dependency_perm.py

#Compute ground truth correlation:
[r,pv] =stats.pearsonr(feat_set[:,1],feat_set[:,0])
#Copy one of the features:
pS = copy.copy(feat_set[:,1])
#Initialize variables:
pR = []
#Choose number of permutations:
p=10000
#Initialize permutation loop:
for i in range(0,p):
  #Shuffle one of the features:
    random.shuffle(pS)
    #Computed permuted correlations and store them in pR:
    pR.append(stats.pearsonr(pS,feat_set[:,0])[0])

#Significance:
p_val = len(np.where(np.abs(pR)>=np.abs(r))[0])/p