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hass.tibber_prices/custom_components/tibber_prices/interval_pool/manager.py
Julian Pawlowski 23b4330b9a fix(coordinator): track API calls separately from cached data usage 
The lifecycle sensor was always showing "fresh" state because
_last_price_update was set on every coordinator update, regardless of
whether data came from API or cache.

Changes:
- interval_pool/manager.py: get_intervals() and get_sensor_data() now
  return tuple[data, bool] where bool indicates actual API call
- coordinator/price_data_manager.py: All fetch methods propagate
  api_called flag through the call chain
- coordinator/core.py: Only update _last_price_update when api_called=True,
  added debug logging to distinguish API calls from cached data
- services/get_price.py: Updated to handle new tuple return type

Impact: Lifecycle sensor now correctly shows "cached" during normal
15-minute updates (using pool cache) and only "fresh" within 5 minutes
of actual API calls. This fixes the issue where the sensor would never
leave the "fresh" state during frequent HA restarts or normal operation.
2025-12-25 18:53:29 +00:00

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"""Interval pool manager - main coordinator for interval caching."""
from __future__ import annotations
import asyncio
import contextlib
import logging
from datetime import datetime, timedelta
from typing import TYPE_CHECKING, Any
from zoneinfo import ZoneInfo
from custom_components.tibber_prices.api.exceptions import TibberPricesApiClientError
from homeassistant.util import dt as dt_utils
from .cache import TibberPricesIntervalPoolFetchGroupCache
from .fetcher import TibberPricesIntervalPoolFetcher
from .garbage_collector import MAX_CACHE_SIZE, TibberPricesIntervalPoolGarbageCollector
from .index import TibberPricesIntervalPoolTimestampIndex
from .storage import async_save_pool_state
if TYPE_CHECKING:
from custom_components.tibber_prices.api.client import TibberPricesApiClient
from custom_components.tibber_prices.coordinator.time_service import (
TibberPricesTimeService,
)
_LOGGER = logging.getLogger(__name__)
_LOGGER_DETAILS = logging.getLogger(__name__ + ".details")
# Interval lengths in minutes
INTERVAL_HOURLY = 60
INTERVAL_QUARTER_HOURLY = 15
# Debounce delay for auto-save (seconds)
DEBOUNCE_DELAY_SECONDS = 3.0
def _normalize_starts_at(starts_at: datetime | str) -> str:
"""Normalize startsAt to consistent format (YYYY-MM-DDTHH:MM:SS)."""
if isinstance(starts_at, datetime):
return starts_at.strftime("%Y-%m-%dT%H:%M:%S")
return starts_at[:19]
class TibberPricesIntervalPool:
"""
High-performance interval cache manager for a single Tibber home.
Coordinates all interval pool components:
- TibberPricesIntervalPoolFetchGroupCache: Stores fetch groups and manages protected ranges
- TibberPricesIntervalPoolTimestampIndex: Provides O(1) timestamp lookups
- TibberPricesIntervalPoolGarbageCollector: Evicts old fetch groups when cache exceeds limits
- TibberPricesIntervalPoolFetcher: Detects gaps and fetches missing intervals from API
Architecture:
- Each manager handles exactly ONE home (1:1 with config entry)
- home_id is immutable after initialization
- All operations are thread-safe via asyncio locks
Features:
- Fetch-time based eviction (oldest fetch groups removed first)
- Protected date range (day-before-yesterday to tomorrow never evicted)
- Fast O(1) lookups by timestamp
- Automatic gap detection and API fetching
- Debounced auto-save to prevent excessive I/O
Example:
manager = TibberPricesIntervalPool(home_id="abc123", hass=hass, entry_id=entry.entry_id)
intervals = await manager.get_intervals(
api_client=client,
user_data=data,
start_time=datetime(...),
end_time=datetime(...),
)
"""
def __init__(
self,
*,
home_id: str,
api: TibberPricesApiClient,
hass: Any | None = None,
entry_id: str | None = None,
time_service: TibberPricesTimeService | None = None,
) -> None:
"""
Initialize interval pool manager.
Args:
home_id: Tibber home ID (required, immutable).
api: API client for fetching intervals.
hass: HomeAssistant instance for auto-save (optional).
entry_id: Config entry ID for auto-save (optional).
time_service: TimeService for time-travel support (optional).
If None, uses real time (dt_utils.now()).
"""
self._home_id = home_id
self._time_service = time_service
# Initialize components with dependency injection
self._cache = TibberPricesIntervalPoolFetchGroupCache(time_service=time_service)
self._index = TibberPricesIntervalPoolTimestampIndex()
self._gc = TibberPricesIntervalPoolGarbageCollector(self._cache, self._index, home_id)
self._fetcher = TibberPricesIntervalPoolFetcher(api, self._cache, self._index, home_id)
# Auto-save support
self._hass = hass
self._entry_id = entry_id
self._background_tasks: set[asyncio.Task] = set()
self._save_debounce_task: asyncio.Task | None = None
self._save_lock = asyncio.Lock()
async def get_intervals(
self,
api_client: TibberPricesApiClient,
user_data: dict[str, Any],
start_time: datetime,
end_time: datetime,
) -> tuple[list[dict[str, Any]], bool]:
"""
Get price intervals for time range (cached + fetch missing).
Main entry point for retrieving intervals. Coordinates:
1. Check cache for existing intervals
2. Detect missing time ranges
3. Fetch missing ranges from API
4. Add new intervals to cache (may trigger GC)
5. Return complete interval list
User receives ALL requested intervals even if cache exceeds limits.
Cache only keeps the most recent intervals (FIFO eviction).
Args:
api_client: TibberPricesApiClient instance for API calls.
user_data: User data dict containing home metadata.
start_time: Start of range (inclusive, timezone-aware).
end_time: End of range (exclusive, timezone-aware).
Returns:
Tuple of (intervals, api_called):
- intervals: List of price interval dicts, sorted by startsAt.
Contains ALL intervals in requested range (cached + fetched).
- api_called: True if API was called to fetch missing data, False if all from cache.
Raises:
TibberPricesApiClientError: If API calls fail or validation errors.
"""
# Validate inputs
if not user_data:
msg = "User data required for timezone-aware price fetching"
raise TibberPricesApiClientError(msg)
if start_time >= end_time:
msg = f"Invalid time range: start_time ({start_time}) must be before end_time ({end_time})"
raise TibberPricesApiClientError(msg)
# Convert to ISO strings for cache operations
start_time_iso = start_time.isoformat()
end_time_iso = end_time.isoformat()
_LOGGER_DETAILS.debug(
"Interval pool request for home %s: range %s to %s",
self._home_id,
start_time_iso,
end_time_iso,
)
# Get cached intervals using index
cached_intervals = self._get_cached_intervals(start_time_iso, end_time_iso)
# Check coverage - find ranges not in cache
missing_ranges = self._fetcher.check_coverage(cached_intervals, start_time_iso, end_time_iso)
if missing_ranges:
_LOGGER_DETAILS.debug(
"Coverage check for home %s: %d range(s) missing - will fetch from API",
self._home_id,
len(missing_ranges),
)
else:
_LOGGER_DETAILS.debug(
"Coverage check for home %s: full coverage in cache - no API calls needed",
self._home_id,
)
# Fetch missing ranges from API
if missing_ranges:
fetch_time_iso = dt_utils.now().isoformat()
# Fetch with callback for immediate caching
await self._fetcher.fetch_missing_ranges(
api_client=api_client,
user_data=user_data,
missing_ranges=missing_ranges,
on_intervals_fetched=lambda intervals, _: self._add_intervals(intervals, fetch_time_iso),
)
# After caching all API responses, read from cache again to get final result
# This ensures we return exactly what user requested, filtering out extra intervals
final_result = self._get_cached_intervals(start_time_iso, end_time_iso)
# Track if API was called (True if any missing ranges were fetched)
api_called = len(missing_ranges) > 0
_LOGGER_DETAILS.debug(
"Pool returning %d intervals for home %s (from cache: %d, fetched from API: %d ranges, api_called=%s)",
len(final_result),
self._home_id,
len(cached_intervals),
len(missing_ranges),
api_called,
)
return final_result, api_called
async def get_sensor_data(
self,
api_client: TibberPricesApiClient,
user_data: dict[str, Any],
home_timezone: str | None = None,
*,
include_tomorrow: bool = True,
) -> tuple[list[dict[str, Any]], bool]:
"""
Get price intervals for sensor data (day-before-yesterday to end-of-tomorrow).
Convenience method for coordinator/sensors that need the standard 4-day window:
- Day before yesterday (for trailing 24h averages at midnight)
- Yesterday (for trailing 24h averages)
- Today (current prices)
- Tomorrow (if available in cache)
IMPORTANT - Two distinct behaviors:
1. API FETCH: Controlled by include_tomorrow flag
- include_tomorrow=False → Only fetch up to end of today (prevents API spam before 13:00)
- include_tomorrow=True → Fetch including tomorrow data
2. RETURN DATA: Always returns full protected range (including tomorrow if cached)
- This ensures cached tomorrow data is used even if include_tomorrow=False
The separation prevents the following bug:
- If include_tomorrow affected both fetch AND return, cached tomorrow data
would be lost when include_tomorrow=False, causing infinite refresh loops.
Args:
api_client: TibberPricesApiClient instance for API calls.
user_data: User data dict containing home metadata.
home_timezone: Optional timezone string (e.g., "Europe/Berlin").
include_tomorrow: If True, fetch tomorrow's data from API. If False,
only fetch up to end of today. Default True.
DOES NOT affect returned data - always returns full range.
Returns:
Tuple of (intervals, api_called):
- intervals: List of price interval dicts for the 4-day window (including any cached
tomorrow data), sorted by startsAt.
- api_called: True if API was called to fetch missing data, False if all from cache.
"""
# Determine timezone
tz_str = home_timezone
if not tz_str:
tz_str = self._extract_timezone_from_user_data(user_data)
# Calculate range in home's timezone
tz = ZoneInfo(tz_str) if tz_str else None
now = self._time_service.now() if self._time_service else dt_utils.now()
now_local = now.astimezone(tz) if tz else now
# Day before yesterday 00:00 (start) - same for both fetch and return
day_before_yesterday = (now_local - timedelta(days=2)).replace(hour=0, minute=0, second=0, microsecond=0)
# End of tomorrow (full protected range) - used for RETURN data
end_of_tomorrow = (now_local + timedelta(days=2)).replace(hour=0, minute=0, second=0, microsecond=0)
# API fetch range depends on include_tomorrow flag
if include_tomorrow:
fetch_end_time = end_of_tomorrow
fetch_desc = "end-of-tomorrow"
else:
# Only fetch up to end of today (prevents API spam before 13:00)
fetch_end_time = (now_local + timedelta(days=1)).replace(hour=0, minute=0, second=0, microsecond=0)
fetch_desc = "end-of-today"
_LOGGER.debug(
"Sensor data request for home %s: fetch %s to %s (%s), return up to %s",
self._home_id,
day_before_yesterday.isoformat(),
fetch_end_time.isoformat(),
fetch_desc,
end_of_tomorrow.isoformat(),
)
# Fetch data (may be partial if include_tomorrow=False)
_intervals, api_called = await self.get_intervals(
api_client=api_client,
user_data=user_data,
start_time=day_before_yesterday,
end_time=fetch_end_time,
)
# Return FULL protected range (including any cached tomorrow data)
# This ensures cached tomorrow data is available even when include_tomorrow=False
final_intervals = self._get_cached_intervals(
day_before_yesterday.isoformat(),
end_of_tomorrow.isoformat(),
)
return final_intervals, api_called
def get_pool_stats(self) -> dict[str, Any]:
"""
Get statistics about the interval pool.
Returns comprehensive statistics for diagnostic sensors, separated into:
- Sensor intervals (protected range: day-before-yesterday to tomorrow)
- Cache statistics (entire pool including service-requested data)
Protected Range:
The protected range covers 4 days at 15-min resolution = 384 intervals.
These intervals are never evicted by garbage collection.
Cache Fill Level:
Shows how full the cache is relative to MAX_CACHE_SIZE (960).
100% is not bad - just means we're using the available space.
GC will evict oldest non-protected intervals when limit is reached.
Returns:
Dict with sensor intervals, cache stats, and timestamps.
"""
fetch_groups = self._cache.get_fetch_groups()
# === Sensor Intervals (Protected Range) ===
sensor_stats = self._get_sensor_interval_stats()
# === Cache Statistics (Entire Pool) ===
cache_total = self._index.count()
cache_limit = MAX_CACHE_SIZE
cache_fill_percent = round((cache_total / cache_limit) * 100, 1) if cache_limit > 0 else 0
cache_extra = max(0, cache_total - sensor_stats["count"]) # Intervals outside protected range
# === Timestamps ===
# Last sensor fetch (for protected range data)
last_sensor_fetch: str | None = None
oldest_interval: str | None = None
newest_interval: str | None = None
if fetch_groups:
# Find newest fetch group (most recent API call)
newest_group = max(fetch_groups, key=lambda g: g["fetched_at"])
last_sensor_fetch = newest_group["fetched_at"].isoformat()
# Find oldest and newest intervals across all fetch groups
all_timestamps = list(self._index.get_raw_index().keys())
if all_timestamps:
oldest_interval = min(all_timestamps)
newest_interval = max(all_timestamps)
return {
# Sensor intervals (protected range)
"sensor_intervals_count": sensor_stats["count"],
"sensor_intervals_expected": sensor_stats["expected"],
"sensor_intervals_has_gaps": sensor_stats["has_gaps"],
# Cache statistics
"cache_intervals_total": cache_total,
"cache_intervals_limit": cache_limit,
"cache_fill_percent": cache_fill_percent,
"cache_intervals_extra": cache_extra,
# Timestamps
"last_sensor_fetch": last_sensor_fetch,
"cache_oldest_interval": oldest_interval,
"cache_newest_interval": newest_interval,
# Fetch groups (API calls)
"fetch_groups_count": len(fetch_groups),
}
def _get_sensor_interval_stats(self) -> dict[str, Any]:
"""
Get statistics for sensor intervals (protected range).
Protected range: day-before-yesterday 00:00 to day-after-tomorrow 00:00.
Expected: 4 days * 24 hours * 4 intervals = 384 intervals.
Returns:
Dict with count, expected, and has_gaps.
"""
start_iso, end_iso = self._cache.get_protected_range()
start_dt = datetime.fromisoformat(start_iso)
end_dt = datetime.fromisoformat(end_iso)
# Count expected intervals (15-min resolution)
expected_count = int((end_dt - start_dt).total_seconds() / (15 * 60))
# Count actual intervals in range
actual_count = 0
current_dt = start_dt
while current_dt < end_dt:
current_key = current_dt.isoformat()[:19]
if self._index.contains(current_key):
actual_count += 1
current_dt += timedelta(minutes=15)
return {
"count": actual_count,
"expected": expected_count,
"has_gaps": actual_count < expected_count,
}
def _has_gaps_in_protected_range(self) -> bool:
"""
Check if there are gaps in the protected date range.
Delegates to _get_sensor_interval_stats() for consistency.
Returns:
True if any gaps exist, False if protected range is complete.
"""
return self._get_sensor_interval_stats()["has_gaps"]
def _extract_timezone_from_user_data(self, user_data: dict[str, Any]) -> str | None:
"""Extract timezone for this home from user_data."""
if not user_data:
return None
viewer = user_data.get("viewer", {})
homes = viewer.get("homes", [])
for home in homes:
if home.get("id") == self._home_id:
return home.get("timeZone")
return None
def _get_cached_intervals(
self,
start_time_iso: str,
end_time_iso: str,
) -> list[dict[str, Any]]:
"""
Get cached intervals for time range using timestamp index.
Uses timestamp_index for O(1) lookups per timestamp.
IMPORTANT: Returns shallow copies of interval dicts to prevent external
mutations (e.g., by parse_all_timestamps()) from affecting cached data.
The Pool cache must remain immutable to ensure consistent behavior.
Args:
start_time_iso: ISO timestamp string (inclusive).
end_time_iso: ISO timestamp string (exclusive).
Returns:
List of cached interval dicts in time range (may be empty or incomplete).
Sorted by startsAt timestamp. Each dict is a shallow copy.
"""
# Parse query range once
start_time_dt = datetime.fromisoformat(start_time_iso)
end_time_dt = datetime.fromisoformat(end_time_iso)
# Use index to find intervals: iterate through expected timestamps
result = []
current_dt = start_time_dt
# Determine interval step (15 min post-2025-10-01, 60 min pre)
resolution_change_dt = datetime(2025, 10, 1, tzinfo=start_time_dt.tzinfo)
interval_minutes = INTERVAL_QUARTER_HOURLY if current_dt >= resolution_change_dt else INTERVAL_HOURLY
while current_dt < end_time_dt:
# Check if this timestamp exists in index (O(1) lookup)
current_dt_key = current_dt.isoformat()[:19]
location = self._index.get(current_dt_key)
if location is not None:
# Get interval from fetch group
fetch_groups = self._cache.get_fetch_groups()
fetch_group = fetch_groups[location["fetch_group_index"]]
interval = fetch_group["intervals"][location["interval_index"]]
# CRITICAL: Return shallow copy to prevent external mutations
# (e.g., parse_all_timestamps() converts startsAt to datetime in-place)
result.append(dict(interval))
# Move to next expected interval
current_dt += timedelta(minutes=interval_minutes)
# Handle resolution change boundary
if interval_minutes == INTERVAL_HOURLY and current_dt >= resolution_change_dt:
interval_minutes = INTERVAL_QUARTER_HOURLY
_LOGGER_DETAILS.debug(
"Retrieved %d intervals from cache for home %s (range %s to %s)",
len(result),
self._home_id,
start_time_iso,
end_time_iso,
)
return result
def _add_intervals(
self,
intervals: list[dict[str, Any]],
fetch_time_iso: str,
) -> None:
"""
Add intervals as new fetch group to cache with GC.
Strategy:
1. Filter out duplicates (intervals already in cache)
2. Handle "touch" (move cached intervals to new fetch group)
3. Add new fetch group to cache
4. Update timestamp index
5. Run GC if needed
6. Schedule debounced auto-save
Args:
intervals: List of interval dicts from API.
fetch_time_iso: ISO timestamp string when intervals were fetched.
"""
if not intervals:
return
fetch_time_dt = datetime.fromisoformat(fetch_time_iso)
# Classify intervals: new vs already cached
new_intervals = []
intervals_to_touch = []
for interval in intervals:
starts_at_normalized = _normalize_starts_at(interval["startsAt"])
if not self._index.contains(starts_at_normalized):
new_intervals.append(interval)
else:
intervals_to_touch.append((starts_at_normalized, interval))
_LOGGER_DETAILS.debug(
"Interval %s already cached for home %s, will touch (update fetch time)",
interval["startsAt"],
self._home_id,
)
# Handle touched intervals: move to new fetch group
if intervals_to_touch:
self._touch_intervals(intervals_to_touch, fetch_time_dt)
if not new_intervals:
if intervals_to_touch:
_LOGGER_DETAILS.debug(
"All %d intervals already cached for home %s (touched only)",
len(intervals),
self._home_id,
)
return
# Sort new intervals by startsAt
new_intervals.sort(key=lambda x: x["startsAt"])
# Add new fetch group to cache
fetch_group_index = self._cache.add_fetch_group(new_intervals, fetch_time_dt)
# Update timestamp index for all new intervals
for interval_index, interval in enumerate(new_intervals):
starts_at_normalized = _normalize_starts_at(interval["startsAt"])
self._index.add(interval, fetch_group_index, interval_index)
_LOGGER_DETAILS.debug(
"Added fetch group %d to home %s cache: %d new intervals (fetched at %s)",
fetch_group_index,
self._home_id,
len(new_intervals),
fetch_time_iso,
)
# Run GC to evict old fetch groups if needed
gc_changed_data = self._gc.run_gc()
# Schedule debounced auto-save if data changed
data_changed = len(new_intervals) > 0 or len(intervals_to_touch) > 0 or gc_changed_data
if data_changed and self._hass is not None and self._entry_id is not None:
self._schedule_debounced_save()
def _touch_intervals(
self,
intervals_to_touch: list[tuple[str, dict[str, Any]]],
fetch_time_dt: datetime,
) -> None:
"""
Move cached intervals to new fetch group (update fetch time).
Creates a new fetch group containing references to existing intervals.
Updates the index to point to the new fetch group.
Args:
intervals_to_touch: List of (normalized_timestamp, interval_dict) tuples.
fetch_time_dt: Datetime when intervals were fetched.
"""
fetch_groups = self._cache.get_fetch_groups()
# Create touch fetch group with existing interval references
touch_intervals = []
for starts_at_normalized, _interval in intervals_to_touch:
# Get existing interval from old fetch group
location = self._index.get(starts_at_normalized)
if location is None:
continue # Should not happen, but be defensive
old_group = fetch_groups[location["fetch_group_index"]]
existing_interval = old_group["intervals"][location["interval_index"]]
touch_intervals.append(existing_interval)
# Add touch group to cache
touch_group_index = self._cache.add_fetch_group(touch_intervals, fetch_time_dt)
# Update index to point to new fetch group using batch operation
# This is more efficient than individual remove+add calls
index_updates = [
(starts_at_normalized, touch_group_index, interval_index)
for interval_index, (starts_at_normalized, _) in enumerate(intervals_to_touch)
]
self._index.update_batch(index_updates)
_LOGGER.debug(
"Touched %d cached intervals for home %s (moved to fetch group %d, fetched at %s)",
len(intervals_to_touch),
self._home_id,
touch_group_index,
fetch_time_dt.isoformat(),
)
def _schedule_debounced_save(self) -> None:
"""
Schedule debounced save with configurable delay.
Cancels existing timer and starts new one if already scheduled.
This prevents multiple saves during rapid successive changes.
"""
# Cancel existing debounce timer if running
if self._save_debounce_task is not None and not self._save_debounce_task.done():
self._save_debounce_task.cancel()
_LOGGER.debug("Cancelled pending auto-save (new changes detected, resetting timer)")
# Schedule new debounced save
task = asyncio.create_task(
self._debounced_save_worker(),
name=f"interval_pool_debounce_{self._entry_id}",
)
self._save_debounce_task = task
self._background_tasks.add(task)
task.add_done_callback(self._background_tasks.discard)
async def _debounced_save_worker(self) -> None:
"""Debounce worker: waits configured delay, then saves if not cancelled."""
try:
await asyncio.sleep(DEBOUNCE_DELAY_SECONDS)
await self._auto_save_pool_state()
except asyncio.CancelledError:
_LOGGER.debug("Auto-save timer cancelled (expected - new changes arrived)")
raise
async def async_shutdown(self) -> None:
"""
Clean shutdown - cancel pending background tasks.
Should be called when the config entry is unloaded to prevent
orphaned tasks and ensure clean resource cleanup.
"""
_LOGGER.debug("Shutting down interval pool for home %s", self._home_id)
# Cancel debounce task if running
if self._save_debounce_task is not None and not self._save_debounce_task.done():
self._save_debounce_task.cancel()
with contextlib.suppress(asyncio.CancelledError):
await self._save_debounce_task
_LOGGER.debug("Cancelled pending auto-save task")
# Cancel any other background tasks
if self._background_tasks:
for task in list(self._background_tasks):
if not task.done():
task.cancel()
# Wait for all tasks to complete cancellation
if self._background_tasks:
await asyncio.gather(*self._background_tasks, return_exceptions=True)
_LOGGER.debug("Cancelled %d background tasks", len(self._background_tasks))
self._background_tasks.clear()
_LOGGER.debug("Interval pool shutdown complete for home %s", self._home_id)
async def _auto_save_pool_state(self) -> None:
"""Auto-save pool state to storage with lock protection."""
if self._hass is None or self._entry_id is None:
return
async with self._save_lock:
try:
pool_state = self.to_dict()
await async_save_pool_state(self._hass, self._entry_id, pool_state)
_LOGGER.debug("Auto-saved interval pool for entry %s", self._entry_id)
except Exception:
_LOGGER.exception("Failed to auto-save interval pool for entry %s", self._entry_id)
def to_dict(self) -> dict[str, Any]:
"""
Serialize interval pool state for storage.
Filters out dead intervals (no longer referenced by index).
Returns:
Dictionary containing serialized pool state (only living intervals).
"""
fetch_groups = self._cache.get_fetch_groups()
# Serialize fetch groups (only living intervals)
serialized_fetch_groups = []
for group_idx, fetch_group in enumerate(fetch_groups):
living_intervals = []
for interval_idx, interval in enumerate(fetch_group["intervals"]):
starts_at_normalized = _normalize_starts_at(interval["startsAt"])
# Check if interval is still referenced in index
location = self._index.get(starts_at_normalized)
# Only keep if index points to THIS position in THIS group
if (
location is not None
and location["fetch_group_index"] == group_idx
and location["interval_index"] == interval_idx
):
living_intervals.append(interval)
# Only serialize groups with living intervals
if living_intervals:
serialized_fetch_groups.append(
{
"fetched_at": fetch_group["fetched_at"].isoformat(),
"intervals": living_intervals,
}
)
return {
"version": 1,
"home_id": self._home_id,
"fetch_groups": serialized_fetch_groups,
}
@classmethod
def from_dict(
cls,
data: dict[str, Any],
*,
api: TibberPricesApiClient,
hass: Any | None = None,
entry_id: str | None = None,
time_service: TibberPricesTimeService | None = None,
) -> TibberPricesIntervalPool | None:
"""
Restore interval pool manager from storage.
Expects single-home format: {"version": 1, "home_id": "...", "fetch_groups": [...]}
Old multi-home format is treated as corrupted and returns None.
Args:
data: Dictionary containing serialized pool state.
api: API client for fetching intervals.
hass: HomeAssistant instance for auto-save (optional).
entry_id: Config entry ID for auto-save (optional).
time_service: TimeService for time-travel support (optional).
Returns:
Restored TibberPricesIntervalPool instance, or None if format unknown/corrupted.
"""
# Validate format
if not data or "home_id" not in data or "fetch_groups" not in data:
if "homes" in data:
_LOGGER.info(
"Interval pool storage uses old multi-home format (pre-2025-11-25). "
"Treating as corrupted. Pool will rebuild from API."
)
else:
_LOGGER.warning("Interval pool storage format unknown or corrupted. Pool will rebuild from API.")
return None
home_id = data["home_id"]
# Create manager with home_id from storage
manager = cls(home_id=home_id, api=api, hass=hass, entry_id=entry_id, time_service=time_service)
# Restore fetch groups to cache
for serialized_group in data.get("fetch_groups", []):
fetched_at_dt = datetime.fromisoformat(serialized_group["fetched_at"])
intervals = serialized_group["intervals"]
fetch_group_index = manager._cache.add_fetch_group(intervals, fetched_at_dt)
# Rebuild index for this fetch group
for interval_index, interval in enumerate(intervals):
manager._index.add(interval, fetch_group_index, interval_index)
total_intervals = sum(len(group["intervals"]) for group in manager._cache.get_fetch_groups())
_LOGGER.debug(
"Interval pool restored from storage (home %s, %d intervals)",
home_id,
total_intervals,
)
return manager
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