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d5mDecodePCgm.py
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d5mDecodePCgm.py
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import cPickle as pickle
import numpy as _N
import matplotlib.pyplot as _plt
import kde as _kde
import scipy.stats as _ss
#import fitMvNormE as fMN
import fitMvNormTet as fMN
import time as _tm
import kdeutil as _ku
import scipy.integrate as _si
import multiprocessing as _mp
# Decode unsorted spike train
# In our case with no history dependence
# p(X_t | dN_t, m_t)
#def parWrap(mvNrm, M, stpos, marks, n1, n2, initPriors):
def parWrapFit(args):
"""
parallel wrapper
"""
mvNrm = args[0]
M = args[1]
stpos = args[2]
marks = args[3]
initPriors = args[4]
telapse = args[5]
n12s = args[6]
if initPriors:
mvNrm.init0(stpos, marks, n12s[0], n12s[1])
mvNrm.fit(M, stpos, marks, n12s[0], n12s[1], init)
mvNrm.set_priors_and_initial_values(telapse=telapse)
return mvNrm
class simDecode():
nTets = 1
bWmaze= False
pX_Nm = None
Lklhd = None
kde = None
lmd = None
lmd0 = None
mltMk = 1 # multiply mark values to
AR = 0.5
AR0 = 1
marksObserved = None # observed in this encode epoch
weighted_occ = False
# xp position grid. need this in decode
xp = None
xpr = None # reshaped xp
dxp = None
# current posterior model parameters
po_us = None
po_covs = None
po_ws = None
# initting fitMvNorm
sepHash = False
sepHashMthd=0
pctH = None
MS = None
kde = False
Bx = None; bx = None; Bm = None
tetfile = "marks.pkl"
usetets = None
utets_str= ""
tt0 = None
tt1 = None
dbgMvt = False
minds = None #
encN = 0 # how many time points used in encode
snpsht_us = []
snpsht_covs= []
snpsht_ms = []
snpsht_gz = []
spdMult = 0.5
def init(self, kde=False, bx=None, Bx=None, Bm=None, makeMarks=None):
oo = self
oo.kde = kde
with open(oo.tetfile, "rb") as f:
lm = pickle.load(f)
f.close()
if oo.usetets is None:
oo.tetlist = lm.tetlist
if makeMarks is not None:
lm.makeMarks(marktype=makeMarks)
oo.marks = lm.marks
oo.utets_str = "all"
else:
stetlist= set(oo.usetets)
mis = [i for i, item in enumerate(lm.tetlist) if item in stetlist]
oo.marks = lm.marks[:, mis]
print mis
for l in xrange(len(mis)):
sc = "" if (l == len(mis)-1) else ","
oo.utets_str += "%(l)s%(c)s" % {"l" : lm.tetlist[mis[l]], "c" : sc}
if oo.mltMk != 1:
nons = _N.equal(oo.marks, None)
for nt in xrange(oo.nTets):
spks = _N.where(nons[:, nt] == False)[0]
for l in xrange(len(spks)):
mklst = oo.marks[spks[l], nt][0]
for ll in xrange(len(mklst)):
mklst[ll] *= oo.mltMk
oo.nTets = oo.marks.shape[1]
oo.Nx = lm.Nx; oo.Nm = lm.Nm
oo.xA = lm.xA; oo.mA = lm.mA
oo.mdim = lm.k#kde.mdim
try:
if lm.minds is not None:
oo.minds = lm.minds
except AttributeError:
pass
#### spatial grid for evaluating firing rates
oo.xp = _N.linspace(-oo.xA, oo.xA, oo.Nx) # space points
oo.xpr = oo.xp.reshape((oo.Nx, 1))
# bin space for occupation histogram. same # intvs as space points
oo.dxp = oo.xp[1] - oo.xp[0]
oo.xb = _N.empty(oo.Nx+1)
oo.xb[0:oo.Nx] = oo.xp - 0.5*oo.dxp
oo.xb[oo.Nx] = oo.xp[-1]+ 0.5*oo.dxp\
####
shp = [oo.Nx] # shape of kde
shp.extend([oo.Nm]*oo.mdim)
#oo.lmd= kde.kde.reshape(shp)
#oo.lmdFLaT = oo.lmd.reshape(oo.Nx, oo.Nm**oo.mdim)
oo.dt = lm.dt
oo.pos = lm.pos
oo.Fp, oo.q2p = 1, 0.003
oo.pX_Nm = _N.zeros((oo.pos.shape[0], oo.Nx))
oo.Lklhd = _N.zeros((oo.nTets, oo.pos.shape[0], oo.Nx))
oo.decmth = "kde"
if not oo.kde:
oo.decmth = "mxn"
oo.mvNrm= []
for nt in xrange(oo.nTets):
oo.mvNrm.append(fMN.fitMvNorm(oo.ITERS, oo.M, oo.mdim + 1))
oo.mvNrm[nt].ITERS = oo.ITERS
oo.mvNrm[nt].AR = oo.AR
oo.mvNrm[nt].AR0 = oo.AR0
def encode(self, t0=None, t1=None, encIntvs=None, initPriors=False, doTouchUp=False, MF=None, kmeansinit=True, telapse=0):
"""
eIntvs: array of times whose spikes we use to create encoding model
"""
oo = self
if encIntvs is None:
encIntvs = _N.array([[t0, t1]])
oo.N = t1-t0
else:
oo.N = encIntvs[-1, 1] - encIntvs[0, 0]
oo.tt0 = encIntvs[0, 0]
oo.tt1 = encIntvs[-1, 1]
tt1 = _tm.time()
oo.encN = _N.sum(encIntvs[:, 1] - encIntvs[:, 0])
if oo.procs > 1:
pool = _mp.Pool(processes=oo.procs)
if initPriors:
oo.all_pos = _N.empty(oo.encN)
print "here"
ii = 0
for ein in xrange(encIntvs.shape[0]):
oo.all_pos[ii:ii+(encIntvs[ein,1]-encIntvs[ein,0])] = oo.pos[encIntvs[ein,0]:encIntvs[ein,1]]
ii += encIntvs[ein,1]-encIntvs[ein,0]
else: # just make this longer
tmp = _N.empty(oo.encN)
ii = 0
for ein in xrange(encIntvs.shape[0]):
tmp[ii:ii+(encIntvs[ein,1]-encIntvs[ein,0])] = oo.pos[encIntvs[ein,0]:encIntvs[ein,1]]
ii += encIntvs[ein,1]-encIntvs[ein,0]
oo.all_pos = _N.array(oo.all_pos.tolist() + tmp.tolist())
dat = _N.empty(oo.N, dtype=list) # include times when no mvt.
stpos = [] # pos @ time of spikes
marks = [] # mark @ time of spikes
oo.marksObserved = _N.zeros(oo.nTets, dtype=_N.int)
for nt in xrange(oo.nTets):
marks.append([])
stpos.append([])
oo.marksObserved[nt] = 0
for ein in xrange(encIntvs.shape[0]):
t0, t1 = encIntvs[ein]
for n in xrange(t0, t1): # oo.marks "list of arrays"
if oo.marks[n, nt] is not None: # [arr(k-dim mark1), arr(k-dim mark2)]
themarks = oo.marks[n, nt]
for l in xrange(len(themarks)):
stpos[nt].append(oo.pos[n])
marks[nt].append(themarks[l])
oo.marksObserved[nt] += 1
print "nspikes tet %(tet)d %(s)d from %(t0)d %(t1)d" % {"t0" : t0, "t1" : t1, "s" : oo.marksObserved[nt], "tet" : nt}
oo.tr_pos = []
oo.tr_marks = []
setprior = True #TEMPORARY. After init, make it a prior
for nt in xrange(oo.nTets):
oo.tr_pos.append(_N.array(stpos[nt]))
oo.tr_marks.append(_N.array(marks[nt]))
currenc_pos = None if not oo.weighted_occ else oo.pos[encIntvs[ein,0]:encIntvs[ein,1]]
if not oo.kde:
# oo.snpsht_us.append([])
# oo.snpsht_covs.append([])
# oo.snpsht_ms.append([])
# oo.snpsht_gz.append([])
tt1 = _tm.time()
if oo.procs == 1:
if initPriors:
for nt in xrange(oo.nTets):
oo.mvNrm[nt].init0(stpos[nt], marks[nt], 0, oo.marksObserved[nt])
for nt in xrange(oo.nTets):
print "encode Doing fit tetrode %d" % nt
#if oo.restrctdClstrs:
# oo.mvNrm[nt].restrctdFit(oo.mvNrm[nt].M, stpos[nt], marks[nt], 0, nspks[nt], currenc_pos, initPriors)
#else:
oo.mvNrm[nt].fit(oo.mvNrm[nt].M, stpos[nt], marks[nt], 0, oo.marksObserved[nt], currenc_pos, initPriors)
oo.mvNrm[nt].set_priors_and_initial_values(telapse=telapse)
else:
print "multiprocess"
Ms = _N.empty(oo.nTets, dtype=_N.int)
IPs = _N.ones(oo.nTets, dtype=_N.bool)
tes = _N.ones(oo.nTets, dtype=_N.int)
n12s = _N.zeros((oo.nTets, 2), dtype=_N.int)
for nt in xrange(oo.nTets):
Ms[nt] = oo.mvNrm[nt].M
IPs[nt] = initPriors
tes[nt] = telapse
n12s[nt] = 0, oo.marksObserved[nt]
tpl_args = zip(oo.mvNrm, Ms, stpos, marks, IPs, tes, n12s)
sxv = pool.map(parWrapFit, tpl_args)
for nt in xrange(oo.nTets):
oo.mvNrm[nt] = sxv[nt]
# oo.snpsht_us[-1].append(_N.array(oo.mvNrm[nt].us))
# oo.snpsht_covs[-1].append(_N.array(oo.mvNrm[nt].covs))
# oo.snpsht_ms[-1].append(_N.array(oo.mvNrm[nt].ms))
# oo.snpsht_gz[-1].append(_N.array(oo.mvNrm[nt].gz))
tt2 = _tm.time()
print "time for init0 + fit: %.3f" % (tt2-tt1)
oo.setLmd0(oo.marksObserved)
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 = 0.2*_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))*oo.spdMult
# assume Nx is even
#k2 = 0.02
k2 = 0.1
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 setLmd0(self, nspks):
"""
Lmd0.
"""
oo = self
#####
if oo.dbgMvt:
occ = _N.ones(oo.Nx) / oo.Nx
oo.iocc = 1./occ
oo.Lam_xk = _N.zeros((oo.Nx, oo.nTets))
return
oo.lmd0 = _N.empty(oo.nTets)
oo.Lam_xk = _N.ones((oo.Nx, oo.nTets))
ibx2 = 1./ (oo.bx*oo.bx)
#occ = _N.sum(_N.exp(-0.5*ibx2*(oo.xpr - oo.all_pos[t0:t1])**2), axis=1) # this piece doesn't need to be evaluated for every new spike
occ = _N.sum(_N.exp(-0.5*ibx2*(oo.xpr - oo.all_pos)**2), axis=1) # this piece doesn't need to be evaluated for every new spike
Tot_occ = _N.sum(occ)
#oo.iocc = 1./(occ + Tot_occ*0.01)
oo.iocc = 1./occ
if oo.kde:
for nt in xrange(oo.nTets):
oo.Lam_xk[:, nt] = _ku.Lambda(oo.xpr, oo.tr_pos[nt], oo.all_pos, oo.Bx, oo.bx)
else: ##### fit mix gaussian
for nt in xrange(oo.nTets):
cmps = _N.zeros((oo.mvNrm[nt].M, oo.Nx))
for m in xrange(oo.mvNrm[nt].M):
var = oo.mvNrm[nt].covs[m, 0, 0]
ivar = 1./var
cmps[m] = (1/_N.sqrt(2*_N.pi*var)) * _N.exp(-0.5*ivar*(oo.xp - oo.mvNrm[nt].us[m, 0])**2)
y = _N.sum(oo.mvNrm[nt].ms*cmps, axis=0)
MargLam = y * oo.iocc
oo.lmd0[nt] = (nspks[nt] / (oo.encN*0.001)) / _N.trapz(MargLam, dx=oo.dxp)
oo.Lam_xk[:, nt] = oo.lmd0[nt] * MargLam
def scoreDecode(self, dt0, dt1):
oo = self
maxPos = _N.max(oo.pX_Nm[dt0:dt1], axis=1)
inds = _N.empty(dt1-dt0, dtype=_N.int)
for t in xrange(dt0, dt1):
inds[t-dt0] = _N.where(oo.pX_Nm[t] == maxPos[t-dt0])[0][0]
diffPos = oo.xp[inds] - oo.pos[dt0:dt1]
return _N.sum(_N.abs(diffPos)) / (dt1-dt0)
def dump(self, dir):
oo = self
pcklme = {}
pcklme["posteriors"] = oo.pX_Nm
pcklme["Lklhds"] = oo.Lklhd
if not oo.kde:
pcklme["snpsht_us"] = oo.snpsht_us
pcklme["snpsht_covs"] = oo.snpsht_covs
pcklme["snpsht_ms"] = oo.snpsht_ms
dmp = open("%s/dec.dump" % dir, "wb")
pickle.dump(pcklme, dmp, -1)
dmp.close()
# import pickle
# with open("mARp.dump", "rb") as f:
# lm = pickle.load(f)