def getLFPData(probeBase, syncDataset, num_channels=384):
probeTTLDir = os.path.join(probeBase, r'events\\Neuropix-PXI-100.0\\TTL_1')
lfp_data_dir = os.path.join(probeBase, r'continuous\\Neuropix-PXI-100.1')
lfp_data_file = os.path.join(lfp_data_dir, 'continuous.dat')
if not os.path.exists(lfp_data_file):
print('Could not find LFP data at ' + lfp_data_file)
return None, None
lfp_data = np.memmap(lfp_data_file, dtype='int16', mode='r')
lfp_data_reshape = np.reshape(lfp_data,
[int(lfp_data.size / num_channels), -1])
time_stamps = np.load(os.path.join(lfp_data_dir, 'lfp_timestamps.npy'))
bRising, bFalling = get_sync_line_data(syncDataset, channel=0)
bs_t, bs = ecephys.extract_barcodes_from_times(bRising, bFalling)
channel_states = np.load(os.path.join(probeTTLDir, 'channel_states.npy'))
event_times = np.load(os.path.join(probeTTLDir, 'event_timestamps.npy'))
beRising = event_times[channel_states > 0] / 30000.
beFalling = event_times[channel_states < 0] / 30000.
be_t, be = ecephys.extract_barcodes_from_times(beRising, beFalling)
#Compute time shift between ephys and sync
shift, p_sampleRate, m_endpoints = ecephys.get_probe_time_offset(
bs_t, bs, be_t, be, 0, 30000)
time_stamps_shifted = (time_stamps / p_sampleRate) - shift
return lfp_data_reshape, time_stamps_shifted
def plot_barcode_intervals(probe_dirs, syncDataset, FIG_SAVE_DIR, prefix=''):
fig, ax = plt.subplots()
fig.suptitle('Sync Barcode Intervals')
bs_t, bs = probeSync.get_sync_barcodes(syncDataset)
ax.plot(np.diff(bs_t), 'k')
ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
pfig, pax = plt.subplots(1, 2)
pfig.set_size_inches([8, 4])
pfig.suptitle('Probe Barcode Intervals')
for ip, probe in enumerate(probe_dirs):
p_name = probe.split('_')[-2][-1]
be_t, be = probeSync.get_ephys_barcodes(probe)
shift, p_sampleRate, m_endpoints = ecephys.get_probe_time_offset(
bs_t, bs, be_t, be, 0, 30000)
pax[0].plot(np.diff(be_t), probe_color_dict[p_name])
pax[0].set_title('uncorrected')
pax[1].plot(
np.diff(be_t) * (30000. / p_sampleRate), probe_color_dict[p_name])
pax[1].set_title('corrected')
pax[0].yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
pax[1].yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
pax[0].legend([probe.split('_')[-2][-1] for probe in probe_dirs])
save_figure(
fig, os.path.join(FIG_SAVE_DIR, prefix + 'Sync_barcode_intervals.png'))
save_figure(
pfig, os.path.join(FIG_SAVE_DIR,
prefix + 'Probe_barcode_intervals.png'))
def getUnitData(probeBase,syncDataset):
probeSpikeDir = os.path.join(probeBase, r'continuous\\Neuropix-PXI-100.0')
#Get barcodes/times from probe events and sync file
be_t, be = get_ephys_barcodes(probeBase)
bs_t, bs = get_sync_barcodes(syncDataset)
#Compute time shift between ephys and sync
shift, p_sampleRate, m_endpoints = ecephys.get_probe_time_offset(bs_t, bs, be_t, be, 0, 30000)
#Get unit spike times
units = load_spike_info(probeSpikeDir, p_sampleRate, shift)
return units
def probe_sync_report(probe_dirs, syncDataset, FIG_SAVE_DIR, prefix=''):
bs_t, bs = probeSync.get_sync_barcodes(syncDataset)
alignment_dict = {}
for ip, probe in enumerate(probe_dirs):
p_name = probe.split('_')[-2][-1]
alignment_dict[p_name] = {}
be_t, be = probeSync.get_ephys_barcodes(probe)
shift, p_sampleRate, m_endpoints = ecephys.get_probe_time_offset(
bs_t, bs, be_t, be, 0, 30000)
alignment_dict[p_name]['shift'] = np.float(shift)
alignment_dict[p_name]['sample_rate'] = np.float(p_sampleRate)
save_file = os.path.join(FIG_SAVE_DIR,
prefix + 'probe_sync_registration.json')
save_json(alignment_dict, save_file)
def getLFPData(dataDir,
pid,
syncDataset,
probePXIDict,
probeGen='3b',
num_channels=384):
if '3a' in probeGen:
lfp_data_dir = glob.glob(
os.path.join(dataDir, '*probe' + pid + '_sorted', 'continuous',
'Neuropix-3a-100.1'))[0]
events_dir = glob.glob(
os.path.join(dataDir, '*probe' + pid + '_sorted', 'events',
'Neuropix-3a-100.0', 'TTL_1'))[0]
elif '3b' in probeGen:
probeDirName = 'Neuropix-PXI-' + probePXIDict[pid]
lfp_data_dir = os.path.join(dataDir, probeDirName + '-LFP')
events_dir = os.path.join(dataDir, 'events', probeDirName, 'TTL_1')
elif probeGen == 'pipeline':
baseString = os.path.basename(dataDir)
probeBase = os.path.join(dataDir,
baseString + '_probe' + pid + '_sorted')
lfp_data_dir = os.path.join(probeBase,
r'continuous\\\Neuropix-PXI-100.1')
events_dir = os.path.join(probeBase,
r'events\\Neuropix-PXI-100.0\\TTL_1')
lfp_data_file = os.path.join(lfp_data_dir, 'continuous.dat')
if not os.path.exists(lfp_data_file):
print('Could not find LFP data at ' + lfp_data_file)
return None, None
lfp_data = np.memmap(lfp_data_file, dtype='int16', mode='r')
lfp_data_reshape = np.reshape(lfp_data,
[int(lfp_data.size / num_channels), -1])
time_stamps = np.load(os.path.join(lfp_data_dir, 'lfp_timestamps.npy'))
#Get barcodes from sync file
if 'barcode' in syncDataset.line_labels:
bRising, bFalling = get_sync_line_data(syncDataset, 'barcode')
elif 'barcodes' in syncDataset.line_labels:
bRising, bFalling = get_sync_line_data(syncDataset, 'barcodes')
bs_t, bs = ecephys.extract_barcodes_from_times(bRising, bFalling)
#Get barcodes from ephys data
if '03122019' in dataDir and 'slot3' in events_dir:
#files on slot3 for this day saved extra bytes at beginning, must skip them to get the right time stamps
channel_states = np.load(
r"Z:\03122019_416656\events\Neuropix-PXI-slot2-probe1\TTL_1\channel_states.npy"
)
event_times_file = open(
os.path.join(events_dir, 'event_timestamps.npy'), 'rb')
event_times_file.seek(8 * 22 + 1)
event_times = np.fromfile(event_times_file,
dtype='<u8')[:channel_states.size]
lfp_data_reshape = lfp_data_reshape[:time_stamps.size]
elif '06122019' in dataDir:
good_channel_states = np.load(
r"Z:\06122019_423745\events\Neuropix-PXI-slot3-probe1\TTL_1\channel_states.npy"
)
good_event_times = np.load(
r"Z:\06122019_423745\events\Neuropix-PXI-slot3-probe1\TTL_1\event_timestamps.npy"
)
channel_states = np.load(os.path.join(
events_dir, 'channel_states.npy'))[:good_channel_states.size]
event_times = np.load(os.path.join(
events_dir, 'event_timestamps.npy'))[:good_event_times.size]
else:
channel_states = np.load(os.path.join(events_dir,
'channel_states.npy'))
event_times = np.load(os.path.join(events_dir, 'event_timestamps.npy'))
beRising = event_times[channel_states > 0] / 30000.
beFalling = event_times[channel_states < 0] / 30000.
be_t, be = ecephys.extract_barcodes_from_times(beRising, beFalling)
if '03212019' in dataDir:
be_t = be_t[5:]
be = be[5:]
#Compute time shift between ephys and sync
shift, p_sampleRate, m_endpoints = ecephys.get_probe_time_offset(
bs_t, bs, be_t, be, 0, 30000)
if '03212019' in dataDir:
shift = -3.6950408520530686
time_stamps_shifted = (time_stamps / p_sampleRate) - shift
return lfp_data_reshape, time_stamps_shifted
def getUnitData(dataDir, syncDataset, probeID, probePXIDict, probeGen='3b'):
if probeGen == '3a':
probeDir = glob.glob(
os.path.join(dataDir, '*Probe' + probeID + '_sorted'))[0]
probeTTLDir = os.path.join(probeDir,
'events\\Neuropix-3a-100.0\\TTL_1')
probeSpikeDir = os.path.join(probeDir, 'continuous\\Neuropix-3a-100.0')
elif probeGen == '3b':
eventsDir = os.path.join(dataDir, 'events')
probeTTLDir = os.path.join(
os.path.join(eventsDir, 'Neuropix-PXI-' + probePXIDict[probeID]),
'TTL_1')
probeSpikeDir = os.path.join(
dataDir,
'Neuropix-PXI-' + probePXIDict[probeID] + '-AP_sortingResults')
elif probeGen == 'pipeline':
baseString = os.path.basename(dataDir)
probeBase = os.path.join(dataDir,
baseString + '_probe' + probeID + '_sorted')
probeTTLDir = os.path.join(probeBase,
r'events\\Neuropix-PXI-100.0\\TTL_1')
probeSpikeDir = os.path.join(probeBase,
r'continuous\\Neuropix-PXI-100.0')
print(probeTTLDir)
print(probeSpikeDir)
#Get barcodes from sync file
if 'barcode' in syncDataset.line_labels:
bRising, bFalling = get_sync_line_data(syncDataset, 'barcode')
elif 'barcodes' in syncDataset.line_labels:
bRising, bFalling = get_sync_line_data(syncDataset, 'barcodes')
bs_t, bs = ecephys.extract_barcodes_from_times(bRising, bFalling)
#Get barcodes from ephys data
if '03122019' in dataDir and 'slot3' in probeTTLDir:
#files on slot3 for this day saved extra bytes at beginning, must skip them to get the right time stamps
channel_states = np.load(
r"Z:\03122019_416656\events\Neuropix-PXI-slot2-probe1\TTL_1\channel_states.npy"
)
event_times_file = open(
os.path.join(probeTTLDir, 'event_timestamps.npy'), 'rb')
event_times_file.seek(8 * 22 + 1)
event_times = np.fromfile(event_times_file,
dtype='<u8')[:channel_states.size]
elif '06122019' in dataDir:
good_channel_states = np.load(
r"Z:\06122019_423745\events\Neuropix-PXI-slot3-probe1\TTL_1\channel_states.npy"
)
good_event_times = np.load(
r"Z:\06122019_423745\events\Neuropix-PXI-slot3-probe1\TTL_1\event_timestamps.npy"
)
channel_states = np.load(
os.path.join(probeTTLDir,
'channel_states.npy'))[:good_channel_states.size]
event_times = np.load(os.path.join(
probeTTLDir, 'event_timestamps.npy'))[:good_event_times.size]
else:
channel_states = np.load(
os.path.join(probeTTLDir, 'channel_states.npy'))
event_times = np.load(os.path.join(probeTTLDir,
'event_timestamps.npy'))
beRising = event_times[channel_states > 0] / 30000.
beFalling = event_times[channel_states < 0] / 30000.
be_t, be = ecephys.extract_barcodes_from_times(beRising, beFalling)
if '03212019' in dataDir:
be_t = be_t[5:]
be = be[5:]
#Compute time shift between ephys and sync
shift, p_sampleRate, m_endpoints = ecephys.get_probe_time_offset(
bs_t, bs, be_t, be, 0, 30000)
if '03212019' in dataDir:
shift = -3.6950408520530686
#be_t_shifted = (be_t/(p_sampleRate/30000)) - shift #just to check that the shift and scale are right
#Get unit spike times
units = load_spike_info(probeSpikeDir, p_sampleRate, shift)
return units
'Neuropix-PXI-' + pxiDict[probeLabel]), 'TTL_1')
# get barcodes from sync file
bRising, bFalling = get_sync_line_data(syncDataset, 'barcode')
bs_t, bs = ecephys.extract_barcodes_from_times(bRising, bFalling)
# get barcodes from ephys data
channel_states = np.load(os.path.join(probeEventsDir, 'channel_states.npy'))
event_times = np.load(os.path.join(probeEventsDir, 'event_timestamps.npy'))
beRising = event_times[channel_states > 0] / 30000.
beFalling = event_times[channel_states < 0] / 30000.
be_t, be = ecephys.extract_barcodes_from_times(beRising, beFalling)
# compute time shift between ephys and sync
probeShift, probeSampleRate, m_endpoints = ecephys.get_probe_time_offset(
bs_t, bs, be_t, be, 0, 30000)
# unit data
kilosortData = {
key: np.load(os.path.join(probeSpikeDataDir, key + '.npy'))
for key in ('spike_clusters', 'spike_times', 'templates',
'spike_templates', 'channel_positions', 'amplitudes')
}
clusterIDs = pd.read_csv(os.path.join(probeSpikeDataDir,
'cluster_KSLabel.tsv'),
sep='\t')
unitIDs = np.unique(kilosortData['spike_clusters'])
unitData = {}