pyepr.ResonatorProfileAnalysis
==============================

.. py: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:

   .. math:: \nu(t) = a \cos(2\pi f (t - x_0)) e^{-(t - x_0)/      au} + k

   where :math:`a` is the amplitude, :math:`f` is the nutation frequency,
   :math:`\tau` is the decay time, :math:`x_0` is the offset and :math:`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:

       **dataset** : xr.xarray
           The dataset containing the nutations. It must have both a 'LO' axis
           and a 'pulse0_tp' axis.

       **f_lims** : tuple, 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

       **bounds** : tuple, 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])

       **p0** : list, optional
           The initial guess for the fit, by default None. If not given the guess is set to [50e-3,150,1,0]














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   .. py:attribute:: n_files


   .. py:attribute:: t


   .. py:attribute:: f_lims
      :value: (32, 36)



   .. py:attribute:: attenuator
      :value: 0



   .. py:method:: process_nutations(noisedensity = None, threshold = 2, nfft = 1000, **kwargs)

      
      Uses a power series to extract the resonator profile.


      :Parameters:

          **noisedensity** : tuple, 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











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   .. py:method:: _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(2\pi f (t - x_0)) e^{-(t - x_0)/      au} + k
      where :math:`a` is the amplitude, :math:`f` is the nutation frequency,
      :math:`\tau` is the decay time, :math:`x_0` is the offset and :math:`k` is a constant offset.

      :Parameters:

          **R_limit** : float, optional
              The R^2 limit for extracting fits, by default 0.5

          **mask** : np.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

          **debug** : bool, optional
              If True all fits that are below the R_limit are plotted, by default False














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   .. py:method:: 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_threshold** : float, optional
              The difference between two peaks at which they will be merged into one, by default 0.03














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   .. py:method:: plot(fieldsweep=None, axs=None, fig=None)

      
      plot. 


      :Parameters:

          **fieldsweep** : FieldSweepAnalysis, optional
              Overlays the FieldSweep if provided, by default None

          **axs** : matplotlib.Axes, optional
              Axes to plot on, by default None

          **fig** : matplotlib.Figure, optional
              Figure to plot on, by default None



      :Returns:

          Matplotlib.Figure
              matplotlib figure object











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   .. py:method:: _dummy_create(freq_axis, nu_max, Q, fc)
      :classmethod:


      
      Creates a dummy resonator profile for testing purposes.


      :Parameters:

          **freq_axis** : np.ndarray
              The frequency axis of the resonator profile.

          **nu_max** : float
              The maximum nutation frequency.

          **Q** : float
              The quality factor of the resonator.

          **fc** : float
              The center frequency of the resonator.














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