data_sth = raw_data[:370, :]
buffer = 500
pointer = 20000
def space_data(dat):
dat = dat.astype(np.float32)
result = np.array(
([dat[i, :] + (100 * i) for i in np.arange(dat.shape[0])]))
return result
seq_v.graph_range(globals(),
'pointer',
'buffer',
'data_cor',
transform_name='space_data')
seq_v.graph_range(globals(),
'pointer',
'buffer',
'data_hyp',
transform_name='space_data')
seq_v.graph_range(globals(),
'pointer',
'buffer',
'data_th',
transform_name='space_data')
seq_v.graph_range(globals(),
'pointer',
rec1_part2_wav_filename = join(sounds_basic_folder, r'2019_05_06 Recording 1', r'T2019-05-06_17-18-20_002.wav') rec1_both_sounds_wav_filename = join(sounds_basic_folder, r'2019_05_06 Recording 1', r'T2019-05-06_16-47-55_full.wav') rec2_wav_filename = join(sounds_basic_folder, r'2019_05_06 Recording 2', r'T2019-05-06_17-41-22_008.wav') # ---------------------------------------------------------------------------------------------------------------------- # SYNC STUFF sync_sampling_freq = const.SAMPLING_FREQUENCY sync1 = np.fromfile(rec1_sync_filename, dtype=np.uint16).astype(np.int32) sync2 = np.fromfile(rec2_sync_filename, dtype=np.uint16).astype(np.int32) sync = np.fromfile(full_sync_filename, dtype=np.uint16).astype(np.int32) sync -= sync.min() sync_diff = np.diff(sync) ''' # Have a quick look sync_start = 2160000 sync_step = 60000 sv.graph_range(globals(), 'sync_start', 'sync_step', 'sync') sv.graph_range(globals(), 'sync_start', 'sync_step', 'sync_diff') ''' # ---------------------------------------------------------------------------------------------------------------------- # SOUND STUFF rec1_part1 = wavfile.read(rec1_part1_wav_filename) rec1_part2 = wavfile.read(rec1_part2_wav_filename) rec2 = wavfile.read(rec2_wav_filename) sound_sampling_freq = rec1_part1[0]
# 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')
sound_durations = np.array([s[1] - s[0] for s in sounds])
reward_sounds_mask = sound_durations < reward_sound_max_duration
reward_sounds = sounds[reward_sounds_mask]
trial_sounds_mask = sound_durations > reward_sound_max_duration
trial_sounds = sounds[trial_sounds_mask]
trial_start_events = pd.read_pickle(join(events_folder, sync_funcs.event_types[9]+".pkl"))
trial_end_events = pd.read_pickle(join(events_folder, sync_funcs.event_types[8]+".pkl"))
trial_end_events['AmpTimePoints'] - np.array(trial_sounds)[:, 1]
trial_end_events[trial_end_events['Result']=='Food']['AmpTimePoints'] - np.array(reward_sounds)[:, 1]
'Analysis', '\Lfp', 'Downsampling', 'Amplifier_LFPs_Downsampled_x4.npy')
np.save(ds_numpy_filename, downsampled_lfp)
downsampled_lfp = np.load(ds_numpy_filename)
'''
factor = 4 # possible 3, 4, 5, 10
ds_numpy_filename = join(const.base_save_folder, const.rat_folder,
const.date_folders[date], 'Analysis', 'Lfp',
'Downsampling',
'Amplifier_LFPs_Downsampled_x{}.npy'.format(factor))
downsampled_lfp = np.load(ds_numpy_filename)
# ----------------------------------------------------------------------------------------------------------------------
# HAVE A LOOK AT THE RAW DATA
# ----------------------------------------------------------------------------------------------------------------------
'''
def space_data(dat):
dat = dat.astype(np.float32)
result = np.array(([dat[i, :] + (500*i) for i in np.arange(dat.shape[0])]))
return result
timepoint = 100000
buffer = 10000
seq_v.graph_range(globals(), 'timepoint', 'buffer', 'raw_lfp', transform_name='space_data')
'''
# ----------------------------------------------------------------------------------------------------------------------
# TESTING DIFFERENT SPECTRAL DENSITY METHODS
# ----------------------------------------------------------------------------------------------------------------------
timepoint = 0
data_thal = raw_data[:4*120, :]
data_thal_bottom = raw_data[:2*120, :]
data_ca3 = raw_data[4*120:6*120, :]
data_ca1 = raw_data[6*120:8*120, :]
data_cort = raw_data[8*120:, :]
data_half = raw_data[:600, :]
def space(data):
return cdt.space_data(data, 100)
sv.graph_range(globals(), 'timepoint', 'timepoint_step', 'data_thal', transform_name='space')
sv.graph_range(globals(), 'timepoint', 'timepoint_step', 'data_ca1', transform_name='space')
sv.graph_range(globals(), 'timepoint', 'timepoint_step', 'data_half', transform_name='space')
frame = 265
tp = 172000
start = 172200
end = 173200
start = 793200
end = start + 1000
downsampled_lfp = np.load(ds_numpy_filename)
def space_data(dat):
dat = dat.astype(np.float32)
result = np.array(
([dat[i, :] + (500 * i) for i in np.arange(dat.shape[0])]))
return result
timepoint = 100000
buffer = 10000
seq_v.graph_range(globals(),
'timepoint',
'buffer',
'downsampled_lfp',
transform_name='space_data')
# ----------------------------------------------------------------------------------------------------------------------
# RUN THE emd.py TO GENERATE THE IMFS
# ----------------------------------------------------------------------------------------------------------------------
'''
Parameters used
result_dtype = np.int16
num_imfs = 13
ensemble_size = 25
noise_strength = 0.01
S_number = 20
num_siftings = 100
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]
# ----------------------------------
# HAVE A LOOK
# Show the video gui
video_frame = 0
video_file = join(data_folder, 'Video.avi')
sv.image_sequence(globals(), 'video_frame', 'video_file')
# ----------------------------------
# Show the sync trace gui
sync_range = 2000
sync_point = time_point_of_first_video_frame - int(sync_range / 2)
sv.graph_range(globals(), 'sync_point', 'sync_range', 'sync')
# ----------------------------------
# Connect the video gui to the sync trace gui
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(), 'sync_point', 'video_frame',
'time_point_to_frame')
# ----------------------------------
import drop_down
import one_shot_viewer
import video_viewer
# Make some data
data1 = np.random.random((10, 10000))
for i in range(10):
data1[i] += i
data2 = np.random.random(500)
# Have a look at them
position1 = 0
range1 = 1000
sequence_viewer.graph_range(globals(), 'position1', 'range1', 'data1')
position2 = 0
range2 = 10
sequence_viewer.graph_range(globals(), 'position2', 'range2', 'data2')
# Also you can view data in panes
pane = 0
sequence_viewer.graph_pane(globals(), 'pane', 'data1')
# The pane viewer shows te last one or two dimensions of a 2d or 3d data set and itterates over the first one.
# Connect two guis
def pos1_to_pos2(pos1):
if pos1 >= 0 and pos1 <= 4000:
return int(pos1 / 10)