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HomeAssistantVS/custom_components/ha_washdata/cycle_detector.py
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# WashData - Home Assistant integration for appliance cycle monitoring via smart plugs.
# Copyright (C) 2026 Lukas Bandura
# SPDX-License-Identifier: AGPL-3.0-or-later
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
"""Cycle detection logic for WashData."""
from __future__ import annotations
import itertools
import logging
import math
from dataclasses import dataclass
from datetime import datetime
from typing import Any, Callable, cast
import numpy as np
from homeassistant.util import dt as dt_util
from .log_utils import DeviceLoggerAdapter
from .const import (
ANTI_WRINKLE_ELIGIBLE_REASONS,
TerminationReason,
STATE_OFF,
STATE_DELAY_WAIT,
STATE_STARTING,
STATE_RUNNING,
STATE_PAUSED,
STATE_ENDING,
STATE_FINISHED,
STATE_ANTI_WRINKLE,
STATE_INTERRUPTED,
STATE_FORCE_STOPPED,
STATE_UNKNOWN,
DEVICE_TYPE_WASHING_MACHINE,
DEVICE_TYPE_DRYER,
DEVICE_TYPE_WASHER_DRYER,
DEFAULT_MAX_DEFERRAL_SECONDS,
DEFAULT_DEFER_FINISH_CONFIDENCE,
DISHWASHER_END_SPIKE_MIN_PROGRESS,
DISHWASHER_END_SPIKE_QUIET_RELEASE_SECONDS,
DISHWASHER_END_SPIKE_WAIT_SECONDS,
DISHWASHER_SMART_TERMINATION_DEBOUNCE_SECONDS,
WASHER_SMART_TERMINATION_DEBOUNCE_MAX_SECONDS,
STARTING_PAUSED_TRUE_OFF_TIMEOUT_SECONDS,
DISHWASHER_MATCH_FREEZE_QUIET_SECONDS,
DISHWASHER_MIN_CYCLE_DURATION_S,
TERMINAL_DROP_OFF_DELAY_SECONDS,
ENDING_HARD_FINALIZE_RATIO,
ENDING_HARD_FINALIZE_MIN_QUIET_S,
STANDBY_BAND_FINALIZE_DEVICE_TYPES,
STANDBY_BAND_MIN_RATIO,
STANDBY_BAND_WINDOW_S,
STANDBY_BAND_MAX_FRACTION,
STANDBY_BAND_FLATNESS_FRACTION,
STANDBY_BAND_FLATNESS_FLOOR_W,
ANTI_CREASE_FINALIZE_RATIO,
ANTI_CREASE_CONFIRM_WINDOW_S,
)
# The dishwasher end-spike wait window is shared between two code paths
# (Smart Termination's wait branch and _should_defer_finish's no-end-spike
# branch). They MUST release the cycle at the same instant - sanity-check
# that the constants module loaded a sensible value rather than allowing the
# paths to silently drift if one was changed and the other forgotten.
if DISHWASHER_END_SPIKE_WAIT_SECONDS <= 0:
# Runtime check (not assert: asserts are stripped under python -O).
raise ValueError("DISHWASHER_END_SPIKE_WAIT_SECONDS must be positive")
# Opt-in ML end-detection guard (Stage 6). When the manager injects an
# end-confidence provider (only when the user enabled ML models for the device),
# the cycle-end model can defer a *normal* completion if it judges the current
# low-power event to be a pause rather than the true end. This is intentionally
# asymmetric: it can only *delay* a completion, never end a cycle early, and it
# is bounded, so a wrong model can slow a finish but can neither stop one early
# nor hang the cycle. Force-stop / smart-termination / user paths never consult
# it. Overridable emphasis lives here rather than const.py to keep the guard
# self-contained (it is detector-internal policy, not user configuration).
ML_END_GUARD_MIN_CONFIDENCE = 0.5 # P(true end) below this -> treat as a likely pause
ML_END_GUARD_MAX_DEFER_SECONDS = 1800.0 # cap the extra wait the guard may add (30 min)
# The opt-in ML end-guard / terminal-drop providers rebuild the trace and run
# inference on every ENDING-phase evaluation. During a long quiet tail (e.g. a
# dishwasher's up-to-1h soak) that is wasteful, so recompute at most this often
# (data-clock seconds). Safe to cache: the guard only ever *defers* and terminal
# drop only ever *shortens*, so both tolerate a value up to this window stale.
ML_PROVIDER_THROTTLE_SECONDS = 30.0
if not 0 < DISHWASHER_END_SPIKE_MIN_PROGRESS < 1:
raise ValueError("DISHWASHER_END_SPIKE_MIN_PROGRESS must be a fraction in (0, 1)")
from .signal_processing import energy_gap_threshold_s, integrate_wh
_LOGGER = logging.getLogger(__name__)
# After a user/external stop the manual-stop lockout swallows the machine's
# spin-down/drain so it is not logged as a fresh cycle. The lockout normally
# clears as soon as power drops to idle. As a safety net, if power instead stays
# at or above the start threshold for longer than any plausible spin-down, the
# device is running a genuinely new (back-to-back) load: release the lockout so
# the new cycle is detected instead of being pinned until the progress-reset
# window expires (issue #267).
STOP_LOCKOUT_RELEASE_SECONDS = 180.0
@dataclass
class CycleDetectorConfig:
"""Configuration for cycle detection."""
min_power: float
off_delay: int
device_type: str = DEVICE_TYPE_WASHING_MACHINE
smoothing_window: int = 5
interrupted_min_seconds: int = 150
completion_min_seconds: int = 600
start_duration_threshold: float = 5.0
start_energy_threshold: float = 0.005
end_energy_threshold: float = 0.05 # 50 Wh threshold for "still active"
running_dead_zone: int = 0
end_repeat_count: int = 1
min_off_gap: int = 60
start_threshold_w: float = 2.0
stop_threshold_w: float = 2.0
min_duration_ratio: float = 0.8 # Default deferred finish ratio
# Power-based Off detection (issue #284). Carried on the config so the manager
# (the single owner of the terminal -> Off transition) can read them live; the
# detector itself does not act on them. 0 = disabled.
power_off_threshold_w: float = 0.0
power_off_delay: float = 30.0
match_interval: int = 300 # Default profile match interval
profile_duration_tolerance: float = 0.25 # Default tolerance (±25%)
anti_wrinkle_enabled: bool = False
anti_wrinkle_max_power: float = 400.0
anti_wrinkle_max_duration: float = 60.0
anti_wrinkle_exit_power: float = 0.8
delay_detect_enabled: bool = False
# Sustained seconds power must stay in the standby band (between
# stop_threshold_w and start_threshold_w) before DELAY_WAIT engages.
# Tuned to filter out brief menu-navigation peaks at the start of a
# delayed program.
delay_confirm_seconds: float = 60.0
delay_timeout_seconds: float = 28800.0
# Add other fields as needed
def trim_zero_readings(
readings: list[tuple[datetime, float]],
threshold: float = 0.5,
trim_start: bool = True,
trim_end: bool = True,
) -> list[tuple[datetime, float]]:
"""Trim continuous zero/near-zero readings from start and end of cycle.
Args:
readings: List of (timestamp, power) tuples
threshold: Power values below this are considered "zero"
trim_start: Whether to trim zeros from the beginning
trim_end: Whether to trim zeros from the end
Returns:
Trimmed list
"""
if not readings:
return readings
start_idx = 0
if trim_start:
for i, (_, power) in enumerate(readings):
if power > threshold:
start_idx = i
break
else:
# All readings are zero - return single point if list not empty
return readings[:1] if readings else []
end_idx = len(readings) - 1
if trim_end:
# Find last non-zero reading
found_end = False
for i in range(len(readings) - 1, -1, -1):
if readings[i][1] > threshold:
end_idx = i
found_end = True
break
if not found_end and trim_start:
# If all zeros and trim_start was checked, it would return early.
# But if safety fallback needed:
end_idx = start_idx
elif not found_end and not trim_start:
# Trimming end but not start, and all zeros?
# Keep first point
end_idx = 0
# Return trimmed slice (inclusive of end)
return readings[start_idx : end_idx + 1]
class CycleDetector:
"""Detects washing machine cycles based on power usage.
Implements a robust state machine:
OFF -> STARTING -> RUNNING <-> PAUSED -> ENDING -> OFF
"""
def __init__(
self,
config: CycleDetectorConfig,
on_state_change: Callable[[str, str], None],
on_cycle_end: Callable[[dict[str, Any]], None],
profile_matcher: (
Callable[
[list[tuple[datetime, float]]],
tuple[str | None, float, float, str | None],
]
| None
) = None,
device_name: str = "",
end_confidence_provider: (
Callable[[list[tuple[float, float]], float], float | None] | None
) = None,
terminal_drop_provider: (
Callable[[list[tuple[float, float]], float], bool | None] | None
) = None,
) -> None:
"""Initialize the cycle detector."""
self._logger = DeviceLoggerAdapter(_LOGGER, device_name)
self._config = config
self._on_state_change = on_state_change
self._on_cycle_end = on_cycle_end
self._profile_matcher = profile_matcher
# Opt-in ML end-guard: (points, expected_duration) -> P(true end) or None.
# Injected by the manager; None disables the guard (existing behavior).
self._end_confidence_provider = end_confidence_provider
# Opt-in terminal-drop detector: (points, expected_duration) -> bool.
# True means the current low-power event is an anomalously-early hard
# cliff-to-0 (never seen this early on this device), so the cycle may be
# finalized without waiting out the full soak-bridging min_off_gap.
# Injected by the manager; None disables it (existing behavior). Opposite
# asymmetry to the end-guard: it can only ever *shorten* the end wait.
self._terminal_drop_provider = terminal_drop_provider
# Throttle caches for the two providers, scoped to the cycle + expectation:
# (last_reading_ts, expected_duration, cycle_start, result). Reused only
# within the recompute window when expected_duration and cycle_start match.
self._ml_end_cache: tuple[datetime, float, datetime, float | None] | None = None
self._terminal_drop_cache: tuple[datetime, float, datetime, bool] | None = None
# Cycle duration (s) at which the ML guard first deferred the current
# ending episode; bounds how long the guard may keep deferring.
self._ml_defer_start_duration: float | None = None
# State
self._state = STATE_OFF
self._sub_state: str | None = None
self._ignore_power_until_idle: bool = False
# Sustained high-power time accrued while the stop lockout is armed; used
# to release the lockout for a genuinely new back-to-back load (#267).
self._lockout_high_seconds: float = 0.0
# Data
self._power_readings: list[tuple[datetime, float]] = [] # (time, raw_power)
self._current_cycle_start: datetime | None = None
self._last_active_time: datetime | None = None
self._cycle_max_power: float = 0.0
# Accumulators (dt-aware)
self._energy_since_idle_wh: float = 0.0
self._time_above_threshold: float = 0.0
self._time_below_threshold: float = 0.0
self._last_process_time: datetime | None = None
# New State Machine trackers
self._state_enter_time: datetime | None = None
self._matched_profile: str | None = None
self._verified_pause: bool = False
self._last_power: float | None = None
self._time_in_state: float = 0.0
# Smoothing buffer
self._ma_buffer: list[float] = []
# Adaptive Sampling Tracker
self._recent_dts: list[float] = [] # Track last 20 dt values
self._p95_dt: float = 1.0 # Default assumption
# Profile Matching Tracker
self._last_match_time: datetime | None = None
self._expected_duration: float = 0.0
self._last_match_confidence: float = 0.0
self._end_spike_seen: bool = False
self._end_spike_duration: float = 0.0 # cycle duration (s) when _end_spike_seen was last set
self._match_ambiguous: bool = False # last live match was ambiguous (gates predictive end)
self._match_prefix_ambiguous: bool = False # longer candidate with good shape exists (prefix guard)
# Anti-wrinkle tracking (dryers only)
self._anti_wrinkle_candidate_start: datetime | None = None
self._anti_wrinkle_candidate_peak: float = 0.0
self._anti_wrinkle_candidate_start_power: float = 0.0
self._anti_wrinkle_idle_time: float = 0.0 # Track time spent below exit_power while in ANTI_WRINKLE
self._anti_wrinkle_idle_timeout: float = 120.0
# Delayed-start band tracking.
# _delay_band_start anchors the first reading in the standby band
# [stop_threshold_w, start_threshold_w) while still in STATE_OFF.
# _delay_band_seconds mirrors the anchored elapsed time for
# diagnostics and tests.
self._delay_band_start: datetime | None = None
self._delay_band_seconds: float = 0.0
# _delay_band_peak is purely diagnostic - surfaced in the log line
# when the transition fires so users can see what their machine's
# actual standby plateau looked like.
self._delay_band_peak: float = 0.0
# _delay_wait_true_off_seconds tracks sustained "true off" (power
# below stop_threshold_w) inside DELAY_WAIT, so we can drop back to
# OFF only when the machine has clearly been switched off rather
# than briefly dipped.
self._delay_wait_true_off_seconds: float = 0.0
# _starting_paused_off_since anchors the first below-stop reading while
# a user-paused STARTING state is held (issue #306). Elapsed time is
# measured from this anchor, not accumulated per-dt, so a single large
# (but sub-outage) interval cannot prematurely credit minutes of quiet time.
self._starting_paused_off_since: datetime | None = None
# _delay_wait_high_start anchors the first high-power reading
# observed inside DELAY_WAIT. We only transition to STARTING
# when the high-power streak has lasted at least
# start_duration_threshold real seconds - measured between two
# consecutive high readings, not from the dt to the previous
# (low) reading. This prevents a single isolated spike from
# tripping STARTING just because the sampling interval is long.
self._delay_wait_high_start: datetime | None = None
self._delay_wait_high_power: float | None = None
# Preserve a delayed-start candidate across a false STARTING probe
# that drops back into the standby band without the machine truly
# turning off.
self._preserve_delay_band_on_off: bool = False
@property
def _dynamic_pause_threshold(self) -> float:
"""Calculate dynamic pause threshold based on sampling cadence."""
# User requirement: T_pause >= 3 * p95_update_interval
# Default 15s or 3 * p95
return max(15.0, 3.0 * self._p95_dt)
@property
def _dynamic_end_threshold(self) -> float:
"""Calculate dynamic end candidate threshold."""
# Keep this generic for pause->ending transitions across all device types.
base = 3.0 * self._p95_dt
# Ensure end threshold is at least 15s greater than pause threshold
return max(base, self._dynamic_pause_threshold + 15.0)
def _update_cadence(self, dt: float) -> None:
"""Update rolling cadence statistics."""
if dt <= 0.1:
return
self._recent_dts.append(dt)
if len(self._recent_dts) > 20:
self._recent_dts.pop(0)
# Calculate p95 if enough samples
if len(self._recent_dts) >= 5:
self._p95_dt = float(np.percentile(self._recent_dts, 95))
else:
self._p95_dt = max(dt, 1.0)
def _try_profile_match(self, timestamp: datetime, force: bool = False) -> None:
"""Attempt to invoke the profile matcher if conditions are met.
Args:
timestamp: Current timestamp.
force: If True, run match immediately regardless of interval.
"""
if not self._profile_matcher:
return
if not self._power_readings:
return
# Terminal-tail match freeze (dishwashers): once we are in ENDING with a
# profile already matched and power has been sustained-quiet, the active
# cycle is over - only the passive drain/dry tail remains. Re-matching on
# the growing idle tail inflates the observed duration and drifts the
# Stage-4 duration-agreement toward a LONGER near-duplicate profile,
# flipping the label and stalling smart-termination on the ambiguity gate.
# Keep the active-phase match instead. Self-correcting: a real resume sends
# a high reading that leaves ENDING, so this guard stops applying.
if (
self._state == STATE_ENDING
and self._config.device_type == "dishwasher"
and self._matched_profile
and self._time_below_threshold >= DISHWASHER_MATCH_FREEZE_QUIET_SECONDS
):
return
# Terminal-tail match freeze (anti-crease, #296): once a washer/dryer with
# anti-wrinkle enabled is past its expected duration and has settled into
# the low-power tumble tail, re-matching on the growing flat tail drifts the
# label toward a LONGER near-duplicate (its expected duration grows), which
# pushes the anti-crease finalize gate out and breaks Smart Termination -
# the field failure that merges back-to-back washes. Keep the good
# pre-tail match instead. Self-correcting: a new wash's heating burst above
# anti_wrinkle_max_power leaves the tail regime, so this stops applying and
# matching re-arms for the next cycle.
if self._matched_profile and self._in_anticrease_freeze(timestamp):
return
# Rate limiting
if not force and self._last_match_time:
elapsed = (timestamp - self._last_match_time).total_seconds()
if elapsed < self._config.match_interval:
return
self._last_match_time = timestamp
# Call the matcher
try:
result = self._profile_matcher(self._power_readings)
# If synchronous result returned, process it.
# If None returned (async offload), the matcher is responsible for
# calling update_match later.
if result:
self.update_match(result)
except Exception as e: # pylint: disable=broad-exception-caught
self._logger.debug("Profile match failed: %s", e)
# Maximum reasonable cycle duration accepted by the detector. Anything
# longer is rejected as corrupted data and replaced with the
# _SANITIZE_INVALID_SENTINEL so downstream gates fall through to the
# unmatched / no-expected-duration path.
_SANITIZE_MAX_EXPECTED_DURATION = 6 * 3600.0 # 6 hours
_SANITIZE_INVALID_SENTINEL = 0.0 # 0 == "no valid expected_duration"
def _sanitize_expected_duration(
self, raw: Any, *, source: str = "update_match"
) -> float:
"""Coerce ``raw`` into a finite float in (0, 6h] or return 0.0.
The class invariant is that ``self._expected_duration`` is either a
finite, strictly positive float ≤ 6 hours, or 0.0 meaning "no valid
expected duration". Every code path that assigns ``_expected_duration``
(live profile-match callbacks AND restored snapshots) routes through
this helper so the gates in STATE_ENDING and ``_should_defer_finish``
can trust the value without re-validating.
Emits a DEBUG log line distinguishing the rejection reason - the
``<= 0`` and ``> 6h`` markers are part of issue #197's regression
contract and tests assert on them.
"""
try:
value = float(raw)
except (TypeError, ValueError):
self._logger.debug(
"%s: invalid raw_expected_duration %r, defaulting to 0.0",
source, raw,
)
return self._SANITIZE_INVALID_SENTINEL
if not math.isfinite(value):
self._logger.debug(
"%s: invalid raw_expected_duration %r, defaulting to 0.0",
source, raw,
)
return self._SANITIZE_INVALID_SENTINEL
if value <= 0:
self._logger.debug(
"%s: invalid raw_expected_duration %r (<= 0), defaulting to 0.0",
source, raw,
)
return self._SANITIZE_INVALID_SENTINEL
if value > self._SANITIZE_MAX_EXPECTED_DURATION:
self._logger.debug(
"%s: invalid raw_expected_duration %r (> 6h), defaulting to 0.0",
source, raw,
)
return self._SANITIZE_INVALID_SENTINEL
return value
def update_match(self, result: tuple[Any, ...] | list[Any] | Any) -> None: # type: ignore[misc]
"""Process a match result (synchronously).
Can be called by the matcher callback directly or asynchronously.
"""
# Terminal-tail match freeze (anti-crease, #296). This is the single sink
# for ALL match updates - the detector's own _try_profile_match AND the
# manager's async 5-min matcher (manager.py calls update_match directly).
# Once a washer/dryer with anti-wrinkle enabled is past its expected
# duration and has settled into the low-power tumble tail, re-matching on
# the growing flat tail drifts the label toward a LONGER near-duplicate (or
# flips it ambiguous), which pushes out expected_duration and would block
# the anti-crease finalize - the field failure that merges back-to-back
# washes. Keep the good pre-tail match instead. Self-correcting: a new
# wash's heating burst above anti_wrinkle_max_power leaves the tail regime,
# so this stops applying and matching re-arms for the next cycle.
if (
self._matched_profile
and self._power_readings
and self._in_anticrease_freeze(self._power_readings[-1][0])
):
return
# Unpack 5 elements (or 4 for backward compatibility if needed, but wrapper is updated)
# wrapper returns (name, confidence, duration, phase, is_mismatch)
# Or MatchResult object if refactored, but currently wrapper returns tuple.
is_match_mismatch = False
match_name: str | None = None
phase_name: str | None = None
confidence: float = 0.0
expected_duration: float = 0.0
ambiguous: bool = False
if isinstance(result, (list, tuple)): # type: ignore[misc]
result_seq = cast(tuple[Any, ...] | list[Any], result)
# Optional 6th element: whether the live match is ambiguous
# (top-1 vs top-2 within MATCH_AMBIGUITY_MARGIN). Used to gate the
# predictive Smart Termination below.
if len(result_seq) >= 6:
ambiguous = bool(result_seq[5])
if len(result_seq) >= 5:
(
raw_name,
raw_confidence,
raw_expected_duration,
raw_phase_name,
raw_mismatch,
) = result_seq[:5]
match_name = str(raw_name) if raw_name is not None else None
try:
confidence = float(raw_confidence)
if not math.isfinite(confidence):
confidence = 0.0
self._logger.debug("update_match: invalid raw_confidence %r, defaulting to 0.0", raw_confidence)
except (TypeError, ValueError):
confidence = 0.0
self._logger.debug("update_match: invalid raw_confidence %r, defaulting to 0.0", raw_confidence)
expected_duration = self._sanitize_expected_duration(
raw_expected_duration, source="update_match"
)
phase_name = str(raw_phase_name) if raw_phase_name is not None else None
is_match_mismatch = raw_mismatch if isinstance(raw_mismatch, bool) else bool(raw_mismatch)
else:
# Fallback for old signature
if len(result_seq) >= 4:
(
raw_name,
raw_confidence,
raw_expected_duration,
raw_phase_name,
) = result_seq[:4]
match_name = str(raw_name) if raw_name is not None else None
try:
confidence = float(raw_confidence)
if not math.isfinite(confidence):
confidence = 0.0
self._logger.debug("update_match: invalid raw_confidence %r, defaulting to 0.0", raw_confidence)
except (TypeError, ValueError):
confidence = 0.0
self._logger.debug("update_match: invalid raw_confidence %r, defaulting to 0.0", raw_confidence)
expected_duration = self._sanitize_expected_duration(
raw_expected_duration, source="update_match"
)
phase_name = (
str(raw_phase_name) if raw_phase_name is not None else None
)
is_match_mismatch = False
# Store confidence + ambiguity for Smart Termination checks
self._last_match_confidence = confidence or 0.0
self._match_ambiguous = ambiguous
self._match_prefix_ambiguous = bool(result_seq[6]) if len(result_seq) >= 7 else False
else:
# Assume MatchResult object or similar (future proofing)
# But for now wrapper returns tuple
return
if is_match_mismatch and self._matched_profile:
# Confident non-match - revert to detecting if previously matched
self._matched_profile = None
self._match_ambiguous = False
self._match_prefix_ambiguous = False
elif match_name:
# If sanitization rejected the expected_duration, treat the match
# as invalid: setting _matched_profile while _expected_duration is
# the 0.0 sentinel would let Smart Termination fire on the
# `current_duration >= 0` always-true comparison. Drop both so
# the cycle stays in detecting/unmatched mode.
if expected_duration == self._SANITIZE_INVALID_SENTINEL:
self._logger.debug(
"update_match: match %r ignored - expected_duration "
"sanitized to invalid sentinel; treating as unmatched",
match_name,
)
self._matched_profile = None
self._expected_duration = self._SANITIZE_INVALID_SENTINEL
else:
self._matched_profile = match_name
# Sub-state can be set from phase_name if available
if phase_name:
self._sub_state = phase_name
# Wrapper provides it
self._expected_duration = expected_duration
def set_verified_pause(self, verified: bool) -> None:
"""Set or clear the verified pause flag."""
self._verified_pause = verified
def reset(self, target_state: str = STATE_OFF) -> None:
"""Force reset the detector state to target state."""
self._transition_to(target_state, dt_util.now())
self._power_readings = []
self._current_cycle_start = None
self._last_active_time = None
self._cycle_max_power = 0.0
self._ma_buffer = []
self._energy_since_idle_wh = 0.0
self._time_above_threshold = 0.0
# Only reset time_below_threshold if not transitioning to ANTI_WRINKLE
# (ANTI_WRINKLE needs to track idle time to determine true-off)
if target_state != STATE_ANTI_WRINKLE:
self._time_below_threshold = 0.0
self._last_match_time = None
self._matched_profile = None
# Clear stale match state so the next cycle starts with clean defaults.
# _expected_duration left at 0 tells the dishwasher end-spike gate that
# no profile is matched yet; stale non-zero would mis-gate the spike check.
self._expected_duration = 0.0
self._last_match_confidence = 0.0
self._match_ambiguous = False
self._match_prefix_ambiguous = False
self._ignore_power_until_idle = False # Reset lockout
self._lockout_high_seconds = 0.0
# Clear the verified-pause flag so it can't leak into the next cycle (B6):
# a stale True would make an early low-power dip look like a verified pause
# before the first live match of the new cycle runs.
self._verified_pause = False
self._anti_wrinkle_candidate_start = None
self._anti_wrinkle_candidate_peak = 0.0
self._anti_wrinkle_candidate_start_power = 0.0
# Reset idle time tracker for anti-wrinkle
self._anti_wrinkle_idle_time = 0.0
# Reset delayed-start tracking
self._delay_band_seconds = 0.0
self._delay_band_peak = 0.0
self._delay_wait_true_off_seconds = 0.0
self._starting_paused_off_since = None
self._delay_wait_high_start = None
@property
def state(self) -> str:
"""Return current state."""
return self._state
@property
def sub_state(self) -> str | None:
"""Return current sub-state."""
return self._sub_state
@property
def config(self) -> CycleDetectorConfig:
"""Return current configuration."""
return self._config
@property
def matched_profile(self) -> str | None:
"""Return the name of the matched profile, if any."""
return self._matched_profile
@property
def current_cycle_start(self) -> datetime | None:
"""Return the start timestamp of the current cycle."""
return self._current_cycle_start
@property
def samples_recorded(self) -> int:
"""Return the number of power samples recorded in current cycle."""
return len(self._power_readings)
@property
def expected_duration_seconds(self) -> float:
"""Return the expected duration of the current cycle in seconds."""
return self._expected_duration
def process_reading(self, power: float, timestamp: datetime) -> None:
"""Process a new power reading using robust dt-aware logic."""
# Calculate dt (needed by the stop lockout below and the state machine).
dt = 0.0
if self._last_process_time:
dt = (timestamp - self._last_process_time).total_seconds()
# Sanity check for negative dt
if dt < 0:
self._last_process_time = timestamp
return
# Manual Stop Lockout:
# If user/external stop forced an end, ignore the machine's spin-down so
# it is not logged as a new cycle. The lockout clears the moment power
# drops to idle. As a safety net, if power instead stays high far longer
# than any plausible spin-down, treat it as a genuinely new back-to-back
# load and release the lockout so the cycle is detected immediately
# rather than pinned until the progress-reset window expires (#267).
if self._ignore_power_until_idle:
if power < self._config.start_threshold_w:
self._ignore_power_until_idle = False
self._lockout_high_seconds = 0.0
self._logger.debug(
"Power dropped below start threshold. Manual stop lockout cleared."
)
else:
self._lockout_high_seconds += dt
if self._lockout_high_seconds < STOP_LOCKOUT_RELEASE_SECONDS:
# Still within the spin-down window - ignore reading.
self._last_process_time = timestamp
return
self._ignore_power_until_idle = False
self._lockout_high_seconds = 0.0
self._logger.info(
"Manual stop lockout released after sustained power "
"(>= %.1fs at/above start threshold): treating as a new "
"cycle (#267).",
STOP_LOCKOUT_RELEASE_SECONDS,
)
# Fall through: the state machine will start a new cycle.
self._update_cadence(dt)
self._last_process_time = timestamp
# 1. Smoothing (Legacy buffer for debug/display, logic uses raw + time accumulators)
self._ma_buffer.append(power)
if len(self._ma_buffer) > self._config.smoothing_window:
self._ma_buffer.pop(0)
# 2. Accumulators Update
# Hysteresis Logic
if self._state in (STATE_OFF, STATE_DELAY_WAIT, STATE_STARTING, STATE_UNKNOWN):
threshold = self._config.start_threshold_w
else:
threshold = self._config.stop_threshold_w
is_high = power >= threshold
if is_high:
self._time_above_threshold += dt
self._time_below_threshold = 0.0
# Energy integration (trapezoidal approx for this single step)
# prev_p = self._last_power if self._last_power is not None else power
# step_wh = ((power + prev_p) / 2.0) * (dt / 3600.0)
# Simplified: just P * dt for short steps is fine,
# or call integrate_wh on buffer if needed.
# Let's use simple rect/trapz here for running sum
step_wh = power * (dt / 3600.0)
self._energy_since_idle_wh += step_wh
self._last_active_time = timestamp
else:
self._time_below_threshold += dt
self._time_above_threshold = 0.0
self._time_in_state += dt
self._last_power = power
anti_wrinkle_active = (
self._config.anti_wrinkle_enabled
and self._config.device_type in (
DEVICE_TYPE_WASHING_MACHINE,
DEVICE_TYPE_DRYER,
DEVICE_TYPE_WASHER_DRYER,
)
)
# 3. State Machine
if self._state in (
STATE_OFF,
STATE_FINISHED,
STATE_INTERRUPTED,
STATE_FORCE_STOPPED,
STATE_ANTI_WRINKLE,
):
started_from_anti_wrinkle = False
if anti_wrinkle_active and self._state == STATE_ANTI_WRINKLE and is_high:
if self._anti_wrinkle_candidate_start is None:
self._anti_wrinkle_candidate_start = timestamp
self._anti_wrinkle_candidate_peak = power
self._anti_wrinkle_candidate_start_power = power
else:
self._anti_wrinkle_candidate_peak = max(
self._anti_wrinkle_candidate_peak, power
)
candidate_duration = (
timestamp - self._anti_wrinkle_candidate_start
).total_seconds()
exceeds = (
self._anti_wrinkle_candidate_peak
> self._config.anti_wrinkle_max_power
or power > self._config.anti_wrinkle_max_power
or candidate_duration > self._config.anti_wrinkle_max_duration
)
if exceeds:
candidate_start = self._anti_wrinkle_candidate_start
candidate_peak = self._anti_wrinkle_candidate_peak
candidate_start_power = self._anti_wrinkle_candidate_start_power
self._anti_wrinkle_candidate_start = None
self._anti_wrinkle_candidate_peak = 0.0
self._anti_wrinkle_candidate_start_power = 0.0
self._transition_to(STATE_STARTING, timestamp)
started_from_anti_wrinkle = True
self._current_cycle_start = candidate_start or timestamp
# Preserve the anti-wrinkle candidate window instead of dropping ramp-up samples.
if candidate_start and candidate_start < timestamp:
start_power = candidate_start_power if candidate_start_power > 0 else power
self._power_readings = [(candidate_start, start_power), (timestamp, power)]
interval_s = (timestamp - candidate_start).total_seconds()
avg_power = (start_power + power) / 2.0
self._energy_since_idle_wh = max(0.0, avg_power * (interval_s / 3600.0))
else:
self._power_readings = [(timestamp, power)]
self._energy_since_idle_wh = power * (dt / 3600.0) if dt > 0 else 0.0
self._cycle_max_power = max(candidate_peak, power)
elif self._state != STATE_ANTI_WRINKLE:
self._anti_wrinkle_candidate_start = None
self._anti_wrinkle_candidate_peak = 0.0
self._anti_wrinkle_candidate_start_power = 0.0
if self._state == STATE_ANTI_WRINKLE:
# Track time in idle (below exit_power threshold)
effective_exit = max(self._config.anti_wrinkle_exit_power, self._config.stop_threshold_w)
if power < effective_exit:
# Low-power gap invalidates any burst candidate collected while in anti-wrinkle.
self._anti_wrinkle_candidate_start = None
self._anti_wrinkle_candidate_peak = 0.0
self._anti_wrinkle_candidate_start_power = 0.0
self._anti_wrinkle_idle_time += dt
anti_wrinkle_end_threshold = max(
self._dynamic_end_threshold,
self._anti_wrinkle_idle_timeout,
)
if self._anti_wrinkle_idle_time >= anti_wrinkle_end_threshold:
self._transition_to(STATE_OFF, timestamp)
return
else:
# Reset idle timer when power rises (burst detected)
self._anti_wrinkle_idle_time = 0.0
# Exit conditions:
# 1. Idle duration exceeded (handled above), OR
# 2. Safety timeout (2 hours in anti-wrinkle), OR
# 3. External trigger (user_stop, external triggers handled by manager)
if (
self._state_enter_time
and (timestamp - self._state_enter_time).total_seconds() > 7200
):
# Safety timeout: 2 hours in anti-wrinkle
self._transition_to(STATE_OFF, timestamp)
return
# Delayed-start "standby band" detection (only from STATE_OFF).
#
# A machine in delayed-start mode sits in a power band between
# the off-noise floor (stop_threshold_w) and the cycle-start
# threshold (start_threshold_w) - display, electronics, the
# occasional anti-damp tumble - for minutes to hours. We
# track anchored elapsed time while power is in that band; once
# it crosses delay_confirm_seconds we transition to DELAY_WAIT.
#
# Brief high-power excursions (menu navigation, button presses)
# don't break the candidate: they fall through to the normal
# start logic below, and unless they sustain for
# start_duration_threshold they get aborted as a false start
# and we re-enter the band on the next reading. Excursions
# below stop_threshold_w (machine momentarily idle on the noise
# floor) DO reset the candidate, because that's the same
# signal we use to define "off".
if (
self._config.delay_detect_enabled
and self._state == STATE_OFF
and not started_from_anti_wrinkle
and self._config.stop_threshold_w < self._config.start_threshold_w
):
in_band = (
self._config.stop_threshold_w
<= power
< self._config.start_threshold_w
)
if in_band:
if self._delay_band_start is None:
self._delay_band_start = timestamp
self._delay_band_seconds = 0.0
else:
self._delay_band_seconds = (
timestamp - self._delay_band_start
).total_seconds()
self._delay_band_peak = max(self._delay_band_peak, power)
if self._delay_band_seconds >= self._config.delay_confirm_seconds:
self._logger.info(
"Delayed start detected: standby band held for %.0fs "
"(peak %.1fW, current %.1fW) → DELAY_WAIT",
self._delay_band_seconds,
self._delay_band_peak,
power,
)
self._transition_to(STATE_DELAY_WAIT, timestamp)
return
# Stay in OFF while we accumulate evidence - do not
# fall through to the high-power start logic, the
# reading is below threshold by definition.
return
elif power < self._config.stop_threshold_w:
# Machine genuinely idle: forget any band history.
self._delay_band_start = None
self._delay_band_seconds = 0.0
self._delay_band_peak = 0.0
self._preserve_delay_band_on_off = False
# power >= start_threshold_w: fall through to the normal
# start path below. If it turns out to be a brief peak,
# STATE_STARTING will abort it as a false start and we'll
# re-enter the band check on the next sample without
# losing accumulated time (we don't reset on a high
# excursion - most users' "menu navigation" peaks last
# less than a sample interval anyway).
if is_high and not started_from_anti_wrinkle:
# Transition to STARTING
self._preserve_delay_band_on_off = self._delay_band_start is not None
self._transition_to(STATE_STARTING, timestamp)
self._current_cycle_start = timestamp
self._power_readings = [(timestamp, power)]
self._energy_since_idle_wh = power * (dt / 3600.0) if dt > 0 else 0.0
self._cycle_max_power = power
# NOTE: terminal-state expiry (Finished/Interrupted/Force-Stopped -> Off)
# is owned solely by the manager (WashDataManager._handle_state_expiry),
# which has a wall-clock timer that also fires when a change-only power
# sensor stops reporting, plus the opt-in power-based Off (issue #284).
# The detector used to auto-expire here after a hardcoded 30 min, but that
# duplicated the manager timer (a weaker, per-reading subset) and left the
# manager's bookkeeping (progress, clean overlay, notifications) dangling.
# ANTI_WRINKLE -> Off is handled by its own idle/timeout logic above.
elif self._state == STATE_DELAY_WAIT:
if power >= self._config.start_threshold_w:
# Power is in cycle-start territory. Require at least
# two consecutive high readings spanning
# start_duration_threshold real seconds before committing
# to STARTING, so a single isolated spike (a heavy menu
# interaction, an anti-damp pulse briefly crossing the
# threshold) doesn't false-trigger. We anchor on the
# FIRST high reading instead of accumulating dt, because
# dt to the previous (low) reading is unrelated to how
# long the high power has actually persisted.
self._delay_wait_true_off_seconds = 0.0
if self._delay_wait_high_start is None:
self._delay_wait_high_start = timestamp
self._delay_wait_high_power = power
else:
elapsed_high = (
timestamp - self._delay_wait_high_start
).total_seconds()
if elapsed_high >= self._config.start_duration_threshold:
self._logger.info(
"Delayed start: cycle starting (power %.1fW sustained ≥ %.1fW for %.0fs)",
power,
self._config.start_threshold_w,
elapsed_high,
)
self._transition_to(STATE_STARTING, timestamp)
start_timestamp = self._delay_wait_high_start or timestamp
start_power = self._delay_wait_high_power or power
self._current_cycle_start = start_timestamp
self._power_readings = [(start_timestamp, start_power)]
elapsed_from_anchor = (timestamp - start_timestamp).total_seconds()
self._energy_since_idle_wh = (
start_power * (elapsed_from_anchor / 3600.0)
if elapsed_from_anchor > 0
else 0.0
)
if timestamp != start_timestamp:
self._power_readings.append((timestamp, power))
self._cycle_max_power = max(start_power, power)
else:
# Power dropped back below start threshold - clear the
# high-power streak anchor so the next high reading
# starts a fresh confirmation window.
self._delay_wait_high_start = None
self._delay_wait_high_power = None
if power < self._config.stop_threshold_w:
# Power near zero: machine genuinely turned off, not
# just waiting.
self._delay_wait_true_off_seconds += dt
if self._delay_wait_true_off_seconds >= 30.0:
self._logger.info(
"Delayed start cancelled: power dropped to off (%.1fW) for %.0fs",
power,
self._delay_wait_true_off_seconds,
)
self._transition_to(STATE_OFF, timestamp)
return
else:
self._delay_wait_true_off_seconds = 0.0
# Safety timeout
if (
self._state_enter_time
and (timestamp - self._state_enter_time).total_seconds()
>= self._config.delay_timeout_seconds
):
self._logger.info(
"Delayed start timeout after %.0fh → OFF",
self._config.delay_timeout_seconds / 3600.0,
)
self._transition_to(STATE_OFF, timestamp)
elif self._state == STATE_STARTING:
self._power_readings.append((timestamp, power))
self._cycle_max_power = max(self._cycle_max_power, power)
if is_high:
# Power back up - clear any accumulated "true off" hold time.
self._starting_paused_off_since = None
if self._time_above_threshold >= self._config.start_duration_threshold:
if self._energy_since_idle_wh >= self._config.start_energy_threshold:
self._transition_to(STATE_RUNNING, timestamp)
# Abort if power drops below threshold before confirmation.
# Skip the abort when the user has explicitly paused the cycle
# (issue #306): a user pause sets verified_pause=True, which signals
# that the low power is intentional, not a false start.
if not is_high and self._time_below_threshold > 1.0: # 1s grace period
if getattr(self, "_verified_pause", False):
# User pause holds; wait for Resume Cycle (issue #306). But a
# genuinely paused appliance keeps standby power above the stop
# threshold - sustained power *below* it means the machine was
# switched off, so fall back to OFF rather than pinning STARTING
# forever.
if power < self._config.stop_threshold_w:
# An outage-sized gap since the last reading is NOT observed
# quiet (the machine may still be paused, we just lost
# telemetry): reset anchor so only genuinely-sampled
# sustained-off time can cancel a paused STARTING.
if dt > self._outage_threshold_s():
self._starting_paused_off_since = None
elif self._starting_paused_off_since is None:
# First below-stop reading: anchor the timestamp.
# Don't credit the preceding dt interval — we only
# know the device is off *now*, not how long before
# this sample it went quiet.
self._starting_paused_off_since = timestamp
observed_off_s = (
(timestamp - self._starting_paused_off_since).total_seconds()
if self._starting_paused_off_since is not None
else 0.0
)
if observed_off_s >= STARTING_PAUSED_TRUE_OFF_TIMEOUT_SECONDS:
self._logger.info(
"Paused STARTING cancelled: power off (%.1fW) for "
"%.0fs → OFF",
power,
observed_off_s,
)
self._transition_to(STATE_OFF, timestamp)
return
else:
# Power recovered — clear the off anchor.
self._starting_paused_off_since = None
else:
# False start
self._logger.debug(
"False start detected: power dropped after %.2fs",
self._time_above_threshold,
)
# Do NOT reset _delay_band_* here — _transition_to(STATE_OFF) will
# preserve the band via _preserve_delay_band_on_off if it was set
# at STARTING entry (line 838), so a brief high-power peak (menu
# navigation) doesn't restart the delayed-start accumulation from zero.
self._transition_to(STATE_OFF, timestamp)
elif self._state == STATE_RUNNING:
self._power_readings.append((timestamp, power))
self._cycle_max_power = max(self._cycle_max_power, power)
# Anti-crease finalize (#296): a matched cycle past its expected
# duration that has settled into the low-power tumble tail is done -
# finalize into anti-wrinkle now instead of letting the periodic
# bursts keep reviving RUNNING until a second wash merges in.
if self._maybe_finalize_anticrease_tail(timestamp):
return
# Use dynamic threshold
thresh = self._dynamic_pause_threshold
if self._time_below_threshold >= thresh:
self._try_profile_match(timestamp, force=True) # Refine match on pause
self._transition_to(STATE_PAUSED, timestamp)
# Periodic profile matching
self._try_profile_match(timestamp)
# Standby-band stuck finalize (#296): an appliance holding a flat
# low standby draw ABOVE stop_threshold never accumulates
# _time_below_threshold, so it never reaches PAUSED/ENDING. Detect
# the plateau and finalize as a normal completion (so anti-wrinkle
# still engages). Cheaply gated on being well past expected before
# the window scan runs.
if self._is_standby_band_stuck(timestamp):
start_time = self._current_cycle_start or timestamp
current_duration = (timestamp - start_time).total_seconds()
# The plateau sits ABOVE stop_threshold, so it keeps advancing
# _last_active_time and the default keep_tail=False trim would NOT
# remove it - inflating the stored duration/energy with minutes of
# standby. Snap the end back to the last real activity (the last
# reading above the plateau ceiling) and drop the trailing plateau.
level_ceiling = float(self._cycle_max_power) * STANDBY_BAND_MAX_FRACTION
plateau_start_idx = None
for i in range(len(self._power_readings) - 1, -1, -1):
if float(self._power_readings[i][1]) > level_ceiling:
plateau_start_idx = i
break
if (
plateau_start_idx is not None
and plateau_start_idx < len(self._power_readings) - 1
):
self._power_readings = self._power_readings[: plateau_start_idx + 1]
self._last_active_time = self._power_readings[-1][0]
current_duration = (
self._last_active_time - start_time
).total_seconds()
self._logger.info(
"Standby-band finalize: flat plateau ~%.1fW (peak %.0fW) held "
"past expected %.0fs — appliance finished but holds a standby "
"draw above stop_threshold; finalizing (plateau trimmed, "
"duration %.0fs).",
power,
self._cycle_max_power,
self._expected_duration,
current_duration,
)
self._finish_cycle(
timestamp,
status="completed",
termination_reason=TerminationReason.TIMEOUT,
keep_tail=False,
)
return
# Max duration safety
if (
self._current_cycle_start
and (timestamp - self._current_cycle_start).total_seconds() > 28800
): # 8h safety
self._finish_cycle(
timestamp,
status="force_stopped",
termination_reason=TerminationReason.FORCE_STOPPED,
)
elif self._state == STATE_PAUSED:
self._power_readings.append((timestamp, power))
# Anti-crease finalize (#296) - see the RUNNING branch.
if self._maybe_finalize_anticrease_tail(timestamp):
return
if is_high:
# Resume to RUNNING
self._transition_to(STATE_RUNNING, timestamp)
else:
# Periodic profile matching during pause
self._try_profile_match(timestamp)
thresh = self._dynamic_end_threshold
if self._time_below_threshold >= thresh:
self._transition_to(STATE_ENDING, timestamp)
elif self._state == STATE_ENDING:
self._power_readings.append((timestamp, power))
# Hard cap: ENDING must not run longer than RUNNING's 8 h safety limit.
# Without this a standby baseline can hold the state open indefinitely.
if (
self._current_cycle_start
and (timestamp - self._current_cycle_start).total_seconds() > 28800
):
self._finish_cycle(
timestamp,
status="force_stopped",
termination_reason=TerminationReason.FORCE_STOPPED,
)
return
# Anti-crease finalize (#296) - see the RUNNING branch. Fires ahead of
# the is_high end-spike handling so a sub-max_power tail burst finalizes
# into anti-wrinkle instead of reviving RUNNING.
if self._maybe_finalize_anticrease_tail(timestamp):
return
if is_high:
start_time = self._current_cycle_start or timestamp
current_duration = (timestamp - start_time).total_seconds()
is_dishwasher = self._config.device_type == "dishwasher"
# Issue #43: only treat this as a *terminal* end spike (which then
# pre-arms Smart Termination) when it occurs near the end of the
# expected cycle. Mid-cycle spikes - e.g. the dishwasher
# wash→drying drain wind-down at ~50% of expected duration - must
# not arm smart termination, otherwise the cycle finishes at 99%
# of expected *before* the real end-of-cycle pump-out, and that
# pump-out is then misread as a brand-new cycle. Without a
# matched profile (expected==0) the gating is bypassed so the
# legacy "any spike counts" behaviour is preserved for unmatched
# cycles (relied on by the dishwasher unmatched-cap path).
if (
self._expected_duration <= 0
or current_duration
>= self._expected_duration * DISHWASHER_END_SPIKE_MIN_PROGRESS
):
self._end_spike_seen = True
self._end_spike_duration = current_duration
self._logger.debug(
"End spike detected (power high in ENDING state, "
"%.0fs/%.0fs)",
current_duration,
self._expected_duration,
)
else:
self._logger.debug(
"Mid-cycle spike in ENDING ignored for end-spike "
"tracking (%.0fs < %.0f%% of expected %.0fs)",
current_duration,
DISHWASHER_END_SPIKE_MIN_PROGRESS * 100,
self._expected_duration,
)
# Sanity check: if expected_duration is unreasonable (>6 hours), use fallback
max_reasonable = 21600.0 # 6 hours
effective_expected = self._expected_duration
if effective_expected <= 0 or effective_expected > max_reasonable:
# Fallback: use current duration + buffer if we've run > 3 hours
# (Assumes any cycle over 3 hours running is near completion when in ENDING)
if current_duration > 10800: # 3 hours
effective_expected = current_duration * 0.99 # Always past threshold
self._logger.debug(
"End spike check using fallback: expected_duration=%ds is unreasonable, "
"using current_duration=%ds as reference",
int(self._expected_duration), int(current_duration)
)
past_expected = (
effective_expected > 0
and current_duration >= (effective_expected * 0.98)
)
# If ENDING has already lasted long enough, treat any power burst as
# terminal (applies to all device types). Dishwashers additionally check
# proximity to the expected duration.
long_ending_tail = self._time_in_state >= 120.0
terminal_spike = long_ending_tail
if is_dishwasher:
near_expected = (
effective_expected > 0
and current_duration >= (effective_expected * 0.90)
)
terminal_spike = near_expected or long_ending_tail
if terminal_spike:
self._logger.debug(
"End spike kept in ENDING (duration %.0fs/%.0fs, time_in_ending %.0fs)",
current_duration,
effective_expected,
self._time_in_state,
)
return
if past_expected:
self._logger.debug(
"End spike ignored for state transition (past expected duration %.0fs/%.0fs)",
current_duration, effective_expected
)
# Stay in ENDING, the spike is recorded but doesn't resume cycle
else:
# Resume -> RUNNING (spike is genuine mid-cycle activity)
self._transition_to(STATE_RUNNING, timestamp)
else:
# Periodic profile matching during ending
self._try_profile_match(timestamp)
# --- SMART TERMINATION CHECK ---
# If we have a confident profile match and duration meets expectations,
# we terminate early (after appropriate debounce), ignoring long arbitrary timeouts.
if self._matched_profile:
start_time = self._current_cycle_start or timestamp
current_duration = (timestamp - start_time).total_seconds()
# --- ROBUSTNESS UPGRADE ---
# 1. Require higher duration ratio for Smart path
# 2. Require debounce to be measured FROM entry into ENDING state
if self._config.device_type == "dishwasher":
# If the most-recent in-ENDING spike occurred at ≥90% of
# expected, it is the terminal pump-out, not a mid-cycle
# rinse drain. Once that pump-out is confirmed, we don't
# need to wait for 99% of the rolling avg — individual
# cycles can be up to ~7% shorter than avg_duration and
# still terminate cleanly. Keeping the 0.99 gate for
# spikes at <90% prevents premature closes during the
# passive Dry phase that follows the pre-final-rinse drain.
_esp_dur = getattr(self, "_end_spike_duration", 0.0)
if (
getattr(self, "_end_spike_seen", False)
and self._expected_duration > 0
and _esp_dur >= self._expected_duration * 0.90
):
smart_ratio = 0.90 # pump-out confirmed near end
else:
smart_ratio = 0.99 # conservative: wait for expected duration
else:
smart_ratio = 0.98
is_confident_match = (
getattr(self, "_last_match_confidence", 0.0) >= 0.4
)
# Gate the predictive end on match certainty.
# _match_ambiguous: top-1 vs top-2 score gap is too small to
# trust the matched profile's expected duration — fall through
# to the power-based fallback timeout instead.
# _match_prefix_ambiguous: a longer candidate with a similar
# shape score exists in the pool. The current trace may be a
# prefix of that longer program (e.g. Quick 46 min matched
# while the machine is actually running Normal 88 min and
# happens to be in a mid-cycle soak dip at the 46-min mark).
# Blocking Smart Termination here means a true Quick cycle
# waits for the fallback timeout instead of getting an early
# close — an acceptable trade-off against the alternative of
# splitting a Normal wash into two separate cycle records.
if (
current_duration >= (self._expected_duration * smart_ratio)
and is_confident_match
and not self._match_ambiguous
and not self._match_prefix_ambiguous
):
# Dynamic confirmation window
if self._config.device_type == "dishwasher":
# Fixed - NOT off_delay-derived. off_delay is sized to
# bridge the long drying "pause", but must not delay the
# end; see DISHWASHER_SMART_TERMINATION_DEBOUNCE_SECONDS.
smart_debounce = DISHWASHER_SMART_TERMINATION_DEBOUNCE_SECONDS
elif self._config.device_type in (
DEVICE_TYPE_WASHING_MACHINE,
DEVICE_TYPE_WASHER_DRYER,
):
# Washing machines and washer-dryers have soak and
# rinse gaps that can dip for several minutes between
# programme phases. Require quiet time equal to half
# the soak-bridging min_off_gap before committing
# Smart Termination, so a near-duplicate profile
# doesn't cut a long cycle short during a mid-cycle
# power trough. Bounded above so a large suggested /
# hand-set min_off_gap can't inflate the quiet-time
# requirement and starve end-detection (see
# WASHER_SMART_TERMINATION_DEBOUNCE_MAX_SECONDS).
smart_debounce = min(
WASHER_SMART_TERMINATION_DEBOUNCE_MAX_SECONDS,
max(180.0, self._config.min_off_gap * 0.5),
)
else:
smart_debounce = 120.0
if self._time_in_state >= smart_debounce:
# --- END SPIKE WAIT PERIOD (Dishwashers) ---
# Dishwashers should see the real end-of-cycle
# pump-out (which arms _end_spike_seen via the 85%
# progress gate) before Smart Termination fires -
# otherwise the pump-out arrives AFTER the cycle
# has already closed and registers as a brand-new
# "ghost" cycle. User reports (issue #43) showed
# the original 5-min past_wait_period escape hatch
# closing the cycle ~4 min before the real pump-out
# at ~99.5% of expected. Widen the escape hatch
# substantially (DISHWASHER_END_SPIKE_WAIT_SECONDS,
# currently 30 min past expected) so it cannot
# short-circuit a pump-out that fires within a
# reasonable window around expected end, but still
# guarantees the cycle terminates eventually for
# dishwashers that have no pump-out at all.
end_spike_seen = getattr(self, "_end_spike_seen", False)
# Release the pump-out wait once EITHER the cycle has run
# DISHWASHER_END_SPIKE_WAIT_SECONDS past its expected
# duration OR it has already reached its expected duration
# AND power has since stayed sustained-quiet for
# DISHWASHER_END_SPIKE_QUIET_RELEASE_SECONDS. The second arm
# closes cycles that finish shorter than the profile's
# (drifted-up) average and whose terminal pump-out lands
# *before* the drop into ENDING, so no in-ENDING end-spike
# ever arms - without it they hang to the fallback timeout
# (~30-44 min late) and their label can even drift to a longer
# near-duplicate profile. It is gated on
# ``current_duration >= expected`` so it can NOT fire during a
# long passive-drying phase that precedes a genuinely-late
# pump-out (e.g. an ECO cycle quiet from 50%-99% of expected):
# while still short of expected the cycle keeps waiting, and a
# real pump-out at ~99% arms the end-spike first. Takes the
# SOONER of the two anchors, so it can only ever shorten the
# wait, never extend it.
past_wait_period = current_duration >= (
self._expected_duration
+ DISHWASHER_END_SPIKE_WAIT_SECONDS
) or (
current_duration >= self._expected_duration
and self._time_below_threshold
>= DISHWASHER_END_SPIKE_QUIET_RELEASE_SECONDS
)
if (
self._config.device_type == "dishwasher"
and not end_spike_seen
and not past_wait_period
):
self._logger.debug(
"Waiting for end spike (duration %.0fs, "
"expected %.0fs + %.0fs wait)",
current_duration,
self._expected_duration,
DISHWASHER_END_SPIKE_WAIT_SECONDS,
)
return # Don't finish yet, wait for spike
self._logger.info(
"Smart Termination: Profile '%s' match confirmed (duration %.0fs, "
"conf %.2f, spike_seen=%s), ending.",
self._matched_profile,
current_duration,
getattr(self, "_last_match_confidence", 0.0),
end_spike_seen,
)
# Keep tail when smart terminating (matches profile duration)
self._finish_cycle(
timestamp,
status="completed",
termination_reason=TerminationReason.SMART,
keep_tail=True,
)
return
# --- DURATION-ANCHORED HARD FINALIZE (backstop) ---
# Separate safety net for a matched cycle whose Smart
# Termination is blocked (ambiguous / prefix-ambiguous match)
# and whose fallback energy gate is held open by a low standby
# baseline: without this it sits in ENDING until the 8 h cap /
# zombie-kill (#296/#311). Fires only well past the expected
# duration AND after a long *continuous* sub-threshold span, so
# it can never truncate a longer program mismatched to a shorter
# profile (a real longer program has high-power phases that keep
# resetting the quiet timer) — asymmetric, shorten-only. Not
# for user-paused cycles.
required_quiet = max(
ENDING_HARD_FINALIZE_MIN_QUIET_S,
float(max(self._config.off_delay, self._config.min_off_gap)),
)
# Require the required_quiet tail to be actually SAMPLED (no
# outage-sized gap): otherwise a telemetry outage that inflated
# _time_below_threshold could finalize an active cycle early.
# Walk in reverse so we can capture the boundary reading (the
# first reading outside the window) — an outage right before the
# window would be invisible if we only passed in-window timestamps.
quiet_ts: list[datetime] = []
_boundary_q: datetime | None = None
for _ts, _ in reversed(self._power_readings):
if (timestamp - _ts).total_seconds() <= required_quiet:
quiet_ts.append(_ts)
elif quiet_ts:
_boundary_q = _ts
break
if _boundary_q is not None:
quiet_ts.append(_boundary_q)
if (
self._expected_duration > 0
and current_duration
>= self._expected_duration * ENDING_HARD_FINALIZE_RATIO
and self._time_below_threshold >= required_quiet
and not self._verified_pause
and not self._window_has_outage_gap(quiet_ts)
):
self._logger.info(
"Duration-anchored finalize: cycle in ENDING at %.0fs "
"(%.1fx expected %.0fs), quiet %.0fs — Smart Termination "
"was blocked (ambiguous=%s prefix=%s); finalizing.",
current_duration,
current_duration / self._expected_duration,
self._expected_duration,
self._time_below_threshold,
self._match_ambiguous,
self._match_prefix_ambiguous,
)
self._finish_cycle(
timestamp,
status="completed",
termination_reason=TerminationReason.TIMEOUT,
keep_tail=False,
)
return
# --- FALLBACK TIMEOUT CHECK ---
# Rule: To separate cycles, we must wait at least min_off_gap.
effective_off_delay = max(self._config.off_delay, self._config.min_off_gap)
# Energy gate always looks back off_delay seconds by default;
# overridden below for the dishwasher cap case so the window
# is consistent with the shortened effective_off_delay.
gate_window = self._config.off_delay
# Dishwasher-specific: after a terminal end spike (pump-out), an
# unmatched cycle doesn't need to wait the full min_off_gap (up to
# 9000s) before closing. Cap at 30 min so cycle 3 ends cleanly
# ~30 min after the pump-out rather than sitting open for hours.
if (
self._config.device_type == "dishwasher"
and not self._matched_profile
and self._end_spike_seen
):
effective_off_delay = min(effective_off_delay, 1800)
gate_window = effective_off_delay
# Opt-in terminal-drop fast finalize (asymmetric, shorten-only):
# a hard cliff-to-~0 sustained for TERMINAL_DROP_OFF_DELAY_SECONDS
# that began earlier than this device has ever legitimately gone
# quiet is almost certainly a real stop (plug pulled / cancelled),
# not a soak. Finalize now instead of waiting out the full
# soak-bridging min_off_gap. Only consulted when there is a longer
# wait to shorten and the provider is wired (ML/anomaly opt-in);
# the energy/defer gates are bypassed because the sustained sub-
# threshold span already proves the appliance is off, and the
# anomaly check has ruled out a legitimate early pause.
if (
self._terminal_drop_provider is not None
and not self._verified_pause
and effective_off_delay > TERMINAL_DROP_OFF_DELAY_SECONDS
and self._time_below_threshold >= TERMINAL_DROP_OFF_DELAY_SECONDS
and self._is_terminal_drop()
):
start_time = self._current_cycle_start or timestamp
current_duration = (timestamp - start_time).total_seconds()
self._logger.info(
"Terminal drop: anomalously-early power cliff after %.0fs "
"(device never quiet this early) - finalizing without the "
"full %.0fs soak wait.",
current_duration,
effective_off_delay,
)
self._finish_cycle(
timestamp,
status="interrupted",
termination_reason=TerminationReason.TERMINAL_DROP,
keep_tail=False,
)
return
if self._time_below_threshold >= effective_off_delay:
# Walk from the tail — readings are chronological so we can
# break as soon as we exceed the gate window (O(window) not O(n)).
recent_window = []
for r in reversed(self._power_readings):
if (timestamp - r[0]).total_seconds() <= gate_window:
recent_window.append(r)
else:
break
recent_window.reverse()
if not recent_window:
# Check deferred finish for matched profiles
start_time = self._current_cycle_start or timestamp
current_duration = (timestamp - start_time).total_seconds()
if self._should_defer_finish(current_duration):
return
# For dishwashers, use the timeout timestamp as end_time
# (keep_tail=True) so that the stored cycle duration includes
# the passive drying phase. Without this, end_time snaps back
# to _last_active_time which may be set by a terminal drain
# spike mid-ENDING, producing a falsely short cycle duration.
keep_tail = self._config.device_type == "dishwasher"
self._finish_cycle(timestamp, status="completed", keep_tail=keep_tail)
return
# Compute energy in recent window
recent_ts = np.array([r[0].timestamp() for r in recent_window])
recent_p = np.array([r[1] for r in recent_window])
max_gap_s = energy_gap_threshold_s(recent_ts)
recent_e = integrate_wh(recent_ts, recent_p, max_gap_s=max_gap_s)
if recent_e <= self.config.end_energy_threshold:
start_time = self._current_cycle_start or timestamp
current_duration = (timestamp - start_time).total_seconds()
if self._should_defer_finish(current_duration):
return
keep_tail = self._config.device_type == "dishwasher"
self._finish_cycle(timestamp, status="completed", keep_tail=keep_tail)
else:
self._logger.debug(
"Cycle ending prevented by energy gate: %.4fWh > %.4fWh",
recent_e,
self._config.end_energy_threshold,
)
def _transition_to(self, new_state: str, timestamp: datetime) -> None:
"""Handle state transitions."""
if self._state == new_state:
return
old_state = self._state
self._state = new_state
self._state_enter_time = timestamp
self._time_in_state = 0.0
self._sub_state = new_state.capitalize() # Default substate
# Bound each ENDING episode's ML-guard deferral independently: clear the
# tracker whenever we are not in ENDING (e.g. on resume back to RUNNING).
if new_state != STATE_ENDING:
self._ml_defer_start_duration = None
# Reset energy accumulator on transition to OFF
if new_state == STATE_OFF:
self._energy_since_idle_wh = 0.0
# Also reset idle time tracker when leaving ANTI_WRINKLE
self._anti_wrinkle_idle_time = 0.0
if not self._preserve_delay_band_on_off:
self._delay_band_start = None
self._delay_band_seconds = 0.0
self._delay_band_peak = 0.0
self._delay_wait_true_off_seconds = 0.0
self._delay_wait_high_start = None
self._delay_wait_high_power = None
self._preserve_delay_band_on_off = False
# Clear the paused-STARTING true-off accumulator so a later STARTING
# cycle cannot inherit stale hold time and finalize to OFF prematurely
# (this path is also reached via the paused-STARTING cancellation).
self._starting_paused_off_since = None
# Reset end spike tracker when entering ENDING state
if new_state == STATE_ENDING:
self._end_spike_seen = False
self._end_spike_duration = 0.0
elif new_state == STATE_DELAY_WAIT:
# Band-accumulation tracker already played its role getting us
# here; reset it so a future OFF→band cycle starts fresh.
self._delay_band_start = None
self._delay_band_seconds = 0.0
self._delay_band_peak = 0.0
self._delay_wait_true_off_seconds = 0.0
self._delay_wait_high_start = None
self._delay_wait_high_power = None
self._sub_state = "Waiting to Start"
self._preserve_delay_band_on_off = False
elif new_state == STATE_ANTI_WRINKLE:
self._anti_wrinkle_candidate_start = None
self._anti_wrinkle_candidate_peak = 0.0
self._anti_wrinkle_candidate_start_power = 0.0
self._anti_wrinkle_idle_time = 0.0 # Reset idle time when entering ANTI_WRINKLE
self._sub_state = "Anti-Wrinkle"
elif new_state == STATE_STARTING:
# Reset idle time if exiting ANTI_WRINKLE to STARTING (high-power burst resumed)
self._anti_wrinkle_idle_time = 0.0
# Fresh STARTING cycle: never inherit a prior cycle's true-off hold.
self._starting_paused_off_since = None
elif new_state == STATE_RUNNING:
self._delay_band_start = None
self._delay_band_seconds = 0.0
self._delay_band_peak = 0.0
self._preserve_delay_band_on_off = False
self._logger.debug("Transition: %s -> %s at %s", old_state, new_state, timestamp)
self._on_state_change(old_state, new_state)
def _ml_end_confidence(self) -> float | None:
"""P(the current low-power event is the true end) from the opt-in ML guard.
Builds the offset-second trace from the current cycle's readings and asks
the injected provider. Returns None when there is no provider, no cycle
start, or the provider declines (ML off / unmatched / model unavailable),
so the caller keeps the existing power/energy-based behavior.
"""
provider = self._end_confidence_provider
start = self._current_cycle_start
if provider is None or start is None or not self._power_readings:
return None
# Throttle: reuse the last result within the recompute window, but only when
# it was computed for THIS cycle and the same expected_duration (which can
# change under overrun) — otherwise recompute.
now_ts = self._power_readings[-1][0]
exp = float(self._expected_duration)
cache = self._ml_end_cache
if (
cache is not None
and cache[1] == exp
and cache[2] == start
and (now_ts - cache[0]).total_seconds() < ML_PROVIDER_THROTTLE_SECONDS
):
return cache[3]
points = [
((ts - start).total_seconds(), float(power))
for ts, power in self._power_readings
]
try:
result = provider(points, exp)
except Exception: # noqa: BLE001 - ML must never break detection
result = None
self._ml_end_cache = (now_ts, exp, start, result)
return result
def _window_has_outage_gap(self, window_ts: list[datetime]) -> bool:
"""Whether a 'sustained window' contains a data-outage-sized hole.
The span + coverage checks in the standby / anti-crease window scans accept
e.g. three readings spanning the window even if a long unobserved gap sits
between them (a sensor dropout, or a sparse burst next to one old reading).
Finalizing on such a window could wrongly cut an active cycle, so reject it.
The gap ceiling is the sensor's own data-driven outage threshold
(``energy_gap_threshold_s`` over the full trace), so a change-only sensor's
legitimately-sparse stable stretches (tens of seconds between reports) are
NOT rejected while a genuine dropout is.
"""
if len(window_ts) < 2:
return True # too few points to trust as a sustained window
max_gap = self._outage_threshold_s()
ordered = sorted(t.timestamp() for t in window_ts)
return any((b - a) > max_gap for a, b in itertools.pairwise(ordered))
def _outage_threshold_s(self) -> float:
"""Sensor-adaptive gap ceiling (seconds): intervals longer than this are
treated as telemetry outages, not observed quiet. Data-driven from the
trace's own cadence (`energy_gap_threshold_s`), so a change-only sensor's
sparse-but-real stable stretches are not mistaken for a dropout.
"""
all_ts = np.array(
[r[0].timestamp() for r in self._power_readings], dtype=float
)
return energy_gap_threshold_s(all_ts)
def _is_standby_band_stuck(self, timestamp: datetime) -> bool:
"""Whether a RUNNING cycle is stuck on a flat standby plateau (#296).
Returns True only when ALL of the following hold, so this can never end
an active low-power phase:
* the device is a wet appliance where a stuck baseline is unambiguously
anomalous (``STANDBY_BAND_FINALIZE_DEVICE_TYPES``);
* a profile is matched and elapsed >= ``STANDBY_BAND_MIN_RATIO`` x the
expected duration (well past when it should have ended);
* not user-paused;
* the most recent >= ``STANDBY_BAND_WINDOW_S`` of readings are ALL at or
below ``STANDBY_BAND_MAX_FRACTION`` of the cycle's own peak power AND
span no more than ``STANDBY_BAND_FLATNESS_FRACTION`` of the peak (a flat
plateau, not fluctuating activity).
The expensive window scan runs only after the cheap duration gate passes,
so normal cycles never pay for it.
"""
if self._config.device_type not in STANDBY_BAND_FINALIZE_DEVICE_TYPES:
return False
if getattr(self, "_verified_pause", False):
return False
if not (self._matched_profile and self._expected_duration > 0):
return False
start = self._current_cycle_start
if start is None:
return False
current_duration = (timestamp - start).total_seconds()
if current_duration < self._expected_duration * STANDBY_BAND_MIN_RATIO:
return False
peak = float(self._cycle_max_power)
if peak <= 0:
return False
level_ceiling = peak * STANDBY_BAND_MAX_FRACTION
# Walk the tail; readings are chronological so we can break once outside
# the window (O(window), not O(n)).
window: list[float] = []
window_ts: list[datetime] = []
oldest_in_window: datetime | None = None
saw_older = False # a reading older than the window exists -> full coverage
_standby_boundary_ts: datetime | None = None
for ts, p in reversed(self._power_readings):
if (timestamp - ts).total_seconds() <= STANDBY_BAND_WINDOW_S:
window.append(float(p))
window_ts.append(ts)
oldest_in_window = ts
else:
saw_older = True
_standby_boundary_ts = ts # boundary: last reading before the window
break
# The plateau must actually SPAN the required window (data exists from
# before it), not just a couple of recent samples, and have enough points
# to judge. `saw_older` (rather than an exact span >= WINDOW check) is
# robust to sample phase/granularity: with e.g. 30 s sampling the oldest
# in-window reading is typically only ~570-599 s old, which an exact check
# would wrongly reject. A coverage sanity (oldest >= 90% of the window)
# plus an adjacent-gap check (``_window_has_outage_gap``) guard against a
# sparse burst of samples sitting next to one old reading across a dropout.
if (
oldest_in_window is None
or not saw_older
or len(window) < 3
or (timestamp - oldest_in_window).total_seconds()
< STANDBY_BAND_WINDOW_S * 0.9
or self._window_has_outage_gap(
[_standby_boundary_ts, *window_ts]
if _standby_boundary_ts is not None
else window_ts
)
):
return False
hi = max(window)
lo = min(window)
if hi > level_ceiling:
return False # a real active reading in the window - not standby
flatness_limit = max(
STANDBY_BAND_FLATNESS_FLOOR_W, peak * STANDBY_BAND_FLATNESS_FRACTION
)
if (hi - lo) > flatness_limit:
return False # fluctuating - still doing work
return True
def _anticrease_gate_open(self, timestamp: datetime) -> bool:
"""Core anti-crease gate (#296): everything except the current power level
and the low-power-window check. Shared by the match freeze
(``_in_anticrease_freeze``) and the finalise (``_is_anticrease_tail``).
True only when a genuinely energetic, confidently-matched cycle for an
anti-wrinkle device is PAST its expected duration - the discriminator that
separates the post-wash anti-crease tail from a mid-wash low-power trough
(a washer spends most of its cycle below ``anti_wrinkle_max_power``, but a
mid-wash trough is always BEFORE the expected duration, the tail after it).
"""
if not self._config.anti_wrinkle_enabled:
return False
if self._config.device_type not in (
DEVICE_TYPE_WASHING_MACHINE,
DEVICE_TYPE_DRYER,
DEVICE_TYPE_WASHER_DRYER,
):
return False
if getattr(self, "_verified_pause", False):
return False
if not (self._matched_profile and self._expected_duration > 0):
return False
if self._last_match_confidence < 0.4:
return False
if self._match_ambiguous or self._match_prefix_ambiguous:
return False
if self._cycle_max_power <= float(self._config.anti_wrinkle_max_power):
return False # never a hot/energetic cycle - leave low-power programs alone
start = self._current_cycle_start
if start is None:
return False
current_duration = (timestamp - start).total_seconds()
if current_duration < self._expected_duration * ANTI_CREASE_FINALIZE_RATIO:
return False
return True
def _in_anticrease_freeze(self, timestamp: datetime) -> bool:
"""Whether match updates should be frozen (#296): the anti-crease gate is
open AND the most recent reading is in the low-power regime (at or below
``anti_wrinkle_max_power``).
Deliberately lighter than ``_is_anticrease_tail`` - it does NOT wait for the
full ``ANTI_CREASE_CONFIRM_WINDOW_S``, so the confident pre-tail match is
preserved from the instant the cycle crosses its expected duration in a
low-power state, before the window accrues. Without this a match that
degrades to ambiguous within the first window's worth of tail would
deadlock both the freeze and the finalise (both require an unambiguous
match). Self-correcting: a heating burst above ``anti_wrinkle_max_power``
leaves the regime and re-arms matching.
"""
if not self._power_readings:
return False
if float(self._power_readings[-1][1]) > float(
self._config.anti_wrinkle_max_power
):
return False
return self._anticrease_gate_open(timestamp)
def _is_anticrease_tail(self, timestamp: datetime) -> bool:
"""Whether a matched, past-expected cycle has settled into the anti-crease
tumble tail (#296) - the trigger for the finalise into STATE_ANTI_WRINKLE.
Miele-style "Knitterschutz": after the wash proper ends, the machine holds
a constant baseline plus periodic sub-``anti_wrinkle_max_power`` tumble
bursts (no heating) until the door is opened. Because those bursts recur
faster than off_delay they keep reviving the cycle out of ENDING, so the
normal power-off path never finalises it and STATE_ANTI_WRINKLE - which is
built to absorb the tail and split off the next wash - never engages.
Recognising the tail lets us finalise into anti-wrinkle directly.
Requires the core gate (``_anticrease_gate_open``) AND that the most recent
>= ``ANTI_CREASE_CONFIRM_WINDOW_S`` of readings are ALL at or below
``anti_wrinkle_max_power`` (we are in the low-power tail, clear of the final
spin and not mid-heating). The expensive window scan runs only after the
cheap gate passes, so normal cycles never pay for it.
"""
if not self._anticrease_gate_open(timestamp):
return False
max_power = float(self._config.anti_wrinkle_max_power)
# Walk the tail; readings are chronological so we can break once outside the
# window (O(window), not O(n)).
window: list[float] = []
window_ts: list[datetime] = []
oldest_in_window: datetime | None = None
saw_older = False
_ac_boundary_ts: datetime | None = None
for ts, p in reversed(self._power_readings):
if (timestamp - ts).total_seconds() <= ANTI_CREASE_CONFIRM_WINDOW_S:
window.append(float(p))
window_ts.append(ts)
oldest_in_window = ts
else:
saw_older = True
_ac_boundary_ts = ts # boundary: last reading before the window
break
# The low-power tail must actually SPAN the window (data exists from before
# it) and have enough points to judge - not just a couple of recent samples.
# ``saw_older`` plus a coverage sanity and an adjacent-gap check
# (``_window_has_outage_gap``) is robust to sample phase/granularity while
# rejecting a dropout-sized hole (mirrors _is_standby_band_stuck).
if (
oldest_in_window is None
or not saw_older
or len(window) < 3
or (timestamp - oldest_in_window).total_seconds()
< ANTI_CREASE_CONFIRM_WINDOW_S * 0.9
or self._window_has_outage_gap(
[_ac_boundary_ts, *window_ts]
if _ac_boundary_ts is not None
else window_ts
)
):
return False
if max(window) > max_power:
return False # a heating / high-spin reading in the window - still washing
return True
def _maybe_finalize_anticrease_tail(self, timestamp: datetime) -> bool:
"""Finalise a cycle that has entered the anti-crease tail into
STATE_ANTI_WRINKLE (#296). Returns True if the cycle was finalised.
Shared by the RUNNING / PAUSED / ENDING branches so the finalise fires no
matter which state a burst left the detector in. Uses Smart Termination
(in ``ANTI_WRINKLE_ELIGIBLE_REASONS``) so ``_finish_cycle`` routes into
STATE_ANTI_WRINKLE, which then absorbs the tail and splits off any next
wash on its first heating burst above ``anti_wrinkle_max_power``.
"""
if not self._is_anticrease_tail(timestamp):
return False
start_time = self._current_cycle_start or timestamp
current_duration = (timestamp - start_time).total_seconds()
self._logger.info(
"Anti-crease finalize: matched '%s' past expected %.0fs (elapsed %.0fs), "
"settled into the low-power tumble tail — finalizing into anti-wrinkle.",
self._matched_profile,
self._expected_duration,
current_duration,
)
self._finish_cycle(
timestamp,
status="completed",
termination_reason=TerminationReason.SMART,
keep_tail=True,
)
return True
def _is_terminal_drop(self) -> bool:
"""Whether the current low-power event is an anomalously-early hard drop.
Mirrors ``_ml_end_confidence``: builds the offset-second trace from the
current cycle's readings and asks the injected terminal-drop provider.
Returns ``False`` when there is no provider, no cycle start, or the
provider declines/raises (ML off / too little history / not anomalous),
so the caller keeps the proven soak-bridging end-detection.
"""
provider = self._terminal_drop_provider
start = self._current_cycle_start
if provider is None or start is None or not self._power_readings:
return False
# Throttle: reuse within the window, scoped to this cycle + expected_duration.
now_ts = self._power_readings[-1][0]
exp = float(self._expected_duration)
cache = self._terminal_drop_cache
if (
cache is not None
and cache[1] == exp
and cache[2] == start
and (now_ts - cache[0]).total_seconds() < ML_PROVIDER_THROTTLE_SECONDS
):
return cache[3]
points = [
((ts - start).total_seconds(), float(power))
for ts, power in self._power_readings
]
try:
result = bool(provider(points, exp))
except Exception: # noqa: BLE001 - ML must never break detection
result = False
self._terminal_drop_cache = (now_ts, exp, start, result)
return result
def _should_defer_finish(self, duration: float) -> bool:
"""Check if we should defer termination based on expected duration."""
# Check explicit verified pause override from manager
if getattr(self, "_verified_pause", False):
self._logger.debug("Deferring cycle finish: Verified pause active")
return True
# Dishwasher minimum-duration floor: even without a matched profile (e.g.
# first cycle of a program, or the 5-min matcher hasn't fired yet) a
# dishwasher cycle should never end before it has crossed the minimum
# reasonable programme duration. This prevents a dip during the fill or
# early wash phase from being read as the end of a complete cycle.
if (
self._config.device_type == "dishwasher"
and duration < DISHWASHER_MIN_CYCLE_DURATION_S
):
self._logger.debug(
"Deferring dishwasher cycle end: elapsed %.0fs < minimum %.0fs",
duration,
DISHWASHER_MIN_CYCLE_DURATION_S,
)
return True
if not self._matched_profile or self._expected_duration <= 0:
return False
# Safety: Don't defer forever
if duration > (self._expected_duration + DEFAULT_MAX_DEFERRAL_SECONDS):
self._logger.warning(
"Deferral limit exceeded (%.0fs > expected %.0f + %s), allowing finish",
duration,
self._expected_duration,
DEFAULT_MAX_DEFERRAL_SECONDS,
)
return False
# Opt-in ML end-guard (asymmetric anti-premature-stop, bounded). If the
# cycle-end model judges this low-power event to be more likely a pause
# than the true end, defer the normal completion - but only for a bounded
# extra window, so a wrong model can delay, never hang, the cycle. As the
# low-power run lengthens the model's confidence rises, so a genuine end
# is released once the model agrees or the cap is reached.
if (
self._end_confidence_provider is not None
and self._last_match_confidence >= DEFAULT_DEFER_FINISH_CONFIDENCE
):
confidence = self._ml_end_confidence()
if confidence is not None and confidence < ML_END_GUARD_MIN_CONFIDENCE:
if self._ml_defer_start_duration is None:
self._ml_defer_start_duration = duration
if (duration - self._ml_defer_start_duration) < ML_END_GUARD_MAX_DEFER_SECONDS:
self._logger.debug(
"Deferring cycle finish: ML end-guard (P(true end)=%.2f < %.2f)",
confidence,
ML_END_GUARD_MIN_CONFIDENCE,
)
return True
elif confidence is not None:
# Model is confident this is the true end -> stop ML-deferring.
self._ml_defer_start_duration = None
# Dishwasher passive drying protection:
# Dishwashers can have 2+ hour passive drying phases at near-0W. A terminal
# drain spike that fires early in the ENDING state (e.g. at 120 min of a
# 233-min ECO cycle) resets _time_below_threshold, and the subsequent 60-min
# silence timeout would otherwise end the cycle at ~180 min - well before the
# real finish. Defer until the cycle reaches the late-phase threshold (the
# same one used by the end-spike arm gate, so both move together) so that
# smart termination can catch the true end (~99% of expected) instead.
# Confidence may be low this early, so the normal confidence gate is
# bypassed here.
if (
self._config.device_type == "dishwasher"
and self._matched_profile
and self._expected_duration > 0
and duration
< (self._expected_duration * DISHWASHER_END_SPIKE_MIN_PROGRESS)
):
self._logger.debug(
"Deferring cycle finish: dishwasher drying phase protection "
"(%.0fs < %.0f%% of expected %.0fs, profile: %s, conf %.2f)",
duration,
DISHWASHER_END_SPIKE_MIN_PROGRESS * 100,
self._expected_duration,
self._matched_profile,
self._last_match_confidence,
)
return True
# Issue #43: dishwasher end-spike wait protection. Once past the 85%
# passive-drying gate above, we still keep the cycle deferred until
# the real end-of-cycle pump-out fires (sets _end_spike_seen=True via
# the 85% progress gate in STATE_ENDING) or we cross the
# smart-termination wait window (expected + 30 min) - whichever comes
# first. Shares DISHWASHER_END_SPIKE_WAIT_SECONDS with Smart
# Termination's wait branch so the two paths release the cycle at the
# same instant. Beyond the wait window, Smart Termination's
# past_wait_period kicks in and finalises; below it, the fallback
# timeout's energy gate is the safety net for cycles whose pump-out
# never arrives.
# Mirrors the STATE_ENDING pump-out wait so both paths release together.
# Keep deferring while we are still inside the wait window, UNLESS the cycle
# has already reached its expected duration and has since been sustained-quiet
# for DISHWASHER_END_SPIKE_QUIET_RELEASE_SECONDS - in which case any terminal
# pump-out has already happened, so a cycle that finished slightly short of the
# profile's (drifted-up) average is released here instead of hanging to
# expected + 30 min. The ``duration >= expected`` gate keeps a long
# passive-drying phase that still precedes a late pump-out deferred.
quiet_released = (
duration >= self._expected_duration
and self._time_below_threshold
>= DISHWASHER_END_SPIKE_QUIET_RELEASE_SECONDS
)
if (
self._config.device_type == "dishwasher"
and self._matched_profile
and self._expected_duration > 0
and not self._end_spike_seen
and duration
< (self._expected_duration + DISHWASHER_END_SPIKE_WAIT_SECONDS)
and not quiet_released
):
self._logger.debug(
"Deferring cycle finish: dishwasher waiting for end-of-cycle "
"pump-out (%.0fs < expected %.0fs + %.0fs wait, quiet %.0fs, profile: %s)",
duration,
self._expected_duration,
DISHWASHER_END_SPIKE_WAIT_SECONDS,
self._time_below_threshold,
self._matched_profile,
)
return True
# If matched profile, enforce min duration ratio
ratio = self._config.min_duration_ratio
# --- STRICTER DEFERRAL ---
# If we are NOT in a verified pause, but power has been low for a long time (ENDING state),
# we only defer if we are VERY confident this profile is correct.
# This prevents hanging on too-long profiles that matched early but are now diverging.
if self._last_match_confidence < DEFAULT_DEFER_FINISH_CONFIDENCE:
self._logger.debug(
"Not deferring finish: confidence %.2f too low for unverified pause (profile: %s)",
self._last_match_confidence,
self._matched_profile,
)
return False
# Also use profile tolerance to handle variable cycle lengths (e.g. long drying)
# Allow deferral up to Expected * (1 + tolerance)
upper_threshold = self._expected_duration * (
1.0 + self._config.profile_duration_tolerance
)
# Primary check: Is duration significantly below expectation?
if duration < (self._expected_duration * ratio):
self._logger.debug(
"Deferring cycle finish: duration %.0fs < %.0f%% of expected %.0fs (profile: %s, confidence %.2f)",
duration,
ratio * 100,
self._expected_duration,
self._matched_profile,
self._last_match_confidence,
)
return True
# Secondary check: If within valid completion window (ratio to tolerance), allow finish.
if duration <= upper_threshold:
return False
# Tertiary check: If duration exceeded max tolerance, allow finish (failsafe).
return False
def _finish_cycle(
self,
timestamp: datetime,
status: str = "completed",
termination_reason: str = TerminationReason.TIMEOUT,
keep_tail: bool = False,
) -> None:
"""Finalize cycle.
Args:
timestamp: Time of completion
status: Cycle status string
termination_reason: Reason for termination
keep_tail: If True, use current timestamp as end time and preserve
trailing zero readings (e.g. Smart Termination).
If False (default), snap back to last active time and trim
trailing zeros (e.g. Timeout).
"""
# Capture data before reset
if keep_tail:
end_time = timestamp
else:
end_time = self._last_active_time or timestamp
if not self._current_cycle_start:
self.reset()
return
duration = (end_time - self._current_cycle_start).total_seconds()
# "Interrupted" logic (short cycle etc)
if duration < self._config.interrupted_min_seconds:
status = "interrupted"
elif duration < self._config.completion_min_seconds:
status = "interrupted"
# Trim leading/trailing zero readings for cleaner data
# If we keep tail, we explicitly do NOT trim end zeros
trimmed_readings = trim_zero_readings(
self._power_readings,
threshold=self._config.stop_threshold_w,
trim_end=not keep_tail,
)
# Ensure power_data covers the full duration until end_time
# (especially important for manual recordings or drying phases with no sensor updates)
final_readings = list(trimmed_readings)
if final_readings:
last_t, last_p = final_readings[-1]
if last_t < end_time:
final_readings.append((end_time, last_p))
start_ts = self._current_cycle_start.timestamp()
cycle_data: dict[str, Any] = {
"start_time": self._current_cycle_start.isoformat(),
"end_time": end_time.isoformat(),
"duration": duration,
"max_power": self._cycle_max_power,
"status": status,
"termination_reason": termination_reason,
"power_data": [[round(t.timestamp() - start_ts, 1), p] for t, p in final_readings],
}
self._logger.info("Cycle Finished: %s, %.1f min", status, duration / 60)
self._on_cycle_end(cycle_data)
target = STATE_FINISHED
if status == "interrupted":
target = STATE_INTERRUPTED
elif status == "force_stopped":
target = STATE_FORCE_STOPPED
elif (
status == "completed"
and termination_reason in ANTI_WRINKLE_ELIGIBLE_REASONS
and self._config.anti_wrinkle_enabled
and self._config.device_type in (
DEVICE_TYPE_WASHING_MACHINE,
DEVICE_TYPE_DRYER,
DEVICE_TYPE_WASHER_DRYER,
)
):
target = STATE_ANTI_WRINKLE
self.reset(target_state=target)
# Stub methods for compatibility or simpler logic
def force_end(self, timestamp: datetime) -> None:
"""Force the cycle to end immediately."""
if self._state != STATE_OFF:
self._finish_cycle(
timestamp,
status="force_stopped",
termination_reason=TerminationReason.FORCE_STOPPED,
keep_tail=False, # Force stop usually implies snap back to reality
)
self._ignore_power_until_idle = False
def user_stop(self) -> None:
"""Handle user-initiated stop."""
if self._state != STATE_OFF:
now = dt_util.now()
self._finish_cycle(
now,
status="completed",
termination_reason=TerminationReason.USER,
keep_tail=True, # User implies "Done Now"
)
# Prevent immediate restart if power is still high
self._ignore_power_until_idle = True
# Anchor the lockout clock to this stop instant. The next reading's
# dt is measured from the last processed sample, which predates the
# stop, so without this the high-power accumulator would count the
# pre-stop gap and release the lockout early (#267).
self._lockout_high_seconds = 0.0
self._last_process_time = now
def get_power_trace(self) -> list[tuple[datetime, float]]:
"""Return the current power trace."""
return list(self._power_readings)
def get_state_snapshot(self) -> dict[str, Any]:
"""Get a snapshot of the current state for persistence."""
return {
"state": self._state,
"sub_state": self._sub_state,
"current_cycle_start": (
self._current_cycle_start.isoformat()
if self._current_cycle_start
else None
),
"power_readings": [(t.isoformat(), p) for t, p in self._power_readings],
"accumulated_energy_wh": self._energy_since_idle_wh,
"time_above": self._time_above_threshold,
"time_below": self._time_below_threshold,
"cycle_max_power": self._cycle_max_power,
"last_active_time": (
self._last_active_time.isoformat() if self._last_active_time else None
),
"expected_duration": self._expected_duration,
"matched_profile": self._matched_profile,
"state_enter_time": (
self._state_enter_time.isoformat() if self._state_enter_time else None
),
"end_spike_seen": self._end_spike_seen,
"end_spike_duration": self._end_spike_duration,
"match_ambiguous": self._match_ambiguous,
"match_prefix_ambiguous": self._match_prefix_ambiguous,
"ml_defer_start_duration": self._ml_defer_start_duration,
}
def get_elapsed_seconds(self) -> float:
"""Return seconds elapsed in current cycle."""
if self._current_cycle_start:
return (dt_util.now() - self._current_cycle_start).total_seconds()
return 0.0
def is_waiting_low_power(self) -> bool:
"""Return True if we are pending end/pause due to low power."""
return (
self._state in (STATE_RUNNING, STATE_PAUSED, STATE_ENDING)
and self._time_below_threshold > 0
)
def restore_state_snapshot(self, snapshot: dict[str, Any]) -> None:
"""Restore state from snapshot."""
try:
self._state = snapshot.get("state", STATE_OFF)
self._sub_state = snapshot.get("sub_state")
self._energy_since_idle_wh = snapshot.get("accumulated_energy_wh", 0.0)
self._time_above_threshold = snapshot.get("time_above", 0.0)
self._time_below_threshold = snapshot.get("time_below", 0.0)
self._cycle_max_power = snapshot.get("cycle_max_power", 0.0)
# Sanitize via the same helper as update_match so the class
# invariant on _expected_duration holds across restarts and the
# gates in STATE_ENDING / _should_defer_finish can trust the value.
# If sanitization rejects the snapshot's expected_duration, also
# clear the matched_profile so we don't restore a half-valid state
# where Smart Termination can fire on _expected_duration == 0.0.
restored_match = snapshot.get("matched_profile")
sanitized_expected = self._sanitize_expected_duration(
snapshot.get("expected_duration", 0.0),
source="restore_state_snapshot",
)
if (
restored_match is not None
and sanitized_expected == self._SANITIZE_INVALID_SENTINEL
):
self._logger.debug(
"restore_state_snapshot: dropping matched_profile %r "
"because expected_duration sanitized to invalid sentinel",
restored_match,
)
self._matched_profile = None
else:
self._matched_profile = restored_match
self._expected_duration = sanitized_expected
self._end_spike_seen = snapshot.get("end_spike_seen", False)
self._end_spike_duration = float(snapshot.get("end_spike_duration", 0.0))
self._match_ambiguous = snapshot.get("match_ambiguous", False)
self._match_prefix_ambiguous = snapshot.get("match_prefix_ambiguous", False)
self._ml_defer_start_duration = snapshot.get("ml_defer_start_duration")
# Restore state enter time and recompute time_in_state from it
enter_time = snapshot.get("state_enter_time")
if enter_time:
try:
self._state_enter_time = dt_util.parse_datetime(enter_time)
if self._state_enter_time:
elapsed = (dt_util.now() - self._state_enter_time).total_seconds()
self._time_in_state = max(0.0, elapsed)
except Exception: # pylint: disable=broad-exception-caught
self._logger.warning("Failed to parse state enter time")
start = snapshot.get("current_cycle_start")
self._current_cycle_start = None
if start:
try:
dt_start = dt_util.parse_datetime(start)
if dt_start and dt_start.tzinfo is None:
# Fix Naive Timestamp (Legacy Data)
dt_start = dt_start.replace(tzinfo=dt_util.now().tzinfo)
self._logger.warning("Restored Naive start_time, assuming local: %s", dt_start)
self._current_cycle_start = dt_start
except Exception: # pylint: disable=broad-exception-caught
self._logger.warning("Failed to parse start time: %s", start)
readings = snapshot.get("power_readings", [])
self._power_readings = []
# Detect naive readings once
has_naive_readings = False
for r in readings:
if isinstance(r, (list, tuple)):
reading = cast(list[Any] | tuple[Any, ...], r)
if len(reading) < 2:
continue
try:
t = dt_util.parse_datetime(str(reading[0]))
if t:
if t.tzinfo is None:
t = t.replace(tzinfo=dt_util.now().tzinfo)
has_naive_readings = True
value = float(reading[1])
if math.isfinite(value):
self._power_readings.append((t, value))
except (TypeError, ValueError) as exc:
self._logger.debug("Skipping malformed power reading %s: %s", r, exc)
if has_naive_readings:
self._logger.warning(
"Restored %d power readings with Naive timestamps (fixed to local)",
len(self._power_readings),
)
# Restore last active
last_active = snapshot.get("last_active_time")
if last_active:
dt_last = dt_util.parse_datetime(last_active)
if dt_last and dt_last.tzinfo is None:
dt_last = dt_last.replace(tzinfo=dt_util.now().tzinfo)
self._last_active_time = dt_last
else:
self._last_active_time = self._current_cycle_start
except Exception as e: # pylint: disable=broad-exception-caught
self._logger.error("Failed restore: %s", e)
self.reset()
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