def decon_known(df,center):
"""
This method uses multiple make_lor to do a multiple-peak deconvolutoin using
multiple Lorentzians and one arc tangent step function
Notice: parameters are NOT automatically deleted if they are not used in the
deconvolution of data with fewer peaks than the last time
Parameters
----------
df : pandas dataframe
df is a column-wise dataframe that records the data you want to
deconvolve.
center : array
the array recording the centers of all peaks.
Returns
-------
out : lmfit.Model
the composit multiple-peak model for the deconvolution.
"""
arctan_mod=StepModel(form='atan',prefix='arctan_')
paras.update(arctan_mod.make_params())
paras['arctan_center'].set(value=inflection(norm.Energy,df),vary=False,min=0.0)
paras['arctan_amplitude'].set(value=1.0,vary=False)
paras['arctan_sigma'].set(value=1.0,min=0.01)
mod=arctan_mod
for i in range(len(center)):
this=make_lor(df,i,center,2.0)['model']
mod=mod+this
paras.update(make_lor(df,i,center,2.0)['paras'])
out=mod.fit(df,params=paras,x=norm.Energy,weights=df)
return {'out':out}
Returns
-------
float
returns the energy where the point of inflection of df happens.
"""
energy_diff=np.diff(energy)
df_diff=np.diff(df)
slope=(df_diff/energy_diff)
slope_min=np.amin(-1*slope)
index=np.where(-slope_min-slope==0)[0]
return energy[index].item()
#make parameters for the deconvolution of known
arctan_mod=StepModel(form='atan',prefix='arctan_')
paras=arctan_mod.make_params()
#construct the model with 5 arctangents and 5 Lorentzians
atan2=StepModel(form='atan',prefix='atan2_')
atan3=StepModel(form='atan',prefix='atan3_')
atan4=StepModel(form='atan',prefix='atan4_')
atan5=StepModel(form='atan',prefix='atan5_')
atan6=StepModel(form='atan',prefix='atan6_')
lor2=LorentzianModel(prefix='l2_')
lor3=LorentzianModel(prefix='l3_')
lor4=LorentzianModel(prefix='l4_')
lor5=LorentzianModel(prefix='l5_')
lor6=LorentzianModel(prefix='l6_')
model=atan2+atan3+atan4+atan5+atan6+lor2+lor3+lor4+lor5+lor6
model.set_param_hint('l2_amplitude', min=0.0)
model.set_param_hint('l3_amplitude', min=0.0)
model.set_param_hint('l4_amplitude', min=0.0)
b.append(i + 1)
for j in b:
if j not in b_new:
b_new.append(j)
funccenter = funccenter[b_new]
## First fitting attempt
gaussnum = len(funccenter)
funcnum = gaussnum
# initial guess x0F, lower bound lb, upper bound up
x0f = np.zeros((funcnum, 4))
lb = np.zeros((funcnum, 4))
ub = np.zeros((funcnum, 4))
# initial guess for error function
step1 = StepModel(form='arctan', prefix='step1_')
# pars.update(step2.guess(y,x=x))
pars = step1.make_params()
pars['step1_center'].set(e0 + 6, min=e0 + 3, max=e0 + 8)
pars['step1_amplitude'].set(0.5, min=0.1, max=1)
pars['step1_sigma'].set(0.5, min=0.3, max=0.8)
mod = step1
# initial guess for gaussian
x0f[:funcnum, 2] = funccenter[:]
for n0 in range(0, funcnum):
x0f[n0, 0:2] = [0.5, 0.5]
lb[n0, :] = [0.2, 0.2, x0f[n0, 2] - 1, 0]
ub[n0, :] = [3, 0.7, x0f[n0, 2] + 1, 0.1]
gauss = GaussianModel(prefix='g%s_' % int(n0 + 1))
pars.update(gauss.make_params())
pars['g%s_amplitude' % int(n0 + 1)].set(x0f[n0][0],
min=lb[n0][0],
max=ub[n0][0])
