pyepr.CarrPurcellAnalysis
=========================

.. py:class:: pyepr.CarrPurcellAnalysis(dataset, sequence = None)

   

   Analysis and calculation of Carr Purcell decay. 


   :Parameters:

       **dataset**
           _description_

   :Attributes:

       **axis** : xr.DataArray
           The time axis representing the interpulse delay.













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   .. py:attribute:: data


   .. py:attribute:: dataset


   .. py:attribute:: noise


   .. py:method:: fit(type = 'mono', **kwargs)

      
      Fit the experimental CP decay


      :Parameters:

          **type** : str, optional
              Either a mono or double exponential decay model, by default "mono"














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   .. py:method:: plot(norm = True, ci=50, axs=None, fig=None)

      
      Plot the carr purcell decay with fit, if avaliable.


      :Parameters:

          **norm** : bool, optional
              Normalise the fit to a maximum of 1, by default True

          **ci** : int, optional
              The percentage confidence interval to plot, by default 50



      :Returns:

          Figure
              The figure.











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   .. py:method:: check_decay(level=0.1)

      
      Checks that the data has decayed by over 90% in the first half, and less than 90% in the first quarter.


      :Parameters:

          **level** : float, optional
              The level to check the decay, by default 0.05



      :Returns:

          int
              0 if both conditions are met, 1 if a longer decay is needed, and -1 if the decay is too long.











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   .. py:method:: find_optimal(SNR_target, target_time, target_step, averages=None)

      
      Calculate the optimal inter pulse delay for a given total measurment
      time. 


      :Parameters:

          **SNR_target: float,**
              The Signal to Noise ratio target.

          **target_time** : float
              The target time in hours

          **target_shrt** : float
              The shot repettition time of target in seconds

          **target_step: float**
              The target step size in ns.

          **averages** : int, optional
              The total number of shots taken, by default None. If None, the
              number of shots will be calculated from the dataset.



      :Returns:

          float
              The calculated optimal time in us











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   .. py:method:: __call__(x, norm=True, SNR=False, source=None)

      
      Evaluate the fit or data at a given x value.


      :Parameters:

          **x** : float
              The x value to evaluate the data at.

          **norm** : bool, optional
              Normalise the data to the maximum, by default True

          **SNR** : bool, optional
              Return the SNR_per_sqrt(shot) for this data point, by default False
              If True, the data is normalised to the maximum of the data.

          **source** : str, optional
              The source of the data, either 'fit' or 'data', by default None
              If None, the source is determined by the presence of a fit result.














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