:py:mod:`antibunching`
======================

.. py:module:: antibunching

.. autoapi-nested-parse::

   Module for performing antibunching-type calculations.

   Currently only performs simple second-order correlation.
   More advanced functionality might be added in the future.

   Bertus van Heerden,
   University of Pretoria,
   2023



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

Classes
~~~~~~~

.. autoapisummary::

   antibunching.AntibunchingAnalysis




Attributes
~~~~~~~~~~

.. autoapisummary::

   antibunching.__docformat__
   antibunching.logger


.. py:data:: __docformat__
   :value: "'NumPy'"

   

.. py:data:: logger
   :value: "'antibunching'"

   

.. py:class:: AntibunchingAnalysis(particle)


   Performs second-order correlation and stores the result.

   Parameters:
   -----------
   particle : smsh5.particle
       particle object to perform analysis on.

   .. py:attribute:: _particle

      

   .. py:attribute:: uuid

      

   .. py:attribute:: corr_hist
      :value: 'None'

      

   .. py:attribute:: corr_bins
      :value: 'None'

      

   .. py:attribute:: corr_events
      :value: 'None'

      

   .. py:attribute:: has_corr
      :value: 'False'

      

   .. py:method:: correlate_particle(difftime: float, window: float, binsize: float)

      Calculate second-order correlation for this particle.

      Mainly a wrapper around static method `correlate_times`.
      Stores result in class attributes `corr_bins`, `corr_hist` and `corr_events` and sets `has_corr` to `True`.

      Arguments:
      ----------
      difftime : float
          time difference between channels (ch. 1 - ch. 2) in ns
      window : float
          time window for correlation in ns
      binsize : float
          bin size for correlation histogram in ns


   .. py:method:: correlate_times(abstimes1, abstimes2, microtimes1, microtimes2, difftime=0.0, window=500.0, binsize=0.5)
      :staticmethod:

      Calculate second-order correlation based on time-tagged time-resolved photon data.

      The function is a simple nested loop that runs through every photon within a certain window and
      checks for coincidences. Each coincidence gets a relative time, which are all put in a histogram
      to get the second-order correlation. Before the calculation, the arrival times are corrected
      based on the difftime parameter, which accounts for possible delay between two TCSPC cards.

      Arguments:
      ----------
      abstimes1 : 1D array
          absolute times for channel 1 in ns
      abstimes2 : 1D array
          absolute times for channel 2 in ns
      microtimes1 : 1D array
          micro times for channel 1 in ns
      microtimes2 : 1D array
          micro times for channel 2 in ns
      difftime : float
          time difference between channels (ch. 1 - ch. 2) in ns
      window : float
          time window for correlation in ns
      binsize : float
          bin size for correlation histogram in ns

      Returns:
      --------
      bins : 1D array
          correlation histogram bins
      corr : 1D array
          correlation histogram values
      events : 1D array
          difftimes used to construct histogram, returned in case rebinning is needed.


   .. py:method:: rebin_corr(window, binsize)