mirror of
https://github.com/jpawlowski/hass.tibber_prices.git
synced 2026-05-28 18:43:40 +00:00
bbcfdd4443
Recompute merged relaxed periods from raw intervals, harden numeric period option normalization, update day-volatility handling for zero or negative averages, and expose day context on period binary sensors. Add focused regressions for overlap merges, cache invalidation, day statistics, and visible binary sensor attributes. Impact: Best and peak period entities stay consistent on negative-price days, refresh correctly when same-day prices change, and expose the documented day context attributes.
134 lines
5.4 KiB
Python
134 lines
5.4 KiB
Python
"""Regression tests for overlap resolution and merged period summaries."""
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from __future__ import annotations
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from datetime import timedelta
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import pytest
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from custom_components.tibber_prices.coordinator.period_handlers import TibberPricesPeriodConfig
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from custom_components.tibber_prices.coordinator.period_handlers.period_overlap import resolve_period_overlaps
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from custom_components.tibber_prices.coordinator.time_service import TibberPricesTimeService
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from custom_components.tibber_prices.utils.price import calculate_coefficient_of_variation
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from homeassistant.util import dt as dt_util
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def _create_interval(base_time, offset: int, price: float, level: str, difference: float, rating: str) -> dict:
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"""Create one quarter-hour interval for overlap tests."""
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return {
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"startsAt": base_time + timedelta(minutes=offset * 15),
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"total": price,
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"level": level,
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"difference": difference,
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"rating_level": rating,
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}
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@pytest.mark.unit
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class TestResolvePeriodOverlaps:
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"""Validate merged period summaries stay consistent after overlap resolution."""
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def test_merge_recomputes_summary_from_raw_intervals(self) -> None:
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"""Overlapping periods should be rebuilt from the raw union, not glued summaries."""
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base_time = dt_util.parse_datetime("2025-11-22T10:00:00+01:00")
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assert base_time is not None
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all_prices = [
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_create_interval(base_time, 0, 0.10, "CHEAP", -12.0, "LOW"),
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_create_interval(base_time, 1, 0.10, "CHEAP", -11.0, "LOW"),
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_create_interval(base_time, 2, 0.11, "CHEAP", -10.0, "LOW"),
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_create_interval(base_time, 3, 0.11, "CHEAP", -9.0, "NORMAL"),
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_create_interval(base_time, 4, 0.12, "NORMAL", -4.0, "NORMAL"),
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_create_interval(base_time, 5, 0.13, "NORMAL", 0.0, "NORMAL"),
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_create_interval(base_time, 6, 0.13, "NORMAL", 1.0, "NORMAL"),
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_create_interval(base_time, 7, 0.14, "NORMAL", 3.0, "NORMAL"),
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]
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config = TibberPricesPeriodConfig(
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reverse_sort=False,
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flex=0.15,
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min_distance_from_avg=0.0,
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min_period_length=60,
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threshold_low=-10.0,
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threshold_high=10.0,
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)
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time = TibberPricesTimeService(reference_time=base_time + timedelta(hours=1))
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existing_period = {
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"start": base_time,
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"end": base_time + timedelta(minutes=75),
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"duration_minutes": 75,
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"level": "cheap",
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"rating_level": "low",
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"rating_difference_%": -9.2,
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"price_mean": 0.108,
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"price_median": 0.11,
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"price_min": 0.10,
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"price_max": 0.12,
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"price_spread": 0.02,
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"price_coefficient_variation_%": 7.7,
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"volatility": "low",
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"period_interval_count": 5,
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"period_price_diff_from_daily_min": 0.008,
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"period_price_diff_from_daily_min_%": 8.0,
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"period_position": 1,
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"period_count_total": 1,
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"period_count_remaining": 0,
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}
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relaxed_period = {
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"start": base_time + timedelta(minutes=60),
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"end": base_time + timedelta(minutes=120),
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"duration_minutes": 60,
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"level": "normal",
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"rating_level": "normal",
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"rating_difference_%": 0.0,
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"price_mean": 0.13,
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"price_median": 0.13,
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"price_min": 0.12,
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"price_max": 0.14,
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"price_spread": 0.02,
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"price_coefficient_variation_%": 5.8,
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"volatility": "low",
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"period_interval_count": 4,
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"period_price_diff_from_daily_min": 0.03,
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"period_price_diff_from_daily_min_%": 30.0,
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"period_position": 1,
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"period_count_total": 1,
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"period_count_remaining": 0,
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"relaxation_active": True,
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"relaxation_level": "flex=18.0% +level_any",
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}
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merged_periods, periods_added = resolve_period_overlaps(
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existing_periods=[existing_period],
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new_relaxed_periods=[relaxed_period],
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all_prices=all_prices,
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config=config,
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time=time,
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)
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assert periods_added == 1
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assert len(merged_periods) == 1
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merged = merged_periods[0]
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expected_prices = [0.10, 0.10, 0.11, 0.11, 0.12, 0.13, 0.13, 0.14]
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expected_cv = calculate_coefficient_of_variation(expected_prices)
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assert expected_cv is not None
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assert merged["start"] == base_time
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assert merged["end"] == base_time + timedelta(minutes=120)
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assert merged["period_interval_count"] == 8
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assert merged["duration_minutes"] == 120
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assert merged["price_mean"] == 0.1175
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assert merged["price_median"] == 0.115
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assert merged["price_min"] == 0.10
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assert merged["price_max"] == 0.14
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assert merged["price_spread"] == 0.04
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assert merged["price_coefficient_variation_%"] == round(expected_cv, 1)
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assert merged["level"] == "normal"
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assert merged["rating_level"] == "normal"
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assert merged["rating_difference_%"] == -5.25
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assert merged["period_price_diff_from_daily_min"] == 0.0175
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assert merged["period_price_diff_from_daily_min_%"] == 17.5
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assert merged["relaxation_active"] is True
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assert merged["relaxation_level"] == "flex=18.0% +level_any"
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assert "merged_from" in merged
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