""" Service handler for find_cheapest_schedule service. Finds optimal non-overlapping blocks for multiple tasks within a search range. Uses a greedy algorithm: tasks are sorted by duration (longest first), then each task claims the cheapest available contiguous window in the remaining pool. """ from __future__ import annotations import logging import math from datetime import datetime, timedelta from typing import TYPE_CHECKING, Any import voluptuous as vol from custom_components.tibber_prices.const import ( DOMAIN, get_display_unit_factor, get_display_unit_string, ) from custom_components.tibber_prices.utils.price_window import ( calculate_window_statistics, ) from homeassistant.exceptions import ServiceValidationError from homeassistant.helpers import config_validation as cv from homeassistant.util import dt as dt_utils from .helpers import ( INTERVAL_MINUTES, PRICE_LEVEL_ORDER, VALID_SEARCH_SCOPES, build_rating_lookup, build_response_interval, filter_intervals_by_price_level, get_entry_and_data, resolve_home_timezone, resolve_search_range, validate_power_profile_length, validate_price_level_range, ) if TYPE_CHECKING: from zoneinfo import ZoneInfo from homeassistant.core import HomeAssistant, ServiceCall, ServiceResponse _LOGGER = logging.getLogger(__name__) FIND_CHEAPEST_SCHEDULE_SERVICE_NAME = "find_cheapest_schedule" _TASK_SCHEMA = vol.Schema( { vol.Required("name"): cv.string, vol.Required("duration"): vol.All( cv.positive_time_period, vol.Range(min=timedelta(minutes=1), max=timedelta(hours=12)), ), vol.Optional("power_profile"): vol.All( [vol.All(vol.Coerce(int), vol.Range(min=1, max=100000))], vol.Length(min=1, max=48), ), } ) FIND_CHEAPEST_SCHEDULE_SERVICE_SCHEMA = vol.Schema( { vol.Optional("entry_id", default=""): cv.string, vol.Required("tasks"): vol.All( [_TASK_SCHEMA], vol.Length(min=1, max=4), ), vol.Optional("gap_minutes", default=0): vol.All(vol.Coerce(int), vol.Range(min=0, max=120)), vol.Optional("search_start"): cv.datetime, vol.Optional("search_end"): cv.datetime, vol.Optional("search_start_time"): cv.time, vol.Optional("search_start_day_offset", default=0): vol.All(vol.Coerce(int), vol.Range(min=-7, max=2)), vol.Optional("search_end_time"): cv.time, vol.Optional("search_end_day_offset", default=0): vol.All(vol.Coerce(int), vol.Range(min=-7, max=2)), vol.Optional("search_start_offset_minutes"): vol.All(vol.Coerce(int), vol.Range(min=-10080, max=10080)), vol.Optional("search_end_offset_minutes"): vol.All(vol.Coerce(int), vol.Range(min=-10080, max=10080)), vol.Optional("search_scope"): vol.In(VALID_SEARCH_SCOPES), vol.Optional("max_price_level"): vol.In([lvl.lower() for lvl in PRICE_LEVEL_ORDER]), vol.Optional("min_price_level"): vol.In([lvl.lower() for lvl in PRICE_LEVEL_ORDER]), vol.Optional("use_base_unit", default=False): cv.boolean, } ) def _find_cheapest_window_in_pool( pool: list[dict[str, Any]], duration_intervals: int, available: list[bool], ) -> tuple[int, int] | None: """ Find the cheapest contiguous window of `duration_intervals` in available pool slots. Args: pool: Full sorted interval list. duration_intervals: Required contiguous count. available: Boolean mask, same length as pool. True = still available. Returns: (start_index, end_index_exclusive) of the best window, or None if not found. """ n = len(pool) best_sum: float | None = None best_start: int = -1 i = 0 while i <= n - duration_intervals: # Check if a contiguous block starting at i is fully available # and all intervals are contiguous in time (no gaps) block: list[dict[str, Any]] = [] j = i while j < n and len(block) < duration_intervals: if not available[j]: break if block: # Check temporal contiguity prev_start = block[-1]["startsAt"] curr_start = pool[j]["startsAt"] prev_dt = datetime.fromisoformat(prev_start) if isinstance(prev_start, str) else prev_start curr_dt = datetime.fromisoformat(curr_start) if isinstance(curr_start, str) else curr_start if curr_dt - prev_dt != timedelta(minutes=INTERVAL_MINUTES): # Gap in time — can't extend this block, skip to j+1 break block.append(pool[j]) j += 1 if len(block) == duration_intervals: window_sum = sum(iv["total"] for iv in block) if best_sum is None or window_sum < best_sum: best_sum = window_sum best_start = i i += 1 else: # Skip past the blocking unavailable/non-contiguous slot i = j + 1 if best_start == -1: return None return (best_start, best_start + duration_intervals) async def handle_find_cheapest_schedule(call: ServiceCall) -> ServiceResponse: # noqa: PLR0915 """Handle find_cheapest_schedule service call.""" service_label = "find_cheapest_schedule" hass: HomeAssistant = call.hass entry_id: str = call.data.get("entry_id", "") tasks_raw: list[dict[str, Any]] = call.data["tasks"] gap_minutes: int = call.data.get("gap_minutes", 0) use_base_unit: bool = call.data.get("use_base_unit", False) max_price_level: str | None = call.data.get("max_price_level") min_price_level: str | None = call.data.get("min_price_level") # Round gap up to nearest quarter interval gap_intervals = math.ceil(gap_minutes / INTERVAL_MINUTES) if gap_minutes > 0 else 0 entry, coordinator, data = get_entry_and_data(hass, entry_id) rating_lookup = build_rating_lookup(data) home_id = entry.data.get("home_id") if not home_id: raise ServiceValidationError( translation_domain=DOMAIN, translation_key="missing_home_id", ) home_timezone = resolve_home_timezone(coordinator, home_id) home_tz: ZoneInfo from zoneinfo import ZoneInfo # noqa: PLC0415 home_tz = ZoneInfo(home_timezone) now = dt_utils.now().astimezone(home_tz) search_start, search_end = resolve_search_range(call.data, now, home_tz) # Resolve task durations (round up to intervals) tasks: list[dict[str, Any]] = [] for task in tasks_raw: dur_td: timedelta = task["duration"] dur_minutes_req = int(dur_td.total_seconds() / 60) dur_minutes = math.ceil(dur_minutes_req / INTERVAL_MINUTES) * INTERVAL_MINUTES dur_intervals = dur_minutes // INTERVAL_MINUTES validate_power_profile_length(task.get("power_profile"), dur_intervals) tasks.append( { "name": task["name"], "duration_minutes_requested": dur_minutes_req, "duration_minutes": dur_minutes, "duration_intervals": dur_intervals, "power_profile": task.get("power_profile"), } ) # Validate parameter combinations validate_price_level_range(min_price_level, max_price_level) _LOGGER.info( "%s called: %d tasks, gap=%dmin, range=%s to %s", service_label, len(tasks), gap_minutes, search_start, search_end, ) # Fetch intervals api_client = coordinator.api user_data = coordinator._cached_user_data # noqa: SLF001 pool = entry.runtime_data.interval_pool try: price_info, _api_called = await pool.get_intervals( api_client=api_client, user_data=user_data, start_time=search_start, end_time=search_end, ) except Exception as error: _LOGGER.exception("Error fetching price data for %s", service_label) raise ServiceValidationError( translation_domain=DOMAIN, translation_key="price_fetch_failed", ) from error currency = entry.data.get("currency", "EUR") unit_factor = 1 if use_base_unit else get_display_unit_factor(entry) price_unit = f"{currency}/kWh" if use_base_unit else get_display_unit_string(entry, currency) # Apply optional level filter filtered_price_info = filter_intervals_by_price_level(price_info, min_price_level, max_price_level) if not filtered_price_info: return { "home_id": home_id, "search_start": search_start.isoformat(), "search_end": search_end.isoformat(), "currency": currency, "price_unit": price_unit, "all_tasks_scheduled": False, "tasks": [], "total_estimated_cost": None, } # Greedy assignment: longest task first tasks_sorted = sorted(tasks, key=lambda t: t["duration_intervals"], reverse=True) available = [True] * len(filtered_price_info) assignments: list[dict[str, Any]] = [] unscheduled: list[str] = [] for task in tasks_sorted: dur_intervals = task["duration_intervals"] window = _find_cheapest_window_in_pool(filtered_price_info, dur_intervals, available) if window is None: _LOGGER.info("%s: no window found for task '%s'", service_label, task["name"]) unscheduled.append(task["name"]) continue start_idx, end_idx = window task_intervals = filtered_price_info[start_idx:end_idx] # Mark task intervals + trailing gap as unavailable gap_end = min(end_idx + gap_intervals, len(filtered_price_info)) for k in range(start_idx, gap_end): available[k] = False stats = calculate_window_statistics( task_intervals, unit_factor=unit_factor, round_decimals=4, power_profile=task.get("power_profile"), ) first_start = task_intervals[0]["startsAt"] last_start = task_intervals[-1]["startsAt"] first_dt = datetime.fromisoformat(first_start) if isinstance(first_start, str) else first_start last_dt = datetime.fromisoformat(last_start) if isinstance(last_start, str) else last_start end_dt = last_dt + timedelta(minutes=INTERVAL_MINUTES) # Build enriched interval list for this task task_response_intervals = [build_response_interval(iv, unit_factor, rating_lookup) for iv in task_intervals] assignments.append( { "name": task["name"], "start": first_dt.isoformat(), "end": end_dt.isoformat(), "duration_minutes_requested": task["duration_minutes_requested"], "duration_minutes": task["duration_minutes"], **stats, "intervals": task_response_intervals, } ) # Sort final assignments by start time assignments.sort(key=lambda a: a["start"]) # Sum estimated costs total_cost_values: list[float] = [ a["estimated_total_cost"] for a in assignments if a.get("estimated_total_cost") is not None ] total_estimated_cost = round(sum(total_cost_values), 4) if total_cost_values else None all_scheduled = len(unscheduled) == 0 _LOGGER.info( "%s: scheduled %d/%d tasks, total_cost=%s", service_label, len(assignments), len(tasks), total_estimated_cost, ) result: dict[str, Any] = { "home_id": home_id, "search_start": search_start.isoformat(), "search_end": search_end.isoformat(), "currency": currency, "price_unit": price_unit, "all_tasks_scheduled": all_scheduled, "unscheduled_tasks": unscheduled or None, "tasks": assignments, "total_estimated_cost": total_estimated_cost, } return result