mirror of
https://github.com/Akkudoktor-EOS/EOS.git
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NormannandGitHub
5d7198d090
Regression tests were scattered across feature- and bug-specific files, and some suites repeated the same checks. Group tests by responsibility, isolate feature fixtures in test classes, and remove duplicate or obsolete cases. The review covered all 127 collected test modules on main. This reduces them and removes or combines definitions:. No application code changes.
280 lines
12 KiB
Python
280 lines
12 KiB
Python
"""GENETIC0 parameter preparation: time alignment, tariff coverage, and measured SoC."""
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from unittest.mock import AsyncMock, Mock, patch
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import numpy as np
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import pandas as pd
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import pendulum
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import pytest
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import pytest_asyncio
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from akkudoktoreos.core.coreabc import get_ems, get_measurement
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from akkudoktoreos.optimization.genetic0.genetic0params import (
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Genetic0OptimizationParameters,
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)
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from akkudoktoreos.utils.datetimeutil import to_datetime
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class TestGenetic0ParameterTimeAlignment:
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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"start",
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[
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# Normal day: 23 elapsed hours since local midnight.
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pendulum.datetime(2026, 10, 24, 23, tz="Europe/Berlin"),
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# Fall back (CEST -> CET): 24 elapsed hours since local midnight.
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pendulum.datetime(2026, 10, 25, 23, tz="Europe/Berlin"),
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# Spring forward (CET -> CEST): 22 elapsed hours since local midnight.
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pendulum.datetime(2026, 3, 29, 23, tz="Europe/Berlin"),
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],
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ids=["normal", "fall-back", "spring-forward"],
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)
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async def test_parameter_arrays_start_hour_aligned_on_dst_days(self, config_eos, start):
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config_eos.merge_settings_from_dict(
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{
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"general": {"timezone_override": "Europe/Berlin"},
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"prediction": {"hours": 48},
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"optimization": {"genetic0": {"horizon_hours": 24}},
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"feedintariff": {"provider": "FeedInTariffDvhubOnline"},
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"elecfee": {"provider": None},
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"devices": {
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"max_batteries": 0,
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"max_electric_vehicles": 0,
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"max_inverters": 0,
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"max_home_appliances": 0,
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},
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}
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)
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get_ems().set_start_datetime(start)
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ems_start = get_ems().start_datetime
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assert ems_start.hour == 23
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# The tariff is only published from the start slot on, as a live
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# provider would do late in the evening.
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prices = pd.Series(
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np.full(24 * 4, 0.0001),
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index=pd.date_range(ems_start, periods=24 * 4, freq="15min"),
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)
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array_starts = []
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async def read_array(**kwargs):
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array_starts.append(kwargs["start_datetime"])
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return np.full(48, 1.0)
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prediction = Mock(
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update_data=AsyncMock(),
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key_to_array=AsyncMock(side_effect=read_array),
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key_to_raw_series=AsyncMock(return_value=prices),
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)
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with patch.object(Genetic0OptimizationParameters, "prediction", prediction):
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parameters = await Genetic0OptimizationParameters.prepare()
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assert parameters is not None
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# Index ``start.hour`` of every array is the start slot.
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assert array_starts
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for array_start in array_starts:
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assert array_start.add(hours=ems_start.hour) == ems_start
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tariffs = parameters.ems.feed_in_tariff_per_wh
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assert isinstance(tariffs, list)
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assert np.isfinite(tariffs[ems_start.hour : ems_start.hour + 24]).all()
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class TestGenetic0TariffCoverage:
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"""Tariff arrays indexed from UTC midnight, independent of the host timezone."""
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@pytest.mark.asyncio
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@pytest.mark.parametrize("published_hours", [20, 24])
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async def test_dvhub_tariff_requires_real_control_horizon(self,
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config_eos, published_hours, caplog
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):
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config_eos.merge_settings_from_dict(
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{
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"general": {"timezone_override": "UTC"},
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"prediction": {"hours": 48},
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"optimization": {"genetic0": {"horizon_hours": 24}},
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"feedintariff": {"provider": "FeedInTariffDvhubOnline"},
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"elecfee": {"provider": None},
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"devices": {
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"max_batteries": 0,
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"max_electric_vehicles": 0,
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"max_inverters": 0,
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"max_home_appliances": 0,
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},
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}
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)
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start = to_datetime("2026-08-01T00:00:00+00:00", in_timezone="UTC")
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get_ems().set_start_datetime(start)
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prices = pd.Series(
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np.arange(published_hours * 4, dtype=float) / 1_000_000,
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index=pd.date_range(start, periods=published_hours * 4, freq="15min"),
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)
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async def read_array(**kwargs):
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return np.full(48, 1.0)
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prediction = Mock(
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update_data=AsyncMock(),
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key_to_array=AsyncMock(side_effect=read_array),
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key_to_raw_series=AsyncMock(return_value=prices),
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)
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with patch.object(Genetic0OptimizationParameters, "prediction", prediction):
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parameters = await Genetic0OptimizationParameters.prepare()
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prediction.update_data.assert_awaited_once()
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assert config_eos.feedintariff.provider == "FeedInTariffDvhubOnline"
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if published_hours < 24:
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assert parameters is None
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assert "Missing feed-in tariff within the GENETIC0 control horizon" in caplog.text
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else:
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assert parameters is not None
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tariffs = parameters.ems.feed_in_tariff_per_wh
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assert isinstance(tariffs, list)
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assert tariffs[:24] == pytest.approx(
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[(4 * hour + 1.5) / 1_000_000 for hour in range(24)]
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)
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assert np.isnan(tariffs[24:]).all()
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class TestGenetic0MeasuredSOC:
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@pytest_asyncio.fixture
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async def isolated_measurement(self, config_eos):
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"""Keep synthetic records out of the process-wide measurement singleton."""
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measurement = get_measurement()
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await measurement.delete_by_datetime(None, None)
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try:
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yield measurement
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finally:
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await measurement.delete_by_datetime(None, None)
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@pytest.mark.asyncio
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async def test_soc_read_late_in_slot_beats_previous_reading(self, config_eos, isolated_measurement):
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config_eos.merge_settings_from_dict(
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{
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"prediction": {"hours": 48},
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"optimization": {"genetic0": {"horizon_hours": 24}},
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"elecfee": {"provider": None},
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"devices": {
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"max_batteries": 1,
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"max_electric_vehicles": 0,
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"max_inverters": 1,
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"max_home_appliances": 0,
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"batteries": {"storage": {"capacity_wh": 2000, "max_charge_power_w": 1000}},
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"inverters": {"inverter": {"battery_id": "storage", "max_power_w": 1000}},
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},
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}
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)
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# A run at 14:40 belongs to the 14:00 slot. The previous run (13:40) is closer
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# to the slot start than the reading taken now.
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run_time = to_datetime("2026-08-01T14:40:00+00:00")
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get_ems().set_start_datetime(run_time)
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measurement = isolated_measurement
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await measurement.update_value(run_time.subtract(hours=1), "storage-soc-factor", 0.10)
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await measurement.update_value(run_time, "storage-soc-factor", 0.42)
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tariff = pd.Series(
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0.00008, index=pd.date_range("2026-08-01T00:00:00Z", periods=72 * 4, freq="15min")
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)
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async def read_array(**kwargs):
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return np.full(48, 0.0003)
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prediction = Mock(
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update_data=AsyncMock(),
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key_to_array=AsyncMock(side_effect=read_array),
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key_to_raw_series=AsyncMock(return_value=tariff),
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)
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with patch.object(Genetic0OptimizationParameters, "prediction", prediction):
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parameters = await Genetic0OptimizationParameters.prepare()
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assert parameters is not None
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assert parameters.pv_battery is not None
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assert parameters.pv_battery.initial_soc_percentage == 42
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@pytest.mark.asyncio
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async def test_meter_only_record_does_not_mask_soc(self, config_eos, isolated_measurement):
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config_eos.merge_settings_from_dict(
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{
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"prediction": {"hours": 48},
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"optimization": {"genetic0": {"horizon_hours": 24}},
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"elecfee": {"provider": None},
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"measurement": {"load_emr_keys": ["load_meter"]},
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"devices": {
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"max_batteries": 1,
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"max_electric_vehicles": 0,
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"max_inverters": 1,
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"max_home_appliances": 0,
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"batteries": {"storage": {"capacity_wh": 2000, "max_charge_power_w": 1000}},
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"inverters": {"inverter": {"battery_id": "storage", "max_power_w": 1000}},
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},
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}
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)
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run_time = to_datetime("2026-08-01T14:40:00+00:00")
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get_ems().set_start_datetime(run_time)
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measurement = isolated_measurement
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# SoC through the measurement API at 13:30, the adapter's meter reading at the
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# slot start (14:00); the run at 14:40 must still find the SoC.
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await measurement.update_value(run_time.subtract(minutes=70), "storage-soc-factor", 0.42)
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await measurement.update_value(get_ems().start_datetime, "load_meter", 1234.5)
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tariff = pd.Series(
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0.00008, index=pd.date_range("2026-08-01T00:00:00Z", periods=72 * 4, freq="15min")
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)
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async def read_array(**kwargs):
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return np.full(48, 0.0003)
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prediction = Mock(
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update_data=AsyncMock(),
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key_to_array=AsyncMock(side_effect=read_array),
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key_to_raw_series=AsyncMock(return_value=tariff),
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)
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with patch.object(Genetic0OptimizationParameters, "prediction", prediction):
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parameters = await Genetic0OptimizationParameters.prepare()
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assert parameters is not None
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assert parameters.pv_battery is not None
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assert parameters.pv_battery.initial_soc_percentage == 42
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@pytest.mark.asyncio
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async def test_older_missing_value_does_not_spoil_current_soc(self, config_eos, isolated_measurement):
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config_eos.merge_settings_from_dict(
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{
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"prediction": {"hours": 48},
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"optimization": {"genetic0": {"horizon_hours": 24}},
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"elecfee": {"provider": None},
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"devices": {
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"max_batteries": 1,
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"max_electric_vehicles": 0,
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"max_inverters": 1,
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"max_home_appliances": 0,
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"batteries": {"storage": {"capacity_wh": 2000, "max_charge_power_w": 1000}},
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"inverters": {"inverter": {"battery_id": "storage", "max_power_w": 1000}},
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},
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}
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)
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run_time = to_datetime("2026-08-01T14:40:00+00:00")
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get_ems().set_start_datetime(run_time)
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measurement = isolated_measurement
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# An older reading stored as pandas NA (e.g. a JSON null in a Float64 series).
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await measurement.update_value(run_time.subtract(hours=1), "storage-soc-factor", pd.NA)
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await measurement.update_value(run_time, "storage-soc-factor", 0.42)
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tariff = pd.Series(
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0.00008, index=pd.date_range("2026-08-01T00:00:00Z", periods=72 * 4, freq="15min")
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)
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async def read_array(**kwargs):
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return np.full(48, 0.0003)
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prediction = Mock(
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update_data=AsyncMock(),
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key_to_array=AsyncMock(side_effect=read_array),
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key_to_raw_series=AsyncMock(return_value=tariff),
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)
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with patch.object(Genetic0OptimizationParameters, "prediction", prediction):
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parameters = await Genetic0OptimizationParameters.prepare()
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assert parameters is not None
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assert parameters.pv_battery is not None
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assert parameters.pv_battery.initial_soc_percentage == 42
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