Preprocessing Nodes

Preprocessing nodes transform spectra before modeling.

Most preprocessing nodes accept a SpectralDataset port and return a transformed SpectralDataset port. At runtime, those ports carry SherpaDataset objects with spectral axes and processing history, so reports and exported workflows can show what happened before modeling.

Baseline, Smoothing, and Derivatives

Node	Use When	Inputs	Outputs	Key Configuration
Baseline Penalized LS (`baseline.penalized_ls`)	Correct smooth fluorescence, scattering, or instrument baseline while preserving peaks.	`default: SpectralDataset`	corrected spectral dataset	`method` (`als`, `arpls`, `airpls`); `lam`; `p`; `max_iter`; `tol`. Larger `lam` makes a smoother baseline.
Baseline Rubberband (`baseline.rubberband`)	You want convex-hull style baseline correction over selected spectral ranges.	`default: SpectralDataset`	corrected spectral dataset	`ranges`. Requires SpectroChemPy.
Smooth (`preprocess.smooth`)	Reduce noise before derivatives, peak finding, or visualization.	`default: SpectralDataset`	smoothed spectral dataset	`method` (`savitzky_golay`, `whittaker`, `gaussian`); `size`; `order`; `lam`; `d`; `sigma`. For Savitzky-Golay, `size` should be odd and larger than `order`.
Derivative (`preprocess.derivative`)	Remove broad baseline trends or emphasize bands; common for NIR and Raman preprocessing.	`default: SpectralDataset`	derivative spectral dataset	`method` (`savitzky_golay`, `norris_williams`); `deriv`; `size`; `order`; `gap`; `segment`. Derivatives amplify noise, so smooth carefully.
Cosmic Ray Removal (`preprocess.cosmic_ray`)	Remove Raman spike artifacts before modeling or peak finding.	`default: SpectralDataset`	cleaned spectral dataset	`window`; `zscore`. Uses local median and MAD-style spike detection.

SciPy's savgol_filter is the underlying reference point for Savitzky-Golay concepts such as window length, polynomial order, and derivative order: https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.savgol_filter.html.

Range, Alignment, Normalization, and Scaling

Node	Use When	Inputs	Outputs	Key Configuration
Clip Range (`preprocess.clip_range`)	Keep a chemically relevant wavenumber range and drop unneeded variables.	`default: SpectralDataset`	cropped spectral dataset	`min_wavenumber`; `max_wavenumber`. Check axis direction after cropping.
Clip Floor (`preprocess.clip_floor`)	Remove negative values before non-negative methods such as NMF.	`default: SpectralDataset`	clipped spectral dataset	`floor`.
Wavenumber Align (`preprocess.wavenumber_align`)	Align spectra onto a common spectral grid before stacking, transfer, or comparison.	`default: SpectralDataset`	aligned spectral dataset	`method` such as `pchip`; `merge_tolerance`.
Normalize (`preprocess.normalize`)	Normalize each spectrum for scatter, pathlength, or scale differences.	`default: Array2D`	normalized spectral dataset	`method` (`snv`, `msc`, `scale`); `reference`; `scale_method`.
Scale / Center (`preprocess.scale`)	Mean-center, autoscale, Pareto-scale, or max-scale before PCA, PLS, SVR, or KNN.	`default: Array2D`; `reference: Array2D?`	scaled spectral dataset	`method`; `center`; `target_max`. Use a training reference when applying preprocessing consistently to new data.
EMSC (`preprocess.emsc`)	Correct scatter and baseline with a polynomial model and optional constituent spectra.	`default: SpectralDataset`; `constituents: SpectralDataset?`	corrected spectral dataset	`reference`; `poly_order`.
OSC Filter (`preprocess.osc`)	Remove spectral variation orthogonal to the target before calibration.	`X: SpectralDataset`; `y: TargetMatrix?`	filtered spectral dataset	`n_components`; `tol`; `max_iter`. Requires SpectroChemPy. Use only inside validation folds to avoid leakage.

Time-Series Helpers

Node	Use When	Inputs	Outputs	Key Configuration
Moving Window (`time_series.moving_window`)	Analyze time-resolved spectra in rolling windows.	`default: SpectralDataset`	windowed spectral dataset	`window_size`; `step_size`; `aggregation`.
Trend Removal (`time_series.trend_removal`)	Remove drift from sequential spectra before PCA, monitoring, or calibration.	`default: SpectralDataset`	detrended spectral dataset	`method`; `poly_order`; `window_size`.

Calibration Transfer

Node	Use When	Inputs	Outputs	Key Configuration
PDS Transfer (`transfer.pds`)	Standardize spectra from a secondary instrument to a primary/master instrument using local windows.	`X_primary`; `X_secondary`; `X_new`	`X_standardized`; `transfer_error`	`half_window`; `n_components`. Best when paired primary/secondary transfer standards are available.
SBC / DS Transfer (`transfer.sbc`)	Apply global slope/bias correction or direct standardization for instrument transfer.	`X_primary`; `X_secondary`; `X_new`	`X_standardized`; `transfer_error`	`method`; `regularization`. Simpler than PDS, but less local.

Notes

Some preprocessing methods are base-install implementations. Some rely on SpectroChemPy for coordinate-aware spectroscopy routines. When a node or file reader depends on SpectroChemPy, install spectra-sherpa[scp].