'buffer', 'raw_data', transform_name='space_data') sync = np.fromfile(join(data_folder, 'Sync.bin'), dtype=np.uint16).astype(np.int32) sync -= sync.min() video_frame = 0 video_file = join(data_folder, 'Video.avi') seq_v.image_sequence(globals(), 'video_frame', 'video_file') # Create some arrays and constants relating to the events camera_pulses, beam_breaks, sounds = \ sync_funcs.get_time_points_of_events_in_sync_file(data_folder, clean=True, cam_ttl_pulse_period= const.CAMERA_TTL_PULSES_TIMEPOINT_PERIOD) points_per_pulse = np.mean(np.diff(camera_pulses)) camera_frames_in_video = csv_funcs.get_true_frame_array(data_folder) time_point_of_first_video_frame = camera_pulses[camera_frames_in_video][0] def time_point_to_frame(x): return sync_funcs.time_point_to_frame(time_point_of_first_video_frame, camera_frames_in_video, points_per_pulse, x) tr.connect_repl_var(globals(), 'pointer', 'video_frame', 'time_point_to_frame')
sampling_freq = 30000 cam_ttl_pulse_period = 122 reward_sound_max_duration = 3000 # Generate the pickles of DataFrames for most of the csv event files for event_type in sync_funcs.event_types: exec(r'{} = sync_funcs.get_dataframe_of_event_csv_file(data_folder, event_type, 122)'.format(event_type)) print('Done with the {} event'.format(event_type)) # ---------------------------------- # Load the pre generated DataFrames for the event CSVs event_dataframes = ns_funcs.load_events_dataframes(events_folder, sync_funcs.event_types) # Create some arrays and constants relating to the events camera_pulses, beam_breaks, sounds = \ sync_funcs.get_time_points_of_events_in_sync_file(data_folder, clean=True, cam_ttl_pulse_period=cam_ttl_pulse_period) points_per_pulse = np.mean(np.diff(camera_pulses)) camera_frames_in_video = csv_funcs.get_true_frame_array(data_folder) time_point_of_first_video_frame = camera_pulses[camera_frames_in_video][0] # ---------------------------------- sound_bit_on = sync & 8 start=0 step = 5000 sv.graph_range(globals(), 'start', 'step', 'sound_bit_on')