def _get_patterns(self, cell):
def _find_classes(trains,ev):
"""Classify spike trains"""
tr_cl =[x[(x>ev[0]) & (x<ev[-1]) ] for x in trains]
cl = np.array([np.sum(2**(np.unique(np.searchsorted(ev,y))-1)) for y in tr_cl])
cl[cl<0]=0
return cl
def _filter_spikes(spt,stim,win):
i=np.searchsorted(stim,spt);
spt2=(spt-stim[i-1]);
spikes=spt[(spt2<win[1]) & (spt2>=win[0])]
return spikes
dataset = dashboard.read_dataset(self.io_filter, cell)
spt = dataset['spt']
stim = dataset['stim']
ev = np.sort(self.root.h5filter.get_cached_string(cell,
"events"))
try:
win = [ev[0], ev[-1]]
except IndexError:
ev = [0, 30]
win = ev
dataset['events'] = ev
spt = _filter_spikes(spt, stim, win)
dataset['spt'] = spt
trains = basic.SortSpikes(spt, stim, win)
cl = _find_classes(trains, ev)
return dataset, cl
def event_selector(self, cellid):
dataset = dashboard.read_dataset(self.io_filter, cellid)
spt = dataset['spt']
stim = dataset['stim']
psth, time = basic.CalcPSTH(spt, stim)
data = [{'x': x, 'y': int(y)} for x, y in zip(time, psth)]
template = self.env.get_template('event_selector.html')
doc = doc2html(self.__doc__)
return template.render(cellid=str(cellid),
data=json.dumps(data),
doc=doc)
