def plot_cc_empSC_empFC(subjects):
results = []
for subject in subjects:
empSCnorm, abeta, tau, fMRI = AD_Auxiliar.loadSubjectData(subject)
empFC = FC.from_fMRI(fMRI)
corr_SC_FCemp = FC.pearson_r(empFC, empSCnorm)
print("{} -> Pearson_r(SCnorm, empFC) = {}".format(
subject, corr_SC_FCemp))
results.append(corr_SC_FCemp)
plt.figure()
n, bins, patches = plt.hist(
results, bins=6, color='#0504aa',
alpha=0.7) #, histtype='step') #, rwidth=0.85)
plt.grid(axis='y', alpha=0.75)
plt.xlabel('SC weights')
plt.ylabel('Counts')
plt.title("SC histogram", fontweight="bold", fontsize="18")
plt.show()
def findMinMax(arrayValues):
return FC.findMinMax(arrayValues)
def postprocess(FCs):
FCemp = FC.postprocess(FCs)
N = FCemp.shape[0]
FCemp2 = FCemp - np.multiply(FCemp, np.eye(N))
GBCemp = np.mean(FCemp2,1)
return GBCemp
def accumulate(FCs, nsub, signal):
return FC.accumulate(FCs, nsub, signal)
def init(S, N):
return FC.init(S, N)
def from_fMRI(signal, applyFilters = True):
return FC.from_fMRI(signal, applyFilters=applyFilters)
def FC_Similarity(FC1, FC2): # FC Similarity
return FC.FC_Similarity(FC1, FC2)
def pearson_r(x, y):
return FC.pearson_r(x, y)
def characterizeConnectivityMatrix(C):
return FC.characterizeConnectivityMatrix(C)