def trans_netattr(self,subject_scan, atlas_name, feature_name, value): if value.ndim == 1: # 这里要改一下 arr = netattr.Attr(value, atlas.get(atlas_name),subject_scan, feature_name) return arr else: net = netattr.Net(value, atlas.get(atlas_name), subject_scan, feature_name) return net
def trans_dynamic_netattr(self, subject_scan, atlas_name, feature_name, window_length, step_size, value): if value.ndim == 2: # 这里要改一下 arr = netattr.DynamicAttr(value.swapaxes(0,1), atlas.get(atlas_name), window_length, step_size, subject_scan, feature_name) return arr else: net = netattr.DynamicNet(value.swapaxes(0,2).swapaxes(0,1), atlas.get(atlas_name), window_length, step_size, subject_scan, feature_name) return net
def calculate_real_healthy_range_threshold():
"""
This function loads in the healthy networks, calculate graph similarities, and plot them in one graph.
"""
atlasobj = atlas.get('brodmann_lrce')
healthy_group = group_manager.getHealthyGroup()
healthy_scans = [scan.filename for scan in healthy_group.scans]
healthy_nets = [
loader.load_single_network(atlasobj, filename)
for filename in healthy_scans
]
thresh_list = list(range(60, 100))
curve_mat = np.zeros((int(len(healthy_nets) * (len(healthy_nets) - 1) / 2),
len(thresh_list))) # each row is a curve
for count, threshold in enumerate(thresh_list):
print('Threshold: %d' % threshold)
threshold = threshold / 100.0
counter = 0 # counter in curve_mat row index
for i in range(len(healthy_nets)):
for j in range(i + 1, len(healthy_nets)):
# calculate similarities between each pair of networks
curve_mat[counter, count] = compare_two_graphs(
healthy_nets[i].binarize(threshold).data,
healthy_nets[j].binarize(threshold).data, 4, 4)
counter += 1
mdb = mongodb_database.MongoDBDatabase(None)
mdb.put_temp_data(
curve_mat, 'graph kernel inter-healthy',
'The inter-healthy network similarity calculated by graph kernel method with d = 4, h = 4. The binary threshold is varied range(60, 100). The data is of shape (cases, thresh_list length). Each row is a curve'
)
def test_directed_net_strongly_connected_components():
atlasobj = atlas.get('aal')
znet = DirectedNet(np.zeros((atlasobj.count, atlasobj.count)), atlasobj)
znet.data[1, 0] = 1
znet.data[0, 2] = 1
znet.data[2, 1] = 1
znet.data[0, 3] = 1
znet.data[3, 4] = 1
scc, lsize = znet.connected_components()
print(scc, len(scc), lsize)
def test_net_connected_components():
atlasobj = atlas.get('aal')
znet = zero_net(atlasobj)
znet.data[2, 3] = 1
znet.data[3, 2] = 1
znet.data[3, 5] = 1
znet.data[5, 3] = 1
znet.data[6, 7] = 1
znet.data[7, 6] = 1
cc, lsize = znet.connected_components()
print(cc)
print(len(cc))
print(lsize)
def load_attrs(scans, atlasobj, attrname, rootFolder = rootconfig.path.feature_root, csvfilename = None): """ Load static attrs as a list :param rootFolder: :param scans: :param atlasobj: :return: """ if type(atlasobj) is str: atlasobj = atlas.get(atlasobj) # l = AttrLoader(atlasobj, rootFolder) if csvfilename is not None: return AttrLoader(atlasobj, rootFolder).load_multiple_attrs(scans, attrname, csvfilename) return AttrLoader(atlasobj, rootFolder).load_multiple_attrs(scans, attrname)
def get_net(self, scan, atlas_name, feature):
#return to an net object directly
if self.exist_static(scan, atlas_name, feature):
binary_data = self.query_static(scan, atlas_name, feature)['value']
netdata = pickle.loads(binary_data)
atlasobj = atlas.get(atlas_name)
net = netattr.Net(netdata, atlasobj, scan, feature)
return net
else:
print(
"can't find the document you look for. scan: %s, atlas: %s, feature: %s."
% (scan, atlas_name, feature))
raise NoRecordFoundException(scan)
return None
def get_attr(self, scan, atlas_name, feature, comment_dict={}):
#return to an attr object directly
if self.exist_static(scan, atlas_name, feature, comment_dict):
binary_data = self.query_static(scan, atlas_name, feature,
comment_dict)[0]['value']
attrdata = pickle.loads(binary_data)
atlasobj = atlas.get(atlas_name)
attr = netattr.Attr(attrdata, atlasobj, scan, feature)
return attr
else:
print(
"can't find the document you look for. scan: %s, atlas: %s, feature: %s."
% (scan, atlas_name, feature))
raise NoRecordFoundException(scan)
return None
def func(args):
subject = args[0]
atlasname = args[1]
windowLength = args[2]
stepsize = args[3]
volumename = '3mm'
atlasobj = atlas.get(atlasname)
work_path = 'D:/Research/xuquan_FMRI/Dynamic_tfMRI_work/'
outfolder = os.path.join(work_path, subject, atlasname, 'bold_net',
'dynamic_%d_%d' % (stepsize, windowLength))
img = load_nii(os.path.join(work_path, subject, 'pBOLD.nii'))
os.makedirs(outfolder, exist_ok=True)
c = CalcDynamic(atlasobj, volumename, img, outfolder, windowLength,
stepsize)
c.run()
def plot_range_threshold_curve():
atlasobj = atlas.get('brodmann_lrce')
mdb = mongodb_database.MongoDBDatabase(None)
threshold_list = range(5, 100, 5)
h_list = list(range(1, int(atlasobj.count / 2)))
d_list = list(range(2, 10))
plt.figure()
for threshold in threshold_list:
curve = mdb.get_temp_data('gk chen range h threshold %d' %
threshold)['value']
plt.plot(h_list, curve, label='threshold %d' % threshold)
plt.xticks(range(1, int(atlasobj.count / 2), 3),
range(1, int(atlasobj.count / 2), 3))
plt.grid(True)
plt.legend(loc='upper right')
plt.xlabel('Depth (h)')
plt.ylabel('Similarity')
plt.title('Chen Network similarity d = 4')
plt.savefig(WORK_PREFIX + 'Chen range h.png', dpi=300)
plt.close()
def calculate_SCI_vs_healthy():
"""
This function loads in healthy networks and SCI patients networks, calculate each
patients network similarity to each healthy controls, and plot each person's similarity
in one figure.
"""
atlasobj = atlas.get('brodmann_lrce')
healthy_group = group_manager.getHealthyGroup()
healthy_scans = [scan.filename for scan in healthy_group.scans]
healthy_nets = [
loader.load_single_network(atlasobj, filename)
for filename in healthy_scans
]
assistant = analysis_report.GroupAnalysisAssistant('jisuizhenjiaciji',
atlasobj)
study = assistant.study
group1 = study.getGroup('control 1')
scans = [scan.filename for scan in group1.scans]
group2 = study.getGroup('treatment 1')
scans += [scan.filename for scan in group2.scans]
SCI_nets = [loader.load_single_network(atlasobj, scan) for scan in scans]
curve_mat = np.zeros((len(scans) * len(healthy_scans),
len(thresh_list))) # each row is a curve
column_counter = 0
for subj_num, SCI_net in enumerate(SCI_nets):
for healthy_net in healthy_nets:
for count, threshold in enumerate(thresh_list):
print('Threshold: %d, pair: %d/%d' %
(threshold, column_counter, curve_mat.shape[0]))
threshold = threshold / 100.0
curve_mat[column_counter, count] = compare_two_graphs(
SCI_net.binarize(threshold).data,
healthy_net.binarize(threshold).data, 4, 4)
column_counter += 1
mdb = mongodb_database.MongoDBDatabase(None)
mdb.put_temp_data(
curve_mat, 'graph kernel SCI 1 x HC',
'The network similarity of each pair of SCI 1 and HC calculated by graph kernel method with d = 4, h = 4. The binary threshold is varied range(60, 100). The data is of shape (cases, thresh_list length). Each row is a curve.'
)
def calculate_real_range_parameters():
mdb = mongodb_database.MongoDBDatabase(None)
atlasobj = atlas.get('brodmann_lrce')
subject_1 = loader.load_single_network(atlasobj, 'caochangsheng_20161027')
subject_2 = loader.load_single_network(atlasobj, 'caochangsheng_20161114')
subject_3 = loader.load_single_network(atlasobj, 'chenyifan_20150612')
subject_4 = loader.load_single_network(atlasobj, 'chenyifan_20150629')
threshold_list = range(40, 60, 5)
h_list = list(range(1, int(atlasobj.count / 2)))
d_list = list(range(2, 10))
for thresh_count, threshold in enumerate(threshold_list, 1):
threshold = threshold / 100.0
sim_list_1 = np.zeros(len(h_list))
sim_list_2 = np.zeros(len(h_list))
sim_list_3 = np.zeros(len(h_list))
for idx, h in enumerate(h_list):
print('threshold: %d/%d, depth: %d/%d' %
(thresh_count, len(threshold_list), h, h_list[-1]))
sim_list_1[idx] = compare_two_graphs(
subject_1.binarize(threshold).data,
subject_2.binarize(threshold).data, h, 4)
sim_list_2[idx] = compare_two_graphs(
subject_3.binarize(threshold).data,
subject_4.binarize(threshold).data, h, 4)
sim_list_3[idx] = compare_two_graphs(
subject_1.binarize(threshold).data,
subject_3.binarize(threshold).data, h, 4)
threshold = int(threshold * 100.0)
mdb.put_temp_data(
sim_list_1, 'gk cao range h threshold %d' % threshold,
'caochangsheng_20161027 vs caochangsheng_20161114 network similarity measured by graph kernel with brodmann_lrce, threshold = %d, d = 4, h range(1, int(atlasobj.count/2))'
% threshold)
mdb.put_temp_data(
sim_list_2, 'gk chen range h threshold %d' % threshold,
'chenyifan_20150612 vs chenyifan_20150629 network similarity measured by graph kernel with brodmann_lrce, threshold = %d, d = 4, h range(1, int(atlasobj.count/2))'
% threshold)
mdb.put_temp_data(
sim_list_3, 'gk cao vs chen range h threshold %d' % threshold,
'caochangsheng_20161027 vs chenyifan_20150612 network similarity measured by graph kernel with brodmann_lrce, threshold = %d, d = 4, h range(1, int(atlasobj.count/2))'
% threshold)
