def calc_mean(data_dir, seg_file, label_file, sessid_file=None):
if sessid_file == None:
sessid_list = []
sess_dir_list = glob.glob(os.path.join(data_dir, '*'))
for sess_dir in sess_dir_list:
sessid_list.append(os.path.split(sess_dir)[-1])
else:
sessid_list = readsessid(sessid_file)
vmin = 0
vmax = 1
label_img = nib.load(seg_file)
label_dat = label_img.get_data()
label_list = np.unique(label_dat)
label_name_file = label_file
label_name_list = readsessid(label_name_file)[:-1]
figname = 'meants.png'
dataname = 'meants'
for sess in sessid_list:
print sess
sess_dir = os.path.join(data_dir, sess)
outfig = os.path.join(sess_dir, figname)
outdata = os.path.join(sess_dir, dataname)
data_file = os.path.join(sess_dir, 'smwc1'+sess+'_anat.nii')
img = nib.load(data_file)
dat = img.get_data()
#meants = []
meants = np.zeros(len(label_list[1:]))
for label in label_list[1:]:
label_voxel = (label_dat == label)
label_voxel_index = label_voxel.nonzero()
#label_size.append(len(label_voxel_index))
dat_tmp = dat[label_voxel_index]
#dens = stats.kde.gaussian_kde(dat_tmp)
#x_tmp = np.linspace(vmin, vmax, n_pdf)
#freq = dens(x_tmp)
#databank[:, label-1] = freq
meants[label-1] = dat_tmp.mean()
# For plotting each meants plot
plt.plot(range(len(meants)), meants)
plt.title('Mean Measure for Each Node')
#('Distributions for Each Node')
plt.xlabel('#Node')
plt.ylabel('Gray matter Volume')
plt.savefig(outfig, dpi=600)
np.savez(outdata, meants=meants)
def calc_pdfs(data_dir, seg_file, label_file, sessid_file=None):
if sessid_file == None:
sessid_list = []
sess_dir_list = glob.glob(os.path.join(data_dir, '*'))
for sess_dir in sess_dir_list:
sessid_list.append(os.path.split(sess_dir)[-1])
else:
sessid_list = readsessid(sessid_file)
vmin = 0
vmax = 1
n_pdf = 100
label_img = nib.load(seg_file)
label_dat = label_img.get_data()
label_list = np.unique(label_dat)
label_name_file = label_file
label_name_list = readsessid(label_name_file)[:-1]
figname = 'pdfs.png'
dataname = 'databank'
for sess in sessid_list:
print sess
sess_dir = os.path.join(data_dir, sess)
outfig = os.path.join(sess_dir, figname)
outdata = os.path.join(sess_dir, dataname)
data_file = os.path.join(sess_dir, 'smwc1'+sess+'_anat.nii')
img = nib.load(data_file)
dat = img.get_data()
databank = np.zeros((n_pdf, len(label_list[1:])))
for label in label_list[1:]:
label_voxel = (label_dat == label)
label_voxel_index = label_voxel.nonzero()
#label_size.append(len(label_voxel_index))
dat_tmp = dat[label_voxel_index]
dens = stats.kde.gaussian_kde(dat_tmp)
x_tmp = np.linspace(vmin, vmax, n_pdf)
freq = dens(x_tmp)
databank[:, label-1] = freq
# For plotting each freq plot
plt.plot(np.linspace(0,1,100), databank)
plt.title('Distributions for Each Node')
('Distributions for Each Node')
plt.xlabel('Gray Matter Volume')
plt.ylabel('Probability')
plt.savefig(outfig, dpi=600)
np.savez(outdata, databank=databank)
def calc_kls_mat(data_dir, sessid_file=None):
"""
"""
if sessid_file == None:
sessid_list = []
sess_dir_list = glob.glob(os.path.join(data_dir, '*'))
for sess_dir in sess_dir_list:
sessid_list.append(os.path.split(sess_dir)[-1])
else:
sessid_list = readsessid(sessid_file)
databank = 'databank.npz'
mat_fig = 'skl_mat.png'
mat_dat = 'skl_mat'
rmat_fig = 'skl_mat_reordered.png'
rmat_dat = 'skl_mat_reordered'
for sess in sessid_list:
print sess
sessdir = os.path.join(data_dir, sess)
mat_fig_file = os.path.join(sessdir, mat_fig)
mat_dat_file = os.path.join(sessdir, mat_dat)
rmat_fig_file = os.path.join(sessdir, rmat_fig)
rmat_dat_file = os.path.join(sessdir, rmat_dat)
data_file = os.path.join(sessdir, databank)
dat = np.load(data_file)
dat = dat['databank']
dat = np.where(dat == 0, dat[dat!=0].min(), dat)
dat = dat.T
n_roi = dat.shape[0]
n_samp = dat.shape[1]
im = np.zeros((n_roi, n_roi))
for i in range(n_roi):
for j in range(n_roi):
if i < j:
dkl = get_dkl(dat, i, j, n_samp)
skl = np.exp(-dkl)
im[i,j] = skl
im[j,i] = skl
# Save mat
np.savez(mat_dat_file, im=im)
np.savetxt(mat_dat_file+'.txt', im, fmt='%.4e', delimiter=' ')
plt.imshow(im, origin='lower',interpolation='nearest')
plt.colorbar(ticks=[0, 0.5, 1])
plt.savefig(mat_fig_file, dpi=600)
plt.close()
# Reorder the mat
im = reorder_mat(im)
np.savez(rmat_dat_file, im=im)
np.savetxt(rmat_dat_file+'.txt', im, fmt='%.4e', delimiter=' ')
plt.imshow(im, origin='lower',interpolation='nearest')
plt.colorbar(ticks=[0, 0.5, 1])
plt.savefig(rmat_fig_file, dpi=600)
plt.close()
def mbnu_prep(sessid_file, src_dir, tgt_dir):
sessid_list = readsessid(sessfile)
if not os.path.exists(tgt_dir):
os.mkdir(tgt_dir)
for sessid in sessid_list:
filename = 'smwc1' + sessid + '_anat.nii'
src_file = os.path.join(src_dir, filename)
sess_dir = os.path.join(tgt_dir, sessid)
if not os.path.exists(sess_dir):
os.mkdir(sess_dir)
tgt_file = os.path.join(sess_dir, filename)
cp_str = 'ln -s ' + srcfile + ' ' + tgtfile
print cp_str
os.system(cp_str)
