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>

.env

@@ -11,7 +11,7 @@ DOCKER_COMPOSE_DATA_DIR=${HOME}/.local/share/net.akkudoktor.eos
 # -----------------------------------------------------------------------------
 # Image / build
 # -----------------------------------------------------------------------------
-VERSION=0.3.0.dev2607301181843779
+VERSION=0.3.0.dev2608010961051894
 PYTHON_VERSION=3.13.9
 
 # -----------------------------------------------------------------------------

config.yaml

@@ -6,7 +6,7 @@
 # the root directory (no add-on folder as usual).
 
 name: "Akkudoktor-EOS"
-version: "0.3.0.dev2607301181843779"
+version: "0.3.0.dev2608010961051894"
 slug: "eos"
 description: "Akkudoktor-EOS add-on"
 url: "https://github.com/Akkudoktor-EOS/EOS"

docs/_generated/configexample.md

@@ -162,6 +162,7 @@
            }
        },
        "logging": {
+           "api_level": "TRACE",
            "console_level": "TRACE",
            "file_level": "TRACE"
        },

docs/_generated/configlogging.md

@@ -7,8 +7,9 @@
 
 | Name | Environment Variable | Type | Read-Only | Default | Description |
 | ---- | -------------------- | ---- | --------- | ------- | ----------- |
-| console_level | `EOS_LOGGING__CONSOLE_LEVEL` | `str | None` | `rw` | `None` | Logging level when logging to console. |
+| api_level | `EOS_LOGGING__API_LEVEL` | `str | None` | `rw` | `None` | Logging level for API response. |
-| file_level | `EOS_LOGGING__FILE_LEVEL` | `str | None` | `rw` | `None` | Logging level when logging to file. |
+| console_level | `EOS_LOGGING__CONSOLE_LEVEL` | `str | None` | `rw` | `None` | Logging level for logging to console. |
+| file_level | `EOS_LOGGING__FILE_LEVEL` | `str | None` | `rw` | `None` | Logging level for logging to file. |
 | file_path | | `pathlib.Path | None` | `ro` | `N/A` | Computed log file path based on data output path. |
 :::
 <!-- pyml enable line-length -->
@@ -21,6 +22,7 @@
 ```json
    {
        "logging": {
+           "api_level": "TRACE",
            "console_level": "TRACE",
            "file_level": "TRACE"
        }
@@ -36,6 +38,7 @@
 ```json
    {
        "logging": {
+           "api_level": "TRACE",
            "console_level": "TRACE",
            "file_level": "TRACE",
            "file_path": "/home/user/.local/share/net.akkudoktor.eos/output/eos.log"

docs/_generated/openapi.md

@@ -1,6 +1,6 @@
 # Akkudoktor-EOS
 
-**Version**: `v0.3.0.dev2607301181843779`
+**Version**: `v0.3.0.dev2608010961051894`
 
 <!-- pyml disable line-length -->
 **Description**: This project provides a comprehensive solution for simulating and optimizing an energy system based on renewable energy sources. With a focus on photovoltaic (PV) systems, battery storage (batteries), load management (consumer requirements), heat pumps, electric vehicles, and consideration of electricity price data, this system enables forecasting and optimization of energy flow and costs over a specified period.
@@ -143,7 +143,7 @@ Note:
 **Request Body**:
 
 - `application/json`: {
-  "$ref": "#/components/schemas/Genetic0OptimizationParameters"
+  "$ref": "#/components/schemas/Genetic0OptimizationParameters-Input"
 }
 
 **Responses**:
@@ -948,6 +948,14 @@ Merge the measurement data given as dataframe into EOS measurements.
 
 Fastapi Measurement Keys Get
 
+<!-- pyml disable line-length -->
+```python
+"""
+Get a list of available measurement keys.
+"""
+```
+<!-- pyml enable line-length -->
+
 **Responses**:
 
 - **200**: Successful Response
@@ -997,7 +1005,73 @@ Fastapi Measurement Series Get
 <!-- pyml disable line-length -->
 ```python
 """
-Get the measurements of given key as series.
+Get measurements for given key within given date range as series.
+
+Args:
+    key (str): Measurement key
+    start_datetime (Optional[str]): Starting datetime (inclusive).
+        Defaults to datetime of first measurement.
+    end_datetime (Optional[str]: Ending datetime (exclusive).
+        Defaults to datetime after latest measurement.
+    interval (Optional[str]): Time duration for each interval.
+        Defaults to 1 hour.
+    fill_method (str): Method to handle missing values during resampling.
+
+        - 'linear': Linearly interpolate missing values (for numeric data only).
+        - 'time': Interpolate missing values (for numeric data only).
+        - 'ffill': Forward fill missing values.
+        - 'bfill': Backward fill missing values.
+        - Defaults to 'linear' for numeric values, otherwise 'ffill'.
+
+    resample_method (str):
+        Method used to aggregate values within a resampling interval.
+
+        - "first": Use the first value in each interval.
+        - "mean": Compute the arithmetic mean of all samples in each interval.
+        - "interval_mean": Compute the time-weighted mean assuming each
+            value remains valid until the next timestamp (piecewise-constant
+            signal).
+
+    dropna: (bool): Whether to drop NAN/ None values before processing.
+        Defaults to True.
+    boundary (Literal["strict", "context"]): resampling boundary
+        "strict"  → only values inside [start, end)
+        "context" → include one value before and after for proper resampling
+    align_to_interval (bool): When True, snap the resample origin to the nearest
+        UTC epoch-aligned boundary of ``interval`` before resampling.  This ensures
+        that bucket timestamps always fall on wall-clock-round times regardless of
+        when ``start_datetime`` falls:
+
+        - 15-minute interval → buckets on :00, :15, :30, :45
+        - 1-hour interval    → buckets on the hour
+
+        When False (default), the origin is ``query_start`` (or ``"start_day"`` when
+        no start is given), preserving the existing behaviour where buckets are
+        aligned to the query window rather than the clock.
+
+        Set to True when storing compacted records back to the database so that the
+        resulting timestamps are predictable and human-readable.  Leave False for
+        forecast or reporting queries where alignment to the exact query window is
+        more important than clock-round boundaries.
+    processing (SeriesProcessing):
+        Processing mode for the returned series.
+
+        - ``SeriesProcessing.RESAMPLED``: Return a processed series.
+            Measurements are first filtered by ``start_datetime``,
+            ``end_datetime``, and ``dropna``, then resampled according to
+            ``interval`` and ``resample_method``, and finally missing values
+            are filled using ``fill_method``.
+        - ``SeriesProcessing.RAW``: Return the original measurement series.
+            Measurements are filtered by ``start_datetime``,
+            ``end_datetime``, and ``dropna`` only. No resampling or filling is
+            performed, and ``interval``, ``fill_method``,
+            ``resample_method``, ``boundary``, and
+            ``align_to_interval`` are ignored.
+
+        Defaults to ``SeriesProcessing.RAW``.
+
+Returns:
+    Series
 """
 ```
 <!-- pyml enable line-length -->
@@ -1006,6 +1080,24 @@ Get the measurements of given key as series.
 
 - `key` (query, required): Measurement key.
 
+- `start_datetime` (query, optional): Starting datetime (inclusive).
+
+- `end_datetime` (query, optional): Ending datetime (exclusive).
+
+- `interval` (query, optional): Time duration for each interval. Defaults to 1 hour.
+
+- `fill_method` (query, optional): Method to handle missing values during resampling.
+
+- `resample_method` (query, optional): Method used to aggregate values within a resampling interval.
+
+- `dropna` (query, optional): Drop NAN/ None values before processing.
+
+- `boundary` (query, optional): Resampling boundary mode.
+
+- `align_to_interval` (query, optional): Snap resample origin to the nearest UTC epoch-aligned boundary of interval.
+
+- `processing` (query, optional): Processing mode. 'raw' returns original measurement data without resampling or filling.
+
 **Responses**:
 
 - **200**: Successful Response
@@ -1118,7 +1210,7 @@ Args:
             value remains valid until the next timestamp (piecewise-constant
             signal).
 
-    dropna: (bool, optional): Whether to drop NAN/ None values before processing.
+    dropna: (bool): Whether to drop NAN/ None values before processing.
         Defaults to True.
     boundary (Literal["strict", "context"]): resampling boundary
         "strict"  → only values inside [start, end)
@@ -1288,7 +1380,7 @@ Args:
             value remains valid until the next timestamp (piecewise-constant
             signal).
 
-    dropna: (bool, optional): Whether to drop NAN/ None values before processing.
+    dropna: (bool): Whether to drop NAN/ None values before processing.
         Defaults to True.
     boundary (Literal["strict", "context"]): resampling boundary
         "strict"  → only values inside [start, end)
@@ -1423,6 +1515,65 @@ Args:
         Defaults to start datetime of latest prediction.
     end_datetime (Optional[str]: Ending datetime (exclusive).
         Defaults to end datetime of latest prediction.
+    interval (Optional[str]): Time duration for each interval.
+        Defaults to 1 hour.
+    fill_method (str): Method to handle missing values during resampling.
+
+        - 'linear': Linearly interpolate missing values (for numeric data only).
+        - 'time': Interpolate missing values (for numeric data only).
+        - 'ffill': Forward fill missing values.
+        - 'bfill': Backward fill missing values.
+        - Defaults to 'linear' for numeric values, otherwise 'ffill'.
+
+    resample_method (str):
+        Method used to aggregate values within a resampling interval.
+
+        - "first": Use the first value in each interval.
+        - "mean": Compute the arithmetic mean of all samples in each interval.
+        - "interval_mean": Compute the time-weighted mean assuming each
+            value remains valid until the next timestamp (piecewise-constant
+            signal).
+
+    dropna: (bool): Whether to drop NAN/ None values before processing.
+        Defaults to True.
+    boundary (Literal["strict", "context"]): resampling boundary
+        "strict"  → only values inside [start, end)
+        "context" → include one value before and after for proper resampling
+    align_to_interval (bool): When True, snap the resample origin to the nearest
+        UTC epoch-aligned boundary of ``interval`` before resampling.  This ensures
+        that bucket timestamps always fall on wall-clock-round times regardless of
+        when ``start_datetime`` falls:
+
+        - 15-minute interval → buckets on :00, :15, :30, :45
+        - 1-hour interval    → buckets on the hour
+
+        When False (default), the origin is ``query_start`` (or ``"start_day"`` when
+        no start is given), preserving the existing behaviour where buckets are
+        aligned to the query window rather than the clock.
+
+        Set to True when storing compacted records back to the database so that the
+        resulting timestamps are predictable and human-readable.  Leave False for
+        forecast or reporting queries where alignment to the exact query window is
+        more important than clock-round boundaries.
+    processing (SeriesProcessing):
+        Processing mode for the returned series.
+
+        - ``SeriesProcessing.RESAMPLED``: Return a processed series.
+            Measurements are first filtered by ``start_datetime``,
+            ``end_datetime``, and ``dropna``, then resampled according to
+            ``interval`` and ``resample_method``, and finally missing values
+            are filled using ``fill_method``.
+        - ``SeriesProcessing.RAW``: Return the original measurement series.
+            Measurements are filtered by ``start_datetime``,
+            ``end_datetime``, and ``dropna`` only. No resampling or filling is
+            performed, and ``interval``, ``fill_method``,
+            ``resample_method``, ``boundary``, and
+            ``align_to_interval`` are ignored.
+
+        Defaults to ``SeriesProcessing.RAW``.
+
+Returns:
+    Array
 """
 ```
 <!-- pyml enable line-length -->
@@ -1435,6 +1586,20 @@ Args:
 
 - `end_datetime` (query, optional): Ending datetime (exclusive).
 
+- `interval` (query, optional): Time duration for each interval. Defaults to 1 hour.
+
+- `fill_method` (query, optional): Method to handle missing values during resampling.
+
+- `resample_method` (query, optional): Method used to aggregate values within a resampling interval.
+
+- `dropna` (query, optional): Drop NAN/ None values before processing.
+
+- `boundary` (query, optional): Resampling boundary mode.
+
+- `align_to_interval` (query, optional): Snap resample origin to the nearest UTC epoch-aligned boundary of interval.
+
+- `processing` (query, optional): Processing mode. 'raw' returns original measurement data without resampling or filling.
+
 **Responses**:
 
 - **200**: Successful Response

pyproject.toml

@@ -84,6 +84,7 @@ dev = [
     "pytest-cov==7.1.0",
     "pytest-xprocess==1.0.2",
     "coverage==7.15.2",
+    "pypdf==6.14.2",
 ]
 
 [project.urls]

src/akkudoktoreos/core/dataabc.py

@@ -1009,7 +1009,7 @@ class DataSequence(DataABC, DatabaseRecordProtocolMixin[DataRecord]):
         key: str,
         start_datetime: Optional[DateTime] = None,
         end_datetime: Optional[DateTime] = None,
-        dropna: Optional[bool] = None,
+        dropna: bool = True,
     ) -> Dict[DateTime, Any]:
         """Extract a dictionary indexed by the date_time field of the DataRecords.
 
@@ -1020,7 +1020,7 @@ class DataSequence(DataABC, DatabaseRecordProtocolMixin[DataRecord]):
             key (str): The field name in the DataRecord from which to extract values.
             start_datetime (datetime, optional): The start date to filter records (inclusive).
             end_datetime (datetime, optional): The end date to filter records (exclusive).
-            dropna: (bool, optional): Whether to drop NAN/ None values before processing. Defaults to True.
+            dropna: (bool): Whether to drop NAN/ None values before processing. Defaults to True.
 
         Returns:
             Dict[datetime, Any]: A dictionary with the date_time of each record as the key
@@ -1038,8 +1038,6 @@ class DataSequence(DataABC, DatabaseRecordProtocolMixin[DataRecord]):
         end_timestamp = DatabaseTimestamp.from_datetime(end_datetime) if end_datetime else None
 
         # Create a dictionary to hold date_time and corresponding values
-        if dropna is None:
-            dropna = True
         filtered_data = {}
         async for record in self.db_iterate_records(start_timestamp, end_timestamp):
             if (
@@ -1090,7 +1088,7 @@ class DataSequence(DataABC, DatabaseRecordProtocolMixin[DataRecord]):
         key: str,
         start_datetime: Optional[DateTime] = None,
         end_datetime: Optional[DateTime] = None,
-        dropna: Optional[bool] = None,
+        dropna: bool = True,
     ) -> Tuple[list[DateTime], list[Optional[float]]]:
         """Extracts two lists from data records within an optional date range.
 
@@ -1102,7 +1100,7 @@ class DataSequence(DataABC, DatabaseRecordProtocolMixin[DataRecord]):
             key (str): The key of the attribute in DataRecord to extract.
             start_datetime (datetime, optional): The start date for filtering the records (inclusive).
             end_datetime (datetime, optional): The end date for filtering the records (exclusive).
-            dropna: (bool, optional): Whether to drop NAN/ None values before processing. Defaults to True.
+            dropna: (bool): Whether to drop NAN/ None values before processing. Defaults to True.
 
         Returns:
             tuple: A tuple containing a list of datetime values and a list of extracted values.
@@ -1119,8 +1117,6 @@ class DataSequence(DataABC, DatabaseRecordProtocolMixin[DataRecord]):
         end_timestamp = DatabaseTimestamp.from_datetime(end_datetime) if end_datetime else None
 
         # Create two lists to hold date_time and corresponding values
-        if dropna is None:
-            dropna = True
         filtered_records = []
         async for record in self.db_iterate_records(start_timestamp, end_timestamp):
             if (
@@ -1155,48 +1151,51 @@ class DataSequence(DataABC, DatabaseRecordProtocolMixin[DataRecord]):
         async with self._record_lock:
             await self._key_from_lists(key, dates, values)
 
-    async def key_to_series(
+    async def key_to_raw_series(
         self,
         key: str,
         start_datetime: Optional[DateTime] = None,
         end_datetime: Optional[DateTime] = None,
-        dropna: Optional[bool] = None,
+        dropna: bool = True,
     ) -> pd.Series:
-        """Extract a series indexed by the date_time field from data records within an optional date range.
+        """Return the raw time series stored for a key.
+
+        Retrieves the timestamps and values exactly as stored by the underlying data provider.
+        The returned index therefore represents the original timestamps of the stored records.
 
         Args:
-            key (str): The field name in the DataRecord from which to extract values.
+            key: Field name to extract from the stored records.
-            start_datetime (datetime, optional): The start date for filtering the records (inclusive).
+            start_datetime: Inclusive lower bound for timestamps.
-            end_datetime (datetime, optional): The end date for filtering the records (exclusive).
+            end_datetime: Exclusive upper bound for timestamps.
-            dropna: (bool, optional): Whether to drop NAN/ None values before processing. Defaults to True.
+            dropna: Whether to discard records whose value is None or NaN.
 
         Returns:
-            pd.Series: A Pandas Series with the index as the date_time of each record
+            A pandas Series indexed by the original timestamps.
-                        and the values extracted from the specified key.
 
         Raises:
-            KeyError: If the specified key is not found in any of the DataRecords.
+            KeyError: If the key does not exist.
         """
+        # Normalize datetime arguments
+        start_datetime = to_datetime(start_datetime, to_maxtime=False) if start_datetime else None
+        end_datetime = to_datetime(end_datetime, to_maxtime=False) if end_datetime else None
+
         dates, values = await self.key_to_lists(
             key=key, start_datetime=start_datetime, end_datetime=end_datetime, dropna=dropna
         )
-        series = pd.Series(data=values, index=pd.DatetimeIndex(dates), name=key)
+
+        # Construct series
+        index = pd.to_datetime(dates, utc=True)
+        series = pd.Series(values, index=index, name=key)
+
+        # Check for correct series
+        if not isinstance(series.index, pd.DatetimeIndex):
+            raise TypeError(f"Expected pd.DatetimeIndex, but got {type(series.index).__name__}.")
+        if series.index.tz is None:
+            raise TypeError("Expected timezone-aware DatetimeIndex.")
+
         return series
 
-    async def key_from_series(self, key: str, series: pd.Series) -> None:
+    async def key_to_series(
-        """Update the DataSequence from a Pandas Series.
-
-        The series index should represent the date_time of each DataRecord, and the series values
-        should represent the corresponding data values for the specified key.
-
-        Args:
-            series (pd.Series): A Pandas Series containing data to update the DataSequence.
-            key (str): The field name in the DataRecord that corresponds to the values in the Series.
-        """
-        async with self._record_lock:
-            await self._key_from_series(key, series)
-
-    async def key_to_array(
         self,
         key: str,
         start_datetime: Optional[DateTime] = None,
@@ -1204,11 +1203,25 @@ class DataSequence(DataABC, DatabaseRecordProtocolMixin[DataRecord]):
         interval: Optional[Duration] = None,
         fill_method: Optional[FillMethod] = None,
         resample_method: ResampleMethod = "mean",
-        dropna: Optional[bool] = True,
+        dropna: bool = True,
         boundary: BoundaryMode = "context",
         align_to_interval: bool = False,
-    ) -> NDArray[Shape["*"], Any]:
+    ) -> pd.Series:
-        """Extract an array indexed by fixed time intervals from data records within an optional date range.
+        """Return a pandas Series for a data key.
+
+        The method performs all preprocessing required for resampling:
+
+        - validates the input arguments,
+        - extends the query window when context resampling is requested,
+        - loads the raw database values,
+        - inserts boundary values when necessary,
+        - determines the resampling origin,
+        - performs aggregation,
+        - fills missing values,
+        - truncates the result back to the requested interval.
+
+        The DatetimeIndex represents the actual bucket timestamps and should therefore be
+        considered the authoritative timestamp information for the resampled data.
 
         Args:
             key (str): The field name in the DataRecord from which to extract values.
@@ -1232,7 +1245,7 @@ class DataSequence(DataABC, DatabaseRecordProtocolMixin[DataRecord]):
                   value remains valid until the next timestamp (piecewise-constant
                   signal).
 
-            dropna: (bool, optional): Whether to drop NAN/ None values before processing.
+            dropna: (bool): Whether to drop NAN/ None values before processing.
                 Defaults to True.
             boundary (Literal["strict", "context"]): resampling boundary
                 "strict"  → only values inside [start, end)
@@ -1255,14 +1268,12 @@ class DataSequence(DataABC, DatabaseRecordProtocolMixin[DataRecord]):
                 more important than clock-round boundaries.
 
         Returns:
-            np.ndarray: A NumPy Array of the values at the chosen frequency extracted from the
+            A resampled pandas Series whose index contains the correct timestamps
-                specified key.
+            for every returned value.
 
         Raises:
             KeyError: If the specified key is not found in any of the DataRecords.
         """
-        self._validate_key(key)
-
         # Validate fill method
         if fill_method not in ("ffill", "bfill", "linear", "time", "none", None):
             raise ValueError(f"Unsupported fill method: {fill_method}")
@@ -1307,36 +1318,40 @@ class DataSequence(DataABC, DatabaseRecordProtocolMixin[DataRecord]):
                 else:
                     query_end = DatabaseTimestamp.to_datetime(query_end_timestamp).add(seconds=1)
 
-        # Load raw lists (already sorted & filtered)
+        # Load raw series
-        dates, values = await self.key_to_lists(
+        series = await self.key_to_raw_series(
             key=key, start_datetime=query_start, end_datetime=query_end, dropna=dropna
         )
-        values_len = len(values)
 
-        # Bring lists into shape
+        # Ensure we have at least one value
-        if values_len < 1:
+        if series.empty:
-            # No values, assume at least one value set to None
+            dummy_time = (
-            if query_start is not None:
+                query_start - interval if query_start is not None else to_datetime(to_maxtime=False)
-                dates.append(query_start - interval)
+            )
-            else:
+            series = pd.Series(
-                dates.append(to_datetime(to_maxtime=False))
+                [None],
-            values.append(None)
+                index=pd.DatetimeIndex([dummy_time], tz="UTC"),
+                name=key,
+            )
 
         if query_start is not None:
-            start_index = 0
+            idx = series.index
-            while start_index < values_len:
+
-                if compare_datetimes(dates[start_index], query_start).ge:
+            # Number of samples before query_start
-                    break
+            start_index = idx.searchsorted(pd.Timestamp(query_start), side="left")
-                start_index += 1
+
             if start_index == 0:
-                # No value before start
+                # No value before query_start -> prepend dummy
-                # Add dummy value
+                prepend = pd.Series(
-                dates.insert(0, query_start - interval)
+                    [series.iloc[0]],
-                values.insert(0, values[0])
+                    index=pd.DatetimeIndex([query_start - interval], tz="UTC"),
+                    name=key,
+                )
+                series = pd.concat([prepend, series])
+
             elif start_index > 1:
-                # Truncate all values before latest value before query_start
+                # Keep only the last sample before query_start
-                dates = dates[start_index - 1 :]
+                series = series.iloc[start_index - 1 :]
-                values = values[start_index - 1 :]
 
             # Determine resample origin
             if align_to_interval:
@@ -1360,23 +1375,17 @@ class DataSequence(DataABC, DatabaseRecordProtocolMixin[DataRecord]):
             resample_origin = "start_day"
 
         if query_end is not None:
-            if compare_datetimes(dates[-1], query_end).lt:
+            if compare_datetimes(to_datetime(series.index[-1]), query_end).lt:
-                # Add dummy value at query_end
+                append = pd.Series(
-                dates.append(query_end)
+                    [series.iloc[-1]],
-                values.append(values[-1])
+                    index=pd.DatetimeIndex([query_end], tz="UTC"),
-
+                    name=key,
-        # Construct series
+                )
-        index = pd.to_datetime(dates, utc=True)
+                series = pd.concat([series, append])
-        series = pd.Series(values, index=index, name=key)
-        if series.index.inferred_type != "datetime64":
-            raise TypeError(
-                f"Expected DatetimeIndex, but got {type(series.index)} "
-                f"infered to {series.index.inferred_type}: {series}"
-            )
 
         # Check for numeric values
-        numeric = pd.to_numeric(series.dropna(), errors="coerce")
+        numeric_series = pd.to_numeric(series, errors="coerce")  # ensures float64, not object dtype
-        is_numeric = numeric.notna().all()
+        is_numeric = numeric_series.dropna().notna().all()
 
         # Determine default fill method depending on dtype
         if fill_method is None:
@@ -1388,10 +1397,7 @@ class DataSequence(DataABC, DatabaseRecordProtocolMixin[DataRecord]):
         # Perform the resampling
         if is_numeric:
             # Step 1: aggregate — collapses sub-interval data (e.g. 4x 15min → 1h mean).
-            # Produces NaN for buckets where no data existed at all.
+            # numeric_series has NaN for buckets where no data existed at all.
-            numeric_series = pd.to_numeric(
-                series, errors="coerce"
-            )  # ← ensures float64, not object dtype
 
             if resample_method == "first":
                 resampled = numeric_series.resample(
@@ -1433,22 +1439,114 @@ class DataSequence(DataABC, DatabaseRecordProtocolMixin[DataRecord]):
             elif fill_method == "bfill":
                 resampled = resampled.bfill()
 
+        if start_datetime is not None and len(resampled) > 0:
+            resampled = resampled.truncate(before=start_datetime)
+        if end_datetime is not None and len(resampled) > 0:
+            resampled = resampled.truncate(after=end_datetime.subtract(seconds=1))
+
         logger.debug(
-            "Resampled for '{}' with length {}: {}...{}",
+            "Resampled series for '{}' with length {}: {}...{}",
             key,
             len(resampled),
             resampled[:10],
             resampled[-10:],
         )
 
-        # Convert the resampled series to a NumPy array
+        return resampled
-        if start_datetime is not None and len(resampled) > 0:
-            resampled = resampled.truncate(before=start_datetime)
-        if end_datetime is not None and len(resampled) > 0:
-            resampled = resampled.truncate(after=end_datetime.subtract(seconds=1))
-        array = resampled.values
 
-        # Convert NaN to None if there are actually NaNs
+    async def key_from_series(self, key: str, series: pd.Series) -> None:
+        """Update the DataSequence from a Pandas Series.
+
+        The series index should represent the date_time of each DataRecord, and the series values
+        should represent the corresponding data values for the specified key.
+
+        Args:
+            series (pd.Series): A Pandas Series containing data to update the DataSequence.
+            key (str): The field name in the DataRecord that corresponds to the values in the Series.
+        """
+        async with self._record_lock:
+            await self._key_from_series(key, series)
+
+    async def key_to_array(
+        self,
+        key: str,
+        start_datetime: Optional[DateTime] = None,
+        end_datetime: Optional[DateTime] = None,
+        interval: Optional[Duration] = None,
+        fill_method: Optional[FillMethod] = None,
+        resample_method: ResampleMethod = "mean",
+        dropna: bool = True,
+        boundary: BoundaryMode = "context",
+        align_to_interval: bool = False,
+    ) -> NDArray[Shape["*"], Any]:
+        """Extract an array indexed by fixed time intervals from data records within an optional date range.
+
+        Args:
+            key (str): The field name in the DataRecord from which to extract values.
+            start_datetime (datetime, optional): The start date for filtering the records (inclusive).
+            end_datetime (datetime, optional): The end date for filtering the records (exclusive).
+            interval (duration, optional): The fixed time interval. Defaults to 1 hour.
+            fill_method (str): Method to handle missing values during resampling.
+
+                - 'linear': Linearly interpolate missing values (for numeric data only).
+                - 'time': Interpolate missing values (for numeric data only).
+                - 'ffill': Forward fill missing values.
+                - 'bfill': Backward fill missing values.
+                - Defaults to 'linear' for numeric values, otherwise 'ffill'.
+
+            resample_method (str):
+                Method used to aggregate values within a resampling interval.
+
+                - "first": Use the first value in each interval.
+                - "mean": Compute the arithmetic mean of all samples in each interval.
+                - "interval_mean": Compute the time-weighted mean assuming each
+                  value remains valid until the next timestamp (piecewise-constant
+                  signal).
+
+            dropna: (bool): Whether to drop NAN/ None values before processing.
+                Defaults to True.
+            boundary (Literal["strict", "context"]): resampling boundary
+                "strict"  → only values inside [start, end)
+                "context" → include one value before and after for proper resampling
+            align_to_interval (bool): When True, snap the resample origin to the nearest
+                UTC epoch-aligned boundary of ``interval`` before resampling.  This ensures
+                that bucket timestamps always fall on wall-clock-round times regardless of
+                when ``start_datetime`` falls:
+
+                - 15-minute interval → buckets on :00, :15, :30, :45
+                - 1-hour interval    → buckets on the hour
+
+                When False (default), the origin is ``query_start`` (or ``"start_day"`` when
+                no start is given), preserving the existing behaviour where buckets are
+                aligned to the query window rather than the clock.
+
+                Set to True when storing compacted records back to the database so that the
+                resulting timestamps are predictable and human-readable.  Leave False for
+                forecast or reporting queries where alignment to the exact query window is
+                more important than clock-round boundaries.
+
+        Returns:
+            np.ndarray: A NumPy Array of the values at the chosen frequency extracted from the
+                specified key.
+
+        Raises:
+            KeyError: If the specified key is not found in any of the DataRecords.
+        """
+        series = await self.key_to_series(
+            key=key,
+            start_datetime=start_datetime,
+            end_datetime=end_datetime,
+            interval=interval,
+            fill_method=fill_method,
+            resample_method=resample_method,
+            dropna=dropna,
+            boundary=boundary,
+            align_to_interval=align_to_interval,
+        )
+
+        array = series.to_numpy()
+
+        # Convert NaN to None if necessary
         if (
             isinstance(array, np.ndarray)
             and np.issubdtype(array.dtype.type, np.floating)
@@ -1458,7 +1556,11 @@ class DataSequence(DataABC, DatabaseRecordProtocolMixin[DataRecord]):
             array[pd.isna(array)] = None
 
         logger.debug(
-            "Array for '{}' with length {}: {}...{}", key, len(array), array[:10], array[-10:]
+            "Array for '{}' with length {}: {}...{}",
+            key,
+            len(array),
+            array[:10],
+            array[-10:],
         )
 
         return array
@@ -2380,12 +2482,12 @@ class DataContainer(SingletonMixin, DataABC):
                         # Log as warning and continue so the remaining providers still run.
                         logger.warning(error)
 
-    async def key_to_series(
+    async def key_to_raw_series(
         self,
         key: str,
         start_datetime: Optional[DateTime] = None,
         end_datetime: Optional[DateTime] = None,
-        dropna: Optional[bool] = None,
+        dropna: bool = True,
     ) -> pd.Series:
         """Extract a series indexed by the date_time field from data records within an optional date range.
 
@@ -2395,7 +2497,93 @@ class DataContainer(SingletonMixin, DataABC):
             key (str): The field name in the DataRecord from which to extract values.
             start_datetime (datetime, optional): The start date for filtering the records (inclusive).
             end_datetime (datetime, optional): The end date for filtering the records (exclusive).
-            dropna: (bool, optional): Whether to drop NAN/ None values before processing. Defaults to True.
+            dropna: (bool): Whether to drop NAN/ None values before processing.
+                Defaults to True.
+
+        Returns:
+            pd.Series: A Pandas Series with the index as the date_time of each record
+                        and the values extracted from the specified key.
+
+        Raises:
+            KeyError: If the specified key is not found in any of the DataRecords.
+        """
+        series = None
+        for provider in self.enabled_providers:
+            try:
+                series = await provider.key_to_raw_series(
+                    key=key,
+                    start_datetime=start_datetime,
+                    end_datetime=end_datetime,
+                    dropna=dropna,
+                )
+                break
+            except KeyError:
+                continue
+
+        if series is None:
+            raise KeyError(f"No data found for key '{key}'.")
+
+        return series
+
+    async def key_to_series(
+        self,
+        key: str,
+        start_datetime: Optional[DateTime] = None,
+        end_datetime: Optional[DateTime] = None,
+        interval: Optional[Duration] = None,
+        fill_method: Optional[FillMethod] = None,
+        resample_method: ResampleMethod = "mean",
+        dropna: bool = True,
+        boundary: BoundaryMode = "context",
+        align_to_interval: bool = False,
+    ) -> pd.Series:
+        """Extract a series indexed by the date_time field from data records within an optional date range.
+
+        Iterates through providers to find and return the first available series for the specified key.
+
+        Args:
+            key (str): The field name in the DataRecord from which to extract values.
+            start_datetime (datetime, optional): The start date for filtering the records (inclusive).
+            end_datetime (datetime, optional): The end date for filtering the records (exclusive).
+            interval (duration, optional): The fixed time interval. Defaults to 1 hour.
+            fill_method (str): Method to handle missing values during resampling.
+
+                - 'linear': Linearly interpolate missing values (for numeric data only).
+                - 'time': Interpolate missing values (for numeric data only).
+                - 'ffill': Forward fill missing values.
+                - 'bfill': Backward fill missing values.
+                - Defaults to 'linear' for numeric values, otherwise 'ffill'.
+
+            resample_method (str):
+                Method used to aggregate values within a resampling interval.
+
+                - "first": Use the first value in each interval.
+                - "mean": Compute the arithmetic mean of all samples in each interval.
+                - "interval_mean": Compute the time-weighted mean assuming each
+                  value remains valid until the next timestamp (piecewise-constant
+                  signal).
+
+            dropna: (bool): Whether to drop NAN/ None values before processing.
+                Defaults to True.
+            boundary (Literal["strict", "context"]):
+                "strict"  → only values inside [start, end)
+                "context" → include one value before and after for proper resampling
+            align_to_interval (bool): When True, snap the resample origin to the nearest
+                UTC epoch-aligned boundary of ``interval`` before resampling.  This ensures
+                that bucket timestamps always fall on wall-clock-round times regardless of
+                when ``start_datetime`` falls:
+
+                - 15-minute interval → buckets on :00, :15, :30, :45
+                - 1-hour interval    → buckets on the hour
+
+                When False (default), the origin is ``query_start`` (or ``"start_day"`` when
+                no start is given), preserving the existing behaviour where buckets are
+                aligned to the query window rather than the clock.
+
+                Set to True when storing compacted records back to the database so that the
+                resulting timestamps are predictable and human-readable.  Leave False for
+                forecast or reporting queries where alignment to the exact query window is
+                more important than clock-round boundaries.
 
         Returns:
             pd.Series: A Pandas Series with the index as the date_time of each record
@@ -2408,10 +2596,15 @@ class DataContainer(SingletonMixin, DataABC):
         for provider in self.enabled_providers:
             try:
                 series = await provider.key_to_series(
-                    key,
+                    key=key,
                     start_datetime=start_datetime,
                     end_datetime=end_datetime,
+                    interval=interval,
+                    fill_method=fill_method,
+                    resample_method=resample_method,
                     dropna=dropna,
+                    boundary=boundary,
+                    align_to_interval=align_to_interval,
                 )
                 break
             except KeyError:
@@ -2430,7 +2623,7 @@ class DataContainer(SingletonMixin, DataABC):
         interval: Optional[Duration] = None,
         fill_method: Optional[FillMethod] = None,
         resample_method: ResampleMethod = "mean",
-        dropna: Optional[bool] = True,
+        dropna: bool = True,
         boundary: BoundaryMode = "context",
         align_to_interval: bool = False,
     ) -> NDArray[Shape["*"], Any]:
@@ -2460,7 +2653,7 @@ class DataContainer(SingletonMixin, DataABC):
                   value remains valid until the next timestamp (piecewise-constant
                   signal).
 
-            dropna: (bool, optional): Whether to drop NAN/ None values before processing.
+            dropna: (bool): Whether to drop NAN/ None values before processing.
                 Defaults to True.
             boundary (Literal["strict", "context"]):
                 "strict"  → only values inside [start, end)
@@ -2495,7 +2688,7 @@ class DataContainer(SingletonMixin, DataABC):
         for provider in self.enabled_providers:
             try:
                 array = await provider.key_to_array(
-                    key,
+                    key=key,
                     start_datetime=start_datetime,
                     end_datetime=end_datetime,
                     interval=interval,
@@ -2522,13 +2715,13 @@ class DataContainer(SingletonMixin, DataABC):
         interval: Optional[Duration] = None,
         fill_method: Optional[FillMethod] = None,
         resample_method: ResampleMethod = "mean",
-        dropna: Optional[bool] = True,
+        dropna: bool = True,
         boundary: BoundaryMode = "context",
         align_to_interval: bool = False,
     ) -> pd.DataFrame:
         """Retrieve a dataframe indexed by fixed time intervals for specified keys from the data in each DataProvider.
 
-        Generates a pandas DataFrame using the NumPy arrays for each specified key, ensuring a common time index.
+        Generates a pandas DataFrame of data for each specified key, ensuring a common time index.
 
         Args:
             keys (list[str]): The field names in the DataRecords from which to extract values.
@@ -2552,7 +2745,7 @@ class DataContainer(SingletonMixin, DataABC):
                   value remains valid until the next timestamp (piecewise-constant
                   signal).
 
-            dropna: (bool, optional): Whether to drop NAN/ None values before processing.
+            dropna: (bool): Whether to drop NAN/ None values before processing.
                 Defaults to True.
             boundary (Literal["strict", "context"]):
                 "strict"  → only values inside [start, end)
@@ -2575,17 +2768,23 @@ class DataContainer(SingletonMixin, DataABC):
                 more important than clock-round boundaries.
 
         Returns:
-            pd.DataFrame: A DataFrame where each column represents a key's array with a common time index.
+            pd.DataFrame: A DataFrame whose columns contain the (resampled) values
+                for each requested key and whose index is the common (resampled) DatetimeIndex.
 
         Raises:
             KeyError: If no valid data is found for any of the requested keys.
-            ValueError: If any retrieved array has a different time index than the first one.
+            ValueError: If any retrieved series has a different DatetimeIndex than the first one.
         """
+        if not keys:
+            raise ValueError("keys must not be empty.")
+
         # Ensure datetime objects are normalized
         start_datetime = to_datetime(start_datetime, to_maxtime=False) if start_datetime else None
         end_datetime = to_datetime(end_datetime, to_maxtime=False) if end_datetime else None
+
         if interval is None:
             interval = to_duration("1 hour")
+
         if start_datetime is None:
             # Take earliest datetime of all providers that are enabled
             for provider in self.enabled_providers:
@@ -2601,7 +2800,6 @@ class DataContainer(SingletonMixin, DataABC):
                 if end_datetime is None:
                     end_datetime = max_dt
                 elif max_dt and compare_datetimes(max_dt, end_datetime).gt:
-                    min_dt = await provider.min_datetime()
                     end_datetime = max_dt
             if end_datetime:
                 end_datetime = end_datetime.add(seconds=1)
@@ -2611,17 +2809,13 @@ class DataContainer(SingletonMixin, DataABC):
             raise ValueError(
                 f"Can not determine datetime range. Got '{start_datetime}'..'{end_datetime}'."
             )
-        reference_index = pd.date_range(
-            start=start_datetime,
-            end=end_datetime,
-            freq=interval,
-            inclusive="left",
-        )
 
-        data = {}
+        reference_index: Optional[pd.DatetimeIndex] = None
+        data: dict[str, pd.Series] = {}
+
         for key in keys:
             try:
-                array = await self.key_to_array(
+                series = await self.key_to_series(
                     key=key,
                     start_datetime=start_datetime,
                     end_datetime=end_datetime,
@@ -2632,20 +2826,24 @@ class DataContainer(SingletonMixin, DataABC):
                     boundary=boundary,
                     align_to_interval=align_to_interval,
                 )
-
+                if not isinstance(series.index, pd.DatetimeIndex):  # make mypy happy
-                if len(array) != len(reference_index):
+                    raise TypeError(
-                    raise ValueError(
+                        f"Expected DatetimeIndex for key '{key}', got {type(series.index).__name__}"
-                        f"Array length mismatch for key '{key}' (expected {len(reference_index)}, got {len(array)})"
                     )
 
-                data[key] = array
+                if reference_index is None:
+                    reference_index = series.index
+                elif not series.index.equals(reference_index):
+                    raise ValueError(f"Time index mismatch for key '{key}'.")
+
+                data[key] = series
             except KeyError as e:
-                raise KeyError(f"Failed to retrieve data for key '{key}': {e}")
+                raise KeyError(f"Failed to retrieve data for key '{key}': {e}") from e
 
         if not data:
             raise KeyError(f"No valid data found for the requested keys {keys}.")
 
-        return pd.DataFrame(data, index=reference_index)
+        return pd.DataFrame(data)  # Pandas aligns the series in data by their index
 
     async def key_delete_by_datetime(
         self,

src/akkudoktoreos/core/databaseabc.py

@@ -549,7 +549,7 @@ class DatabaseRecordProtocolMixin(
             interval: Optional[Duration] = None,
             fill_method: Optional[FillMethod] = None,
             resample_method: ResampleMethod = "mean",
-            dropna: Optional[bool] = True,
+            dropna: bool = True,
             boundary: BoundaryMode = "context",
             align_to_interval: bool = False,
         ) -> NDArray[Shape["*"], Any]: ...

src/akkudoktoreos/core/logsettings.py

@@ -15,10 +15,18 @@ from akkudoktoreos.core.logabc import LOGGING_LEVELS
 class LoggingCommonSettings(SettingsBaseModel):
     """Logging Configuration."""
 
+    api_level: Optional[str] = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Logging level for API response.",
+            "examples": LOGGING_LEVELS,
+        },
+    )
+
     console_level: Optional[str] = Field(
         default=None,
         json_schema_extra={
-            "description": "Logging level when logging to console.",
+            "description": "Logging level for logging to console.",
             "examples": LOGGING_LEVELS,
         },
     )
@@ -26,7 +34,7 @@ class LoggingCommonSettings(SettingsBaseModel):
     file_level: Optional[str] = Field(
         default=None,
         json_schema_extra={
-            "description": "Logging level when logging to file.",
+            "description": "Logging level for logging to file.",
             "examples": LOGGING_LEVELS,
         },
     )

src/akkudoktoreos/optimization/genetic/genetic.py

@@ -1221,39 +1221,20 @@ class GeneticOptimization(OptimizationBase):
         else:
             discharge = discharge.tolist()
 
-        # Visualize the results in PDF
-        try:
-            from akkudoktoreos.utils.visualize import prepare_visualize
-
-            visualize = {
-                "ac_charge": ac_charge_hours,
-                "dc_charge": dc_charge_hours,
-                "discharge_allowed": discharge,
-                "ev_charge_hours_float": ev_charge_hours_float,
-                "result": GeneticSimulationResult(**simulation_result).model_dump(),
-                "ev_obj": self.simulation.ev.to_dict() if self.simulation.ev else None,
-                "start_solution": start_solution,
-                "washingstart": washingstart_int,
-                "extra_data": extra_data,
-                "fitness_history": self.fitness_history,
-                "fixed_seed": self.fix_seed,
-            }
-
-            prepare_visualize(parameters, visualize, start_hour=start_hour)
-
-        except Exception as ex:
-            error_msg = f"Visualization failed: {ex}"
-            logger.error(error_msg)
-
         return GeneticSolution(
             **{
+                "parameters": parameters,
                 "ac_charge": ac_charge_hours,
                 "dc_charge": dc_charge_hours,
                 "discharge_allowed": discharge,
                 "ev_charge_hours_float": ev_charge_hours_float,
                 "result": GeneticSimulationResult(**simulation_result),
                 "ev_obj": self.simulation.ev,
+                "start_hour": start_hour,
                 "start_solution": start_solution,
                 "washingstart": washingstart_int,
+                "extra_data": extra_data,
+                "fitness_history": self.fitness_history,
+                "fixed_seed": self.fix_seed,
             }
         )

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})

src/akkudoktoreos/optimization/genetic/geneticvisualize.py

@@ -1,7 +1,7 @@
 import json
-import os
 import textwrap
 from collections.abc import Sequence
+from io import BytesIO
 from typing import Callable, Optional, Union
 
 import matplotlib
@@ -12,7 +12,7 @@ import pendulum
 from matplotlib.backends.backend_pdf import PdfPages
 
 from akkudoktoreos.core.coreabc import ConfigMixin, get_ems
-from akkudoktoreos.optimization.genetic.genetic import GeneticOptimizationParameters
+from akkudoktoreos.optimization.genetic.geneticsolution import GeneticSolution
 from akkudoktoreos.utils.datetimeutil import DateTime, to_datetime
 
 matplotlib.use(
@@ -23,39 +23,29 @@ matplotlib.use(
 debug_visualize: bool = False
 
 
-class VisualizationReport(ConfigMixin):
+class GeneticVisualizationReport(ConfigMixin):
     def __init__(
         self,
-        filename: str = "visualization_results.pdf",
         version: str = "0.0.1",
-        create_img: bool = True,
     ) -> None:
-        # Initialize the report with a given filename and empty groups
+        # Initialize the report with empty groups
-        self.filename = filename
         self.groups: list[list[Callable[[], None]]] = []  # Store groups of charts
         self.current_group: list[
             Callable[[], None]
         ] = []  # Store current group of charts being created
-        self.pdf_pages = PdfPages(filename, metadata={})  # Initialize PdfPages without metadata
+
+        # file like buffer to create the PDF
+        self._buffer = BytesIO()
+        self.pdf_pages: PdfPages | None = None
+
         self.version = version  # overwrite version as test for constant output of pdf for test
         self.current_time = to_datetime(
             as_string="YYYY-MM-DD HH:mm:ss", in_timezone=self.config.general.timezone
         )
-        self.create_img = create_img
 
     def add_chart_to_group(self, chart_func: Callable[[], None], title: str | None) -> None:
         """Add a chart function to the current group and save it as a PNG and SVG."""
         self.current_group.append(chart_func)
-        if self.create_img and title:
-            server_output_dir = self.config.cache.path()
-            server_output_dir.mkdir(parents=True, exist_ok=True)
-            fig, ax = plt.subplots()
-            chart_func()
-            plt.tight_layout()  # Adjust the layout to ensure titles are not cut off
-            sanitized_title = "".join(c if c.isalnum() else "_" for c in title)
-            chart_filename_base = os.path.join(server_output_dir, f"chart_{sanitized_title}")
-            fig.savefig(f"{chart_filename_base}.svg")
-            plt.close(fig)
 
     def finalize_group(self) -> None:
         """Finalize the current group and prepare for a new group."""
@@ -66,22 +56,15 @@ class VisualizationReport(ConfigMixin):
         self.current_group = []  # Reset current group for new charts
 
     def _initialize_pdf(self) -> None:
-        """Create the output directory if it doesn't exist and initialize the PDF."""
+        """Initialize the PDF."""
-        output_dir = self.config.general.data_output_path
+        self._buffer = BytesIO()
-
+        self.pdf_pages = PdfPages(self._buffer, metadata={})  # Initialize PdfPages without metadata
-        # If self.filename is already a valid path, use it; otherwise, combine it with output_dir
-        if os.path.isabs(self.filename):
-            output_file = self.filename
-        else:
-            output_dir.mkdir(parents=True, exist_ok=True)
-            output_file = os.path.join(output_dir, self.filename)
-
-        self.pdf_pages = PdfPages(
-            output_file, metadata={}
-        )  # Re-initialize PdfPages without metadata
 
     def _save_group_to_pdf(self, group: list[Callable[[], None]]) -> None:
         """Save a group of charts to the PDF."""
+        if self.pdf_pages is None:  # make mypy happy
+            raise RuntimeError("Report not initialized")
+
         fig_count = len(group)  # Number of charts in the group
 
         if fig_count == 0:
@@ -305,7 +288,7 @@ class VisualizationReport(ConfigMixin):
     def create_bar_chart(
         self,
         labels: list[str],
-        values_list: Sequence[Union[int, float, list[Union[int, float]]]],
+        values_list: Sequence[Sequence[int | float]],
         title: str,
         ylabel: str,
         xlabels: Optional[list[str]] = None,
@@ -371,6 +354,9 @@ class VisualizationReport(ConfigMixin):
         """Add a page with text content to the PDF."""
 
         def chart() -> None:
+            if self.pdf_pages is None:  # make mypy happy
+                raise RuntimeError("Report not initialized")
+
             fig = plt.figure(figsize=(8.5, 11))  # Create a standard page size
             plt.axis("off")  # Turn off axes for a clean page
             wrapped_text = textwrap.fill(text, width=80)  # Wrap text to fit the page width
@@ -398,6 +384,9 @@ class VisualizationReport(ConfigMixin):
         """
 
         def chart() -> None:
+            if self.pdf_pages is None:  # make mypy happy
+                raise RuntimeError("Report not initialized")
+
             # Convert JSON object to a formatted string
             json_str = json.dumps(json_obj, indent=4)
 
@@ -424,31 +413,37 @@ class VisualizationReport(ConfigMixin):
 
         self.add_chart_to_group(chart, title)  # Treat the JSON page as a "chart" in the group
 
-    def generate_pdf(self) -> None:
+    def generate_pdf(self) -> bytes:
-        """Generate the PDF report with all the added chart groups."""
+        """Generate the PDF report with all the added chart groups.
+
+        Returns:
+            PDF report as bytes object
+        """
         self._initialize_pdf()  # Initialize the PDF
 
+        buffer = BytesIO()
+        self.pdf_pages = PdfPages(buffer, metadata={})
+
         for group in self.groups:
-            self._save_group_to_pdf(group)  # Save each group to the PDF
+            self._save_group_to_pdf(group)
 
-        self.pdf_pages.close()  # Close the PDF to finalize the report
+        self.pdf_pages.close()
+        return buffer.getvalue()
 
 
-def prepare_visualize(
+def genetic_prepare_visualize(
-    parameters: GeneticOptimizationParameters,
+    solution: GeneticSolution,
-    results: dict,
+) -> bytes:
-    filename: str = "visualization_results.pdf",
-    start_hour: int = 0,
-) -> None:
     global debug_visualize
 
-    report = VisualizationReport(filename)
+    report = GeneticVisualizationReport()
     next_full_hour_date = get_ems().start_datetime
+    start_hour = solution.start_hour
     # Group 1:
     report.create_line_chart_date(
         next_full_hour_date,
         [
-            parameters.ems.total_load[start_hour:],
+            solution.parameters.ems.total_load[start_hour:],
         ],
         title="Load Profile",
         # xlabel="Hours", # not enough space
@@ -458,7 +453,7 @@ def prepare_visualize(
     report.create_line_chart_date(
         next_full_hour_date,
         [
-            parameters.ems.pv_forecast_wh[start_hour:],
+            solution.parameters.ems.pv_forecast_wh[start_hour:],
         ],
         title="PV Forecast",
         # xlabel="Hours", # not enough space
@@ -469,10 +464,10 @@ def prepare_visualize(
         next_full_hour_date,
         [
             np.full(
-                len(parameters.ems.total_load) - start_hour,
+                len(solution.parameters.ems.total_load) - start_hour,
-                parameters.ems.feed_in_tariff_per_wh[start_hour:]
+                solution.parameters.ems.feed_in_tariff_per_wh[start_hour:]
-                if isinstance(parameters.ems.feed_in_tariff_per_wh, list)
+                if isinstance(solution.parameters.ems.feed_in_tariff_per_wh, list)
-                else parameters.ems.feed_in_tariff_per_wh,
+                else solution.parameters.ems.feed_in_tariff_per_wh,
             )
         ],
         title="Remuneration",
@@ -480,11 +475,11 @@ def prepare_visualize(
         ylabel="amount/Wh",
         x2label=None,  # not enough space
     )
-    if parameters.temperature_forecast:
+    if solution.parameters.temperature_forecast:
         report.create_line_chart_date(
             next_full_hour_date,
             [
-                parameters.temperature_forecast[start_hour:],
+                solution.parameters.temperature_forecast[start_hour:],
             ],
             title="Temperature Forecast",
             # xlabel="Hours", # not enough space
@@ -495,13 +490,13 @@ def prepare_visualize(
 
     # Group 2:
     report.create_line_chart_date(
-        next_full_hour_date,  # start_date
+        start_date=next_full_hour_date,  # start_date
-        [
+        y_list=[
-            results["result"]["load_wh_per_hour"],
+            solution.result.load_wh_per_hour,
-            results["result"]["home_appliance_wh_per_hour"],
+            solution.result.home_appliance_wh_per_hour,
-            results["result"]["grid_feed_in_wh_per_hour"],
+            solution.result.grid_feed_in_wh_per_hour,
-            results["result"]["grid_consumption_wh_per_hour"],
+            solution.result.grid_consumption_wh_per_hour,
-            results["result"]["losses_per_hour"],
+            solution.result.losses_per_hour,
         ],
         title="Energy Flow per Hour",
         # xlabel="Date", # not enough space
@@ -520,8 +515,8 @@ def prepare_visualize(
 
     # Group 3:
     report.create_line_chart_date(
-        next_full_hour_date,  # start_date
+        start_date=next_full_hour_date,
-        [results["result"]["battery_soc_per_hour"], results["result"]["ev_soc_per_hour"]],
+        y_list=[solution.result.battery_soc_per_hour, solution.result.ev_soc_per_hour],
         title="Battery SOC",
         # xlabel="Date", # not enough space
         ylabel="%",
@@ -532,8 +527,8 @@ def prepare_visualize(
         markers=["o", "x"],
     )
     report.create_line_chart_date(
-        next_full_hour_date,  # start_date
+        start_date=next_full_hour_date,  # start_date
-        [parameters.ems.electricity_price_per_wh[start_hour:]],
+        y_list=[solution.parameters.ems.electricity_price_per_wh[start_hour:]],
         # title="Electricity Price", # not enough space
         # xlabel="Date", # not enough space
         ylabel="Electricity Price (amount/Wh)",
@@ -550,11 +545,11 @@ def prepare_visualize(
     labels = labels[start_hour:] + labels
 
     report.create_bar_chart(
-        labels,
+        labels=labels,
-        [
+        values_list=[
-            results["ac_charge"][start_hour:],
+            solution.ac_charge[start_hour:],
-            results["dc_charge"][start_hour:],
+            solution.dc_charge[start_hour:],
-            results["discharge_allowed"][start_hour:],
+            solution.discharge_allowed[start_hour:],
         ],
         title="AC/DC Charging and Discharge Overview",
         ylabel="Relative Power (0-1) / Discharge (0 or 1)",
@@ -570,8 +565,8 @@ def prepare_visualize(
     report.create_line_chart_date(
         next_full_hour_date,  # start_date
         [
-            results["result"]["costs_per_hour"],
+            solution.result.costs_per_hour,
-            results["result"]["revenue_per_hour"],
+            solution.result.revenue_per_hour,
         ],
         title="Financial Balance per Hour",
         # xlabel="Date", # not enough space
@@ -579,21 +574,22 @@ def prepare_visualize(
         labels=["Costs", "Revenue"],
     )
 
-    extra_data = results["extra_data"]
+    extra_data = solution.extra_data
-    report.create_scatter_plot(
+    if extra_data:
-        extra_data["losses"],
+        report.create_scatter_plot(
-        extra_data["balance"],
+            x=np.array(extra_data["losses"]),
-        title="Scatter Plot",
+            y=np.array(extra_data["balance"]),
-        xlabel="losses",
+            title="Scatter Plot",
-        ylabel="balance",
+            xlabel="losses",
-        c=extra_data["constraints"],
+            ylabel="balance",
-    )
+            c=np.array(extra_data["constraints"]),
+        )
 
     values_list = [
         [
-            results["result"]["total_costs"],
+            solution.result.total_costs,
-            results["result"]["total_revenue"],
+            solution.result.total_revenue,
-            results["result"]["total_balance"],
+            solution.result.total_balance,
         ]
     ]
     labels = ["Total Costs [amount]", "Total Revenue [amount]", "Total Balance [amount]"]
@@ -609,69 +605,77 @@ def prepare_visualize(
     report.finalize_group()
 
     # Group 1: Scatter plot of losses vs balance with color-coded constraints
-    f1 = np.array(extra_data["losses"])  # Losses
+    if extra_data:
-    f2 = np.array(extra_data["balance"])  # Balance
+        f1 = np.array(extra_data["losses"])  # Losses
-    n1 = np.array(extra_data["constraints"])  # Constraints
+        f2 = np.array(extra_data["balance"])  # Balance
+        n1 = np.array(extra_data["constraints"])  # Constraints
 
-    # Filter data where 'constraints' < 0.01
+        # Filter data where 'constraints' < 0.01
-    filtered_indices = n1 < 0.01
+        filtered_indices = n1 < 0.01
-    filtered_losses = f1[filtered_indices]
+        filtered_losses = f1[filtered_indices]
-    filtered_balance = f2[filtered_indices]
+        filtered_balance = f2[filtered_indices]
 
-    # Group 2: Violin plot for filtered losses
+        # Group 2: Violin plot for filtered losses
-    if filtered_losses.size > 0:
+        if filtered_losses.size > 0:
-        report.create_violin_plot(
+            report.create_violin_plot(
-            data_list=[filtered_losses],  # Data for filtered losses
+                data_list=[filtered_losses],  # Data for filtered losses
-            labels=["Filtered Losses"],  # Label for the violin plot
+                labels=["Filtered Losses"],  # Label for the violin plot
-            title="Violin Plot for Filtered Losses (Constraint < 0.01)",
+                title="Violin Plot for Filtered Losses (Constraint < 0.01)",
-            xlabel="Losses",
+                xlabel="Losses",
-            ylabel="Values",
+                ylabel="Values",
-        )
+            )
-    else:
+        else:
-        print("No data available for filtered losses violin plot (Constraint < 0.01)")
+            print("No data available for filtered losses violin plot (Constraint < 0.01)")
 
     # Group 3: Violin plot for filtered balance
-    if filtered_balance.size > 0:
+    if extra_data:
-        report.create_violin_plot(
+        if filtered_balance.size > 0:
-            data_list=[filtered_balance],  # Data for filtered balance
+            report.create_violin_plot(
-            labels=["Filtered Balance"],  # Label for the violin plot
+                data_list=[filtered_balance],  # Data for filtered balance
-            title="Violin Plot for Filtered Balance (Constraint < 0.01)",
+                labels=["Filtered Balance"],  # Label for the violin plot
-            xlabel="Balance",
+                title="Violin Plot for Filtered Balance (Constraint < 0.01)",
-            ylabel="Values",
+                xlabel="Balance",
-        )
+                ylabel="Values",
-    else:
+            )
-        print("No data available for filtered balance violin plot (Constraint < 0.01)")
+        else:
+            print("No data available for filtered balance violin plot (Constraint < 0.01)")
+
+        if filtered_balance.size > 0 or filtered_losses.size > 0:
+            report.finalize_group()
+
+    if solution.fitness_history:
+        if debug_visualize or solution.fixed_seed:
+            report.create_line_chart(
+                start_hour=0,
+                y_list=[
+                    np.array(solution.fitness_history["avg"]),
+                    np.array(solution.fitness_history["max"]),
+                    np.array(solution.fitness_history["min"]),
+                ],
+                title=f"DEBUG: Generation Fitness for seed {solution.fixed_seed}",
+                xlabel="Generation",
+                ylabel="Fitness",
+                labels=[
+                    "avg",
+                    "max",
+                    "min",
+                ],
+                markers=[".", ".", "."],
+            )
+            report.finalize_group()
 
-    if filtered_balance.size > 0 or filtered_losses.size > 0:
-        report.finalize_group()
-    if debug_visualize or results["fixed_seed"]:
-        report.create_line_chart(
-            0,
-            [
-                results["fitness_history"]["avg"],
-                results["fitness_history"]["max"],
-                results["fitness_history"]["min"],
-            ],
-            title=f"DEBUG: Generation Fitness for seed {results['fixed_seed']}",
-            xlabel="Generation",
-            ylabel="Fitness",
-            labels=[
-                "avg",
-                "max",
-                "min",
-            ],
-            markers=[".", ".", "."],
-        )
-        report.finalize_group()
     # Generate the PDF report
-    report.generate_pdf()
+    pdf = report.generate_pdf()
+
+    # Return pdf (a bytes object)
+    return pdf
 
 
-def generate_example_report(filename: str = "example_report.pdf") -> None:
+def genetic_generate_example_report(filename: str = "example_report.pdf") -> None:
     """Generate example visualization report."""
     global debug_visualize
 
-    report = VisualizationReport(filename, "test")
+    report = GeneticVisualizationReport("test")
     x_hours = 0  # Define x-axis start values (e.g., hours)
 
     # Group 1: Adding charts to be displayed on the same page
@@ -785,9 +789,13 @@ def generate_example_report(filename: str = "example_report.pdf") -> None:
         ylabel="test",
     )
     report.finalize_group()
-    # Generate the PDF report
+    # Generate the PDF report (a bytes object)
-    report.generate_pdf()
+    pdf = report.generate_pdf()
+
+    # write to file
+    with open(filename, "wb") as f:
+        f.write(pdf)
 
 
 if __name__ == "__main__":
-    generate_example_report()
+    genetic_generate_example_report()

src/akkudoktoreos/optimization/genetic0/genetic0.py

@@ -1067,7 +1067,7 @@ class Genetic0Optimization(OptimizationBase):
         # Start hour has to be in sync with energy management
         if start_hour != self.ems.start_datetime.hour:
             raise ValueError(
-                f"Start hour not synced. EMS {self.ems.start_datetime.hour} vs. GENETIC {start_hour}."
+                f"Start hour not synced. EMS {self.ems.start_datetime.hour} vs. GENETIC0 {start_hour}."
             )
 
         # Set the number of generations
@@ -1224,39 +1224,20 @@ class Genetic0Optimization(OptimizationBase):
         else:
             discharge = discharge.tolist()
 
-        # Visualize the results in PDF
-        try:
-            from akkudoktoreos.utils.visualize import prepare_visualize
-
-            visualize = {
-                "ac_charge": ac_charge_hours,
-                "dc_charge": dc_charge_hours,
-                "discharge_allowed": discharge,
-                "ev_charge_hours_float": ev_charge_hours_float,
-                "result": Genetic0SimulationResult(**simulation_result).model_dump(),
-                "ev_obj": self.simulation.ev.to_dict() if self.simulation.ev else None,
-                "start_solution": start_solution,
-                "washingstart": washingstart_int,
-                "extra_data": extra_data,
-                "fitness_history": self.fitness_history,
-                "fixed_seed": self.fix_seed,
-            }
-
-            prepare_visualize(parameters, visualize, start_hour=start_hour)
-
-        except Exception as ex:
-            error_msg = f"Visualization failed: {ex}"
-            logger.error(error_msg)
-
         return Genetic0Solution(
             **{
+                "parameters": parameters,
                 "ac_charge": ac_charge_hours,
                 "dc_charge": dc_charge_hours,
                 "discharge_allowed": discharge,
                 "ev_charge_hours_float": ev_charge_hours_float,
                 "result": Genetic0SimulationResult(**simulation_result),
                 "ev_obj": self.simulation.ev,
+                "start_hour": start_hour,
                 "start_solution": start_solution,
                 "washingstart": washingstart_int,
+                "extra_data": extra_data,
+                "fitness_history": self.fitness_history,
+                "fixed_seed": self.fix_seed,
             }
         )

src/akkudoktoreos/optimization/genetic0/genetic0solution.py

@@ -1,6 +1,6 @@
 """Genetic0 algorithm optimisation solution."""
 
-from typing import Any, Optional
+from typing import Any, Optional, Union
 
 import numpy as np
 import pandas as pd
@@ -29,6 +29,9 @@ from akkudoktoreos.devices.genetic0.genetic0battery import Genetic0Battery
 from akkudoktoreos.optimization.genetic0.genetic0devices import (
     Genetic0ParametersBaseModel,
 )
+from akkudoktoreos.optimization.genetic0.genetic0params import (
+    Genetic0OptimizationParameters,
+)
 from akkudoktoreos.optimization.optimization import OptimizationSolution
 from akkudoktoreos.utils.datetimeutil import to_datetime, to_duration
 from akkudoktoreos.utils.utils import NumpyEncoder
@@ -244,8 +247,9 @@ class Genetic0SimulationResult(Genetic0ParametersBaseModel):
         return NumpyEncoder.convert_numpy(field)[0]
 
 
-class Genetic0Solution(ConfigMixin, Genetic0ParametersBaseModel):
+# Legacy GENETIC solution dataset. To keep the /optimize endpoint compatible.
-    """**Note**: The first value of "load_wh_per_hour", "grid_feed_in_wh_per_hour", and "grid_consumption_wh_per_hour", will be set to null in the JSON output and represented as NaN or None in the corresponding classes' data returns. This approach is adopted to ensure that the current hour's processing remains unchanged."""
+class Genetic0SolutionLegacy(Genetic0ParametersBaseModel):
+    """GENETIC0 solution legacy dataset."""
 
     model_config = ConfigDict(populate_by_name=True, extra="ignore")
 
@@ -317,6 +321,109 @@ class Genetic0Solution(ConfigMixin, Genetic0ParametersBaseModel):
             return Genetic0ElectricVehicleResult(**field.to_dict())
         return field
 
+
+class Genetic0Solution(ConfigMixin, Genetic0ParametersBaseModel):
+    """**Note**: The first value of "load_wh_per_hour", "grid_feed_in_wh_per_hour", and "grid_consumption_wh_per_hour", will be set to null in the JSON output and represented as NaN or None in the corresponding classes' data returns. This approach is adopted to ensure that the current hour's processing remains unchanged."""
+
+    model_config = ConfigDict(populate_by_name=True, extra="ignore")
+
+    parameters: Genetic0OptimizationParameters = 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."
+        }
+    )
+    dc_charge: list[float] = Field(
+        json_schema_extra={
+            "description": "Array with DC charging values as relative power (0-1), other values set to 0."
+        }
+    )
+    discharge_allowed: list[int] = Field(
+        json_schema_extra={
+            "description": "Array with discharge values (1 for discharge, 0 otherwise)."
+        }
+    )
+    ev_charge_hours_float: Optional[list[float]] = Field(
+        validation_alias=AliasChoices("ev_charge_hours_float", "eautocharge_hours_float"),
+        json_schema_extra={
+            "description": "Array with EV charging values as relative power (0.0-1.0), or `null` if no EV is optimized."
+        },
+    )
+    result: Genetic0SimulationResult
+    ev_obj: Optional[Genetic0ElectricVehicleResult] = Field(
+        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={
+            "description": "An array of binary values (0 or 1) representing a possible starting solution for the simulation."
+        },
+    )
+    washingstart: Optional[int] = Field(
+        default=None,
+        json_schema_extra={
+            "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})
+    def eautocharge_hours_float(self) -> Optional[list[float]]:
+        """Deprecated: Use ev_charge_hours_float instead."""
+        return self.ev_charge_hours_float
+
+    @computed_field(json_schema_extra={"deprecated": True})
+    def eauto_obj(self) -> Optional[Genetic0ElectricVehicleResult]:
+        """Deprecated: Use ev_obj instead."""
+        return self.ev_obj
+
+    @field_validator(
+        "ac_charge",
+        "dc_charge",
+        "discharge_allowed",
+        mode="before",
+    )
+    def convert_numpy(cls, field: Any) -> Any:
+        return NumpyEncoder.convert_numpy(field)[0]
+
+    @field_validator(
+        "ev_obj",
+        mode="before",
+    )
+    def convert_eauto(cls, field: Any) -> Any:
+        if isinstance(field, Genetic0Battery):
+            return Genetic0ElectricVehicleResult(**field.to_dict())
+        return field
+
     def _battery_device_id(self) -> str:
         """Get battery device id."""
         try:

src/akkudoktoreos/optimization/genetic0/genetic0visualize.py

@@ -0,0 +1,801 @@
+import json
+import textwrap
+from collections.abc import Sequence
+from io import BytesIO
+from typing import Callable, Optional, Union
+
+import matplotlib
+import matplotlib.dates as mdates
+import matplotlib.pyplot as plt
+import numpy as np
+import pendulum
+from matplotlib.backends.backend_pdf import PdfPages
+
+from akkudoktoreos.core.coreabc import ConfigMixin, get_ems
+from akkudoktoreos.optimization.genetic0.genetic0solution import Genetic0Solution
+from akkudoktoreos.utils.datetimeutil import DateTime, to_datetime
+
+matplotlib.use(
+    "Agg"
+)  # non-interactive backend that can only write to files, backend needed to stay in main thread.
+
+
+debug_visualize: bool = False
+
+
+class Genetic0VisualizationReport(ConfigMixin):
+    def __init__(
+        self,
+        version: str = "0.0.1",
+    ) -> None:
+        # Initialize the report with empty groups
+        self.groups: list[list[Callable[[], None]]] = []  # Store groups of charts
+        self.current_group: list[
+            Callable[[], None]
+        ] = []  # Store current group of charts being created
+
+        # file like buffer to create the PDF
+        self._buffer = BytesIO()
+        self.pdf_pages: PdfPages | None = None
+
+        self.version = version  # overwrite version as test for constant output of pdf for test
+        self.current_time = to_datetime(
+            as_string="YYYY-MM-DD HH:mm:ss", in_timezone=self.config.general.timezone
+        )
+
+    def add_chart_to_group(self, chart_func: Callable[[], None], title: str | None) -> None:
+        """Add a chart function to the current group and save it as a PNG and SVG."""
+        self.current_group.append(chart_func)
+
+    def finalize_group(self) -> None:
+        """Finalize the current group and prepare for a new group."""
+        if self.current_group:  # Check if current group has charts
+            self.groups.append(self.current_group)  # Add current group to groups
+        else:
+            print("Finalizing an empty group!")  # Warn if group is empty
+        self.current_group = []  # Reset current group for new charts
+
+    def _initialize_pdf(self) -> None:
+        """Initialize the PDF."""
+        self._buffer = BytesIO()
+        self.pdf_pages = PdfPages(self._buffer, metadata={})  # Initialize PdfPages without metadata
+
+    def _save_group_to_pdf(self, group: list[Callable[[], None]]) -> None:
+        """Save a group of charts to the PDF."""
+        if self.pdf_pages is None:  # make mypy happy
+            raise RuntimeError("Report not initialized")
+
+        fig_count = len(group)  # Number of charts in the group
+
+        if fig_count == 0:
+            print("Attempted to save an empty group to PDF!")
+            return
+
+        # Check for special charts before creating layout
+        special_keywords = {"add_text_page", "add_json_page"}
+        for chart_func in group:
+            if any(keyword in chart_func.__qualname__ for keyword in special_keywords):
+                chart_func()  # Special chart functions handle their own rendering
+                return
+
+        # Create layout only if no special charts are detected
+        if fig_count == 3:
+            fig = plt.figure(figsize=(14, 10))
+            ax1 = fig.add_subplot(2, 1, 1)
+            ax2 = fig.add_subplot(2, 2, 3)
+            ax3 = fig.add_subplot(2, 2, 4)
+            axs = [ax1, ax2, ax3]
+        else:
+            cols = 2 if fig_count > 1 else 1
+            rows = (fig_count + 1) // 2
+            fig, axs = plt.subplots(rows, cols, figsize=(14, 7 * rows))
+            axs = list(np.array(axs).reshape(-1))
+
+        # Add footer text with current time to each page
+        if self.version == "test":
+            current_time = "test"
+        else:
+            current_time = self.current_time
+        fig.text(
+            0.5,
+            0.02,
+            f"Generated on: {current_time} with version: {self.version}",
+            ha="center",
+            va="center",
+            fontsize=10,
+        )
+
+        # Render each chart in its corresponding axis
+        for idx, chart_func in enumerate(group):
+            plt.sca(axs[idx])  # Set current axis
+            chart_func()  # Render the chart
+
+        # Save the figure to the PDF and clean up
+        for idx in range(fig_count, len(axs)):
+            axs[idx].set_visible(False)
+
+        self.pdf_pages.savefig(fig)  # Save the figure to the PDF
+        plt.close(fig)
+
+    def create_line_chart_date(
+        self,
+        start_date: DateTime,
+        y_list: list[Union[np.ndarray, list[Optional[float]], list[float]]],
+        ylabel: str,
+        xlabel: Optional[str] = None,
+        title: Optional[str] = None,
+        labels: Optional[list[str]] = None,
+        markers: Optional[list[str]] = None,
+        line_styles: Optional[list[str]] = None,
+        x2label: Optional[Union[str, None]] = "Hours Since Start",
+    ) -> None:
+        """Create a line chart and add it to the current group."""
+
+        def chart() -> None:
+            timestamps = [
+                start_date.add(hours=i) for i in range(len(y_list[0]))
+            ]  # 840 timestamps at 1-hour intervals
+
+            for idx, y_data in enumerate(y_list):
+                label = labels[idx] if labels else None  # Chart label
+                marker = markers[idx] if markers and idx < len(markers) else "o"  # Marker style
+                line_style = line_styles[idx] if line_styles and idx < len(line_styles) else "-"
+                plt.plot(
+                    timestamps, y_data, label=label, marker=marker, linestyle=line_style
+                )  # Plot line
+
+            # Format the time axis
+            plt.gca().xaxis.set_major_formatter(
+                mdates.DateFormatter("%Y-%m-%d", tz=self.config.general.timezone)
+            )  # Show date and time
+            plt.gca().xaxis.set_major_locator(
+                mdates.DayLocator(interval=1, tz=self.config.general.timezone)
+            )  # Major ticks every day
+            plt.gca().xaxis.set_minor_locator(
+                mdates.HourLocator(interval=2, tz=self.config.general.timezone)
+            )
+            # Minor ticks every 6 hours
+            plt.gca().xaxis.set_minor_formatter(
+                mdates.DateFormatter("%H", tz=self.config.general.timezone)
+            )
+            # plt.gcf().autofmt_xdate(rotation=45, which="major")
+            # Auto-format the x-axis for readability
+
+            # Move major tick labels further down to avoid collision with minor tick labels
+            for plt_label in plt.gca().get_xticklabels(which="major"):
+                plt_label.set_y(-0.04)
+
+            # Add labels, title, and legend
+            if xlabel:
+                plt.xlabel(xlabel)
+            plt.ylabel(ylabel)
+            if title:
+                plt.title(title)
+            if labels:
+                plt.legend()
+            plt.grid(True)
+
+            # Add vertical line for the current date if within the axis range
+            current_time = pendulum.now(self.config.general.timezone)
+            if timestamps[0].subtract(hours=2) <= current_time <= timestamps[-1]:
+                plt.axvline(current_time, color="r", linestyle="--", label="Now")
+                plt.text(current_time, plt.ylim()[1], "Now", color="r", ha="center", va="bottom")
+
+            # Add a second x-axis on top
+            ax1 = plt.gca()
+            ax2 = ax1.twiny()
+            ax2.set_xlim(ax1.get_xlim())  # Align the second axis with the first
+
+            # Generate integer hour labels
+            hours_since_start = [(t - timestamps[0]).total_seconds() / 3600 for t in timestamps]
+            # ax2.set_xticks(timestamps[::48])  # Set ticks every 12 hours
+            # ax2.set_xticklabels([f"{int(h)}" for h in hours_since_start[::48]])
+            # ax2.set_xticks(timestamps[:: len(timestamps) // 24])  # Select 10 evenly spaced ticks
+            ax2.set_xticks(timestamps[:: len(timestamps) // 12])  # Select 10 evenly spaced ticks
+            # ax2.set_xticklabels([f"{int(h)}" for h in hours_since_start[:: len(timestamps) // 24]])
+            ax2.set_xticklabels([f"{int(h)}" for h in hours_since_start[:: len(timestamps) // 12]])
+            if x2label:
+                ax2.set_xlabel(x2label)
+
+            # Ensure ax1 and ax2 are aligned
+            # assert ax1.get_xlim() == ax2.get_xlim(), "ax1 and ax2 are not aligned"
+
+        self.add_chart_to_group(chart, title)  # Add chart function to current group
+
+    def create_line_chart(
+        self,
+        start_hour: Optional[int],
+        y_list: list[Union[np.ndarray, list[Optional[float]], list[float]]],
+        title: str,
+        xlabel: str,
+        ylabel: str,
+        labels: Optional[list[str]] = None,
+        markers: Optional[list[str]] = None,
+        line_styles: Optional[list[str]] = None,
+    ) -> None:
+        """Create a line chart and add it to the current group."""
+
+        def chart() -> None:
+            nonlocal start_hour  # Allow modifying `x` within the nested function
+            if start_hour is None:
+                start_hour = 0
+            first_element = y_list[0]
+            x: np.ndarray
+            # Case 1: y_list contains np.ndarray elements
+            if isinstance(first_element, np.ndarray):
+                x = np.arange(
+                    start_hour, start_hour + len(first_element)
+                )  # Start at x and extend by ndarray length
+            # Case 2: y_list contains float elements (1D list)
+            elif isinstance(first_element, float):
+                x = np.arange(
+                    start_hour, start_hour + len(y_list)
+                )  # Start at x and extend by list length
+            # Case 3: y_list is a nested list of floats
+            elif isinstance(first_element, list) and all(
+                isinstance(i, float) for i in first_element
+            ):
+                max_len = max(len(sublist) for sublist in y_list)
+                x = np.arange(
+                    start_hour, start_hour + max_len
+                )  # Start at x and extend by max sublist length
+            else:
+                print(f"Unsupported y_list structure: {type(y_list)}, {y_list}")
+                raise TypeError(
+                    "y_list elements must be np.ndarray, float, or a nested list of floats"
+                )
+
+            for idx, y_data in enumerate(y_list):
+                label = labels[idx] if labels else None  # Chart label
+                marker = markers[idx] if markers and idx < len(markers) else "o"  # Marker style
+                line_style = (
+                    line_styles[idx] if line_styles and idx < len(line_styles) else "-"
+                )  # Line style
+                plt.plot(x, y_data, label=label, marker=marker, linestyle=line_style)  # Plot line
+
+            plt.title(title)  # Set title
+            plt.xlabel(xlabel)  # Set x-axis label
+            plt.ylabel(ylabel)  # Set y-axis label
+            if labels:
+                plt.legend()  # Show legend if labels are provided
+            plt.grid(True)  # Show grid
+            plt.xlim(x[0] - 0.5, x[-1] + 0.5)  # Adjust x-limits
+
+        self.add_chart_to_group(chart, title)  # Add chart function to current group
+
+    def create_scatter_plot(
+        self,
+        x: np.ndarray,
+        y: np.ndarray,
+        title: str,
+        xlabel: str,
+        ylabel: str,
+        c: Optional[np.ndarray] = None,
+    ) -> None:
+        """Create a scatter plot and add it to the current group."""
+
+        def chart() -> None:
+            scatter = plt.scatter(x, y, c=c, cmap="viridis")  # Create scatter plot
+            plt.title(title)  # Set title
+            plt.xlabel(xlabel)  # Set x-axis label
+            plt.ylabel(ylabel)  # Set y-axis label
+            if c is not None:
+                plt.colorbar(scatter, label="Constraint")  # Add colorbar if color data is provided
+            plt.grid(True)  # Show grid
+
+        self.add_chart_to_group(chart, title)  # Add chart function to current group
+
+    def create_bar_chart(
+        self,
+        labels: Sequence[str],
+        values_list: Sequence[Sequence[int | float]],
+        title: str,
+        ylabel: str,
+        xlabels: Optional[list[str]] = None,
+        label_names: Optional[list[str]] = None,
+        colors: Optional[list[str]] = None,
+        bar_width: float = 0.35,
+        bottom: Optional[int] = None,
+    ) -> None:
+        """Create a bar chart and add it to the current group."""
+
+        def chart() -> None:
+            num_groups = len(values_list)  # Number of data groups
+            num_bars = len(labels)  # Number of bars (categories)
+            # Calculate the positions for each bar group on the x-axis
+            x = np.arange(num_bars)  # x positions for bars
+            offset = np.linspace(
+                -bar_width * (num_groups - 1) / 2, bar_width * (num_groups - 1) / 2, num_groups
+            )  # Bar offsets
+            for i, values in enumerate(values_list):
+                bottom_use = None
+                if bottom == i + 1:  # Set bottom if specified
+                    bottom_use = 1
+                color = colors[i] if colors and i < len(colors) else None  # Bar color
+                label_name = label_names[i] if label_names else None  # Bar label
+                plt.bar(
+                    x + offset[i],
+                    values,
+                    bar_width,
+                    label=label_name,
+                    color=color,
+                    zorder=2,
+                    alpha=0.6,
+                    bottom=bottom_use,
+                )  # Create bar
+            if xlabels:
+                plt.xticks(x, labels)  # Add custom labels to the x-axis
+            plt.title(title)  # Set title
+            plt.ylabel(ylabel)  # Set y-axis label
+
+            if colors and label_names:
+                plt.legend()  # Show legend if colors are provided
+            plt.grid(True, zorder=0)  # Show grid in the background
+            plt.xlim(-0.5, len(labels) - 0.5)  # Set x-axis limits
+
+        self.add_chart_to_group(chart, title)  # Add chart function to current group
+
+    def create_violin_plot(
+        self, data_list: list[np.ndarray], labels: list[str], title: str, xlabel: str, ylabel: str
+    ) -> None:
+        """Create a violin plot and add it to the current group."""
+
+        def chart() -> None:
+            plt.violinplot(data_list, showmeans=True, showmedians=True)  # Create violin plot
+            plt.xticks(np.arange(1, len(labels) + 1), labels)  # Set x-ticks and labels
+            plt.title(title)  # Set title
+            plt.xlabel(xlabel)  # Set x-axis label
+            plt.ylabel(ylabel)  # Set y-axis label
+            plt.grid(True)  # Show grid
+
+        self.add_chart_to_group(chart, title)  # Add chart function to current group
+
+    def add_text_page(self, text: str, title: Optional[str] = None, fontsize: int = 12) -> None:
+        """Add a page with text content to the PDF."""
+
+        def chart() -> None:
+            if self.pdf_pages is None:  # make mypy happy
+                raise RuntimeError("Report not initialized")
+
+            fig = plt.figure(figsize=(8.5, 11))  # Create a standard page size
+            plt.axis("off")  # Turn off axes for a clean page
+            wrapped_text = textwrap.fill(text, width=80)  # Wrap text to fit the page width
+            y = 0.95  # Start at the top of the page
+
+            if title:
+                plt.text(0.5, y, title, ha="center", va="top", fontsize=fontsize + 4, weight="bold")
+                y -= 0.05  # Add space after the title
+
+            plt.text(0.5, y, wrapped_text, ha="center", va="top", fontsize=fontsize, wrap=True)
+            self.pdf_pages.savefig(fig)  # Save the figure as a page in the PDF
+            plt.close(fig)  # Close the figure to free up memory
+
+        self.add_chart_to_group(chart, title)  # Treat the text page as a "chart" in the group
+
+    def add_json_page(
+        self, json_obj: dict, title: Optional[str] = None, fontsize: int = 12
+    ) -> None:
+        """Add a page with a formatted JSON object to the PDF.
+
+        Args:
+            json_obj (dict): The JSON object to display.
+            title (Optional[str]): An optional title for the page.
+            fontsize (int): The font size for the JSON text.
+        """
+
+        def chart() -> None:
+            if self.pdf_pages is None:  # make mypy happy
+                raise RuntimeError("Report not initialized")
+
+            # Convert JSON object to a formatted string
+            json_str = json.dumps(json_obj, indent=4)
+
+            fig = plt.figure(figsize=(8.5, 11))  # Standard page size
+            plt.axis("off")  # Turn off axes for a clean page
+
+            y = 0.95  # Start at the top of the page
+            if title:
+                plt.text(0.5, y, title, ha="center", va="top", fontsize=fontsize + 4, weight="bold")
+                y -= 0.05  # Add space after the title
+
+            # Split the JSON string into lines and render them
+            lines = json_str.splitlines()
+            for line in lines:
+                plt.text(0.05, y, line, ha="left", va="top", fontsize=fontsize, family="monospace")
+                y -= 0.02  # Move down for the next line
+
+                # Stop if the text exceeds the page
+                if y < 0.05:
+                    break
+
+            self.pdf_pages.savefig(fig)  # Save the figure as a page in the PDF
+            plt.close(fig)  # Close the figure to free up memory
+
+        self.add_chart_to_group(chart, title)  # Treat the JSON page as a "chart" in the group
+
+    def generate_pdf(self) -> bytes:
+        """Generate the PDF report with all the added chart groups.
+
+        Returns:
+            PDF report as bytes object
+        """
+        self._initialize_pdf()  # Initialize the PDF
+
+        buffer = BytesIO()
+        self.pdf_pages = PdfPages(buffer, metadata={})
+
+        for group in self.groups:
+            self._save_group_to_pdf(group)
+
+        self.pdf_pages.close()
+        return buffer.getvalue()
+
+
+def genetic0_prepare_visualize(
+    solution: Genetic0Solution,
+) -> bytes:
+    global debug_visualize
+
+    report = Genetic0VisualizationReport()
+    next_full_hour_date = get_ems().start_datetime
+    start_hour = solution.start_hour
+    # Group 1:
+    report.create_line_chart_date(
+        next_full_hour_date,
+        [
+            solution.parameters.ems.total_load[start_hour:],
+        ],
+        title="Load Profile",
+        # xlabel="Hours", # not enough space
+        ylabel="Load (Wh)",
+        labels=["Total Load (Wh)"],
+    )
+    report.create_line_chart_date(
+        next_full_hour_date,
+        [
+            solution.parameters.ems.pv_forecast_wh[start_hour:],
+        ],
+        title="PV Forecast",
+        # xlabel="Hours", # not enough space
+        ylabel="PV Generation (Wh)",
+    )
+
+    report.create_line_chart_date(
+        next_full_hour_date,
+        [
+            np.full(
+                len(solution.parameters.ems.total_load) - start_hour,
+                solution.parameters.ems.feed_in_tariff_per_wh[start_hour:]
+                if isinstance(solution.parameters.ems.feed_in_tariff_per_wh, list)
+                else solution.parameters.ems.feed_in_tariff_per_wh,
+            )
+        ],
+        title="Remuneration",
+        # xlabel="Hours", # not enough space
+        ylabel="amount/Wh",
+        x2label=None,  # not enough space
+    )
+    if solution.parameters.temperature_forecast:
+        report.create_line_chart_date(
+            next_full_hour_date,
+            [
+                solution.parameters.temperature_forecast[start_hour:],
+            ],
+            title="Temperature Forecast",
+            # xlabel="Hours", # not enough space
+            ylabel="°C",
+            x2label=None,  # not enough space
+        )
+    report.finalize_group()
+
+    # Group 2:
+    report.create_line_chart_date(
+        start_date=next_full_hour_date,  # start_date
+        y_list=[
+            solution.result.load_wh_per_hour,
+            solution.result.home_appliance_wh_per_hour,
+            solution.result.grid_feed_in_wh_per_hour,
+            solution.result.grid_consumption_wh_per_hour,
+            solution.result.losses_per_hour,
+        ],
+        title="Energy Flow per Hour",
+        # xlabel="Date", # not enough space
+        ylabel="Energy (Wh)",
+        labels=[
+            "Load (Wh)",
+            "Household Device (Wh)",
+            "Grid Feed-in (Wh)",
+            "Grid Consumption (Wh)",
+            "Losses (Wh)",
+        ],
+        markers=["o", "o", "x", "^", "^"],
+        line_styles=["-", "--", ":", "-.", "-"],
+    )
+    report.finalize_group()
+
+    # Group 3:
+    report.create_line_chart_date(
+        start_date=next_full_hour_date,
+        y_list=[solution.result.battery_soc_per_hour, solution.result.ev_soc_per_hour],
+        title="Battery SOC",
+        # xlabel="Date", # not enough space
+        ylabel="%",
+        labels=[
+            "Battery SOC (%)",
+            "Electric Vehicle SOC (%)",
+        ],
+        markers=["o", "x"],
+    )
+    report.create_line_chart_date(
+        start_date=next_full_hour_date,  # start_date
+        y_list=[solution.parameters.ems.electricity_price_per_wh[start_hour:]],
+        # title="Electricity Price", # not enough space
+        # xlabel="Date", # not enough space
+        ylabel="Electricity Price (amount/Wh)",
+        x2label=None,  # not enough space
+    )
+
+    labels = list(
+        item
+        for sublist in zip(
+            list(str(i) for i in range(0, 23, 2)), list(str(" ") for i in range(0, 23, 2))
+        )
+        for item in sublist
+    )
+    labels = labels[start_hour:] + labels
+
+    report.create_bar_chart(
+        labels=labels,
+        values_list=[
+            solution.ac_charge[start_hour:],
+            solution.dc_charge[start_hour:],
+            solution.discharge_allowed[start_hour:],
+        ],
+        title="AC/DC Charging and Discharge Overview",
+        ylabel="Relative Power (0-1) / Discharge (0 or 1)",
+        label_names=["AC Charging (relative)", "DC Charging (relative)", "Discharge Allowed"],
+        colors=["blue", "green", "red"],
+        bottom=3,
+        xlabels=labels,
+    )
+    report.finalize_group()
+
+    # Group 4:
+
+    report.create_line_chart_date(
+        next_full_hour_date,  # start_date
+        [
+            solution.result.costs_per_hour,
+            solution.result.revenue_per_hour,
+        ],
+        title="Financial Balance per Hour",
+        # xlabel="Date", # not enough space
+        ylabel="Amount",
+        labels=["Costs", "Revenue"],
+    )
+
+    extra_data = solution.extra_data
+    if extra_data:
+        report.create_scatter_plot(
+            x=np.array(extra_data["losses"]),
+            y=np.array(extra_data["balance"]),
+            title="Scatter Plot",
+            xlabel="losses",
+            ylabel="balance",
+            c=np.array(extra_data["constraints"]),
+        )
+
+    values_list = [
+        [
+            solution.result.total_costs,
+            solution.result.total_revenue,
+            solution.result.total_balance,
+        ]
+    ]
+    labels = ["Total Costs [amount]", "Total Revenue [amount]", "Total Balance [amount]"]
+
+    report.create_bar_chart(
+        labels=labels,
+        values_list=values_list,
+        title="Financial Overview",
+        ylabel="Amount",
+        xlabels=["Total Costs [amount]", "Total Revenue [amount]", "Total Balance [amount]"],
+    )
+
+    report.finalize_group()
+
+    # Group 1: Scatter plot of losses vs balance with color-coded constraints
+    if extra_data:
+        f1 = np.array(extra_data["losses"])  # Losses
+        f2 = np.array(extra_data["balance"])  # Balance
+        n1 = np.array(extra_data["constraints"])  # Constraints
+
+        # Filter data where 'constraints' < 0.01
+        filtered_indices = n1 < 0.01
+        filtered_losses = f1[filtered_indices]
+        filtered_balance = f2[filtered_indices]
+
+        # Group 2: Violin plot for filtered losses
+        if filtered_losses.size > 0:
+            report.create_violin_plot(
+                data_list=[filtered_losses],  # Data for filtered losses
+                labels=["Filtered Losses"],  # Label for the violin plot
+                title="Violin Plot for Filtered Losses (Constraint < 0.01)",
+                xlabel="Losses",
+                ylabel="Values",
+            )
+        else:
+            print("No data available for filtered losses violin plot (Constraint < 0.01)")
+
+    # Group 3: Violin plot for filtered balance
+    if extra_data:
+        if filtered_balance.size > 0:
+            report.create_violin_plot(
+                data_list=[filtered_balance],  # Data for filtered balance
+                labels=["Filtered Balance"],  # Label for the violin plot
+                title="Violin Plot for Filtered Balance (Constraint < 0.01)",
+                xlabel="Balance",
+                ylabel="Values",
+            )
+        else:
+            print("No data available for filtered balance violin plot (Constraint < 0.01)")
+
+        if filtered_balance.size > 0 or filtered_losses.size > 0:
+            report.finalize_group()
+
+    if solution.fitness_history:
+        if debug_visualize or solution.fixed_seed:
+            report.create_line_chart(
+                start_hour=0,
+                y_list=[
+                    np.array(solution.fitness_history["avg"]),
+                    np.array(solution.fitness_history["max"]),
+                    np.array(solution.fitness_history["min"]),
+                ],
+                title=f"DEBUG: Generation Fitness for seed {solution.fixed_seed}",
+                xlabel="Generation",
+                ylabel="Fitness",
+                labels=[
+                    "avg",
+                    "max",
+                    "min",
+                ],
+                markers=[".", ".", "."],
+            )
+            report.finalize_group()
+
+    # Generate the PDF report
+    pdf = report.generate_pdf()
+
+    # Return pdf (a bytes object)
+    return pdf
+
+
+def genetic0_generate_example_report(filename: str = "example_report.pdf") -> None:
+    """Generate example visualization report."""
+    global debug_visualize
+
+    report = Genetic0VisualizationReport("test")
+    x_hours = 0  # Define x-axis start values (e.g., hours)
+
+    # Group 1: Adding charts to be displayed on the same page
+    report.create_line_chart(
+        x_hours,
+        [np.array([10, 20, 30, 40])],
+        title="Load Profile",
+        xlabel="Hours",
+        ylabel="Load (Wh)",
+    )
+    report.create_line_chart(
+        x_hours,
+        [np.array([5, 15, 25, 35])],
+        title="PV Forecast",
+        xlabel="Hours",
+        ylabel="PV Generation (Wh)",
+    )
+    report.create_line_chart(
+        x_hours,
+        [np.array([5, 15, 25, 35])],
+        title="PV Forecast",
+        xlabel="Hours",
+        ylabel="PV Generation (Wh)",
+    )
+    # Note: If there are only 3 charts per page, the first is as wide as the page
+
+    report.finalize_group()  # Finalize the first group of charts
+
+    # Group 2: Adding more charts to be displayed on another page
+    report.create_line_chart(
+        x_hours,
+        [np.array([0.2, 0.25, 0.3, 0.35])],
+        title="Electricity Price",
+        xlabel="Hours",
+        ylabel="Price (€/Wh)",
+    )
+    report.create_bar_chart(
+        ["Costs", "Revenue", "Balance"],
+        [[500.0], [600.0], [100.0]],
+        title="Financial Overview",
+        ylabel="Euro",
+        label_names=["AC Charging (relative)", "DC Charging (relative)", "Discharge Allowed"],
+        colors=["red", "green", "blue"],
+    )
+    report.create_scatter_plot(
+        np.array([5, 6, 7, 8]),
+        np.array([100, 200, 150, 250]),
+        title="Scatter Plot",
+        xlabel="Losses",
+        ylabel="Balance",
+        c=np.array([0.1, 0.2, 0.3, 0.4]),
+    )
+    report.finalize_group()  # Finalize the second group of charts
+
+    # Group 3: Adding a violin plot
+    data = [np.random.normal(0, std, 100) for std in range(1, 5)]  # Example data for violin plot
+    report.create_violin_plot(
+        data,
+        labels=["Group 1", "Group 2", "Group 3", "Group 4"],
+        title="Violin Plot",
+        xlabel="Groups",
+        ylabel="Values",
+    )
+    data = [np.random.normal(0, 1, 100)]  # Example data for violin plot
+    report.create_violin_plot(
+        data, labels=["Group 1"], title="Violin Plot", xlabel="Group", ylabel="Values"
+    )
+
+    report.finalize_group()  # Finalize the third group of charts
+
+    debug_visualize = True  # set level for example report
+
+    if debug_visualize:
+        report.create_line_chart(
+            x_hours,
+            [np.array([0.2, 0.25, 0.3, 0.35])],
+            title="DEBUG",
+            xlabel="DEBUG",
+            ylabel="DEBUG",
+        )
+        report.finalize_group()  # Finalize the third group of charts
+
+    report.add_text_page(
+        text=" Bisher passierte folgendes:"
+        "Am Anfang wurde das Universum erschaffen."
+        "Das machte viele Leute sehr wütend und wurde allent-"
+        "halben als Schritt in die falsche Richtung angesehen...",
+        title="Don't Panic!",
+        fontsize=14,
+    )
+    report.finalize_group()
+
+    sample_json = {
+        "name": "Visualization Report",
+        "version": 1.0,
+        "charts": [
+            {"type": "line", "data_points": 50},
+            {"type": "bar", "categories": 10},
+        ],
+        "metadata": {"author": "AI Assistant", "date": "2025-01-11"},
+    }
+
+    report.add_json_page(json_obj=sample_json, title="Formatted JSON Data", fontsize=10)
+    report.finalize_group()
+
+    report.create_line_chart_date(
+        pendulum.now().subtract(hours=0),
+        [list(np.random.random(840))],
+        title="test",
+        xlabel="test",
+        ylabel="test",
+    )
+    report.finalize_group()
+    # Generate the PDF report (a bytes object)
+    pdf = report.generate_pdf()
+
+    # write to file
+    with open(filename, "wb") as f:
+        f.write(pdf)
+
+
+if __name__ == "__main__":
+    genetic0_generate_example_report()

src/akkudoktoreos/prediction/elecpriceenergycharts.py

@@ -217,7 +217,7 @@ class ElecPriceEnergyCharts(ElecPriceProvider):
         # Determine if update is needed and how many days
         past_days = 35
         if self.highest_orig_datetime:
-            history_series = await self.key_to_series(
+            history_series = await self.key_to_raw_series(
                 key="elecprice_marketprice_wh", start_datetime=self.ems_start_datetime
             )
             # If history lower, then start_datetime

src/akkudoktoreos/prediction/feedintariffenergycharts.py

@@ -170,7 +170,7 @@ class FeedInTariffEnergyCharts(FeedInTariffProvider):
         past_days = 35
         needs_history_refresh = False
         if self.highest_orig_datetime:
-            raw_history = await self.key_to_series(
+            raw_history = await self.key_to_raw_series(
                 key="feed_in_tariff_wh",
                 end_datetime=to_datetime(self.highest_orig_datetime).add(seconds=1),
             )
@@ -243,7 +243,7 @@ class FeedInTariffEnergyCharts(FeedInTariffProvider):
             logger.error(error_msg)
             raise ValueError(error_msg)
 
-        raw_series = await self.key_to_series(
+        raw_series = await self.key_to_raw_series(
             key="feed_in_tariff_wh",
             end_datetime=to_datetime(self.highest_orig_datetime).add(seconds=1),
         )

src/akkudoktoreos/prediction/weatherbrightsky.py

@@ -132,7 +132,7 @@ class WeatherBrightSky(WeatherProvider):
             error_msg = f"No WeatherDataRecord key for '{description}'"
             logger.error(error_msg)
             raise ValueError(error_msg)
-        series = await self.key_to_series(key)
+        series = await self.key_to_raw_series(key)
         return series
 
     async def _description_from_series(self, description: str, data: pd.Series) -> None:

src/akkudoktoreos/prediction/weatheropenmeteo.py

@@ -218,7 +218,7 @@ class WeatherOpenMeteo(WeatherProvider):
             error_msg = f"No WeatherDataRecord key for '{description}'"
             logger.error(error_msg)
             raise ValueError(error_msg)
-        series = await self.key_to_series(key)
+        series = await self.key_to_raw_series(key)
         return series
 
     async def _description_from_series(self, description: str, data: pd.Series) -> None:

src/akkudoktoreos/server/rest/error.py

@@ -1,4 +1,109 @@
 import html
+import traceback
+from dataclasses import dataclass
+
+from fastapi import FastAPI, Request
+from fastapi.exceptions import HTTPException, RequestValidationError
+from fastapi.responses import JSONResponse
+from loguru import logger
+
+from akkudoktoreos.core.coreabc import get_config
+
+
+@dataclass(slots=True)
+class EOSProblem(Exception):
+    """Application exception returned as RFC 7807 Problem Details."""
+
+    status: int
+    title: str
+    detail: str
+    type: str = "about:blank"
+    cause: Exception | None = None
+
+    def __str__(self) -> str:
+        return self.detail
+
+
+def _problem_response(
+    *,
+    request: Request,
+    status: int,
+    title: str,
+    detail: str,
+    type: str,
+    cause: Exception | None = None,
+) -> JSONResponse:
+    body = {
+        "type": type,
+        "title": title,
+        "status": status,
+        "detail": detail,
+        "instance": request.url.path,
+    }
+
+    if cause is not None:
+        logger.exception(cause)
+
+        if get_config().logging.api_level in ("TRACE", "DEBUG"):
+            body["traceback"] = traceback.format_exception(cause)
+
+    return JSONResponse(
+        status_code=status,
+        media_type="application/problem+json",
+        content=body,
+    )
+
+
+async def eos_problem_handler(request: Request, exc: EOSProblem) -> JSONResponse:
+    return _problem_response(
+        request=request,
+        status=exc.status,
+        title=exc.title,
+        detail=exc.detail,
+        cause=exc.cause,
+        type=exc.type,
+    )
+
+
+async def http_exception_handler(request: Request, exc: HTTPException) -> JSONResponse:
+    return _problem_response(
+        request=request,
+        status=exc.status_code,
+        title="HTTP Error",
+        detail=str(exc.detail),
+        cause=exc,
+        type="about:blank",
+    )
+
+
+async def unexpected_exception_handler(request: Request, exc: Exception) -> JSONResponse:
+    return _problem_response(
+        request=request,
+        status=500,
+        title="Internal Server Error",
+        detail=str(exc),
+        cause=exc,
+        type="/problems/internal-server-error",
+    )
+
+
+async def validation_handler(request: Request, exc: RequestValidationError) -> JSONResponse:
+    return _problem_response(
+        request=request,
+        status=422,
+        title="Validation Error",
+        detail="Request validation failed.",
+        cause=exc,
+        type="/problems/validation",
+    )
+
+
+def register_problem_handlers(app: FastAPI) -> None:
+    app.add_exception_handler(EOSProblem, eos_problem_handler)
+    app.add_exception_handler(HTTPException, http_exception_handler)
+    app.add_exception_handler(Exception, unexpected_exception_handler)
+    app.add_exception_handler(RequestValidationError, validation_handler)
+
 
 ERROR_PAGE_TEMPLATE = """
 <!DOCTYPE html>

tests/test_dataabcsequence.py

@@ -180,11 +180,11 @@ class TestDataSequence:
         retrieved_record = await sequence.get_by_datetime(dt1)
         assert retrieved_record.data_value == 0.9  # Record should have merged with new value
 
-    async def test_key_to_series(self, sequence):
+    async def test_key_to_raw_series(self, sequence):
         dt = to_datetime(datetime(2023, 11, 6))
         record = self.create_test_record(dt, 0.8)
         await sequence.insert_by_datetime(record)
-        series = await sequence.key_to_series("data_value")
+        series = await sequence.key_to_raw_series("data_value")
         assert isinstance(series, pd.Series)
 
         retrieved_record = await sequence.get_by_datetime(dt)
@@ -228,7 +228,7 @@ class TestDataSequence:
         assert retrieved_record2 is not None
         assert retrieved_record2.data_value == 8.0
 
-        series = await sequence.key_to_series(
+        series = await sequence.key_to_raw_series(
             key="data_value", start_datetime=start_datetime, end_datetime=end_datetime
         )
         assert len(series) == 2

tests/test_databaseabc.py

@@ -265,7 +265,7 @@ class SampleSequence(DatabaseRecordProtocolMixin[SampleRecord]):
         interval: Optional[Duration] = None,
         fill_method: Optional[FillMethod] = None,
         resample_method: ResampleMethod = "mean",
-        dropna: Optional[bool] = True,
+        dropna: bool = True,
         boundary: BoundaryMode = "context",
         align_to_interval: bool = False,
     ) -> NDArray[Shape["*"], Any]:

tests/test_elecpricetibber.py

@@ -393,7 +393,7 @@ class TestElecPriceTibber:
         assert forecast_call["forecast_slots"] == 22
 
         # (a)+(c) Stored records keep the native 15-min grid across today and the forecast.
-        stored = await tibber_provider.key_to_series(
+        stored = await tibber_provider.key_to_raw_series(
             "elecprice_marketprice_wh",
             start_datetime=to_datetime("2026-07-09T00:00:00+00:00"),
             end_datetime=to_datetime("2026-07-09T06:15:00+00:00"),

tests/test_feedintariffenergycharts.py

@@ -93,7 +93,7 @@ class TestFeedInTariffEnergyCharts:
         with patch.object(provider, "_request_forecast", return_value=energy_charts_data):
             await provider._update_data(force_update=True)
 
-        result = await provider.key_to_series(
+        result = await provider.key_to_raw_series(
             key="feed_in_tariff_wh",
             start_datetime=start,
             end_datetime=start.add(hours=provider.config.prediction.hours),

tests/test_genetic0optimize.py

@@ -1,9 +1,12 @@
 import json
+from io import BytesIO
 from pathlib import Path
 from typing import Any
 from unittest.mock import patch
 
 import pytest
+from pydantic import ValidationError
+from pypdf import PdfReader
 
 from akkudoktoreos.config.config import ConfigEOS
 from akkudoktoreos.core.cache import CacheEnergyManagementStore
@@ -13,14 +16,14 @@ from akkudoktoreos.optimization.genetic0.genetic0params import (
     Genetic0OptimizationParameters,
 )
 from akkudoktoreos.optimization.genetic0.genetic0solution import Genetic0Solution
-from akkudoktoreos.utils.datetimeutil import to_datetime
+from akkudoktoreos.optimization.genetic0.genetic0visualize import (
-from akkudoktoreos.utils.visualize import (
+    genetic0_prepare_visualize,
-    prepare_visualize,  # Import the new prepare_visualize
 )
+from akkudoktoreos.utils.datetimeutil import to_datetime
 
 ems_eos = get_ems(init=True) # init once
 
-DIR_TESTDATA = Path(__file__).parent / "testdata"
+DIR_TESTDATA = Path(__file__).parent / "testdata" / "genetic0"
 
 
 def compare_dict(actual: dict[str, Any], expected: dict[str, Any]):
@@ -40,11 +43,11 @@ def compare_dict(actual: dict[str, Any], expected: dict[str, Any]):
 @pytest.mark.parametrize(
     "fn_in, fn_out, ngen, break_even",
     [
-        ("genetic0optimize_input_1.json", "genetic0optimize_result_1.json", 3, 0),
+        ("optimize_input_1.json", "optimize_result_1.json", 3, 0),
-        ("genetic0optimize_input_2.json", "genetic0optimize_result_2.json", 3, 0),
+        ("optimize_input_2.json", "optimize_result_2.json", 3, 0),
-        ("genetic0optimize_input_2.json", "genetic0optimize_result_2_full.json", 400, 0),
+        ("optimize_input_2.json", "optimize_result_2_full.json", 400, 0),
-        ("genetic0optimize_input_1.json", "genetic0optimize_result_1_be.json", 3, 1),
+        ("optimize_input_1.json", "optimize_result_1_be.json", 3, 1),
-        ("genetic0optimize_input_2.json", "genetic0optimize_result_2_be.json", 3, 1),
+        ("optimize_input_2.json", "optimize_result_2_be.json", 3, 1),
     ],
 )
 async def test_optimize(
@@ -90,20 +93,10 @@ async def test_optimize(
     )
 
     # Load input and output data
-    file = DIR_TESTDATA / fn_in
+    parameter_file = DIR_TESTDATA / fn_in
-    with file.open("r") as f_in:
+    with parameter_file.open("r") as f_in:
         input_data = Genetic0OptimizationParameters(**json.load(f_in))
 
-    file = DIR_TESTDATA / fn_out
-    # In case a new test case is added, we don't want to fail here, so the new output is written
-    # to disk before
-    try:
-        with file.open("r") as f_out:
-            expected_data = json.load(f_out)
-            expected_result = Genetic0Solution(**expected_data)
-    except FileNotFoundError:
-        pass
-
     # Fake energy management run start datetime
     ems_eos.set_start_datetime(to_datetime().set(hour=fixed_start_hour))
 
@@ -116,27 +109,37 @@ async def test_optimize(
     if ngen > 10 and not is_finalize:
         pytest.skip()
 
-    visualize_filename = str((DIR_TESTDATA / f"new_{fn_out}").with_suffix(".pdf"))
+    # Call the optimization function
-
+    genetic0_solution = genetic0_optimization.optimize_ems(
-    with patch(
+        parameters=input_data, start_hour=fixed_start_hour, ngen=ngen
-        "akkudoktoreos.utils.visualize.prepare_visualize",
+    )
-        side_effect=lambda parameters, results, *args, **kwargs: prepare_visualize(
-            parameters, results, filename=visualize_filename, **kwargs
-        ),
-    ) as prepare_visualize_patch:
-        # Call the optimization function
-        genetic0_solution = genetic0_optimization.optimize_ems(
-            parameters=input_data, start_hour=fixed_start_hour, ngen=ngen
-        )
-        # The function creates a visualization result PDF as a side-effect.
-        prepare_visualize_patch.assert_called_once()
-        assert Path(visualize_filename).exists()
 
     # Write test output to file, so we can take it as new data on intended change
     TESTDATA_FILE = DIR_TESTDATA / f"new_{fn_out}"
     with TESTDATA_FILE.open("w", encoding="utf-8", newline="\n") as f_out:
         f_out.write(genetic0_solution.model_dump_json(indent=4, exclude_unset=True))
 
+    solution_file = DIR_TESTDATA / fn_out
+    # In case a new test case is added, we don't want to fail here, so the new output is written
+    # to disk before
+    try:
+        with solution_file.open("r") as f_out:
+            expected_data = json.load(f_out)
+            expected_result = Genetic0Solution(**expected_data)
+    except ValidationError:
+        # Expected genetic solution data does not fit to Genetic0Solution data schema
+        # Possibly the Genetic0Solution class changed.
+        pytest.fail(
+            f"ValidationError: Can not load expected solution from {solution_file}\n"
+            f"cp {TESTDATA_FILE} {solution_file}\n"
+        )
+    except FileNotFoundError:
+        # Should not happen
+        pytest.fail(
+            f"FileNotFoundError: Can not load expected solution from {solution_file}\n"
+            f"cp {TESTDATA_FILE} {solution_file}\n"
+        )
+
     assert genetic0_solution.result.Gesamtbilanz_Euro == pytest.approx(
         expected_result.result.Gesamtbilanz_Euro
     )
@@ -153,3 +156,16 @@ async def test_optimize(
     # Check the correct generic energy management plan is created
     plan = genetic0_solution.energy_management_plan()
     # @TODO
+
+    # Check visualization works
+    pdf = genetic0_prepare_visualize(
+        solution=genetic0_solution,
+    )
+    assert pdf.startswith(b"%PDF-")
+
+    reader = PdfReader(BytesIO(pdf))
+    assert len(reader.pages) == 6
+
+
+    # Everything passed, remove generated files
+    TESTDATA_FILE.unlink()

tests/test_genetic0visualize.py

@@ -0,0 +1,37 @@
+from pathlib import Path
+
+from matplotlib.testing.compare import compare_images
+
+from akkudoktoreos.optimization.genetic0.genetic0visualize import (
+    genetic0_generate_example_report,
+)
+
+filename = "example_report.pdf"
+
+
+DIR_TESTDATA = Path(__file__).parent / "testdata" / "genetic0"
+reference_file = DIR_TESTDATA / "test_example_report.pdf"
+reference_png_file = DIR_TESTDATA / "test_example_report_pdf.png"
+output_file = DIR_TESTDATA / "test_example_report_new.pdf"
+output_png_file = DIR_TESTDATA / "test_example_report_new_pdf.png"
+
+
+
+def test_generate_pdf_example(config_eos):
+    """Test generation of example visualization report."""
+    # Generate PDF
+    genetic0_generate_example_report(filename=str(output_file))
+
+    # Check if the file exists
+    assert output_file.exists()
+
+    # Compare the generated file with the reference file
+    comparison = compare_images(str(reference_file), str(output_file), tol=0)
+
+    # Assert that there are no differences
+    assert comparison is None, f"Images differ: {comparison}"
+
+    # Everything passed, remove compare file
+    reference_png_file.unlink()
+    output_file.unlink()
+    output_png_file.unlink()

tests/test_geneticoptimize.py

@@ -1,9 +1,12 @@
 import json
+from io import BytesIO
 from pathlib import Path
 from typing import Any
 from unittest.mock import patch
 
 import pytest
+from pydantic import ValidationError
+from pypdf import PdfReader
 
 from akkudoktoreos.config.config import ConfigEOS
 from akkudoktoreos.core.cache import CacheEnergyManagementStore
@@ -13,14 +16,14 @@ from akkudoktoreos.optimization.genetic.geneticparams import (
     GeneticOptimizationParameters,
 )
 from akkudoktoreos.optimization.genetic.geneticsolution import GeneticSolution
-from akkudoktoreos.utils.datetimeutil import to_datetime
+from akkudoktoreos.optimization.genetic.geneticvisualize import (
-from akkudoktoreos.utils.visualize import (
+    genetic_prepare_visualize,
-    prepare_visualize,  # Import the new prepare_visualize
 )
+from akkudoktoreos.utils.datetimeutil import to_datetime
 
 ems_eos = get_ems(init=True) # init once
 
-DIR_TESTDATA = Path(__file__).parent / "testdata"
+DIR_TESTDATA = Path(__file__).parent / "testdata" / "genetic"
 
 
 def compare_dict(actual: dict[str, Any], expected: dict[str, Any]):
@@ -90,20 +93,10 @@ async def test_optimize(
     )
 
     # Load input and output data
-    file = DIR_TESTDATA / fn_in
+    parameter_file = DIR_TESTDATA / fn_in
-    with file.open("r") as f_in:
+    with parameter_file.open("r") as f_in:
         input_data = GeneticOptimizationParameters(**json.load(f_in))
 
-    file = DIR_TESTDATA / fn_out
-    # In case a new test case is added, we don't want to fail here, so the new output is written
-    # to disk before
-    try:
-        with file.open("r") as f_out:
-            expected_data = json.load(f_out)
-            expected_result = GeneticSolution(**expected_data)
-    except FileNotFoundError:
-        pass
-
     # Fake energy management run start datetime
     ems_eos.set_start_datetime(to_datetime().set(hour=fixed_start_hour))
 
@@ -116,27 +109,37 @@ async def test_optimize(
     if ngen > 10 and not is_finalize:
         pytest.skip()
 
-    visualize_filename = str((DIR_TESTDATA / f"new_{fn_out}").with_suffix(".pdf"))
+    # Call the optimization function
-
+    genetic_solution = genetic_optimization.optimize_ems(
-    with patch(
+        parameters=input_data, start_hour=fixed_start_hour, ngen=ngen
-        "akkudoktoreos.utils.visualize.prepare_visualize",
+    )
-        side_effect=lambda parameters, results, *args, **kwargs: prepare_visualize(
-            parameters, results, filename=visualize_filename, **kwargs
-        ),
-    ) as prepare_visualize_patch:
-        # Call the optimization function
-        genetic_solution = genetic_optimization.optimize_ems(
-            parameters=input_data, start_hour=fixed_start_hour, ngen=ngen
-        )
-        # The function creates a visualization result PDF as a side-effect.
-        prepare_visualize_patch.assert_called_once()
-        assert Path(visualize_filename).exists()
 
     # Write test output to file, so we can take it as new data on intended change
     TESTDATA_FILE = DIR_TESTDATA / f"new_{fn_out}"
     with TESTDATA_FILE.open("w", encoding="utf-8", newline="\n") as f_out:
         f_out.write(genetic_solution.model_dump_json(indent=4, exclude_unset=True))
 
+    solution_file = DIR_TESTDATA / fn_out
+    # In case a new test case is added, we don't want to fail here, so the new output is written
+    # to disk before
+    try:
+        with solution_file.open("r") as f_out:
+            expected_data = json.load(f_out)
+            expected_result = GeneticSolution(**expected_data)
+    except ValidationError:
+        # Expected genetic solution data does not fit to GeneticSolution data schema
+        # Possibly the GeneticSolution class changed.
+        pytest.fail(
+            f"ValidationError: Can not load expected solution from {solution_file}\n"
+            f"cp {TESTDATA_FILE} {solution_file}\n"
+        )
+    except FileNotFoundError:
+        # Should not happen
+        pytest.fail(
+            f"FileNotFoundError: Can not load expected solution from {solution_file}\n"
+            f"cp {TESTDATA_FILE} {solution_file}\n"
+        )
+
     assert genetic_solution.result.Gesamtbilanz_Euro == pytest.approx(
         expected_result.result.Gesamtbilanz_Euro
     )
@@ -153,3 +156,16 @@ async def test_optimize(
     # Check the correct generic energy management plan is created
     plan = genetic_solution.energy_management_plan()
     # @TODO
+
+    # Check visualization works
+    pdf = genetic_prepare_visualize(
+        solution=genetic_solution,
+    )
+    assert pdf.startswith(b"%PDF-")
+
+    reader = PdfReader(BytesIO(pdf))
+    assert len(reader.pages) == 6
+
+
+    # Everything passed, remove generated files
+    TESTDATA_FILE.unlink()

tests/test_geneticvisualize.py

@@ -2,20 +2,24 @@ from pathlib import Path
 
 from matplotlib.testing.compare import compare_images
 
-from akkudoktoreos.utils.visualize import generate_example_report
+from akkudoktoreos.optimization.genetic.geneticvisualize import (
+    genetic_generate_example_report,
+)
 
 filename = "example_report.pdf"
 
 
-DIR_TESTDATA = Path(__file__).parent / "testdata"
+DIR_TESTDATA = Path(__file__).parent / "testdata" / "genetic"
 reference_file = DIR_TESTDATA / "test_example_report.pdf"
+reference_png_file = DIR_TESTDATA / "test_example_report_pdf.png"
 output_file = DIR_TESTDATA / "test_example_report_new.pdf"
+output_png_file = DIR_TESTDATA / "test_example_report_new_pdf.png"
 
 
 def test_generate_pdf_example(config_eos):
     """Test generation of example visualization report."""
     # Generate PDF
-    generate_example_report(filename=str(output_file))
+    genetic_generate_example_report(filename=str(output_file))
 
     # Check if the file exists
     assert output_file.exists()
@@ -25,3 +29,8 @@ def test_generate_pdf_example(config_eos):
 
     # Assert that there are no differences
     assert comparison is None, f"Images differ: {comparison}"
+
+    # Everything passed, remove compare file
+    reference_png_file.unlink()
+    output_file.unlink()
+    output_png_file.unlink()

tests/test_pvforecastakkudoktor.py

@@ -346,7 +346,7 @@ async def test_timezone_behaviour(
     other_end_datetime = other_start_datetime + to_duration("24 hours")
     expected_end_datetime = to_datetime("2024-10-07T00:00:00+0200", in_timezone=other_timezone)
     assert compare_datetimes(other_end_datetime, expected_end_datetime).equal
-    forecast_temps = await provider.key_to_series(
+    forecast_temps = await provider.key_to_raw_series(
         "pvforecastakkudoktor_temp_air", other_start_datetime, other_end_datetime
     )
     assert len(forecast_temps) == 23  # 24-1, first temperature is null
@@ -355,7 +355,7 @@ async def test_timezone_behaviour(
 
     # Test fetching AC power forecast
     other_end_datetime = other_start_datetime + to_duration("48 hours")
-    forecast_measured = await provider.key_to_series(
+    forecast_measured = await provider.key_to_raw_series(
         "pvforecastakkudoktor_ac_power_measured", other_start_datetime, other_end_datetime
     )
     assert len(forecast_measured) == 1

tests/test_system.py

@@ -45,7 +45,13 @@ class TestSystem:
             result = requests.post(f"{server}/v1/prediction/update/BrightSky")
             assert result.status_code == HTTPStatus.OK
 
-            result = requests.get(f"{server}/v1/prediction/series?key=weather_temp_air")
+            result = requests.get(
+                f"{server}/v1/prediction/series",
+                params={
+                    "key": "weather_temp_air",
+                    "processing": "raw",
+                }
+            )
             assert result.status_code == HTTPStatus.OK
 
             data = result.json()
@@ -72,7 +78,13 @@ class TestSystem:
             result = requests.post(f"{server}/v1/prediction/update/ClearOutside")
             assert result.status_code == HTTPStatus.OK, f"Failed: {result.headers} {result.text}"
 
-            result = requests.get(f"{server}/v1/prediction/series?key=weather_temp_air")
+            result = requests.get(
+                f"{server}/v1/prediction/series",
+                params={
+                    "key": "weather_temp_air",
+                    "processing": "raw",
+                }
+            )
             assert result.status_code == HTTPStatus.OK
 
             data = result.json()
@@ -103,7 +115,13 @@ class TestSystem:
             result = requests.post(f"{server}/v1/prediction/update/PVForecastAkkudoktor")
             assert result.status_code == HTTPStatus.OK, f"Failed: {result.headers} {result.text}"
 
-            result = requests.get(f"{server}/v1/prediction/series?key=pvforecast_ac_power")
+            result = requests.get(
+                f"{server}/v1/prediction/series",
+                params={
+                    "key": "pvforecast_ac_power",
+                    "processing": "raw",
+                }
+            )
             assert result.status_code == HTTPStatus.OK
 
             data = result.json()
@@ -134,7 +152,13 @@ class TestSystem:
             result = requests.post(f"{server}/v1/prediction/update/ElecPriceAkkudoktor")
             assert result.status_code == HTTPStatus.OK
 
-            result = requests.get(f"{server}/v1/prediction/series?key=elecprice_marketprice_wh")
+            result = requests.get(
+                f"{server}/v1/prediction/series",
+                params={
+                    "key": "elecprice_marketprice_wh",
+                    "processing": "raw",
+                }
+            )
             assert result.status_code == HTTPStatus.OK
 
             data = result.json()
@@ -161,7 +185,13 @@ class TestSystem:
             result = requests.post(f"{server}/v1/prediction/update/LoadAkkudoktor")
             assert result.status_code == HTTPStatus.OK
 
-            result = requests.get(f"{server}/v1/prediction/series?key=loadforecast_power_w")
+            result = requests.get(
+                f"{server}/v1/prediction/series",
+                params={
+                    "key": "loadforecast_power_w",
+                    "processing": "raw",
+                }
+            )
             assert result.status_code == HTTPStatus.OK
 
             data = result.json()
@@ -318,7 +348,13 @@ class TestSystem:
         # ----------------------------------------------------------------------
         # 4. GET /v1/measurement/series
         # ----------------------------------------------------------------------
-        result = requests.get(f"{server}/v1/measurement/series", params={"key": "pv1_emr"})
+        result = requests.get(
+            f"{server}/v1/measurement/series",
+            params={
+                "key": "pv1_emr",
+                "processing": "raw",
+            }
+        )
         assert result.status_code == HTTPStatus.OK, f"Failed to GET series: {result.text}"
 
         series_response = result.json()
@@ -330,7 +366,11 @@ class TestSystem:
 
         # Non-existent key must be rejected
         result = requests.get(
-            f"{server}/v1/measurement/series", params={"key": "non_existent_key"}
+            f"{server}/v1/measurement/series",
+            params={
+                "key": "non_existent_key",
+                "processing": "raw",
+            }
         )
         assert result.status_code == HTTPStatus.NOT_FOUND, (
             f"Expected 404 for unknown series key, got {result.status_code}"
@@ -362,7 +402,13 @@ class TestSystem:
         assert len(series_response["data"]) >= 3
 
         # Verify the data round-trips correctly
-        result = requests.get(f"{server}/v1/measurement/series", params={"key": "pv2_emr"})
+        result = requests.get(
+            f"{server}/v1/measurement/series",
+            params={
+                "key": "pv2_emr",
+                "processing": "raw",
+            }
+        )
         assert result.status_code == HTTPStatus.OK
         fetched = result.json()
         fetched_values = list(fetched["data"].values())
@@ -400,7 +446,13 @@ class TestSystem:
 
         # Verify data was loaded for both columns
         for key in ("pv1_emr", "load1_emr"):
-            result = requests.get(f"{server}/v1/measurement/series", params={"key": key})
+            result = requests.get(
+                f"{server}/v1/measurement/series",
+                params={
+                    "key": key,
+                    "processing": "raw",
+                }
+            )
             assert result.status_code == HTTPStatus.OK, f"Failed to verify series for {key}"
             series_response = result.json()
             assert len(series_response["data"]) >= 3, f"Expected >=3 data points for {key}"
@@ -443,7 +495,13 @@ class TestSystem:
             ("load1_emr", [60.5, 55.3]),
             ("pv2_emr", [150.8, 175.2]),
         ):
-            result = requests.get(f"{server}/v1/measurement/series", params={"key": key})
+            result = requests.get(
+                f"{server}/v1/measurement/series",
+                params={
+                    "key": key,
+                    "processing": "raw",
+                }
+            )
             assert result.status_code == HTTPStatus.OK, f"Failed to verify {key} after data PUT"
             fetched = result.json()
             fetched_values = list(fetched["data"].values())
@@ -581,7 +639,10 @@ class TestSystem:
         # ----------------------------------------------------------------------
         result = requests.get(
             f"{server}/v1/measurement/series",
-            params={"key": "pv1_emr_kwh"},
+            params={
+                "key": "pv1_emr_kwh",
+                "processing": "raw",
+            },
         )
         assert result.status_code == HTTPStatus.OK
 
@@ -727,7 +788,10 @@ class TestSystem:
         # ----------------------------------------------------------------------
         result = requests.get(
             f"{server}/v1/measurement/series",
-            params={"key": "pv1_emr_kwh"},
+            params={
+                "key": "pv1_emr_kwh",
+                "processing": "raw",
+            },
         )
         assert result.status_code == HTTPStatus.OK
 

tests/testdata/docs/_generated/configexample.md

@@ -162,6 +162,7 @@
            }
        },
        "logging": {
+           "api_level": "TRACE",
            "console_level": "TRACE",
            "file_level": "TRACE"
        },

tests/testdata/docs/_generated/configlogging.md

@@ -7,8 +7,9 @@
 
 | Name | Environment Variable | Type | Read-Only | Default | Description |
 | ---- | -------------------- | ---- | --------- | ------- | ----------- |
-| console_level | `EOS_LOGGING__CONSOLE_LEVEL` | `str | None` | `rw` | `None` | Logging level when logging to console. |
+| api_level | `EOS_LOGGING__API_LEVEL` | `str | None` | `rw` | `None` | Logging level for API response. |
-| file_level | `EOS_LOGGING__FILE_LEVEL` | `str | None` | `rw` | `None` | Logging level when logging to file. |
+| console_level | `EOS_LOGGING__CONSOLE_LEVEL` | `str | None` | `rw` | `None` | Logging level for logging to console. |
+| file_level | `EOS_LOGGING__FILE_LEVEL` | `str | None` | `rw` | `None` | Logging level for logging to file. |
 | file_path | | `pathlib.Path | None` | `ro` | `N/A` | Computed log file path based on data output path. |
 :::
 <!-- pyml enable line-length -->
@@ -21,6 +22,7 @@
 ```json
    {
        "logging": {
+           "api_level": "TRACE",
            "console_level": "TRACE",
            "file_level": "TRACE"
        }
@@ -36,6 +38,7 @@
 ```json
    {
        "logging": {
+           "api_level": "TRACE",
            "console_level": "TRACE",
            "file_level": "TRACE",
            "file_path": "/home/user/.local/share/net.akkudoktor.eos/output/eos.log"

tests/testdata/genetic0optimize_result_1.json

@@ -1,772 +0,0 @@
-{
-    "ac_charge": [
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        1.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0
-    ],
-    "dc_charge": [
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0
-    ],
-    "discharge_allowed": [
-        0,
-        0,
-        0,
-        0,
-        0,
-        0,
-        0,
-        0,
-        0,
-        0,
-        1,
-        0,
-        1,
-        1,
-        1,
-        0,
-        1,
-        0,
-        1,
-        0,
-        1,
-        0,
-        1,
-        1,
-        1,
-        1,
-        0,
-        1,
-        0,
-        0,
-        1,
-        0,
-        1,
-        1,
-        1,
-        1,
-        1,
-        0,
-        1,
-        0,
-        1,
-        0,
-        1,
-        0,
-        0,
-        0,
-        1,
-        0
-    ],
-    "eautocharge_hours_float": null,
-    "result": {
-        "Last_Wh_pro_Stunde": [
-            1053.07,
-            1063.91,
-            1320.56,
-            1132.03,
-            1163.67,
-            1176.82,
-            1216.22,
-            1103.78,
-            1129.12,
-            1178.71,
-            1050.98,
-            988.56,
-            912.38,
-            704.61,
-            516.37,
-            868.05,
-            694.34,
-            608.79,
-            556.31,
-            488.89,
-            506.91,
-            804.89,
-            1141.98,
-            1056.97,
-            992.46,
-            1155.99,
-            827.01,
-            1257.98,
-            1232.67,
-            871.26,
-            860.88,
-            1158.03,
-            1222.72,
-            1221.04,
-            949.99,
-            987.01,
-            733.99,
-            592.97
-        ],
-        "EAuto_SoC_pro_Stunde": [
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0,
-            54.0
-        ],
-        "Einnahmen_Euro_pro_Stunde": [
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.19391086083906173,
-            0.18681973047764083,
-            0.12880892587597292,
-            0.02404700392596282,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.12500582038027028,
-            0.14608958812480227,
-            0.09047346757070289,
-            0.010404817872487553,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0
-        ],
-        "Gesamt_Verluste": 2878.660271824896,
-        "Gesamtbilanz_Euro": 1.053854835771092,
-        "Gesamteinnahmen_Euro": 0.9055602150669013,
-        "Gesamtkosten_Euro": 1.9594150508379933,
-        "Home_appliance_wh_per_hour": [
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0
-        ],
-        "Kosten_Euro_pro_Stunde": [
-            0.0,
-            0.004569992000000018,
-            0.0,
-            0.0,
-            0.0,
-            4.482711108977355e-14,
-            0.0,
-            0.016809914659344942,
-            0.0,
-            0.05480703000000003,
-            0.0,
-            0.291163892,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.22802125600000003,
-            0.0,
-            0.182970359,
-            0.162995926,
-            0.0,
-            0.26411047,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.010682755832597498,
-            0.0,
-            0.0003442778967139274,
-            0.0,
-            0.028137079449292023,
-            0.0,
-            0.08231598,
-            0.174597189,
-            0.293043269,
-            0.0,
-            0.16484566
-        ],
-        "Netzbezug_Wh_pro_Stunde": [
-            0.0,
-            20.660000000000082,
-            0.0,
-            0.0,
-            0.0,
-            2.236881790906864e-10,
-            0.0,
-            74.05248748610107,
-            0.0,
-            171.54000000000008,
-            0.0,
-            980.68,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            694.34,
-            0.0,
-            556.31,
-            488.89,
-            0.0,
-            799.85,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            56.853410498124,
-            0.0,
-            1.7179535764168035,
-            0.0,
-            123.95189184710142,
-            0.0,
-            257.64,
-            566.69,
-            987.01,
-            0.0,
-            592.97
-        ],
-        "Netzeinspeisung_Wh_pro_Stunde": [
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            2770.155154843739,
-            2668.8532925377262,
-            1840.127512513899,
-            343.5286275137546,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            1785.797434003861,
-            2086.994116068604,
-            1292.4781081528986,
-            148.64025532125078,
-            0.0,
-            0.0,
-            0.0,
-            0.0,
-            0.0
-        ],
-        "Verluste_Pro_Stunde": [
-            16.744090909090914,
-            0.0,
-            29.157272727272726,
-            3.7179773678125034,
-            582.6180000000041,
-            188.65138141872444,
-            10.782457846871594,
-            0.0,
-            59.810111380126784,
-            0.0,
-            99.72409090909093,
-            0.0,
-            124.41545454545451,
-            96.08318181818186,
-            70.41409090909087,
-            118.37045454545455,
-            0.0,
-            83.01681818181817,
-            0.0,
-            0.0,
-            69.12409090909085,
-            0.0,
-            109.96227272727276,
-            47.952272727272714,
-            16.01263877609336,
-            55.946263193163475,
-            161.62968357967037,
-            14.209990740225123,
-            538.2984000000038,
-            227.42215349036655,
-            10.13011689299087,
-            0.0,
-            44.377460775206174,
-            0.0,
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tests/testdata/genetic0optimize_result_2.json

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tests/testdata/genetic0optimize_result_2_be.json

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tests/testdata/genetic0optimize_result_2_full.json

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uv.lock

@@ -62,6 +62,7 @@ dev = [
     { name = "myst-parser" },
     { name = "pandas-stubs" },
     { name = "pre-commit" },
+    { name = "pypdf" },
     { name = "pytest" },
     { name = "pytest-asyncio" },
     { name = "pytest-cov" },
@@ -112,6 +113,7 @@ requires-dist = [
     { name = "pydantic", specifier = "==2.13.4" },
     { name = "pydantic-extra-types", specifier = "==2.11.2" },
     { name = "pydantic-settings", specifier = "==2.14.2" },
+    { name = "pypdf", marker = "extra == 'dev'", specifier = "==6.14.2" },
     { name = "pytest", marker = "extra == 'dev'", specifier = "==9.1.1" },
     { name = "pytest-asyncio", marker = "extra == 'dev'", specifier = "==1.4.0" },
     { name = "pytest-cov", marker = "extra == 'dev'", specifier = "==7.1.0" },
@@ -145,11 +147,11 @@ wheels = [
 
 [[package]]
 name = "annotated-doc"
-version = "0.0.4"
+version = "0.0.5"
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