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Generic etl

generic etl development, to be rebalanced with the remind_coupling package

The ETL operations are governed by the config file. Allowed fields are defined by the rpycpl.etl.Transformation class and are name: str method: Optional[str] frames: Dict[str, Any] params: Dict[str, Any] filters: Dict[str, Any kwargs: Dict[str, Any] dependencies: Dict[str, Any]

The sequence of operations matters: Dependencies represents previous step outputs.

ETLRunner

Container class to execute ETL steps.

Source code in workflow/scripts/remind_coupling/generic_etl.py 
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class ETLRunner:
    """Container class to execute ETL steps."""

    @staticmethod
    def run(
        step: Transformation,
        frames: dict[str, pd.DataFrame],
        previous_outputs: dict[str, Any] = None,
        **kwargs,
    ) -> pd.DataFrame:
        """Run the ETL step using the provided frames and extra arguments.
        Args:
            step (Transformation): The ETL step to run.
            frames (dict): Dictionary of loaded frames.
            previous_outputs (dict, optional): Dictionary of outputs from previous
                steps that can be used as inputs.
            **kwargs: Additional arguments for the ETL method.
        Returns:
            pd.DataFrame: The result of the ETL step.
        """
        method = step.name if not step.method else step.method
        func = ETL_REGISTRY.get(method)
        if not func:
            raise ValueError(f"ETL method '{method}' not found in registry.")

        # Handle dependencies on previous outputs if specified in the step
        if hasattr(step, "dependencies") and step.dependencies and previous_outputs:
            for output_key in step.dependencies:
                if output_key in previous_outputs:
                    # Add the dependency to frames with the specified key
                    frames[output_key] = previous_outputs[output_key]
                else:
                    msg = f"Dependency '{output_key}' not found in previous outputs"
                    msg += f" for step '{step.name}'"
                    raise ValueError(msg)

        kwargs.update(step.kwargs)
        if kwargs:
            return func(frames, **kwargs)
        else:
            return func(frames)

run(step, frames, previous_outputs=None, **kwargs) staticmethod

Run the ETL step using the provided frames and extra arguments. Args: step (Transformation): The ETL step to run. frames (dict): Dictionary of loaded frames. previous_outputs (dict, optional): Dictionary of outputs from previous steps that can be used as inputs. **kwargs: Additional arguments for the ETL method. Returns: pd.DataFrame: The result of the ETL step.

Source code in workflow/scripts/remind_coupling/generic_etl.py 
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@staticmethod
def run(
    step: Transformation,
    frames: dict[str, pd.DataFrame],
    previous_outputs: dict[str, Any] = None,
    **kwargs,
) -> pd.DataFrame:
    """Run the ETL step using the provided frames and extra arguments.
    Args:
        step (Transformation): The ETL step to run.
        frames (dict): Dictionary of loaded frames.
        previous_outputs (dict, optional): Dictionary of outputs from previous
            steps that can be used as inputs.
        **kwargs: Additional arguments for the ETL method.
    Returns:
        pd.DataFrame: The result of the ETL step.
    """
    method = step.name if not step.method else step.method
    func = ETL_REGISTRY.get(method)
    if not func:
        raise ValueError(f"ETL method '{method}' not found in registry.")

    # Handle dependencies on previous outputs if specified in the step
    if hasattr(step, "dependencies") and step.dependencies and previous_outputs:
        for output_key in step.dependencies:
            if output_key in previous_outputs:
                # Add the dependency to frames with the specified key
                frames[output_key] = previous_outputs[output_key]
            else:
                msg = f"Dependency '{output_key}' not found in previous outputs"
                msg += f" for step '{step.name}'"
                raise ValueError(msg)

    kwargs.update(step.kwargs)
    if kwargs:
        return func(frames, **kwargs)
    else:
        return func(frames)

RemindLoader

Load Remind symbol tables from csvs or gdx

Source code in workflow/scripts/remind_coupling/generic_etl.py 
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class RemindLoader:
    """Load Remind symbol tables from csvs or gdx"""

    def __init__(self, remind_dir: PathLike, backend="csv"):
        self.remind_dir = remind_dir

        supported_backends = ["csv", "gdx"]
        if backend not in supported_backends:
            raise ValueError(f"Backend {backend} not supported. Available: {supported_backends}")
        self.backend = backend

    def _group_split_frames(self, keys, pattern: str = r"_part\d+$") -> dict[str, list[str]]:
        """Chat gpt regex magic to group split frames
        Args:
            keys (list): list of keys
            pattern (str, optional): regex pattern to group split frames by. Defaults to r"_part\\d+$"."
        Returns:
            dict[str, list[str]]: dictionary with base name as key and list of keys as value
        """
        grouped = {}
        for k in keys:
            base = re.sub(pattern, "", k)
            grouped.setdefault(base, []).append(k)
        return grouped

    def load_frames_csv(self, frames: dict[str, str]) -> dict[str, pd.DataFrame]:
        """Remind Frames to read
        Args:
            frames (dict): (param: remind_symbol_name) to read
        Returns:
            dict[str, pd.DataFrame]: dictionary (param: dataframe)
        """
        paths = {k: os.path.join(self.remind_dir, v + ".csv") for k, v in frames.items() if v}
        return {k: read_remind_csv(v) for k, v in paths.items()}

    def load_frames_gdx(
        self, frames: dict[str, str], gdx_file: PathLike
    ) -> dict[str, pd.DataFrame]:

        raise NotImplementedError("GDX loading not implemented yet")

    def merge_split_frames(self, frames: dict[str, pd.DataFrame]) -> dict[str, pd.DataFrame]:
        """In case several REMIND parameters are needed, group them by their base name
        Args:
            frames (dict): Dictionary with all dataframes
        Example:
            frames = {eta: 'pm_dataeta', eta_part2: 'pm_eta_conv'}
            merge_split_frames(frames)
            >> {eta: pd.concat([pm_dataeta, pm_eta_conv], axis=0).drop_duplicates()}
        """

        grouped = self._group_split_frames(frames)
        unmerged = {k: v for k, v in grouped.items() if len(v) > 1}
        merged = {k: pd.concat([frames[v] for v in multi], axis=0) for k, multi in unmerged.items()}
        merged = {k: v.drop_duplicates().reset_index(drop=True) for k, v in merged.items()}

        to_drop = [item for sublist in unmerged.values() for item in sublist]
        frames = {k: v for k, v in frames.items() if k not in to_drop}
        frames.update(merged)
        return frames

    def auto_load(
        self, frames: dict[str, str], filters: dict[str, str] = None
    ) -> dict[str, pd.DataFrame]:
        """Automatically load, merge, and filter frames in one step.

        Args:
            frames: Dictionary mapping parameter names to REMIND symbol names.
            filters: Optional dictionary of filter expressions to apply to frames.

        Returns:
            Dictionary of processed DataFrames ready for transformation.
        """
        # Load raw frames
        if self.backend == "gdx":
            raw_frames = self.load_frames_gdx(frames, os.path.join(self.remind_dir, "gdx"))
        elif self.backend == "csv":
            raw_frames = self.load_frames_csv(frames)

        # Merge split frames
        processed_frames = self.merge_split_frames(raw_frames)

        # Apply filters if any
        if filters:
            for k, filter_expr in filters.items():
                if k in processed_frames:
                    processed_frames[k] = processed_frames[k].query(filter_expr)
                else:
                    logger.warning(f"Filter specified for non-existent frame: {k}")

        return processed_frames

auto_load(frames, filters=None)

Automatically load, merge, and filter frames in one step.

Parameters:

Name Type Description Default
frames dict[str, str]

Dictionary mapping parameter names to REMIND symbol names.

 required
filters dict[str, str]

Optional dictionary of filter expressions to apply to frames.

 None

Returns:

Type Description
dict[str, DataFrame]

Dictionary of processed DataFrames ready for transformation.
Source code in workflow/scripts/remind_coupling/generic_etl.py 
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def auto_load(
    self, frames: dict[str, str], filters: dict[str, str] = None
) -> dict[str, pd.DataFrame]:
    """Automatically load, merge, and filter frames in one step.

    Args:
        frames: Dictionary mapping parameter names to REMIND symbol names.
        filters: Optional dictionary of filter expressions to apply to frames.

    Returns:
        Dictionary of processed DataFrames ready for transformation.
    """
    # Load raw frames
    if self.backend == "gdx":
        raw_frames = self.load_frames_gdx(frames, os.path.join(self.remind_dir, "gdx"))
    elif self.backend == "csv":
        raw_frames = self.load_frames_csv(frames)

    # Merge split frames
    processed_frames = self.merge_split_frames(raw_frames)

    # Apply filters if any
    if filters:
        for k, filter_expr in filters.items():
            if k in processed_frames:
                processed_frames[k] = processed_frames[k].query(filter_expr)
            else:
                logger.warning(f"Filter specified for non-existent frame: {k}")

    return processed_frames

load_frames_csv(frames)

Remind Frames to read Args: frames (dict): (param: remind_symbol_name) to read Returns: dict[str, pd.DataFrame]: dictionary (param: dataframe)

Source code in workflow/scripts/remind_coupling/generic_etl.py 
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def load_frames_csv(self, frames: dict[str, str]) -> dict[str, pd.DataFrame]:
    """Remind Frames to read
    Args:
        frames (dict): (param: remind_symbol_name) to read
    Returns:
        dict[str, pd.DataFrame]: dictionary (param: dataframe)
    """
    paths = {k: os.path.join(self.remind_dir, v + ".csv") for k, v in frames.items() if v}
    return {k: read_remind_csv(v) for k, v in paths.items()}

merge_split_frames(frames)

In case several REMIND parameters are needed, group them by their base name Args: frames (dict): Dictionary with all dataframes Example: frames = {eta: 'pm_dataeta', eta_part2: 'pm_eta_conv'} merge_split_frames(frames) >> {eta: pd.concat([pm_dataeta, pm_eta_conv], axis=0).drop_duplicates()}

Source code in workflow/scripts/remind_coupling/generic_etl.py 
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def merge_split_frames(self, frames: dict[str, pd.DataFrame]) -> dict[str, pd.DataFrame]:
    """In case several REMIND parameters are needed, group them by their base name
    Args:
        frames (dict): Dictionary with all dataframes
    Example:
        frames = {eta: 'pm_dataeta', eta_part2: 'pm_eta_conv'}
        merge_split_frames(frames)
        >> {eta: pd.concat([pm_dataeta, pm_eta_conv], axis=0).drop_duplicates()}
    """

    grouped = self._group_split_frames(frames)
    unmerged = {k: v for k, v in grouped.items() if len(v) > 1}
    merged = {k: pd.concat([frames[v] for v in multi], axis=0) for k, multi in unmerged.items()}
    merged = {k: v.drop_duplicates().reset_index(drop=True) for k, v in merged.items()}

    to_drop = [item for sublist in unmerged.values() for item in sublist]
    frames = {k: v for k, v in frames.items() if k not in to_drop}
    frames.update(merged)
    return frames