DatasetTransformFunctionProtocol

Protocol defining the interface for Dataset-level processing functions.

This protocol specifies the required signature for functions that can be used
with the Dataset.process() method. These functions can operate across multiple
signals and create new signals with cross-parameter relationships.

Dataset transform functions are ideal for operations that require multiple
parameters simultaneously, such as:
- Calculating derived parameters (e.g., BOD/COD ratios)
- Multivariate analysis and modeling
- Cross-parameter quality control
- System-wide fault detection
- Process efficiency calculations

The protocol ensures that new signals created by dataset processing maintain
proper metadata inheritance and processing lineage from their input signals.

Note:
    New signals created by dataset processing will have their project property
    automatically updated to match the parent dataset's project. The transform
    function is responsible for setting appropriate signal names, units,
    provenance parameters, and purposes.

Method Signature

def __call__(self, input_signals, input_series_names, args, kwargs)

call

Process input signals and return new signals with processing metadata.

Args: input_signals (list[Signal]): List of Signal objects containing input data input_series_names (list[str]): Specific time series names to use from input signals args: Function-specific positional arguments *kwargs: Function-specific keyword arguments

Returns: list[Signal]: List of new Signal objects created by processing