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esphome/esphome/components/sensor/__init__.py

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import math
import esphome.codegen as cg
import esphome.config_validation as cv
from esphome import automation
from esphome.components import mqtt
from esphome.const import CONF_ABOVE, CONF_ACCURACY_DECIMALS, CONF_ALPHA, CONF_BELOW, \
CONF_EXPIRE_AFTER, CONF_FILTERS, CONF_FROM, CONF_ICON, CONF_ID, CONF_INTERNAL, \
CONF_ON_RAW_VALUE, CONF_ON_VALUE, CONF_ON_VALUE_RANGE, \
CONF_SEND_EVERY, CONF_SEND_FIRST_AT, CONF_TO, CONF_TRIGGER_ID, \
CONF_UNIT_OF_MEASUREMENT, \
CONF_WINDOW_SIZE, CONF_NAME, CONF_MQTT_ID
from esphome.core import CORE, coroutine, coroutine_with_priority
from esphome.util import Registry
IS_PLATFORM_COMPONENT = True
def validate_send_first_at(value):
send_first_at = value.get(CONF_SEND_FIRST_AT)
send_every = value[CONF_SEND_EVERY]
if send_first_at is not None and send_first_at > send_every:
raise cv.Invalid("send_first_at must be smaller than or equal to send_every! {} <= {}"
"".format(send_first_at, send_every))
return value
FILTER_REGISTRY = Registry()
validate_filters = cv.validate_registry('filter', FILTER_REGISTRY)
def validate_datapoint(value):
if isinstance(value, dict):
return cv.Schema({
cv.Required(CONF_FROM): cv.float_,
cv.Required(CONF_TO): cv.float_,
})(value)
value = cv.string(value)
if '->' not in value:
raise cv.Invalid("Datapoint mapping must contain '->'")
a, b = value.split('->', 1)
a, b = a.strip(), b.strip()
return validate_datapoint({
CONF_FROM: cv.float_(a),
CONF_TO: cv.float_(b)
})
# Base
sensor_ns = cg.esphome_ns.namespace('sensor')
Sensor = sensor_ns.class_('Sensor', cg.Nameable)
SensorPtr = Sensor.operator('ptr')
# Triggers
SensorStateTrigger = sensor_ns.class_('SensorStateTrigger', automation.Trigger.template(cg.float_))
SensorRawStateTrigger = sensor_ns.class_('SensorRawStateTrigger',
automation.Trigger.template(cg.float_))
ValueRangeTrigger = sensor_ns.class_('ValueRangeTrigger', automation.Trigger.template(cg.float_),
cg.Component)
SensorPublishAction = sensor_ns.class_('SensorPublishAction', automation.Action)
# Filters
Filter = sensor_ns.class_('Filter')
MedianFilter = sensor_ns.class_('MedianFilter', Filter)
SlidingWindowMovingAverageFilter = sensor_ns.class_('SlidingWindowMovingAverageFilter', Filter)
ExponentialMovingAverageFilter = sensor_ns.class_('ExponentialMovingAverageFilter', Filter)
LambdaFilter = sensor_ns.class_('LambdaFilter', Filter)
OffsetFilter = sensor_ns.class_('OffsetFilter', Filter)
MultiplyFilter = sensor_ns.class_('MultiplyFilter', Filter)
FilterOutValueFilter = sensor_ns.class_('FilterOutValueFilter', Filter)
ThrottleFilter = sensor_ns.class_('ThrottleFilter', Filter)
DebounceFilter = sensor_ns.class_('DebounceFilter', Filter, cg.Component)
HeartbeatFilter = sensor_ns.class_('HeartbeatFilter', Filter, cg.Component)
DeltaFilter = sensor_ns.class_('DeltaFilter', Filter)
OrFilter = sensor_ns.class_('OrFilter', Filter)
CalibrateLinearFilter = sensor_ns.class_('CalibrateLinearFilter', Filter)
CalibratePolynomialFilter = sensor_ns.class_('CalibratePolynomialFilter', Filter)
SensorInRangeCondition = sensor_ns.class_('SensorInRangeCondition', Filter)
unit_of_measurement = cv.string_strict
accuracy_decimals = cv.int_
icon = cv.icon
SENSOR_SCHEMA = cv.MQTT_COMPONENT_SCHEMA.extend({
cv.OnlyWith(CONF_MQTT_ID, 'mqtt'): cv.declare_id(mqtt.MQTTSensorComponent),
cv.GenerateID(): cv.declare_id(Sensor),
cv.Optional(CONF_UNIT_OF_MEASUREMENT): unit_of_measurement,
cv.Optional(CONF_ICON): icon,
cv.Optional(CONF_ACCURACY_DECIMALS): accuracy_decimals,
cv.Optional(CONF_EXPIRE_AFTER): cv.All(cv.requires_component('mqtt'),
cv.Any(None, cv.positive_time_period_milliseconds)),
cv.Optional(CONF_FILTERS): validate_filters,
cv.Optional(CONF_ON_VALUE): automation.validate_automation({
cv.GenerateID(CONF_TRIGGER_ID): cv.declare_id(SensorStateTrigger),
}),
cv.Optional(CONF_ON_RAW_VALUE): automation.validate_automation({
cv.GenerateID(CONF_TRIGGER_ID): cv.declare_id(SensorRawStateTrigger),
}),
cv.Optional(CONF_ON_VALUE_RANGE): automation.validate_automation({
cv.GenerateID(CONF_TRIGGER_ID): cv.declare_id(ValueRangeTrigger),
cv.Optional(CONF_ABOVE): cv.float_,
cv.Optional(CONF_BELOW): cv.float_,
}, cv.has_at_least_one_key(CONF_ABOVE, CONF_BELOW)),
})
def sensor_schema(unit_of_measurement_, icon_, accuracy_decimals_):
# type: (str, str, int) -> cv.Schema
return SENSOR_SCHEMA.extend({
cv.Optional(CONF_UNIT_OF_MEASUREMENT, default=unit_of_measurement_): unit_of_measurement,
cv.Optional(CONF_ICON, default=icon_): icon,
cv.Optional(CONF_ACCURACY_DECIMALS, default=accuracy_decimals_): accuracy_decimals,
})
@FILTER_REGISTRY.register('offset', OffsetFilter, cv.float_)
def offset_filter_to_code(config, filter_id):
yield cg.new_Pvariable(filter_id, config)
@FILTER_REGISTRY.register('multiply', MultiplyFilter, cv.float_)
def multiply_filter_to_code(config, filter_id):
yield cg.new_Pvariable(filter_id, config)
@FILTER_REGISTRY.register('filter_out', FilterOutValueFilter, cv.float_)
def filter_out_filter_to_code(config, filter_id):
yield cg.new_Pvariable(filter_id, config)
MEDIAN_SCHEMA = cv.All(cv.Schema({
cv.Optional(CONF_WINDOW_SIZE, default=5): cv.positive_not_null_int,
cv.Optional(CONF_SEND_EVERY, default=5): cv.positive_not_null_int,
cv.Optional(CONF_SEND_FIRST_AT, default=1): cv.positive_not_null_int,
}), validate_send_first_at)
@FILTER_REGISTRY.register('median', MedianFilter, MEDIAN_SCHEMA)
def median_filter_to_code(config, filter_id):
yield cg.new_Pvariable(filter_id, config[CONF_WINDOW_SIZE], config[CONF_SEND_EVERY],
config[CONF_SEND_FIRST_AT])
SLIDING_AVERAGE_SCHEMA = cv.All(cv.Schema({
cv.Optional(CONF_WINDOW_SIZE, default=15): cv.positive_not_null_int,
cv.Optional(CONF_SEND_EVERY, default=15): cv.positive_not_null_int,
cv.Optional(CONF_SEND_FIRST_AT, default=1): cv.positive_not_null_int,
}), validate_send_first_at)
@FILTER_REGISTRY.register('sliding_window_moving_average', SlidingWindowMovingAverageFilter,
SLIDING_AVERAGE_SCHEMA)
def sliding_window_moving_average_filter_to_code(config, filter_id):
yield cg.new_Pvariable(filter_id, config[CONF_WINDOW_SIZE], config[CONF_SEND_EVERY],
config[CONF_SEND_FIRST_AT])
@FILTER_REGISTRY.register('exponential_moving_average', ExponentialMovingAverageFilter, cv.Schema({
cv.Optional(CONF_ALPHA, default=0.1): cv.positive_float,
cv.Optional(CONF_SEND_EVERY, default=15): cv.positive_not_null_int,
}))
def exponential_moving_average_filter_to_code(config, filter_id):
yield cg.new_Pvariable(filter_id, config[CONF_ALPHA], config[CONF_SEND_EVERY])
@FILTER_REGISTRY.register('lambda', LambdaFilter, cv.returning_lambda)
def lambda_filter_to_code(config, filter_id):
lambda_ = yield cg.process_lambda(config, [(float, 'x')],
return_type=cg.optional.template(float))
yield cg.new_Pvariable(filter_id, lambda_)
@FILTER_REGISTRY.register('delta', DeltaFilter, cv.float_)
def delta_filter_to_code(config, filter_id):
yield cg.new_Pvariable(filter_id, config)
@FILTER_REGISTRY.register('or', OrFilter, validate_filters)
def or_filter_to_code(config, filter_id):
filters = yield build_filters(config)
yield cg.new_Pvariable(filter_id, filters)
@FILTER_REGISTRY.register('throttle', ThrottleFilter, cv.positive_time_period_milliseconds)
def throttle_filter_to_code(config, filter_id):
yield cg.new_Pvariable(filter_id, config)
@FILTER_REGISTRY.register('heartbeat', HeartbeatFilter, cv.positive_time_period_milliseconds)
def heartbeat_filter_to_code(config, filter_id):
var = cg.new_Pvariable(filter_id, config)
yield cg.register_component(var, {})
yield var
@FILTER_REGISTRY.register('debounce', DebounceFilter, cv.positive_time_period_milliseconds)
def debounce_filter_to_code(config, filter_id):
var = cg.new_Pvariable(filter_id, config)
yield cg.register_component(var, {})
yield var
def validate_not_all_from_same(config):
if all(conf[CONF_FROM] == config[0][CONF_FROM] for conf in config):
raise cv.Invalid("The 'from' values of the calibrate_linear filter cannot all point "
"to the same value! Please add more values to the filter.")
return config
@FILTER_REGISTRY.register('calibrate_linear', CalibrateLinearFilter, cv.All(
cv.ensure_list(validate_datapoint), cv.Length(min=2), validate_not_all_from_same))
def calibrate_linear_filter_to_code(config, filter_id):
x = [conf[CONF_FROM] for conf in config]
y = [conf[CONF_TO] for conf in config]
k, b = fit_linear(x, y)
yield cg.new_Pvariable(filter_id, k, b)
CONF_DATAPOINTS = 'datapoints'
CONF_DEGREE = 'degree'
def validate_calibrate_polynomial(config):
if config[CONF_DEGREE] >= len(config[CONF_DATAPOINTS]):
raise cv.Invalid("Degree is too high! Maximum possible degree with given datapoints is "
"{}".format(len(config[CONF_DATAPOINTS]) - 1), [CONF_DEGREE])
return config
@FILTER_REGISTRY.register('calibrate_polynomial', CalibratePolynomialFilter, cv.All(cv.Schema({
cv.Required(CONF_DATAPOINTS): cv.All(cv.ensure_list(validate_datapoint), cv.Length(min=1)),
cv.Required(CONF_DEGREE): cv.positive_int,
}), validate_calibrate_polynomial))
def calibrate_polynomial_filter_to_code(config, filter_id):
x = [conf[CONF_FROM] for conf in config[CONF_DATAPOINTS]]
y = [conf[CONF_TO] for conf in config[CONF_DATAPOINTS]]
degree = config[CONF_DEGREE]
a = [[1] + [x_**(i+1) for i in range(degree)] for x_ in x]
# Column vector
b = [[v] for v in y]
res = [v[0] for v in _lstsq(a, b)]
yield cg.new_Pvariable(filter_id, res)
@coroutine
def build_filters(config):
yield cg.build_registry_list(FILTER_REGISTRY, config)
@coroutine
def setup_sensor_core_(var, config):
cg.add(var.set_name(config[CONF_NAME]))
if CONF_INTERNAL in config:
cg.add(var.set_internal(config[CONF_INTERNAL]))
if CONF_UNIT_OF_MEASUREMENT in config:
cg.add(var.set_unit_of_measurement(config[CONF_UNIT_OF_MEASUREMENT]))
if CONF_ICON in config:
cg.add(var.set_icon(config[CONF_ICON]))
if CONF_ACCURACY_DECIMALS in config:
cg.add(var.set_accuracy_decimals(config[CONF_ACCURACY_DECIMALS]))
if CONF_FILTERS in config:
filters = yield build_filters(config[CONF_FILTERS])
cg.add(var.set_filters(filters))
for conf in config.get(CONF_ON_VALUE, []):
trigger = cg.new_Pvariable(conf[CONF_TRIGGER_ID], var)
yield automation.build_automation(trigger, [(float, 'x')], conf)
for conf in config.get(CONF_ON_RAW_VALUE, []):
trigger = cg.new_Pvariable(conf[CONF_TRIGGER_ID], var)
yield automation.build_automation(trigger, [(float, 'x')], conf)
for conf in config.get(CONF_ON_VALUE_RANGE, []):
trigger = cg.new_Pvariable(conf[CONF_TRIGGER_ID], var)
yield cg.register_component(trigger, conf)
if CONF_ABOVE in conf:
template_ = yield cg.templatable(conf[CONF_ABOVE], [(float, 'x')], float)
cg.add(trigger.set_min(template_))
if CONF_BELOW in conf:
template_ = yield cg.templatable(conf[CONF_BELOW], [(float, 'x')], float)
cg.add(trigger.set_max(template_))
yield automation.build_automation(trigger, [(float, 'x')], conf)
if CONF_MQTT_ID in config:
mqtt_ = cg.new_Pvariable(config[CONF_MQTT_ID], var)
yield mqtt.register_mqtt_component(mqtt_, config)
if CONF_EXPIRE_AFTER in config:
if config[CONF_EXPIRE_AFTER] is None:
cg.add(mqtt_.disable_expire_after())
else:
cg.add(mqtt_.set_expire_after(config[CONF_EXPIRE_AFTER]))
@coroutine
def register_sensor(var, config):
if not CORE.has_id(config[CONF_ID]):
var = cg.Pvariable(config[CONF_ID], var)
cg.add(cg.App.register_sensor(var))
yield setup_sensor_core_(var, config)
@coroutine
def new_sensor(config):
var = cg.new_Pvariable(config[CONF_ID])
yield register_sensor(var, config)
yield var
SENSOR_IN_RANGE_CONDITION_SCHEMA = cv.All({
cv.Required(CONF_ID): cv.use_id(Sensor),
cv.Optional(CONF_ABOVE): cv.float_,
cv.Optional(CONF_BELOW): cv.float_,
}, cv.has_at_least_one_key(CONF_ABOVE, CONF_BELOW))
@automation.register_condition('sensor.in_range', SensorInRangeCondition,
SENSOR_IN_RANGE_CONDITION_SCHEMA)
def sensor_in_range_to_code(config, condition_id, template_arg, args):
paren = yield cg.get_variable(config[CONF_ID])
var = cg.new_Pvariable(condition_id, template_arg, paren)
if CONF_ABOVE in config:
cg.add(var.set_min(config[CONF_ABOVE]))
if CONF_BELOW in config:
cg.add(var.set_max(config[CONF_BELOW]))
yield var
def _mean(xs):
return sum(xs) / len(xs)
def _std(x):
return math.sqrt(sum((x_ - _mean(x)) ** 2 for x_ in x) / (len(x) - 1))
def _correlation_coeff(x, y):
m_x, m_y = _mean(x), _mean(y)
s_xy = sum((x_ - m_x) * (y_ - m_y) for x_, y_ in zip(x, y))
s_sq_x = sum((x_ - m_x) ** 2 for x_ in x)
s_sq_y = sum((y_ - m_y) ** 2 for y_ in y)
return s_xy / math.sqrt(s_sq_x * s_sq_y)
def fit_linear(x, y):
assert len(x) == len(y)
m_x, m_y = _mean(x), _mean(y)
r = _correlation_coeff(x, y)
k = r * (_std(y) / _std(x))
b = m_y - k * m_x
return k, b
def _mat_copy(m):
return [list(row) for row in m]
def _mat_transpose(m):
return _mat_copy(zip(*m))
def _mat_identity(n):
return [[int(i == j) for j in range(n)] for i in range(n)]
def _mat_dot(a, b):
b_t = _mat_transpose(b)
return [[sum(x*y for x, y in zip(row_a, col_b)) for col_b in b_t] for row_a in a]
def _mat_inverse(m):
n = len(m)
m = _mat_copy(m)
id = _mat_identity(n)
for diag in range(n):
# If diag element is 0, swap rows
if m[diag][diag] == 0:
for i in range(diag+1, n):
if m[i][diag] != 0:
break
else:
raise ValueError("Singular matrix, inverse cannot be calculated!")
# Swap rows
m[diag], m[i] = m[i], m[diag]
id[diag], id[i] = id[i], id[diag]
# Scale row to 1 in diagonal
scaler = 1.0 / m[diag][diag]
for j in range(n):
m[diag][j] *= scaler
id[diag][j] *= scaler
# Subtract diag row
for i in range(n):
if i == diag:
continue
scaler = m[i][diag]
for j in range(n):
m[i][j] -= scaler * m[diag][j]
id[i][j] -= scaler * id[diag][j]
return id
def _lstsq(a, b):
# min_x ||b - ax||^2_2 => x = (a^T a)^{-1} a^T b
a_t = _mat_transpose(a)
x = _mat_inverse(_mat_dot(a_t, a))
return _mat_dot(_mat_dot(x, a_t), b)
@coroutine_with_priority(40.0)
def to_code(config):
cg.add_define('USE_SENSOR')
cg.add_global(sensor_ns.using)
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