chore: adapt pdf visualization (#1205)

Change PDF visualization to be created on demand and per optimization algorithm. The PDF
for the GENETIC0 optimization is provided by the /visualization_results.pdf endpoint.
There is no change in the interface.

By this the optimization algorithm is offloaded from the PDF generation which spares some
time.

To cope with several users may call the /visualization_results.pdf endpoint at the same
time the PDF is generated on the fly without any intermediate file taking the stored
GENETIC0 solution as an input. SVG picture generation is removed as this would again
create intermediate files. Chart pictures can easily be taken from the PDF.

To allow on demand creation of the optimization results visualization the optimisation
solution stored is extended by several new attributes. To keep the deprecated
/optimize endpoint compatible the optimization solution is stripped to the legacy
content before returned. Due to the extension of the solution the optimization tests were
adapted to cover the extended content.

The optimization tests are adapted to test the generated visualization report by
the pypdf reader. Pypdf is added to the development dependencies.

Besides the adaptation several fixes and improvements are added:

* feat: extend /v1/prediction/series endpoint by resampling and filling

  Add parameters for resampling and filling. Add the processing parameter
  to control wether raw data or resampled data shall be returned.

* feat: extend /v1/measurement/series endpoint by resampling and filling

  Add parameters for resampling and filling: Add the processing parameter
  to control wether raw data or resampled data shall be returned.

* feat: standardize and improve API error response

  Use FASTApi exception handlers to provide a standardized API exception handling.
  All exceptions are logged.

  Exception traces are only returned if the new logging configuration parameter
  logging.api_logging_level is set to "DEBUG" or "TRACE". Avoids unwanted leackage
  of server internals on exceptions.

* fix: align to intervall when resampling

  Ensure resampling is aligned to interval also when the buckets are shifted due to the
  align_to_intervall parameter is set.

* chore: make dropna mandatory and default to True

* chore: refactor key_to_xxx data management methods

  Make key_to_series the central method for data resampling and fill.
  Add a new key_to_raw_series to retrieve the data as it is stored
  (without resampling and filling).

  Users of key_to_series were mostly moved to key_to_raw_series as this resembles
  the former interface. Especially in predictions and tests this was done.

* chore: create test data sub-directory for each optimization algorithm

  To prevent cluttering the test data directory and ease test data management for
  optimization algorithms each algorithm got it's own sub-directory. The current
  test data was moved to these sub-directories.

* chore: update version

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

src/akkudoktoreos/optimization/genetic/geneticsolution.py

@@ -1,6 +1,6 @@
 """Genetic algorithm optimisation solution."""
 
-from typing import Any, Optional
+from typing import Any, Optional, Union
 
 import numpy as np
 import pandas as pd
@@ -27,6 +27,9 @@ from akkudoktoreos.devices.devicesabc import (
 )
 from akkudoktoreos.devices.genetic.battery import Battery
 from akkudoktoreos.optimization.genetic.geneticdevices import GeneticParametersBaseModel
+from akkudoktoreos.optimization.genetic.geneticparams import (
+    GeneticOptimizationParameters,
+)
 from akkudoktoreos.optimization.optimization import OptimizationSolution
 from akkudoktoreos.utils.datetimeutil import to_datetime, to_duration
 from akkudoktoreos.utils.utils import NumpyEncoder
@@ -247,6 +250,9 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
 
     model_config = ConfigDict(populate_by_name=True, extra="ignore")
 
+    parameters: GeneticOptimizationParameters = Field(
+        json_schema_extra={"description": "Optimization parameters used to generate solution."}
+    )
     ac_charge: list[float] = Field(
         json_schema_extra={
             "description": "Array with AC charging values as relative power (0.0-1.0), other values set to 0."
@@ -273,6 +279,10 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
         validation_alias=AliasChoices("ev_obj", "eauto_obj"),
         json_schema_extra={"description": "Electric vehicle state after optimization."},
     )
+    start_hour: int = Field(
+        default=0,
+        json_schema_extra={"description": "Start hour."},
+    )
     start_solution: Optional[list[float]] = Field(
         default=None,
         json_schema_extra={
@@ -285,6 +295,28 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
             "description": "Can be `null` or contain an object representing the start of washing (if applicable)."
         },
     )
+    extra_data: Optional[dict[str, Union[list[int], list[float]]]] = Field(
+        default=None,
+        json_schema_extra={
+            "description": ("Dictionary of balance: TBD, losses: TBD, constraints: TBD.")
+        },
+    )
+    fitness_history: Optional[dict[str, Union[list[int], list[float]]]] = Field(
+        default=None,
+        json_schema_extra={
+            "description": (
+                "Dictionary of "
+                "gen: Generation numbers (X-axis), "
+                "avg: Average fitness for each generation (Y-axis), "
+                "max: Maximum fitness for each generation (Y-axis), "
+                "min: Minimum fitness for each generation (Y-axis)."
+            )
+        },
+    )
+    fixed_seed: Optional[int] = Field(
+        default=None,
+        json_schema_extra={"description": "Fixed seed."},
+    )
 
     # Computed fields for backward compatibility (deprecated German names)
     @computed_field(json_schema_extra={"deprecated": True})