def decode(self, t0, t1):
oo = self
## each
oo.pX_Nm[t0] = 1. / oo.Nx
if oo.dbgMvt:
oo.pX_Nm[t0, 20:30] = 151/5.
A = _N.trapz(oo.pX_Nm[t0], dx=oo.dxp)
oo.pX_Nm[t0] /= A
oo.intgrd= _N.empty(oo.Nx)
oo.intgrd2d= _N.empty((oo.Nx, oo.Nx))
oo.intgrl = _N.empty(oo.Nx)
oo.xTrs = _N.zeros((oo.Nx, oo.Nx)) # Gaussian
x = _N.linspace(-oo.xA, oo.xA, oo.Nx)
## (xk - a xk1)
i = 0
if not oo.bWmaze:
for x1 in x:
j = 0
for x0 in x: # j is current position
oo.xTrs[i, j] = _N.exp(-((x1-oo.Fp*x0)**2)/(2*oo.q2p))
j += 1
i += 1
else:
grdsz = (12./oo.Nx)
spdGrdUnts = _N.diff(oo.pos) / grdsz # unit speed ( per ms ) in grid units
# avg. time it takes to move 1 grid is 1 / _N.mean(_N.abs(spdGrdUnts))
# p(i+1, i) = 1/<avg spdGrdUnts>
p1 = _N.mean(_N.abs(spdGrdUnts))*1
# assume Nx is even
#k2 = 0.02
k2 = 0.2
k3 = 0.1
for i in xrange(0, oo.Nx/2):
oo.xTrs[i, i] = 1-p1
if i > 0:
oo.xTrs[i-1, i] = p1
if i > 1: ## next nearest neighbor
oo.xTrs[i-2, i] = p1*k2
oo.xTrs[i+1, i] = p1*k2*k3
elif i == 1:
oo.xTrs[oo.Nx/2-1, 1] = p1*k2/2
oo.xTrs[oo.Nx/2, 1] = p1*k2/2
oo.xTrs[i+1, i] = p1*k2*k3
oo.xTrs[oo.Nx/2-1, 0] = p1/2
oo.xTrs[oo.Nx/2, 0] = p1/2
for i in xrange(oo.Nx/2, oo.Nx):
oo.xTrs[i, i] = 1-p1
if i < oo.Nx - 1:
oo.xTrs[i+1, i] = p1
if i < oo.Nx - 2:
oo.xTrs[i-1, i] = p1*k2*k3
oo.xTrs[i+2, i] = p1*k2
elif i == oo.Nx-2:
oo.xTrs[i-1, i] = p1*k2*k3
oo.xTrs[oo.Nx/2-1, oo.Nx-2] = p1*k2/2
oo.xTrs[oo.Nx/2, oo.Nx-2] = p1*k2/2
oo.xTrs[oo.Nx/2-1, oo.Nx-1] = p1/2
oo.xTrs[oo.Nx/2, oo.Nx-1] = p1/2
#oo.xTrs[:, j] += _N.mean(oo.xTrs[:, j])*0.01
for i in xrange(oo.Nx):
A = _N.trapz(oo.xTrs[:, i])*((2.*oo.xA)/oo.Nx)
oo.xTrs[:, i] /= A
# keep in mind that k_{k-1} is not treated as a value with a correct answer.
# integrate over all possible values of x_{k-1}
# Need value of integrand for all x_{k-1}
# I will perform integral L times for each time step
# multiply integral with p(\Delta N_k, m_k | x_k)
pNkmk0 = _N.exp(-oo.dt * oo.Lam_xk) # one for each tetrode
pNkmk = _N.ones(oo.Nx)
dims = _N.ones(oo.mdim, dtype=_N.int)*oo.Nm
fxdMks = _N.empty((oo.Nx, oo.mdim+1)) # for each pos, a fixed mark
fxdMks[:, 0] = oo.xp
pNkmk = _N.empty((oo.Nx, oo.nTets))
tStart = _tm.time()
if oo.kde:
occ = 1./oo.iocc
iBx2 = 1. / (oo.Bx * oo.Bx)
sptl = []
for nt in xrange(oo.nTets):
sptl.append(-0.5*iBx2*(oo.xpr - oo.tr_pos[nt])**2) # this piece doesn't need to be evalu
if not oo.kde:
for nt in xrange(oo.nTets):
oo.mvNrm[nt].iSgs = _N.linalg.inv(oo.mvNrm[nt].covs)
oo.mvNrm[nt].i2pidcovs = 1/_N.sqrt(2*_N.pi*_N.linalg.det(oo.mvNrm[nt].covs))
oo.mvNrm[nt].i2pidcovsr= oo.mvNrm[nt].i2pidcovs.reshape((oo.mvNrm[nt].M, 1))
for t in xrange(t0+1,t1): # start at 1 because initial condition
#tt1 = _tm.time()
for nt in xrange(oo.nTets):
oo.Lklhd[nt, t] = pNkmk0[:, nt]
# build likelihood
if (oo.marks[t, nt] is not None) and (not oo.dbgMvt):
nSpks = len(oo.marks[t, nt])
for ns in xrange(nSpks):
fxdMks[:, 1:] = oo.marks[t, nt][ns]
if oo.kde:
#(atMark, fld_x, tr_pos, tr_mks, all_pos, mdim, Bx, cBm, bx)
#_ku.kerFr(fxdMks[0, 1:], fxdMks[:, 0], oo.tr_pos, oo.tr_mks, oo.mvpos, oo.mdim, oo.Bx, oo.cBm, oo.bx)
oo.Lklhd[nt, t] *= _ku.kerFr(fxdMks[0, 1:], sptl[nt], oo.tr_marks[nt], oo.mdim, oo.Bx, oo.Bm, oo.bx)* oo.iocc*oo.dt
else:
oo.Lklhd[nt, t] *= oo.mvNrm[nt].evalAtFxdMks_new(fxdMks)*oo.lmd0[nt] * oo.iocc * oo.dt
ttt1 =0
ttt2 =0
ttt3 =0
#tt2 = _tm.time()
# transition convolved with previous posterior
_N.multiply(oo.xTrs, oo.pX_Nm[t-1], out=oo.intgrd2d)
oo.intgrl = _N.trapz(oo.intgrd2d, dx=oo.dxp, axis=1)
#for ixk in xrange(oo.Nx): # above trapz over 2D array
# oo.intgrl[ixk] = _N.trapz(oo.intgrd2d[ixk], dx=oo.dxp)
#tt3 = _tm.time()
oo.pX_Nm[t] = oo.intgrl * _N.product(oo.Lklhd[:, t], axis=0)
A = _N.trapz(oo.pX_Nm[t], dx=oo.dxp)
oo.pX_Nm[t] /= A
#tt4 = _tm.time()
#print "%(1).3e %(2).3e %(3).3e" % {"1" : (tt2-tt1), "2" : (tt3-tt2), "3" : (tt4-tt3)}
#print "%(1).3e %(2).3e" % {"1" : ttt1, "2" : ttt2}
tEnd = _tm.time()
print "decode %(1).3e" % {"1" : (tEnd-tStart)}
def decodeKDE(self, t0, t1):
"""
decode activity from [t0:t1]
"""
oo = self
oo.init_pX_Nm(t0) # flat pX_Nm init cond at start of decode
oo.LmdMargOvrMrks(0, t0)
## each
# k_{k-1} is not treated as a value with a correct answer.
# integrate over all possible values of x_{k-1}
# Need value of integrand for all x_{k-1}
# I will perform integral L times for each time step
# multiply integral with p(\Delta N_k, m_k | x_k)
pNkmk0 = _N.exp(-oo.dt * oo.Lam_MoMks) # one for each tetrode
pNkmk = _N.ones(oo.Nx)
fxdMks = _N.empty((oo.Nx, oo.mdim+1)) # fixed mark for each field pos.
fxdMks[:, 0] = oo.xp
pNkmk = _N.empty((oo.Nx, oo.nTets))
tStart = _tm.time()
ibx2 = 1. / (oo.bx * oo.bx)
sptl = []
dens = []
for nt in xrange(oo.nTets):
sptl.append(-0.5*ibx2*(oo.xpr - oo.tr_pos[nt])**2) # this piece doesn't need to be evalu
for t in xrange(t0+1,t1): # start at 1 because initial condition
#tt1 = _tm.time()
for nt in xrange(oo.nTets):
oo.Lklhd[nt, t] = pNkmk0[:, nt]
if (oo.mkpos[nt][t, 1] == 1):
fxdMks[:, 1:] = oo.mkpos[nt][t, 2:]
#(atMark, fld_x, tr_pos, tr_mks, all_pos, mdim, Bx, cBm, bx)
#dens.append(_ku.kerFr(fxdMks[0, 1:], sptl[nt], oo.tr_marks[nt], oo.mdim, oo.Bx, oo.Bm, oo.bx, oo.dxp, oo.occ))
oo.Lklhd[nt, t] *= _ku.kerFr(fxdMks[0, 1:], sptl[nt], oo.tr_marks[nt], oo.mdim, oo.Bx, oo.Bm, oo.bx, oo.dxp, oo.occ)
ttt1 =0
ttt2 =0
ttt3 =0
#tt2 = _tm.time()
# transition convolved with previous posterior
_N.multiply(oo.xTrs, oo.pX_Nm[t-1], out=oo.intgrd2d)
oo.intgrl = _N.trapz(oo.intgrd2d, dx=oo.dxp, axis=1)
#for ixk in xrange(oo.Nx): # above trapz over 2D array
# oo.intgrl[ixk] = _N.trapz(oo.intgrd2d[ixk], dx=oo.dxp)
#tt3 = _tm.time()
oo.pX_Nm[t] = oo.intgrl * _N.product(oo.Lklhd[:, t], axis=0)
A = _N.trapz(oo.pX_Nm[t], dx=oo.dxp)
oo.pX_Nm[t] /= A
#tt4 = _tm.time()
#print "%(1).3e %(2).3e %(3).3e" % {"1" : (tt2-tt1), "2" : (tt3-tt2), "3" : (tt4-tt3)}
#print "%(1).3e %(2).3e" % {"1" : ttt1, "2" : ttt2}
tEnd = _tm.time()
print "decode %(1).3e" % {"1" : (tEnd-tStart)}