def load_events(file_path, channels=None):
"""
return time stamps in seconds of each digital channel
:param file_path:
:param channels: name of channels
:return: dictionary, {channel: {'rise':[timestamps of rising events in seconds],
'fall':[timestamps of falling events in seconds}}
"""
print('\n')
if file_path[-7:] != '.events':
raise LookupError('The input file: ' + file_path + ' is not a .events file!')
with open(file_path) as f:
header = oe.readHeader(f)
fs = float(header['sampleRate'])
events = oe.loadEvents(file_path)
detected_channel_number = int(max(events['channel']) + 1)
real_channels = ['ch_' + ft.int2str(c, 3) for c in range(detected_channel_number)]
if channels is not None:
if detected_channel_number != len(channels):
warning_msg = '\nThe number of digital channels detected: ' + str(detected_channel_number) + \
' does not match input channel number: ' + str(len(channels))
warnings.warn(warning_msg)
if len(channels) <= detected_channel_number:
real_channels[0:len(channels)] = channels
else:
real_channels = channels[0:detected_channel_number]
output = {}
for i, ch in enumerate(real_channels):
output.update({ch : {'rise' : [],
'fall' : []}
})
rise = events['timestamps'][np.logical_and(events['channel'] == i, events['eventId'] == 1)]
output[ch]['rise'] = np.array(rise.astype(np.float32) / fs).astype(np.float32)
fall = events['timestamps'][np.logical_and(events['channel'] == i, events['eventId'] == 0)]
output[ch]['fall'] = np.array(fall.astype(np.float32) / fs).astype(np.float32)
print('events loaded.\n')
return output
#lowpassフィルタがかけられた波形を抽出
waveform = glia.nucleus.Waveform(lowpass(tmp["data"], 30000, 450, 600,
10, 30),
rate=glia.Frequency(30000))
#tmp(['header', 'timestamps', 'data', 'recordingNumber'])が記載されている
#timestampsはサンプリングレートで記録された記録点
timestamps = tmp["timestamps"][0]
if num < 10:
glia.save(f"./waveform/{dirname}/0{num}.lfp", waveform)
else:
glia.save(f"./waveform/{dirname}/{num}.lfp", waveform)
del tmp, waveform
gc.collect()
num += 1
events = OpenEphys.loadEvents(pathname + "/all_channels.events")
#eventsにはトリガーの始点・終点が入っているので、始点だけ取り出す
e_tmp = (events["timestamps"] - timestamps)[0::2]
trigger = []
#millisecondのスケールでトリガー情報をtriggerに格納
for i in (e_tmp / 30):
trigger.append(glia.millisecond(i))
#numpy配列に変換
trigger = np.array(trigger)
glia.save(f"./waveform/{dirname}/{dirname}.trigger", trigger)
print((events["timestamps"] - timestamps)[0::2])
#この状態で.lfpファイルにはそれぞれのチャネルからの計測電圧が入る
#.triggerファイルではトリガーファイルが入る
#刺激パターンをjsonファイルから読み込んで、.stimorderファイルに格納
DataFiles = glob.glob(''.join([RecFolder,'/*.continuous']))
DataFiles.sort()
#%% Generate TTLs
os.makedirs('DataArrays', exist_ok=True)
DirList = glob.glob('OpenEphysFiles/*'); DirList.sort()
for RecFolder in DirList:
DataFiles = glob.glob(''.join([RecFolder,'/*.continuous']))
EventsFile = ''.join([RecFolder,'/all_channels.events'])
DataFiles.sort()
TempData = OpenEphys.loadContinuous(DataFiles[0]) # Any ch would work ;)
Events = OpenEphys.loadEvents(EventsFile)
# Draw TTL
print('Drawing TTL channel...')
if len(Events['timestamps']) > 100: # arbitrary value, I never use < 100 stimulation pulses
TTLSound = [0] * len(TempData['data'])
for EvTS in range(len(Events['timestamps'])):
if Events['eventType'][EvTS] == 3: # if event is a TTL
if Events['eventId'][EvTS] == 1: # if TTL is on
TTLSound(find(TempTimestamps==EventsTimestamps(EvTS)): ...
find(TempTimestamps==EventsTimestamps(EvTS+1))) = 1;
end
end
end
else
TTLSound = zeros(size(TempData, 1), size(TempData, 2));
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 11 13:26:20 2018
@author: behrenslab
"""
import data_import as di
import OpenEphys as op
import Esync as es
import numpy as np
session = di.Session(
'/home/behrenslab/ephys/2018-06-05-reversal_learning_3_tasks_recording/m483-2018-06-07-161545.txt'
)
rsync_events = [
event.time for event in session.events if event.name in ['Rsync']
]
rsync_ephys = op.loadEvents(
'/home/behrenslab/ephys/2018-06-07_16-15-43/all_channels.events')
rsync_timestamps = rsync_ephys['timestamps']
rsync_timestamps = np.array(rsync_timestamps)[::2]
rsync_events = np.array(rsync_events)
rsync_timestamps = rsync_timestamps / 30
aligner = es.Rsync_aligner(rsync_events, rsync_timestamps, plot=True)
times_B = aligner.A_to_B(rsync_events) #A pycontrol
times_A = aligner.B_to_A(rsync_timestamps)
pycontrol_to_ephys = aligner.A_to_B
".lfp") and os.path.isfile(
"./waveform/" + filename[4:-4] +
".trig"):
print("Load lfp, trig")
else:
wave = []
for j in channel_map:
tmp = OpenEphys.loadContinuous(filename + "/101_CH" + str(j) +
".continuous")
#gliaの形式にそってローパスフィルタをかけた波形の出力
wave.append(
glia.nucleus.Waveform(lowpass(tmp["data"], 30000, 450, 600, 10,
30),
rate=glia.Frequency(30000)))
timestamp = tmp["timestamps"][0]
events = OpenEphys.loadEvents(filename + "/all_channels.events")
e_tmp = (events["timestamps"] - timestamp)[0::2]
trigger = []
for i in (e_tmp / 30):
trigger.append(glia.millisecond(i))
trigger = np.array(trigger)
glia.save("./waveform/" + filename[4:] + ".lfp", wave)
glia.save("./waveform/" + filename[4:] + ".trig", trigger)
if not reload and os.path.isfile("./waveform/" + filename[4:-4] +
".stimorder"):
print("Load stimorder")
else:
with open("json/" + jsonfile, encoding="utf-8") as json_fp:
stim_pattern = json.load(json_fp)
#pandas配列に刺激パターンを格納
table = pandas.DataFrame(stim_pattern)
for file_ephys in files_ephys:
if file_ephys in m486:
match_ephys = re.search(r'\d{4}-\d{2}-\d{2}', file_ephys)
date_ephys = datetime.strptime(match_ephys.group(), '%Y-%m-%d').date()
date_ephys = match_ephys.group()
for file_behaviour in files_behaviour:
match_behaviour = re.search(r'\d{4}-\d{2}-\d{2}', file_behaviour)
date_behaviour = datetime.strptime(match_behaviour.group(), '%Y-%m-%d').date()
date_behaviour = match_behaviour.group()
if date_ephys == date_behaviour:
behaviour_path = behaviour_filename+'/'+file_behaviour
behaviour_session = di.Session(behaviour_path)
ephys_path = ephys_data_folder+'/'+file_ephys + '/' + subject
print(behaviour_path)
print(ephys_path)
ephys_events = op.loadEvents(os.path.join(ephys_path,'all_channels.events'))
data_folder = file_ephys
check_if_kilosort_exists = os.listdir(ephys_path)
check_if_npy_exists = os.listdir(spikes_df_csv_out_folder)
file_ephys_npy = file_ephys +'.npy'
if file_ephys_npy not in check_if_npy_exists:
# For multiple files in one session add the requirement of cluster_groups.csv file
for file in check_if_kilosort_exists:
if fnmatch.fnmatch(file, '*.csv'):
# Get offset in samples of Kilosort spike data relative to TTL events.
with open(os.path.join(ephys_path,'messages.events')) as f:
message = f.read()
recording_start_sample, sampling_rate = [
int(x) for x in message.split('start time: ')[1].split('Hz')[0].split('@')]
