nirs4all.synthesis.reconstruction.pipeline module

Complete reconstruction pipeline for end-to-end workflow.

Provides a unified interface for: 1. Dataset configuration and preprocessing detection 2. Global calibration 3. Batch inversion 4. Parameter distribution learning 5. Synthetic generation 6. Validation

class nirs4all.synthesis.reconstruction.pipeline.DatasetConfig(wavelengths: ndarray, signal_type: Literal['absorbance', 'reflectance', 'unknown'] = 'absorbance', preprocessing: Literal['none', 'first_derivative', 'second_derivative', 'snv', 'msc', 'unknown'] = 'none', domain: str = 'unknown', sg_window: int = 15, sg_polyorder: int = 2, name: str = 'dataset')[source]

Bases: object

Configuration for a dataset to be reconstructed.

Captures all dataset-specific information needed for reconstruction: - Wavelength grid - Signal type (absorbance, reflectance) - Preprocessing applied - Application domain (for component selection)

wavelengths

Wavelength grid in nm.

Type:: numpy.ndarray

signal_type

Signal type (‘absorbance’, ‘reflectance’).

Type:: Literal[‘absorbance’, ‘reflectance’, ‘unknown’]

preprocessing

Detected or specified preprocessing type.

Type:: Literal[‘none’, ‘first_derivative’, ‘second_derivative’, ‘snv’, ‘msc’, ‘unknown’]

domain

Application domain for component selection.

Type:: str

sg_window

Savitzky-Golay window (for derivatives).

Type:: int

sg_polyorder

Savitzky-Golay polynomial order.

Type:: int

name

Optional dataset name.

Type:: str

domain: str = 'unknown'

classmethod from_data(X: ndarray, wavelengths: ndarray, name: str = 'dataset') → DatasetConfig[source]

Create configuration by auto-detecting properties from data.

Parameters:

X – Spectra matrix (n_samples, n_wavelengths).
wavelengths – Wavelength grid.
name – Dataset name.

Returns:

DatasetConfig with detected properties.

name: str = 'dataset'

preprocessing: Literal['none', 'first_derivative', 'second_derivative', 'snv', 'msc', 'unknown'] = 'none'

sg_polyorder: int = 2

sg_window: int = 15

signal_type: Literal['absorbance', 'reflectance', 'unknown'] = 'absorbance'

wavelengths: ndarray

class nirs4all.synthesis.reconstruction.pipeline.PipelineResult(config: DatasetConfig, calibration: 'CalibrationResult' | None = None, inversion_results: List['InversionResult'] | None = None, distribution: 'DistributionResult' | None = None, X_synthetic: np.ndarray | None = None, validation: 'ValidationResult' | None = None, forward_chain: 'ForwardChain' | None = None)[source]

Bases: object

Result of reconstruction pipeline.

Contains all outputs from the reconstruction workflow: - Calibration results - Inversion results - Learned distributions - Generated synthetic data - Validation metrics

config

Dataset configuration used.

Type:: DatasetConfig

calibration

Global calibration result.

Type:: Optional[‘CalibrationResult’]

inversion_results

Per-sample inversion results.

Type:: Optional[List[‘InversionResult’]]

distribution

Learned parameter distributions.

Type:: Optional[‘DistributionResult’]

X_synthetic

Generated synthetic spectra.

Type:: Optional[np.ndarray]

validation

Validation result.

Type:: Optional[‘ValidationResult’]

forward_chain

Calibrated forward chain.

Type:: Optional[‘ForwardChain’]

X_synthetic: np.ndarray | None = None

calibration: 'CalibrationResult' | None = None

config: DatasetConfig

distribution: 'DistributionResult' | None = None

forward_chain: 'ForwardChain' | None = None

inversion_results: List['InversionResult'] | None = None

summary() → str[source]: Generate pipeline summary.

validation: 'ValidationResult' | None = None

class nirs4all.synthesis.reconstruction.pipeline.ReconstructionPipeline(config: DatasetConfig, component_names: List[str] | None = None, canonical_resolution: float = 0.5, baseline_order: int = 5, continuum_order: int = 3, n_prototypes: int = 5, fit_environmental: bool = False, verbose: bool = True)[source]

Bases: object

Complete reconstruction pipeline.

Orchestrates the full workflow: 1. Configuration and component selection 2. Prototype selection and global calibration 3. Per-sample inversion (optionally with environmental parameters) 4. Parameter distribution learning 5. Synthetic generation 6. Validation

config

Dataset configuration.

Type:: nirs4all.synthesis.reconstruction.pipeline.DatasetConfig

component_names

Components to use (auto-selected if None).

Type:: List[str] | None

canonical_resolution

Resolution of canonical grid (nm).

Type:: float

baseline_order

Baseline polynomial order.

Type:: int

n_prototypes

Number of prototypes for calibration.

Type:: int

fit_environmental

Whether to fit environmental parameters.

Type:: bool

verbose

Print progress.

Type:: bool

__post_init__()[source]: Initialize components if not provided.

baseline_order: int = 5

canonical_resolution: float = 0.5

component_names: List[str] | None = None

config: DatasetConfig

continuum_order: int = 3

fit(X: ndarray, max_samples: int | None = None) → PipelineResult[source]

Run full reconstruction pipeline.

Parameters:

X – Spectra matrix (n_samples, n_wavelengths).
max_samples – Max samples to invert (for speed).

Returns:

PipelineResult with all outputs.

fit_environmental: bool = False

generate(n_samples: int, result: PipelineResult, random_state: int | None = None) → ndarray[source]

Generate additional synthetic samples using fitted pipeline.

Parameters:

n_samples – Number of samples to generate.
result – PipelineResult from fit().
random_state – Random seed.

Returns:

Synthetic spectra matrix.

n_prototypes: int = 5

verbose: bool = True

nirs4all.synthesis.reconstruction.pipeline.reconstruct_and_generate(X: ndarray, wavelengths: ndarray, n_synthetic: int | None = None, domain: str = 'unknown', component_names: List[str] | None = None, fit_environmental: bool = False, verbose: bool = True) → Tuple[ndarray, PipelineResult][source]

Convenience function for end-to-end reconstruction and generation.

Parameters:

X – Real spectra matrix.
wavelengths – Wavelength grid.
n_synthetic – Number of synthetic samples (default: same as X).
domain – Application domain.
component_names – Components to use.
fit_environmental – Whether to fit environmental parameters (temperature, water activity, scattering).
verbose – Print progress.

Returns:

Tuple of (X_synthetic, PipelineResult).