fix: optimization fail after restart (#1007)

Fix documentation for the loadforecast_power_w key.

Fix documentation to explain the usage of import file/ JSON string to
primarily initialise prediction data.

Fix code scanning alert no. 6: URL redirection from remote source

Enable to automatically save the configuration to the configuration file
by default, which is a widespread user expectation.

Make the genetic parameters non optional for better pydantic compliance.

Update:
- bump pytest to 9.0.3
- bump pillow to 12.2.0
- bump platformdirs to 4.9.6
- bump typespyyaml to 6.0.12.20260408
- bump tzfpy to 1.2.0
- bump pydantic to 2.13.0
- bump types-requests to 2.33.0.20260408

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Co-authored-by: Normann <github@koldrack.com>

src/akkudoktoreos/optimization/genetic/geneticparams.py

@@ -200,24 +200,15 @@ class GeneticOptimizationParameters(
             )
             cls.config.optimization.interval = 3600
         # Check genetic algorithm definitions
-        if cls.config.optimization.genetic is None:
-            logger.info(
-                "Genetic optimization configuration not configured - defaulting to demo config."
-            )
-            cls.config.optimization.genetic = {
-                "individuals": 300,
-                "generations": 400,
-                "seed": None,
-                "penalties": {
-                    "ev_soc_miss": 10,
-                },
-            }
         if cls.config.optimization.genetic.individuals is None:
             logger.info("Genetic individuals unknown - defaulting to 300.")
             cls.config.optimization.genetic.individuals = 300
         if cls.config.optimization.genetic.generations is None:
             logger.info("Genetic generations unknown - defaulting to 400.")
             cls.config.optimization.genetic.generations = 400
+        if "ev_soc_miss" not in cls.config.optimization.genetic.penalties:
+            logger.info("Genetic penalties unknown - defaulting to ev_soc_miss = 10.")
+            cls.config.optimization.genetic.penalties["ev_soc_miss"] = 10
 
         # Get start solution from last run
         start_solution = None

src/akkudoktoreos/optimization/optimization.py

@@ -1,6 +1,6 @@
 from typing import Optional, Union
 
-from pydantic import Field, computed_field, model_validator
+from pydantic import Field, computed_field
 
 from akkudoktoreos.config.configabc import SettingsBaseModel
 from akkudoktoreos.core.coreabc import get_ems
@@ -18,7 +18,7 @@ class GeneticCommonSettings(SettingsBaseModel):
         default=300,
         ge=10,
         json_schema_extra={
-            "description": "Number of individuals (solutions) to generate for the (initial) generation [>= 10]. Defaults to 300.",
+            "description": "Number of individuals (solutions) in the population [>= 10]. Defaults to 300.",
             "examples": [300],
         },
     )
@@ -27,7 +27,7 @@ class GeneticCommonSettings(SettingsBaseModel):
         default=400,
         ge=10,
         json_schema_extra={
-            "description": "Number of generations to evaluate the optimal solution [>= 10]. Defaults to 400.",
+            "description": "Number of generations to evolve [>= 10]. Defaults to 400.",
             "examples": [400],
         },
     )
@@ -36,21 +36,21 @@ class GeneticCommonSettings(SettingsBaseModel):
         default=None,
         ge=0,
         json_schema_extra={
-            "description": "Fixed seed for genetic algorithm. Defaults to 'None' which means random seed.",
-            "examples": [None],
+            "description": "Random seed for reproducibility. None = random.",
+            "examples": [None, 42],
         },
     )
 
+    # --- Penalties (existing) -------------------------------------------------
+
     penalties: dict[str, Union[float, int, str]] = Field(
         default_factory=lambda: {
             "ev_soc_miss": 10,
             "ac_charge_break_even": 1.0,
         },
         json_schema_extra={
-            "description": "A dictionary of penalty function parameters consisting of a penalty function parameter name and the associated value.",
-            "examples": [
-                {"ev_soc_miss": 10},
-            ],
+            "description": "Penalty parameters used in fitness evaluation.",
+            "examples": [{"ev_soc_miss": 10}],
         },
     )
 
@@ -58,7 +58,7 @@ class GeneticCommonSettings(SettingsBaseModel):
 class OptimizationCommonSettings(SettingsBaseModel):
     """General Optimization Configuration."""
 
-    horizon_hours: Optional[int] = Field(
+    horizon_hours: int = Field(
         default=24,
         ge=0,
         json_schema_extra={
@@ -67,23 +67,26 @@ class OptimizationCommonSettings(SettingsBaseModel):
         },
     )
 
-    interval: Optional[int] = Field(
+    interval: int = Field(
         default=3600,
         ge=15 * 60,
         le=60 * 60,
         json_schema_extra={
-            "description": "The optimization interval [sec].",
+            "description": "The optimization interval [sec]. Defaults to 3600 seconds (1 hour)",
             "examples": [60 * 60, 15 * 60],
         },
     )
 
-    algorithm: Optional[str] = Field(
+    algorithm: str = Field(
         default="GENETIC",
-        json_schema_extra={"description": "The optimization algorithm.", "examples": ["GENETIC"]},
+        json_schema_extra={
+            "description": "The optimization algorithm. Defaults to GENETIC",
+            "examples": ["GENETIC"],
+        },
     )
 
-    genetic: Optional[GeneticCommonSettings] = Field(
-        default=None,
+    genetic: GeneticCommonSettings = Field(
+        default_factory=GeneticCommonSettings,
         json_schema_extra={
             "description": "Genetic optimization algorithm configuration.",
             "examples": [{"individuals": 400, "seed": None, "penalties": {"ev_soc_miss": 10}}],
@@ -109,14 +112,14 @@ class OptimizationCommonSettings(SettingsBaseModel):
             key_list = df.columns.tolist()
         return sorted(set(key_list))
 
-    # Validators
-    @model_validator(mode="after")
-    def _enforce_algorithm_configuration(self) -> "OptimizationCommonSettings":
-        """Ensure algorithm default configuration is set."""
-        if self.algorithm is not None:
-            if self.algorithm.lower() == "genetic" and self.genetic is None:
-                self.genetic = GeneticCommonSettings()
-        return self
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def horizon(self) -> int:
+        """Number of optimization steps."""
+        if self.interval is None or self.interval == 0 or self.horizon_hours is None:
+            return 0
+        num_steps = int(float(self.horizon_hours * 3600) / self.interval)
+        return num_steps
 
 
 class OptimizationSolution(PydanticBaseModel):