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* fix: improve error handling for provider updates Distinguishes failures of active providers from inactive ones. Propagates errors only for enabled providers, allowing execution to continue if a non-active provider fails, which avoids unnecessary interruptions and improves robustness. * fix: add provider settings validation for forecast requests Prevents potential runtime errors by checking if provider settings are configured before accessing forecast credentials. Raises a clear error when settings are missing to help with debugging misconfigurations. * refactor(load): move provider settings to top-level fields Transitions load provider settings from a nested "provider_settings" object with provider-specific keys to dedicated top-level fields.\n\nRemoves the legacy "provider_settings" mapping and updates migration logic to ensure backward compatibility with existing configurations. * docs: update version numbers and documantation --------- Co-authored-by: Normann <github@koldrack.com>
250 lines
8.0 KiB
Python
250 lines
8.0 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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"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.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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