def calc_attr(theattr):
img = load_nii('../iso2.0_dtifitresult_{}.nii.gz'.format(theattr))
atlasobj = atlas.getbywd()
atlasname = atlasobj.name
atlasimg = load_nii(os.path.join('nativespace', 'wtemplate_2.nii.gz'))
res = niicalc.calc_region_mean(img, atlasobj, atlasimg)
save_csvmat(os.path.join('nativespace', 'mean{}.csv'.format(theattr)), res)
def gen_net(self):
ts = self.gen_timeseries()
save_csvmat(self.outpath('timeseries.csv'), ts)
timepoints = ts.shape[1] # number of total timepoints
start = 0
while start + self.windowLength < timepoints:
tscorr = np.corrcoef(ts[:, start:start + self.windowLength])
save_csvmat(self.outpath('corrcoef-%d.%d.csv' % (start, start + self.windowLength)), tscorr)
start += self.stepsize
def gen_net(self):
# print(self.get_total_time_points())
for start in range(0, self.get_total_time_points()-self.windowLength+1, self.stepSize):
ts = self.gen_timeseries(start)
if ts is None:
# print(start,)
raise Exception('Dynamic sliding window exceeds total time points')
save_csvmat(self.outpath('timeseries-%d-%d.csv' % (start, start+self.windowLength)), ts)
tscorr = np.corrcoef(ts)
save_csvmat(self.outpath('corrcoef-%d-%d.csv' % (start, start+self.windowLength)), tscorr)
def save(self, outfile, addticks=True):
"""Save the attr, can add ticks defined in atlasobj."""
if addticks is False:
save_csvmat(outfile, self.data)
else:
path.makedirs_file(outfile)
with open(outfile, 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(('Region', 'Value'))
for tick, value in zip(self.atlasobj.ticks, self.data):
writer.writerow((tick, value))
def gen_net_csv():
netpickle = 'net_gen_net.pickle'
net = pickles.load_pickle(netpickle)
M = net.M
atlasdir = os.path.dirname(os.getcwd())
atlasname = os.path.basename(atlasdir)
atlasobj = atlas.get(atlasname)
idx = atlasobj.regions
realM = M[idx][:, idx]
save_csvmat('dwinetraw.csv', realM)
save_csvmat('dwinet.csv', np.log1p(realM))
return realM
def save(self, outfile, addticks=True):
"""Save the net, can add ticks defined in atlasobj."""
if addticks is False:
save_csvmat(outfile, self.data)
else:
path.makedirs_file(outfile)
with open(outfile, 'w', newline='') as f:
writer = csv.writer(f)
ticks = self.atlasobj.ticks
firstrow = ['Region']
firstrow.extend(ticks)
writer.writerow(firstrow)
for tick, datarow in zip(self.atlasobj.ticks, self.data):
currow = [tick]
currow.extend(datarow)
writer.writerow(currow)
def gen_net(self):
ts = self.gen_timeseries()
save_csvmat(self.outpath('timeseries.csv'), ts)
tscorr = np.corrcoef(ts)
save_csvmat(self.outpath('corrcoef.csv'), tscorr)
import os
import t1_niicalc as niicalc
from mmdps.proc import atlas
from mmdps.util.loadsave import load_nii, save_csvmat
if __name__ == '__main__':
img = load_nii('../grey.hdr')
curatlas = atlas.getbywd()
atlasimg = load_nii(curatlas.get_volume('1mm')['niifile'])
res = niicalc.calc_region_mean(img, curatlas, atlasimg)
save_csvmat(os.path.join('t1mean', 'greyvolume.csv'), res)
loadsave.load_csvmat(
'Y:/BOLD/xiezhihao_20180416/brodmann_lrce/bold_net/corrcoef.csv'),
atlasobj)
net2 = netattr.Net(
loadsave.load_csvmat(
'Y:/BOLD/xiezhihao_20180524/brodmann_lrce/bold_net/corrcoef.csv'),
atlasobj)
net2.data -= net1.data
# set a threshold mask
netList = sorted(abs(net2.data.ravel()))
threshold = netList[int(0.8 * len(netList))]
net2.data[abs(net2.data) < threshold] = 0
loadsave.save_csvmat(
'E:/Changgung works/jixieshou_20180703/xiezhihao/xiezhihao 21 link.edge',
net2.data,
delimiter='\t')
mstr = gen_matlab(
'E:/MMDPSoftware/mmdps/atlas/brodmann_lrce/brodmann_lrce_samesize.node',
'E:/Changgung works/jixieshou_20180703/xiezhihao/xiezhihao 21 link.edge',
'abc',
'E:/Changgung works/jixieshou_20180703/xiezhihao/xiezhihao 21 link.png',
get_mesh('ch2cere'),
'E:/Changgung works/jixieshou_20180703/BNV_diffedge_options.mat')
j = job.MatlabJob('bnv', mstr)
j.run()
exit()
jobList = []
for i in range(1, len(subject_list)):
# load in the given subject's net
def yujia_method():
atlasobj = atlas.getbywd()
track_path = 'tractogram_probabilistic.trk'
roi_feature = ROI_atlas(atlasobj, track_path=track_path)
loadsave.save_csvmat('tract_count_yujia.csv', roi_feature)
def dipy_method():
atlasobj = atlas.getbywd()
track_path = '../tractogram_probabilistic.trk'
roi_feature = ROI_atlas_dipy(atlasobj, track_path)
loadsave.save_csvmat('tract_count.csv', roi_feature)
sigConnections = [] # a list of tuples
with open(
'Z:/changgeng/jixieshou/controlexperimental/bold_net/net_ttest/original_value/patientE_after-before_paired_ttest_report.csv'
) as f:
reader = csv.DictReader(f, delimiter=',')
for row in reader:
connection = (row['RegionA'], row['RegionB'])
reversedConnection = (row['RegionB'], row['RegionA'])
if connection in sigConnections or reversedConnection in sigConnections:
continue
sigConnections.append(connection)
# load in the given subject's net
atlasobj = atlas.get('brodmann_lrce')
net = netattr.Net(
loadsave.load_csvmat(
'Y:/BOLD/wangwei_20171107/brodmann_lrce/bold_net/corrcoef.csv'),
atlasobj)
# find the appropriate nodes and links and output as BNV expected
plotNet = netattr.Net(np.zeros((atlasobj.count, atlasobj.count)),
atlasobj) # all zero matrix
for connection in sigConnections:
plotNet.data[atlasobj.ticks.index(connection[0]),
atlasobj.ticks.index(connection[1])] = 1.0
plotNet.data[atlasobj.ticks.index(connection[1]),
atlasobj.ticks.index(connection[0])] = 1.0
loadsave.save_csvmat(
'E:/Changgung works/jixieshou_20180703/wangwei_20171107_link.edge',
plotNet.data)
import os
import t1_niicalc as niicalc
from mmdps.proc import atlas
from mmdps.util.loadsave import load_nii, save_csvmat
if __name__ == '__main__':
img = load_nii('../grey.hdr')
curatlas = atlas.getbywd()
atlasimg = load_nii(curatlas.get_volume('1mm')['niifile'])
res = niicalc.calc_binary_density(img, curatlas, atlasimg)
save_csvmat(os.path.join('t1mean', 'greydensity.csv'), res)