pyfli.simulator.separate.model_simulator#

Generate mono- and bi-exponential model parameters and simulated TCSPC observations.

This module belongs to pyfli.simulator.separate and is part of PyFLI synthetic FLI/FLIM data generation, hardware noise modeling, calibration, and validation tools. Public API includes classes FLIModelSimulator.

Shared IRF/timing setup, n_cycles range validation, and TCSPC photon-binning logic live in pyfli.simulator.sim_engine_common.

Classes

FLIModelSimulator(irf_full[, tau2, ...])

Run the flimodel simulator routine.
class FLIModelSimulator(irf_full, tau2=(1, 0.5), tau2_dist='normal', tau2_beta_range=(4.8, 0.2), efficiency=(5, 5), A1_fraction=(5, 5), photo_count=(1.0, 1.0), mono_fraction=0.2, bit=8, n_cycles=800_000, dcr=0.05, laser_feq=80, pileup_mode='wrap', seed=None, **kwargs)[source]#

Bases: BaseFLIEngine

Run the flimodel simulator routine. observations. The class is useful for controlled model-generation experiments independent of image geometry.

Parameters:

  • irf_full (np.ndarray) – Full instrument response function sampled over the decay window.

  • tau2 (tuple[int, float]) – Long lifetime component.

  • tau2_dist (str) – Distribution used to sample donor-only lifetimes.

  • tau2_beta_range (tuple[float, ]) – Shape-parameter range for beta-distributed tau2 values.

  • efficiency (tuple[int, ]) – FRET transfer efficiency used to derive simulated lifetime components.

  • A1_fraction (tuple[int, ]) – Amplitude fraction assigned to the first exponential component.

  • photo_count (tuple[float, int]) – Expected photon count used to scale the simulated decay.

  • mono_fraction (float) – Fraction of pixels or events assigned to the mono-exponential component.

  • bit (int) – Bit depth or quantization setting for simulated detector output.

  • n_cycles (int | tuple[int, int]) – Number of excitation cycles used when constructing the simulated decay. A bare int is the upper bound (lower bound fixed at 1000); a (low, high) tuple sets both bounds, and both must be >= 1000. The per-call cycle count is drawn from a Beta distribution (shaped by photo_count) over that range.

  • dcr (float) – Detector dark-count rate used by the noise model.

  • laser_feq (int) – Laser repetition frequency used by the simulation.

  • pileup_mode (str) – ‘wrap’ (default) folds photons back via modulo; ‘truncate’ drops them.

  • seed (int | None) – Seed for reproducible random sampling.

  • **kwargs (Any) – Additional keyword arguments forwarded to the underlying implementation.

sample_mono_params()[source]#

Samples the lifetime parameter for a single pixel (pure single-exponential).

Return type:

dict[Any, Any]

sample_bi_params()[source]#

Samples lifetime and fraction parameters for a single pixel.

Return type:

dict[Any, Any]

sample_params()[source]#

Sample params.

Returns:

Object produced by sample params.

Return type:

Any

get_model_analytical_decay(p)[source]#

Return model analytical decay.

Parameters:

p (Any) – Detector parameter object or fitted parameter vector.

Returns:

Object produced by get model analytical decay.

Return type:

Any

simulate_model_tcspc(p, n_cycles, mu_per_cycle)[source]#

Simulate model TCSPC.

Parameters:

  • p (Any) – Detector parameter object or fitted parameter vector.

  • n_cycles (int) – Number of simulated laser cycles.

  • mu_per_cycle (np.ndarray) – Expected photons per laser cycle in the TCSPC simulation.

Returns:

Object produced by simulate model TCSPC.

Return type:

Any