nirs4all.data.synthetic.sources module

Multi-source dataset generation for synthetic NIRS data.

This module provides tools for generating synthetic datasets with multiple data sources, such as combining NIR spectra with molecular markers or auxiliary measurements.

Example

>>> from nirs4all.data.synthetic.sources import MultiSourceGenerator
>>>
>>> generator = MultiSourceGenerator(random_state=42)
>>>
>>> dataset = generator.generate(
...     n_samples=500,
...     sources=[
...         {"name": "NIR_low", "type": "nir", "wavelength_range": (1000, 1700)},
...         {"name": "NIR_high", "type": "nir", "wavelength_range": (1700, 2500)},
...         {"name": "markers", "type": "aux", "n_features": 15},
...     ]
... )

class nirs4all.data.synthetic.sources.MultiSourceGenerator(random_state: int | None = None)[source]

Bases: object

Generate synthetic multi-source NIRS datasets.

This class creates datasets combining multiple data sources, such as: - Multiple NIR spectral ranges (e.g., visible-NIR + shortwave-NIR) - NIR spectra + molecular markers - NIR spectra + auxiliary measurements

The generated sources share common underlying structure through component concentrations, creating realistic inter-source correlations.

rng: NumPy random generator.

Parameters:: random_state – Random seed for reproducibility.

Example

>>> generator = MultiSourceGenerator(random_state=42)
>>>
>>> result = generator.generate(
...     n_samples=500,
...     sources=[
...         {
...             "name": "NIR",
...             "type": "nir",
...             "wavelength_range": (1000, 2500),
...             "complexity": "realistic"
...         },
...         {
...             "name": "markers",
...             "type": "aux",
...             "n_features": 20,
...             "correlation_with_target": 0.7
...         }
...     ],
...     target_range=(0, 100)
... )
>>>
>>> print(result.source_names)
['NIR', 'markers']

create_dataset(n_samples: int, sources: List[SourceConfig | Dict[str, Any]], *, train_ratio: float = 0.8, target_range: Tuple[float, float] | None = None, name: str = 'multi_source_synthetic') → SpectroDataset[source]

Create a SpectroDataset from multi-source generation.

Parameters:

n_samples – Number of samples to generate.
sources – List of source configurations.
train_ratio – Proportion of samples for training.
target_range – Optional (min, max) for target scaling.
name – Dataset name.

Returns:

SpectroDataset with multiple sources configured.

Example

>>> dataset = generator.create_dataset(
...     n_samples=500,
...     sources=[
...         {"name": "NIR", "type": "nir", "wavelength_range": (1000, 2500)},
...         {"name": "markers", "type": "aux", "n_features": 10}
...     ],
...     train_ratio=0.8
... )

generate(n_samples: int, sources: List[SourceConfig | Dict[str, Any]], *, target_range: Tuple[float, float] | None = None, concentration_method: str = 'dirichlet', n_components: int = 5) → MultiSourceResult[source]

Generate a multi-source dataset.

All sources share underlying component concentrations, which creates realistic correlations between sources. NIR sources generate spectra from these concentrations, while auxiliary sources create features correlated with the same underlying structure.

Parameters:

n_samples – Number of samples to generate.
sources – List of source configurations (SourceConfig or dict).
target_range – Optional (min, max) for scaling target values.
concentration_method – Method for generating component concentrations.
n_components – Number of underlying components.

Returns:

MultiSourceResult containing all generated data.

Example

>>> result = generator.generate(
...     n_samples=300,
...     sources=[
...         {"name": "VIS-NIR", "type": "nir", "wavelength_range": (400, 1100)},
...         {"name": "SWIR", "type": "nir", "wavelength_range": (1100, 2500)},
...     ]
... )

class nirs4all.data.synthetic.sources.MultiSourceResult(sources: ~typing.Dict[str, ~numpy.ndarray], targets: ~numpy.ndarray, source_configs: ~typing.List[~nirs4all.data.synthetic.sources.SourceConfig], wavelengths: ~typing.Dict[str, ~numpy.ndarray] = <factory>, metadata: ~typing.Dict[str, ~typing.Any] | None = None)[source]

Bases: object

Container for multi-source generation results.

sources

Dictionary mapping source names to feature arrays.

Type:: Dict[str, numpy.ndarray]

targets

Target values.

Type:: numpy.ndarray

source_configs

Source configuration objects.

Type:: List[nirs4all.data.synthetic.sources.SourceConfig]

wavelengths

Dictionary mapping NIR source names to wavelength arrays.

Type:: Dict[str, numpy.ndarray]

metadata

Optional metadata dictionary.

Type:: Dict[str, Any] | None

get_combined_features() → ndarray[source]: Concatenate all sources into single feature matrix.

metadata: Dict[str, Any] | None = None

property n_features_total: int: Get total number of features across all sources.

property n_samples: int: Get number of samples.

source_configs: List[SourceConfig]

property source_names: List[str]: Get list of source names.

sources: Dict[str, ndarray]

targets: ndarray

wavelengths: Dict[str, ndarray]

class nirs4all.data.synthetic.sources.SourceConfig(name: str, source_type: Literal['nir', 'vis', 'aux', 'markers'] = 'nir', n_features: int | None = None, wavelength_start: float | None = None, wavelength_end: float | None = None, wavelength_step: float = 2.0, components: List[str] | None = None, complexity: Literal['simple', 'realistic', 'complex'] = 'simple', distribution: Literal['normal', 'uniform', 'lognormal'] = 'normal', correlation_with_target: float = 0.5)[source]

Bases: object

Configuration for a single data source.

name

Unique identifier for the source.

Type:: str

source_type

Type of source (‘nir’, ‘vis’, ‘aux’, ‘markers’).

Type:: Literal[‘nir’, ‘vis’, ‘aux’, ‘markers’]

n_features

Number of features (auto-calculated for NIR sources).

Type:: int | None

# NIR-specific

wavelength_start

Start wavelength for NIR sources.

Type:: float | None

wavelength_end

End wavelength for NIR sources.

Type:: float | None

wavelength_step

Wavelength step for NIR sources.

Type:: float

components

Component names for NIR sources.

Type:: List[str] | None

complexity

Complexity level for NIR sources.

Type:: Literal[‘simple’, ‘realistic’, ‘complex’]

# Auxiliary-specific

distribution

Distribution for auxiliary features.

Type:: Literal[‘normal’, ‘uniform’, ‘lognormal’]

correlation_with_target

How correlated aux features are with target.

Type:: float

complexity: Literal['simple', 'realistic', 'complex'] = 'simple'

components: List[str] | None = None

correlation_with_target: float = 0.5

distribution: Literal['normal', 'uniform', 'lognormal'] = 'normal'

classmethod from_dict(config: Dict[str, Any]) → SourceConfig[source]: Create SourceConfig from dictionary.

n_features: int | None = None

name: str

source_type: Literal['nir', 'vis', 'aux', 'markers'] = 'nir'

wavelength_end: float | None = None

wavelength_start: float | None = None

wavelength_step: float = 2.0

nirs4all.data.synthetic.sources.generate_multi_source(n_samples: int, sources: List[Dict[str, Any]] | None = None, *, random_state: int | None = None, target_range: Tuple[float, float] | None = None, as_dataset: bool = True, train_ratio: float = 0.8, name: str = 'multi_source_synthetic') → SpectroDataset | MultiSourceResult[source]

Convenience function for generating multi-source datasets.

Parameters:

n_samples – Number of samples.
sources – List of source configurations. If None, uses default single NIR source with wavelength range (1000, 2500).
random_state – Random seed.
target_range – Target value range.
as_dataset – If True, returns SpectroDataset.
train_ratio – Training set proportion.
name – Dataset name.

Returns:

SpectroDataset or MultiSourceResult depending on as_dataset.

Example

>>> dataset = generate_multi_source(
...     n_samples=500,
...     sources=[
...         {"name": "NIR", "type": "nir", "wavelength_range": (1000, 2500)},
...         {"name": "markers", "type": "aux", "n_features": 15}
...     ],
...     random_state=42
... )