def oscPer(setname,
fltPrms=[5, 13, 1, 20],
t0=None,
t1=None,
tr0=0,
tr1=None,
trials=None,
fn=None,
showHist=True,
osc=None):
"""
find period of oscillation
"""
if t0 is None:
t0 = 0
if t1 is None:
t1 = Na
if setname is not None:
_dat = _N.loadtxt(resFN("xprbsdN.dat", dir=setname))
# modulation histogram. phase @ spike
Na, cols = _dat.shape
p = _re.compile("^\d{6}") # starts like "exptDate-....."
m = p.match(setname)
bRealDat = True
COLS = 4
sub = 1
if m == None:
bRealDat = False
COLS = 3
sub = 0
TR = cols / COLS
if trials is None:
if tr1 is None:
tr1 = TR
trials = _N.arange(tr0, tr1)
N = t1 - t0
dat = _dat[t0:t1, :]
elif osc is not None:
TR, N = osc.shape
trials = _N.arange(TR)
bRealDat = False
COLS = 3
sub = 0
dat = _N.empty((N, COLS * TR))
dat[:, sub::COLS] = osc.T
Ts = []
for tr in trials:
x = dat[:, tr * COLS + sub]
if fltPrms is None:
fx = x
elif len(fltPrms) == 2:
fx = lpFilt(fltPrms[0], fltPrms[1], 500, x)
else: # 20, 40, 10, 55 #(fpL, fpH, fsL, fsH, nyqf, y):
fx = bpFilt(fltPrms[0], fltPrms[1], fltPrms[2], fltPrms[3], 500, x)
intvs = _N.where((fx[1:] < 0) & (fx[0:-1] >= 0))
Ts.extend(_N.diff(intvs[0]))
if showHist:
fig = _plt.figure()
_plt.hist(Ts, bins=range(min(Ts) - 1, max(Ts) + 1))
mn = _N.mean(Ts)
std = _N.std(Ts)
print "mean Hz %(f).3f cv: %(cv).3f" % {
"cv": (std / mn),
"f": (1000 / mn)
}
def modhistAll(setname,
shftPhase=0,
fltPrms=[3.3, 11, 1, 15],
t0=None,
t1=None,
tr0=0,
tr1=None,
trials=None,
fn=None,
maxY=None,
yticks=None,
normed=False,
surrogates=1,
color=None,
nofig=False,
flatten=False,
filtCol=0,
xlabel=None,
smFnt=20,
bgFnt=22):
"""
shftPhase from 0 to 1.
yticks should look like [[0.5, 1, 1.5], ["0.5", "1", "1.5"]]
"""
_dat = _N.loadtxt(resFN("xprbsdN.dat", dir=setname))
# modulation histogram. phase @ spike
Na, cols = _dat.shape
t0 = 0 if (t0 is None) else t0
t1 = Na if (t1 is None) else t1
dat = _dat[t0:t1, :]
N = t1 - t0
p = _re.compile("^\d{6}") # starts like "exptDate-....."
m = p.match(setname)
bRealDat, COLS, sub, phC = True, 4, 2, 3
if m == None:
bRealDat, COLS, sub = False, 3, 1
print "realDat is %s" % str(bRealDat)
TR = cols / COLS
tr1 = TR if (tr1 is None) else tr1
trials = _N.arange(tr0, tr1) if (trials is None) else trials
if type(trials) == list:
trials = _N.array(trials)
phs = []
cSpkPhs = []
sSpkPhs = []
for tr in trials:
if fltPrms is not None:
x = dat[:, tr * COLS]
if len(fltPrms) == 2:
fx = lpFilt(fltPrms[0], fltPrms[1], 500, x)
elif len(fltPrms) == 4:
# 20, 40, 10, 55 #(fpL, fpH, fsL, fsH, nyqf, y):
fx = bpFilt(fltPrms[0], fltPrms[1], fltPrms[2], fltPrms[3],
500, x)
else:
fx = dat[:, tr * COLS + filtCol]
ht_x = _ssig.hilbert(fx)
ph_x = (_N.arctan2(ht_x.imag, ht_x.real) + _N.pi) / (2 * _N.pi)
ph_x = _N.mod(ph_x + shftPhase, 1)
ispks = _N.where(dat[:, tr * COLS + (COLS - sub)] == 1)[0]
cSpkPhs.append(_N.cos(2 * _N.pi * ph_x[ispks]))
sSpkPhs.append(_N.sin(2 * _N.pi * ph_x[ispks]))
phs.append(ph_x[ispks])
if nofig:
if not flatten:
return phs
else:
fl = []
for i in xrange(len(phs)):
fl.extend(phs[i])
return fl
return figCircularDistribution(phs,
cSpkPhs,
sSpkPhs,
trials,
surrogates=surrogates,
normed=normed,
fn=fn,
maxY=maxY,
yticks=yticks,
setname=setname,
color=color,
xlabel=xlabel,
smFnt=smFnt,
bgFnt=bgFnt)
def _histPhase0_phaseInfrdAll(TR,
N,
x,
_mdn,
t0=None,
t1=None,
bRealDat=False,
trials=None,
fltPrms=None,
maxY=None,
yticks=None,
fn=None,
normed=False,
surrogates=1,
shftPhase=0,
color=None):
"""
what is the inferred phase when ground truth phase is 0
"""
pInfrdAt0 = []
if (fltPrms is not None) and (not bRealDat):
_fx = _N.empty((TR, N))
for tr in xrange(TR):
if len(fltPrms) == 2:
_fx[tr] = lpFilt(fltPrms[0], fltPrms[1], 500, x[tr])
elif len(fltPrms) == 4:
# 20, 40, 10, 55 #(fpL, fpH, fsL, fsH, nyqf, y):
_fx[tr] = bpFilt(fltPrms[0], fltPrms[1], fltPrms[2],
fltPrms[3], 500, x[tr])
else:
_fx = x
gk = gauKer(1)
gk /= _N.sum(gk)
if trials is None:
trials = _N.arange(TR)
TR = TR
else:
TR = len(trials)
trials = _N.array(trials)
#trials, TR = range(mARp.TR), mARp.TR if (trials is None) else trials, len(trials)
nPh0s = _N.zeros(TR)
t1 = t1 - t0 # mdn already size t1-t0
t0 = 0
mdn = _mdn
fx = _fx
if _mdn.shape[0] != t1 - t0:
mdn = _mdn[:, t0:t1]
if _fx.shape[0] != t1 - t0:
fx = _fx[:, t0:t1]
itr = 0
phs = [] # phase 0 of inferred is at what phase of GT or LFP?
cSpkPhs = []
sSpkPhs = []
for tr in trials:
itr += 1
cv = _N.convolve(mdn[tr, t0:t1] - _N.mean(mdn[tr, t0:t1]),
gk,
mode="same")
ht_mdn = _ssig.hilbert(cv)
ht_fx = _ssig.hilbert(fx[tr, t0:t1] - _N.mean(fx[tr, t0:t1]))
ph_mdn = (_N.arctan2(ht_mdn.imag, ht_mdn.real) + _N.pi) / (2 * _N.pi)
ph_mdn = _N.mod(ph_mdn + shftPhase, 1)
ph_fx = (_N.arctan2(ht_fx.imag, ht_fx.real) + _N.pi) / (2 * _N.pi)
ph_fx = _N.mod(ph_fx + shftPhase, 1)
# phase = 0 is somewhere in middle
inds = _N.where((ph_mdn[0:t1 - t0 - 1] < 1)
& (ph_mdn[0:t1 - t0 - 1] > 0.5)
& (ph_mdn[1:t1 - t0] < 0.25))[0]
cSpkPhs.append(_N.cos(2 * _N.pi * ph_fx[inds + t0]))
sSpkPhs.append(_N.sin(2 * _N.pi * ph_fx[inds + t0]))
phs.append(ph_fx[inds + t0])
#for i in xrange(t0-t0, t1-t0-1):
# if (ph_mdn[i] < 1) and (ph_mdn[i] > 0.5) and (ph_mdn[i+1] < -0.5):
# pInfrdAt0.append(ph_fx[i]/2.)
return figCircularDistribution(phs,
cSpkPhs,
sSpkPhs,
trials,
surrogates=surrogates,
normed=normed,
fn=fn,
maxY=maxY,
yticks=yticks,
color=color,
xlabel=xlabel)
def histPhase0_phaseInfrd(mARp,
_mdn,
t0=None,
t1=None,
bRealDat=False,
trials=None,
filtParams=None,
maxY=None,
yticks=None,
fn=None,
normed=False):
# what is the inferred phase when ground truth phase is 0
pInfrdAt0 = []
#if (filtParams is not None) and (not bRealDat):
if not bRealDat:
_fx = _N.empty((mARp.TR, mARp.N + 1))
for tr in xrange(mARp.TR):
_fx[tr] = lpFilt(30, 40, 1000, mARp.x[tr])
else:
_fx = mARp.x
if bRealDat:
_fx = mARp.fx
gk = gauKer(1)
gk /= _N.sum(gk)
if trials is None:
trials = range(mARp.TR)
TR = mARp.TR
else:
TR = len(trials)
nPh0s = _N.zeros(TR)
#t1 = t1-t0 # mdn already size t1-t0
#t0 = 0
mdn = _mdn
fx = _fx
#if _mdn.shape[0] != t1 - t0:
# mdn = _mdn[:, t0:t1]
#if _fx.shape[0] != t1 - t0:
# fx = _fx[:, t0:t1]
itr = 0
for tr in trials:
itr += 1
cv = _N.convolve(mdn[tr, t0:t1] - _N.mean(mdn[tr, t0:t1]),
gk,
mode="same")
#cv = mdn[tr, t0:t1] - _N.mean(mdn[tr, t0:t1])
ht_mdn = _ssig.hilbert(cv)
#ht_fx = _ssig.hilbert(fx[tr, t0:t1] - _N.mean(fx[tr, t0:t1]))
ht_fx = _ssig.hilbert(fx[tr, t0:t1] - _N.mean(fx[tr, t0:t1]))
ph_mdn = _N.arctan2(ht_mdn.imag, ht_mdn.real) / _N.pi
ph_fx = ((_N.arctan2(ht_fx.imag, ht_fx.real) / _N.pi) + 1)
# phase = 0 is somewhere in middle
for i in xrange(t0 - t0, t1 - t0 - 1):
if (ph_mdn[i] < 1) and (ph_mdn[i] > 0.5) and (ph_mdn[i + 1] <
-0.5):
nPh0s[itr - 1] += 1
pInfrdAt0.append(ph_fx[i] / 2.)
pInfrdAt0.append((ph_fx[i] + 2) / 2.)
bgFnt = 22
smFnt = 20
fig, ax = _plt.subplots(figsize=(6, 4.2))
pInfrdAt0A = _N.array(pInfrdAt0[::2]) # 0 to 2
Npts = len(pInfrdAt0A)
R2 = (1. / (Npts * Npts)) * (_N.sum(_N.cos(2 * _N.pi * pInfrdAt0A))**2 +
_N.sum(_N.sin(2 * _N.pi * pInfrdAt0A))**2)
_plt.hist(pInfrdAt0,
bins=_N.linspace(0, 2, 41),
color=mC.hist1,
edgecolor=mC.hist1,
normed=normed)
print "maxY!!! %f" % maxY
if (maxY is not None):
_plt.ylim(0, maxY)
_plt.xlabel("phase", fontsize=bgFnt)
_plt.ylabel("frequency", fontsize=bgFnt)
_plt.xticks(fontsize=smFnt)
if yticks is not None:
_plt.yticks(yticks)
_plt.yticks(fontsize=smFnt)
if yticks is not None:
_plt.yticks(yticks)
if normed:
_plt.yticks([0.25, 0.5, 0.75, 1], ["0.25", "0.5", "0.75", "1"])
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.yaxis.set_ticks_position("left")
ax.xaxis.set_ticks_position("bottom")
for tic in ax.xaxis.get_major_ticks():
tic.tick1On = tic.tick2On = False
fig.subplots_adjust(left=0.17, bottom=0.17, right=0.95, top=0.9)
if fn is None:
fn = "fxPhase1_phaseInfrd,R=%.3f.eps" % _N.sqrt(R2)
else:
fn = "%(1)s,R=%(2).3f.eps" % {"1": fn, "2": _N.sqrt(R2)}
_plt.savefig(fn, transparent=True)
_plt.close()
return pInfrdAt0, _N.sqrt(R2), nPh0s