def fisher_z2r(Z):
X = ne.evaluate('exp(2*Z)')
return ne.evaluate('(X - 1) / (X + 1)')
# get averaged (upper triangular) 1D correlation (Fisher r2z transformed)
ave_filena = '/data/pt_mar006/subjects_group/corrFisherR2Z_upper.h5'
ave_array = np.array(h5py.File(ave_filena, 'r')['data'])
###### Step #5: Fisher's z2r transform ###############################
print "Fisher z2r transform..."
ave_array = fisher_z2r(ave_array)
# get the full matrix
N_orig = hcp_corr.N_original(ave_array)
ave_array.resize([N_orig, N_orig])
corr = hcp_corr.upper_to_down(ave_array)
###### Step #6: threshold each row of corr matrix at 90th percentile ##
print "thresholding each row at its 90th percentile..."
perc = np.array([np.percentile(x, 90) for x in corr])
for i in range(corr.shape[0]):
#print "Row %d" % i
corr[i, corr[i,:] < perc[i]] = 0
# Check for minimum & maximum value
print "Minimum value is %f" % corr.min()
print "Maximum value is %f" % corr.max()
## Count negative values per row
#neg_values = np.array([sum(corr[i,:] < 0) for i in range(N_orig)])
for i in range(0, matrix.shape[0]):
if np.count_nonzero(matrix[i, :]) == 0:
cnt_zeros += 1
return cnt_zeros, matrix
#### Step 1, get all connectivity matrices of given subject #########
corr_All = []
for image_rest in glob.glob(data_dir + '/' + subject_id + '*' +
'/preprocessed/func/' +
'rest_preprocessed2mni_sm.nii.gz' ):
[voxel_zeros, t_series] = mask_check(image_rest, image_mask)
corr_upper = corrcoef_upper(t_series)
N_orig = N_original(corr_upper)
corr_upper.resize([N_orig, N_orig])
corr = upper_to_down(corr_upper)
print image_rest, corr.shape
corr_All.append(corr)
corr_All = np.array(corr_All)
corr_All = corr_All.T
print 'input data size...', corr_All.shape
##### Step 2, get concordance value per voxel ########################
W_voxels = []
p_voxels = []
Fdist_voxels = []
ccc_voxels = []
# get mean correlation
SUM = ne.evaluate("SUM / N")
# Fisher z to r transform on the (averaged) upper triangular
print "do Fisher z2r..."
# Fisher z to r transform on average , now this is back to correlation array
SUM = fisher_z2r(SUM)
# transform correlation array into similarity array
SUM += 1.0
SUM /= 2.0
# get full similartiy matrix
N = hcp_corr.N_original(SUM)
SUM.resize([N, N])
hcp_corr.upper_to_down(SUM)
print "SUM.shape", SUM.shape
# output prefix
out_prfx = args.outprfx
print "writing-out data in HDF5 format"
h = h5py.File(out_prfx, "w")
h.create_dataset("sum", data=SUM)
h.close()
# set NaN entries to 0
# SUM[np.where(np.isnan(SUM) == True)] = 0
# ignore zero entries?
# ind = np.where(np.sum(SUM,axis=1) != 1)
X = ne.evaluate('exp(2*Z)')
return ne.evaluate('(X - 1) / (X + 1)')
# get averaged (upper triangular) 1D correlation (Fisher r2z transformed)
ave_filena = '/data/pt_mar006/subjects_group/corrFisherR2Z_upper.h5'
ave_array = np.array(h5py.File(ave_filena, 'r')['data'])
###### Step #5: Fisher's z2r transform ###############################
print "Fisher z2r transform..."
ave_array = fisher_z2r(ave_array)
# get the full matrix
N_orig = hcp_corr.N_original(ave_array)
ave_array.resize([N_orig, N_orig])
corr = hcp_corr.upper_to_down(ave_array)
###### Step #6: threshold each row of corr matrix at 90th percentile ##
print "thresholding each row at its 90th percentile..."
perc = np.array([np.percentile(x, 90) for x in corr])
for i in range(corr.shape[0]):
#print "Row %d" % i
corr[i, corr[i, :] < perc[i]] = 0
# Check for minimum & maximum value
print "Minimum value is %f" % corr.min()
print "Maximum value is %f" % corr.max()
## Count negative values per row
#neg_values = np.array([sum(corr[i,:] < 0) for i in range(N_orig)])
