Labber examples

import quantum_fitter as qf

path = 'Z:/KianG/Resonator_Fitting_Test_Data/Al_on_sapphire_chip3/Power_sweeps_all_res_40dB_att_TWPA_on.hdf5'

qf.fit_all_labber_resonator(file_loc=path,
                            frequency=6.4972218e9,
                            power=list(range(-50, -40, 2)),  # Doesn't include 8
                            attenuation=40,
                            plot_all=True)

-42

C:\Users\rbcma\anaconda3\envs\fitter\lib\site-packages\scipy\signal\_savitzky_golay.py:333: ComplexWarning: Casting complex values to real discards the imaginary part
  x = x.astype(np.float64)

-44

C:\Users\rbcma\anaconda3\envs\fitter\lib\site-packages\scipy\signal\_savitzky_golay.py:333: ComplexWarning: Casting complex values to real discards the imaginary part
  x = x.astype(np.float64)

-46

C:\Users\rbcma\anaconda3\envs\fitter\lib\site-packages\scipy\signal\_savitzky_golay.py:333: ComplexWarning: Casting complex values to real discards the imaginary part
  x = x.astype(np.float64)

-48

C:\Users\rbcma\anaconda3\envs\fitter\lib\site-packages\scipy\signal\_savitzky_golay.py:333: ComplexWarning: Casting complex values to real discards the imaginary part
  x = x.astype(np.float64)

-50

C:\Users\rbcma\anaconda3\envs\fitter\lib\site-packages\scipy\signal\_savitzky_golay.py:333: ComplexWarning: Casting complex values to real discards the imaginary part
  x = x.astype(np.float64)

#import matplotlib.pyplot as plt
#import numpy as np


path = 'Z:/KianG/Resonator_Fitting_Test_Data/Al_on_sapphire_chip3/Power_sweeps_all_res_40dB_att_TWPA_on.hdf5'
t5 = qf.LabberData(filePath=path)

# method with 'lc', 'aw', 'lcaw' or None
t5.pull_data(frequency=6.4972218e9, power=[-28])
t5.fit_data(model='ResonatorModel', method='lc', resonator_plot=True, window=0.05, sigma=0.15, verbose=1)
#plt.show()

[[-2.8000000e+01  6.4972218e+09]]

C:\Users\rbcma\anaconda3\envs\fitter\lib\site-packages\scipy\signal\_savitzky_golay.py:333: ComplexWarning: Casting complex values to real discards the imaginary part
  x = x.astype(np.float64)

[[Model]]
    Model(S21funct)
[[Fit Statistics]]
    # fitting method   = least_squares
    # function evals   = 119
    # data points      = 20002
    # variables        = 8
    chi-square         = 8.6363e-04
    reduced chi-square = 4.3194e-08
    Akaike info crit   = -339177.053
    Bayesian info crit = -339113.824
[[Variables]]
    f_0:       6.49723104 +/- 9.5398e-09 (0.00%) (init = 6.497222)
    Qi:        932.619276 +/- 1.76881908 (0.19%) (init = 518119.8)
    Qe_mag:    220.856382 +/- 0.11551741 (0.05%) (init = 114655.4)
    Qe_theta: -0.67037302 +/- 5.2380e-04 (0.08%) (init = 0)
    A:         0.01903253 +/- 1.4910e-05 (0.08%) (init = 0.01910321)
    alpha:     180.180610 +/- 16.3862554 (9.09%) (init = 0)
    phi1:     -455.682198 +/- 2.52483947 (0.55%) (init = 0)
    phi2:      2963.53586 +/- 16.4044590 (0.55%) (init = 3.007161)
[[Correlations]] (unreported correlations are < 0.100)
    C(phi1, phi2)      = -1.000
    C(A, phi2)         = 0.984
    C(A, phi1)         = -0.984
    C(f_0, Qe_theta)   = -0.835
    C(Qi, Qe_theta)    = 0.557
    C(Qe_mag, A)       = -0.518
    C(Qe_mag, phi1)    = 0.497
    C(Qe_mag, phi2)    = -0.497
    C(Qe_theta, alpha) = 0.496
    C(f_0, Qi)         = -0.495
    C(Qe_theta, A)     = 0.442
    C(f_0, alpha)      = -0.437
    C(Qe_theta, phi2)  = 0.388
    C(Qe_theta, phi1)  = -0.388
    C(Qe_mag, alpha)   = 0.385
    C(Qi, alpha)       = 0.355
    C(Qi, Qe_mag)      = 0.289
    C(f_0, A)          = -0.287
    C(f_0, phi2)       = -0.246
    C(f_0, phi1)       = 0.246
    C(f_0, Qe_mag)     = -0.107
    C(Qi, phi2)        = 0.101
    C(Qi, phi1)        = -0.101

<quantum_fitter._fit.QFit at 0x1e21ce03ac0>