:py:mod:`autodeer.ResPro`
=========================

.. py:module:: autodeer.ResPro



Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   autodeer.ResPro.ResonatorProfileAnalysis



Functions
~~~~~~~~~

.. autoapisummary::

   autodeer.ResPro.ceil
   autodeer.ResPro.floor
   autodeer.ResPro.calc_overlap
   autodeer.ResPro.BSpline_extra
   autodeer.ResPro.optimise_spectra_position



.. py:class:: ResonatorProfileAnalysis(dataset, f_lims=(32, 36))


   

   Analysis and calculation of resonator profiles.


   :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)














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   .. py:attribute:: dataset

      

   .. py:attribute:: n_files

      

   .. py:attribute:: t

      

   .. py:attribute:: f_lims

      

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

      
      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)


   .. 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:function:: ceil(number, decimals=0)


.. py:function:: floor(number, decimals=0)


.. py:function:: calc_overlap(x, func1, func2)

   
   Calcuates the overlap between two functions.


   :Parameters:

       **x** : np.ndarray
           The x axis of the functions

       **func1** : function
           The first function

       **func2** : function
           The second function



   :Returns:

       float
           The overlap between the two functions.











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.. py:function:: BSpline_extra(tck_s)


.. py:function:: optimise_spectra_position(resonator_profile, fieldsweep, verbosity=0)