def rabiFit(t, x):
from analysis.fitting import Cosinus_phase, Cosinus_phaseEstimator, fit
parameters = Cosinus_phaseEstimator(t, x)
fitParameters = fit(t, x, Cosinus_phase, parameters)
print "Period = %f and frequency = %f " % (fitParameters[2],
fitParameters[2]**(-1) * 1e9)
return Cosinus_phase(*fitParameters)
def fit_z(self):
if self.fit_method_z == 'Maximum':
zp = self.Z[self.data_z.argmax()]
self.zfit = zp
return zp
elif self.fit_method_z == 'Gaussian':
x = self.Z
y = self.data_z
def GaussianEstimator(x, y):
n = len(x)
dx = np.abs(x[1] - x[0])
c = y.min()
center = x[y.argmax()]
amp = y.max() - c
integral = (y.sum() - n * c) * dx
width = integral / amp * (2 * np.pi)**-0.5
return c, amp, center, width
p = fit(x, y, Gaussian, GaussianEstimator)
self.zfit_p = p
offset, amp, center, width = p
self.zfit = center
self.zfit_ind = center
self.zfit_val = Gaussian(*p)(center)
self.data_fit_z = Gaussian(*p)(x)
elif self.fit_method_z == 'Spline':
from scipy.interpolate import UnivariateSpline
sort_map = self.Z.argsort()
x = self.Z[sort_map]
y = self.data_z[sort_map]
x1 = x[::2]
y1 = y[::2]
x2 = x[1::2]
y2 = y[1::2]
s1 = UnivariateSpline(x1, y1, k=3, s=self.smoothing)
s2 = UnivariateSpline(x2, y2, k=3, s=self.smoothing)
ind1 = s1(x1).argmax()
ind2 = s2(x2).argmax()
x0 = 0.5 * (x1[ind1] + x2[ind2])
y0 = 0.5 * (y1[ind1] + y2[ind2])
center = x0
self.zfit = x0
self.zfit_ind = x0
self.zfit_val = y0
self.data_fit_z = 0.5 * (s1(self.Z) + s2(self.Z))
else:
raise ValueError('Unknown fit method')
return center
def hahnFit(t, x):
from analysis.fitting import fit
parameters = Cosinus_phaseEstimator(t, x)
fitParameters = fit(t, x, cosinusExp, parameters)
print "T1 = %f " % (fitParameters[-1])
return cosinusExp(*fitParameters)