def hanger_func_complex_SI_Guess(data, f, fit_window=None):
## This is complete garbage, just to get some return value
xvals = f
abs_data = np.abs(data)
peaks = a_tools.peak_finder(xvals, abs_data)
# Search for peak
if peaks['dip'] is not None: # look for dips first
f0 = peaks['dip']
amplitude_factor = -1.
elif peaks['peak'] is not None: # then look for peaks
f0 = peaks['peak']
amplitude_factor = 1.
else: # Otherwise take center of range
f0 = np.median(xvals)
amplitude_factor = -1.
min_index = np.argmin(abs_data)
max_index = np.argmax(abs_data)
min_frequency = xvals[min_index]
max_frequency = xvals[max_index]
amplitude_guess = max(dm_tools.reject_outliers(abs_data))
# Creating parameters and estimations
S21min = (min(dm_tools.reject_outliers(abs_data)) /
max(dm_tools.reject_outliers(abs_data)))
Q = f0 / abs(min_frequency - max_frequency)
Qe = abs(Q / abs(1 - S21min))
guess_dict = {'f0': {'value': f0*1e-9,
'min': min(xvals)*1e-9,
'max': max(xvals)*1e-9},
'A': {'value': amplitude_guess},
'Qe': {'value': Qe,
'min': 1,
'max': 50e6},
'Ql': {'value': Q,
'min': 1,
'max': 50e6},
'theta': {'value': 0,
'min': -np.pi/2,
'max': np.pi/2},
'alpha': {'value':0,
'vary':True},
'phi_0': {'value':0,
'vary':True},
'phi_v': {'value':0,
'vary':True}}
return guess_dict
def SlopedHangerFuncAmplitudeGuess(data, f, fit_window=None):
xvals = f
peaks = a_tools.peak_finder(xvals, data)
# Search for peak
if peaks['dip'] is not None: # look for dips first
f0 = peaks['dip']
amplitude_factor = -1.
elif peaks['peak'] is not None: # then look for peaks
f0 = peaks['peak']
amplitude_factor = 1.
else: # Otherwise take center of range
f0 = np.median(xvals)
amplitude_factor = -1.
min_index = np.argmin(data)
max_index = np.argmax(data)
min_frequency = xvals[min_index]
max_frequency = xvals[max_index]
amplitude_guess = max(dm_tools.reject_outliers(data))
# Creating parameters and estimations
S21min = (min(dm_tools.reject_outliers(data)) /
max(dm_tools.reject_outliers(data)))
Q = f0 / abs(min_frequency - max_frequency)
Qe = abs(Q / abs(1 - S21min))
guess_dict = {'f0': {'value': f0*1e-9,
'min': min(xvals)*1e-9,
'max': max(xvals)*1e-9},
'A': {'value': amplitude_guess},
'Q': {'value': Q,
'min': 1,
'max': 50e6},
'Qe': {'value': Qe,
'min': 1,
'max': 50e6},
'Qi': {'expr': 'abs(1./(1./Q-1./Qe*cos(theta)))',
'vary': False},
'Qc': {'expr': 'Qe/cos(theta)',
'vary': False},
'theta': {'value': 0,
'min': -np.pi/2,
'max': np.pi/2},
'slope': {'value':0,
'vary':True}}
return guess_dict