Data Nodes

Data nodes introduce datasets into a workflow.

Core Data Nodes

Node	Use When	Inputs	Outputs	Key Configuration
Data Source (`data.source`)	You need an example dataset, uploaded experiment file, library entry, or direct file path.	none	`default: SpectralDataset`; `target: TargetMatrix` when available	`source`; source-specific selectors such as `example_dataset`, `sklearn_dataset`, `eigenvector_dataset`, `synthetic_dataset`, `experiment_id`, `file_id`, `stage`, `library_id`, `file_path`.
File Load (`data.file_load`)	You want a specific uploaded experiment file from a project dataset.	none	spectral dataset	`experiment_id`; `file_id`; `stage` such as raw or processed.
My Dataset (`data.my_dataset`)	You want to load all files in a saved dataset collection.	none	`default: SpectralDataset`; `target: TargetMatrix` if the dataset has targets	`dataset_id`.
NIST Library (`data.nist_library`)	You need reference spectra from the bundled/reference NIST library.	none	`SherpaDataset`	`library_id`; `compound_name`.
Load Group (`data.load_group`)	You need to stack many files from a folder into one dataset.	none	grouped spectral dataset	`folder_path`; `pattern`; `recursive`; `sort_by`; `validate_axes`; `group_title`. Requires SpectroChemPy for spectroscopy-native grouped file loading.
Attach Target (`data.attach_target`)	You already have spectra and a target vector or matrix and want downstream supervised nodes to see them together.	`X: SpectralDataset`; `y: TargetMatrix`	`default: SpectralDataset` with attached targets	`target_type` such as continuous or categorical.
Train/Test Split (`data.train_test_split`)	You need a simple train/test split before modeling or holdout evaluation.	`X: SpectralDataset`; `y: TargetMatrix?`	`X_train`; `X_test`; `y_train`; `y_test`	`test_size`; `split_method`; `random_seed`; `shuffle`.
Filter Samples (`data.filter_samples`)	You need to subset rows by sample label, class, target, metadata, row number, or intensity rule.	`X: SpectralDataset`	filtered `SpectralDataset`	`field`; `pattern`; `match_mode`; `case_sensitive`; `invert`; sample-table and intensity threshold settings.

Synthesis and Mixture Helpers

These nodes are useful for examples, simulation, and method development. They should not be mistaken for measured calibration data.

Node	Use When	Inputs	Outputs	Key Configuration
Species (`synthesis.species`)	Mark a spectrum as a component/species before mixture generation.	`default: SpectralDataset`	spectral dataset with species metadata	`species_name`; `molar_absorptivity`.
Blend (`synthesis.blend`)	Create synthetic mixtures from component spectra and concentration curves.	`default: SpectralDataset`	synthetic spectral dataset	`n_timepoints`; `model_type`; `pathlength`; `noise_level`; `species_config`.
Merge Spectra (`synthesis.merge`)	Stack or combine spectra into one dataset.	`default: SpectralDataset`	merged spectral dataset	`align_wavenumbers`.
Synthetic Curve (`data.synthetic_curve`)	Generate concentration/time curves for synthetic examples.	none	synthetic curve dataset	`curve_type`; `n_points`; `max_concentration`; `center`; `width`.
Concentration Curve (`custom.concentration_curve`)	Generate simple concentration curves for custom blending workflows.	none	array	`curve_type`; `n_points`; `max_concentration`; `center`; `width`.
Catmull-Rom Curve (`custom.catmull_rom_curve`)	Generate smoother custom curves from control points.	none	array	`n_points`; `max_concentration`; `control_points`.
Noise Injection (`custom.noise_injection`)	Stress-test workflows with controlled synthetic noise.	`default: SpectralDataset`	spectral dataset	`noise_level`; `noise_type`; `seed`.
System Saturation (`custom.system_saturation`)	Simulate detector or system saturation in synthetic spectra.	`default: SpectralDataset`	spectral dataset	`s_system`; `p_system`.
Linear Calibration (`custom.linear_calibration`)	Simulate Beer-Lambert style response with saturation capping.	`spectrum: SpectralDataset`; `concentrations: Any`	spectral dataset	`s_max`; `concentration_unit`; `reference_confirmed`.
Saturation Model (`custom.saturation_model`)	Simulate nonlinear high-concentration response.	`spectrum: SpectralDataset`; `concentrations: Any`	spectral dataset	`validate_params`; `concentration_unit`; `warn_extrapolation`.
Hybrid Model Selector (`custom.hybrid_selector`)	Choose linear or saturation output per variable.	`linear_result`; `saturation_result`; `concentrations`	spectral dataset	`auto_select`; `saturation_threshold`.
Golden Grid Align (`custom.golden_grid_align`)	Align spectra to a common wavenumber grid for synthetic or imported sets.	`default: SpectralDataset`	aligned spectral dataset	`method`; `merge_tolerance`.

Deployment I/O Helpers

Node	Use When	Inputs	Outputs	Key Configuration
Deploy Input (`deploy.input`)	Building a headless prediction workflow that receives external data.	none	`default: SpectralDataset`	`stream_name`.
Deploy Output (`deploy.output`)	Formatting headless prediction results for a service response.	`default: Any`	scalar or structured response payload	`output_format`; `key_value_separator`; `end_of_message_tag`.

Data Checks Before Modeling

Confirm sample count, variable count, spectral axis units, sample IDs, and target alignment before connecting to supervised nodes. A model can train on row-misaligned spectra and targets, but the results are scientifically invalid.

Import Boundary

Base file support and optional SpectroChemPy-backed file support are documented in Supported File Types.