nirs4all.data.synthetic.domains module

Application domain configurations for synthetic NIRS data generation.

This module provides domain-specific priors and configurations for generating realistic synthetic NIRS data tailored to specific application areas such as agriculture, pharmaceutical, food processing, petrochemical, and others.

Each domain configuration includes: - Typical spectral components (chemical compounds) - Concentration distributions specific to the domain - Wavelength ranges commonly used - Typical number of components in samples - Domain-specific noise and artifact characteristics

Key Features:

15+ predefined application domains
Domain-aware component selection
Realistic concentration priors
Easy integration with generators

Example

>>> from nirs4all.data.synthetic.domains import (
...     get_domain_config,
...     APPLICATION_DOMAINS,
...     DomainConfig
... )
>>>
>>> # Get configuration for agricultural samples
>>> config = get_domain_config("agriculture_grain")
>>> print(config.typical_components)
['starch', 'protein', 'moisture', 'lipid', 'cellulose']

References

Burns, D. A., & Ciurczak, E. W. (2007). Handbook of Near-Infrared Analysis (3rd ed.). CRC Press.
Williams, P. C., & Norris, K. H. (2001). Near-Infrared Technology in the Agricultural and Food Industries (2nd ed.). AACC International.
Reich, G. (2005). Near-Infrared Spectroscopy and Imaging: Basic Principles and Pharmaceutical Applications. Advanced Drug Delivery Reviews.

class nirs4all.data.synthetic.domains.ConcentrationPrior(distribution: str = 'uniform', params: Dict[str, float]=<factory>, min_value: float = 0.0, max_value: float = 1.0)[source]

Bases: object

Prior distribution for component concentrations.

distribution

Distribution type (‘uniform’, ‘normal’, ‘lognormal’, ‘beta’).

Type:: str

params

Parameters for the distribution (distribution-specific).

Type:: Dict[str, float]

min_value

Minimum allowed concentration.

Type:: float

max_value

Maximum allowed concentration.

Type:: float

distribution: str = 'uniform'

max_value: float = 1.0

min_value: float = 0.0

params: Dict[str, float]

sample(rng: Generator, n_samples: int = 1) → ndarray[source]: Sample from the concentration prior.

class nirs4all.data.synthetic.domains.DomainCategory(value)[source]

Bases: str, Enum

Top-level domain categories.

AGRICULTURE = 'agriculture'

BEVERAGE = 'beverage'

BIOMEDICAL = 'biomedical'

ENVIRONMENTAL = 'environmental'

FOOD = 'food'

PETROCHEMICAL = 'petrochemical'

PHARMACEUTICAL = 'pharmaceutical'

POLYMER = 'polymer'

TEXTILE = 'textile'

class nirs4all.data.synthetic.domains.DomainConfig(name: str, category: DomainCategory, description: str = '', typical_components: List[str] = <factory>, component_weights: Dict[str, float] | None=None, concentration_priors: Dict[str, ~nirs4all.data.synthetic.domains.ConcentrationPrior]=<factory>, wavelength_range: Tuple[float, float]=(1000, 2500), n_components_range: Tuple[int, int]=(3, 8), noise_level: str = 'medium', measurement_mode: str = 'reflectance', typical_sample_types: List[str] = <factory>, complexity: str = 'realistic', additional_params: Dict[str, ~typing.Any]=<factory>)[source]

Bases: object

Configuration for a specific application domain.

Encapsulates all domain-specific parameters needed for generating realistic synthetic NIRS data.

name

Human-readable domain name.

Type:: str

category

Domain category (agriculture, pharmaceutical, etc.).

Type:: nirs4all.data.synthetic.domains.DomainCategory

description

Brief description of the domain.

Type:: str

typical_components

List of predefined component names commonly found.

Type:: List[str]

component_weights

Relative importance of each component (for selection).

Type:: Dict[str, float] | None

concentration_priors

Per-component concentration distributions.

Type:: Dict[str, nirs4all.data.synthetic.domains.ConcentrationPrior]

wavelength_range

Typical measurement range (nm).

Type:: Tuple[float, float]

n_components_range

Range of number of components per sample.

Type:: Tuple[int, int]

noise_level

Typical noise level (‘low’, ‘medium’, ‘high’).

Type:: str

measurement_mode

Typical measurement geometry.

Type:: str

typical_sample_types

Examples of sample types in this domain.

Type:: List[str]

complexity

Overall complexity level for generation.

Type:: str

additional_params

Domain-specific additional parameters.

Type:: Dict[str, Any]

additional_params: Dict[str, Any]

category: DomainCategory

complexity: str = 'realistic'

component_weights: Dict[str, float] | None = None

concentration_priors: Dict[str, ConcentrationPrior]

description: str = ''

get_component_weights() → Dict[str, float][source]: Get normalized component weights for selection.

measurement_mode: str = 'reflectance'

n_components_range: Tuple[int, int] = (3, 8)

name: str

noise_level: str = 'medium'

sample_components(rng: Generator, n_components: int | None = None) → List[str][source]

Sample components for a sample based on domain priors.

Parameters:

rng – Random number generator.
n_components – Number of components. If None, samples from range.

Returns:

List of component names.

sample_concentrations(rng: Generator, components: List[str], n_samples: int = 1) → ndarray[source]

Sample concentrations for selected components.

Parameters:

rng – Random number generator.
components – List of component names.
n_samples – Number of samples.

Returns:

Concentration matrix (n_samples, n_components).

typical_components: List[str]

typical_sample_types: List[str]

wavelength_range: Tuple[float, float] = (1000, 2500)

nirs4all.data.synthetic.domains.create_domain_aware_library(domain_name: str, n_samples: int = 100, random_state: int | None = None) → Tuple[List[str], ndarray][source]

Create component selection and concentrations based on domain priors.

This function samples components and their concentrations according to domain-specific distributions.

Parameters:

domain_name – Name of the domain.
n_samples – Number of samples to generate concentrations for.
random_state – Random seed for reproducibility.

Returns:

Tuple of (component_names, concentration_matrix).

Example

>>> components, concentrations = create_domain_aware_library(
...     "food_dairy",
...     n_samples=50,
...     random_state=42
... )
>>> print(components)
['water', 'lactose', 'casein', 'lipid']
>>> print(concentrations.shape)
(50, 4)

nirs4all.data.synthetic.domains.get_domain_components(domain_name: str) → List[str][source]

Get typical components for a domain.

Parameters:: domain_name – Name of the domain.
Returns:: List of component names.

Example

>>> get_domain_components("food_dairy")
['water', 'lactose', 'casein', 'lipid', 'moisture', 'protein']

nirs4all.data.synthetic.domains.get_domain_config(domain_name: str) → DomainConfig[source]

Get configuration for a specific domain.

Parameters:: domain_name – Name of the domain (key in APPLICATION_DOMAINS).
Returns:: DomainConfig for the specified domain.
Raises:: ValueError – If domain is not found.

Example

>>> config = get_domain_config("agriculture_grain")
>>> print(config.name)
'Grain and Cereals'

nirs4all.data.synthetic.domains.get_domains_for_component(component_name: str) → List[str][source]

Find domains that typically contain a specific component.

Parameters:: component_name – Name of the component.
Returns:: List of domain names containing this component.

Example

>>> get_domains_for_component("protein")
['agriculture_grain', 'food_meat', 'biomedical_tissue', ...]

nirs4all.data.synthetic.domains.list_domains(category: DomainCategory | None = None) → List[str][source]

List available domain names.

Parameters:: category – Optional category filter.
Returns:: List of domain names.

Example

>>> list_domains(DomainCategory.AGRICULTURE)
['agriculture_grain', 'agriculture_forage', ...]