parameters['rs'] = 0.95
parameters['recoil_prob'] = 0.025
#load obs data
peaklist = get_peaks()
if False:
# test the theoretical model
dt_output = 60
tmax = 6*60*60
npts = tmax/dt_output
dfmod = tm.detector_model_wrapper(timestep=dt_output,
initial_state=np.zeros(tm.N_state),
external_radon_conc=np.ones(npts) * onemBq / 100.0,
internal_airt_history=np.ones(npts)*(273.15+20),
parameters=parameters)
chains_list = []
parameters.pop('transform_radon_timeseries')
parameters.pop('total_efficiency')
parameters.pop('total_efficiency_frac_error')
count = 0
for dfss in peaklist:
count += 1
import emcee_deconvolve_tm
parameters['variable_parameter_lower_bounds'] = np.array([0.0, 0.0, 0.0, 0.0, -np.inf, 0.0, 3600*4])
parameters['variable_parameter_upper_bounds'] = np.array([np.inf, np.inf, 2.0, np.inf, np.inf, np.inf, 3600*6])
variable_parameters_mu_prior = np.array(
[parameters[k] for k in variable_parameter_names])
variable_parameters_sigma_prior = np.array([parameters['Q_external'] * 0.02,
parameters['Q']*0.02,
0.05,
1/100.0,
60.,
0.2,
10*60.0])
# how does the initial guess look?
Y = tm.detector_model_wrapper(np.diff(t)[0],
initial_state=np.array([0.,0,0,0,0]),
external_radon_conc=dfss.cal_radon_conc.values/lamrn*0.0,
internal_airt_history=dfss.airt.values,
parameters=parameters,
interpolation_mode=1,
return_full_state=False)
dfss['lldmod'] = np.r_[0.0, Y.Acc_counts.diff().values[1:]] + \
expected_background*tres
f, ax = plt.subplots()
dfss[['lld','lldmod']].plot(ax=ax)
ax.set_title(dfss.index.to_pydatetime()[0].date().strftime('%b %Y'))
plt.show()
# fit to obs
if True:
fit_ret = emcee_deconvolve_tm.fit_parameters_to_obs(t, observed_counts=dfss.lld.values,
