fix: pydantic extra keywords deprecated (#753)

Pydantic deprecates using extra keyword arguments on Field.
Used json_schema_extra instead.

Deprecated in Pydantic V2.0 to be removed in V3.0.

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>

src/akkudoktoreos/optimization/optimization.py

@@ -16,30 +16,38 @@ class GeneticCommonSettings(SettingsBaseModel):
     individuals: Optional[int] = Field(
         default=300,
         ge=10,
-        description="Number of individuals (solutions) to generate for the (initial) generation [>= 10]. Defaults to 300.",
-        examples=[300],
+        json_schema_extra={
+            "description": "Number of individuals (solutions) to generate for the (initial) generation [>= 10]. Defaults to 300.",
+            "examples": [300],
+        },
     )
 
     generations: Optional[int] = Field(
         default=400,
         ge=10,
-        description="Number of generations to evaluate the optimal solution [>= 10]. Defaults to 400.",
-        examples=[400],
+        json_schema_extra={
+            "description": "Number of generations to evaluate the optimal solution [>= 10]. Defaults to 400.",
+            "examples": [400],
+        },
     )
 
     seed: Optional[int] = Field(
         default=None,
         ge=0,
-        description="Fixed seed for genetic algorithm. Defaults to 'None' which means random seed.",
-        examples=[None],
+        json_schema_extra={
+            "description": "Fixed seed for genetic algorithm. Defaults to 'None' which means random seed.",
+            "examples": [None],
+        },
     )
 
     penalties: Optional[dict[str, Union[float, int, str]]] = Field(
         default=None,
-        description="A dictionary of penalty function parameters consisting of a penalty function parameter name and the associated value.",
-        examples=[
-            {"ev_soc_miss": 10},
-        ],
+        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},
+            ],
+        },
     )
 
 
@@ -49,28 +57,33 @@ class OptimizationCommonSettings(SettingsBaseModel):
     horizon_hours: Optional[int] = Field(
         default=24,
         ge=0,
-        description="The general time window within which the energy optimization goal shall be achieved [h]. Defaults to 24 hours.",
-        examples=[24],
+        json_schema_extra={
+            "description": "The general time window within which the energy optimization goal shall be achieved [h]. Defaults to 24 hours.",
+            "examples": [24],
+        },
     )
 
     interval: Optional[int] = Field(
         default=3600,
         ge=15 * 60,
         le=60 * 60,
-        description="The optimization interval [sec].",
-        examples=[60 * 60, 15 * 60],
+        json_schema_extra={
+            "description": "The optimization interval [sec].",
+            "examples": [60 * 60, 15 * 60],
+        },
     )
 
     algorithm: Optional[str] = Field(
         default="GENETIC",
-        description="The optimization algorithm.",
-        examples=["GENETIC"],
+        json_schema_extra={"description": "The optimization algorithm.", "examples": ["GENETIC"]},
     )
 
     genetic: Optional[GeneticCommonSettings] = Field(
         default=None,
-        description="Genetic optimization algorithm configuration.",
-        examples=[{"individuals": 400, "seed": None, "penalties": {"ev_soc_miss": 10}}],
+        json_schema_extra={
+            "description": "Genetic optimization algorithm configuration.",
+            "examples": [{"individuals": 400, "seed": None, "penalties": {"ev_soc_miss": 10}}],
+        },
     )
 
     @model_validator(mode="after")
@@ -85,57 +98,71 @@ class OptimizationCommonSettings(SettingsBaseModel):
 class OptimizationSolution(PydanticBaseModel):
     """General Optimization Solution."""
 
-    id: str = Field(..., description="Unique ID for the optimization solution.")
+    id: str = Field(
+        ..., json_schema_extra={"description": "Unique ID for the optimization solution."}
+    )
 
-    generated_at: DateTime = Field(..., description="Timestamp when the solution was generated.")
+    generated_at: DateTime = Field(
+        ..., json_schema_extra={"description": "Timestamp when the solution was generated."}
+    )
 
     comment: Optional[str] = Field(
-        default=None, description="Optional comment or annotation for the solution."
+        default=None,
+        json_schema_extra={"description": "Optional comment or annotation for the solution."},
     )
 
     valid_from: Optional[DateTime] = Field(
-        default=None, description="Start time of the optimization solution."
+        default=None, json_schema_extra={"description": "Start time of the optimization solution."}
     )
 
     valid_until: Optional[DateTime] = Field(
-        default=None,
-        description="End time of the optimization solution.",
+        default=None, json_schema_extra={"description": "End time of the optimization solution."}
     )
 
     total_losses_energy_wh: float = Field(
-        description="The total losses in watt-hours over the entire period."
+        json_schema_extra={"description": "The total losses in watt-hours over the entire period."}
     )
 
-    total_revenues_amt: float = Field(description="The total revenues [money amount].")
+    total_revenues_amt: float = Field(
+        json_schema_extra={"description": "The total revenues [money amount]."}
+    )
 
-    total_costs_amt: float = Field(description="The total costs [money amount].")
+    total_costs_amt: float = Field(
+        json_schema_extra={"description": "The total costs [money amount]."}
+    )
 
-    fitness_score: set[float] = Field(description="The fitness score as a set of fitness values.")
+    fitness_score: set[float] = Field(
+        json_schema_extra={"description": "The fitness score as a set of fitness values."}
+    )
 
     prediction: PydanticDateTimeDataFrame = Field(
-        description=(
-            "Datetime data frame with time series prediction data per optimization interval:"
-            "- pv_energy_wh: PV energy prediction (positive) in wh"
-            "- elec_price_amt_kwh: Electricity price prediction in money per kwh"
-            "- feed_in_tariff_amt_kwh: Feed in tariff prediction in money per kwh"
-            "- weather_temp_air_celcius: Temperature in °C"
-            "- loadforecast_energy_wh: Load mean energy prediction in wh"
-            "- loadakkudoktor_std_energy_wh: Load energy standard deviation prediction in wh"
-            "- loadakkudoktor_mean_energy_wh: Load mean energy prediction in wh"
-        )
+        json_schema_extra={
+            "description": (
+                "Datetime data frame with time series prediction data per optimization interval:"
+                "- pv_energy_wh: PV energy prediction (positive) in wh"
+                "- elec_price_amt_kwh: Electricity price prediction in money per kwh"
+                "- feed_in_tariff_amt_kwh: Feed in tariff prediction in money per kwh"
+                "- weather_temp_air_celcius: Temperature in °C"
+                "- loadforecast_energy_wh: Load mean energy prediction in wh"
+                "- loadakkudoktor_std_energy_wh: Load energy standard deviation prediction in wh"
+                "- loadakkudoktor_mean_energy_wh: Load mean energy prediction in wh"
+            )
+        }
     )
 
     solution: PydanticDateTimeDataFrame = Field(
-        description=(
-            "Datetime data frame with time series solution data per optimization interval:"
-            "- load_energy_wh: Load of all energy consumers in wh"
-            "- grid_energy_wh: Grid energy feed in (negative) or consumption (positive) in wh"
-            "- costs_amt: Costs in money amount"
-            "- revenue_amt: Revenue in money amount"
-            "- losses_energy_wh: Energy losses in wh"
-            "- <device-id>_operation_mode_id: Operation mode id of the device."
-            "- <device-id>_operation_mode_factor: Operation mode factor of the device."
-            "- <device-id>_soc_factor: State of charge of a battery/ electric vehicle device as factor of total capacity."
-            "- <device-id>_energy_wh: Energy consumption (positive) of a device in wh."
-        )
+        json_schema_extra={
+            "description": (
+                "Datetime data frame with time series solution data per optimization interval:"
+                "- load_energy_wh: Load of all energy consumers in wh"
+                "- grid_energy_wh: Grid energy feed in (negative) or consumption (positive) in wh"
+                "- costs_amt: Costs in money amount"
+                "- revenue_amt: Revenue in money amount"
+                "- losses_energy_wh: Energy losses in wh"
+                "- <device-id>_operation_mode_id: Operation mode id of the device."
+                "- <device-id>_operation_mode_factor: Operation mode factor of the device."
+                "- <device-id>_soc_factor: State of charge of a battery/ electric vehicle device as factor of total capacity."
+                "- <device-id>_energy_wh: Energy consumption (positive) of a device in wh."
+            )
+        }
     )