be_inds = _N.array(_N.linspace(beginInd, endInd, sections + 1),
dtype=_N.int)
time_lags = _N.linspace(-(slideby / 300) * lags, lags * (slideby / 300),
2 * lags + 1)
dNGS_acs = _N.empty((sections, 2 * lags + 1))
fac01s = _N.empty((nStates, sections, 2 * lags + 1))
wgt_states = _N.zeros((nStates, sections))
for hlf in range(sections):
i0 = be_inds[hlf]
i1 = be_inds[hlf + 1]
x, dNGS_ac = _eu.autocorrelate(
cwtl_avg_behv_sig_interp[i0:i1] -
_N.mean(cwtl_avg_behv_sig_interp[i0:i1]), lags)
acdNGS_AMP = _N.std(dNGS_ac[0:lags - 5])
acdNGS_mn = _N.mean(dNGS_ac[0:lags - 5])
dNGS_acs[hlf] = (dNGS_ac - acdNGS_mn) / acdNGS_AMP
dNGS_timescales[hlf] = find_osc_timescale(dNGS_ac[lags - 5:], lags)
toobig = _N.where(dNGS_acs[hlf] > 2.8)[0]
dNGS_acs[hlf, toobig] = 2.8 # cut it off
for ns in range(nStates):
rmpd_lab = _rmpd_lab
stateInds = _N.where(rmpd_lab == ns)[0]
be_inds = _N.array(_N.linspace(beginInd, endInd, sections + 1),
dtype=_N.int)
time_lags = _N.linspace(-(slideby / 300) * lags,
lags * (slideby / 300), 2 * lags + 1)
dNGS_acs = _N.empty((sections, 2 * lags + 1))
fac01s = _N.empty((nStates, sections, 2 * lags + 1))
wgt_states = _N.zeros((nStates, sections))
for hlf in range(sections):
i0 = be_inds[hlf]
i1 = be_inds[hlf + 1]
x, dNGS_ac = _eu.autocorrelate(
cwtl_avg_behv_sig_interp[i0:i1] -
_N.mean(cwtl_avg_behv_sig_interp[i0:i1]), lags)
acdNGS_AMP = _N.std(dNGS_ac[0:lags - 5])
acdNGS_mn = _N.mean(dNGS_ac[0:lags - 5])
dNGS_acs[hlf] = (dNGS_ac - acdNGS_mn) / acdNGS_AMP
dNGS_timescales[hlf] = find_osc_timescale(dNGS_ac[lags - 5:], lags)
toobig = _N.where(dNGS_acs[hlf] > 2.8)[0]
dNGS_acs[hlf, toobig] = 2.8 # cut it off
for ns in range(nStates):
rmpd_lab = _rmpd_lab
stateInds = _N.where(rmpd_lab == ns)[0]
wins = _N.where(_hnd_dat[0:TO-2, 2] == 1)[0]
ww = _N.where(_hnd_dat[wins+1, 2] == 1)[0]
win_aft_win[pid-1] = len(ww) / len(wins)
loses = _N.where(_hnd_dat[0:TO-2, 2] == -1)[0]
lw = _N.where(_hnd_dat[loses+1, 2] == 1)[0]
win_aft_los[pid-1] = len(lw) / len(loses)
dmp = depickle(getResultFN("%(rpsm)s/%(lb)d/WTL_1.dmp" % {"rpsm" : partID, "lb" : label}))
_prob_mvs = dmp["cond_probs"][0]
prob_mvs = _prob_mvs[:, 0:_hnd_dat.shape[0] - win] # is bigger than hand by win size
prob_mvs = prob_mvs.reshape((3, 3, prob_mvs.shape[1]))
dbehv = _emp.get_dbehv(prob_mvs, gk)
maxs = _N.where((dbehv[0:TO-11] >= 0) & (dbehv[1:TO-10] < 0))[0] + (win//2) # 3 from label71
MLAG = 20
tlag, AC = _eu.autocorrelate(dbehv, MLAG)
# dAC = _N.diff(AC)
# AC_pks = _N.where((dAC[0:-1] > 0) & (dAC[1:] <= 0))[0]
# coherence[pid-1] = _N.std(_N.diff(AC_pks))
decr = True
for i in range(2, MLAG-1):
if decr:
if (AC[MLAG+i-1] >= AC[MLAG+i]) and (AC[MLAG+i] <= AC[MLAG+i+1]):
decr = False
iLow = i+MLAG
else:
if (AC[MLAG+i-1] <= AC[MLAG+i]) and (AC[MLAG+i] >= AC[MLAG+i+1]):
iHigh = i+MLAG
break
coherence[pid-1] = AC[iHigh] - AC[iLow]
ACmin = _N.min(AC[MLAG:])
})
for ns in range(nStates):
rmpd_lab = _rmpd_lab #shift_correlated_shuffle(_rmpd_lab, low=2, high=4, local_shuffle=True, local_shuffle_pcs=(4*sections)) if shf > 0 else _rmpd_lab
stateInds = _N.where(rmpd_lab == ns)[0]
stateBin[:] = 0
stateBin[stateInds] = 1 # neural signal
k_stateBin = _N.convolve(stateBin, gk, mode="same")
k_rps_ts = _N.convolve(rps_times, gk, mode="same")
for hlf in range(sections):
i0 = be_inds[hlf]
i1 = be_inds[hlf + 1]
ac1 = _eu.autocorrelate(
k_stateBin[i0:i1] - _N.mean(k_stateBin[i0:i1]), lags)
ac2 = _eu.autocorrelate(k_rps_ts[i0:i1] - _N.mean(k_rps_ts[i0:i1]),
lags)
ac1[1][lags] = 0
ac2[1][lags] = 0
std1 = _N.std(ac1[1][lags + 3:])
std2 = _N.std(ac2[1][lags + 2:])
fig.add_subplot(nStates, sections, sections * ns + hlf + 1)
if ns == 0:
_plt.title("section %d" % (hlf + 1))
if hlf == 0:
_plt.ylabel("pattern %d" % ns)
#_plt.plot(time_lags[0:lags-1], (ac1[1][0:lags-1] - _N.mean(ac1[1]))*4 + 0.2, lags, color="black")
_plt.plot(time_lags[lags + 3:],
(ac1[1][lags + 3:] - _N.mean(ac1[1])) * (0.4 / std1),