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https://github.com/Akkudoktor-EOS/EOS.git
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aa334d0b61
* Update utilities in utils submodule. * Add base configuration modules. * Add server base configuration modules. * Add devices base configuration modules. * Add optimization base configuration modules. * Add utils base configuration modules. * Add prediction abstract and base classes plus tests. * Add PV forecast to prediction submodule. The PV forecast modules are adapted from the class_pvforecast module and replace it. * Add weather forecast to prediction submodule. The modules provide classes and methods to retrieve, manage, and process weather forecast data from various sources. Includes are structured representations of weather data and utilities for fetching forecasts for specific locations and time ranges. BrightSky and ClearOutside are currently supported. * Add electricity price forecast to prediction submodule. * Adapt fastapi server to base config and add fasthtml server. * Add ems to core submodule. * Adapt genetic to config. * Adapt visualize to config. * Adapt common test fixtures to config. * Add load forecast to prediction submodule. * Add core abstract and base classes. * Adapt single test optimization to config. * Adapt devices to config. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
76 lines
2.6 KiB
Python
76 lines
2.6 KiB
Python
import json
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from pathlib import Path
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from typing import Any
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from unittest.mock import patch
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import pytest
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from akkudoktoreos.config.config import get_config
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from akkudoktoreos.optimization.genetic import (
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OptimizationParameters,
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OptimizeResponse,
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optimization_problem,
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)
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DIR_TESTDATA = Path(__file__).parent / "testdata"
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def compare_dict(actual: dict[str, Any], expected: dict[str, Any]):
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assert set(actual) == set(expected)
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for key, value in expected.items():
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if isinstance(value, dict):
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assert isinstance(actual[key], dict)
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compare_dict(actual[key], value)
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elif isinstance(value, list):
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assert isinstance(actual[key], list)
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assert actual[key] == pytest.approx(value)
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else:
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assert actual[key] == pytest.approx(value)
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@pytest.mark.parametrize(
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"fn_in, fn_out, ngen",
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[
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("optimize_input_1.json", "optimize_result_1.json", 3),
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("optimize_input_2.json", "optimize_result_2.json", 3),
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("optimize_input_2.json", "optimize_result_2_full.json", 400),
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],
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)
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@patch("akkudoktoreos.optimization.genetic.visualisiere_ergebnisse")
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def test_optimize(
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visualisiere_ergebnisse_patch, fn_in: str, fn_out: str, ngen: int, is_full_run: bool
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):
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"""Test optimierung_ems."""
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# Assure configuration holds the correct values
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config_eos = get_config()
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config_eos.merge_settings_from_dict({"prediction_hours": 48, "optimization_hours": 24})
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# Load input and output data
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file = DIR_TESTDATA / fn_in
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with file.open("r") as f_in:
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input_data = OptimizationParameters(**json.load(f_in))
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file = DIR_TESTDATA / fn_out
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with file.open("r") as f_out:
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expected_result = OptimizeResponse(**json.load(f_out))
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opt_class = optimization_problem(fixed_seed=42)
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start_hour = 10
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if ngen > 10 and not is_full_run:
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pytest.skip()
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# Call the optimization function
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ergebnis = opt_class.optimierung_ems(parameters=input_data, start_hour=start_hour, ngen=ngen)
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# with open(f"new_{fn_out}", "w") as f_out:
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# f_out.write(ergebnis.model_dump_json(indent=4, exclude_unset=True))
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# Assert that the output contains all expected entries.
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# This does not assert that the optimization always gives the same result!
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# Reproducibility and mathematical accuracy should be tested on the level of individual components.
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compare_dict(ergebnis.model_dump(), expected_result.model_dump())
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# The function creates a visualization result PDF as a side-effect.
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visualisiere_ergebnisse_patch.assert_called_once()
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