mirror of
https://github.com/Akkudoktor-EOS/EOS.git
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f61665669f
* Migrate from Flask to FastAPI
* FastAPI migration:
- Use pydantic model classes as input parameters to the
data/calculation classes.
- Interface field names changed to constructor parameter names (for
simplicity only during transition, should be updated in a followup
PR).
- Add basic interface requirements (e.g. some values > 0, etc.).
* Update tests for new data format.
* Python requirement down to 3.9 (TypeGuard no longer needed)
* Makefile: Add helpful targets (e.g. development server with reload)
* Move API doc from README to pydantic model classes (swagger)
* Link to swagger.io with own openapi.yml.
* Commit openapi.json and check with pytest for changes so the
documentation is always up-to-date.
* Streamline docker
* FastAPI: Run startup action on dev server
* Fix config for /strompreis, endpoint still broken however.
* test_openapi: Compare against docs/.../openapi.json
* Move fastapi to server/ submodule
* See #187 for new repository structure.
81 lines
2.5 KiB
Python
81 lines
2.5 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.class_optimize 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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from akkudoktoreos.config import AppConfig
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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.class_optimize.visualisiere_ergebnisse")
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def test_optimize(
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visualisiere_ergebnisse_patch,
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fn_in: str,
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fn_out: str,
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ngen: int,
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is_full_run: bool,
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tmp_config: AppConfig,
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):
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"""Test optimierung_ems."""
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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_output_data = json.load(f_out)
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opt_class = optimization_problem(tmp_config, 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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# from akkudoktoreos.class_numpy_encoder import NumpyEncoder
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# json_data_str = NumpyEncoder.dumps(ergebnis)
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# json.dump(json.loads(json_data_str), f_out, indent=4)
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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, expected_output_data)
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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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OptimizeResponse(**ergebnis)
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