nirs4all.data.synthetic.detectors module

Detector simulation for synthetic NIRS data generation.

This module provides detailed simulation of NIR detector characteristics including spectral response curves, noise models, and nonlinearity effects.

Key Features:

Detector spectral response curves (Si, InGaAs, PbS, etc.)
Shot noise, thermal noise, read noise, and 1/f noise models
Detector nonlinearity simulation
Temperature-dependent behavior

References

Rogalski, A. (2002). Infrared Detectors: An Overview. Infrared Physics & Technology, 43(3-5), 187-210.
Vincent, J. D., Hodges, S., Vampola, J., Stegall, M., & Pierce, G. (2015). Fundamentals of Infrared and Visible Detector Operation and Testing. Wiley.
Burns, D. A., & Ciurczak, E. W. (2007). Handbook of Near-Infrared Analysis. CRC Press.

class nirs4all.data.synthetic.detectors.DetectorConfig(detector_type: ~nirs4all.data.synthetic.instruments.DetectorType = DetectorType.INGAAS, temperature_k: float = 293.0, integration_time_ms: float = 100.0, gain: float = 1.0, noise_model: ~nirs4all.data.synthetic.detectors.NoiseModelConfig = <factory>, apply_response_curve: bool = True, apply_nonlinearity: bool = False, nonlinearity_coefficient: float = 0.02)[source]

Bases: object

Complete detector configuration.

detector_type

Type of detector.

Type:: nirs4all.data.synthetic.instruments.DetectorType

temperature_k

Operating temperature in Kelvin.

Type:: float

integration_time_ms

Integration time in milliseconds.

Type:: float

gain

Amplifier gain.

Type:: float

noise_model

Noise model configuration.

Type:: nirs4all.data.synthetic.detectors.NoiseModelConfig

apply_response_curve

Whether to apply spectral response.

Type:: bool

apply_nonlinearity

Whether to apply detector nonlinearity.

Type:: bool

nonlinearity_coefficient

Quadratic nonlinearity coefficient.

Type:: float

apply_nonlinearity: bool = False

apply_response_curve: bool = True

detector_type: DetectorType = 'ingaas'

gain: float = 1.0

integration_time_ms: float = 100.0

noise_model: NoiseModelConfig

nonlinearity_coefficient: float = 0.02

temperature_k: float = 293.0

class nirs4all.data.synthetic.detectors.DetectorSimulator(config: DetectorConfig | None = None, random_state: int | None = None)[source]

Bases: object

Simulate detector effects on NIR spectra.

Applies detector spectral response, noise models, and nonlinearity to synthetic spectra.

config: Detector configuration.

rng: Random number generator.

Example

>>> config = DetectorConfig(detector_type=DetectorType.INGAAS)
>>> simulator = DetectorSimulator(config, random_state=42)
>>> spectra_out = simulator.apply(spectra, wavelengths)

apply(spectra: ndarray, wavelengths: ndarray, base_signal_level: float = 1.0) → ndarray[source]

Apply detector effects to spectra.

Parameters:

spectra – Input spectra (n_samples, n_wavelengths).
wavelengths – Wavelength array (nm).
base_signal_level – Reference signal level for noise scaling.

Returns:

Spectra with detector effects applied.

class nirs4all.data.synthetic.detectors.DetectorSpectralResponse(detector_type: DetectorType, wavelengths: ndarray, response: ndarray, peak_wavelength: float, cutoff_wavelength: float, short_cutoff: float, peak_qe: float = 0.7)[source]

Bases: object

Spectral response curve for a detector.

Defines the wavelength-dependent sensitivity (quantum efficiency) of the detector.

detector_type

Type of detector.

Type:: nirs4all.data.synthetic.instruments.DetectorType

wavelengths

Wavelength grid for response curve (nm).

Type:: numpy.ndarray

response

Relative response at each wavelength (0-1).

Type:: numpy.ndarray

peak_wavelength

Wavelength of peak response (nm).

Type:: float

cutoff_wavelength

Long-wavelength cutoff (nm).

Type:: float

short_cutoff

Short-wavelength cutoff (nm).

Type:: float

peak_qe

Peak quantum efficiency (0-1).

Type:: float

cutoff_wavelength: float

detector_type: DetectorType

get_response_at(wavelengths: ndarray) → ndarray[source]

Get detector response at specified wavelengths.

Parameters:: wavelengths – Wavelengths to evaluate (nm).
Returns:: Detector response at each wavelength.

peak_qe: float = 0.7

peak_wavelength: float

response: ndarray

short_cutoff: float

wavelengths: ndarray

class nirs4all.data.synthetic.detectors.NoiseModelConfig(shot_noise_enabled: bool = True, thermal_noise_enabled: bool = True, read_noise_enabled: bool = True, flicker_noise_enabled: bool = False, quantization_noise_enabled: bool = False, shot_noise_factor: float = 1.0, thermal_noise_factor: float = 1.0, read_noise_electrons: float = 50.0, flicker_corner_freq: float = 100.0, adc_bits: int = 16, full_scale: float = 3.0)[source]

Bases: object

Configuration for detector noise model.

shot_noise_enabled

Enable shot (photon) noise.

Type:: bool

thermal_noise_enabled

Enable thermal (Johnson) noise.

Type:: bool

read_noise_enabled

Enable readout noise.

Type:: bool

flicker_noise_enabled

Enable 1/f (flicker) noise.

Type:: bool

quantization_noise_enabled

Enable ADC quantization noise.

Type:: bool

shot_noise_factor

Scaling factor for shot noise.

Type:: float

thermal_noise_factor

Scaling factor for thermal noise.

Type:: float

read_noise_electrons

Read noise in electrons.

Type:: float

flicker_corner_freq

1/f noise corner frequency (Hz).

Type:: float

adc_bits

ADC resolution in bits.

Type:: int

full_scale

Full-scale signal level.

Type:: float

adc_bits: int = 16

flicker_corner_freq: float = 100.0

flicker_noise_enabled: bool = False

full_scale: float = 3.0

quantization_noise_enabled: bool = False

read_noise_electrons: float = 50.0

read_noise_enabled: bool = True

shot_noise_enabled: bool = True

shot_noise_factor: float = 1.0

thermal_noise_enabled: bool = True

thermal_noise_factor: float = 1.0

nirs4all.data.synthetic.detectors.get_default_noise_config(detector_type: DetectorType) → NoiseModelConfig[source]

Get default noise model configuration for a detector type.

Parameters:: detector_type – Type of detector.
Returns:: NoiseModelConfig with appropriate defaults.

nirs4all.data.synthetic.detectors.get_detector_response(detector_type: DetectorType) → DetectorSpectralResponse[source]

Get spectral response curve for a detector type.

Parameters:: detector_type – Type of detector.
Returns:: DetectorSpectralResponse object.

nirs4all.data.synthetic.detectors.get_detector_wavelength_range(detector_type: DetectorType) → Tuple[float, float][source]

Get the effective wavelength range for a detector type.

Parameters:: detector_type – Type of detector.
Returns:: Tuple of (min_wavelength, max_wavelength) in nm.

nirs4all.data.synthetic.detectors.list_detector_types() → List[str][source]

List available detector types.

Returns:: List of detector type names.

nirs4all.data.synthetic.detectors.simulate_detector_effects(spectra: ndarray, wavelengths: ndarray, detector_type: DetectorType = DetectorType.INGAAS, include_response: bool = True, include_noise: bool = True, random_state: int | None = None) → ndarray[source]

Apply detector effects to spectra with simple API.

Parameters:

spectra – Input spectra (n_samples, n_wavelengths).
wavelengths – Wavelength array (nm).
detector_type – Type of detector to simulate.
include_response – Whether to apply spectral response.
include_noise – Whether to apply noise.
random_state – Random seed.

Returns:

Spectra with detector effects applied.

Example

>>> spectra_out = simulate_detector_effects(
...     spectra, wavelengths,
...     detector_type=DetectorType.PBS
... )