def read_spikes(self, params):
self.__read_metadata()
p = self.__check_params(params)
from NeuroTools.signals import spikes
data = self.get_data()
result = spikes.SpikeList(data, p['id_list'], p['t_start'], p['t_stop'], p['dims'])
del data
return result
def setUp(self):
self.spikes = []
nb_cells = 10
frequencies = [nb_cells for _ in xrange(10)]
for idx in xrange(nb_cells):
param = 1. / frequencies[idx]
isi = numpy.random.exponential(param, 1000)
pspikes = numpy.cumsum(isi) * 1000. # convert to ms
for spike in pspikes:
self.spikes.append((idx, spike))
self.spk = spikes.SpikeList(self.spikes, range(10))
def testSelect_Ids(self):
spks = []
nb_cells = 3
frequencies = [5, 40, 40]
for idx in xrange(nb_cells):
param = 1. / frequencies[idx]
isi = numpy.random.exponential(param, 100)
pspikes = numpy.cumsum(isi) * 1000. # convert to ms
for spike in pspikes:
spks.append((idx, spike))
spk = spikes.SpikeList(spks, range(3), 0, 1000)
assert len(spk.select_ids("cell.mean_rate() < 20")) == 1
def read_spikes(self, params):
from NeuroTools.signals import spikes
contents = numpy.load(self.fileobj)
spike_data = contents['data'][:, (1,0)]
self.metadata = M = {}
for k,v in contents['metadata']:
M[k] = eval(v)
id_list = range(M['first_id'], M['last_id'])
t_stop = params['t_stop']
# really need to check the agreement between file metadata and
# params for all metadata items
return spikes.SpikeList(spike_data, id_list, t_start=0.0, t_stop=t_stop,
dims=M['dimensions'])
def setUp(self):
self.spikes = []
nb_cells = 50
frequencies = numpy.random.uniform(0, 50, nb_cells)
for idx in xrange(nb_cells):
param = 1. / frequencies[idx]
isi = numpy.random.exponential(param, 100)
pspikes = numpy.cumsum(
isi) * 1000. # To convert the spikes_time in ms
for spike in pspikes:
self.spikes.append((idx, spike))
self.spk = spikes.SpikeList(self.spikes, range(nb_cells), 0, 4000)
self.spk.save("tmp2.txt")
def setUp(self):
self.spikes = []
nb_cells = 50
frequencies = numpy.random.uniform(0, 50, 50)
for idx in xrange(nb_cells):
param = 1. / frequencies[idx]
isi = numpy.random.exponential(param, 100)
pspikes = numpy.cumsum(
isi) * 1000. # To convert the spikes_time in ms
for spike in pspikes:
self.spikes.append((idx, spike))
self.spk = spikes.SpikeList(self.spikes, range(50), 0, 4000)
try:
os.mkdir("Plots")
except Exception:
pass
def testTime_Slice(self):
spk = spikes.SpikeList(self.spikes, range(10), t_start=0)
new_spk = spk.time_slice(0, 1000.)
assert (new_spk.t_start == spk.t_start) and (new_spk.t_stop == 1000.)
def testMerge(self):
spk2 = spikes.SpikeList(self.spikes, range(50, 60))
self.spk.merge(spk2)
assert len(self.spk) == 20
