def run(fquadrnts=None):
quadrnts = None
n_qdrnts = 1
if fquadrnts is not None:
if K == 4:
quadrnts = [[int(g_Ms[0]*fquadrnts[0][0])], [int(g_Ms[1]*fquadrnts[1][0])], [int(g_Ms[2]*fquadrnts[2][0])], [int(g_Ms[3]*fquadrnts[3][0])]]
n_qdrnts = (len(quadrnts[0])+1) * (len(quadrnts[1])+1) * (len(quadrnts[2])+1) * (len(quadrnts[3])+1)
elif K == 1:
quadrnts = [[int(g_Ms[0]*fquadrnts[0][0])]]
n_qdrnts = (len(quadrnts[0])+1)
run_os, run_es, run_vs, lftovr_os, lftovr_es = _Gu.get_obs_exp_v(g_Ms, expctd, chi2_boxes_mk, 6., quadrnts=quadrnts)
fp = open("%(df)s/exp_obs%(qd)s.txt" % {"df" : outdirN, "qd" : n_qdrnts}, "w+")
fp.write("# quadrants %s\n" % str(fquadrnts))
fp.write("# ks D, pv\n")
fp.write("%(d).3e %(pv).3e -1\n" % {"d" : ks_D, "pv" : ks_pv})
fp.write("# chi2, pv, dg freedom # quadrnt#, \n")
for nq in xrange(n_qdrnts):
reslt = _ss.chisquare(run_os[nq], run_es[nq])
fp.write("%(chi2).3e %(pv).3e %(n)d # nq %(nq)d\n" % {"chi2" : reslt[0], "pv" : reslt[1], "n" : len(run_es[nq]), "nq" : nq})
fp.close()
lftovr = _N.empty((n_qdrnts, 2))
lftovr[:, 0] = lftovr_os
lftovr[:, 1] = lftovr_es
_U.savetxtWCom("%(df)s/lftovrs%(qd)d.txt" % {"df" : outdirN, "qd" : n_qdrnts}, lftovr, fmt="%d %.3f", delimiter=" ", com=("# observed, expeceted, quadrants %s" % str(fquadrnts)))
bfn = "u" if (mvPat == UNIF) else "n"
if (mvPat == NUNIF) and (Amx > 0):
bfn = "b"
bfn += "n" if (mSM > 0) else "s"
iInd = 0
while not bFnd:
iInd += 1
fn = "../DATA/%(bfn)s%(iI)d.dat" % {"bfn" : bfn, "iI" : iInd}
fnocc="../DATA/%(bfn)s%(iI)docc.png" % {"bfn" : bfn, "iI" : iInd}
if not os.access(fn, os.F_OK): # file exists
bFnd = True
dat = _N.zeros((NT, 5))
dat[:, 0] = pths
dat[sts, 1] = 1
dat[:, 2] = ctr
dat[:, 3] = sx
dat[:, 4] = l0
com = "# f=%(mx).3f q2=%(q2).4f l0=%(l0).4f" % {"mx" : mx, "q2" : _N.mean(sx), "l0" : _N.mean(l0)}
_U.savetxtWCom("%s" % fn, dat, fmt="%.4f %d %.4f %.4f %.4f", delimiter=" ", com=com)
print "created %s" % fn
fig = _plt.figure()
_plt.hist(dat[:, 0], bins=_N.linspace(0, 3, 61), color="black")
_plt.savefig(fnocc)
_plt.close()
def stochasticAssignment(oo, epc, it, Msc, M, K, l0, f, q2, u, Sg, _f_u, _u_u, _f_q2, _u_Sg, Asts, t0, mASr, xASr, rat, econt, gz, qdrMKS, freeClstr, hashthresh, cmp2Existing, nthrds=1):
# Msc Msc signal clusters
# M all clusters, including nz clstr. M == Msc when not using nzclstr
# Gibbs sampling
# parameters l0, f, q2
# mASr, xASr just the mark, position of spikes btwn t0 and t1
#qdrMKS2 = _N.empty(qdrMKS.shape)
t1 = _tm.time()
nSpks = len(Asts)
twpi = 2*_N.pi
Kp1 = K+1
#rat = _N.zeros(M+1)
pc = _N.zeros(M)
ur = u.reshape((M, 1, K))
fr = f.reshape((M, 1)) # centers
#print q2
iq2 = 1./q2
iSg = _N.linalg.inv(Sg)
iq2r = iq2.reshape((M, 1))
try:
## warnings because l0 is 0
isN = _N.where(q2 <= 0)[0]
if len(isN) > 0:
q2[isN] = 0.3
is0 = _N.where(l0 <= 0)[0]
if len(is0) > 0:
l0[is0] = 0.001
pkFR = _N.log(l0) - 0.5*_N.log(twpi*q2) # M
except RuntimeWarning:
print "WARNING"
print l0
print q2
mkNrms = _N.log(1/_N.sqrt(twpi*_N.linalg.det(Sg)))
mkNrms = mkNrms.reshape((M, 1)) # M x 1
rnds = _N.random.rand(nSpks)
pkFRr = pkFR.reshape((M, 1))
dmu = (mASr - ur) # mASr 1 x N x K, ur is M x 1 x K
N = mASr.shape[1]
#t2 = _tm.time()
#_N.einsum("mnj,mjk,mnk->mn", dmu, iSg, dmu, out=qdrMKS)
#t3 = _tm.time()
_fm.multi_qdrtcs_par_func(dmu, iSg, qdrMKS, M, N, K, nthrds=nthrds)
# fr is M x 1, xASr is 1 x N, iq2r is M x 1
#qdrSPC = (fr - xASr)*(fr - xASr)*iq2r # M x nSpks # 0.01s
qdrSPC = _N.empty((M, N))
_hcb.hc_bcast1(fr, xASr, iq2r, qdrSPC, M, N)
### how far is closest cluster to each newly observed mark
# mAS = mks[Asts+t0]
# xAS = x[Asts + t0] # position @ spikes
if cmp2Existing: # compare only non-hash spikes and non-hash clusters
# realCl = _N.where(freeClstr == False)[0]
# print freeClstr.shape
# print realCl.shape
abvthrEachCh = mASr[0] > hashthresh # should be NxK of
abvthrAtLeast1Ch = _N.sum(abvthrEachCh, axis=1) > 0 # N x K
newNonHashSpks = _N.where(abvthrAtLeast1Ch)[0]
newNonHashSpksMemClstr = _N.ones(len(newNonHashSpks), dtype=_N.int) * (M-1) # initially, assign all of them to noise cluster
#print "spikes not hash"
#print abvthrInds
abvthrEachCh = u[0:Msc] > hashthresh # M x K (M includes noise)
abvthrAtLeast1Ch = _N.sum(abvthrEachCh, axis=1) > 0
knownNonHclstrs = _N.where(abvthrAtLeast1Ch & (freeClstr == False) & (q2[0:Msc] < wdSpc))[0]
#print "clusters not hash"
# Place prior for freeClstr near new non-hash spikes that are far
# from known clusters that are not hash clusters
nNrstMKS_d = _N.sqrt(_N.min(qdrMKS[knownNonHclstrs], axis=0)/K) # dim len(sts)
nNrstSPC_d = _N.sqrt(_N.min(qdrSPC[knownNonHclstrs], axis=0))
# for each spike, distance to nearest non-hash cluster
# print nNrstMKS_d
# print nNrstSPC_d
# print "=============="
s = _N.empty((len(newNonHashSpks), 3))
# for each spike, distance to nearest cluster
s[:, 0] = newNonHashSpks
s[:, 1] = nNrstMKS_d[newNonHashSpks]
s[:, 2] = nNrstSPC_d[newNonHashSpks]
_N.savetxt(resFN("qdrMKSSPC%d" % epc, dir=oo.outdir), s, fmt="%d %.3e %.3e")
dMK = nNrstMKS_d[newNonHashSpks]
dSP = nNrstSPC_d[newNonHashSpks]
### assignment into
farMKinds = _N.where(dMK > 4)[0] #
# mean of prior for center - mean of farMKinds
# cov of prior for center - how certain am I of mean?
farSPinds = _N.where(dSP > 4)[0] # 4 std. deviations away
farMKSPinds = _N.union1d(farMKinds, farSPinds)
print farMKinds
print newNonHashSpks
## points in newNonHashSpks but not in farMKinds
notFarMKSPinds = _N.setdiff1d(_N.arange(newNonHashSpks.shape[0]), farMKSPinds)
farMKSP = _N.empty((len(farMKSPinds), K+1))
farMKSP[:, 0] = xASr[0, newNonHashSpks[farMKSPinds]]
farMKSP[:, 1:] = mASr[0, newNonHashSpks[farMKSPinds]]
notFarMKSP = _N.empty((len(notFarMKSPinds), K+1))
notFarMKSP[:, 0] = xASr[0, newNonHashSpks[notFarMKSPinds]]
notFarMKSP[:, 1:] = mASr[0, newNonHashSpks[notFarMKSPinds]]
# farSP = _N.empty((len(farSPinds), K+1))
# farMK = _N.empty((len(farMKinds), K+1))
# farSP[:, 0] = xASr[0, farSPinds]
# farSP[:, 1:] = mASr[0, farSPinds]
# farMK[:, 0] = xASr[0, farMKinds]
# farMK[:, 1:] = mASr[0, farMKinds]
minK = 1
maxK = farMKSPinds.shape[0] / K
maxK = maxK if (maxK < 6) else 6
freeClstrs = _N.where(freeClstr == True)[0]
if maxK >= 2:
print "coming in here"
#labs, bics, bestLab, nClstrs = _oT.EMBICs(farMKSP, minK=minK, maxK=maxK, TR=1)
labs, labsH, clstrs = emMKPOS_sep1B(farMKSP, None, TR=1, wfNClstrs=[[1, 4], [1, 4]], spNClstrs=[[1, 4], [1, 3]])
nClstrs = clstrs[0]
bestLab = labs
cls = clrs.get_colors(nClstrs)
_U.savetxtWCom(resFN("newSpksMKSP%d" % epc, dir=oo.outdir), farMKSP, fmt="%.3e %.3e %.3e %.3e %.3e", com=("# number of clusters %d" % nClstrs))
_U.savetxtWCom(resFN("newSpksMKSP_nf%d" % epc, dir=oo.outdir), notFarMKSP, fmt="%.3e %.3e %.3e %.3e %.3e", com=("# number of clusters %d" % nClstrs))
L = len(freeClstrs)
unqLabs = _N.unique(bestLab)
upto = nClstrs if nClstrs < L else L # this should just count large clusters
ii = -1
fig = _plt.figure()
for fid in unqLabs[0:upto]:
iths = farMKSPinds[_N.where(bestLab == fid)[0]]
ths = newNonHashSpks[iths]
for w in xrange(K):
fig.add_subplot(2, 2, w+1)
_plt.scatter(xASr[0, ths], mASr[0, ths, w], color=cls[ii])
if len(ths) > K:
ii += 1
im = freeClstrs[ii] # Asts + t0 gives absolute time
newNonHashSpksMemClstr[iths] = im
_u_u[im] = _N.mean(mASr[0, ths], axis=0)
u[im] = _u_u[im]
_f_u[im] = _N.mean(xASr[0, ths], axis=0)
f[im] = _f_u[im]
q2[im] = _N.std(xASr[0, ths], axis=0)**2 * 9
# l0 = Hz * sqrt(2*_N.pi*q2)
l0[im] = 10*_N.sqrt(q2[im])
_f_q2[im] = 1
_u_Sg[im] = _N.cov(mASr[0, ths], rowvar=0)*25
print "ep %(ep)d new cluster # %(m)d" % {"ep" : epc, "m" : im}
print _u_u[im]
print _f_u[im]
print _f_q2[im]
else:
print "too small this prob. doesn't represent a cluster"
_plt.savefig("newspks%d" % epc)
# ####### known clusters
# for fid in unqLabs[0:upto]:
# iths = farMKSPinds[_N.where(bestLab == fid)[0]]
# ths = newNonHashSpks[iths]
# for w in xrange(K):
# fig.add_subplot(2, 2, w+1)
# _plt.scatter(xASr[0, ths], mASr[0, ths, w], color=cls[ii])
# if len(ths) > K:
# ii += 1
# im = freeClstrs[ii] # Asts + t0 gives absolute time
# newNonHashSpksMemClstr[iths] = im
# _u_u[im] = _N.mean(mASr[0, ths], axis=0)
# u[im] = _u_u[im]
# _f_u[im] = _N.mean(xASr[0, ths], axis=0)
# f[im] = _f_u[im]
# q2[im] = _N.std(xASr[0, ths], axis=0)**2 * 9
# # l0 = Hz * sqrt(2*_N.pi*q2)
# l0[im] = 10*_N.sqrt(q2[im])
# _f_q2[im] = 1
# _u_Sg[im] = _N.cov(mASr[0, ths], rowvar=0)*25
# print "ep %(ep)d new cluster # %(m)d" % {"ep" : epc, "m" : im}
# print _u_u[im]
# print _f_u[im]
# print _f_q2[im]
# else:
# print "too small this prob. doesn't represent a cluster"
# _plt.savefig("newspks%d" % epc)
else: # just one cluster
im = freeClstrs[0] # Asts + t0 gives absolute time
_u_u[im] = _N.mean(mASr[0, newNonHashSpks[farMKSPinds]], axis=0)
_f_u[im] = _N.mean(xASr[0, newNonHashSpks[farMKSPinds]], axis=0)
_u_Sg[im] = _N.cov(mASr[0, newNonHashSpks[farMKSPinds]], rowvar=0)*16
_f_q2[im] = _N.std(xASr[0, newNonHashSpks[farMKSPinds]], axis=0)**2 * 16
# ## kernel density estimate
# xs = _N.linspace(-6, 6, 101)
# xsr = xs.reshape(101, 1)
# isg2= 1/(0.1**2) # spatial kernel bandwidth
# # fig = _plt.figure(figsize=(6, 9))
# # fig.add_subplot(1, 2, 1)
# # _plt.scatter(xASr[0, newNonHashSpks[farMKinds]], mASr[0, newNonHashSpks[farMKinds], 0])
# # fig.add_subplot(1, 2, 2)
# # _plt.scatter(xASr[0, newNonHashSpks[farSPinds]], mASr[0, newNonHashSpks[farSPinds], 0])
# freeClstrs = _N.where(freeClstr == True)[0]
# L = len(freeClstrs)
# jjj = 0
# if (len(farSPinds) >= Kp1) and (len(farMKinds) >= Kp1):
# jjj = 1
# l1 = L/2
# for l in xrange(l1): # mASr is 1 x N x K
# im = freeClstrs[l] # Asts + t0 gives absolute time
# _u_u[im] = _N.mean(mASr[0, newNonHashSpks[farMKinds]], axis=0)
# y = _N.exp(-0.5*(xsr - xASr[0, newNonHashSpks[farMKinds]])**2 * isg2)
# yc = _N.sum(y, axis=1)
# ix = _N.where(yc == _N.max(yc))[0][0]
# _f_u[im] = xs[ix]
# _u_Sg[im] = _N.cov(mASr[0, newNonHashSpks[farMKinds]], rowvar=0)*30
# _f_q2[im] = _N.std(xASr[0, newNonHashSpks[farMKinds]], axis=0)**2 * 30
# # _plt.figure()
# # _plt.plot(xs, yc)
# for l in xrange(l1, L):
# im = freeClstrs[l] # Asts + t0 gives absolute time
# _u_u[im] = _N.mean(mASr[0, newNonHashSpks[farSPinds]], axis=0)
# y = _N.exp(-0.5*(xsr - xASr[0, newNonHashSpks[farSPinds]])**2 * isg2)
# yc = _N.sum(y, axis=1)
# ix = _N.where(yc == _N.max(yc))[0][0]
# _f_u[im] = xs[ix]
# _u_Sg[im] = _N.cov(mASr[0, newNonHashSpks[farSPinds]], rowvar=0)*30
# _f_q2[im] = _N.std(xASr[0, newNonHashSpks[farSPinds]], axis=0)**2 * 30
# # _plt.figure()
# # _plt.plot(xs, yc)
# elif (len(farSPinds) >= Kp1) and (len(farMKinds) < Kp1):
# jjj = 2
# for l in xrange(L):
# im = freeClstrs[l] # Asts + t0 gives absolute time
# _u_u[im] = _N.mean(mASr[0, newNonHashSpks[farSPinds]], axis=0)
# y = _N.exp(-0.5*(xsr - xASr[0, newNonHashSpks[farSPinds]])**2 * isg2)
# yc = _N.sum(y, axis=1)
# ix = _N.where(yc == _N.max(yc))[0][0]
# _f_u[im] = xs[ix]
# _u_Sg[im] = _N.cov(mASr[0, newNonHashSpks[farSPinds]], rowvar=0)*30
# _f_q2[im] = _N.std(xASr[0, newNonHashSpks[farSPinds]], axis=0)**2 * 30
# # _plt.figure()
# # _plt.plot(xs, yc)
# elif (len(farSPinds) < Kp1) and (len(farMKinds) >= Kp1):
# jjj = 3
# for l in xrange(L):
# im = freeClstrs[l] # Asts + t0 gives absolute time
# _u_u[im] = _N.mean(mASr[0, newNonHashSpks[farMKinds]], axis=0)
# y = _N.exp(-0.5*(xsr - xASr[0, newNonHashSpks[farMKinds]])**2 * isg2)
# yc = _N.sum(y, axis=1)
# ix = _N.where(yc == _N.max(yc))[0][0]
# _f_u[im] = xs[ix]
# _u_Sg[im] = _N.cov(mASr[0, newNonHashSpks[farMKinds]], rowvar=0)*30
# _f_q2[im] = _N.std(xASr[0, newNonHashSpks[farMKinds]], axis=0)**2 * 30
# # _plt.figure()
# # _plt.plot(xs, yc)
"""
print "^^^^^^^^"
print freeClstrs
print "set priors for freeClstrs %d" % jjj
#print _u_u[freeClstrs]
#print _u_Sg[freeClstrs]
print _f_u[freeClstrs]
print _f_q2[freeClstrs]
"""
#if len(farSPinds) > 10:
# set the priors of the freeClusters to be near the far spikes
#### outside cmp2Existing here
# (Mx1) + (Mx1) - (MxN + MxN)
#cont = pkFRr + mkNrms - 0.5*(qdrSPC + qdrMKS)
cont = _N.empty((M, N))
_hcb.hc_qdr_sum(pkFRr, mkNrms, qdrSPC, qdrMKS, cont, M, N)
mcontr = _N.max(cont, axis=0).reshape((1, nSpks))
cont -= mcontr
_N.exp(cont, out=econt)
for m in xrange(M):
rat[m+1] = rat[m] + econt[m]
rat /= rat[M]
"""
# print f
# print u
# print q2
# print Sg
# print l0
"""
# print rat
M1 = rat[1:] >= rnds
M2 = rat[0:-1] <= rnds
gz[it] = (M1&M2).T
if cmp2Existing:
# gz is ITERS x N x Mwowonz (N # of spikes in epoch)
gz[it, newNonHashSpks] = False # not a member of any of them
gz[it, newNonHashSpks, newNonHashSpksMemClstr] = True
def create(Lx,
Hx,
N,
mvPat,
RTs,
frqmx,
Amx,
pT,
l_sx_chpts,
l_l0_chpts,
l_ctr_chpts,
mk_chpts,
Covs,
LoHis,
km,
bckgrdLam=None,
script="no info",
addShortStops=False,
stops=10,
stopDur=500,
thresh=None):
"""
km tells me neuron N gives rise to clusters km[N] (list)
bckgrd is background spike rate (Hz)
"""
global UNIF, NUNIF
##### First check that the number of neurons and PFs all consistent.
nNrnsSX = len(l_sx_chpts)
nNrnsL0 = len(l_l0_chpts)
nNrnsCT = len(l_ctr_chpts)
nNrnsMK = len(mk_chpts)
nNrnsMKA = LoHis.shape[0]
nNrnsMKC = Covs.shape[0]
if not (nNrnsSX == nNrnsL0 == nNrnsCT == nNrnsMK == nNrnsMKA == nNrnsMKC):
print "Number of neurons not consistent"
return None
nNrns = nNrnsSX
if not (LoHis.shape[1] == Covs.shape[1] == Covs.shape[2]):
print "Covariance of LoHis not correct"
return None
K = LoHis.shape[1]
PFsPerNrn = _N.zeros(nNrns, dtype=_N.int)
sx_chpts = []
l0_chpts = []
ctr_chpts = []
M = 0
nrnNum = []
for nrn in xrange(nNrns):
# # of place fields for neuron nrn
nPFsSX = len(l_sx_chpts[nrn])
nPFsL0 = len(l_l0_chpts[nrn])
nPFsCT = len(l_ctr_chpts[nrn])
sx_chpts.extend(l_sx_chpts[nrn])
l0_chpts.extend(l_l0_chpts[nrn])
ctr_chpts.extend(l_ctr_chpts[nrn])
if not (nPFsSX == nPFsL0 == nPFsCT):
print "Number of PFs for neuron %d not consistent" % nrn
return None
M += len(l_ctr_chpts[nrn])
nrnNum += [nrn] * nPFsSX
PFsPerNrn[nrn] = nPFsSX
# M = # of clusters (in mark + pos space)
# nNrns = # of neurons
#### build data
Ns = _N.empty(RTs, dtype=_N.int)
if mvPat == NUNIF:
for rt in xrange(RTs):
Ns[rt] = N * ((1 - pT) + pT * _N.random.rand())
else:
Ns[:] = N
NT = _N.sum(Ns) # total time we have data
pths = _N.empty(NT)
x01 = _N.linspace(0, 1, len(pths))
x01 = x01.reshape((1, NT))
plastic = False
########## nonstationary center width
# sxt should be (M x NT)
sxt = _N.empty((M, NT))
for m in xrange(M): # sxts time scale
sxt[m] = createSmoothedPath(sx_chpts[m], NT)
if len(sx_chpts[m]) > 1: plastic = True
sx = sxt**2 # var of firing rate function
########## nonstationary center height l0
# f is NT x M
l0 = _N.empty((M, NT))
for m in xrange(M):
l0[m] = createSmoothedPath(l0_chpts[m], NT)
if len(l0_chpts[m]) > 1: plastic = True
f = l0 / _N.sqrt(2 * _N.pi * sx) # f*dt
########## nonstationary center location
ctr = _N.empty((M, NT))
for m in xrange(M):
ctr[m] = createSmoothedPath(ctr_chpts[m], NT)
if len(ctr_chpts[m]) > 1: plastic = True
if K > 0:
########## nonstationary marks
mk_MU = _N.empty((nNrns, NT, K))
for n in xrange(nNrns):
mk_MU[n] = createSmoothedPathK(mk_chpts[n], NT, K, LoHis[n])
if len(mk_chpts[n]) > 1: plastic = True
if mvPat == NUNIF:
now = 0
for rt in xrange(RTs):
N = Ns[rt] # each traverse slightly different duration
rp = _N.random.rand(N / 100)
x = _N.linspace(Lx, Hx, N)
xp = _N.linspace(Lx, Hx, N / 100)
r = _N.interp(x, xp, rp) # creates a velocity vector
# create movement without regard for place field
frqmxR = _N.abs(frqmx * (1 + 0.25 * _N.random.randn()))
_N.linspace(0, 1, N, endpoint=False)
rscld_t = _N.random.rand(N) # rscld_t
rscld_t /= (_N.max(rscld_t) * 1.01)
rscld_t.sort()
phi0 = _N.random.rand() * 2 * _N.pi
r += _N.exp(Amx * _N.sin(2 * _N.pi * rscld_t * frqmxR + phi0))
pth = _N.zeros(N + 1)
for n in xrange(1, N + 1):
pth[n] = pth[n - 1] + r[n - 1]
pth /= (pth[-1] - pth[0])
pth *= (Hx - Lx)
pth += Lx
pths[now:now + N] = pth[0:N]
now += N
else:
now = 0
x = _N.linspace(Lx, Hx, N)
for rt in xrange(RTs):
N = Ns[rt]
pths[now:now + N] = x
now += N
if addShortStops:
for ist in xrange(stops):
done = False
while not done:
t0 = int(_N.random.rand() * NT)
t1 = t0 + int(stopDur * (1 + 0.1 * _N.random.randn()))
if _N.abs(_N.max(_N.diff(pths[t0:t1]))) < 0.05 * (Hx - Lx):
done = True # not crossing origin
pths[t0:t1] = _N.mean(pths[t0:t1])
### now calculate firing rates
dt = 0.001
fdt = f * dt
# change place field location
Lam = f * dt * _N.exp(-0.5 * (pths - ctr)**2 / sx)
#_N.savetxt("lam", Lam.T)
#_N.savetxt("pths", pths)
rnds = _N.random.rand(M, NT)
#dat = _N.zeros((NT, 2 + K))
dat = _N.zeros((NT, 2 + K))
dat[:, 0] = pths
datNghbr = _N.zeros(
(NT, 2 + K)) ## if spk in bin already exists, nxt door
datNghbr[:, 0] = pths
for m in xrange(M):
sts = _N.where(rnds[m] < Lam[m])[0] # spikes from this neuron
alrdyXst = _N.where(dat[:, 1] == 1)[0] # places where synchronous
ndToMove = _N.intersect1d(alrdyXst, sts)
dntMove = _N.setdiff1d(sts, ndToMove) # empty slots
nonsynch = _N.empty(len(sts))
datNghbr[dntMove, 1] = 1
nonsynch[0:len(dntMove)] = dntMove
print len(ndToMove)
iStart = len(dntMove) # in nonsynch
for iOcpd in ndToMove: # occupied
bDone = False
while not bDone:
iOcpd += 1
if datNghbr[iOcpd, 1] == 0:
datNghbr[iOcpd, 1] = 1
nonsynch[iStart] = iOcpd
iStart += 1
bDone = True
snonsynch = _N.sort(nonsynch)
dat[sts, 1] = 1
nrn = nrnNum[m]
if K > 0:
for t in xrange(len(sts)):
obsMrk = _N.random.multivariate_normal(mk_MU[nrn, sts[t]],
Covs[nrn],
size=1)
dat[sts[t], 2:] = obsMrk
datNghbr[nonsynch[t], 2:] = obsMrk
# now noise spikes
if bckgrdLam is not None:
nzsts = _N.where(rnds[m] < (bckgrdLam * dt) / float(M))[0]
dat[nzsts, 1] = 1
nrn = nrnNum[m]
if K > 0:
for t in xrange(len(nzsts)):
dat[nzsts[t],
2:] = _N.random.multivariate_normal(mk_MU[nrn,
nzsts[t]],
Covs[nrn],
size=1)
if thresh is not None:
sts = _N.where(dat[:, 1] == 1)[0]
nID, nC = _N.where(dat[sts, 2:] < thresh)
swtchs = _N.zeros((len(sts), K))
swtchs[nID, nC] = 1 # for all cells, all components below hash == 1
swtchsK = _N.sum(swtchs, axis=1)
blw_thrsh_all_chs = _N.where(swtchsK == K)[0]
abv_thr = _N.setdiff1d(_N.arange(len(sts)), blw_thrsh_all_chs)
print "below thresh in all channels %(1)d / %(2)d" % {
"1": len(blw_thrsh_all_chs),
"2": len(sts)
}
dat[sts[blw_thrsh_all_chs], 1:] = 0
bFnd = False
## us un uniform sampling of space, stationary or non-stationary place field
## ns nn non-uni sampling of space, stationary or non-stationary place field
## bs bb biased and non-uni sampling of space
bfn = "" if (M == 1) else ("%d" % M)
if mvPat == UNIF:
bfn += "u"
else:
bfn += "b" if (Amx > 0) else "n"
bfn += "n" if plastic else "s"
iInd = 0
while not bFnd:
iInd += 1
dd = os.getenv("__EnDeDataDir__")
fn = "%(dd)s/%(bfn)s%(iI)d.dat" % {"bfn": bfn, "iI": iInd, "dd": dd}
fnocc = "%(dd)s/%(bfn)s%(iI)docc.png" % {
"bfn": bfn,
"iI": iInd,
"dd": dd
}
fnprm = "%(dd)s/%(bfn)s%(iI)d_prms.pkl" % {
"bfn": bfn,
"iI": iInd,
"dd": dd
}
if not os.access(fn, os.F_OK): # file exists
bFnd = True
smk = " %.4f" * K
_U.savetxtWCom("%s" % fn,
dat,
fmt=("%.4f %d" + smk),
delimiter=" ",
com="# script=%s.py" % script)
_U.savetxtWCom("%s_NS.dat" % fn[:-4],
datNghbr,
fmt=("%.4f %d" + smk),
delimiter=" ",
com="# script=%s.py" % script)
pcklme = {}
pcklme["l0"] = l0[:, ::100]
pcklme["f"] = ctr[:, ::100]
pcklme["sq2"] = sx[:, ::100]
pcklme["u"] = mk_MU[:, ::100]
pcklme["covs"] = Covs
pcklme["intv"] = 100
pcklme["km"] = km
dmp = open(fnprm, "wb")
pickle.dump(pcklme, dmp, -1)
dmp.close()
print "created %s" % fn
fig = _plt.figure()
_plt.hist(dat[:, 0], bins=_N.linspace(Lx, Hx, 101), color="black")
_plt.savefig(fnocc)
_plt.close()
def create(Lx, Hx, N, mvPat, RTs, frqmx, Amx, pT, l_sx_chpts, l_l0_chpts, l_ctr_chpts, mk_chpts, Covs, LoHis, km, bckgrdLam=None, script="no info", addShortStops=False, stops=10, stopDur=500, thresh=None):
"""
km tells me neuron N gives rise to clusters km[N] (list)
bckgrd is background spike rate (Hz)
"""
global UNIF, NUNIF
##### First check that the number of neurons and PFs all consistent.
nNrnsSX = len(l_sx_chpts)
nNrnsL0 = len(l_l0_chpts)
nNrnsCT = len(l_ctr_chpts)
nNrnsMK = len(mk_chpts)
nNrnsMKA= LoHis.shape[0]
nNrnsMKC= Covs.shape[0]
if not (nNrnsSX == nNrnsL0 == nNrnsCT == nNrnsMK == nNrnsMKA == nNrnsMKC):
print "Number of neurons not consistent"
return None
nNrns = nNrnsSX
if not (LoHis.shape[1] == Covs.shape[1] == Covs.shape[2]):
print "Covariance of LoHis not correct"
return None
K = LoHis.shape[1]
PFsPerNrn = _N.zeros(nNrns, dtype=_N.int)
sx_chpts = []
l0_chpts = []
ctr_chpts = []
M = 0
nrnNum = []
for nrn in xrange(nNrns):
# # of place fields for neuron nrn
nPFsSX = len(l_sx_chpts[nrn])
nPFsL0 = len(l_l0_chpts[nrn])
nPFsCT = len(l_ctr_chpts[nrn])
sx_chpts.extend(l_sx_chpts[nrn])
l0_chpts.extend(l_l0_chpts[nrn])
ctr_chpts.extend(l_ctr_chpts[nrn])
if not (nPFsSX == nPFsL0 == nPFsCT):
print "Number of PFs for neuron %d not consistent" % nrn
return None
M += len(l_ctr_chpts[nrn])
nrnNum += [nrn]*nPFsSX
PFsPerNrn[nrn] = nPFsSX
# M = # of clusters (in mark + pos space)
# nNrns = # of neurons
#### build data
Ns = _N.empty(RTs, dtype=_N.int)
if mvPat == NUNIF:
for rt in xrange(RTs):
Ns[rt] = N*((1-pT) + pT*_N.random.rand())
else:
Ns[:] = N
NT = _N.sum(Ns) # total time we have data
pths = _N.empty(NT)
x01 = _N.linspace(0, 1, len(pths))
x01 = x01.reshape((1, NT))
plastic = False
########## nonstationary center width
# sxt should be (M x NT)
sxt = _N.empty((M, NT))
for m in xrange(M): # sxts time scale
sxt[m] = createSmoothedPath(sx_chpts[m], NT)
if len(sx_chpts[m]) > 1: plastic = True
sx = sxt**2 # var of firing rate function
########## nonstationary center height l0
# f is NT x M
l0 = _N.empty((M, NT))
for m in xrange(M):
l0[m] = createSmoothedPath(l0_chpts[m], NT)
if len(l0_chpts[m]) > 1: plastic = True
f = l0/_N.sqrt(2*_N.pi*sx) # f*dt
########## nonstationary center location
ctr = _N.empty((M, NT))
for m in xrange(M):
ctr[m] = createSmoothedPath(ctr_chpts[m], NT)
if len(ctr_chpts[m]) > 1: plastic = True
if K > 0:
########## nonstationary marks
mk_MU = _N.empty((nNrns, NT, K))
for n in xrange(nNrns):
mk_MU[n] = createSmoothedPathK(mk_chpts[n], NT, K, LoHis[n])
if len(mk_chpts[n]) > 1: plastic = True
if mvPat == NUNIF:
now = 0
for rt in xrange(RTs):
N = Ns[rt] # each traverse slightly different duration
rp = _N.random.rand(N/100)
x = _N.linspace(Lx, Hx, N)
xp = _N.linspace(Lx, Hx, N/100)
r = _N.interp(x, xp, rp) # creates a velocity vector
# create movement without regard for place field
r += Amx*(1.1+_N.sin(2*_N.pi*_N.linspace(0, 1, N, endpoint=False)*frqmx))
pth = _N.zeros(N+1)
for n in xrange(1, N+1):
pth[n] = pth[n-1] + r[n-1]
pth /= (pth[-1] - pth[0])
pth *= (Hx-Lx)
pth += Lx
pths[now:now+N] = pth[0:N]
now += N
else:
now = 0
x = _N.linspace(Lx, Hx, N)
for rt in xrange(RTs):
N = Ns[rt]
pths[now:now+N] = x
now += N
if addShortStops:
for ist in xrange(stops):
done = False
while not done:
t0 = int(_N.random.rand()*NT)
t1 = t0 + int(stopDur*(1+0.1*_N.random.randn()))
if _N.abs(_N.max(_N.diff(pths[t0:t1]))) < 0.05*(Hx-Lx):
done = True # not crossing origin
pths[t0:t1] = _N.mean(pths[t0:t1])
### now calculate firing rates
dt = 0.001
fdt = f*dt
# change place field location
Lam = f*dt*_N.exp(-0.5*(pths-ctr)**2 / sx)
rnds = _N.random.rand(M, NT)
#dat = _N.zeros((NT, 2 + K))
dat = _N.zeros((NT, 2 + K))
dat[:, 0] = pths
for m in xrange(M):
sts = _N.where(rnds[m] < Lam[m])[0]
dat[sts, 1] = 1
nrn = nrnNum[m]
if K > 0:
for t in xrange(len(sts)):
dat[sts[t], 2:] = _N.random.multivariate_normal(mk_MU[nrn, sts[t]], Covs[nrn], size=1)
# now noise spikes
if bckgrdLam is not None:
nzsts = _N.where(rnds[m] < (bckgrdLam*dt)/float(M))[0]
dat[nzsts, 1] = 1
nrn = nrnNum[m]
if K > 0:
for t in xrange(len(nzsts)):
dat[nzsts[t], 2:] = _N.random.multivariate_normal(mk_MU[nrn, nzsts[t]], Covs[nrn], size=1)
if thresh is not None:
sts = _N.where(dat[:, 1] == 1)[0]
nID, nC = _N.where(dat[sts, 2:] < thresh)
swtchs = _N.zeros((len(sts), K))
swtchs[nID, nC] = 1 # for all cells, all components below hash == 1
swtchsK = _N.sum(swtchs, axis=1)
blw_thrsh_all_chs = _N.where(swtchsK == K)[0]
abv_thr = _N.setdiff1d(_N.arange(len(sts)), blw_thrsh_all_chs)
print "below thresh in all channels %(1)d / %(2)d" % {"1" : len(blw_thrsh_all_chs), "2" : len(sts)}
dat[sts[blw_thrsh_all_chs], 1:] = 0
bFnd = False
## us un uniform sampling of space, stationary or non-stationary place field
## ns nn non-uni sampling of space, stationary or non-stationary place field
## bs bb biased and non-uni sampling of space
bfn = "" if (M == 1) else ("%d" % M)
if mvPat == UNIF:
bfn += "u"
else:
bfn += "b" if (Amx > 0) else "n"
bfn += "n" if plastic else "s"
iInd = 0
while not bFnd:
iInd += 1
dd = os.getenv("__EnDeDataDir__")
fn = "%(dd)s/%(bfn)s%(iI)d.dat" % {"bfn" : bfn, "iI" : iInd, "dd" : dd}
fnocc="%(dd)s/%(bfn)s%(iI)docc.png" % {"bfn" : bfn, "iI" : iInd, "dd" : dd}
fnprm = "%(dd)s/%(bfn)s%(iI)d_prms.pkl" % {"bfn" : bfn, "iI" : iInd, "dd" : dd}
if not os.access(fn, os.F_OK): # file exists
bFnd = True
smk = " %.4f" * K
_U.savetxtWCom("%s" % fn, dat, fmt=("%.4f %d" + smk), delimiter=" ", com="# script=%s.py" % script)
pcklme = {}
pcklme["l0"] = l0[:, ::100]
pcklme["f"] = ctr[:, ::100]
pcklme["sq2"] = sx[:, ::100]
pcklme["u"] = mk_MU[:, ::100]
pcklme["covs"]= Covs
pcklme["intv"]= 100
pcklme["km"] = km
dmp = open(fnprm, "wb")
pickle.dump(pcklme, dmp, -1)
dmp.close()
print "created %s" % fn
fig = _plt.figure()
_plt.hist(dat[:, 0], bins=_N.linspace(Lx, Hx, 101), color="black")
_plt.savefig(fnocc)
_plt.close()
def create(Lx,
Hx,
N,
mvPat,
RTs,
frqmx,
Amx,
pT,
l_sx_chpts,
l_l0_chpts,
l_ctr_chpts,
mk_chpts,
Covs,
LoHis,
km,
bckgrdLam=None,
script="no info",
addShortStops=False,
stops=10,
stopDur=500,
thresh=None,
x_mvt=None,
nz_mvt=None,
spc_dim=1,
segs=None):
"""
km tells me neuron N gives rise to clusters km[N] (list)
bckgrd is background spike rate (Hz)
"""
global UNIF, NUNIF
##### First check that the number of neurons and PFs all consistent.
nNrnsSX = len(l_sx_chpts)
nNrnsL0 = len(l_l0_chpts)
nNrnsCT = len(l_ctr_chpts)
nNrnsMK = len(mk_chpts)
nNrnsMKA = LoHis.shape[0]
nNrnsMKC = Covs.shape[0]
if not (nNrnsSX == nNrnsL0 == nNrnsCT == nNrnsMK == nNrnsMKA == nNrnsMKC):
print("Number of neurons not consistent")
return None
nNrns = nNrnsSX
if not (LoHis.shape[1] == Covs.shape[1] == Covs.shape[2]):
print("Covariance of LoHis not correct")
return None
K = LoHis.shape[1]
PFsPerNrn = _N.zeros(nNrns, dtype=_N.int)
sx_chpts = []
l0_chpts = []
ctr_chpts = []
M = 0 # of place fields total. a neuron may have > 1 PFs.
nrnNum = []
for nrn in range(nNrns):
# # of place fields for neuron nrn
nPFsSX = len(l_sx_chpts[nrn])
nPFsL0 = len(l_l0_chpts[nrn])
nPFsCT = len(l_ctr_chpts[nrn])
sx_chpts.extend(l_sx_chpts[nrn])
l0_chpts.extend(l_l0_chpts[nrn])
ctr_chpts.extend(l_ctr_chpts[nrn])
if not (nPFsSX == nPFsL0 == nPFsCT):
print("Number of PFs for neuron %d not consistent" % nrn)
return None
M += len(l_ctr_chpts[nrn])
nrnNum += [nrn] * nPFsSX
PFsPerNrn[nrn] = nPFsSX
# M = # of clusters (in mark + pos space)
# nNrns = # of neurons
if x_mvt is None:
#### build data
Ns = _N.empty(RTs, dtype=_N.int)
if mvPat == NUNIF:
for rt in range(RTs):
Ns[rt] = N * ((1 - pT) + pT * _N.random.rand())
else:
Ns[:] = N
NT = _N.sum(Ns) # total time we have data
pths = _N.empty(NT)
else:
NT = x_mvt.shape[0] # total time we have data
plastic = False
########## nonstationary center width
# sx_chpts is flattened version of l_sx_chpts, which is a list per neuron
# of place field change points. neur#1 has 1 pf, neur#2 has 5 pfs = 6 pfs
# sxt should be (M x NT)
sx = _N.empty((M, NT)) if spc_dim == 1 else _N.empty((M, NT, 2, 2))
isx = _N.empty((M, NT)) if spc_dim == 1 else _N.empty((M, NT, 2, 2))
for m in range(M): # sxts time scale
if spc_dim == 1:
sx[m] = createSmoothedPath(
sx_chpts[m],
NT)**2 # sx_chpts[m] is an ndaray. sx_chpts is a list
if len(sx_chpts[m]) > 1: plastic = True
isx[m] = 1. / sx[m]
else:
sx[m, :, 0,
0] = createSmoothedPath(sx_chpts[m][:, _N.array([0, 1])], NT)**2
sx[m, :, 1,
0] = createSmoothedPath(sx_chpts[m][:, _N.array([0, 2])], NT)**2
sx[m, :, 0, 1] = sx[m, :, 1, 0]
sx[m, :, 1,
1] = createSmoothedPath(sx_chpts[m][:, _N.array([0, 3])], NT)**2
isx[m] = _N.linalg.inv(sx[m])
########## nonstationary center height l0
# f is NT x M
l0 = _N.empty((M, NT))
for m in range(M):
l0[m] = createSmoothedPath(l0_chpts[m], NT)
if len(l0_chpts[m]) > 1: plastic = True
f = l0 / _N.sqrt(2 * _N.pi * sx) if spc_dim == 1 else l0 / _N.sqrt(
(2 * _N.pi) * (2 * _N.pi) * _N.linalg.det(sx))
########## nonstationary center width
# sx_chpts is flattened version of l_sx_chpts, which is a list per neuron
# of place field change points. neur#1 has 1 pf, neur#2 has 5 pfs = 6 pfs
# sxt should be (M x NT)
ctr = _N.empty((M, NT)) if spc_dim == 1 else _N.empty((M, NT, 2))
for m in range(M): # sxts time scale
if spc_dim == 1:
ctr[m] = createSmoothedPath(
ctr_chpts[m],
NT) # sx_chpts[m] is an ndaray. sx_chpts is a list
if len(ctr_chpts[m]) > 1: plastic = True
else:
ctr[m, :,
0] = createSmoothedPath(ctr_chpts[m][:, _N.array([0, 1])], NT)
ctr[m, :,
1] = createSmoothedPath(ctr_chpts[m][:, _N.array([0, 2])], NT)
if K > 0:
########## nonstationary marks
mk_MU = _N.empty((nNrns, NT, K))
print("------- ")
for n in range(nNrns):
mk_MU[n] = createSmoothedPathK(mk_chpts[n], NT, K, LoHis[n])
if len(mk_chpts[n]) > 1: plastic = True
if x_mvt is None:
if mvPat == NUNIF:
now = 0
for rt in range(RTs):
N = Ns[rt] # each traverse slightly different duration
rp = _N.random.rand(N // 100)
x = _N.linspace(Lx, Hx, N)
xp = _N.linspace(Lx, Hx, N // 100)
r = _N.interp(x, xp, rp) # creates a velocity vector
# create movement without regard for place field
frqmxR = _N.abs(frqmx * (1 + 0.25 * _N.random.randn()))
_N.linspace(0, 1, N, endpoint=False)
rscld_t = _N.random.rand(N) # rscld_t
rscld_t /= (_N.max(rscld_t) * 1.01)
rscld_t.sort()
phi0 = _N.random.rand() * 2 * _N.pi
r += _N.exp(Amx * _N.sin(2 * _N.pi * rscld_t * frqmxR + phi0))
pth = _N.zeros(N + 1)
for n in range(1, N + 1):
pth[n] = pth[n - 1] + r[n - 1]
pth /= (pth[-1] - pth[0])
pth *= (Hx - Lx)
pth += Lx
pths[now:now + N] = pth[0:N]
now += N
else: # x_mvt is not None
now = 0
x = _N.linspace(Lx, Hx, N)
for rt in range(RTs):
N = Ns[rt]
pths[now:now + N] = x
now += N
if addShortStops:
for ist in range(stops):
done = False
while not done:
t0 = int(_N.random.rand() * NT)
t1 = t0 + int(stopDur * (1 + 0.1 * _N.random.randn()))
if _N.abs(_N.max(_N.diff(pths[t0:t1]))) < 0.05 * (Hx - Lx):
done = True # not crossing origin
pths[t0:t1] = _N.mean(pths[t0:t1])
else:
pths = x_mvt
### now calculate firing rates
dt = 0.001
fdt = f * dt
# change place field location
if spc_dim == 1:
Lam = f * dt * _N.exp(-0.5 * (pths - ctr)**2 / sx)
else: # spc_dim == 2
Lam = _N.empty((M, NT))
for m in range(M):
dif = pths - ctr[m]
Lam[m] = f[m] * dt * _N.exp(
-0.5 * _N.einsum("ni,nij,nj->n", dif, isx[m], dif))
_N.savetxt("lam", Lam.T)
_N.savetxt("pths", pths)
rnds = _N.random.rand(M, NT)
if spc_dim == 1:
dat = _N.zeros((NT, 2 + K + 1))
dat[:, 0] = pths
if segs is not None:
dat[:, 2 + K] = segs
else:
dat[:, 2 + K] = 1
else:
dat = _N.zeros((NT, 3 + K + 2))
if nz_mvt is not None:
dat[:, 0:2] = nz_mvt
else:
dat[:, 0:2] = pths
dat[:, 7:9] = segs
spkind = 1 if spc_dim == 1 else 2
mrk_frm = 2 if spc_dim == 1 else 3
for m in range(M):
sts = _N.where(rnds[m] < Lam[m])[0] # spikes from this neuron
print("neuron %(m)d %(s)d spks" % {"m": m, "s": len(sts)})
alrdyXst = _N.where(dat[:,
spkind] == 1)[0] # places where synchronous
ndToMove = _N.intersect1d(alrdyXst, sts)
dntMove = _N.setdiff1d(sts, ndToMove) # empty slots
nonsynch = _N.empty(len(sts))
nonsynch[0:len(dntMove)] = dntMove
print(len(ndToMove))
iStart = len(dntMove) # in nonsynch
# for iOcpd in ndToMove: # occupied
# bDone = False
# while not bDone:
# iOcpd += 1
# if datNghbr[iOcpd, 1] == 0:
# datNghbr[iOcpd, 1] = 1
# nonsynch[iStart] = iOcpd
# iStart += 1
# bDone = True
snonsynch = _N.sort(nonsynch)
dat[sts, spkind] = 1
nrn = nrnNum[m]
if K > 0:
for t in range(len(sts)):
obsMrk = _N.random.multivariate_normal(mk_MU[nrn, sts[t]],
Covs[nrn],
size=1)
dat[sts[t], mrk_frm:mrk_frm + K] = obsMrk
# now noise spikes
if bckgrdLam is not None:
nzsts = _N.where(rnds[m] < (bckgrdLam * dt) / float(M))[0]
dat[nzsts, spkind] = 1
nrn = nrnNum[m]
if K > 0:
for t in range(len(nzsts)):
dat[nzsts[t], mrk_frm:mrk_frm +
K] = _N.random.multivariate_normal(mk_MU[nrn,
nzsts[t]],
Covs[nrn],
size=1)
if thresh is not None:
sts = _N.where(dat[:, spkind] == 1)[0]
nID, nC = _N.where(dat[sts, mrk_frm:mrk_frm + K] < thresh)
swtchs = _N.zeros((len(sts), K))
swtchs[nID, nC] = 1 # for all cells, all components below hash == 1
swtchsK = _N.sum(swtchs, axis=1)
blw_thrsh_all_chs = _N.where(swtchsK == K)[0]
abv_thr = _N.setdiff1d(_N.arange(len(sts)), blw_thrsh_all_chs)
print("below thresh in all channels %(1)d / %(2)d" % {
"1": len(blw_thrsh_all_chs),
"2": len(sts)
})
dat[sts[blw_thrsh_all_chs], spkind] = 0
bFnd = False
## us un uniform sampling of space, stationary or non-stationary place field
## ns nn non-uni sampling of space, stationary or non-stationary place field
## bs bb biased and non-uni sampling of space
bfn = "" if (M == 1) else ("%d" % M)
if mvPat == UNIF:
bfn += "u"
else:
bfn += "b" if (Amx > 0) else "n"
bfn += "n" if plastic else "s"
iInd = 0
while not bFnd:
iInd += 1
dd = os.getenv("__JIFIDataDir__")
fn = "%(dd)s/%(bfn)s%(iI)d.dat" % {"bfn": bfn, "iI": iInd, "dd": dd}
fnocc = "%(dd)s/%(bfn)s%(iI)docc.png" % {
"bfn": bfn,
"iI": iInd,
"dd": dd
}
fnprm = "%(dd)s/%(bfn)s%(iI)d_prms.pkl" % {
"bfn": bfn,
"iI": iInd,
"dd": dd
}
if not os.access(fn, os.F_OK): # file exists
bFnd = True
smk = " %.2f" * K
if spc_dim == 1:
smk += " %d"
_U.savetxtWCom("%s" % fn,
dat,
fmt=("%.4f %d" + smk),
delimiter=" ",
com="# script=%s.py" % script)
else:
smk += " %d %d"
_U.savetxtWCom("%s" % fn,
dat,
fmt=("%.2f %.2f %d" + smk),
delimiter=" ",
com="# script=%s.py" % script)
pcklme = {}
pcklme["l0"] = l0[:, ::100]
pcklme["u"] = mk_MU[:, ::100]
pcklme["covs"] = Covs
pcklme["intv"] = 100
pcklme["km"] = km
pcklme["f"] = ctr[:, ::100]
pcklme["sq2"] = sx[:, ::100]
dmp = open(fnprm, "wb")
pickle.dump(pcklme, dmp, -1)
dmp.close()
print("created %s" % fn)
def done():
"""
come here after 6 landmarks chosen
"""
global r, seg_ts, segs, Nsgs, inout, a_inout, lindist, lin_lr, lin_inout, lin_lr_inout, lr, raw_lindist
global scxMin, scxMax, scyMin, scyMax
global an, day, ep
hdir = _N.empty(2)
vdir = _N.empty(2)
linp = _N.empty(2)
"""
L5 L0 L3
|| || ||
|| || ||
5 1 3
|| || ||
|| || ||
L4===4===L1===2===L2
"""
scxMin, scxMax, scyMin, scyMax = get_boundaries(r)
segs_from_landmarks(segs, landmarks, length)
e = inout_dir(segs, Nsgs)
a_s, b_s, c_s = slopes_of_segs(segs)
_plt.plot([segs[0, 0, 0], segs[0, 1, 0]], [segs[0, 0, 1], segs[0, 1, 1]],
lw=3,
color="black")
_plt.plot([segs[1, 0, 0], segs[1, 1, 0]], [segs[1, 0, 1], segs[1, 1, 1]],
lw=3,
color="black")
_plt.plot([segs[2, 0, 0], segs[2, 1, 0]], [segs[2, 0, 1], segs[2, 1, 1]],
lw=3,
color="black")
_plt.plot([segs[3, 0, 0], segs[3, 1, 0]], [segs[3, 0, 1], segs[3, 1, 1]],
lw=3,
color="black")
_plt.plot([segs[4, 0, 0], segs[4, 1, 0]], [segs[4, 0, 1], segs[4, 1, 1]],
lw=3,
color="black")
segsr = segs.reshape((10, 2))
clrs = ["blue", "orange", "red", "green", "yellow", "black", "brown"]
fillin_unobsvd(r)
N = r.shape[0]
seg_ts = _N.empty(N, dtype=_N.int)
lindist = _N.empty(N)
lin_lr = _N.empty(N)
lin_inout = _N.empty(N)
lin_lr_inout = _N.empty(N)
lr = _N.ones(N, dtype=_N.int) * -3
inout = _N.empty(N, dtype=_N.int)
a_inout = _N.empty(N)
gk = gauKer(30)
gk /= _N.sum(gk)
fx = _N.convolve(0.5 * (r[:, 1] + r[:, 3]), gk, mode="same")
fy = _N.convolve(0.5 * (r[:, 2] + r[:, 4]), gk, mode="same")
xp = fx
yp = fy
xpyp = _N.empty((N, 2))
xpyp[:, 0] = xp
xpyp[:, 1] = yp
_xpyp = _N.repeat(xpyp, Nsgs * 2, axis=0)
rxpyp = _xpyp.reshape((N, Nsgs * 2, 2))
dv = segsr - rxpyp
dists = _N.sum(dv * dv, axis=2) # closest point on maze from field points
rdists = dists.reshape((N, Nsgs, 2))
print rdists.shape
online = _N.empty(Nsgs, dtype=bool)
mins = _N.empty(Nsgs)
for n in xrange(N):
x0 = xpyp[n, 0]
y0 = xpyp[n, 1]
# xcs, ycs: pt on all line segs closest to x0, y0 (may b byond endpts)
xcs = (b_s *
(b_s * x0 - a_s * y0) - a_s * c_s) / (a_s * a_s + b_s * b_s)
ycs = (-a_s *
(b_s * x0 - a_s * y0) - b_s * c_s) / (a_s * a_s + b_s * b_s)
find_clsest(n, x0, y0, segs, rdists, seg_ts, Nsgs, online, offset, xcs,
ycs, mins, linp)
# fig = _plt.figure()
# _plt.plot(seg_ts)
# clean_seg_ts(seg_ts)
# _plt.plot(seg_ts)
raw_lindist = _N.zeros(N)
lindist_x0y0(N, xpyp, segs, rdists, seg_ts, Nsgs, online, offset, a_s, b_s,
c_s, mins, linp, raw_lindist)
smooth_lindist(raw_lindist, lindist)
# fig = _plt.figure(figsize=(10, 4))
# _plt.plot(lindist)
# gk = gauKer(8) # don't want to make this too large. if we just pass through the choice point, we can miss it.
# gk /= _N.sum(gk)
# flindist = _N.convolve(lindist, gk, mode="same")
# lindist = flindist
# rm_lindist_jumps(N, lindist, seg_ts)
fig = _plt.figure(figsize=(10, 4))
_plt.plot(lindist)
spd_thr = 0.35
a_inout_x0y0(N, a_inout, inout, r, seg_ts, spd_thr, e)
#_plt.plot([x0, x0], [y0, y0], ms=10, marker=".", color=clr)
make_lin_inout(N, lindist, inout, lin_inout)
make_lin_lr(N, lr, lindist, seg_ts, r)
build_lin_lr_inout(N, lin_lr_inout, lindist, lr, inout, gkRWD)
# inout
cp_lr, cp_inout = cpify_LR_inout(lr, inout)
sday = ("0%d" % day) if (day < 10) else ("%d" % day)
fn = _edd.datFN("lindist.dat",
dir="linearize/%(an)s%(dy)s0%(ep)d" % {
"dy": sday,
"ep": (ep + 1),
"an": anim2
},
create=True)
_N.savetxt(fn, lindist, fmt="%.3f")
fn = _edd.datFN("cp_lr.dat",
dir="linearize/%(an)s%(dy)s0%(ep)d" % {
"dy": sday,
"ep": (ep + 1),
"an": anim2
})
_U.savetxtWCom(
fn,
cp_lr,
fmt="%d %d",
com=
("# N=%d. 1st column time, 2nd column - inout value from this time until time in next row"
% N))
fn = _edd.datFN("cp_inout.dat",
dir="linearize/%(an)s%(dy)s0%(ep)d" % {
"dy": sday,
"ep": (ep + 1),
"an": anim2
})
_U.savetxtWCom(
fn,
cp_inout,
fmt="%d %d",
com=
("# N=%d. 1st column time, 2nd column - inout value from this time until time in next row"
% N))
fn = _edd.datFN("lin_lr_inout.dat",
dir="linearize/%(an)s%(dy)s0%(ep)d" % {
"dy": sday,
"ep": (ep + 1),
"an": anim2
})
_N.savetxt(fn, lin_lr_inout, fmt="%.3f")
"""
"""
t0 = 0
winsz = 1000
t1 = 0
iw = -1
while t1 < N:
iw += 1
t0 = iw * winsz
t1 = (iw + 1) * winsz if (iw + 1) * winsz < N else N - 1
#btwnfigs(anim2, day, ep, t0, t1, inout, [-1.1, 1.1], seg_ts+1, [0.9, 5.1], r, 1, 2, scxMin, scxMax, scyMin, scyMax)
btwnfigs(anim2, day, ep, t0, t1, inout, "INOUT", [-1.1, 1.1], lr, "LR",
[-1.1, 1.1], lin_lr_inout, "lin_lr_inout", [-6.1, 6.1], r, 1,
2, scxMin, scxMax, scyMin, scyMax)
