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

.. py:module:: tonic.functional.to_frame


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


Functions
~~~~~~~~~

.. autoapisummary::

   tonic.functional.to_frame.to_frame_numpy



.. py:function:: to_frame_numpy(events, sensor_size, time_window=None, event_count=None, n_time_bins=None, n_event_bins=None, overlap=0.0, include_incomplete=False, start_time=None, end_time=None)

   Accumulate events to frames by slicing along constant time (time_window), constant number of
   events (event_count) or constant number of frames (n_time_bins / n_event_bins).

   :param events: ndarray of shape [num_events, num_event_channels]
   :param sensor_size: size of the sensor that was used [W,H,P]
   :param time_window: window length in us.
   :type time_window: None
   :param event_count: number of events per frame.
   :type event_count: None
   :param n_time_bins: fixed number of frames, sliced along time axis.
   :type n_time_bins: None
   :param n_event_bins: fixed number of frames, sliced along number of events in the recording.
   :type n_event_bins: None
   :param overlap: overlap between frames defined either in time in us, number of events or number of bins.
   :type overlap: 0.
   :param include_incomplete: if True, includes overhang slice when time_window or event_count is specified. Not valid for bin_count methods.
   :type include_incomplete: False

   :returns: numpy array with dimensions (TxPxHxW)