Amin = -2000
Amax = -40000
Avals = np.linspace(Amin, Amax, 101)
# background
Bmin = 275000
Bmax = 225000
Bvals = np.linspace(Bmin, Bmax, 151)
# Pack the parameters into a tuple and send it to the BOE
# note that the order must correspond to how the values are unpacked in the model_function
myOBE.pars = (x0vals, Avals, Bvals)
# define the known constants
# keeping the peak width constant in this example
dtrue = .1
myOBE.cons = (dtrue, )
# Settings, parameters, constants and model all defined, so set it all up
myOBE.config()
"""
MEASUREMENT SIMULATION
"""
# pick the parameters of the true resonance. myOBE will have to "discover" these.
x0true = (x0max -
x0min) * np.random.rand() + x0min # pick a random resonance x0
Btrue = (Bmax - Bmin) * np.random.rand() + Bmin # pick a random background
# Btrue = 250000
Atrue = (Amax - Amin) * np.random.rand() + Amin # pick a random amplitude
# Rabi freuquency B1min = 1 B1max = 5 B1 = np.linspace(B1min, B1max, 71) fc_min = -7 fc_max = 7 f_center = np.linspace(fc_min, fc_max, 71) # baseline = np.linspace(50000, 60000, 51) # contrast = np.linspace(.05, .15, 11) myOBE.pars = (B1, f_center) param_extent = (B1min, B1max, fc_min, fc_max) baseline = 100000 contrast = .01 T1 = .5 myOBE.cons = (baseline, contrast, T1) # Settings, parameters, constants and model all defined, so set it all up myOBE.config() # put in a prior B1prior = np.exp(-(B1 - 3.0)**2 / 2 / 2.0**2) fcprior = np.exp(-(f_center)**2 / 2 / 4.0**2) myOBE.set_pdf(probvalarrays=(B1prior, fcprior)) """ MEASUREMENT SIMULATION """ # secret stuff - to be used only by the measurement simulator # pick the parameters of the true resonance