def plot_mriscan_weak(fu, mriscan, netname):
print(mriscan)
net = fu.nets.load(mriscan, netname)
netfilepath = fu.nets.loadfilepath(mriscan, netname)
netfiledir = os.path.dirname(netfilepath)
atlasobj = fu.atlasobj
attrdata = np.mean(net.data, axis=0)
attr = netattr.Attr(attrdata, atlasobj)
outfilepath = os.path.join(netfiledir, netname + '_circos_weak')
title = '{}\n{}\n'.format(mriscan, netname)
if netname == 'bold_net':
p = WeakBOLDNetCircosPlot(atlasobj, title, outfilepath)
p.add_circosvalue(braincircos.CircosValue(attr, (-1, 1)))
p.add_circoslink(WeakCircosLink(net, 0.5, (-1, 1)))
elif netname == 'dwi_net':
p = WeakDWINetCircosPlot(atlasobj, title, outfilepath)
p.add_circosvalue(braincircos.CircosValue(attr, (-100, 100)))
p.add_circosvalue(
braincircos.CircosValue(fu.attrs.load(mriscan, 'dwi_MD'),
(-0.003, 0.003)))
p.add_circosvalue(
braincircos.CircosValue(fu.attrs.load(mriscan, 'dwi_FA'), (-1, 1)))
p.add_circoslink(WeakCircosLink(net, 2, (-6, 6)))
else:
return
p.plot()
def plot_mriscan(fu, mriscan):
netname = 'bold_net'
title = 'TestCircos'
outfilepath = 'boldnet'
atlasobj = fu.atlasobj
net = fu.nets.load(mriscan, netname)
attr = fu.attrs.load(mriscan, 'bold_interWD')
builder = braincircos.CircosPlotBuilder(atlasobj, title, outfilepath)
builder.add_circosvalue(braincircos.CircosValue(attr))
builder.add_circosvalue(braincircos.CircosValue(attr, (0, 50)))
builder.add_circosvalue(braincircos.CircosValue(attr, (0, 100)))
builder.add_circoslink(braincircos.CircosLink(net))
builder.plot()
ChanggungPatientNets = io_utils.loadSpecificNets(
mmdps_locale.ChanggungAllFullPath,
atlasobj,
subjectList=os.path.join(mmdps_locale.ChanggungRootPath,
'CS_subjects.txt'))
ChanggungHealthyNets = io_utils.loadSpecificNets(
mmdps_locale.ChanggungAllFullPath,
atlasobj,
subjectList=os.path.join(mmdps_locale.ChanggungRootPath,
'normal_subjects.txt'))
sig_connections = stats_utils.filter_sigdiff_connections_Bonferroni(
ChanggungPatientNets, ChanggungHealthyNets)
sigDiffNet = netattr.Net(np.zeros((atlasobj.count, atlasobj.count)), atlasobj)
for conn in sig_connections:
sigDiffNet.data[conn[0], conn[1]] = 1
title = 'CS_signet'
outfilepath = 'E:/Results/CS_signet/test.png'
builder = braincircos.CircosPlotBuilder(atlasobj, title, outfilepath)
builder.add_circoslink(braincircos.CircosLink(sigDiffNet))
builder.add_circosvalue(
braincircos.CircosValue(
netattr.Attr(np.random.uniform(size=atlasobj.count), atlasobj)))
builder.customizeSize('0.80', '10p')
builder.plot()
# net2.data = abs(net2.data) - abs(net1.data)
wd2.data -= wd1.data
# set a threshold mask
# netList = sorted(abs(net2.data.ravel()))
# threshold = netList[int(0.95*len(netList))]
# net2.data[abs(net2.data) < threshold] = 0
builder = braincircos.CircosPlotBuilder(
atlasobj,
'%s 5th minus\n%s 1th all neg\nratio = %1.3f' % (subject_list[-1].replace(
'_', ' '), subject_list[0].replace('_', ' '), ratio),
'%s/%s 51 circos all neg test.png' %
(subject_list[0].split('_')[0], subject_list[0].split('_')[0]))
builder.add_circoslink(braincircos.CircosLink(net2, threshold=0))
builder.add_circosvalue(braincircos.CircosValue(wd2))
builder.plot()
exit()
for i in range(1, len(subject_list)):
# load in the given subject's net
net1 = netattr.Net(
loadsave.load_csvmat('Y:/BOLD/%s/brodmann_lrce/bold_net/corrcoef.csv' %
subject_list[i - 1]), atlasobj)
net2 = netattr.Net(
loadsave.load_csvmat('Y:/BOLD/%s/brodmann_lrce/bold_net/corrcoef.csv' %
subject_list[i]), atlasobj)
sigRegions = stats_utils.row_wise_ttest(net1, net2)
sigAttr = netattr.Attr(sigRegions, atlasobj)
net1, net2, ratio = neg2pos(net1, net2)