'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')
