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()
from mmdps.vis import braincircos
import numpy as np
atlasobj = atlas.get('brodmann_lrce')
subject_list = [
'tanenci_20170601', 'tanenci_20170706', 'tanenci_20170814',
'tanenci_20170922', 'tanenci_20171117'
]
# subject_list = ['wangwei_20171107', 'wangwei_20171221', 'wangwei_20180124', 'wangwei_20180211', 'wangwei_20180520']
# subject_list = ['xiezhihao_20180416', 'xiezhihao_20180524']
for i in range(0, 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)
# net1.data = abs(net1.data)
# set a threshold mask
netList = sorted(abs(net1.data.ravel()))
threshold = netList[int(0.95 * len(netList))]
net1.data[abs(net1.data) < threshold] = 0
builder = braincircos.CircosPlotBuilder(
atlasobj,
'%s %dth orig top 5%%' % (subject_list[i].replace('_', ' '), i + 1),
'%s/%s %dth circos orig top 5%%.png' %
(subject_list[i].split('_')[0], subject_list[i].replace('_',
' '), i + 1))
builder.add_circoslink(braincircos.CircosLink(net1, threshold=0))
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()
loadsave.load_csvmat('Y:/BOLD/%s/brodmann_lrce/bold_net/corrcoef.csv' %
subject_list[0]), atlasobj)
net1.data = abs(net1.data)
net2 = netattr.Net(
loadsave.load_csvmat('Y:/BOLD/%s/brodmann_lrce/bold_net/corrcoef.csv' %
subject_list[-1]), atlasobj)
net2.data = abs(net2.data)
net2.data -= net1.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 2th minus\n%s 1th abs top5%%' %
(subject_list[-1].replace('_', ' '), subject_list[0].replace('_', ' ')),
'xiezhihao/xiezhihao 21 circos abs top5.png')
builder.add_circoslink(braincircos.CircosLink(net2, threshold=0))
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)
net1.data = abs(net1.data)
net2 = netattr.Net(
loadsave.load_csvmat('Y:/BOLD/%s/brodmann_lrce/bold_net/corrcoef.csv' %
subject_list[i]), atlasobj)
net2.data = abs(net2.data)
subject_list[-1]), atlasobj)
# net2.data = abs(net2.data)
net1, net2, ratio = all_neg(net1, net2)
net2.data -= net1.data
# 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)
ret1.data[mask] = 0
ret2.data[mask] = 0
return(ret1, ret2, ratio)
def all_neg(net1, net2):
ret1 = copy.deepcopy(net1)
ret2 = copy.deepcopy(net2)
mask = np.logical_not(np.logical_and(net1.data < 0, net2.data < 0))
ratio = (mask.size - np.count_nonzero(mask))/float(mask.size)
ret1.data[mask] = 0
ret2.data[mask] = 0
return(ret1, ret2, ratio)
atlasobj = atlas.get('brodmann_lrce')
# scan_list = ['tanenci_20170601', 'tanenci_20170706', 'tanenci_20170814', 'tanenci_20170922', 'tanenci_20171117']
scan_list = ['wangwei_20171107', 'wangwei_20171221', 'wangwei_20180124', 'wangwei_20180211', 'wangwei_20180520']
# scan_list = ['xiezhihao_20180416', 'xiezhihao_20180524']
net_list = io_utils.loadAllNets('Y:/BOLD/', atlasobj, scanList = scan_list)
for i in range(1, len(scan_list)):
net_list[i-1], net_list[i], _ = all_pos(net_list[i-1], net_list[i])
# net_list[-1] contains the final all same sign net
# net_list[-1].data[net_list[-1].data != 0] = 1
# plot this net
builder = braincircos.CircosPlotBuilder(atlasobj, '%s all positive' % (scan_list[0].split('_')[0]), '%s/%s all positive.png' % (scan_list[0].split('_')[0], scan_list[0].split('_')[0]))
builder.add_circoslink(braincircos.CircosLink(net_list[-1], threshold = 0))
builder.plot()