Node Library

Workflow nodes are the building blocks of SpectraSherpa analyses. They are grouped by what they do rather than by implementation package.

This section documents the built-in nodes from the current workflow registry. Each category page lists the node's expected inputs, outputs, and configuration knobs so you can decide whether it belongs in a workflow before wiring it.

Reading Node Tables

Most spectroscopy workflows pass SpectralDataset objects between nodes. A SpectralDataset is a numeric matrix with sample rows, spectral-variable columns, axis metadata, and processing history. Supervised nodes also consume targets such as concentrations or class labels.

Term	Meaning
SpectralDataset	Spectral matrix, usually samples by wavenumbers or Raman shifts, with axis metadata.
TargetMatrix	Continuous target values such as concentration, property value, or response matrix.
Categorical	Class labels, sample groups, or QC categories.
ScoreMatrix	Scores from PCA, PLS, PCR, clustering embeddings, or similar latent-variable methods.
LoadingMatrix	Loadings or component vectors associated with latent-variable models.
FittedModel	Generic trained model object.
RegressionModel	Trained model that predicts continuous targets.
ClassificationModel	Trained model that predicts class labels or class distances.
Visualization	Plot-ready payload consumed by output nodes and the report view.
ValidationResult	Metrics, limits, diagnostics, or validation tables.

Optional inputs are marked with ? in the tables. The default port is the normal single input or output when a node does not need a named port.

Choosing Nodes

• Start with Data Nodes to load files, project datasets, library references, synthetic curves, or saved datasets.
• Use Preprocessing Nodes to correct baseline, smooth, crop, align, normalize, or transfer spectra before modeling.
• Use Exploratory Nodes for PCA, clustering, MCR-ALS, EFA, SIMPLISMA, peak finding, and library comparison.
• Use Regression Nodes when the target is quantitative.
• Use Classification Nodes when the target is a class or QC decision.
• Use Selection and Validation Nodes for sample splitting, variable selection, CV, holdout metrics, and outlier flags.
• Use Output Nodes to make results visible or exportable.
• Check Optional SpectroChemPy Nodes when a node or file reader requires spectra-sherpa[scp].

Categories

• data, synthesis, and deployment helpers
• preprocessing and calibration transfer
• exploratory, clustering, and decomposition
• regression
• classification
• selection and validation
• output
• optional SpectroChemPy-backed nodes