def fc_visual(fcfile_pickle):
with open(fcfile_pickle, 'rb') as handle:
fc = pickle.load(handle)
imcohs = fc['imcohs']
pvals = fc['pvals']
chnAreas = fc['chnAreas']
# 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(imcohs.shape)
if len(rows) > 0:
weight[rows, cols] = imcohs[rows, cols]
for co in ['normal', 'mild', 'moderate']:
if co in fcfile_pickle:
cond = co
save_prefix = 'all'
folder, filename = os.path.split(fcfile_pickle)[0], os.path.split(
fcfile_pickle)[1]
saveFCGraph = os.path.join(
folder,
'visual_' + filename[:-len('.pickle')] + '_' + save_prefix + '.png')
texts = dict()
texts[cond] = [-80, 40, 15]
texts[animal] = [80, 20, 20]
weight_visual_save(weight,
chnInf=assign_coord2chnArea(
area_coord_file=area_coord_file, chnAreas=chnAreas),
savefile=saveFCGraph,
texts=texts,
threds_edge=None)
def fc_visual_subAreas(fcfile_pickle,
subareas=['M1', 'STN', 'GP'],
subtitle='M1DBS'):
with open(fcfile_pickle, 'rb') as handle:
fc = pickle.load(handle)
imcohs = fc['imcohs']
pvals = fc['pvals']
chnAreas = fc['chnAreas']
idxs_remain = []
chnAreas_new = []
for ci, carea in enumerate(chnAreas):
for sarea in subareas:
if sarea.lower() in carea.lower():
idxs_remain.append(ci)
chnAreas_new.append(carea)
idxs_remain = np.array(idxs_remain)
tmp = imcohs[idxs_remain, :]
tmp = tmp[:, idxs_remain]
imcohs = tmp
tmp = pvals[idxs_remain, :]
tmp = tmp[:, idxs_remain]
pvals = tmp
chnAreas = chnAreas_new
# 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(imcohs.shape)
if len(rows) > 0:
weight[rows, cols] = imcohs[rows, cols]
for co in ['normal', 'mild', 'moderate']:
if co in fcfile_pickle:
cond = co
folder, filename = os.path.split(fcfile_pickle)[0], os.path.split(
fcfile_pickle)[1]
saveFCGraph = os.path.join(
folder,
'visual_' + filename[:-len('.pickle')] + '_' + subtitle + '.png')
texts = dict()
texts[cond] = [-80, 40, 15]
texts[animal] = [80, 20, 20]
weight_visual_save(weight,
chnInf=assign_coord2chnArea(
area_coord_file=area_coord_file, chnAreas=chnAreas),
savefile=saveFCGraph,
texts=texts,
threds_edge=None)
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 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)