# this function is a wrapper so that myOBE can access the model
def my_model_function(sets, pars, cons):
# unpack the experimental settings
x = sets[0]
# unpack model parameters
x0 = pars[0]
A = pars[1]
B = pars[2]
# unpack model constants
d = cons[0]
return lorentz_peak(x, x0, A, B, d)
"""Connect it to myOBE"""
myOBE.model_function = my_model_function
"""
Settings and Parameters and Constants (oh My!)
"""
# define the measurement setting space
# 50 values between 1.5 and 4.5 (GHz)
xvals = np.linspace(1.5, 4.5, 200)
# tell it to the BOE
# sets, pars, cons are all expected to be tuples
myOBE.sets = (xvals, )
# define the parameter space where the peak could be found
# resonance values x0 (like NV frequency) around 3 GHz
x0min = 2
x0max = 4
x0vals = np.linspace(x0min, x0max, 201)
# a straight line
return m * x + b
def mxplsub_wrapper(sets, pars, cons):
# unpack the experimental settings
x = sets[0]
# unpack model parameters
m = pars[0]
b = pars[1]
# unpack model constants
# N/A
return mxplusb(x, m, b)
myOBE.model_function = mxplsub_wrapper
# settings, parameters and constants
# define the measurement setting space
# 101 possible x values
xsettings = np.linspace(0, 1, 201)
# sent it to myOBE packaged as a tuple
myOBE.sets = (xsettings, )
# define the parameter space
mvals = np.linspace(-1, 1, 501)
bvals = np.linspace(-1, 1, 501)
# package as a tuple and send
myOBE.pars = (mvals, bvals)