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 showTrajectory(dec, t0, t1, ep, setname, dir):
fig = _plt.figure(figsize=(14, 7))
ax = fig.add_subplot(1, 1, 1)
_plt.imshow(dec.pX_Nm[t0:t1].T, aspect=(0.5*(t1-t0)/50.), cmap=_plt.get_cmap("Reds"))
_plt.plot(_N.linspace(t0-t0, t1-t0, t1-t0), (dec.xA+dec.pos[t0:t1])/dec.dxp, color="grey", lw=3, ls="--")
#_plt.plot(_N.linspace(float(t0)/1000., float(t1)/1000., t1-t0), (dec.xA+dec.pos[t0:t1])/dec.dxp, color="red", lw=2)
#print (float(t0)/1000)
#print (float(t1)/1000)
_plt.xlim(0, t1-t0)
_plt.ylim(-(dec.nTets*4), 50)
#_plt.xticks(_N.arange(0, t1-t0, 2000), _N.arange(t0, t1, 2000, dtype=_N.float)/1000)
dt = int((((int(t1/1000.)*1000) - (int(t0/1000.)*1000))/4.)/1000.)*1000
stT0 = t0 - int(t0/1000.)*1000
enT1 = t1 - int(t1/1000.)*1000
#_plt.xticks(_N.arange(0, t1-t0, dt), _N.arange(t0, t1, dt, dtype=_N.float)/1000)
_plt.xticks(_N.arange(stT0, t1-t0, dt), _N.arange(int(t0/1000.)*1000, int(t1/1000.)*1000, dt, dtype=_N.int)/1000)
#_plt.locator_params(nbins=6, axis="x")
_plt.yticks(_N.linspace(0, 50, 5), [-6, -3, 0, 3, 6])
mF.arbitaryAxes(ax, axesVis=[False, False, False, False], x_tick_positions="bottom", y_tick_positions="left")
mF.setLabelTicks(_plt, xlabel="Time (sec.)", ylabel="Position", xtickFntSz=30, ytickFntSz=30, xlabFntSz=32, ylabFntSz=32)
x = []
y = []
for nt in xrange(dec.nTets):
x.append([])
y.append([])
for t in xrange(t0, t1):
for nt in xrange(dec.nTets):
if dec.marks[t, nt] is not None:
x[nt].append(t-t0)
y[nt].append(-1.5 - 3*nt)
for nt in xrange(dec.nTets):
_plt.plot(x[nt], y[nt], ls="", marker="|", ms=15, color="black")
fig.subplots_adjust(bottom=0.15, left=0.15)
_plt.savefig(resFN("decode_%(uts)s_%(mth)s_win=%(e)d.eps" % {"e" : (ep/2), "mth" : dec.decmth, "uts" : dec.utets_str, "dir" : dir}, dir=setname, create=True))
_plt.close()
def showTrajectory(dec, t0, t1, ep, setname, dir):
fig = _plt.figure(figsize=(14, 7))
ax = fig.add_subplot(1, 1, 1)
_plt.imshow(dec.pX_Nm[t0:t1].T,
aspect=(0.5 * (t1 - t0) / 50.),
cmap=_plt.get_cmap("Reds"))
_plt.plot(_N.linspace(t0 - t0, t1 - t0, t1 - t0),
(dec.xA + dec.pos[t0:t1]) / dec.dxp,
color="grey",
lw=3,
ls="--")
#_plt.plot(_N.linspace(float(t0)/1000., float(t1)/1000., t1-t0), (dec.xA+dec.pos[t0:t1])/dec.dxp, color="red", lw=2)
#print (float(t0)/1000)
#print (float(t1)/1000)
_plt.xlim(0, t1 - t0)
_plt.ylim(-(dec.nTets * 4), 50)
#_plt.xticks(_N.arange(0, t1-t0, 2000), _N.arange(t0, t1, 2000, dtype=_N.float)/1000)
dt = int((((int(t1 / 1000.) * 1000) -
(int(t0 / 1000.) * 1000)) / 4.) / 1000.) * 1000
stT0 = t0 - int(t0 / 1000.) * 1000
enT1 = t1 - int(t1 / 1000.) * 1000
#_plt.xticks(_N.arange(0, t1-t0, dt), _N.arange(t0, t1, dt, dtype=_N.float)/1000)
_plt.xticks(
_N.arange(stT0, t1 - t0, dt),
_N.arange(
int(t0 / 1000.) * 1000, int(t1 / 1000.) * 1000, dt, dtype=_N.int) /
1000)
#_plt.locator_params(nbins=6, axis="x")
_plt.yticks(_N.linspace(0, 50, 5), [-6, -3, 0, 3, 6])
mF.arbitaryAxes(ax,
axesVis=[False, False, False, False],
x_tick_positions="bottom",
y_tick_positions="left")
mF.setLabelTicks(_plt,
xlabel="Time (sec.)",
ylabel="Position",
xtickFntSz=30,
ytickFntSz=30,
xlabFntSz=32,
ylabFntSz=32)
x = []
y = []
for nt in range(dec.nTets):
x.append([])
y.append([])
for t in range(t0, t1):
for nt in range(dec.nTets):
if dec.marks[t, nt] is not None:
x[nt].append(t - t0)
y[nt].append(-1.5 - 3 * nt)
for nt in range(dec.nTets):
_plt.plot(x[nt], y[nt], ls="", marker="|", ms=15, color="black")
fig.subplots_adjust(bottom=0.15, left=0.15)
_plt.savefig(
resFN("decode_%(uts)s_%(mth)s_win=%(e)d.eps" % {
"e": (ep / 2),
"mth": dec.decmth,
"uts": dec.utets_str,
"dir": dir
},
dir=setname,
create=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()
