Add more optimization tests (#171)

* Integrated single_test_optimization into pytest to run a basic optimization test with tolerance set to 1e-6, ensuring quick detection of deviations.
* Added a long-run test (400 generations, like single_test_optimization), which can be triggered using --full-run in pytest.
* Mocked PDF creation in optimization tests and added a new PDF generation test with image comparison validation.

Note: Current tolerance is set to 1e-6; feedback on whether this tolerance is tight enough is welcome.

---------

Co-authored-by: Normann <github@koldrack.com>
Co-authored-by: Michael Osthege <michael.osthege@outlook.com>

tests/test_class_optimize.py

@@ -1,16 +1,39 @@
 import json
 from pathlib import Path
+from typing import Any
+from unittest.mock import patch
 
 import pytest
 
 from akkudoktoreos.class_optimize import optimization_problem
-from akkudoktoreos.config import output_dir
 
 DIR_TESTDATA = Path(__file__).parent / "testdata"
 
 
-@pytest.mark.parametrize("fn_in, fn_out", [("optimize_input_1.json", "optimize_result_1.json")])
-def test_optimize(fn_in, fn_out):
+def compare_dict(actual: dict[str, Any], expected: dict[str, Any]):
+    assert set(actual) == set(expected)
+
+    for key, value in expected.items():
+        if isinstance(value, dict):
+            assert isinstance(actual[key], dict)
+            compare_dict(actual[key], value)
+        elif isinstance(value, list):
+            assert isinstance(actual[key], list)
+            assert actual[key] == pytest.approx(value)
+        else:
+            assert actual[key] == pytest.approx(value)
+
+
+@pytest.mark.parametrize(
+    "fn_in, fn_out, ngen",
+    [
+        ("optimize_input_1.json", "optimize_result_1.json", 3),
+        ("optimize_input_2.json", "optimize_result_2.json", 3),
+        ("optimize_input_2.json", "optimize_result_2_full.json", 400),
+    ],
+)
+@patch("akkudoktoreos.class_optimize.visualisiere_ergebnisse")
+def test_optimize(visualisiere_ergebnisse_patch, fn_in: str, fn_out: str, ngen: int, is_full_run):
     # Load input and output data
     with open(DIR_TESTDATA / fn_in, "r") as f_in:
         input_data = json.load(f_in)
@@ -23,16 +46,20 @@ def test_optimize(fn_in, fn_out):
     )
     start_hour = 10
 
+    if ngen > 10 and not is_full_run:
+        pytest.skip()
+
     # Call the optimization function
-    ergebnis = opt_class.optimierung_ems(parameter=input_data, start_hour=start_hour, ngen=3)
-    # with open("new.json", "w") as f_out:
-    #     json.dump(ergebnis, f_out, indent=4)
+    ergebnis = opt_class.optimierung_ems(parameter=input_data, start_hour=start_hour, ngen=ngen)
+    # with open(f"new_{fn_out}", "w") as f_out:
+    #     from akkudoktoreos.class_numpy_encoder import NumpyEncoder
+    #     json_data_str = NumpyEncoder.dumps(ergebnis)
+    #     json.dump(json.loads(json_data_str), f_out, indent=4)
 
     # Assert that the output contains all expected entries.
     # This does not assert that the optimization always gives the same result!
     # Reproducibility and mathematical accuracy should be tested on the level of individual components.
-    assert set(ergebnis) == set(expected_output_data)
+    compare_dict(ergebnis, expected_output_data)
 
     # The function creates a visualization result PDF as a side-effect.
-    fp_viz = Path(output_dir) / "visualization_results.pdf"
-    assert fp_viz.exists()
+    visualisiere_ergebnisse_patch.assert_called_once()