nirs4all.controllers.data.resampler module

Controller for wavelength resampling operations.

This controller handles the Resampler operator, extracting wavelengths from dataset headers and managing the resampling process across multiple sources.

class nirs4all.controllers.data.resampler.ResamplerController[source]

Bases: OperatorController

Controller for Resampler operators.

This controller: 1. Extracts wavelengths from dataset headers 2. Validates that headers are convertible to float (wavelengths in cm-1) 3. Fits the resampler with original wavelengths 4. Transforms all data to the target wavelength grid 5. Updates dataset with new features and headers 6. Supports multi-source datasets with per-source or shared parameters

execute(step_info: ParsedStep, dataset: SpectroDataset, context: ExecutionContext, runtime_context: RuntimeContext, source: int = -1, mode: str = 'train', loaded_binaries: List[Tuple[str, Any]] | None = None, prediction_store: Any | None = None) → Tuple[ExecutionContext, List][source]

Execute resampling operation.

Parameters:

  • step_info – Pipeline step configuration

  • dataset – Dataset to operate on

  • context – Pipeline execution context

  • runtime_context – Runtime context

  • source – Data source index (-1 for all sources)

  • mode – Execution mode (“train” or “predict”)

  • loaded_binaries – Pre-loaded binary objects for prediction mode

  • prediction_store – External prediction store (unused)

Returns:

Tuple of (updated_context, fitted_resamplers)

classmethod matches(step: Any, operator: Any, keyword: str) → bool[source]

Match Resampler objects.

priority: int = 5
classmethod supports_prediction_mode() → bool[source]

Resampler supports prediction mode.

classmethod use_multi_source() → bool[source]

Resampler supports multi-source datasets.