def read_segment(self, lazy=False, cascade=True):
seg = Segment(name=self.filename[-4])
seg.file_origin = os.path.basename(self.filename)
with open(self.filename, 'r') as nf:
for i, l in enumerate(nf):
if len(l) <= 2:
continue
spikes = map(float, l[:-1].split(", "))
seg.spiketrains.append(
SpikeTrain(spikes, name=str(i),
units=pq.s, t_stop=np.amax(spikes)))
return seg
def read_segment(self, lazy=False, cascade=True):
seg = Segment(name=self.filename[-4])
seg.file_origin = os.path.basename(self.filename)
# Just one pass through the file! Do it live!
with open(self.filename, 'r') as nf:
for i, l in enumerate(nf):
if i == 0:
# First line, header. Set up basic data structures.
cols = l[:-1].split(",")
data = [[] for _ in xrange(len(cols))]
lastdata = [None for _ in xrange(len(cols))]
types = []
for col in cols:
if col == "time":
types.append("Time")
elif col.endswith("_spikes"):
types.append("SpikeTrains")
elif col.endswith("_event"):
types.append("EventArray")
elif col.endswith("_states"):
types.append("EpochArray")
else:
types.append("AnalogSignal")
continue
ll = l[:-1].split(",")
if i == 1:
# Second line. Get number of dimensions for everything.
for j, t in enumerate(types):
if t == "SpikeTrains" or t == "AnalogSignal":
dims = len(ll[j].split(';'))
data[j] = [[] for _ in xrange(dims)]
elif t == "EventArray":
data[j] = [[] for _ in xrange(3)]
# No continue, keep going
time = float(ll[0])
for j, lll in enumerate(ll):
if types[j] == "Time":
data[j].append(float(lll))
elif types[j] == "AnalogSignal":
for k, v in enumerate(lll.split(";")):
data[j][k].append(float(v))
elif types[j] == "EventArray":
curdata = float(lll)
if curdata == 1.0 and curdata != lastdata[j]:
data[j][0].append(time)
elif curdata == 0.0 and curdata != lastdata[j]:
data[j][1].append(time)
elif curdata == -1.0 and curdata != lastdata[j]:
data[j][2].append(time)
lastdata[j] = curdata
elif types[j] == "SpikeTrains":
for k in [k for k, s in
enumerate(lll.split(";")) if s == '1']:
data[j][k].append(time)
t_start = data[0][0]
t_stop = data[0][-1]
period = pq.s * (t_stop - t_start) / float(len(data[0]))
# File closed now, process each column and add to segment
# for col, typ, dat in izip(cols, types, data):
for col, typ, dat in zip(cols, types, data):
if typ == "SpikeTrains":
for i, times in enumerate(dat):
seg.spiketrains.append(
SpikeTrain(times, name=col + '_' + str(i),
units=pq.s, t_stop=t_stop,
sampling_rate=1.0 / period))
elif typ == "AnalogSignal":
for i, v in enumerate(dat):
seg.analogsignals.append(
AnalogSignal(v, units=pq.mV, sampling_period=period,
name=col + '_' + str(i)))
elif typ == "EventArray":
name = col[:-5] + 'on'
labels = np.array([name] * len(dat[0]), dtype='S')
seg.eventarrays.append(
EventArray(times=dat[0], labels=labels, channel_name=name))
name = col[:-5] + 'zero'
labels = np.array([name] * len(dat[0]), dtype='S')
seg.eventarrays.append(
EventArray(times=dat[1], labels=labels, channel_name=name))
name = col[:-5] + 'off'
labels = np.array([name] * len(dat[0]), dtype='S')
seg.eventarrays.append(
EventArray(times=dat[2], labels=labels, channel_name=name))
return seg