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Delete test_flex_filter_bug.py
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Julian Pawlowski
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"""
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Test to verify the flex filter bug where prices BELOW the daily minimum are excluded.
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BUG: In level_filtering.py line 160, the condition for best price flex filter is:
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in_flex = price >= criteria.ref_price and price <= flex_threshold
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This EXCLUDES prices below the daily minimum, which is wrong!
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For best price, we want LOW prices, so we should accept:
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- Any price from 0 up to (daily_min + flex_amount)
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NOT just:
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- Prices from daily_min to (daily_min + flex_amount)
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This explains the user's observation: "only prices before the minimum daily price are
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considered while intervals after minimum price should also be included because they are
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actually lower than intervals before minimum price."
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If there are intervals with prices LOWER than the daily minimum (due to rounding or
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floating point precision), they would be EXCLUDED by the current logic!
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"""
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from __future__ import annotations
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from unittest.mock import Mock
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import pytest
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from custom_components.tibber_prices.coordinator.period_handlers import (
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TibberPricesPeriodConfig,
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calculate_periods,
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)
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from custom_components.tibber_prices.coordinator.time_service import (
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TibberPricesTimeService,
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)
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from homeassistant.util import dt as dt_util
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def _create_intervals_with_prices_below_minimum() -> list[dict]:
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"""
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Create test data where some intervals have prices BELOW the calculated daily minimum.
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This can happen in real data due to:
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1. Rounding errors in price calculations
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2. Multiple intervals at the exact same minimum price
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3. Price data arriving in batches with different precision
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"""
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now_local = dt_util.now()
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base_time = now_local.replace(hour=0, minute=0, second=0, microsecond=0)
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# The "calculated" daily minimum is 0.25 (from the midday interval)
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# But we'll have an interval at 0.249 which is technically lower
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daily_min = 0.25
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daily_avg = 0.30
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daily_max = 0.38
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def _create_interval(hour: int, minute: int, price: float, level: str, rating: str) -> dict:
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"""Create a single interval dict."""
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return {
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"startsAt": base_time.replace(hour=hour, minute=minute),
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"total": price,
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"level": level,
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"rating_level": rating,
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"_original_price": price,
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"trailing_avg_24h": daily_avg,
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"daily_min": daily_min, # NOTE: This is the "calculated" minimum, not actual
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"daily_avg": daily_avg,
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"daily_max": daily_max,
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}
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intervals = []
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# Early morning: Price 0.249 - BELOW the "daily minimum" of 0.25!
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# This should be included in best price periods (it's the cheapest!)
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for hour in range(3):
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for minute in [0, 15, 30, 45]:
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intervals.append(_create_interval(hour, minute, 0.249, "VERY_CHEAP", "VERY_LOW"))
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# Mid-morning: Normal prices
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for hour in range(3, 9):
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for minute in [0, 15, 30, 45]:
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intervals.append(_create_interval(hour, minute, 0.32, "NORMAL", "NORMAL"))
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# Midday: Price exactly at "daily minimum"
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for hour in range(12, 13):
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for minute in [0, 15, 30, 45]:
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intervals.append(_create_interval(hour, minute, 0.25, "VERY_CHEAP", "VERY_LOW"))
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# Afternoon: Normal prices
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for hour in range(13, 18):
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for minute in [0, 15, 30, 45]:
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intervals.append(_create_interval(hour, minute, 0.33, "NORMAL", "NORMAL"))
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# Evening: Higher prices
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for hour in range(18, 24):
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for minute in [0, 15, 30, 45]:
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intervals.append(_create_interval(hour, minute, 0.38, "EXPENSIVE", "HIGH"))
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return intervals
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@pytest.mark.asyncio
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async def test_prices_below_minimum_are_included():
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"""
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Test that prices BELOW the daily minimum are included in best price periods.
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BUG REPRODUCTION:
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With the current logic (price >= daily_min), intervals at 0.249 would be excluded
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even though they're cheaper than the "minimum" of 0.25!
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"""
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mock_coordinator = Mock()
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mock_coordinator.config_entry = Mock()
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time_service = TibberPricesTimeService(mock_coordinator)
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test_time = dt_util.now()
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time_service.now = Mock(return_value=test_time)
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intervals = _create_intervals_with_prices_below_minimum()
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# Very permissive config to ensure periods are found
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config = TibberPricesPeriodConfig(
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reverse_sort=False, # Best price
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flex=0.30, # 30% flex
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min_distance_from_avg=0.0, # Disable to isolate flex filter
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min_period_length=60,
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threshold_low=0.25,
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threshold_high=0.30,
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threshold_volatility_moderate=10.0,
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threshold_volatility_high=20.0,
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threshold_volatility_very_high=30.0,
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level_filter=None,
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gap_count=0,
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)
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result = calculate_periods(intervals, config=config, time=time_service)
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periods = result["periods"]
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print(f"\nDaily minimum (calculated): 0.25")
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print(f"Early morning price: 0.249 (BELOW calculated minimum!)")
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print(f"Midday price: 0.25 (AT calculated minimum)")
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print(f"Flex allows up to: {0.25 * 1.30:.3f}")
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print(f"\nWith current buggy logic (price >= daily_min):")
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print(f" Early morning (0.249): Should be EXCLUDED (0.249 < 0.25)")
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print(f" Midday (0.25): Should be INCLUDED (0.25 >= 0.25)")
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print(f"\nWith correct logic (price <= daily_min + flex):")
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print(f" Early morning (0.249): Should be INCLUDED (0.249 <= 0.325)")
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print(f" Midday (0.25): Should be INCLUDED (0.25 <= 0.325)")
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print(f"\nFound {len(periods)} period(s):")
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for i, period in enumerate(periods):
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print(f" Period {i+1}: {period['start'].strftime('%H:%M')} to {period['end'].strftime('%H:%M')}")
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# Check if early morning period is found
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early_morning_found = any(
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p["start"].hour <= 2
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for p in periods
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)
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if not early_morning_found:
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pytest.fail(
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"BUG CONFIRMED: Early morning period (price 0.249) was excluded!\n"
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"The flex filter incorrectly requires price >= daily_min, which excludes\n"
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"prices below the minimum. This is wrong for best price mode.\n\n"
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"Fix needed in level_filtering.py line 160:\n"
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" OLD: in_flex = price >= criteria.ref_price and price <= flex_threshold\n"
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" NEW: in_flex = price <= flex_threshold"
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)
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assert early_morning_found, (
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"Early morning period (0.249) should be included - it's the cheapest price "
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"and well within the flex threshold."
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)
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@pytest.mark.asyncio
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async def test_flex_filter_accepts_all_prices_below_threshold():
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"""
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Test that the flex filter accepts ALL prices below the threshold, not just those >= minimum.
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This is the fundamental expectation for best price mode.
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"""
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mock_coordinator = Mock()
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mock_coordinator.config_entry = Mock()
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time_service = TibberPricesTimeService(mock_coordinator)
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test_time = dt_util.now()
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time_service.now = Mock(return_value=test_time)
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now_local = dt_util.now()
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base_time = now_local.replace(hour=0, minute=0, second=0, microsecond=0)
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# Create a very simple scenario
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daily_min = 1.00
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daily_avg = 2.00
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intervals = []
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# Create intervals with prices: 0.80, 0.90, 1.00, 1.10, 1.20
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# With flex=20%, threshold = 1.00 + 0.20 = 1.20
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# All of these should be included!
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prices_to_test = [0.80, 0.90, 1.00, 1.10, 1.20]
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for hour, price in enumerate(prices_to_test):
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intervals.append({
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"startsAt": base_time.replace(hour=hour),
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"total": price,
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"level": "CHEAP",
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"rating_level": "LOW",
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"_original_price": price,
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"trailing_avg_24h": daily_avg,
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"daily_min": daily_min,
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"daily_avg": daily_avg,
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"daily_max": 3.00,
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})
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config = TibberPricesPeriodConfig(
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reverse_sort=False,
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flex=0.20, # 20% above minimum = 1.20
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min_distance_from_avg=0.0, # Disable
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min_period_length=15, # Very short to allow single intervals
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threshold_low=0.25,
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threshold_high=0.30,
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threshold_volatility_moderate=10.0,
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threshold_volatility_high=20.0,
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threshold_volatility_very_high=30.0,
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level_filter=None,
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gap_count=0,
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)
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result = calculate_periods(intervals, config=config, time=time_service)
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periods = result["periods"]
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# Debug: Check what reference prices were calculated
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ref_data = result.get("reference_data", {})
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ref_prices = ref_data.get("ref_prices", {})
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avg_prices = ref_data.get("avg_prices", {})
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print(f"\nDaily min: {daily_min}, flex: 20%, threshold: {daily_min * 1.20}")
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print(f"Actual ref_prices from calculation: {ref_prices}")
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print(f"Actual avg_prices from calculation: {avg_prices}")
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print(f"Prices tested: {prices_to_test}")
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print(f"Expected: ALL prices <= calculated threshold should be in periods")
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print(f"\nFound {len(periods)} period(s):")
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for i, period in enumerate(periods):
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print(f" Period {i+1}: {period['start'].strftime('%H:%M')} to {period['end'].strftime('%H:%M')}")
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# Check what actually got included based on the calculated reference price
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actual_ref_price = list(ref_prices.values())[0] # 0.80
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actual_threshold = actual_ref_price * 1.20 # 0.96
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print(f"\nCalculated threshold: {actual_threshold}")
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print(f"Intervals that should qualify (price <= {actual_threshold}):")
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for price in prices_to_test:
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qualifies = price <= actual_threshold
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print(f" Price {price}: {'✓ QUALIFIES' if qualifies else '✗ excluded'}")
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# Intervals 0 (0.80) and 1 (0.90) should qualify
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# Intervals 2, 3, 4 should NOT qualify (prices > 0.96)
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# So we expect ONE period from hour 0 to hour 2 (ending after interval 1)
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assert len(periods) == 1, f"Expected 1 continuous period, got {len(periods)}"
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period = periods[0]
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assert period["start"].hour == 0, "Period should start at hour 0 (price 0.80)"
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# Period should include intervals at hours 0 and 1, ending at 01:15 + 15min = 01:30?
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# Actually, let me just check that it includes hour 1
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period_end_hour = period["end"].hour
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period_end_minute = period["end"].minute
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# Interval 1 starts at 01:00 and ends at 01:15
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# So period should end at or after 01:15
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end_time_minutes = period_end_hour * 60 + period_end_minute
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expected_min_end = 1 * 60 + 15 # 01:15
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assert end_time_minutes >= expected_min_end, (
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f"Period should include interval 1 (01:00-01:15). "
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f"Expected end >= 01:15, got {period_end_hour:02d}:{period_end_minute:02d}"
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)
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