Helpers¶

evaluate_model¶

Evaluate a model TAC from explicit parameters:

pred = kinepet.evaluate_model(
    time,
    aif,
    model="rev",
    K1=0.12,
    k2=0.16,
    k3=0.09,
    k4=0.04,
    vB=0.05,
    delay=0.0,
    dispersion=0.0,
)

time and aif must be one-dimensional arrays with the same length. The returned array has shape (T,).

default_bounds¶

Inspect the active parameter bounds used by the solver:

bounds = kinepet.default_bounds(
    model="rev",
    fit_vb=True,
    fit_delay=True,
    fit_dispersion=True,
)

print(bounds["K1"])
print(bounds["k4"])
print(bounds["vB"])

Current defaults:

Parameter	Bounds
K1	[0, 10]
k2	[0, 10]
k3	[0, 5]
k4	[0, 1] when active
vB	[0, 1] when fitted
delay	[-0.2, 0.2] minutes when fitted
dispersion	[0, 0.1] minutes when fitted

default_frame_weights¶

Generate the solver's default frame weights for a time vector:

weights = kinepet.default_frame_weights(time)
fit = kinepet.fit_tacs(tacs, time, aif, weights=weights, model="rev")

If weights=None, the batch fitter uses default frame weights internally.