def dailyfc_visual(files):
for onefile in files:
lfpdata, chnAreas, fs = lfp_extract([onefile])
if lfpdata.shape[2] < 80:
continue
print(onefile)
ciCOHs = calc_ciCOHs_rest(lfpdata)
# permutation test: use the lfp data whose ciCOHs are the largest to get distribution
[i, j] = np.unravel_index(np.argmax(ciCOHs), shape=ciCOHs.shape)
lfp1, lfp2 = lfpdata[i, :, :], lfpdata[j, :, :]
_, mu, std = pval_permciCOH_rest(lfp1,
lfp2,
ciCOHs[i, j],
shuffleN=1000)
pvals = norm.sf(abs(ciCOHs), loc=mu, scale=std) * 2
# multiple comparison correction, get weights
reject, pval_corr = fdr_correction(pvals, alpha=0.05, method='indep')
[rows, cols] = np.where(reject == True)
weight = np.zeros(ciCOHs.shape)
if len(rows) > 0:
weight[rows, cols] = ciCOHs[rows, cols]
# visual and save
filename = os.path.basename(onefile)
datestr = re.search('[0-9]{8}', filename).group()
cond = re.search('_[a-z]*_[0-9]{8}', filename).group()[1:-9]
freqstr = 'freq' + re.search('_filtered[0-9]*_[0-9]*',
filename).group()[len('_filtered'):]
save_prefix = 'all'
saveFCGraph = os.path.join(
savefolder,
freqstr + '_' + cond + '_' + save_prefix + '_' + datestr + '.png')
texts = dict()
texts[cond + ',' + datestr] = [-80, 50, 15]
weight_visual_save(weight,
chnInf=assign_coord2chnArea(
area_coord_file=area_coord_file,
chnAreas=chnAreas),
savefile=saveFCGraph,
texts=texts,
threds_edge=None)
del texts, datestr, cond, weight
def subArea_dailyfc_visual(files):
for onefile in files:
lfpdata, chnAreas, fs = lfp_extract([onefile])
if lfpdata.shape[2] < 80:
continue
print(onefile)
ciCOHs = calc_ciCOHs_rest(lfpdata)
# permutation test: use the lfp data whose ciCOHs are the largest to get distribution
[i, j] = np.unravel_index(np.argmax(ciCOHs), shape = ciCOHs.shape)
lfp1, lfp2 = lfpdata[i, :, :], lfpdata[j, :, :]
_, mu, std = pval_permciCOH_rest(lfp1, lfp2, ciCOHs[i, j], shuffleN = 1000)
cond = re.search('_[a-z]*_[0-9]{8}', files[0]).group()[1:-9]
datestr = re.search('[0-9]{8}', os.path.basename(onefile)).group()
### left thalamus and SMA/M1 ###
save_prefix = 'leftThaCor_'
areas_used = ['lVA', 'lVLo/VPLo', 'lSMA', 'rSMA','M1']
# subareas selection
ciCOH_new, chnAreas_new = ciCOH_select(ciCOHs, chnAreas, areas_used)
# multiple comparison correction, get weight matrix
pvals = norm.sf(abs(ciCOH_new), loc = mu, scale = std) * 2
reject, pval_corr = fdr_correction(pvals, alpha = 0.05, method='indep')
[rows, cols]= np.where(reject == True)
weight = np.zeros(ciCOH_new.shape)
if len(rows) > 0:
weight[rows, cols] = ciCOH_new[rows, cols]
# visual and save
saveFCGraph = os.path.join(savefolder, cond + '_' + save_prefix + '_' + datestr + '.png')
texts = dict()
texts[datestr] = [80, 50, 15]
weight_visual_save(weight, chnInf = assign_coord2chnArea(area_coord_file, chnAreas_new),
savefile = saveFCGraph, texts = None, threds_edge = None)
del ciCOH_new, chnAreas_new, save_prefix, areas_used
del saveFCGraph, weight
def pval_permciCOH_rest(lfp1, lfp2, actciCOH, shuffleN=1000):
"""
Arg:
lfp1, lfp2: ntemp * nsegs(ntrials)
shuffleN: the total shuffle times
actciCOH: an actual ciCOH value (positive or negative)
Return:
pval: the p-value base on permutation test
mu, std: the mu and std of the fitted normal distribution
"""
permlfp1, permlfp2 = lfp1.copy(), lfp2.copy()
permciCOHs = np.zeros(shape=(shuffleN, ))
for i in range(shuffleN):
# shuffle permlfp2
permlfp2 = np.transpose(permlfp2, axes=(1, 0))
np.random.shuffle(permlfp2)
permlfp2 = np.transpose(permlfp2, axes=(1, 0))
permlfp = np.concatenate((np.expand_dims(
permlfp1, axis=0), np.expand_dims(permlfp2, axis=0)),
axis=0)
ciCOHM = calc_ciCOHs_rest(permlfp)
permciCOHs[i] = ciCOHM[0, 1]
del ciCOHM, permlfp
# Fit a normal distribution to the data:
mu, std = norm.fit(permciCOHs)
pval = norm.sf(abs(actciCOH), loc=mu, scale=std)
return pval, mu, std
def segfc_visual(onefile):
# lfpdata: nchns * ntemp * nsegs
lfpdata, chnAreas, fs = lfp_extract([onefile])
nchns, _, nsegs = lfpdata.shape
seg_ciCOHs = np.zeros(shape=(nchns, nchns, nsegs))
for segi in range(nsegs):
seg_ciCOHs[:, :, segi] = calc_ciCOHs_rest(
np.expand_dims(lfpdata[:, :, segi], axis=2))
# permutation test: use the lfp data whose ciCOHs are the largest to get distribution
[i, j] = np.unravel_index(np.argmax(ciCOHs), shape=ciCOHs.shape)
lfp1, lfp2 = lfpdata[i, :, :], lfpdata[j, :, :]
_, mu, std = pval_permciCOH_rest(lfp1, lfp2, ciCOHs[i, j], shuffleN=1000)
pvals = norm.sf(abs(ciCOHs), loc=mu, scale=std) * 2
# multiple comparison correction, get weights
reject, pval_corr = fdr_correction(pvals, alpha=0.05, method='indep')
[rows, cols] = np.where(reject == True)
weight = np.zeros(ciCOHs.shape)
if len(rows) > 0:
weight[rows, cols] = ciCOHs[rows, cols]
# visual and save
filename = os.path.basename(onefile)
datestr = re.search('[0-9]{8}', filename).group()
cond = re.search('_[a-z]*_[0-9]{8}', filename).group()[1:-9]
save_prefix = 'all'
saveFCGraph = os.path.join(
savefolder, cond + '_' + save_prefix + '_' + datestr + '.png')
weight_visual_save(weight,
chnInf=assign_coord2chnArea(
area_coord_file=area_coord_file, chnAreas=chnAreas),
savefile=saveFCGraph,
texts=None,
threds_edge=None)