Improve Configuration and Prediction Usability

pyproject.toml

@@ -51,9 +51,16 @@ select = [
               # Google convention via `convention = "google"`, below.
 ]
 ignore = [
-    # On top of `Pyflakes (F)` to prevent errors for existing sources. Should be removed!!!
+   # Prevent errors due to ruff false positives
+   # ------------------------------------------
+   # On top of `Pyflakes (F)` to allow numpydantic Shape forward annotation
+   "F722",    # forward-annotation-syntax-error: forward annotations that include invalid syntax.
+
+    # Prevent errors for existing sources. Should be removed!!!
+    # ---------------------------------------------------------
+    # On top of `Pyflakes (F)`
     "F841",   # unused-variable: Local variable {name} is assigned to but never used
-    # On top of `pydocstyle (D)` to prevent errors for existing sources. Should be removed!!!
+    # On top of `pydocstyle (D)`
     "D100",   # undocumented-public-module: Missing docstring in public module
     "D101",   # undocumented-public-class: Missing docstring in public class
     "D102",   # undocumented-public-method: Missing docstring in public method

requirements.txt

@@ -1,11 +1,15 @@
 numpy==2.1.3
+numpydantic==1.6.4
 matplotlib==3.9.2
 fastapi[standard]==0.115.5
+python-fasthtml==0.9.1
 uvicorn==0.32.1
-pydantic==2.10.1
 scikit-learn==1.5.2
 timezonefinder==6.5.5
 deap==1.4.1
 requests==2.32.3
 pandas==2.2.3
+pendulum==3.0.0
+platformdirs==4.3.6
+pvlib==0.11.1
 pydantic==2.10.1

single_test_optimization.py

@@ -4,7 +4,7 @@
 
 import numpy as np
 
-from akkudoktoreos.config import get_working_dir, load_config
+from akkudoktoreos.config.config import get_config
 from akkudoktoreos.optimization.genetic import (
     OptimizationParameters,
     optimization_problem,
@@ -281,9 +281,9 @@
 start_time = time.time()
 
 # Initialize the optimization problem using the default configuration
-working_dir = get_working_dir()
-config = load_config(working_dir)
-opt_class = optimization_problem(config, verbose=True, fixed_seed=42)
+config_eos = get_config()
+config_eos.merge_settings_from_dict({"prediction_hours": 48, "optimization_hours": 24})
+opt_class = optimization_problem(verbose=True, fixed_seed=42)
 
 # Perform the optimisation based on the provided parameters and start hour
 ergebnis = opt_class.optimierung_ems(parameters=parameters, start_hour=start_hour)