nirs4all.data.synthetic.reconstruction.generator module

Synthetic data generation from learned parameter distributions.

Generates realistic synthetic spectra by: 1. Sampling physical parameters from learned distributions 2. Running the forward model chain 3. Adding appropriate noise

class nirs4all.data.synthetic.reconstruction.generator.GenerationResult(X: ndarray, concentrations: ndarray, path_lengths: ndarray, baseline_coeffs: ndarray, wavelengths: ndarray, noise_level: float = 0.0, wl_shifts: ndarray | None = None, temperature_deltas: ndarray | None = None, water_activities: ndarray | None = None, scattering_powers: ndarray | None = None, scattering_amplitudes: ndarray | None = None)[source]

Bases: object

Result of synthetic generation.

X

Generated spectra (n_samples, n_wavelengths).

Type:: numpy.ndarray

concentrations

Sampled concentrations (n_samples, n_components).

Type:: numpy.ndarray

path_lengths

Sampled path lengths (n_samples,).

Type:: numpy.ndarray

baseline_coeffs

Sampled baseline coefficients.

Type:: numpy.ndarray

wavelengths

Wavelength grid.

Type:: numpy.ndarray

noise_level

Applied noise level.

Type:: float

wl_shifts

Per-sample wavelength shifts.

Type:: numpy.ndarray | None

temperature_deltas

Per-sample temperature deviations (°C).

Type:: numpy.ndarray | None

water_activities

Per-sample water activity values.

Type:: numpy.ndarray | None

scattering_powers

Per-sample scattering exponents.

Type:: numpy.ndarray | None

scattering_amplitudes

Per-sample scattering amplitudes.

Type:: numpy.ndarray | None

X: ndarray

baseline_coeffs: ndarray

concentrations: ndarray

property n_samples: int: Number of generated samples.

property n_wavelengths: int: Number of wavelengths.

noise_level: float = 0.0

path_lengths: ndarray

scattering_amplitudes: ndarray | None = None

scattering_powers: ndarray | None = None

temperature_deltas: ndarray | None = None

water_activities: ndarray | None = None

wavelengths: ndarray

wl_shifts: ndarray | None = None

class nirs4all.data.synthetic.reconstruction.generator.ReconstructionGenerator(noise_level: float = 0.001, multiplicative_noise: float = 0.01, add_noise: bool = True, noise_type: str = 'both')[source]

Bases: object

Generate synthetic spectra from learned parameter distributions.

Uses the calibrated forward chain and learned parameter distributions to generate realistic synthetic data that matches the statistical properties of the original dataset.

forward_chain: Calibrated forward chain.

sampler: Parameter sampler with learned distributions.

noise_estimator: Estimated noise level from inversion residuals.

add_noise

Whether to add noise to generated spectra.

Type:: bool

noise_type

Type of noise (‘additive’, ‘multiplicative’, ‘both’).

Type:: str

add_noise: bool = True

generate(n_samples: int, forward_chain: ForwardChain, sampler: ParameterSampler, random_state: int | None = None) → GenerationResult[source]

Generate synthetic spectra.

Parameters:

n_samples – Number of samples to generate.
forward_chain – Calibrated forward chain.
sampler – Parameter sampler.
random_state – Random seed.

Returns:

GenerationResult with generated spectra and parameters.

generate_matched(X_real: np.ndarray, forward_chain: ForwardChain, sampler: ParameterSampler, random_state: int | None = None) → GenerationResult[source]

Generate synthetic data matched to real data statistics.

Generates same number of samples as real data and optionally adjusts noise level based on estimated residuals.

Parameters:

X_real – Real data matrix for reference.
forward_chain – Calibrated forward chain.
sampler – Parameter sampler.
random_state – Random seed.

Returns:

GenerationResult.

multiplicative_noise: float = 0.01

noise_level: float = 0.001

noise_type: str = 'both'

nirs4all.data.synthetic.reconstruction.generator.estimate_noise_from_residuals(inversion_results: List['InversionResult']) → Tuple[float, float][source]

Estimate noise parameters from inversion residuals.

Parameters:: inversion_results – List of inversion results with residuals.
Returns:: Tuple of (additive_noise_std, multiplicative_noise_std).

nirs4all.data.synthetic.reconstruction.generator.generate_synthetic_dataset(forward_chain: ForwardChain, distribution_result: DistributionResult, n_samples: int, noise_level: float = 0.001, multiplicative_noise: float = 0.01, random_state: int | None = None) → GenerationResult[source]

Complete pipeline to generate synthetic dataset.

Parameters:

forward_chain – Calibrated forward chain.
distribution_result – Fitted parameter distributions.
n_samples – Number of samples to generate.
noise_level – Additive noise level.
multiplicative_noise – Multiplicative noise level.
random_state – Random seed.

Returns:

GenerationResult with synthetic spectra.