def victorDistance(ds, dpathPred='/gspikes', dpathDat="/spikes", cost=.01, searchOffset=20):
'''calculate the victor/purpura spike metric between the two spike sets, with the indicated cost'''
if not searchOffset:
d1 = ds.getSubData(dpathPred).getData()[:,0]
d2 = ds.getSubData(dpathDat).getData()[:,0]
dist = sc.victorDistance(d1, d2, cost)
print dist
else:
offsets = range(-searchOffset, searchOffset)
distances = []
d1 = ds.getSubData(dpathPred).getData()[:,0]
d2 = ds.getSubData(dpathDat).getData()[:,0]
for i, o in enumerate(offsets):
td = d1 + o
dist = sc.victorDistance(td, d2, cost)
distances.append(dist)
mdi = distances.index(min(distances))
print "Offset: %i, Distance: %g" % (offsets[mdi], distances[mdi])
dist = distances[mdi]
z = ds.getSubData(dpathDat).getData().shape[0]
r = ds.attrib('real_victordist')
r = r * z
s = dist - r
print z, r
s = 100 - 100*s / (z-r)
print "percentage = %g" % s
def _spikeDistance(e1d, e2d, cost): if cost==0: print "0 cost" d=abs(e1d.shape[0]-e2d.shape[0]) elif cost>=2: print "Inf cost" d=union1d(e1d, e2d).shape[0] elif not (e1d.shape[0] and e2d.shape[0]): print "One sequence empty" d=max(e1d.shape[0], e2d.shape[0]) elif abs(e1d.shape[0]-e2d.shape[0])>3*e1d.shape[0]: print "excessive miss" d=2*abs(e1d.shape[0]-e2d.shape[0]) else: d=sc.victorDistance(e1d, e2d, cost) return d
def randomVictorDistance(ds, dpathPred='/gspikes', dpathDat="/spikes", cost=.01, searchOffset=20): rd = [] d1 = ds.getSubData(dpathPred).getData()[:,0] d2 = ds.getSubData(dpathDat).getData()[:,0] ran = (min(min(d2), min(d1)), max(max(d2), max(d1))) nspikes = d1.shape[0] for j in range(20): offsets = range(-searchOffset, searchOffset) distances = [] d1 = randint(ran[0], ran[1], nspikes) for i, o in enumerate(offsets): td = d1 + o dist = sc.victorDistance(td, d2, cost) distances.append(dist) mdi = distances.index(min(distances)) rd.append(distances[mdi]) rd = array(rd) print "Mean %g, Std %d" % (mean(rd), std(rd))
def calcRealVictorDist(ds, dpathg='/rg', noise=.1, cost=.01):
sd = ds.getSubData(dpathg)
fs = ds.fs()
g = sd.getData()[:,0]
match = []
nspikes = []
for i in range(5):
evts1 = _gtoSpikes(g, noise, fs)
evts2 = _gtoSpikes(g, noise, fs)
dist = sc.victorDistance(evts1, evts2, cost)
print dist
ns = (evts1.shape[0] + evts2.shape[0])/2
m = dist / ns
print m
match.append(m)
m = mean(array(match))
print m
ds.setAttrib('real_victordist', m)
