def loaddframebyarband (lcol,skipbipolar=True,skipcsd=False,FoctTH=1.5,ERPscoreTH=0.8,ERPDurTH=[75,300]):
# loads the pandas data frames split up by frequency band
lar = ['A1', 'Thal']
based = 'data/nhpdat/spont/oscout/'
ddf = {'A1':{'s2':{},'g':{},'i1':{}},'Thal':{'Th':{}}}
for ar,lschan in zip(lar,[['s2','g','i1'],['Th']]):
for schan in lschan:
for b in lband:
ddf[ar][schan][b]={k:[] for k in lcol}
for ar in lar:
for fn in os.listdir(based+ar):
if getorigsampr('data/nhpdat/spont/'+ar+'/'+fn.split('_')[0]) != 44e3: continue
if not fn.endswith('dframe.pkl'): continue
if skipbipolar and fn.count('bipolar_True')>0: continue
if skipcsd and fn.count('bipolar_False')>0: continue
df = pickle.load(open(based+ar+'/'+fn,'rb'))
print(fn)
lchan = list(set(df['chan']))
lchan.sort()
if ar == 'A1':
s2,g,i1 = lchan
lschan = ['s2','g','i1']
else:
th = lchan[0]
lschan = ['Th']
for band in lband:
for chan,schan in zip(lchan,lschan):
dfs = df[(df.band==band) & (df.Foct<FoctTH) & (df.chan==chan) & ((df.ERPscore<ERPscoreTH)|(df.dur<ERPDurTH[0])|(df.dur>ERPDurTH[1]))]
for k in lcol:
lx = dfs[k]
for x in lx: ddf[ar][schan][band][k].append(x)
return ddf
def avgERPOverChan(dd, noiseth=0.75):
from nhpdat import getflayers, getdownsampr, getorigsampr, getTriggerIDs, closestfile, hasBBNStim, IsCortex, IsThal
from nhpdat import getStimIntensity, getTriggerTimes
ddx = {'s2': [], 'g': [], 'i1': [], 'tt': None}
for k in dd:
if getorigsampr('data/nhpdat/bbn/' + k) != 44000.0: continue
ddf = dd[k]
lsc = ddf['lchan']
for idx, c in enumerate([ddf[lsc[0]], ddf[lsc[1]], ddf[lsc[2]]]):
if max(abs(ddf['avgCSD'][c, :])) > noiseth: continue
ddx[lsc[idx]].append(ddf['avgCSD'][c, :])
if ddx['tt'] is None: ddx['tt'] = ddf['ttavg']
for c in lsc:
ddx[c + 'avg'] = mean(np.array(ddx[c]), axis=0)
s = std(np.array(ddx[c]), axis=0)
s /= sqrt(len(ddx[c]))
ddx[c + 'stderr'] = s
return ddx
def loaddlcoh (lband = ['delta','theta','alpha','beta','gamma','hgamma'], skipbipolar = True,\
ar='A1', bdir='data/nhpdat/spont/laggedcoh/A1',origdir='data/nhpdat/spont/A1/',lschan=['s2','g','i1']):
# loads lagged coherence output into dictionaries
from nhpdat import getorigsampr
ddlcoh = {}
ddlcoh[ar] = {}
lfn = os.listdir(bdir)
for fn in lfn:
if skipbipolar and fn.count('bipolar_True') > 0: continue
origfn = origdir+fn.split('_')[0]
if ar == 'A1' and getorigsampr(origfn) < 44e3: continue
if fn.endswith('.pkl'): ddlcoh[ar][fn] = pickle.load(open(bdir+'/'+fn,'rb'))
dlcoh = {ar:{schan:{} for schan in lschan}}
for c in lschan:
for b in lband:
dlcoh[ar][c][b]=[]
for k in ddlcoh[ar].keys():
for chan,schan in zip(ddlcoh[ar][k].keys(),lschan):
for b in lband:
for x in ddlcoh[ar][k][chan][b]: dlcoh[ar][schan][b].append(x)
return ddlcoh,dlcoh
def getAvgERPInDir (based, stimIntensity, needBBN, needCX, needThal,\
swindowms=0, ewindowms=150,
dbpath='data/nhpdat/spont/A1/19apr4_A1_spont_LayersForSam.csv',
useBIP=False):
from nhpdat import getflayers, getdownsampr, getorigsampr, getTriggerIDs, closestfile, hasBBNStim, IsCortex, IsThal
from nhpdat import getStimIntensity, getTriggerTimes
dd = {}
for fn in os.listdir(based):
if not fn.endswith('.mat'): continue
FN = os.path.join(based, fn)
if stimIntensity > 0 and getStimIntensity(FN) != stimIntensity:
continue
if needBBN and not hasBBNStim(FN): continue
if needCX and not IsCortex(FN): continue
if needThal and not IsThal(FN): continue
s2, g, i1 = -1, -1, -1
lchan = []
if IsCortex(FN):
s2, g, i1 = getflayers(closestfile(fn, dbpath=dbpath)[0],
abbrev=True)
if s2 < 0: continue # no layer/channel information
lchan = [s2, g, i1]
samprds = getdownsampr(FN)
divby = getorigsampr(FN) / samprds
trigtimes = [
int(round(x)) for x in np.array(getTriggerTimes(FN)) / divby
]
trigIDs = getTriggerIDs(FN)
if useBIP:
sampr, dat, dt, tt, CSD, MUA, BIP = loadfile(FN,
samprds,
getbipolar=True)
else:
sampr, dat, dt, tt, CSD, MUA = loadfile(FN, samprds)
ttrigtimes = [index2ms(t, sampr) for t in trigtimes]
if useBIP:
ttavg, avgBIP = getAvgERP(BIP, sampr, trigtimes, swindowms,
ewindowms)
ddf = {'fn': fn, 'ttavg': ttavg, 'avgBIP': avgBIP, 'sampr': sampr}
else:
ttavg, avgCSD = getAvgERP(CSD, sampr, trigtimes, swindowms,
ewindowms)
ddf = {'fn': fn, 'ttavg': ttavg, 'avgCSD': avgCSD, 'sampr': sampr}
print(fn, lchan)
if s2 >= 0:
if useBIP:
s2 += 1
g += 1
i1 += 1
ddf['s2'] = s2
ddf['g'] = g
ddf['i1'] = i1
else:
th = int(CSD.shape[0] / 2)
lchan = [th]
if useBIP: th += 1
ddf['th'] = th
if useBIP:
ddf['lchan'] = [x + 1 for x in lchan]
else:
ddf['lchan'] = lchan
dd[fn] = ddf
return dd