def show_posmarks(dec, setname, ylim=None, win=None, singles=False, baseFN=None):
MTHR = 0.001 # how much smaller is mixture compared to maximum
for nt in xrange(dec.nTets):
if not singles:
fig = _plt.figure(figsize=(7, 5))
for k in xrange(1, dec.mdim+1):
if singles:
fig = _plt.figure(figsize=(4, 3))
ax = fig.add_subplot(1, 1, 1)
else:
ax = fig.add_subplot(2, 2, k)
"""
for l in xrange(dec.tt0, dec.tt1):
if (dec.marks[l, nt] is not None):
x.append(dec.pos[l])
y.append(dec.marks[l, nt][0][k-1])
"""
if dec.marksObserved[nt] > 0:
_plt.scatter(dec.tr_pos[nt], dec.tr_marks[nt][:, k-1], color="black", s=2)
#_plt.scatter(dec.mvNrm[nt].us[:, 0], dec.mvNrm[nt].us[:, k], color="red", s=30)
mThr = MTHR * _N.max(dec.mvNrm[nt].ms)
for m in xrange(dec.M):
if dec.mvNrm[nt].ms[m, 0] >= mThr:
ux = dec.mvNrm[nt].us[m, 0] # position
uy = dec.mvNrm[nt].us[m, k]
ex_x = _N.sqrt(dec.mvNrm[nt].covs[m, 0, 0])
ex_y = _N.sqrt(dec.mvNrm[nt].covs[m, k, k])
_plt.plot([ux-ex_x, ux+ex_x], [uy, uy], color="red", lw=2)
_plt.plot([ux, ux], [uy-ex_y, uy+ex_y], color="red", lw=2)
_plt.scatter(dec.mvNrm[nt].us[m, 0], dec.mvNrm[nt].us[m, k], color="red", s=30)
_plt.xlim(-6, 6)
if ylim is not None:
_plt.ylim(ylim[0], ylim[1])
if singles:
_plt.suptitle("k=%(k)d t0=%(2).2fs : t1=%(3).2fs" % {"2" : (dec.tt0/1000.), "3" : (dec.tt1/1000.), "k" : k})
fn= baseFN if (dec.usetets is None) else "%(bf)s_tet%(t)s" % {"bf" : baseFN, "t" : dec.usetets[nt]}
mF.arbitraryAxes(ax)
mF.setLabelTicks(_plt, xlabel="position", ylabel="mark", xtickFntSz=14, ytickFntSz=14, xlabFntSz=16, ylabFntSz=16)
fig.subplots_adjust(left=0.2, bottom=0.2, top=0.85)
_plt.savefig(resFN("%(1)s_win=%(w)d.png" % {"1" : fn, "w" : win}, dir=setname), transparent=True)
_plt.close()
if not singles:
_plt.suptitle("t0=%(2)d,t1=%(3)d" % {"2" : dec.tt0, "3" : dec.tt1})
fn= baseFN if (dec.usetets is None) else "%(bf)s_tet%(t)s" % {"bf" : baseFN, "t" : dec.usetets[nt]}
_plt.savefig(resFN("%(1)s_win=%(w)d.png" % {"1" : fn, "w" : win}, dir=setname, create=True), transparent=True)
_plt.close()
def show_posmarksCNTR(dec, setname, mvNrm, ylim=None, win=None, singles=False, showScatter=True, baseFN="look", scatskip=1):
for nt in xrange(dec.nTets):
if not singles:
fig = _plt.figure(figsize=(7, 5))
for k in xrange(1, dec.mdim+1):
if singles:
fig = _plt.figure(figsize=(4, 3))
ax = fig.add_subplot(1, 1, 1)
else:
ax = fig.add_subplot(2, 2, k)
"""
for l in xrange(dec.tt0, dec.tt1):
if (dec.marks[l, nt] is not None):
x.append(dec.pos[l])
y.append(dec.marks[l, nt][0][k-1])
"""
_plt.xlim(-6, 6)
if ylim is not None:
_plt.ylim(ylim[0], ylim[1])
else:
ylim = _N.empty(2)
ylim[0] = _N.min(dec.tr_marks[nt][:, k-1])
ylim[1] = _N.max(dec.tr_marks[nt][:, k-1])
yAMP = ylim[1] - ylim[0]
ylim[0] -= 0.1*yAMP
ylim[1] += 0.1*yAMP
if showScatter and dec.marksObserved[nt] > 0:
_plt.scatter(dec.tr_pos[nt][::scatskip], dec.tr_marks[nt][::scatskip, k-1], color="grey", s=1)
img = mvNrm.evalAll(1000, k-1, ylim=ylim)
_plt.imshow(img, origin="lower", extent=(-6, 6, ylim[0], ylim[1]), cmap=_plt.get_cmap("Reds"))
if singles:
_plt.suptitle("k=%(k)d t0=%(2).2fs : t1=%(3).2fs" % {"2" : (dec.tt0/1000.), "3" : (dec.tt1/1000.), "k" : k})
fn= baseFN if (dec.usetets is None) else "%(fn)s_tet%(tets)s" % {"fn" : baseFN, "tets" : dec.usetets[nt]}
mF.arbitraryAxes(ax)
mF.setLabelTicks(_plt, xlabel="position", ylabel="mark", xtickFntSz=14, ytickFntSz=14, xlabFntSz=16, ylabFntSz=16)
fig.subplots_adjust(left=0.2, bottom=0.2, top=0.85)
_plt.savefig(resFN("%(1)s_win=%(w)d.png" % {"1" : fn, "w" : win}, dir=setname), transparent=True)
_plt.close()
if not singles:
_plt.suptitle("t0=%(2)d,t1=%(3)d" % {"2" : dec.tt0, "3" : dec.tt1})
fn= baseFN if (dec.usetets is None) else "%(fn)s_tet%(tets)s" % {"fn" : baseFN, "tets" : dec.usetets[nt]}
_plt.savefig(resFN("%(1)s_win=%(w)d.png" % {"1" : fn, "w" : win}, dir=setname, create=True), transparent=True)
_plt.close()
def pos_timeline(bfn,
datfn,
itvfn,
outfn="timeline",
ch1=0,
ch2=1,
xL=0,
xH=3,
yticks=[0, 1, 2, 3],
yticksD=[0, 1, 2, 3],
thin=1,
t0=None,
t1=None,
skp=1,
maze=_mkd.mz_CRCL):
d = _N.loadtxt(datFN("%s.dat" % datfn)) # marks
t0 = 0 if (t0 is None) else t0
t1 = d.shape[0] if (t1 is None) else t1
itv = _N.loadtxt(datFN("%s.dat" % itvfn))
N = d.shape[0]
epochs = itv.shape[0] - 1
ch1 += 2 # because this is data col
ch2 += 2
_sts = _N.where(d[t0:t1, 1] == 1)[0] + t0
if thin == 1:
sts = _sts
else:
sts = _sts[::thin]
wvfmMin = _N.min(d[t0:t1, 2:], axis=0)
wvfmMax = _N.max(d[t0:t1, 2:], axis=0)
#fig = _plt.figure(figsize=(4, 2.2))
fig = _plt.figure(figsize=(10, 2.2))
#######################
ax = _plt.subplot2grid((1, 3), (0, 0), colspan=3)
_plt.scatter(_N.arange(d[t0:t1:100].shape[0], dtype=_N.float) / 10.,
d[t0:t1:100, 0],
s=9,
color="black")
_plt.scatter(sts[::skp] / 1000., d[sts[::skp], 0], s=1, color="orange")
if maze == _mkd.mz_W:
_plt.axhline(y=-6, ls="--", lw=1, color="black")
_plt.axhline(y=-3, ls=":", lw=1, color="black")
_plt.axhline(y=0, ls="--", lw=1, color="black")
_plt.axhline(y=3, ls=":", lw=1, color="black")
_plt.axhline(y=6, ls="--", lw=1, color="black")
mF.arbitraryAxes(ax,
axesVis=[True, True, False, False],
xtpos="bottom",
ytpos="left")
#itv[-1] = 0.97
for ep in range(epochs):
_plt.axvline(x=(itv[ep + 1] * N / 1000.), color="red", ls="-.")
mF.setTicksAndLims(xlabel="time (s)",
ylabel="position",
xticks=None,
yticks=yticks,
xticksD=None,
yticksD=yticksD,
xlim=[t0 / 1000., t1 / 1000.],
ylim=[xL - 0.3, xH + 0.3],
tickFS=15,
labelFS=18)
choutfn = "%(of)s_%(1)d,%(2)d" % {
"of": outfn,
"1": (ch1 - 1),
"2": (ch2 - 1)
}
fig.subplots_adjust(bottom=0.28, left=0.1, top=0.96, right=0.99)
_plt.savefig(resFN("%s.pdf" % choutfn, dir=bfn), transparent=True)
_plt.close()
def timeline_v2(bfn,
datfn,
itvfn,
outfn="timeline",
ch1=0,
ch2=1,
xL=0,
xH=3,
yticks=[0, 1, 2, 3],
thin=1,
allFR=None,
tcksz=19,
lblsz=21,
t0=None,
t1=None,
figw=10,
figh=10,
ytpos="left"): # 25Hz
d = _N.loadtxt(datFN("%s.dat" % datfn)) # marks
N = d.shape[0] # ALL spikes
itv = _N.loadtxt(datFN("%s.dat" % itvfn))
epochs = itv.shape[0] - 1
ch1 += 2 # because this is data col
ch2 += 2
wvfmMin = _N.min(d[:, 2:], axis=0)
print(wvfmMin)
wvfmMax = _N.max(d[:, 2:], axis=0)
print(wvfmMax)
Aint = _N.array(itv * N, dtype=_N.int)
Asts = _N.where(d[Aint[0]:Aint[1], 1] == 1)[0]
N1 = len(Asts) # num spikes in 1st epoch
_x = _N.empty((N1, 5))
_x[:, 0] = d[Asts, 0]
_x[:, 1:] = d[Asts, 2:]
T1 = Aint[1] - Aint[0]
hz = _x.shape[0] / float(T1 * 0.001)
if allFR is not None:
unonhash, hashsp, hashthresh = sepHash(_x,
BINS=20,
blksz=5,
xlo=xL,
xhi=xH)
rh = len(hashsp) / float(N1)
rnh = len(unonhash) / float(N1)
#(m x rh + rnh)*hz = allFR
#allFR - rnh x hz = m x rh x hz
# m = (rnh x hz - allFR) / (rh x hz)
m = (allFR - rnh * hz) / (rh * hz)
# ((m*len(hashsp) + len(unonhash)) / (T1*0.001)) == allFR (target FR)
# how much of hashsp should we remove?
# Find lowest (1 - m)
_x[:, 1:].sort(axis=0)
chmins = channelMins(_x, 100, 4, int((1 - m) * len(hashsp)))
#spk_n, chs = _N.where(_x[:, 1:] > chmins)
#unique, counts = _N.unique(spk_n, return_counts=True)
#print len(_N.where(counts > 0)[0]) # at least 1 channel above
#print (len(_N.where(counts > 0)[0]) / float(T1*0.001))
_sts = _N.where(d[:, 1] == 1)[0] # ALL spikes in ALL epochs
spk_n, chs = _N.where(d[_sts, 2:] > chmins)
unique_spk_IDs, counts = _N.unique(spk_n, return_counts=True)
sts = _sts[unique_spk_IDs[_N.where(counts > 0)[0]]]
if allFR is None:
_sts = _N.where(d[:, 1] == 1)[0]
if thin == 1:
sts = _sts
else:
sts = _sts[::thin]
fig = _plt.figure(figsize=(figw, figh))
#######################
#ax =_plt.subplot2grid((5, 3), (0, 0), colspan=3)
ax = _plt.subplot2grid((33, 3), (0, 0), colspan=3, rowspan=5)
_plt.scatter(sts / 1000., d[sts, 0], s=2, color="black")
if ytpos == "left":
mF.arbitraryAxes(ax,
axesVis=[True, True, False, False],
xtpos="bottom",
ytpos="left")
mF.setTicksAndLims(xlabel="time (s)",
ylabel="position",
yticks=[-6, -5, -3, -1, 0, 1, 3, 5, 6],
xticks=None,
yticksD=[
"H ", "C", "L ", "C", "H ", "C",
"R ", "C", "H "
],
xticksD=None,
xlim=[0, N / 1000.],
ylim=[xL - 0.3, xH + 0.3],
tickFS=tcksz,
labelFS=lblsz)
else:
mF.arbitraryAxes(ax,
axesVis=[False, True, True, False],
xtpos="bottom",
ytpos="right")
mF.setTicksAndLims(
xlabel="time (s)",
ylabel="position",
yticks=[-6, -5, -3, -1, 0, 1, 3, 5, 6],
xticks=None,
yticksD=["H", " C", "L", " C", "H", " C", "R", " C", "H"],
xticksD=None,
xlim=[0, N / 1000.],
ylim=[xL - 0.3, xH + 0.3],
tickFS=tcksz,
labelFS=lblsz)
for ep in range(epochs):
_plt.axvline(x=(itv[ep + 1] * N / 1000.), color="red", ls="--")
#######################
#ax = _plt.subplot2grid((5, 3), (1, 0), colspan=3)
ax = _plt.subplot2grid((33, 3), (10, 0), colspan=3, rowspan=5)
print(len(sts))
_plt.scatter(sts / 1000., d[sts, 2], s=2, color="black")
if ytpos == "left":
mF.arbitraryAxes(ax,
axesVis=[True, True, False, False],
xtpos="bottom",
ytpos="left")
mF.setTicksAndLims(xlabel=None,
ylabel="channel 1\nmark (mV)",
xticks=None,
yticks=[],
xticksD=None,
yticksD=None,
xlim=[0, N / 1000.],
ylim=[wvfmMin[0], wvfmMax[0]],
tickFS=tcksz,
labelFS=lblsz)
else:
mF.arbitraryAxes(ax,
axesVis=[False, True, True, False],
xtpos="bottom",
ytpos="right")
mF.setTicksAndLims(xlabel=None,
ylabel="channel 1\nmark (mV)",
xticks=None,
yticks=[],
xticksD=None,
yticksD=None,
xlim=[0, N / 1000.],
ylim=[wvfmMin[0], wvfmMax[0]],
tickFS=tcksz,
labelFS=lblsz)
for ep in range(epochs):
_plt.axvline(x=(itv[ep + 1] * N / 1000.), color="red", ls="--")
#######################
ax = _plt.subplot2grid((33, 3), (16, 0), colspan=3, rowspan=5)
#ax = _plt.subplot2grid((5, 3), (2, 0), colspan=3)
_plt.scatter(sts / 1000., d[sts, 3], s=2, color="black")
if ytpos == "left":
mF.arbitraryAxes(ax,
axesVis=[True, True, False, False],
xtpos="bottom",
ytpos="left")
mF.setTicksAndLims(xlabel=None,
ylabel="channel 2\nmark (mV)",
xticks=None,
yticks=[],
xticksD=None,
yticksD=None,
xlim=[0, N / 1000.],
ylim=[wvfmMin[1], wvfmMax[1]],
tickFS=tcksz,
labelFS=lblsz)
else:
mF.arbitraryAxes(ax,
axesVis=[False, True, True, False],
xtpos="bottom",
ytpos="right")
mF.setTicksAndLims(xlabel=None,
ylabel="channel 2\nmark (mV)",
xticks=None,
yticks=[],
xticksD=None,
yticksD=None,
xlim=[0, N / 1000.],
ylim=[wvfmMin[1], wvfmMax[1]],
tickFS=tcksz,
labelFS=lblsz)
for ep in range(epochs):
_plt.axvline(x=(itv[ep + 1] * N / 1000.), color="red", ls="--")
#######################
#ax = _plt.subplot2grid((5, 3), (3, 0), colspan=3)
ax = _plt.subplot2grid((33, 3), (22, 0), colspan=3, rowspan=5)
print(len(sts))
_plt.scatter(sts / 1000., d[sts, 4], s=2, color="black")
if ytpos == "left":
mF.arbitraryAxes(ax,
axesVis=[True, True, False, False],
xtpos="bottom",
ytpos="left")
mF.setTicksAndLims(xlabel=None,
ylabel="channel 3\nmark (mV)",
xticks=None,
yticks=[],
xticksD=None,
yticksD=None,
xlim=[0, N / 1000.],
ylim=[wvfmMin[2], wvfmMax[2]],
tickFS=tcksz,
labelFS=lblsz)
else:
mF.arbitraryAxes(ax,
axesVis=[False, True, True, False],
xtpos="bottom",
ytpos="right")
mF.setTicksAndLims(xlabel=None,
ylabel="channel 3\nmark (mV)",
xticks=None,
yticks=[],
xticksD=None,
yticksD=None,
xlim=[0, N / 1000.],
ylim=[wvfmMin[2], wvfmMax[2]],
tickFS=tcksz,
labelFS=lblsz)
for ep in range(epochs):
_plt.axvline(x=(itv[ep + 1] * N / 1000.), color="red", ls="--")
#######################
ax = _plt.subplot2grid((33, 3), (28, 0), colspan=3, rowspan=5)
#ax = _plt.subplot2grid((5, 3), (4, 0), colspan=3)
_plt.scatter(sts / 1000., d[sts, 5], s=2, color="black")
if ytpos == "left":
mF.arbitraryAxes(ax,
axesVis=[True, True, False, False],
xtpos="bottom",
ytpos="left")
mF.setTicksAndLims(xlabel="time (s)",
ylabel="channel 4\nmark (mV)",
xticks=None,
yticks=[],
xticksD=None,
yticksD=None,
xlim=[0, N / 1000.],
ylim=[wvfmMin[3], wvfmMax[3]],
tickFS=tcksz,
labelFS=lblsz)
else:
mF.arbitraryAxes(ax,
axesVis=[False, True, True, False],
xtpos="bottom",
ytpos="right")
mF.setTicksAndLims(xlabel="time (s)",
ylabel="channel 4\nmark (mV)",
xticks=None,
yticks=[],
xticksD=None,
yticksD=None,
xlim=[0, N / 1000.],
ylim=[wvfmMin[3], wvfmMax[3]],
tickFS=tcksz,
labelFS=lblsz)
for ep in range(epochs):
_plt.axvline(x=(itv[ep + 1] * N / 1000.), color="red", ls="--")
#fig.subplots_adjust(left=0.08, bottom=0.08, right=0.95, top=0.95, wspace=0.9, hspace=0.9)
if ytpos == "left":
fig.subplots_adjust(left=0.16,
bottom=0.08,
right=0.98,
top=0.95,
wspace=0.4,
hspace=0.4)
if ytpos == "right":
fig.subplots_adjust(left=0.05,
bottom=0.08,
right=0.84,
top=0.95,
wspace=0.4,
hspace=0.4)
epochs = len(itv) - 1
for fmt in ["eps"]:
choutfn = "%(of)s.%(fmt)s" % {"of": outfn, "fmt": fmt}
_plt.savefig(resFN(choutfn, dir=bfn), transparent=True)
_plt.close()
def timeline(bfn,
datfn,
itvfn,
outfn="timeline",
ch1=0,
ch2=1,
xL=0,
xH=3,
yticks=[0, 1, 2, 3],
thin=1,
allFR=None): # 25Hz
d = _N.loadtxt(datFN("%s.dat" % datfn)) # marks
itv = _N.loadtxt(datFN("%s.dat" % itvfn))
N = d.shape[0] # ALL spikes
epochs = itv.shape[0] - 1
ch1 += 2 # because this is data col
ch2 += 2
wvfmMin = _N.min(d[:, 2:], axis=0)
wvfmMax = _N.max(d[:, 2:], axis=0)
Aint = _N.array(itv * N, dtype=_N.int)
Asts = _N.where(d[Aint[0]:Aint[1], 1] == 1)[0]
N1 = len(Asts) # num spikes in 1st epoch
_x = _N.empty((N1, 5))
_x[:, 0] = d[Asts, 0]
_x[:, 1:] = d[Asts, 2:]
T1 = Aint[1] - Aint[0]
hz = _x.shape[0] / float(T1 * 0.001)
if allFR is not None:
unonhash, hashsp, hashthresh = sepHash(_x,
BINS=20,
blksz=5,
xlo=xL,
xhi=xH)
rh = len(hashsp) / float(N1)
rnh = len(unonhash) / float(N1)
#(m x rh + rnh)*hz = allFR
#allFR - rnh x hz = m x rh x hz
# m = (rnh x hz - allFR) / (rh x hz)
m = (allFR - rnh * hz) / (rh * hz)
# ((m*len(hashsp) + len(unonhash)) / (T1*0.001)) == allFR (target FR)
# how much of hashsp should we remove?
# Find lowest (1 - m)
_x[:, 1:].sort(axis=0)
chmins = channelMins(_x, 100, 4, int((1 - m) * len(hashsp)))
#spk_n, chs = _N.where(_x[:, 1:] > chmins)
#unique, counts = _N.unique(spk_n, return_counts=True)
#print len(_N.where(counts > 0)[0]) # at least 1 channel above
#print (len(_N.where(counts > 0)[0]) / float(T1*0.001))
_sts = _N.where(d[:, 1] == 1)[0] # ALL spikes in ALL epochs
spk_n, chs = _N.where(d[_sts, 2:] > chmins)
unique_spk_IDs, counts = _N.unique(spk_n, return_counts=True)
sts = _sts[unique_spk_IDs[_N.where(counts > 0)[0]]]
if allFR is None:
_sts = _N.where(d[:, 1] == 1)[0]
if thin == 1:
sts = _sts
else:
sts = _sts[::thin]
fig = _plt.figure(figsize=(10, 12))
#######################
ax = _plt.subplot2grid((4, 3), (0, 0), colspan=3)
_plt.scatter(sts / 1000., d[sts, 0], s=2, color="black")
mF.arbitraryAxes(ax,
axesVis=[True, True, False, False],
xtpos="bottom",
ytpos="left")
mF.setTicksAndLims(xlabel="time (s)",
ylabel="position",
xticks=None,
yticks=yticks,
xticksD=None,
yticksD=None,
xlim=[0, N / 1000.],
ylim=[xL - 0.3, xH + 0.3],
tickFS=15,
labelFS=18)
for ep in range(epochs):
_plt.axvline(x=(itv[ep + 1] * N / 1000.), color="red", ls="--")
#######################
ax = _plt.subplot2grid((4, 3), (1, 0), colspan=3)
print(len(sts))
_plt.scatter(sts / 1000., d[sts, ch1], s=2, color="black")
mF.arbitraryAxes(ax,
axesVis=[True, True, False, False],
xtpos="bottom",
ytpos="left")
mF.setTicksAndLims(xlabel="time (s)",
ylabel=("mk elctrd %d" % (ch1 - 1)),
xticks=None,
yticks=[0, 3, 6],
xticksD=None,
yticksD=None,
xlim=[0, N / 1000.],
ylim=[wvfmMin[0], wvfmMax[0]],
tickFS=15,
labelFS=18)
for ep in range(epochs):
_plt.axvline(x=(itv[ep + 1] * N / 1000.), color="red", ls="--")
#######################
ax = _plt.subplot2grid((4, 3), (2, 0), colspan=3)
_plt.scatter(sts / 1000., d[sts, ch2], s=2, color="black")
mF.arbitraryAxes(ax,
axesVis=[True, True, False, False],
xtpos="bottom",
ytpos="left")
mF.setTicksAndLims(xlabel="time (s)",
ylabel=("mk elctrd %d" % (ch2 - 1)),
xticks=None,
yticks=[0, 3, 6],
xticksD=None,
yticksD=None,
xlim=[0, N / 1000.],
ylim=[wvfmMin[1], wvfmMax[1]],
tickFS=15,
labelFS=18)
for ep in range(epochs):
_plt.axvline(x=(itv[ep + 1] * N / 1000.), color="red", ls="--")
##############
ax = _plt.subplot2grid((4, 3), (3, 0), colspan=1)
_plt.scatter(d[sts, ch1], d[sts, ch2], s=2, color="black")
mF.arbitraryAxes(ax,
axesVis=[True, True, False, False],
xtpos="bottom",
ytpos="left")
mF.setTicksAndLims(xlabel=("mk elctrd %d" % (ch1 - 1)),
ylabel=("mk elctrd %d" % (ch2 - 1)),
xticks=[0, 3, 6],
yticks=[0, 3, 6],
xticksD=None,
yticksD=None,
xlim=[wvfmMin[0], wvfmMax[0]],
ylim=[wvfmMin[1], wvfmMax[1]],
tickFS=15,
labelFS=18)
##############
ax = _plt.subplot2grid((4, 3), (3, 1), colspan=1)
_plt.scatter(d[sts, 0], d[sts, ch1], s=2, color="black")
mF.arbitraryAxes(ax,
axesVis=[True, True, False, False],
xtpos="bottom",
ytpos="left")
mF.setTicksAndLims(xlabel="pos",
ylabel=("mk elctrd %d" % (ch1 - 1)),
xticks=_N.linspace(xL, xH, 3),
yticks=[0, 3, 6],
xticksD=None,
yticksD=None,
xlim=[xL, xH],
ylim=None,
tickFS=15,
labelFS=18)
##############
ax = _plt.subplot2grid((4, 3), (3, 2), colspan=1)
_plt.scatter(d[sts, 0], d[sts, ch2], s=2, color="black")
mF.arbitraryAxes(ax,
axesVis=[True, True, False, False],
xtpos="bottom",
ytpos="left")
mF.setTicksAndLims(xlabel="pos",
ylabel=("mk elctrd %d" % (ch2 - 1)),
xticks=_N.linspace(xL, xH, 3),
yticks=[0, 3, 6],
xticksD=None,
yticksD=None,
xlim=[xL, xH],
ylim=None,
tickFS=15,
labelFS=18)
##############
fig.subplots_adjust(left=0.15, bottom=0.15, wspace=0.38, hspace=0.38)
epochs = len(itv) - 1
choutfn = "%(of)s_%(1)d,%(2)d" % {
"of": outfn,
"1": (ch1 - 1),
"2": (ch2 - 1)
}
_plt.savefig(resFN(choutfn, dir=bfn), transparent=True)
_plt.close()
def show_posmarksCNTR(dec,
setname,
mvNrm,
ylim=None,
win=None,
singles=False,
showScatter=True,
baseFN="look",
scatskip=1):
for nt in range(dec.nTets):
if not singles:
fig = _plt.figure(figsize=(7, 5))
for k in range(1, dec.mdim + 1):
if singles:
fig = _plt.figure(figsize=(4, 3))
ax = fig.add_subplot(1, 1, 1)
else:
ax = fig.add_subplot(2, 2, k)
"""
for l in range(dec.tt0, dec.tt1):
if (dec.marks[l, nt] is not None):
x.append(dec.pos[l])
y.append(dec.marks[l, nt][0][k-1])
"""
_plt.xlim(-6, 6)
if ylim is not None:
_plt.ylim(ylim[0], ylim[1])
else:
ylim = _N.empty(2)
ylim[0] = _N.min(dec.tr_marks[nt][:, k - 1])
ylim[1] = _N.max(dec.tr_marks[nt][:, k - 1])
yAMP = ylim[1] - ylim[0]
ylim[0] -= 0.1 * yAMP
ylim[1] += 0.1 * yAMP
if showScatter and dec.marksObserved[nt] > 0:
_plt.scatter(dec.tr_pos[nt][::scatskip],
dec.tr_marks[nt][::scatskip, k - 1],
color="grey",
s=1)
img = mvNrm.evalAll(1000, k - 1, ylim=ylim)
_plt.imshow(img,
origin="lower",
extent=(-6, 6, ylim[0], ylim[1]),
cmap=_plt.get_cmap("Reds"))
if singles:
_plt.suptitle("k=%(k)d t0=%(2).2fs : t1=%(3).2fs" % {
"2": (dec.tt0 / 1000.),
"3": (dec.tt1 / 1000.),
"k": k
})
fn = baseFN if (
dec.usetets is None) else "%(fn)s_tet%(tets)s" % {
"fn": baseFN,
"tets": dec.usetets[nt]
}
mF.arbitraryAxes(ax)
mF.setLabelTicks(_plt,
xlabel="position",
ylabel="mark",
xtickFntSz=14,
ytickFntSz=14,
xlabFntSz=16,
ylabFntSz=16)
fig.subplots_adjust(left=0.2, bottom=0.2, top=0.85)
_plt.savefig(resFN("%(1)s_win=%(w)d.png" % {
"1": fn,
"w": win
},
dir=setname),
transparent=True)
_plt.close()
if not singles:
_plt.suptitle("t0=%(2)d,t1=%(3)d" % {"2": dec.tt0, "3": dec.tt1})
fn = baseFN if (dec.usetets is None) else "%(fn)s_tet%(tets)s" % {
"fn": baseFN,
"tets": dec.usetets[nt]
}
_plt.savefig(resFN("%(1)s_win=%(w)d.png" % {
"1": fn,
"w": win
},
dir=setname,
create=True),
transparent=True)
_plt.close()
def show_posmarks(dec,
setname,
ylim=None,
win=None,
singles=False,
baseFN=None):
MTHR = 0.001 # how much smaller is mixture compared to maximum
for nt in range(dec.nTets):
if not singles:
fig = _plt.figure(figsize=(7, 5))
for k in range(1, dec.mdim + 1):
if singles:
fig = _plt.figure(figsize=(4, 3))
ax = fig.add_subplot(1, 1, 1)
else:
ax = fig.add_subplot(2, 2, k)
"""
for l in range(dec.tt0, dec.tt1):
if (dec.marks[l, nt] is not None):
x.append(dec.pos[l])
y.append(dec.marks[l, nt][0][k-1])
"""
if dec.marksObserved[nt] > 0:
_plt.scatter(dec.tr_pos[nt],
dec.tr_marks[nt][:, k - 1],
color="black",
s=2)
#_plt.scatter(dec.mvNrm[nt].us[:, 0], dec.mvNrm[nt].us[:, k], color="red", s=30)
mThr = MTHR * _N.max(dec.mvNrm[nt].ms)
for m in range(dec.M):
if dec.mvNrm[nt].ms[m, 0] >= mThr:
ux = dec.mvNrm[nt].us[m, 0] # position
uy = dec.mvNrm[nt].us[m, k]
ex_x = _N.sqrt(dec.mvNrm[nt].covs[m, 0, 0])
ex_y = _N.sqrt(dec.mvNrm[nt].covs[m, k, k])
_plt.plot([ux - ex_x, ux + ex_x], [uy, uy],
color="red",
lw=2)
_plt.plot([ux, ux], [uy - ex_y, uy + ex_y],
color="red",
lw=2)
_plt.scatter(dec.mvNrm[nt].us[m, 0],
dec.mvNrm[nt].us[m, k],
color="red",
s=30)
_plt.xlim(-6, 6)
if ylim is not None:
_plt.ylim(ylim[0], ylim[1])
if singles:
_plt.suptitle("k=%(k)d t0=%(2).2fs : t1=%(3).2fs" % {
"2": (dec.tt0 / 1000.),
"3": (dec.tt1 / 1000.),
"k": k
})
fn = baseFN if (dec.usetets is None) else "%(bf)s_tet%(t)s" % {
"bf": baseFN,
"t": dec.usetets[nt]
}
mF.arbitraryAxes(ax)
mF.setLabelTicks(_plt,
xlabel="position",
ylabel="mark",
xtickFntSz=14,
ytickFntSz=14,
xlabFntSz=16,
ylabFntSz=16)
fig.subplots_adjust(left=0.2, bottom=0.2, top=0.85)
_plt.savefig(resFN("%(1)s_win=%(w)d.png" % {
"1": fn,
"w": win
},
dir=setname),
transparent=True)
_plt.close()
if not singles:
_plt.suptitle("t0=%(2)d,t1=%(3)d" % {"2": dec.tt0, "3": dec.tt1})
fn = baseFN if (dec.usetets is None) else "%(bf)s_tet%(t)s" % {
"bf": baseFN,
"t": dec.usetets[nt]
}
_plt.savefig(resFN("%(1)s_win=%(w)d.png" % {
"1": fn,
"w": win
},
dir=setname,
create=True),
transparent=True)
_plt.close()
def compareWF(mARp,
ests,
Xs,
spkHists,
oscMn,
dat,
gkW=20,
useRefr=True,
dspW=None):
"""
instead of subplots, plot 3 different things with 3 largely separated y-values
"""
glmsets = len(ests) # horrible hack
TR = oscMn.shape[0]
infrdAll = _N.zeros((TR, mARp.N + 1))
dt = 0.001
gk = _flt.gauKer(gkW)
gk /= _N.sum(gk)
paramss = []
ocifss = []
stTs = []
for gs in xrange(glmsets): # for the 2 glm conditions
params = _N.array(ests[gs].params)
X = Xs[gs]
spkHist = spkHists[gs]
stTs.append(spkHist.LHbin *
(spkHist.nLHBins +
1)) # first stT spikes used for initial history
ocifss.append(
_N.empty((spkHist.endTR - spkHist.startTR,
spkHist.t1 - spkHist.t0 - stTs[gs])))
for tr in xrange(spkHist.endTR - spkHist.startTR):
ocifss[gs][tr] = _N.exp(_N.dot(X[tr], params)) / dt
stT = min(stTs)
#cglmAll = _N.zeros((TR, mARp.N+1))
xt = _N.arange(stT, mARp.N + 1)
xts = [_N.arange(stTs[0], mARp.N + 1), _N.arange(stTs[1], mARp.N + 1)]
lss = [":", "-"]
lws = [3.8, 2]
cls = [myC.infrdM]
for tr in xrange(spkHist.startTR, TR):
_gt = dat[stT:, tr * 3]
gt = _N.convolve(_gt, gk, mode="same")
gt /= _N.std(gt)
infrd = oscMn[tr, stT:] / _N.std(oscMn[tr, stT:])
infrd /= _N.std(infrd)
infrdAll[tr, stT:] = infrd
fig = _plt.figure(figsize=(12, 8))
ax = fig.add_subplot(1, 1, 1)
_plt.plot(xt, gt, color=myC.grndTruth, lw=4)
#_plt.plot(xt, infrd, color="brown", lw=4)
up1 = _N.max(gt) - _N.min(gt)
## mirror
_plt.plot(xt, gt + up1 * 1.25, color=myC.grndTruth, lw=4)
_plt.plot(xt, infrd + up1 * 1.25, color=myC.infrdM, lw=2)
for gs in xrange(glmsets):
ocifs = ocifss[gs]
glm = (ocifs[tr] - _N.mean(ocifs[tr])) / _N.std(ocifs[tr])
cglm = _N.convolve(glm, gk, mode="same")
cglm /= _N.std(cglm)
_plt.plot(xts[gs], cglm, color=myC.infrdM, lw=lws[gs], ls=lss[gs])
MINx = _N.min(infrd)
#MAXx = _N.max(infrd)
MAXx = _N.max(gt) + up1 * 1.35
AMP = MAXx - MINx
ht = 0.08 * AMP
ys1 = MINx - 0.5 * ht
ys2 = MINx - 3 * ht
for n in xrange(stT, mARp.N + 1):
if mARp.y[tr, n] == 1:
_plt.plot([n, n], [ys1, ys2], lw=2.5, color="black")
_plt.ylim(ys2 - 0.05 * AMP, MAXx + 0.05 * AMP)
if dspW is None:
_plt.xlim(stT, mARp.N + 1)
else:
_plt.xlim(dspW[0], dspW[1])
mF.arbitraryAxes(ax,
axesVis=[False, False, False, False],
xtpos="bottom",
ytpos="none")
mF.setLabelTicks(_plt,
yticks=[],
yticksDsp=None,
xlabel="time (ms)",
ylabel=None,
xtickFntSz=24,
xlabFntSz=26)
fig.subplots_adjust(left=0.05, right=0.95, bottom=0.2, top=0.85)
_plt.savefig("cmpGLMAR_%(tr)d.eps" % {"tr": tr}, transparent=True)
_plt.close()
def compare(mARp, est, X, spkHist, oscMn, dat, gkW=20, useRefr=True):
dt = 0.001
gk = _flt.gauKer(gkW)
gk /= _N.sum(gk)
TR = oscMn.shape[0]
# params, stT
params = _N.array(est.params)
stT = spkHist.LHbin * (spkHist.nLHBins + 1
) # first stT spikes used for initial history
ocifs = _N.empty(
(spkHist.endTR - spkHist.startTR, spkHist.t1 - spkHist.t0 - stT))
##
sur = "refr"
if not useRefr:
params[spkHist.endTR:spkHist.endTR +
spkHist.LHbin] = params[spkHist.endTR + spkHist.LHbin]
sur = "NOrefr"
for tr in xrange(spkHist.endTR - spkHist.startTR):
ocifs[tr] = _N.exp(_N.dot(X[tr], params)) / dt
cglmAll = _N.zeros((TR, mARp.N + 1))
infrdAll = _N.zeros((TR, mARp.N + 1))
xt = _N.arange(stT, mARp.N + 1)
for tr in xrange(spkHist.startTR, TR):
_gt = dat[stT:, tr * 3]
gt = _N.convolve(_gt, gk, mode="same")
gt /= _N.std(gt)
glm = (ocifs[tr] - _N.mean(ocifs[tr])) / _N.std(ocifs[tr])
cglm = _N.convolve(glm, gk, mode="same")
cglm /= _N.std(cglm)
infrd = oscMn[tr, stT:] / _N.std(oscMn[tr, stT:])
infrd /= _N.std(infrd)
pc1, pv1 = _ss.pearsonr(glm, gt)
pc1c, pv1c = _ss.pearsonr(cglm, gt)
pc2, pv2 = _ss.pearsonr(infrd, gt)
cglmAll[tr, stT:] = cglm
infrdAll[tr, stT:] = infrd
fig = _plt.figure(figsize=(12, 4))
ax = fig.add_subplot(1, 1, 1)
_plt.plot(xt, infrd, color=myC.infrdM, lw=2)
_plt.plot(xt, cglm, color=myC.infrdM, lw=2., ls="--")
#_plt.plot(xt, glm, color=myC.infrdM, lw=2., ls="-.")
_plt.plot(xt, gt, color=myC.grndTruth, lw=4)
MINx = _N.min(infrd)
MAXx = _N.max(infrd)
AMP = MAXx - MINx
ht = 0.08 * AMP
ys1 = MINx - 0.5 * ht
ys2 = MINx - 3 * ht
for n in xrange(stT, mARp.N + 1):
if mARp.y[tr, n] == 1:
_plt.plot([n, n], [ys1, ys2], lw=2.5, color="black")
_plt.ylim(ys2 - 0.05 * AMP, MAXx + 0.05 * AMP)
_plt.xlim(stT, mARp.N + 1)
mF.arbitraryAxes(ax,
axesVis=[False, False, False, False],
xtpos="bottom",
ytpos="none")
mF.setLabelTicks(_plt,
yticks=[],
yticksDsp=None,
xlabel="time (ms)",
ylabel=None,
xtickFntSz=24,
xlabFntSz=26)
fig.subplots_adjust(left=0.05, right=0.95, bottom=0.2, top=0.85)
_plt.savefig("cmpGLMAR_%(ur)s_%(tr)d.eps" % {"tr": tr, "ur": sur})
_plt.close()
corrs[tr] = pc1, pc1c, pc2
mF.histPhase0_phaseInfrd(mARp,
cglmAll,
t0=stT,
t1=(mARp.N + 1),
bRealDat=False,
normed=True,
maxY=1.8,
fn="smthdGLMPhaseGLM%s" % sur)
mF.histPhase0_phaseInfrd(mARp,
infrdAll,
t0=stT,
t1=(mARp.N + 1),
bRealDat=False,
normed=True,
maxY=1.8,
fn="smthdGLMPhaseInfrd")
print _N.mean(corrs[:, 0])
print _N.mean(corrs[:, 1])
print _N.mean(corrs[:, 2])
fig = _plt.figure(figsize=(8, 3.5))
ax = fig.add_subplot(1, 2, 1)
_plt.hist(corrs[:, 1],
bins=_N.linspace(-0.5,
max(corrs[:, 2]) * 1.05, 30),
color=myC.hist1)
mF.bottomLeftAxes(ax)
ax = fig.add_subplot(1, 2, 2)
_plt.hist(corrs[:, 2],
bins=_N.linspace(-0.5,
max(corrs[:, 2]) * 1.05, 30),
color=myC.hist1)
mF.bottomLeftAxes(ax)
fig.subplots_adjust(left=0.05,
bottom=0.1,
right=0.95,
top=0.88,
wspace=0.2,
hspace=0.2)
_plt.savefig("cmpGLMAR_hist")
_plt.close()
# the parts of the model in which making a proposal distribution is # difficult. #us = -1 us = 0 std= 1.3#.5 tcksz=15 labsz=17 us = us + std*_N.random.randn(20000) ps = 1/(1 + _N.exp(-us)) fig = _plt.figure(figsize=(5, 10)) ax = fig.add_subplot(3, 1, 1) _plt.hist(ps, bins=_N.linspace(0, 1, 1001)) mF.arbitraryAxes(ax, axesVis=[True, True, False, False], xtpos="bottom", ytpos="left") mF.setTicksAndLims(xlabel="ps", ylabel="prob", xticks=None, yticks=None, xticksD=None, yticksD=None, xlim=None, ylim=None, tickFS=tcksz, labelFS=labsz) ax = fig.add_subplot(3, 1, 2) _plt.hist(1-ps, bins=_N.linspace(0, 1, 1001)) mF.arbitraryAxes(ax, axesVis=[True, True, False, False], xtpos="bottom", ytpos="left") mF.setTicksAndLims(xlabel="ps", ylabel="prob", xticks=None, yticks=None, xticksD=None, yticksD=None, xlim=None, ylim=None, tickFS=tcksz, labelFS=labsz) ax = fig.add_subplot(3, 1, 3) _plt.hist(1/(1-ps), bins=_N.linspace(1, 5, 1001)) mF.arbitraryAxes(ax, axesVis=[True, True, False, False], xtpos="bottom", ytpos="left") mF.setTicksAndLims(xlabel="ps", ylabel="prob", xticks=None, yticks=None, xticksD=None, yticksD=None, xlim=None, ylim=None, tickFS=tcksz, labelFS=labsz) fig.subplots_adjust(left=0.15, bottom=0.15, wspace=0.3, hspace=0.3)
def timeline(bfn, datfn, itvfn, outfn="timeline", ch1=0, ch2=1, xL=0, xH=3, yticks=[0, 1, 2, 3], thin=1):
d = _N.loadtxt(datFN("%s.dat" % datfn)) # marks
itv = _N.loadtxt(datFN("%s.dat" % itvfn))
N = d.shape[0]
epochs = itv.shape[0]-1
ch1 += 2 # because this is data col
ch2 += 2
_sts = _N.where(d[:, 1] == 1)[0]
if thin == 1:
sts = _sts
else:
sts = _sts[::thin]
wvfmMin = _N.min(d[:, 2:], axis=0)
wvfmMax = _N.max(d[:, 2:], axis=0)
fig = _plt.figure(figsize=(10, 12))
#######################
ax =_plt.subplot2grid((4, 3), (0, 0), colspan=3)
_plt.scatter(sts/1000., d[sts, 0], s=2, color="black")
mF.arbitraryAxes(ax, axesVis=[True, True, False, False], xtpos="bottom", ytpos="left")
mF.setTicksAndLims(xlabel="time (s)", ylabel="position", xticks=None, yticks=yticks, xticksD=None, yticksD=None, xlim=[0, N/1000.], ylim=[xL-0.3, xH+0.3], tickFS=15, labelFS=18)
for ep in xrange(epochs):
_plt.axvline(x=(itv[ep+1]*N/1000.), color="red", ls="--")
#######################
ax = _plt.subplot2grid((4, 3), (1, 0), colspan=3)
_plt.scatter(sts/1000., d[sts, ch1], s=2, color="black")
mF.arbitraryAxes(ax, axesVis=[True, True, False, False], xtpos="bottom", ytpos="left")
mF.setTicksAndLims(xlabel="time (s)", ylabel=("mk tet%d" % (ch1-1)), xticks=None, yticks=[0, 3, 6], xticksD=None, yticksD=None, xlim=[0, N/1000.], ylim=[wvfmMin[0], wvfmMax[0]], tickFS=15, labelFS=18)
for ep in xrange(epochs):
_plt.axvline(x=(itv[ep+1]*N/1000.), color="red", ls="--")
#######################
ax = _plt.subplot2grid((4, 3), (2, 0), colspan=3)
_plt.scatter(sts/1000., d[sts, ch2], s=2, color="black")
mF.arbitraryAxes(ax, axesVis=[True, True, False, False], xtpos="bottom", ytpos="left")
mF.setTicksAndLims(xlabel="time (s)", ylabel=("mk tet%d" % (ch2-1)), xticks=None, yticks=[0, 3, 6], xticksD=None, yticksD=None, xlim=[0, N/1000.], ylim=[wvfmMin[1], wvfmMax[1]], tickFS=15, labelFS=18)
for ep in xrange(epochs):
_plt.axvline(x=(itv[ep+1]*N/1000.), color="red", ls="--")
##############
ax = _plt.subplot2grid((4, 3), (3, 0), colspan=1)
_plt.scatter(d[sts, ch1], d[sts, ch2], s=2, color="black")
mF.arbitraryAxes(ax, axesVis=[True, True, False, False], xtpos="bottom", ytpos="left")
mF.setTicksAndLims(xlabel=("mk tet%d" % (ch1-1)), ylabel=("mk tet%d" % (ch2-1)), xticks=[0, 3, 6], yticks=[0, 3, 6], xticksD=None, yticksD=None, xlim=[wvfmMin[0], wvfmMax[0]], ylim=[wvfmMin[1], wvfmMax[1]], tickFS=15, labelFS=18)
##############
ax = _plt.subplot2grid((4, 3), (3, 1), colspan=1)
_plt.scatter(d[sts, 0], d[sts, ch1], s=2, color="black")
mF.arbitraryAxes(ax, axesVis=[True, True, False, False], xtpos="bottom", ytpos="left")
mF.setTicksAndLims(xlabel="pos", ylabel=("mk tet%d" % (ch1-1)), xticks=_N.linspace(xL, xH, 3), yticks=[0, 3, 6], xticksD=None, yticksD=None, xlim=[xL, xH], ylim=None, tickFS=15, labelFS=18)
##############
ax = _plt.subplot2grid((4, 3), (3, 2), colspan=1)
_plt.scatter(d[sts, 0], d[sts, ch2], s=2, color="black")
mF.arbitraryAxes(ax, axesVis=[True, True, False, False], xtpos="bottom", ytpos="left")
mF.setTicksAndLims(xlabel="pos", ylabel=("mk tet%d" % (ch2-1)), xticks=_N.linspace(xL, xH, 3), yticks=[0, 3, 6], xticksD=None, yticksD=None, xlim=[xL, xH], ylim=None, tickFS=15, labelFS=18)
##############
fig.subplots_adjust(left=0.15, bottom=0.15, wspace=0.38, hspace=0.38)
epochs = len(itv)-1
choutfn = "%(of)s_%(1)d,%(2)d" % {"of" : outfn, "1" : (ch1-1), "2" : (ch2-1)}
_plt.savefig(resFN(choutfn, dir=bfn), transparent=True)
_plt.close()
