def setup_epocharrays(self):
epocharrname11 = 'epocharr 1 1'
epocharrname12 = 'epocharr 1 2'
epocharrname21 = 'epocharr 2 1'
epocharrname22 = 'epocharr 2 2'
epocharrtime11 = np.arange(0, 10) * pq.ms
epocharrtime12 = np.arange(10, 20) * pq.ms
epocharrtime21 = np.arange(20, 30) * pq.s
epocharrtime22 = np.arange(30, 40) * pq.s
epocharrdur11 = np.arange(1, 11) * pq.s
epocharrdur12 = np.arange(11, 21) * pq.s
epocharrdur21 = np.arange(21, 31) * pq.ms
epocharrdur22 = np.arange(31, 41) * pq.ms
self.epocharrnames1 = [epocharrname11, epocharrname12]
self.epocharrnames2 = [epocharrname21, epocharrname22]
self.epocharrnames = [epocharrname11,
epocharrname12, epocharrname21, epocharrname22]
epocharr11 = EpochArray(epocharrtime11, epocharrdur11,
label=epocharrname11, name=epocharrname11)
epocharr12 = EpochArray(epocharrtime12, epocharrdur12,
label=epocharrname12, name=epocharrname12)
epocharr21 = EpochArray(epocharrtime21, epocharrdur21,
label=epocharrname21, name=epocharrname21)
epocharr22 = EpochArray(epocharrtime22, epocharrdur22,
label=epocharrname22, name=epocharrname22)
self.epocharr1 = [epocharr11, epocharr12]
self.epocharr2 = [epocharr21, epocharr22]
self.epocharr = [epocharr11, epocharr12, epocharr21, epocharr22]
def read_epocharray(self,
lazy=False,
cascade=True,
channel_index=0,
t_start=0.,
segment_duration=0.):
"""function to read digital timestamps. this function reads the event
onset and offset and outputs onset and duration. to get only onsets use
the event array function"""
if lazy:
epa = EpochArray(file_origin=self.filename,
times=None,
durations=None,
labels=None)
else:
#create temporary empty lists to store data
tempNames = list()
tempTimeStamp = list()
durations = list()
#get entity from file
digEntity = self.fd.get_entity(channel_index)
#transform t_start into index (reading will start from this index)
startat = digEntity.get_index_by_time(
t_start, 0) #zero means closest index to value
#get the last index to read, using segment duration and t_start
endat = digEntity.get_index_by_time(
float(segment_duration + t_start),
-1) #-1 means last index before time
#run through entity using only odd "i"s
for i in range(startat, endat + 1, 1):
if i % 2 == 1:
#get in which digital bit was the trigger detected
tempNames.append(digEntity.label[-8:])
#get the time stamps of even events
tempData, onOrOff = digEntity.get_data(i - 1)
#if this was an onset event, save it to the list
#on triggered recordings it seems that only onset events are
#recorded. On continuous recordings both onset(==1)
#and offset(==255) seem to be recorded
#if onOrOff == 1:
#append the time stamp to them empty list
tempTimeStamp.append(tempData)
#get time stamps of odd events
tempData1, onOrOff = digEntity.get_data(i)
#if onOrOff == 255:
#pass
durations.append(tempData1 - tempData)
epa = EpochArray(file_origin=self.filename,
times=np.array(tempTimeStamp) * pq.s,
durations=np.array(durations) * pq.s,
labels=np.array(tempNames, dtype="S"),
description="digital events with duration")
return epa
def read_segment(
self,
lazy=False,
cascade=True,
):
fid = open(self.filename, 'rb')
globalHeader = HeaderReader(fid, GlobalHeader).read_f(offset=0)
#~ print globalHeader
#~ print 'version' , globalHeader['version']
seg = Segment()
seg.file_origin = os.path.basename(self.filename)
seg.annotate(neuroexplorer_version=globalHeader['version'])
seg.annotate(comment=globalHeader['comment'])
if not cascade:
return seg
offset = 544
for i in range(globalHeader['nvar']):
entityHeader = HeaderReader(
fid, EntityHeader).read_f(offset=offset + i * 208)
entityHeader['name'] = entityHeader['name'].replace('\x00', '')
#print 'i',i, entityHeader['type']
if entityHeader['type'] == 0:
# neuron
if lazy:
spike_times = [] * pq.s
else:
spike_times = np.memmap(
self.filename,
np.dtype('i4'),
'r',
shape=(entityHeader['n']),
offset=entityHeader['offset'],
)
spike_times = spike_times.astype(
'f8') / globalHeader['freq'] * pq.s
sptr = SpikeTrain(
times=spike_times,
t_start=globalHeader['tbeg'] / globalHeader['freq'] * pq.s,
t_stop=globalHeader['tend'] / globalHeader['freq'] * pq.s,
name=entityHeader['name'],
)
if lazy:
sptr.lazy_shape = entityHeader['n']
sptr.annotate(channel_index=entityHeader['WireNumber'])
seg.spiketrains.append(sptr)
if entityHeader['type'] == 1:
# event
if lazy:
event_times = [] * pq.s
else:
event_times = np.memmap(
self.filename,
np.dtype('i4'),
'r',
shape=(entityHeader['n']),
offset=entityHeader['offset'],
)
event_times = event_times.astype(
'f8') / globalHeader['freq'] * pq.s
labels = np.array([''] * event_times.size, dtype='S')
evar = EventArray(times=event_times,
labels=labels,
channel_name=entityHeader['name'])
if lazy:
evar.lazy_shape = entityHeader['n']
seg.eventarrays.append(evar)
if entityHeader['type'] == 2:
# interval
if lazy:
start_times = [] * pq.s
stop_times = [] * pq.s
else:
start_times = np.memmap(
self.filename,
np.dtype('i4'),
'r',
shape=(entityHeader['n']),
offset=entityHeader['offset'],
)
start_times = start_times.astype(
'f8') / globalHeader['freq'] * pq.s
stop_times = np.memmap(
self.filename,
np.dtype('i4'),
'r',
shape=(entityHeader['n']),
offset=entityHeader['offset'] + entityHeader['n'] * 4,
)
stop_times = stop_times.astype(
'f') / globalHeader['freq'] * pq.s
epar = EpochArray(times=start_times,
durations=stop_times - start_times,
labels=np.array([''] * start_times.size,
dtype='S'),
channel_name=entityHeader['name'])
if lazy:
epar.lazy_shape = entityHeader['n']
seg.epocharrays.append(epar)
if entityHeader['type'] == 3:
# spiketrain and wavefoms
if lazy:
spike_times = [] * pq.s
waveforms = None
else:
spike_times = np.memmap(
self.filename,
np.dtype('i4'),
'r',
shape=(entityHeader['n']),
offset=entityHeader['offset'],
)
spike_times = spike_times.astype(
'f8') / globalHeader['freq'] * pq.s
waveforms = np.memmap(
self.filename,
np.dtype('i2'),
'r',
shape=(entityHeader['n'], 1,
entityHeader['NPointsWave']),
offset=entityHeader['offset'] + entityHeader['n'] * 4,
)
waveforms = (waveforms.astype('f') * entityHeader['ADtoMV']
+ entityHeader['MVOffset']) * pq.mV
t_stop = globalHeader['tend'] / globalHeader['freq'] * pq.s
if spike_times.size > 0:
t_stop = max(t_stop, max(spike_times))
sptr = SpikeTrain(
times=spike_times,
t_start=globalHeader['tbeg'] / globalHeader['freq'] * pq.s,
#~ t_stop = max(globalHeader['tend']/globalHeader['freq']*pq.s,max(spike_times)),
t_stop=t_stop,
name=entityHeader['name'],
waveforms=waveforms,
sampling_rate=entityHeader['WFrequency'] * pq.Hz,
left_sweep=0 * pq.ms,
)
if lazy:
sptr.lazy_shape = entityHeader['n']
sptr.annotate(channel_index=entityHeader['WireNumber'])
seg.spiketrains.append(sptr)
if entityHeader['type'] == 4:
# popvectors
pass
if entityHeader['type'] == 5:
# analog
timestamps = np.memmap(
self.filename,
np.dtype('i4'),
'r',
shape=(entityHeader['n']),
offset=entityHeader['offset'],
)
timestamps = timestamps.astype('f8') / globalHeader['freq']
fragmentStarts = np.memmap(
self.filename,
np.dtype('i4'),
'r',
shape=(entityHeader['n']),
offset=entityHeader['offset'],
)
fragmentStarts = fragmentStarts.astype(
'f8') / globalHeader['freq']
t_start = timestamps[0] - fragmentStarts[0] / float(
entityHeader['WFrequency'])
del timestamps, fragmentStarts
if lazy:
signal = [] * pq.mV
else:
signal = np.memmap(
self.filename,
np.dtype('i2'),
'r',
shape=(entityHeader['NPointsWave']),
offset=entityHeader['offset'],
)
signal = signal.astype('f')
signal *= entityHeader['ADtoMV']
signal += entityHeader['MVOffset']
signal = signal * pq.mV
anaSig = AnalogSignal(
signal=signal,
t_start=t_start * pq.s,
sampling_rate=entityHeader['WFrequency'] * pq.Hz,
name=entityHeader['name'],
channel_index=entityHeader['WireNumber'])
if lazy:
anaSig.lazy_shape = entityHeader['NPointsWave']
seg.analogsignals.append(anaSig)
if entityHeader['type'] == 6:
# markers : TO TEST
if lazy:
times = [] * pq.s
labels = np.array([], dtype='S')
markertype = None
else:
times = np.memmap(
self.filename,
np.dtype('i4'),
'r',
shape=(entityHeader['n']),
offset=entityHeader['offset'],
)
times = times.astype('f8') / globalHeader['freq'] * pq.s
fid.seek(entityHeader['offset'] + entityHeader['n'] * 4)
markertype = fid.read(64).replace('\x00', '')
labels = np.memmap(
self.filename,
np.dtype('S' + str(entityHeader['MarkerLength'])),
'r',
shape=(entityHeader['n']),
offset=entityHeader['offset'] + entityHeader['n'] * 4 +
64)
ea = EventArray(times=times,
labels=labels.view(np.ndarray),
name=entityHeader['name'],
channel_index=entityHeader['WireNumber'],
marker_type=markertype)
if lazy:
ea.lazy_shape = entityHeader['n']
seg.eventarrays.append(ea)
create_many_to_one_relationship(seg)
return seg
def read_segment(
self,
cascade=True,
lazy=False,
):
"""
Arguments:
"""
f = struct_file(self.filename, 'rb')
#Name
f.seek(64, 0)
surname = f.read(22)
while surname[-1] == ' ':
if len(surname) == 0: break
surname = surname[:-1]
firstname = f.read(20)
while firstname[-1] == ' ':
if len(firstname) == 0: break
firstname = firstname[:-1]
#Date
f.seek(128, 0)
day, month, year, hour, minute, sec = f.read_f('bbbbbb')
rec_datetime = datetime.datetime(year + 1900, month, day, hour, minute,
sec)
f.seek(138, 0)
Data_Start_Offset, Num_Chan, Multiplexer, Rate_Min, Bytes = f.read_f(
'IHHHH')
#~ print Num_Chan, Bytes
#header version
f.seek(175, 0)
header_version, = f.read_f('b')
assert header_version == 4
seg = Segment(
name=firstname + ' ' + surname,
file_origin=os.path.basename(self.filename),
)
seg.annotate(surname=surname)
seg.annotate(firstname=firstname)
seg.annotate(rec_datetime=rec_datetime)
if not cascade:
return seg
# area
f.seek(176, 0)
zone_names = [
'ORDER', 'LABCOD', 'NOTE', 'FLAGS', 'TRONCA', 'IMPED_B', 'IMPED_E',
'MONTAGE', 'COMPRESS', 'AVERAGE', 'HISTORY', 'DVIDEO', 'EVENT A',
'EVENT B', 'TRIGGER'
]
zones = {}
for zname in zone_names:
zname2, pos, length = f.read_f('8sII')
zones[zname] = zname2, pos, length
#~ print zname2, pos, length
# reading raw data
if not lazy:
f.seek(Data_Start_Offset, 0)
rawdata = np.fromstring(f.read(), dtype='u' + str(Bytes))
rawdata = rawdata.reshape((rawdata.size / Num_Chan, Num_Chan))
# Reading Code Info
zname2, pos, length = zones['ORDER']
f.seek(pos, 0)
code = np.fromfile(f, dtype='u2', count=Num_Chan)
units = {
-1: pq.nano * pq.V,
0: pq.uV,
1: pq.mV,
2: 1,
100: pq.percent,
101: pq.dimensionless,
102: pq.dimensionless
}
for c in range(Num_Chan):
zname2, pos, length = zones['LABCOD']
f.seek(pos + code[c] * 128 + 2, 0)
label = f.read(6).strip("\x00")
ground = f.read(6).strip("\x00")
logical_min, logical_max, logical_ground, physical_min, physical_max = f.read_f(
'iiiii')
k, = f.read_f('h')
if k in units.keys():
unit = units[k]
else:
unit = pq.uV
f.seek(8, 1)
sampling_rate, = f.read_f('H') * pq.Hz
sampling_rate *= Rate_Min
if lazy:
signal = [] * unit
else:
factor = float(physical_max -
physical_min) / float(logical_max -
logical_min + 1)
signal = (rawdata[:, c].astype('f') -
logical_ground) * factor * unit
anaSig = AnalogSignal(signal,
sampling_rate=sampling_rate,
name=label,
channel_index=c)
if lazy:
anaSig.lazy_shape = None
anaSig.annotate(ground=ground)
seg.analogsignals.append(anaSig)
sampling_rate = np.mean(
[anaSig.sampling_rate for anaSig in seg.analogsignals]) * pq.Hz
# Read trigger and notes
for zname, label_dtype in [('TRIGGER', 'u2'), ('NOTE', 'S40')]:
zname2, pos, length = zones[zname]
f.seek(pos, 0)
triggers = np.fromstring(
f.read(length),
dtype=[('pos', 'u4'), ('label', label_dtype)],
)
ea = EventArray(name=zname[0] + zname[1:].lower())
if not lazy:
keep = (triggers['pos'] >= triggers['pos'][0]) & (
triggers['pos'] < rawdata.shape[0]) & (triggers['pos'] !=
0)
triggers = triggers[keep]
ea.labels = triggers['label'].astype('S')
ea.times = (triggers['pos'] / sampling_rate).rescale('s')
else:
ea.lazy_shape = triggers.size
seg.eventarrays.append(ea)
# Read Event A and B
# Not so well tested
for zname in ['EVENT A', 'EVENT B']:
zname2, pos, length = zones[zname]
f.seek(pos, 0)
epochs = np.fromstring(f.read(length),
dtype=[
('label', 'u4'),
('start', 'u4'),
('stop', 'u4'),
])
ep = EpochArray(name=zname[0] + zname[1:].lower())
if not lazy:
keep = (epochs['start'] > 0) & (
epochs['start'] < rawdata.shape[0]) & (epochs['stop'] <
rawdata.shape[0])
epochs = epochs[keep]
ep.labels = epochs['label'].astype('S')
ep.times = (epochs['start'] / sampling_rate).rescale('s')
ep.durations = ((epochs['stop'] - epochs['start']) /
sampling_rate).rescale('s')
else:
ep.lazy_shape = triggers.size
seg.epocharrays.append(ep)
seg.create_many_to_one_relationship()
return seg
def read_segment(self,
lazy = False,
cascade = True,
):
fid = open(self.filename, 'rb')
globalHeader = HeaderReader(fid , GlobalHeader ).read_f(offset = 0)
#~ print globalHeader
#~ print 'version' , globalHeader['version']
seg = Segment()
seg.file_origin = os.path.basename(self.filename)
seg.annotate(neuroexplorer_version = globalHeader['version'])
seg.annotate(comment = globalHeader['comment'])
if not cascade :
return seg
offset = 544
for i in range(globalHeader['nvar']):
entityHeader = HeaderReader(fid , EntityHeader ).read_f(offset = offset+i*208)
entityHeader['name'] = entityHeader['name'].decode().replace('\x00','')
#print 'i',i, entityHeader['type']
if entityHeader['type'] == 0:
# neuron
if lazy:
spike_times = [ ]*pq.s
else:
spike_times= np.memmap(self.filename , np.dtype('i4') ,'r' ,
shape = (entityHeader['n'] ),
offset = entityHeader['offset'],
)
spike_times = spike_times.astype('f8')/globalHeader['freq']*pq.s
sptr = SpikeTrain( times= spike_times,
t_start = globalHeader['tbeg']/globalHeader['freq']*pq.s,
t_stop = globalHeader['tend']/globalHeader['freq']*pq.s,
name = entityHeader['name'],
)
if lazy:
sptr.lazy_shape = entityHeader['n']
sptr.annotate(channel_index = entityHeader['WireNumber'])
seg.spiketrains.append(sptr)
if entityHeader['type'] == 1:
# event
if lazy:
event_times = [ ]*pq.s
else:
event_times= np.memmap(self.filename , np.dtype('i4') ,'r' ,
shape = (entityHeader['n'] ),
offset = entityHeader['offset'],
)
event_times = event_times.astype('f8')/globalHeader['freq'] * pq.s
labels = np.array(['']*event_times.size, dtype = 'S')
evar = EventArray(times = event_times, labels=labels, channel_name = entityHeader['name'] )
if lazy:
evar.lazy_shape = entityHeader['n']
seg.eventarrays.append(evar)
if entityHeader['type'] == 2:
# interval
if lazy:
start_times = [ ]*pq.s
stop_times = [ ]*pq.s
else:
start_times= np.memmap(self.filename , np.dtype('i4') ,'r' ,
shape = (entityHeader['n'] ),
offset = entityHeader['offset'],
)
start_times = start_times.astype('f8')/globalHeader['freq']*pq.s
stop_times= np.memmap(self.filename , np.dtype('i4') ,'r' ,
shape = (entityHeader['n'] ),
offset = entityHeader['offset']+entityHeader['n']*4,
)
stop_times = stop_times.astype('f')/globalHeader['freq']*pq.s
epar = EpochArray(times = start_times,
durations = stop_times - start_times,
labels = np.array(['']*start_times.size, dtype = 'S'),
channel_name = entityHeader['name'])
if lazy:
epar.lazy_shape = entityHeader['n']
seg.epocharrays.append(epar)
if entityHeader['type'] == 3:
# spiketrain and wavefoms
if lazy:
spike_times = [ ]*pq.s
waveforms = None
else:
spike_times= np.memmap(self.filename , np.dtype('i4') ,'r' ,
shape = (entityHeader['n'] ),
offset = entityHeader['offset'],
)
spike_times = spike_times.astype('f8')/globalHeader['freq'] * pq.s
waveforms = np.memmap(self.filename , np.dtype('i2') ,'r' ,
shape = (entityHeader['n'] , 1,entityHeader['NPointsWave']),
offset = entityHeader['offset']+entityHeader['n'] *4,
)
waveforms = (waveforms.astype('f')* entityHeader['ADtoMV'] + entityHeader['MVOffset'])*pq.mV
t_stop = globalHeader['tend']/globalHeader['freq']*pq.s
if spike_times.size>0:
t_stop = max(t_stop, max(spike_times))
sptr = SpikeTrain( times = spike_times,
t_start = globalHeader['tbeg']/globalHeader['freq']*pq.s,
#~ t_stop = max(globalHeader['tend']/globalHeader['freq']*pq.s,max(spike_times)),
t_stop = t_stop,
name = entityHeader['name'],
waveforms = waveforms,
sampling_rate = entityHeader['WFrequency']*pq.Hz,
left_sweep = 0*pq.ms,
)
if lazy:
sptr.lazy_shape = entityHeader['n']
sptr.annotate(channel_index = entityHeader['WireNumber'])
seg.spiketrains.append(sptr)
if entityHeader['type'] == 4:
# popvectors
pass
if entityHeader['type'] == 5:
# analog
timestamps= np.memmap(self.filename , np.dtype('i4') ,'r' ,
shape = (entityHeader['n'] ),
offset = entityHeader['offset'],
)
timestamps = timestamps.astype('f8')/globalHeader['freq']
fragmentStarts = np.memmap(self.filename , np.dtype('i4') ,'r' ,
shape = (entityHeader['n'] ),
offset = entityHeader['offset'],
)
fragmentStarts = fragmentStarts.astype('f8')/globalHeader['freq']
t_start = timestamps[0] - fragmentStarts[0]/float(entityHeader['WFrequency'])
del timestamps, fragmentStarts
if lazy :
signal = [ ]*pq.mV
else:
signal = np.memmap(self.filename , np.dtype('i2') ,'r' ,
shape = (entityHeader['NPointsWave'] ),
offset = entityHeader['offset'],
)
signal = signal.astype('f')
signal *= entityHeader['ADtoMV']
signal += entityHeader['MVOffset']
signal = signal*pq.mV
anaSig = AnalogSignal(signal=signal, t_start=t_start * pq.s,
sampling_rate=
entityHeader['WFrequency'] * pq.Hz,
name=entityHeader['name'],
channel_index=entityHeader['WireNumber'])
if lazy:
anaSig.lazy_shape = entityHeader['NPointsWave']
seg.analogsignals.append( anaSig )
if entityHeader['type'] == 6:
# markers : TO TEST
if lazy:
times = [ ]*pq.s
labels = np.array([ ], dtype = 'S')
markertype = None
else:
times= np.memmap(self.filename , np.dtype('i4') ,'r' ,
shape = (entityHeader['n'] ),
offset = entityHeader['offset'],
)
times = times.astype('f8')/globalHeader['freq'] * pq.s
fid.seek(entityHeader['offset'] + entityHeader['n']*4)
markertype = fid.read(64).replace('\x00','')
labels = np.memmap(self.filename, np.dtype('S' + str(entityHeader['MarkerLength'])) ,'r',
shape = (entityHeader['n'] ),
offset = entityHeader['offset'] + entityHeader['n']*4 + 64
)
ea = EventArray( times = times,
labels = labels.view(np.ndarray),
name = entityHeader['name'],
channel_index = entityHeader['WireNumber'],
marker_type = markertype
)
if lazy:
ea.lazy_shape = entityHeader['n']
seg.eventarrays.append(ea)
seg.create_many_to_one_relationship()
return seg
def read_segment(self, cascade = True, lazy = False,):
"""
Arguments:
"""
f = struct_file(self.filename, 'rb')
#Name
f.seek(64,0)
surname = f.read(22)
while surname[-1] == ' ' :
if len(surname) == 0 :break
surname = surname[:-1]
firstname = f.read(20)
while firstname[-1] == ' ' :
if len(firstname) == 0 :break
firstname = firstname[:-1]
#Date
f.seek(128,0)
day, month, year, hour, minute, sec = f.read_f('bbbbbb')
rec_datetime = datetime.datetime(year+1900 , month , day, hour, minute, sec)
f.seek(138,0)
Data_Start_Offset , Num_Chan , Multiplexer , Rate_Min , Bytes = f.read_f('IHHHH')
#~ print Num_Chan, Bytes
#header version
f.seek(175,0)
header_version, = f.read_f('b')
assert header_version == 4
seg = Segment( name = firstname+' '+surname,
file_origin = os.path.basename(self.filename),
)
seg.annotate(surname = surname)
seg.annotate(firstname = firstname)
seg.annotate(rec_datetime = rec_datetime)
if not cascade:
return seg
# area
f.seek(176,0)
zone_names = ['ORDER', 'LABCOD', 'NOTE', 'FLAGS', 'TRONCA', 'IMPED_B', 'IMPED_E', 'MONTAGE',
'COMPRESS', 'AVERAGE', 'HISTORY', 'DVIDEO', 'EVENT A', 'EVENT B', 'TRIGGER']
zones = { }
for zname in zone_names:
zname2, pos, length = f.read_f('8sII')
zones[zname] = zname2, pos, length
#~ print zname2, pos, length
# reading raw data
if not lazy:
f.seek(Data_Start_Offset,0)
rawdata = np.fromstring(f.read() , dtype = 'u'+str(Bytes))
rawdata = rawdata.reshape(( rawdata.size/Num_Chan , Num_Chan))
# Reading Code Info
zname2, pos, length = zones['ORDER']
f.seek(pos,0)
code = np.fromfile(f, dtype='u2', count=Num_Chan)
units = {-1: pq.nano*pq.V, 0:pq.uV, 1:pq.mV, 2:1, 100: pq.percent, 101:pq.dimensionless, 102:pq.dimensionless}
for c in range(Num_Chan):
zname2, pos, length = zones['LABCOD']
f.seek(pos+code[c]*128+2,0)
label = f.read(6).strip("\x00")
ground = f.read(6).strip("\x00")
logical_min , logical_max, logical_ground, physical_min, physical_max = f.read_f('iiiii')
k, = f.read_f('h')
if k in units.keys() :
unit = units[k]
else :
unit = pq.uV
f.seek(8,1)
sampling_rate, = f.read_f('H') * pq.Hz
sampling_rate *= Rate_Min
if lazy:
signal = [ ]*unit
else:
factor = float(physical_max - physical_min) / float(logical_max-logical_min+1)
signal = ( rawdata[:,c].astype('f') - logical_ground )* factor*unit
anaSig = AnalogSignal(signal, sampling_rate=sampling_rate,
name=label, channel_index=c)
if lazy:
anaSig.lazy_shape = None
anaSig.annotate(ground = ground)
seg.analogsignals.append( anaSig )
sampling_rate = np.mean([ anaSig.sampling_rate for anaSig in seg.analogsignals ])*pq.Hz
# Read trigger and notes
for zname, label_dtype in [ ('TRIGGER', 'u2'), ('NOTE', 'S40') ]:
zname2, pos, length = zones[zname]
f.seek(pos,0)
triggers = np.fromstring(f.read(length) , dtype = [('pos','u4'), ('label', label_dtype)] , )
ea = EventArray(name =zname[0]+zname[1:].lower())
if not lazy:
keep = (triggers['pos']>=triggers['pos'][0]) & (triggers['pos']<rawdata.shape[0]) & (triggers['pos']!=0)
triggers = triggers[keep]
ea.labels = triggers['label'].astype('S')
ea.times = (triggers['pos']/sampling_rate).rescale('s')
else:
ea.lazy_shape = triggers.size
seg.eventarrays.append(ea)
# Read Event A and B
# Not so well tested
for zname in ['EVENT A', 'EVENT B']:
zname2, pos, length = zones[zname]
f.seek(pos,0)
epochs = np.fromstring(f.read(length) ,
dtype = [('label','u4'),('start','u4'),('stop','u4'),] )
ep = EpochArray(name =zname[0]+zname[1:].lower())
if not lazy:
keep = (epochs['start']>0) & (epochs['start']<rawdata.shape[0]) & (epochs['stop']<rawdata.shape[0])
epochs = epochs[keep]
ep.labels = epochs['label'].astype('S')
ep.times = (epochs['start']/sampling_rate).rescale('s')
ep.durations = ((epochs['stop'] - epochs['start'])/sampling_rate).rescale('s')
else:
ep.lazy_shape = triggers.size
seg.epocharrays.append(ep)
seg.create_many_to_one_relationship()
return seg
def generate_one_simple_segment(
seg_name='segment 0',
supported_objects=[],
nb_analogsignal=4,
t_start=0. * pq.s,
sampling_rate=10 * pq.kHz,
duration=6. * pq.s,
nb_spiketrain=6,
spikerate_range=[.5 * pq.Hz, 12 * pq.Hz],
event_array_types={
'stim': ['a', 'b', 'c', 'd'],
'enter_zone': ['one', 'two'],
'color': ['black', 'yellow', 'green'],
},
event_array_size_range=[5, 20],
epoch_array_types={
'animal state': ['Sleep', 'Freeze', 'Escape'],
'light': ['dark', 'lighted']
},
epoch_array_duration_range=[.5, 3.],
):
seg = Segment(name=seg_name)
if AnalogSignal in supported_objects:
for a in range(nb_analogsignal):
anasig = AnalogSignal(rand(int(sampling_rate * duration)),
sampling_rate=sampling_rate,
t_start=t_start,
units=pq.mV,
channel_index=a,
name='sig %d for segment %s' % (a, seg.name))
seg.analogsignals.append(anasig)
if SpikeTrain in supported_objects:
for s in range(nb_spiketrain):
spikerate = rand() * np.diff(spikerate_range)
spikerate += spikerate_range[0].magnitude
#spikedata = rand(int((spikerate*duration).simplified))*duration
#sptr = SpikeTrain(spikedata,
# t_start=t_start, t_stop=t_start+duration)
# #, name = 'spiketrain %d'%s)
spikes = rand(int((spikerate * duration).simplified))
spikes.sort() # spikes are supposed to be an ascending sequence
sptr = SpikeTrain(spikes * duration,
t_start=t_start,
t_stop=t_start + duration)
sptr.annotations['channel_index'] = s
seg.spiketrains.append(sptr)
if EventArray in supported_objects:
for name, labels in iteritems(event_array_types):
ea_size = rand() * np.diff(event_array_size_range)
ea_size += event_array_size_range[0]
labels = np.array(labels, dtype='S')
labels = labels[(rand(ea_size) * len(labels)).astype('i')]
ea = EventArray(times=rand(ea_size) * duration, labels=labels)
seg.eventarrays.append(ea)
if EpochArray in supported_objects:
for name, labels in iteritems(epoch_array_types):
t = 0
times = []
durations = []
while t < duration:
times.append(t)
dur = rand() * np.diff(epoch_array_duration_range)
dur += epoch_array_duration_range[0]
durations.append(dur)
t = t + dur
labels = np.array(labels, dtype='S')
labels = labels[(rand(len(times)) * len(labels)).astype('i')]
epa = EpochArray(
times=pq.Quantity(times, units=pq.s),
durations=pq.Quantity([x[0] for x in durations], units=pq.s),
labels=labels,
)
seg.epocharrays.append(epa)
# TODO : Spike, Event, Epoch
return seg