def detector_model_observed_counts(timestep, initial_state, external_radon_conc,
internal_airt_history,
parameters, interpolation_mode=1):
"""just return the observed_counts timeseries"""
params = fast_detector.parameter_array_from_dict(parameters)
soln = fast_detector.detector_model(timestep, interpolation_mode,
external_radon_conc, internal_airt_history,
initial_state, params)
return np.diff(soln[:,-1])
def detector_model_wrapper(timestep, initial_state, external_radon_conc,
internal_airt_history,
parameters, interpolation_mode=1,
return_full_state=False):
"""
TODO:
"""
t = np.arange(0, timestep*len(external_radon_conc), timestep, dtype=np.float)
params = fast_detector.parameter_array_from_dict(parameters)
soln = fast_detector.detector_model(timestep, interpolation_mode,
external_radon_conc,
internal_airt_history,
initial_state, params)
df = pd.DataFrame(index=t/60.0, data=soln)
df.columns = 'Nrnd,Nrnd2,Nrn,Fa,Fb,Fc,Acc_counts'.split(',')
eff = parameters['eff']
df['count rate'] = eff*(df.Fa*lama + df.Fc*lamc)
if return_full_state:
#TODO
assert(False)
return df