def weight(self, gid):
# soma does not allow syn. weight
if gid_is_mitral(gid) or \
gid_is_mtufted(gid) or \
gid_is_granule(gid):
return None
# init
try:
winit = self.initweights[gid]
except KeyError:
winit = 0
if gid % 2 != 0 and self.hebbian:
tpre = [0]
try:
tpre += self.retrieve(gid)
except KeyError: pass
tpost = [0]
try:
tpost += self.retrieve(gid+1)
except KeyError: pass
return hebbian.weights(tpre, tpost, winit)
else:
return nonhebbian.weights(self.retrieve(gid), winit, gid % 2 == 0)
def soma_prefix(gid):
if bulbdef.gid_is_mitral(gid):
return ' MC'
elif bulbdef.gid_is_mtufted(gid):
return 'mTC'
elif bulbdef.gid_is_granule(gid):
return ' GC'
elif bulbdef.gid_is_blanes(gid):
return 'dSAC'
return None
def printinfo(self):
print '\n\nelement information'
for gid in sorted(set(self.gids)):
if bulbdef.gid_is_mitral(gid):
print 'MC %d' % gid
elif bulbdef.gid_is_mtufted(gid):
print 'mTC %d' % gid
elif bulbdef.gid_is_granule(gid):
print 'GC %d' % gid
else:
ci = bulbdict.query(gid)
if bulbdef.gid_is_mitral(ci[0]):
s = 'MC '
else:
s = 'mTC'
x = (gid, s) + ci[:3] + (ci[3], )
print '%d:\t %s %d, %d, %.1g\t<-> GC %d' % x
def __init__(self, obj, spkr, dt=25.0, MinFR=0, MaxFR=200.0, MinW=0, MaxW=1):
self.obj = obj
def retrieve(func, gid, Tvals, tstop):
try:
t, y = func(gid)
except KeyError:
t = [0]
y = [0]
t.append(tstop)
y.append(y[-1])
return np.interp(Tvals, t, y)
def normalize(y, miny, maxy):
y = np.divide(np.subtract(y, miny), maxy - miny)
y = np.floor(np.multiply(y, len(self.obj.palette)))
y[np.where(y < 0)] = 0
y[np.where(y > (len(self.obj.palette)-1))] = len(self.obj.palette)-1
return y
from bulbvis import GC, Segment, Soma
# time to visualize
Tvals = np.linspace(0, spkr.tstop, int(spkr.tstop/dt)+1)
if len(self.obj.gids) == 0:
self.Wexc_data = [ (0, 0) ]
self.Winh_data = [ (0, 0) ]
self.FRdata = [ (0, 0) ]
elif isinstance(self.obj, Soma):
self.Wexc_data = [ (0, 0) ]
self.Winh_data = [ (0, 0) ]
#print normalize(retrieve(spkr.frequency, self.obj.gids[0], Tvals, spkr.tstop),MinFR, MaxFR)
self.FRdata = compress(Tvals,
normalize(retrieve(spkr.frequency, self.obj.gids[0], Tvals, spkr.tstop), \
MinFR, MaxFR))
else:
# interpolated weight
Wexc_interp = []
Winh_interp = []
# interpolated frequencies
FRinterp = []
# gather all data
for gid in self.obj.gids:
# don't show weights for somas
if bulbdef.gid_is_mitral(gid) or \
bulbdef.gid_is_mtufted(gid) or \
bulbdef.gid_is_granule(gid):
continue
# FR interp
FRinterp.append(retrieve(spkr.frequency, gid, Tvals, spkr.tstop))
# excitatory
Wexc_interp.append(retrieve(spkr.weight, gid, Tvals, spkr.tstop))
# if it is granule do not visualize WExc inverted
if isinstance(self.obj, GC):
Winh_interp.append(Wexc_interp[-1])
else:
Winh_interp.append(retrieve(spkr.weight, gid-1, Tvals, spkr.tstop))
# get max for granule cell
# avg for mitral cell segment
if isinstance(self.obj, GC):
Wexc_interp = np.matrix(Wexc_interp).max(0)
Winh_interp = np.matrix(Winh_interp).max(0)
else:
Wexc_interp = np.matrix(Wexc_interp).mean(0)
Winh_interp = np.matrix(Winh_interp).mean(0)
# clean
Wexc_interp = np.array(Wexc_interp)[0]
Winh_interp = np.array(Winh_interp)[0]
FRinterp = np.array(np.matrix(FRinterp).mean(0))[0]
# normalizations
Wexc_interp = normalize(Wexc_interp, MinW, MaxW)
Winh_interp = normalize(Winh_interp, MinW, MaxW)
FRinterp = normalize(FRinterp, MinFR, MaxFR)
# compress
self.Wexc_data = compress(Tvals, Wexc_interp)
self.Winh_data = compress(Tvals, Winh_interp)
self.FRdata = compress(Tvals, FRinterp)
def gid_is_soma(gid):
return bulbdef.gid_is_mitral(gid) or \
bulbdef.gid_is_mtufted(gid) or \
bulbdef.gid_is_granule(gid) or \
bulbdef.gid_is_blanes(gid)