def get_spikes(self):
spikes = {}
for pop in chain(self._neuronal_populations, self._source_populations):
spike_arr = pop.getSpikes()
spikes[pop.label] = signals.SpikeList(spike_arr,
id_list=range(pop.size))
return spikes
def process_spikes(ev, tmax, tmin=0.):
"""
Converts NEST events to dictionary of numpy arrays
:param ev: NEST events
:param tmax: max time until when to convert
:param tmin: min time from which to convert
:return: dict of parsed spikes
"""
senders = ev['senders']
times = ev['times']
idxs = np.where(times >= tmin)
ev['senders'] = senders[idxs]
ev['times'] = times[idxs]
senders = senders[idxs]
times = times[idxs]
spikelist = [(senders[i], ev['times'][i]) for i in range(len(senders))]
if len(senders) > 0:
ids = np.arange(senders.min(), senders.max() + 1)
else:
ids = np.array([])
spike_trains = signals.SpikeList(spikelist, ids, t_start=tmin, t_stop=tmax)
return {"ids": ids, "spike_list": spikelist, "spike_trains": spike_trains, 'ev': ev}
def get_rates(self, t_start, t_stop):
"""Calculates and returns mean rates for e and i populations.
This uses the internally saved spikes which are available after calling run()
:param t_start: start time
:param t_stop: end time
:return: dictionary of two rates
"""
print "Returning rates from %f to %f" % (t_start, t_stop)
if t_start >= t_stop:
raise ValueError("t_stop should be bigger than t_start.")
ex_spiketrains_base = signals.SpikeList(self.spikes["e"]["spike_list"],
self.spikes["e"]["ids"],
t_start=t_start,
t_stop=t_stop)
ex_base = ex_spiketrains_base.mean_rate()
in_spiketrains_base = signals.SpikeList(self.spikes["i"]["spike_list"],
self.spikes["i"]["ids"],
t_start=t_start,
t_stop=t_stop)
in_base = in_spiketrains_base.mean_rate()
return {"e": ex_base, "i": in_base}
def read_spikes(self, params):
"""
Read a SpikeList object from a file and return the SpikeList object, created from the File and
from the additional params that may have been provided
Examples:
>> params = {'id_list' : range(100), 't_stop' : 1000}
>> handler.read_spikes(params)
SpikeList Object (with params taken into account)
"""
p = self.__check_params(params)
from NeuroTools import signals
data = self.get_data()
data, p = self._fix_id_list(data, p)
return signals.SpikeList(data, p['id_list'], p['t_start'], p['t_stop'], p['dims'])
def spikestats():
ff = open('./data/0/spikes.dat', 'r')
fdata = ff.readlines()
lst = []
nid = 0
for l in fdata:
ar = numpy.fromstring(l, sep=' ')
for a in ar:
lst.append((nid, a))
nid += 1
slist = signals.SpikeList(lst, range(0, nid))
slist.raster_plot()
#pylab.figure()
#pylab.imshow(spraster , interpolation='nearest', aspect='auto',cmap='hot')
#pp = numpy.correlate(spraster[100, :], spraster[102,:], 'full');
#print pp;
#pylab.plot(pp)
pylab.show()