def append(self, et_fn):
for etle in self.evt_filenames:
if etle.fn == et_fn:
return False
self.evt_filenames.append(EventTableListEntry(fn=et_fn))
#self.evt_filenames.sort(cmp=lambda x,y: cmp(x.short_fn,y.short_fn))
self.evts.append(eegpy.EventTable(str(et_fn)))
def extract_part_from_eeg(fn_in, fn_out, start, end, et_in=None, et_out=None):
"""Extract a part of an EEG, write it to new file"""
eeg = eegpy.open_eeg(fn_in)
out = eegpy.F32(fn_out,
"w+",
shape=(end - start, eeg.shape[1]),
cNames=eeg.channel_names,
Fs=eeg.Fs)
out[:] = eeg[start:end, :]
if not et_in == None:
if et_out == None:
et_out = os.path.join(os.path.splitext(fn_out)[0], ".evt")
et = eegpy.EventTable(et_in)
et -= start
et.save(et_out)
def _load(self):
print "Load from EventTable"
extension = "evt"
wildcard = "EventTable|*.evt|VA MarkerFile|*.vmrk"
fileDialog = FileDialog(action='open',
title='Load EventTable',
wildcard=wildcard)
fileDialog.open()
if fileDialog.path == '' or fileDialog.return_code == CANCEL:
return False
else:
print "Opening", fileDialog.path
#et = eegpy.load(str(fileDialog.path))
et = eegpy.EventTable(str(fileDialog.path))
for k in et.keys():
for t in et[k]:
#print k,t
self.append(t, k, False, False)
self.markers.sort(cmp=self.cmp_markers)
def __main__(fn):
et = eegpy.EventTable(fn)
plot_hypnogram(et)
p.show()
np.array(stage_map.values()).max() + 1))
#print [x for x in stage_map.keys()],[stage_map[x] for x in stage_map.keys()]
del stage_map["mask"]
p.yticks([stage_map[x] for x in stage_map.keys()],
[x for x in stage_map.keys()])
return xs, ys
def __main__(fn):
et = eegpy.EventTable(fn)
plot_hypnogram(et)
p.show()
if __name__ == "__main__":
#__main__(sys.argv[1])
events = []
for i in range(20):
events.append("awake")
events.append("REM")
events.append("Stage 1")
events.append("Stage 2")
events.append("Stage 3")
events.append("foo")
np.random.shuffle(events)
et = eegpy.EventTable()
for i, e in enumerate(events):
et.add_trigger(e, i * 20000)
plot_hypnogram(et)
def _fn_changed(self):
print "EventTableListEntry._fn_changed"
print self.fn
self.short_fn = os.path.split(self.fn)[1]
self.et = eegpy.EventTable(str(self.fn))
self.marker_count = len(self.et.get_all_events())
#res = analyze(d5,Fs=200.0)
#p.imshow(n.abs(res)**2.,aspect="auto",origin="lower",interpolation="bilinear")
#p.colorbar()
#p.show()
#print freqs
#print res
#print res.shape
#ar = n.random.random((1024+512,10))
#wtp1 = wavepower_lin(ar[512:],"db4",normalise=False)
#wtp2 = wavepower_lin(ar,"db4",normalise=True,as_dB=True)
#wtp3 = wavepower_lin(ar,"db4",normalise=True,as_dB=False)
import pylab as p
import eegpy
from eegpy.plot.wavelet import plot_wavedec_lin
eeg = eegpy.F32("/media/story/SchlafGed/iEEG/data/canseven_bp.f32")
et = eegpy.EventTable(
"/media/story/SchlafGed/iEEG/data/evts/canseven_bp_manuell.evt")
data = eeg.get_data_for_events(
et.events_by_names(*["T %i" % i for i in range(239, 256)]),
start_end=(-512, 1024),
channels=[10])
wts = [0, 0, 0]
wts[0] = wavepower_lin(data[512:, 0, :], "db4", normalise=False)
wts[1] = wavepower_lin(data[:, 0, :], "db4", normalise=True, as_dB=False)
wts[2] = wavepower_lin(data[:, 0, :], "db4", normalise=True, as_dB=True)
for i in range(3):
p.subplot(3, 1, i + 1)
plot_wavedec_lin(wts[i].mean(axis=1))
p.colorbar()