pyfli.solver.forward_model#

Evaluate exponential decay kernels and convolved NumPy forward models.

This module belongs to pyfli.solver and is part of PyFLI least-squares, maximum- likelihood, CPU, GPU, binned, and global FLI fitting routines. Public API includes functions decay_kernel() and model_numpy().

Functions

decay_kernel(t, params, model_type[, h_shift])

Return (kernel, v_shift).

model_numpy(t, irf, params, model_type)

Evaluate the NumPy FLI forward model.
decay_kernel(t, params, model_type, h_shift=0.0)[source]#

Return (kernel, v_shift).

The temporal delay h_shift (in the same units as t, i.e. ns) is applied directly to the exponential argument so no IRF array manipulation is needed.

Parameters:
Return type:

tuple

model_numpy(t, irf, params, model_type)[source]#

Evaluate the NumPy FLI forward model.

Parameters:

  • t (np.ndarray) – Time axis or acquisition period used by the calculation.

  • irf (np.ndarray) – Instrument response function aligned with the decay signal.

  • params (Any) – Model, detector, or plotting parameters used by the routine.

  • model_type (str) – FLI model family, such as mono- or bi-exponential.

Returns:

Model decay evaluated with NumPy for the supplied parameters.

Return type:

np.ndarray