:py:mod:`autodeer.criteria`
===========================

.. py:module:: autodeer.criteria



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

Classes
~~~~~~~

.. autoapisummary::

   autodeer.criteria.Criteria
   autodeer.criteria.TimeCriteria
   autodeer.criteria.SNRCriteria
   autodeer.criteria.DEERCriteria




Attributes
~~~~~~~~~~

.. autoapisummary::

   autodeer.criteria.log


.. py:data:: log

   

.. py:class:: Criteria(name, test, description = '', end_signal=None)


   
   A class for defining criteria for terminating experiments. This should
   only be subclassed and not used directly.
















   ..
       !! processed by numpydoc !!
   .. py:attribute:: name

      

   .. py:attribute:: description

      

   .. py:attribute:: test

      

   .. py:attribute:: end_signal

      

   .. py:method:: __add__(__o)



.. py:class:: TimeCriteria(name, end_time, description = '', night_hours = None, **kwargs)


   Bases: :py:obj:`Criteria`

   A class for defining criteria for terminating experiments. This should
   only be subclassed and not used directly.


   Criteria testing for a specific finishing time. The finishing time 
   is given as absolute time in the locale of the computer, it is *not* 
   how the long the measurment continues for. 


   :Parameters:

       **name** : str
           Name of the criteria

       **end_time** : float
           Finishing time in seconds since epoch

       **description** : str, optional
           A description of the criteria, by default None

       **night_hours** : tuple, optional
           A tuple of two integers specifying the start and end of night hours.
           The criteria will always return False during these hours, by default None














   ..
       !! processed by numpydoc !!


.. py:class:: SNRCriteria(SNR_target, description = '', verbosity=0, **kwargs)


   Bases: :py:obj:`Criteria`

   A class for defining criteria for terminating experiments. This should
   only be subclassed and not used directly.


   Criteria testing for signal to noise ratio. This checks the SNR of 
   the normalised absolute data using the deerlab SNR noise estimation
   which is based on the work by Stoher et. al. [1]


   :Parameters:

       **name** : str
           _description_

       **SNR_target** : int
           The mimimum SNR value.

       **description** : str, optional
           _description_, by default None












   .. rubric:: References

   [1] F. Stoehr, R. White, M. Smith, I. Kamp, R. Thompson, D. Durand,
   W. Freudling, D. Fraquelli, J. Haase, R. Hook, T. Kimball, M. Kummel,
   K. Levay, M. Lombardi, A. Micol, T. Rogers DERSNR: A Simple & General
   Spectroscopic Signal-to-Noise Measurement Algorithm Astronomical Data
   Analysis Software and Systems XVII, ASP Conference Series, Vol. 30,
   2008, p5.4

   .. only:: latex

      


   ..
       !! processed by numpydoc !!


.. py:class:: DEERCriteria(mode='Speed', model=None, verbosity=0, update_func=None, **kwargs)


   Bases: :py:obj:`Criteria`

   A class for defining criteria for terminating experiments. This should
   only be subclassed and not used directly.


   Criteria for running DEER experiments.


   :Parameters:

       **tau1** : _type_
           _description_

       **tau2** : _type_
           _description_

       **tau3** : _type_, optional
           _description_, by default None

       **mode** : str, optional
           _description_, by default "Speed"



   :Returns:

       _type_
           _description_











   ..
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   .. py:attribute:: name
      :value: "'DEERCriteria'"

      

   .. py:attribute:: description
      :value: "'Criteria for terminating DEER experiments.'"