pyepr.ResonatorProfileAnalysis¶
- class pyepr.ResonatorProfileAnalysis(dataset, f_lims=(32, 36), attentuator=0, R_limit=0.2, bounds=None, p0=None, **kwargs)¶
Analysis and calculation of resonator profiles.
Fitting fuction:
\[\nu(t) = a \cos(2\pi f (t - x_0)) e^{-(t - x_0)/ au} + k\]where \(a\) is the amplitude, \(f\) is the nutation frequency, \(\tau\) is the decay time, \(x_0\) is the offset and \(k\) is a constant offset. The fit is performed for each frequency in the dataset. The fit is performed using the curve_fit function from scipy.optimize.
- Parameters:
- datasetxr.xarray
The dataset containing the nutations. It must have both a ‘LO’ axis and a ‘pulse0_tp’ axis.
- f_limstuple, optional
The frequency limits of the resonator profile, by default (33,35)
- attenuator: int
The value of the main attentuator in dB, by default 0
- R_limit: float, optional
The R^2 limit for extracting fits, by default 0.2
- boundstuple, optional
The bounds for the fit in the form ([lower bounds],[upper bounds]), by default None. If not given the bounds are set to ([5e-3,10,0,-1,-5],[0.3,2000,2,1,5])
- p0list, optional
The initial guess for the fit, by default None. If not given the guess is set to [50e-3,150,1,0]
- n_files¶
- t¶
- f_lims = (32, 36)¶
- attenuator = 0¶
- process_nutations(noisedensity=None, threshold=2, nfft=1000, **kwargs)¶
Uses a power series to extract the resonator profile.
- Parameters:
- noisedensitytuple, optional
If not given the first trace is assumed to be so far off resonance that it is just noise.
- nfft: int, optional
The length of the fft to be used, zero padded if requred, default is 1000.
- threshold: int, optional
The multiples above the noise a single must be to not be excluded, default is 2.
- Returns:
- prof_data: np.ndarray
The resonator profile, give in nutation frequency (GHz)
- prof_frqs: np.ndarray
The frequency axis in GHz
- Parameters:
noisedensity (float)
threshold (int)
nfft (int)
- _process_fit(R_limit=0.5, mask=None, debug=False)¶
Processes the nutation data by fitting a cosine function to each frequency in the dataset.
Function used for fitting: .. math:: nu(t) = a cos(2pi f (t - x_0)) e^{-(t - x_0)/ au} + k where \(a\) is the amplitude, \(f\) is the nutation frequency, \(\tau\) is the decay time, \(x_0\) is the offset and \(k\) is a constant offset.
- Parameters:
- R_limitfloat, optional
The R^2 limit for extracting fits, by default 0.5
- masknp.ndarray, optional
A mask to apply to the dataset, by default None. If not given the whole dataset is used. The mask is applied along the ‘pulse0_tp’ axis, so it should be a 1D array
- debugbool, optional
If True all fits that are below the R_limit are plotted, by default False
- fit(f_diff_threshold=2, cores=1, multi_mode=False, fc_guess=None)¶
Fit the resonator profile with a sum of lorentzians.
- Parameters:
- f_diff_thresholdfloat, optional
The difference between two peaks at which they will be merged into one, by default 0.03
- plot(fieldsweep=None, axs=None, fig=None)¶
plot.
- Parameters:
- fieldsweepFieldSweepAnalysis, optional
Overlays the FieldSweep if provided, by default None
- axsmatplotlib.Axes, optional
Axes to plot on, by default None
- figmatplotlib.Figure, optional
Figure to plot on, by default None
- Returns:
- Matplotlib.Figure
matplotlib figure object
- classmethod _dummy_create(freq_axis, nu_max, Q, fc)¶
Creates a dummy resonator profile for testing purposes.
- Parameters:
- freq_axisnp.ndarray
The frequency axis of the resonator profile.
- nu_maxfloat
The maximum nutation frequency.
- Qfloat
The quality factor of the resonator.
- fcfloat
The center frequency of the resonator.
- Parameters:
freq_axis (numpy.ndarray)
nu_max (float)
Q (float)
fc (float)