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https://github.com/jpawlowski/hass.tibber_prices.git
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1d065b11cd
_resolve_time_with_day_offset() was calling dt_util.now() internally instead of using the injected now parameter. This caused incorrect date calculations in tests and any caller that passes a specific reference time. Also add missing price_rank_* sensor keys to TIME_SENSITIVE_ENTITY_KEYS in coordinator/constants.py so quarter-hour refresh is registered for all 11 price rank sensors (current/next/previous interval and hour variants). Rename dt as dt_utils → dt as dt_util (ICN001) across 11 files to follow the project-wide import alias convention. Apply ruff auto-fixes for import ordering and collapsing single-item imports throughout the codebase. Released-Bug: no
227 lines
9.4 KiB
Python
227 lines
9.4 KiB
Python
"""Test Bug #8: Average functions return None instead of 0.0 when no data available."""
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from datetime import UTC, datetime, timedelta
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import pytest
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from custom_components.tibber_prices.coordinator.time_service import TibberPricesTimeService
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from custom_components.tibber_prices.utils.average import calculate_leading_24h_mean, calculate_trailing_24h_mean
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@pytest.fixture
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def time_service() -> TibberPricesTimeService:
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"""Create a TibberPricesTimeService instance for testing."""
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return TibberPricesTimeService()
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@pytest.fixture
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def sample_prices() -> list[dict]:
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"""Create sample price data for testing."""
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base_time = datetime(2025, 11, 22, 12, 0, tzinfo=UTC)
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return [
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{"startsAt": base_time - timedelta(hours=2), "total": -10.0},
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{"startsAt": base_time - timedelta(hours=1), "total": -5.0},
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{"startsAt": base_time, "total": 0.0},
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{"startsAt": base_time + timedelta(hours=1), "total": 5.0},
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{"startsAt": base_time + timedelta(hours=2), "total": 10.0},
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]
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def test_trailing_avg_returns_none_when_empty(time_service: TibberPricesTimeService) -> None:
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"""
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Test that calculate_trailing_24h_avg returns None when no data in window.
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Bug #8: Previously returned 0.0, which with negative prices could be
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misinterpreted as a real average value.
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"""
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interval_start = datetime(2025, 11, 22, 12, 0, tzinfo=UTC)
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empty_prices: list[dict] = []
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avg, _median = calculate_trailing_24h_mean(empty_prices, interval_start, time=time_service)
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assert avg is None, "Empty price list should return (None, None), not 0.0"
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assert _median is None, "Empty price list should return (None, None), not 0.0"
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def test_leading_avg_returns_none_when_empty(time_service: TibberPricesTimeService) -> None:
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"""
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Test that calculate_leading_24h_avg returns None when no data in window.
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Bug #8: Previously returned 0.0, which with negative prices could be
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misinterpreted as a real average value.
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"""
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interval_start = datetime(2025, 11, 22, 12, 0, tzinfo=UTC)
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empty_prices: list[dict] = []
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avg, _median = calculate_leading_24h_mean(empty_prices, interval_start, time=time_service)
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assert avg is None, "Empty price list should return (None, None), not 0.0"
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assert _median is None, "Empty price list should return (None, None), not 0.0"
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def test_trailing_avg_returns_none_when_no_data_in_window(
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sample_prices: list[dict],
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time_service: TibberPricesTimeService,
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) -> None:
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"""
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Test that calculate_trailing_24h_avg returns None when data exists but not in the window.
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This tests the case where we have price data, but it doesn't fall within
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the 24-hour trailing window for the given interval.
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"""
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# Sample data spans 10:00-14:00 UTC on 2025-11-22
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# Set interval_start to a time where the 24h trailing window doesn't contain this data
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# For example, 2 hours after the last data point
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interval_start = datetime(2025, 11, 22, 16, 0, tzinfo=UTC)
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avg, _median = calculate_trailing_24h_mean(sample_prices, interval_start, time=time_service)
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# Trailing window is 16:00 - 24h = yesterday 16:00 to today 16:00
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# Sample data is from 10:00-14:00, which IS in this window
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assert avg is not None, "Should find data in 24h trailing window"
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# Average of all sample prices: (-10 + -5 + 0 + 5 + 10) / 5 = 0.0
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assert avg == pytest.approx(0.0), "Average should be 0.0"
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def test_leading_avg_returns_none_when_no_data_in_window(
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sample_prices: list[dict],
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time_service: TibberPricesTimeService,
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) -> None:
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"""
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Test that calculate_leading_24h_avg returns None when data exists but not in the window.
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This tests the case where we have price data, but it doesn't fall within
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the 24-hour leading window for the given interval.
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"""
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# Sample data spans 10:00-14:00 UTC on 2025-11-22
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# Set interval_start far in the future, so 24h leading window doesn't contain the data
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interval_start = datetime(2025, 11, 23, 15, 0, tzinfo=UTC)
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avg, _median = calculate_leading_24h_mean(sample_prices, interval_start, time=time_service)
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# Leading window is from 15:00 today to 15:00 tomorrow
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# Sample data is from yesterday, outside this window
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assert avg is None, "Should return (None, None) when no data in 24h leading window"
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assert _median is None, "Should return (None, None) when no data in 24h leading window"
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def test_trailing_avg_with_negative_prices_distinguishes_zero(
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sample_prices: list[dict],
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time_service: TibberPricesTimeService,
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) -> None:
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"""
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Test that calculate_trailing_24h_avg correctly distinguishes 0.0 average from None.
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Bug #8 motivation: With negative prices, we need to know if the average is
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truly 0.0 (real value) or if there's no data (None).
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"""
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# Use base_time where we have data
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interval_start = datetime(2025, 11, 22, 12, 0, tzinfo=UTC)
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avg, _median = calculate_trailing_24h_mean(sample_prices, interval_start, time=time_service)
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# Should return an actual average (negative, since we have -10, -5 in the trailing window)
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assert avg is not None, "Should return average when data exists"
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assert isinstance(avg, float), "Should return float, not None"
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assert avg != 0.0, "With negative prices, average should not be exactly 0.0"
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def test_leading_avg_with_negative_prices_distinguishes_zero(
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sample_prices: list[dict],
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time_service: TibberPricesTimeService,
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) -> None:
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"""
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Test that calculate_leading_24h_avg correctly distinguishes 0.0 average from None.
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Bug #8 motivation: With negative prices, we need to know if the average is
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truly 0.0 (real value) or if there's no data (None).
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"""
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# Use base_time - 2h to include all sample data in leading window
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interval_start = datetime(2025, 11, 22, 10, 0, tzinfo=UTC)
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avg, _median = calculate_leading_24h_mean(sample_prices, interval_start, time=time_service)
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# Should return an actual average (0.0 because average of -10, -5, 0, 5, 10 = 0.0)
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assert avg is not None, "Should return average when data exists"
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assert isinstance(avg, float), "Should return float, not None"
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assert avg == 0.0, "Average of symmetric negative/positive prices should be 0.0"
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def test_trailing_avg_with_all_negative_prices(time_service: TibberPricesTimeService) -> None:
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"""
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Test calculate_trailing_24h_avg with all negative prices.
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Verifies that the function correctly calculates averages when all prices
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are negative (common scenario in Norway/Germany with high renewable energy).
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"""
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base_time = datetime(2025, 11, 22, 12, 0, tzinfo=UTC)
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all_negative = [
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{"startsAt": base_time - timedelta(hours=3), "total": -15.0},
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{"startsAt": base_time - timedelta(hours=2), "total": -10.0},
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{"startsAt": base_time - timedelta(hours=1), "total": -5.0},
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]
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avg, _median = calculate_trailing_24h_mean(all_negative, base_time, time=time_service)
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assert avg is not None, "Should return average for all negative prices"
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assert avg < 0, "Average should be negative"
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assert avg == pytest.approx(-10.0), "Average of -15, -10, -5 should be -10.0"
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def test_leading_avg_with_all_negative_prices(time_service: TibberPricesTimeService) -> None:
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"""
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Test calculate_leading_24h_avg with all negative prices.
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Verifies that the function correctly calculates averages when all prices
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are negative (common scenario in Norway/Germany with high renewable energy).
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"""
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base_time = datetime(2025, 11, 22, 12, 0, tzinfo=UTC)
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all_negative = [
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{"startsAt": base_time, "total": -5.0},
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{"startsAt": base_time + timedelta(hours=1), "total": -10.0},
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{"startsAt": base_time + timedelta(hours=2), "total": -15.0},
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]
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avg, _median = calculate_leading_24h_mean(all_negative, base_time, time=time_service)
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assert avg is not None, "Should return average for all negative prices"
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assert avg < 0, "Average should be negative"
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assert avg == pytest.approx(-10.0), "Average of -5, -10, -15 should be -10.0"
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def test_trailing_avg_returns_none_with_none_timestamps(time_service: TibberPricesTimeService) -> None:
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"""
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Test that calculate_trailing_24h_avg handles None timestamps gracefully.
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Price data with None startsAt should be skipped, and if no valid data
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remains, the function should return None.
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"""
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interval_start = datetime(2025, 11, 22, 12, 0, tzinfo=UTC)
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prices_with_none = [
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{"startsAt": None, "total": 10.0},
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{"startsAt": None, "total": 20.0},
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]
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avg, _median = calculate_trailing_24h_mean(prices_with_none, interval_start, time=time_service)
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assert avg is None, "Should return (None, None) when all timestamps are None"
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assert _median is None, "Should return (None, None) when all timestamps are None"
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def test_leading_avg_returns_none_with_none_timestamps(time_service: TibberPricesTimeService) -> None:
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"""
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Test that calculate_leading_24h_avg handles None timestamps gracefully.
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Price data with None startsAt should be skipped, and if no valid data
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remains, the function should return None.
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"""
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interval_start = datetime(2025, 11, 22, 12, 0, tzinfo=UTC)
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prices_with_none = [
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{"startsAt": None, "total": 10.0},
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{"startsAt": None, "total": 20.0},
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]
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avg, _median = calculate_leading_24h_mean(prices_with_none, interval_start, time=time_service)
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assert avg is None, "Should return (None, None) when all timestamps are None"
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assert _median is None, "Should return (None, None) when all timestamps are None"
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