def btwnfigs(anim, day, ep, t0, t1, someplt1, label1, lims1, someplt2, label2, lims2, someplt3, label3, lims3, r, x, y, scxMin, scxMax, scyMin, scyMax):
print "btwnfigs %(ep)d %(1)d %(2)d" % {"ep" : ep, "1" : t0, "2" : t1}
fig = _plt.figure(figsize=(13, 10))
fig.add_subplot(2, 2, 1)
_plt.scatter(r[::10, x], r[::10, y], s=5, color="grey")
_plt.scatter(r[t0:t1:2, x], r[t0:t1:2, y], s=9, color="black")
_plt.plot(r[t0, x], r[t0, y], ms=40, color="blue", marker=".")
_plt.plot(r[t1, x], r[t1, y], ms=40, color="red", marker=".")
_plt.xlim(scxMin, scxMax)
_plt.ylim(scyMin, scyMax)
fig.add_subplot(2, 2, 2)
_plt.plot(range(t0, t1), someplt1[t0:t1], color="black", lw=4)
_plt.xlim(t0, t1)
_plt.xticks(_N.arange(t0, t1, (t1-t0)/5))
_plt.ylabel(label1)
_plt.ylim(lims1[0], lims1[1])
_plt.grid()
fig.add_subplot(2, 2, 3)
_plt.plot(range(t0, t1), someplt2[t0:t1], color="black", lw=4)
_plt.xlim(t0, t1)
_plt.ylim(lims2[0], lims2[1])
_plt.ylabel(label2)
_plt.xticks(_N.arange(t0, t1, (t1-t0)/5))
_plt.grid()
fig.add_subplot(2, 2, 4)
_plt.plot(range(t0, t1), someplt3[t0:t1], color="black", lw=4)
_plt.xlim(t0, t1)
_plt.ylim(lims3[0], lims3[1])
_plt.ylabel(label3)
_plt.xticks(_N.arange(t0, t1, (t1-t0)/5))
_plt.grid()
fig.subplots_adjust(left=0.1, bottom=0.1, top=0.95, right=0.99)
sday = ("0%d" % day) if (day < 10) else ("%d" % day)
#print _edd.datFN("btwn_%(dy)d%(ep)d_%(1)d_%(2)d" % {"dy" : day, "ep" : (ep+1), "1" : t0, "2" : t1}, create=True)
#_edd.datFN("lindist.dat", dir="linearize/%(an)s%(dy)s0%(ep)d" % {"dy" : sday, "ep" : (ep+1), "an" : anim2}, create=True)
_plt.savefig(_edd.datFN("btwn_%(dy)d%(ep)d_%(1)d_%(2)d" % {"dy" : day, "ep" : (ep+1), "1" : t0, "2" : t1}, dir="linearize/%(an)s%(dy)s0%(ep)d" % {"dy" : sday, "ep" : (ep+1), "an" : anim2}, create=True))
#_plt.savefig("btwn_%(an)s%(dy)d%(ep)d_%(1)d_%(2)d" % {"dy" : day, "ep" : (ep+1), "1" : t0, "2" : t1, "an" : anim})
_plt.close()
def cmpGTtoFIT(basefn, datfn, itvfn, M, epc, xticks=None, yticks=None):
dmp = open(_ed.resFN("posteriors.dump", dir=basefn), "rb")
gt = _N.loadtxt(_ed.datFN("%s_prms.dat" % datfn))
_intvs = _N.loadtxt(_ed.datFN("%s.dat" % itvfn))
intvs = _N.array(gt.shape[0] * _intvs, dtype=_N.int)
pckl = pickle.load(dmp)
dmp.close()
N = 300
x = _N.linspace(0, 3, N)
smpls = pckl["cp%d" % epc]
mds = pckl["md"]
l0s = smpls[0] # GIBBS ITER x M
fs = smpls[1]
q2s = smpls[2]
frm = 200
Nsmp = l0s.shape[0] - frm
SMPS = 1000
rfsmps= _N.empty((SMPS, N, M)) # samples
rs = _N.random.rand(SMPS, 3, M)
for m in xrange(M):
for ss in xrange(SMPS):
i_l0 = int((Nsmp-frm)*rs[ss, 0, m]) # one of the iters
i_f = int((Nsmp-frm)*rs[ss, 1, m])
i_q2 = int((Nsmp-frm)*rs[ss, 2, m])
l0 = l0s[frm+i_l0, m]
f = fs[frm+i_f, m]
q2 = q2s[frm+i_q2, m]
rfsmps[ss, :, m] = (l0 / _N.sqrt(2*_N.pi*q2)) * _N.exp(-0.5*(x - f)*(x-f)/q2)
#_plt.plot(x, rfsmps[ss], color="black")
Arfsmps = _N.sum(rfsmps, axis=2)
srtd = _N.sort(Arfsmps, axis=0)
fig = _plt.figure(figsize=(5, 4))
ax = fig.add_subplot(1, 1, 1)
#_plt.fill_between(x, srtd[50, :], srtd[950, :], alpha=0.3)
_plt.fill_between(x, srtd[50, :], srtd[950, :], color="#CCCCFF")
fr_s = _N.zeros(N)
fr_e = _N.zeros(N)
Mgt = gt.shape[1]/3
print Mgt
for m in xrange(Mgt):
l0_s = gt[intvs[epc], 2+3*m]
l0_e = gt[intvs[epc+1]-1, 2+3*m]
f_s = gt[intvs[epc], 3*m]
f_e = gt[intvs[epc+1]-1, 3*m]
q2_s = gt[intvs[epc], 1+3*m]
q2_e = gt[intvs[epc+1]-1, 1+3*m]
fr_s += (l0_s / _N.sqrt(2*_N.pi*q2_s)) * _N.exp(-0.5*(x - f_s)*(x-f_s)/q2_s)
fr_e += (l0_e / _N.sqrt(2*_N.pi*q2_e)) * _N.exp(-0.5*(x - f_e)*(x-f_e)/q2_e)
_plt.plot(x, 0.5*(fr_s+fr_e), lw=3, color="black")
fr_m = _N.zeros(N)
for m in xrange(M):
l0_m = mds[epc, 3*m]
f_m = mds[epc, 1+3*m]
q2_m = mds[epc, 2+3*m]
fr_m += (l0_m / _N.sqrt(2*_N.pi*q2_m)) * _N.exp(-0.5*(x - f_m)*(x-f_m)/q2_m)
_plt.plot(x, fr_m, lw=3, color="blue")
mF.setTicksAndLims(xlabel="position", ylabel="Hz", xticks=xticks, yticks=yticks, tickFS=22, labelFS=24)
mF.arbitaryAxes(ax, axesVis=[True, True, False, False])
fig.subplots_adjust(left=0.2, bottom=0.2, right=0.95, top=0.95)
_plt.savefig(_ed.resFN("cmpGT2FIT%d.eps" % epc, dir=basefn))
for epc in range(4, 5):
ep=epc+1;
_pts=mLp["linpos"][0,ex][0,ep]
if (_pts.shape[1] > 0): # might be empty epoch
pts = _pts["statematrix"][0][0]["time"][0,0].T[0]
a = mLp["linpos"][0,ex][0,ep]["statematrix"][0,0]["segmentIndex"]
r = mRp["rawpos"][0,ex][0,ep]["data"][0,0]
fillin_unobsvd(r)
scxMin, scxMax, scyMin, scyMax = get_boundaries(r)
sday = ("0%d" % day) if (day < 10) else ("%d" % day)
fn = _edd.datFN("cp_lr.dat", dir="linearize/%(an)s%(dy)s0%(ep)d" % {"dy" : sday, "ep" : (ep+1), "an" : anim2})
cp_lr = _N.loadtxt(fn, dtype=_N.int)
fn = _edd.datFN("cp_inout.dat", dir="linearize/%(an)s%(dy)s0%(ep)d" % {"dy" : sday, "ep" : (ep+1), "an" : anim2})
cp_inout = _N.loadtxt(fn, dtype=_N.int)
lr, inout = thaw_LR_inout(27901, cp_lr, cp_inout)
fn = _edd.datFN("lindist.dat", dir="linearize/%(an)s%(dy)s0%(ep)d" % {"dy" : sday, "ep" : (ep+1), "an" : anim2})
lindist = _N.loadtxt(fn)
N = len(lindist)
lin_lr_inout = _N.empty(N)
build_lin_lr_inout(N, lin_lr_inout, lindist, lr, inout, gkRWD)
t0 = 0
winsz = 1000
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)
