def _update_fit(self):
if self.perform_fit:
N = self.number_of_resonances
if N != 'auto':
N = int(N)
try:
p = fitting.fit_multiple_lorentzians(self.frequency,
self.counts,
N,
threshold=self.threshold *
0.01)
except Exception:
logging.getLogger().debug('ODMR fit failed.', exc_info=True)
p = np.nan * np.empty(4)
else:
p = np.nan * np.empty(4)
self.fit_parameters = p
self.fit_frequencies = p[1::3]
self.fit_line_width = p[2::3]
N = len(p) / 3
contrast = np.empty(N)
c = p[0]
pp = p[1:].reshape((N, 3))
for i, pi in enumerate(pp):
a = pi[2]
g = pi[1]
A = np.abs(a / (np.pi * g))
if a > 0:
contrast[i] = 100 * A / (A + c)
else:
contrast[i] = 100 * A / c
self.fit_contrast = contrast
def update_fit(self):
if self.perform_fit:
N = self.number_of_resonances
if N != 'auto':
N = int(N)
try:
p = fitting.fit_multiple_lorentzians(self.frequency,
self.counts,
N,
threshold=self.threshold *
0.01)
except Exception:
logging.getLogger().debug('ODMR fit failed.', exc_info=True)
p = np.nan * np.empty(4)
else:
p = np.nan * np.empty(4)
self.fit_parameters = p
self.fit_frequencies = p[1::3]
self.fit_line_width = p[2::3].mean()
self.fit_contrast = -100 * p[3::3].mean() / (np.pi * p[2::3].mean() *
p[0])