def testEpochsFromSpiketrain(self):
"""test for epoch generation from a spiketrain"""
st = sp.array([50, 100, 150])
cut = (5, 5)
st_ep = sp.array([
[45, 55],
[95, 105],
[145, 155],
])
assert_equal(epochs_from_spiketrain(st, cut), st_ep)
assert_equal(epochs_from_spiketrain(st, cut, end=150), st_ep[:-1])
assert_equal(epochs_from_spiketrain(st, cut),
epochs_from_spiketrain(st, sum(cut)))
def testEpochsFromSpiketrain(self):
"""test for epoch generation from a spiketrain"""
st = sp.array([50, 100, 150])
cut = (5, 5)
st_ep = sp.array([
[45, 55],
[95, 105],
[145, 155],
])
assert_equal(epochs_from_spiketrain(st, cut), st_ep)
assert_equal(epochs_from_spiketrain(st, cut, end=150), st_ep[:-1])
assert_equal(epochs_from_spiketrain(st, cut),
epochs_from_spiketrain(st, sum(cut)))
print "retrieving multiunit spike set @tf=%d" % tf
spks_info = []
spks = []
for id_trl in trial_ids:
trial_st = None
try:
trial_st = db.get_unit_data(id_mu, id_trl)["spiketrain"]
if trial_st.size == 0:
print "\tno spiketrain for %s" % db.get_fname_for_id(id_trl)
continue
trial_spks, trial_st = get_aligned_spikes(
data[id_trl], trial_st, tf, align_at=align_at, mc=False, kind="min"
)
end = data[id_trl].shape[0]
nep = epochs_from_spiketrain(trial_st, tf, end=end)
nep = invert_epochs(nep, end=end)
nep = merge_epochs(nep)
ndet.update(data[id_trl], epochs=nep)
spks.append(trial_spks)
spks_info.append(sp.vstack([[id_trl] * trial_st.size, trial_st]).T)
print "\tprocessed %s" % db.get_fname_for_id(id_trl)
except Exception, e:
raise RuntimeError("error processing %s\n%s" % (db.get_fname_for_id(id_trl), e))
finally:
del trial_st
spks_info = sp.vstack(spks_info)
spks = sp.vstack(spks)
print "found %d spikes in total" % spks.shape[0]
print "done."