def write_to_gifti(result, giftis, script_name, zero_mask, cifti=True):
example_array_L = giftis[0].darrays[0]
example_array_R = giftis[1].darrays[0]
L_shape = len(giftis[0].agg_data()[0])
padded_result = np.zeros([result.shape[0], L_shape * 2])
padded_result[:, zero_mask] = result
L_result = padded_result[:, :L_shape]
R_result = padded_result[:, L_shape:]
L_gifti_image = GiftiImage(meta=giftis[0].meta)
R_gifti_image = GiftiImage(meta=giftis[1].meta)
for row_L, row_R in zip(L_result, R_result):
gifti_array_L = GiftiDataArray(row_L,
intent=example_array_L.intent,
datatype=example_array_L.datatype,
meta=example_array_L.meta)
gifti_array_R = GiftiDataArray(row_R,
intent=example_array_R.intent,
datatype=example_array_R.datatype,
meta=example_array_R.meta)
L_gifti_image.add_gifti_data_array(gifti_array_L)
R_gifti_image.add_gifti_data_array(gifti_array_R)
nb.save(L_gifti_image, f'{script_name}.L.func.gii')
nb.save(R_gifti_image, f'{script_name}.R.func.gii')
if cifti:
os.system(f'./utils/giftis_to_cifti.sh {script_name}.L.func.gii '
f'{script_name}.R.func.gii {script_name}.dtseries.nii')
def _write_gifti(pd, opth):
# TODO: what about pointdata?
from nibabel.gifti.gifti import GiftiDataArray
if not pd.has_only_triangle:
raise ValueError('GIFTI writer only accepts triangles.')
points = GiftiDataArray(data=pd.Points, intent=INTENT_POINTS)
cells = GiftiDataArray(data=pd.GetCells2D(), intent=INTENT_CELLS)
# if data is not None:
# data_array = GiftiDataArray(data=data, intent=INTENT_POINTDATA)
# gii = nb.gifti.GiftiImage(darrays=[points, cells, data_array])
# else:
g = nb.gifti.GiftiImage(darrays=[points, cells])
nb.save(g, opth)
def save_gifti_texture(self, filename):
""" save the texture of the surface in a gifti (BrainVisa) format"""
gifti_image = GiftiImage()
if surface_master is None:
vertices = np.zeros(np.max(self.label.vertices))
else:
vertices = np.zeros(self.pos_length)
vertices[list(self.label.vertices)] = 1
# create gifti information
darray = GiftiDataArray.from_array(vertices, intent='NIFTI_INTENT_LABEL',
endian='LittleEndian')
# location
darray.intent = 0
giftiImage.add_gifti_data_array(darray)
gifti.giftiio.write(gifti_image, filename)
def remove_gii(centers):
for center in centers:
regs = gii_regs(center)
pathes, *_ = center.get_cortical_thickness_pathes()
x = create_x(center)
for xi, path in zip(x, pathes):
ct_gii = nib.load(path)
newpath = path.replace('resampled_32k', 'resampled_32k.removed')
ct_darray = ct_gii.get_arrays_from_intent(0)[0]
data = ct_darray.data
shape = data.shape
data = np.nan_to_num(data)
data = data.flatten()
new_data = np.zeros_like(data)
index = data != np.nan
for (reg, i) in zip(regs, index):
new_data[i] = data[i] - np.dot(xi[:4], reg.coef_[:4])
new_data = np.reshape(new_data, newshape=shape)
gdarray = GiftiDataArray.from_array(new_data, intent=0)
ct_gii.remove_gifti_data_array_by_intent(0)
ct_gii.add_gifti_data_array(gdarray)
nib.save(ct_gii, newpath)
def meta_ct(label_eg,
label_cg,
p_thres=0.001,
topn=0.3,
save_gii=True,
save_nii=False,
mask=None,
csv_prefix='roi_ct_removed',
csv_dir_prefix='./data/meta_csv',
out_dir_prefix='./results/meta'):
models = {}
surfix = '{}_{}/{}'.format(label_eg, label_cg, csv_prefix)
out_dir = os.path.join(out_dir_prefix, surfix)
if not os.path.isdir(out_dir):
os.mkdir(out_dir)
csv_dir = os.path.join(csv_dir_prefix, surfix)
csvs = os.listdir(csv_dir)
for f in csvs:
csv_path = os.path.join(csv_dir, f)
model = csv_meta_analysis(csv_path, model_type='random')
models[f[:-4]] = model
if save_gii:
for annot, surf, lr in zip(annots, surfs, l_r):
a = surface.load_surf_data(annot_dir.format(annot))
a = a.astype(np.float32)
b = surf_dir.format(surf)
tmp_gii = nib.load(b)
cor_model, _ = bon_cor(models, thres=p_thres)
ll = np.unique(a).tolist()
_, sorted_models = sort_models(cor_model, descend=False)
top_es = sorted_models[int(len(sorted_models) *
topn)].total_effect_size
for k, v in cor_model.items():
_id = np.float32(k)
if v.total_effect_size <= top_es:
a[a == _id] = v.total_effect_size
if _id in ll:
ll.remove(_id)
for i in ll:
a[a == i] = 0
gdarray = GiftiDataArray.from_array(a, intent=0)
tmp_gii.remove_gifti_data_array_by_intent(0)
tmp_gii.add_gifti_data_array(gdarray)
path = os.path.join(
out_dir,
'es_{}_bon{}_top{}.gii'.format(lr,
str(p_thres)[2:],
str(topn)[1:]))
nib.save(tmp_gii, path)
if save_nii:
nii_array = mask.data.astype(np.float32)
p_array = nii_array
ll = mask.labels.tolist()
for k, v in models.items():
_id = int(k)
nii_array[nii_array == _id] = v.total_effect_size
p_array[p_array == _id] = v.p
ll.remove(_id)
for i in ll:
nii_array[nii_array == i] = 0
path = os.path.join(out_dir, 'es.nii')
p_path = os.path.join(out_dir, 'p.nii')
utils.gen_nii(nii_array, mask.nii, path)
utils.gen_nii(p_array, mask.nii, p_path)
return models
def meta_gii(centers, label_eg, label_cg, out_dir='./results/meta/{}_{}'):
out_dir = out_dir.format(label_eg, label_cg)
if not os.path.isdir(out_dir):
os.mkdir(out_dir)
out_dir = os.path.join(out_dir, 'surf')
if not os.path.isdir(out_dir):
os.mkdir(out_dir)
with open(os.path.join(out_dir, 'centers.txt'), "w") as text_file:
center_mean_dict = {}
center_std_dict = {}
center_count_dict = {}
for center in centers:
n1 = len(center.get_by_label(label_eg))
n2 = len(center.get_by_label(label_cg))
if n1 == 0 or n2 == 0:
continue
print('{}: e:{}, c:{}'.format(center.name, n1, n2), file=text_file)
group_mean_dict = {}
group_std_dict = {}
group_count_dict = {}
for label in [label_eg, label_cg]:
m, s, n = center.load_msn_array(label, _dir='surf')
group_mean_dict[label] = m
group_std_dict[label] = s
group_count_dict[label] = n
center_mean_dict[center.name] = group_mean_dict
center_std_dict[center.name] = group_std_dict
center_count_dict[center.name] = group_count_dict
results = voxelwise_meta_analysis(label_eg,
label_cg,
center_mean_dict=center_mean_dict,
center_std_dict=center_std_dict,
center_count_dict=center_count_dict,
dtype=np.float32)
es = results[0]
p = results[-1]
es_l = es[:32492]
es_r = es[32492:]
p_l = p[:32492]
p_r = p[32492:]
es_l = es_l[p_l < 0.001]
es_r = es_r[p_r < 0.001]
path = os.path.join(out_dir, 'es_l_bon001.gii')
gii_path = temp_dir.format('lh.central.freesurfer.gii')
ct_gii = nib.load(gii_path)
gdarray = GiftiDataArray.from_array(es_l, intent=0)
ct_gii.remove_gifti_data_array_by_intent(0)
ct_gii.add_gifti_data_array(gdarray)
nib.save(ct_gii, path)
path = os.path.join(out_dir, 'es_r_bon001.gii')
gii_path = temp_dir.format('rh.central.freesurfer.gii')
ct_gii = nib.load(gii_path)
gdarray = GiftiDataArray.from_array(es_r, intent=0)
ct_gii.remove_gifti_data_array_by_intent(0)
ct_gii.add_gifti_data_array(gdarray)
nib.save(ct_gii, path)
result_names = ['es', 'var', 'se', 'll', 'ul', 'q', 'z', 'p']
for result, name in zip(results, result_names):
result_l = result[:32492]
result_r = result[32492:]
result_list = [result_l, result_r]
for _result, surf, lr in zip(result_list, surfs, l_r):
path = os.path.join(out_dir, '{}_{}.gii'.format(name, lr))
gii_path = temp_dir.format(surf)
ct_gii = nib.load(gii_path)
gdarray = GiftiDataArray.from_array(_result, intent=0)
ct_gii.remove_gifti_data_array_by_intent(0)
ct_gii.add_gifti_data_array(gdarray)
nib.save(ct_gii, path)
temp_dir = r'./data/mask/BN_Atlas_freesurfer/fsaverage/fsaverage_LR32k/{}'
surfs = [
'fsaverage.L.inflated.32k_fs_LR.surf.gii',
'fsaverage.R.inflated.32k_fs_LR.surf.gii'
]
for test in tests:
voxel_path = os.path.join(path, test, 'surf')
for lr, surf in zip(l_r, surfs):
es_path = os.path.join(voxel_path, 'es_{}.gii'.format(lr))
p_path = os.path.join(voxel_path, 'p_{}.gii'.format(lr))
es_array = load_surf_data(es_path)[-1]
p_array = load_surf_data(p_path)[-1]
voxel_count = np.size(p_array) * 2
for p in ps:
corrected_array = voxelwise_correction(es_array,
p_array,
voxel_count,
thres=p)
new_f = os.path.join(voxel_path,
'es_bon_{}_{}.gii'.format(lr,
str(p)[2:]))
ct_gii = nib.load(temp_dir.format(surf))
gdarray = GiftiDataArray.from_array(corrected_array, intent=0)
ct_gii.add_gifti_data_array(gdarray)
nib.save(ct_gii, new_f)
# %%