def read_segment(
self,
# the 2 first keyword arguments are imposed by neo.io API
lazy=False,
cascade=True,
# all following arguments are decied by this IO and are free
segment_duration=15.,
num_analogsignal=4,
num_spiketrain_by_channel=3,
):
"""
Return a fake Segment.
The self.filename does not matter.
In this IO read by default a Segment.
This is just a example to be adapted to each ClassIO.
In this case these 3 paramters are taken in account because this function
return a generated segment with fake AnalogSignal and fake SpikeTrain.
Parameters:
segment_duration :is the size in secend of the segment.
num_analogsignal : number of AnalogSignal in this segment
num_spiketrain : number of SpikeTrain in this segment
"""
sampling_rate = 10000. #Hz
t_start = -1.
#time vector for generated signal
timevect = np.arange(t_start, t_start + segment_duration,
1. / sampling_rate)
# create an empty segment
seg = Segment(name='it is a seg from exampleio')
if cascade:
# read nested analosignal
for i in range(num_analogsignal):
ana = self.read_analogsignal(lazy=lazy,
cascade=cascade,
channel_index=i,
segment_duration=segment_duration,
t_start=t_start)
seg.analogsignals += [ana]
# read nested spiketrain
for i in range(num_analogsignal):
for _ in range(num_spiketrain_by_channel):
sptr = self.read_spiketrain(
lazy=lazy,
cascade=cascade,
segment_duration=segment_duration,
t_start=t_start,
channel_index=i)
seg.spiketrains += [sptr]
# create an EventArray that mimic triggers.
# note that ExampleIO do not allow to acess directly to EventArray
# for that you need read_segment(cascade = True)
eva = EventArray()
if lazy:
# in lazy case no data are readed
# eva is empty
pass
else:
# otherwise it really contain data
n = 1000
# neo.io support quantities my vector use second for unit
eva.times = timevect[(np.random.rand(n) *
timevect.size).astype('i')] * pq.s
# all duration are the same
eva.durations = np.ones(n) * 500 * pq.ms
# label
l = []
for i in range(n):
if np.random.rand() > .6: l.append('TriggerA')
else: l.append('TriggerB')
eva.labels = np.array(l)
seg.eventarrays += [eva]
create_many_to_one_relationship(seg)
return seg
def read_segment(self,
# the 2 first keyword arguments are imposed by neo.io API
lazy = False,
cascade = True,
# all following arguments are decied by this IO and are free
segment_duration = 15.,
num_analogsignal = 4,
num_spiketrain_by_channel = 3,
):
"""
Return a fake Segment.
The self.filename does not matter.
In this IO read by default a Segment.
This is just a example to be adapted to each ClassIO.
In this case these 3 paramters are taken in account because this function
return a generated segment with fake AnalogSignal and fake SpikeTrain.
Parameters:
segment_duration :is the size in secend of the segment.
num_analogsignal : number of AnalogSignal in this segment
num_spiketrain : number of SpikeTrain in this segment
"""
sampling_rate = 10000. #Hz
t_start = -1.
#time vector for generated signal
timevect = np.arange(t_start, t_start+ segment_duration , 1./sampling_rate)
# create an empty segment
seg = Segment( name = 'it is a seg from exampleio')
if cascade:
# read nested analosignal
for i in range(num_analogsignal):
ana = self.read_analogsignal( lazy = lazy , cascade = cascade ,
channel_index = i ,segment_duration = segment_duration, t_start = t_start)
seg.analogsignals += [ ana ]
# read nested spiketrain
for i in range(num_analogsignal):
for _ in range(num_spiketrain_by_channel):
sptr = self.read_spiketrain(lazy = lazy , cascade = cascade ,
segment_duration = segment_duration, t_start = t_start , channel_index = i)
seg.spiketrains += [ sptr ]
# create an EventArray that mimic triggers.
# note that ExampleIO do not allow to acess directly to EventArray
# for that you need read_segment(cascade = True)
eva = EventArray()
if lazy:
# in lazy case no data are readed
# eva is empty
pass
else:
# otherwise it really contain data
n = 1000
# neo.io support quantities my vector use second for unit
eva.times = timevect[(np.random.rand(n)*timevect.size).astype('i')]* pq.s
# all duration are the same
eva.durations = np.ones(n)*500*pq.ms
# label
l = [ ]
for i in range(n):
if np.random.rand()>.6: l.append( 'TriggerA' )
else : l.append( 'TriggerB' )
eva.labels = np.array( l )
seg.eventarrays += [ eva ]
seg.create_many_to_one_relationship()
return seg
