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))
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 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(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()
# 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)
for p in xrange(4):
print "#### 3 %d" % p
ax = fig.add_subplot(2, 2, p + 1)
maxY = _N.max(rscldA[:, 1 + p])
minY = _N.min(rscldA[:, 1 + p])
A = maxY - minY
_plt.scatter(rscldA[:, 0], rscldA[:, 1 + p], color="black", s=10)
mF.arbitraryAxes(ax,
axesVis=[True, True, False, False],
xtpos="bottom",
ytpos="left")
mF.setTicksAndLims(xlabel="rescaled time",
ylabel=("mark channel %d" % (p + 1)),
xticks=[0, 3000, 6000],
yticks=None,
xticksD=None,
yticksD=None,
xlim=[-1, maxT * 1.03],
ylim=[minY - 0.01 * A, maxY + 0.01 * A],
tickFS=25,
labelFS=27)
if tets is not None:
_plt.savefig("%(df)s/rescltets%(ep)d%(ts)s%(ptrb)s" % {
"df": outdirN,
"ep": epch,
"ts": tetstr,
"ptrb": sPrtrb
},
transparent=True)
_plt.savefig("%(df)s/rescltets%(ep)d%(ts)s%(ptrb)s.eps" % {
"df": outdirN,
"ep": epch,
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()
marker=".",
ms=12,
color="black")
_plt.text(u[iP1m2] + 0.2, j[iP1m2] + yoff + 0.02, "0.01")
_plt.plot([u[iP5m2], u[iP5m2]], [j[iP5m2], j[iP5m2]],
marker=".",
ms=12,
color="black")
_plt.text(u[iP5m2] + 0.2, j[iP5m2] + 0.02, "0.05")
_plt.plot([u[iP5m2], u[iP5m2]], [j[iP5m2] + yoff, j[iP5m2] + yoff],
marker=".",
ms=12,
color="black")
_plt.text(u[iP5m2] + 0.2, j[iP5m2] + yoff + 0.02, "0.05")
mF.arbitraryAxes(ax,
axesVis=[True, True, False, False],
xtpos="bottom",
ytpos="left")
mF.setTicksAndLims(xlabel="u",
ylabel="j(u)",
yticks=[0, 0.5, 1, yoff, yoff + 0.5, yoff + 1],
yticksD=["0", "0.5", "1", "0", "0.5", "1"],
xlim=None,
ylim=None,
tickFS=18,
labelFS=20)
fig.subplots_adjust(left=0.12, bottom=0.12, right=0.95, top=0.95)
_plt.savefig("jx.pdf")