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https://github.com/Akkudoktor-EOS/EOS.git
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6498c7dc32
The database supports backend selection, compression, incremental data load, automatic data saving to storage, automatic vaccum and compaction. Make SQLite3 and LMDB database backends available. Update tests for new interface conventions regarding data sequences, data containers, data providers. This includes the measurements provider and the prediction providers. Add database documentation. The fix includes several bug fixes that are not directly related to the database implementation but are necessary to keep EOS running properly and to test and document the changes. * fix: config eos test setup Make the config_eos fixture generate a new instance of the config_eos singleton. Use correct env names to setup data folder path. * fix: startup with no config Make cache and measurements complain about missing data path configuration but do not bail out. * fix: soc data preparation and usage for genetic optimization. Search for soc measurments 48 hours around the optimization start time. Only clamp soc to maximum in battery device simulation. * fix: dashboard bailout on zero value solution display Do not use zero values to calculate the chart values adjustment for display. * fix: openapi generation script Make the script also replace data_folder_path and data_output_path to hide real (test) environment pathes. * feat: add make repeated task function make_repeated_task allows to wrap a function to be repeated cyclically. * chore: removed index based data sequence access Index based data sequence access does not make sense as the sequence can be backed by the database. The sequence is now purely time series data. * chore: refactor eos startup to avoid module import startup Avoid module import initialisation expecially of the EOS configuration. Config mutation, singleton initialization, logging setup, argparse parsing, background task definitions depending on config and environment-dependent behavior is now done at function startup. * chore: introduce retention manager A single long-running background task that owns the scheduling of all periodic server-maintenance jobs (cache cleanup, DB autosave, …) * chore: canonicalize timezone name for UTC Timezone names that are semantically identical to UTC are canonicalized to UTC. * chore: extend config file migration for default value handling Extend the config file migration handling values None or nonexisting values that will invoke a default value generation in the new config file. Also adapt test to handle this situation. * chore: extend datetime util test cases * chore: make version test check for untracked files Check for files that are not tracked by git. Version calculation will be wrong if these files will not be commited. * chore: bump pandas to 3.0.0 Pandas 3.0 now performs inference on the appropriate resolution (a.k.a. unit) for the output dtype which may become datetime64[us] (before it was ns). Also numeric dtype detection is now more strict which needs a different detection for numerics. * chore: bump pydantic-settings to 2.12.0 pydantic-settings 2.12.0 under pytest creates a different behaviour. The tests were adapted and a workaround was introduced. Also ConfigEOS was adapted to allow for fine grain initialization control to be able to switch off certain settings such as file settings during test. * chore: remove sci learn kit from dependencies The sci learn kit is not strictly necessary as long as we have scipy. * chore: add documentation mode guarding for sphinx autosummary Sphinx autosummary excecutes functions. Prevent exceptions in case of pure doc mode. * chore: adapt docker-build CI workflow to stricter GitHub handling Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
254 lines
8.1 KiB
Python
254 lines
8.1 KiB
Python
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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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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"provider_settings": {
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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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}
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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.provider_settings.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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"provider_settings": {
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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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"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.provider_settings.LoadAkkudoktor.loadakkudoktor_year_energy_kwh == 1000
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return LoadAkkudoktorAdjusted()
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@pytest.fixture
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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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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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assert compare_datetimes(measurement.min_datetime, to_datetime("2024-01-01T00:00:00")).equal
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assert compare_datetimes(measurement.max_datetime, 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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def test_loadakkudoktor_settings_validator():
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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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def test_loadakkudoktor_provider_id(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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def test_load_data_from_mock(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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def test_load_data_from_file(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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def test_update_data(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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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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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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def test_calculate_adjustment(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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assert measurement_eos.min_datetime == to_datetime("2024-01-01T00:00:00")
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assert measurement_eos.max_datetime == to_datetime("2024-01-02T00:00:00")
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# Use same calculation as in _calculate_adjustment
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compare_start = measurement_eos.max_datetime - to_duration("7 days")
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if compare_datetimes(compare_start, measurement_eos.min_datetime).lt:
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# Not enough measurements for 7 days - use what is available
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compare_start = measurement_eos.min_datetime
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compare_end = measurement_eos.max_datetime
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compare_interval = to_duration("1 hour")
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load_total_kwh_array = 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 = 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 = 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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def test_provider_adjustments_with_mock_data(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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loadakkudoktoradjusted._update_data()
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assert mock_adjust.called
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