mirror of
https://github.com/jpawlowski/hass.tibber_prices.git
synced 2026-05-28 18:43:40 +00:00
e01cc5d447
Add entity_resolver module that lets all service parameters accept HA entity references in place of literal values. The entity's current state (or a specific attribute via the @attr syntax) is resolved at call time and coerced to the expected Python type. Syntax: "sensor.washing_duration" → uses entity state "sensor.washing_duration@run_minutes" → uses entity attribute Apply or_entity_ref() and resolve_entity_references() to all five service handlers (get_price, find_cheapest_block, find_cheapest_hours, find_cheapest_schedule, get_chartdata) for every parameter where a dynamic value from another entity is useful (duration, start/end times, offsets, etc.). Add five new translation keys for entity-resolution error messages (invalid_entity_reference, entity_not_found, entity_attribute_not_found, entity_state_unavailable, entity_value_conversion_failed) across all five language files. Fix pytest warning filter to suppress AsyncMock cleanup noise, and update test_resource_cleanup to mock hass.config_entries.async_entries so the blueprint-removal path in async_remove_entry does not raise. Impact: Automations and scripts can pass sensor entity IDs as service parameters (e.g. duration from a sensor) instead of having to use template-based workarounds.
538 lines
21 KiB
Python
538 lines
21 KiB
Python
"""
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Service handler for find_cheapest_hours and find_most_expensive_hours services.
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Finds the cheapest (or most expensive) N minutes of intervals within a search range.
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Intervals need not be contiguous — designed for flexible loads
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(battery charging, EV, water heater with thermostat).
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"""
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from __future__ import annotations
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from datetime import datetime, time as dt_time, timedelta
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import logging
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import math
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from typing import TYPE_CHECKING, Any
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import voluptuous as vol
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from custom_components.tibber_prices.const import DOMAIN, get_display_unit_factor, get_display_unit_string
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from custom_components.tibber_prices.utils.price_window import calculate_window_statistics, find_cheapest_n_intervals
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from homeassistant.exceptions import ServiceValidationError
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from homeassistant.helpers import config_validation as cv
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from homeassistant.util import dt as dt_util
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from .entity_resolver import or_entity_ref, resolve_entity_references
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from .helpers import (
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INTERVAL_MINUTES,
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PRICE_LEVEL_ORDER,
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VALID_SEARCH_SCOPES,
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apply_must_finish_by,
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build_rating_lookup,
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build_response_interval,
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calculate_search_range_avg,
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check_min_distance_from_avg,
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filter_intervals_by_price_level,
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get_entry_and_data,
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resolve_home_timezone,
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resolve_search_range,
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restore_original_prices,
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smooth_service_intervals,
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validate_power_profile_length,
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validate_price_level_range,
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validate_search_params,
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)
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from .relaxation import (
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MIN_RELAXED_DURATION_INTERVALS,
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calculate_max_duration_reduction_intervals,
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generate_relaxation_steps,
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)
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if TYPE_CHECKING:
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from zoneinfo import ZoneInfo
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from homeassistant.core import HomeAssistant, ServiceCall, ServiceResponse
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_LOGGER = logging.getLogger(__name__)
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FIND_CHEAPEST_HOURS_SERVICE_NAME = "find_cheapest_hours"
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# Parameter types for entity reference resolution
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_HOURS_ENTITY_PARAMS: dict[str, type] = {
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"duration": timedelta,
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"min_segment_duration": timedelta,
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"search_start": datetime,
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"search_end": datetime,
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"search_start_time": dt_time,
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"search_end_time": dt_time,
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"search_start_day_offset": int,
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"search_end_day_offset": int,
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"search_start_offset_minutes": int,
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"search_end_offset_minutes": int,
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"min_distance_from_avg": float,
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"duration_flexibility_minutes": int,
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"must_finish_by": datetime,
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}
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_COMMON_HOURS_SCHEMA = {
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vol.Optional("entry_id", default=""): cv.string,
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vol.Required("duration"): or_entity_ref(
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vol.All(cv.positive_time_period, vol.Range(min=timedelta(minutes=1), max=timedelta(hours=24))),
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),
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vol.Optional("search_start"): or_entity_ref(cv.datetime),
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vol.Optional("search_end"): or_entity_ref(cv.datetime),
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vol.Optional("search_start_time"): or_entity_ref(cv.time),
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vol.Optional("search_start_day_offset", default=0): or_entity_ref(
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vol.All(vol.Coerce(int), vol.Range(min=-7, max=2)),
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),
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vol.Optional("search_end_time"): or_entity_ref(cv.time),
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vol.Optional("search_end_day_offset", default=0): or_entity_ref(
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vol.All(vol.Coerce(int), vol.Range(min=-7, max=2)),
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),
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vol.Optional("search_start_offset_minutes"): or_entity_ref(
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vol.All(vol.Coerce(int), vol.Range(min=-10080, max=10080)),
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),
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vol.Optional("search_end_offset_minutes"): or_entity_ref(
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vol.All(vol.Coerce(int), vol.Range(min=-10080, max=10080)),
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),
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vol.Optional("min_segment_duration"): or_entity_ref(
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vol.All(cv.positive_time_period, vol.Range(min=timedelta(minutes=1), max=timedelta(hours=4))),
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),
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vol.Optional("search_scope"): vol.In(VALID_SEARCH_SCOPES),
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vol.Optional("max_price_level"): vol.In([lvl.lower() for lvl in PRICE_LEVEL_ORDER]),
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vol.Optional("min_price_level"): vol.In([lvl.lower() for lvl in PRICE_LEVEL_ORDER]),
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vol.Optional("include_comparison_details", default=False): cv.boolean,
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vol.Optional("power_profile"): vol.All(
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[vol.All(vol.Coerce(int), vol.Range(min=1, max=100000))],
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vol.Length(min=1, max=96),
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),
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vol.Optional("include_current_interval", default=True): cv.boolean,
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vol.Optional("use_base_unit", default=False): cv.boolean,
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vol.Optional("smooth_outliers", default=True): cv.boolean,
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vol.Optional("min_distance_from_avg"): or_entity_ref(
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vol.All(vol.Coerce(float), vol.Range(min=0.1, max=50.0)),
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),
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vol.Optional("allow_relaxation", default=True): cv.boolean,
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vol.Optional("duration_flexibility_minutes"): or_entity_ref(
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vol.All(vol.Coerce(int), vol.Range(min=0, max=120)),
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),
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vol.Optional("must_finish_by"): or_entity_ref(cv.datetime),
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}
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FIND_CHEAPEST_HOURS_SERVICE_SCHEMA = vol.Schema(_COMMON_HOURS_SCHEMA)
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def _determine_no_intervals_reason(
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price_info: list[dict],
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filtered_price_info: list[dict],
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total_intervals: int,
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*,
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level_filter_active: bool,
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) -> str:
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"""Classify why no interval selection could be found."""
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if not price_info:
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return "no_data_in_range"
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if level_filter_active and not filtered_price_info:
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return "no_intervals_matching_level_filter"
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if len(filtered_price_info) < total_intervals:
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return "insufficient_intervals_after_filter"
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return "insufficient_intervals_for_constraints"
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def _attempt_find_hours(
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price_info: list[dict],
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*,
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max_price_level: str | None,
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min_price_level: str | None,
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total_intervals: int,
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min_segment_intervals: int,
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smooth_outliers: bool,
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min_distance_from_avg: float | None,
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reverse: bool,
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) -> tuple[dict | None, str]:
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"""Attempt to find hours with specific filter parameters.
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Returns:
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(result_dict, "") on success or (None, reason_code) on failure.
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"""
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level_filter_active = min_price_level is not None or max_price_level is not None
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filtered = filter_intervals_by_price_level(price_info, min_price_level, max_price_level)
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if smooth_outliers and filtered:
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search_data = smooth_service_intervals(filtered)
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else:
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search_data = filtered
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result = find_cheapest_n_intervals(search_data, total_intervals, min_segment_intervals, reverse=reverse)
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if result is None:
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return None, _determine_no_intervals_reason(
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price_info, filtered, total_intervals, level_filter_active=level_filter_active
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)
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# Restore original prices (smoothing only affects scoring)
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if smooth_outliers:
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result["intervals"] = restore_original_prices(result["intervals"])
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for seg in result["segments"]:
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seg["intervals"] = restore_original_prices(seg["intervals"])
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# Check distance constraint
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if min_distance_from_avg is not None:
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range_avg = calculate_search_range_avg(price_info)
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window_mean = sum(iv["total"] for iv in result["intervals"]) / len(result["intervals"])
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if range_avg is not None and not check_min_distance_from_avg(
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window_mean, range_avg, min_distance_from_avg, reverse=reverse
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):
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return None, "selection_above_distance_threshold" if not reverse else "selection_below_distance_threshold"
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return result, ""
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def _build_found_response(
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*,
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result: dict,
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comparison_result: dict | None,
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reverse: bool,
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home_id: str,
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search_start: datetime,
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search_end: datetime,
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total_minutes_requested: int,
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total_minutes: int,
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min_segment_minutes_requested: int,
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min_segment_minutes: int,
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currency: str,
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price_unit: str,
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unit_factor: int,
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service_label: str,
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rating_lookup: dict[str, str | None],
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include_comparison_details: bool = False,
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power_profile: list[int] | None = None,
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) -> dict:
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"""Build the service response when intervals are found."""
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stats = calculate_window_statistics(
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result["intervals"], unit_factor=unit_factor, round_decimals=4, power_profile=power_profile
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)
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# Calculate price comparison (difference to opposite-direction selection)
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price_comparison: dict[str, float | str | None] = {}
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if comparison_result is not None:
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comparison_stats = calculate_window_statistics(
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comparison_result["intervals"], unit_factor=unit_factor, round_decimals=4
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)
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own_mean = stats.get("price_mean")
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comp_mean = comparison_stats.get("price_mean")
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if own_mean is not None and comp_mean is not None:
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diff = round(float(comp_mean) - float(own_mean), 4)
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if reverse:
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diff = -diff
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price_comparison = {
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"comparison_price_mean": comp_mean,
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"price_difference": abs(round(diff, 4)),
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}
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if include_comparison_details:
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price_comparison["comparison_price_min"] = comparison_stats.get("price_min")
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price_comparison["comparison_price_max"] = comparison_stats.get("price_max")
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response_intervals = [build_response_interval(iv, unit_factor, rating_lookup) for iv in result["intervals"]]
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response_segments = []
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for seg in result["segments"]:
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seg_stats = calculate_window_statistics(seg["intervals"], unit_factor=unit_factor, round_decimals=4)
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last_start = seg["intervals"][-1]["startsAt"]
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if isinstance(last_start, str):
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seg_end = datetime.fromisoformat(last_start) + timedelta(minutes=INTERVAL_MINUTES)
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else:
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seg_end = last_start + timedelta(minutes=INTERVAL_MINUTES)
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response_segments.append(
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{
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"start": seg["start"],
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"end": seg_end.isoformat() if hasattr(seg_end, "isoformat") else seg_end,
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"duration_minutes": seg["duration_minutes"],
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"interval_count": seg["interval_count"],
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"price_mean": seg_stats.get("price_mean"),
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"intervals": [build_response_interval(iv, unit_factor, rating_lookup) for iv in seg["intervals"]],
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}
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)
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actual_minutes = len(result["intervals"]) * INTERVAL_MINUTES
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_LOGGER.info(
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"%s: found %d intervals in %d segments, mean=%.4f %s",
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service_label,
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len(result["intervals"]),
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len(response_segments),
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stats.get("price_mean", 0) or 0,
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price_unit,
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)
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return {
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"home_id": home_id,
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"search_start": search_start.isoformat(),
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"search_end": search_end.isoformat(),
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"total_minutes_requested": total_minutes_requested,
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"total_minutes": total_minutes,
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"min_segment_minutes_requested": min_segment_minutes_requested,
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"min_segment_minutes": min_segment_minutes,
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"currency": currency,
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"price_unit": price_unit,
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"intervals_found": True,
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"schedule": {
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"total_minutes": actual_minutes,
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"interval_count": len(result["intervals"]),
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**stats,
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"segment_count": len(response_segments),
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"segments": response_segments,
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"intervals": response_intervals,
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},
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"price_comparison": price_comparison or None,
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}
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async def _handle_find_hours(
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call: ServiceCall,
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*,
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reverse: bool = False,
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) -> ServiceResponse:
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"""
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Core handler for finding price hours (cheapest or most expensive).
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Finds the cheapest/most expensive N intervals (not necessarily contiguous)
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within the search range. Results are grouped into contiguous segments for
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scheduling convenience.
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"""
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service_label = "find_most_expensive_hours" if reverse else "find_cheapest_hours"
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hass: HomeAssistant = call.hass
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# Resolve entity references
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data, resolved_refs = resolve_entity_references(hass, call.data, _HOURS_ENTITY_PARAMS)
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entry_id: str = data.get("entry_id", "")
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duration_td: timedelta = data["duration"]
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min_segment_td: timedelta | None = data.get("min_segment_duration")
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use_base_unit: bool = data.get("use_base_unit", False)
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max_price_level: str | None = data.get("max_price_level")
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min_price_level: str | None = data.get("min_price_level")
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include_comparison_details: bool = data.get("include_comparison_details", False)
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power_profile: list[int] | None = data.get("power_profile")
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smooth_outliers: bool = data.get("smooth_outliers", True)
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min_distance_from_avg: float | None = data.get("min_distance_from_avg")
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allow_relaxation: bool = data.get("allow_relaxation", True)
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duration_flexibility_minutes: int | None = data.get("duration_flexibility_minutes")
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total_minutes_requested = int(duration_td.total_seconds() / 60)
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min_segment_minutes_requested = int(min_segment_td.total_seconds() / 60) if min_segment_td else INTERVAL_MINUTES
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# Round up to nearest quarter-hour intervals
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total_minutes = math.ceil(total_minutes_requested / INTERVAL_MINUTES) * INTERVAL_MINUTES
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min_segment_minutes = math.ceil(min_segment_minutes_requested / INTERVAL_MINUTES) * INTERVAL_MINUTES
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entry, coordinator, data = get_entry_and_data(hass, entry_id)
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rating_lookup = build_rating_lookup(data)
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home_id = entry.data.get("home_id")
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if not home_id:
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raise ServiceValidationError(
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translation_domain=DOMAIN,
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translation_key="missing_home_id",
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)
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# Resolve timezone
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home_timezone = resolve_home_timezone(coordinator, home_id)
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home_tz: ZoneInfo
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from zoneinfo import ZoneInfo # noqa: PLC0415
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home_tz = ZoneInfo(home_timezone)
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# Handle must_finish_by: convert deadline to search_end
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validate_search_params(data)
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effective_data, must_finish_by_dt = apply_must_finish_by(data, home_tz)
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# Resolve search range (priority: explicit datetime > time+offset > minutes offset > default)
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now = dt_util.now().astimezone(home_tz)
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search_start, search_end = resolve_search_range(effective_data, now, home_tz)
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total_intervals = total_minutes // INTERVAL_MINUTES
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min_segment_intervals = min_segment_minutes // INTERVAL_MINUTES
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# Validate parameter combinations
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if min_segment_minutes > total_minutes:
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raise ServiceValidationError(
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translation_domain=DOMAIN,
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translation_key="min_segment_exceeds_duration",
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translation_placeholders={
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"min_segment_minutes": str(min_segment_minutes),
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"duration_minutes": str(total_minutes),
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},
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)
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validate_price_level_range(min_price_level, max_price_level)
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validate_power_profile_length(power_profile, total_intervals)
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_LOGGER.info(
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"%s called: total=%dmin, min_segment=%dmin, range=%s to %s",
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service_label,
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total_minutes,
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min_segment_minutes,
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search_start,
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search_end,
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)
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# Fetch intervals via pool
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api_client = coordinator.api
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user_data = coordinator._cached_user_data # noqa: SLF001
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pool = entry.runtime_data.interval_pool
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try:
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price_info, _api_called = await pool.get_intervals(
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api_client=api_client,
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user_data=user_data,
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start_time=search_start,
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end_time=search_end,
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)
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except Exception as error:
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_LOGGER.exception("Error fetching price data for %s", service_label)
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raise ServiceValidationError(
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translation_domain=DOMAIN,
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translation_key="price_fetch_failed",
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) from error
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# Determine currency and unit
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currency = entry.data.get("currency", "EUR")
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unit_factor = 1 if use_base_unit else get_display_unit_factor(entry)
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price_unit = f"{currency}/kWh" if use_base_unit else get_display_unit_string(entry, currency)
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# --- Attempt with original parameters ---
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effective_total = total_intervals
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result, reason = _attempt_find_hours(
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price_info,
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max_price_level=max_price_level,
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min_price_level=min_price_level,
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total_intervals=effective_total,
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min_segment_intervals=min_segment_intervals,
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smooth_outliers=smooth_outliers,
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min_distance_from_avg=min_distance_from_avg,
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reverse=reverse,
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)
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relaxation_applied = False
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relaxation_steps = 0
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# --- Relaxation loop ---
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if result is None and allow_relaxation:
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max_reduction = calculate_max_duration_reduction_intervals(total_intervals, duration_flexibility_minutes)
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min_dur = max(MIN_RELAXED_DURATION_INTERVALS, min_segment_intervals)
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steps = generate_relaxation_steps(
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min_distance_from_avg=min_distance_from_avg,
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max_price_level=max_price_level,
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min_price_level=min_price_level,
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total_intervals=total_intervals,
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min_duration_intervals=min_dur,
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max_duration_reduction_intervals=max_reduction,
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reverse=reverse,
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)
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for step in steps:
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effective_total = total_intervals - step.duration_reduction
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result, reason = _attempt_find_hours(
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price_info,
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max_price_level=step.max_price_level,
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min_price_level=step.min_price_level,
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|
total_intervals=effective_total,
|
|
min_segment_intervals=min_segment_intervals,
|
|
smooth_outliers=smooth_outliers,
|
|
min_distance_from_avg=step.min_distance_from_avg,
|
|
reverse=reverse,
|
|
)
|
|
if result is not None:
|
|
relaxation_applied = True
|
|
relaxation_steps = step.step_number
|
|
_LOGGER.info(
|
|
"%s: relaxation succeeded at step %d (phase=%s, intervals=%d)",
|
|
service_label,
|
|
step.step_number,
|
|
step.phase,
|
|
effective_total,
|
|
)
|
|
break
|
|
else:
|
|
reason = "relaxation_exhausted"
|
|
|
|
effective_total_minutes = effective_total * INTERVAL_MINUTES
|
|
|
|
if result is None:
|
|
_LOGGER.info(
|
|
"%s: no interval selection found (reason=%s, need %d, have %d in range)",
|
|
service_label,
|
|
reason,
|
|
effective_total,
|
|
len(price_info),
|
|
)
|
|
response: dict[str, Any] = {
|
|
"home_id": home_id,
|
|
"search_start": search_start.isoformat(),
|
|
"search_end": search_end.isoformat(),
|
|
"must_finish_by": must_finish_by_dt.isoformat() if must_finish_by_dt else None,
|
|
"total_minutes_requested": total_minutes_requested,
|
|
"total_minutes": effective_total_minutes,
|
|
"min_segment_minutes_requested": min_segment_minutes_requested,
|
|
"min_segment_minutes": min_segment_minutes,
|
|
"currency": currency,
|
|
"price_unit": price_unit,
|
|
"intervals_found": False,
|
|
"reason": reason,
|
|
"relaxation_applied": relaxation_applied,
|
|
"schedule": None,
|
|
}
|
|
if relaxation_applied:
|
|
response["relaxation_steps"] = relaxation_steps
|
|
if resolved_refs:
|
|
response["_resolved"] = resolved_refs
|
|
return response
|
|
|
|
# Find opposite-direction selection for price comparison (from full unfiltered list)
|
|
comparison_result = find_cheapest_n_intervals(
|
|
price_info, effective_total, min_segment_intervals, reverse=not reverse
|
|
)
|
|
|
|
found_response = _build_found_response(
|
|
result=result,
|
|
comparison_result=comparison_result,
|
|
reverse=reverse,
|
|
home_id=home_id,
|
|
search_start=search_start,
|
|
search_end=search_end,
|
|
total_minutes_requested=total_minutes_requested,
|
|
total_minutes=effective_total_minutes,
|
|
min_segment_minutes_requested=min_segment_minutes_requested,
|
|
min_segment_minutes=min_segment_minutes,
|
|
currency=currency,
|
|
price_unit=price_unit,
|
|
unit_factor=unit_factor,
|
|
service_label=service_label,
|
|
rating_lookup=rating_lookup,
|
|
include_comparison_details=include_comparison_details,
|
|
power_profile=power_profile,
|
|
)
|
|
found_response["relaxation_applied"] = relaxation_applied
|
|
found_response["must_finish_by"] = must_finish_by_dt.isoformat() if must_finish_by_dt else None
|
|
if relaxation_applied:
|
|
found_response["relaxation_steps"] = relaxation_steps
|
|
|
|
# Add seconds_until_start (time until first segment starts)
|
|
schedule = found_response.get("schedule")
|
|
if schedule and schedule.get("segments"):
|
|
first_seg_start = schedule["segments"][0]["start"]
|
|
first_seg_dt = datetime.fromisoformat(first_seg_start)
|
|
schedule["seconds_until_start"] = max(0, int((first_seg_dt - now).total_seconds()))
|
|
last_seg_end = schedule["segments"][-1]["end"]
|
|
last_seg_end_dt = datetime.fromisoformat(last_seg_end)
|
|
schedule["seconds_until_end"] = max(0, int((last_seg_end_dt - now).total_seconds()))
|
|
|
|
if resolved_refs:
|
|
found_response["_resolved"] = resolved_refs
|
|
|
|
return found_response
|
|
|
|
|
|
async def handle_find_cheapest_hours(call: ServiceCall) -> ServiceResponse:
|
|
"""Handle find_cheapest_hours service call."""
|
|
return await _handle_find_hours(call, reverse=False)
|