def fit_S21_resonance_complex(*args,**kw):
'''
input:
x,y
returns fitresults with:
fr[0]=(A,f0,Q,s21min,slope,theta)
'''
#print 'no slope'
if type(args[0])==Data:
dat = args[0]
xy = dat.get_data()
x= xy[:,0]
y= xy[:,1]
else:
x=args[0]
y=args[1]
silent = kw.pop('silent',True)
p0i=param_estimator_complex(x,y)
if not silent:
print('init guess: ',p0i)
pars = S21p0i2paramsc(x,p0i)
fr = fit.fitxy(pars,y, fn.disp_hanger_S21_complex_sloped, ret_fit=True)
if not silent:
fit.print_fitres(pars)
fitpars=params2tuple(pars)
fitparserr = paramserr2tuple(pars)
output = {'fitpars':fitpars,'fitparserr':fitparserr,'fit':fr[1],'fitparsobj':pars,'parsinit':p0i}
return output
def fit_lorentzian(x, y, plot=True):
y0 = (y[0] + y[-1]) / 2.
peak = np.abs((np.max(y) - y0)) > np.abs((np.min(y) - y0))
print('peak = ', peak, y0, np.max(y), np.min(y))
if peak:
A = np.max(y) - y0
f0 = x[y[:].tolist().index(max(y[:]))]
find_y2 = (y - y0) > A / 2
else:
A = np.min(y) - y0
f0 = x[y[:].tolist().index(min(y[:]))]
find_y2 = (y - y0) < A / 2
ind_df = find_y2.tolist().index(True)
df = 2 * np.abs(x[ind_df] - f0)
pars = fit.Parameters()
pars.add('f', value=x, vary=False)
pars.add('A', value=A)
pars.add('f0', value=f0)
pars.add('df', value=df)
pars.add('y0', value=y0)
fit.print_fitres(pars)
fit.fitxy(pars, y, fn.Lorentzian)
fit.print_fitres(pars)
yfit = fn.Lorentzian(pars)
if plot:
plt.plot(x,
yfit,
label='fit: f0 = %.4f, df = %.1f' % (pars['f0'].value, 1e3 *
(pars['df'].value)))
plt.legend()
return pars, fn.Lorentzian(pars)
def fit_lorentzian(x,y, plot=True):
y0 = (y[0]+y[-1])/2.
peak = np.abs((np.max(y)-y0)) > np.abs((np.min(y)-y0))
print('peak = ',peak, y0, np.max(y),np.min(y))
if peak:
A = np.max(y)-y0
f0 = x[y[:].tolist().index(max(y[:]))]
find_y2 = (y-y0)>A/2
else:
A = np.min(y)-y0
f0 = x[y[:].tolist().index(min(y[:]))]
find_y2 = (y-y0)<A/2
ind_df = find_y2.tolist().index(True)
df = 2*np.abs(x[ind_df] - f0)
pars = fit.Parameters()
pars.add('f',value = x, vary = False)
pars.add('A',value = A)
pars.add('f0',value = f0)
pars.add('df',value = df)
pars.add('y0',value = y0)
fit.print_fitres(pars)
fit.fitxy(pars,y,fn.Lorentzian)
fit.print_fitres(pars)
yfit = fn.Lorentzian(pars)
if plot:
plt.plot(x,yfit, label = 'fit: f0 = %.4f, df = %.1f'%(pars['f0'].value,1e3*(pars['df'].value)))
plt.legend()
return pars, fn.Lorentzian(pars)
def fit_S21_resonance(*args, **kw):
'''
input:
x,y
returns fitresults with:
fr[0]=(A,f0,Q,s21min,slope,theta)
'''
# print 'no slope'
if type(args[0]) == Data:
dat = args[0]
xy = dat.get_data()
x = xy[:, 0]
y = xy[:, 1]
else:
x = args[0]
y = np.abs(args[1])
p0i = param_estimator(x, y)
silent = kw.pop('silent', True)
if not silent:
print('init guess: ', p0i)
pars = S21p0i2params(x, p0i)
# print 'init_guess', p0i
fr = fit.fitxy(pars, y, fn.S21_resonance_sloped, ret_fit=True)
if not silent:
fit.print_fitres(pars)
fitpars = params2tuple(pars)
fitparserr = paramserr2tuple(pars)
output = {
'fitpars': fitpars,
'parserr': fitparserr,
'fit': fr[1],
'fitparsobj': pars,
'parsinit': p0i
}
return output