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31bd2de18b
Extend single_test_optimization.py to be able to use real world data from new prediction classes. - .venv/bin/python single_test_optimization.py --real_world --verbose Can also be run with profiling "--profile". Add single_test_prediction.py to fetch predictions from remote prediction providers - .venv/bin/python single_test_prediction.py --verbose --provider-id PVForecastAkkudoktor | more - .venv/bin/python single_test_prediction.py --verbose --provider-id LoadAkkudoktor | more - .venv/bin/python single_test_prediction.py --verbose --provider-id ElecPriceAkkudoktor | more - .venv/bin/python single_test_prediction.py --verbose --provider-id BrightSky | more - .venv/bin/python single_test_prediction.py --verbose --provider-id ClearOutside | more Can also be run with profiling "--profile". single_test_optimization.py is an example on how to retrieve prediction data for optimization and use it to set up the optimization parameters. Changes: - load: Only one load provider at a time (vs. 5 before) Bug fixes: - prediction: only use providers that are enabled to retrieve or set data. - prediction: fix pre pendulum format strings - dataabc: Prevent error when resampling data with no datasets. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
100 lines
3.2 KiB
Python
100 lines
3.2 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.config.config import get_config
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from akkudoktoreos.core.ems import get_ems
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from akkudoktoreos.prediction.loadakkudoktor import (
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LoadAkkudoktor,
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LoadAkkudoktorCommonSettings,
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)
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config_eos = get_config()
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ems_eos = get_ems()
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@pytest.fixture
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def load_provider(monkeypatch):
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"""Fixture to create a LoadAkkudoktor instance."""
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settings = {
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"load_provider": "LoadAkkudoktor",
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"load_name": "Akkudoktor Profile",
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"loadakkudoktor_year_energy": "1000",
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}
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config_eos.merge_settings_from_dict(settings)
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return LoadAkkudoktor()
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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`."""
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settings = LoadAkkudoktorCommonSettings(loadakkudoktor_year_energy=1234)
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assert isinstance(settings.loadakkudoktor_year_energy, float)
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assert settings.loadakkudoktor_year_energy == 1234.0
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settings = LoadAkkudoktorCommonSettings(loadakkudoktor_year_energy=1234.56)
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assert isinstance(settings.loadakkudoktor_year_energy, float)
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assert settings.loadakkudoktor_year_energy == 1234.56
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def test_loadakkudoktor_provider_id(load_provider):
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"""Test the `provider_id` class method."""
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assert load_provider.provider_id() == "LoadAkkudoktor"
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@patch("akkudoktoreos.prediction.loadakkudoktor.Path")
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@patch("akkudoktoreos.prediction.loadakkudoktor.np.load")
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def test_load_data_from_mock(mock_np_load, mock_path, mock_load_profiles_file, load_provider):
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"""Test the `load_data` method."""
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# Mock path behavior to return the test file
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mock_path.return_value.parent.parent.joinpath.return_value = mock_load_profiles_file
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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 = load_provider.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(load_provider):
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"""Test `load_data` loads data from the profiles file."""
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data_year_energy = load_provider.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, load_provider):
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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.set_start_datetime(pendulum.datetime(2024, 1, 1))
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# Assure there are no prediction records
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load_provider.clear()
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assert len(load_provider) == 0
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# Execute the method
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load_provider._update_data()
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# Validate that update_value is called
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assert len(load_provider) > 0
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