS]:
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input_var = await cg.get_variable(ch[CONF_BINARY_SENSOR])
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cg.add(var.add_channel(input_var, ch[CONF_VALUE]))
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