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A battery profile describes a battery model: the charge and discharge limit
maps over temperature and SoC, the charge and discharge efficiency curves
over C-rate, the technical SoC bounds, the hard temperature cutoffs and,
for a model, its nominal capacity and power limits. EOS packages the
generic profile generic-lfp; users add their own profiles.
A battery or electric vehicle selects its profile by battery_profile and
describes how the installation differs from it in profile_adjustment
(capacity, power limits of the installation, periodic full charge). The
effective values are resolved from both at the measured operating point
(temperature, SoC, power) and shown as effective_* fields.
The GENETIC simulation uses the limits and efficiencies of the profile over
SoC, with precomputed tables as a fast path; GENETIC0 gets the values at the
operating point. Optimization parameters are always valid: a battery whose
profile is missing, unreadable or invalid uses the packaged generic-lfp
profile, and the former defaults only if that is not usable either; each is
logged as error, and an optimization run is never cancelled because of a
battery.
Additional changes
- Add a generic device profile store: user and packaged profiles, atomic
saves, compiled profiles cached per file and checked again at most once a
second, plain file names as profile names ('schema' is reserved). On
Windows, a save or read that collides with the other is retried briefly.
- Add the REST API /v1/profile to list the profile kinds and to list, read
(document and JSON schema), save and delete profiles.
- Check the hard temperature cutoffs on the exact temperature.
- Resolve inhibited charging or discharging to a power of 0, e.g. for a cold
battery; give GENETIC0 its own discharge limit for that.
- Limit generic profiles without own power limits to the former 5000 W.
- Accept only the implemented units; current limits need the nominal
voltage.
- Migrate legacy battery settings into the profile adjustment, keep the
former charge power limit as discharge limit (also for a Home Assistant
entity on it) and report dropped efficiencies as errors.
- Limit GENETIC battery discharge by the discharge power.
- Allow battery profile lookups while the configuration is created.
- Resolve a battery once per configuration dump.
- Package the bundled profiles, document battery profiles and regenerate
the configuration and OpenAPI documentation.
- Ignore the report PDFs written by the visualization tests.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Assisted-by: Claude Code (claude-opus-5-5)
322 lines
11 KiB
Python
322 lines
11 KiB
Python
import asyncio
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from unittest.mock import patch
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import numpy as np
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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.measurement.measurement import MeasurementDataRecord
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from akkudoktoreos.prediction.loadakkudoktor import (
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LoadAkkudoktor,
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LoadAkkudoktorAdjusted,
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LoadAkkudoktorCommonSettings,
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)
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from akkudoktoreos.utils.datetimeutil import compare_datetimes, to_datetime, to_duration
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@pytest.fixture
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def loadakkudoktor(config_eos):
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"""Fixture to initialise the LoadAkkudoktor instance."""
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settings = {
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"load": {
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"provider": "LoadAkkudoktor",
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"loadakkudoktor": {
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"loadakkudoktor_year_energy_kwh": "1000",
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},
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},
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}
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config_eos.merge_settings_from_dict(settings)
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assert config_eos.load.provider == "LoadAkkudoktor"
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assert config_eos.load.loadakkudoktor.loadakkudoktor_year_energy_kwh == 1000
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return LoadAkkudoktor()
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@pytest.fixture
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def loadakkudoktoradjusted(config_eos):
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"""Fixture to initialise the LoadAkkudoktorAdjusted instance."""
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settings = {
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"load": {
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"provider": "LoadAkkudoktorAdjusted",
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"loadakkudoktor": {
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"loadakkudoktor_year_energy_kwh": "1000",
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},
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},
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"measurement": {
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"load_emr_keys": ["load0_mr", "load1_mr"]
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}
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}
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config_eos.merge_settings_from_dict(settings)
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assert config_eos.load.provider == "LoadAkkudoktorAdjusted"
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assert config_eos.load.loadakkudoktor.loadakkudoktor_year_energy_kwh == 1000
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return LoadAkkudoktorAdjusted()
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@pytest_asyncio.fixture
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async def measurement_eos():
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"""Fixture to initialise the Measurement instance."""
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# Load meter readings are in kWh
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measurement = get_measurement()
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load0_mr = 500.0
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load1_mr = 500.0
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dt = to_datetime("2024-01-01T00:00:00")
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interval = to_duration("1 hour")
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for i in range(25):
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await measurement.insert_by_datetime(
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MeasurementDataRecord(
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date_time=dt,
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load0_mr=load0_mr,
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load1_mr=load1_mr,
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)
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)
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dt += interval
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# 0.05 kWh = 50 Wh
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load0_mr += 0.05
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load1_mr += 0.05
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min_dt = await measurement.min_datetime()
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max_dt = await measurement.max_datetime()
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assert min_dt is not None
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assert max_dt is not None
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assert compare_datetimes(min_dt, to_datetime("2024-01-01T00:00:00")).equal
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assert compare_datetimes(max_dt, to_datetime("2024-01-02T00:00:00")).equal
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return measurement
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@pytest.fixture
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def mock_load_profiles_file(tmp_path):
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"""Fixture to create a mock load profiles file."""
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load_profiles_path = tmp_path / "load_profiles.npz"
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np.savez(
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load_profiles_path,
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yearly_profiles=np.random.rand(365, 24), # Random load profiles
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yearly_profiles_std=np.random.rand(365, 24), # Random standard deviation
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)
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return load_profiles_path
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@pytest.mark.asyncio
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class TestLoadAkkudoktor:
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async def test_loadakkudoktor_settings_validator(self):
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"""Test the field validator for `loadakkudoktor_year_energy_kwh`."""
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settings = LoadAkkudoktorCommonSettings(loadakkudoktor_year_energy_kwh=1234)
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assert isinstance(settings.loadakkudoktor_year_energy_kwh, float)
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assert settings.loadakkudoktor_year_energy_kwh == 1234.0
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settings = LoadAkkudoktorCommonSettings(loadakkudoktor_year_energy_kwh=1234.56)
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assert isinstance(settings.loadakkudoktor_year_energy_kwh, float)
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assert settings.loadakkudoktor_year_energy_kwh == 1234.56
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async def test_loadakkudoktor_provider_id(self, loadakkudoktor):
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"""Test the `provider_id` class method."""
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assert loadakkudoktor.provider_id() == "LoadAkkudoktor"
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@patch("akkudoktoreos.prediction.loadakkudoktor.np.load")
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async def test_load_data_from_mock(self, mock_np_load, mock_load_profiles_file, loadakkudoktor):
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"""Test the `load_data` method."""
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# Mock numpy load to return data similar to what would be in the file
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mock_np_load.return_value = {
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"yearly_profiles": np.ones((365, 24)),
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"yearly_profiles_std": np.zeros((365, 24)),
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}
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# Test data loading
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data_year_energy = loadakkudoktor.load_data()
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assert data_year_energy is not None
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assert data_year_energy.shape == (365, 2, 24)
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async def test_load_data_from_file(self, loadakkudoktor):
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"""Test `load_data` loads data from the profiles file."""
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data_year_energy = loadakkudoktor.load_data()
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assert data_year_energy is not None
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@patch("akkudoktoreos.prediction.loadakkudoktor.LoadAkkudoktor.load_data")
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async def test_update_data(self, mock_load_data, loadakkudoktor):
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"""Test the `_update` method."""
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mock_load_data.return_value = np.random.rand(365, 2, 24)
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# Mock methods for updating values
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ems_eos = get_ems()
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ems_eos.set_start_datetime(pendulum.datetime(2024, 1, 1))
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# Assure there are no prediction records
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await loadakkudoktor.delete_by_datetime(start_datetime=None, end_datetime=None)
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assert len(loadakkudoktor) == 0
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# Execute the method
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await loadakkudoktor._update_data()
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# Validate that update_value is called
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assert len(loadakkudoktor) > 0
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@pytest.mark.asyncio
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class TestLoadAkkudoktorAdjusted:
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async def test_calculate_adjustment(self, loadakkudoktoradjusted, measurement_eos):
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"""Test `_calculate_adjustment` for various scenarios."""
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data_year_energy = np.random.rand(365, 2, 24)
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# Check the test setup
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assert loadakkudoktoradjusted.measurement is measurement_eos
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min_dt = await measurement_eos.min_datetime()
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assert min_dt == to_datetime("2024-01-01T00:00:00")
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max_dt = await measurement_eos.max_datetime()
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assert max_dt == to_datetime("2024-01-02T00:00:00")
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# Use same calculation as in _calculate_adjustment
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compare_start = max_dt - to_duration("7 days")
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if compare_datetimes(compare_start, min_dt).lt:
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# Not enough measurements for 7 days - use what is available
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compare_start = min_dt
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compare_end = max_dt
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compare_interval = to_duration("1 hour")
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load_total_kwh_array = await measurement_eos.load_total_kwh(
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start_datetime=compare_start,
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end_datetime=compare_end,
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interval=compare_interval,
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)
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np.testing.assert_allclose(load_total_kwh_array, [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
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# Call the method and validate results
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weekday_adjust, weekend_adjust = await loadakkudoktoradjusted._calculate_adjustment(data_year_energy)
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assert weekday_adjust.shape == (24,)
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assert weekend_adjust.shape == (24,)
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data_year_energy = np.zeros((365, 2, 24))
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weekday_adjust, weekend_adjust = await loadakkudoktoradjusted._calculate_adjustment(data_year_energy)
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assert weekday_adjust.shape == (24,)
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expected = np.array(
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[
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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]
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)
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np.testing.assert_allclose(weekday_adjust, expected)
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assert weekend_adjust.shape == (24,)
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expected = np.array(
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[
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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]
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)
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np.testing.assert_array_equal(weekend_adjust, expected)
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async def test_provider_adjustments_with_mock_data(self, loadakkudoktoradjusted):
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"""Test full integration of adjustments with mock data."""
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with patch(
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"akkudoktoreos.prediction.loadakkudoktor.LoadAkkudoktorAdjusted._calculate_adjustment"
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) as mock_adjust:
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mock_adjust.return_value = (np.zeros(24), np.zeros(24))
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# Test execution
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await loadakkudoktoradjusted._update_data()
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assert mock_adjust.called
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@pytest_asyncio.fixture
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async def empty_measurement(config_eos):
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"""Own, empty measurement store (the singleton is shared between tests)."""
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measurement = get_measurement()
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await measurement.delete_by_datetime(None, None)
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config_eos.merge_settings_from_dict(
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{"devices": {"max_batteries": 1, "batteries": {"storage": {"profile_adjustment": {"nominal_capacity_wh": 2000}}}}}
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)
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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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async def _adjustment_window(adjusted, measurement):
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measurement_class = type(measurement)
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original = measurement_class.load_total_kwh
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calls = []
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async def spy(self, *args, **kwargs):
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calls.append(kwargs)
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return await original(self, *args, **kwargs)
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with patch.object(measurement_class, "load_total_kwh", spy):
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result = await adjusted._calculate_adjustment(np.random.rand(365, 2, 24))
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return result, calls
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@pytest.mark.asyncio
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async def test_adjustment_buckets_stay_on_full_hours(loadakkudoktoradjusted, empty_measurement):
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"""A newer off-hour record (e.g. a device SoC) must not shift the hourly buckets (#1447)."""
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dt = to_datetime("2024-01-01T00:00:00")
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for hour in range(25):
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await empty_measurement.update_value(dt.add(hours=hour), "load0_mr", 500.0 + hour)
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await empty_measurement.update_value(
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to_datetime("2024-01-02T00:11:00"), "storage-soc-factor", 0.42
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)
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_, calls = await _adjustment_window(loadakkudoktoradjusted, empty_measurement)
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assert calls
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assert calls[0]["start_datetime"] == to_datetime("2024-01-01T00:00:00")
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assert calls[0]["end_datetime"] == to_datetime("2024-01-02T00:00:00")
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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"records",
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[
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[("14:15", "load0_mr", 1000.0), ("14:26", "storage-soc-factor", 0.42)],
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[("14:11", "storage-soc-factor", 0.42)],
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],
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)
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async def test_adjustment_within_first_hour_returns_zero(
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loadakkudoktoradjusted, empty_measurement, records
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):
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"""Less than one full hour of data yields no adjustment instead of an error."""
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for time, key, value in records:
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await empty_measurement.update_value(to_datetime(f"2026-08-01T{time}:00"), key, value)
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(weekday, weekend), calls = await _adjustment_window(loadakkudoktoradjusted, empty_measurement)
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assert calls == []
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assert not weekday.any() and not weekend.any()
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