:py:mod:`tonic.functional.to_averaged_timesurface`
==================================================

.. py:module:: tonic.functional.to_averaged_timesurface


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


Functions
~~~~~~~~~

.. autoapisummary::

   tonic.functional.to_averaged_timesurface.to_averaged_timesurface_numpy



.. py:function:: to_averaged_timesurface_numpy(events, sensor_size, cell_size, surface_size, time_window, tau, decay)

   Representation that creates averaged timesurfaces for each event for one recording.

   Taken from the paper
   Sironi et al. 2018, HATS: Histograms of averaged time surfaces for robust event-based object classification
   https://openaccess.thecvf.com/content_cvpr_2018/papers/Sironi_HATS_Histograms_of_CVPR_2018_paper.pdf
   :param cell_size: size of each square in the grid
   :type cell_size: int
   :param surface_size: has to be odd
   :type surface_size: int
   :param time_window: how far back to look for past events for the time averaging. Expressed in microseconds.
   :type time_window: int
   :param tau: time constant to decay events around occuring event with. Expressed in microseconds.
   :type tau: int
   :param decay: can be either 'lin' or 'exp', corresponding to linear or exponential decay.
   :type decay: str

   :returns: array of histograms (numpy.Array with shape (n_cells, n_pols, surface_size, surface_size))